TON_IoT_Framework_Pipeline.ipynb

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      "mount_file_id": "1kKDalxY2fLIt4ud0HAshTJCrzkufhxhh",
      "authorship_tag": "ABX9TyNiYBvGj0YgxrVrSAHPnNKH",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/goldboy225/Intelligent-IoT-Security-Frameworks/blob/main/TON_IoT_Framework_Pipeline.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mount the google drive"
      ],
      "metadata": {
        "id": "ryZ6Lizrvrot"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "bLAwqE-FvstK",
        "outputId": "bde8725e-fbaa-4af9-e2e1-382a5918c3bf"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "error",
          "ename": "MessageError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mMessageError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-d5df0069828e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, readonly)\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadonly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m   \u001b[0;34m\"\"\"Mount your Google Drive at the specified mountpoint path.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m   return _mount(\n\u001b[0m\u001b[1;32m    101\u001b[0m       \u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m       \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral, readonly)\u001b[0m\n\u001b[1;32m    121\u001b[0m       'TBE_EPHEM_CREDS_ADDR'] if ephemeral else _os.environ['TBE_CREDS_ADDR']\n\u001b[1;32m    122\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mephemeral\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m     _message.blocking_request(\n\u001b[0m\u001b[1;32m    124\u001b[0m         'request_auth', request={'authType': 'dfs_ephemeral'}, timeout_sec=None)\n\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mblocking_request\u001b[0;34m(request_type, request, timeout_sec, parent)\u001b[0m\n\u001b[1;32m    169\u001b[0m   request_id = send_request(\n\u001b[1;32m    170\u001b[0m       request_type, request, parent=parent, expect_reply=True)\n\u001b[0;32m--> 171\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m    100\u001b[0m         reply.get('colab_msg_id') == message_id):\n\u001b[1;32m    101\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;34m'error'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mMessageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'error'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mMessageError\u001b[0m: Error: credential propagation was unsuccessful"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Data Collection"
      ],
      "metadata": {
        "id": "BCR4hzI9qCw1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.1 Import libraries"
      ],
      "metadata": {
        "id": "d9Hl17Oxjhri"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# install 3rd party lib for lightweight pandas\n",
        "# https://modin.readthedocs.io/en/stable/ https://github.com/modin-project/modin\n",
        "\n",
        "pip install modin"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "HUF0Nc4SEyH9",
        "outputId": "c480edf1-b420-4987-8c5b-eae81e07b7d8"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-3-4681af189b19>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    pip install modin\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# data cleaning and plots\n",
        "import pandas as pd\n",
        "#import modin.pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')\n",
        "%matplotlib inline\n",
        "\n",
        "# sklearn: data preprocessing\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder\n",
        "import missingno\n",
        "\n",
        "# sklearn: train model\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score, cross_validate, StratifiedKFold\n",
        "from sklearn.metrics import precision_recall_curve, precision_score, recall_score, f1_score, accuracy_score\n",
        "from sklearn.metrics import roc_curve, auc, roc_auc_score, confusion_matrix, classification_report, plot_roc_curve\n",
        "\n",
        "# sklearn classifiers\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "# optimization algorithms\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "import itertools\n",
        "import matplotlib.gridspec as gridspec \n",
        "\n",
        "# timestamp for noting time resource operation\n",
        "from pandas import Timestamp\n",
        "\n",
        "# os lib\n",
        "import os  # print (\"current directory : %s\" % os.getcwd())\n",
        "\n",
        "# Ignore warnings\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "5e1ZA9r9jTX5"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2 Load the dataset"
      ],
      "metadata": {
        "id": "aomav1RkUU4-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "W9UnRdy9SWba"
      },
      "outputs": [],
      "source": [
        "# load the original dataset, which have not been processed by the authors\n",
        "df = pd.read_csv('/content/drive/MyDrive/Datasets/TON-IoT/Train_Test_Network.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.3 Data Sanity Check"
      ],
      "metadata": {
        "id": "waZSHoAyUcw8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the dataframe\n",
        "\n",
        "df.head(10)"
      ],
      "metadata": {
        "id": "InuVVx7Fj47n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "oqJUDUwmoi8I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check the size of the data\n",
        "\n",
        "print(df.shape)"
      ],
      "metadata": {
        "id": "3xQSqLvUW2O_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the columns of the data\n",
        "\n",
        "print(df.columns)"
      ],
      "metadata": {
        "id": "QHVkA3etW44S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include = 'all').T"
      ],
      "metadata": {
        "id": "Ra6vJ3jBozTP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Exploration Data Analysis (EDA)"
      ],
      "metadata": {
        "id": "1BwTJOAiRcyZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1 Check the count and distribution of target classes"
      ],
      "metadata": {
        "id": "_jb5DP852ada"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# to install matplotlib in order to show the figure in the following steps\n",
        "\n",
        "#! python -m pip uninstall matplotlib\n",
        "#! pip install matplotlib==3.1.3\n",
        "\n",
        "\n",
        "# Check the count of normal and attack class\n",
        "\n",
        "counts = df.value_counts('label')\n",
        "print(counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QB28lJ5aYCdB",
        "outputId": "5d48e6ff-cfae-4143-b21d-d542ecf5df7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label\n",
            "0    300000\n",
            "1    161043\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's see the distribution of our target normal/attack by bar chart\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "ax = sns.countplot(x = 'label', data = df, palette = 'Set2')\n",
        "for container in ax.containers:\n",
        "    ax.bar_label(container)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "RTVoFsEKzbtB",
        "outputId": "dfed43d2-59ed-470c-d447-b60534353c9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-aa0a1fe09dd5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'label'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Set2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcontainer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontainers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontainer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'AxesSubplot' object has no attribute 'bar_label'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the count of category class\n",
        "\n",
        "counts = df.value_counts('type')\n",
        "print(counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wryL5Ihg05CM",
        "outputId": "ce2d264a-1a0b-431c-e119-74a4140bfcbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "type\n",
            "normal        300000\n",
            "backdoor       20000\n",
            "ddos           20000\n",
            "dos            20000\n",
            "injection      20000\n",
            "password       20000\n",
            "ransomware     20000\n",
            "scanning       20000\n",
            "xss            20000\n",
            "mitm            1043\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's see the distribution of our target category by bar chart\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "ax = sns.countplot(x = 'type', data = df, palette = 'Set2')\n",
        "for container in ax.containers:\n",
        "    ax.bar_label(container)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "qkf7yrQu1Igf",
        "outputId": "e8045712-adc2-4488-8630-66f969a83422"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-1f5eb2dac20e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'type'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Set2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcontainer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontainers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontainer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'AxesSubplot' object has no attribute 'bar_label'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# helper function for drawing mulitple charts.\n",
        "\n",
        "def bake_pies(data_list,labels):\n",
        "    list_length = len(data_list)\n",
        "    \n",
        "    # setup for mapping colors\n",
        "    color_list = sns.color_palette()\n",
        "    color_cycle = itertools.cycle(color_list)\n",
        "    cdict = {}\n",
        "    \n",
        "    # build the subplots\n",
        "    fig, axs = plt.subplots(1, list_length,figsize=(18,10), tight_layout=False)\n",
        "    plt.subplots_adjust(wspace=1/list_length)\n",
        "    \n",
        "    # loop through the data sets and build the charts\n",
        "    for count, data_set in enumerate(data_list): \n",
        "        # update our color mapt with new values\n",
        "        for num, value in enumerate(np.unique(data_set.index)):\n",
        "            if value not in cdict:\n",
        "                cdict[value] = next(color_cycle)\n",
        "       \n",
        "        # build the wedges\n",
        "        wedges,texts = axs[count].pie(data_set,\n",
        "                           colors=[cdict[v] for v in data_set.index])\n",
        "\n",
        "        # build the legend\n",
        "        axs[count].legend(wedges, data_set.index,\n",
        "                           title=\"Flags\",\n",
        "                           loc=\"center left\",\n",
        "                           bbox_to_anchor=(1, 0, 0.5, 1))\n",
        "        # set the title\n",
        "        axs[count].set_title(labels[count])\n",
        "        \n",
        "    return axs"
      ],
      "metadata": {
        "id": "Cs-G_cvvzezf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use pie chart to show the distribution of the target classes based on the protocols\n",
        "\n",
        "normal_protocol = df.loc[df.label == 0].proto.value_counts()\n",
        "attack_protocol = df.loc[df.label == 1].proto.value_counts()\n",
        "\n",
        "protocol_axs = bake_pies([normal_protocol, attack_protocol], ['Normal','Attack'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "eRuEECECzgsz",
        "outputId": "18e353a8-669f-4240-cb49-3b3aa64da5f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x720 with 2 Axes>"
            ],
            "image/png": 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tqchxHGC07pS0ur4h2OU6CAAAkhQKx+ZL+jdJS1xnAUZph6S/aGqsu991ECAbKDw8JhqJL1DqDfgDrrMAGbRJ0mX1DUGmXgIAnAqFY5+T9B1JVa6zABn0iKQvNjXWveU6CJBJFB4eEo3EPy/p/0mqdJ0FyIKEpJsl3VTfEGSjLQBAToXCsQlKLQH4hOssQJbskXRFU2Pdw66DAJlC4eEB0Uh8kqR/lnSh6yxADvxaqdkeG10HAQD4Qygcq5P0r5JmuM4CZJmV9C1Jf93UWJdwHQYYLQqPAheNxD8s6S5JU11nAXKoW9JX6xuCP3AdBADgXaFwrFjSTZK+Jsk4jgPk0pOSLmlqrNvpOggwGhQeBSoaiQckfVPSX4k3YPjXvZKu4vS1AIBMC4Vj0yX9RNKZrrMAjuyU9Kmmxrq1roMAI0XhUYCikfhESfdJOs91FiAPvCTpovqG4OuugwAAvCEUjp0n6ceSal1nARxLKHX2x1ubGuv44IiCQ+FRYKKR+GJJ/yFpjussQB5pl/SZ+obgI66DAAAKVygcC0j6hqS/lhRwmwbIK49IurypsW6P6yDAkaDwKCDRSPxzkr4nqdx1FiAPWaWWeX2jviGYdB0GAFBYQuFYrVJLWOpcZwHy1BZJn2hqrHvBdRDgcFF4FIBoJF6q1Pne611nAQrAf0m6tL4hyBEIAMBhCYVjCyQ9Jmm+6yxAnuuX1NDUWHeH6yDA4aDwyHPRSLxW0oOSlrrOAhSQTZJW1TcEN7gOAgDIb6Fw7ANKTdef5DoLUEDulXRVU2MdG8cjr1F45LFoJH60UkerOdoAHLk9kj5e3xB80nUQAEB+CoVjH1VqGcsY11mAAvSspFVNjXVtroMA74fNmPJUNBJfKulpUXYAIzVe0uPRSPxy10EAAPknFI59QdIDouwARmqJpHXpUzgDeYkZHnkoGolfJOnfxBswkCn/t74heJPrEAAA90LhmJF0i6Svuc4CeMQWSec3NdZtdB0EOBCFR56JRuJ/Kek2MfsGyLQfSPpifUMw4ToIAMCNUDhWKumHki51nQXwmGZJH25qrPut6yDA/ig88kQ0EjdKFR2rXWcBPOxhSZ+sbwj2ug4CAMitUDg2VqklLJx2FsiODkkfbWqsY/805A0KjzwQjcQDku6U9FnHUQA/eELSBfUNwW7XQQAAuREKx2YotRH8Ca6zAB7XJ+mSpsa6B10HASSWTTgXjcSLJN0jyg4gV86R9Fg0Eq92HQQAkH2hcOx4pc4mQdkBZF+5pP8IhWOfdR0EkCg8nIpG4iVKnQrt066zAD6zXNL/RCPxca6DAACyJxSOLZX0K0kzXWcBfKRI0g9D4di1roMALGlxJBqJl0r6d0kfdZ0F8LHfSDqvviHI+eMBwGNC4djJSi1jpNwG3Pn7psa6v3IdAv5F4eFANBIvV2rTrJWuswDQi5I+WN8QbHEdBACQGaFw7BhJ6yRNdp0FgP5FUn1TY13SdRD4D0taciwaiY9R6kwRlB1AfjhR0tpoJD7FdRAAwOiFwrE5kv5HlB1Avvi8pDtch4A/UXjkUDQSL5Z0v6QPus4C4D2Ckh5nTw8AKGyhcGyqpP+VNMt1FgDvcXUoHLvVdQj4D4VHjkQjcSPpR5L+xHUWAMM6UdIj6VlYAIACEwrHxkt6XNIC11kADOtroXDsOtch4C8UHrlzuzgbC5Dvlkm6Pz0bCwBQIELhWJWk/xKnngXy3d+HwrGrXIeAf1B45EA0Er9B0ldc5wBwWP5E0o/Ss7IAAHkuFI6VS3pI0gdcZwFwWL4fCscudh0C/kDhkWXRSPxLkr7hOgeAI/JppWZlAQDyWCgcK5b0M0nnuM4C4LAFJN0TCsfOdR0E3kfhkUXRSPxTkr7rOgeAEflKenYWACAPhcKxgKR/lXSB6ywAjlippP8IhWPHuw4Cb6PwyJJoJL5CqU1KmRYPFK5vRCPxz7kOAQAY1h2SPuU6BIARGyvpF6FwbLrrIPAuY611ncFzopH4PElNkia6zgJg1AYknVvfEPyV6yAAgJRQOPY3km5ynQNARvxO0llNjXWdroPAeyg8MiwaiVdLekbSca6zAMiYFkmh+obgVtdBAMDvQuHYhyU9KmYqA17y35I+0tRYN+Q6CLyFN4oMikbiAUn3irID8JpaSQ9FI/Eq10EAwM9C4dhcST8WY1jAaz4k6fuuQ8B7eLPIrFslfcR1CABZcaKkf+N0tQDgRvr0s/8haYLrLACy4spQOHa16xDwFgqPDIlG4pdLanSdA0BWfUzSza5DAIBPfU/Sqa5DAMiq20Ph2ImuQ8A72MMjA6KR+BJJayWVOY4CIDc+Xd8QvM91CADwi/RR36jrHABy4jVJi5sa67pcB0HhY4bHKEUj8QmSfibKDsBP/jkaiR/jOgQA+EEoHDtd0ndd5wCQMwtFwYkMofAYvbslzXIdAkBOVUr6WTQSL3cdBAC8LBSOTZJ0v6RS11kA5NSnQ+HYVa5DoPBReIxCNBJvkHSB6xwAnDhR0u2uQwCAV4XCsSJJPxEHlgC/uj0Ujp3gOgQKG4XHCEUj8ZCkv3edA4BTV0cj8U+6DgEAHvVNSee6DgHAmTGS/j0UjlW5DoLCReExAtFIfKxSRxxKXGcB4NwPopH4fNchAMBLQuHYhZK+5joHAOeOkXSH6xAoXBQeI/Mvko5yHQJAXqiR9NNoJM76cgDIgFA4drSkH0kyrrMAyAuXhcKxK12HQGGi8DhC0Uj8GkmfcJ0DQF5ZLJa4AcCopfft+FelymQA2Oe7oXDseNchUHgoPI5Aetr6t13nAJCX/jIaiZ/lOgQAFLivSlriOgSAvLNvP49K10FQWCg8DlM0Eg8odQpa/icDMBwj6c5oJF7hOggAFKJQOHaMpL91nQNA3lok6R9dh0BhofA4fH8pabnrEADy2gJJf+c6BAAUmlA4FpB0l6Ry11kA5LXPhsKxD7oOgcJB4XEYopH40ZJudp0DQEH4SjQSX+Y6BAAUmK9KWuo6BICC8E+hcKzMdQgUBgqPQ4hG4kaps7KMcZ0FQEEISPphNBLnNQMADkN6KctNrnMAKBgLJV3nOgQKA4XHoV0jiY0IARyJhZK+6ToEAOS7UDhmJP1ALGUBcGS+HgrH5rsOgfxH4XEQ0Uh8pjjVJICRWR2NxDnTAAAc3OfEgSUAR65cbGCKw0DhcXC3Sap2HQJAQQpI+l76DE8AgAOEwrFJkr7lOgeAgvXhUDj2CdchkN8YiL+PaCR+jqQ/c50DQEE7RdLVrkMAQJ76tqSJrkMAKGj/LxSOcYAa74vCYxjRSLxY0ndc5wDgCTdHI/EJrkMAQD4JhWNnS7rCdQ4ABW+GpL91HQL5i8JjeF+UdLzrEAA8YYLYwBQA3hEKx0olfd91DgCe8ZVQOHay6xDITxQeB4hG4rWSbnSdA4Cn1Ecjcd6IASBltaRjXIcA4BlFku5In/UJeA8Kjz/2d5LGuQ4BwFMCkr7rOgQAuBYKx8ZK+prrHAA8Z4mkq1yHQP6h8NhPNBI/TdKfu84BwJOWRyPxS12HAADHrlVqqR8AZNqtoXCs1nUI5BcKj/f6B/F3AiB7bolG4mWuQwCAC+nT0K52nQOAZ40XWxPgAHy4T4tG4h+SdJbrHAA8bZaketchAMCRv5LE6SMBZNOVoXBspusQyB8UHu+6yXUAAL7w9WgkXuE6BADkUigcmyHpS65zAPC8UrFPEPZD4SEpGolfKCnkOgcAX5gi6S9chwCAHPsbSeWuQwDwhatC4dh01yGQH3xfeEQj8YCY3QEgt66LRuJjXYcAgFwIhWPzJF3pOgcA3yiTdJ3rEMgPvi88JF0i6XjXIQD4ynilzlQAAH7wDUklrkMA8JWrQ+HYVNch4J6vC49oJF6s1JswAOTa6mgkPsl1CADIplA4FpR0mescAHxnjKRG1yHgnq8LD0mXSzradQgAvlSt1BkLAMDL/laMNwG4cU0oHJvsOgTc8u0bUDQSN2IHXwBu1Ucj8fGuQwBANoTCsVMlXeQ6BwDfqpC0xnUIuOXbwkPSRyUtdB0CgK9VidM0AvCub0oyrkMA8LUvhsIxlhD7mJ8LD9o+APngL6KR+BjXIQAgk0LhWEjSStc5APhepdgo3td8WXhEI/EPSFruOgcASKqV9FnXIQAgw77oOgAApH05FI5NdB0Cbviy8BA79gLIL6vT+woBQMELhWPjJX3SdQ4ASKuS1OA6BNzwXeERjcSPkvRx1zkAYD8LJa1yHQIAMuQKpU4JCQD54iuhcKzadQjknu8KD0lflT+fN4D89lXXAQAgQ65xHQAADlAj6dOuQyD3fPXBPxqJj5P0Odc5AGAY50Qj8RNdhwCA0QiFY+dIOsZ1DgAYRr3rAMg9XxUeki5T6nzMAJCPrnIdAABG6QuuAwDA+zglfQYp+IjfCo/Puw4AAAdxaTQSL3cdAgBGIhSOTZV0oescAHAQzPLwGd8UHtFIfLGkk1znAICDGC/pT12HAIARulJSiesQAHAQl4TCsRrXIZA7vik8xOwOAIWB1yoABScUjgUkXe06BwAcQqVS2xzAJ3xReEQj8QpJn3KdAwAOw4poJL7AdQgAOEKrJM12HQIADgMHl3zEF4WHpD+TNNZ1CAA4DEapaeEAUEg4FS2AQnFKKBw7wXUI5IZfCg9aPACF5IpoJF7sOgQAHI5QODZX0odd5wCAI3C56wDIDc8XHtFI/GhJy13nAIAjME18eABQOK6WD8aUADzl0lA4VuQ6BLLPD29OF7sOAAAjwGsXgELxadcBAOAITZP0QdchkH1+KDw+4ToAAIzAR6OReKnrEABwMKFw7CRJc1znAIARYFmLD3i68Eif6eBk1zkAYATGSjrfdQgAOISPug4AACN0YSgcq3YdAtnl6cJDzO4AUNh4DQOQ7yg8ABSqCkkfcR0C2eX1wuPPXAcAgFH4WDQSL3EdAgCGEwrHZkha7DoHAIzCStcBkF2eLTyikfhRkk51nQMARmGc2FALQP66QJJxHQIARuH8UDjG65iHebbwEFPBAXgDM9UA5CuWswAodFMkneI6BLLHy4XHn7oOAAAZcGE0Euc88QDySigcq5JU5zoHAGQAy1o8zJOFRzQSnyTpNNc5ACADxks63XUIADjAhySVuQ4BABnwYdcBkD2eLDwknSvvPjcA/sPpaQHkG5azAPCKJaFwbKzrEMgOr5YCfDgA4CW8pgHIG6FwrEjSKtc5ACBDiiWd5zoEssOrhQf/YAF4yenRSJwjDwDyxTJJE12HAIAMYlmLR3mu8IhG4oskzXKdAwAyqFjSOa5DAEAay1kAeA2Fh0d5rvAQU78BeBOvbQDyBYUHAK+ZEQrHTnAdAplH4QEAhYHXNgDOhcKx2ZKOdp0DALKAWR4e5KnCIxqJl0ha4ToHAGTB/GgkfpTrEAB87wzXAQAgSyg8PMhThYekkyVVuQ4BAFmyzHUAAL631HUAAMiS5aFwjM+SHuO1wmOJ6wAAkEW8xgFwjcIDgFeVSjrTdQhkltcKjw+4DgAAWcRrHABnQuHYGKVm0wKAV53iOgAyi8IDAArHidFIvNx1CAC+dZqkEtchACCLKHU9xjOFRzQSnyhpgescAJBFJZIWuw4BwLdYzgLA605yHQCZ5ZnCQ8zuAOAPvNYBcIXXHwBetyAUjlW6DoHMKXYdIIN4EwbgBwX/WvfCCy8ESktLrysqKjpW3ireD5RMJBKvDAwMfGvx4sVJ12GADGBtOwCvC0g6QdKzroOMBmOtd1F4AEBhKfgztZSWll5XU1NzcUlJiedLgMHBwRM6Ojok6VbXWYDRCIVj4yTNc50DAHLgZBV44cFY611eantY1w7AD2an9ywqWEVFRcf64Q1YkkpKSpLpoytAoWMjPwB+UfD7eDDWepcnCo9oJD5V0iTXOQAgRwr9A7Qn3nuOgN+eL7yJ5SwA/MILBa/fxh7v+3y98hdxnOsAAJBDhV54ACg8FB4A/OKEUDjmlc/JvueVPTyOdx0AAHIo6DpAPpg1a9biefPm9e77/q677np98+bNZd///ven3H///a+7zAZ4EIUHAL+olLRA0muug7jmhbGWVwoPBv8A/IQZHpJKS0uT69ati+9/3ebNm8tc5QG8Ko1/0LcAACAASURBVBSOlUpa5DoHAOTQSaLw8MRYyytTdXgTBuAnlLyH4emnn64477zzFq1YsSJ4/vnnL3r55ZfLJKmrqytw2WWXHbV06dLjLrnkkvnnnnvuomeffbZiaGhIV1111dxly5Ydt3z58uA//MM/THb9HIA8MUPeOUgGAIfDC/t4ZF0hjLW88ua10HUAAMihWdFIvKq+IdjlOohLAwMDgbPOOisoSdOnT+//yU9+smn/24PBYN8vfvGLV0tKSvTYY49V33zzzTPvvffeTXfccUdtTU1N4plnnnn5d7/7XfkFF1xwnCS98MILFc3NzSVPPfXUy5LU1tZWlPtnBeSlWa4DAECOFfyZWjLBC2Otgi88opF4jaSprnMAQI4tkvS86xAuDTfNcn/t7e1F11xzzby33nqr3Bhjh4aGjCQ9//zzVZ///OebJenkk0/umz9/fo8kLViwoH/79u1lq1evnvXBD35w78qVKzty80yAvEfhAcBvKDzkjbGWF5a0HO06AAA4wFK+Q7j55ptnLFmypPOpp556+Uc/+tHrAwMDB33PmzhxYiIWi8WXLl3aec8999R+4QtfmJujqEC+m+06AADk2IxQOFbiOkS+K4SxlhcKjzmuAwCAA3wAOYTOzs6iadOmDUjSPffcM2nf9YsXL+568MEHx0vSiy++WP7GG2+MkaRdu3YVJ5NJffKTn2y//vrrt7/66qsVbpIDeYcZHgD8xkhiL69DKISxVsEvaVFqIy0A8Bte+w7hS1/60s5rr7123ve+973pZ511Vvu+67/whS+0XHPNNXOXLl163Ny5c/vmzp3bN3bs2MS2bdtKrr322rnJZNJIUmNj4zZ36YG8QuEBwI+mStruOkQ+K4SxlrHWZvt3ZFU0Er9V0tdc5wCAHHuoviH4MdchRiIej98zbtw4Z6fWHRoa0uDgoBkzZox97bXXyj71qU8tfPrpp/9QVlaWlTfE9vb2V4LB4Gey8dhALoTCsd+J9ewA/OcjTY11j7oOMRKMtd7lhRkeM10HAAAHmOExQt3d3YELL7zwmH0ba33jG9/Ymq03YMAjmOEBwI+muA5QqPJprOWFwoNBPwA/4rVvhMaOHZt84oknXnGdAygEoXCsQtIE1zkAwAHOBDpC+TTW8sKmpQz6AfjR5Ggkzu7hALKN2R0A/IrCwwMoPACgMAUkTXMdAoDnUXgA8CuWtHhAQRce0Uh8nCROGwjAr9jDCEC2UXgA8CtmeHhAQRceonUD4G+1rgMA8DwKDwB+xWdNDyj0TUtrXAcAAIc88Rr48E/bF2fy8S745LgXMvE4mzZtKr388suPfuqpp17OxOMBBYrCA4BfeWaGx8d+/GpGx1oPXrrooGOttra2onvvvXfCl7/85ZZM/t6RKPQZHp4Y7APACPEaCCDbJrkOAACOjA2FY+WuQxSiPXv2FN13332TXeeQCn+GR7XrAADgEK+BI3Tg7I1wODylu7u7aOXKle1r1qyZK0lLly7t2Hf/O++8c+Ljjz8+rqurq7ilpaVk1apVu2+44YYdjuIDuVTmOgAAODRF0lbXIQrNjTfeOPPtt98uO+uss4JLlizpmDBhwtAjjzwywRij5cuX773lllu2r1q16piFCxf2vPDCC9WJRMKEw+HNZ5xxRk+mszDDAwAKF6+BGbZmzZq5N95445vr16+PH3hbPB6vvPvuu19/4oknXn788ccnPPvss2yaDT/g9NcA/Mwzy1py6YYbbtg2ffr0/nXr1sXPPPPMjlgsNu6xxx57df369fFrr71257779fX1BdatWxe/6aabtjY2Ns7LRpZCLzw4ugnAzyg8Mqijo6Oos7OzqK6urkuSLrnkkt3733766ad31NbWJiorK+2555675+mnn65ykxTIKQoPAH7GBvGjtG7dupqLLrqotaqqKilJkyZNSuy77eMf/3ibJNXV1XV1d3cH2traijL9+wu98GCwD8DPKH1HqLi42CaTyXe+7+/vP+T7oTHmoN8DHlXqOgAAOMSyvizKxdiq0AsPBvsA/IzSd4SmTZs21N7eXtzS0lLU19dn1q5dO7ampiZRXV2dWLt2bZUk/exnP5uw/88899xzNa2trUXd3d3miSeeGLd06dIuN+mBnGKGBwA/K/Q9L52oqalJ9PT0BCRpxYoVHQ888MCkrq6ugCS1tra+M4vj5z//+XhJWrt2bVVVVVVi/PjxieEfceQK/T8ghQcAP/PEa2CmTiN7JEpLS219ff2OlStXHltbWzs4b968Pkn69re/vWXNmjVzjTHv2bRUkoLBYPcVV1wxv6WlpXTVqlW7lyxZkvGNtYA8xAwPAH5W6J+XJR36NLKZVltbmzjppJO6li1bdtyyZcv2nnPOOe3nnXfesSUlJfbMM8/ce/PNN2+XpLKyMrtixYrg0NCQCYfDm7ORpdD/AxZ6fgAYDV4DR2H16tXNq1evbj7w+gM2LN227w9Tp04dvP322zflJByQP5jhAcDPGGuN0N133/2eAuP666/feeB9Lr744t233XbbW9nMUehLWgDAzzK+sRMAHIAZHgD8jMKjwPEfEAAKF6V1jlx55ZW7Je0+5B0B72GGBwA/4/Nyljz66KMbcvF7+A8IjJC11pqhvrZSO8ipGuCElWEPCQDZRuEBZ4yUKLPaK1nJ2vR1ScnKBmRllZSx1hhrJVmZ9P0YmCFTrDHWdQaMTqEXHvwDRE512cTOjbZ306Zkr2lXYqFkJ44f7Gyd0dPcPLt7V+fs7p39M3qaNblvz5jqwe6xZcnBqUYa6zo3PKtKX3vedQYA3saSFjhjpaIpgbKtSwM1g5UKnGaMObyZjakCJGmstZJNGtmkrLX7vqZuS1oja2Vtcr+v2nfbvvunH8Mam0z/nJWUTJrU7en7pe9vrYySyfdclyphksYmlbp/6qtJX5d+7H2/V/v9vgPun5TSmU3qKVgja1L3T8rYpE3/2Zh0lncfJ6lUvqRJZ06XQ0m9+3usMUpaY61J/Zw1Sn//bs733KbU87Um/XzNu4/7zvVK/53uK6X2+7rv1PD7XZf6WbPvZyW9c12q0FJA+98n9d86ICn1+2Qlq8A7tyl1m6wNpO9s9M5jaN91+/5NBd69/7u3DRWXG+mCI/yXi3xS6IUHkFVD1vZusX3x12xv1y47MCMhLZA09Z07GKM9ZTW1e8pqav8wfsGwj1Ex1Ncxrbd116zune1zunb2zurZlZzSu7t03EBXTXmif1JAdooRByMwIhk/dRcAHIAZHnBqq+0/ZWuiRWNVtHVZoGbrNFN6sjHm4KdlN0aSCaQ/9hZxhBSjEKh3nQCjQuEB7Mdaa5s1uHFDsnfHm7a/pk/JoKTFo3nMnuLymk3VM2s2Vc8c9vbi5NDAlL62HTO7d7XN6d7ZPbt7V2Jqb2vRxP69lZVDfROKbGKa4QgbhkfhASDbeP9BXtirxJxfJPfMKZHpPC1Q9eQiUzG3yJg5rnPB84ZcB8DoUHjA97ptonmj7d34erLPtGvoaEkL05ecGAoUl26vmDxne8XkOc/VnvDHd7DWTuzfu3NGT3Pr7O5dHXO6dw7M6GkOTOpvH1M92D2uNDk0xUgHP9IBr/JE4fGxH786qlLxQIc613xbW1vRvffeO+HLX/5ySyZ/L+BRzPBAXhmUrX4m2bniGXUmjzZjfn16oKp4jCk61XUueJYnxloP/7Q9o2OtCz457qBjrcOxadOm0ssvv/zop5566uVMZHo/hV540LjhiA1Z27c1tUylc6cdmJ6QjpY02XWu92WM2V0+buru8nFTX5wwfA9TOdizd3pv687Z3Tv3zuna0Tuzp1lT+trKxg50VZcnBiYHZGtznBq54Yk34Vzbs2dP0X333TeZwgM4LL2SqlyHAIYR2Gh7T9+Y6NVEFb++vKhmxySVnGaMGeM6GDyFz5sFrtALj07XAVAYmu3Axg3J3rfftP3VvallKp46EtBdUjF2Y8nssRtrZg97e0lisG9q3+4ds1LLZnpnde8amtbbWjy+v6OqItE3qcgmp5rCfz3wI87SMgI33njjzLfffrvsrLPOCi5ZsqRjwoQJQ4888sgEY4yWL1++95Zbbtm+atWqYxYuXNjzwgsvVCcSCRMOhzefccYZ/H3Dj9pE4YE8t1tDCx5MtC0ol2k7PVDz3AJTfkzAmGmuc8ETeO8fgQNnb4TD4Snd3d1FK1eubF+zZs1cSVq6dGnHvvvfeeedEx9//PFxXV1dxS0tLSWrVq3afcMNN+zIRJZC/4DTcei7wI96bKIlvUzFtmtogU3N4jjadS5XBotKyt+qnDrvrcqp857WSX90u7HJ5KT+vTtm9DS3zOna0Tm7e+fgjJ6WwKT+vRVVQz3jS5JDU41U6SA6Dq7LdYBCdMMNN2y7/PLLx6xbty7+8MMP13z3u9+d/thjj71aVVWVbG1tLdp3v76+vsC6devisVisqrGxcV62p1wCeapN0vBtOpBn+mQnrEvuPXu99g4FTcUzpwaqqspMYJj1wsBh4/NmBq1Zs2bujTfe+GZdXV3Xdddd954NDuPxeGUsFnu5oqIief755wc/9KEP7V2yZMmoCycKD3hCwtr+N21/fIPt6dhpB6YNpcoNlnEcJmsCgZby8dNaysdP+92EY4a9T/VAV9uM3pZds7t37Z3dtWNgZk+LndzXVjZ2sGtsWWJgckCamOPYYJbbqK1bt67moosuaq2qqkpK0qRJk95ZJvTxj3+8TZLq6uq6vvrVrwba2tqKJkyYwDIi+E2b6wDAkbJS8cu2Z+nLiR5NVUn8jKKaPeNVfLoxhj1pcKT2ug7gFR0dHUWdnZ1FdXV1XZJ0ySWX7H7qqafG7rv99NNP76itrU1I0rnnnrvn6aefrqLwoPDwtRY7+PqGZM/2rba/Kr1M5RTXmbyss7RqwqulVRNeHTtv2NvLEgM903pbd858d9lMYlpva8n4gc7qMUP9kwJKTjFS0bA/jJGi8MgiY8xBvwd8gsIDBW2nBoMPJHarQoFdSwM1r8w1ZccbYya5zoWCwefNESguLrbJZPKd7/v7+wOH+plsjbsKvfBgsO8jvTbRutH2vfZ6stfu0dB8Ky1Q6oI80F9UWrGlavpRW6qmH/WrYW4PJBNDtf3t22Z279o9p3tn1+zunYPTU6ffraga7JlQbBNTjcRGY0eG18ARqKmpSfT09AQkacWKFR3f+c53pl922WVt+5a07Jvl8fOf/3z8eeed17l27dqqqqqqxPjx45ndAT+i8IAn9Cg55ZfJ9ikBqf8kU/mrEwOVtSUmMPy0VuBdzPAYgWnTpg21t7cXt7S0FFVXVyfXrl07dvny5R3V1dWJtWvXVp199tldP/vZzybs/zPPPfdcTWtra9GYMWOSTzzxxLhwOLwlE1kKvfCgcfOwhLUDb9n++Abbu3eHHZgyJHuMpDNc58LIJANFxbvGTJy5a8zEmS9MCg57n3EDnS0zeppbZqVOv9s/o7tZk/vaymsGu8eVJQenGGlcjmPnuz2uA2TCoU4jm2m1tbWJk046qWvZsmXHLVu2bO8555zTft555x1bUlJizzzzzL0333zzdkkqKyuzK1asCA4NDZlwOLw5lxmBPLLbdQAgk5JS2W9t9/LfJro1y5T9fmmgurdaRSFjDLNQMRxPFB6ZOI3skSgtLbX19fU7Vq5ceWxtbe3gvHnz+iTp29/+9pY1a9bMNca8Z9NSSQoGg91XXHHF/JaWltJVq1btzsRyFonCA3mm1Q5u2pDs3b7V9lX0pJapnOw6E3KnvbS6tr20uvblcfOHvX3MUF/ntN7WXbO6d+1JL5tJTunbXTrunWUzdoqRDjllzkM48jpCd99993sKjOuvv37ngfe5+OKLd992221v5S4VkJd4nYFnvWX7T3or0a8aFW07I1CzaYYpPdkYM/bQPwmfSNQ3BLtdhyhUq1evbl69enXzgdevX78+vt+32/b9YerUqYO33377pkznKPTCwxONm5/12mTb67Z3w+vJ3kRbapnKfKUuwB/pLS6vfqN6ZvUb1TOHvb0omRic0te2b9lMz6zuXYPTe1uKJvTvrawc6puYXjZTluPY2eSJGR4A8hqFBzyvQ4mZjyX3zCyW6T41ULXuOFMxq8iY4Tctg59wcN0DCr3w+KPGCPktae1geplK+9t2YHJ6mcpS17ngDYlAUcnbFbWz366onf3r2uP/+A7W2gkDHc0zelpaZnfv7JjTtaN/Rk9LoLZ/T3n1YM/40uTgZCMV0pEdPohkyaOPPrrBdQYgT/A6A98Ykq38dbLzrF+r08435c+fHqhWhQKLDbtW+1Wr6wB+ceWVV+5WlpZQFnThUd8Q7IxG4p2Sql1nwftrs4ObNyR739pi+8Z0K3mspJNcZ4JPGWPaysZObisbO/ml8cPvd1sx1Lt3ek/Lrtndu9rndO/ondndrCl9baXjBjpryhMDtUa21kj5MvBpcR0AgOdReMCPzCbbd9qmRJ8mqPiNZUU12yarZLExptJ1MOTUtkPfBfmuoAuPtG2SjnUdAu/qs8k9r9veV19P9iZ2a2ieleYpdQHyXk/xmLGv18we+3rN7GFvL04O9U/t3b1zZk/z7jldO7pnp06/WzxhYG9VxVDfxCKbnGqkkhzF3Z6j3wPAvyg84GttGjrq4UTbUaUye08PVD+50IyZHzBm+LW18BrGWR7ghcJjuyg8nEpaO7jN9r+ywfbuedsO1A7KLhLLVOBRQ4Hism2VU+Zsq5wy59naE/7odmOTyYn9e3fM6GlundO9s3N2186BGb0tRZP62surh3rGlySHphqpKgNRds969Pm+DDwOABwMhQcgaUB27K+SHSueUkdikRnz7OJA9ZhyE2DWsrdReHiAFwoPpho5sMcObtmQ7H1zs+0rTy9TOdF1JiAfWBMItJaPn9ZaPn7a7yccM+x9qge796SXzXTM7t7ZO7Nnl6b0tpWNHeyqKUsMTg7ITjqMX8VrH4BcoPAA9mOlolds75JXEr2arJJXlxXVtE5QccgY46VN0ZHCWMsDvFB40LzlQJ9Ntm+yva9uTPYN7tbgPCvNVeoC4Ah1llSO3zC2cvyGsXOHvb00MdA7tXf3zlk9u9rmdO3smdW9MzGtd3fJ+IGO6opE/4SATU4zkmdOl5q46qOLM/l4Rf/80CHPNX/++ecvevzxx1/N5O8FvKipsa4vFI51KTMz0wBPadbgov9M7NYYBVqWBKpfnmfKgwFjJrvOhYzxzOdMP4+1KDwwrKS1Q9vtwCsbbE/bdjswKb1MZYnrXIAfDBSVjnmzatq8N6umzXtqmGFTwCYT4/s7NjyW+2iekQ9vwEABeUPM5ATeV6+StU8k9579pPYOnGAqnzo5UDmhxARYcl/4mOExCvky1vJC4cE/xAxpt0Nvbkj2bNls+8q7lFwk6Y83KADgXNIEinaXj3vbdY5CNn/+/FM2bdr0W0m69dZbpz7yyCMTjDFavnz53ltuuWX7qlWrjlm0aFHPb37zm6q+vr7Abbfdtvk73/nOtE2bNo350Ic+1HbTTTe9vWnTptJLL7306EWLFvVs2LCh4qijjuqNRqNbqqqqkq6fH5Bhr4nCAzikpFT6e9u97PeJbs00pS8uDdR016jodGNMketsGBEOrI9Cvoy1Atl7ijmz1XWAQtVvk3tfSfY89+DQ7vU/HNr55v2J1tkv2Z6zupQ8XVKN63wADmqT6wBe8PDDD9fEYrFxjz322Kvr16+PX3vttTv33VZSUpJ84oknXrn44otbrr766gXhcPjNdevWvfzQQw9NamlpKZKkt956q/xzn/tc8zPPPPNyZWVl8o477qh192yArNnoOgBQaLbZgRP/PdG69KeJ1l1vJvvXWmv3uM6EI9Inaech74VDcj3W8sIMj9ckJeWN8iarktYm3rYDr2ywvbu32/6JA7LHSvqA61wARoTCIwPWrVtXc9FFF7XuO1IwadKkxL7bVq5c2S5Jxx13XO9RRx3VO3PmzEFJmj59ev/WrVtLx48fn6itrR1YsWJFtyR94hOf2H3nnXdOlrTLwVMBsuk11wGAQtWlxPTHk3umF0s9Jweq1h9vKqcXGzPfdS4c0mv1DUFmbGaA67FWwRce9Q3BvmgkvkXSUa6z5KO9duitDcneLZttX2mnEoskHe86E4CMeMN1AK8rKyuzkhQIBFRSUmL3XR8IBDQ0NGQkyRjznp858HvAIyg8gFEakiqeT3ad+by6NM+Uv/CBQHWyUoHTDG8c+eoV1wH8IBdjLa/Mioi7DpAvBmyy49Vkz68fGtq97odDO9/890TrrBdt95mdSnxA0ljX+QBkxM6mxroe1yG8YMWKFR0PPPDApK6uroAktba2HtE66+bm5tL169dXStIDDzww4bTTTuvKRk7AMQoPIIM2277FP0m0hP4j0bp1hx140lrLe0f+ofDIENdjrYKf4ZH2iqSPuA7hQtLaxA478OoG29u6zfZPSC9TOd11LgBZ5anlLIdzarNs+chHPtLx0ksvVZx33nnHlpSU2DPPPHPvzTfffNiblM2aNavvhz/84eTrrruuYt68eX3XXHNNSzbzAi40Nda1hsKxNkkTXGcBvKRdibmPJtrmlsh0hALVTy4yY44KGDPLdS5IkvLiDCOZ4uexlrHWHvpeeS4aiX9W0l2uc+RKhx3atiHZu/kN21eSXqYyznUmADn1g6bGunrXIUYqHo/fM27cuII/Xd+mTZtKL7/88qOfeuqplw92v/b29leCweBncpULyIZQOLZe0nLXOQCPSx5jxjSdFqguHWMCp7gO43Mn1TcEX3QdYqQYa73LSzM8PGvAJjs3275XXkv29rVocHZSmitpputcAJxpch0AgO+8KAoPINsCG2zvBzYkelWrkteWFdXsmqjikDGm3HUwn0mKpXyeQeGRh6y1yfQylZZttn98P8tUALzXr10HgDR//vyBQx1xADykYI90AoWoRYMLf57YvbBcgd1LAtXPHWXKjwkYM9V1Lp/YUt8Q7HMdApkZa3mi8KhvCHZEI/FtKuBZD5126O0Nyd433rB9RR2pZSpB15kA5KUeSYX+Idtvp3nz2/OFN73kOgDgR31KTlyb3LviSe0dPN5UPH1KoGpsqQkc5zqXx3nh9c5vY4/3fb6eKDzSXlABFR6DNtm92fbFX0v29jZrcFZSmidpuutcAPLeb5oa6xKHvlv+SiQSrwwODp5QUlLi+TfjwcHBQCKR8NQsRPjWS5KsJE6hCThgpZKXbM8ZLyV6NM2UvrwsULN3rIpCxpgS19k86HnXAUaLsda7vFR4PCfpY65DvB9rrd2pwVc3JHua37L949LLVEKucwEoOAW/nGVgYOBbHR0dKioqOlbeOT36cJKJROKVgYGBb7kOAoxWU2NdZygc26LUARoADu2wA8fdn2hVpQI7zgjUbJhtyk4wxkx0nctDnJ3RJFMYa73LS4XHs64DHKjTJna8luzZ9IbtK9qrxEJJx6YvADBSBb9h6eLFi5OSbnWdA8ARe1EUHkDe6FZy2v8k26cVSX0nBarWn2gqpxYbc7TrXB5Q8DM8GGu9y0uFR5NSa3ecNViD1vZssX3x12xvT7MdmJGQ5kua5ioPAE8q+BkeAArWr5XHs2kBv0pI5b9Jdp35G3Vpjin77ZJAzUCVAiFjjJeP7GfLlvqGYIvrEMgczxQe9Q3Brmgk/rKkE3L1O621dpcGN2xI9u56y/aN7ZMNSjotV78fgO+0NjXWveE6BADfirkOAODgttr+U7YmWjRWRW8uC9RsnmZKTzHG1LjOVUDybtUARsczhUfac8py4dFlEzs32t5Nm5K9pj21TGVR+gIA2VbwUywBFLQmSR2S+PAE5Lm9Ssz+RXLP7BKZzsWBqiePNRVzioyZ6zpXAXjGdQBkltcKj2clfT6TDzhkbW96mUrXrtQylQWSOAc2ABdYzgLAmabGukQoHFsn6SOuswA4PIOy1c8mO1c8q057tClvCgWqAxWmaLHrXHmMGR4e47XC47nRPoC11jZrcOOGZO+ON21/TZ+SQUm8KADIBwW/YSmAghcThQdQiMxG2xfamOjTRBW/vqyoZketSk4zxoxxHSyPdEn6resQyCyvFR5xSW2SJhzJD3XbRPNG27vx9WSfadfQ0ZIWpi8AkE+Y4QHAtV+6DgBgdHZraMFDibYFZTJ7PhCofm6BGbMwYMx017nywLr6huCg6xDILE8VHvUNwWQ0El8r6aKD3W/I2r6tqWUqnTvtwPSEdLSkyTkJCQAjs6Wpsa7ZdQgAvveSpBZJta6DABidftnx65IdZ69Xx9CxpuKZxYGqyjITONF1Lof+13UAZJ6nCo+0/9UwhUezHdi4Idn79pu2v7o3tUzl1NxHA4AR+y/XAQCgqbHOhsKxJyRd7DoLgMywUnHc9iyNJ3o0VSXxM4pq9oxXccgYU+o6W44xg82DvFh4/FKSemyiJb1MxbZraIFNzeI42nE2ABiph10HAIC0mCg8AE/aqcHgA4ndqlCgeWmgJj7XlB1njPHDjK5dSs1gg8d4rvCobwi+tjz8xJP9smeJ6ZYAvKFbqQ8YAJAPOAoKeFyPkpN/mWyfHJD6TzSVvzopUDmpxAQWuc6VRbH6hqB1HQKZF3AdIBv6ZV+RZFznAIAM+Z+mxrp+1yEAQJKaGutel/Sm6xwAsi8plf3Odi//UaJ50X8n9rzYYYeetdYmXOfKAopcj/Jk4SHpF64DAEAGPeQ6AAAcgFlngM+8ZftP/FmidcnPEq07tiX7n7TW7nWdKYPYsNSjvFp4/FISR0MBeEFS0qOuQwDAASg8AJ/qVGLmY8k9K36UaC5+Mdm9LmHtG64zjdIf6huCW12HQHZ4svBoaqzrkfSk6xwAkAG/5nS0APIQ078BnxuSrfx1svOsuxK75j2RaH+h2yaet9YW4j4YD7gOgOzxZOGRxrIWAF7A2VkA5J2mo+dL+QAAF5hJREFUxrq3Jf3BdQ4AecFssn2L70u0nPZAYveWXXZgnbW223WoI/CfrgMgeyg8ACC/UXgAyFf3uQ4AIL/s0dC8hxNtZ92TaB56JdnzZNLaba4zHcIb9Q3B37kOgezxbOHR1Fi3Uf+/vXuPurSs6z/+5pAi/lJsVSqFWktribkycyvqz7BLO9gPzIzCBFPLAAkPqBci5SEVT1uBkhJBf4MchgEkQMRQYAPCAHKlECAwgGDoiHOeYZgDM89+dn/cmxhwzvPs/b33fb9faz1rWDDMvP1Dnnk+z31fF9wc3SFJO+CHJSfvhJdUV2cCk/j4uqQRW8fgyXOnH9h3Vn/B06/pr7h+7WD6v6KbNsGnOxqusYPH0GnRAZK0A3y6Q1JtlZz+G7g6ukNSfQ1glzsGa/Y5o7/wt782tWTeksH6awaDQZ0ul3DwaLimDx5nAk28J1pSOzh4SKq7M6IDJE2Ghaz/zfP7S/7v7P6iB34wvebK6cFgQXDST4Frgxs0Yo0ePEpOPwW+Gd0hSdthJd42Jan+zgXq9N1aSTW3hulfumJ6xStn9Rf8wg39lXPXDaZvC0o5/9Aj9/a1vIZr9OAx5GstkibROSWnddERkrQ5JaflwNejOyRNngH83M2DVS8/rb9w7//oL71lxWDqusFgMDXGhNPH+HspSBsGjwuB5dERkrSNTo4OkKSt5GstknbI/MG655/bX/zSs/uLF943vfbKwWCwbMS/5bxDj9z7uhH/HqqBxg8eJae1VI9bStKkuKnkdEN0hCRtpW8AS6MjJE2+B+nv+a3p5a88tb9gt5umH7x6ajC4e0S/1akj+nVVM40fPIa+Eh0gSdvglOgASdpaw9fv/OaSpBnThyf85/SDrzi1v+DZl/eXfe/BQf+GwWAwU+dtTOPrLK3RisGj5DQX+EF0hyRthdX4eLikyeN/tySNxL2Dh144p7/oxef1F993/2DdVYPBYOUO/pKXHnrk3vNnJE6114rBY8jDSyVNgrNLTg9ER0jSNpoL3BsdIam5ltN/5sX9pfue1l84uG161bf7g8F92/lLnTqTXaq3Ng0epwNeOySp7nydRdLEKTkNgDOjOyQ133oGT7p2euXvzeov+NVv91fcsGbQv3Eb/vXlwAWjalP9tGbwKDndC1wd3SFJm3FLyckTwyVNKl9rkTROO985WPPiM/uLfueCqcV3LRqsv3owGKzdwr9zxqFH7r2ln6MGac3gMeThpZLqzKc7JE2sktM84JroDknts5ip51zYX/KKM/sLV981veaq6cHg/o38tAFw4rjbFKttg8fZwJLoCEnaiDV4YrikyfeZ6ABJ7bWWwS9cNb1i31n9Bb90ff+Ba9cNpm/d4B9fduiRe88Li1OIVg0eJadVwOejOyRpI84tOS2PjpCkHfR14NYt/ixJGqEB7HrrYPXLTusv/K2L+0u/v2wwNbc/GPh1YAu1avAY+jywKjpCkh7j5OgASdpRw8NLfcpDUm3cP1j3vPP6i586q7/g4ugWjV/rBo+S01L8wkJSvXy/5DQ3OkKSZshZwPZeFylJo3B8yWk6OkLj17rBY+hzwLroCEka+kR0gCTNlJLTFNWftSSpDhYDs6IjFKOVg0fJaT7eFS+pHu4A5kRHSNIM+xLVFxmSFO0LJac10RGK0crBY+jTgI81SYr2UR+xlNQ0JafVeP2jpHhr8b9FrdbawWN4V/wF0R2SWu12quuyJamJPCheUrTTS04LoyMUp7WDx9AnowMktdrHfLpDUlMND4o/JbpDUmtNA8dFRyhWqwePktN/ApdHd0hqJZ/ukNQGnwPWR0dIaqWvlJzuiI5QrFYPHkM+5SEpgk93SGq8ktOPgdnRHZJaZw3wwegIxWv94FFyuhwo0R2SWsWnOyS1yaeBQXSEpFY5fngzp1qu9YPH0LHRAZJaxZtZJLVGyel2PChe0vgsohpaJQcPgJLThcAV0R2SWuE24JzoCEkas2OAqegISa3wsZLTA9ERqgcHj0e8G+hHR0hqPM/ukNQ6w4MDvxDdIanx7gJOio5QfTh4DJWcbgZOju6Q1Gg+3SGpzT4CLIuOkNRox5ScvBlK/8vB49E+iJ+IJY3Oe326Q1JblZyWAh+L7pDUWNeXnL4aHaF6cfDYQMlpCfDh6A5JjXReyemS6AhJCnYi1SPnkjTT3hcdoPpx8PhZXwC+Hx0hqVEepDonSJJabfio+VHRHZIa54KS09zoCNWPg8djlJym8AsTSTPrwyWnH0dHSFIdlJwuAC6N7pDUGFPA0dERqicHj40oOV0GXBjdIakRbgb+JTpCkmrmCGBddISkRjil5DQvOkL15OCxae8BHoqOkDTRBsDbh0+OSZKGSk53Ap+N7pA08VZQ3QAlbZSDxyaUnO4Bjo/ukDTRvlxyujY6QpJq6ljgv6MjJE20d5ecFkZHqL4cPDbvWOAn0RGSJtJi4P3REZJUVyWn1cC7ojskTayLSk6nRkeo3hw8NqPk9CBebyRp+xxVcloaHSFJdVZyuhD4enSHpImzFDg0OkL15+CxBSWns4BzojskTZRrgFOjIyRpQrwDWBkdIWmivKPkdH90hOrPwWPrHAbMj46QNBGmqA4qHUSHSNIkKDn9kOrWFknaGv9ecpodHaHJ4OCxFUpOy4A3U924IEmbc1zJ6dboCEmaJCWn04A50R2Sam8R1Tejpa3i4LGVSk6XA/8c3SGp1m4EPhQdIUkT6jC8tUXS5h1ecloUHaHJ4eCxbY4G/M6tpI15EDiw5PRQdIgkTaKS0wrgIKAf3SKpls4uOX01OkKTxcFjGwy/kDkI8AsaSY91WMnprugISZpkJae5wLHRHZJqZwHw99ERmjwOHtuo5HQz8I/RHZJqZVbJ6czoCElqiI8C10VHSKqVQ0pOS6IjNHkcPLbPccAV0RGSauF2vF1AkmZMyalP9UTtA9Etkmrh9JLT16IjNJkcPLZDyWma6taW5dEtkkKtpTq3Y3V0iCQ1ScnpXuDw6A5J4eYD74yO0ORy8NhOJacf4XtkUtu9u+R0S3SEJDXR8FVBXxeU2ush4M9LTn6TWdvNwWMHlJxmA7OjOySFOLfk9MXoCElquMOBe6MjJIU4pOT0negITTYHjx33d8D3oiMkjdW9VP/flySNUMnpAbyqVmqj40pOp0VHaPI5eOyg4bv7rwXuj26RNBbrgTeUnFZEh0hSG5ScrgP+IbpD0th8EzgqOkLN4OAxA0pO84E/BdZEt0gauWNKTjdER0hSm5ScPg18ObpD0sjdSfWNJZ/q0oxw8JghJacCvAUYBKdIGp1ZJafPRkdIUksdBlwaHSFpZB4A/tRDSjWTHDxmUMnpHOCfojskjcS3gEOiIySprUpOU8ABwK3RLZJm3DTwVyWnO6JD1Cw7DQY+kDDTOt3eWcAbojskzZibgN8rOa2MDpGktut0e88ArgeeHt0iacYcPXx1TZpRPuExGm8FfMdfaob7gD9x7JCkeig53QfsB6yKbpE0I2Y7dmhUHDxGoOS0Fngd8OPoFkk7ZDnwmpKTtzBJUo2UnL4H/BXVY/CSJtd3gbdFR6i5HDxGZPgF0mvxuw/SpFoHvK7kdFt0iCTpZ5WcLgLeFd0habv9lOrPWt50qZFx8BihktONwJvw5hZp0gyAN5ecrooOkSRtWsnpROCE6A5J22wl8NqSk0/Ea6QcPEas5HQ+cGR0h6RtcnTJaU50hCRpq7wXuCA6QtJWWwPsV3Iq0SFqPm9pGZNOt/ce4HPRHZK26F9LTkdER0iStl6n29sduBLoBKdI2rx1VE92fDM6RO3gEx5jUnI6DsjRHZI26wLgndERkqRtU3JaDewP3BXdImmTpoADHTs0Tg4eY1Ry+ixwVHSHpI26AnhjyckT/yVpApWcFgD7Ah42LdXPNPCWkpOvn2msHDzGrOTUBd4f3SHpUS4F/p+nhEvSZBvekvdK4KbgFEmPGACHlZzOjA5R+zh4BCg5fQb4QHSHJAAuoXqX1LFDkhqg5LQISMAN0S2SGACHlJxOiQ5ROzl4BCk5fQo4JrpDarmLqe5/XxsdIkmaOSWnZcCrgaujW6QWmwb+tuT0pegQtZeDR6CS0yeBf4jukFrqQuD1JaeHokMkSTOv5LQS+GPg8ugWqYWmgbeWnGZFh6jdHDyClZw+AXwwukNqmbOBvyg5rYsOkSSNzvD2lv2onuiTNB594K9LTqdFh0gOHjVQcvo48KHoDqklTqK6jWV9dIgkafSGry3+GXBedIvUAuuBgz2gVHWx02AwiG7QUKfbewdwPLBLdIvUUJ8oOfkamSS1UKfb2wX4CnBQdIvUUMuAA0pOvegQ6WEOHjXT6fb+BJgD/Hx0i9QgA+B9JafjokMkSXE63d7OwMnA30a3SA1zN7BfyWledIi0IQePGup0e78FfB14ZnSL1AB94O88NEuSBNDp9nYCTgDeGd0iNcRVVAfBL40OkR7LwaOmOt3eU4ELgH2iW6QJtgQ4sOTkCf2SpEcZvkp8HLBrdIs0wU4FDvUgeNWVg0eNdbq93YBZwBuiW6QJ9F/A60pOP4wOkSTVU6fbezVwDvCU6BZpwgyAD5ScPh0dIm2Ot7TU2PBU8TcCH41ukSbMWcDLHDskSZtTcroMeAlwR3SLNEFWUx1O6tih2vMJjwnR6fbeCPx/4PHRLVKN9YGjS06fjQ6RJE2OTrf3ZKqx/DXRLVLN/QR4bcnpu9Eh0tZw8JggnW7vZcD5wC9Ht0g1tJTqvI7LokMkSZNneIPLp4Ac3SLV1I3A/iWn+dEh0tZy8JgwnW7vWVQ3uDwvOEWqk5upzuu4NzpEkjTZOt3e66nOUHtSdItUIxcCB5WcVkWHSNvCMzwmzPBMgpcCZwSnSHVxNvBSxw5J0kwoOf078CLglugWqQamgI9QXTvr2KGJ4xMeE6zT7b0J+Dfg/0S3SAH6VKeDd6NDJEnN0+n2dge+CBwc3SIFuQs4uOR0Q3SItL0cPCZcp9t7DjAHeGF0izRGC4A3lZwujQ6RJDVbp9t7O3AC8LjoFmmMTgbe41MdmnQOHg3Q6fYeB3wSOBLYKThHGrXZwDtKTkujQyRJ7dDp9p5Pda7H70a3SCO2EHhbyemi6BBpJjh4NEin2/tDqqtrfyW6RRqBhcBhJafzo0MkSe3T6fZ2pbrB5cPA44NzpFG4iGrsWBgdIs0UB4+G6XR7ewAnAgdFt0gzaA5wRMlpSXSIJKndOt3ec6m+wbRPdIs0Q1YBR5acTokOkWaag0dDdbq9A4AvAL8Y3SLtgEXA20tO50WHSJL0sE63tzPVq8QfA54QnCPtiO9QHUx6d3SINAoOHg3W6faeCpwC7B/dIm2Hc4HDS06Lo0MkSdqY4eHxXwZeEd0ibaMp4OPAsSWnqegYaVQcPFqg0+0dCHSBvaJbpK2wCPj7ktO50SGSJG1Jp9vbCTiC6gD5JwbnSFvjDuDNXjerNnDwaInhXfJHUx22tVtwjrQpX6V6qmNRdIgkSdui0+39GvAlIEW3SJuwDPgn4N9KTuujY6RxcPBomU639yzgc8Drg1OkDd0NHOUNLJKkSTZ82uMQ4DPAk4JzpIdNAV8EPuwB8GobB4+W6nR7rwL+GXhedItabTHwUeAkv9MgSWqKTre3J9X1tX8D7Bqco3b7JvCektNt0SFSBAePFhveJ3841aNtewTnqF3WAicAnyo5rYiOkSRpFDrd3m8AxwIHRLeode4A3lty+kZ0iBTJwUN0ur1fpPpk/DZg5+AcNds0cAbwjyWnH0XHSJI0Dp1u70XAp4BXRbeo8ZYBH6E6p8PbV9R6Dh76X51u73eAzwMvj25RI10G5JLTTdEhkiRF6HR7f0A1fLwwukWNMwWcRHVOx9LoGKkuHDz0Mzrd3v7AMcA+0S1qhFuoDiS9JDpEkqRow4NN/xL4OPDs4Bw1wyVU53TcHh0i1Y2Dhzap0+29EvgA8IfBKZpM84EPAaeWnKajYyRJqpPhWWpvozrc9GnBOZpMlwCfLDl9OzpEqisHD21Rp9v7Xarh48/wjA9t2c3A8cDsktO66BhJkuqs0+3tDrwbOAp4cnCO6m8a+CrVwe83RsdIdefgoa3W6fZ+E3g/cDDwc8E5qpcB8A3g+JLT5dExkiRNmk6392TgrVQ36D0nOEf1sw44HfhMyenO6BhpUjh4aJt1ur29gPdRPYa5e3COYq0BvgKcUHKaFx0jSdKkG57x8UfAEcBr8OnatlsKnAycWHKaHx0jTRoHD2234XW276L6hLxHcI7G637gX4GTSk5LomMkSWqiTrf361RPfPwN8JTgHI3XbcC/AKeXnFZHx0iTysFDO6zT7T0ReD3wFuD3gZ1CgzRKN1GdzzHH8zkkSRqPTrf3BOCNVN9kekFwjkZnQHUQ6Qklp29Fx0hN4OChGdXp9p4BvAl4M75/2hSrga8Bp5ScetExkiS1WafbeznV8PHneKZaU8wDzqI68P2u6BipSRw8NDKdbu9lVMPHgXjq+KRZB/wHMAe4qOS0KrhHkiRtoNPtPQ04lOp1l2cE52jb/Yjqz1lneduKNDoOHhq5Tre3G/A6qvHjD4BdYou0CX3gcqpPvueXnJYH90iSpC0YHnL6EuAvhh97xRZpMxYD5wKzgbklJ78Qk0bMwUNj1en29qS61vZg4PnBOareFb2GauQ4t+S0KLhHkiRtpw3Gj78EDsDxow5WAudTvbJyWclpKrhHahUHD4XpdHu/SnXt2h8Br8bTx8fpu1SfeM8uOf04OkaSJM2s4fjxImD/4YeHnY7PWuAbVE9yXFxyWhvcI7WWg4dqodPt7QJ0eGQAeTG++jKT7gauAK4Eriw5/SQ2R5IkjVOn29sL2I9q/EjA42OLGmUdcAPVn7OuAq71KlmpHhw8VEudbu8pwKt4ZADxkcxt8wOqT7pXUA0c82NzJElSXXS6vScC+wL7DD9ejAfMb4uHgO9QjRtXAteVnNaEFknaKAcPTYROt/dcquHjJVSPZP4GsHNoVL3cy6Of4PhRbI4kSZoUw9dfnssjA8g+wPPwz1oPWwtczyMDx/W+piJNBgcPTaROt7c71aGnL9jg4/nAEyO7xqAP3APcDtwGfB/4dsnpvtAqSZLUKJ1u7+epXjd+eAB5CfDLoVHjsRa4C5gH3EI1clxfcnootErSdnHwUGN0ur2dgWfz6BHkBcDTI7u200PAnTwybNw+/LjTT7iSJClCp9v7darXX54NPGuDj72Ax0V1baf5VKPGPOCODf76vpLTdGSYpJnj4KHG63R7ewC/QjV87LmJH58OPGGMWauBhcCCDT7u4ZFx456SU3+MPZIkSdtl+E2nPYFn8ugh5OGPZzD+QWQlsHj4cQ+PHjfuLDk9OOYeSQEcPKSh4TCy4QiyB9VNMbsOf9zUXz/2760FVmziYzGw0E+ykiSpLYZnhOwJPI3qG0y7PeZjY3/vsf+sD6wCHnzMj6uoxo0lVH/OWgIsKTmtH8//Okl15uAhSZIkSZIax5OXJUmSJElS4zh4SJIkSZKkxnHwkCRJkiRJjePgIUmSJEmSGsfBQ5IkSZIkNY6DhyRJkiRJahwHD0mSJEmS1DgOHpIkSZIkqXEcPCRJkiRJUuM4eEiSJEmSpMZx8JAkSZIkSY3j4CFJkiRJkhrHwUOSJEmSJDWOg4ckSZIkSWocBw9JkiRJktQ4Dh6SJEmSJKlxHDwkSZIkSVLjOHhIkiRJkqTGcfCQJEmSJEmN4+AhSZIkSZIax8FDkiRJkiQ1joOHJEmSJElqHAcPSZIkSZLUOA4ekiRJkiSpcRw8JEmSJElS4zh4SJIkSZKkxnHwkCRJkiRJjePgIUmSJEmSGsfBQ5IkSZIkNY6DhyRJkiRJahwHD0mSJEmS1DgOHpIkSZIkqXEcPCRJkiRJUuM4eEiSJEmSpMZx8JAkSZIkSY3j4CFJkiRJkhrHwUOSJEmSJDWOg4ckSZIkSWocBw9JkiRJktQ4Dh6SJEmSJKlxHDwkSZIkSVLjOHhIkiRJkqTGcfCQJEmSJEmN4+AhSZIkSZIax8FDkiRJkiQ1joOHJEmSJElqnP8B5w2Vq1rlSgwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's see the target classses using another bar chart\n",
        "\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.countplot(y='label' , data=df, order = df['label'].value_counts().index)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "cf79PipYzsZH",
        "outputId": "ad811e0a-d4d0-48b2-f80c-690c7a2785a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4a9404670>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2 Check the corrlelation between variables"
      ],
      "metadata": {
        "id": "bQ-8zkgLymYp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the correlation matrix between among the attributes\n",
        "\n",
        "corr = df.corr()\n",
        "#sns.heatmap(corr, annot=True, cmap='coolwarm')\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(16,16))         # Sample figsize in inches\n",
        "sns.heatmap(corr, annot = True, cmap='coolwarm', linewidths=.5, ax=ax)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "D2Px8iyh4vaf",
        "outputId": "70d73e0a-2d88-4ba9-8bd8-4bd75a394fbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4a57fe2e0>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x1152 with 2 Axes>"
            ],
            "image/png": 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iotnoSEFDWiM2AAe4aZbF7XMsxvav2wno2dltuOWE6OTHRVPnuMZF10wTH22QGzyuNeKLpvJzgPwi6iybEAMpSQYZ9UzjOxutlQ/jomBID4PP05o2ElkZT5RRo3OYV+jUPb5RteIprDqOsZE144mNpFFuvnRYONniznke5k+yGs2X57rcVje8j8m2Aw0f99bOe40Z3c9k75GmH+uEGINTBVXpTxfYJMTW/M0JsQanq6cptEkIdgQ6J5r07Wbxg+uiuPvqKHp0rtu0GtXPIuNkoLKD3VRxUZBXXGtf1O68VNvXjuNecIkKb9r6h/Y0mnwLw5nIP32c+A5V5964Dl0oOH28xbdTn/Z8PmvN+Bqr5zwW3DnP4va5VuXFvaaIj3aneVfGUUi9sVY/D+YWurf31HbBAJPdGWdYAJroRE4uXZITK//unJTIiVM1B252HUjnWPYppo0ZVuPzAb26s3rjdvyBAJknstl5IIPj2adaJU45P6jz/N/jc+BrXq/3l8AIn8/XxLE1yTt1kmPpuxnYwlO2W8Izi04wfGA0f/lZH4YPiCbrVDm23fINoTMWDME0ISXZ4OVVAV58z89FIy2S48A0DVKSDD7fbfP4237K/Q4XDj931ZFpuPeT/WePzVPLA5T7Yeqw9lMdPrcywFPLA/zzgwDjBpr07Fzz++G9TLYfbJ1GSFOFecA73cOKDQHKzuDe0nMqmA/njrd4d6NNW5cMJ0QApuGWiw27bZ5c5paLaeewXIQqtxUs0x01/uJQ2+bB+lw43MS2abRj39JME6Ij4M//LuatNaXcNqfmVZKuSSbzp0bgW1XPkHkba63O83mlnZ/PmlLP/fk1P08uC/DaxwHmjLfocHaT1M7ajFEWtgNb9rVNnWHbNn96eTE/uPHKOt8tuGginZMSuOXnD/HQi68zckBvrPN0yra0DN3z/F/C5/Ot9nq904HLgee8Xu+ffD7fC7XTeb3eO4E7AW49xzG2hoQOXTidc6zy79ycYyR06NzIEnVt/WwFw8ZdguUJC524CebN6MBl09wrpHsOFtMxKQz2uZeRkxM9ZJ9q+n1JObl+fvt4JgCREQZTxsZRWNy0k9OEQSZjB7gniCPZNa8U177qDXWvjsdHQ34wTWGxO82sIPj/iiljeUVQXOpQ7q+aptUlyeDwcYe8Iiqnun5xqOHGRn6xQ0Lt7RbXjC2/yCEhBvKL3Xu+IsOguNRNFx9DnWXzitzYMrPdz3cets+q89wasbnrdT8rKoW0dHfK6uET7neG4U77e2p56OOcX0Sd45pf65bLvCL3N+QXOTXjKyL429ztxkVTuaxpgHe6xbaDNrvSm9fwPhf5sFuywbXT3SHc6AgY0N3CdgL1xj5+oMnY/lXxJMRUTWmNjzHqHt/iWvHEVB3HgpJa8YToO+UVBctFcCR/xyGHqbXKRVuW2+x8d7n+3Q2O5jQ+NbS18l5jRvU1GNDd4IX3Qs8FnTYijMnD3CbQ4RM2HWJNDuCWqcRYk9yCmvsxt8AhMbbqWCTGmJUjcqcLHLbuc7d5+LjbeYmJdPdnQozB1+dF8vK7JWTnnXlZyS8meFtEtX1R63bsin1dUcdEhEFxWd111dY5EQwTjrXCIFtcYhfyTlWde/NPHSc2sUvLbyiovZ/P2rqeq8gzpwrg4DGHlCSDUwX158eJQ0zGD3TXk5HlBGdYuGnjY6g31oRqvychpubI+pj+JoN6mDyzvPWusnZOSuB49unKv0/knKZzh6ob/YtKStmXfpS77n8UgOzcPP7noaf40w/vYGjfnvzw5qsr0379l3+mZ9czayd+mehVVc2nSyvnt3wgDsDr9fYCjvt8vqeAfwBj61vA5/M96fP5xvl8vnHnLszWk9p3ONnHDpFzIgO/v4wt65YzZOzMM1rH5rVLGT15XovFtOzDU9xz/wHuuf8A6zYXMGuSW8EP6hNJUbFd773NDYmPsTCC9eB1czry3prTjS9Qzfo09yr542/72XnYZnQ/tzpI7WhQUu5UTgurUFAMpeUOqR3dDY7uZ1Z2PtIyqpZ3P3cbobvSbXp2NjAN9wEq3TsaZOU6FJS4U2CT4911900xOJlb/4k8MxuS4gwSY9wr/8N6meyu9XCbtEyHkX3d7Q/taXDguPv97gyHYb1MLBMSY9z1ZGa7jY28IipH0/p0NTl5Frfmt0ZsYZb7IC5w/903xeBEtcPat6tBdl7dBnT98TkkV4+vt0larWlzuzOcyumH1eNLy7AZ1rsqvuQ4o7JTt2CyRVau0+BTls/EuciHf3nDz19ed//bcchh6Wf1d5wBPt9t88QyP08s87Mrw2ZkH3d93TsalJY1HE/3YDwj+1TFszvDZlTw2I/qa5KW3vj+KixxpytXlIs+KW55Odf7q6FyW2FEb5NtBxr/La2V9xrSL8Vg6lCLf30YaPQe3wqfbCvnD/8q5g//Kmbbfj/jh7iFrlcXk+Iyp94OQkmZQ68u7v4aP8TDtv1uXb1tv58BqW5no1Oie19zYYk7xffOBZG8vbaUA0fPrqwcyYEOce4tEqbp7qc9mTVj23PEYURvdz8OSTU4dLxpnfShPQ12hHhuwtlK6TWCUycOcjornYC/jJ0bltJ/5KxW2Ra0//NZW9ZzkeFU3msfHeE+76Oh8y3AZzttHn2znEffLGfnIZsx/d283aOTQWlZPRdvit1bBXp0cmMd09+qfJDggO4G00dYvPheeYvd31yfoX17kn7sJJknsin3+1m5biPTLxhe+X1sdBTvP/EASx7+BUse/gXD+/eu7DiXlJZRXOJe2Vy3bReWadV50JhIdRp5Po/5fL5sr9e7xuv1bgdigEKv11sOFAC3nIsYRr/4EMkXTSC8YwdmHfiIPfc9Qvqzi87FpgGwLA8Lb/0ZT//+DmzbZvxFV9E1dQArFz1Cap9hDL1gFun7tvHCX75HcVEeOzd9wLuvPcoPH1wCQM7JTHJzjtFn8PhWiW/D9gLGjYjhyfv7ua+qev5o5XcP/7wP99x/AIDbru7MRRPiiQg3ePZ3/Vn5yWleeTuL4YOiufXKzjg4fLGnmMdeOdbQphq1J9NhYHeHe67yuK/k+bTqLPfNKzyVT+lc+pnNlVOsylfyVDTiPt5u451uMba/h9OFDv/+yF0+K9d9YMi35ntwHPd1NRUdwWXrA1wzzcKy3AcPVd9mdY4DyzfY3DjLvVCweZ/NyVyYMdLkSLbD7kyHTXsdrppicPcCi+JSeG2Nu66TubDjkM23rnCnjC3fYFdOnV2+IcBVU91XjpwqcHhrnXuyH5RqMHe8SXQEfHWGxfFTDi9/UH/DtzVii4lyR3XBHeHdfrDqFVYAw3oZbG/idFnHgWWfB7jpYk/d+HIcdmc4bNxrc9VUi+8u9FBc6rDok5rxfXu+Bzu4HsdxG0ij+pocP+Vw1zy3Rfb+5gB7jzgM7mEwd5xFdCTcMNPi2CmHl1c1vcXUWvnwbO3JdBjQzeG7C9143lxbtb675nl4YlkwnvVuPB4L9h6x2Rt80u0n222uvdBiTD8PuYUO//7YXT4mEu6c6yEizB3PmTTY5G9v+ykrh+WfB7h6qoVluiND1bd5rvZXY+U2zAP9uhksWdd4p6s18h7A1dMsencxiI6AH1zl4cOtATbtc5g3wS3LN1/sNmsysmyWrm9aOdlxMMCQXhY/vyWasnKHV96vmiLw469E8Yd/ub2FRR+W1nhV1c5Dbryf7fDz1Ysj+MkNUfgD8M/33OWnjQyjY4LJZePDuSx4CnnszZLKB4o1hePAyo02X7nIfR3elv3uk7anD3dH//ccgc37HRZMMvnmPPep24vXVv3ub19hEuFxO08Duxv86yO78kndQ3oY+Fa3zjRa0/Iw+yv34nvkdhw7wIgp19Cp2wA+XvIwXXsOZ8Coizl6cCuvP3E3pUV57N32AZ+8/Qi337u02dtuz+ez1oyvIZ0SDOZPsnAcd2bCJ9sDTb5YnJZhM7CHyf9cG0653+H1j6su7t+9MIxH33RHk9/61M810z14LIM9GXblvc3zJ4dhmfD1y9yZe+knHd781F3Hj64LJyLYsR/SK5xn3ynn5Omzu5jjsSx+fNs1fPfBxwjYNgsumkS/1BQeX7SMIX16cNEFIxpcNicvn7sffBzTMOjcIYH7vnXTWcUg/z0MJ9RNWPJfa2nYoHabOS4vT2Px5614GbMZrhzvdnzm37WzjSOp35InhvCLF9rrTarwq1vCuO/l5r/zsTXce6PbMG/v8f3qpfZ5fH9xU1i7zXu/usVt3LXnfQe06/3X3vfd9x8paONI6veX77o3oD7wavs8p/3v9RbPrAqdri18PTiY3Z7LBbTv+H72TPu7F7/Cb74eQf6GFW0dRr3ixs0B+NLNgf5oyOh227av7aKdm9vl/tW0bREREREREZEQ1HkWERERERERCUGdZxEREREREZEQ9MAwERERERGR85xhtcvbiL9UNPIsIiIiIiIiEoI6zyIiIiIiIiIhaNq2iIiIiIjIec7UtO1m08iziIiIiIiISAjqPIuIiIiIiIiEoGnbIiIiIiIi5znD1LTt5tLIs4iIiIiIiEgI6jyLiIiIiIiIhKDOs4iIiIiIiEgIuudZRERERETkPGdYGjdtLu1BERERERERkRDUeRYREREREREJQdO2RUREREREznOmpVdVNZdGnkVERERERERCUOdZREREREREJATDcZy2jkHaL2UOEREREZG6vnRzoNdNnPCladtP+mx9u9y/uudZRERERETkPGeY7bI/+qWizrM0aPHngbYOoUFXjrdYGjaorcOo1+XlaQBc/6NDbRxJ/V79Yy/ufb6srcNo0H23hvOrl8rbOox6/eKmMIB2H999L/vbOJL63Xujh58/1z7z3v23hQO0+/jaa9n9MpTb2355vI0jqd9zv+wCwP8+XdrGkdTvgW9E8OtX2med8n9fdZux7bnOg/Z9zmivxxbc4/vosvY5UHr3PHVC/1vpnmcRERERERGREDTyLCIiIiIicp7Tq6qaTyPPIiIiIiIiIiGo8ywiIiIiIiISgqZti4iIiIiInOcMTdtuNo08i4iIiIiIiISgzrOIiIiIiIhICOo8i4iIiIiIiISge55FRERERETOc4apcdPm0h4UERERERERCUGdZxEREREREZEQNG1bRERERETkPGeYelVVc2nkWURERERERCQEdZ5FREREREREQlDnWURERERERCQE3fMsIiIiIiJynjMt3fPcXBp5FhEREREREQlBnef/Il6vN9Hr9X67reMQERERERH5stG07Tbm9Xotn88XOAfb8QCJwLeBv7fkutO2fMxbL/4Wxw4wfsa1zFxwR43v9+/awJIXf8ux9N189e4/MnLCZQDs2/EZS176XWW6k0cPcMN3/siwcZe0ZHiNGvnUA3SeN4OyE9msHjP/nG23utsWdmDMkChKyxweezWbA5llddJcPyeR6eNiiI0yufVn6TW+mzQqmusuTcBx4NCRch75Z1aLxjdvgsWA7iblfoc31gQ4muPUSZOSZHD1NAuPZbAn02bZejdLD+tlMHO0RccEgyeX+jmSXXfZhsRHw5VTLGIjDRxg4x6bz9LsGmlG9DaYOswCoMzvsPSzAMdPn9nvmzbMZEx/E9uBFZ8H2HfUjfGeKz2UloPjgO04PLW8/mLaL8VgzngL04CNe23WfFEzRst0f0e3ZIOiUodFHwfILWx82wsmWQxMNSgsgcfe9leu65ppFh3j3SlXkeFQUgZPLPPTmH4pBpeNMzEN2LTXZs2OmsfAjc8kJcmguBQWfVIV39RhBmP6ufG9s8Fm31HHPS6TTWKiDBzH/c3r09x1XjTCZEx/g6ISd/lVW2z2Hmn6MQe4fILFwFQ3v732Sf35rVuym9/CLIPdGTZLq+W3WaMtOiUaPP523fyWEAPfuzKMVZsDdY5TW8WWGAv3XBlGVp77d/pJh7fWNv+U0FbltrpzWTa6dIArJri/xXYclq63zzpugBvnxjFyQDhl5Q7/WJzHoaN1y9k1s2KYMiqKmCiDbz5wsvLzyyZHM31sFLbtkF9o8/SbeWTnnnl+q+6KSRaDeliU+R1eW13/MemWbHDtdA9hHoO09ABvr3OP55zxFkN6mnzKn7cAACAASURBVPhtyMlzeO1jPyVlbt77wTXhnMwN5r0TDm9+2nh9UqFfisFlY00MAzbts/l0Z916ZeGkqnrltU+r1StDDUb3NXEcWPEfm/3H3GUnDHTrG8OAjfuq6pUKkwYbzB5j8cfX/BTXPU3WjK0F6zzLhNtmW1gWmAbsPOzw0Tb3eI4faDBxsElSnMEfFvkpLm04ppYuCw2ts3cXg0svsLBMOJLt8Na6AI4Dg1INZo6yKs9pKzaEzpNne5yjwuHaaSbdkgy2HHBY8R93Wx4Lrp1q0iHOPX/sznRYtaV5ZaPCoZ0fs/qN3+A4NkMnXsu4S+6s8X3AX8bKl3/CyYwviIxOZM6tfyI+KZW0/yxh46qnK9NlHU3jKz98nU7dh7RIXO2NXlXVfOo8tzKv1xsD+IBUwAJ+DTwIvArMBn7v9XpPAw8Ev8/y+XwXN7CuXwL9gP5AR+D3Pp/vKa/XawC/B+YCDnC/z+d71ev1zghu7xQwGNgI9PN6vZuBd30+34+b+/tsO8Di5+/n9p/+g4SkLjx67/UMvWAmXbr3r0yTmJyC964HWL3s2RrL9hs6ke8/8AYARQWn+f0P5zBgxNTmhnRGMp5/nYN/f4nRzzx4TrdbYfTgSLp2CuOe3x1hQM9wvnFNEj//67E66TbuKOKdNfk8/NNuNT7v2tHDlbMSuPfR4xQW28THtuxkkgHdDZLjDB5+o5zUjgbzJ1k8WU9nbf5kizc/DZCR5XDzxR4GdDfYk+lw/LTDKx/4WTD5zKsa24GVGwMcy4FwD9w5z8O+YzZZuVVpThXAc++6DcH+3QyumGTx9Iqmdzw6JsCw3iZ/X+InLgpuvsTDo2/5cYLtg+ffa7gxBGAYbiflxff95BXBHXM9pGXUjHFMf5OSModH3gwwrJfBJWMsXvsk0Oi2N++3Wb/b4aopNffba59U/bZLx5qUlDf++wwD5o43eWlVgLwiuH2ORVpGgKy8avH1Mygug0ffqojP5LVPbDrGw7BeJo+9HSAuCm662OJvSwLYNqzcaHPslHtc7phrsf9o1To/22WzdufZdVoGdjdIjjf48+vlpHYyWDDZ4omldfPbgkkWiz8NkHHS4ZZLqvLbiWB+Wzil/vw2d7yHPZntL7acfIe/vdW0TktTtGW5rXCuy8bsMRYfbXMv1vTvZjB7rMnz757dRYiRA8LpkmTxk79m0y81jFsuj+fX/8ipk27z7jLeW1/Mg99LrvH5oaPl/OrJIsrKYea4KLyz43hsUW6d5ZtqYKpJcrzJQ/8uo0cng4VTPDy2pG7hXzjVwxuf+Ek/6XDrpWEMTHXYnWGz94jNyg0BbAcuG29x0SiLdz53901OvsOji0NUJLUYBsy5wOTlDwLkFcPtl1rszqxZr4zua1BSBn97O8CwngYXjzJ5/dNgvdLT5PFlbr1y40yLvy8N0DEexvQzeXplgIANN8ww2ZPpcKrAXV98NPTtanC6sPHy2xp1XsCGF94PUO53O89fu9Ri7xHIzHYvdu3ODHDrJVajcbV0WWhsnVdOsXjhPT85+TBjpMnovgab9jnsP+aQluEu2zkRrruw8fLdnOPsD8CHW206JRp0TqjZWVu7y+HQCRvThJtnWvRLMSovCJwt2w7w4Wv3ceU3nyE2sQuv/vk6+g6fRVLXqrboF+sWERkVzy0/W8nujUtZs+Qh5t76ZwZdMJ9BF7iDJ1lH0lj6zN3nbcdZWoambbe+OcARn883yufzDQdWBD/P9vl8Y4H3gaeAa3w+3yjguhDrGwnMAiYD93q93m7A1cBoYBRwCfAHr9ebEkw/FrjH5/MNBH4K7PP5fKNbouMMkL5vG8ldepLcuQceTzijJs1lx39W1UiT1Kk7KT0HYRgNZ7dt61cyaNSFhEdEtURYTZbzyQbKc86+UdNc44dFs3qD2zrYc7iMmEiTxLi6J+E9h8s4nV+3IXjxxFhWrsmnsNi9cptX0DJXcCsM7mGyeb+7zowsh8hwg9hahyg2CiLCDDKy3JPf5v02g3u4xzorF7LzOCsFxXAs2F4t88PJXIf4qJon4Ywsh5Kyqn/HR1d9P6KPwe1zLO6a5+GKie6V8zq/L9Xki4M2ARtOF7oNye7JTb8q2z3ZICff4XQB2DZ8cdBmcGrNfD4o1WDLfnff7Djs0LerEXLbh084jXbaAYb2Mtl+sPHj3T0ZTlWP75DNoB41f9+gVIOtwWO847BDny7u94N6GHxxqCq+U/kO3ZOhoASOnXKXLfNDVm7N/d4cQ3qabN4XzG8nG8lv4QYZJ4P5bZ/N0J7uPj+ZS42GXc11G5wqcDux7S22ltaW5bbCuS4bDhAR5v47Mhzyi84+9jGDIlizxZ0+sS+jnOhIg4R6Lkzuyygnt546d9fBcsrKq9IkxTevqTW0l8mmvW79n37SITIc4modz7goiAxzvwfYtDfA0F7udvdmOtjBbJ9+wiGhmeW1WxKcKnA4XRg8todtBqXWrVe2HAjWK+kOfYLHdlCqwReHq9UrBQ7dkqBjvEFmtoM/4M72OXzCYXC1uurSMSbvb7bdA92I1qjzAMqD155M0/2vwrFTVI4QN6aly0JD5Ss6AgI25OS7691/1GFIsA4qr3b9LNxjhNqVzTrO5QFIzwJ/rWaLPwCHTrhbtm04esqdzdRcxw9vJbFjTxI69sDyhDNwzDz2b3+/RpoD299n8IQrAeg/6jIy9qzFcWruhd2bljJwzLzmByTnNXWeW982YLbX633Q6/Ve6PP5Knpqrwb/PwlY7fP5DgD4fL66l7dretPn8xX7fL4s4ANgAjANeMXn8wV8Pt9x4CNgfDD9+op1t4bcU8dJTOpa+XdCUldyT5044/VsWbec0ZMvb8nQvhQ6JFhkn646u2Tn+klKaPwKdnUpncJI6eThvu904f7vdmXUoMgWjS8+2iC32pX+vKK6HaX4aIO86mkKW64zVSEhxp1imtHINMwx/czKKcIVIwjPvBPgiWV+bNud4l1bXDTkFlWtM7/I/QzcNtrNF3u4Y66Hsf3r/z1x0ZBXrZGeV+RULl8hPtqo3IbjQEk5REU0vu1QenY2KCxxKhtIDYmLMsitEZ/7Wc3fYFQ2/mrEF2WQV9j4sgkx0DWpqgMGMH6gyV3zLOZPMokMb9rvqRlL43mpdn7LLXSIC5Hfwj1w4XCLDzaf/XTo1ooNoEOswbfne/jGHA+9Oje/7LSHcnuuy8Y7GwLMHmvx/as8zB5r8X4zjnWHeIucvKrlT+UF6HCWHeDpY6PYureROcZNEB9NreMJ8TG1jmeMUaMT5x7Puuu6YKDJ7oyqDn+HWIO7rwzjjnlh9O7StOMfH23UOrb11CtRVWkcx73FJCq85ueVvyXa4GSuQ89OBlHh7tTe/t3Myvw4sLtBXjFNuiWnteo8w4A751r86BqL/UcdMrNDx1JdS5eFhspXUak7Op6S5MY9tJdRo1wP7mHwnfkebphphbw9pDnHuSkiwtxje+BY80adAQpPHyc2MaXy79iErhTkHq+RpiD3BHHBNKblITwyjpLCmplqz6blDBx7frdFDdP80vzXXmnadivz+Xy7vV7vWGAecL/X6624FNaEa5X1ql3LhKp1zmg7Xq/3TuBOgBt+/MqZLHrW8k6d5Fj6bgae4ynb5wPThK4dw/jVY8dJSvTwy2934cd/PEJRSfNPRu1FmAe80z2s2BCoHM2prXcXgzH9TZ59x7203qere6/VHXPdKs7jgcJSm9DFpcqz7/jJL4boCHeqXFZeqz+aoMlG9DbYfrBtj3GYB6670OKd/9iUBUc0NuyxWb3dbUTNHGUye6zJknUtOxvibMwabfHpjqo425P8IvjDonKKS917Vm+c5eGvi8spPbOZtP/1xg00eWdDgJ3pDkN7GiyYZPHi+21bZiePjKRPNw+/ffZUm8ZRYcYoC9umcgZFfhE8+GpZZd676ZIwHn69rE3yXlYefLrT5saZFmV+OHbKwXHcjvS0oSYvf9i2x9Jx4MnlASLC4PrpJp0S3Nkk7dFrnwS4bJyJx4R9Rx2qD67uSnfYle6nZ2eDmaParnNiGHD1FJP1u21On21ruIUdO7SFsPBIklMGtnUo0s6p89zKgtOqc3w+30vBe5tvr5VkHfB3r9fbx+fzHfB6vUkhRp8Xer3e3wIxwAzcqdgWcJfX630eSAKmAz/Gvc+5unwgrrF4fT7fk8CTAIs/D4RsnSd06MLpnKp7dHNzjpHQoXOoxWrY+tkKho27BMsTdkbLfVldOiWWiye6h2FfeinJiVUjzckJHnJym95IyMkNsPdwKQEbTub4OXqynJROYexLP/uRjgmDTC4Y6J5UM7McEmIMKjqd7pXomtkir8ipMQoSH1M3zdkyDfBOt9h20GZXev3r7JwI8ydZvLyq6iEyhgFb9tvuNL9qBvcwuGiEu7/fWhcgv4jgFEZ33XHRVdM984vd/xeVwq50u97p3PlF1BjdiY826kwXzStyp0nmFzkYhju1sriURrfdGMNwp+U+uTx0TzC/uOYUzfho97Oav8EhIcb9vTXiK3aIj6HeZU0DvBe608arH5fCkqr0G/fafHVG6FkUEwebjGsov9WTl2rnt4QYd982JrWTwbDeJpeNs4gMdxvC/oB7f3ZbxxawqZyGfCTbISffITneOOOHXbWncgvnvmyM6muyYoNbJnYcdlgw6cxG0S8eH8VFF7hzoQ9klpMUbwFuL7JDvMWpvDO7CDS0bzjzL4zht8/l1Jm62hSThpiMG+SWn7rHkxqzBsD9O6F6eY2pOWo4doDJ4J4mTy+r6hnXl/c6JhhkZjWeD2rPZKi3Xil2gp8H65VwKC6r+rz6shX5bvN+h8373Z01c6RJXpFDUqz7YLM751iV6e+YY/H0ykCN+qb6dlujzqtQWg4Hj7v31Vc8aK0pWqMsNLTOjCyH51a6+7FvikFyfN04D59w6BDbeBlpznEO5YoJJjn51Hko3NmKSexCwemjlX8X5B4jNqFLjTSxCZ3JP32U2MSu2AE/ZSX5RMYkVn6/Z+MyBow5v0edpWW03zHx88cIYH3wIV2/AO6v/qXP5zuJO9L7utfr3ULVdO6GbMWdrr0O+LXP5zsCvBH8fAuwCvh/Pp+vzlOnfD5fNrDG6/Vu93q9f2jez3Kl9h1O9rFD5JzIwO8vY8u65QwZO/OM1rF57VJGT/7vucdk5acF/OTPR/nJn4/y+RfFTB8XC8CAnuEUldj13tvckM+3FzG0nztVOy7aJKVTGMezmze8tj7N5rElfh5b4mfXYZvRfd1qIrWjQUm5Q0FxzfQFxVBa7pDa0T3Jju5rsiu9ZUYbF0y2yMp1WLez/vXFR8P1F3l4Y02gxhTm/cdshvR07/8C94SeEONedX9imZ8nlvk5muOQlmEzrLeJZUJiDCTHuffdhVnuVF+AMMt94mh998pmZjskxxkkxrizAIb1NknLqBnr7gyHUX2DU+h6Ghw47q6noW2H0rerQVae06SOdmY2JFWPr5fJ7oya20jLdBgZPMbV49ud4TCsV1V8SXFG5VTF+ZNMTubBul011xVb7a6BwT3q32e1fbbL5m9v+fnbW352HLYZ3S+Y3zoZlJY1kN/KHFI7BfNbP5OdhxvPb/9Y7uehReU8tKictTtsPtoaCNlxPlexRUdQeT9+h1g3H5zKP/MGZXsqt3Duy0Z+MfQKTjvu09UgO8QtDbW9/3kx9z6ew72P57BxVylTR7mZuV9qGMWlTr33NjekZ1cPt10Rx8OvnCY/xAOuGrJup82ji8t5dHE5Ow7ZjOnvdh57dDIoKa+6uFchv9id6tsjmPfG9LfYcciNeUB3g+kjLF58t5zyaqeXmMhqeS8OkuMNcvJCx3skp1a90rNuvbI702FUn2C90sPgYPV6pWfNeuVIcLigor6Oj3brj+2HHE7kwp/eCPDIEve/vCJ4akX9HWdonTovOqLqfnqPBX1TzDN+fkFLl4XGylfFfrRMmDrUZMNu9/MOsVXb65oEVohrm805zo2ZMcIkIgze2dhy9U2XHiM4ffIQudkZBPxl7N60jD7DZtVI02f4LHatXwzA3i3vkNp/EkawADi2zZ4tyxmozrM0gUaeW5nP53sHeKfWx71rpVkOLG/iKrf6fL5bai3v4I40/7jW5x8CH9b67IYmbqdJLMvDwlt/xtO/vwPbthl/0VV0TR3AykWPkNpnGEMvmEX6vm288JfvUVyUx85NH/Dua4/ywweXAJBzMpPcnGP0GTw+xJZax+gXHyL5ogmEd+zArAMfsee+R0h/dtE52/6mncWMGRzFwz/tRlm5+6qqCg/+IIWf/Nm9knrj5YlMHRNDeJjB33/enVXrC1i0MpctaSWMHBjFQz9Owbbh5bdPUVDUciek3ZkOA1Idvn91WOUrbyp8a76Hx5a4HfW31wW4aqpFmMd95U3FE42H9DSYN8FDTCTcdLGHYzkOL7zXtM59j04Go/qaHD/lcNc89+T8/uZAcAQG/rPH5qKRFlHh7iuEoOqVUlm58MGWADdf7MEw3BGWZZ8Hatw3CO60ux2HbL4934PtuGkcB2Ki3E45uKOs2w/a9T4N1Akuc1NwO5v32ZzMdZ9weiTHYXeGw8a9NldNtfjuQg/FpQ6Lgk/MbmjbAFdPs+jdxSA6An5wlYcPtwbYtM/9cnhvs8lTth0Hlm+wuXGWVTe+bIfdmQ6b9jpcNcXg7gWW+6qRNTXj+9YVFnZwPY4DPTpReVzunOvu94pXUl0y1qRLBwMcOF3osPSzM8uLuzMcBnZ3+J+rwygLOLxe7eni31ngqXwi9VvrAlxT8TqoTJvd1fLbFRPd/HbLJR6O5jg8/27LzNVurdh6dzW4eLS7jx0H3lzb+Gt4mhRrG5bbCue6bCxZF2DOOAvTBH/A4e3Pzv64b9lTxsgBEfz+e8mUljs8/WZVT+m+byZx7+Nub887O5ZJIyIJDzP40/90ZPXGYhZ/WMj1l8YSEW7wHW8CANm5Ng+/cobv0KsmLd1mUKrJD68Ld1+T9nHVb7v7yrDKp2W/9amfa6d78ARfk1Zxb/OCKWFYJnxtjtsDrHglVe+uJpeMtQjYwby3pml5z3FgxQabG2ZYlbN8Tua5r6o7mhOsV/Y5XDnZ4DtXWBSXwesV9Uoe7Dhs8815VmX9VHFsr5tmERXhPkhq+Qb7rKaPt0adFxsFCye7r4QyDDdNRVmZMMhgylCT2Ej45jyLPUcc3q6n3muNslDfOgGmDjMZ0N19SOaG3XZlh3ZoT5ORfU1sG8oD7uuwvn5Zw92A5hxngO/Ot4gIczvxg1ItXv4gQGk5XDjcJCvX4Y7gbILPd9ts3t+8EWjT8nDRNf/HW098A9u2GTrxGpJTBrBu+V/p3GM4fYfPYujEa3n35f/HC7+5lIjoBObc/KfK5TP3f05sYgoJHXs0K44vA72qqvmM2k+ak/Yr+KqqAp/P98dzsb2mTNtuK1eOt1gaNqitw6jX5eVpAFz/o0NtHEn9Xv1jL+59vpmt81Z0363h/Oql9nnD5y9uchuf7T2++15uhzf3Avfe6OHnz7XPvHf/be5Tbtp7fO217H4Zyu1tvzweImXbeO6X7vTS/306xCP228gD34jg16+0zzrl/77qdv7ac50H7fuc0V6PLbjH99Fl7bMpevc8A+BL1xPddsXM9rlD6zHi7Q/a5f7VyHM75PV6vwbcU+vjNT6f7zttEY+IiIiIiMh/O3We2yGfz/cs8GxbxyEiIiIiIucH02qXg7lfKnpgmIiIiIiIiEgI6jyLiIiIiIiIhKDOs4iIiIiIiEgIuudZRERERETkPKdXVTWfRp5FREREREREQlDnWURERERERCQETdsWERERERE5zxmmxk2bS3tQREREREREJAR1nkVERERERERC0LRtERERERGR85yett18GnkWERERERERCUGdZxEREREREZEQ1HkWERERERERCUH3PIuIiIiIiJzndM9z82nkWURERERERCQEdZ5FREREREREQjAcx2nrGKT9UuYQEREREanrSzcHevdX53xp2vYDX1nRLvevRp5FREREREREQtADw6RB8+/a2dYhNGjJE0O4/keH2jqMer36x14ALA0b1MaR1O/y8jTue9nf1mE06N4bPfzihfK2DqNev7olDKDdx/erl9pnfL+4KYwHXg20dRj1+t/rLQB+62uf8f1/Xje+3/yrfcb3s69Y7bZeufdGt6lz7/NlbRxJ/e67NRyA7/4lr40jqd8j34/nmw+eausw6vX4TzoA7bvOg/YdX3uNDdz4ps3/qK3DqNcnSy5q6xCkjWjkWURERERERCQEjTyLiIiIiIic5wxT46bNpT0oIiIiIiIiEoI6zyIiIiIiIiIhaNq2iIiIiIjIec602uXbn75UNPIsIiIiIiIiEoI6zyIiIiIiIiIhaNq2iIiIiIjIec4wNW27uTTyLCIiIiIiIhKCOs8iIiIiIiIiIajzLCIiIiIiIhKC7nkWERERERE5zxmmxk2bS3tQREREREREJAR1nkVERERERERC0LRtERERERGR85xeVdV8GnkWERERERERCUGdZxEREREREZEQ1HkWERERERERCUH3PJ9DXq/3l0CBz+f7Yz3fzQDKfD7fp624/d7AFJ/P98+WXved13fhguGxlJbZPPzcUfall9RJc/PCTsyclEBstIX3nrTKzzslebjn1m7Ex1oUFAZ46JkjZJ/2t2h8ty3swJghUZSWOTz2ajYHMsvqpLl+TiLTx8UQG2Vy68/Sa3w3aVQ0112agOPAoSPlPPLPrBaNryEjn3qAzvNmUHYim9Vj5rfadvqlGFw2zsQ0YNNemzU7nBrfWyZcOcUkJcmguBQWfRIgt9D9buowgzH9TGwH3tlgs++oQ3w0XDnZJCbKwHFg416b9WnuOi8aYTKmv0FRMIus2mKz90jN7dVn7niTAd1NygOweI2fozl106QkwVVTPXgs2JNps/xzG4CocLhuukVirMHpAgff6gAl1bJAt2SD2+daLFodYMdhh95dDOaMtyq/75gAi1YHzmlsvbsYfHWmxakCd/mdh20+2uous3CKxcDuBoUl8PclocvKnHHB+PyweK2fYw3Et3CyhzCPG9+KDe62IsPh2gstEmMMThc6LPrYjW9Eb4Opw9x9VOZ3WPpZgOOnq9ZnGHDHXA/5RQ6vfNjwvquub1eYPcbEMGDLfoe1u+rmw/kTDbp2MCgug8Wf2uQWufvw6ikmKUmw9aDDyo3ucuEeuHlW1TXiuGjYfsjhvU2h81t9sV0y2i0jmw84rKsntismGKRUxLbWja13F5gxwsQyIWDDB1ttDp1wl7n+QpOYKDANSM9y43bOPLTK+C4d6+67zfsd1u6sG9+CSVX77o1PbXILg/tuqkm3JNh6wOGdjVXLmSbMucCgZycDB/hwq01aRtNjaul6xTLhttkWluXus52HHT7aZleub+Yok6E9DWwH/rOnqs5pinkTrGAZcXhjTYCjOXWXTUkyuHqahccy2JNps2y9m6+H9TKYOdqiY4LBk0v9HMl2l42KgK9c5KFbR4PN+2yWfta0clCfay6KYFifMMrKHV5aWUzGSbtOmh6dTW66NIowj8EXB8p57aNSAL42L4rOHcxgTAbFpQ4PvlzIoJ4WC6ZG4rHAH4A3Py5hd8bZxei9OIrh/dz4nl9WRPrxuuvp2cXi1stjCPPA9n3l+N4vBiC1s8UNl0UTZoFtwyvvFnHwaIDoCINb5kXTMdHE74cXlhdyJKvu767Pua7zFkyyGJjq1smPvd02dXJyPCycbJGSZLBqs83anW765Hi4dlpVc79DrFsPfbar4X15LuOrcDbnjNruubMfky9IpqQ0wAMPp7F7X0GN7yMiTH79k6F0T4nCth3WrM/m8ecPAPDd2/sxdkSi+xsiTBITwpn71TVnFUd7pnuem0+d5/ZjBlAAtErn2ev1eoDewA1Ai3aeLxgeQ7fO4dz1f/sY1CeSb93YlR/97mCddOu35vP2B6d44tf9anz+9Wu7sGptLqvW5TJyUDS3XtWZPz17pMXiGz04kq6dwrjnd0cY0DOcb1yTxM//eqxOuo07inhnTT4P/7Rbjc+7dvRw5awE7n30OIXFNvGx527CRsbzr3Pw7y8x+pkHW20bhuF2/l5aFSCvCG6fY5GWESArryrNmH5ug/vRtwIM62VwyRiT1z6x6RgPw3qZPPZ2gLgouOlii78tCWDbsHKjzbFTbgfmjrkW+49WrfOzXXadxn1jBnQ3SI43+OtiP6kdDa6YaPHU8ron1ysmWby1NkBGlsNNF1v072aw94jDtOEm+485fLI9wLThJhcON3l3o135+2ePNdlXrQN/8LjD48EGUFQ4fO8qT43vz1Vsh044/HNV3XVt3muzfpfDVVNDV+H9uxkkxRk88qaf7h0NLp9g8fSKuuu8fILFks8CZGY53DCzWnzDTA4cc1jzRYCpw0ymDTN5b5PNqQJ47l0/JWXuNq6YVHO9EwebZOU6RISFDBFwj8NlF5i88qFNXjF8bbbJniNOjXw4qq9BSRk8vsxmaA+DmaMMFq918Afgo+02nRIMOiVUpS/zw9MrqxpoX5ttkpZx5r1Tw3A7pv/6yI3ttkvc2LKrx9bHoKQcHl9uM6SHwYyRBm+uc4KdQpuCEugYD1+ZbvLo225Mb6y1KQu2s6+aYjI4FXamn118c8aZ/PMDN76vzzbZk1lz340O7rvHltoM7Wkwa5TBG58G9902m8619h3AtKFuZ+DxZVUXes4kppauVwI2vPB+gHK/23n+2qUWe49AZrabN+Kj4W9L3DwYHdH0WAd0N0iOM3j4jXJSOxrMn2Tx5LK6HaD5ky3e/NQtwzdf7GFAd4M9mQ7HTzu88oGfBZNrlkd/AN7fHKBzokGXDmffYB3a20PnDhb3PVdA764W118cxUP/KqyT7vpZUbzyXgkHjwX4o7aDwgAAIABJREFU1pXRDO3tYcdBP88uK65Mc9WFERSXuXmssNjhibeKyCt0SEk2+fZV0fzfPwrqrDeU4X09dE6yuPfJPPp0s7jh0mgefDG/TrobLo3mpRWFHDgS4O7rYhnW18MX+/1cPSOKpWuK+WK/n+F9PVw9I4o/vVLAnMmRpJ8I8PgbhXRJMvnq7Gj+8mro+Nqiztu832b9boerprRdnVxcCis22AxOrZnXsvPgiWB+Ngz4n6s97EpvuON8ruOrcKbnjNomXZBEj27RfOWu9QwbFMePvjWAO3+0qU66V97IYNO203g8Bg/fP4pJFySx7j85PPKPfZVprrmiGwP7xp5dIHLe07TtVub1en/m9Xp3e73eT4BBwc++5/V6d3i93q1er/dfwRHhbwI/8Hq9m71e74UNrOs5r9f7uNfr3RBc5xXBzyO9Xu+zXq93m9fr3eT1emcGP7/N6/W+5fV6VwHvA78DLgxu4wct9RsnjYpj1bpcANIOlBATZdIhvu4JJO1ACafy6jZIeqZEsDXNbQhsTSti4qiWrbDGD4tm9Qb3hLvn8P/P3n2HR3GdDR/+nZlVr0iAEJLoSPTeqzHYphtcxjWxU1xSnJ43eeO8TuwkTpzyJY7jnsQ9tscNm2LcbYwB0wyY3otElYQaEkK7M98fZ7WrVUECVchzXxcX2t0pz5yZOWXOmZkzxEQaJMaZNabbdfAMBcU1C4ipo2N597NiTpXpwqaopGFXvptC/vK1VOQXNus60pLhZLFLQYm+6r/lgENWRmjhlpWu2LRXb/fWgy7dU/TvWRmKLQccfA4UnNLLSUuGktNw9KSe94wXcgtd4qPPv/LYJ0P33ABk57pEhitio0KniY2CiDBFdq6uGG7Y49C3i/LPbwTm37DHoU9GMOsb3cdg20GXUzUHSwDQr6tid45LRR0XwpsztrocOK4bZQ3RJ0OxaZ9efk498eX449u0z6FPRuU+Ntjo3/cb9zpk+ePLznUDvffZuaH7Ny4aendWrN/d8HOlcxKcLNbHkePo46x3Wugxk9lZ8eV+HeO2bD1CAKDCB9m5urFSl6RYiImEQycaHFJobCXB2LYddMnsHBpb7zTFZn9s26vEdqxAnw8AuUXgMXWPKxBoOBsq+N356JwE+dXSLjOtZnyb9vnT7lAtaVfLrhrcQ7GiSm9xWc0BO3VqjnwFoKIyzQz9r9KI3gbLqvRClzbw/AD/Obj3HM/hvcFzNbeQkAsplSq8cPC4e9bjsiEG9vSweptO/P1HfUSFUyM/jY9WRIbr3wFWbzvDwJ41y+GhmWGs26ETMfuEQ9EpvT1H8hzCPApPzaKxXoN6h7Nqs07wfYd9REUo4mOqxRejiIxQ7Dus41u1uZzBvfXVGBeIDNfTR0boETgAqe1NdhzQsR7Ld0hOMIhrQDnSGnnewTaQJ5eWw+E8F99Zrr9176TIL3YDIzzaSnznU2ZUN3FMMks/1B0jW3YUExvjIbld6BW/8nKHL77UwwW8Xpede4rpkFzzquC0SR15b9l5FBbiv4I0npuRZVnDgeuBIcBMYKT/p58DQ23bHgTcadv2fuAx4K+2bQ+xbfvTsyy2GzAKmAU8ZllWJPAdwLVteyBwA/CM/3uAYcA1tm1P9q/3U/86/tpU25mc6CE3vyLwOa/AS3K7hg9q2Jd9mrFD4wAYOzSO6CiTuJjzKMHr0C7BJK8gWHvJK/SSlNDw5ad2CCO1g4f7vpPCb+/qxOCsyPpnuoDERSkKS4Ofi0r1dyHTRKtAYeu6cLpCD0mMi1IUnTr7vAkx0CkpWOkEGJlpcMdMkzljDCIb0JsVF60oComxZmM8PlpRVOpWmYZARSsmCkr8nS8lZfqz3nbom6FYs6PuAntAN4Mv99X9e3PFBpDRQfGt2R5unmrW6BVsqLgoFVJRKjrl1ty/UdXiOxXcj7GRofHF1nL4D+1phAy9nz7c5P0vfJxLH2pcFBSVBecoLtXfhUwTTSCtXRfKKxreG9qvi2LrwfMbEx0bRUj6FJfVEltU/bFlpcPRAj18u9J1kwy+d6XBmQqX7efRK1657uKq++8846uqsvdn8kDFNy43uGqcQcw59OY2V76iFNw+w+QnV5vsPeKSk6enaRenh09/c7rJjVMMkuIaHmt8tKLwVNXzs45zuOo0pxp3QfBcJMYoThYH111Q4pIQWy2fjQ02OgEKil0SqzVge6aZFJe6nCiomZ8N6eUh+7jvvBr6ibGKk0XBZRYUOyTGhVYvE+MMThZXncYl0b8Nr3xQytVTorn/WwlcMyWaBZ/oDCf7uJehmfpA7JZqkpRg0C6u/mpra+R556Il4qvLgK5G4CJfW4rvfMqM6tonR3A8N3gF43heOe1raRhXio0xGT8qmXUbC0K+T+kQQWpKJOs3nWxENG2XMowL5l9b1XYjuzhMBN6wbbvUtu0i4C3/95uAFyzLuhk415t7bdu2Hdu2dwF7gT7ABOB5/4/bgQNApn/692zbruVuldpZlnW7v2d77TnGdd7+/epxBmRG87e7uzOgdzS5JytwnMZkoU3LMKBT+zDuffQYD76Qy+3XJhMdKfeMNESYB66daPLOuuDw1LW7HB56y8fjS3yUlOkh0y3Of3jNGGny3nqnzgI7NgpS2qnzriSdF/+qjuS7/PU1L48u8vL5docbprSNu2yq35PbLUUxtJfB++t1rbt3muLUabfW+75bU2Maz02hfTxMGWQE7gus9PIyh4fecjBNRdeOrRRcLQylG4zZuS7/etchO89l6tDWz/dcF55428df3/CRlkzgopLH0CMP/rnUx/rdLnPHNN0F2IvF8Kww1u2oqPF9pySDuRMieemDslrman6ThkTwygel/OLRQl75sJSvzIgG4J1Vp4mKVNx9axyXDIvg0DEfrVE1qC/Pa20NfU6CYejRHlsPttzoOag/vtYoM0wDfv3TfryyMIfDx0KHnU2b1JGPP8vFadlkEheQtlEb++8zC5gEzAHutixr4DnMWz0bqi/bPMvgnJps234CeAJgzh1135Q685J2XDFBP1hh1/4y2ieFwR5d8CYnesg72fBrAvmFXn7/WA6gh2yNGxYXGCJ9vi4fF8vU0brrYc+hcpITgxWp5AQP+YUNL/TyC33sPliOz4ET+V6OnKggtUMYew6dwxjGNqy4zCWhSg9KfLT+LmSaUpeEGN3jphREhkFZuZ4uPoZa5zUUWBMNNu932F7lPs6qw6PX73a44ZLaK7mjsgyG9dYN68N5+iFkwfWEXvWGmr1F8dHB3rhTZbohXOL/vzKGzsmKaybp9UdHQO80E8f1BeId0FUP6a5eYWuJ2Mqr1HF35bjMGq1jbMhw1JGZBsN6BeNLiAkOV46PUTX3b1m1+GKC+7HkdLX4qqy/YyLMGWPywofewJDeLh0UWen6YTMeU/dgzh9v8sZnZz/nissgPkpRmaXFRevvQqYprTzG9HEYEdawocQdE0EZwVsJzlVJWeUwWX9sUbXEVlZ3bHFRcPV4g4WfOxTUkiP7HL2PMzsr9h8799ZBcVnlSAY9b/w5xlebsjP6oUjb/Q8I23bIZUgPg/qLnMr1NU++Uqm8Qj+boFdnxYlCl6JSAuft9kMuc8ecPb5RWQbDM/U5kpPrkhBTJf3qOodjqp4jNadpShMHhTFuoO41O3jUR7u44LoTYxWFJaHrLiwJ9uQCJMbphzVVMhQM7unhTy+GHoCJsYrb5kTx3Dtl5BY2fHsmD41gwmAd34GjPtrFG5Dj86/boKA4tAwvKHZCeo0T44I95WMHRgQeHrZuewU3T9c7//QZeHZJcPjC7+6MJ7eg9nykNfO8hmip+M6md2fFkfzab1FqzfjOt8wAuGpmZ+ZckQrAtl3FdGwfHB7TMTmC3Lzad9L/fDeTQ4dLeeWtnBq/TZ3Ygf/32O561y3+e0njuXktA562LOv36LSeAzwJZNi2/ZH/PujrgVigGIhvwDKvtSzrGaA70APYAXwK3AR8aFlWJtDF//2wavMWA+cwmK1uSz4+yZKPdU10xIBYZk9px7I1RWR1j6S0zKn13ua6xMeYFJf6cF24dnp73v+soP6Z6vHuihLeXaHvcx7aN4orxsexYkMpvbuEU3raqfXe5rqs2VzK+KExfLzmFHHRBqkdwjiW17RPA29NOXmQFKdIjNHDPft3NWoUWjtyXAb1MMjO1Q8b2uev4O/Mdpk/3mTVNv1gn6Q4FRhGOWeMwYkiajyVODYyeA9onwzF8YLaK2yrdzis9g+n7p2mGN3HYPN+H+ntFacr3MCwsEolZVBe4ZLeXveWDelpBJ4muiPbYUhPg+Wb9f+VD0v52xvB/ThvnMnO7NCG/oDuivfX17yQ0xKxVU2ntGSFUg2/j3PNToc1O4PxjczU8aW1V5SfqTu+tPb6HrZB3Y3A9u3Mdhjcw+CzLfr/Hf744qPhuske3vjMR36VZwN9sMHhgw16mq4pinF9ax5PtTmcr4feVjam+nVRvLkyNO13HXYZ2E2Rk+fSN11xoIENzX5dFFsPnH9D53C+fkJtZWx9uyjeWlUztgH+2PqkKw4c1+uLCINrJxp8tMkJnBugR2WEe/TFEqWgZ6q+9/h840uqlnYLqqddjsug7v60y2hYI31XjkvXjnDgOHRPUeSew+MXmiNfiY7QFxrKK/S94z1SDT7bWnke6fu4N+x16dpRkVfzeVUhqp7DmWmK0X1MvtznNPwc7mGwanvz9Tx+uqmCTzfpK2j9u3mYNCScdTu8dOtkcvoMtTbuT5+Bbp1M9h/1MapvOJ9sCDYcsrqYHDvphAztjoqAO6+M5q3l5ew7cm7b8skX5Xzyhc6QBvTwcMnwSNZuq6B7Z5PT5W7IEHfQQ39Pl7t072yy77CPMQMi+HidzuAKShwyMzzsPOQlq6uH4yd9/vgUZypcfA5MGBzOrkPekLckVNWaeV5DtER89RnQTV/MbmvxnW+ZAfD6ksO8vkQ/YHbsiCSunp3G+8tO0D8rjpJSL3knax4wt93cjZgYD394aGeN37qkRxEXG8bm7bU8xOAiIU/bbjxpPDcj27bXW5b1MrAROA6sQV/aft6yrARAAX+3bbvAsqyFwKuWZV0J3HWW+54PAqvRDe07bds+bVnWI8CjlmV9iR4Gfqtt2+WWZVWfdxPgsyxrI/B0U933vHZzCSMGxvDEb3vqV1U9cyTw24O/7M73f6tfA3DrVR2ZPCqeiHDFU3/oxbvLC3hxUS4DsqK5ZV5HXFy27Crj0RdrPgm7Mb7YVsbQPlE8+PPOnKnQr6qq9MAPU/nZX3W8N81KZPzQGMLDFI/8Mo0PV5fw6ruFbNxxmkGZUfzlp6k4Dryw6CQlpS0znmfIc38hefIowtu349J9n7Drvoc49NSrTboO14W31zrcdKmpX3Ozx+FEIVwyyOBwnsvOHJcvdrvMH6f47lyTsnJ4zV+wnSiErQccvjXbxPEvx3UhowMM7mFw7KTL7TN0z27lK6mmDTP0k2ddKDjlsvjz+tNyV45LZprL9+d79KszVgQL1jtnewJPxl78ucO8cWbg1Rm7cnQF7tPNDtYkk2G9PBSccnnlk/oL5sQYSIipv4HWXLH162owMsvAcaDC54a8KuuaiSbdUhTRkfCjqz18vFEPVa0rvt6dXe66Usf35srgcu6Y6Qk8hXXxah2fx4Tdh4OvD1u+2eGaiSZDe3ooPOXyyqd6/smDTKLC9RNXARzXrfUp4w3luvoJ7ddP1q822rhXPy160gDdW7LrsH4F09wxBnfO1E+OrtpA/PZsgwiPHo6XmaZ46RMn8GTnvhkKe9n5n7OuC++td7h+kn4V1KZ9OraJ/RVHTrrsPqzjnTPa4M4Z+gnSb/ob18N7KdrFwoR+BhP66eW95I/l2gn6FVZK6YfArd9zfg1814V31jncMNnAMOpOuyvHGHxrlk67N1YE0+M7c6qkXbrixY912n24Uc8TEa4v3CxqwLlaNaamzldio/Srbgyl02zrgeB5tHyLw1XjDUb3NaiogEWrGn4s7sxx6Z3u8oOrwgKvqqr0rTkeHvW/Dm7RKh/zx5uEeVTIOdy3i2LmKA8xkXDzVA9H812efV/P88Orw4gI02nbJ8Pg2fcqOHGOz4Dcst9Lv+4e7rk1lgqvflVVpZ/dFMMDL+je5Jc/LAu8qmrbfi9b9wcvDtY2ZHvS4HDaJxpMHxPB9DG6x+7h10spKTu343DzXi8Devr4ze3xnPHCM0uCvdt33xrH757WLc3/vFfKLTNjCPfAlr0VbN6r43v+7VNY06IxDf2QtReW6t7mTskGt86KwXXhSK6P594urbnyWrRGnnfVBH+eHAE/nO/h400+vqjjfG6u+GIi4fYZHiLCdEVzTB+Dhxd5OVMBYSb0SFUs+rxhF81aOr6msHJtPmNHJPHyE6MCr6qq9NSDw/na99fRITmcW67ryv5Dp/j334YD8NriHBa9q+ud0yZ25INPjzdNQOKipdzzfamkaHGWZT0NLLJtu2lbT3U427Dt1rbw8b5c95MDrR1GrV7+c1cAFodltXIktZtVsYP7Xmi7Pef33OThV882UWnaxO79qn54TVuP797n22Z8v7o5jPtfbhv3CVb3i+t0hfj3dtuM738tHd/vXmqb8d19vdlm85V7btL9BPc80zZvtbnvFj38+a6/tc3erod+EM+dD7TNhyc99rN2QNvO86Btx9dWYwMd34Q5n7R2GLVavnAy6E6wC8qhb1/dZuv21WU88lqbTF95YJgQQgghhBBCCFEPGbbdBlmWdTdwbbWvX7Ft+9ZWCEcIIYQQQghxgWvLr4C6UEjjuQ2ybft3wO9aOw4hhBBCCCGEEJpcfhBCCCGEEEIIIeohPc9CCCGEEEIIcbFTbfIZXBcU6XkWQgghhBBCCCHqIY1nIYQQQgghhBCiHtJ4FkIIIYQQQggh6iH3PAshhBBCCCHERU4ZF9c9z5ZlTQceBEzgn7Zt/6Ha712AZ4BE/zQ/t217SWPWKT3PQgghhBBCCCEuGJZlmcDDwAygH3CDZVn9qk32S8C2bXsocD3wSGPXK41nIYQQQgghhBAXklHAbtu299q2fQZ4Cbiy2jQuEO//OwE43NiVyrBtIYQQQgghhLjIKePC6Te1LOt24PYqXz1h2/YTVT6nAYeqfM4GRldbzK+Bdy3LuguIAaY1Ni5pPAshhBBCCCGEaDP8DeUn6p3w7G4AnrZt+y+WZY0FnrMsa4Bt2875LvDCufwghBBCCCGEEEJADpBR5XO6/7uqvgHYALZtrwQigfaNWan0PAshhBBCCCHERe4ie9r2GqC3ZVnd0Y3m64Ebq01zEJgKPG1ZVl904/lEY1YqPc9CCCGEEEIIIS4Ytm17ge8C7wDb9Ff2Fsuy7rMsa65/sh8Dt1mWtRF4EbjVtm23MeuVnmchhBBCCCGEEBcU/zubl1T77p4qf28FxjflOqXnWQghhBBCCCGEqIf0PAshhBBCCCHERe5CelVVW6Vct1HDvsXFTQ4OIYQQQggharrgnr519Kc3XzB1+05/er5Npq/0PIs6/erZitYOoU73fjWMe54509ph1Oq+W8L1/y94WzmS2t1zk4fFYVmtHUadZlXs4P6Xfa0dRq1+cZ0JwG9ebJv79v9u0Fl6Wz72tu/Jbu0watWnZzoAC9e1zbSbM1zv2xNbV7dyJLXr0G8UC9a0zfN23kh93t77fNss0351c5j+v42Wufd+NaxNxwZtf9+25fjaamzQtuOr3Lfiv480noUQQgghhBDiIneRvaqqVcjAdyGEEEIIIYQQoh7SeBZCCCGEEEIIIeohjWchhBBCCCGEEKIecs+zEEIIIYQQQlzk5J7nxpOeZyGEEEIIIYQQoh7SeBZCCCGEEEIIIeohw7aFEEIIIYQQ4mJnSL9pY0kKCiGEEEIIIYQQ9ZDGsxBCCCGEEEIIUQ8Zti2EEEIIIYQQFzml5GnbjSU9z0IIIYQQQgghRD2k8SyEEEIIIYQQQtRDGs9CCCGEEEIIIUQ95J5nIYQQQgghhLjIKXlVVaNJCgohhBBCCCGEEPWQxrMQQgghhBBCCFEPGbYthBBCCCGEEBc5ZcirqhpLGs8txLKsXwMltm3/uZHLSQRutG37Ef/nzsDfbdu+pvFR1m/GSIPeaQYVPljwmZcj+TWnSU2C+eM9eEzYlePw9hoHgKhwuHaSSWKsoqDExV7m4/QZPU+3FMX0kSamAaWnXZ561wdAZBjMHWfSMVGBCwtW+MjOdc857pmjTB231+WNz3wcya+5jNQkxVUTTDymYleOw5LVOob+XRVThpi0T1A8sdjL4bxzX3/PVMUVIwwMBV/sdvhsa+gyTAPmjTNITVKUlcOry30UntK/je+vGNrTwHHhnbUOe464xEfDvLEGMVEK14X1ux1W79DLnDzQYGgvRelpPf+HGx12Hz73mBti0JP303HmJZw5nseyoXOaZR3V9egElw01UAo27nVZub1mWs4ZrejUTlF2BhascCgs1cffVeMMUpNg036Xd9cH5+uboRjfT6EU7D7s8tGmpkmvnqmKK4bpWL/Y47BiW81YrxwT3O+vrdD7PSocrplg0DlJsXGfy9J1TuNiaMJjzzTg1stMTBMMBdsOunzypY5vZKZidB+DpDjFn171UlZ+3mGzfu1qnnz8YRzH4bIrZnKNdUPI728vXsjbi97EMA0iI6P49vd+SJcu3QK/nzh+jO/e+XWuv+kW5l9tnX8gddi+8VPefPYPOI6P0VOu5tK5t4X8vmfbWt567g8cObiTm+76E4NHXxH4bdF//sy2L5bhui6ZA8dy5Vf/t0nfvblq/SYe/NdzOI7D7GmX8JWraz83P165hl/+8e/880/30qdXD7bu3MMfH/03AC4uX7/uKiaPGdFkcVXasfFT3nru97iOj5GXXMOUamm3d/taFj73e44e2skN3/0zg0bptNuz9XMWPv+HwHQnjuzjxu/8mf4jpjVovT1TdTljKJ1nfrYl9LzS54JJ52RFabnLq58Gz4UJ/Q2G9tLnwtI1PvYccRu0zOkjDIb2NPj9y14Ahvc2GJlp4Lpwxuuy8HNfnfE2R3mblaG4dIiJ64LjuCxd63DwuN6WhBiYO9YkIVrhAi984KXgVN3p2RzxDeyumDDARAHlFS6LPvdx7KRe1ti+BsN667Q7XuCy4LOaaRcRBnPH6DqDC7y1MrTOEBmuf0+KU3h9Lm+u9HGisO5trE1dx8L353kor0Cnrevy5NvB+Fry2BuZaTCmr86H//hKRUg+3DVFMX24iWFAaXkwXVoyvivHmnRNUZT7638LVno5dhLG9TMY2E0PiDUMaB/f8rEBXDrYoF9XPc/anQ6rdzgkx+u4U5MUH25wWLnt/MtkceGTxnMbZFmWx7Ztbx0/JwLfBh4BsG37MNAiDefeaYrkeMXfF3hJb6+YPdoMKRwqzR5jBgqsm6ea9Oqs2H3YZcIAg71HXZZv9jFhgMHEAQbvrXeIDINZo02e/8BL4SmIiQwua8Yok905DvYnutIeZp5n3HGKB9+oIL29Ys4YkyeW1EzeOWNN3vQ3zr8y1UPvNMWuHJdjBS4vfuRl7tjzO12U0pWM5z/0UVQK35xusiPbR25RcJqhPXVD7x9v+ejfVTFtqMFryx3ax0P/rgaPLvIRFwU3TzV5eKEPx4F31zscPQnhHrhthsneI8Flfr7dYeW25mkwV5X9zOvsf+R5hvz7gWZfF+i0vGK4wYsfOxSVwdcuM9h12A1Jy8E9FKfPwGNLHPplKKYMVixY6eL1wSebHTokKDokBKePCodLByuees+htBxmj1J06wj7jzc+1unDDV74yEdRGXzzcpOdOaH7fYg/1ocX+ejfRTF1sMHrKxy8Pvh4k0OHREXHhPNvVDXHsedz4NkPfFR4deP5a5eb7D4MOXlw6ITLzhwft0w7jxO1Cp/Px+OP/J17f/dHktt34Cc/+DajxowNaRxPnnIpM2bpRuHnq1bw7ycf49e/CTas/vXkowwbMapRcdTFcXy88dTvuP1/nyQhOYUHf3kd/YZNoVN6r8A07dqnct2dv+OTRU+HzLt/5xfs3/kFP37gDQAe/vVX2LNtDb36NU2sPp/D/3viGf7665/RMTmJb/7PPUwYNYzuGWkh05WWlfHKonfol9kz8F2Prun888/34TFNcvMLuPWHv2D8yKF4zMbtz6ocx8eCZ37LN3/+TxKSUvjHPdfRb/gUUtKCaZeYnIp1x/0sW/JUyLw9+43mB/frdCstKeCPP55O74HjG7zumaNMnvvAS1Ep3DbDw45sh9wqDaehvQxOn3F56M3Kc8HkteU+2idA/24Gjyz0EhcFX5nm4R9veetdZmqSIjI89Pz9cr/Dul264p2ZrrhieO1p21zl7b4jLo8e0rGnJMK1kz384039ef54k2VfOuw94hLu0Y3AujRXfAUl8NQ7Xk6fgV6dFXPH6OXGRcHoPgb/eMuL16cb3gO618wbp48w2X3E4ZVPXYxa6gwTBxgcO6kb68nxMHOkyXMf1H0Bo7q6joXKtHrm/dovGrbksXfohMPOHIdbLwuts0SEwayRJs9/qOeJjoCfXqsv7rb0ufHeeh/bDoYeYCu2OqzY6j830hRj+hp076RaNLYhPRTxMSowXXSEXkdZOSxd69AnXXpthTSem5VlWXcDtwDHgUPAOsuyPgZ+Ytv2Wsuy2gNrbdvuZlnWrcBVQCxgWpY1C3gTaAeEAb+0bftN4A9AT8uyNgDvAQ8Di2zbHmBZViTwKDAC8AI/sm37I/+y5wLRQE/gDdu2/+dct6dPhmLDHp2xZee6RIYrYqOgpCw4TWwURISpwJXeDXsc+nbRhWWfDIOn3vUGvv/a5R7eW+8wsIfBtoNO4ErhKX+PaUQYdO2oeOMzvSyfo/+de9wGG/aeY9x7HfpkGOzK8YVk0ucjLRlOFrsUlOjPWw44ZGUocrezG3koAAAgAElEQVQEC46sdBXovdt60GXGCH31NStDseWAg8+BglN6OWnJkJ0LJf50OuOF3EKX+GhFblHzN5iryl++lqiuafVP2EQ6J8HJYgK9IVsPuvROC93uzM6KT/1Xkbdlu1w+3ABcKnw63drFhi4zMRZOlkCpv8Kz/5hO9/3HG5eWnZPgZIkbiHXLQYesdEXu1jr2+yGX6f79XuGDQ7nQLq5RITTbsVfhv/ZkGPpfpaMnGxdvpV07t9OpcxqdUjsDMHHSFFavXBHSeI6Ojgn8XX76NFWrNKtWLCelUyqREVWuxDWhg7u/JDklg+SUDACGjJ3JlnUfhTSekzro86LmEDlFxZkz+LwVuK6Lz+clLiG5yWLbtmsP6akppHXqCMC0CWNYvnpdjcbzk/95jZvmz+Y/CxYHvouMiAj8fabiTJP2hlc6tOdLklO6kNxRp93gMTPYuu7DkMZzIO1U3Y9l+XL1u2QNnkh4RFSD151f9VzY79An3WB5YbBQyUpXfLIpeC7MHKm3v0+6wZb9wXMhv9glLVmddZlKwWXDDF7/zEffjGBV60xFMJ6zNVCbq7w9U+W6cZhHj+gC6JCgz+W9/p65M3Vdvm/m+A6dCCZIdq5LfEzwGKxsDDsOhHmguDQ0pogw3bP65kq9DMeB8mp1hvYJis826y/ziiAxVhETqesdA7srRmcZmIYiJ89h8Wqnxv6p61iob0RcSx57deXDA7sbbDvkUORPt8oyLy1ZtWh8DTGgm8Hm/Q7dOxktGtuITIPXlgcvplSmUWm57qnvnSaNZyEPDGs2lmUNB64HhgAzgZENmG0YcI1t25OB08B827aHAVOAv1iWpYCfA3ts2x5i2/ZPq83/HcC1bXsgcAPwjL9BjT+O64CBwHWWZWWc6zbFRatApgtQVKobbFXFRyuKSt0q0+j5AGKqFKwlZfozQHKc7v279XKTO2Z5GNxDT98uFk6Vu8wbZ3LnbA9zx5qEncflnvhoReGpqjHVEXfVaU7VnOZ8xUUpCkPSTX8XMk20Clw8cF04XQFREXq6olNnnzchBjolhRbeIzMN7phpMmeMQWR4k2xGmxAXBUVlwe0sLtXfhUwTTeA4dV0or9DHV11OFkNSHCRE657azDRFfFTj9318jfOllv0epUJiPX3m7LGeq+Y69pSC22eY/ORqk71HXHLymi5mgLy8XNq37xD4nNy+A3l5uTWmW7xwAXd8/Wae/vcT3HbndwEoKyvj9Vdf4vobv9q0QVVRePIYicmpgc+JSSkU5h9r0LzdMofQq/8o7v32Jdz37UvIGjSelLSe9c/YQCfyT9KxfVLgc4fkJE7khdamd+zZz/HcPMaNGFJj/i07d3Pz937OLT/4BT+582tN2usM/rRL6hT4nJDUicKT5z7MY+OqtxkydtY5zVO9/IqLDv09PlpR6C+/Qs6FaALfgz/fiQ7Na6ovc1Smwc5sN6QxWWlkpsFdV3qYNtRk6draez2bq7wF3fD97pUebppqsmCFXn9yvB4Fc91kXd5ePlz3SNalOeOrNKyXwa4cPX9xGazY4vDDqz385FoP5WcIDMGtlBirb/u6cqzJ7TM9zBlj1uh5PnbSpW8XHUPnZEViDMRHExhp8+93fDy+xIvjwMBuNROgrmMB9HWIr0z1cNsMD8N6hc7bksdeXSrrWrdcZnLbDA+D/D33DVlWU8d36RCTO2d5uGK4gVmtJeIx9aiDrf6e6ZaMrV2cYkA3g9tmmNw4xSSpkRew26TKq94Xwr82Snqem89EdA9vKYBlWW81YJ73bNuuvGtIAfdbljUJcIA0IKWe+ScADwHYtr3dsqwDQKb/tw9s2y70x7IV6IruDW89/jzNMCA1WfHMez7CTPjmDA/ZJ7wYhiI1SbFktY+cXJcZI/XQrg83yL0mlcI8cO1Ek3fWBXsU1u5yWLZZFyJTBhtcNsxg4SpJs7qcroCl6xzmjdP30uXkuSTGyNXls3FdeOJtHxFhcN0kgw4JnPN9g01h1px5zJozj08++gD7pef5wY9/zksvPMPcedcQFdXwHsmWlHv0AMdy9vJ///gAgCfuv42929fRo8/wFlm/4zg89NQL3P2922v9vX9mL57/+x/YfyiH3/39CcYMG0REeNu6Ald08gRHD+0k8xyGbLek2Cjo11Xx9Hu1N4zX7HRYs9NhQDfFxAFNe3GiTlXamdsPuWw/5KVrR8WlQw2efc+HofRIr8cW6dunrp1kMrSnYv3uFhrNVG013VIUw3oZ/OsdXbBFhuvRMH97XQ/ptiabgcZfJUPpOsPba3zk5OmRPBMGGHy0MVj+Ld/iMH2EyR0zPRwrcDly0sVxoXsn/YyJ22boarHHA6fKnZqBncVT73gpLtNDfb8yzUNuUcOHg7cEw9C3Ejz7vg+PB75xRes0AT7Y4KOkTN+rPHu0yfj+Bsu+DO1JPnjCDTwTpyV5DPD64Mm3ffTJUMwda/L0u21rP4rWJ43nlucl2ONffUxh1Udz3AR0AIbbtl1hWdb+WqY/F1XvwvFRx763LOt24HaAvrNfYFSWfkAHwOE8/aCqStWvKkPNq8/x0VDsn+ZUGYFhXbFRweHZRaVQVu5S4dXDQQ8cc0lJUhw85lJUCjn+HtUtB1wmDmjYlahRWQbDM/W0ObkuCTGKykKwzrirNJjiY2pOc76Ky1wSqqdJWeiyi0tdEmL01XWl9IPSysr1dPHB0akh8xoKrIl6aNP2Q8HlVaYr6Adh3HBJC1XOWkBxGf5eYb29cdH6u5BpSivTSadlRBiU1VMI7z4Muw/rwntID4XjNH7f13ouVN/vZW5IrJHh9cd6Lprr2KtUXgH7j7n06qw4Udh0lezk5Pbk5p4IfM7LPUFycvs6p584eQqPPfwgADt3bGPF8mU88+8nOHWqBKUMwsPDmTVnXpPFl9AuhYK8I4HPBfnHSEiq79qm9uWaD+jaaxARkTpxs4ZM4MCuDU3WeO6Q1I7jucEnN53Iy6dDcrvA59Ky0+w7mM1dv7wfgPyCQn52/1954Bc/pE+vHoHpumWkERUZwb6D2SHfN1ZCuxQK8o8GPhfmHyWhXcdzWsamz5fSf8Q0TE/YOc1XvfyqPuy3qFSfL8Wlbui5UIr/PKqS75TWvczUJEVSnOJ7V+piNswDd13p4aE3Q8dCb97vMmtU8PxsifK2qgPHXdrFKqIjdDl8NN/lpH8o67ZDDhntg9vckvGlJMKV40yer3L/cI9URUGV22u2HXTI6Fi9d9dfZ/A/1HPrAZfx1eoMZyr0Q8QqfX+eh5Ml0LUjbNzr8EG1i/N9MhSTB+oy9K1VvrMeC5VlUWk5bD/kBIYIV2571bRqrmPvbAJ1LZ++NejgcZfkeBUoM1sqvsoRBz5H3yI3rm/oPurfVddrWiPtikr1sQX6ItOVY+VCuqip7faJX/iWAfMsy4qyLCsOqHzc6X6gspZ0tgd9JQDH/Q3nKeieYoBioK6BJJ+iG91YlpUJdAF2nEvQtm0/Ydv2CNu2RwCs3uHw2CIvjy3ysu2gw5Ce+pBJb684XVFzSFpJmX5CZnp7neEM6WkEGnY7soPz6+8rMyiHLh0VhtL3M6W1V+QWupSc1sOnk/1PXOyR2vAK+uodDo8u9PLoQi/bDzoM6XGOcfcIxtdYOXmQFKeHhxmGLhh2Zodux44cl0H+GPt1Uew7pn/fme3Sv6se1pQYo5dTOUR2zhiDE0WwqtrTpmOrXGLpk6E4XtCy90E3p8P5+j7gBH9a9uuiAsP6Ku067AaG2/VNVxw4Vv/2Vz4UJDIMhvdSbNzb+DQ7nF9tv3epud935rgM7u7f7xmK/Q2I9Vw0x7EXHaEvSIAeXtcj1Qh5AFlT6J3ZhyOHczh29AgVFRV8uuwjRo0ZFzLN4ZzswN9r16witbO+T/b3f3qQJ5/+D08+/R/mXHk111x3Y5M2nAEyeg4g9+hB8o5n4/WeYcPKJfQfPqVB87Zrn8rebWvx+bz4vBXs3baWjp2brnHap3cPDh05yuFjx6mo8PL+8lWMHzks8HtsTDSLn32UV5/4K68+8Vf6ZfYMNJwPHzuO16cbFkeP53Ig5widOnaoa1XnJb3HAPKOHiDfn3YbV71N32ENS7tKG1YuZsjYmee87uSq50I3gx3ZoXn8zmw3cNtQ1XNhR7ZD/27BcyE5TpGT55KT59a6zF05Ln95zcuDC/S/Ci+BhnPVYaCZafpe00otUd5WXX9qkj6HS8t1gzMyXAXywh6djBqjSVoivoQYuO4SD68v95FXHFxW4SlI76ACw7B7pBo1nkdy6rQepltZZ+iequsSVUWEBUeDDuulOHDc5UwF7D3q0LeLESwLwnUs2w+5PL7Ey+NLvBzJd+s8FsJMfQ876HpMz9TQsreljr2z2XHIIaODfquEx1/XAhq0rKaML7bqLQTpoekUEaZHHew41Dppt/2QQ7dOelldUxR5xRdP/amSMtQF86+tkp7nZmLb9nrLsl4GNqIfGLbG/9OfAdvfw7u4rvmBF4CFlmV9CawFtvuXm2dZ1meWZW0G3kY/MKzSI8Cj/nm8wK22bZdbVtO8pmVXjktmmsv353uo8BK4VwrgztkeHlukKweLP3eYN07fn7wrxwk0bj7d7GBNMhnWy0PBKZdXPtHz5xbq1wN9a44n8Nql4wV6uUtW+7h6golpKk4WuyHrbKidOS69011+cFVY4FVVlb41x8OjC3Xci1b5mD/eJMyjQuLu20Uxc5SHmEi4eaqHo/kuz75fz9NUqnBdeHutw02XmiilH45yohAuGWRwOM9lZ47LF7td5o9TfHeuqV9Z5I/xRCFsPeDwrdkmjn85rgsZHWBwD/3U0Ntn6NpE5Suppg0zSGmnHwRTcMpl8efNN2R7yHN/IXnyKMLbt+PSfZ+w676HOPTUq822PtfVTxm/frJ+9dLGvfpJ25MGKI7ku+w6DBv2uswdY3DnTH0P34KVwe3/9myDCI8eLpaZpnjpE4fcIrhsqCIlUWfUy7e45Jc0TaxL1zrceInpf62Ww4ki/SqxI/n+/b7HZd5YxXdmm5SdgderHJt3zTGJCNOxZqWbvPCR75wbqc1x7MVG6Vd2GEr3VG89EDxXRmUpxvUziI2EO2ea7Drssug8jj/TNLn9W3fx61/+DMdxmHr5DLp07cYLzz1Fr95ZjB4zjsULF7Bxw3o8Hg8xsbH84Mc/O+f1nC/T9DD/1rt58g+34zoOIy+ZT6f0Xix95SEyevSn//BLObjnS5756/cpPVXE1vUf8+6rD/PTP73FoNGXs3vL5/zlZ/NBQZ9BExrc8G4Ij2nyo9u+yo/u/ROO4zBr6iR6dEnnn/95jT69ujNh1LA65920bSfPv74Ij2liGIof33ELifFNe9OfaXq48pa7+dcfb8NxHEZOnk+n9N68++pDpHfvT7/hl3Joz5c8+7fvUVZaxLYvPuK91/7Bjx9YCED+iRwK84/SvU9DHiMSaskaHzdP9dQ8F/Jddma7rN/tMH+8yV1Xeigrd3l1eei58O05HhxXL6fyQVK1LfNsRmUZdO9k4DhQdkaXad+eU7Mfo7nK235dDAb3NPA54PW5vLJMf++68M46H7dc7kGhe5Yrnwpem+aKb/Igk+gI/QYO0K/TemKJvnVr6wGHO2Z7cBzdS752p8PMUaEjq95e4+Oq8SamoThZol9FNdzfW75ul37bwrxxJi5wosDlrVXBeshHG318xb8vfY7et1WflwJ1HwsxUXDdZF2lNhRs3u+E3JPdksfeqCyD8f0MYqPgW7M87DrssnCVLj/2HHH51iwPLrquNX2Efm1ZS8Z31XiT6AjdiD96Ur+OrFKfDMWeI7p3vDXSbvkWh6smmIzpo/Sr5PyjFGIi4fYZHiLCdD/2mD7S9/jfTLlnexeB+K/2q2cr2uzBce9Xw7jnmVa4IaYB7rtF3x943wsNb2C3pHtu8rA4LKu1w6jTrIod3P9y27zH6BfX6Yrab15sm/v2/27Qlbe2fOxt35Nd/4StoE/PdAAWrmubaTdnuN63J7aubuVIateh3ygWrGmb5+28kfq8vff5inqmbB2/ulkP5fjVs20zvnu/GtamY4O2v2/bcnxtNTZo2/H5923b7R6tQ/5v72izdfvqkn75eJtMX+l5FkIIIYQQQoiL3NleASgaRlJQCCGEEEIIIYSohzSehRBCCCGEEEKIekjjWQghhBBCCCGEqIfc8yyEEEIIIYQQF7s2/AqoC4X0PAshhBBCCCGEEPWQxrMQQgghhBBCCFEPGbYthBBCCCGEEBc5ZUi/aWNJCgohhBBCCCGEEPWQxrMQQgghhBBCCFEPaTwLIYQQQgghhBD1kHuehRBCCCGEEOIip+RVVY0mPc9CCCGEEEIIIUQ9pPEshBBCCCGEEELUQ4ZtCyGEEEIIIcTFTkm/aWNJCgohhBBCCCGEEPWQxrMQQgghhBBCCFEPGbYthBBCCCGEEBc5edp240nPsxBCCCGEEEIIUQ/lum5rxyDaLjk4hBBCCCGEqOmC68Yt+n8/uGDq9vE/+lubTF/peRZCCCGEEEIIIeoh9zyLOt33gre1Q6jTPTd5uPf5itYOo1a/ujlM//9s24zv3q+Gcf/LvtYOo06/uM5kcVhWa4dRq1kVOwA4/fIfWzmS2kVe9z8AvL+pvJUjqd20QRH85sW2ma/83w26OGyr58YvrjMB+L3dNuP7X8tss2XGPTfpfdvWy4y2HF9bjg3adtpB246vrcYGOr7fvdQ287y7rzdbO4TzY0i/aWNJCgohhBBCCCGEEPWQxrMQQgghhBBCCFEPGbYthBBCCCGEEBc5pdrkM7guKNLzLIQQQgghhBBC1EMaz0IIIYQQQgghRD1k2LYQQgghhBBCXOzkaduNJikohBBCCCGEEELUQxrPQgghhBBCCCFEPaTxLIQQQgghhBBC1EPueRZCCCGEEEKIi5wy5FVVjSU9z0IIIYQQQgghRD2k8SyEEEIIIYQQQtRDhm0LIYQQQgghxMVOSb9pY0kKCiGEEEIIIYQQ9ZDGsxBCCCGEEEIIUQ9pPAshhBBCCCGEEPWQe56FEEIIIYQQ4mInr6pqNOl5FkIIIYQQQggh6iE9z22MZVm/Bkps2/5zA6cfAnS2bXtJswYG9ExVXDHCwFDwxW6Hz7a6Ib+bBswbZ5CapCgrh1eX+yg8pX8b318xtKeB48I7ax32HNHzRoTBnDEGHRMULrBwlY/sXOjbRTF5oEGHBPjnUh9H8muPKT4a5o0ziY3U86/f5fD5DidkmoHdFOP7mwCc8bos/tzHsYJz2/YJ/Q2G9tLxL13jC8T//XkeyivAdcFxXZ5821fnMmaMNOidZlDhgwWfeWvdptQkmD/eg8eEXTkOb6/R2xIVDtdOMkmMVRSUuNjLfJw+E5yvc7LimzNMXl3mY+tBl24piukjzcDv7RPg1WU+th9yq6+yhh6d4LKhBkrBxr0uK7fX3M9zRis6tVOUnYEFKxwKS3WMV40zSE2CTftd3l0fnK9vhmJ8P4VSsPuwy0eb6o+jsQY9eT8dZ17CmeN5LBs6p9nXV5vPdmXzwJJVOK7D/GFZfGPS4BrTvLN5L4999AUAWZ2S+MO1UwK/lZw+w/x/vMaUPl35xexxTRrbli+W8+pTD+A4DuOnXsXl878R8vsHC59lxQevY5gmsfHtuPnb95HcoTOH9m3n5Sd/S1nZKQzDYPpVtzF8/PQmialnquKKYfrY+2KPw4ptNY+9K8cE85jXVug8Jiocrplg0DlJsXGfy9J1+rzxmHDNeIN2cQrXhZ05Lh9udGpbdb3a+nnRoxNMG6Lz5w37XFbVEt/sUYrUyvhW6vi6pcAlAw1MA3wOfLTJ4cBxPY9hwOVDFV076vT75EuHHTkNj6k5yozvXWlS7gXXAcfV5QPA5IEGQ3spSk/r+T/c6LD7cM30nD7Cnw97YcFKL0fryIevHOshzKPz4aVr9TETGQ7XTDRJjFEUnHJ59dNgPlzXcuOjYe4Yk/gYBS688JGXwlPQvZPismEmZ+sDao5Yz1Yezh1jkpmuOHUaHl3kPUtkzRdfcjxcOdYkNUnx4QaHldv09MnxcM2EYJW1Xaw+VuvSOVnxjStMXl3uY9vB0OMg3ANfuzy4rPho2LTP4Z11Dc8bEmPg6okm0RGKw3kub6zw4TgwuIfer8WlNedpaF2kW4ri8uEmpgGH81zeWuXDPYesoa607ZqiuH6ySUGJnm7bodDtbclzI6UdzBplEhGm85ZPN/vYckBv5NwxJqnJKnBuhHmgoo7DsUcnuNxfZmzY67KyljJj7phgvvzGCidQZlw13qBzEmza5/LO+poJfO1Eg8QYeHLp+ZUZ4uIljecWYFmWadt23a2qxhkCjACatfGslG78Pf+hj6JS+OZ0kx3ZPnKLgtMM7akzp3+85aN/V8W0oQavLXdoHw/9uxo8ushHXBTcPNXk4YW6MJg+wmDPYZdXP3UwDAjzt/dOFLi8sszHrNFm7QH5OS68u97H0XxdIN4+08Oeow65hcFpTpbA0+95OX0GenVWzB5j8q+lDd8d7ROgfzeDRxZ6iYuCr0zz8I+3vIHC7Jn3vZSVn30ZvdMUyfGKvy/wkt5eMXu0WWtDe/YYk7dW+sjOdbl5qkmvzordh10mDDDYe9Rl+WYfEwYYTBxg8N56naErBZcN0+lYaf8xl8f8lZ+ocPjefE/I73VRCq4YbvDixw5FZfC1ywx2HXZD9vPgHorTZ+CxJQ79MhRTBisWrHTx+uCTzQ4dEhQdEoLTR4XDpYMVT73nUFquK/DdOsL+4/WG0yjZz7zO/keeZ8i/H2jeFdXB5zjcv2gFj98ynZT4GG58/C0u6dOFnh3bBaY5kFfIv5Zt5JlvziY+KoK8krKQZTz84TqGd+3U5LE5Ph/2v+7nrv97gsSkFP74vzcwcMQlpGb0DEyT0b0PP3vgRcIjolj2zssseO6vfONHfyI8IpKv3vU7OqZ2pSD/OA/87Hr6DhlHdEx8o2JSCqYPN3jhIx9FZfDNy0125oTmMUP8x97Di3z076KYOtjg9RUOXh98vMmhQ6KiY0Joc2TldpcDx3X+8pUpJj1TVaAhdi6xteXzQildgXzpEx3frdN0fHlV4+uuOF0Bj73t0DdDcckgxZurXH+j1aHkNLSPh+snGfxjkc5bxvdVlJbD428HL+KdS0zNUWYAPPu+r9Y89/PtTo3Kc1W9OiuS4hQPveklrb1i1qjay4JZo0wWfu4jJ9flxilV8uH+BvuOuny2xcf4/gYT+hu8/4Vz1uXOH2/y6ZcOe4+6hHkIbMOsUSYvfewltwh+dXNYi8V6tvJww16H1Ttd5o+rv2rYXPGVlcPStQ590kPP47wieHyJLtOUgh9d5WH7IYfpI2rWEZSCaUONOs/zM97gsgBum+FhWwMuLFc1bZjJqm0OWw64zBplMKynwdpd+jzZciB44bvqvm1oXWTeOJNn3/eSXwyXDDIY0kPxxZ6Gx1dX2gIcPO7y4sfBdU4ZrNOvpc+NCi8sWOEjvxhio3TdbfdhL+UVsHSdjzMVBNJvRG9V63mtlK5D/ucjne99/TKDXTlurWXGo4sd+nVRXDpY8cYKf778pUPHavlypax0fYGDs17eujApeVVVo0njuQlYlhUD2EA6YAK/AR4AXgYuA/5oWVYBcL//91zbtqeeZZGDLctaCbQH/mjb9pOWZT0LvG7b9gL/Ol/wr/M+IMqyrAnA74FFwEPAACAM+LVt229altUfeAoIRw/Xv9q27V0N3ca0ZDhZ7AauWG454JCVocjdEszQstIVn3ypM+itB11mjNAnaFaGYssBB58DBaf0ctKS4UQhdOmoeHOlnsdxoNx/ga9q5nc2JWX6H+gC8UShS3yUIrcwGFd2bujf8dHBzHBgd8XoLAPTUOTkOSxe7dS4wtsn3WDL/mD8+cUuackqZLn16ZOh2LDHCcQQGa6IjQrGDroAiQgLLnfDHoe+XXTB1CfD4Kl3vYHvv3a5J9B4Ht3HYNtBl87JtWfy/boqdue4VDTgekHnJDhZrLcT9H7snabILQpua2Znxadb9Lq3ZbtcPtwA9PKzc3WPQFWJsbrSUOqv7O4/po+J/cebt/c5f/laorqmNes6zmZz9gkykuJJT9KNyukDe/Dx9oMhjefX1+7g+tH9iI+KACA5Nirw29bDueSVlDG+dzpbcnKbNLb9uzfToVMX2qekAzB8/HQ2rf0opPGcOWBU4O/umYNY8+liAFI6dwt8n5jUkbiEJEqKTja68dw5CU6WuIFjb8tBh6x0Re7WOvKYQy7T/XlMhQ8O5UK7uNBlen1wwH+cOQ4cOekSH32esbXh80KnXTC+bQddMjsrVlaJr3eaYrk/vu3ZLpcP0/FV7fnKLdK99ZW90IO6K554O9jrUlZltEt9mqPMyG7kadAnQ7Fpn15fTj35cI4/H960z6FPhs6HszIMnnlP58Mb9zrccpmH979w6lxuZLi+vXDvUb2sqr1nLno9+q+Wi/Vs5eHB4y4JMa2blqXlUFquz6+6dO+kyC92A6MUqhuVdfYysaqkOIiJ1NsOEB0Bs0abgXR4Z63DoRM191H3FMVry11//C6TBxmsradGdba0rxQdoc+9/GL9ee8RffH8iz0+wkyYMdKkYyIYhuKTTT52ZNeMra60PZuWPjcqtw/0Ok6d1vuhvIJAwzmgjiyxc5JeTtV8ObNavtw7TfHpZn++fMjlimr5clJczeWGeWB0lsGSNQ7zx118jWfReHL5oWlMBw7btj3Ytu0BwFL/93m2bQ8DPgCeRDdYBwPX1rO8QcClwFjgHsuyOgP/Am4FsCwrARgHLAbuAV62bXuIbdsvA3cDH9q2PQqYAvzJ37i/E3jQtu3Knursc9nAuChFYZVhSEWl+ruQaaJVoDBzXThdAVERerqiUzXnTYyF0tMwd4zBbTNMZo82Aj3P5yMhBqwAkA8AACAASURBVFKTFNl5dVc+h/Y0AkP5Kns3/v2Oj8eXeHEcPayqxrZHQ2FpcJnFpfo70Hn6V6Z6uG2Gh2G96s5k46IVRSHpV7PgjI9WFFVZT1Gpng8gpkohVlKmPwPERemhn2t21F0wDuhm8OW+hg07iouCorJq2xpVbZpoAtviurqwO1uP1MliXUAlROsrxZlpivioi79AOl5cSqcqNdGO8dEcKwqt7R3IK+RAbiG3PLmQm594i8926dPScVz+svRzfnzF6GaJrSD/GO2SUwKfE5NSKMiru8tzxQdv0G/ohBrf79/1JV5vBe1TMhodU3yNc6SWPCZKhRx7p880vDc0Ikwfe/uOnnvjtK2fF7FRhOQdxWW1xBdVf3xZ6XC0QFfeI/wdZpMGKL52mcG8sQbREQ2PqTnKDNB57s2Xmnxzulkjzx2ZaXDHTJM5Ywwia0n7uCgV0uAqOuXWcYxVyYdPBdcdGxmaD8dGnn25yXG618uaZHL7TA+X+YeXAixc6ePGS01+OL/2PozmirWqquXhuWqJ+OoyoKvB5v21xx0XpRuCa3Y2rMwb0M1gy4HgtNNH6B7lf77tw/7Ex5wxNSslURH6WK280F69PO/bxeDOWR6unVh3haautC8t1xdcUpP08vp1VYFlTxxosO+Ywz+X+njmPS+XDTNrrTOdLW3TOyjumOXhxilmSI9rS58bVXVOVphGaIN67liTH1+tz401u+re18VV4znPfK+6yQMVn293GtThIP47Sc9z0/gS+ItlWQ8Ai2zb/tSyLNA9zwBjgGW2be8DsG27jjt4A960bbsMKLMs6yNglG3bCyzLesSyrA7A1cBrtm17/eup6nJgrmVZP/F/jgS6ACuBuy3LSkf3YNd6jdSyrNuB2wEGXPmfhm7/edEFhB6ilZOnh0WO72/w8VnuY6pLmAesSR6WrvXVvGrp1y1FMbSXwVPv6Kuj3TvpeyRvm6FPA48HTpU71HmZsxZPveOluExfLf7KNA+5RS2U2/pDnDHS5L31Tp0Rx0ZBSjt13hWkpnC6Apauc5g3zsB1ISfPJTHm4m88N4TXcTmQX8Q/vz6LY0Wn+Pq/FvPqd+azeNMeJvTOIKWh3UDNaPWyRRzcu4Uf3PtUyPeFJ0/wzEO/4Kvf/S2G0bavwyql7ztevdMJ9FK0trZ2XrSPhymD9NBv0PlzfLQiO8/hg40uIzMVUwcrFq5uvbwE4Ol3fYE89+apJrlFPg4eh7W7HJZt1hXkKYMNLhtmsHBV896rWN99qIahR1c9vkTf53zNRDMwBHdMX4P/fOgjJ8+tddh2c8davTxsbQ29p9cw9IiFDzbUXtZeMcKst5e1qgFdDd5YEUyDHqmKDgnBFmlE2Nnvua1uZ7bL5v1efA4M723Qr2vNaepL+9eW+7hihIHHgD1H3EDa9ExVZKUbjOurP3tM3WlQ30i9yvmP5Lv87Q0vFV49TPu6yc3XBGjo/oyN0rc2LFgRuj/fWulDKbjnpjD6dVFs2tcy+U5KIrSLVbz/RcNHYVxw5GnbjSaN5yZg2/ZOy7KGATOB31qW9YH/p/OtplXPJSo/PwvcDFwPfK2OeRW6h3tHte+3/X/27jw+ivp+/PhrZjaEJCSBBIiQg5sA4RYQBLm0ghwiFsez9rRqrT1sv1/7baut+mvt8bV+1Vrvaq14jCjKjQdYDgU55D5CuBPOHOS+dmd+f3w22d1cmxCSLPh+Ph48NLszn33vZ2feM5/5fOYzpmluBGYCy0zTvNuyrFU1V7Ys60XgRYBH57ur4ygsdYj1u7IaE6le81dYopJNYak6WW0fBqXlarkYvyRUtW5BiboimJXjDfCYzfi0pp+E65q6sr/ziF3vhFhdO8LssQbzV7mrhx6qiX9sPt0WeKAdkKwxaYg6eC7a4KGwBO93V2VHR1I9GUih90prSTnsO26T6DdMbEyqzsh+6vucyAkcLlqzlxlqX72OifRdVS0upXoIVYcINcQJ1BXbeRNVrJHh0C/RwHZ8E4MN7qGGr9mNPO4UluLt/fL7rqU1limp+g1VHYaHBR/OmXECMk6oeh7eW8NubEAXsa7RkZzyu+R+pqCEhJjAo3FCTBRDkroQZugkdYqmR3wMx3IL2HH8DFuPnsLatJeSikoqPTaR7cL42bWjL0hsHeMSyMs5Xf33udzTdIzvWmu5fTs2sOL9l/j5I/8kLMx3ub60pIjnHr+P2bfeT6/+tSdBOx91bv81c0ypE7DttW/XuKHEs8bo5BbCl/vPb7sL9f2iqBRv3Xnji6gjvtL644uOgG+O11m80XdxobRC3fO33ztGad9xh2G9dBp7cbEljhlV3wNUzt1/XN1Cc+yMU50TAbZm2Nw6WeXFUf19MRSVqsbG8bPecqO0erYxv7ijfJ9dVFYjD5f7fdc6ytV1jVN5vqHr+4/bJHXW2J/pkNBJI6vGKKnR/XVG9vUdM1oiVqj7eNgYrRVfQ/p11ziZG/h7A9w9Q53OhreDeRNqHBPtuoc3J3RUjXH/yTs14OUVqvHr7/apamLSE7kOizd4aB+mtlnHCTye+9fn1gybWd55W+6e4WL+ajeR4cHrPjPb4bWPVGOydzeN+Jiq2DWsNe6AuQxA9dJ266Tq/83Vnnrr1r9jIeOEg/duIe6e4Wqx37O+fQOgXRjcNsXFqm2e6qHg/qoa4AOS6m48F5ZWjc5T78U0Me/VJbGzRrc4uG+2mugwKhzumKrzxiqZNEz4hHZ3wUXCO6y6xLKsN4C/AiNrLLIBmGiaZi/v8nFBipxjmmZ70zTjgcnAJu/rrwE/A7Asa4/3tULA/66NlcD9pmlq3s8a4f1vb+CQZVlPAx+ihoY3WlYOxEVrdIxSB5u0HjrpNQ5G+7MchvZWm9SgFI3Dp9X76ZkOaT3UbK4do1Q5WTmqAVhQAvHe6HtdpnM2nya7fpxBdr7Dhr11J7eYSLh5kouF6z0Bw4IOnbIZmOIbiti+nTqx2nfc4YVlbl5Y5uZkrsP+TJu0nr7446PVSU+YoSYpAzXRWZ9uGmfO+erky/02zy9x8/wSN3uP2Qzvo+omqbNGWaVDjbmhKCqF8kqHpM7q4DS8j17dCN6f6Vtfva6+6/8tdPN/76t/e46q2Tv9LyAM7qU1esg2wIlcdd9orPd3HpSicSAr8Hc+cMKpHt4+MEnj6OngJ9PVdRwGl/fV2H7o0m88pyV24VhuAZl5hVS6PazYeYhJA1IClpk6sAebj5wEIK+4jKM5BSR1iubxeZNZ+YtbWP7AzTwwbQyzhvW9YA1ngB590zhz8ijZpzNxV1ayZf0KhoyaHLDM8cN7eevFR7nnwaeJjo2vft1dWcmLf/0ZYybNZuS4ay9YTCdya+SYlNo5Jj2rqgEHg5I1jjRi25s8RCc8DFZuPf+Tn1DfL07kqnuqq+IbmKJx4ETt+AZ74xuQpFXfCx4epmaVXb3Drr6QWSXjhEMP7zWVnglao+eigJY5ZtTMub27aZzx3rPtPzR1QLIvF29O933mvkybod7tJ7GzRnlF/Xk40ZuHh/by5eH0TJth3niH9dbZ783D+zOdOss9kaPu8az6nXt6j3GlFeo3r3m/5aZ0u/rY01Kx1nc8bIzWiC+YwT11dh2pvWxVXE9/4OYp7789xxyWfll3w7m+sg6edLhigO/UOME7RcX8Ver2rsUbVKP28GmHQSmaN36t+jP8pq0g1W/SsxeWudG1xtV91fZi6DB+kM5m7xD0gydtxqT6YrvMG9uiL1Rsb65WsdVXt1F++0j3eK26N70lf8/69g1dh5snGmw/ZNeaDb3m/BA5hXX/fuqYEZiX02vm5SyHob28ebkRx4ytGQ5Pf2jz7GKb1z+1ySlEGs6iFul5vjCGoO4ttoFK4F5gQdWblmWd9Q6Hft80TR04g5pIrD47gNWoCcMesyzrhLec06Zp7gU+8Ft2NfAr0zS3oSYMewz4P2CH97MOA7MAE/iWaZqVwCnU5GWN5jiwfLPN7VMN9UiAgzZn89VMkCdyHNKzHL7KcJh7pcaPrzfUY2TWq0R+Nh/2HLW5d5aB7S2n6ori8s0e5o5Xj2TIK3JY5B1ml5qkcd1o1bC9dbLB6TyH+atrJ7DkLhrDeuucznO4e4ZK0J9u8xDrHf645YDNpKEGEe3ULJHge6RUdj6s3u7hW1e70DR1n9+yTR7yiwOTa1X8P5rtwnbUMo6j7juuGvaka7DriF3v7J4Hshz6Jzr8dK6repbJKvfMclXPjL10o80NVxrVj4GoOkFfu8vGnGgwsq+Lc8UO7/4n+PDwjlEQG9W4k/gqjgMfbbW5ZZK66rr9kJq5cuJgdbX/wAn1OIjrx+rcM0Pdz/fBF77f5UezdMJd6qDfP1Hj7f/YZBfAN0ZoJHRUv8m63Q65RY0O6bwN//cTxE8aQ7vOnZh6+D8cePQZjr+6IPiKF4jL0PmfmeO49/UV2LbDDSP707drJ579dAtpiZ2ZPKAHV/ZN5POMTOY+8x66pvHzaaPpGNmEm//Ok2G4ML//a579w73YtodxU26ge3Jflrz9LCl9BjF09BQW/vtvlJeV8PIT6g6QuM6Xcc+vnmHrFyvJ2LuV4sJ8NqxeBMC37nuM5F4DmhWT46hbOG6bbFSPCjlboB5BdDLXm2MOOtwwTuO+WQalFfD+et9+cP9sg/Awte2lJhnMX+2hvBKuGqyTne9w13S1/29Kt9nWxEZqqO8XjgMfb7W5ZaK6p3bHYRXfVWkaJ/McMk6omGdfoXPPdWqG6w+9ufbyvhqdOsCEQToTBqny3l6jJm5avUOtc81w1dO7dFPjTyJb4pgRFaFGGUHtnHvNSJ2ETupxUOeKHZZurB3rgSyHft0d7p+j8vCHX/i2n7tnuKpnYF76pcrDLkONDKi67WXdLpt5VxmM6OMiv9jh3bWeBst1HPh4i4c7r1HHiZO5Dlsy1HdZvNGDOdFVbz9+S8Va3/EQ4MYJBj0TVGP/53NdfLbDU+8szy0VX1R7+OF1LsLDVH/i2AE6zy5xU1Hpu2CyZOP5XWTyjwvUBZ35qwOHTi/f7GHGaIN7ZrrQNTh6Rk0kWtMnX3mYN8Fg6nCVA77KUPFfkarTP0k9Yq20PDDOhur+tikGizZ4KCqF8Wnq0U6aBpvT7eoG35qdNtNHqfupNQ3OFQXOnF2lvrodlKIzqr+Kze1Wj5P6/nS1bbb2vpHWQ6NHgkZkuMZwb6P7gy/cnM5Ts42Hh2nV8wOs3VX37+04sHKLza2TdHS9/rw8Z6zOvTNVXl74ue+3vG+2X15O0njrM7tJFwjF15fmNOXhcaJNmaYZibq/eqRlWefRR9s0/sO2Q83Dt7t45I16bm5uY1X3r/3u9dCM75E7w/jjO6E7E8avbzZYGpba1mHUaWaluhui7J2/tHEkdWt/838D8MmORo6BbGXXDA3nsbdC4x7Lmh66VZ1Ehuq+8eub1Qn34y321MPm+R/T4NH5ofnbPny7+m1D/ZgRyvGFcmwQ2nUHoR1fqMYGKr4/vB2aOe83txhwET7LquSVh0P23L6myO8/GpL1K8O2LxKmaV4D7AWeaY2GsxBCCCGEEEIIHxm23UZM0/wu8NMaL6+3LOu+upa3LOsToI55G4UQQgghhBBCtDRpPLcRy7JeBV4NuqAQQgghhBBCNJcWkiOhLyoybFsIIYQQQgghhAhCGs9CCCGEEEIIIUQQ0ngWQgghhBBCCCGCkHuehRBCCCGEEOJSp0u/aXNJDQohhBBCCCGEEEFI41kIIYQQQgghhAhChm0LIYQQQgghxKVOHlXVbNLzLIQQQgghhBBCBCGNZyGEEEIIIYQQIggZti2EEEIIIYQQlzhNZttuNqlBIYQQQgghhBAiCGk8CyGEEEIIIYQQQUjjWQghhBBCCCGECELueRZCCCGEEEKIS50m/abNJTUohBBCCCGEEEIEoTmO09YxiNAlG4cQQgghhBC1aW0dQFOVvvHHi+bcPuKOX4dk/cqwbSGEEEIIIYS41Okh2R69qEjjWdTr0fnutg6hXg/f7uKRNyrbOow6/e6OMPXf10MzvkfuDOOxt0L3t33oVhdl7/ylrcOoU/ub/xuApWGpbRxJ3WZW7gdgwUa7jSOp27wr9JDNKw/frg6HoZ5XQrn+/vC2p63DqNNvbjGA0P9tQzm+UI4NQrvugJA95j50qytkcwpcHHlFfP3IPc9CCCGEEEIIIUQQ0ngWQgghhBBCCCGCkGHbQgghhBBCCHGJ0+RRVc0mNSiEEEIIIYQQQgQhjWchhBBCCCGEECIIGbYthBBCCCGEEJc6eVRVs0nPsxBCCCGEEEIIEYQ0noUQQgghhBBCiCBk2LYQQgghhBBCXOpktu1mkxoUQgghhBBCCCGCkMazEEIIIYQQQggRhDSehRBCCCGEEEKIIOSeZyGEEEIIIYS41GnyqKrmkp5nIYQQQgghhBAiCGk8CyGEEEIIIYQQQciwbSGEEEIIIYS41OnSb9pcUoNCCCGEEEIIIUQQ0ngWQgghhBBCCCGCkGHbF5hpmr8HiizL+t863psMVFiW9XkD678GLLEsa0EjPy9omRdKn24a00bp6Bp8lWGzfo8T8L6hww1X6nSL0ygthwXrPOQXq/fGp2mM6KNjO7Bys83Bk2rdn8wxKHeDY4PtwMsrPAB8c4JOfLSaEbB9OyirgBeXexqMbfpoA12DrRk263fbdcRm0D1eo6TcYcFaX2wT0nRG9FWxrdjkqY7t+rEG/ZM0isvguSXu6rK+OcGgc0xgbC8scxPMdaN1+iXqVHrgg/VuTubWXqZbHMwd78JlwIEsm+Wb1PeIaAc3TTTo2EHjXJGDtcZDWQX0TNC4dYpBXpFaf+8xm//sUOvMudKgf6KK/x+Lg8dXlz7dNKaN1NE0+Oqgzed7a//mc8b6fvP3Plf1GtEO5k3Q6R6nsf2ww4otdj2f0DzrD2Ty52UbsB2buSNT+f7EYbWWWbnrEM+v/gqA1Mvi+NNNU6rfKyqrYO7f32PKgB78etaVLRJjfYa+9Ee6zphMxZkc1oyY3aqfDZC+Yy1L3/gjtm0zatI8Js2+K+D9w/s2sXT+45w+ns7NP3qCwWOmVb93LvsEC//5EPk5p0DT+PYvXqBTl8TziqMl8kp4GMweq9M1VsMBFm/wkJntK3PsAI1rLzf46wI3peUNx9ZaeWVQisakoQZdYuGl5R5O5gbWQ2vUnaHDd75hYBiga7D3mMN/dqrvPPdKnW7xGrYNWTkOSzfa2MFDrNb7MrjWm0u2HXL4oo5ccv1Yjcs6aZRWwMLP7epccuN4ne5xsOOww8qttT/0pqt0OkbBSyuC55npo7x52A0ffOHmVD15eM44F2EulYdXbFbltm8H864y6Bilca5Y/d5lFQ2XGxOpfvOYKA0cmL/aXf0bVK3XGC0R95CeGuPTDAAq3A5LN3o4fa5R4bRYTPExMGecQbc4jVXbbL7Y6/tNw8NUXXbtqPbrRV/Uf07QPV7j+9MMFqzzsPdY7W3m9qkGHSI0dA2OnbFZtsnGacL2XN/3mjRUZ2RfnZKy+tc93+MqwPhBGsN76zgOrNhic+iUWndMf7U/axpsPWjz5X71+sBkjUlDdDrHwCsfeeo872hIa+aY89ESeeWWSTod2qvRzcfPOqzY4jRp2xCXPmk8t67JQBFwIRu6LVFmLZqmGn9vrPJQUAI/mG6wP9NDdoFvmRF9VHL6+yIPaT00rhmh8946m84xkNZD57klHqIj4I6rDZ5d7KlORq9/4ql1AvveOl8y/cZInfKK+jOXpsGMMQb//tRNQQncdZ2L/Zk22fl+sfXVKatweObDqtgM3lvnoXMspPXU+cdiN9ER8K1rXPx9kRvHgW2HbL5Md5h7ZeBu8t463wH72pE6ZZXB669fokZ8jMbTH7hJ6qwx6wqDl+q4GDBrrMGiLzxkZjvccbVB3+4aGSccJgzWOXTKYd0uDxMG61w1WOfjraqOjp5xeHNV7bK2Zdh8uc9h7vjz2801DaZfrjN/tYeCUvjBtQbpWYG/+fDeGmUV8OwSD2kpGlcP03n/cxu3Bz7bYdOlo0bX2JZ5LILHtvnjks954dvTSYiJ4rYXFjF5QAp9unaqXuZoTj6vrNnOv34wi5iIcHKKSgPKeHbVFi7vcVmLxBdM5r/e58g/3mD4P//c6p9t2x4Wv/4Y3/3vV4iJS+C535kMHDmFrol9q5fpGN+deXc9ztrl/6y1/oIXf8Xk6++m7+DxlJcVo2nnN4ippfLK9FE6B084LFhro+sQZvjKi4lUJ4Tnihs+G2rtvHLmnIO1xs2sKwwaoyXqzmPD6596qHSrE9vvXmuQcQKycmDnEYeFn6ucc+N4nRF9NbYcaNwZpaap3+TN1TYFpfC9b+gcyHLqzCXPLbUZlKIxdZjGws8d3B74z06brrEaXWJrl52apBp+EDzP9O2uERet8cyHbhI7a8wcY/DKitq5c+YYg8UbPWRlO9w2xS8Pp+kcPuWwfreH8Wk6E9J0PvnKbrDcueMN1u5UjZwwFwEn4d3iNNq3a7u484rgtY/dlFWoz5g1tu5yWzOm0nJYsdlmQFLtepk+yiDjpM27a51a+7U/TYNrRujVF6zq8u5aDxXeY/dNEw0GpWjsPtr4FlJ93wtgw15fo/93d4TViu18j6udYyAtRef5ZWq/vX2KwT+WeugcAyP66LzykdqHb5us9q+8Ijib7/DuWg8zRjcur9SMtTVzzPnE1xJ55f31NhXe65rfHK8zMBn21HEB5qJ1nsdr4SM1eAGYpvkb0zTTTdNcB6R6X/uJaZp7TNPcYZrm26Zp9gTuAX5umuY20zSvaqDIa0zT3Owtc5a3vDWmaQ73+8x1pmkOq1mmaZpdTNN8zzTNTd5/473LT/Ius800za9M04xuyndMjIe8QodzRWDbsPuoTWpy4MEtNUljxyF1wNhzzKFXgno/NVlj91Ebjw3nilU5ifGN/+xBKRq7GjioJcZr5PrHdsRmQFLgpp2apLH9kFMdW+/LVGwDknR2H/HFllvokBiv3jt2xmmwVwpgUA+dXUeCXzUdkKyx7aBaLjPboX07jQ4Rgct0iIDwMI3MbBXntoM2A1O8cSbr1etvO2gzIDn4rnu0EfE3pHsc5BU5nCv21usxm9Sk2r/59sPe3/y4Qy9vvVZ64Hg2uBt3HnZedmWeJTkuhqS4GMJcBtOH9OazfccClnl/835uuWIQMRHhAMT7VfqeE9nkFJUyru/59Zg2V+66zVTm5gdfsAVkHtxBXNcU4rom43K1Y+jYGezduipgmU5dErksJbVWw/hMVga2x0PfweMBCG8fRbvwGhtzI7VEXgkPg5SuGl8dVPuRbUO53wWuay9XJ+kEORdq7bySXQA5BbVfrz++lsnJld6TRl0PnFemqmEAquc5JrLxF8W6x0FuIdW5ZM8xh/6Jgev3S9TYcVh9xt7jDj0TfLkkMxvcdaTZMBdckaqzfnfjTmwHJGvs8OarrCB5OMubh3ccthmQXFVvOtu99bn9kE2qNw/XV27nWNVAqOodrHT7cqKmqQvDn3wVPEm2VNyZ2U51z3lmdtN+05aKqaQcTuQ4eGr8pOFh0CNB46uMuvdrf2NSdfYecyhuoPe3quGsa6p3skqnDqpX+q7rXHznWoP4mNrrNvS9gmnOcTU1SWP3Mb/9tsihexx0jtHIylENQsdROaYqnuwCyClsVGi1tHaOaaqWyitVDeeqbUN6nUVN0nhuJtM0LwduAYYDM4DR3rd+BYywLGsocI9lWUeA54EnLcsablnW2gaK7QmMAWYCz5um2R54BfiO9zP7A+0ty9peR5lPef8eDXwTeNlb5i+B+yzLGg5cBQR2wQURHaGRX+L7u6BEvRawTKRWPVzHcaCsEiLC1XIFxXWv6wB3TDX4wXSDkX1rH3xSukJxmUqQ9cYWqcr0le8QHRm4TEykRn6JUzu2SKpfBygsoda69UnpqlFc5jQYmy9GrVaMNU9UYiI1CvxiKShR6wFERUBVp2lRqfq7SnIXjXtnubjjaqPOnpnzFVMr5jp+8wjfMo6jhrBHtLtwMTTkTGEJl8VGVf/dNSaS0/4bGqrn+Wh2Pt9+aTF3vLiI9QcyAbBthydWbOQX065onWBDTEHeGWLjfT3uMXEJ5OedbtS62aeO0D4ymvlP3c/ff3sjy9/6K7Z9fldJWiKvdOwAJWVw/Vidu64zmHWFXt1D1T9Jo7CERg1Nbau80lgtlZM1DX54ncEvv2lw6KRTq0dI12BoL52DJxo/1DI6Agr9c1upeq3mMv65pLwyeC6ZNERj4z6bykZuftERWsCQ6YJip56c5hdrsa9uOrQPzMMd2jdcbny06vUyJxr8cIaLb3iHlwKM6a+TnulQ1IgjcUvF7W9EHz3gAkkoxORP7dcOc8apupw91qiz5zk6QjXsN6UH3z5vn2rwy3kuKip9PYuzrjBYvsnDS8vdfLzFZuaY2h/S0PcC1Xi/Z6aL68fWXrc5x1X/16vWjYnUOJvvkNJFI6IduAzo211v0oWQ+rRVjml8fC2TV0AN3f7ZXJ1yt8O+TGk9i0AybLv5rgIWWpZVAmCa5iLv6zuA+aZpfgB80MQyLcuybOCAaZqHgAHAu8BDpmn+F/A94LV61r0GGGSaZtXfMaZpdgDWA38zTXM+8L5lWZl1rWya5g+BHwIMnvNmE8Nuutc+8lBYCpHhalhPdoGHY2d87w9uZM9uWxjSU2PXkTZKqt6PPZnr8OR7birc6grrrVNcPP3B+d3ffCly2w5Hcwt4+XszOV1QzPdeWcqC++aydMdBJvRLJsGv8S0ax/Z4OJK+hR8/9j6x8d1459kH2Lp2IaMmzWvr0ADVuOsWp4Z+ZuXAtMt1xqfprNttc1WaGoIo6uc4an6J8DC4eaJOl1g46zdAYsYYnaNnHI6dbbsYARI6QqcOGp985dBWu3GwHildVxdZX1im7L6FhgAAIABJREFU7nOed5XB8N4aB044DOqh8drHbbMt1oy7Z4LGiL46r65su2NH0LrUNLrFaSzf5CErx2H6KJ0Jg2v3/0wbZaiRJY0wf5UHQ4cbJxj0StA4nu2Q3EXjpqt8p8ZGE0c7b063WbNT3T89dVjr9E9lF8Dne21un2JQ4YZTeaF9j26wHBMK3v6Pre7nHqfTs6vD4cZdW7446C1zK93XiTSeW85MYCIwG/iNaZpDmrBuzbTnWJZVYprmx8AcwAQur2ddHRhrWVbNAUt/Mk1zKap3fL1pmtMsy9pXc2XLsl4EXgR4dL67Oo7CUodYvyuZMZHqNX+FJeokprBUXVlsHwal5Wq5GL+TG/91C71XnkvKYf9xNbTx2Bn1nqapK8gvLW/4QFhYosr0la96l/wVlKj4C0ucwNhK8H4v9ZnRkdRaty4qNp0Xl9d/sjEmVWdkP3XwPJHj1IrR/8p1VYwxNevYu0xxqRoqVuT9b9VwNP9haweyHGZeoS5ElDRjuHaD8dT8zUsd7+ve37wdlFY0/7Mbo2t0JKf8uj7OFJSQEBN4Fp0QE8WQpC6EGTpJnaLpER/DsdwCdhw/w9ajp7A27aWkopJKj01kuzB+du3omh9zSYrp1FVN9uVVkHua2E4JjVs3LoFuKQOI65oMwMDLr+Z4xnaY1PQ4WiKvFJSonoaq3oy9x2zGp+nERaveq7tnGNXL//A6g5dXeOoc3tkWeaUpWionVymvhCOnHfp2Vz1bABOHaESGw5KNTbugWVhaNYpGlRMT4cv9/sv455LwsIZzSWJnjW5xcN9sNZlRVDjcMVXnjVWBsV3uN6KpqBRio+C4t+EfE6XVk9P86jXKVzdFZTXycLlvnbrK1XWNU3lq2CvA/uM2SZ01isogLlrjJ3PqPwUb3V9NPAXq+NEScQN07QizxxrMX+UOmrtbK6a6FJQ43v3ae5vEUYfxfo3nu2eougxvB/MmqH08Mhz6JRrYtof99fQeemz1u6Qma2TmOJRV1p4AVPWUqvL3Z9psPmDX+738c8mWDJurhgS2vptzXK163X/dqvOIbYccth1SF2OmDNVrnV+cj7bIMU2L78LnFX8eG9Kz1FDww6dD+GqEaHUybLv51gA3mKYZ4b2PeDaqXpMty1oNPAjEAh2AQqAx9xrfZJqmbppmH6A3sN/7+svA08Amy7LyvK/VLPMj4P6qP6rukzZNs49lWTsty/ozsAnVm91oWTnqYN8xSl1NT+uhhpz525/lMLS32qQGpfiSTXqmQ1oPHUOHjlGqnKwcNdlHO++5Q5gBvbtpnPEbTtn7Mo2cgtrJsHZsDvH+sfXU2Z8ZeAKVnukwrLdWK7b9mTZpPX2xxUdr1QfnhvS+TCO7wGnwhPjL/TbPL3Hz/BI3e4/ZDO+j6iaps0ZZZe3hekWlUF7pkNRZxTm8j86+4744q9ZXr6vv5z/ULTFeQ9MuTMMZ4ERujd88pfZvnp7lMKyX9zdP1jjSigeYtMQuHMstIDOvkEq3hxU7DzFpQErAMlMH9mDzkZMA5BWXcTSngKRO0Tw+bzIrf3ELyx+4mQemjWHWsL5fm4YzQGLvIeScPkru2Uzc7gp2bFjGgBFTgq8IJPUeQllJIcUFatrWQ3s20jWxz3nF0RJ5pbhMNZ7jvVmx12U6Z/PhzDl44j0PT3+o/hWUqN6P+u6LbIu80hQtUXeR4erkEtTwz97d9OrJd0b00ejTTef99U0fCaRyiWq46rqKJT0rMNYDWQ5De6m6HNiIXLI1w+HpD22eXWzz+qc2OYXUajgDbMnwlbMv02aoN18ldtYor6g/Dyd68/DQXr48nJ5pM8xbn8N66+z35uH9mU6d5Z7IUfcCR6opF+jp3RYPZDk88Z6bpz5Q/+qyKd3mhWVuXljmbrG4YyLh5kkuFq73NOr2o9aIqT7FZepWiKp7kHt108j2a3BVxfX0B7563XPMYemXtRvOYS6q78/WNOiXqJOdr+6DPlfkMCjF12BM6Kh6SqvK/2yH3eD38r/ve2Adc5M057ianumQlhK4357wzp5dtY3FRKpOh4bmiWms1s4xTdUSeSXM5Tuv0jTo2/387xkXly7peW4my7K2mqb5DrAdOINqmDrAG6ZpxqKmAH3asqxzpmkuBhaYpjkHuL+B+56PAV8CMaj7pcu8n7XFNM0C4FW/ZQPKBH4CPGua5g7U77sGNanYz0zTnALYwG5geVO+p+PA8s02t0811CMBDtqczYfJQ3VO5DikZzl8leEw90qNH19vqMcrrFdXQc/mw56jNvfOMrC95TiOum/XnKiuyuoa7DpiB8yOmdZDY9fR4CdqjgPLNnm442pX7dhyHdIzHbZm2Mwdb3D/HBel5Q4L1gXG9qPZLmxvOVXDnW6cYNAzQZ34/Hyui892eKonIRrcU2/SkO0DWQ79Ex1+OtelHuvxuW+43j2zXDzvfWTN0o02N1xpVD/+4oD3QLB2l4050WBkXxfnih3e/Y9af1APndGpOrYNlR6HBWt85c67yht/e3jgmy4+2+5ha0bjY3YcNfT1tsnqN99+yOZsAUwaonMy1/ubH3S4YZzGfbMMSivg/fW+z79/tkF4mJpwIzXJYP5qz3kfJOviMnT+Z+Y47n19BbbtcMPI/vTt2olnP91CWmJnJg/owZV9E/k8I5O5z7yHrmn8fNpoOkYGubmulQz/9xPETxpDu86dmHr4Pxx49BmOv9qoJ9Q1m2G4mH3nb3ntLz/AcWxGTryRhKR+fPLe0yT2GszAkVPJPLST+U/dT2lxAfu+Ws2nC5/hp48vQdcNrrv1v3jlz98Fx6F7zzRGTb7pvOJoibwCsHyzh7njDQxdTaqzaEPTG3ytnVcGJGtcN8ogsj3cNsXgVJ7D/AaGmLdE3XWIUI8J0jV14rjnqC8HzRyjc64Yvnetytn7jtus2dW4fOI4sHKLza2TdHQdth9SM+JOHKxxMtfhwAnVczZnrM69M9V9wlUze4PqXQ53qVzSP0njrc/s88olB7Ic+nV3uH+OysMf+j3q6O4Zruoex6VfqjzsMiDjhF19L/C6XTbzrjIY0cdFfrGaxbihch0HPt7i4c5r1KnWyVyHLRlN3xZbKu5JQw0i2lF9X6/tOHU+BaI1Y4pqr3p5w8PUidTYATrPLnFTUQnLN3m4cbyBoWvkFTl8+IWHKwc1flx1VVztXHDLZAOXri44HzmlepMB3l/nYeYVBlcN0TA02HXU5vS52r9Zfd/rmhEGl3VSjbW6ZvRvznH1bAHsOWZzzwyjev+vyis3TTCICFcTZy3fbFePSktN0ph+uU5kONwyyeB0nsObnzVuG2ztHNNULZFXSsvVo+8MQ528Hz3jBFyAuyTIbNvNpjmhfGOECGCaZnfgM2CA957oFuU/bDvUPHy7i0feaMQzotpA1aMpfvd6aMb3yJ1hPPZW6N4X/dCtLsre+Utbh1Gn9jf/NwBLw1LbOJK6zaxUg1QWNHFYbWuZd4XOo/NDc9t7+HbVwAn1vBLK9feHt0PzfvLf3KIaWKH+24ZyfKEcG4R23QEhe8x96FZXyOYUuCjyykV3A3HZh38P2XP7mtrP+XFI1q/0PF8kTNO8E/gD8EBrNJyFEEIIIYQQIlSZpjkd9aQhA3jZsqw/1bGMCfweNaBlu2VZtzXnM6Xx3EZM0/wNUHOs47uWZf2hruUty3odeL3FAxNCCCGEEEKIEGaapgE8C3wDyAQ2maa5yLKsPX7L9AP+BxhvWVaeaZpdm/u50nhuI95Gcp0NZSGEEEIIIYS4oLSQHAl9vsYAGZZlHQIwTfNt1FOJ9vgtcxfwbNVEy5ZlnalVShNJ41kIIYQQQgghRMgwTfOHwA/9XnrR+0jdKonAcb+/M4ErahTT31vWetTQ7t9blrWiOXFJ41kIIYQQQgghRMjwNpRfDLpgw1xAP2AykASsMU1ziGVZ5xpcK0iBQgghhBBCCCEuZfol9aiqLCDZ7+8k72v+MoGNlmVVAodN00xHNaY3ne+HSuNZCCGEEEIIIcTFZBPQzzTNXqhG8y1AzZm0PwBuBV41TbMzahj3oeZ86CV1+UEIIYQQQgghxKXNsiw38GNgJbBXvWTtNk3zUdM0r/cuthLIMU1zD7Aa+C/LsnKa87nS8yyEEEIIIYQQ4qJiWdYyYFmN1x72+38HeMD774KQxrMQQgghhBBCXOourUdVtQkZti2EEEIIIYQQQgQhjWchhBBCCCGEECIIGbYthBBCCCGEEJc6TfpNm0tqUAghhBBCCCGECEIaz0IIIYQQQgghRBAybFsIIYQQQgghLnW69Js2l9SgEEIIIYQQQggRhDSehRBCCCGEEEKIIKTxLIQQQgghhBBCBKE5jtPWMYjQJRuHEEIIIYQQtWltHUBTla185aI5t28/7fshWb8yYZio1yNvVLZ1CPX63R1hPDrf3dZh1Onh29VuFar1F8p1B6r+PtlR3tZh1OmaoeEALNhot3EkdZt3hRpMtDQstY0jqdvMyv0hvV9AaO+3ENrxhWpeuRhyMoR2fKEcG4R23QH8eUFoHjMenKfz2Fuhud8CPHSrK+Tzivj6kWHbQgghhBBCCCFEEHLZRAghhBBCCCEudZr0mzaX1KAQQgghhBBCCBGENJ6FEEIIIYQQQoggpPEshBBCCCGEEEIEIfc8CyGEEEIIIcSlTgvJpz9dVKTnWQghhBBCCCGECEIaz0IIIYQQQgghRBAybFsIIYQQQgghLnW69Js2l9SgEEIIIYQQQggRhDSehRBCCCGEEEKIIGTYthBCCCGEEEJc4hyZbbvZpOdZCCGEEEIIIYQIQhrPQgghhBBCCCFEENJ4FkIIIYQQQgghgpB7noUQQgghhBDiUqdJv2lzSQ0KIYQQQgghhBBBSONZCCGEEEIIIYQIQoZti0br001j+mgDXYOtGTbrd9sB7xs63HClQfd4jZJyhwVrPeQXq/cmpOmM6KtjO7Bik4eDJx1iItXyHdprOMDWAzYb96syB6VoTBpq0CUWXlru4WSuEzS2aaN0dA2+yrBZvydweRWbTrc4jdJyWLDOF9v4NI0RfVRsKzfbvtjG6URFaDiO+r5f7ldlThqiM6KvRkmZWn/VdpuMEw3HBzB9lE6/RJ1KN3zwhZtTubWX6RYHc8a5CHPBgSybFZtVfbRvB/OuMugYpXGuWNVtWQUM6akxPs0AoMLtsHSjh9PnfOVpGtx1nYvCEoe3PvO0Wv0ZOnznGwaGAboGe485/Gen+i6j+2tcMUAnLlrjrwvclJYHrbp67f5qHQte/TO2bTP+6hu5du73A97/dPHrfP7p++iGQYeYTtzxo0eJ79Kd44f38c5L/4/S0mJ0XWf6jXdx+fjp5x9IHdJ3rGXpG3/Etm1GTZrHpNl3Bbx/eN8mls5/nNPH07n5R08weMy06vfOZZ9g4T8fIj/nFGga3/7FC3TqknhB4wtm6Et/pOuMyVScyWHNiNmt8pmtmWNaK4aGyrx+rEG3eA0NyClw+OALD5VumHa5Ts8EdW07zAVR7ds+1l6XaXxjpIGGyjUffO4hr6jBqrzgeQXgJ3MMyt3g2GA78PIKldcam5dbIg/XV25CJ5g5xiA8TB1H1u7ysPuoiqlngsa1lxsYNbowWjM+gGtGqNcB1uz0xTe6v87YgSpPt1V8PRM0pl1uVJfbORYWrPWwP9Nh7ni1XVfRNbU9xMfAnHEG3eI0Vm2z+WJv3fv7+WzP/jpGwTevMogM1ziR47Dwcw+2DcN6q3ILSxpXTmb6WjYs/SOObdN/1DyGTQo8TnjcFaxZ8CDZWXsIj+zIlFv+RnSnRLIy1rN55d+wPZXoRhijp/8X3fuMBeDQjmVs/+wFHMdDcupkRk//ZeO/WAP6dNOYNlJH0+Crgzaf7629P88Z69uf3/tc7c8R7WDeBJ3ucRrbDzus2NJwDg4aQ4jllIuODNtuNqnBepimeb1pmr9qobK/Y5rm3xt4/zXTNOc1obzJpmleeWGiq5umwYwxBvNXuXl2sZvBPXU6xwYuM6KvTlmFwzMfutmw1+aaEeqg1zkW0nrq/GOxm/mfupkxxkDzHug+2urhH0vcvLLCzehUX5lnzjlYa9wcPRM8UWkaXDda583VHv6xxENaT53OMTVi66NRWgF/X+Rhwz6ba0aoTb9zDKT10HluiYc3V3m4brQ6MNg2fLTV5rklHv650sPo/oFlbtxn8+JyDy8u9zQqmfbtrhEXrfHMh24Wb/Qwc4xR53Izxxgs3ujhmQ/dxEVr9O2uTg4mpOkcPuXw90VuDp9ymJCm4s8rgtc+dvP8UjdrdtrMGhtY7hUDdLLzG46vJerPY8Prn3p4cZn617e7RmK8Kuv4WYd/f+rhXFHzDkK2x4P1yh+57zfP8dCTH7B5/XJOHj8YsExyrwE8+Oe3+M0T7zFi7Df44N9PAtAuvD133v8HHnpyIff95jkWvPYXSooLmhVPQGy2h8WvP8a3f/kiP/3TYnZsWMqZrIyAZTrGd2feXY8zdNzMWusvePFXXDXje/zsz0u59/fvEBUTd8Fia6zMf73Pl7N+0Gqf19o5pj4XOoaGvteKLR5eWKr23/wShzGpar9aucXmhWVuXljm5sv9NnuP1d5XWqK+Gipz5hiD99epmHYecZg4pO4c5h/fhc4rVV7/ROXeqpPcKsHyckvl4frKrXTDB597eG6JmzdWuZk2yiA8TH3GDVcaLFjr5rkl7jaLr1+ixmVxGs8vdfPycjfjBum088Z3/KzN65+4A/J0a8d35LRTvR/86xM3lW6qGzw7D9s8u8hXdyP7qrJKy2HFZpsv9jTcQGvq9lzTNSMNNuy1eeZDN2UVDiP7+E6ndx/17b8NsW0PXyx+jGu//SI3/nQxh3YsJe9M4HEiffMC2rWP5aZfrGTw+DvZvPJ/AWgf2YlvfOs55v5kERPnPc6adx8EoKwkj00r/pfp33+VG3+6hNKibE4c/KJJ360umgbTL9d58zMPzy3zMLhH7f15eG+Nsgp4domHjfttrh6m6sTtgc922Hy87fwbzVUxhFpOEV9P0niuh2VZiyzL+lNbx9FIk4EWbTwnxmvkFjqcK1INy91HbAYkBW4+qUka2w+p5LLnmEPvy1RmGpCks/uIjceGc8WQW+iQGK9RVEr1VesKN5zNd4iJUOtkF0BOI9syifGQ5x/bUZvU5MCr5alJGjsO2dWx9UpQ76cma+w+6ostr9AhMR6KyuBUni+27HyHmMjzf7D8gGSNHYfV52dlO7Rvp9EhInCZDhEQHqaRla3qcMdhmwHJVXHqbPfGv/2QTWqyqvvMbKf6yn1mdmCM0ZHQr7vG1oyGD1gtUX+gThwBdF39q3Iqj+orwc1xJGMXXS5LoXNCEq6wMC4fP50dm1cHLNN/8BjahauK7tV/KOdyTwOQ0L0nXbv1AKBjXFeiY+MoKshrflBemQd3ENc1hbiuybhc7Rg6dgZ7t64KWKZTl0QuS0lFq3EV+ExWBrbHQ9/B4wEIbx9V/R1aU+66zVTm5rfa57V2jqnPhY6hoe9VUekrN8wA6jg3G9xTY9eR2vtwS9RXQ2U6qPwE0D4MCksbPpFsqbzSHC2Vh+srN7dQ/QMoKoXiMjWKIDJcXWCseq+t4usSq3HsjIPjQKUHzuRB326qrLrydGvH529QisaBEw5ub9umZkMmJlL9t6QcTuQ4eIK0c+rbniPD4aaJBj+4Tv1L7lJ3vuiVoLHHe1Fr+yGn1rbdGNmZO4iJSyEmLhnD1Y7eQ2dwbG/gceLY3lX0GzkHgJ5p0zhxcAOO4xDffRCRMV0B6Ni1H253OR53BYW5mcTE9yAiSl1w7d5nHEd2fdTk2GrqHgd5RQ7nir378zGb1KTa+/N27++457hDL2++qfTA8Wyqf7vzFYo5RXw9fS2HbZum2RNYAWxANTo3Aa8CjwBdgduBQcAoy7J+bJrmTcDvAA+Qb1nWRNM007zrtENdhPimZVkHTNO8A/iJ9/WNwI8sy/KYpvld4H+Ac8B2INhg1Wu8Pd8xwAOWZS0xTXMN8BPLsrZ5v8c64D7gHsDj/ez7gX3A80CKt6yfWZa13jTNScBT3tccYKJlWTUO33WLjoQCv2FIBSUOiZ0Dk1ZMpEZ+iTqYOA6UVUJEuFo3M9t3JCssUa/5i42CbnEamTlNv7IXHaGRHxCbOrH0PxONjtSqTwQCYovwHeSr1o2OCFw3Ngoui9PIzPadwI7urzO0F5zIdfh4q13dgG0wxmK/zyl2iI7QKPI7AY2O0Cgo8V+mKhbo0F6dfIH6b4c6hnGO6KMHnFBMv9zgk688tAtr+KDeUvWnaXDXdIO4aNiU7pCV02AYTXYu9zSd4hOq/+4Yl8CRAzvrXf7zTxcyaMSEWq8fObATt7uSzgnJFyy2grwzxMZfVv13TFwCxw/uaNS62aeO0D4ymvlP3U/e2Sz6pI1j2s0PoOtN6xm52IRKjmmJGBoq8/pxBv26a5zNd1i5JfDsMjYKOnbQOHy6dswtVV/1lbn4Cw+3TTVwu6G8El5e2XCvWkvlFQe4Y6pRfTvN1gzfcsHyckvl4caU2z1ew9B9DWZdU9uj/y1JrR3fqTyHSUN0Pt+jbg/omaBxNj/wNwqV+hvcU693CDbUbkwHU9/2PH2U6lE+flbd9nHH1S7+sThwW48IV9uq4/3IgpLAC9cDU3R6dNXJKWg4puKCM0TF+o4TUTEJnD2+o8Yyp4mK7QaAbrho1z6a8pJztI/qVL3Mkd0fEd99IIarHTHxKeRnH6YwL4uomASO7v0U211Jc8VEajVyQx37c4RvGceBsgo1ZLs0yPlRY4ViThFfT1/nnue+wBPAAO+/24AJwC+BX9dY9mFgmmVZw4Drva/dAzxlWdZwYBSQaZrmQOBmYLz3dQ9wu2ma3VAN8/HezxjUiPh6AmOAmcDzpmm2B14BvgNgmmZ/oL1lWdtRDeUnLcsablnWWlQD+UnLskYD3wRe9pb5S+A+b2xXAaWNiKPFhbnAnOhixWZPQE9MKAhzwU1XGazcYlPhPX5uPmDzzCIPLyzzUFQK3xjZ+ruRU+OY3DNBY0RfnU+2qpPvfokaxWUOJ+u4H621OA68uNzDkws9JMZDlyDDZVvSl2uWcOzQbq65/jsBr+fnneVfz/yab/3oUXQ9NNKh7fFwJH0L193639z7iEXe2eNsXbuwrcO6qIVyjln0hYe/ve8mO99hcI/Axu/gHjp7j9q19ve2MHagzpurPDy50M22Q3bAvait6bWPPLy03MObqz2M6q+Tojrf2iQvN/Z36RABc8cbfPiF7+LIe+s8TBul84PpLVePweI7dNIh44TD96e5+OYEg+PZDnYrbmtNqb+uHTUONtBAPna2aYHXtz337qYxY7TB3TNc3DrZRXiYyh+NlZ7p8NRCdSvGoVMtX5l5pw+weeUTjJ/zCADhEbFcef3vWP32Ayx96Q46dExEu8QvvDZXKOWU1uBo2kXzL1RdGlvC+TlsWdZOy7JsYDfwqWVZDrAT1XD1tx54zTTNu4CqLPQF8GvTNB8EeliWVQpcDVwObDJNc5v3797AFcBnlmWdtSyrAninEfFZlmXZlmUdAA6hGvjvArNM0wwDvge8Vs+61wB/98awCIgxTbOD93v8zTTNnwAdLcuq1XVgmuYPTdPcbJrmZv/XC0t8w6JAXYWsOSFGQYlDrPfqq6apoVCl5Wrd2BrDiavW1TUwJxrsPGKz7/j5HWgKSx1iA2KrPaSwsMQhNorasZU6xETVva6ugXmVzq4asRWX+Q76WzNs75XP2kb317l7hou7Z7goKqX68wFiorTaMZYGXr2OifLFUlRG9TC2DhFQ7DduoWtHmD3W4O3P3NVXeFO6aKQm6fz0BhfzJhj0ukxj7vi6D6AtVX9VyivVvWtV971dKB3jEsjLOV3997nc03SM71pruX07NrDi/Ze458GnCQtrV/16aUkRzz1+H7NvvZ9e/Ydd0NhiOnVVk315FeSeJrZTQgNr+K0bl0C3lAHEdU3GMFwMvPxqThzZc0HjC0WhkmMudAyN+V6OA7uOOgxMCTwkp/XU2Xmk7phbor7qKzMyHBI6aWR5e+13HbFJ7tzw/txSeaXQe8m3pBz2H3eq8299eXlUf1+cLZWHC0udesttFwa3TXGxapsnoOcrM9vhtY881cPq2yq+tbvU/blvfOqpnrjOX9U90C15HGsoPoC0FJ19x2s37CcN8e0vVcfamsO969LQ9qwBL69wV9+z/OT76l7r26eqBvXssQal5WpbrTq3V72yqqzSCjUkHwh6y1RUTFeK833HieKC00TGJtRYJoHi/JMA2B43FWWFhEd2VMvnn+LT+fczcd6fiIlPqV4nZeAUrr/3HWbf8zaxnXsR27lH8EoJQvWu+/6uc38u9S2jaWqCuAvV61xVfijkFCG+zo1n/2HTtt/fNjWGs1uWdQ/wWyAZ2GKaZrxlWW+ieqFLgWWmaU5F5d1/eXuAh1uWlWpZ1u/PM76aZ0yOZVklwMfAHMAE5tezrg6M9Ysj0bKsIu893D8AIoD1pmkOqLmiZVkvWpY1yrKsUf6vZ+U4xEdrdIxS96+m9dTZnxl4YEjPdBjWWyWXQSm+oYb7M23SeuoYupqhMj7ad9C6fpxBdr7DhgaGYwWTlQNx/rH10EnPDKy+/VkOQ3vrtWJLz3RI6+GLLS5aqx5ePHusztkC2LAvsCz/IdMDkjXOnKv75HZTum/SkH2ZNkN7qc9P7KxRXuFUD1+rUlQK5ZW+YZJDe+nVJ/vpmTbDvPEP662z/7iqr5hIuHmSi4XrPQH3z326zebJhW6e+sDNgnUeDp9yWLi+7huOWqL+IsOpnhjHZUDvbjrZF24+LgB69E3jzMmjZJ/OxF1ZyZb1KxgyanLAMscP7+WtFx/lngefJjrWd4OTu7KSF//6M8ZMms3Icdde2MCAxN5DyDl9lNyzmbhwfOcGAAAgAElEQVTdFezYsIwBI6Y0at2k3kMoKymkuEANGzi0ZyNdE/tc8BhDTajkmAsdQ0Pfq1MHX7mpSRrZfg2X+Bg15NF/eHVL11d9ZZZWqJPQuGhVbp9uGmeDDEltibwSZkA779E5zFC9hGe8TxeoLy9vTvd9Zkvl4f2ZTp3l6jrcPNFg+6Hak75Fhqv/+jewWjs+TVPbGKiLsAmdtOoJuapUjdRoyeNYffFVqeu+/xF9Nfr4XZCtOtbWjKcuDW3PB086XDHAd2qc4B0dPX+VhxeWuVm8QR1HD592GJSieb+Lxn7vtu3feK95T3BNnROHkJ9zlMLcTDzuCg7tWEbKgMDjRPLAKRzY+iEAR3avpFvvsWiaRnlpAR+9fg+jpj1AQo+Rgd+vSJ3ElJfms2/jW/Qf1ej5Z+t1IrfG/pxSe39Oz3IY5v0dByVrHKnjdpPmCJWcIsTX8p7npjJNs49lWRuBjaZpXgckm6YZCxyyLOtp0zRTgKHAR8CHpmk+aVnWGdM044Bo1L3PT5mmGQ8UADeh7ntuyE2maf4L6IXqvd7vff1lYDGw1rKsqhmOClH3Rlf5CHXv81+98Q+3LGub93vsBHaapjka1Zu9rzF14DiwbJOHO652oWmw7aDN2XyYPFTnRK5DeqbD1gybueMN7p/jorTcYcE6dZA5mw97jtr8aLYL21uO40ByF41hvXVO5zncPUMlu0+3qRkNByRrXDfKILI93DbF4FSew/xVdTf+HAeWb7a5fapRO7Ych/Qsh68yHOZeqfHj69VV4/fWB8Z27ywD21uOio3q2H54neqxrXpMwTUjdRI6aeDAuWKHpRuDn5QfyHLo193h/jkuKt0EDN+7e4arelbOpV/a3HClgcuAjBO+xyKs22Uz7yqDEX1c5Bc7vLvW+yiFoQYR7aiendR2HF5a3rRZOVqi/jpEqMeF6Jo6Qdtz1OZAlvouY1I1rhyk06E93DPD4MAJhyWNqMOaDMOF+f1f8+wf7sW2PYybcgPdk/uy5O1nSekziKGjp7Dw33+jvKyEl59Qj+qI63wZ9/zqGbZ+sZKMvVspLsxnw+pFAHzrvsdI7lXretJ5MQwXs+/8La/95Qc4js3IiTeSkNSPT957msRegxk4ciqZh3Yy/6n7KS0uYN9Xq/l04TP89PEl6LrBdbf+F6/8+bvgOHTvmcaoyTddkLiaYvi/nyB+0hjade7E1MP/4cCjz3D81QUt9nmtnWPqc6FjqK9MUDMuh4dpaBqcynNY+qVv3x3cU69zorCWrK+GYl280YM50YUDlFU4ATmsvvgudF6JilCjCECNKNh1xPe4mcbk5ZbKw/WVm9ZDo0eCRmS4xnDvCf0HX7g5nQfj09SjmfxHJrZ2fLoG371WnQaWVzq8v95dvR2MSdUZP0ivbgzOHmuweIOnVeMD1dMdE1W7ITZrjME5vwnNJg7RWbPTJqo9/PA6NdzaAcYO0Hl2iZuKSnUusWiDGoJb3/a8fLOHGaMN7pnpQtfg6BmbpV/W3pY++crDvAkGU4er+9a/ylDrX5Gq0z9JPQ6ptLzhxpZuuBg3+7esfE0dJ/qNvJFOCf3Y+snTdE4cTMrAqfS/fB5rFjzIu09MIzwilsm3PAHA3g3zKcw5xrZVz7Ft1XMATPvuy0R0iGfD0j+Sd1KdMg6fei+xnXs1GEdjOI6axfy2yWp/3n7I5myB6v0/mevdnw863DBO475ZBqUV8L7fBfv7Z6uZ5g0dUpMM5q/2NPmCeijmlIuSPKqq2TQnFG6mamXeCcOWWJY12Pv3a96/F1S9B/wvvgnD3gf6oXqWPwV+BjwIfAuoBE4Bt1mWlWua5s2oicF073v3WZa1ocaEYduACsuyflxPfK8BZah7qasnDPN7fx9qErAV3r/7AwtQveb3A3uBZ4GBqAskayzLusc0zWeAKd7ldgPfsSyr3onLHnmjMmQ3jt/dEcaj8xuesKatPHy7Ohl55I0Qu7nSK5TrDlT9fbKjGQ9/bkHXDFXdRQtC9CA67wp1UFwaltrGkdRtZuX+kN4vILT3Wwjt+EI1r1wMORlCO75Qjg1Cu+4A/rwgNI8ZD87Teeyt0NxvAR661RXqeeWiG8tdssYK2XP7miInmiFZv1/LnmfLso4Ag/3+/k49773mfe3GOor5k/dfzbLfoY57mi3LehU1O3dj4vtOfe+Zptkd1TD/yG/5dFTPt7+b6yj3/sZ8vhBCCCGEEEKIQF/LxvPFyjTNO4E/oHqiQ/MyphBCCCGEECL0hPAs1hcLaTy3IdM0f4O6/9nfu5Zl/aGu5S3Leh14vcUDE0IIIYQQQggRQBrPbcjbSK6zoSyEEEIIIYQQInTIlGtCCCGEEEIIIUQQ0vMshBBCCCGEEJc6XfpNm0tqUAghhBBCCCGECEIaz0IIIYQQQgghRBAybFsIIYQQQgghLnGOPKqq2aTnWQghhBBCCCGECEIaz0IIIYQQQgghRBDSeBZCCCGEEEIIIYKQe56FEEIIIYQQ4lKnSb9pc0kNCiGEEEIIIYQQQUjjWQghhBBCCCGECEKGbQshhBBCCCHEJc6RYdvNJjUohBBCCCGEEEIEIY1nIYQQQgghhBAiCBm2LYQQQgghhBCXOk1r6wgueprjOG0dgwhdsnEIIYQQQghR20XXEi3auPiiObfvcMXskKxfGbYthBBCCCGEEEIEIcO2Rb1+93plW4dQr0fuDOO3r1W0dRh1+n/faQfAH9/xtHEkdfv1zQb7Dma2dRj1GtAnicfecrd1GHV66FaVMh+dH5rxPXy7iu+RN0Jz3/3dHWEsDUtt6zDqNLNyP8D/Z+/Ow6uo7sePv2fmBkhCQkjYsgBhX8K+byKgouzgMlprra2tS9X2181vN7Vaa6vWqiiKYEWtW0eUfbUCsoV9lS3skLAmgRBIQnJn5vfHuVlutpsQQiL9vJ7HR3LvzJnPnDtzljlnZrg45ckajqR09R97CYDNSek1HEnpereP5OudOTUdRqlu6loPqN3nBdTu+GpzbFC78w5qd3ugtta3oOrc2toWffb+oJoOQdQQ6TwLIYQQQgghxHVOXlVVdZKDQgghhBBCCCFEANJ5FkIIIYQQQgghApBp20IIIYQQQghxvZNXVVWZjDwLIYQQQgghhBABSOdZCCGEEEIIIYQIQDrPQgghhBBCCCFEAHLPsxBCCCGEEEJc7+RVVVUmOSiEEEIIIYQQQgQgnWchhBBCCCGEECIAmbYthBBCCCGEENc5V15VVWUy8iyEEEIIIYQQQgQgnWchhBBCCCGEECIAmbYthBBCCCGEENc7edp2lUkOCiGEEEIIIYQQAUjnWQghhBBCCCGECEA6z0IIIYQQQgghRAByz7MQQgghhBBCXOdc5FVVVSWd5+uAaZrDgN9YljW2lM9zLctaWx3bHdVXp12sTp4Ns9d4OZlecpnoSJg02IPHgP0pDos2OgAE14G7hhpE1Nc4f9HFWmmTk1u4XkyUxk9GGcxcabP7mHtF8Y3pZ9A+TifP6/LFapuT6SXTiYnSuH2IQZChkZTssGCDDUBCS40RPQwaR2hMne/lRJr/ug1C4ecTg1i2zWbNLqdScbVuBrf01NE02H7IJXGvf9qGDuP6azRrqJGdC7PXOmRkqTy7fZBOdCTsOOKydItar44HfjCicBJJWAh8e9Tlv1uvLN+K2rJpA9PfmYLjONxy62juNL/n9/2iBfNYNH8OuqFTr14wP/v5L2nRIr7g+7NnTvP4Iz/mnu//kEl3mFWOB6BNtMatvVT+bT3osHZPyfybMEAnOlIj+zJ8sdYm45LKvzuH6MREamw/7LJ4s/rdPAbcOVinYZiG60JSisuy7RX/TdtEa9zaR0fXYOsBhzW7S8YzcVBhPDNXq3gABido9Gyj47iwZJPDwZNq3bpBMG6ATpMGGi4wb51NcmphmgM6aozsbfDyTC/ZlyuXd7f1NdA12HLAKXHsqlgNYqI0si67zFxVGOuQBJ2ebVWsizfaHDzpEh6ilq9fT8W5Zb/D+n2VOx+uRLfpL9Bk9DByz6Sxsue4at9eIGuPnOYfK3diuzAxoQU/6tPe7/tXVu5kk+8HzPHapGdd5ptHxlRbPNs3J/Lh9NdwHJvht4xn/F33+32/YPanrFg6F90wCA+P4KFf/JHGTaIB+PT9KWzdqKqMSff8iIE33HzV49u1dQ2fz3gR13EYdNMkbp30oN/3X8/7kDVfz0LXDcLCG3LfY88S1TiG44f38tn0v5KTdRFNN7jtjp/QZ/Btldr2bX189ZYXZid6OVVGvTVhoIcgj6q3Fm9Sx3S9OnDnDQYRoRrnL6nzI7/eKi3dpg1VPVQ3SJUtq7612XVUnePxTdU5bJQz/686Yo0KhwkDDaIjNZZtc0jcU3i+DuioznGA0+dd5qy1scs5nWtDfEVVpDzqGq8xOMEAINfrsmC9zenzZe9jaUorC0GV2+MHGDSJUNufm2iXnxBXvz0A0Km5xuDOGpoGB064LN9x5W2Bq13fAgzvptM1XiO4Drw4M3AeBVLb26Li+iPTtmuYaZpGNSY/DBhUHQm3i9WICteYPNvLvESbsf1L342xAwzmJtpMnu0lKlyjbYy64jWki86hUy6TZ3s5dMrlhi6Fh6KmwS29dA6euPKCqr0vvle/zGN2os34gaXHN36Awey1Nq9+mUdUuEa7WBXfmfMuny73cvR06TGM6uthf0rl49M0uLW3zn9WOkxb7NC5pUajcP9lurfWyMmFqQsdNu5zGd5dxeS14ZtvHb7e7r/dXC/8a6lT8F/GJdiXXPVC3rZt3nlrMs889zfenPoeq75ZxrFjR/yWuXH4CCa//S6vvTmNSXfezXvTp/p9/6/pb9OrT78qx5JP0+C23jqfrLB5e6FNl5Z6ifzr4cu/KfNt1u9zuKm7Ora8NqzY4fDVtpKtwcS9Lm8vsJm22KZ5I4020RW7MqtpquL+ZLnNW/NtEuJLxtOzjWr0vDnXZt1eh5t7qngahUNCS52359t8ssxmVF/VQAHVKD14wuWt+TbvLLQ5m1GYXniIatCcv1S531jTYHQ/g4+XeZkyz0uXeJ1GDYrF2lYnJ9fljTle1u1xuLmnOm8aNYCEeJ235nn5+Gsvo/sZaBo4LizdYvPWfC//Wuylb4eSaVaH5A++ZMPYn1T/hirAdlz+vmIHkycMZOZ9I1iSlMKhtAt+y/x6aFc+vXc4n947nLu7t2ZE25hqi8exbWZMfYUn//xPXp7yKWtXfkXyscN+y8S3bs/z/5zBi298RL/BI/h0xhQAtm5cw+GD+/jb5A947pV3WfDlJ2RlXbrq8f3n3Rd4/I9v8dSrs9i0ejEnjx/0WyauVUd+9+In/OmfM+k58BZm/ftVAOrUrccPn3iep16bxeN/eouZM14m69KF0jZTqrYxGpFhGm/M8TJvvc2YfqXXC2P6Gcxbb/PGHC+RYUXqrQSdw6dc3pzr5fAplyEJernp5nlh9lqbt+d7+WiZl1v7GNQNUtuYOMhg5iovb8/3XtNYsy/D4k0Oibv9y8GwYOjXUWf6IhWTrkGX+LLLwdoYX0XKo3MX4f2vvExd4GXlToexAyrXBCurLAS4rY/BgZMOU+ap9M9mlF9GV0d7ILgOjOiu8ckKh+mLHULrQXyTSu2iX3zVUd8mpTi8t7TqnWao/W1RcX2SkedqZppmKGABcYAB/AV4EfgPcAvwkmma54EXfN+nWpZ1Uxlp/RloA7QFGgEvWZY1vdgyfYFpwJ3AI4BtmuZ9wBNAM+AZwAYyLMsaeqX71bG5xraDqlBMTnWpV0ejfjBczC5cpn4w1A3SSE5VBc+2gw6dWmgcOOHSsbnOjKXegs9/NNLDV1tUev076uw55hITdeVTSzq10AvjO1tOfHU0ks8Wxte5hc7+FP/OSsm0Nc5ddMkrvc1TrphIOJcJ533t0d3HXNrFaqReKCyc28dorPKNCO5JdhnZWwdc8mxIToWG9ctOP7I+hNaD42crH1tx+5P20iwmlmbRqqF/w9DhbEhc6zeyHBISWvDvyzk5fpOB1q1dTdNm0dSrW6/qwfjERMK5i25B/u065tAhTiO1yGhvhziNb3aq/Nt93OW2PqoyzLPheCo0DPNP02vD0TNqfceBk+fUiGpFxEbBuUyX8xd98Rx16NBcI3VXGfEccxnli6dDc41dRx1sRx0P5zJdYqPgbAa0aKIxJ9EpiOlykfbHyN46/93qcPeNlWv0xUZppBeN9YhDxzid1RmFiXeI0/hmR2Gso/uqX7RjnM6uI4Wxpme6xEapczv/nMr1wtkMl/BgjdQAjcaqSl+9ieCWsdW6jYradfoczSNCiWugzoWR7WJZcegUraPCS11+yb5kHh7QsdriObB/N02j42jaTOXPwKE3s3n9SuJatCpYJqFb74J/t+uQwJoViwFIPn6Yjgk9MAwPhuGhRas27NicyICrOPp85MC3NG7WnEZN4wDoPfg2tm9cQXTzNgXLdOhSeMGtVbuubFi5AICmMfEFn0dENiGsQSQXL5wjJLT0vC6uY3ONHYfV8Z0SoN5K8dVbOw47dGyu6q0OzXU++EoV/NsPOfzwFg//3eqUmW56ZmG6F7PhUo4qnw0dbMf/+2sVa9ZlyLrsFlwoLkrX1Ewc24EgAzKzSyxSq+O7mE3A8ii/PZL/7/CQwu10baXRv4OOoWukpDks2ODgFivKyioLz2a4tGyqMSexsC65XLLf6Kc62gMR9dUFgizfjKQjp1Vdc+RM5cvk6qhvAVLSKh1KmWp7W7Q2cuVVVVUmOVj9bgNOWJbV3bKsLsBi3+dplmX1Ar4GpgN3WJbVHbgrQHrdgBHAQOBp0zQLhjBM0xwETAUmWJZ10PfvVy3L6mFZ1irgaeBW33bGV2WnwkI0LmQV/n0hy78SAggP0biQ5RZZRq0HEFqkcLuYrf4GdXW5U3ONjVWc+hkWopFRZGTuwqUy4iuyTMYltyC+stTxwA1dDJZvu7KrpmHBcCG7cJuZWeoz/9gpyFvXhct56mpyRXRuoV21qUVpaak0atS44O+oRo1JS0stsdyCebN5+Mf38f570/jpI48DkJ2dzZczP+Oee+8vsXxVhJc47iAs2P83CwvW/PIvJ7fi+Vc3SM1aOHyqYnkYFqyRESieEK1g6rPrQk4eBNf1xXmp5LoR9SErB8YP0PnpKIOx/XWCfP3k9nEamVlUepqhioMS52xYsYsE4SEaGb5z1i/WEAo+B99xW2zdBqEQHamRnFa9Hefa5szFHJrWLzyJm9YP5uylnFKXPXkhi5QLWfSNa1zq91fDubSzRDUqHGqKjGpCelrZV9OWfzWP7r0HAtAyvh07tqzjck4OFzLOs2vHFtJSz1zV+M6nn6Fho2YFfzeMakJG+ukyl1+7bBYJPQeX+PzI/p14vXk0atq8wtsOCy48F0HVC6WXH0XrjsJzun49/3qrfr2KpxsTpWHoqsOcdVl1BKMjy65vqivWsmRmQ+Juh19O8vDrOzzk5MGhk2Wfy7U9voqURz3b6BzwjSrmzwR6b4nNOwu9OI6a4l1iv8soC1W57TJhoMFDoz2MG2AUlNtlqY72wLlMiAyDBiFq5LR9rEZ48JV1/qq7vr0aantbVFyfZOS5+u0EXjFN80VgvmVZq0zTBDXyDDAAWGlZ1mEAy7JKuVvDzxzLsrKBbNM0lwP9gPNAJ9SI80jLsk6Use4a4H3TNC3gy9IWME3zIeAhgE5jP67gLl4FvnJtVF+Dr7Y41Nbm94geBmt3O+RewajztdC5hcbc9de2sB8zbiJjxk3km+VfY332Ef/v17/js48/YPzEOwkODg6cQC2haeoesg1JTsGV9pqgGtVq6mJKmprWNzhBZ/UuhxsSdD5adnWmu11NQR4wh3pYvMkmN6+mo6m9liSlcHPbGAy9doxkrF6+mMMH9vLU394CoFuv/hzav4c/P/kQYQ0iaNexC7pec9fY16+cz9GDu/nlc+/5fZ5x7izvv/FHfvj48zUaX/FRybLUD4ZJg9UtQvm+WG1zax8dzzUKP1Cs9eqoEcrXZ3vJyVX3gXZtpbHz8LWpja80vtJUpDyKb6rRs63OjCWqMm/VTN2f+9NRqlns8cClyw5UsDWiaxrRkRqLNtqkpKkR2CFdrv2xmZMHizc7TByk47qQkuYSEVo7ypvvhO9IW1TULOk8VzPLspJM0+wFjAaeN03za99XV9o8L34u5/99EqgH9ARK7TxblvWIaZr9gTHAZtM0e1uWlVZsmWmoTjjPfJjnt61+HXR6tVOVwYk0/6mtxa/sQckrgOEhkOlb5lI2BVNr6ger6Wygrs7fOVRdrg2pC+1iDRzXZu/xwEVY/446fdqr+FJSXRqEagXZEx5aRnxFKpUGoVpBfGWJa6yREK9zax+DenVUhe+1Yf3einVYM7PxXQVW2wkLKTn1LDPLl1fZqkNXNwiyc0umVVyTCNB0OHWuQqEEFBXViNTUwhGrtNSzREU1KnP5G24cztQprwOQtG8Pa1ev5IP3pnHp0kU0TadOnTqMGTexSjGVekxl+/9mmdmuX/7Vq1Ox/BvbTyc9Ezbsq3h1mZnt0iBQPFkuDUKLxBOk7uvLzHYJL5z1XrDuhSx1ZTx/atueYw6DE3Qiw9ToxsOjjYLlHxpl8O5imzIGOovFQYlzNjPLf5kLWWp/MrNc/1iz8O1nkePWt66ugTnUYOcRp0Ln6fWmSf16nC4yR/D0xWwah5Y+jLY0KYX/G96tWuNpGNXYb7Q4Pe0MkVElR7p3btvAbOt9nvrbWwQFFQ4VTbz7ASbe/QAAb778NM1iW1zV+CIim3Au9VTB3+fSztAgsmmJ5fbuWMfiL97lV8/9yy++7KyLvPXC44z/3hO0ah84L+MiC4f/LmarEcn821rCQ7Uyyo8i53Ro4Tl9MadYvXW5cJ2y0q0TBPcO97Bsm10wfRnUFNP3ffd9PnOfqrf6ttfp1bawjq2OWMvSupnG+SLTffccc2jeyL/zXBvjK64i5VGTCBg3QD3/Ib9uUA/scvi62D26HZtr3NhVHUNz19llloUXslxfua0+333UZXCAznN1tQcOnIADJ9R+9Git4ThXVi5XZ31bFbW9LSqufzJtu5r5plVnWZb1EfAy0KvYIuuAoaZptvItHxkgyQmmadYzTTMK9UCwjb7Pz6M6xX/zPWUbIBMouOPENM02lmWttyzraeAsUPH5bsCGfQ5T53uZOt/LnmMOPdqowyeukUZOnut3jwmowuhynkucr4Lr0UYvKHj2JReurz5XBf1rs7y89qX6b/dR9STMihZW6/c6TJnrZcpcL7uLxtdY43JuGfHlusQ1Loxvz7HyO8HvLvLyysw8XpmZR+Juh2922BXuOAOcSFf3ADUIBV1XI8XFHzy2/4RbMF2sU5xW5kPLiuvcQmP30atXsLdr35GTJ1I4feokeXl5rFq5nH4D/J8/dyIlueDfmzauIzpG3Wf5t5dfZ/r7nzD9/U8YN+EO7rz73ip3nEHlX2SYRoQv/xJa6CQVezhaUopL91bqt+/cXONIBfJvWFedukGwZEvlRu1T0orF07JkPPtSXLq19sXTQuOwL56kZJeEljqGDhGhKp2UNFV5X8iCKN+Z26qZztkMOHMeXvnCZvIc9d+FLJi2qGIdZxWrS1TRWON19iX7729Sskv31lqJWPclOyTEF8YaFaYVNBLHDzRIzXBZt+d/c3pb56YRHD9/iZSMS+TZDkv3p3Bj62YlljucnsmFy7l0a9awWuNp064Tp04c58ypE3jz8khc+V9697vBb5kjB/fxrykv8eunXqZBRGGV49g2mRfUAx+OHT7AsSMH6dbz6j3wD6Bl2wTOnDxG6ulkvHl5bF6zmG59b/Rb5vihPXzyzl949HevE9YgquBzb14e0176Jf1vHEevgbdUaHvJ6YWjvXuTHbr5yobYRuXUC3kusb56q1urwnorKdmhu+9c7t5aZ5+v3tqX7Jaarq7D3UMNth9y2FPsdpqQuur/RZ+2vTHJ4Z2FXt5Z6K22WMuScUltx+O71tCqmU5qsWex1fb4IHB5FB4Cd9/oYdYa2++e80OnHDq10At+l3p1VD2997hbsM8n090yy8JLOWo6d/6jDlpFB372Q3W1Bwr2IQh6t9XYfujK2gXVVd9WVW1vi9Z6mv7d+a+WkpHn6tcVeNk0TQfIAx4FZuZ/aVnWWd9U6S9N09SBM6gHiZVlB7Ac9cCwv1iWdcI0zfa+tE6bpjkWWGSa5o+BecBM0zQnoB4Y9kvTNNsBGupe6+1XulP7U1zax7r8YpKn4Imi+R4Z62Gq7wmiC9Y7TBxkFLymIr9iWPWtgznUoFdbD+cvuXz+zdWdipqUrOL71e1B5NouX64uTP+x8R6mzFXxzV1nc0f+q6pSHJJ88XVqoTG2v4fQenD/zR5OprsFDzepCteFpVsc7rlRvdpo+yGX1AswtIvGyXSX/Sdg2yGX8QN0HhmtnmI5O7GwEfCzsTp1ParB1T5W47NvnIIGRKfmGtbKq9eBMQyDhx59gj//6f9wHIebRo6iRct4Pv73DNq260D/AYNYMG8227dtwePxEFq/Pv/v1/931bZfGtdV05nvHWYUjBScvQA3dtU5me6SlOKy9aDLxIEaj401yM6FL9cU/vZPjFNPuzV06BBn8PFym8t5cEMXndQMl5/eplpmG5MctlWgweG6sGiTw/dHqHi2HXQ4mwHDuumcSPPFc8Bl0iCNx8cb6lUevnjOZsDuow6PjjVwfOnkT11ctMlm0mD1GptzF13mrqv67+q6sHCjzX03eUrGmu6SlOyy5YDDpMEGT0zwkH3ZZeZq/1h/Ns6D40vHdaF5Y43urXVOn3N5eLSq6L7eZhfcR1hdevz7FaJu7EedRg0Zcfgb9j/3BsdnzAy8YjXw6DpPDuvG43MSsR2XCQktaBMVztvr9tC5SQQ3tlavgHZLrjYAACAASURBVFqalMLI9rFoWsnRsqvJMDw88Miv+fsz/w/HcRh281jiWrbm84+m0bpdJ3r3v4GPZ7xJTk4Wk//+RwCiGjflN0+9jNf28tzvHgEgOCSUn/36GQzj6jYTDMPD3T/5PW8+/yiO4zBwxERimrdl3mdTaNkmgW59h/Hlv1/lck4W777yWwAaNmrGo7+bzObEJezfs4VLFzNYt2IuAD947Dmat6rYA9j2p7i0i3F5YoKqt+YUeZXQw6M9vLPQV29tUPWWx1AjePnH8+pvHe68waBnGw8Zl1w+X2WXm25CS42WTTVC6mr08HUUZyd6OX0OBieo1+uUdThUV6yh9eChUR7qBqnxzgEddabM95KS5rLnmMPDo9U5fjLdZfP+ssud2hLfqL6FMwvKKo8a+GaYbd7vcGM3g+A6FDwd3HFdpi+ySc2A5dttfuArH21HlXMZxd5qUFZZCLBoo83tgw0MXT1UdE6izaDOZd/4XF3tgVt6ajSNUPu8epdL+sUyQyhXddS3qRfgph46XVpqBHngFxMMth50WfntldVxtb0tKq5PmlvRm3ZEjfM9bfuiZVn/uBbbKz5tuzZ59v4g/vR+Nc8NukLPP6CmGL7wn9pZCP/hboO9B5MDL1hDOraJ4y+f1s6byp/6nupIPPdx7Yzv6e+r+J79qHbeePzMfUEsCOpQ02GUakzePgAuTnmyhiMpXf3HXgJgc1Kgx2LUjN7tI/l6ZwWnQVxjN3VV0+hr83kBtTu+2hwb1O68g9rdHqit9S2oOveZD2vnb/vs/UEA1Xs1tBqc37ai1rbti4voMaxW5q+MPAshhBBCCCHEdc6t5tlP/wuk81wLmab5I+AXxT5eY1nWYzURjxBCCCGEEEL8r5POcy1kWdYMYEZNxyGEEEIIIYQQQpHOsxBCCCGEEEJc59xa/BTr7wrJQSGEEEIIIYQQIgDpPAshhBBCCCGEEAFI51kIIYQQQgghhAhA7nkWQgghhBBCiOudvKqqymTkWQghhBBCCCGECEA6z0IIIYQQQgghRAAybVsIIYQQQgghrnPyqqqqkxwUQgghhBBCCCECkM6zEEIIIYQQQggRgHSehRBCCCGEEEKIAOSeZyGEEEIIIYS4zrnIq6qqSkaehRBCCCGEEEKIAKTzLIQQQgghhBBCBCDTtoUQQgghhBDiOievqqo6zXXdmo5B1F5ycAghhBBCCFHSd+4G4rO71n9n2vaNE/rXyvyVyw9CCCGEEEIIIUQAMm1blOnZj/JqOoQyPXNfEH96P7emwyjV8w/UAeBvll3DkZTu96bBvM3emg6jTON6e3jhP7Uz7/5wtwHU3nPjmfuCgNod38UpT9Z0GKWq/9hLACwI6lDDkZRuTN4+AHYdOFnDkZQuoW007yyt6ShK9/BI9f/afF5A7Y6vNscGtTvvAJ77uHbWuU9/31Nr8w5U/j3zYe2M79n7g2o6hCuj1crB3O8UGXkWQgghhBBCCCECkM6zEEIIIYQQQggRgHSehRBCCCGEEEKIAOSeZyGEEEIIIYS4zrkyblplkoNCCCGEEEIIIUQA0nkWQgghhBBCCCECkGnbQgghhBBCCHGdc+VVVVUmI89CCCGEEEIIIUQA0nkWQgghhBBCCCECkM6zEEIIIYQQQggRgNzzLIQQQgghhBDXOVeTcdOqkhwUQgghhBBCCCECkM6zEEIIIYQQQggRgEzbFkIIIYQQQojrnIu8qqqqZORZCCGEEEIIIYQIQDrPQgghhBBCCCFEADJtWwghhBBCCCGuc/K07aqTHBRCCCGEEEIIIQKQkedqYprmeKCzZVl/r+R6DwB9LMt6vILLxwODLMv6pNJBXoHb+ui0i9XJ88LsRC+n0ksuEx0JEwZ6CPLA/hSHxZscAOrVgTtvMIgI1Th/yWXmKpucXIgKhwkDDaIjNZZtc0jc4xSkVTcIxg8waBKh4QJzE22SU90KxTqmn0H7OJ08r8sXq21OppdcLyZK4/YhBkGGRlKyw4INNgAJLTVG9DBoHKExdb6XE2lq3Yj68IuJQaReUH8fP+syN9GuTBbSuhnc3ENH12DbYZd1e/3jMnQY208juqFGdi7MTnTIyIL4pjCsq46hg+3A8h0OR8+ode6+QSc0GHQNjqe6LN3i4lYsm8q1d/sq5nz4dxzHpv/wOxgx/qd+3x/cs4m5//47J48l8f0nXqZ7/1sLvpv/yT/Ys3UlruvSvutAJtz/ezSt6g+qaN0Mbumpo2mw/ZBLYin5N66/RrP8/Fur8i+4Dtw+SCc6EnYcUXmUr1NzjcGdNTQNDpxwWb6j4pnXJlrjtr4GugZbDjis2eX4fW/oMHGQQUyURtZlddxnXFLfDUnQ6dlWx3Fh8UabgyfVdscPMGgfp3EpB96e7y1Iq3MLjRu7GTRuANMXlX5MV1dMZaU5foBBdJSGBqRdcJmdaJPnhVt768Q3VddngzwQWq/CWVrC2iOn+cfKndguTExowY/6tPf7/pWVO9mUnApAjtcmPesy3zwy5so3WEXdpr9Ak9HDyD2Txsqe46759rdsWs97097EcWxuHjmG283v+32/ZOEcFs2fja7r1AsO5tEnfkPzFvFs27qJj2ZMw+vNw+MJ4ocPPkLX7r2uSkyHd69kxRd/xXEcug68i34jH/L73puXy+J/P8np47sIDo1gzI9epUFUHLY3l/9+9gynjn2LpmkMv/OPNG/XH4DV815l94bZXM66wBOvbK1QHNVRhwVKt04QPDbWw95kh0Ub/c/Fe4YZ1zTW8urb/h10erVT5+yWAw7r9zolN1iD8ZXWHiiqrHKzuJgojQdvNZi52mbPscpVlGXt81P3ejhzXv07I8vlsxVltwvaRGvc2ke1AbYecFizu2QdNnGQTnSkRvZlmLm6sHwenKDRs40qn5dscgrKZwBNg5/cZpCZ7fLZCpVvkwbpREdpOA6kpLksWO/gBNjla1mnNY2AMf0N6njUcfDlGpvcvPLjK25UX99vYsPsNV5OlnEcThrswWOo4zD/PAyuA3cNNYior3H+oou1Uh2H8U01vjfc4NxFtf6eYw7f7Cj/fBD/O2TkuRJM0yy7livGsqy5le04X6F44N5rsB3axmhEhmm8McfLvPU2Y/qVnh1j+hnMW2/zxhwvkWEabWNUh2lIgs7hUy5vzvVy+JTLkAR1+GVfhsWbHBJ3lyyYbutjcOCkw5R5XqYu8HI2o2IVXftYjahwjVe/zGN2os34gaXHOn6Awey1Nq9+mUdUuEa7WBXrmfMuny73cvR0ye2lZ7pMmetlylxvpTvOmgYje+lYqxymLXHo3EIjKtx/me6tNHLyYOoihw1JLsO6qZhUJerwr6UO8zc4jOtXePrOSnR4b6nDu0scQupqdIyreifVcWxmzfgrP3lyKr99eS5b1y7kVPIBv2UaNorm7kf+Ss9B/h2VI0lbOZK0lV+/OIvfvDSb4we/5eCejVWOSdNUp+w/Kx2mLXbo3FKjUfH8a62RkwtTFzps3OcyvLvKC68N33zr8PV2/980uA6M6K7xyQqH6YsdQutBfJOKxzO6n8HHy7xMmeelS7xOowb+y/Rsq5OT6/LGHC/r9jjc3FMdi40aQEK8zlvzvHz8tZfR/Qzyry1sO+Tw0bKSjb8z512slV6Onin/PLjaMZW3n4s327yzQJ2fGVku/Tqo43LJZod3Fnp5Z6GXDfucSjdS89mOy99X7GDyhIHMvG8ES5JSOJR2wW+ZXw/tyqf3DufTe4dzd/fWjGgbc0XbulqSP/iSDWN/UiPbtm2b6W+/zp+efZHX3/6AVSuXcfzYEb9lbhh2M6+9NYN/vvkvJt7xPWZMnwJAeHgD/vDMC7z21gye+NXveP2VF65KTI5js+zz55j06Ls88McF7N08n7ST/mXJt4mfUy8knAef+Ypewx9g1Zx/ALBz7ecA/PAP87jz8Rl8M+tFXEfVFa27DOfe33xe4Tiqqw4LlO6I7nqp52zH5lqZHYVrXd82bgC92ulMX6TO5faxGg3rl5GRNRAfBG4PlFVuFqVpcHNP3a/TWVHl7bPXpqC8K6/jrGmqs/fJcpu35tskxOsl6rCebdSF3zfn2qzb63BzT5U3jcIhoaXO2/NtPllmM6qvTtHr0f07aAUX9vPtPOLy1jybqQtsggzo2bb8tsG1rtPGDTT4eqvD1AVe9h53GNy5ct2Sdr623uTZXuYl2oztX/pxOHaAwdxEm8mzvUSFFzkOu+gcOuUyebaXQ6dcbuhSuP2jZ1ymzvcydb5XOs61mGmat5mmuc80zQOmaf6unOXuME3TNU2zT1W3KSPPRZimGQpYQBxgAH8BXgT+A9wCvGSa5nngBd/3qZZl3VRGWg/gG0E2TfN9IAfoA4QDv7Isa345oTQ3TXMFEAt8ZFnWs6ZpPgekW5b1mi/9vwJngO8BnUzT3AZ8AEwG/g4MA+oCUyzLesc0zWjffoSjfvdHLctaVZn86dhcY8dhVYCkpLrUq6NRPxguZhcuUz8Y6gZppPhGh3ccdujYXOPACZcOzXU++EoVntsPOfzwFg//3eqQdRmyLrsFHdd8dYOgZVONOYkqLceByxUsvzq10Nl2UC2cfLacWOtoJJ9V6W876NC5hc7+FJuzGZXJmYqLiYRzF+G87yrtnmMu7WM0EotUeO1iNVb7rvTuTXYZ2UsHXE6fL0wn9QJ4DApGoXN9dZKuqc+uhmMHdhLVtDlRTZsD0GPgaHZtXk6zuLYFy0Q2jgVA04tXyBp5ubnY3jxc18W2vYQ1iKpyTDGRcC6zMP92H1PHTdEGQ/sYjVW+/NuT7DKyt8q/PBuSUynRIIyor36TrMvq7yOnoUNzjSMBOqgAsVEa6Zku531Xp3cdcegYp7M6o/BA7RCnFVS8u4+5jO6r8qpjnM6uIw62o/YnPdMlNkojOdXl2BmXBqElt5d6oeRnpbnaMZWXZtHGf5ABlJJtXeI1Vmx36Nm28gfnrtPnaB4RSpwvQ0a2i2XFoVO0Ln7VyWfJvmQeHtCx0tu5mtJXbyK4ZWyNbPtA0l6iY2JpFq0uIAwZOoIN69bQvEV8wTIhIYUH1+WcnIIZIa3btCv4vEXLVuRevkxeXi5BQXWqFNOpozuIaNSSiEaqLOnYewwHd35NVHRhWXJw5zIGjlYTrtr3uJVlnz+H67qknTpA8/ZqpDkkLIq6wWGcOvYt0fHdiGnVo1JxVFcdVl660ZEQWk/jwAmHmKjCcjLIAwM76cxbb9O1Vcnz4lrXt40bqHS8vn7f0TMunVrorC2lE1sT8VWkPVBWuVlUvw46e465fr8FwKDOOp1banh0jb3HHVaU0lmqyD4HEhsF54qWpUcdOjTXSN1VWHB2iNP4Zmdh+Tyqjzo+OjTX2HW0sHw+l+kSG6XqtbBg1XZY9a3LgE6F+3bgRGG6KWku4SEapRbSBfFd2zotKkwruLB06KTLfSMMlm+veEe1Y3OtsK0X4DjMn7W47aBDpxbqOOzYXGfGUm/B5z8a6eGrLdd3R9m9CjMAawvfoOYUVB8tGdhomuZcy7J2F1suDPgFsP5qbFdGnv3dBpywLKu7ZVldgMW+z9Msy+oFfA1MB+6wLKs7cFcl0o4H+gFjgKmmaZY3ibEfcAfQDbjLd5XkPeB+ANM0deAe4CPgd8Aqy7J6WJb1KvAgkGFZVl+gL/BT0zRboUanl1iW1QPoDmyrROwAhAVrBVNzAC5ccgkL1koscyHLLbIMBcvUr1dYoF3MVn+XJ6I+ZOW4TBho8NBoD+MGGKpxXpFYQzQyLhWNI7/SKBQeonGhyDIZl1zCQgIXKg3ra/xsnIcHb/PQsknlCqH6wfjlT2a2qvT8Yg+GC1nq364Ll/PU6GhRHeLg1HnVcc5391Cdn0/Qyc1z2Ztc9TnbGedOExEVXfB3RGRTMtJPV2jd+PY9aJvQj2d/NoznfjaMDt0G0zS2TZVjCguGC9lF8i+rlPwLCZx/RZ3LhMgwaBCirrq3j9UID67Y71p0W6B+27AQ/2XCQzQyfL+560JOHgTXVetmZBXbl2LrXqmrHVOg/Rw/0ODXd3iICtdYv8+/4dEgFCLqaxwuZRZHRZy5mEPT+oU/ctP6wZy9lFPqsicvZJFyIYu+cY2vaFvXg7S0s0Q1Ktz/qEaNSU87W2K5RfNn8eiD9/LhjKk8+PDPS3yfuOYbWrdpV+WOM8DF86cJa9is4O/6EU3JPO9fllzMOE1YhCpvdMND3eAwci6do3FsRw7uXIZje8lIPc6Z47vIPH/yiuKorjqsvHRH9jZYuqXkSOSI7jqJexzyyhgovdb17ZnzLi2aaATXURdm28ZoNCinPPoutQcK41GdrY1J/mVU62g1ovzuIpupC7xER2q0KKVuL2+fPQb8dJTBg7cadChn5ldYsEaGX1lKyXwLKdyOX/kcrHHhUunr3tpH579bnTJv19I16NZK5+CJ8juG17pOO5vhFuRX55Y64QEufpSMVysRb6ltvaLHYRYFbb3QYP/jMLRIe6J5Y41Hx3q47yZ1q5SolfoBByzLOmRZVi7wGTChlOXyB0NLbzxUkow8+9sJvGKa5ovAfMuyVpmmCWrEFmAAsNKyrMMAlmWVcmdFmSzLshxgv2mah4COlN2B/cqyrDQA0zS/BIZYlvWaaZpppmn2BJoCWy3LSvPFV9RIoJtpmnf6/m4AtAM2Au+ZphkEzLYsq9Rtm6b5EPAQQML4jyuxe5UX6J5cXdOIjtRYtNEmJc3ltj46Q7rolboqebVlZsHLM/PIvqzum/r+CA+TZ+dxuZL36FRFo3AY3k3ns2/88+E/Kx0MHcYP0GnZxOVIxfq51SL11FFOpxziqTe/BmDaCz/l0N7NtO7Yu+aCKkNOHize7DBxkI7rqqvzEaHXz5XZa2Fuoq2mI/bR6dJSY9uhwpO7S0udPUfLbtRdTUuSUri5bQxGiZkQorhRYycxauwkVq74LzP/829+/qvfF3x37Ohh/j1jGs88/3INRqh0GXAH6acO8vHLdxAeGUN0q57oWiV7TdUk0DHdt73O/hSXzCz/z5s2hIZhGks2OwFHSq+WQLGmXoA1u2zuu8lDntfl9Dk34L2xV9OVtgcq49Y+Bv/dWrL90CZao020xsOjVZO4ThBEhcGxMxVP+7VZXjKzVSf/hzd7OHPeW3C/bHVrF6vuJT6ZDi3LuOVodD9168CxktfRatScRJtRfQyGdlXPnbFretDXdxyeTHd59QsvuV6Vv98b7mHy7PJvCRBXX9E+ic80y7KmFfk7Fjhe5O9koH+xNHoBzS3LWmCa5m+vRlzSeS7CsqwkXyaPBp43TfNr31eXylmtoopXDeVVFWUt+y7wANAMNRJdGg14wrKsJcW/ME1zKGrk+33TNP9pWdaHxZfxHZTTAJ79KM/t216nl2+q5Yk0Ne3muK/wDQ/VyMz2DzUz2/+qX3goBctczKFgOk39YLh0ucz9B9QVxAtZqjMDsPuoy+ByKsv+HXX6tFffp6S6NAgtnJ4UHup/5TE//fAinaQGoRqZWeXX4Laj7skClR/pmS5R4VrBA8UCuZiN37SpsGA1+lxUZjaEh6j/a5qarpbteyhNWDDcMVhn3nqnYOpy8fj2p6ip4EeucKQvX4OGTTmfVjjCcz79NA0im1Zo3Z0bv6Zl227Uradahh16DOHo/m1V7jxnZuMbFfblX0gp+ZdVdv6V5cAJOOC7It+jtYZTwVZj/rbyhYdoJRrKF7JcGoSoY0vToF6QOoYys6BBSLF9KbbulaqOmAKl6brw7VGXwZ11th0qHGlLiNdZuKFyzwYoqkn9epwuMgfv9MVsGpfx9LGlSSn83/BuV7yt60FUVGPSUgtbyGmpZ4mMKnskfsjQEUyb8mrB36mpZ3jx+af4+a9/T7PoqzP1vH5EUzLPnSr4++L504RF+Jcl9Rs0JfP8ScIaNsOxvVzOzqReaEM0TWPYHX8oWO7Tf95DwybxFd52QvPCf1/MplrqsMzs0uvGuMY6LZto9G2vU8ejbqnJzYOMSxATqfGLiR6KXuepyfoWYOtBl60HVQdhRA/db0SvpuOrbHugNDFRGncOURdeQupCu1gDx7HRgNW7HDbv9++5Fd3fj5d7y/yd1b6qz85fhCOnXZpFapy7WLIeycx2fWWsL40QSuZbltpOfh1WUD5nu34js/nrto/T6RCn0S7GwGOoOm/iIJ3Za9X+DO2qEVIX5q8P3DO91nVa2gX4aJmqHyLDKDFdvzT9ijzY7kSaWyLeUtt6xfPct8yl7GLHoW9csuiAyP4UlzH91TGTVYHzqLZz+e5cXC7aJ7kSvpm6/0T1na4ambZdhGmaMUCWZVkfAS8DxR8zug4Y6psGjWmakZVI/i7TNHXTNNsArYF95Sx7i2makaZpBgMTgTW+z2ehppb3BfI7x5lAWJF1lwCP+kaYMU2zvWmaoaZptgROW5Y1HdUJr9AjVDcmFT70Z2+yQzffvVmxjTQu57ol7vW5mA2X81xiG6mTs1srnb3HVSGVlOzQvbVav3trnX3Hyy/IL+WoKUD5tza2itZILeeBYev3OgUP8tp9zKFHG7WtuMblxJrrEtdYxdqjjc6eY+XHFFKXggdgNKyv7tc5l1nxTuqJdLVeg1DQdejUQmP/Cf/1959w6RKffw9R4f1AdYPgrht0lu9wSEkrXL7ok4w1DdpEQ1pmhUMqU/M2XUg9dYy0M8l4vblsS1xIQu/hFVq3YaNoDu3ZhG17sb15HNqziSYxrasc04l0aBhWmH+dW2jsTymZf119+dcpTiv1oW/FhdRV/68XBL3bamw/VLHfNCXNJSpMI8IXT0K8zr5k/2MoKdmle2vftLQWhdOX9yU7JMSrp6dHhKpjKaWCF2ECudoxlbefRe8h7xDnf/95VLiaMl/RJ+SXpnPTCI6fv0RKxiXybIel+1O4sXWzEssdTs/kwuVcujVreMXbuh60bd+BkynJnD51kry8PFavXEbf/oP8ljmRklzw780b1xEdozrJly5m8tc//54fPPAQnTp3vWoxNWvRlfNnj5CRehzbm8vezQto3XWE3zJtuo5g9/pZACRtW0KL9gPQNI283GzyLqsW+NG9a9B1w+9e6UB2FRmTqK46bF+yW2q6s9bYvDbLy+uzvSzdYrP9sMPX2xw27Xf455fq8/eWFo5m1WR9C4XlYHgIdGqus/Ow/zrfpfZAaSbPVnn++mwvu4+5LNhgsy/Z5cBJlx5tNIJ8w0lhwSoviu7vxeyyf+d6dQqfNRJcV033LevhpilpEFm0LG2pk1TsNqt9KS7dfHlTtHxOSnZJaFlYPkeGaaSkwbJtDq/Nspk8x+aL1Q6HT7sFHeeebTTaROt8uaZiQ7rXuk7LP+YAhnY12LQ/cJwb9jkFD/LaU7St10gjJ6/s4zCuUWFbL/843JdcuL76XG2/6G0EsVHqTRzXQ8f5OpQCFLlESpzvs3xhQBdghWmaR1AziOdW9aFhMvLsryvwsmmaDpAHPArMzP/SsqyzvikEX/quZpxB3aReEceADagHdj1iWVZ58+43AF+gDoKPLMva5Nt+rmmay4HzlmXlD+XsAGzTNLcD7wOvo+6v3mKapgacRXXAhwG/NU0zD7iI7/7pytif4tIuxuWJCR7yvGq6Tb6HR3t4Z6FqBCzY4DBxkLoCeuCEU/DAitXfOtx5g0HPNh4yLrl8vkqtH1oPHhrloW6Qul45oKPOlPlecvNg0Uab2wcbGLq6ijungk+3Tkp2aR/r8qvbg8i1Xb5cXbjeY+M9TJmrYp27zuaO/FdVpTgk+TpinVpojO3vIbQe3H+zh5PpLh985SW+mcZNPQwcV420zUn0BhzVLMp14astDvcMVU/J3HHYJfUC3JCgcfKcy4ET6vVL4/rrPDJKPXFzzjpVmPduq55+OqSzzpDOKr3PVqrv7hqiKixNUw962XKw6p0ww/Aw6YE/Mv3vD+E6Dn2HTaJZXFsWf/4GzVsnkNB7BMcO7uSDV39B1qUL7N6ygqUzp/Dbl+fSrf9IDuxazyv/Nwk06NhtSIU73uVxXVi6xeGeG9VrPrYfUvk3tIvGyXSX/Sdg2yGX8QN0Hhmtnro9O7GwMv7ZWJ26vhGg9rEan33jkHoBbump0TRCVayrd7mkV3C6nevCwo1qqqOmqQeOnM2AYd10TqS7JCW7bDngMGmwwRMTPGRfdpnpOxbPZsDuow4/G+fB8aWTP3Xx9iEG8U3VaMEvJ3lYscNm60GXjs01RvUxCKkH9w43OHXO5eNlJc+J6oiptDRBvbKkbpBqXJw65xa87g2gS7zOt0eqNg/Po+s8Oawbj89JxHZcJiS0oE1UOG+v20PnJhHc2FrdJ7s0KYWR7WOvyuvQqqrHv18h6sZ+1GnUkBGHv2H/c29wfMbMwCteBYbh4SeP/oLnnvotjuNw0y2jaNGyFZ/++z3atOtAvwGDWTR/Fju2bcYwDOrXD+MJ35TthfNncepECtanH2B9+gEATz//DyIiqnZBQjc8DL/rab546ye4rk2XAXfQKLodaxa8TrMWXWjT9Sa6DLyTRR/+ln89ewv1Qhow5kdqNDwrM40v33oQTdOp36Apo+5/qSDdlbNfYu/m+eTlZTPtqaF0GXgXg0Y/UWYc1VWHlZfulaqJ+ta80SCkjobtO/fLux2ptrQHBnUunMJfWrmp+4b2i48qF3XopEvjcJcHb1VN4lyvy6w1donOUln73ChcY2x/Axc19W/NLpvUMh466rqwaJPD90cYJcvnNJekFJetB1wmDdJ4fLxB9mX4Yo1/+fzoWNUGWbQp8O0wY/rpnL8EPx6p8mnvcYeV35a90rWu07rG6/T1vaFhzzGHbZVsu+xPUW29X0xSv8nstYXH4SNjPUz1vRZrwXp1HOa/Mi3/ovuqbx3MoQa92no4f8nl82/U+p1bqrgcB/Jsl5krq35Oi2qxEWjnG9RMQT0PquAN7t1ijwAAIABJREFURJZlZQCN8v/2PYz5N/n9qiuludfiRrT/cb6nbc+3LKtKrSdfh30LcJdlWfuvRmzlefajvFp7cDxzXxB/er8SvdZr6PkH1AN2/mbVzsL296bBvM21996dcb09vPCf2pl3f7hbNUCe/ega3uReCc/cFwTU7vguTnmypsMoVf3HVKdsQVCHGo6kdGPy1GSlXQeu7GFZ1S2hbTTvLK3pKEr38Ej1/9p8XkDtjq82xwa1O+8Anvu4dta5T3/fU2vzDlT+PfNh7Yzv2fuDgO/QHGif5KRyrp7UMnHtuwTMX9M0RwOvod6C9J5lWX/1vaFok2VZc4stu4Kr0HmWkefvCNM0OwPzgVnXouMshBBCCCGEuH642vV1x65lWQuBhcU+e7qMZYddjW1K57mKTNP8EerdYUWtsSzrsfw/LMt6oJT1bkU9Nr2ow5ZlTSptO753llX9plEhhBBCCCGEEJUmnecqsixrBjDjCtZbQuFDv4QQQgghhBBC1GLSeRZCCCGEEEKI69x36VVVtdX1NfFdCCGEEEIIIYSoBtJ5FkIIIYQQQgghApBp20IIIYQQQghxnbvenrZdEyQHhRBCCCGEEEKIAKTzLIQQQgghhBBCBCCdZyGEEEIIIYQQIgC551kIIYQQQgghrnPyqqqqk5FnIYQQQgghhBAiAOk8CyGEEEIIIYQQAci0bSGEEEIIIYS4zsmrqqpOclAIIYQQQgghhAhAOs9CCCGEEEIIIUQA0nkWQgghhBBCCCECkHuehRBCCCGEEOI6J6+qqjoZeRZCCCGEEEIIIQLQXNet6RhE7SUHhxBCCCGEECV954ZxDx08+J1p27du06ZW5q9M2xZleubDvJoOoUzP3h/E0x/k1nQYpXruh3UA+Otndg1HUro/3mNwdveGmg6jTI079+NvVu3Mu9+bBgDPfeyt4UhK9/T3VZH+7Ee189x95r4gNiel13QYperdPhKAXQdO1nAkpUtoGw3AgqAONRxJ6cbk7SP128SaDqNUjboMBGr3eQG1O77aHBvU7rwD+MuntbPOeOp7nlpbn4Gq02prW/TZ+4NqOoQr4mq1sj/6nSLTtoUQQgghhBBCiACk8yyEEEIIIYQQQgQg07aFEEIIIYQQ4jrnujJtu6pk5FkIIYQQQgghhAhAOs9CCCGEEEIIIUQA0nkWQgghhBBCCCECkHuehRBCCCGEEOI658q4aZVJDgohhBBCCCGEEAFI51kIIYQQQgghhAhApm0LIYQQQgghxHXORV5VVVUy8iyEEEIIIYQQQgQgnWchhBBCCCGEECIA6TwLIYQQQgghhBAByD3PQgghhBBCCHGdk3ueq05GnoUQQgghhBBCiACk8yyEEEIIIYQQQgQg07aFEEIIIYQQ4jon07arTkaehRBCCCGEEEKIAGTkWVTKqL467WJ18myYvcbLyfSSy0RHwqTBHjwG7E9xWLTRASC4Dtw11CCivsb5iy7WSpucXLVOfFON2/oaGDpk5bjMWGoTFQ53DS08RBvWh+XbHdbtcSod9+h+horb6zJrjc3JdLeUuDVuH2LgMTT2pzgs3GADkNBSY3gPg0YNNKYt8HIireS6ldG6GYzspaNpsO2QS+Ie//QMHcYP0GjWUCM7F2atdci4pPLv9sE6MZGw47DLki2F6+k63NZbo0VjDRdYscNhX3KVwgRg3ZYdvP6vf+M4DmNvHsYP7hhX6nIrEjfyp5cm8+7Lz9KxbWt2Jx3kpbffA8DF5cd3386NA/pUPSBU/t3cQ0fXYNthl3V7S+bf2H4a0b78m53okJEF8U1hWFcdQwfbgeU7HI6eUevoOozsqdGyiYbrwjc7HfalVCyeNtEat/ZR8Ww94LBmd8l4Jg7SiY7UyL4MM1fbZFxS3w1O0OjZRsdxYckmh4MnXQwdHrjFwDBA12DPMZdvdqpjftIgnegoDceBlDSXBesdnACHY5todW7pGmw54LBml//5o+IziInSyLrsMnNVYXxDEnR6tlXxLd5oc/CkW26arZpp3NLLQANyvS6z19qcu1ixfCxu++ZEPpz+Go5jM/yW8Yy/636/7xfM/pQVS+eiGwbh4RE89Is/0rhJNACfvj+FrRvXqjy750cMvOHmKwuiHFs2ree9aW/iODY3jxzD7eb3/b5fsnAOi+bPRtd16gUH8+gTv6F5i3i2bd3ERzOm4fXm4fEE8cMHH6Fr915XPb7ydJv+Ak1GDyP3TBore5Z+TlendVt38Np7n+A4DuNuGsoPbh9b6nLLEzfyp39M4d0Xn6FT21YFn586m8Z9/+8P/NicyL0TRl1xHLf18dVnXpid6OVUGfXZhIEegjyqPlu8SR3r9erAnTcYRIRqnL+kzpv8+qy0dOObatza2yhIt1EDmLnKZl9y2SdwdcTXNV5jcIKKI9frsmC9zenzKq3xAwzax2lcyoG353trJP+iwmHCQIPoSI1l2xwSi9X3mgY/HeUhM8vl0xV2ie3VDYJJgw0ahGroGiTuttl2qOw8vmeYQcP6WoX2tyL7BdCvg07f9nqZZXObaI1bfW2ArQcd1pbSBpgwoLDO+GJtkTqjs0aP1jquC4s3Oxw6pdbt30HVJa4LZzJc5q5zsB2YOFAnJlLDduBEusuCDRWrM65mnRYeouIIDVb165YDDhv2qTRv7KrTs61GVo5af9l2hwMnArexqqstChATpfGTUQYzV9rsPqZiuaWXTvs4HQ04eLIwLfG/SUaeryHTNP9smuZvyvhumGmag64w3QdM03yzlM8nmqbZ+UrSLE27WI2ocI3Js73MS7QZ298odbmxAwzmJtpMnu0lKlyjbYyaIjKki86hUy6TZ3s5dMrlhi7q8KsXBGP6G3y63MuUuV6slapCTLsAU+d7mTrfyzsLvOTZsOdY5QusdrEaUWEar8/KY26izbgBpcc9bqDBnLU2r8/KIypMo12sivv0eZdPl3s5erpqnWZQFf9tfXQ++8bhnUUOCS00GoX7L9OjtUZOLry9QFUwI7qrOLy26tR9va1kHEM6qwbP1IUO7yx0OHamyqFi2w7/nPYB/3jqt3w0+UX+uzqRw8dL9iizsrP5fP4SOrdvU/BZ65ZxvPuP53j/1b/yylNP8vLb7+G1SzZ0KkvT1IUHa5XDtCUOnVtoRBXLv+6tNHLyYOoihw1JLsO6qfxTlbzDv5Y6zN/gMK5fYfE3uJNG1mV4Z5HDtMUOx85WPJ5RfXU+WW7z1nybhHi9xO/Zs43qxL8512bdXoebe6rtNgqHhJY6b8+3+WSZzai+qjFlO/Dh1zbTFqr/2sZoxEaptHYecXlrns3UBTZBBvRsW/70K01TF44+XuZlyjwvXeJ1GjUoFl9bnZxclzfmeFm3x+Hmnur8aNQAEuJ13prn5eOvvYzuZ6Bp5ac5pp/Bl6u9vLPQy84jLkO7ln6uBeLYNjOmvsKTf/4nL0/5lLUrv/r/7N13fBRl/sDxz8xsSE8gCQRIAoQOoTepoiCKFBHRsRye3p311PNOr/g77/T0Tj3b2Xuv6IiAUkRF9BBEmiC9l5CEloT0kGR35vfHs8lm00kgCdz3/XrxCjv77Mx3n515yjzPzJCSvM8vTafO3fnXf97i0efeZ9ioccx66wUA1q9Zwb49O3jk2Xd48MnXWTjnQwoK8usVR3U8Hg+vvfQMf3vgUZ556R2+X7aUg8n7/dKMOe8Cnn7xLf7z/BtcOuNq3npNxRcREclf73+Yp198izvuuodnnnz4lMZWFynvzGH1lBsafbugypUnX3uPJ++9iw+efpgly1dVWa7kFxbyycKv6d2tc6X3nnt7FsMH9m1QHF3ba0SFazz3mZv5qzxMHlb1vjp5mMH8VR6e+8xNVHi5+ixJZ99hh+c/d7PvsMPoJL3G9e4/4vDKInVsvLPETYmbspNRjRnf8Tx4+2s3Ly90s2yTzZRy9eGGvTbvL61bJ/J0xVdYBIvX2qzcWnVdf05PnfTs6vNtaHf1/isL3bzztZsLBxvo1bR0eyZoFJfU6evW+Xt1itXoEa/x8kJ3lR1yTYOJg3U+/M7DS4s89OlYuc4obQO8sMDDqh024/uXqzM66Ly8yMOH33m4eIiqM8KD1fd+/UsPr3zhQdfUSX+AzfsdXlyolrsMVR/V5HTUabYNX/1k89ICD29+6WFod/91rtpu8+oXHl79wlOnjvPpaouWfv8Jg3T2lIsjobVGhzYaL85XdV5cjEan2DN36rODdsb8a66k89x8nAfUq/Ncg0uBU9Z57pmgsWGPqtBS0h2CWmiEBfunCQuGwACNlHRV8GzYY9Org+b9vF72+Q17bHomqN2vb2edbcl22ZnL/BOVt925rcbxXKcszcnFrbNh70nGvdcXX3q26sifCu2jIDMXsvJVhbI12aF7nH8B0S1OY+M+Fce2g05ZIV3igZR0cFfRpujfWeOHcmeHC4srpzlZ23btIb5dLHFt2xAQ4OKC0cNZvnpdpXSvffgpv5g+hRYBAWXLggIDcRne0Y2SYjTt1BSC7aNU4680/7YlO3RvXzn/Nu9XebE9xZd/R7Igz7tvpeeAy1Bn0AH6JWp+MwDqmn9x0XA81yErT8Wz5YBNjwT/eHrEa2z07n9bkx0SvfH0SNDYckCNDmTlq/WUdpJLvG0uXcev4Ve+YZGa4RARUnO+xkVrZJaPb79Nz3j/Yr9HvMbP3pGZrckOndt6j9d4nS37ffFl5jrERWs1rtNBHUegTorlFtbvhNPuXVuJbRdPbNs4XAEBjDj3AtatWuaXJqnfYAKDggDo1iOJzAx1xijl4D56Jg3AMFwEBQXTIbELG9etrFcc1ca3czvt2sfRtl17AgICGH3uOFb/uMIvTUhIaNn/i06cKDsGOnfpRlR0DAAdOiZSXFRESckpOGBPQubytZRkZjfqNktt272X+La+cmX86HP4fs36SulemzWHmdMnEdgiwG/5slXraNcmhsSEuAbF0TNBY+M+dVym1lIvpHrrhY37bHomlB6/Oj97j+uf99r08NYXdVlv7w4au9Ic3DWcTzxd8aWkO2WjbCnp/mVI8lGHwqLqY2qM+AqKIC3DwVNF0REeAt3aa/y0u+aT6C1cvr+FxaqcqijABSN66Szb7P8jtAqDX4wzuPFiF9dfaFQ6OVvb9xrSXWf5FlVuVkXVYU5ZHbYl2aZHfOU642dv3m496JDoLZN7xGtsSS5XZ+Q5tI9Sn9E1Vadp3r95hWr57nInaNLqVGec+jot7wQcPq4+W+yG9Oza46jJ6WqLgjo5sy3Z8WuHOg64DA1DB5eu8jrvRMMHU8SZS6Ztn2amad4LXAccBQ4C60zT/B1wC+AGtgL3eF97TNOcCdxhWdb3VazrbeAEMASIAO6yLGtBhTSTgb8BfwEuAcaapvk3YAYwufx2Lcu66mS+S3iIRk6Br8DIKVAFYF65BnJEpTTqcwChwb4CPa9QvQaIDledmOsvNAgM0Phxm6esMV+qT6LOpn31K6wiQjSy8301WbVx55eLO98hIuTUn1sKD4bc8vlTCHFRldPkFKj/Ow4UlahpRtV16AK9bcuxfdW04+N58OU6m/w6NoKqcyzzOG1ifMG1jo5i6849fml27NnP0fQMRg4ZwIfzFvq9t2Xnbh55/nWOHEvnb3feUtaZboiwYPz2r9xCyhoPpeqSfz3i4XCWGuUtzb9z+6izy8fz1FnygjrkX3iwRnaB73VOgeqwqm6kN02IVnbSx3HgRAkEB6rPlja+Sj8bHqw+q2lw40SDqHBYs9MhNcN/u7oG/RJ1vlxb82h+eIgvL9Q2HOJi/BstESEa2d489YsvhLKGB0BugVpWGmtV65y/0sM14wzcbpXvr395clMhSx3POEZ0TJuy11HRbdi9c0u16b/9ej79B48AoGOnbsz56A0mX3oNRUUn2LLxJ+ISEqv9bH1kZBwjOqZ12evomNbs2rG1UrovFszl87mf4HaX8MDDT1V6f+WK/9K5SzcCAlqc0vias4rlSpuoVmzZtdcvzY69+zmansnIwQP48LMvypYXFJ7g/XmLePq+PzHr8y9oiPBgjewKZX54sH+9EB5coT7LLz1GISzIvz4LC6r7evt00itNR26s+Mob2EWv00hfU8VX0cTBBkvWe2gRUH3Ha/UOm6vOM7hrhotAl5pSXJVx/dVvUFKhiJpyjsHC1R4yc1VZPnmYwbtL/NdR0/eKDlf18LgBepUnR1Qbyfe6yjojWPOrw04UqzosPFgjNcO/zogIUct+3G5z5yUGJR7Ye9gpm85dStegbyedL3+qpc44TXVaqchQaBulkZLu2/+Hdtfpl6imlX/9k+03hbrKGE9TWzQ8GHolaLz9lYdpI33tlZR0h32Hbf54hQsNWL3dJr1pzj2KZkJGnk8j0zQHA1cBA4BJwFDvW/cAAy3L6gfcYlnWfuBl4CnLsgZU1XEupxMwDNURftk0zbIqxzTN6d51T7IsaxnwOfAn7zr3VNzuqfum9eQt13Qd2kVrfLDUw3tL3IztZxAd7ktm6N4zrgfkGpOq6JqqKFLSHd74yiYlw2H8wNM/3cW2bZ576wNu/9U1Vb6f1L0r7z/7b1577AHe/3Q+RcWNO7pWnZgIOL+fXnaNWln+ZTi89bVNaobD+P5NO13IceDVLzw8NddDXDS0rjDVetIwnQNHnTpPL28sw3vpfLjUw1Nz3WzYa/td43m6LP92Mft2b2fKZeqa436DzmHA4JH848838fwT99GtZx/06uZtnmYXT5nOS298yLW/upnZH7/n917ygX2899ar3HLH3U0SW3Nl2zbPvT2LO66vfG73TWseV065iJDgOvS0GplTxz5oWDC0aan5TQttDBXj6xSrMbCrzpJaOlONpbb86xankX/CqfLa1vK6tNc4ctzhP5+qqekXDzWoMHmB2FbQKlxj+0H/jQa41BTdK8a4uHmSiynnGIQFn1xdoOuqo/vGYg9f/9Q4bZagAOger/HcfA9Pz/PQwqWubS/v4iE6ycccDjZhnRHggivGGHy5zqbYe9Ji7S6b5z738MoiD3mFasp0o/PuBhcPNfj6J5uKu2JUOLSO1PjPbDdPznaT2E6daBf/u2Tk+fQaA8y1LKsAwDTNz73LNwIfmKY5D5h3kuu0LMuygV2mae4FenqXj0ONSF9oWVZ1k4xr3a5pmjcBNwH0mvIBw3roDOqmCjM15ceXtuKZPfCdAfSl8Y205heqhkOe92/ptJicAigscihxq+mqB444xEZpZOSqz3WN0ziU6VQ5nbs6w3roDO6u4k5Nd4gM9Z39rDbu0HJxh1ZOcyrkFpae/fTGEqyWVUwTEaL+apoaGa1pGnFhsbrxy3bvDcK2HXQY0FmHSlXAyWkd1Yqj6b6WyrGMTFpHtyp7XVB4gn3JKdzxN3XNZmZWNn95+Cke/esf6NnVd51ip4Q4goMC2Zec4re8PvIK8e5f6ruFn2T+hQfDjFE681fZZHnPnJfmX+kN1rYfdOifWLf8yy10iKy4v1eYqpxb4BAZ6osnKEBd15db6BDhm9lb5WeLStS1kl3baxzzXud3bl+NkEBYsKr2hlluAZWO2dwC/zQ5Beo75BY4/vEV4P1u3rwOoeyzVa0zJBBiW/lGRjbvt5k5rn5VTKvo1mSk+y7cz8w4SlR060rpNm1YzTzrbf7+yIt+o7eXXnk9l155PQDPP34fbeM61CuO6kRHtyYj3dcKzUg/VmV8pUafO45XX/CNPKenH+XRf/2d3939f7Rt17Dpx2eaiuXK0czjlcqVvcmp3H7fvwFvufLvZ3j0njvZsmsv365cw4vvfUxefgGartMiIIDLJ9XthnBDu+sM6uqrzyJDKetMRIRqlY/dwgr1WajvGM07UaE+K/J9pqb1JnXQ2X7QqfKmTY0RH0CbljB1uLpvwclc4tNY8VWlQ2uNHvHqJlEuw3djsFI3T1JlTWGxw3c/q7LxeB5k5TnERGh+N/pMiFE30brzUhe6BqFBcN0Eg1nfeThRAq8s8h+O1jS46WK1/h0pNmt32dV+r5wCh23eTnlVNxetso1UZb6VqzO8M6dKl5f/bE6BmtadlUfZbKntBx3iYzQ2eS9fOrePRmgQWN/Xoc44TXWaroE5RmfzftvvpEX5dt1Pu22uPq/qE66N0RZtH61x+blq+yGB0C3OwHY8REdopBxzyjr8u1IdElqfuZ1nxzlzY28uZOS5aUwGXgAGAWtM0zyZFmbF0rj09R4gHOjekO1alvWqZVlDLMsaAmoKVOlNu7Yl2wzoonaZ+BiNEyVO2dSXUnmFUFSiCm6AAV30soJyR4rv82q5Ksi3H7Tp0EbdGTPAgLgYze+GIH076Wzad3JncFfvsHlpvpuX5rvZnmx7O5MnEXdnX3ynUlqmOosZGarOUPfuoLEz1f8n3ZXq0C9RxdErQWN/HW5UtivVoaN3lmtirHZKphT17NaZg4cOk3bkKCUlbpYs/5FRQ313BQ4LDWHhuy8x+9WnmP3qU/Tu3qWs45x25GjZDcIOH03nQOoh2rapvnNRV2mZ6pq00vzr5b12sLxdaQ59OpVet6tx4Kh6PzAArhij8+1Gu9I06N1pvvzrFKuRXsdr3FMzICpco6U3nqSOOjsr3D13R6pDP+/+17uDxj7v77kzxSGpo7r7d8tQtZ7UDFVpl04ldxnQuZ1eFs/ALhpd2unMWVG3fTM1wyG6fHyddHak+H92Z4pD/85apfh2pNgkdfLFFx2uOsbVrbOwWDWioryzRrq00ziWU78TOF269eJw2kGOHk7DXVLCymVLGDxsjF+a/Xt28MYLj3H33x8nsqVvGrDt8ZCbow6A5H27Sd6/h34Dh9Urjup07d6DQ6kpHDl8iJKSEpYvW8rQc/xvWZGW6rvd/bo1P9Kuveok5+fl8tA//o9rr7+JXr0bdtOrM1HPromkHDpC2pFjlJS4+Wb5KkYPGVj2flhoCIvefp5PX36ST19+kqTuXXj0njvp1TWRl/7117Ll5pQL+eVlU+rccQZYs9Muu2nX9hSbfonquIyL0Sgqrr5eKL0soV+irz7bmWLT33tc9++ss8NbX+xIcWpcb59OGpv3V338NkZ8ESFw5VgXc1eoqcknozHiq843G2yemuvmmXluZi/3sO+wenJGqdK4MnIcEtt5p+cGQXSExvE8/3Jo7S6b/8xR63rzKzcZufDO1x6KS1Rnu3cHX+citqUaFS9d/3cb7Rq/1/Zy9ymJCqcS1QYoV352qFxn7EwtPYELvcu1AXamOCR18K8z0jIhu0C1XVzefmentr421IDOGp3b6sz5oa51xqmv0wCmDtc5lkOlp2OUn67fM0HjaFbVdUZjtEWfnuvm6Tnq39YD6k702w86ZOU7dGyr2qi6ptoIx2q4aZ04+8nI8+m1DHjbNM1HUHk9FXgNSLAs61vTNJejpnWHAbmo65hrc4Vpmu8AiUBnYAcwEDgA/AmYY5rmFZZlbfGuMxzANE29mu1m1fXL7Ep16B7ncOd0l3o0xQ++iuuWKS5e9t5ZcuEqm0tHGmWPcNjl7Rx+v9nGPNdgUFcXWfkOn/xXfT49W3Vebp3qKnuMwVFvVAEuNQ1r/o/1L6h2pjp0i3f4/WUBZY+qKnXrVBcvzVdxL/jRw/RRBgEuzS/uXh00Jg1zERoEM8e7OJzp8O6S+l3L6TjqeuSrx+roOvy81yE9R50ZPpTpsCtNPb5q2nCdWyerO27OLVfp3TZVJ9ClprJ3j9eY9Z1Neg4s/Vl9JrCFOvtcl1HJ2rgMg7tu/CV3PfA4tm0zefy5dO4Qz+sffkrPromMHlb943U2btvJ+3MW4DIMdF3j7puvo2VEFS2Jk+Q48PVPNledq+7iuXGfyr8xSRqHjjvsTlN5OvUcnVsuVncE/exHlReDu2q0CoPRvXVGe2+j99EydW3ztxvVZy4YoPJvYR0fQ+E48MVam1+MU3ei3rDH5lg2nNdPJy3DYWeqw/rdDtNHatx+iaEeO+Ld/45lw9YDNrdOMbC963EcdSZ82gj1GChNU2lK98XJw3Sy8uHXF6pW0vaDNss2V39sOA4sWuNh5nhX5fgyHXamOPy022b6KIM7prkoLHLKrhEsje+3U13Y3vWUTq2sap0A81d5MM914QAnih0+W1m/KaGG4eL6W+7m3/f/Htu2Oe+CKcR37Mwn779K5269GHzOGD5463lOnCjg2X/fC0B061j++PfHcXvcPHiPuiolOCSU3959P4Zxaqs6w3Bxw6138uDf/4Rt24yfcDEdOiYy67036dKtB8OGj+KLBXPZuGEdhmEQFhbOHXf9HwCLFszlcFoq1qx3sGa9A8B9/3qCli1b1bTJU2rAe08SPXYYLWJaMW7ff9n14HMcfGt2o2zbZRj84YaZ3PXPJ/DYNlPGjaFzhzhemzWHnl0TGTN0YO0rOQV2pTp0a+9wxzRVn5XfV2+e5CobfVy4WtVnLgN2p/keo7N8s83lYwwGdnGRne/wyfeeWtcbGapGaOt6QvR0xDe2n0FwC8rujm07Dq99od67bLRBp1g1s+UP0118t9HD+j1Vx3q64gsNUqO8gQFqZGB4T50XFrjrfFfs/26yuXSEwS2TVfm0ZL2n7CZo5eOqzpzlHiafYzCmr4ahweYDNkeyKtcH1X2v9Xtspo0wuHWKq8qbhjmOupv4NeepOuPnvTbHctQjmw5leuuMPQ6XjtC4bYpBYTHMKa0zcmBrss0tk4yyusdxIC1D3TzzxokGtg2Hjzv85P3dJg9VdcavJvjqjO+31FxnnOo6LaG1OkFy5LjDTRerOEofSXXBIJ3YVho4kJWvHr9Ym9PVFq3O1gMOnds6/Haqqtt2p9mVTiiI/y2aU9cLdUS9VLhhWDKwHpgGRAIa8L5lWf82TbM7MBuwOckbhpmmeT0wxLKs203THAh8gOqot0V11otQneU3Km63ptjvf7ek2e4cD/wygPveaR4DAL1bAAAgAElEQVTX0Fb04HVq+uhDHzWPa8kquvcqg2NbVzd1GNVq3XsYj1jNM+/+z1QV/4Mf1O/kyel23y9UJ/GB9+v5/JXT7P6ZAazbWctFi01kcHc1er1l96EmjqRqSV3VM6wXBvRo4kiqNrlkB+mbT+1dzU+VmD7qZnLN+biA5h1fc44NmnfeAfxzVvOsM/5+tavZ1meg6rT7322ev+0DvwwAmvHzlKqxZXcNz8hrZpK6tmuW+Ssjz6eZZVkPAQ9VWPx4Fel2Av3qsMollmX53ezLsqy3gbe9/1+P7/FUe/B/VNXoOgUthBBCCCGEEMKPXPMshBBCCCGEEELUQkaemyHvVO8rKiz+xLKs65sgHCGEEEIIIcQZzjnzZpo3O9J5boaqmeothBBCCCGEEKKJyLRtIYQQQgghhBCiFtJ5FkIIIYQQQgghaiHTtoUQQgghhBDiLCfXPDecjDwLIYQQQgghhBC1kM6zEEIIIYQQQghRC5m2LYQQQgghhBBnOceRadsNJSPPQgghhBBCCCFELaTzLIQQQgghhBBC1EI6z0IIIYQQQgghRC3kmmchhBBCCCGEOMvZ8qiqBpORZyGEEEIIIYQQohbSeRZCCCGEEEIIIWoh07aFEEIIIYQQ4iznyLTtBpORZyGEEEIIIYQQohbSeRZCCCGEEEIIIWqhOY7T1DGI5kt2DiGEEEIIISo74+ZA/7Qz44xp2w/qHt0s81eueRbVeuD9kqYOoVr3zwxotvHdPzMAgAc/cDdxJFW77xcu5q3xNHUY1bp0qNGs8w7goY+aZ/7de5UBNO9975tNJ5o6jCqN7xsEwCtfNXEg1bj5QvU3ffPKpg2kGjF9RrAwoEdTh1GlySU7gOZbp5XWGc05vuYcGzTvvAN4Zn7z7K/cOVVrtvUZqDqtuf+24n+PTNsWQgghhBBCCCFqIZ1nIYQQQgghhBCiFjJtWwghhBBCCCHOcvKoqoaTkWchhBBCCCGEEKIW0nkWQgghhBBCCCFqIdO2hRBCCCGEEOIs5zgybbuhZORZCCGEEEIIIYSohXSehRBCCCGEEEKIWkjnWQghhBBCCCGEqIVc8yyEEEIIIYQQZzl5VFXDycizEEIIIYQQQghRC+k8CyGEEEIIIYQQtZBp20IIIYQQQghxlpNHVTWcjDwLIYQQQgghhBC1kM6zEEIIIYQQQghRC5m2LYQQQgghhBBnObupAzgLyMizEEIIIYQQQghRC+k8CyGEEEIIIYQQtZDOsxBCCCGEEEIIUQu55rkapmn+A8izLOuJKt47Dyi2LOuHGj5/C1BgWda7p2q71aQfALS3LGvRyWynPrq005g41EDX4KfdNiu2+F85Yehw6UiD9tEaBUUOs7/3kJ2v3hudpDOwq47twOI1HvYccgC4ZLhB93iN/BPw0gJ32bomDNLpHqfjsSEz1+GzlR6KSppPfLGtYMowA5ehYTsOC1fbpGU4NcZ20RAdXYP1u21WbPVPq2LTaRelUVgEs5f7YhuVpDGwi4rty7U2ew45GDpcP8HAMEDXYFuyw383+b7v+f11enfQsB1Yt8tm9Y7qY6vJjp+/5/P3HsGxPQw973LOv+RGv/f3bl/L/Pce4fDBnVx9+xP0G3YRAHu2rmL++/8uS3fs0D6uue0JkoZcUK84TnX+AfxumkGRGxwbbAdeX+wBYGxfnYFdNQpOqM8v/dlmd1rd8q9zW7hwkI6mwYa9Diu3VY7zkuEabVtpFBbD3B9ssvMhuAVcNkqnfRRs3Ofw5U+Vt3fFGJ2WofDa4pO7YulMybuKtqxfwSdvPYpj24wcP52Lpv/G7/1v5r/Lim/mousG4RGtmHnbA0S3bs/Bfdv56LWHOFGQh6YbTJxxA0NGTaxXDOXt27qM7z59CNu26TviCoZdeJPf++6SYha/92eOHNxCcGhLJv/qKSKj4/G4i1ny0f0cTt6Mpmmcf/m9JHQ7B4Dl859i6+p5FBXkcMeT6xscY6kf12/k6Tc/xLZtpo4/l2svm1Jlum9XruFvT7zA64/eT6+uiWXLDx/LYObv/8qvzUu5ZtrFpyyuuuj32sO0mXQexUczWDZw6mnd1sQhOt3idErcMG+lm8OZldO0i4JpI1wEuGBXqs3iter4C2oBl48xaBmqkZWv6pMTxTWv94KBarmmwd5DvnU1Znx9O2mMSjIAKHY7LFzl4UiWKgd+daGBYWjeOsXmu42NH190BEwbYdAuSmPpBpuV21T66Ai4fLSvydoqDL6tIr6RvXX6dlLjQroOMRHw+Gx32W9Tavoo1RawbUhNd1iwyoN9EkVVTb9/x1iNiYMN9DoMTyVv/57ln6lypfc5lzNonH+54nEXs2TWXziWsoWgkJZceO1/iIiKJyczhVmPTaZlG3Xcxnboz3mXPwDArg2LWPfNyzi2Tade5zFiyh/r/sUqONV1WgsX/HK8L2PCQ2Dzfoev19c98xtzv6vPcdHcyaOqGk46z/VzHpAHVNt5tizr5UaKZQAwBDitnWdNg0nDDN77xk1OAdx4sYsdKTbp2b40A7vqnCh2eO4zD0kdNS4YaPDpcg8xkZDUSefF+W7Cg+HaC1w8/7kbx4ENe21W73SYPtJ/V9xzyGHJepXmgoE6Y/roLFlffYHV2PFNGGjw302qY9C1vcaEQTrvfO2pNraLh+q8v9RDTgHcMNFgR4qH9JxysXVRFc/zn5fGpvPpcpuYCEjqqPPSAg/hwTBzvMEL8z14bHj3Gw8lbtV5/tWFBrvTIDUD+nfWiAiBF+areEIC6/gjV2DbHua98y9uuOd1IqNief6+K+k9+Hxi47qWpWkZ3Q7z5odZtugtv8926X0Ov394LgAFeVk8dvdEuvUdVa84Tkf+Od56+t0lHgqLKm9z1Xa7UiOhLnFOHKLz4bc2OYXw6wk6u1IdvzgHdNY4UQwvLbTp3UFjXH+NuT84uD3w3002bSI1WkdWXnePeNXQhZOr9M6UvKvI9nj4+PWH+d19r9AyKpZH77mGfkPOo11Cl7I08Yk9uefRD2kRGMyyLy3mvvcUN9z1OC0Cg7jujn/Rpl1HsjKP8u8/X03vASMJCY2ofzy2h6WfPMiM294ivGUsHzx+OV36jiO6ne9Y2LzyE4JCIvjN/V+zfd1Cvv/sCab8+mk2/fAJANf9dT4FuRnMeelGfvHH2Wi6Tuc+5zPg3F/w1oMX1T+zKvB4bJ587T2evu9PtImO4oa/PMDooQNJTIjzS5dfWMgnC7+md7fOldbx3NuzGD6w7ymL6WSkvDOH/S++z4A3Hz2t2+naXiMqXOO5z9zExWhMHmbwxuLKZfjkYQbzV3lITXe45nyDru01dqc5jE7S2XfYYcUWD6OSdEYnqTqquvXGx2gktNZ4eaE6CfurCw06xmocOFL1sXK64jueB29/rTqTXdtrTBmu1uux4Z0l5eqUiwx2pWmkpjdufIVFsHitTc94/7IuIwdeWaTyTtPgrstcbD9oM3GI4Zfuh602P2xVbYXucRrDe+mVOs4Am/bZzF2hvttlow0GddVZu6vunaLq4g8MgMlDDd5fqtoi988MqHYdtu1h2dwHmXrTm4RFxjL7mSvo1HscUW195cq2VbMJDI5g5v99xa71C1m58EkuuvYpACKjO3DlXfP81nki/zgrFzzOFb//lOCwKL6Z9RdSdq0kvtuIOn+3UqejTit2w+tf+vL51xfqbE+pe33R2PvdyR4X4n+DTNsuxzTNe03T3Gma5nKgh3fZ70zT3Gqa5kbTND8yTbMTcAvwB9M0N5imOaaadf3DNM0/ev//nWmaz3jTbzZNc1gtofQ3TXOlaZq7TNO80buOd03TvLTc+j8wTXMa8CBwpXfdV5qmGWqa5pumaa42TXO9Nw2maSZ5l23wfpduJ5M3cdEambkOWXlg27Blv03PeP/dp0e8xs97VYGyNdmhc1tVCPWM19my38ZjQ1a+GkmOi1bvJR91qmyA7z3klDXSU9IdwkNq7jQ0dnwOEOitE4NaQG5BTbHB8fKxHbDpkeD/fXrEa2zca5fFlhir3u+RoLHlgC+247kOcdHqMyXegXBdx+8M95BuOsvKjUIXVBF/XRzcs4no2A5Et0nA5WpB/+EXs3XdUr80Ua3jaNehB5pWfVGyafVX9Og/hhaBwfWK43Tl36nWPgoyc9V2bFvF0T3OP85ucRob96l9cNtBh07eOEs8kJIO7irabgEuOKeHzootJ19Znyl5V9H+3Ztp3TaBmNh4XAEBDB41kZ/XfOcfd59hZftUYre+ZGUcBSC2fSfatOsIQMuoNoRHRpGXc7xB8Rw+sJGWMR1pGZOA4WpBz8GT2bPpG780ezYtpfc50wHoPuAikneuxHEcMg7vJqG7GmkOCY8mMDicw8mbAWifOICwyDYNiq2ibbv3Et82lri2bQgIcDF+9Dl8v6byqPZrs+Ywc/okAlv4N+6XrVpHuzYxlTrbjSVz+VpKMrNrT9hAPRM0Nu5T+31qukNQC42wCkVUWDAEBvgayhv32fRMKD0+dH72Hjc/77XpkaDXul6XoWHolP3LL6z+mD5d8aWkO2WdyZR0h4hydWv5OsXQNVXRNXJ8BUWQluHgqWHbiW1VfV86Q6Y6fTrpbN5fdYe4/IyYtHSHiBD1/wBDzTi7YaLBTZNc9Iivuu1RXfx9E3W2HbTJqaFNUOpo8kYiozsQGa3Kla4DJrFvi3+5sm/LN/Qcopp+XfpdROouVa5UJzsjhciYjgSHRQEQ330kezZ+VXswVThddVqpqHAIDYKDx+oeU1PsdydzXIj/DdJ59jJNczBwFWokdxIw1PvWPcBAy7L6AbdYlrUfeBl4yrKsAZZlfV/HTYRYljUA+C3wZi1p+wHjgBHAfaZptgfeAK73xhoJjAQWAvcBH3tj+Ri4F1hqWdYw4HzgcdM0Q1Ed/me8MQwBUuoYN6Cm1pSvDHIKHMJD/NNEhGhkF6hSxXHgRAkEB6rPli4H1dGs+NmaDOiiszut5jPCjR3fl2s9TBhk8PvpLiYMMvhmQ9WjzgDhwRrZfrGpZf7xa2UNAb/YgjVy8qv+rKbBTRcb/HGGwd5DDqkZKk2rcEjqqHHDRINrzteJCq/5u1Qn+/gRWka1LXsdGdWW7ONHT3o9P//4BQNGTK5fEJy+/HOAmeNUI2lQV//1De2uc/Mkg6nDdYJa1DVOyC23H+UUqmUV05Tup44DRSVqeltNxvbVWLXdpqT6XayGmM6MvKsoK/MorWJ8+16r6DZkZx6pNv0PS+eSNLDyzIb9uzbhdpcQE5tQv0C88rKOEN7KF09Yy1hys/zjycs+QnjLdgDohovA4HBO5B+ndVxP9mxaiu1xk51+kKMHt5CbdahB8dTkWOZx2sRElb1uE9WKYxn+Jw927N3P0fRMRg4e4Le8oPAE789bxK9952nPWuHBml/nKyffqXxsBGvklD+m833HQFgQ5BWq5XmF6nVN601Jd9h/xObuGS7unuFiT5r/CF5jxVfewC66XydS0+DmSS7+dLmLvYdsUmu4FKkx4qtOn446m/fX3INxGWqUcmtyzel0Dfp19uXDmL46+47YvL7Ywztfu5kwyCDAqPy56uKPDldl+nUTDG68uObJnfnZRwjzlhkAYS3bkp99pEKao2VpdMNFi+BwThRkAZCTmYL1n+nMe3EmaXvXAhAZ04GsY/vIyUzB9rjZt3kJefUsb05XnVaqd4faf5/KMTX+fncyx8WZwEE7Y/41VzJt22cMMNeyrAIA0zQ/9y7fCHxgmuY8YF51H66DWQCWZS0zTTPCNM2WlmVlVZP2M8uyCoFC0zS/BYZZljXPNM0XTdNsDcwAPrUsy22aZsXPXghcUjrqDQQBHYCVwL2macYDcyzL2lXVhk3TvAm4CSDpkg/q/21PkTF9dGwbNu1rXoXVkO46X671sO2gQ+8OGpcMN3jvm3r0bhrAceDVLzwEBsCV5+q0joRj2eDSwe1R16H2TFCxvV3NlPLTLef4MQ4f3En3ek7ZPp3e/spDbqGa1j5zvEF6jofko7B2l82yzSp/z++vM2GQzvwfm+Yap9iW0CpMY8l6h8jQJgmhSs0p71YtW8CBPVv5w4P+5ySzjx/j7efu5brb/4Vel4sPT5M+w2eQeXgPHzw+g4io9rRLHIiuVdEabyS2bfPc27O49/YbKr33pjWPK6dcREjwSfRkBAA1DAYC6hrdmEiN/8xRw1jXjjfokKaRfKxx6raK8XWK1RjYVeetL91+aV5Z5FZ1ylijrE5piviqo+tqtkxNJ6xBpUk+5lQ5Zbu8ycMMDhyxy36HLu00esTrjOyl3ncZEBlKjSc6ysev69AuSuPdJR5cLvjzFdVP226I0Ig2/PJvSwkKbcXRlM188dbtXP2nBQSFRDL2svv56r270HSNth0HkpNx8LTE0FC9O2h83kR1a6m67HdNeVyI5kk6z7WbDJwLTEV1Put7IVjFQ7SmQ7a6tO8CM1Ej5L+q5rMaMMOyrB0Vlm8zTXMV6vssMk3zZsuyllb8sGVZrwKvAjzwfklZHLkFlE1rAjWKW3Gqck6BQ2SIRm6Bg6ZBUAAUFqnPRoZoZV8jPKTmac6l+nfW6BanKqHaNHZ8/TvrLF6rGh1bkx0uGV79GbLcQse7/tLY1DL/+FXnKLcQ/9gKHSLKdZqq+mxRCew/oq69PpbtkFMA2w+qNNsPOlwyvObvUp3IVrFkZR4ue52deZjIVic3xXTjqsUkDbkAw1X/BsTpyr9c79nngiLYcVBN1U8+6pB/wpf+p902V59Xt45ObiHeywvU+iOCfdson0bFoOIMDIDCGhp3cTEa7aLgtqnqhl+hgTBznM77S+vW4DhT8q6illFtOJ7u2/eOZxwlMiq2UrrtG39k8aevc9eDbxAQ4BvuKCzI48WHb+eSq+8gsXu/esVQXljLWHKP++LJyzpCeEv/eMIiY8nNOkR4q7bYHjdFhbkEhbZC0zTOm/HXsnSz/nMVrdp0anBM1Wkd1Yqj6b476BzNPE7r6FZlrwsKT7A3OZXb71M39MvMyuYv/36GR++5ky279vLtyjW8+N7H5OUXoOk6LQICuHxS/W7019wM7a4zqKs6kZKWofb70imjEaFa5WOj0H9ac0So7xjIO6Gmh+YVqr/5Rb7PVLXefok6qelO2RTQ3WkO8a39O8+NER9Am5YwdbjBB0vdVZY/vjpF51i2r6xprPhq0q29xqFM/7IG1MggwAffuskrVPdrqG7KdqmxfXVCgmD+f8un07CWucmo0Fm+ZIRBu1bqO374rafa+HMKoLDIocRDrbOFQiNj/UaF87IOExoZWyFNG/KyDhHWUpUrxYW5BIW0RNM0DJcq89rE9yEyOoGsY/tok9CXTknj6JQ0DoAtP36MptevHD4ddVqpNi3VqP/hOlxR0xz2O6j+uBD/e2Tats8y4FLTNINN0wxHdZZ1IMGyrG+BvwCRQBiQC5zsZNgrAUzTHA1kW5ZV03mraaZpBpmmGY26Odka7/K3gd8DWJa11busYixfAneYpql5tzfQ+7czsNeyrGeBz1BTw+ssNcMhOlyjZag6s5rUSWdHin/hsTPFoX9nVWD17qCxz3sjlB0pNkmddAwdWoZCdLhW67SXLu00RvU2+Og7D+46DJo2dny5heqOmqCuv8rIrSk2iCofW0ednRVukLEj1aFfZ71SbDtTHJI6+mKLCtdIzVAjfqXXXLsM6NxOLzszviPFd91RxzY1x1aT+M59yDh8gMyjKbjdxfz84xf0GnT+Sa1jw8qFDBgxqX4BeJ2O/Asw1F0/Qf2/czuNo955IOWncfVM0DiaVbchkbRMdQ1XpDfO3h00dqb6f3ZXqkO/RPXb9ErQ2F/NzYJK/bTb4dnPbF6Yb/PuNzYZudS54wxnTt5V1LFrEkcPJZN+JAV3SQnrViym39CxfmkO7t3Gh6/8k1vveYbwSN/F2O6SEl597A+cM3Yqg0ZMqNf2K2rboS9Zx/aTnX4Qj7uY7esW0rnvOL80XfqOY+sqdZO8nRu+pEP34WiaRklxISVF6mzcge0r0HXD70Zjp1rPromkHDpC2pFjlJS4+Wb5KkYPGVj2flhoCIvefp5PX36ST19+kqTuXXj0njvp1TWRl/7117Ll5pQL+eVlU86ajjPAmp02ryxy88oiN9tTbPolqv0+LkajqNgpm85ZKq8Qikoc4mLUMdsvUS87Mbkzxaa/97jp31lnx0F1XO5Icapcb3a+Q8c2GpqmOg0d22ikZ/sfH40RX0QIXDnWxdwVHjLL1Q2V6xSN9JzGj6821V3HXBpXXqH6Hp1iNXYcrL78GdhVo0t7jU+X+zcw9hyyGdbD1zRu6z3v9PlKD68scvPht54a499x0CahtfqdXbX0Wdsk9CU7/QA5GSl43MXs3rCIxCT/cqVT0ji2r1WTHvds/JK4rqpcKczLxLZVLNkZB8lOP0BEtLo8pSBXXcN1oiCbzT/Movc5l9ccSDVOR51WKukkpmw35X5Xl+NC/O+RkWcvy7J+Mk3zY+Bn4Ciqw+oA73uvMdaAZy3LyjJNcz4w23szrjvqeN3zCdM01wMBwK9rSbsR+BaIAf5pWVaaN8Yjpmluw3/6+LfAPaZpbgAeAf4JPA1sNE1TB/YBUwATuNY0zRLgMPBwHWIu4ziwaI2HmeNd6pEFe2yOZcN5/XTSMh12pjj8tNtm+iiDO6a5KCxymO2tlI5lw9YDNr+d6sL2rqd0qsxlow06xWqEBMIfprv4bqOH9XscJg0zMHS4drzaRVPSbRaurr6Qa+z45v/oYeIQ9SgKt8dhwSp3jbF9sdbmF+OMyrFlOOxMdVi/22H6SI3bLzEoLIJPV/jHdusUA9u7HsdRZ0ynjVCP5dI0lWaXt1JbvsXmslE65/TSKSmBBT/Wb8q2YbiYdt29vPHYjdi2zdCx02kb342vZj9HfGISvQeP4+CeTbz79O8oLMhh2/pv+frT57n70fkAZB5LJTvzMIk9h9aypZqdjvwLDQbzXNWy0TXYvN/3GKYLBunEtlI3BcnKd1i4qm6NOseBL9fZXD1WR9fh573qmsZz+6iRkl1p6lEf04br3DpZ3aF07g++dd82VSfQpW4k1D1eY9Z3dq1TBc+WvKvIMFxcecP/8fy/bsW2bUaMu5T2CV2Z/9ELdOySRL+h5zHnvacoOlHA60/+CYBWMW259Z5nWbfyS3Zt+4n8vGx+/E5dfXPtbQ+SkNiz3vmoGy7Ov+I+Pn3xBhzHQ5/hM4hp140VC5+hbYc+dOk7nj4jLueLd//EGw9MICgkksm/UnfELcjNYM6Lv0HTdMIiY7n4l4+VrXfZvMfYvm4BJSWFvPr3c+kz4gpGTrqj3nECuAyDP9wwk7v++QQe22bKuDF07hDHa7Pm0LNrImOGDqx9JU1owHtPEj12GC1iWjFu33/Z9eBzHHxr9infzq5Uh27tHe6Y5qLEDZ+t9JWTN09yld3ZeeFqm0tHGrgM2J3me/Ta8s02l48xGNjFRXa+wyffe2pc79Zkh8S2DrdOcYEDuw/ZlToijRHf2H4GwS3UdGUA23F47QsPYcFw6UhXWZ2ypVyd0pjxhQbBTRe7CAxQDbDhPXVeWOCmuMR3sm7Bqpo7Lz0TNPYcciqN/F5zvsHnP3rIK1SPmszKh99cpNoY2w7aLNuk/k0conPLZNWWyMpzmPVd5Tq0uvjTc9TTQm6d7Kr1vlK64WLM9L8z/7Xf4Dg2PYfOIKptN1YvfpbWCX1ITBpHr2GX882sP/P+IxcSFBLJhJn/ASBt7xpWf/kcuuFC03TGzvgHQSEtVWyfPURGmpp8OGTCb2nZOrHaGGpyOuu0Xh00Pv7vydcPjb3fnexxcSaQR1U1nFbTXfvEqWGa5nfAHy3LWtvA9YQAm4BBtYxcnxLlp203N/fPDOCB92t58HMTKX00xYMfVN+hbkr3/cLFvDVNcw10XVw61GjWeQfw0EfNM//uvUo1iJtz/n2z6UTtCZvA+L5q2PyV+t2Y9rS7+UL1N33zyqYNpBoxfUawMKBHU4dRpcklqiPR3OuM5hxfc44NmnfeATwzv3k2p+6cqjXb+gxUndbMf9szrie6Ymte89wZqzCqd1izzF+Ztn2GME3zAmAb8FxjdJyFEEIIIYQQQvjItO0GMk3zXuCKCos/sSzrodIXlmWdV8XnfgXcWWHxCsuybqtqO5ZlLQE6NixaIYQQQgghxP+i5vwIqDOFdJ4byNtJfqjWhJU/9xbw1qmPSAghhBBCCCHEqSbTtoUQQgghhBBCiFrIyLMQQgghhBBCnOXsM+Z2Yc2XjDwLIYQQQgghhBC1kM6zEEIIIYQQQghRC+k8CyGEEEIIIYQQtZBrnoUQQgghhBDiLCePqmo4GXkWQgghhBBCCCFqIZ1nIYQQQgghhBCiFjJtWwghhBBCCCHOco4j07YbSkaehRBCCCGEEEKIWkjnWQghhBBCCCGEqIV0noUQQgghhBBCiFrINc9CCCGEEEIIcZZznKaO4MwnI89CCCGEEEIIIUQtpPMshBBCCCGEEELUQnNk/F5UT3YOIYQQQgghKjvjnvv0zaYTZ0zbfnzfoGaZvzLyLIQQQgghhBBC1EJuGCaq9fvn8po6hGo9fUcY1//jSFOHUaW3/xELwH3vFDdxJFV78LoWPPB+SVOHUa37ZwY02/junxkAIPHVk/y29XcmxNecYwNYGNCjiSOp2uSSHQA89qndxJFU7c8z9GZdn0HzPi6gecfXXGMDFd/dL+Y3dRhVevK3oU0dgmgi0nkWQgghhBBCiLOc4zTLmdBnFJm2LYQQQgghhBBC1EI6z0IIIYQQQgghRC2k8yyEEEIIIYQQQtRCrnkWQgghhBBCiLOcPKG44WTkWQghhBBCCCGEqIV0noUQQgghhBBCiFrItG0hhBBCCCGEOMs5yKOqGkpGnoUQQgghhBBCiFpI51kIIYQQQgghhMU+m+4AACAASURBVKiFdJ6FEEIIIYQQQohayDXPQgghhBBCCHGWs8+yR1WZpjkReAYwgNcty/p3hffvAm4A3MAx4NeWZR1oyDZl5FkIIYQQQgghxBnDNE0DeAG4GOgNXG2aZu8KydYDQyzL6gfMBh5r6HZl5FkIIYQQQgghxJlkGLDbsqy9AKZpfgRMA7aWJrAs69ty6X8EZjZ0o9J5FkIIIYQQQoiznOOcOY+qMk3zJuCmcotetSzr1XKv44CD5V6nAOfUsMrfAF80NC7pPAshhBBCCCGEaDa8HeVXa01YB6ZpzgSGAGMbui7pPAshhBBCCCGEOJOkAgnlXsd7l/kxTfMC4F5grGVZRQ3dqHSehRBCCCGEEOIs55xdd9teA3QzTTMR1Wm+CrimfALTNAcCrwATLcs6eio2Kp3nejJN8x9AnmVZTzTydjsBCyzL6tOY263KZee2oFdHFyVuhw+XFJFyzK6UJr61zjUXBBLg0th2wM2cZcVl743pF8DofgHYtsPW/R7m/1BM9wSDqSNbYOjgseHzFcXsSvE0ONZfXBxOv24tKC5xeH1eDgcOuSulmTEulJH9gwkN1rjl4WNlyy8aEcK5g4KxbYfcfJs3PsshI7vydz0Zk4YZdIvTKXE7zF3h4VBm5dKsXZTGZaMNXIbGrlSbRatVPiR11Dh/gEFMpMarC92kZajPBgfCVWNdtI/R2LDHZuGqmvOtSzuNiUMNdA1+2m2zYov/dzJ0uHSkQftojYIih9nfe8jOV++NTtIZ2FXHdmDxGg97Djl1WufEIToDu+g88rHK/8HddIZ213EcKHY7zC8X88QhujePYN5KN4czK3+HdlEwbYSLABfsSrVZvFZtL6gFXD7GoGWoRla+iv1Ecc3rjQiBS4YbRIRq4MAH37rJzofEthoTBhlUvEqoucRXPp6mii+2FUweZhAYoOE48P1mD1sOqH2iU6zGhYMNDG94mgYXDW6esTXVbwvQIgBum+Jie4rNF2v8j5urzjMqB9AEsXaK1bhosC+WmEiY/b2HHSnVt8YaOy8vGKiWaxrsPeRb16nS77WHaTPpPIqPZrBs4NRTuu7qpOz8nh8XPIxt2/QYejn9x97o977HXcx/P/kL6albCQppyflX/4fwVnEcO7iR5fPuV4kch4Hjb6NT0gQAPn5sPAGBoWi6ga4bTLttdoNibA51WnmNud9FhsKVY11ogK7D6h0263ZV3u+aS5k8tLvO8F46UeHVX/t6OmKNjoBpIwzaRWks3WCzcpsvj+681EVRierY2Y7Da1/U/be+dHQLenU0KHbDR98UkZpedVv0qnGBBLhg2wEP85YX+70/tr+LS0YFct+b+eSfUN/hmgsCaRWmoesa320oYc32yu1G0bQsy3Kbpnk78CXqUVVvWpa1xTTNB4G1lmV9DjwOhAGfmKYJkGxZ1iUN2a50nkW99Opo0LqlzkPvFdAxVueK8wJ56pPCSumuOD+Qj5cWceCIzc2XBNGro8G2Ax66xhn06Wzw2IcFeGwIC1aFeH6hw2sLTpCT79A2SueWaUH8462CBsXar1sLYqMM/vJsBl3iA/jl5Aj++XrlmmDDzmKWrC7k0d9F+y0/cKiEB14toLgEzh8SjDkhnJdmZ9c7nm5xGtHhGs/MLSE+RmPqcINXF1UulKeOMPjsBw8p6Q7XjnfRLU5jV6rDkSyHWd+6uWSE/+Hr9sA3Gzy0aakR26rmG0JommrsvPeNm5wCuPFiFztSbNLLfa2BXXVOFDs895mHpI4aFww0+HS5h5hISOqk8+J8N+HBcO0FLp7/XMVf0zrbRWkEtfCPa9N+XyOje7yvYd61vUZUuMZzn7mJi9GYPMzgjcWVK9PJwwzmr/KQmu5wzfkGXdtr7E5zGJ2ks++ww4otHkYl6YxO0lmy3q5xvdNHGXy/yWbvYYcAl+/s7ORhBh995yY9B+6fGdDs4qsqbxs7vhI3zPvBQ2YuhAXDTZNc7E5zU1SiTsC8u8RNZq7KvwsH6c02tqb6bQHG9dc5cLRyh6NngkZxSaXFTRLr/iMOr3jLqqAW8LtprrITZ80hvvgYjYTWGi8vVDH+6kKDjrGn9uY4Ke/MYf+L7zPgzUdP6XqrY9sefvj8n0z89RuERsTy+YsmHXqeT6vYrmVpdqydTWBwJOYfv2TPzwtZs/gJxl39FK1iuzHtt5+gGy4Kco4y97npdOh5Prqh6o5JN7xDUGirBsfYHOq08hp7v8sthDcWu/HYEOCC305RdV9eYdPFVFO5d/CYzc5Um+snuGgZVjlfT1eshUWweK1Nz/iqf8t3lrgpPMkJtT07qJMuj3xQSIdYnRljW/DspycqpZtxbgus74pIPmJzw+RAenYw2J6svlPLMI0eCQaZub5O96g+ARzJtHlzUQmhQXDPNSH8tFP9xqJ5sSxrEbCowrL7yv3/glO9Tek8nwTTNO8FrgOOou7uts40ze+AVcD5QEvgN5ZlfW+aZhLwFtAC9TztGZZl7arrei3LesI0za7Ay0BrwANc4f1b+rlOwHtAqHfR7ZZl/WCaZjvgYyAC9RvfCvwAvIG6WN5BnZ15qr550bezizXbVOV44IhNcKBGRIhGToGvIRURohr0B46o0mbNNjd9O7vYdsDDqL4uvllXUlYQ5RWqz5U/Y3g40ybApZWNQtfXwB6BrPhZFaZ7UkoICdKIDNPJzvNf6Z6Uqluo2/eX+KUZ2S+o/sEAPRN0NuxV205JdwhqoREWjF9FGxYMgQEaKekqXzbstemZoLMr1ePXwS2vxA3JR50azyaXiovWyMx1yMpTr7fst+kZr7O83Ih6j3iN/25Ur7cmO0waqtbbM15ny34bjw1Z+ZCZ6xAXrd6rbp2aBhMG6cxZ4aFXgq/YKd8paFGuQ9gzQWPjPrXt1FryKNWbRxv32fRMUJV3jwSdd75W++fPe22um+BiyXq72vUGtQBdg72HnbK8LOWgtqP+1/ziK5+3/TvrTRJfZq5vvXmFkH8CQoMoO3bLv9+rg86S9Z5mGVtT5F1eoRrBCQ3S2J1m0z7ad/wGuGBEL535qzz0TawwPN5EsZbq3UFjV5qDu4YBosaOD8BlaGUzCQxdnZA9lTKXryW4Y9wpXWdNjqVsJCK6AxFR6rK+zv0mkbxtqV/nOXnbUgaOvw2AxD4XsXL+v3AcB1eL4LI0HncxVJo/c2o0hzrNP56mOy5cuiqTmzqm6sq9ohI4fLxp8q+gCAqKHLrFnbr9sE+iwbodalvJR2yCW2iEh2jklmuLhnvbosnetui6HW76JPo6z5eMasH8lSX8+uLAss84QKD3hHRggJp9Z0vHWXhVro1FlUzTHIyaSz8AmAQMLfe2y7KsYcDvAe8cKW4BnrEsawCqw5pSj/V+ALxgWVZ/YCRwqMLHjwITLMsaBFwJPOtdfg3wpXfb/YEN3vXHWZbVx7KsvqiOfb1FhmocL9f5zMqziaxwBjMyTCOrfJp8m8hQlaZNS53O7Q3+cEUwt18WTEKbyrti/y4GKcc8DT7T1yrCIDPH18I7nuOhVUT9dv1zBwWzcXdx7QlrEBGikZ3vK9hzChwiQrRKaXLKp8mvnKYhwkMgp9yAfk6BQ3hIFXF6KyDHgRMlahpdeAhlywFyC9SymtY5rLvOzhTHr/ItNbS7zh3TXFww0GDxWvU7hQdrflOSc/IdwoP9v394sP/Jmpx8ytKEBfkq+rxC9bqm9UaHa5woBvNcg5smuZgwSC9rAM1f6eGacQZ/mO7r9Den+KrK28aOr7z20arzkpkLBUWq098uypcmKIBmG1td13Oq47twsMFXP1XuhY7rr7Nym+13sqSpYy3Vp5PO5v01F86NHV9KusP+IzZ3z3Bx9wwXe9Ic0nNqDLHZK8g+Smhk27LXIZGx5Occ8UuTn32EsMh2AOiGixZB4RQVZAFw9ODPfPr0FOY8O41Rl95fNuqMprH4rd8w7/kZbF9tNSjG5lCnldcUx0VECNwy2cUfLnOxYotdqa5rLmVyXZyuWGviANeOd3HjxS4Gda37fhEZqpGV54sjO98pa2dWlyarXJqkTgbZ+Q6HMvzLshWbSohtpXP/dcH88apg5i0v5tSehms6NtoZ86+5kpHnuhsDzLUsqwDANM3Py703x/t3HdDJ+/+VwL2macYDc6obda5uvaZphqM6u3MBLMs64V1e/rMBwPOmaQ5AjUh39y5fA7xpmmYAMM+yrA2mae4FOpum+RywEPiqqmDKP1Ot/Zg3a8yQhtB1CAmEpz5RU22unxjEP9/19bzaRulMHRXIS/Oq6G01kRH9gkhs7+KRt2o5bSv8hAVD744ab3/9/+zdd5Qc1Z3//XdV9+SoUc45C0UkCwSInEHEAmOMccLYa+/au96fd+1dcHjM2mubNWCbZBuMweASQQJJ5CCSAAWEJCSUwwRlaXLsrnr+uD0905N6FGa6JX9e5+hoprvq1ndupXvrhmq7iWr5Jo/lmzwmDbM4c1L7YzuPRbwJMmwbhvSxeHCJGUd83ZkBpo6w+Hirz+zxNn97I0zxQT/atTdZ4ttc4neYt90VX6PsDNO9fMH7TfE8826Yi061CUaeVx3vAsjxjK0rxItv5hibzcU+FS1Gp/TtAT1yLF5e6ZGX1fa6x9uR5GWffIutJd1bnIwXX49s6JVncfez5mnDF88LMKQkeQtg3aHP4Clc+91FlO7bytKn/5NBY84imJLG5bc9QVZeX2oqD/LSn79KXu/h9B8+M36C/4A6c16UV8MDi0NkZ8CNcwOs32VaexMZE7R93etunYn1kZdDVNSYcuEXzw9yoDzMrjaGsRxPKUE4b0YKD73QekeNHRyg+IDH/Qvr6Zlr8Y0r09lWUkNdO0No5B+LKs/HR+MojTCRPHVd92+O43wIXAYscRznG67rvnGct/s9YC+mddkGaiPbfttxnLMi237UcZy7Xdd9zHGcKcBFmFZxB/hKywSbv1Ptu/dVxly5zjglhdMmmkNm1z6PHtk22zFP6/KzbcpiF6es0ic/u6lUmp9lR59Ol1b6rNlqLua79nr4mC5FVbXmKeFXLk3niVdrOVh+dBfP82ZmMHeG6bK2vbiBgtwAYK56PXIDHC4/subsCSNSueLMLP7n0UMddlNsz6yxNjPGmLwoPtD41NP8bS27u0PkyX2zp6e5Wa2XORYV1eZJeTT9TKtV4b282icv0v3JskyLYU2dWTcvsyn+nEyi67aVZv8CM37qn+eZYyclCN+ZF+S+hbHNaRmpFpOHm7+5ssZMwlK4v+nvr2jR/bKiJrblIjeL6DKVtUS7mWVnQFVd0zptpWvbFnsON3U531joMaiXxcYin749LIoP+swc03QsJ0t8lbVE8za1Wb2+u+MDM+HVTecEeWN1ONqVD0w3zk93+EwfZfKvpi65Ynv0lTAzx9gM7JWYvBvU22ZoH4uZY2xSg6arcX2DaZ0fUGDxL1cFsZvV/2aOsaN5WXKw/b+5q/ISYOIQm88Kfbw2LkmJjG/ycJviA360pX5Lic+g3id25Tkzrw9VZXuiv1eX7SUrt2/MMll5faks201WXj+8cIj62grSMvNjlsnvM5KU1EwO791M70GTyMozaWRk92TohPM5ULT2iCrPyXZPS4bzAsw6+0rNw87sdCvm82S57iUq/zpSEWknqa6Dzwo9Bva02q08z5kU5HMTTHmicJ8XM247Lyu2FwSY1ujmy+RHlumZa1GQY/Nvjikr5mVbfO/6DO55upaZ44O8scqUGQ+W+xwq9+nTw6Zwn/pui7ptH4m3gascx8mItAp3OM2m4zgjgG2u694LLAQmH0m6rutWAEWO41wVSS/NcZwWHWvJA3a7rusBX8TMNIfjOEOBva7rPgz8EZjuOE4vwHZd9xngv4DpR/j38+7aBn71VA2/eqqGtdtCzBxvLl5D+9rU1Ptt3ixr632G9jWH2czxQdZuM6WatdtCjB5kWhl755suRVW1kJEKt12ZzqJldWzfffQXqdeX13DHA4e444FDrPqsjjlTTL+hkYNSqKnzW4137siQfkFuvTyHe54spaLq6G72H230uP+FEPe/EOKzXR5TI2NTB/WyqG1o3Z25sgbqGnwG9TIX/KkjbD4rPH4X7eKDPj1zLPKzTKvmxGE2G4ti099U5DNlhNn+hCEW2/eav31jkcfEYTYBG/KzoGeOqVy2l+bmYp/fPBPingXmX0OIaMW5IKdpe2VVPnsOm218VuQxOTLGc2Avi7r69vNoYCSPJg83BXoTuxcd/ztlhM3GSN5tLPLbTLfkoBnXlRkZ8jSsn83+MqipNw8NCnJMC3mjZImved7+an7Tw4jujs+24YazAnyyzWPDrthzJDPN5N0fXzLxrdySXLGBmRk+UXn33Hthfvuc2YevrArzyXaP11d7rNjscfez5vM/v9K0b5dv8nhwSYgHl4S6PdZGk4ZZ7XbZTmR8ZVU+Q/tYWJbpkj+0j8WBshO7s2XvgadQfmAnFYeKCIfq2bZmCUPGnxOzzJBx57Bl1UIAtq97mQEjZmNZFhWHivDC5tipOFxM2f5t5PQYSEN9NfV1pl9uQ301xVveo0ff0UcUV7Ld0xJ53OVkQjDSaSo91VScD5b7Cb1ndHTdS1T+tSclYOY8afx5ZH+LfaXtx/zeuhB3u7Xc7daybnuYGWPNykP6mklOK1qURSsiZdEhkbLojLFB1m0Ps+eQz48frebnj9fw88drKKv0+b/5NVTU+JRW+NEyamNPm0NH2OiSrHz/xPmXrNTy3Emu665yHOfvwCeYscbL46ziAF90HKcB2APcdRTpfhF4MDLlegNmwrDmZ+8fgGccx7kFeAloHKVyNvDvkW1XArcAA4FHHMdpfGDyn3H/6A6s3xFm/NAA/3VLJvUNPk++3vRo8d9vzOBXT5kr7dNv1cW8qmrDTtNs++H6EJ8/L40f3JRBKAx/e82sf8bkFHrl2Vw0M5WLIg/B719YG51Q7Gh8srmeyaPT+N9/7kldg8+fFjYNgvvp7QXc8YCZedu5IJvZp6STmmJx97/24u1VNSx4q4obLswmLdXin5w8AA6WedzzZOlRx7Op2Gf0IJ/vXpMSfa1Ho29eEeT+F0xhZ9EHYa6eEyAlaF7rsbnY5MH4IRaXzgqSlQ43nxdkzyGfx14z63zv2hTSUkzr1bjBNo+92sD+NiZj8X1YsjzMzecFsSxYvdVjfxmcPdmm5JDPpiKfVVs8rp4T4DvzgtTU+Tz9rolzfxms3+nxrSuCeJF0Gi9ybaXZkVljbYb3s/E8qKn3WfB+mG9dYbqxjh7g8515QRpCsHBZUx5949JgdMbfxR95XHV6gGAAtpR4bIl0I313ncd1ZwaYNjJIWZXP/HfM+u2l6/vw6sowt5xvLom7D/ms3OLh+/DCh2Gcs4Ix3Y2TJb72dHd8E4daDO1rkZlmRQvRC5aF2HsY5kxsenUQwHufeuRl2kkZWyLy7lgkIta8LNMStWNv/Gtyd8e3fpfP8H4+37w8CD5s2e2xqfj4lsCm/vU39Jw7i9RePTh3+1I2//Q+Ch85ttc8dcQOBDntyv/ipUe+hu97jJlxDT36jmblq/fSa9Akho4/lzGnXsfS+T/A/fVFpGXmcc6NvwFgz86VrFn6MHYgBcuyOG3eHaRn9aD8UCGvP/4dADwvxMgplzNozJlHHWMy3NOa6+7jrneueeWdj5mS7f31Yfa1KCIk0zV51libORPs6CR7V8wO8MIHTfF0VaxZ6XDbJUHSUkwfhdnjbH6/KERmmnnVF5iHXut2eB3O4t/chp1hxg8J8J9fyKAhBE+90VQW/VcnnbvNiEeeebs++qqqz3aFo5OFtefVFQ3ceF4a37/BxLXog/ou7YYvJxbLT+aq/T+gRL0/ui0tu20nk99+J5tbf7w3/oIJ8OiPTXe4O/5ybBOLdZWffimVnzyevAN37rw5JWnjaxzzrPiOjvbt0TsR4kvm2AAWp4xNcCRtu6xhIwD/+0xytmz9v2vtpL6fQXKfF5Dc8SVrbGDi+7c/VMVfMAF+860s6Kop7LvQCytDSVu2b+mKGcGkzF912xYRERERERGJQ922u4njOD2B19v46jzXdQ82/uK67o+7LSgREREREfmH4PtJ2Zh7QlHluZtEKshTEx2HiIiIiIiIHDl12xYRERERERGJQy3PIiIiIiIiJznvhJkuLHmp5VlEREREREQkDlWeRUREREREROJQt20REREREZGTnK9u28dMLc8iIiIiIiIicajyLCIiIiIiIhKHKs8iIiIiIiIicWjMs4iIiIiIyEnOx0p0CCc8tTyLiIiIiIiIxKHKs4iIiIiIiEgc6rYtIiIiIiJykvP0qqpjppZnERERERERkThUeRYRERERERGJQ5VnERERERERkTg05llEREREROQk52vM8zGzfOWitE8Hh4iIiIhIayfcS5Pnf3DiTBl2/Ww7KfNXLc/Srrv+Hk50CO364Q0BfvinukSH0aa7vpoGwHd+W57gSNp233dzufOxhkSH0a6f3JLCTx5PzvjuvDkFQPEdpTtv1r49WidCfMkcG8D/PuMlOJK2/b9rzQi6xSljExxJ2y5r2MjPngwlOow2/ffnTTE22Y+9ZI4v2csDN/1HUaLDaNPffjEo0SFIgqjyLCIiIiIicpJTh+NjpwnDREREREREROJQ5VlEREREREQkDnXbFhEREREROcl5flLOwXVCUcuziIiIiIiISByqPIuIiIiIiIjEocqziIiIiIiISBwa8ywiIiIiInKS06uqjp1ankVERERERETiUOVZREREREREJA512xYRERERETnJqdv2sVPLs4iIiIiIiEgcqjyLiIiIiIiIxKHKs4iIiIiIiEgcGvMsIiIiIiJykvM05vmYqeVZREREREREJA5VnkVERERERETiULdtERERERGRk5zvW4kO4YSnyrMclRH94IJpNpYFn2zzWfZZ7CCKgA1XfM6iXw+LmnpY8L5HWTVkpMI1p9v0L4A1O3xeWWXWSw3CF89t6giRkwnrdvq89vHRD864fHaAsYMD1Id8nnk7RMnB1mkN6Glx3VlBUoIWGwvDLPogDMDFMwOMH2IT8uBQuc8z74SorYf8bPjetansLzNpFe7zWfh+6Ihju3ZuGhOHp1Df4PP4KzUU7fdaLTO4j83NF2aQErT4dHsDzyytA+DLl2bQp4fJq4w0i5o6n18+UcXYIQGunJNOMAChMCx8p5ZNReFOx3TJTJvRA20awrDgvRC7D7Vepn8BXD0nSDAAm4s9Xlxu4s5IhevPCpCfbVFa6eO+Haa2HsYOtjh3agDfB8/zeWmFx659Ju/ysuDK0wLkZVr4wBOvhyitaj++i0+NxBeCBctC7GknvnmnBUkJmvheWmHiS0+F684MkJ9lUVrl8/Q7Jr5ThlnMmRgAoD7ks/jDMHtLTVpXzg4wZpBFVS3cv6jz+7i74+zOODpK9/xp5nOAt9eG+XSn2c8zx9jMHm9TkNP+DbsrYu2ZC/NOC9C/wOKN1R7LNjSdY7PH2UwbZWLdW+qz8P32z5Puju1zY22mjzaxrdri8eFnra8NXR3f8TwvujO+gA1fvjBAIGBhW7Bhl8dbazrOP4CiTe/wwaK78DyPsTOvY8rcr8d8Hw7Vs3T+DzhQvJ70zHzO+fzd5PQYyP7CNby74E6zkO8z7bx/YtjECwD4+/+eR0paFpYdwLYDzPunp+PGcTxMfvgu+lx6NvX7DvL2tCu6bDsj+1tcNN2UAT7e6vH+htZlgHmzbfoXWNTUwTPvhymLXN/nTLCYOsLG9+GllR7b9vj0zIFr5gSi6/fIhrfWeny00efsU2zGDLLwfaiq9Xn+Q4/KmtYxJcs1r3k8iYwvN9Ocr7lZFvjwxJuh6D5oK77muqI80GhAT4uvXRLg6bfDrN9l8uzOm4PRa0xZlc+Tb3a+7NLcLVfkMXVsBvUNHg/MP8yOkoZWyzgX5nLm9EyyMmy+cmdJ9POzZmRy0yV5HCo3235lWSVvLa8+qjjk5KbK83HgOM6PgUrXdX/dhdu4FXjFdd2SeMt2NcuCi2bYPPmWR3kNfPkCm80lPgfKm5aZMsKith4eWOIxYbDFOVMsFizzCYVh6TqP3nkWvfOalq8PwZ9eaSrkfPkCm41FR19xHjPIpmeuzW/m1zO4t8W804Pc/0Lri+i8OUGeezdE4X6fL12YwphBPpuKPLaUeLyyIoznw0UzA8ydEuDl5eaCeqjC53cLWqfVWROGBenTI8BPH61kWL8AN5yXwW+eal1rvOHcDJ58rZYde8J886pMJgwLsn5HiEeWNJUarj4zjZp6k09VNT4PPl9NeZVP/54237o6k//+Y2WnYho90KJnrsW9C0IM6mVx+ecCPPxi65vX5bMDPL8sTNEBn5vPCzBqgMWWEp8zJtls2+Pz7rowZ0yyOXOSzaurPLbv9rm/0BSw++bD9XOD/G6h+f3qOQHeXuuxbbdPahD8Dnb3qAEWBTkW9y0MMbCXxWWzAvzppdbxXTYrwAsfhik+4HPTOc3im2izfY/Pe5+GmTPR5oyJNq997HG4Eh591TwYGTXA4vLZTemu3ubx0Safq0/v/GUyEXF2ZxztpTt6oEW/AosHFocI2vClCwNsLglT3wCF+z02FXvcekGQ/OzWFeiuirWmDl5a4TFuUOw2czJg1jibP7wQIhQ2BdJJw9qu2Hd3bL3zYPpom4dfDBH24OZzA2wqgsPtnMbJfl50d3xhD/7yWpiGENgWfPmiAJtLOm5l8bww7z//My7+yp/Iyu3L839wGDLuHHr0HRVdZuOKp0nLyMP5/sts/WQxy1/6Ned+/v/o0Xc08741HzsQpLp8H8/ddzVDxp2DHTB5c+nX/kJ6Vo+4+XQ8Ff3lWXb84XGm/vmXXbYNy4KLZ9g88WaY8hr42oUBNhWHY8oAUyNlgN8vCjNxiMV5U2yefd+jVy5MkpwSwAAAIABJREFUHGLzwJIwORnwhXMC/GFxmIMV8HDk2LAs+O68ABsLzU3h/Q0eb6016c4cY3HWRJslK2IfiiTTNQ+gf4FFeqqVsPjA3GPfWWseTqS0uMe2jK+5rioPNO7bC6bbbC2JveE3hOGBI3hI3ZapY9Pp1yuFf/31HkYNTuUrV/Xgjj/sa7Xcqg21vLKskru/36/Vdx+sqeHR5+M8mZZ/eBrzfOK4FRiQ6CAABhTA4QoorQLPg/W7fEYPjL0IjxlgsXaHuThuKPIZ1td83xCGogOmZbQ9BdmQlQ6F+48+xglDbT7eYjZSuN8nPdUUmpvLyYD0FPM9wMdbwkwYak6JLcV+dEbCwn0+eZnHr5vLKSODfLTBPIbdsSdMRirktkg/N9MiPdV8D/DRhnpOGdm6sDptTAorN5obTtF+j/IqE/Tugx4pQYtgoNUqbRo32GL1VnNzKzrgk55qkd0iv7IzIC3FouiA2cbqrR7jh1iR9e3o+qu3eowbbPKxvtm9MCVonn6DqSTYNmzb7UeXa+jgmBg32GLNdpN+cZz4iiPxrdnuMW6wiW/sYJtPtpn1P9nmMTYSX9EBP/pEvOiAH7Mfdu3zqalrP6ZkibM742gv3d55Frv2+fi+2Y/7DsOo/iatPYeJae3orlir66DkoE+4jYcytgXBgCnIpQSgoo1WrETE1jvPpBMKm4Luzn0+44e0f5tO9vMiEfE1RK45tg0Bu+ma0579RWvI7TmE3ILBBIKpjJh8Kbs2vBGzzK4NbzBq+jwAhk+6iJKtH+D7PsHUjGhFORyqBxLfHfLQuytoOFTWpdsYUACHK/1oGeDTXR5jWzwIGjvI4pPIvl9f6DO8nxX9/NNdHmHPlCEOV/oMKIhNf3hfi8OVUBZp9Gt+H0kNtr1Lk+ma11hBfO3jpptad8fXK89c57btMWk1hJrKXW3F1zIvu6I8APC5cTYbdvlU1ba56WMyY0I676wyN5sthfVkZljk57S+fm4prKe0In6PlJOV7584/5KVWp6PkuM4PwK+BOwDCoGVjuO8BXwInAPkA191Xfcdx3EmAo8AqZgHFte6rru5s+kCO4BTgSccx6kBfgR83XXdqyLrXAB8y3Xdqx3HqQQeBi4E9gA3uq6733GckcDvgd5AdWT9z47mb8/JgPKapqO6ohoG9GyxTCaUR258vg91DaYrT009cU0YYkW78hyt3EzT9adReTXkZllUNIs7N8uKKdSXV/nkZrZOa8YYm7Xbmi60PbItvn1VCnX18OrKEDv2Hlms+VkWhyua1imt9MnLtiivbvosL9LdKbpMhU9+VmzhZOTAABXVPvtLW98Epo4KUrQv3OFDiuZyMmO3X15tCqSVzfOr1TJmPYCsDKLd6CprzO+Nxg22OH96gKx0eOJ1E1DPXNMqccPcAD1yLLbt9nh1ldfuxTInw4rdn1U+ORmx8eVktIivynwGkJ0eG192euttTBtps6Xk2I67ZImzq+JoL909h33mnmLz/npICcKwvhb7yyzi1ly6MNb2VNTAsvUe37s6SEMYtu72ow9xEh3bvlKfc6daZKSaAvmoARa72xhu0p3xHct5kYj4LAtuuyRIQQ4s3+RR3EH+AVSX7SMrr6kFKjOvL/sL18QsU1W2l+y8/gDYgSCp6TnUVZeSntWDfYWf8M4zP6KydDdzr/9FtDKNZfHSI18FLMbNuoFxs5wO4ziRmHtB0+/l1TCwZ+z5bvar+dn3obbelAFyMqyYfVJebdJr/tnEoRbrdsbe186ZbHPKMIu6BvjrG61vbMl0zZs1xmZTkR/Ttby748vNNHnuRLpPb9/j8drH5h7bVnwxedlF5YGcDBg/2OLRV8LMOz32yX4wALddGsDz4d11Hp8VHvk1p0dugEOlTcfGobIwPXIDR1RRnjkpg3HDU9l9IMRfF5VxqOzouo/LyU0tz0fBcZwZwI3AVOBSYGazr4Ou684CvgvcGfnsduAe13WnYirBRUeSruu6TwMrgC9E0lgCjHMcp3dk1S8Df478nAWscF13IrC0WQwPAd9xXXcG8H3gD0edAV3seFSej5ezpwTwPKJPUSuq4Zd/r+d3CxpY/GEI5+wU0lISE9uMsSms3Ni6+3i/Apsrz0jnqdfbuTN2h2a777NCn98tDPHUm2HOnWYuObYFQ/tYvLIyzEOLQ/TItpg2svtabVpW0of1tZg2yua1Vcl1o0yWOOM9Ad6222dLic9XLwpy7RkBCg/4CXuXZLxY01PNWPx7FoS4+5kQqUE4ZXj3HHvxYjtQDu99Gubm84LcfG6AvYe7Nx+T5XhrT2fi8314cEmIu58NMaBn7PCgrtBn8BSu/e4i5n3L5ZOlDxNqMM3yl9/2BFd9+1kuuvUhNnzwN3ZvX961gZwkbBvGDLTY0KLy9OYaj3ufD7Nup8fM0V1fdD3aa152BkwYavHhxq5t2YwXn23DkD4Wr6wK8/CLIfKzLaaOsLotvhiRWC+ZGTAPydtY5P+eCfHQkjDPvBPm4pkBemR3X3iNVm2o5V9+uZv/uGcfazfX8U2ne4dcyIlDLc9H50zgOdd1qwEcx3m+2XfPRv5fCQyL/LwM+JHjOIOAZ9trdY6TbpTrur7jOH8FbnYc5xHgNOCWyNce8PfIz48DzzqOkw2cDsx3nOjT77S20nYc5zbgNoCp1z7ZZpAVNZCb0fSUOSezdbfHimrT+ltRY1oC0lI61+rcJx8s23T1PFKzx9ucOtY8zSw+4JOX1RRjbibRLs2Nyqt88rKafs/Nin2aPn20zbghNn9a0lRBDXtEuyyWHPQ5VOHTK6+pm1V7zpycwumnpAKwa0+YHs0mT8rPtiirjF2/rNKPGR+an2MmCWlkWzBlZJBfPRnbHzY/2+LrV2Tw15drOFDWcUyzmk1MVHIwttW95VNlaHr63LQMVESWqaoxhYbKyP9tdcnauc+nR7ZFZpp5Sr3nkB8dy7mh0GNwr9iWi5ljbKaPaoovL6upK3/LXgQAFTUt4ssiukxlbYv4mnU77ZMPV8wO8MQboU4doy0lS5wzxzQVKCtr6JI4Kmra//veWefxzjpTILtmToCD5e0ff92VZ20Z0c+itNJ0nQYzqZQ59hIfG8DHW30+3mr6qZ471Y65JnVnfEd7XiRLfHUNsGOvz6gBHVe0MvP6UFW2J/p7ddlesnL7xiyTldeXyrLdZOX1wwuHqK+tIC0zP2aZ/D4jSUnN5PDezfQeNImsPJNGRnZPhk44nwNFa+k/fCYngzbvBW3u16YyQHqk51nj583XbX6vGdXfYveh9rv1rt3h8/m5NkvXwamjm2JIlmte/wIzBvmf5wVJbfZgvbvjs23TOl4aucduLPQY1MuispZofI3++aogH2zwurw8YCZoNWW0zDQYPTCA54f5rNCPliEPV8KOPSYfD1d2XIYBuGB2FufMMgW5bUX1FOQHYKf5riAvwOHyzj/4q6xueqDw5vIqbrq0i5+8yQlLLc/HX+PtPUzk4YTrun8DrgRqgCWO45x7HLbzCHAz8Hlgvuu67c204GP2c6nrulOb/Rvf1sKu6z7kuu6pruue2t6GSw5BjxxzI7Bt01K8uTj2Ire5xOeUyCQ84wdZ7Oxk1+YJQyzW7+zcsi19sMHjdwsa+N2CBtbv9Jg2ylykB/e2qG1oo4JfA7UN5nuAaaMCrI90FRs90OKsUwL89dWGmLG4WemmIAAmD3rmWhzqoJLQ6J01DfzyiSp++UQVa7aGmDXeVKSH9QtQW0+bN6baevM9wKzxqazd2rSLxw4JsPewF9O1OyMNbp+XyfPv1rF9d/wbxkcbPR5YFOKBRSE27PKYOtJcDgb1sqhtaN2lq7IG6hp8BkUqGlNH2tGuVRuLmtY3n5t8LMhpWr9/gemaVV0HxQfNOKrMyCOcEf1s9rcYprd8k8eDS0I8uCTEZ0Uek4eb9Af2sqirbz++gZH4Jg9vim9TkceUEWb9KSNsNkbiy82EG+YGee69MIcq4mZZm5IlzuWbmm78XRXHxiK/zXQty3TJBFOp6dvDYms7XaG7K8/aU1ZlttM4H8DwfnbMREeJjA2InhO5mTB+sM3a7bHrJMvx1p5ExpeZRrQnUDAAI/pbHIhzfe498BTKD+yk4lAR4VA929YsYcj4c2KWGTLuHLasWgjA9nUvM2DEbCzLouJQEV7YXJcrDhdTtn8bOT0G0lBfTX2debDZUF9N8Zb36NF39JFkY1IrOWQqYPmRMsDEIaYbcHObin2mRPb9hMFWdHjTpiKfiUNsAjbkZ5l0SprN5DxpqNVq1uqCZq2QYwda0QdzKzY3LZcs17zNxT6/eSbEPQtC/Gp+0z27u+MraXGPHRa5xzaP754FJr57F4S6pTzw2+dC/PZZ82/9TjNL/meFZk6aQKQ2kplmWsz3x3n43+jVD6r44b37+OG9+1jxaS1nTjcV6VGDU6mp9Y+oy3bz8dEzJqRTvO/oJ4ZNZp5/4vxLVmp5PjpvA486jvM/mDy8AniwvYUdxxkBbHNd917HcYYAk4E32li0o3QrgGhVxHXdEsdxSoD/As5vloYNXAc8BdwEvOu6brnjONsdx7nedd35juNYwGTXdT85mj/e9+GVVR43zrWxI6+qOlAOZ00yT4w3l8DqbT5Xzra5/VIztnXBsqYL2Lcut0kLmovlmIEWTy31ooXX8YMt3LePvTvRxkKPsYNs/u36VBpC5lVTjb59VUp0tuzn3w9x3VlBggGLTUUem4rMtq88PcW89uRiUxJrfCXVsH42508PEPZMPix878hbKz/dEWLC8CB33JpNQ8i8qqrRD76QxS+fMIWuv79RE31V1YYdIdbvaPob2uqyfdaUVHrl21w8O42LZ5s75u+frY4Zp9SezcU+Ywb6/MvVQfO6i2av7rn98mB0FszFH3pcdXog+hqNxocm76zzcM4KMH1UkNIqn/lLzfoThthMGWkT9iAU9pn/tvnc9+HllWG+dGEQC3OjX7m5/f2+udhn9ACf78wz8S1c1hTfNy4N8uCSSHwfmfiCAdhS4kXHQr67zuO6MwNMGxmkrMpn/jtm/bmTA2SkmhlOATzfj84qes0ZAYb1NYWP710d5K018R9IJCLO7oyjvXRtC758obmd1DX4PPteKNqtcNZYmzkT7OiEM1fMDvDCB03xdFWsWelm7GtainmCOHucze8XhSg+6LNhl8c3Lg3i+bD7kDn2LpnZena97o6tvgGcuQEyUy3CPixZHqaug/JbspwXH29t+xrT3fFlZ8BVpwexLVO5+XSn1+rBbkt2IMhpV/4XLz3yNXzfY8yMa+jRdzQrX72XXoMmMXT8uYw59TqWzv8B7q8vIi0zj3Nu/A0Ae3auZM3Sh7EDKViWxWnz7iA9qwflhwp5/fHvmNi8ECOnXM6gMWd2GMfxMvWvv6Hn3Fmk9urBuduXsvmn91H4yPF9TZbvm9nibzo7EHldpcf+cph7is3uQz6bin0+3upz1WkW/3R5gJp6ePY9s+/2l8P6XR63X2peYfjiiqa5LlICMLyfxeLlsfeCc6fa9MwxrzQsq/JZsrz1vSKZrnlt6e74fB9eXRnmlvNNjLsP+azc0rmyVVeVB9rTO8/iitnmeLAseHdduNXD9M5YvbGWqePS+b9/70ddg8+D85ueytz1z3344b1m5u3PX5LH6VMzSE2xuO8/+/HW8mqeea2ci07PZsaEDMKeT2W1x4Pzj6ILpPxDsPxkns4sibWY2GsXsAq4HPi+67orHMfphRl7PMxxnP8Avgg0YCbxusl13Tbemtd2uq7r/tpxnGuBuzCt16e5rlvjOM6NwHdd153dbP1KzPjmCyNp3BCZMGw4cD/QH0gBnnJd96cd/Y13/b2teWqTww9vCPDDPx3hVMjd5K6vmorrd35bHmfJxLjvu7nc+VjyPlH9yS0p/OTx5IzvzpvNwxTFd3TuvFn79midCPElc2wA//tMcs6w+/+uNS1ei1PGJjiStl3WsJGfPXlsrxHqKv/9eVM5TPZjL5njS/bywE3/0eY0QQn3t18MgmSYYv8IPfpWJ2byTBK3np2c+auW56Pkuu7PgZ+3+PjXzb4/QGTMs+u6vwB+caTpRt4f3fj5M8AzLRY/AzOzdss0/rWNz7YDF3cmBhERERERObmozfTYqfJ8gnIcZyVQBfxbomMRERERERE52anynACO4/QEXm/jq/Nc1z3Y+Ivruj9uL43IK6fa+jwBE/yLiIiIiIic3FR5ToBIBXlqouMQERERERGRzlHlWURERERE5CSnMc/HTu95FhEREREREYlDlWcRERERERGRONRtW0RERERE5CTnqdv2MVPLs4iIiIiIiEgcqjyLiIiIiIiIxKFu2yIiIiIiIic5zbZ97NTyLCIiIiIiIhKHKs8iIiIiIiIicajyLCIiIiIiIhKHxjyLiIiIiIic5Dwv0RGc+NTyLCIiIiIiIhKHKs8iIiIiIiIicajbtoiIiIiIyElOr6o6dmp5FhEREREREYnD8vUIQtqng0NEREREpDUr0QEcqQdePnHK9rdflJz5q27b0q4/v5HoCNr3lXPhZ0+GEh1Gm/778+a0uv2XhxMcSdse+EEP7nysIdFhtOsnt6Twk8eTM747b04BUHxH6c6btW+P1okQXzLHBnDHX+oTHEnbfvqlVCC572mLU8YmOow2XdawEUju8wKSO75kLw/8jxtOdBht+k8nkOgQJEFUeRYRERERETnJqcPxsdOYZxEREREREZE4VHkWERERERERiUPdtkVERERERE5ynrptHzO1PIuIiIiIiIjEocqziIiIiIiISBzqti0iIiIiInKS80+o6baT8jXPankWERERERERiUeVZxEREREREZE4VHkWERERERERiUNjnkVERERERE5yJ9SQ5ySllmcRERERERGROFR5FhEREREREYlD3bZFREREREROcp6X6AhOfGp5FhEREREREYlDlWcRERERERGROFR5FhEREREREYlDY55FREREREROcnpV1bFTy7OIiIiIiIhIHGp5Pg4cx/kxUOm67q8THUsjx3GGAYtc153Undvd9unbvO7+HM/3mDLnemZfdFvM94Wbl/P6/LvYV7yRK796N+OmX3zctj2yv8VF020sCz7e6vH+htjHawEb5s226V9gUVMHz7wfpqzKfDdngsXUETa+Dy+t9Ni2x6w7a4zFtJEmzVVbPT7aGJvm7HEWF0wL8OtnQtTUdz5W57wMJo1Mob7B5y9LqincG261zJC+Ab50WRYpQVi3tQH39RoABvUJcNNFmaQEzKyJT75azY7dYTLTLG65NJNe+TahEDz2YhUlBzo/reIlM21GD7RpCMOC90LsPtR6mf4FcPWcIMEAbC72eHG5ST8jFa4/K0B+tkVppY/7dpjaejhluMUZkwJYQF2Dz6IPw+w9bNI6bbzN9NEmz/eV+ix4L0yog3AvPjUSXwgWLAuxp5345p0WJCVo4ntphUkwPRWuOzNAfpZFaZXP0++Y+HrmwrzTAvQvsHhjtceyDWb5nrlw3RlNl8ce2fDmmvaD687YANJS4MrZAfrkW/jA88vCFB2IPTa7Iqb20h3W1+KiGYFour3y4Ol3wmws8rl6ToABPa3oDJ+2BV6LJ9/dnX+zx9lMG2WeHe8t9Vn4fuvzL1GxdWbfJjI+AMuCr18SpKLa58m32s+77o7vSM/b5i6dFYjE6fPce2F2H2qd5/0LLK45I0AwYLG52GPJR+ZvnzjU4pypAXrlWTy0OETJQbNuRhrcODfIgF4Wq7d6LP6w47xq7njfz3rmwDVzms7RHtnw1lpzTzv7FJsxgyx8H6pqfZ7/0KOyptOhdtrkh++iz6VnU7/vIG9Pu+K4p58s17x5pwUY2teirkWZoDvjA/jvm4LsKzU/l1X7PBU5V2eOsZk93qYgx2o3L7uiPDB2sMW5UwP4Pniez0srPHbtM8d1XhZceVqAvExz3Xvi9RClVe2GB8CIfnD+VBvbgtXbfT74rPU5cvksi/49LGrqYcEyj7JqGNYXzj7FJmBD2DPXiJ37zDq2DRdOsxjax5wPS9d6bCzuOA75x6OW5yTlOI7lOM4JtX88L8yrT/2U67/9R752x2LWL1/Egd1bYpbJLejPpbf8DxNmXn5ct21ZcPEMm7+9Feb+JWEmDbXplRu7zNQRFrX18PtFYT7c6HHeFJO9vXJh4hCbB5aE+dtbYS451RRYeufBtJE2f3olzIMvhhk9wKJHdrO/JRNG9DM3siMxaUSQPgUB7nionCderuamCzPbXO6mCzN5/KUq7nionD4FASaOMIXCa87OYPF7Nfz80QpeeLeGa87OAODi09Ip3Bfm/3ukgkcWV+Gc13a6bRk90KJnrsW9C0K8sCzM5Z8LtLnc5bMDPL8szL0LQvTMtRg1wNx8z5hks22Pz70LQmzb43PmJJO3pZXwyMsh/vBCiKVrPK6cbdLNyYDPjbN5cLH5zrJg0vD2b+SjBlgU5FjctzDECx+GuWxW2/FdNivACx+GuW9hiIKcZvFNtNm+x+d3z4fYvsfnjIkmvpo6eGmFx7L1sQXsg+Xw4JIQDy4J8dCLIRrC8Flh24Xw7o4N4OJTA2zZ7fH7F0I8sDjE/rLYY7CrYmov3R17/Wh+/eW1EA0h2LrbxLR2u8fvnw9x/6IQANNHxV7Wujv/cjJg1jibh180MdkWTBrW9rGXjPs20fGBOXcPdBBXouI7kvO2udEDLXrmWNzzXAPPLwtzxey247zitAAL3w9zz3MN9MyxGD3QxLm31OfJN0Ps3BubJ6EwvL46zMsrOl9phq65nx2sgIdfCvPwS2H++HKYhhBsLDTxvr/B46EXzXebS3zOmtg1RY+ivzzLR5d/rUvSTqZrHsCrq8I8uCSUsPjAHH+NMT7V7CFX4X6Px14LUVrZ9jncVeWB7bt97n8hxAOLQix8P8yVpzWle/WcAO996vG750M8vCREVW2bm4yyLLhwuo37jsdDL3tMGGLRs8U5MmW4RW0DPPCix0ebfM6ebOKrqYOn3/X40yseiz7yuGJW0/E+Z7xFdR08+KLHQy957NrfcRwnIs8/cf4lqxOqcpZMHMf5keM4mxzHeRcYG/nsLcdxfuk4zkeR786MfD4x8tlqx3HWOI4zup00hzmOs9FxnMeAdcBgx3F+4DjOWsdxPnEc5xeR5aY6jvNBJK3nHMfpEfl8RmS5T4B/apZuwHGcXzmOszyyzje6Ik9271hDfu+h5PceTCCYyvhTL2PzJ6/HLJPXcxB9Bo3Dso7voTegAA5X+pRWmdbYT3d5jB0UWyAeO8jik+2mILW+0Gd4Pyv6+ae7PMIelFaZdAYUQK9ci+KDPqGwGSOya5/PuMFNaV44zeb11R4c4Qk+eXQqH6yrA2B7SZiMNIvcrNhYc7Ms0tMstpeYG94H6+qYMjoVMJtLTzXLp6dZ0Rtg/14BNu40N+u9hzx65tnkZLZfIW1u3GDTMgJQdMAnPdUiOyN2mewMSEuxoq1gq7d6jB9iRda3o+uv3uoxbrDZv4X7/ejT8aIDfszfaduQEjAtkSlBqKjuOL41kX1XHCe+4kh8a7Z70f01drDNJ9vM+p9s8xgbia+6DkoO+oQ72IfD+1kcqvCjrTqJji0tBYb2tfh4i/nC86CuoXti6ky6E4ZYbC4x5w3AlpLYPyC3xTOdROxb24JgwBTAUgJQ0U4rWzLu20TGB5CTCaMHWKzaEr9SmsznbWycNqu3HeH1b1vTde5Amam4t9QQMveN0JHVnbvkftbc8L4WhyuhLHLNrW+q45EaPOJbWqcdencFDYfKuiTtZLrmJVt8Le05TIfnRVeVB5ofZylBK3qg9c4z5YFtkYcP9SFoiHPOmHOE6DmyYZfPmAGx58jogRbrdpg0PyvyGda38WEXVEYq5wfKzb0gECmSTh5usaxZL48j6VEo/zhUeT4KjuPMAG4EpgKXAjObfR10XXcW8F3gzshntwP3uK47FTgVKOog+dHAH1zXnQhMAOYBn3Nddwrwv5FlHgN+4LruZGBts+08AnwnsmxzXwXKXNedGYn1647jDD/CPzuuitK95PboF/09p0dfKkv3Hu/NtCk306K8WeWrvBpyMmIvpDkZTcv4PtTWm+5FzT9vXDc302J/mc+Q3hYZqebiOmqATW6kMjpmoEV5jbkIH6n8bIvD5U0Fz9IKj/yc2FMxP8fmcEXzZXzys822579ezbXnZHLXN/O47pxMFiw1Jf+ifSGmjUkBYFj/AAV5Nj1yOneK57TKPz/6tzYyeew3W4Zo5Twrg2g3v8oa83tL00fZbC4261fUwPufenzv2iDfvz5IXX3sU/tW8WVYMTf78iq/nf3bLL6qpmMgOz02vuz0djfVyqShdvQGnAyx5WdDda3pGnjbpUGumB0gpUXDQFfF1Jl0Jw2zWbejdcXKjizWsjLd3flXUQPL1nt87+og/3ZtkNqGpkJbS8m4bxMZH8DFMwK89nG4UxWsZD5vm8vNtCiran5ta+f613yZqtbLHC9dcT9rbuJQi3U7Y8/Rcybb/POVASYNtVm6tvPDfZJFsl3zzp0a4PbLmoYQJCK+YAC+fkmAr14UaPXwpSNdWR4YN9ji2/OCfOG8AAsiw2V65ppeFDfMDXD75UEunGF6S3QkO4OY7VfUmF5FMX9HBjHnSF2DOUeaGzsI9pSa7ttppvjEWZMsvnyBzVWn2WSmdRyH/GNS5fnonAk857puteu65cDzzb57NvL/SmBY5OdlwA8dx/kBMNR13Y5GE+10XfeDyM/nA4+4rlsN4LruIcdx8oB813WXRpb5C3CW4zj5kc/fjnz+12ZpXgjc4jjOauBDoCemkt6K4zi3OY6zwnGcFR1lwD+CA+WmO9sXzglw09kB9hz28X1zQzpjQuIKGGdNTWP+69X88P4y5r9RzRcvMU15L39QS0a6xY9uzeHs6WkU7g3NErd4AAAgAElEQVQnrttLi+0O62sxfZTNq6vMzTI91Yx/+u2zIX49P0RK0Dzx7bbwOpkvtm1actbv6r59HS8227LoX2CxYpPHQ0tCNIR8zpjUtZfyzuZXdgb0ybfYWtJ6hcbuhLv2d+1BGS/WxmPvngUh7n4mRGrQjM3vDsm4b5uLF9/ogRZVtX6b4x+7QzKftycK2zYPfzcUxmbmm2s87n0+zLqdHjNHq2h4LNe811eH+f3zIR5+MdTBmsemM/H99rkQD78Y5pn3wlx8aiBm2Fm3ahbrZ4U+v1sY4qk3w5w7zRxntgVD+1i8sjLMQ4tD9Mi2mDay66/JvXLNQ6PGceW2ZR4KFB30eeRVj+KDPudN6b5ySXfx/RPnX7LShGHHX13k/zCR/HVd92+O43wIXAYscRznG67rvtHO+p3oZHbELEyL9MvxFnRd9yHgIYA/v3Fkvbdy8vtSfnhP9PeKw3vJzu97hKEenZZPRnMzoaImNvyKGj/yuemumZ5quuQ0ft583cYnmqu3+azeZip850y2Ka/2Kcg2LUS3XRyILv/1iwP86ZVwu+N05k5L44wp5pHnzj1heuTaUGzSzc+xKa2ILeSVVngxrcb5OU3ds087JS06edjKzxq4+eIswLQ8PLak6XHxz2/P5UBp+32fZo01E3aB6QIZmwexT5WhnTyOLFNVYwoRlZH/m+dD33yYd3qAx18LURM5O0b0tyitNN0vATbs8hjcx2LN9qZtzhxjR8fHlhz0ycuCwsj4o9wsq5392yy+rKZjoLK2RXx1dMroARa7D/mt9msiYyuv9imvhuLIpETrd/rMmWQzc0zT8VJZQ5fEVFHT8d86cYjNZ4V+q4c2c0+xyWzWapjI/BvRr41jr1dT2sm4b5tLZHxDeluMHWQmEgoGTEvN1XMCPPde03Ummc/b5maNtZkROWeKD/jkZVk0lvLbvf5lNY+z9TLHS1fdzwBG9e84b9bu8Pn8XJul647rn9Qlhn7zpujPyXTNa2x1bT6/QyLiaxyOUlppxmj3K7A43M445+4qDzTauc+nR7ZFZpppHd5zyOdwpfluQ2HjNbn986uyprFHhVkmJ6P18JuKGmLOkbSUpm7YORlw7RybFz70ohOT1dRDfchnY6Rv6GeFPlOG2x3GIf+Y9Hjx6LwNXOU4TobjODlAh9NGOo4zAtjmuu69wEJgcie38yrwZcdxMiPpFLiuWwYcbhxPDXwRWOq6bilQ6jjOGZHPv9AsnZeBbzqOkxJJZ4zjOFmdjKHT+g89hcP7dlB6oJBwqJ4NKxYzavK5x3szbSo5BAU5FvlZ5sn6xCE2m4piL3ibihsvhDBhsMWOyOQum4p8Jg4xMy/mZ5l0SiItK41ddnIzTXejdTt99pXB3c+Fue8F86+82kzE0lFBbenHdfz80Qp+/mgFqzfVM3uSSXj4gAC1dX5Md0AwXa9q63yGDzAV9NmT0liz2Vz1Sys9xgw2z73GDg2y77ApuGakWdFxO2dMSWVzYSg63rgtH230eGCRmbxjwy6PqSPNyoN6WdQ2+K1mW62sMTNmD4pUNKaONIUGgI1FTeubz83DgLwsuOHsIM++G+ZgRVNaZVUwqLcV7ZI6or/NgRZD4ZZv8qKTnXxW5DE5su8G9rKoq28/voGR+CYPb4pvU5HHlBFm/SkjbDZ2YhIhaL8LciJjq6o1M6c2To4yvL/FgTKf5Zua1uuqmDYW+R2mO2mY1Sq/po2yGDnA4pl3mypYicy/siqznWDk2Bvez+ZAs/Gqybhvm0tkfK+v9vi/50LcsyDE0++G2b7Hj6k4Jzq+Ru2dt819tNHj/hdC3P9CiM92eUwdcYTXvxF2pyYjOxpddT8DmDTU4tOdsWkVNGuRHDvQ4mD5iVFZ2Hn/36I/J9M1r3F8cCKvyempTeN4M9JgcG+rw8kHu6M8UJDTtH7/AtOLr7rOPCxMT7Wi5a0R/Wz2xxkaX3LIzBifFzlHxkfGnTe3ucSPTgY5bpDFzsjM3mkpcP2ZNm+u8Sg+GJvulhKfoX3Mz8P6WjH3BpFGank+Cq7rrnIc5+/AJ8A+YHmcVRzgi47jNAB7gLs6uZ2XHMeZCqxwHKceWAL8EPgS8ECkUr0N+HJklS8Df3YcxwdeaZbUHzFdyFc5jmMB+4GrOhPDkbADQS648Q7c+76G74U55fRr6T1gNO+8cA/9hkxi9JTz2L1jDc8++G3qqsvZsvZN3l10H1+7Y/Exb9v3zeyrN50dwLLMhBr7y02L1+5DPpuKfT7e6nPVaRb/dHmAmnp4NlLo218O63d53H6peYXCiyu8aHeR688IkJFmJqR4cYXX4eQ9nbVuW4hJI8P87LZc6kPwlyVNnQ1+dGsOP3/U1DL/9mo1X7o0i9QgfLqtgXXbTBewx1+swjk/k4BtJqR54iXT2tyvp82tl2Xh+7D7QJi/vtjBDFwtbC72GTPQ51+uDprXXTR7dc/tlwd5IDJT8uIPPa46PRB9jUbjGOZ31nk4ZwWYPipIaZXP/KVm/bmTA2SmwWWR2To9z+ehJWGKD/is3+nxjcuDeJ556rxiU/sF0c3FPqMH+Hxnnolv4bKm+L5xaTA6q+nij0x8wQBsKfGi42vfXedx3ZkBpo0MUlblM/8ds35WOtx2SZC0FPNsefY4m98vClHfYCaSGtHfYtGHHRckExHbi8vDXDMnQMA2LQnNt9mVMXWUbl6WafXY0WLG4ctnBSitgq9eZG43Z51i83azIQ/dnX/FB3027PL4xqVBPB92H/JZudnjkpmtBxcn475NdHxHIpnP2+Y2FfuMHuTz3WtSoq+qavTNK4Lc/4KJc9EHYa6eEyAlaMVc/8YPsbh0VpCsdLj5vCB7Dvk89ppZ53vXppCWYioy4wbbPPZqQ9yKQVfdz1ICZiK1xctbjs+16ZljXhFUVuWzZHnXPBSY+tff0HPuLFJ79eDc7UvZ/NP7KHzk6eOSdjJd866ZEyAzzYoZt9vd8fXKtbj8cwF8TNfD9z4NRx9QzxprM2eCHa3kX3mamTW7eaxdUR6YMMRmykibsAehsM/8t83nvg8vrwzzpQuDWJiW75WbOz4GfR9eXeVx41lmfPSa7T4HyuHMiRa7D/tsKYFPtvlc8Tmb2y8xr6pa+IFJc8Yo8+aUMybYnDHBpPfU2x7VdfDmGrPO+VNNxb7luSICYPnJ3KlcEupIu213p6+cCz97suvGEx2L//68qSTc/svDCY6kbQ/8oAd3PnYcngJ0kZ/cksJPHk/O+O682cwooviOzp03a98erRMhvmSODeCOvyTn1Lk//ZIZ0pPM97TFKWMTHUabLmvYCCT3eQHJHV+ylwf+xz3C6eq7yX86ATDPJk4ov342mV8CFev719hJmb/qti0iIiIiIiISh7ptJ4DjOD2B19v46jzXdQ+28bmIiIiIiIgkkCrPCRCpIE9NdBwiIiIiIvKP4cTptJ281G1bREREREREJA5VnkVERERERETiUOVZREREREREJA6NeRYRERERETnJ6Q3Fx04tzyIiIiIiIiJxqPIsIiIiIiIiEoe6bYuIiIiIiJzkPL2r6pip5VlEREREREQkDlWeRUREREREROJQt20REREREZGTnGbbPnZqeRYRERERERGJQ5VnERERERERkThUeRYRERERERGJQ2OeRURERERETnIa83zs1PIsIiIiIiIiEofl6xGEtE8Hh4iIiIhIa1aiAzhSP38qfMKU7X90YyAp81fdtkVERERERE5ynhpNj5kqz9KuOx9rSHQI7frJLSn89IlQosNo0x1fMKfVTx5Pzvy78+aUpI0Nkju+O29OAZJ730Jyx5fMsUFy5x0kd3zJHBskd95BcseXzLEBLE4Zm+BI2nZZw0Ygufftu+urEh1Gu86YkJW0ZdGf3JKS6BAkQTTmWURERERERCQOVZ5FRERERERE4lC3bRERERERkZOc7yU6ghOfKs8iIiIiIiJyQnEc52LgHiAA/NF13V+0+D4NeAyYARwEbnBdd8exbFPdtkVEREREROSE4ThOAPg9cAkwAfi84zgTWiz2VeCw67qjgP8Dfnms21XlWURERERE5CTn+/4J868TZgFbXNfd5rpuPfAUMK/FMvOAv0R+fho4z3GcY3p/tLpti4iIiIiISNJwHOc24LZmHz3kuu5DzX4fCBQ2+70I+FyLZKLLuK4bchynDOgJHDjauFR5FhERERERkaQRqSg/FHfBbqbKs4iIiIiIyEnOO7lm2y4GBjf7fVDks7aWKXIcJwjkYSYOO2qqPIuIiIiIiMiJZDkw2nGc4ZhK8o3ATS2WeR74ErAMuA54w3XdTg2obo8mDBMREREREZEThuu6IeDbwMvABvOR+6njOD91HOfKyGJ/Ano6jrMF+FfgP451u2p5FhERERERkROK67pLgCUtPruj2c+1wPXHc5uqPIuIiIiIiJzkOvkKKOmAum2LiIiIiIiIxKHKs4iIiIiIiEgc6rYtIiIiIiJykvPUa/uYqeVZREREREREJA5VnkVERERERETiOKZu247jDAMWua47qcXntwKvuK5bEvn9u8BDrutWH8v2EslxnKuATa7rru9gmbeA77uuu+Iot7EDONV13QNHsM4w2tgHXeWSmTajB9o0hGHBeyF2H2q9TP8CuHpOkGAANhd7vLjcAyAjFa4/K0B+tkVppY/7dpjaerPOsL4WF88MELChutbnkVfCAKSnwJWnB+iTb4EPC94PU3Sg7T4nI/tbXHSqjW3Bx1s83lsfu1zAhqtOt+lfYFFTB0+/G6asynw3Z6LFtJE2ng8vr/DYutsnYMOtFwQIBMC2YMMun6Vrzd8yc4zF58bZFORY/OrpEDV18fPu4lMjeReCBctC7Gkn7+adFiQlaPLupRVme+mpcN2ZAfKzLEqrfJ5+x+TdKcMs5kwMAFAf8ln8YZi9pSatK2cHGDPIoqoW7l8UajOmtBSzXJ98Cx94flls/qanmu8LcixCYZ+Fy8LsL4v/tzZ3xkSbaaNM3r60PMzW3Sb9f7kqSF0D+D54vs/DL4bbTWNAT4uvXhTg6XfDbNgVu19Tg/DlC5suZbmZsGa7x8srvU7HmJ8F154ZIDPNouSgz3Pvh/E8mDLC4oLpASriXLk6ig/gC+cGyM6wsC3Ytc9jyXKPI5nwsr3jYu5km+mjbKprW6/TMxfmnRagf4HFG6s9lm1oOz+G9zN/o4U5hha8H+ZwZedj62zefbSpaf/+/+zdd3gU17n48e/MrHoDCRASvRdRbcA0G4MbzRQbj7sdJ66JU25uyv3lJnHsXPsmN82J47jHiW2MM8Y2mGJwAYzpxoAxvRdJNAlQQULSzszvj7Or3ZV2tQIhJJP38zw8aHdnzrx75szZc+acmUlNhGkjDZLjVbnbsNth7c7Q+Ooq2/UVqeyFK/fB6nPsQPT9XpdI9cEv7vBw3Pc9i8pc3lpW+7iIi4HpowzSklSZWr3NZtO+yNu/7WqDlsland8lnEjlDmBYL52hPVXe7s4L3Xcj++r076zOzes6tEqF3832Vtf3ftNHGWRnaDgO5BW4zF9rn9OUwkj1IkCnTI3xlxvodQwRNEadHCndtCS4dYwHzZcn63Y6fLG79jF5MWMCuHaweh9g+Vc2Ww+G7oDxQ/Rary9WfJ0zNW643KhOt1UazP7MZmeuy9QRBp0yterPUgf2pvjLHbWDOQcDXnqKNhOvpvJ4IcsH39igtGrqlqXaOLoGG/Y4rNwauu9V+0QdD2UVKm/87ZNI9VikNDtnalx/uWpP5Re6vL/GxnXr/5sQyVcbVjLrld/jOjZXXjudiTffF/L54rlv8NnH72EYBsmpLbnv0cdo1SYbgPtvHkL7jt0BSG/dlu/97OlzzsNgjdEW7dVBY9wgQ7VJHJdF6x0OHQ8cD3Ex8J0pHnYcdli47tzyTlxaGuua528AW4B83+sfAG8ADeo8m6bp8T0QuylMA+YDETvPl7oe7TQyUjX+MsdL+1Yak68wwnZ4Jg83qjthd11j0D1bY0++y+h+OvuOuqzYYjO6n86V/XQ+2uAQHwOTrjB44xMvRWcgKT6Q1oRhBnvyHKxPVWc2xqi1OQA0TVWmbyyxKS6D+8cb7My1KSgOLDO4m0Z5Jfz1fZucThrXDtZ5Z4VDq1TI6aTz3HyblAS46xqDZ+fZ2A689olNlVd1nu+73mBPPuQVwuETLrvybO69NkJANXTP1khP0Xhmrpd2rTQmDTN4ZVHtvJs0zGDeWpu8Apc7xgblXY7O/qMuK7fajMrRGZ2j8/FGh1Ol8I+PVKO0e7bG5OGBdDftc1i3y2X6yMiH+fghBnuOOLz9mYseJn+v7Kdz7JT6cclIhYlDDV7/JHInt6ZWaZDTWedv87ykJMDd13r46/ve6o7jPz+OfuJB01QDz99gqKnSCy8sDFQLD0zwsP3wuXVkrr3MYM12h60HXSYN07msm856X8N268HAj+5jd8Wcc3wAb39mU1ml/r7lKoO+HbVajdS6RCoXAGu2BxpBwfGVV8Ci9Q6922th0wxO+61lXgqKYUhPnav6G8xdXf99XN+8A5gyXP3vuPDhBpujJ9XJjwcneth71KEg6MRMXWW7Puoqe9HKfX2Onfrs90jqqg+8dmh5DmdoT52CItWxToyDR6d42HzAixOmPde7g1Zd9s5VpHLXOVOjV3uN5xd4sR1IjKP6RAfAqm0Oq7apYHq20xjeR6/VcQb4ar/DeytV/t002uCy7oGyUx+R6sW4GJg01OCNJV6Ky8Ift41VJ0dKt6QcXlmk8ivGA9+e7GFnrkNpedPF1KOdRtt0tR89Otx7vcHu/EBdlZWuER+rNVl8B4651cdCfCx8b6on5Hj7aIONeZU6RhvacQbI/ee7HPjbGwz6+28bnFZNE4cZvP6JKo8PTFD7Pri+G9xd52ylyzNz/e0Tg3dW2BHrsbrSnDbS4LWPvZwsgasH6AzqqrFxr1vv34RwHNtm5ou/5T9/9TdaZmTy65/cxaBhY8ju0LV6mU5de3H1798gLi6BpYveZvZrf+bhH6m8jI2N41d/eqthmejTWG3R/Udcnjus8jazBdwyxsNf5wbq4nGDdA4eP/f6vrlx5aLnBrsQnWfDNM2XgJFAHvA6MASYaZpmOfAqkA0sNU2zwLKssaZplgIvAdcDR4HbLMs6ES5x32juJmA0MMv3+o9AMlAAfMOyrCOmaV4O/N232ofABMuy+vlGwYdYlvWoL735wO8ty1pmmub1wONAHLAXuM+yrFLTNH8DTAG8vrTe9b0eY5rmz4GbLcvaGyE/7jZN82VU3n7Tsqx1pmmm+2LrijqB8KBlWZtN08wAZgHtgNWA5ovxCeCkZVlP+14/CRy3LOvPde0I0zQN4DfA1b7v9KxlWS+Ypnk18CtffvUDvgDusizrnI6g3h00Nu1VDZvcApf4WI3kBEJ+/JMTIC5Gqx693LTXoU9HVWH17qDz6ofe6vfvu97DRxsc+nfV2X7IqT7LesY3ihYXA53aaNWNK9tR/8JplwGnSlxO+0bMth506NVBo2Br4Cv2aq9VjxxvO+QywXdGvVcHja0HHWwHTp9R6bTLgNwCqPLVm7pOyAjG0VPnknMq7zbvV9vOi5J3eb6827zfoXcHlXe9Ouj88yMVzJf7HO69zsPHG52QUeLcApfUxMCP4qHjLmlJkWOKi1GjM3NXqzQcBypq5G+rNI2VW9SbhcXQIlkjKV7to/5dNK7opWPoGnmFDgvW1R5N7d1eZ+uBQN6eLHFpl6FFnD0QzrBeOtsPuWRnRP/BT09RJ1/8Z4sT49SJGX8+LF7vcPhE7W13ydR4Z4V6/8t9LmMG6KzffeHi8zdGdU2NMPi1TFYNoMQ4jSrbZd4am8Li0HXrKhd1KauAsgqXHu3qzjcXlT64xMdASXnj511peaDsV3rhRJFLaoJGQVFoeQ7+O7hsN6TsnShyo5b7aMcORN7vI/vq9O2k4dE1dhx2WLa5dqVVn/ogmlhP4P/ySsJ2nGM8MKKPzry1NrdcGfi5b2i5G9JTZ8VWp7o+LqvjBFi/zjpbDoSvuIPLcH6BS2qiL24DJgw1aNMCdF3j081qtLGmSPVi/y462w87FNdxur6x6uT6pOvR1cmXpo6pdZrGoeMurgtVNhw/Bd2zNLYdctE0uO4ynXdX2gzsqjd5nvXtqLE738Vb//Nn5+zkivUkdGrXOGkHt08OOPRur7OiKHBc9Gqv8enmQPtk4lBVQCLVY5HS3HBWLXuyRL2/74jqLG7ca9f7NyGcfbu30CarPa3btgdg2Ogb2LhuWUjnuXf/odV/d+3ZnzWfLjzn7dRHY7VFK4POWcZ41GxHv6x0SIrX2JPv1KstIi5tF+Ka5x6oTloOcBpV3NYDd1qWNcjX4csHxlqWNda3ThKw3rfOp8BjUbYRa1nWEOAvwDPADMuy/J3lJ33LvAp817KsgfUJ2jTNVsDPgWsty7rMF/MPfR3a6UCOZVkDgP+xLGsV8D7wY993itRxBki0LGsQ8G0CnfnHgY2+9H4GvOZ7/zFghS8f3gM6+t7/O3CPL04duA01ch/Nt4Aiy7KGAkOBB0zT7OL7bDBqBkBfVCd+VD3SC5GSqIU0RorLQhu0AKmJGsVlbtAyaj2ApKDKrbRcvQbISFHTaL5xvcFDkzwM7KqWb5kMZypcpo00eHiyhykjDGIinO5JSdAoColNvVczfn8H3XXhbBUkxKnlis+EX1fT4MEJBj+62WDfEZe8wvrkVIT4grdxxq0dX0KNvDsTiCM5PjTvkuOpZXA3PWqHKliLZDVFfuoIgwcnerhxuFFrBO7YKZc+HVUM2RkaLZLUlFv/aP3fF9u8sFCNePXvXPsHJSVRTT31KylT74GqKO6+xsMDEzxc1j38j1FKgvqh/HxX/Uaj+nXW2XowsOz4IWpU9OUPbKxPbW4cXnumQEKcKgv+zlfNct2no87DkzzccmXtdc8lvjvHGfxohofKKtU4Aph8hcEHn9u89IGXj75wmDQs3DYilwtQnbiHJ3mYEua71ce81TZ3jDP4j+keBnRRnSK48Hnn7xjVlJakRrhyCyOX3eCy3dCyV59yH02k/d41S42gvfyBzfMLvGSla3RsEya2OuoDjwEPTDD41g0GvSKMEK3b6dAqTeOHN3t4ZLKHRevD9yjGDdRZvd2pPgno19Byl5Gi0amNxrfGG9x7nRGxMekx1GjltihT2nUNBnQN7OMr++vsP+bw8iKbf37k5brLwu+jSPWi/zfl3usMHpgQ/kejserkutJNTYSHJ3n4j5s8rNzq1DpZcrFjOnrKpVuWhsdQx3LnTI20JJXWsJ46u3LdkBibIs/8wp2EGTcoUCj02NqzC5qTmm2nlBr1YWqiVl1fhbRPItRjKYnh0yyrUMdTVrrKv76dtFrttPNx+uQJ0lu1rX7dMqMNpwuPR1x+xcdz6HdZoJlZVVnJEz+6kyd/eg8b1i5tUCyN1RYFVa8/OtXDndcYzFml6lUNuGGIwYdfNOKZG/G1ciFGnvdblrXJ9/cXQOd6rOMA//L9/QZqZLcu/mV7oUZOPzJNE8AAjpim2QJoYVnWct9yrwMToqQ5HNWRXOlLKxY1+lsEnAVe8Y1Sz6/H9wk2C8CyrOWmaab6YhsN3Ox7f4lpmhmmaaYCVwE3+d5fYJrmKd/fB0zTLDRNczCQiep416fbdj0wwDTNGb7XaaiTG5XAOsuycgFM09yE2k8raiZgmuaDwIMAfSbPPMevfo589ZquQ1aGxj8/sokx4P4JHnJPeNF1jax0jYXr1PSvCUPV9Jolmy7etSauCy9+YBMXA7depdM6jXO+5rex4grWOVNjcHedVxfX/6oGXVP5+8HnNnmFLuOH6Izup7P0y0D+rtjqMH6IwUMTPRw77XLklIvjQpe2OtnpWnXD1OOBMxUOIadqo3h1sZeScjXCefe1HgqK7ZDri0D9YH28sf77u18nnfdWBfKga5ZG67RAAysuRo3G1exMRLIr12XLATXV8vIeOn07hX5+LvHNXGJj6Gp6apdMjcMFLh1aayEjgsY5duLW73JY/pUadR038PzOhQ7vo/PmElUGRvbVueFyg3lr7Aued9NG1v5yMR4wr1Kdv0hTi2uW7YaWvUjl/lxE2u/dsjS6ZWk8NFHFFhujOnKHIrcxa3n6PXVctEiGe6/1cPy0t9Y16N2yNY6dcnntY5uWyer4OXjcG5KHmS2hZYrG4i+ckFH0GA8NLne6rjqnryyyyc7QmBHmxBKo0bRDJ9ywU7aDTRpmcPCYw6ETgWs5e7XXGdlHfe4x1EmWguI6EiFQL+q66jy89rGNxwM/uaXxO1b1uYdBcRk8v8BLcgLcNsZg26HATKumiGnfEZd2GS7fusHDmQqXwwWqfk9OUJ2uf3zUuJ2F+t73ITkB2rTQ2Bt0cviTTTal5YEp+V1//CB7nny2McL82nlnhc0NQ3Q8Ouw94p7T/TUuhNXLFnBg7zZ+8j8vV7/3fy8uoGVGG04czeV3v3yI9h270yarw8UNLJKg/Nlx2GXHYS+d2miMG6zz2kc2Q3vp7M5z65zJ8nVyscvDpehCdJ6DJ2zZQEKkBesQbVf6z0lqwFbLskYEf+jroEbiJXSE3T9mpwEfWZZ1e80VTNMcBlwDzAAeBcZFiS9Yze9yvsX0ZdS1420JjGBHo6FG3xcHv+mbtl1zP4Xd95ZlvQi8CPDYa1XusF46l/VQ2Zdf6IaMHtU8swe1zwCmJkKJb5kz5VRPrUlOCDQaisugvMKlyqsa5gePuWSmaxw6pior//SvrQddrozQyC0pd0mrud3y0NhKytRUzJJyNaIcH6OuCy0pd0kNalyGW7eiCg4cc+meraZ91sfQnupmTqDyLi0JDvsuTkhN0mrHV14j75ICcZSerZF3QXuzTQu4cbjBzCVeyqM0UoMVl/ny1zfit+2gy6ga+VtZFXozpYk2VTYAACAASURBVO9P83CqFDq1UdPuPqlxIqN3B40x/VVD+v01NiVl+PaL2kZKItU3kCrxnfktq4Adhx3aZWjVnWd/5yMuFmaMVuklxkGPdgaOE34KZ2YL1WgOvnGIBrzsu84w2J3j1M2q8k+qKavxMapMuG5ouQ7Ozw17HCZfYZx3fKAuO9h5WF1SkFvocraq9vWtaraDSn9nrsP63U7EchHc8P5ij8OV/QPxzVzqjToNODEOMltq1WVgywGHu8Z5GiXvrh0cWuXoGphXGXx1wGFHhGvUw5VtTWtY2atPuY8mO0MLu9811AmnmjeCCq4LZi71qvoqQn3gPy5Ol6o6p226xqnSQP48NNFDeaXLMt9JrlOlcLrUpVWqumGbX4dW6iTD96d50DV1OcO91xnMWmY3uNwVl7nV9xXILww0zv3Hhb/s5XSKPGXbb0x/ncR4mPdp8HIa1nJvrankU0YYZLVUefXmUjtivVj9m2Kr6cjh9kNj1cl17Vu/0nI4fho6ttFIjtdC3r/YMX22xeEz36U5N40yKCx2yUpXMyi+N9WDf0D3v2/3sHlf0+RZTkedHYfdkJvJ1azbWgztT3NWs+1U8yaUxWWqDVNS5oa2T+r4DY2UZm6Byz98N13tmqWRkdrw3lKL9NacLDha/fpU4XFaZLSptdy2L9eyYPYr/OR/XiYmJrb6/Za+ZVu3bU+vfkM4tH/nOXWeL0ZbNNjB4y4tkzUS49TJxo6ZGkN76cR61KVXlfUfpxCXoMZ6VFUJkFLHax3VMQW4gzAjoBHsBFqbpjkCwDTNGNM0cyzLOg2cNk1ztG+5O4PWOQAMMk1TN02zAzDM9/4aYJRpmt19aSWZptnTNM1kIM2yrIXAfwD+aeA1v0Mkt/rSG42aQl0EfOaPydeRLbAsqxhY7vv+mKY5AWgZlM57wHjU9OuQznAdFgOPmKYZ40uzp2maUa7cq9u6nQ7Pz/fy/Hwv2w85DOqmikz7Vhpnq9xaP2Cl5VBR5dK+laq0BnXTqxvGO3MD66v31Q/2jsMOHduou8bGGNCulbr2sfSsmr6VkarS7poVueOaVwjpKWpasa6rRtuuGh2YnXkuA3zXbvXtqLH/mPp8V65LTicdQ1d3Dk5P0cgrVI3iOF/DwWNA1yw96shHsM93Obyw0MsLC73syHUY0EVtu10rjYrKyHnXzpd3A7oE8m5XrlN93dnArjo7fXmXmqju4PreSrv6Gqf6OnNWTQfz52+XrNBrTkF9f/+13pd11zh43KWyCvYddejTUScxTn0WH6safTsOu9Xf+chJl525DjmdA3mbkaI6ajFG4JrNGEONNB0/Hdi2P42/zPHyZ9+/bYdcFqyL3DENN61v7xGXK3oHqrlM3xE2c4ma8jtvjWpg7D/m0tc3PX1gV616G8lBpwKDp9CeS3wxnkA6mgY92ukUFKkTE6dLA9sFdQLAdQPpL9vs1FkuguPr0yHwPV9YGL3jDKqDGx+jrhUHtR9OFLuNknc1y9aUEQYFRS5rItzxNVLZbmjZq0+5jybSft9zxGVQN6368pKUBFWPBNcFpeWwM9cNWx/ExwauiU/wNdpq1nkvLPRSWOzSJcs3BTEeMlJDO9gA63c7/PFdFePfP/RSWAL//Mi+IOVux2F10zBQZccfc/B3jItRswZ21nHzvsHdNbpla7yzInSEc+8Rh2G9AmWvra/svb9alb03l6rlI9WLOw87dGitoWmq7va7GHVypH2bkhiIJT5WdZwLi92Qqf8XOyZNUzMIQJ2oymypsfeIy+48lz+8o8rO795WvYQnZ138PPPr11mrVbcn1ximKdlaz5tUNJGM4PZJZ52duaHfZ1euW33JWnD7JFI9llfoRkzTXzcaOozqq7O+npc91aVLjxyOHTnMiWN5eKuqWLdiMYOGjglZ5uC+Hbz23JN892dPk9oivfr9M6XFVFWps58lxafYs2NTyLXS9XEx2qLpQS38rHR1vJZVqJH8P73j5el3vXz4hc2X+xw+3iB32/531lh32/4H8LzvhmEjUCOZi0zTzPdd93wGGOa7+dZxfB3OaCzLqvRNSf6LaZppqPifBrYC9wF/N03TRd3ky28lsB91l+ztwAZfWid8NxObZZqmr6rh56hO8lzTNONRgy8/9H32FvCSaZrfQ11zHem657OmaW4EYoBv+t77lS+2zagbht3re/9x3/a3AquAQzW+61LgtGVZ9Z079TJqOvYG0zQ14ATqLuEXxO48l57tXL4/3aMeJ7EqENbDkz0873sMyoK1DtNGGtWPqdidFzjDbV5lcFl3D6fPuLz9qVq/oEjdOOaRGz24rhql8j+qZeE6m5tHGxiGxqkSN2SbwVwXPljvcOc4A01TN4E4UaTuNJlf6LIrz2XjHpfpIzUenWJQXgHvrFRpnSiCbQcdHpls4PjScX1T16aOUI+B0DS1jP+7DOulMbKvTnI8PDzRYHe+y/y1kSvT3XkuPbJdvjtV5V3w3YwfmuipHgVasE7lnceAPflO9TWAK7Y4zLjSYHA3D0VnXN7+TK0/ZoBBQizV1ywGP/LpptEGnTPVmdP/mO5h2WabjXtDG7IffG5z0ygDQ1eN77mrbS73nd39YrdD6zSNaSMNXODEafXIC/8+W/qlzd3XeNA0NaK68HObojOh6fvz9ts3enBctYzrqmuMbh2jqh9dUyOe53rX4uB8A3XCZObS0NPBH6y3mTjU4OFJavTt4HF1c6maPt5oM2O0wbhBGkdOumzco77nFb10erZXjwgprzi/+GI96lFBHl015g8cdarvKPzuCptJVxhc2V/D0GDLQYdjp2vHF6lcXDvYoG1L1Tg4XSPvk+LVSGJcjBqzGN5b59n5amrvHWMN3l+jpj3OW6vuWOsCZyvd6rJ5ofNuzmqbR6eostWhtcbArupO7g9NVO99ssmuvt7yi91OxLLd0LIH4cv9yL6BXla4Y0fXA7FFsu+IS+tUNQ0W1CO23ltp17qhVqT6oFWqunOsi/rxWbnVDrkjr9+nXzlMG6H2jaapfeC/a33N4yKchpa7jXsdpo4weGSyB9tRvwX3XhfanOjdQXXEqmpU2cFlb/Iwg9NnqM6v7YfVZQjLv3IYP0Sv/n6nS11mhXlkV6R6saBYnfx5ZJIn4tSvxqqTI6XbOlU9Psi/b1dts6t/55oqJvUUCZX3FVUu76701jml82LHB+rEWGqSxoFjoYHdNErd8M5vz1PPRQ68nga9/gcyxgwjtlVLxu3/lN1PPMPhV2c3OF1Q9c9dvjorpH1y0mVXrsuGPQ7TRxl8d6qH8gqX2StC2yfh6rFwaQKMylGPcdI0dWmPP+/C/SbUl2F4uPOBn/Knx7+D4ziMvmYK7Tp2Y86bz9G5e18GDRvD2/98moqzZTz3u58AgUdSHcndz2vPPYmma7iOy8Sb7jvnznOwxmqL9u2oM7Cbju2A13Z5e/mleY2zI3fbbjDNbYLJ76ZpllqWldyI6XfmIj77uDH4bhS2AbjFsqwmOaX62GtVzfYIe/yeGJ6Y2TznzfzyTtUYefyN83w+TCN77K6YZhsbNO/4/NfXSXznR/bt+fs6xNecY4PmnXfQvONrzrEBLIjp1cSRhDepaifQvPftim1noi/YREb3TeKx15pn3j1+Twz4npLzdfJfL51ttm37mn7zQHyzzN/GmrYtGsA0zb7AHuCTpuo4CyGEEEIIIYQIaKxp23UKN+psmuaz1H580p8ty3r1PNI/gLord6O4kLGGY1nWNtTjpIK32R91F/FgFZZlXXEhtimEEEIIIYQQIrIm6TyHY1nWd5o6hvpqilgty/oKGHSxtyuEEEIIIYT4+muKy3UvNTJtWwghhBBCCCGEiEI6z0IIIYQQQgghRBTNZtq2EEIIIYQQQojG4cojqhtMRp6FEEIIIYQQQogopPMshBBCCCGEEEJEIZ1nIYQQQgghhBAiCrnmWQghhBBCCCEucY48qqrBZORZCCGEEEIIIYSIQjrPQgghhBBCCCFEFDJtWwghhBBCCCEuca5M224wGXkWQgghhBBCCCGikM6zEEIIIYQQQggRhUzbFkIIIYQQQohLnOPItO2GkpFnIYQQQgghhBAiCuk8CyGEEEIIIYQQUUjnWQghhBBCCCGEiEKTW5aLOkjhEEIIIYQQojatqQM4Vz94pvRr07Z/+rvJzTJ/5YZhIqLHXqtq6hAievyeGB5/o3nG99hdMQDNOr7mGhs07/j8+/bXs7xNHEl4v7hdVem/ne00cSTh/XSGzlP/sps6jLB+dqsBwBMzm+e+/eWdat8257L353nNs032/RtV+6u51yvNOb7mHBs077wDWBDTq4kjCW9S1U5eX97UUUR291XNf9+Kfz8ybVsIIYQQQgghhIhCRp6FEEIIIYQQ4hLnyqOqGkxGnoUQQgghhBBCiCik8yyEEEIIIYQQQkQhnWchhBBCCCGEECIKueZZCCGEEEIIIS5xjjyiuMFk5FkIIYQQQgghhIhCOs9CCCGEEEIIIUQUMm1bCCGEEEIIIS5x8qiqhpORZyGEEEIIIYQQIgrpPAshhBBCCCGEEFHItG0hhBBCCCGEuMTJtO2Gk5FnIYQQQgghhBAiCuk8CyGEEEIIIYQQUUjnWQghhBBCCCGEiEKueRZCCCGEEEKIS5xc8txwMvIshBBCCCGEEEJEIZ1nIYQQQgghhBAiCpm2Lc7JhKE6PdrpVNkwZ6WXIydrL5OVDtNHefAYsDvP4YPPHQASYuGWqwxaJGucLnWxltucrQysl52hcf8Eg9nLbbYdUvNKHrvLw7HT6vOiMy6zltrVy3fL0hg/1EDXYMMeh5VbnZA4DB2mjTTIztAoq3CZ/ZlN0Rn12egcncHddRwXFn1us/eIW2eanTM1rr/cwNAhv9Dl/TU2rgu92muMHWjguuC4LovWh8YQbPwQX955Yc5qL0cj5N3UER5iPCrv/OnFx8KMKw1aJGmcPqO+y9lKyEiFqSMMstI1lmxyWL1dLZ+RCjNGBw7vlsmwdLPD2h2B+C5m/g3tqTO8j056isb/vV1FeUVgO50yNcZfbqDrUFYROp+of2eNUTkGAJVelwVr7eryECzS/qmvSPnbKVPjtjEGp0sjr9stS+OGy3Q0DTbudVi1PXTDhg5Th+tkpWuUV8A7qwL5OKqvxqCuOq4Li75w2HdUrTusp8bgbirNDXsd1u1U7/fpoDGmv06rVHjlQzvs8VeX3F2fsWbBU7iOQ88hMxg45oGQz21vJctn/5SCvG3EJbZg7G1/JKVlO/L2rGT94j/i2FXoRgxDx/+Y7G7DAdi3eSFfLnsB17Xp0Otqho7/0bkFFaRrW7husPreX+5zWb2jdl7eeIVG25Ya5ZUwZ5VDUZmqW24aqZOVDpsPuHy4IbBenw4ao/pqaBrsyXdZurn+BaNblsYNQ3R0DTbucVi5rXY800YG9u3sFUH7NkftQ8eFxeud6mMEQNPg/vEGJeUuby1Tx8j0kTpZGRqOA3mFLgvWOlGn1zVG2buil4rbdeF4kcv7axxsB6aN0MlO17AdyD/psmBd9PiCHdrxGSvmPonjOPS9YgaXjXsw5HPbW8nHs37KidytxCe24Pq7/0hqenuKT+Yy6/8m0aJNFwAyOw7k6hmPA7B700K++OR5XMehc5+rGTE5etlrjHo4UrqZLWHSMIO4GA3Xhc+22Gw9qDItuE5sqvgAUhNhynCD1CQNXJi51FtdRvzrNWV8v7jDw3F/G6DM5a1ldkj++TWH9kCk3+MLZcBLT9Fm4tVUHi9k+eAbL2ja9bF3y3IWv/UkruMw6MpbGDUh9Bg+uOtzPvrXUxzL3clND/6RPpePB+Dooe18MPNXVJSXous6oyY9Qs7QiecdR2OUwUhtDUOH+643MAwNXYPthxyWbb6w+/Vik0dVNVzUkWfTNDubprklzPvfME0zO+j1D0zTTLzQAX6dmKb5K9M0z7vlaJrmP0zTnHEe6x0wTbPV+W63vnq008hI1fjLHC/zVttMvsIIu9zk4Qbvr7b5yxwvGaka3bNV42B0P519R13+MsfLvqMuV/YLFD9Ng+su09mbH3pQV9nw/Hwvz8/3hnScASYOM5i5xMuz87z066zTKi00jsHddc5Wujwz18ua7Q7XDlbxtkqDnM46f5vnZeYnXiYOM9A0FUOkNKeNNJj9mZfn5nspOuMyqKv6TvuOujy/wMsLC73MXW0zZXj4POmerZGeovHMXC/z1tpMGhZ+uUnDDOattXlmrpf0lKC8y9HZf9Tlr+972X/UZXSOyrvyCli03mH1ttDKvLAYXlio4nrxAy9VNuw4HFimru/aGPl3+ITDax97OV0aun/jYmDSUINZy1Tevr08dB+fKoV/fOTl+QVeln/lMDlC/kbaP/UVKX8BDh13q/OyJk2D8ZfrvLnM5rmFNv06qY5tsEFdNc5WwrPzbdbudLhmoEq7VSrkdNR5fqHNm8tsJgxRnaDWaTC4m84rH9q88IFNj2yNlskqrRNFLm9/ZnPw+Dl9PQAcx2b1vF9z/b0vctP357Fv8wJOHd8Tssyu9bOJjU/jlv9cTL9R97B+8e8BiE9syXV3P8f0773PVTP+l+Vv/xSAs2Wn+HzR7xn/rVe56fvzKS8tIH/v6nMPDpWXN1yu86/lDi8ucujbSauVlwN9efn8QofPd7qMHaj2s9eGT7c4fPJlaPlKiIVxAzXeXObw0iKHpHjo3Kb+8UwYqvPmUpu/zbfJ6Vx73w7upjrxf33fZs0Oh2sHB+3bTjrPzbd5c4nNhKFq3/pd0UujoDg01q8OuPxtns3zC2xiDBjcve4y3BhlLyVBdUpeXqzKnq5BTicVx5YDLn9boN73GOq715fj2Cx/7wkm3f8St/94Prs3LuDk0dCyt33tbOISUrnr/33IwKvuZfWCP1R/lpbRkVt/OIdbfzinuuN89swpVs//HVMf+ge3/3g+ZSUnyN1dd9lrrHo4UrpVXpizyua5+V7eWOLlhiEGcTFqG+HqxIsdH8D0UQartjn8bZ6XlxZ5OXM2sJ2sdI342MB+bor4vHbgt8zfcQ7OP7/m0B6I9Ht8oeT+813WTb6/UdKOxnFsPnjzCW7//ss8/MQCtq6bz4n80GM4LT2LG+/7X/oNmxzyfkxsPFO/+VsefmIBt//gZT7611OcLSs+rzgaqwxGamvYDvzzY5sXFnh5YYGXbtka7VqdW/tCXHoaMm37G0B20OsfAA3uPJumKaPhzVTvDhqb9qofhdwCl/hYjeSE0GWSEyAuRiO3QDUINu116NNR862vV6+/aa9D7w6B4ndFb53th9yQH+5oTpa4nC4Fx4GtBxx6tw8tzr3aa3y5T8Wx7ZBL17a+ONrrbD2gRlNOn1HptMvQaJehhU0zMU5VoCdLVLr7jrj06ai2VRXUn4r1aEQ6n9e7g8bm/eq750XJuzxf3m3e79C7g4q5VwedL/ep9b/c59DLl3dlFerMt13HicQubdX3Ch5NiPRdGyP/AI6eImT7fv276Gw/7FBcRvX3CZZb4FaPTOQWuKQm1v7Rqmv/xBhqVOX+8QYPTvTQq334H71I+RtNdjqcKnU5fcb3nQ85tbbRq73Gl759v+2wSxdfPvZqr7H1UCAfT5W6ZKdDq1SNvEIXrw2uqzrv/nJQUAyFJfUKrZaC3M2kpnckNb0DhieWrgMmcmj7kpBlDm1fQo/LpgLQOecG8veuwXVdMrL7kpiqep0t2vTA663A9lZScjKX1IxOJCSlq/zoNoIDWz48r/iy0+FUCdV5ue2QS492oXnZM1vjqwOqTG7PdemcqT6vsiG3QDW0g7VIVo0if7k6cAx6dahfw6ddBpwKLs8HnVrr9mqvsdlXbrYdcumS6T9eNbYeDNq3JS7tMtQ6KQnqROTGPaEH7Z6gE4d5heHLerDGKHsAugYeQ3UePAaUlvviCxo5z69HfMGOH9pMWkZH0jJU2es+aCL7t34Sssz+rZ/Qe8g0ALoNuIG83atx65g+UlSYS1qrTiQkq8Db9xzJ3s11l73GqocjpXuyJFAvlZbDmbOQFK9eh6sTL3Z8rdLU/vbPOqjyBo4h/wntjzcGDqqLHV9dauZfc2gP1Of3uCFOrlhP1cmixkk8ivz9m0lv3YmWrdUxnDN0Ers2hR7DLVq1J7N9bzQtNO8z2nYhPbMzACktMklMSaes5BynTfk0Vhmsq63hb+fpOhi6mqEh/r3Vt6NqmKb5EjASyANeB4YAM03TLAdeRXWkl5qmWWBZ1ljTNEuBl4DrgaPAbZZlnQiXuGmay4BNwGhglu/1H4FkoAD4hmVZR0zT/B7wMOAFtlmWdZtpmr8CugHdgVbA/1mW9ZJpmhrwf8AEVFH/H8uy/mWa5tXAr3zp9gO+AO6yLMs1TfM3wBRf+h9alvUj0zRbA88DHX3h/sCyrJV15NVA0zRX1zMWDXgGuA44DFT68mMc8D3Lsqb5Xl8HfNuyrOl1bNefl3cB3wNigbW+9Wzf/vgzMBkoB6ZalnUsWnrBUhI1issCtUZxmapgSssD76XWWkatB5CUEGiIlZar16Aak306aPzjQ5upI0PPInoMeHCigePCii0OOw6Hph0cS82zgamJGkW+WFwXzlZBQhykJFLduQcoKVPvRUqzrEI1MLLSNY6cdOnbSQupWHt30LhmkEFSPLy51OZb42sfVikJGkVngmI/45KSEJp3KQk18u6Meg8gOT4075Lja20ion6ddLYcCK3tUxIvXv7VJSNFTYu69zqDWI/G2h12xGUHd9NDOhh+de2fK/vr7D/m8P4al7gYeGCCh31H1Eh8sLryt31rjYcmeSgpq71tVd6Dv7M6MRH866r2q/rbdeFspRoRTUlQneTgdVMTNU4UuYwdoJMQqzqF3bN18k+6NPQX+0zxcZLS2la/TkrN5MThzTWWOUZSWhYAuuEhNj6FirLTxCe1rF7mwNYPycjug+GJJTWjI0UF+yk5lUdSaiYHt3+C4606r/hSEqC4PLRcZWfUWCao3LouVFSpvCyvJKxTJZCeAmmJUFwOPdtpGDrUJy9TEjSKou3bRK26AR9yjCQEGm7+ddWx7HLDEJ2PNzrEesIfG7oGA7roLF4f+ViAxil7eYUua3Y4fH+KQZWtOlX+jlVwfP076yzeUHd8wc4UHSO5RVb16+QWbTl28MsayxyvXkY3PMQmpHC2TM3XLT6Zi/XH6cTGJzFs/A/I7jqEtFYdOX1iP8Unc0lOa8v+LR9jRyl7jVUP1yfd7AxV9k7WcfLrYseXmqjKhOm7nGr/UYePNzq4LgzrqbMr161Or6nyz2PAAxMMHAdWbHXYmRv+2G0u7YFLVcnpY6SmB34/Ulpmkr9/cx1rhJe3fzO2t4qWrTtGXziMi9GWqtnW0DR4cIKH9BT4fJcTUnd+HdV1UlLUT31HnnsAz1qWlQOcRv06rwfutCxrkGVZfwbygbGWZY31rZMErPet8ynwWJRtxFqWNQT4C6pDOcOyrMuBvwNP+pb5L2CwZVkDUJ1ovwHAOGAE8EvfdPKbgEHAQOBa4Hemafp/vQejRsr7Al2BUaZpZgDTgRxf+v/jW/bPwJ8syxoK3Ay8HOV7nEss04FevjjuQZ2cAFgK9PZ13AHu8+VDnUzT7APcCoyyLGsQYAN3+j5OAtZYljUQWA48ED6Vi8h3/E4YavDRBidsc/ZP73h5caHNO5/ZjB9qVE9fvdjeWWFzwxCd+8cbVFQRcj3tjsMuz87z8tanNmMHXpx78NW37tN1dcZ926HmeY2OrqtGyJtLbN5Y4uWq/uGnYHXO1BjcXefjCA32SPunW5bG6ByDhyZ6+MZ16jr8tKTocfnXP3LS5en31HStdTsvTh4WFMOq7Q53jjW442qDo6fcc7p+uzGdOrab9Yv/wKipaupsXEIaI6c8xtK3fsiCl+4iuUU7ND38PmwKZ6vU9bzTRurcPU6nqMxt0sd09GinceYsdV6rPnGYzsHjLofCnmpuXPEx0LO9xjPzbJ6eYxPrUdcCBpswROfQCZfDFym+pNQ23PPzJZg/fI+RU/6Lj2b+iMqzpcQnpjHmpsf48PUf8t7f7iSlZTv0i1z26ntcJieo6dFzV9f/hMOFEC0+XYeObTQ+3GDz0gdeWiRrDOqqRvL6dtJY28h1Xn3y7+n3vLz0gc07K23GD2m6NkCwutoDIrKS08eZ+8qPufEb/4umN4/7Fdfcd+HaGq6rLh3447tesjM0Wte4JED8+6nvyPN+y7I2+f7+Auhcj3Uc4F++v98A3o2yvH/ZXqgR4Y9M0wQwgCO+zzajRrvnAHOC1p1rWVY5UG6a5lJgGL5RbMuybOCYaZqfAkOBYmCdZVm5AKZpbvJ9nzXAWeAV0zTnA/N9aV8L9PXFApBqmmayZVmRbiF0LrFcFfR+vmmaSwB8o+CvA3eZpvkqqiN+T5T8A7gGuBz43BdvAuC/OrIy6Dt9gRrtrsU0zQeBBwH6TJ7JsF46l/VQlZyaqhdYtuYoMwRGowPLUD1id6ZcNSJKff/7p2hnZ2jMuEo1ehLjoEc7A8e12XHYpcR3hvBUKRw46pKVrnHKd41YzVhKgs4S+2NJS9QoKXPRNNUwLK9QZ5bTEgMjNCmJVK8bKc3cApd/fKgq065ZGhmptX8tDx13aZkc+O5De+pc1j2Qd2lJVDc4U5M0SspD0ygpr5F3SVQvU3q2Rt7VmN4cSY9sdXa85nT4krKLm3+RFJdBeYVLla1GWQ8dd8lI1XhooqqaZi71khgHNw5X155FGmGMvH80rOVeCmtcXjVlhEFWS7UP3lxqR8zfyqCBrHCj3mHLe9j9CiXl6gx2vG+k1P9+8Lr+42nTPpdN+9T3GTtAr3WcnY+k1DacKTpa/fpM8TES0zJrLJPJmaIjJKW1xbG9VJ4tIS6xhVq+6CifzPwuV834DakZgVGDjn3G0rGPOme6Y52Ffp6NopJy4SKegwAAIABJREFUSE2oUa7KayxTRkhexsVEHnX225MPe/JVJ2BQVw2nnr3nknLXV86VsPu2TB3X1fvWf4yUu6QGnaTxr9uzvU6v9ho9sg08hop/2kidOatUfFf110iMg/lro3daGqPsdWmrcTpomvuOwy7tWwWmyl/VTyMpHqzPzq1TlZSWSenpI9WvS08fJalm2UtrQ+npIyS38JW98hLiE1ugaRqGJxaANu37kZbRgdMn9tOmQ38654yjc844ALau+VfYEzcDOgf+Li2nUerhkvLI9XtsDNwx1sOSTXbIbAS/2Jimi0/XNY6ecqtviLjzsEP7VhqlZyE9ReN7Uz3V8f337R4272uc37G68s9fB5wuhQPHXNoGtQGCNbf2wKUmpUUmxScDvx8lp46R0iKzjjVCVZSX8q9nHmLs9P+gfbdB57Tti9WWatOi7rZGRZUqg92zdU4UNc8BCXFx1LeVE9xUtzm/u3RHq138V69owFbfiPYgy7L6W5Z1ve+zScCzwGWoDqI/jpppR9tWre9jWZYX1dGdjZravMj3uQ4MD4qnXR0d5/OJJZJXgbuA24G3ffFFowH/DIq1l2VZv/J9VmVZlj+WiPvQsqwXLcsa4psFwLqdTvUNu7YfchjUTRWZ9q00zlaFTukCVSFVVKkGF8Cgbnr1VOuduYH11fuq8nn6PS9Pv6v+bTuo7nK447BLfCy+KZaqU92xjZrS6peRotEiSZ09z+msszM3tDLblesy0Hcjj74dNfYfC8SR01nH0KFFkkonr9Alr9CNmGZinErT0GFUX531u9T7wWfB26aDEdR2+3yXU32jkx25DgO6qC/TrpVGRWXkvPNPNxvQJZB3u3IdBnZV6w/sqrPzcP0q7n6ddbYcqL1sXd+1MfIvkp2HHTq01qqvr/R/d3++6RrcOsbDeyvtOqc7Rto/e484DOsVqOba+mYfv7/a5oWFXt703YQuUv4mBU3pys6oPTUv/6RqZFZ/545qqmNIPua5DPTt+74dNA4c8+9Tl5yOgXxMT9HIPxn6fVIT1WUBWw42vHHWql1/igoPUnIyF9tbyb7NC+nYe2zIMh36jGX3hrkAHNi6mKyuw9E0jYryYj587WGG3PBDMjtdFrJOeWkhABXlRexYO4ueQ875noeAysuWKarzoOuqzO3OC/3eu/Pd6pHQPu01Dh6Lni/+vIyPgcu7B657jCavsMa+7VR73+7McxngKzfBx8iuXJecTqH7Nq8QlmxyePo9m7/MtXlnhcP+Y251x3lwN41uWTrvrqzfsd0YZa+oTNXdHl891rmtRoGvzh3UVaNrW513V517o7FNh/4UFRykuFCVvT2bFtLF1+n165wzjh3r1TnxvZsX0667KnvlpSdxHHWcFhUepqjgIKkZHQAoK1Fl72xZEVtWzaLvFbXL3uYDgb8bqx7emeuGTVfX4darDL7c57D9UPhyF3yC7mLHl1+orhn1HyOd2+qcKILdeS5/eMfLn+d4+d3bqunx5KzG+x2LFF9wGyAhDjq0Dm0DBGsO7YFLWXbn/pw8foBTJw5jeyvZ+vkCeg4cF31F1J303/7bd+g/Ymr1HbjPxcVoS6Umhm9rJMZRfZM/j6FOltS82aP499OQm3OVAClhXhf4XuvADOAt4A5gRT3T3Qm0Nk1zhGVZq03TjAF6AtuBDpZlLTVNcwVwG+qaaICppmn+L2pq8tWo6d0G8JBpmv8E0lGjvD8GeofbqGmayUCiZVkLTdNcCezzffQh8F3gd77lBgWNwodzLrF4gt5vA4wF3gSwLCvfNM184Oeo0e/6+ASYa5rmnyzLOm6aZjqQYlnWwXquX6fdeS4927l8f7qn+i6ifg9P9vD8fPUju2Ctw7SRRvUjAvwN4M+2OJhXGVzW3cPpMy5vf1r3FLbWaRo3DlePgdI0WLHF5kTQvTIWfm5z1zUeNE3dgOxEEVw9QF0fuivXZcMeh+mjDL471UN5hcvsFWp7J4pg20GHb9/owXFVOv6pO+HSBBiVox6NoGmwfpdT3Qjt21FnQFcdx4EqWz324Js31D6sdue59Mh2+e5UlXfB0/cemuipvpPzgnUq7zyGGi3zj3au2OIw40qDwd08FJ1Rd1wG1bl7cIKHuBh1lmZ4b51n53uprFI3y+qapTF/be2K3nUvbv4N66Uzqq9OcgI8MsnD7nyXeWtsCoph7xGXRyZ5cFGPAxk/JHAGYswAg4RYqu+o6bguL32g4rhjrMH7a2xKyyPvn+VfOYwfovPwJBXT6VKXWctql7tI+du3o86QnuoRJl5v+HxctN7hjqsN3+OVHE4Uw5j+OkdOuuzKc9m412XaCI3vTDYor4R3V/rysRi2HXJ4eKIq4x+sd6rz8ZbRBglx6kY1H6x3qPA1sHu11xh/ubppzW1jDI6dcnlzWf0abrrhYcSNP2fxP+7HdR16XHYTLTN7sOHjv9CqXT869hlHz8tnsHz2T3n7DzcQl5DG1bepOx5vXzOTksJDbFryHJuWPAfADfe9TEJyBmsWPMWpIzsBGDTuEdJadalXPOHy8sMNDreNUY+G+nKfS0GxGu08ctJld74akZ8yXOfhieou0nNWB777tyfrxHlUg7ZnO423PnUoKIbrBmtktlCNqBVbXU7WdeqzRjwfrHe4c5xR+xgp9O3bPS7TR2o8OsVQj4JaGXqMPDJZ3a8heN9GMmmYzukz8M3rVVnfcdhh+ZbIKzVG2csvhO2HXB4Yr64xPXrKZcNeFcOkoSq++64LxPfZ1vo1InXDw5XTf8G8l76F6zr0Hnoz6W17sG7RX2jdoR9dcsbRZ9gMPpn1E9743+uJT0zjurv+CED+vs9Zt/gZdMODpumMuflXxPtmQ6yY+ySF+arsDbnu27RoXXfZa6x6OFK6OZ00OmVqJMapx4KBerTOsVOhdWJTxee68NEXNvdcq36zjpx0+WJP5PrkYsfXKlVj8hUGLmpkYOVWm4Iavyl+hSVuk7cHwv0eX0iDXv8DGWOGEduqJeP2f8ruJ57h8KuzL+g2ItEND+Pv+CWznr4fx7UZNOpmWrfrwbK5fya7Uz96DrqG/P2beftvj3K2rJjdm5fy6dxnePiJBWxb/wGHdq+nvPQ0m1e+B8CN9/2Gth37nHMcjVUGI7U1khNg2kgPuqbaoVsPOrVO6n7d1Hf2lYhMi3bhuGmanYH5lmX1873+EarT+hXwFOrmUyOA+4FHgfygG4a9iLph2HHg1ig3DPuRZVnrfa8Hoa59TkN1MJ8G/oG6FjgNVY++YVnWb3w3DOuKui67vjcM+5FlWZN92/or6vrtxcBcIN6X/u8ty/qn7xFQzwJ9fLEstywr+Hrr4O9xrrEE3zDsEFAF/N2yrNm+9G5D3aBseKT941vuADDEsqwC0zRvBf4f6uRFFfAdy7LWmKZZallWsm/5GcBky7K+UVe6j71W1WyPsMfvieHxN87vxkSN7bG71GnK5hxfc40Nmnd8/n3761n1mQhy8f3idtUI/u3s5jkS8tMZOk/96+Je91lfP7tVNZqemNk89+0v71T7tjmXvT/Pa54/Gd+/UZ08ae71SnOOrznHBs077wAWxPRq4kjCm1S1k9eXN3UUkd19VbPft1+7u8U98NTX545nL/0szJS/ZiDqyLNlWQdQ1yD7X/8+6ON3gv5+xvcveN0f1icIy7KurvF6E2p0tqbREZLYbFlWyDXBvinKP/b9C35/GbAs6PWjQR8PCxNbAeomXFEFTZGu+X6kWFzUCYdIRqPuWB5tu52D/v4XgevHg5dJDvp7Nmp6uhBCCCGEEEKIepBnKjdTpml+gboO/D+bOhYhhBBCCCHE15s8qqrhGq3zHDzS6Wea5rPAqBpv/9myrFcbsJ1fne+658s0zfuA79d4e6VlWd+5UNvwPaar5nbXAnE13r7bsqyvLtR2hRBCCCGEEELUdlFHni9k57Ip+Tr7593hb8B2r7jY2xRCCCGEEEIIUf9HVQkhhBBCCCGEEP+25JpnIYQQQgghhLjEufKoqgaTkWchhBBCCCGEECIK6TwLIYQQQgghhBBRyLRtIYQQQgghhLjEybTthpORZyGEEEIIIYQQIgrpPAshhBBCCCGEEFHItG0hhBBCCCGEuMQ5rkzbbigZeRZCCCGEEEIIIaKQzrMQQgghhBBCCBGFdJ6FEEIIIYQQQogo5JpnIYQQQgghhLjEyaOqGk5GnoUQQgghhBBCiCik8yyEEEIIIYQQQkQh07aFEEIIIYQQ4hLnyqOqGkyTTBR1kMIhhBBCCCFEbVpTB3Cu7vnFka9N2/61X2c1y/yVkWcR0X//vaKpQ4joyW/G8etZ3qYOI6xf3K4Oq8ffqGriSMJ77K6YZhsbNO/4HrsrBoAnZjbPsvfLO1XZa87HRnOODZr3cQvNu+w9+Zbd1GGE9d+3GUDz37ePvdY843v8nphmHRvAim1nmjiS8Eb3TQLg9eVNHEgEd18FC2J6NXUYEU2q2tnsy5749yPXPAshhBBCCCGEEFHIyLMQQgghhBBCXOIceVRVg8nIsxBCCCGEEEIIEYV0noUQQgghhBBCiChk2rYQQgghhBBCXOJcmbbdYDLyLIQQQgghhBBCRCGdZyGEEEIIIYQQIgqZti2EEEIIIYQQlzjXlWnbDSUjz0IIIYQQQgghRBTSeRZCCCGEEEIIIaKQzrMQQgghhBBCCBGFXPMshBBCCCGEEJc413GaOoSvPRl5FkIIIYQQQgghopDOsxBCCCGEEEIIEYVM2xZCCCGEEEKIS5zjyKOqGkpGnoUQQgghhBBCiCik8yyEEEIIIYQQQkQhnWchhBBCCCGEECIKueZZnLdJVxj06mBQ5XV55zMv+YW1r6PIztC4+UoPMR6NnYdtFqy1ARg/1KB3Bx3bgZMlav2zlZAQB3eMi6FdK42Nux3mrfGeV2zdsjRuuExH02DjXodV20NjM3SYOlwnK12jvALeWWVTdAYSYmHGaJ3sdI0v97ss+kLd0t9jwIxROi1TNFwXduW5LPny3G73P36ITo92OlVemLPay9GTtZfJSoepIzzEeGB3nsOi9Wob8bEw40qDFkkap8+4zP7M5mwlZKTC1BEGWekaSzY5rN4eGpOmwQMTPJSUucxaZtfKo/FDDXQNNuxxWLk1dF1Dh2kjDbIzNMoq1DaLzqjPRufoDO6u47iw6HObvUfcOtOcOsKgU6ZGRaVaf85qL8dOwci+Ov07q3N4ug6tUhs3vyKlm9kSJg0ziItR+/ezLTZbD6rvNGW4QVaGhlbHvg3O0xuG6OgabNzjsHJb7XI3bWSg3M1eEcjTUTkag7upPF283mHvERdDh29cZ2AYoGuw/ZDLp19dmMdMXOhjpLnGNHaATv/OGgmx8NvZds1NRo3nQh8jU4Yb9GyvceYsPDc/UL9ltlB1aqxHldl3V9pUVtUjvy5geUtNhGkjdJIS1HGwYY/Dup0qzTH9dQZ31yg7q9Zf8qXDnvz6XzvXtS1c79u3m/a5rA6zb6cM12jbUqO8Et5b5VTv25tG6WSnw+b9Los3qPViPXDPNYHz/ymJsOWAy0cb6x/Txa6Tvz/NQ0UVuC44rstLH9RdHicM9cVnw5yVXo5EiG/6KA8eQ8X3wedqewmxcMtVBi2SNU6XuljLA3UgqN/m+ycYzF5us+2QyrPH7vJw7LT6vOiMy6ylFz++Xh00xg0yVB45LovWOxw6ruJLS4IpIwzSEjVcYOYn594++GrDSma98ntcx+bKa6cz8eb7Qj5fPPcNPvv4PQzDIDm1Jfc9+hit2mQDcP/NQ2jfsTsA6a3b8r2fPX3O26/L3i3LWfzWk7iOw6Arb2HUhAdDPj+463M++tdTHMvdyU0P/pE+l48H4Oih7Xww81dUlJei6zqjJj1CztCJFzS2aAa89BRtJl5N5fFClg++sVG31RjlrnOmxu1jDU6VqvW3H3L4dLNDaiLcNNogKV61AL7Y5bBmx9f7UU+uK9c8N9TXqvNsmmZnYL5lWf1qvP8N4EPLsvJ9r38AvGhZVtkF2u7VQKVlWasuxHIXi2maB4AhlmUVXOi0e7bXaZWm88fZlXRorTFlpIfn59Vu6U0d6WHOSi+HT7jce30MPdu77Mp12JPn8OF6G8eFG4YYjBlgsHi9jdeGjzd4yWyhkdny/CZGaBqMv1xn5lKb4nK4/3qDXXk2BcWBZQZ11ThbCc/Ot8npqHHNQJ13Vzl4bVi22aF1C402aaHdpdU7XA4ed9B1uHusQbcsrbpBHE33bI30FI1n5npp10pj0jCDVxbVbphMGmYwb61NXoHLHWMNumdr7Ml3GZ2js/+oy8qtNqNydEbn6Hy80aG8Ahatd+jdPnzX7oreOgVFLnExtfNo4jCD1z/xUlymOtg7cx0KigLLDO6uc7bS5Zm5NjmdNK4dbPDOCptWaZDTWedv87ykJMDd13r46/uqEVNXmh9tsNl+KDS/Vm1zWLVN/RD1bKcxvI9Ol7Zao+VXpHSrvDBnlc3JEkhOgAcnetiT76WiChZ9EejEPHZXTK0YgvN0wlCdN/4/e/cdH1WV/3/8dWcmCUlIAgkdQofQpIqiKCiWtWH32FhdXeuq62/Xdft3Xd11d/1+19517WX1iAUVsFesFAGR3knooSUkpMy9vz/OncxM2iSQ5N7g5/l45JHMzJ0779w295R77kdh9pTAFScFWZYfv92N6mcKBg+8GVmmAV6dZdMhE4b2CvDw22EyUmHKcUEefCtM2IZnPzT5AhZcdmKQlRuhoLDOGA3SXPuIHzMtL7CZvRyuOy3Y6DxNvY84DsxfbfPtcoezjoz/+p18RJD359qs2+owsp/F+CEBPq6ngq45tjfbhvfm2WzeaQqnV54cZPWm6Dy/WWrXKPQ2dFmedGiAFz+22VMKl58QYEWBU+u6fXi6zZCeFpNGWLz+pUNlGD793qZTlkXHrOj05ZXwn3ejy+fyEwMszW94Nq+Oyc98UElpWeJ8A7pb5GRa3PdGJT06WJx2eLDWwvZp44K8+VWY/O0OU46LyTcswOrNDrMWhTlqWICjhwV4f55ZXpYFJ4wOsKpa5UdFGB55u2EF0ubKt2aTw8MbTIbO7eC8iSEemGYenzU+yGff26ze5JAcMpUQjWGHw7zw2B3c9NeHaJ/Tmb/9dgojD5tIt9y+VdP06pvHMf9+npSUVD5+5xWmPnsv1/zmDgCSk1P4690vNe5DG5rNDjPzxdu4+FdPkdm+M0/cfi4DR0yiY7f+VdNkZXdl8mX/5Ot3n4x7b1JyG864/A6yO/emaNcWnvj7OfQbehRt0jKrf0yzyX/mNdY+9Dwjn7yjWT+nOfeLdVsdXvwofl6mcjHMph3mmHj1aSFWbbLZtrvGR4ofkYOl2/bPgG4xj/8fkNaE8z8GOLIJp2v1BvcM8N1Kc5DZsM2hTTJkpMZPk5EKKUnmdYDvVoYZ3NNscis3OkQG/NuwzSEz3ZxoVFTCui0OFY1rIIrTLRt2Fjvs2gu2DT+st8mrdiKT18NiwRpzwFy8waFPF/fzw7BhO1RW+/zKsDmwgpnnpp2mlaahBuVaLHQ/r2C7Q5tki7bVllfbVEhJsijYbj5n4RqbQbkmV15ugAWrzfsXrLbJyzXLsaQMNhY6hGs5ichIgwHdLOatrHkC3j3HYkeRw65idxmttRnUI/5wkNfDYsFqM+PF6x36ustoUI8AP6y1Cduwa6/pOdA9x2rQPOszrHeARWvtZl1edc13R5H5ASguhb37IL2NeZyo9S+iew7sjP3/19nk5dbc7ha6uRavd+jTOZLX4od10WW6s8ihe455T4V7LhsImJ+m0Bz7iF8zFRRC8b7G52mOfQRg/Van1sJTToZVdYxZvclhcG79K7s5trfifbB5p3lveSVs3+2QmXbgFSTdss3+FVm3i9c7DOweP98B3S0WrjH//5INDr07R9dt/naorKexJzvD7K8btjU8kxfH5MYYlGsxf5WZf36CfPluvvmrbAb3dLfB3EDV++evshkUsz0dPijAkvUOe/djv2jufOUxZfekkAXucuyYZY5/q90K6/JKGn2esHrFIjp17UHHLj0IJSVx2FE/4btvP4n/vw4ZS0qK+Uf6DjyEnYVbG/ch+2njmoVkd+xF+465BEPJDB17Ksvnfxg3TbsOPejcYxCWFX9syOnSh+zOvQHIaNeZtIxsSopqaY5tRjtmzaFiR/OXKJtzv6hNcSlVLduRY2JGExwTRevWqlqeXUGl1OOYQmoB8BxwKPCCUqoUeApTkP5YKbVda32sUqoYeBw4EdgMXKC1rvVrVin1S+AaoBJYDPzefRxWSk0BbgDaAX8GkoFC4GIgtZbpfo5pKZ/qzrtYa91WKdUVeBnIxKyDa7XWn9eR5yTgH0AQ2K61Pk4plQ08CfQFSoCrtNYLlVI5wH+B7sBXEO1p6mb6pZv5G+AXWuv9Pv3NTDPduiL27IXMNIuiUidmGovdMW3/u/fWXuAcMyBQdRLTFDLTLPbEfO6eEtwT12i2jNToNI6D6TKeDKXlJJSSZFpJv13W8MwZqVa15eWQkWpRXFo9U/wyzUg1q7BtG3MQB/O7bZvEn3nSmCAffBcmOanmgT4jjWrLyKF7h/jpzPozeRwH9lWYbvUZaVR9KQEUlZjnzHzqnuekkUEmHgJrNtt88J05cY8IBU1L0IzZDpPHNd/yash8u+VYBAPRwjSYroIDutX/hZmRGr+917rdpVlV3Wbjlmlq9AQ98l7zvzim6/1JQbIzYPZy54BbnaH595GDIVNz7SN12bbbIa+HxbJ8hyG9AmSmJ8jXTNtbRFY6dMm2yN8e3VHHDgwwvA9s3OHw/jw7rhtw/VmhKHZfLYXu2TWniV23ZRUNX7dDelpVXY8byotjsgP89LgQjgNzV4SZt7LuzBlp1T67xFRkFFf7jo2fhqoT+/TU+HzpqZH/CQbnWjz9XpgzjozvjREKwlWnBLEdmLXIZumGls8HpoB0/Ogg6W3ghQ/NaUpOpumZcP7EIO0zLFZvsqtaDBtq145tZHfoUvW4fU4n1ixfVOf0sz54g2Gjx1c9rigv57bfXEwwGOTksy9j9OHHNurz61O0awuZ2dFsGe07s3HNwkbPp2DNQsKVFbTv2LPJsvlJc253uR0trj0tRFGpw7tzwjVal9u5x8TYY2dr5Mitqg5Ya2x5HgA8qLUeCuzCfB/NAS7WWo/UWt8LbASO1VpHjmzpwBz3PZ8Ct9Qz/98Do7TWw4FrtNZrgUeAu935fw7MAsZprUcBLwG/rWO6ulwEvKu1HgmMAObXNpFSqiOm0H+O1noEcJ770q3Ad27GPwLPus/fAsxy/8/XgZ7ufAYD5wPj3c8MYwr8njtmhPmiXrCqdVxDYllw9pEBvl1us2uvdzkSdVcb0N1i7z6n1muBvPDh/DAPvlnJ4zMraZNsMX5ozRa89ducBp+MN1ZDu/e1TTVdA6d9FV+v9OZXYe56bf+uvz9QjgOPzQxz9+thuucQ13VVHDymfRVm7MAAV54cIiVEXOVSS0sKwXlHB3l3rl3VEjhnhc39b4Z5dEaY4lLT7dcvhvS0qsYo8EpDjjFPvVvJYzMqeeGjSsbmBenZqQVbsNx8J48N8v48m9ri3v1qJY/NCPPq52FOGhukfduWixcbaOkGhwemVfLSx2EmjXLHxLCgVyeL9+aGeWx6Je3bWozq13zL76tPprN21WJOOvOSquf+97Hp/OXfL3DVr/7BS0/8m62bNjTb5++Pol1bmfbEzUz+2T+xmqqb0sHO3e427XC4+9VKHn67km+W2lx4bHzbYnIIzj8mxDuzw5Q1sDeaOHi1xpbnNVrrSGFzLtC7Ae+xMS29AM8Dr9Uz7UJMK/YbwBt1TNMDeNltQU4G1jQgQ6zZwJNKqSTgjZj/p7pxwGda6zUAWutIUego4Bz3uY+UUjlKqUxgAnC2+/x0pZTbAY/jgDHAbKUUmFbyWvsiKaWuAq4CGHDSc3GvHT44wNiBpqY6f7tDVnq0pSIznbiaPjCPs2JaW7LS41uWRvUPkJcb4MmZTXskitRERmSmEdciDuaxed4UiNs0sIXjtMMC7CiiahCd+owdGGB0f/MFtrHQISs92q0wM92qI1NM7vRo7uJ9pmBXXGp+701wzVzPjhZ5PcygGqGgaS0/a3yQ178whcKiEuJ6AWSmWRRVGyHArD+LohLT+tkmCUrLzHuz0qLrPiONqvfWNc9ITW/YNtd8Hjk4/ot9aK8AJWUOV58Sqpq+OZZXUWnd6yE5CS46NsRH88O11iwnOjkuKnXc5RJdFjUyl5jPr9ruIsu01IlraaztvWUVsHaLQ/9uFtt2H1hBoTn3kYMlU3PtI3Up3APPu9fbZWeYCrB68zXT9hawQB1tLqGIbXmM7eI7b6XNhcc0/BryotJIy4/7fZFqnqs+Tey6TUlq2Lrt1M5kjnQ3r4+Xx+TI/wima/fSDTbdc6yqwbAADssLMHpANF/17a+279ga+4w7zd7Savnc9dctx+LcCWbdpaXAgO5BbCfM0g1OVb6dxbB2s0PXbIudxS2bL9a6rQ7t21qkpZjWw807nOiAThtscjs0rvDcLrsjO7Zvrnq8s3Ar7XI61Zhu8YJvmD71CX779/+QlJRc9Xx7d9qOXXqQN+xQ1q9ZRqeuuY3KUJeMdp3ZsyOarWjnFjLadW7w+8tKi3n5/qs59qxf0aPfyCbJ5Bctsd3FFohXFDicerjZP0rKzPHl/GOCLFxt1xi3Rfw4tcaqqdivqDD7VwFQ39Z/KvAgMBpT2Kxt/vcDD2itDwGuBurqsFWJu4yVUgFMQRut9WeYgm4B8LRS6pI63t9ULOAZt0V8pNY6T2v919om1Fo/prU+VGt9aPXXvlli88C0Ch6YVsGSdTaj+psv4NyOZhTl2k6GyirM6wCj+gdZst40pwzobjHhkCDPfVBxQNc312bjDsjOsGiXbq4cC4xLAAAgAElEQVSRGtozwPJqA8ksL3AY0cds/kNyLdZuSXxAPOaQAClJ8G4Du4rNXm7z6IxKHp1RydJ8m+Hu53XvYFFW7lQVKCOKS6GsIto1dHifQNXJ6/J8mxF9zftH9A2wbEP9GT6cb3P365Xc+0YlU2eFWbPZqSo4AxQUOuTELqPeAZblx89zeb7DiL4my5CeFmvcZbQs32Zo7wDBgOnGlJNhUVDo1DvP2GuSBvWw2LorurxTksxIlzO/NcsLaLbltSzfqXW+gQCcPyHIglq+HBva+lJQWG2761Vzu1tW4DDczRW7TJfnOwztFV2m2RkWBYXmyzsy2FsoCH27BuIGWdpfzbWPHEyZmmMfqU9aSvTvCYcEmbOi/n28ObY3gMnjAmzbA18vjZ9XbLfkQbnx+3AiZt2aCrFAwGRZXhD//hUFDsP7mGU5uBHrdmgjumx7eUxOCprWq8jf/brWXIbfLrN55O1KHnm7kiXrbUb2M/Pv0cFiX0Xd+Xq4+Ub2i+Zblh99v3ne5Lvn9Uruec38LF7nMP0bU3Buk2xGPAezLfbsVLOSriXyZWdE39812xz3SsrM/tgm2araT/p2CTR60KY+A4ayZdMGtm0poLKigm9nvcvIsRPjplm3einPPnw7N/zxHjLbRa8t2Fu8h4oKU5tTtGcnK5fOjxto7EB1630IO7auZee2DYQry/lh9nQGjpjUoPeGK8t55aHrOOSIM6pG4D6YtMR2F3t8655jYVlmuwM448gg23Y5Ne5m0lo5ttNqfvyqNbY816YIyKjlcWSU6QBwLqaL9UWYbtc1uAXcXK31x0qpWcAFQFt3frHDFmZhCr4Al1b73Njp1mJafDVwOpDkfk4vIF9r/bhSKgVTUH+Wmr4GHlJK9dFar1FKZbutz59jul3/zR3he7vWeo9S6jP3//u7UupkoL07nw+BaUqpu7XWW91rpjO01utqWw4NsSzfZmBugF+fm0xFpcNrn0e7tF5/RhIPTDPVeG9+Wck5E0KEghYr8m2Wuyefk49IIhiAy39iSgYbtjlM+9LM4zfnJZPifpEP7pXMU+9WsK0RJ2qOY0Y7veiYIJZlBnPZtsfcamXTDoflBQ7frXI48wiL604LUloOr8UULG+YHCQlyXx+Xo8gL3xsuukcPcyMXH3lSabSYPZym/mrG5ZrRYHDgG4ON5wRoqKSuG7BV58Sqio0Tv/W5swjg4SCsHJj9FYwsxbZnHt0kFH9Quze6/DK5+b96W3gqpNDpCSZGqFxgwI8+HZlwkGuHAdmzA4z5biQuXXMKjN65DHDA2zc4bA832HeSpuzxge54YwQpWUOU2eZz9y2Gxavs/nF5BC2O59Iq2xt8wQ4e3yQtBTzhbR5p8Pb30T//0G5ZtTy2EqU5lpedc13aC+LXp0t0lIsRronxJHbaZ15pLmFlZWgkcNxYOYcm4snBWsu00J3u1vpcNaRFtefHjS3Wvoifplee5q5jGHmHBvHMZUOZxxhbpVkWWaaFQUH/oXSHPvIgRbqmyvTcSMDDOtlkRSCG88I8t0qh88WJT4Jaq595OyjgvTubAoBvzorxCcLw3y3yuGQ3gHG5pltb8l6m/mr6l/PzbG95XY0BcEtOx2uOtkc5yK3pDp+dIDO7c0ATrv2Okz/puEnko4D7861uXBigEAAFqw2I21PGGaxaYfDio3m9lVnjAtw7anm2tbXv4zO/7rJAVJCZt0O7GHx30/squ1tcE+Llz9t/EltSx+T01Lg/InmlCtgwaK1dr13a1hR4DCwu8ONZ4Wq7gYQcc1poapRsad/Y/JFbqUVOT58vshGTQgyun+IXXsdXvm0/lrqjlkWk8eZW0RZFsxaVPOaz5bIN6RngBH9zG0sK8MOr3xmnjfbUJhLTwxhYVog566wOfXwhveACAZDXHzl77j71uuwbZujjjud7j378caLD9O7/xBGHjaRV565h7J9JTz8f78Forek2pS/hmcfvh0rYOHYDqecfVmTFp4DwRAnXfQX/nvPFdhOmJHjz6Fj9wF8Mu1euvUaxsCRx7FxzUJeeeh69pXsYcXCj/l02v1cc9t0Fs+ZyfoVcygt3sXCL14HYPJl/6JLz8FNli+Rkc/dSc7Ew0ju0J5Jaz5lxW33s+GpqU3+Oc223fUyx1/bhoqww1R3u+vZyWJkvwCbdzpcc5o5Pn/4XbhJvodF62W1pvt9Vb9VlVLqN5jC7feYQbVKgSOAK4DrgY0xA4Y9hhkwbCtwfm0DhrndqD/GFI4t4Hmt9b+UUgOBqZju3zcA2cDdwE7gI2Cs1vqYWqZbDkzDdJN+B7jOHTDsUuBmoAIoBi6JdM2uJdPJ7v8WALZqrU9o4IBhX7r/7xit9Xal1PnAH9z5VLhZvq5vef/pyTLfbhy3X57C3/7rzTWoifzPheYE6dbn/XlhzC1TknybDfydL3Krqtte8Oe295eLzbbn533Dz9nA3/st+Hvbu/2lJu5G1ET+dIEpYPl93d7yrD/z3XpJkq+zAcxa7OEgJPU4aoi5PuK5zzwOUoefToDpSXlex6jTqRXL/L7ttbqht8+9sYGtPj4w9d6+vly+rarl2R2Ua1jM43/HvPxqzN/3uz+x7/11A+ZfgbmeuPrzy4Hh1Z6e1sDpxsX8/Tt3umeAZxLlcaedCcys9twO4Mxapi3EFJhrm8/LRK/7FkIIIYQQQgjRCK2q8CyEEEIIIYQQovFs5+C4dttLP4rCs9a6xpA/SqkHgfHVnr5Xa/1Uy6SqkecbIKXa0z/VWn/vRR4hhBBCCCGEEFE/isJzbbTW13mdIZbW+nCvMwghhBBCCCGEqN2PtvAshBBCCCGEED8Wfr4FVGvRGu/zLIQQQgghhBBCtCgpPAshhBBCCCGEEAlI4VkIIYQQQgghhEhArnkWQgghhBBCiIOcXPN84KTlWQghhBBCCCGESEAKz0IIIYQQQgghRALSbVsIIYQQQgghDnKOI922D5S0PAshhBBCCCGEEAlI4VkIIYQQQgghhEhAum0LIYQQQgghxEHOtm2vI7R60vIshBBCCCGEEEIkIIVnIYQQQgghhBAiAem2LYQQQgghhBDioKCUygZeBnoDawGltd5ZbZqRwMNAJhAGbtdav5xo3tLyLIQQQgghhBAHOcd2Ws3PAfo98KHWegDwofu4uhLgEq31UOAk4B6lVLtEM7bkfl+iHrJxCCGEEEIIUZPldYDGOu3Kxa3m3P7tx4fs9/JVSi0DjtFab1JKdQU+0VrnJXjPAuBcrfWK+qaTbttCCCGEEEIIIXxDKXUVcFXMU49prR9r4Ns7a603uX9vBjon+KzDgGRgVaIZS+FZ1KlozjteR6hTxqEn8cAMf1aeXX+KqSg7avKnHiep3ay3JnLr8xVex6jTLVOSuP2lsNcxavWnC4IAvs932wuVHiep3V8uDnHLs/7c9m69JAlA8u2nWy9J8u1x5ZYpZtnd9NBej5PU7s5fpANw0e/zPU5Suxf/1YN/an8e8/6gzDHPz/sF4Ot9w6/LDszym55Ub2OhZ06tWOZ1hP3iOK3nVlVuQbnOwrJS6gOgSy0v/anafBylVJ2FBrdl+jngUq11wgUkhWchhBBCCCGEEK2G1vr4ul5TSm1RSnWN6ba9tY7pMoHpwJ+01l835HNlwDAhhBBCCCGEEAeLN4FL3b8vBaZVn0AplQy8DjyrtZ7a0BlLy7MQQgghhBBCiIPFvwCtlPo5sA5QAEqpQ4FrtNZXuM9NAHKUUj9z3/czrfX8+mYshWchhBBCCCGEOMg1wS2gWgWtdSFwXC3PzwGucP9+Hni+sfOWbttCCCGEEEIIIUQCUngWQgghhBBCCCESkG7bQgghhBBCCHGQ+7F0225O0vIshBBCCCGEEEIkIIVnIYQQQgghhBAiAem2LYQQQgghhBAHOduxvY7Q6knLsxBCCCGEEEIIkYAUnoUQQgghhBBCiASk8CyEEEIIIYQQQiQg1zwLIYQQQgghxEFOblV14KTlWQghhBBCCCGESEAKz0IIIYQQQgghRALSbVsIIYQQQgghDnKOLbeqOlDS8iyEEEIIIYQQQiQgLc8+p5Qq1lq3ref13sDbWuthjZjn0+57ph54QvhywRL+/dxr2LbNmceM42enn1DrdB9+O5/f3fsUz/7tJob07UlFZSX/eOJlFq/eQCBgcdNPz+bQIQOaIlKcdUs+57PXb8dxbIYcfi6HHn9V3OvhynLee+F3bMv/gTZp7Tjp0rvIzO7BsrlvMe+jJ6qm275pGRfc9Boduw9u0nw3XtWPI8bksK8szD/uXcbyVcVxr6ekBPjb74bQvWsqtu3wxbeFPPLMGgBuuKIfow9pB0CblADtspI5+cIvGvS5/bpanDQ2SMCCeSttvvghvjYyGIAzjwzSLceipMxh6udhdu81rx01NMCo/gFsB96ZHWbVJifhPCeNCDCkl3nPnOU23y6zycmEM44I0jXb4qP5Nl8tSVwj2rcLnDg6gGXB/NUOXy2JH/wiGIDTx1l0aW9RWg6vf2mzey+kJsPZ4wN0y4aFaxzenVdz0Izzjg7QLh0ef2f/amabI9sFEwO0bQOBAGzY5vDOXAenEeN99Otq8ZNDAwQs+G6lzReLa2Y688gAXbMtSstg6qzoeh4/1GJUP7PO3p1jV63nX54RpKwSHBtsB/7zThiAiYcEGNXfomSfef9HC2xWbmzc4CQnjw0woHuAijC88UUlm3bUnKZrNpw1PkQoCCsKbGbONusrNRnOmxCkXVuLXcUO+rMw+8qj7+uWY3HFyUGmfhZm8frGD5rSHNl6d7a48NggO93dfsl6m08XNnz7a+nldcLoAAN7BLCAVZui86rLSYe6+Srhja8q2VxHvjOOCJEUMvnemWPm2SYZzj06SLt0i117zTFoXzl1HjeCAbjsxCDBoEXAMsvyk0YsyzOPSmZwryDllfDSh2UUbK/53h4dA1wwKYWkECxZF+aNWeVxr08cEeL08Sn85cm97N1n/oeLjk+hfVuLQMDik/kVzF5a2eBMtblkchYj81Ipr7B55JWdrN1YUWMadWImR49OIz01wOW3bKx6fsKYNC46OYsde8w++95XxXwyu+SA8vTtAsePNMeY+Wscvl5a8xhz2mEWXd3j3htf2ewugd6d4ZhDAgQDELbh44U267aa9wQCcOIoi16dLBwHPv3eZllBwzM1x36Rl2sxaWQQxwHbdnhnjs36rdH/NSUJrjs9xNINNjO+bfn94pDeFuOHBgEor3SY/k2YLbv2b79oyWNdZhqcfVSQ9DYWAHOX23y9tGlbR4c//g86nXIM5VsL+WzU5Cadt/jxkcKzOCBh2+aOp1/hwT/8gs7Z7bjkf+5kwuhD6NujS9x0e0v38dI7nzGsX6+q517/6CsAXr7j9+zYXcQv//cRnv3bTQQCTdchwrbDfPLqbZx5zZO0bdeZl+8+j77DJpHdpX/VND98PZU2qZlc8qf3WD5vOl+8dScnX3o3eWMmkzfGHGS3b1zG9Cevb/KC87gx2eR2S+OCq79laF4Gv7l2AFf95rsa0/339Xy++34XoZDFvX8fwbgx2Xw9dwf3/2dV1TTnnNaNgX3rrGeJY1lwymFBnvuwkj0lcOXJIZbl22zfHZ1mVP8A+8od7p8WZmgvi+NHBXl1VpgOWTC0d4CH3qokIxV+enyIB940J4N1zXNkX4vMdKtqurQU8xmlZfDOHJtBPawG5z7p0AAvfmyzpxQuPyHAigKH7Xui04zsa7GvHB6ebjOkp8WkERavf+lQGTYnYJ2yLDpm1Zx3Xg9zwgENy9JS2V77wqbcPdc+Z3yAwbk0uOBnWeYk6PmPwuwpgStOCrIsPxyXaVQ/c0L7wJuR9Rzg1Vk2HTJhaK8AD78dJiMVphwX5MG3wlUF92c/CFNaVvMzv1lq16g0aKgB3S1yMi3ue6OSHh0sTjs8yOMzwzWmO21ckDe/CpO/3WHKcUH6d7NYudHhqGEBVm92mLUozFHDAhw9LMD78+yqZXHC6ACrGlmYb4ls67Y6vPhRzXl5mam25ZXb0aJnJ4uH3jIb5M9PCtK7s8XaLbUv0/7dLLIzLO6fVkn3DhanHhbkiXdq5jv1sCBvfROmYLvDRcfG5BsaYM1mhy9+CDN+aICjhgb44Du7zuNG2IZnPghTUQkBCy77SZAVGxu2Pw/qGaRDlsU/XyilZ+cA50xM5r5X99WY7pwJyehPyli/xeaKU1MY1DPI0vXmf2rX1iIvN8iOouiJ//hhSWzZYfPkjArS28DvL0pj3vJKwvtZNhiZ14YuHZL49b830z83mcvPbM9fHtpaY7p5S/bx3lfF3PWbLjVe+3phKU+/uWv/AlRjWabC8KVPzXHvZ8cHWLHRoTDmGDOij8W+Cnhkps3gXItjhltM+9pxK+tsivdBh0y4YEKAB942C2b8YIuSMnh0ZrRA1lDNtV+s2eTw8Aaz7XduB+dNDPHAtGhFyKSRAdZtTXx8aa79YmcxPP1+JfvKzWecNs7Mt679omB77Vlb+lhnKmfDbNoBySG4+rQQqzbZbNtd4yP3W/4zr7H2oecZ+eQdTTdT8aMl3bZbCaVUW6XUh0qpeUqp75VSZ8S8HFJKvaCUWqKUmqqUSnPfM0Yp9alSaq5S6l2lVNemzvXDqnXkdu5Ij04dSAqFOHHcaD6d+32N6R6ZOoNLJx9HcnJS1XNrCjZz6JCBAGRnZZCRnsriNRuaNN+W9Qtp16EnWR1yCYaSGTjqFFYv+jBumjWLPmTQYWcC0H/ET8hf8RVOtaa95d9NZ+CoU5o0G8DR43J456PNAPywrIi26SFy2sefJZSV2Xz3vTnRqax0WL6qiI45Nc8kjp/Qifc/29agz+2eY7GjyGFXMdg2/LDWZlCP+MNBXg+LBavNcli83qFvF3MSOqhHgB/W2oRt2LUXdhQ5dM+x6p3noQMDfLow+oVZUhb9vbHQIdzA8ky3bNhRZD7Xtk2ugd3jT44HdLdYuMbMcMkGh96dzesVYcjfDpW1nLQmheDwvABf/LD/t3BormyRgnPAMi0IjWl17p4DO2PXyTqbvNz4THk9LBauNh+8eL1DHzdTXq7FD+ui63lnkUP3nIZ/9v4YlGsxf5XJkr/doU2yRdvU+GnapkJKkkW+e+I3f5XN4J7utpkbqHr//FU2g3Kj2/ThgwIsWe+wt2Z5yPNs+6ull5fjQChoEQxAKGC2yeJ9dW+Qg3ItFq4x8y9IkC9yIr9wjc2g3Mg2GGCBu20uWG2T5+ar77hREdlfAhAMWNDA/WVYnyBzl5k3r99ik5pskZEWv69kpFm0SbZYv8VkmruskmF9glWvnz4+mbe+qoj7TAdISTbzSUkyvXgO5JLDMUPa8Pk80zVk5YZy0lIt2mXU3JZWbihnV1HTtt7Vpls27CyOHveWrHcY2K3mcW/RWrNQluZHj3tbdkGxu31t3wOhoDnGAQzvY8VVwpXGN/DXq7n2i/KYDgNJofhtq2s2pLexWLUx8TJvrv0if7tT1XMkf7tDZsz225j9oqWPdcWlVLVsl1fC9t1OjX3vQO2YNYeKHU1YGm/FHNtpNT9+JYXn1mMfcJbWejRwLHCnUipydMkDHtJaDwb2AL9QSiUB9wPnaq3HAE8Ctzd1qK07dtM5p13V407Z7di6M/4AtXTNBjYX7uSoUUPjnh/QqzufzVtEZThMwdZClqzJZ0vhzibNt3fXFtq2i9YZtM3qQvHuLXHTFO/eSoY7TSAYIrlNBvv2xtfKr/huJgNHn9qk2QA65KSwdXu0+W5rYRkdaikYR7RNDzL+sBzmLojP17ljCl07t2HewoYtv4w02BPTU29PiUNGWvw0mWkWu0vMwctxYF8FpKaY90aeBygqMc/VN8/2GRbDege48uQgFx0bJDujQTFr5k6FopjP3lNqnqs+TSSH40BZReJWi4mHWHyz1Kai8Y1/zZ4NTNft/3dWgLJKh6X5Df9CyUi12B23TsxzcdOkWVXdtOPWc6rFnr21v9cBpkwKcsVJQUb3j5/f2IEBrj4lyORxAdo0orUokqX6NpRZ7SQqM81iT+xyLqHqRCs91ZyIgfmdnhpZDjA412L2sv0vTDRXNjAtuteeFmLKccFae0W0dKa6llf+doc1m21+c16I35wXYtVGJ663So18qdFtC2DPXqfm9pdaLd/e6HbWtk18vrZt6v6sCMuCq08JcfO5IVZvsikobNj+kpVuuphG7N7rkJVu1TvNrphphvYOsnuvw6bC+GX2xfcVdG4f4JZLU/nNBam8Mau8oeX5WrXPDLJjV/RAtWN3mPaZwXreUdPYYan868ZO3HhxNtlZjXtvdW1TiVt/Rft53MvrAZt3md4DKW4d+4RhFpedEODMIwJVvZUaojn31UG5FtefEeLi44K88aVZDxbwk0ODvDe3YV8gLbFfjOoXiLtkpjH7hZfHunbp0CW77lZxIfxAum23HhbwD6XUBMAGugOd3dc2aK0jF7o+D/wSeAcYBryvlAIIApsSfYhS6irgKoAnfnv5AYe2bZu7XniDv159UY3XTp94OGsKNnPJn++kS4f2DB/Qm2ATdtluKpvXLSApuQ05XQd6miMYgL/ePIRX3ipg45b45rPjJ3Tiky+2H1CLRnMKBaAyDI/PDDMo1+L0I4I8/d4BlFSbUOd20L6txQffOWSle52mdi99aptrk48I0LuTw5otid/TnJ5+L0xRqel+P+W4INv3hFm/FeassPlskTlBPnZEgBNGB3jraw83Svf86+SxQd6fZx9QoaXJuWE27XC4+9VKyitNC92Fx4a4740Duyb2QDPVtbyyM6BjlsVdU02+S04I0nOjFXfdZ7PGa8DHOA48OqOSlCQ4f2LjKiP2V1IIjhuTxGNv1ezWkJcbpGC7zcPTysnJtLj69Das3lhKWc3LlFvEvCX7+HJ+CZVhmHRYOteq9tz++HZvwrg6ZMKxw03XbzA9GjLTLPILbT5c4DB2oMVxIyze+tajPTjmY5ducFi6oZJenSwmjQrw7PthxuaZS3T2HNil4/sfr9pi6d3ZYlT/AE+9Wxk3TfX9oim7Rdcf0PxKdKxLDsH5x4R4Z3bYs/1DiIaQwnPrcTHQERijta5QSq0FIvWN1b9RIhdu/qC1PqIxH6K1fgx4DKBozjsJv6k6ZWexpTDaCrp1xy46tY+erZTsK2PVhk1c/fcHACjcvYdf3/k4d910JUP69uSmn55dNe3lf72bnl06NSZuQuntOlO8K1pnULx7M22zOsdN0zarE0W7NtG2XRfscCXl+4pokx5tTV8xbwYDRjVdq/PZp3Rj8k9MS/eSFUV06hCtUu+Uk8L2wtr7p/32+oFs2FjCK2/WHDXluKM7ctcjKxucoagEMmNamjPTLIqqffHvKXHISrMoKnGwLGiTZK5RLiqBrDSLyGaXkUbVe+ua554SMzgImJOPM47Yvy5ZRaWR2m3z2Zmp5rnq02Smmd+WZVox6uvy172DRddsuG6yGfAmPQWmTArw/EeNK/Q1R7ZYYRuWF5iu4GvquMa0ZibHXVeG+ez49xaVmEqDSKaq9VzqkBlTmRD73sj/VVIGyzaYbvvrt8Z38Z230ubCYxK3ah2WF2D0AFNptrHQqbENxbZuQM1WkMy0aIv/3lLTElbs/o7k6ZZjce4EkyUtBQZ0D2I7YZZuqH85tkS22JPEFQUOpx5uMpbUcj15S2Wqa3nlZFrkb3Oquq+uKHDI7RhfeB47MMDo/tF8Wemwwb2aJDPdqrn9lVbLlx7dzor3VctXxzKpTVkFrN3i0L9b3RWy44eFOHyIOQ3asNWmXdtojqx0i91747Pu3uvETdPOnSYn0yI7I8BNyjSxZbW1+NV5qdw7dR9jB4f4aJ5ZyYV7HHbscejUPsCGrQ0/vpwwLp1jDzM74+r8crLbBWGdeS07K8jOPQ2viCwuiX7ux7P3ctEpB1a7UFyKu/7c74NGHvcyUs1YDm99Y7PLbY0tLTfjTyzLN4+XbnAY0SdAfX2NW2K/iLVuq0P7thZpKe5YAJ0txuYFSA6Ziu7ySvhgXnRZt9R+0akdTB4X5IWPKmv9bondL7btjubz+lgXsOD8Y4IsXG2zZD8GcxQN5zg+bWVpRfzXzCfqkgVsdQvOxwK9Yl7rqZSKFJIvAmYBy4COkeeVUklKqfh+001gSN+ebNi8jYKthVRUVvLe1/OYMCY68HfbtFQ+fPQfvHXvLbx17y0M69+7quC8r6yc0n3mqP/190sJBoI1Bho7UJ1zD2HXtnXsLswnXFnO8u9m0GfopLhp+gybxNJv3wBg5YJ36dF/HJbldk+1bVYsmMnAJiw8vzZjI5fdOJfLbpzL519v56RJ5n8empdBcUklhTtrfuNdOaU36ekh7nt8VY3XevZIJaNtEouW7qnxWl0KCh1yMizapZtroIb2DrAsP/6AujzfYURfsxyG9IwW2Jbl2wztbUZIbZcOORkWBYVOvfNcusGmt3vNdK/OFoVF+/fluHGHaf3Kcj9jSE+L5QXx81pR4DC8j/mswbl1D2YUMW+lw33TbB58y+bZD20Ki2h0wbm5siWFol3yLAv6d4PCooZnKiiE7Nh10ivA8mrdvpcVOAzva74KYtfz8nyHob2i6zk7w6KgEJKCpoUAzN99u1psdevPYrsPDsq12Lor8Xr+dpnNI29X8sjblSxZbzOyn8nSo4PFvgqnqvtfRHEplFU49OhgluPIfoGqQvCy/Oj7zfNmPd7zeiX3vGZ+Fq8zo9AmKji3VLbYZdY9x8Ky6i44e728du116NXFjNgbsEwL17bd8ctx9nKbR2dU8uiMSpbm2wzvY+bfvYNFWXnd+bq7+Yb3ieZbnm8zwt02R/QNsGxD/ftlWkq0y2/I3Ta376l7PX+xqJK79D7u0vtYtCbMmDyzYffsbAZLLKpWYCgqcdhX7tCzs8k0Ji/EojVhNu9w+OvTJeyPAwUAACAASURBVNz+fCm3P1/K7mKHu18ppajUYVeRw4AepiKibSp0amexY0/jji/vf72XP963lT/et5U5P+zj6NGmIN0/N5nSfU6jrm2OvT56zJA2FGw9sCa+jTugfdvocW9wT4sV1QblW7HRYVjvyJgZVtWgWilJ5g4HHy+0KSiMn+/KjQ693Lr03p2tuEEOa9MS+0Xs5UZds802VlIGr84Kc/erZn95b26YBavtuIIztMx+kZkG508M8foXYXbEfE80ZL/w+lh3xpFBtu1yGnTHDSG8Ji3PrccLwFtKqe+BOcDSmNeWAdcppZ4EFgMPa63LlVLnAvcppbIw6/oe4IemDBUKBrn5Z+dwwx0PE7ZtTp84jn49uvLI1BkM7pPLxDGH1PneHXuKuP6ORwhYFp3aZ3HbtVOaMhpgrmGeeM7/8OajP8e2bYYcfg45XQfw9cz76JQ7jL7DJjHk8HN5/4Xf8uztJ5KSlsVJP72r6v0Fq2fTtl1XsjrkNnk2gK/m7OCIQ7N5+bHDqm5VFfHUvWO47Ma5dMxJ5tLze7F2w16evGcMAK9OL+Dt98xAY8cf3YkPP6852mp9HAdmzA4z5biQua3SKjOy5THDA2zc4bA832HeSpuzxge54YwQpWUOU2eZ1o1tu2HxOptfTA5hu/OJdBurbZ4As36wOfuoIOMGWZRXOrz1lZlXehu46uQQKUmmTWHcoPrr8xwH3p1rc+HEAIEALFhtRrOeMMxi0w6HFRvNLaLOGBfg2lPNyNavfxn9Mr5ucoAUt2VgYA+L/35iJzwpa8wybepspWXm5DIYNF1J1m11mLuy4RUPjgMz59hcPClYcz0XOiwvcPhupcNZR1pcf3qQ0jJ49Yv49XztaUFsdz6OY65fU26rZMCCRWujt7A6fnSAzu3NYDS79jpM/6ZxJ0IrChwGdne48ayQuYXLl9EWtWtOC/HI26bJc/o3NmceGay6hcsKt5Li80U2akKQ0f1D7Nrr8MqnTXdpQHNlG9IrwNi8ALYNFWGHqZ81PHNLL6/F6xz6dnH4xeQQDrByo12jMqZ6vgHdHG44w+Sb9lV0/lefEuLRGW6+b02+UNDMM3Kt5qxFNuceHWRUvxC79zq88nndx40H366kbSqceWSIgGUqm35YF/1fE1myLszgnkH+cHEqFZXw0kfRGoxfqzbcpU3z2auflVfdqmrp+nDVSNt1eX9OBRccl8JvzjenW29/Xb7fg9YBzF+2j5GD2nD3zV0oq3B49JXoPYT+8ctO/PE+811w4clZHDkyleQki/v/0IVPZpfw6gd7+MmRbRkzJJWw7VBcYvPoKwc2zojjwPvzbC6YYG7Rt3CNOe4dPdRi006HlRvNsXDy4QGuOdmM7D/NvZRjTH+L9m3hqCEBjhpi5vfSZzYlZfDxQvOe40eaAtb0BLdEi9Vs+2rPACP6BQjbUBl2eKUR+2r1fM2xX0wcHiQ12YzSDWA7Do/PDDd6v2jpY13PThYj+wXYvNPhmtPMOcCH34UbvO82xMjn7iRn4mEkd2jPpDWfsuK2+9nwVJPcrVX8CFnVRxUWIqIh3ba9knHoSTwww5/xrj/F1MQeNflTj5PUbtZbE7n1ef9eUHTLlCRuf8kf10NX96cLzEmJ3/Pd9oJH18wm8JeLQ9zyrD+3vVsvMU0zkm//3HpJkm+PK7dMMcvupof2JpjSG3f+wrQkX/T7fI+T1O7Ff/Xgn9qfx7w/KHPM8/N+Afh63/DrsgOz/KYn5Xkdo1anViyD/b23pYeOVd/48+S5Fh/rw325fKXbthBCCCGEEEIIkYAUnoUQQgghhBBCiASk8CyEEEIIIYQQQiQgA4YJIYQQQgghxEHOsWVE8wMlLc9CCCGEEEIIIUQCUngWQgghhBBCCCESkG7bQgghhBBCCHGQc+xWc6cq35KWZyGEEEIIIYQQIgEpPAshhBBCCCGEEAlI4VkIIYQQQgghhEhArnkWQgghhBBCiIOc48itqg6UtDwLIYQQQgghhBAJSOFZCCGEEEIIIYRIQLptCyGEEEIIIcRBTm5VdeCk5VkIIYQQQgghhEhACs9CCCGEEEIIIUQC0m1bCCGEEEIIIQ5yji2jbR8oaXkWQgghhBBCCCESkMKzEEIIIYQQQgiRiOM48iM/LfJz3nnnXeV1htaYTfIdvNkk38GbTfIdvNkk38GbTfIdvNlaQz75aR0/0vIsWtJVXgeoh5+zgeQ7EH7OBpLvQPg5G0i+A+HnbCD5DoSfs4HkOxB+zgb+zydaASk8CyGEEEIIIYQQCUjhWQghhBBCCCGESEAKz6IlPeZ1gHr4ORtIvgPh52wg+Q6En7OB5DsQfs4Gku9A+DkbSL4D4eds4P98ohWwHMfxOoMQQgghhBBCCOFr0vIshBBCCCGEEEIkIIVnIYQQQgghhBAiASk8CyGEEEIIIYQQCUjhWQghhBBCCCGESCDkdQBx8FJKjQfma633KqWmAKOBe7XW6zyOhlLqDq317xI95yWl1I1a63sTPecVpVR3oBcxxxGt9WfeJYpSSvUD8rXWZUqpY4DhwLNa613eJjOUUulAqdbaVkoNBAYBM7XWFR5HA/yfL0Ip1R7I1Vov9DpLhJ+Pe9UppQJAW631Hq+zQKvYb28EngKKgP8Ao4Dfa63f8zSYy91XHwY6a62HKaWGA6drrf/ucTQAlFK9gAFa6w+UUqlASGtd5HWuCKXUkUBv4r/TnvUsUAylVEfgSmrmu9yrTLGUUhZwMdBXa32bUqon0EVr/a2Hmc6u73Wt9WstlUUcXKTlWTSnh4ESpdQI4CZgFeCLLyLghFqeO7nFU9Tv0lqe+1lLh6iNUuoO4Avgz8DN7s9vPA0V71UgrJTqj7k1RS7woreR4nwGtHErIN4Dfgo87WmieL7Np5T6RCmVqZTKBuYBjyul7vI6Vww/H/dQSr3oLr90YBGwWCl1s9e5XH7fby93KxpOBNpj9ot/eRspzuPAH4AKALdS6QJPE7mUUlcCU4FH3ad6AG94lyieUuo54N/AUcBY9+dQT0PFmwZkAR8A02N+/OIh4AjgQvdxEfCgd3EAmFzPz2ke5hKtnLQ8i+YU1lo7SqkzgAe01k8opX7uZSCl1LXAL4B+SqnY1qoMTGHQc0qpC4GLgL5KqTdjXsoAdniTqoYzgTytdZnXQepga60rlVJnAfdrre9XSn3ndagYlta6xN0fHtJa/69Sar7XoWL4OV+W1nqPUuoKTKvkLdX2Za9V+u24V80Qd/ldDMwEfg/MBf7P21hAK9hv3d+nAM9prX9wW9z8Ik1r/a1SKva5Sq/CVHMdcBjwDYDWeoVSqpO3keIcitk3/Hr/1jQ/9YyrxeFa69GR/VVrvVMplexlIK31ZV5+vjh4ScuzaE57lFJ/AKYA090ugl5X2LyIqXWcRnwt5Bit9RQvg8X4ErgTWOL+jvzcBPzEw1yxVgNJXoeoR4VbCXEp8Lb7nJ/yWkqpIzDd3CKtB0EP81Tn53whpVRXQBFdt35S5B73fkr0uOenbS9JKZWEqQB702dd8f2+385VSr2HKTy/q5TKAGyPM8Xa7nZ9dwCUUucCm7yNVKVMa10eeaCUCuHm9IlFQBevQ9TjbaXUKV6HqEeFUipIdNvriE/2DaVUZ6XUE0qpme7jIT6r0BStjNcFGXFwWwaUAT/XWm92r4FJ9zKQ1nq3UqoYGOXHaxABtNbrlFL5wD6t9ade56lDCTBfKfUhZh0DoLX+pXeR4lwGXAPcrrVeo5TqAzzncaZY/w/TvfJ1t/WqL/Cxx5li+TnfrcC7wCyt9Ww32wqPM8U6H9Nz5PKY454fWnUjHgXWAguAz9zrUHd7mijK7/vtz4GRwGq3Z0Y2JrNfXIfp7j5IKVUArMFUgPnBp0qpPwKpSqkTMD3A3vI4U6wOmEsYviX+O+107yLFuRH4o1KqHCjH9IJwtNaZ3saqch/wOtBJKXU7cC7msi4/eBozVsGf3MfLgZeBJ7wKJFo3y3H8VPEnDiZKqXla69HVnvtea32IV5lickwDbtBar/c6S13cgunZWmu/nNhWUUrVdj02WutnWjpLXdwBaXpqrZd5naUuSqk0rXWJ1znq4sd8SqnxWusvEj3npWoDI6UBQb8MjKSU6qO1XhPz2AL6a619UQHh5/22tQwG517PHvDLNgdVg9P9HHO9uIWpAPuPX7pJK6Um1va8jyuwfUcpNQg4DrN+P9RaL/E4EgBKqdla67FKqe+01qPc5+ZrrUd6nU20TtLyLJpczHXFff16XTFmsJcf3FrmvZEnfVTLDFAMfK+Uep/4jJ637mqtn3GvZxroPrXMT90/lVKTMYO/JAN9lFIjgdv8sn7dLtFPAG2Bnu7gUldrrX/hbTLD5/nuxxRaEj3nCXdgpKuAbKAf0B14BHNS6QevErOs3OuzXwLGeBfJ8Pt+ixkMbkTMYHD/wQwGV2vBq6UppXKAWzCDXjlKqVmY5VfobTLQWtuYAc0e9zpLbbTWn9ZW6eV1rggVHc26j9b6b0qpXKCrl6NZu7myYx5uBf4b+5rW2g/jtOx1941Il/Jx+Ke3jWiFpPAsmsOLmIFo/okZjCaiyCcHUoD/8TpAA7zm/viOMreReQbT/dMCcpVSl/rlVlXAXzGD03wCoLWe73bv9Yt7MNevvwmgtV6glJrgbaQ4vsvnFuiPBDoqpX4d81ImPjrJxacDI7mtQkOBLBV/C5dMoI03qWr4K/7eb/0+GNxLmJHyz3EfX4zpnnq8V4GUUt9Tz7XNWuvhLRinTq2g0ushzDXEk4C/YSrXH8SMCu6luZj1awE9gZ3u3+2A9UAf76JV+TXmu6yfUuoLoCOmW7kQ+0UKz6LJud2MdxO9ZYHvuLXMnYl+8Xyrtd7qZabqfN66eydwYqRrpTL3F/0vPmi9clW417fHPueLwUsitNYbquULe5WlNj7Ml4xpCQ9herFE7MFfJ0JlWuvyyLLz0cBIeZjbs7TDDJIYUYS5f6wf+H2/jQwGNwWY4MPB4Lpqrf8W8/jvSqnzPUtjRG4JdJ37O3IN+xT8sV9E+LLSK4bvRrN2c/QBUEo9jhkjY4b7+GTMoISe01rPc7vl52EK9n46lxKtkBSexY+SMmdn/4dp4bCA+5VSN2utp3oaLIbPW3eTYq9J1Fovd0fw9YsflFIXAUGl1ADgl5hRzP1ig1LqSEzXyiTMYDC+uD7M5bt87rWHnyqlntNar459TSnldetLLF8OjKS1ngZMU0odrbX+PPY1d2AuP/D7fhsZDC52EEw/DQb3nlLqAkC7j8/FXFvsmcj14EqpEyLXm7p+p5SaR3zvNC/5tdIrwrejWbvGaa2rKuG01jOVUv/rZaAIpVQbzHH4KMzy+1wp9YjWep+3yURrJYVn8WP1J2BspLXZ/SL6APBN4Rl/t+7OUUr9B3jefXwxMMfDPNXdgFnHZZjLCN7FdHXzi2uAezFdAwuA9zBf7n7h53yvKKVO11oXALjdyR8EPB+I0PV7zMBI3wNXAzO01n66zvNfSqmTtdZ7AJRSg4FXgGHexgJ8vt9qrTcDd8U8Xo+55tkvrsSMlB9p3Q1irve8Gu9HZrZiB/ZzK+f8dLtUX1Z6xfDzaNYAG5VSfyb+nGCjh3liPYvpYXO/+/gizD5ynmeJRKsmhWfxYxWo1k27EH99kYO/W3evxXRziwxe9jnmmiy/OFVr/Seit6ZAKXUeppDgB3la67hbyLgj+fplQD0/57sGeMMdXGo0ZmwFP93/9Aat9b3EDIyklLrRfc4P/gG8pZQ6FdON8Vn8czsjX++37kBD9wODMZcRBIFirXWWp8FcWuuMxFN55ufAk0qpLExPqp3A5d5GilOj0gszIJwvaK1fUErNJTqa9Zl+Gc3adSFmsLrX3cef4Z9L94ZprYfEPP5YKbXYszSi1ZPCs/ixekcp9S7RkSHPx3xZ+olvW3e11mWYFpi7Ek3rkT9Q84S7tue84usRo/FxPvfezr/EtIbvA47XWm/zOFasSzGt9rF+VstzntBaT3cr4d7DXDt+ltZ6ucexIvy+3z4AXIDJcyhwCdExKTynlHoVM0r+O+7o1r6htZ6LGak8y33sq9GO/ToaeCsZzRo3x41KqQxML4dirzPFmKeUGqe1/hpAKXU4PjmXEq2TFJ7Fj5LW+mZ3xNmj3Kce01q/Xt97POC71l2llNZaq7pGUPV65FR3kJJTgO5KqftiXsoEKr1JFeX3EaP9nE8p9Rbx21waZmDCJ5RSnt9mTil1IaY7YB+l1JsxL2UAnp/gKqXuJ375ZQGrgOvd5efZLfD8vt/G0lqvVEoFtdZh4Cl3AKc/eJ3L9TBwGWYMj1eAp/xyv2y30HwLMMF9/CnmNlqeFqJbwWjgsaNZQzSr5f7ti9HolVKHYHqxZLuPtwOXaq0XeZgpsm6TgC+VUuvdx72ApV7lEq2fFJ7Fj9mXmBGEbWC2x1lq0FqXKaUeAD7EZFymtS73ONaN7u/T6p3KOxsxNcqnY046IoqAX3mSKJ7fR4z2c75/e/z5iXwJbAI6YMYriCgCFtb6jpZVvaVlbq1TecPv+21EiTvC8Xx3MKRN+OhyH631B8AHbkH1QvfvDZjW1Oc9HmH4SWAREBlK/afAU8DZdb6jZfj1uwyIjmadiFJqqNb6h+bOU49HgV9rrT928xwDPIapjPWKr9etaL0sx/HTYIJCtAyl1BXAX4CPMDW4EzG14E96GiyGe03iI5jWIQtzv8SrtdYzPQ0GKKXu0Fr/LtFzXlFKnQ687beuixFKqeFaaz8UqGrVCvJ1wdxWxgFmuwM5+YLbpfw5rfVOr7PUxS0ADsIsPz9UygGtYr/thek6m4Qp1GcBD2mtV3oaLIZSKgdzG6ifYiolXsD0sDpEa32Mh7nma61HJnrOr5RSX2mtj/A6R12UUvO01p5dVqOUWqC1HpHoOS8pc+uxqnvauwP+CdFo0vIsfqxuBkZprQuh6oTjS0ztuF/cCRwbOTFTSvUDpgOeF56BE4DqBeWTa3nOKwq4270G8Emttd+6aD2olEoBngZe8LrrYi18m6+Wiq/7lVJ+qvjqBMx2b8PzJPCu1to3tdRKqVMwrURVlXJKKV9UyuHz/TZy2yWgFLjVyyy1UUq9jhkE7jlgstZ6k/vSy0opr6/xLFVKHaW1ngVVAxCWepypMdoknsRTVuJJmtVqpdT/EH8f79X1TN9i3Eq5O4FumMqvXphbLw71MpdovaTwLH6sCjFdAiOK3Of8pKhai8Zq4jO3OKXUtZhbePRVSsW2TGbgj5GYAdBaT1FKZWK6Lj6tlHIwXQT/q7X2dBkCaK2Pdm89dhkwVyn1Leb6xPc9jgb4Pp+vK7601n92TyJPxCy/B5RSGnhCa73K23SAGeTPl5Vyft1vW8F1sRGPa63jBr5USqVorcu01od6Fcp1LfBMZMAwzGjbP/MuTqP5pgKsDl7nuxxTofSam+Vz/DOa+t+AccAHWutRSqljMYV7IfaLFJ7Fj9VK4Bul1DTMgf4MYGFkkCSttR9GkZ6jlJoBaEzG8zAtWmcDaK1f8yDTi5iT7H9ibu0RUeSXUT8jtNZ7lFJTgVTMvU/PAm5WSt2ntb6//nc3P/fWY3/GXOt5HzBKKWUBf/Ro3cbxcT7fV3xprR2l1GZgM2bAq/bAVKXU+1rr33qbzn+VcrF8ut+2lmsn/07Nu0Z8hT9GyZ+PGW070328x+NIogm5l6l4NuhgAhVa60KlVEApFdBaf6yUusfrUKL1ksKz+LFa5f5ETHN/++k+mW2ALZjrsQG2YU4oJ2MK0y1egHG77+7GvX9jzDVEbZVSbf1yDZHbTesyoD9mBNDDtNZblVJpwGLMbZe8zDfczXcq8D6mi+U8pVQ3zMmup4Vnn+fzdcWXUupGzC2MtmPuE3uz1rpCKRUAVgBeF579WCkH+He/jXTXVkr1ATZprfe5j1OBzl5kiuWOAdAdSFVKjSLahTcTMyq955RS/wD+V2u9y33cHrhJa/1nb5M1mNfdohPxdNwCpdT7wHnV1u9LWuufeJnLtUsp1RZz7+kXlFJbgb0eZxKtmBSexY+S1rre69WUUvdrrW9oqTy10VpfVt/rSqk/6P/f3p1HWV7XZx5/N4iiYhMX3DAo4hwdMoASUBQ0QjRoUFHRBx1wEDfQGHDDPWERNIrGQDOjqMiioj64BFQWI7IIegYERVwmM1E0RsnRIEKLAenumj++v1/VrdtVXdXadb/fW/d5nVPn1u9XVdRzqrlV97t9Pva7RpVn6Hs/k7L9s9UzRAcA77d9+eBN27+V9NJKmQatovRjfavt6XN/tn/erfbW1nK+1ie+7gM8d+B8LFD6yEpqYQWzuUm5Aa0/b89hdvXgtd293evEmbYvZQv0Qyi/l3urgbfWCDSHp9uezmL75u78fe3fJwBI+mtKRfL5Cv29aJR5hkn6HOV38gVzFdSzvcfoU81yv37gDNP/vvevGWjA/sDtlCJ/B1EK/R1XNVGMtVTbjphD7cqVi1Ezo6TrgH0YOkNku4UXuEDbFZmh3YrHvdbzzaeFiS9Ju1IqHE8BV9q+tmaejVFzUq77/s0+b+epGN1MRWFJB9j+bO0cc+lqZOxu+47u+u7AN203MeEq6XjgBUCrhf6eQtmVsQdlwqaZHt4Akq4BntPvPusq03++9ddREb+PrDxHjK+a28iaPkPUrVIdTaMVmRuveNx8vgXsWfObd8XCxMwK7umSzrF9fMVYG+P5lJoGI9f68xb4paRn2T4PQNL+lO35rXhof3xhLpWPNHwCuFjS6d31ocCZFfPM0nqhP7fdwxvgbcAVki6jPHefCLyiZiBJq5m7kNoKYMr2yhFHimUig+eI8VVzVrz1M0RvpOGKzDRc8bjTer6WHQzsMnAu9u+Ab1OKOY2DmpNyrT9vD6f8vjuF8nP6KeV8eyt2o2whP6+7fiZwFeWsfTVdocGzgeuAp3S332H7onqp1td4ob/hHt7fYqaH9yHAk+slA9sXdjtu+u3jr7E9PbEk6U9sf2/EmVo5yhPLTAbPEXNrvTgI1M24P6VHZ6tniFqvyNx0xWPaz9eyn1POFd/eXd8N+Fm9OBut5qRc08/bbgVyj27iENu/qRxp2EOAXfu2XpKOAb5ku2pbnm5Qer7tnYALa2aZT+uF/tR2D28AusHyF+f58MdooOp7xKaQwXPE3E6qHUDSnrav3MC9cyrEQtLmwBdt7w2so6GtdwNbFuesyFwtWKevaMw8FY+rBeu0nm+RqkwqSVpF+VndAnyvqz47BTyVsvo3Lkb+82v9edsb3hItadbHa1d6p1T+HqxN8DsaqAbeuVbS7rZb/T3SeqG/k21fMtcHGujhvRjjsCARsSgZPMdEWqitgu0zKsbrrWL9mdrpe7bfOfJE5fuulbRO0tZd66qW9Nu05qvIXNszB94frni85ejjrKf1fItRa+KrX/25Bvj8wP1LRx9lfo1OyrX+vO01uS16wFnAVd0qJcCzgTPqxZnlccBBkn5COeLTnzvduWYoSffp3j1p6BoA27+y/YORB+sMTGjOer9Xs7XcRmqm+FrEHyqD55hUzbZVkPR4SjuUbYZWOlYCm9dJtZ7fANd3kxDTZ51tH1Ev0sItyHq1KjIv1H6sV6vicev5uu/d5MSX7UXtwJD0WdsHLHWeDWhuUq715+2AJrdF92yfIOkCSrEmgENtf6v/uKR7b6AV01LbYL/fitmuoQzsVgDbATd37/8R8K/A9hUyDeonNO9PeV3w1e56b0o9gHEZPEcsGxk8x6RaJ2m7obYKrcyM3hXYivL8HCx4cSvwvCqJ1vc5xvuPdtWKzItQreLxItXM1+zE1yI9vMY3HZNJuYXUft62vC0agK4t2nyt0S6m0rnT4e3Qc6iSzfb2AJI+TGmtdH53/XTKyn1V/YRmN2m4Y3/WWdKDaGdXwWKMRavDiMXI4Dkm1VtprK1Cz/ZlwGWSzuhfcHRFS7ayfWvddMViV9ni99b6+bCa+Vqe+FqMWlnHYVKudS1vi16Mln+v1M62h+2X9xe2L5D0npqBhjxkoEgYlGM129UKM0zSnsC3bd8m6WDKRMhJ/WsY23ts8D8QMUYyeI6J0w1Et6b8cp+zrUIj3iXpcGAtpVjTSkkn2T6xci4k3cAcgwDbVVbVlqHWB4M18zU78dWycZiUa13j26IXo+XfK7Wz/VzS24GPd9cHUSrnt+JiSRcBn+yuDwS+UjHPsA8Au0jaBXg9pWL5WczUzYhYNjJ4jonTVc98o20zf1uFFuxo+1ZJB1H6676Zcj6r+uCZUjintyVlG+995vncFtVe5VhI8s1hjCa+NqT2v22zk3KLUPtn1+y26PiDvRA4mplif5d395pg+9WSngM8qbv1Iduf39DXjNiariXZ/sAptk+T9NLaoSKWQgbPMam+IukNwKeZXfDqV/UirWcLSVtQtgae0vWcrJ0JANvDvVf/QdI1wN/WyPN7qNqKrNGKxxujSr5xmfiSdFfgUZTVtH+2PXje7011Uk1reVIOAEkrKZWYh3uLV28huIDqg/sFtJyvarbub/+Rku5F+X+vtR7eUAqEraH8Xmmt/d1qSW8BDgae1E10blE5U8SSyOA5JtWB3eNfMXu7WEvbjj8I/Bi4Dri8O9vZRGsoSYOrK5tRVqKb+X3SakXmAc1VPIZZvYrn1FdTr5Wv0/TEl6T9KM/dH1IGBNtLOsz2BQC2v1wzHw1PyknaHfgo5Uz2Ckm/Bl5i+xpo4nm7kKpbjyXtAPyb7TskPRnYGThroMDenyfb3CTtRNlmfJ/u+j+AQ2x/t2aunsqT9ERK67sVwCpJR9n+TNVgMw4E/jvwUtv/Lmk7GpqQi9iUmnmxGzFibwIu7FZg/oYyaHlH5UwADFXC/fvu8bWUQeo/jj7RnN7HzAvFNZRB/vOrpVlfkxWZx6Dicd+reE9gR8oAFcq/7ferJFpf8QmYWwAAE4FJREFU6xNf7wP2tv0vMD1o+BJllbcFzU7KAacBr7L9NQBJewGnUwZasbDPArtJegTwIUqf7LOBv4TqE0wtZwM4FXid7UsAugH+hyi/r1vwNmB3278AkLQN5cxzE4Nn2//OzOsVuoKOZ9VLFLF0MniOSfV22+5enO0DvJdS8OJxdWMBM5VwHwnsTnmRsYLS77HqVq2BAd8XmemNSff+Mxj441lZqxWZm6543FdRl/RKYC/ba7rrDwJfq5ltQLMTX53V/cC58yNgePvxyI3JpNzafuAMYPsKSWtqBtpItbdFr7O9pjsbu8r2KknfWvCrRqPlbAD37AfOALYvlXTPmoGGbNYPnDs3UZ67TZC0mpm/sXelbNn+je2t66WKWBoZPMekWts97gd82PaXJB1fM1DP9rEAki4Hdu3P/Uk6hrKCVVOzA/shTVZkHqOKx/emrIb3q0Fbdfda0PLEF8A3JZ0PmPJi8vnA1ZKeC2C7Vn/0cXjuXibpVEpF4SnKLoNL+2MiXbGualrfegzcKemFwCGUf1do59xpy9kAftRNxn2suz6YMvHVigvnqLZ9fsU8s9iengyWtALYn5mijhHLSgbPMal+1r1Ieyrwbkl3o6FZ3M4DgMFCQ7/r7lXT+MAeGJuKzK1XPP474FuSLqEMsJ4EHFM10YxmJ746W1J6sPYtWn4J3J0yYJgCqgyex+G5C+zSPR49dP8xlJ/dPqONs57Wtx4fChwOnGD7BknbMzMYrK3lbAAvAY5l5vn5te5eE2wf1U3A7dXdaq3a9jTbU8A/SjqaUpAwYlnJ4DkmlYCnAe+1/WtJDwKOqpxp2FnAVZL6P5DPBs6oF2eW5gb2vTGpyNx0xWPbp3f9bPvV3Dd1Z9pa0PTEl+1Da2dYQMvP3b1rZ1hA01uPbX8fOGLg+gbg3fUSzWg5G5S6GMARkram/DtXP2oxh69TJg/XUSZdm9HvrOn0RURvrxQnYkll8BwTyfZvGVgBsn0jcGO9ROuzfUI3gHlid+tQ2628UGt5YA+NV2Sm4YrHML3t7inAw20fJ2k7SY+13cL23iYnvroJm/fMV7G8r1TegOaeu5IOtv3xoXPZ02y3Ukuh6a3Hkvak7BB5KOX13QpK26XqxfRazgbrVXpH0i0MVHqvTdLLKK0gv8pMte3jbH+0brJpzxx4vy8iun+dKBFLK4PniIZ1Z/yqnvObS+MDe2i/InPLFY8B/hdldWMf4DhKwavPUs7KVtXwxNcPusdvbvCzKmv0udsXZrrXBj+rvta3Hp9GKQJ3DTPHG1rRcjZov9L7UcBjbN8EIOm+lJXoVgbPH7F95eCNbsLkF/N8fsTYyuA5In4vrQ7sO01WZB6TiscAj7O9a78ltWv1ddfaoVpm+wvd45kb+jxJq2z/9WhSza21567tU7vHYzf0eZLeYvtdo0m1vta3HgO39P3EG9RyNmi/0vtNzK7av7q714pVlL+zC92LGHsZPEfEctRqReZxqHgMZXvq5nSr9l1P0XV1Iy0be9YOMMaeD1QbPLe+9Ri4RNKJlJ0Zd/Q3a1cp7zSZra/kzjyV3mvl6g1MuP4L8L8lnUvJtz/wnWrBOpIeT+mFvc3Q5PBKYPM6qSKWVgbPEbEcNVmReUwqHgOcDHweuL+kEyg9qN9eN1JE9T7KrW897icH/7R7XEEbVcqh3WzvG7oerPS+Xu2CCvoJ1x92b71zK2SZy10prQzvwuxjF7dS/m5ELDsZPEfEctR0RWYarngMYPsTkq6h9K1dATzb9g8W+LKIpVZ7MNPk1uOBFb8vUn5Gg5MMVX9mLWeDxVd4l3TIQkcylsJCRxl6tY6D2L6Msmp/hu2fjPr7R9SQwXNELEdNVmQe0FzF40GSdgBusP0/JT0ZeKqkG23/unK05aD26uk4q/2za3LrMW0fB2k528Y4Ehj54Hkj1D4O8tvuufEnlF73ANiuvbMgYpPL4Dkilp2GKzIDzVY8HvRZYDdJjwBOBc4Dzgb+smqq5eGk2gFaJWnPuSr2Dtw7p0KsQU1uPW75OEjL2TZS7Ymb1n2C0hryGZSK9IcAv6yaKGKJZPAcEVFBaxWPh6yzvUbScyl9qFf1lbdjwyT9E/D8fpVe0r2BT9neF8D2GRXjtW6DFXttv3PkiWh/6/GAlo+DtJxtMVr6d27RfW2fJunIga3cV9cOFbEUMniOiIhhd0p6IfA/KNsrAbaomGec3G9we3vX5uv+NQO1bgwq9o7L1uOWj4O0nG0xWl95rp3vzu7xRkn7AT8H7lMxT8SSaamATkREtOFQ4PHACbZvkLQ98LHKmcbFOknb9ReSHkpWrRYyXLG3f2uiYq/tY7vtxw+hbD1+g+3XU7Zvb7fhrx4d2ydQnrs3d2+H1uyLPajlbADd77gN3bty+OM1SLrHPB+qfRzkeElbA68H3gB8BHhN3UgRSyMrzxERMYvt7wNHwPS243vZfnfdVGPjrcAVki6jrAY9EXhF3Uhtm6tir6TNgK1s31o33SzNbz1u+ThIy9kodR6Gjwx8hu58u+1XjzzRAElPoAxItwK2k7QLcJjtV3X5zqgYD+Bm27cAtwB7w3Rf9IhlJ4PniIiYRdKlwLMofyOuAX4h6Urbr9vgF064bsC3NeVF+B7d7dfY/o96qcbKuyQdTumhfDWwUtJJtk+snKs37luPY4ikR1EqRG/d1XjorWSganQD3g/sSyneiO3rJD2pbqRZNlivIGI5yeA5IiKGbW37VkkvA86yfbSk79QO1Trb6yS90bYpxaVi4+zY/X93EHAB8GbK5E0Tg+cxqJIfG++RlArRf8RMfQeA1cDLqySah+2fShq8tbZWlt4Y1CuI2OQyeI6IiGF36XpjC3hb7TBj5iuS3kBp23Jbf9P2r+pFGhtbSNqCsqJ7iu07hwYL1TW+9Tg2ku1zgXMlPd72N2rn2YCfdlu3p7rnyJHADypngvXrFfSaqFcQsRQyeI6IiGHHARcBV9i+WtLDgf9XOdO4OLB7/CtmFwp7eIUs4+aDwI+B64DLu2Jrt1RNFJPiOZK+B/wncCGwM/Ba2x+vG2va4ZSiYNsCPwO+TPkdU9UY1SuI2GRWTE2lCGhERMSmoLJUemG3/fhvKGf+3tGtWMYchrZ79i13pigdQaZsv2/0qWKSSPq27UdLeg5lG/frgMtt71I52liQdDZlgD9drwBoqV5BxCaTleeIiACgO6/7HkmrmKO9ku0jKsQaN2+3bUl7AfsA7wU+ADyubqymjUsf5Vi++j72+wHn2L6lpSMDkt4DHE+7K+NN1yuI2JTS5zkiInr9GbpvzvMWC+uL+OwHfNj2lyjnAmMe49JHOZa1L0j6P5T/5y6WtA1we+VMg/6i2wb9DMrRhkcAR1VNNNtgvYLzbN9J+tvHMpXBc0REAGD7C9273weeA7yW8gLtKOANtXKNmZ9JOpVy9vl8SXcjf2sXq/k+yrE82X4zpWr0bt3A77fA/nVTzdLvFJ1eGa8ZZg6nUgb192SmXkHOPMeylG3bEREx7OOUAfP1wLrKWcaNgKcB77X9665qeUsrRC1LH+WoQtI9gFdRdjq8Angw5RhBKy3nvtitjP8n8MrWVsZtnwyc3F9L+ldg74HrQ2yfWSNbxKaWwXNERAz7pe3zaocYR7Z/C3xu4PpG4MZ6icZH+ihHRadTzug+obv+GXAOjQyebb+5O/d8i+21km6jrZXxWWxPAWsGbh0JZPAcy0IGzxERMexoSR8BLgbu6G/a/tz8XxLxh0sf5ahkB9sHSnohlEkwSSsW+qIRexTwMEmDr93PqhVmI7X2s4z4vWXwHBERww6lvFDbgplt21MMrKhGRCwjv5N0d7oiV5J2YGDisDZJHwN2AL7NTFHCKcZn8JziYbFsZPAcERHDdrf9yNohIiJG5GhKC6g/lvQJYE/gxVUTzbYbpR3UuA5Cs/Icy0YqgEZExLCvS9qxdoiIiFGw/U/AcykD5k9Sqm5fWjPTkO8CD6wd4g9wZe0AEZvKiqmpcZ3EioiIpSDpB5QtgjdQti6uAKZs71w1WETEEpD0pLnu27581FnmIukS4NHAVcyuQ/GsaqEGSLovcAxlxX4KuAI4zvZNNXNFLIVs246IiGFPqx0gImKEBtvJbQk8llJ9e586cdZzTO0AC/gUcDlwQHd9EPBp4CnVEkUskaw8R0RERER0JP0x8A+2D1jwk0dE0gOA3bvLq2z/omaeQZK+a/u/Dd273vZOtTJFLJWsPEdEREREzPg34L/WDtGTJOBE4FLKMZpVko6y/ZmqwWZ8WdILAHfXzwMuqpgnYslk8BwRERERE0vSKmbaKW1GOV/cUr/xt1G6IPwCQNI2wFeAVgbPLwdeA3ysu94cuE3SYZR6GSurJYvYxDJ4joiIiIhJ9s2B99cAn7TdUoXozYa2ad9EQx1zbN+rdoaIUcngOSIiIiImlu0za2dYwIWSLqK00QI4EDi/Yp5ZJF1s+88XuhexHKRgWERERERMLEnXM7Nte1AzbfokPRfYq7v8mu3P18wDIGlL4B7AJcCTKT8vgJXAhbYfVSlaxJLJynNERERETLILusf+zO5B3eMHKmSZz9eBtcA64OrKWXqHUc46P5jS2qsfPN8KnFIrVMRSyspzREREREwsSd+y/Zihe9fa3rVWpkGSXgb8LfBVygD1z4DjbH+0arCOpCNsnzx0726276iVKWKpNFNsICIiIiKighWS9uwvJD2Btl4jHwU8xvaLbR8C/CnwpsqZBr14jnvfGHWIiFHItu2IiIiImGQvBT4qaevu+tfASyrmGXYTsHrgenV3rypJDwS2Be4uaXCVfiXlLHTEspNt2xEREREx8frBs+1bamcZJOksYCfgXEphs/2B73Rv2P77SrkOoaw678bsc9irgTNaKGoWsall5TkiIiIiJpakBwDvBB5s++mSdgQeb/u0ytF6P+zeeud2j1X7K3ctvs6UdDBlUP8wZsYWOwEZPMeyk8FzREREREyyM4DTgbd11/8X+DTQxODZ9rH9+5I2A7ayfWvFSMNeBNwMXAvcXjlLxJLK4DkiIiIiJtn9bFvSWwBsr5G0tnaonqSzgcMpraquBlZKOsn2iXWTTdvW9r61Q0SMQkuVBCMiIiIiRu02SfelbD1G0h5AS+eed+xWmp9N6Um9PWW1txVfl7RT7RARo5CV54iIiIiYZK8DzgN2kHQlsA3wvLqRZtlC0haUwfMptu+UVL3ir6TrKRMOdwEOlfQj4A5KL+op2zvXzBexFDJ4joiIiIiJJGlz4M+6t0dSBn7/bPvOqsFmOxX4MXAdcLmkhwItnHl+Ru0AEaOWVlURERERMbEkXWX7sbVzbAxJd7G9pnaOiEmTleeIiIiImGRXSjqFUmH7tv6m7WvrRZoxXystGqkGHjFJMniOiIiIiEn26O7xuIF7U8A+FbLM5QwabqUVMUkyeI6IiIiIiWV77w19XNIhts8cVZ45NN1KK2KSpFVVRERERMT8jqz8/VtvpRUxMbLyHBERERExvxWVv3/rrbQiJkYGzxERERER86vWmmZMWmlFTIwMniMiIiIi5ldt5dn2WkkvtP1+4Hu1ckREkcFzRERERMT8rqz9/VtupRUxSTJ4joiIiIiJ1RXjOgbYk7JF+wrgONs3Adh+db10QPuttCImRgbPERERETHJPgVcDhzQXR9EWeV9SrVEA8aglVbExMjgOSIiIiIm2YNsv2Pg+nhJB1ZLs/GOBDJ4jhiBDJ4jIiIiYpJ9WdILAHfXzwMuqphnY9VupRUxMTarHSAiIiIioqKXA2cDd3RvnwIOk7Ra0q1Vky1OtVZaEZMmK88RERERMbFs36t2hj9QVp4jRiQrzxERERExsSRdvJh7DavdSitiYqyYmspOj4iIiIiYLJK2BO4BXAI8mZkV3JXAhbYfVSnaLAu10oqI0cm27YiIiIiYRIcBrwEeDFzDzOD5VuCUWqHm0HQrrYhJksFzREREREwc2ycBJ0k6wvbJgx+TdLdKseYy7q20IpaNDJ4jIiIiYpK9GDh56N43gF1HH2VO495KK2LZyJnniIiIiJg4kh4IbAt8nLIVurcS+GBDZ55XA/cE1na3Ngdu696fsr2ySrCICZSV54iIiIiYRPtSVp0fArx34P5q4C01As1lGbTSilg20qoqIiIiIiaO7TNt7w28EjgNuBi4DLgW2KlmtkHLoJVWxLKRleeIiIiImGQvAm6mDJpvr5xl2kArrftJujezW2ltWy1YxATL4DkiIiIiJtm2tvetHWIO49JKK2JipGBYREREREwsSR8CVtm+vnaWuczXSsv2HbUyRUyqrDxHRERExMSRdD0wRXk9fKikHwF3UFZ4p2zvXDPfgBfTdiutiImRwXNERERETKJn1A6wIQOttO4uaXCgvJJyFjoiRiyD54iIiIiYOLZ/UjvDAsailVbEJMmZ54iIiIiIRkk6mLK9/GHMLHxN2T6uWqiICZWV54iIiIiIdjXZSitiEmXwHBERERHRrlZbaUVMnM1qB4iIiIiIiHl9XdJOtUNERM48R0REREQ0Z6iV1n8BWm2lFTExsm07IiIiIqI9TbfSiphEWXmOiIiIiIiIWEDOPEdEREREREQsIIPniIiIiIiIiAVk8BwRERERERGxgAyeIyIiIiIiIhaQwXNERERERETEAv4/LyXbVVdS4wYAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's see protocol type vs. our target(attack) by bar chart\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "ax = sns.countplot(x='proto', hue='label', data = df, palette ='Set2')\n",
        "for container in ax.containers:\n",
        "    ax.bar_label(container)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "Y9meAXpF54Dm",
        "outputId": "cae902d9-eb06-4efe-9ec7-b317bf12b284"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-811ebdf7c985>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'proto'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;34m'Set2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcontainer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontainers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontainer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'AxesSubplot' object has no attribute 'bar_label'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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ETMzM58rlgxdKfSWwb0u7SaW2Eji6R/3bpT6pl/ab28c7ZOZcYG6ZbaxevXqrPpRGLo+JwefPWKpHR0dH5bI6Lz0sAmaX6dnAwpb6aRHRFhFTgVfK5YPFwIyI2K0MYpwBLC7LXo2IqeWOidN6bKu3fUiSpD4Yqtsjb6V5NqA9Ijpp3r1wFZARcSbwUyBK8ztp3hq5jObtkWcAZOaaiPgC8HBpd0VmbhogeTZv3R55V/liM/uQJEl90NZoDOrQgO1Vo6ura6tWvGbpPQPcleHrouWP192FIdU96/xa9juajqkLD5ledxekUalcemjrbZlPZpQkSZUMCpIkqZJBQZIkVepzUIiIz1XULxi47kiSpOGkP2cU/riifllFXZIkbee2eHtkRBxTJsdGxG/x9lGRvw78fDA6JkmS6teX5yjMK993Am5uqTdovj/hMwPdKUmSNDxsMShk5v4AEbEgM08b/C5JkqThos9PZmwNCRExpseyjQPZKUmSNDz0OShExAeB64EP0LwMAc3xCg1g7MB3TZIk1a0/73qYD3wT+DTNdzBIkqQRrj9B4b3ApZnpyyEkSRol+vMchX+i+WpnSZI0SvTnjMJOwD9FxL/QvC3yTd4NIUnSyNSfoPDj8iVJkkaJ/twe+SeD2RFJkjT89Of2yGOqlmXmfQPTHUmSNJz059LDvB7zvwrsCHTSfOeDJEkaYfpz6WH/1vmIGEvzzZG+FEqSpBGqP7dHvk1mvgF8Efj8wHVHkiQNJ1sdFIrfBnzPgyRJI1R/BjOuoPleh012oflshbMHulOSJGl46M9gxt/rMf8L4CeZ+eoA9keSJA0j/RnM+B148xXTewGrBuL10hHxh8Dv0zxb8SPgDGAicBuwB/Ao8KnM3BAR44EFwGHAS8BJmbm8bOcS4EzgDeCzmbm41GcC19F8w+VNmXnVtvZZkqTRos9jFCLi3RGxAPglsBL4ZUTMj4hdt3bnEbEP8FngQ5l5MM0/5icDVwPXZuaBwFqaAYDyfW2pX1vaERFTynrvB2YCN0TE2HJnxvXAccAU4JTSVpIk9UF/BjP+FfArwG8AO5fvuwB/uY19GAfsHBHjyvaeA44Bbi/L5wMnlulZZZ6yfFpEtJX6bZm5PjOfBZYBR5SvZZn5TGZuoHmWYtY29leSpFGjP0FhJs1LAD8pf5B/QvMywcyt3XlmrgT+HPgZzYDwCs1LDS9nZndp1gnsU6b3AVaUdbtL+z1a6z3WqapLkqQ+6M9gxnU0n8b405ZaO7B+a3ceEbvR/B/+/sDLwNfZhuCxLSJiDjAHIDNpb2+voxsaxjwmBp8/Y2n46U9QuAlYEhF/QTMsvBf4Q+Cvt2H/04FnM/NFgIi4A/gwMCEixpWzBpNojomgfN8X6CyXKnalOahxU32T1nWq6m+TmXOBuWW2sXr16m34WBqJPCYGnz9jqR4dHR2Vy/oTFL5I84/sJ4EOoAv408zs+Q6I/vgZMDUidqE5SHIa8AhwP/AJmmMKZgMLS/tFZf5fy/L7MrMREYuAfyghpgOYDDwEtAGTI2L/0veTgVO3ob+SJI0q/RmjcB3wVGZOz8wpmTkdeDIivry1O8/MB2kOSvwBzVsjx9D8X/1FwAURsYzmGIRNYWQesEepXwBcXLbzBJDAj4G7gXMy841yRuJcYDHwZLNpPrG1/ZUkabTpzxmFU4DP9ag9CnwDOH9rO5CZlwOX9yg/Q/OOhZ5t1wG/W7GdL9I869Gzfidw59b2T5Kk0aw/ZxQaNJ9z0GpsP7chSZK2I/35I/9/gC+UJzNuekLj/yx1SZI0AvXn0sN5wLeA5yLip8Cv0Xz2we8MRsckSVL9+nxGITM7gQ/SfO7Bn9F8WuJhpS5Jkkag/pxRoLwE6oHyJUmSRjgHIkqSpEoGBUmSVMmgIEmSKhkUJElSJYOCJEmqZFCQJEmVDAqSJKmSQUGSJFUyKEiSpEoGBUmSVMmgIEmSKhkUJElSJYOCJEmqZFCQJEmVDAqSJKmSQUGSJFUyKEiSpEoGBUmSVGlc3R2IiAnATcDBQAP4NPAU8DVgP2A5EJm5NiLagOuA44HXgNMz8wdlO7OBy8pmr8zM+aV+GHALsDNwJ3BeZjaG4rNJkrS9Gw5nFK4D7s7M/wAcAjwJXAzcm5mTgXvLPMBxwOTyNQe4ESAidgcuB44EjgAuj4jdyjo3Ame1rDdzCD6TJEkjQq1BISJ2Bf4rMA8gMzdk5svALGB+aTYfOLFMzwIWZGYjMx8AJkTEROBYYElmrsnMtcASYGZZ9p7MfKCcRVjQsi1JkrQFdV962B94EfibiDgEeBQ4D9grM58rbZ4H9irT+wArWtbvLLXN1Tt7qb9DRMyheZaCzKS9vX3rP5VGJI+JwefPWBp+6g4K44APAp/JzAcj4jreuswAQGY2ImLQxxRk5lxgbpltrF69erB3qe2Mx8Tg82cs1aOjo6NyWd1jFDqBzsx8sMzfTjM4rCqXDSjfXyjLVwL7tqw/qdQ2V5/US12SJPVBrUEhM58HVkTE+0ppGvBjYBEwu9RmAwvL9CLgtIhoi4ipwCvlEsViYEZE7FYGMc4AFpdlr0bE1HLHxGkt25IkSVtQ96UHgM8Afx8ROwLPAGfQDDAZEWcCPwWitL2T5q2Ry2jeHnkGQGauiYgvAA+Xdldk5poyfTZv3R55V/mSJEl90NZo+EiBXjS6urq2asVrlt4zwF0Zvi5a/njdXRhS3bPOr2W/o+mYuvCQ6XV3QRqVyhiFtt6W1T1GQZIkDWMGBUmSVMmgIEmSKhkUJElSJYOCJEmqZFCQJEmVDAqSJKmSQUGSJFUyKEiSpEoGBUmSVMmgIEmSKhkUJElSJYOCJEmqZFCQJEmVDAqSJKmSQUGSJFUyKEiSpEoGBUmSVMmgIEmSKhkUJElSJYOCJEmqZFCQJEmVxtXdAYCIGAs8AqzMzBMiYn/gNmAP4FHgU5m5ISLGAwuAw4CXgJMyc3nZxiXAmcAbwGczc3GpzwSuA8YCN2XmVUP64SRJ2o4NlzMK5wFPtsxfDVybmQcCa2kGAMr3taV+bWlHREwBTgbeD8wEboiIsSWAXA8cB0wBTiltJUlSH9QeFCJiEvDfgJvKfBtwDHB7aTIfOLFMzyrzlOXTSvtZwG2ZuT4znwWWAUeUr2WZ+UxmbqB5lmLW4H8qSZJGhtqDAvBl4PPAxjK/B/ByZnaX+U5gnzK9D7ACoCx/pbR/s95jnaq6JEnqg1rHKETECcALmfloRBxdc1/mAHMAMpP29vY6u6NhyGNi8Pkzloafugczfhj4aEQcD+wEvIfmwMMJETGunDWYBKws7VcC+wKdETEO2JXmoMZN9U1a16mqv01mzgXmltnG6tWrt/GjaaTxmBh8/oylenR0dFQuq/XSQ2ZekpmTMnM/moMR78vMTwL3A58ozWYDC8v0ojJPWX5fZjZK/eSIGF/umJgMPAQ8DEyOiP0jYseyj0VD8NEkSRoRhsMYhd5cBFwQEctojkGYV+rzgD1K/QLgYoDMfAJI4MfA3cA5mflGOSNxLrCY5l0VWdpKkqQ+aGs0GnX3YThqdHV1bdWK1yy9Z4C7MnxdtPzxurswpLpnnV/LfkfTMXXhIdPr7oI0KpVLD229LRuuZxQkSdIwYFCQJEmVDAqSJKmSQUGSJFUyKEiSpEoGBUmSVMmgIEmSKhkUJElSJYOCJEmqZFCQJEmVDAqSJKmSQUGSJFUyKEiSpEoGBUmSVMmgIEmSKhkUJElSJYOCJEmqZFCQJEmVDAqSJKmSQUGSJFUyKEiSpEoGBUmSVMmgIEmSKo2rc+cRsS+wANgLaABzM/O6iNgd+BqwH7AciMxcGxFtwHXA8cBrwOmZ+YOyrdnAZWXTV2bm/FI/DLgF2Bm4EzgvMxtD8gElSdrO1X1GoRu4MDOnAFOBcyJiCnAxcG9mTgbuLfMAxwGTy9cc4EaAEiwuB44EjgAuj4jdyjo3Ame1rDdzCD6XJEkjQq1BITOf23RGIDN/DjwJ7APMAuaXZvOBE8v0LGBBZjYy8wFgQkRMBI4FlmTmmsxcCywBZpZl78nMB8pZhAUt25IkSVtQ9xmFN0XEfsB/Ah4E9srM58qi52lemoBmiFjRslpnqW2u3tlLXZIk9UGtYxQ2iYh3Af8InJ+Zr0bEm8sysxERgz6mICLm0LycQWbS3t4+2LvUdsZjYvD5M5aGn9qDQkTsQDMk/H1m3lHKqyJiYmY+Vy4fvFDqK4F9W1afVGorgaN71L9d6pN6af8OmTkXmFtmG6tXr97aj6QRymNi8PkzlurR0dFRuazWSw/lLoZ5wJOZ+RctixYBs8v0bGBhS/20iGiLiKnAK+USxWJgRkTsVgYxzgAWl2WvRsTUsq/TWrYlSZK2oO4zCh8GPgX8KCL+rdT+CLgKyIg4E/gpsOlaxJ00b41cRvP2yDMAMnNNRHwBeLi0uyIz15Tps3nr9si7ypckSeqDtkbDRwr0otHV1bVVK16z9J4B7srwddHyx+vuwpDqnnV+LfsdTcfUhYdMr7sL0qhULj209bZs2Nz1IEmShh+DgiRJqmRQkCRJlQwKkiSpkkFBkiRVMihIkqRKBgVJklTJoCBJkioZFCRJUiWDgiRJqlT3ux4k6U3jFn657i4MqboeCy71h2cUJElSJYOCJEmqZFCQJEmVDAqSJKmSQUGSJFUyKEiSpEoGBUmSVMnnKEjSKHLN0nvq7sKQuvCQ6XV3YbvnGQVJklTJoCBJkioZFCRJUiWDgiRJqjQqBjNGxEzgOmAscFNmXlVzlyRJ2i6M+DMKETEWuB44DpgCnBIRU+rtlSRJ24cRHxSAI4BlmflMZm4AbgNm1dwnSZK2C6MhKOwDrGiZ7yw1SZK0BaNijEJfRMQcYA5AZtLR0bFV27mm47SB7JbkMaUB5fGk/hoNQWElsG/L/KRSe5vMnAvMHapOjQQR8Uhmfqjufmjk8JjSQPOY2najISg8DEyOiP1pBoSTgVPr7ZIkSduHET9GITO7gXOBxcCTzVI+UW+vJEnaPoyGMwpk5p3AnXX3YwTyUo0GmseUBprH1DZqazQadfdBkiQNUyP+0oMkSdp6BgVtVkRMiIiz6+6HRo+I2C8iHq+7Hxq+IuL7dfdhNDEoaEsmAAYFScNGZv6XuvswmoyKwYzaJlcBB0TEvwFLgNXA7wEbgbsy8+KI+DawFDiK5jH16cx8qKb+ahiLiP2Ab2XmwWX+c8C7gG8CN5dm/9zS/nTgvwO70nyi6t9l5p8MYZc1DEXE/8vMd5Xpi+j9d9JjwEeAXwFOAy4BfgP4WmZeVo7Fu4FHgQ8CTwCnZeZrQ/xxhj3PKGhLLgaezsxDgftovifjyMw8BPjTlna7lDZn89YvfKmv/gb4TDmuejoC+DjwAeB3I8KH5wiAiDiO6t9JG8qDlv4XsBA4BzgYOD0i9iht3gfckJn/EXgVz572yqCg/pgO/M2mxJ2Za1qW3Vpq3wXeExETauiftk8TgAnl2AH42x7Ll2TmS5n5S+AO4DeHtHcazjb3O2lR+f4j4InMfC4z1wPP8NbTeldk5vfK9N/hsdUrg4IGSs/7bL3vVr3p5u2/d3bqwzoeW9oa68v3jS3Tm+Y3XXb32OoDg4K25OfAu8v0EuCMiNgFICJ2b2l3Uqn9JvBKZr4ypL3U9mIVsGdE7BER44ETgJeBl8uxA/DJHuv8dkTsHhE7AycC30Nq2tzvpL74tYj4z2X6VOBfBrJzI4VBQZuVmS8B3yu3q02jeTrvkTK48XMtTddFxGM0rweeOfQ91fYgM18HrgAeovlL/t/LojOA68tx1dZjtYeAfwR+CPxjZj4yRN3VMJeZd1P9O6kvngLOiYgngd2AGwe4iyOCT2bUNisjjD/nL3ANtHLXw4cy89y6+6KRpecdOKrmGQVJklTJMwqSJKmSZxQkSVIlg4IkSapkUJAkSZUMCpIkqZJBQdKwFBFHR0Rn3f2QRjuDgqRaRIRvr5W2A94eKWlARcRy4KvAp4CJwDeAPwCm0nzxzl8Bf0jzyYy/D1wNRFk9gYtoPot/NTAe2PTa34OAl3prX172I2kQeEZB0mD4JHAscADNP/CXlfrewO7Ae4E5wKU0A8ShwCE0Xyl9WZ5Za+gAAAElSURBVGb+AjgO6MrMd5Wvrqr2Q/WhpNHIoCBpMHwlM1eU1/5+ETil1DcCl2fm+vLa6E8CV2TmC5n5IvAnNM9EVOlve0nbyKAgaTCsaJn+KdBRpl/MzHUtyzrK8t7a9qa/7SVtI4OCpMGwb8v0rwFdZbrnoKgumpch+tJ2S+0lDQJHHUsaDOdExLdoDkS8FPhaRbtbgcsi4mGaweCPaQ54BFgF7BERu2bmK31oL2kQGBQkDYZ/AP6Z5mWBhcCVNAce9nQl8B7gh2X+66VGZv57RNwKPBMRY4Epm2svaXB4e6SkAVVuj/z9zLyn7r5I2naOUZAkSZUMCpIkqZKXHiRJUiXPKEiSpEoGBUmSVMmgIEmSKhkUJElSJYOCJEmqZFCQJEmV/j9uY9+EGwsEAQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.3 Check the distribution for the features"
      ],
      "metadata": {
        "id": "9OQP-49rzAHg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define history plot function for statistic features\n",
        "\n",
        "def plot_hist(df, cols, title):\n",
        "    grid = gridspec.GridSpec(10, 2, wspace=0.5, hspace=0.5) \n",
        "    fig = plt.figure(figsize=(20,30)) \n",
        "    \n",
        "    for n, col in enumerate(df[cols]):         \n",
        "        ax = plt.subplot(grid[n]) \n",
        "\n",
        "        ax.hist(df[col], bins=20) \n",
        "        #ax.set_ylabel('Count', fontsize=12)\n",
        "        ax.set_title(f'{col} distribution', fontsize=15) \n",
        "    fig.suptitle(title, fontsize=20)\n",
        "    grid.tight_layout(fig, rect=[0, 0, 1, 0.97])\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "I1DF4wGCt6rN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# using plot_hist function to present plot history of selected statistic features\n",
        "\n",
        "hist_cols = [ 'duration', 'src_bytes', 'dst_bytes', 'conn_state', 'missed_bytes', \\\n",
        "              'src_pkts', 'src_ip_bytes', 'dst_pkts', 'dst_ip_bytes', 'dns_query', \\\n",
        "              'dns_qclass', 'dns_qtype', 'dns_rcode', 'dns_AA', 'dns_RD', 'dns_RA', \\\n",
        "              'dns_rejected', 'ssl_version', 'ssl_cipher', 'ssl_resumed', \\\n",
        "#              'ssl_established', 'ssl_subject', 'ssl_issuer', 'http_trans_depth', \\\n",
        "#              'http_method', 'http_uri', 'http_version', 'http_request_body_len', \\\n",
        "#              'http_response_body_len', 'http_status_code', 'http_user_agent', \\\n",
        "#              'http_orig_mime_types', 'http_resp_mime_types', 'weird_name', \\\n",
        "#              'weird_addl', 'weird_notice'\n",
        "            ]\n",
        "    \n",
        "plot_hist(df, hist_cols, 'Distributions of Statistic Features in Data Set')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hq9P4CGavTij",
        "outputId": "b6a197dc-6073-41bb-cf7f-4425099dfb94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x2160 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check distribution of each feature towards attack\n",
        "\n",
        "numeric_features = [i for i in df.columns if df[i].dtype!='O']\n",
        "categorical_features = [i for i in df.columns if df[i].dtype =='O']\n",
        "\n",
        "plt.figure(figsize=(18,18))\n",
        "plt.suptitle('Univariate Analysis of Numerical Features With Attack', fontsize=20, fontweight='bold', alpha=0.8, y=1.)\n",
        "\n",
        "for i in range(0, len(numeric_features)):\n",
        "    plt.subplot(7, 2, i+1)\n",
        "    sns.kdeplot(x= df[numeric_features[i]], shade=True, hue='label' ,data=df)\n",
        "    plt.xlabel(numeric_features[i])\n",
        "    plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6u_zQCdax4x2",
        "outputId": "211946e9-5497-4b13-d572-3cca94a45dab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-aae1814b5c9a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnumeric_features\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshade\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'label'\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_features\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msubplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1028\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1029\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1030\u001b[0;31m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1031\u001b[0m     \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1032\u001b[0m     \u001b[0mbyebye\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36madd_subplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1417\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1418\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1419\u001b[0;31m             \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprojection_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1420\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1421\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_axes_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/axes/_subplots.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnum\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mnum\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m                     raise ValueError(\n\u001b[0m\u001b[1;32m     66\u001b[0m                         f\"num must be 1 <= num <= {rows*cols}, not {num}\")\n\u001b[1;32m     67\u001b[0m                 self._subplotspec = GridSpec(\n",
            "\u001b[0;31mValueError\u001b[0m: num must be 1 <= num <= 14, not 15"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x1296 with 14 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in df.columns:\n",
        "    plt.figure(figsize=(18,18))\n",
        "    sns.displot(data= df,x=i,hue='label',kind='hist')"
      ],
      "metadata": {
        "id": "-615A63F0c7h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.4 Check the outlier of each feature"
      ],
      "metadata": {
        "id": "pJ3clayQ-kru"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#plot the outlier of the feature\n",
        "\n",
        "plt.boxplot(df['src_bytes'])\n",
        "plt.title('Outlier of src_bytes')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "4fibiU7F-uR2",
        "outputId": "b9562fae-300c-462f-fdcf-db3253476d07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Data Pre-processing"
      ],
      "metadata": {
        "id": "CnZzCGrtCTS2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1 Handle missing value#"
      ],
      "metadata": {
        "id": "c6740kuoYqzX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check the count of missing value\n",
        "\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3rdv3M-KZO3U",
        "outputId": "a921cc6e-a7c1-40e5-ee59-f2eb33c34b40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ts                        0\n",
              "src_ip                    0\n",
              "src_port                  0\n",
              "dst_ip                    0\n",
              "dst_port                  0\n",
              "proto                     0\n",
              "service                   0\n",
              "duration                  0\n",
              "src_bytes                 0\n",
              "dst_bytes                 0\n",
              "conn_state                0\n",
              "missed_bytes              0\n",
              "src_pkts                  0\n",
              "src_ip_bytes              0\n",
              "dst_pkts                  0\n",
              "dst_ip_bytes              0\n",
              "dns_query                 0\n",
              "dns_qclass                0\n",
              "dns_qtype                 0\n",
              "dns_rcode                 0\n",
              "dns_AA                    0\n",
              "dns_RD                    0\n",
              "dns_RA                    0\n",
              "dns_rejected              0\n",
              "ssl_version               0\n",
              "ssl_cipher                0\n",
              "ssl_resumed               0\n",
              "ssl_established           0\n",
              "ssl_subject               0\n",
              "ssl_issuer                0\n",
              "http_trans_depth          0\n",
              "http_method               0\n",
              "http_uri                  0\n",
              "http_version              0\n",
              "http_request_body_len     0\n",
              "http_response_body_len    0\n",
              "http_status_code          0\n",
              "http_user_agent           0\n",
              "http_orig_mime_types      0\n",
              "http_resp_mime_types      0\n",
              "weird_name                0\n",
              "weird_addl                0\n",
              "weird_notice              0\n",
              "label                     0\n",
              "type                      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check the count of missing value\n",
        "\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ok0Pj-nKdGId",
        "outputId": "3e8baeb1-f915-4b7c-da56-7aecec070310"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ts                        0\n",
              "src_ip                    0\n",
              "src_port                  0\n",
              "dst_ip                    0\n",
              "dst_port                  0\n",
              "proto                     0\n",
              "service                   0\n",
              "duration                  0\n",
              "src_bytes                 0\n",
              "dst_bytes                 0\n",
              "conn_state                0\n",
              "missed_bytes              0\n",
              "src_pkts                  0\n",
              "src_ip_bytes              0\n",
              "dst_pkts                  0\n",
              "dst_ip_bytes              0\n",
              "dns_query                 0\n",
              "dns_qclass                0\n",
              "dns_qtype                 0\n",
              "dns_rcode                 0\n",
              "dns_AA                    0\n",
              "dns_RD                    0\n",
              "dns_RA                    0\n",
              "dns_rejected              0\n",
              "ssl_version               0\n",
              "ssl_cipher                0\n",
              "ssl_resumed               0\n",
              "ssl_established           0\n",
              "ssl_subject               0\n",
              "ssl_issuer                0\n",
              "http_trans_depth          0\n",
              "http_method               0\n",
              "http_uri                  0\n",
              "http_version              0\n",
              "http_request_body_len     0\n",
              "http_response_body_len    0\n",
              "http_status_code          0\n",
              "http_user_agent           0\n",
              "http_orig_mime_types      0\n",
              "http_resp_mime_types      0\n",
              "weird_name                0\n",
              "weird_addl                0\n",
              "weird_notice              0\n",
              "label                     0\n",
              "type                      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# show the features that contain '-' value, which can be regarded as missing value\n",
        "\n",
        "features_with_missing_value = df.columns[df.isin(['-']).any()]\n",
        "features_with_missing_value"
      ],
      "metadata": {
        "id": "QHuCP6uEmACC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ffb0055-0ac1-4ac2-ea63-379780da407d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['service', 'dns_query', 'dns_AA', 'dns_RD', 'dns_RA', 'dns_rejected',\n",
              "       'ssl_version', 'ssl_cipher', 'ssl_resumed', 'ssl_established',\n",
              "       'ssl_subject', 'ssl_issuer', 'http_trans_depth', 'http_method',\n",
              "       'http_uri', 'http_version', 'http_user_agent', 'http_orig_mime_types',\n",
              "       'http_resp_mime_types', 'weird_name', 'weird_addl', 'weird_notice'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**=> 'service', \"dns_xxx\", \"ssl_xxx\", \"http_xxx\" and \n",
        "\"weird_xxx\" features have \"-\" value, so the next step is to figure out the meaning of the features and why \"-\" exists in the data.**\n",
        "\n"
      ],
      "metadata": {
        "id": "9nXzkl2G4vz0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# show the instances that contain '-' value, which can be regarded as missing value\n",
        "mask = (df == '-').any(axis=1)\n",
        "indices = df.loc[mask].index\n",
        "indices"
      ],
      "metadata": {
        "id": "3RNR7RVyjXlN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1efc417a-b693-47d6-ef69-d4dd19981048"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([     0,      1,      2,      3,      4,      5,      6,      7,\n",
              "                 8,      9,\n",
              "            ...\n",
              "            461033, 461034, 461035, 461036, 461037, 461038, 461039, 461040,\n",
              "            461041, 461042],\n",
              "           dtype='int64', length=461043)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "=> The output shows that each instance has at least one \"-\" value in any feature"
      ],
      "metadata": {
        "id": "ro_GXaGx4int"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"service\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "goPnAxmXsCOK",
        "outputId": "27cbd302-cbc7-4b0b-e828-291a5871a061"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-             280216\n",
              "dns           116480\n",
              "http           60720\n",
              "ssl             2070\n",
              "ftp             1065\n",
              "gssapi           184\n",
              "dce_rpc          136\n",
              "smb              108\n",
              "dhcp              46\n",
              "smb;gssapi        18\n",
              "Name: service, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"dns_query\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-BhuFXBsQ5R",
        "outputId": "f3c090fb-f585-450b-b060-d28a21a5acae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-                                                  366019\n",
              "a2z3kk2ebqzso7.iot.ap-southeast-2.amazonaws.com     13700\n",
              "arpa.whois-servers.net                               2198\n",
              "_sleep-proxy._udp.local                              2187\n",
              "testphp.vulnweb.com                                  1848\n",
              "                                                    ...  \n",
              "94.157.81.140.in-addr.arpa                              1\n",
              "125.48.34.73.in-addr.arpa                               1\n",
              "101.192.178.247.in-addr.arpa                            1\n",
              "254.68.212.in-addr.arpa                                 1\n",
              "loadm.exelator.com                                      1\n",
              "Name: dns_query, Length: 14149, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"dns_AA\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j4vgczsCset1",
        "outputId": "36d20612-df0c-4bd7-bba9-3f566ebafc5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-    365158\n",
              "F     81279\n",
              "T     14606\n",
              "Name: dns_AA, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"dns_RD\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YWL2nuKkslYs",
        "outputId": "6a2dd203-af75-43cb-bba1-440c749d721f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-    365158\n",
              "F     63106\n",
              "T     32779\n",
              "Name: dns_RD, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"dns_rejected\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jGsYhWY6srLT",
        "outputId": "71d1fc72-0e3f-40f7-e746-949df0eb6528"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-    365158\n",
              "F     89392\n",
              "T      6493\n",
              "Name: dns_rejected, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"ssl_version\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ttWthOpws0SF",
        "outputId": "58a3a4e1-a165-41a2-d1cb-994326cbaf27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-         460737\n",
              "TLSv12       188\n",
              "TLSv13       116\n",
              "TLSv10         2\n",
              "Name: ssl_version, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"ssl_cipher\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rTv6VK37s5sJ",
        "outputId": "c7790777-29b1-4cc7-b43b-d897bda5d651"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-                                          460737\n",
              "TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256         176\n",
              "TLS_AES_128_GCM_SHA256                        116\n",
              "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384          11\n",
              "TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA              2\n",
              "TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256         1\n",
              "Name: ssl_cipher, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"ssl_resumed\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "li0xCqnVtDNV",
        "outputId": "ed791532-649d-4534-fc4a-ea48a187b567"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-    460352\n",
              "F       605\n",
              "T        86\n",
              "Name: ssl_resumed, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"ssl_established\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aLUz3d1DtI8I",
        "outputId": "9a7c9b0f-c335-4df8-80c2-9e5cea155ca7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-    460352\n",
              "F       596\n",
              "T        95\n",
              "Name: ssl_established, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"ssl_subject\"].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6HSQTajdtOa2",
        "outputId": "307d9130-c35d-4833-fbba-f4f08dc38da9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-                                                                                           461034\n",
              "CN=*.wns.windows.com                                                                             4\n",
              "CN=Mr Shepherd;OU=Security Shepherd Project;O=OWASP;L=Dublin;ST=Ireland;C=IE                     2\n",
              "CN=settings-win.data.microsoft.com;OU=WSE;O=Microsoft;L=Redmond;ST=WA;C=US                       1\n",
              "CN=sls.update.microsoft.com;OU=DSP;O=Microsoft;L=Redmond;ST=WA;C=US                              1\n",
              "CN=*.events.data.microsoft.com;OU=Microsoft;O=Microsoft Corporation;L=Redmond;ST=WA;C=US         1\n",
              "Name: ssl_subject, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"ssl_issuer\"].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Adrv6s_itY62",
        "outputId": "d58617ab-b4bf-4f33-b5dd-63f0c8a8715b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-                                                                                                461034\n",
              "CN=Microsoft IT TLS CA 5;OU=Microsoft IT;O=Microsoft Corporation;L=Redmond;ST=Washington;C=US         4\n",
              "CN=Microsoft Secure Server CA 2011;O=Microsoft Corporation;L=Redmond;ST=Washington;C=US               2\n",
              "CN=Mr Shepherd;OU=Security Shepherd Project;O=OWASP;L=Dublin;ST=Ireland;C=IE                          2\n",
              "CN=Microsoft Update Secure Server CA 2.1;O=Microsoft Corporation;L=Redmond;ST=Washington;C=US         1\n",
              "Name: ssl_issuer, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"http_trans_depth\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eh7qNwAXtdkN",
        "outputId": "b2b74bd0-96fa-446e-832d-8ce979c7c7c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-     460796\n",
              "1        214\n",
              "2         25\n",
              "10         1\n",
              "3          1\n",
              "4          1\n",
              "5          1\n",
              "6          1\n",
              "7          1\n",
              "8          1\n",
              "9          1\n",
              "Name: http_trans_depth, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"http_method\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JUnltY7htixX",
        "outputId": "79b52574-650b-4509-e0cf-e06b2cd40f02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-       460809\n",
              "GET        215\n",
              "POST        15\n",
              "HEAD         4\n",
              "Name: http_method, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"http_uri\"].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ElbaWPVStna-",
        "outputId": "9c97369c-5ac1-458f-f7ff-a87412be0e1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-                                                                                                                                                                                         460809\n",
              "/no_nonce_string/                                                                                                                                                                             15\n",
              "/dvwa/login.php-r                                                                                                                                                                             13\n",
              "/no_nonce_string/ContentDirectory/scpd.xml                                                                                                                                                    12\n",
              "/filestreamingservice/files/1f41d40e-6e49-42f0-a4dc-3d35029e2a5d?P1=1554315773&P2=402&P3=2&P4=D2MtshBIyXICLz+f8ls5Sa4mWcxA1B670oj1WRainhnd91v77n9XO6/xoc4QeEYZdwdKSG+6cFNvkBVYbJdXpw==         9\n",
              "                                                                                                                                                                                           ...  \n",
              "/icon_LRG.jpg                                                                                                                                                                                  1\n",
              "/msdownload/update/v3/static/trustedr/en/disallowedcertstl.cab?e793f3590addb8f9                                                                                                                1\n",
              "/filestreamingservice/files/846ab63f-c24f-4c71-bc98-6d8fa3f5e5d7/pieceshash                                                                                                                    1\n",
              "/filestreamingservice/files/1f41d40e-6e49-42f0-a4dc-3d35029e2a5d/pieceshash                                                                                                                    1\n",
              "/webupd8team/java/ubuntu/dists/bionic/InRelease                                                                                                                                                1\n",
              "Name: http_uri, Length: 74, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"http_version\"].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Z4DTTrOtvOS",
        "outputId": "989b6006-88ec-40a8-a726-3d5bca45d99f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-      460801\n",
              "1.1       242\n",
              "Name: http_version, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"http_user_agent\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MJfHz1Eqt0QL",
        "outputId": "dd3d70c5-3a26-437f-8243-4ffe22286772"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-                                                                                                                                    460809\n",
              "Microsoft-Delivery-Optimization/10.0                                                                                                     57\n",
              "Microsoft-Windows/10.0 UPnP/1.0                                                                                                          32\n",
              "Ruby                                                                                                                                     31\n",
              "DAFUPnP                                                                                                                                  19\n",
              "User-Agent: Microsoft-DLNA DLNADOC/1.50                                                                                                  19\n",
              "Windows-Update-Agent/10.0.10011.16384 Client-Protocol/1.91                                                                               11\n",
              "MICROSOFT_DEVICE_METADATA_RETRIEVAL_CLIENT                                                                                                9\n",
              "Microsoft NCSI                                                                                                                            8\n",
              "Microsoft-Windows/10.0 UPnP/1.0 Microsoft-DLNA DLNADOC/1.50                                                                               8\n",
              "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:66.0) Gecko/20100101 Firefox/66.0                                                              4\n",
              "Debian APT-HTTP/1.3 (1.7.4)                                                                                                               4\n",
              "Microsoft-CryptoAPI/10.0                                                                                                                  4\n",
              "Microsoft-WNS/10.0                                                                                                                        3\n",
              "sqlmap/1.2#stable (http://sqlmap.org)                                                                                                     2\n",
              "Microsoft BITS/7.8                                                                                                                        2\n",
              "hacking                                                                                                                                   2\n",
              "Mozilla/5.0 (X11; U; Linux i686; de-DE; rv:1.6) Gecko/20040207 Firefox/0.8                                                                1\n",
              "Mozilla/4.08 [en] (WinNT; I ;Nav)                                                                                                         1\n",
              "Mozilla/5.0 (Windows NT 6.1; rv:21.0) Gecko/20130401 Firefox/21.0                                                                         1\n",
              "Mozilla/1.22 (compatible; MSIE 10.0; Windows 3.1)                                                                                         1\n",
              "Mozilla/5.0 (Windows NT 6.3; rv:36.0) Gecko/20100101 Firefox/36.0                                                                         1\n",
              "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML; like Gecko) Chrome/53.0.2785.143 Safari/537.36                 1\n",
              "DataCha0s/2.0                                                                                                                             1\n",
              "Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML; like Gecko) Chrome/53.0.2785.143 Safari/537.36                             1\n",
              "Comos/0.9_(robot@xyleme.com)                                                                                                              1\n",
              "DonutP; Windows98SE                                                                                                                       1\n",
              "Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.1; Trident/4.0; InfoPath.2; SV1; .NET CLR 2.0.50727; WOW64)                              1\n",
              "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/534.30 (KHTML; like Gecko) Version/4.0 Oupeng/10.2.1.86910 Safari/534.30                      1\n",
              "Mozilla/5.0 (SMART-TV; Linux; Tizen 2.3) AppleWebkit/538.1 (KHTML; like Gecko) SamsungBrowser/1.0 Safari/538.1                            1\n",
              "Mozilla/5.0 (Windows NT 6.1; WOW64; rv:49.0) Gecko/20100101 Firefox/49.0                                                                  1\n",
              "Mozilla/5.0 (Windows NT 6.1; Win64; x64; rv:5.0) Whistler/20110021 myibrow/5.0.0.0                                                        1\n",
              "Mozilla/5.0 (iPhone; U; CPU iOS 2_0 like Mac OS X; en-us)                                                                                 1\n",
              "Mozilla/5.0 (Macintosh; Intel Mac OS X 10.11; rv:49.0) Gecko/20100101 Firefox/49.0                                                        1\n",
              "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML; like Gecko) Chrome/64.0.3282.140 Safari/537.36 Edge/18.17763         1\n",
              "Debian APT-HTTP/1.3 (1.6.6)                                                                                                               1\n",
              "Name: http_user_agent, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"http_orig_mime_types\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sY5DgClut9c3",
        "outputId": "becca0d2-07ef-409f-e775-b40b4c872292"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-                       461029\n",
              "application/xml              8\n",
              "application/soap+xml         6\n",
              "Name: http_orig_mime_types, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"http_resp_mime_types\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MEHpGFEzuEZG",
        "outputId": "bb4e1f5d-aeec-4ca8-a503-aa09087921c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-                                    460883\n",
              "application/xml                          63\n",
              "text/html                                41\n",
              "image/png                                15\n",
              "application/vnd.ms-cab-compressed        13\n",
              "text/plain                               11\n",
              "application/ocsp-response                 8\n",
              "text/json                                 4\n",
              "image/jpeg                                4\n",
              "application/x-debian-package              1\n",
              "Name: http_resp_mime_types, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"weird_name\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MVWHumj4uJwl",
        "outputId": "400c2ba6-8b1a-4097-bcf7-e4f762c74004"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-                                   459749\n",
              "DNS_RR_unknown_type                    753\n",
              "active_connection_reuse                275\n",
              "data_before_established                109\n",
              "bad_UDP_checksum                        68\n",
              "bad_TCP_checksum                        52\n",
              "connection_originator_SYN_ack           20\n",
              "above_hole_data_without_any_acks         6\n",
              "inappropriate_FIN                        3\n",
              "dnp3_corrupt_header_checksum             3\n",
              "possible_split_routing                   3\n",
              "TCP_ack_underflow_or_misorder            2\n",
              "Name: weird_name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"weird_addl\"].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E--9LZwLuOke",
        "outputId": "86fb456d-ce3e-4561-df30-378bb5528b7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-     460290\n",
              "46       707\n",
              "48        38\n",
              "43         8\n",
              "Name: weird_addl, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the value counts for the features\n",
        "\n",
        "df[\"weird_notice\"].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_cW9HJEuuUQK",
        "outputId": "566590f0-3649-40a4-f4ef-94b950c1fc8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-    459749\n",
              "F      1294\n",
              "Name: weird_notice, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After checking for the features that contain \"-\" value (missing value), nearly all the instance have missing value for these features, so we need do imputation for the missing values.\n",
        "Then what's the imputation mechanism to be used for that? Need to understand the features and why the value is missing? (type of missing value)"
      ],
      "metadata": {
        "id": "qqmM5Ljlua-G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.2 Handle duplicate instance"
      ],
      "metadata": {
        "id": "KAB1cxKGY4NN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the duplicated instance\n",
        "\n",
        "df.duplicated().sum()\n",
        "\n",
        "#Reference\n",
        "#https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.duplicated.html"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2wmTaznmvwy",
        "outputId": "2c5a0fa7-451c-4dd7-ae1e-d436c860a26b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11071"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# List instances duplicated\n",
        "\n",
        "duplicates = df[df.duplicated()]\n",
        "duplicates\n",
        "\n",
        "#duplicates = df[df.duplicated(keep = False)]\n",
        "#keep : {‘first’, ‘last’, False}, default ‘first’\n",
        "\n",
        "#'first' : Mark duplicates as True except for the first occurrence.\n",
        "#'last' : Mark duplicates as True except for the last occurrence.\n",
        "#False : Mark all duplicates as True."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "PGj_uW9s_cFt",
        "outputId": "7bd14129-b9ad-4be8-8811-8eb20431e1b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                ts         src_ip  src_port         dst_ip  dst_port proto  \\\n",
              "12679   1554220499  192.168.1.250      5353    224.0.0.251      5353   udp   \n",
              "12699   1554220514  192.168.1.133      5353    224.0.0.251      5353   udp   \n",
              "12813   1554220595  192.168.1.133      5353    224.0.0.251      5353   udp   \n",
              "12996   1554220744   192.168.1.79     43029  192.168.1.255     15600   udp   \n",
              "13124   1554220864  192.168.1.195       138  192.168.1.255       138   udp   \n",
              "...            ...            ...       ...            ...       ...   ...   \n",
              "420021  1556423639  192.168.1.195     49884    13.107.4.50        80   tcp   \n",
              "420022  1556423639  192.168.1.195     49885    13.107.4.50        80   tcp   \n",
              "420023  1556423639  192.168.1.195     49884    13.107.4.50        80   tcp   \n",
              "420024  1556423639  192.168.1.195     49885    13.107.4.50        80   tcp   \n",
              "420025  1556423639  192.168.1.195     49884    13.107.4.50        80   tcp   \n",
              "\n",
              "       service  duration  src_bytes  dst_bytes  ... http_response_body_len  \\\n",
              "12679      dns       0.0          0          0  ...                      0   \n",
              "12699      dns       0.0          0          0  ...                      0   \n",
              "12813      dns       0.0          0          0  ...                      0   \n",
              "12996        -       0.0          0          0  ...                      0   \n",
              "13124        -       0.0          0          0  ...                      0   \n",
              "...        ...       ...        ...        ...  ...                    ...   \n",
              "420021       -       0.0          0          0  ...                      0   \n",
              "420022       -       0.0          0          0  ...                      0   \n",
              "420023       -       0.0          0          0  ...                      0   \n",
              "420024       -       0.0          0          0  ...                      0   \n",
              "420025       -       0.0          0          0  ...                      0   \n",
              "\n",
              "        http_status_code  http_user_agent  http_orig_mime_types  \\\n",
              "12679                  0                -                     -   \n",
              "12699                  0                -                     -   \n",
              "12813                  0                -                     -   \n",
              "12996                  0                -                     -   \n",
              "13124                  0                -                     -   \n",
              "...                  ...              ...                   ...   \n",
              "420021                 0                -                     -   \n",
              "420022                 0                -                     -   \n",
              "420023                 0                -                     -   \n",
              "420024                 0                -                     -   \n",
              "420025                 0                -                     -   \n",
              "\n",
              "        http_resp_mime_types  weird_name weird_addl  weird_notice  label  \\\n",
              "12679                      -           -          -             -      0   \n",
              "12699                      -           -          -             -      0   \n",
              "12813                      -           -          -             -      0   \n",
              "12996                      -           -          -             -      0   \n",
              "13124                      -           -          -             -      0   \n",
              "...                      ...         ...        ...           ...    ...   \n",
              "420021                     -           -          -             -      0   \n",
              "420022                     -           -          -             -      0   \n",
              "420023                     -           -          -             -      0   \n",
              "420024                     -           -          -             -      0   \n",
              "420025                     -           -          -             -      0   \n",
              "\n",
              "          type  \n",
              "12679   normal  \n",
              "12699   normal  \n",
              "12813   normal  \n",
              "12996   normal  \n",
              "13124   normal  \n",
              "...        ...  \n",
              "420021  normal  \n",
              "420022  normal  \n",
              "420023  normal  \n",
              "420024  normal  \n",
              "420025  normal  \n",
              "\n",
              "[11071 rows x 45 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d5266a7c-b573-483e-9b36-7af69953e63c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ts</th>\n",
              "      <th>src_ip</th>\n",
              "      <th>src_port</th>\n",
              "      <th>dst_ip</th>\n",
              "      <th>dst_port</th>\n",
              "      <th>proto</th>\n",
              "      <th>service</th>\n",
              "      <th>duration</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>...</th>\n",
              "      <th>http_response_body_len</th>\n",
              "      <th>http_status_code</th>\n",
              "      <th>http_user_agent</th>\n",
              "      <th>http_orig_mime_types</th>\n",
              "      <th>http_resp_mime_types</th>\n",
              "      <th>weird_name</th>\n",
              "      <th>weird_addl</th>\n",
              "      <th>weird_notice</th>\n",
              "      <th>label</th>\n",
              "      <th>type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12679</th>\n",
              "      <td>1554220499</td>\n",
              "      <td>192.168.1.250</td>\n",
              "      <td>5353</td>\n",
              "      <td>224.0.0.251</td>\n",
              "      <td>5353</td>\n",
              "      <td>udp</td>\n",
              "      <td>dns</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12699</th>\n",
              "      <td>1554220514</td>\n",
              "      <td>192.168.1.133</td>\n",
              "      <td>5353</td>\n",
              "      <td>224.0.0.251</td>\n",
              "      <td>5353</td>\n",
              "      <td>udp</td>\n",
              "      <td>dns</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12813</th>\n",
              "      <td>1554220595</td>\n",
              "      <td>192.168.1.133</td>\n",
              "      <td>5353</td>\n",
              "      <td>224.0.0.251</td>\n",
              "      <td>5353</td>\n",
              "      <td>udp</td>\n",
              "      <td>dns</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12996</th>\n",
              "      <td>1554220744</td>\n",
              "      <td>192.168.1.79</td>\n",
              "      <td>43029</td>\n",
              "      <td>192.168.1.255</td>\n",
              "      <td>15600</td>\n",
              "      <td>udp</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13124</th>\n",
              "      <td>1554220864</td>\n",
              "      <td>192.168.1.195</td>\n",
              "      <td>138</td>\n",
              "      <td>192.168.1.255</td>\n",
              "      <td>138</td>\n",
              "      <td>udp</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420021</th>\n",
              "      <td>1556423639</td>\n",
              "      <td>192.168.1.195</td>\n",
              "      <td>49884</td>\n",
              "      <td>13.107.4.50</td>\n",
              "      <td>80</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420022</th>\n",
              "      <td>1556423639</td>\n",
              "      <td>192.168.1.195</td>\n",
              "      <td>49885</td>\n",
              "      <td>13.107.4.50</td>\n",
              "      <td>80</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420023</th>\n",
              "      <td>1556423639</td>\n",
              "      <td>192.168.1.195</td>\n",
              "      <td>49884</td>\n",
              "      <td>13.107.4.50</td>\n",
              "      <td>80</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420024</th>\n",
              "      <td>1556423639</td>\n",
              "      <td>192.168.1.195</td>\n",
              "      <td>49885</td>\n",
              "      <td>13.107.4.50</td>\n",
              "      <td>80</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420025</th>\n",
              "      <td>1556423639</td>\n",
              "      <td>192.168.1.195</td>\n",
              "      <td>49884</td>\n",
              "      <td>13.107.4.50</td>\n",
              "      <td>80</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11071 rows × 45 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5266a7c-b573-483e-9b36-7af69953e63c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see, there are 11071 rows are duplicated in the data set. we need consider the mechanism to handle. If there is no any important information, we may directly drop the duplicate instances."
      ],
      "metadata": {
        "id": "ScSG1SxGwEj3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.3 Check non-numerical value"
      ],
      "metadata": {
        "id": "qwt3_312Y_ub"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the type of each feature\n",
        "\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8MyfltuZnDQw",
        "outputId": "a1ba2b91-9ada-4c59-b59d-4016789f06cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 461043 entries, 0 to 461042\n",
            "Data columns (total 45 columns):\n",
            " #   Column                  Non-Null Count   Dtype  \n",
            "---  ------                  --------------   -----  \n",
            " 0   ts                      461043 non-null  int64  \n",
            " 1   src_ip                  461043 non-null  object \n",
            " 2   src_port                461043 non-null  int64  \n",
            " 3   dst_ip                  461043 non-null  object \n",
            " 4   dst_port                461043 non-null  int64  \n",
            " 5   proto                   461043 non-null  object \n",
            " 6   service                 461043 non-null  object \n",
            " 7   duration                461043 non-null  float64\n",
            " 8   src_bytes               461043 non-null  int64  \n",
            " 9   dst_bytes               461043 non-null  int64  \n",
            " 10  conn_state              461043 non-null  object \n",
            " 11  missed_bytes            461043 non-null  int64  \n",
            " 12  src_pkts                461043 non-null  int64  \n",
            " 13  src_ip_bytes            461043 non-null  int64  \n",
            " 14  dst_pkts                461043 non-null  int64  \n",
            " 15  dst_ip_bytes            461043 non-null  int64  \n",
            " 16  dns_query               461043 non-null  object \n",
            " 17  dns_qclass              461043 non-null  int64  \n",
            " 18  dns_qtype               461043 non-null  int64  \n",
            " 19  dns_rcode               461043 non-null  int64  \n",
            " 20  dns_AA                  461043 non-null  object \n",
            " 21  dns_RD                  461043 non-null  object \n",
            " 22  dns_RA                  461043 non-null  object \n",
            " 23  dns_rejected            461043 non-null  object \n",
            " 24  ssl_version             461043 non-null  object \n",
            " 25  ssl_cipher              461043 non-null  object \n",
            " 26  ssl_resumed             461043 non-null  object \n",
            " 27  ssl_established         461043 non-null  object \n",
            " 28  ssl_subject             461043 non-null  object \n",
            " 29  ssl_issuer              461043 non-null  object \n",
            " 30  http_trans_depth        461043 non-null  object \n",
            " 31  http_method             461043 non-null  object \n",
            " 32  http_uri                461043 non-null  object \n",
            " 33  http_version            461043 non-null  object \n",
            " 34  http_request_body_len   461043 non-null  int64  \n",
            " 35  http_response_body_len  461043 non-null  int64  \n",
            " 36  http_status_code        461043 non-null  int64  \n",
            " 37  http_user_agent         461043 non-null  object \n",
            " 38  http_orig_mime_types    461043 non-null  object \n",
            " 39  http_resp_mime_types    461043 non-null  object \n",
            " 40  weird_name              461043 non-null  object \n",
            " 41  weird_addl              461043 non-null  object \n",
            " 42  weird_notice            461043 non-null  object \n",
            " 43  label                   461043 non-null  int64  \n",
            " 44  type                    461043 non-null  object \n",
            "dtypes: float64(1), int64(17), object(27)\n",
            "memory usage: 158.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the output, there are many non-numerical features, or object type for the features."
      ],
      "metadata": {
        "id": "yXkmNFMvxR3w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3Zw2EzQonUHr",
        "outputId": "f7ad6db1-d9fd-45ee-90cc-7aed955b222a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       0              1                 2  \\\n",
              "ts                            1554198358     1554198358        1554198359   \n",
              "src_ip                       3.122.49.24   192.168.1.79     192.168.1.152   \n",
              "src_port                            1883          47260              1880   \n",
              "dst_ip                     192.168.1.152  192.168.1.255     192.168.1.152   \n",
              "dst_port                           52976          15600             51782   \n",
              "proto                                tcp            udp               tcp   \n",
              "service                                -              -                 -   \n",
              "duration                     80549.53026            0.0               0.0   \n",
              "src_bytes                        1762852              0                 0   \n",
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              "conn_state                           OTH             S0               OTH   \n",
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              "src_ip_bytes                    14911156             63                 0   \n",
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              "dst_ip_bytes                         236              0                 0   \n",
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              "label                                  0              0                 0   \n",
              "type                              normal         normal            normal   \n",
              "\n",
              "                                    3                 4  \n",
              "ts                         1554198359        1554198362  \n",
              "src_ip                  192.168.1.152     192.168.1.152  \n",
              "src_port                        34296             46608  \n",
              "dst_ip                  192.168.1.152     192.168.1.190  \n",
              "dst_port                        10502                53  \n",
              "proto                             tcp               udp  \n",
              "service                             -               dns  \n",
              "duration                          0.0          0.000549  \n",
              "src_bytes                           0                 0  \n",
              "dst_bytes                           0               298  \n",
              "conn_state                        OTH               SHR  \n",
              "missed_bytes                        0                 0  \n",
              "src_pkts                            0                 0  \n",
              "src_ip_bytes                        0                 0  \n",
              "dst_pkts                            0                 2  \n",
              "dst_ip_bytes                        0               354  \n",
              "dns_query                           -                 -  \n",
              "dns_qclass                          0                 0  \n",
              "dns_qtype                           0                 0  \n",
              "dns_rcode                           0                 0  \n",
              "dns_AA                              -                 -  \n",
              "dns_RD                              -                 -  \n",
              "dns_RA                              -                 -  \n",
              "dns_rejected                        -                 -  \n",
              "ssl_version                         -                 -  \n",
              "ssl_cipher                          -                 -  \n",
              "ssl_resumed                         -                 -  \n",
              "ssl_established                     -                 -  \n",
              "ssl_subject                         -                 -  \n",
              "ssl_issuer                          -                 -  \n",
              "http_trans_depth                    -                 -  \n",
              "http_method                         -                 -  \n",
              "http_uri                            -                 -  \n",
              "http_version                        -                 -  \n",
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              "weird_name                          -  bad_UDP_checksum  \n",
              "weird_addl                          -                 -  \n",
              "weird_notice                        -                 F  \n",
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              "      <td>OTH</td>\n",
              "      <td>SHR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>missed_bytes</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>src_pkts</th>\n",
              "      <td>252181</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>src_ip_bytes</th>\n",
              "      <td>14911156</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dst_pkts</th>\n",
              "      <td>2</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dst_ip_bytes</th>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dns_query</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>dns_qclass</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>dns_qtype</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>dns_rcode</th>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>dns_AA</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "      <th>dns_RD</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>dns_RA</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>dns_rejected</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>ssl_version</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>ssl_cipher</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>ssl_resumed</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>ssl_established</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>ssl_subject</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>ssl_issuer</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>http_trans_depth</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
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              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>http_uri</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>http_user_agent</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>http_orig_mime_types</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>http_resp_mime_types</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weird_name</th>\n",
              "      <td>bad_TCP_checksum</td>\n",
              "      <td>-</td>\n",
              "      <td>bad_TCP_checksum</td>\n",
              "      <td>-</td>\n",
              "      <td>bad_UDP_checksum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weird_addl</th>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>weird_notice</th>\n",
              "      <td>F</td>\n",
              "      <td>-</td>\n",
              "      <td>F</td>\n",
              "      <td>-</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>label</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>type</th>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.4 Feature Elimination by understanding the dataset"
      ],
      "metadata": {
        "id": "rd_87t0aCitv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After understanding each feature in the dataset, we decide to drop following features:"
      ],
      "metadata": {
        "id": "Zoqvr2nbxvHK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop the columns (features) that has no relationship with the pattern we try to find\n",
        "# 'pkSeqID', 'stime', 'saddr', 'sport', 'daddr', 'dport', 'ltime', 'seq'\n",
        "\n",
        "df_fe1 = df.drop(['pkSeqID', 'stime', 'saddr', 'sport', 'daddr', 'dport', 'ltime', 'seq'], axis=1)"
      ],
      "metadata": {
        "id": "4wXGbc6fCdxA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_fe1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "YkJmO0MLC-2M",
        "outputId": "38a836bb-7994-4e5e-d37c-94b49814f30d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  flgs  flgs_number proto  proto_number  pkts  bytes state  state_number  \\\n",
              "0    e            1   tcp             1     8   1980   RST             1   \n",
              "1    e            1   arp             2     2    120   CON             2   \n",
              "2    e            1   tcp             1     8   2126   RST             1   \n",
              "3    e            1   tcp             1     8   2024   RST             1   \n",
              "4    e            1   tcp             1     8   2319   RST             1   \n",
              "\n",
              "        dur      mean  ...  AR_P_Proto_P_DstIP  N_IN_Conn_P_DstIP  \\\n",
              "0  7.056393  0.068909  ...             1.12704               96.0   \n",
              "1  0.000131  0.000131  ...         15267.20000                1.0   \n",
              "2  7.047852  0.064494  ...             1.12704               96.0   \n",
              "3  7.047592  0.064189  ...             1.12704               96.0   \n",
              "4  7.046841  0.063887  ...             1.12704               96.0   \n",
              "\n",
              "   N_IN_Conn_P_SrcIP  AR_P_Proto_P_Sport  AR_P_Proto_P_Dport  \\\n",
              "0               75.0            1.133720            1.129970   \n",
              "1                2.0            0.005142            0.005142   \n",
              "2               75.0            1.135100            1.129970   \n",
              "3               75.0            1.135140            1.129970   \n",
              "4               75.0            1.135260            1.129970   \n",
              "\n",
              "   Pkts_P_State_P_Protocol_P_DestIP  Pkts_P_State_P_Protocol_P_SrcIP  attack  \\\n",
              "0                             770.0                            602.0     1.0   \n",
              "1                               2.0                              6.0     1.0   \n",
              "2                             770.0                            602.0     1.0   \n",
              "3                             770.0                            602.0     1.0   \n",
              "4                             770.0                            602.0     1.0   \n",
              "\n",
              "   category  subcategory  \n",
              "0       DoS         HTTP  \n",
              "1       DoS         HTTP  \n",
              "2       DoS         HTTP  \n",
              "3       DoS         HTTP  \n",
              "4       DoS         HTTP  \n",
              "\n",
              "[5 rows x 38 columns]"
            ],
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              "      <th></th>\n",
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              "      <td>CON</td>\n",
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              "      <td>2.0</td>\n",
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              "      <td>0.064494</td>\n",
              "      <td>...</td>\n",
              "      <td>1.12704</td>\n",
              "      <td>96.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>1.135100</td>\n",
              "      <td>1.129970</td>\n",
              "      <td>770.0</td>\n",
              "      <td>602.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>DoS</td>\n",
              "      <td>HTTP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>e</td>\n",
              "      <td>1</td>\n",
              "      <td>tcp</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>2024</td>\n",
              "      <td>RST</td>\n",
              "      <td>1</td>\n",
              "      <td>7.047592</td>\n",
              "      <td>0.064189</td>\n",
              "      <td>...</td>\n",
              "      <td>1.12704</td>\n",
              "      <td>96.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>1.135140</td>\n",
              "      <td>1.129970</td>\n",
              "      <td>770.0</td>\n",
              "      <td>602.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>DoS</td>\n",
              "      <td>HTTP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>e</td>\n",
              "      <td>1</td>\n",
              "      <td>tcp</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>2319</td>\n",
              "      <td>RST</td>\n",
              "      <td>1</td>\n",
              "      <td>7.046841</td>\n",
              "      <td>0.063887</td>\n",
              "      <td>...</td>\n",
              "      <td>1.12704</td>\n",
              "      <td>96.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>1.135260</td>\n",
              "      <td>1.129970</td>\n",
              "      <td>770.0</td>\n",
              "      <td>602.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>DoS</td>\n",
              "      <td>HTTP</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 38 columns</p>\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_fe1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_z_ZBpX5DjZC",
        "outputId": "f28735fa-6f75-4747-f120-a5a4956e975f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 249056 entries, 0 to 249058\n",
            "Data columns (total 38 columns):\n",
            " #   Column                            Non-Null Count   Dtype  \n",
            "---  ------                            --------------   -----  \n",
            " 0   flgs                              249056 non-null  object \n",
            " 1   flgs_number                       249056 non-null  int64  \n",
            " 2   proto                             249056 non-null  object \n",
            " 3   proto_number                      249056 non-null  int64  \n",
            " 4   pkts                              249056 non-null  int64  \n",
            " 5   bytes                             249056 non-null  int64  \n",
            " 6   state                             249056 non-null  object \n",
            " 7   state_number                      249056 non-null  int64  \n",
            " 8   dur                               249056 non-null  float64\n",
            " 9   mean                              249056 non-null  float64\n",
            " 10  stddev                            249056 non-null  float64\n",
            " 11  sum                               249056 non-null  float64\n",
            " 12  min                               249056 non-null  float64\n",
            " 13  max                               249056 non-null  float64\n",
            " 14  spkts                             249056 non-null  float64\n",
            " 15  dpkts                             249056 non-null  float64\n",
            " 16  sbytes                            249056 non-null  float64\n",
            " 17  dbytes                            249056 non-null  float64\n",
            " 18  rate                              249056 non-null  float64\n",
            " 19  srate                             249056 non-null  float64\n",
            " 20  drate                             249056 non-null  float64\n",
            " 21  TnBPSrcIP                         249056 non-null  float64\n",
            " 22  TnBPDstIP                         249056 non-null  float64\n",
            " 23  TnP_PSrcIP                        249056 non-null  float64\n",
            " 24  TnP_PDstIP                        249056 non-null  float64\n",
            " 25  TnP_PerProto                      249056 non-null  float64\n",
            " 26  TnP_Per_Dport                     249056 non-null  float64\n",
            " 27  AR_P_Proto_P_SrcIP                249056 non-null  float64\n",
            " 28  AR_P_Proto_P_DstIP                249056 non-null  float64\n",
            " 29  N_IN_Conn_P_DstIP                 249056 non-null  float64\n",
            " 30  N_IN_Conn_P_SrcIP                 249056 non-null  float64\n",
            " 31  AR_P_Proto_P_Sport                249056 non-null  float64\n",
            " 32  AR_P_Proto_P_Dport                249056 non-null  float64\n",
            " 33  Pkts_P_State_P_Protocol_P_DestIP  249056 non-null  float64\n",
            " 34  Pkts_P_State_P_Protocol_P_SrcIP   249056 non-null  float64\n",
            " 35  attack                            249056 non-null  float64\n",
            " 36  category                          249056 non-null  object \n",
            " 37  subcategory                       249056 non-null  object \n",
            "dtypes: float64(28), int64(5), object(5)\n",
            "memory usage: 74.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Since the attributes are the same for 'flgs' and 'flgs_number', 'proto' and 'proto_number', 'state' and 'state_number'\n",
        "# So we just need one of each pair.\n",
        "# Drop the columns (features) 'flgs_number', 'proto_number', 'state_number'.\n",
        "\n",
        "df_fe2 = df_fe1.drop(['flgs_number', 'proto_number', 'state_number'], axis=1)\n"
      ],
      "metadata": {
        "id": "b9SDCzEzuBOO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_fe2.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DvSUKdAou_DV",
        "outputId": "c38bf75a-6392-4939-8d87-1f5c76060fae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 249056 entries, 0 to 249058\n",
            "Data columns (total 35 columns):\n",
            " #   Column                            Non-Null Count   Dtype  \n",
            "---  ------                            --------------   -----  \n",
            " 0   flgs                              249056 non-null  object \n",
            " 1   proto                             249056 non-null  object \n",
            " 2   pkts                              249056 non-null  int64  \n",
            " 3   bytes                             249056 non-null  int64  \n",
            " 4   state                             249056 non-null  object \n",
            " 5   dur                               249056 non-null  float64\n",
            " 6   mean                              249056 non-null  float64\n",
            " 7   stddev                            249056 non-null  float64\n",
            " 8   sum                               249056 non-null  float64\n",
            " 9   min                               249056 non-null  float64\n",
            " 10  max                               249056 non-null  float64\n",
            " 11  spkts                             249056 non-null  float64\n",
            " 12  dpkts                             249056 non-null  float64\n",
            " 13  sbytes                            249056 non-null  float64\n",
            " 14  dbytes                            249056 non-null  float64\n",
            " 15  rate                              249056 non-null  float64\n",
            " 16  srate                             249056 non-null  float64\n",
            " 17  drate                             249056 non-null  float64\n",
            " 18  TnBPSrcIP                         249056 non-null  float64\n",
            " 19  TnBPDstIP                         249056 non-null  float64\n",
            " 20  TnP_PSrcIP                        249056 non-null  float64\n",
            " 21  TnP_PDstIP                        249056 non-null  float64\n",
            " 22  TnP_PerProto                      249056 non-null  float64\n",
            " 23  TnP_Per_Dport                     249056 non-null  float64\n",
            " 24  AR_P_Proto_P_SrcIP                249056 non-null  float64\n",
            " 25  AR_P_Proto_P_DstIP                249056 non-null  float64\n",
            " 26  N_IN_Conn_P_DstIP                 249056 non-null  float64\n",
            " 27  N_IN_Conn_P_SrcIP                 249056 non-null  float64\n",
            " 28  AR_P_Proto_P_Sport                249056 non-null  float64\n",
            " 29  AR_P_Proto_P_Dport                249056 non-null  float64\n",
            " 30  Pkts_P_State_P_Protocol_P_DestIP  249056 non-null  float64\n",
            " 31  Pkts_P_State_P_Protocol_P_SrcIP   249056 non-null  float64\n",
            " 32  attack                            249056 non-null  float64\n",
            " 33  category                          249056 non-null  object \n",
            " 34  subcategory                       249056 non-null  object \n",
            "dtypes: float64(28), int64(2), object(5)\n",
            "memory usage: 68.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.5 Handling Non-numerical Values"
      ],
      "metadata": {
        "id": "niVr9Gd0DV9h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# let's encoding our categorical features using one-hot encoding\n",
        "\n",
        "df_fe2_he = pd.get_dummies(df_fe2, columns = ['flgs', 'proto', 'state'])"
      ],
      "metadata": {
        "id": "BuVyIDMcDM6b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_fe2_he.head().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iKZHlXu9D8xr",
        "outputId": "755dbd98-c36f-4f6a-b42f-ac4482571394"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                                          0            1          2  \\\n",
              "pkts                                      8            2          8   \n",
              "bytes                                  1980          120       2126   \n",
              "dur                                7.056393     0.000131   7.047852   \n",
              "mean                               0.068909     0.000131   0.064494   \n",
              "stddev                             0.068909          0.0   0.064494   \n",
              "sum                                0.137818     0.000131   0.128988   \n",
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              "max                                0.137818     0.000131   0.128988   \n",
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              "category                                DoS          DoS        DoS   \n",
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              "flgs_e                                    1            1          1   \n",
              "flgs_e *                                  0            0          0   \n",
              "flgs_e d                                  0            0          0   \n",
              "flgs_e g                                  0            0          0   \n",
              "flgs_e s                                  0            0          0   \n",
              "flgs_eU                                   0            0          0   \n",
              "proto_arp                                 0            1          0   \n",
              "proto_tcp                                 1            0          1   \n",
              "proto_udp                                 0            0          0   \n",
              "state_ACC                                 0            0          0   \n",
              "state_CON                                 0            1          0   \n",
              "state_INT                                 0            0          0   \n",
              "state_REQ                                 0            0          0   \n",
              "state_RST                                 1            0          1   \n",
              "\n",
              "                                          3          4  \n",
              "pkts                                      8          8  \n",
              "bytes                                  2024       2319  \n",
              "dur                                7.047592   7.046841  \n",
              "mean                               0.064189   0.063887  \n",
              "stddev                             0.064189   0.063887  \n",
              "sum                                0.128378   0.127774  \n",
              "min                                     0.0        0.0  \n",
              "max                                0.128378   0.127774  \n",
              "spkts                                   5.0        5.0  \n",
              "dpkts                                   3.0        3.0  \n",
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              "dbytes                               1330.0     1330.0  \n",
              "rate                               0.993247   0.993353  \n",
              "srate                               0.56757    0.56763  \n",
              "drate                             15.578993  15.652637  \n",
              "TnBPSrcIP                          157426.0   157426.0  \n",
              "TnBPDstIP                          201841.0   201841.0  \n",
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              "N_IN_Conn_P_DstIP                      96.0       96.0  \n",
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              "attack                                  1.0        1.0  \n",
              "category                                DoS        DoS  \n",
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              "flgs_e *                                  0          0  \n",
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              "      <td>2126</td>\n",
              "      <td>2024</td>\n",
              "      <td>2319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dur</th>\n",
              "      <td>7.056393</td>\n",
              "      <td>0.000131</td>\n",
              "      <td>7.047852</td>\n",
              "      <td>7.047592</td>\n",
              "      <td>7.046841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.068909</td>\n",
              "      <td>0.000131</td>\n",
              "      <td>0.064494</td>\n",
              "      <td>0.064189</td>\n",
              "      <td>0.063887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stddev</th>\n",
              "      <td>0.068909</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.064494</td>\n",
              "      <td>0.064189</td>\n",
              "      <td>0.063887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sum</th>\n",
              "      <td>0.137818</td>\n",
              "      <td>0.000131</td>\n",
              "      <td>0.128988</td>\n",
              "      <td>0.128378</td>\n",
              "      <td>0.127774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000131</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.137818</td>\n",
              "      <td>0.000131</td>\n",
              "      <td>0.128988</td>\n",
              "      <td>0.128378</td>\n",
              "      <td>0.127774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spkts</th>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dpkts</th>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sbytes</th>\n",
              "      <td>650.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>796.0</td>\n",
              "      <td>694.0</td>\n",
              "      <td>989.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dbytes</th>\n",
              "      <td>1330.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>1330.0</td>\n",
              "      <td>1330.0</td>\n",
              "      <td>1330.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rate</th>\n",
              "      <td>0.992008</td>\n",
              "      <td>7633.588379</td>\n",
              "      <td>0.99321</td>\n",
              "      <td>0.993247</td>\n",
              "      <td>0.993353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>srate</th>\n",
              "      <td>0.566862</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.567549</td>\n",
              "      <td>0.56757</td>\n",
              "      <td>0.56763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>drate</th>\n",
              "      <td>14.511893</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.505319</td>\n",
              "      <td>15.578993</td>\n",
              "      <td>15.652637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TnBPSrcIP</th>\n",
              "      <td>157426.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>157426.0</td>\n",
              "      <td>157426.0</td>\n",
              "      <td>157426.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TnBPDstIP</th>\n",
              "      <td>201841.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>201841.0</td>\n",
              "      <td>201841.0</td>\n",
              "      <td>201841.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TnP_PSrcIP</th>\n",
              "      <td>602.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>602.0</td>\n",
              "      <td>602.0</td>\n",
              "      <td>602.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TnP_PDstIP</th>\n",
              "      <td>770.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>770.0</td>\n",
              "      <td>770.0</td>\n",
              "      <td>770.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TnP_PerProto</th>\n",
              "      <td>772.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>772.0</td>\n",
              "      <td>772.0</td>\n",
              "      <td>772.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TnP_Per_Dport</th>\n",
              "      <td>772.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>772.0</td>\n",
              "      <td>772.0</td>\n",
              "      <td>772.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AR_P_Proto_P_SrcIP</th>\n",
              "      <td>1.14297</td>\n",
              "      <td>0.003857</td>\n",
              "      <td>1.14297</td>\n",
              "      <td>1.14297</td>\n",
              "      <td>1.14297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AR_P_Proto_P_DstIP</th>\n",
              "      <td>1.12704</td>\n",
              "      <td>15267.2</td>\n",
              "      <td>1.12704</td>\n",
              "      <td>1.12704</td>\n",
              "      <td>1.12704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>N_IN_Conn_P_DstIP</th>\n",
              "      <td>96.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>96.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>N_IN_Conn_P_SrcIP</th>\n",
              "      <td>75.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>75.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AR_P_Proto_P_Sport</th>\n",
              "      <td>1.13372</td>\n",
              "      <td>0.005142</td>\n",
              "      <td>1.1351</td>\n",
              "      <td>1.13514</td>\n",
              "      <td>1.13526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AR_P_Proto_P_Dport</th>\n",
              "      <td>1.12997</td>\n",
              "      <td>0.005142</td>\n",
              "      <td>1.12997</td>\n",
              "      <td>1.12997</td>\n",
              "      <td>1.12997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pkts_P_State_P_Protocol_P_DestIP</th>\n",
              "      <td>770.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>770.0</td>\n",
              "      <td>770.0</td>\n",
              "      <td>770.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pkts_P_State_P_Protocol_P_SrcIP</th>\n",
              "      <td>602.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>602.0</td>\n",
              "      <td>602.0</td>\n",
              "      <td>602.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>attack</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>category</th>\n",
              "      <td>DoS</td>\n",
              "      <td>DoS</td>\n",
              "      <td>DoS</td>\n",
              "      <td>DoS</td>\n",
              "      <td>DoS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>subcategory</th>\n",
              "      <td>HTTP</td>\n",
              "      <td>HTTP</td>\n",
              "      <td>HTTP</td>\n",
              "      <td>HTTP</td>\n",
              "      <td>HTTP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flgs_e</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flgs_e *</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flgs_e d</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flgs_e g</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flgs_e s</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flgs_eU</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>proto_arp</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>proto_tcp</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>proto_udp</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state_ACC</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state_CON</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state_INT</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state_REQ</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state_RST</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_fe2_he.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QCWkxUPtwftE",
        "outputId": "0de02696-7cc3-42bb-dec8-f18561a50050"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 249056 entries, 0 to 249058\n",
            "Data columns (total 46 columns):\n",
            " #   Column                            Non-Null Count   Dtype  \n",
            "---  ------                            --------------   -----  \n",
            " 0   pkts                              249056 non-null  int64  \n",
            " 1   bytes                             249056 non-null  int64  \n",
            " 2   dur                               249056 non-null  float64\n",
            " 3   mean                              249056 non-null  float64\n",
            " 4   stddev                            249056 non-null  float64\n",
            " 5   sum                               249056 non-null  float64\n",
            " 6   min                               249056 non-null  float64\n",
            " 7   max                               249056 non-null  float64\n",
            " 8   spkts                             249056 non-null  float64\n",
            " 9   dpkts                             249056 non-null  float64\n",
            " 10  sbytes                            249056 non-null  float64\n",
            " 11  dbytes                            249056 non-null  float64\n",
            " 12  rate                              249056 non-null  float64\n",
            " 13  srate                             249056 non-null  float64\n",
            " 14  drate                             249056 non-null  float64\n",
            " 15  TnBPSrcIP                         249056 non-null  float64\n",
            " 16  TnBPDstIP                         249056 non-null  float64\n",
            " 17  TnP_PSrcIP                        249056 non-null  float64\n",
            " 18  TnP_PDstIP                        249056 non-null  float64\n",
            " 19  TnP_PerProto                      249056 non-null  float64\n",
            " 20  TnP_Per_Dport                     249056 non-null  float64\n",
            " 21  AR_P_Proto_P_SrcIP                249056 non-null  float64\n",
            " 22  AR_P_Proto_P_DstIP                249056 non-null  float64\n",
            " 23  N_IN_Conn_P_DstIP                 249056 non-null  float64\n",
            " 24  N_IN_Conn_P_SrcIP                 249056 non-null  float64\n",
            " 25  AR_P_Proto_P_Sport                249056 non-null  float64\n",
            " 26  AR_P_Proto_P_Dport                249056 non-null  float64\n",
            " 27  Pkts_P_State_P_Protocol_P_DestIP  249056 non-null  float64\n",
            " 28  Pkts_P_State_P_Protocol_P_SrcIP   249056 non-null  float64\n",
            " 29  attack                            249056 non-null  float64\n",
            " 30  category                          249056 non-null  object \n",
            " 31  subcategory                       249056 non-null  object \n",
            " 32  flgs_e                            249056 non-null  uint8  \n",
            " 33  flgs_e *                          249056 non-null  uint8  \n",
            " 34  flgs_e d                          249056 non-null  uint8  \n",
            " 35  flgs_e g                          249056 non-null  uint8  \n",
            " 36  flgs_e s                          249056 non-null  uint8  \n",
            " 37  flgs_eU                           249056 non-null  uint8  \n",
            " 38  proto_arp                         249056 non-null  uint8  \n",
            " 39  proto_tcp                         249056 non-null  uint8  \n",
            " 40  proto_udp                         249056 non-null  uint8  \n",
            " 41  state_ACC                         249056 non-null  uint8  \n",
            " 42  state_CON                         249056 non-null  uint8  \n",
            " 43  state_INT                         249056 non-null  uint8  \n",
            " 44  state_REQ                         249056 non-null  uint8  \n",
            " 45  state_RST                         249056 non-null  uint8  \n",
            "dtypes: float64(28), int64(2), object(2), uint8(14)\n",
            "memory usage: 66.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# change the order of the columns that added by hot encoding\n",
        "\n",
        "df_fe2_he1 = df_fe2_he[['flgs_e', 'flgs_e *', 'flgs_e d', 'flgs_e g', 'flgs_e s', 'flgs_eU', 'proto_arp', 'proto_tcp', \\\n",
        "           'proto_udp', 'state_ACC', 'state_CON', 'state_INT', 'state_REQ', 'state_RST', \\\n",
        "           'pkts', 'bytes', 'dur', 'mean', 'stddev', 'sum', 'min', \\\n",
        "           'max', 'spkts', 'dpkts', 'sbytes', 'dbytes', 'rate', 'srate', 'drate', \\\n",
        "           'TnBPSrcIP', 'TnBPDstIP', 'TnP_PSrcIP', 'TnP_PDstIP', 'TnP_PerProto', \\\n",
        "           'TnP_Per_Dport', 'AR_P_Proto_P_SrcIP', 'AR_P_Proto_P_DstIP', \\\n",
        "           'N_IN_Conn_P_DstIP', 'N_IN_Conn_P_SrcIP', 'AR_P_Proto_P_Sport', \\\n",
        "           'AR_P_Proto_P_Dport', 'Pkts_P_State_P_Protocol_P_DestIP', \\\n",
        "           'Pkts_P_State_P_Protocol_P_SrcIP', 'attack', 'category', 'subcategory' ]]\n"
      ],
      "metadata": {
        "id": "8nTwMxqvxVay"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_fe2_he1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WSJcAtO9zzfe",
        "outputId": "14e4f78e-6207-47df-c762-879a376b0822"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 249056 entries, 0 to 249058\n",
            "Data columns (total 46 columns):\n",
            " #   Column                            Non-Null Count   Dtype  \n",
            "---  ------                            --------------   -----  \n",
            " 0   flgs_e                            249056 non-null  uint8  \n",
            " 1   flgs_e *                          249056 non-null  uint8  \n",
            " 2   flgs_e d                          249056 non-null  uint8  \n",
            " 3   flgs_e g                          249056 non-null  uint8  \n",
            " 4   flgs_e s                          249056 non-null  uint8  \n",
            " 5   flgs_eU                           249056 non-null  uint8  \n",
            " 6   proto_arp                         249056 non-null  uint8  \n",
            " 7   proto_tcp                         249056 non-null  uint8  \n",
            " 8   proto_udp                         249056 non-null  uint8  \n",
            " 9   state_ACC                         249056 non-null  uint8  \n",
            " 10  state_CON                         249056 non-null  uint8  \n",
            " 11  state_INT                         249056 non-null  uint8  \n",
            " 12  state_REQ                         249056 non-null  uint8  \n",
            " 13  state_RST                         249056 non-null  uint8  \n",
            " 14  pkts                              249056 non-null  int64  \n",
            " 15  bytes                             249056 non-null  int64  \n",
            " 16  dur                               249056 non-null  float64\n",
            " 17  mean                              249056 non-null  float64\n",
            " 18  stddev                            249056 non-null  float64\n",
            " 19  sum                               249056 non-null  float64\n",
            " 20  min                               249056 non-null  float64\n",
            " 21  max                               249056 non-null  float64\n",
            " 22  spkts                             249056 non-null  float64\n",
            " 23  dpkts                             249056 non-null  float64\n",
            " 24  sbytes                            249056 non-null  float64\n",
            " 25  dbytes                            249056 non-null  float64\n",
            " 26  rate                              249056 non-null  float64\n",
            " 27  srate                             249056 non-null  float64\n",
            " 28  drate                             249056 non-null  float64\n",
            " 29  TnBPSrcIP                         249056 non-null  float64\n",
            " 30  TnBPDstIP                         249056 non-null  float64\n",
            " 31  TnP_PSrcIP                        249056 non-null  float64\n",
            " 32  TnP_PDstIP                        249056 non-null  float64\n",
            " 33  TnP_PerProto                      249056 non-null  float64\n",
            " 34  TnP_Per_Dport                     249056 non-null  float64\n",
            " 35  AR_P_Proto_P_SrcIP                249056 non-null  float64\n",
            " 36  AR_P_Proto_P_DstIP                249056 non-null  float64\n",
            " 37  N_IN_Conn_P_DstIP                 249056 non-null  float64\n",
            " 38  N_IN_Conn_P_SrcIP                 249056 non-null  float64\n",
            " 39  AR_P_Proto_P_Sport                249056 non-null  float64\n",
            " 40  AR_P_Proto_P_Dport                249056 non-null  float64\n",
            " 41  Pkts_P_State_P_Protocol_P_DestIP  249056 non-null  float64\n",
            " 42  Pkts_P_State_P_Protocol_P_SrcIP   249056 non-null  float64\n",
            " 43  attack                            249056 non-null  float64\n",
            " 44  category                          249056 non-null  object \n",
            " 45  subcategory                       249056 non-null  object \n",
            "dtypes: float64(28), int64(2), object(2), uint8(14)\n",
            "memory usage: 66.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.6 Data normalization (feature scaling)"
      ],
      "metadata": {
        "id": "31J1r24EE09R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Scaling using Min-Max Scaling\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# instantiate the MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Set predictor and target variables\n",
        "X = df_fe2_he1.drop(['attack', 'category', 'subcategory'], axis =1)\n",
        "y1 = df_fe2_he1['attack']\n",
        "y2 = df_fe2_he1['category']\n",
        "y3 = df_fe2_he1['subcategory']\n",
        "\n",
        "# fit the scaler to the data\n",
        "scaler.fit(X)\n",
        "\n",
        "# transform the data using the fitted scaler\n",
        "X_fsmm = scaler.transform(X)\n",
        "\n",
        "# Put the tranformed X into dataframe\n",
        "df_fe2_he1_fsmm = pd.DataFrame(X_fsmm, index = X.index, columns = X.columns)"
      ],
      "metadata": {
        "id": "MduDBpMwE4Y0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_fe2_he1_fsmm.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "3EBlYEn12PN2",
        "outputId": "ebcf0c5c-e535-4d88-869b-cf8e27e467fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   flgs_e  flgs_e *  flgs_e d  flgs_e g  flgs_e s  flgs_eU  proto_arp  \\\n",
              "0     1.0       0.0       0.0       0.0       0.0      0.0        0.0   \n",
              "1     1.0       0.0       0.0       0.0       0.0      0.0        1.0   \n",
              "2     1.0       0.0       0.0       0.0       0.0      0.0        0.0   \n",
              "3     1.0       0.0       0.0       0.0       0.0      0.0        0.0   \n",
              "4     1.0       0.0       0.0       0.0       0.0      0.0        0.0   \n",
              "\n",
              "   proto_tcp  proto_udp  state_ACC  ...  TnP_PerProto  TnP_Per_Dport  \\\n",
              "0        1.0        0.0        0.0  ...      0.514019       0.514019   \n",
              "1        0.0        0.0        0.0  ...      0.004005       0.004005   \n",
              "2        1.0        0.0        0.0  ...      0.514019       0.514019   \n",
              "3        1.0        0.0        0.0  ...      0.514019       0.514019   \n",
              "4        1.0        0.0        0.0  ...      0.514019       0.514019   \n",
              "\n",
              "   AR_P_Proto_P_SrcIP  AR_P_Proto_P_DstIP  N_IN_Conn_P_DstIP  \\\n",
              "0            0.000072            0.000074           0.959596   \n",
              "1            0.000000            1.000000           0.000000   \n",
              "2            0.000072            0.000074           0.959596   \n",
              "3            0.000072            0.000074           0.959596   \n",
              "4            0.000072            0.000074           0.959596   \n",
              "\n",
              "   N_IN_Conn_P_SrcIP  AR_P_Proto_P_Sport  AR_P_Proto_P_Dport  \\\n",
              "0           0.747475            0.000085            0.000085   \n",
              "1           0.010101            0.000000            0.000000   \n",
              "2           0.747475            0.000085            0.000085   \n",
              "3           0.747475            0.000085            0.000085   \n",
              "4           0.747475            0.000085            0.000085   \n",
              "\n",
              "   Pkts_P_State_P_Protocol_P_DestIP  Pkts_P_State_P_Protocol_P_SrcIP  \n",
              "0                          0.512684                         0.400534  \n",
              "1                          0.000000                         0.002670  \n",
              "2                          0.512684                         0.400534  \n",
              "3                          0.512684                         0.400534  \n",
              "4                          0.512684                         0.400534  \n",
              "\n",
              "[5 rows x 43 columns]"
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              "      <th></th>\n",
              "      <th>flgs_e</th>\n",
              "      <th>flgs_e *</th>\n",
              "      <th>flgs_e d</th>\n",
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          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We need generate the data with normalised X combined with target y.\n",
        "\n",
        "normalized_data = pd.concat([df_fe2_he1_fsmm, y1, y2, y3], axis=1)"
      ],
      "metadata": {
        "id": "BEf7BFDQ3bHv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check the normalized data\n",
        "\n",
        "normalized_data.head().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "529CZGR330Jx",
        "outputId": "ef82cc9b-bc2e-4fb5-b1f2-5886c25f6d43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         0         1         2         3  \\\n",
              "flgs_e                                 1.0       1.0       1.0       1.0   \n",
              "flgs_e *                               0.0       0.0       0.0       0.0   \n",
              "flgs_e d                               0.0       0.0       0.0       0.0   \n",
              "flgs_e g                               0.0       0.0       0.0       0.0   \n",
              "flgs_e s                               0.0       0.0       0.0       0.0   \n",
              "flgs_eU                                0.0       0.0       0.0       0.0   \n",
              "proto_arp                              0.0       1.0       0.0       0.0   \n",
              "proto_tcp                              1.0       0.0       1.0       1.0   \n",
              "proto_udp                              0.0       0.0       0.0       0.0   \n",
              "state_ACC                              0.0       0.0       0.0       0.0   \n",
              "state_CON                              0.0       1.0       0.0       0.0   \n",
              "state_INT                              0.0       0.0       0.0       0.0   \n",
              "state_REQ                              0.0       0.0       0.0       0.0   \n",
              "state_RST                              1.0       0.0       1.0       1.0   \n",
              "pkts                              0.111111       0.0  0.111111  0.111111   \n",
              "bytes                             0.270388       0.0  0.291612  0.276784   \n",
              "dur                               0.004536       0.0   0.00453   0.00453   \n",
              "mean                              0.014031  0.000027  0.013132   0.01307   \n",
              "stddev                            0.028178       0.0  0.026372  0.026247   \n",
              "sum                                0.00564  0.000005  0.005279  0.005254   \n",
              "min                                    0.0  0.000027       0.0       0.0   \n",
              "max                               0.027817  0.000026  0.026035  0.025912   \n",
              "spkts                             0.148148       0.0  0.148148  0.148148   \n",
              "dpkts                             0.107143  0.035714  0.107143  0.107143   \n",
              "sbytes                            0.101514       0.0  0.126635  0.109085   \n",
              "dbytes                            0.791667  0.035714  0.791667  0.791667   \n",
              "rate                              0.000126  0.969466  0.000126  0.000126   \n",
              "srate                             0.226081       0.0  0.226355  0.226363   \n",
              "drate                             0.882091       0.0  0.942475  0.946953   \n",
              "TnBPSrcIP                         0.760432   0.00116  0.760432  0.760432   \n",
              "TnBPDstIP                         0.975138       0.0  0.975138  0.975138   \n",
              "TnP_PSrcIP                        0.400534   0.00267  0.400534  0.400534   \n",
              "TnP_PDstIP                        0.512684       0.0  0.512684  0.512684   \n",
              "TnP_PerProto                      0.514019  0.004005  0.514019  0.514019   \n",
              "TnP_Per_Dport                     0.514019  0.004005  0.514019  0.514019   \n",
              "AR_P_Proto_P_SrcIP                0.000072       0.0  0.000072  0.000072   \n",
              "AR_P_Proto_P_DstIP                0.000074       1.0  0.000074  0.000074   \n",
              "N_IN_Conn_P_DstIP                 0.959596       0.0  0.959596  0.959596   \n",
              "N_IN_Conn_P_SrcIP                 0.747475  0.010101  0.747475  0.747475   \n",
              "AR_P_Proto_P_Sport                0.000085       0.0  0.000085  0.000085   \n",
              "AR_P_Proto_P_Dport                0.000085       0.0  0.000085  0.000085   \n",
              "Pkts_P_State_P_Protocol_P_DestIP  0.512684       0.0  0.512684  0.512684   \n",
              "Pkts_P_State_P_Protocol_P_SrcIP   0.400534   0.00267  0.400534  0.400534   \n",
              "attack                                 1.0       1.0       1.0       1.0   \n",
              "category                               DoS       DoS       DoS       DoS   \n",
              "subcategory                           HTTP      HTTP      HTTP      HTTP   \n",
              "\n",
              "                                         4  \n",
              "flgs_e                                 1.0  \n",
              "flgs_e *                               0.0  \n",
              "flgs_e d                               0.0  \n",
              "flgs_e g                               0.0  \n",
              "flgs_e s                               0.0  \n",
              "flgs_eU                                0.0  \n",
              "proto_arp                              0.0  \n",
              "proto_tcp                              1.0  \n",
              "proto_udp                              0.0  \n",
              "state_ACC                              0.0  \n",
              "state_CON                              0.0  \n",
              "state_INT                              0.0  \n",
              "state_REQ                              0.0  \n",
              "state_RST                              1.0  \n",
              "pkts                              0.111111  \n",
              "bytes                             0.319669  \n",
              "dur                                0.00453  \n",
              "mean                              0.013009  \n",
              "stddev                            0.026124  \n",
              "sum                               0.005229  \n",
              "min                                    0.0  \n",
              "max                                0.02579  \n",
              "spkts                             0.148148  \n",
              "dpkts                             0.107143  \n",
              "sbytes                            0.159842  \n",
              "dbytes                            0.791667  \n",
              "rate                              0.000126  \n",
              "srate                             0.226387  \n",
              "drate                              0.95143  \n",
              "TnBPSrcIP                         0.760432  \n",
              "TnBPDstIP                         0.975138  \n",
              "TnP_PSrcIP                        0.400534  \n",
              "TnP_PDstIP                        0.512684  \n",
              "TnP_PerProto                      0.514019  \n",
              "TnP_Per_Dport                     0.514019  \n",
              "AR_P_Proto_P_SrcIP                0.000072  \n",
              "AR_P_Proto_P_DstIP                0.000074  \n",
              "N_IN_Conn_P_DstIP                 0.959596  \n",
              "N_IN_Conn_P_SrcIP                 0.747475  \n",
              "AR_P_Proto_P_Sport                0.000085  \n",
              "AR_P_Proto_P_Dport                0.000085  \n",
              "Pkts_P_State_P_Protocol_P_DestIP  0.512684  \n",
              "Pkts_P_State_P_Protocol_P_SrcIP   0.400534  \n",
              "attack                                 1.0  \n",
              "category                               DoS  \n",
              "subcategory                           HTTP  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f354e228-2d65-4e4b-8f80-0c9ca5f69915\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>flgs_e</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flgs_e *</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flgs_e d</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flgs_e g</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flgs_e s</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flgs_eU</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>proto_arp</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>proto_tcp</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>proto_udp</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state_ACC</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state_CON</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state_INT</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state_REQ</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state_RST</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pkts</th>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.111111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bytes</th>\n",
              "      <td>0.270388</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.291612</td>\n",
              "      <td>0.276784</td>\n",
              "      <td>0.319669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dur</th>\n",
              "      <td>0.004536</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00453</td>\n",
              "      <td>0.00453</td>\n",
              "      <td>0.00453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.014031</td>\n",
              "      <td>0.000027</td>\n",
              "      <td>0.013132</td>\n",
              "      <td>0.01307</td>\n",
              "      <td>0.013009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stddev</th>\n",
              "      <td>0.028178</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.026372</td>\n",
              "      <td>0.026247</td>\n",
              "      <td>0.026124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sum</th>\n",
              "      <td>0.00564</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>0.005279</td>\n",
              "      <td>0.005254</td>\n",
              "      <td>0.005229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000027</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.027817</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.026035</td>\n",
              "      <td>0.025912</td>\n",
              "      <td>0.02579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spkts</th>\n",
              "      <td>0.148148</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.148148</td>\n",
              "      <td>0.148148</td>\n",
              "      <td>0.148148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dpkts</th>\n",
              "      <td>0.107143</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.107143</td>\n",
              "      <td>0.107143</td>\n",
              "      <td>0.107143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sbytes</th>\n",
              "      <td>0.101514</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.126635</td>\n",
              "      <td>0.109085</td>\n",
              "      <td>0.159842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dbytes</th>\n",
              "      <td>0.791667</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.791667</td>\n",
              "      <td>0.791667</td>\n",
              "      <td>0.791667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rate</th>\n",
              "      <td>0.000126</td>\n",
              "      <td>0.969466</td>\n",
              "      <td>0.000126</td>\n",
              "      <td>0.000126</td>\n",
              "      <td>0.000126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>srate</th>\n",
              "      <td>0.226081</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.226355</td>\n",
              "      <td>0.226363</td>\n",
              "      <td>0.226387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>drate</th>\n",
              "      <td>0.882091</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.942475</td>\n",
              "      <td>0.946953</td>\n",
              "      <td>0.95143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TnBPSrcIP</th>\n",
              "      <td>0.760432</td>\n",
              "      <td>0.00116</td>\n",
              "      <td>0.760432</td>\n",
              "      <td>0.760432</td>\n",
              "      <td>0.760432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TnBPDstIP</th>\n",
              "      <td>0.975138</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.975138</td>\n",
              "      <td>0.975138</td>\n",
              "      <td>0.975138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TnP_PSrcIP</th>\n",
              "      <td>0.400534</td>\n",
              "      <td>0.00267</td>\n",
              "      <td>0.400534</td>\n",
              "      <td>0.400534</td>\n",
              "      <td>0.400534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TnP_PDstIP</th>\n",
              "      <td>0.512684</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.512684</td>\n",
              "      <td>0.512684</td>\n",
              "      <td>0.512684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TnP_PerProto</th>\n",
              "      <td>0.514019</td>\n",
              "      <td>0.004005</td>\n",
              "      <td>0.514019</td>\n",
              "      <td>0.514019</td>\n",
              "      <td>0.514019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TnP_Per_Dport</th>\n",
              "      <td>0.514019</td>\n",
              "      <td>0.004005</td>\n",
              "      <td>0.514019</td>\n",
              "      <td>0.514019</td>\n",
              "      <td>0.514019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AR_P_Proto_P_SrcIP</th>\n",
              "      <td>0.000072</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000072</td>\n",
              "      <td>0.000072</td>\n",
              "      <td>0.000072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AR_P_Proto_P_DstIP</th>\n",
              "      <td>0.000074</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000074</td>\n",
              "      <td>0.000074</td>\n",
              "      <td>0.000074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>N_IN_Conn_P_DstIP</th>\n",
              "      <td>0.959596</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.959596</td>\n",
              "      <td>0.959596</td>\n",
              "      <td>0.959596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>N_IN_Conn_P_SrcIP</th>\n",
              "      <td>0.747475</td>\n",
              "      <td>0.010101</td>\n",
              "      <td>0.747475</td>\n",
              "      <td>0.747475</td>\n",
              "      <td>0.747475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AR_P_Proto_P_Sport</th>\n",
              "      <td>0.000085</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000085</td>\n",
              "      <td>0.000085</td>\n",
              "      <td>0.000085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AR_P_Proto_P_Dport</th>\n",
              "      <td>0.000085</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000085</td>\n",
              "      <td>0.000085</td>\n",
              "      <td>0.000085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pkts_P_State_P_Protocol_P_DestIP</th>\n",
              "      <td>0.512684</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.512684</td>\n",
              "      <td>0.512684</td>\n",
              "      <td>0.512684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pkts_P_State_P_Protocol_P_SrcIP</th>\n",
              "      <td>0.400534</td>\n",
              "      <td>0.00267</td>\n",
              "      <td>0.400534</td>\n",
              "      <td>0.400534</td>\n",
              "      <td>0.400534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>attack</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>category</th>\n",
              "      <td>DoS</td>\n",
              "      <td>DoS</td>\n",
              "      <td>DoS</td>\n",
              "      <td>DoS</td>\n",
              "      <td>DoS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>subcategory</th>\n",
              "      <td>HTTP</td>\n",
              "      <td>HTTP</td>\n",
              "      <td>HTTP</td>\n",
              "      <td>HTTP</td>\n",
              "      <td>HTTP</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f354e228-2d65-4e4b-8f80-0c9ca5f69915')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f354e228-2d65-4e4b-8f80-0c9ca5f69915 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f354e228-2d65-4e4b-8f80-0c9ca5f69915');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save the transformed data to csv file\n",
        "\n",
        "normalized_data.to_csv('BoT-IoT_normalized.csv', index=False)"
      ],
      "metadata": {
        "id": "dQ75z6Vr4w2i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Till now, basic data-preprocessing involving, handing missing value, duplicate instance, non-numerical value, invalid feature elimination by understanding the data, and normalization by min-max scaling.**"
      ],
      "metadata": {
        "id": "lsMxRPO44J_S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Data Processing"
      ],
      "metadata": {
        "id": "9sBh3vNZ_xDr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.1 Feature ranking"
      ],
      "metadata": {
        "id": "zQQSY31G9_rT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import chi2\n",
        "\n",
        "data = normalized_data\n",
        "\n",
        "# Set predictor and target variables\n",
        "X = data.drop(['attack', 'category', 'subcategory'], axis =1)\n",
        "y1 = data['attack']\n",
        "y2 = data['category']\n",
        "y3 = data['subcategory']\n",
        "\n",
        "#apply SelectKBest class to extract top 10 best features\n",
        "bestfeatures = SelectKBest(score_func=chi2, k=10)\n",
        "fit = bestfeatures.fit(X,y2)\n",
        "dfscores = pd.DataFrame(fit.scores_)\n",
        "dfcolumns = pd.DataFrame(X.columns)\n",
        "\n",
        "#concat two dataframes for better visualization \n",
        "featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n",
        "featureScores.columns = ['Specs','Score']  #naming the dataframe columns\n",
        "print(featureScores.nlargest(10,'Score'))  #print 10 best features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "t4ZnQpgQ_0Sz",
        "outputId": "e7ab8207-2273-427b-86d6-8fd4d225be9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-e99d77bf33ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mchi2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnormalized_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Set predictor and target variables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'normalized_data' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jHvTVxrv3yGu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}