MLP_Binário_Zero_Day.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gabrieladamasceno/Attacks_5G/blob/main/MLP_Bin%C3%A1rio_Zero_Day.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cuurjZdWJk6h",
        "outputId": "a0d8a0b0-553b-4751-f653-99689091aed5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import pandas as pd\n",
        "\n",
        "# Acessar dataset\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "# Carregar o dataset\n",
        "encoded = pd.read_csv(\"/content/gdrive/MyDrive/Datasets/Attacks/Encoded/Encoded.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install imbalanced-learn\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8S3nVM7mjjwA",
        "outputId": "f1eb8b6f-7a55-4180-c0bb-c10a9cbededa"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.11/dist-packages (0.13.0)\n",
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          ]
        }
      ]
    },
    {
      "source": [
        "!pip install --upgrade tensorflow\n",
        "!pip install --upgrade keras"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NRYPuBoL3xT4",
        "outputId": "45475b96-d35b-4f09-ee97-9cba6e5829e0"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.pipeline import Pipeline\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Input\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, precision_score, recall_score, f1_score\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "7VCtN5o8jsGr"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "biNnSG49XmU0",
        "outputId": "c41e24a5-bfee-4b38-b48e-2d437c91b499"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Número de amostras: 1215890\n",
            "Número de amostras (usando len): 1215890\n"
          ]
        }
      ],
      "source": [
        "# Usando shape\n",
        "num_amostras = encoded.shape[0]\n",
        "print(f\"Número de amostras: {num_amostras}\")\n",
        "\n",
        "# Usando len()\n",
        "num_amostras_len = len(encoded)\n",
        "print(f\"Número de amostras (usando len): {num_amostras_len}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ULAbhjQIYl8b",
        "outputId": "bc83cf90-629e-4a1a-8273-66edd2aefe62"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nomes das colunas:\n",
            "Unnamed: 0\n",
            "Seq\n",
            "Dur\n",
            "RunTime\n",
            "Mean\n",
            "Sum\n",
            "Min\n",
            "Max\n",
            "sTos\n",
            "dTos\n",
            "sTtl\n",
            "dTtl\n",
            "sHops\n",
            "dHops\n",
            "TotPkts\n",
            "SrcPkts\n",
            "DstPkts\n",
            "TotBytes\n",
            "SrcBytes\n",
            "DstBytes\n",
            "Offset\n",
            "sMeanPktSz\n",
            "dMeanPktSz\n",
            "Load\n",
            "SrcLoad\n",
            "DstLoad\n",
            "Loss\n",
            "SrcLoss\n",
            "DstLoss\n",
            "pLoss\n",
            "SrcGap\n",
            "DstGap\n",
            "Rate\n",
            "SrcRate\n",
            "DstRate\n",
            "SrcWin\n",
            "DstWin\n",
            "sVid\n",
            "dVid\n",
            "SrcTCPBase\n",
            "DstTCPBase\n",
            "TcpRtt\n",
            "SynAck\n",
            "AckDat\n",
            "Label\n",
            "Attack Type\n",
            "Attack Tool\n",
            " *        \n",
            " *    V   \n",
            " *    f   \n",
            " e        \n",
            " e    f   \n",
            " e &      \n",
            " e *      \n",
            " e d      \n",
            " e g      \n",
            " e i      \n",
            " e r      \n",
            " e s      \n",
            " eU       \n",
            "e        \n",
            "arp\n",
            "icmp\n",
            "ipv6-icmp\n",
            "llc\n",
            "lldp\n",
            "sctp\n",
            "tcp\n",
            "udp\n",
            "ACC\n",
            "CON\n",
            "ECO\n",
            "FIN\n",
            "INT\n",
            "NRS\n",
            "REQ\n",
            "RSP\n",
            "RST\n",
            "TST\n",
            "URP\n",
            "Shutdown\n",
            "Start\n",
            "Status\n",
            "39\n",
            "4\n",
            "52\n",
            "54\n",
            "af11\n",
            "af12\n",
            "af41\n",
            "cs0\n",
            "cs4\n",
            "cs6\n",
            "cs7\n",
            "ef\n",
            "nan\n"
          ]
        }
      ],
      "source": [
        "# Exibir os nomes de todas as colunas\n",
        "colunas = encoded.columns.tolist()  # Converte o Index em uma lista\n",
        "print(\"Nomes das colunas:\")\n",
        "for coluna in colunas:\n",
        "    print(coluna)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Selecionar apenas colunas numéricas\n",
        "numeric_cols = encoded.select_dtypes(include=['number']).columns\n",
        "\n",
        "# Aplicar a suavização apenas nas colunas numéricas\n",
        "for col in numeric_cols:\n",
        "    encoded[col] = encoded[col].fillna(encoded[col].mean())"
      ],
      "metadata": {
        "id": "F7xFWWJ6f72N"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# columns with null values\n",
        "\n",
        "columns_null = []\n",
        "columns = encoded.columns\n",
        "for column in columns:\n",
        "    c = encoded[column].isnull().sum()\n",
        "    if c != 0:\n",
        "        print(column, 'has {} null values'.format(c))\n",
        "        columns_null.append(column)"
      ],
      "metadata": {
        "id": "MTktQ-fDi4cS"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "REG482ke4ujI"
      },
      "source": [
        "Features\n",
        "* Seq: Sequência de pacotes.\n",
        "* Offset: Deslocamento em bytes.\n",
        "* sTtl: Time to Live da fonte.\n",
        "* tcp: Indica se o protocolo TCP está sendo usado.\n",
        "* AckDat: Dados de reconhecimento.\n",
        "* RST: Sinalizador de reinício TCP.\n",
        "* INT: Interrupções no fluxo de dados.\n",
        "* TcpRtt: Tempo de ida e volta do TCP.\n",
        "* icmp: Indica se o protocolo ICMP está presente.\n",
        "* sMeanPktSz: Tamanho médio do pacote da fonte.\n",
        "* FIN: Sinalizador de finalização de conexão.\n",
        "* sHops: Número de saltos até o destino.\n",
        "* Mean: Valor médio de um conjunto de dados.\n",
        "* dTtl: Time to Live do destino.\n",
        "* SrcBytes: Bytes enviados da fonte.\n",
        "* TotBytes: Total de bytes.\n",
        "* dMeanPktSz: Tamanho médio do pacote do destino.\n",
        "* Status: Estado da conexão.\n",
        "* cs0: Parâmetro de controle.\n",
        "* SrcWin: Janela de origem TCP.\n",
        "* sTos: Tipo de serviço.\n",
        "* CON: Conexões.\n",
        "* REQ: Requisições.\n",
        "* Loss: Taxa de perda de pacotes."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import pandas as pd\n",
        "\n",
        "# Selecionar apenas as colunas 'Label' e 'Attack Type'\n",
        "icmp_flood_samples = encoded[['Label', 'icmp', 'Attack Type']]\n",
        "\n",
        "icmp_flood_samples"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "K0q9lfe5EWEK",
        "outputId": "a1db1a8b-6a12-428e-87aa-6d24130e5f55"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Label  icmp Attack Type\n",
              "0        Benign   1.0      Benign\n",
              "1        Benign   1.0      Benign\n",
              "2        Benign   0.0      Benign\n",
              "3        Benign   0.0      Benign\n",
              "4        Benign   0.0      Benign\n",
              "...         ...   ...         ...\n",
              "1215885  Benign   0.0      Benign\n",
              "1215886  Benign   0.0      Benign\n",
              "1215887  Benign   0.0      Benign\n",
              "1215888  Benign   0.0      Benign\n",
              "1215889  Benign   0.0      Benign\n",
              "\n",
              "[1215890 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae32a51f-bcdb-445e-a3b0-7dc260311311\" 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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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>icmp</th>\n",
              "      <th>Attack Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Benign</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Benign</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Benign</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Benign</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Benign</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1215885</th>\n",
              "      <td>Benign</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1215886</th>\n",
              "      <td>Benign</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1215887</th>\n",
              "      <td>Benign</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1215888</th>\n",
              "      <td>Benign</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1215889</th>\n",
              "      <td>Benign</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1215890 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-ae32a51f-bcdb-445e-a3b0-7dc260311311 button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-ae32a51f-bcdb-445e-a3b0-7dc260311311');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        document.querySelector('#df-525e82b3-405f-4358-b656-24bfc4365e49 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_4404905f-62e8-4586-b46c-c6488a109e76\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
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              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('icmp_flood_samples')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <script>\n",
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              "        document.querySelector('#id_4404905f-62e8-4586-b46c-c6488a109e76 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('icmp_flood_samples');\n",
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              "  </div>\n",
              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "icmp_flood_samples"
            }
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import psutil\n",
        "import time\n",
        "from keras.callbacks import Callback\n",
        "from keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
        "\n",
        "# Função para monitorar recursos\n",
        "def monitor_resource_usage():\n",
        "    process = psutil.Process()\n",
        "    memory_info = process.memory_info()\n",
        "    cpu_percent = psutil.cpu_percent(interval=0.1)\n",
        "    memory_usage_mb = memory_info.rss / (1024 * 1024)\n",
        "    return cpu_percent, memory_usage_mb\n",
        "\n",
        "# Callback para monitorar CPU e Memória\n",
        "class ResourceMonitor(Callback):\n",
        "    def on_train_begin(self, logs=None):\n",
        "        self.cpu_usage = []\n",
        "        self.memory_usage = []\n",
        "        self.start_time = time.time()\n",
        "        print(\"\\n[Início do Treinamento]\")\n",
        "\n",
        "    def on_epoch_begin(self, epoch, logs=None):\n",
        "        cpu, mem = monitor_resource_usage()\n",
        "        self.cpu_usage.append(cpu)\n",
        "        self.memory_usage.append(mem)\n",
        "        print(f\"\\n[Época {epoch + 1} - Início] CPU: {cpu:.2f}%, Memória: {mem:.2f} MB\")\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        cpu, mem = monitor_resource_usage()\n",
        "        self.cpu_usage.append(cpu)\n",
        "        self.memory_usage.append(mem)\n",
        "        print(f\"[Época {epoch + 1} - Fim] CPU: {cpu:.2f}%, Memória: {mem:.2f} MB\")\n",
        "\n",
        "    def on_train_end(self, logs=None):\n",
        "        total_time = time.time() - self.start_time\n",
        "        avg_cpu = np.mean(self.cpu_usage)\n",
        "        avg_mem = np.mean(self.memory_usage)\n",
        "\n",
        "        print(\"\\n[Fim do Treinamento]\")\n",
        "        print(f\"Tempo total: {total_time:.2f} segundos\")\n",
        "        print(f\"Uso médio de CPU: {avg_cpu:.2f}%\")\n",
        "        print(f\"Consumo médio de memória: {avg_mem:.2f} MB\")\n",
        "\n",
        "# Instanciar o callback\n",
        "resource_monitor = ResourceMonitor()\n",
        "\n",
        "# Parâmetros para early stopping\n",
        "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.0001)"
      ],
      "metadata": {
        "id": "s4yCHc3I-s6b"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from sklearn.metrics import classification_report\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from keras.regularizers import l2\n",
        "\n",
        "# Configuração das features e rótulos\n",
        "features = [\n",
        "    'Seq', 'Offset', 'sTtl', 'tcp', 'AckDat', 'RST', 'INT', 'TcpRtt', 'icmp',\n",
        "    'sMeanPktSz', 'FIN', 'sHops', 'Mean', 'dTtl', 'SrcBytes', 'TotBytes',\n",
        "    'dMeanPktSz', 'Status', 'cs0', 'SrcWin', 'sTos', 'CON', 'REQ', 'Loss', 'Attack Type'\n",
        "]\n",
        "\n",
        "X = encoded[features]\n",
        "y = encoded['Label']  # Coluna de rótulos\n",
        "\n",
        "# Divida os dados normalmente\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Identifique as amostras ICMPFlood no conjunto de treino\n",
        "train_zero_day_indices = X_train[X_train['Attack Type'] == 'ICMPFlood'].index\n",
        "\n",
        "# Remova essas amostras de ICMPFlood do conjunto de treino\n",
        "X_train_clean = X_train.drop(train_zero_day_indices)\n",
        "y_train_clean = y_train.drop(train_zero_day_indices)\n",
        "\n",
        "# Identifique as amostras ICMPFlood no conjunto de teste\n",
        "test_zero_day_indices = X_test[X_test['Attack Type'] == 'ICMPFlood'].index\n",
        "\n",
        "# Mantenha as amostras de ICMPFlood no conjunto de teste\n",
        "X_test_zero_day = X_test.loc[test_zero_day_indices]\n",
        "y_test_zero_day = y_test.loc[test_zero_day_indices]\n",
        "\n",
        "# Verificar a distribuição das classes no conjunto de teste\n",
        "print(f\"Zero-day presente no conjunto de teste: {any(test_zero_day_indices.isin(X_test_zero_day.index))} \\n\")\n",
        "\n",
        "# Verificar as distribuições\n",
        "print(\"Distribuição do conjunto de treino binário:\", pd.Series(y_train_clean).value_counts())\n",
        "print(\"Distribuição do conjunto de teste binário:\", pd.Series(y_test_zero_day).value_counts())\n",
        "\n",
        "# Remover a coluna 'Attack Type' das features de treino e teste\n",
        "X_train_clean = X_train_clean.drop(columns=['Attack Type'])\n",
        "X_test = X_test.drop(columns=['Attack Type'])\n",
        "X_test_zero_day = X_test_zero_day.drop(columns=['Attack Type'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5xisgI82tprr",
        "outputId": "4717fedc-30f9-4b41-e41f-69ecc63f47fd"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Zero-day presente no conjunto de teste: True \n",
            "\n",
            "Distribuição do conjunto de treino binário: Label\n",
            "Malicious    589852\n",
            "Benign       381943\n",
            "Name: count, dtype: int64\n",
            "Distribuição do conjunto de teste binário: Label\n",
            "Malicious    238\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from imblearn.over_sampling import SMOTE\n",
        "import pandas as pd\n",
        "\n",
        "# Configurar o SMOTE\n",
        "smote = SMOTE(random_state=42)\n",
        "\n",
        "# Aplicar SMOTE no conjunto de treinamento\n",
        "X_train_balanced, y_train_balanced = smote.fit_resample(X_train_clean, y_train_clean)\n",
        "\n",
        "# Verificar distribuição das classes após o SMOTE\n",
        "print(f\"Distribuição após SMOTE:\\n{pd.Series(y_train_balanced).value_counts()}\")\n",
        "\n",
        "# Normalizar os dados após o SMOTE\n",
        "scaler = StandardScaler()\n",
        "X_train_balanced = scaler.fit_transform(X_train_balanced)\n",
        "\n",
        "# Normalizar o conjunto de teste com o mesmo scaler ajustado no treino\n",
        "X_test_combined = scaler.transform(X_test)\n",
        "y_test_combined = y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n7CTpEG903J5",
        "outputId": "d741d510-58d0-4f36-d491-fb4461da4519"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distribuição após SMOTE:\n",
            "Label\n",
            "Malicious    589852\n",
            "Benign       589852\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar a dimensão do teste\n",
        "print(X_test_combined.shape)\n",
        "print(y_test_combined.shape)\n",
        "print(f\"Distribuição Teste:\\n{pd.Series(y_test_combined).value_counts()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wZy0q2ZFIgc3",
        "outputId": "de8f426a-c620-4658-af7c-33f749ab1bd5"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(243178, 24)\n",
            "(243178,)\n",
            "Distribuição Teste:\n",
            "Label\n",
            "Malicious    147384\n",
            "Benign        95794\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Criar o codificador\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "# Ajustar o codificador aos rótulos de treinamento e teste\n",
        "y_train_balanced = label_encoder.fit_transform(y_train_balanced)\n",
        "y_test_combined = label_encoder.transform(y_test_combined)\n",
        "\n",
        "# Verificar os tipos após a transformação\n",
        "print(y_train_balanced.dtype)\n",
        "print(y_test_combined.dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ph39Wxjncpv",
        "outputId": "6b0a3aad-5663-4696-91c4-9df78a3194d5"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "int64\n",
            "int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Input, Dense, Dropout\n",
        "from tensorflow.keras.regularizers import l2\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# Construção do Modelo\n",
        "model = Sequential()\n",
        "model.add(Input(shape=(X_train_balanced.shape[1],)))\n",
        "model.add(Dense(128, activation='relu', kernel_regularizer=l2(0.001)))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Dense(64, activation='relu', kernel_regularizer=l2(0.001)))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Dense(32, activation='relu', kernel_regularizer=l2(0.001)))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "# Compilar o modelo\n",
        "model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Treinar o modelo\n",
        "history = model.fit(\n",
        "    X_train_balanced, y_train_balanced,\n",
        "    validation_data=(X_test_combined, y_test_combined),\n",
        "    epochs=10,\n",
        "    batch_size=32,\n",
        "    callbacks=[early_stopping, resource_monitor, reduce_lr]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "33yGtClgG9bE",
        "outputId": "e57b3809-d8f3-4e23-a926-461e8f54f3f8"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "[Início do Treinamento]\n",
            "\n",
            "[Época 1 - Início] CPU: 0.20%, Memória: 3759.94 MB\n",
            "Epoch 1/10\n",
            "\u001b[1m36860/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9665 - loss: 0.1914[Época 1 - Fim] CPU: 0.10%, Memória: 3601.58 MB\n",
            "\u001b[1m36866/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 2ms/step - accuracy: 0.9665 - loss: 0.1914 - val_accuracy: 0.9948 - val_loss: 0.0471 - learning_rate: 1.0000e-04\n",
            "\n",
            "[Época 2 - Início] CPU: 0.20%, Memória: 3601.58 MB\n",
            "Epoch 2/10\n",
            "\u001b[1m36866/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9947 - loss: 0.0425[Época 2 - Fim] CPU: 1.50%, Memória: 3629.77 MB\n",
            "\u001b[1m36866/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2ms/step - accuracy: 0.9947 - loss: 0.0425 - val_accuracy: 0.9972 - val_loss: 0.0359 - learning_rate: 1.0000e-04\n",
            "\n",
            "[Época 3 - Início] CPU: 2.10%, Memória: 3629.77 MB\n",
            "Epoch 3/10\n",
            "\u001b[1m36848/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9967 - loss: 0.0314[Época 3 - Fim] CPU: 0.40%, Memória: 3641.92 MB\n",
            "\u001b[1m36866/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2ms/step - accuracy: 0.9967 - loss: 0.0314 - val_accuracy: 0.9981 - val_loss: 0.0330 - learning_rate: 1.0000e-04\n",
            "\n",
            "[Época 4 - Início] CPU: 0.40%, Memória: 3641.92 MB\n",
            "Epoch 4/10\n",
            "\u001b[1m36854/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9973 - loss: 0.0270[Época 4 - Fim] CPU: 0.50%, Memória: 3656.71 MB\n",
            "\u001b[1m36866/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2ms/step - accuracy: 0.9973 - loss: 0.0270 - val_accuracy: 0.9981 - val_loss: 0.0310 - learning_rate: 1.0000e-04\n",
            "\n",
            "[Época 5 - Início] CPU: 0.40%, Memória: 3656.71 MB\n",
            "Epoch 5/10\n",
            "\u001b[1m36850/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9978 - loss: 0.0241[Época 5 - Fim] CPU: 0.00%, Memória: 3666.68 MB\n",
            "\u001b[1m36866/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2ms/step - accuracy: 0.9978 - loss: 0.0241 - val_accuracy: 0.9978 - val_loss: 0.0309 - learning_rate: 1.0000e-04\n",
            "\n",
            "[Época 6 - Início] CPU: 0.10%, Memória: 3666.68 MB\n",
            "Epoch 6/10\n",
            "\u001b[1m36854/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9981 - loss: 0.0219[Época 6 - Fim] CPU: 0.10%, Memória: 3678.72 MB\n",
            "\u001b[1m36866/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2ms/step - accuracy: 0.9981 - loss: 0.0219 - val_accuracy: 0.9979 - val_loss: 0.0296 - learning_rate: 1.0000e-04\n",
            "\n",
            "[Época 7 - Início] CPU: 0.70%, Memória: 3678.72 MB\n",
            "Epoch 7/10\n",
            "\u001b[1m36852/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9981 - loss: 0.0207[Época 7 - Fim] CPU: 0.10%, Memória: 3684.50 MB\n",
            "\u001b[1m36866/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2ms/step - accuracy: 0.9981 - loss: 0.0207 - val_accuracy: 0.9976 - val_loss: 0.0297 - learning_rate: 1.0000e-04\n",
            "\n",
            "[Época 8 - Início] CPU: 0.40%, Memória: 3684.50 MB\n",
            "Epoch 8/10\n",
            "\u001b[1m36864/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9983 - loss: 0.0194[Época 8 - Fim] CPU: 0.00%, Memória: 3696.00 MB\n",
            "\u001b[1m36866/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2ms/step - accuracy: 0.9983 - loss: 0.0194 - val_accuracy: 0.9983 - val_loss: 0.0292 - learning_rate: 1.0000e-04\n",
            "\n",
            "[Época 9 - Início] CPU: 0.20%, Memória: 3696.00 MB\n",
            "Epoch 9/10\n",
            "\u001b[1m36848/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9984 - loss: 0.0184[Época 9 - Fim] CPU: 0.30%, Memória: 3705.93 MB\n",
            "\u001b[1m36866/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 2ms/step - accuracy: 0.9984 - loss: 0.0184 - val_accuracy: 0.9984 - val_loss: 0.0277 - learning_rate: 1.0000e-04\n",
            "\n",
            "[Época 10 - Início] CPU: 1.20%, Memória: 3705.93 MB\n",
            "Epoch 10/10\n",
            "\u001b[1m36845/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9984 - loss: 0.0180[Época 10 - Fim] CPU: 0.30%, Memória: 3715.81 MB\n",
            "\u001b[1m36866/36866\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2ms/step - accuracy: 0.9984 - loss: 0.0180 - val_accuracy: 0.9983 - val_loss: 0.0276 - learning_rate: 1.0000e-04\n",
            "\n",
            "[Fim do Treinamento]\n",
            "Tempo total: 828.98 segundos\n",
            "Uso médio de CPU: 0.46%\n",
            "Consumo médio de memória: 3669.97 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fazer previsões diretas e arredondar (sem precisar de threshold manual)\n",
        "y_pred = model.predict(X_test_combined).round()\n",
        "\n",
        "# Gerar o relatório de classificação\n",
        "print(\"\\n--- Relatório de Classificação ---\")\n",
        "print(classification_report(y_test_combined, y_pred, target_names=[\"Benigno\", \"Malicioso\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_aBqD0P35_jJ",
        "outputId": "69c04adc-0260-49be-a194-d074a53d9730"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m7600/7600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 850us/step\n",
            "\n",
            "--- Relatório de Classificação ---\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     Benigno       1.00      1.00      1.00     95794\n",
            "   Malicioso       1.00      1.00      1.00    147384\n",
            "\n",
            "    accuracy                           1.00    243178\n",
            "   macro avg       1.00      1.00      1.00    243178\n",
            "weighted avg       1.00      1.00      1.00    243178\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Tamanho de X_test_zero_day: {X_test_zero_day.shape}\")\n",
        "print(f\"Tamanho de y_test_zero_day: {y_test_zero_day.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pMO9qzt_QomL",
        "outputId": "9a1f4d75-e842-4c82-fab2-ea3443a0d7b1"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tamanho de X_test_zero_day: (238, 24)\n",
            "Tamanho de y_test_zero_day: (238,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prever com o modelo para as amostras zero-day\n",
        "\n",
        "y_pred_zero_day = model.predict(X_test_zero_day)\n",
        "\n",
        "accuracy_zero_day = (y_pred_zero_day == 1).mean()  # Supondo que '1' é a classe de maligno"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5DNpHpzdQp8n",
        "outputId": "cb46fb2d-b065-4e2e-a743-e69a81277363"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_test_zero_day)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNZuW6YuXCK2",
        "outputId": "051541fe-765b-4a14-a54d-e128eaedc0aa"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "765058    Malicious\n",
            "35586     Malicious\n",
            "36988     Malicious\n",
            "766062    Malicious\n",
            "35511     Malicious\n",
            "            ...    \n",
            "35882     Malicious\n",
            "765081    Malicious\n",
            "35852     Malicious\n",
            "36923     Malicious\n",
            "766407    Malicious\n",
            "Name: Label, Length: 238, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_zero_day = [0. if label == 'Benign' else 1.0 for label in y_test_zero_day]\n",
        "print(y_test_zero_day)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0w34fOh1U4sp",
        "outputId": "2a9c5b44-a819-47ae-fd91-b666c00b1ed9"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_pred_zero_day)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "73susrYfXDMo",
        "outputId": "6c53f008-092b-4473-b521-a9c2a949e3a4"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.52046907]\n",
            " [0.        ]\n",
            " [0.52046907]\n",
            " [0.52046907]\n",
            " [0.        ]\n",
            " [0.52046907]\n",
            " [0.52046907]\n",
            " [0.52046907]\n",
            " [0.52046907]\n",
            " [0.52046907]\n",
            " [0.        ]\n",
            " [0.52046907]\n",
            " [0.52046907]\n",
            " [0.52046907]\n",
            " [0.        ]\n",
            " [0.52046907]\n",
            " [0.52046907]\n",
            " [0.52046907]\n",
            " [0.52046907]\n",
            " [0.52046907]\n",
            " [0.52046907]\n",
            " [0.52046907]\n",
            " [0.        ]\n",
            " [0.        ]\n",
            " [0.        ]\n",
            " [0.52046907]\n",
            " [0.        ]\n",
            " [0.52046907]\n",
            " [0.52046907]\n",
            " [0.52046907]\n",
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            " [0.52046907]\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_zero_day = [0.0 if label == 0. else 1.0 for label in y_pred_zero_day]\n",
        "print(y_pred_zero_day)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gjXHDoahV2Wo",
        "outputId": "9f42ac0f-67c1-40c1-d9f1-0a036ac563a6"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_zero_day = np.array(y_pred_zero_day, dtype='float32')"
      ],
      "metadata": {
        "id": "x3pcO7EVW7Dl"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Comparando previsões com os rótulos reais\n",
        "correct_classifications = (y_pred_zero_day == y_test_zero_day).sum()\n",
        "total_zero_day_samples = len(y_test_zero_day)\n",
        "\n",
        "print(f\"Zero-day samples classified correctly: {correct_classifications}/{total_zero_day_samples}\")\n",
        "print(f\"Accuracy for zero-day samples: {correct_classifications / total_zero_day_samples * 100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7CioSBDCQs5a",
        "outputId": "cfc45f26-9399-4a82-cd7b-09e7fffa4649"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Zero-day samples classified correctly: 173/238\n",
            "Accuracy for zero-day samples: 72.69%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy Plot\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Training Accuracy\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['accuracy'], label='Training')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation')\n",
        "plt.title('Accuracy per Epoch')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "# Loss Plot\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['loss'], label='Training')\n",
        "plt.plot(history.history['val_loss'], label='Validation')\n",
        "plt.title('Loss per Epoch')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "NbJqXntM-C0q",
        "outputId": "c2943ea5-c9f7-4cc3-fc80-0abe599bc662"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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ft4nUF3Xr43gROavc3Fy+/vprrr32Wm666aYSt3HjxpGZmck333wDOHdu+f3331m4cGGJc7lmugwdOpTU1NRSZxi5xkRGRuLh4cGPP/5Y7Pv/+Mc/yly76xOpk2fYGIZRYovesLAw+vTpw4cffkhiYmKp9biEhoZy9dVX88knnzB//nwGDhxIaGhomWt67733KCwsdD9+5513KCoq4uqrrwZgwIABBAUF8dJLLxUb55KSklLmn3WuFi1aRFJSkvvx2rVr+e2339y1NmnShI4dO/LRRx8VCw+3bt3K999/776ws1qtDB48mP/85z+sX7++xM/RDCgREZHy8/Dw4KqrruLf//63e9kZwOHDh/n000/p1auXe9n/kSNHij03ICCA1q1bk5+fD0BOTg55eXnFxsTExBAYGOgeczYnX9cZhsGsWbPw8vLiiiuuAOCWW27Bbrfz/PPPl3huUVFRiQ8iq9LZrsf69++PzWbjrbfeKnad8s9//pP09HSuueYaAC6++GKioqKYMWNGifp1fSNSNTRTSqSe+eabb8jMzOT6668v9fuXXHIJYWFhzJ8/n2HDhvHoo4/y1VdfcfPNN3PnnXfSuXNn0tLS+Oabb5g9ezYdOnRgxIgRfPzxx0yYMIG1a9fSu3dvsrOzWbZsGX//+9+54YYbCA4O5uabb2bmzJlYLBZiYmL473//W6LfwJnExsYSExPDI488QlJSEkFBQSxYsKDUtftvvfUWvXr14uKLL2bMmDFERUWxZ88evv32WzZv3lxs7IgRI7jpppsASr2wOpOCggKuuOIKbrnlFnbu3Mk//vEPevXq5X5/g4KCeOedd7j99tu5+OKLufXWWwkLCyMxMZFvv/2WSy+99JyXC3733Xfs2LGjxPGePXsW+zSwdevW9OrVi7Fjx5Kfn8+MGTNo1KgRjz32mHvMa6+9xtVXX02PHj0YPXo0ubm5zJw5k+DgYCZPnuwe99JLL/H999/Tt29fxowZQ7t27Th48CBffvklP//8c4nmoCIiIuL04Ycfsnjx4hLHx48fzwsvvMDSpUvp1asXf//73/H09OTdd98lPz+fV1991T22ffv29OvXj86dO9OwYUPWr1/PV1995W5OvmvXLvf1Sfv27fH09GThwoUcPnyYW2+99aw1+vj4sHjxYkaOHEn37t357rvv+Pbbb3nyySfdy/L69u3LPffcw9SpU9m8eTNXXXUVXl5e/PXXX3z55Ze8+eab7uuriigqKjrtjK4hQ4bg7+/vfny267GwsDAmTpzIc889x8CBA7n++uvd47p27ere4MZqtfLOO+9w3XXX0bFjR0aNGkWTJk3YsWMHf/75J0uWLKnw6xGR0zBp1z8RMcl1111n+Pj4GNnZ2acdc8cddxheXl5GamqqYRiGceTIEWPcuHFGs2bNDJvNZjRv3twYOXKk+/uGYRg5OTnGU089ZURFRRleXl5G48aNjZtuuqnYlsYpKSnG0KFDDT8/PyMkJMS45557jK1btxqAMWfOHPe4kSNHGv7+/qXWtm3bNqN///5GQECAERoaatx9993G77//XuIchmEYW7duNYYMGWI0aNDA8PHxMdq2bWs888wzJc6Zn59vhISEGMHBwUZubm5Z3kb3FsSrVq0yxowZY4SEhBgBAQHGbbfdZhw5cqTE+BUrVhgDBgwwgoODDR8fHyMmJsa44447jPXr15fpdZ+phtPdXO9HQkKCe1voadOmGS1atDC8vb2N3r17G7///nuJ8y5btsy49NJLDV9fXyMoKMi47rrrjG3btpUYt3fvXmPEiBFGWFiY4e3tbURHRxv33XefkZ+fX6y+U7e+XrFihQEYK1asKPNrFRERqe3O9u/2vn37DMMwjI0bNxoDBgwwAgICDD8/P+Oyyy4zVq9eXexcL7zwgtGtWzejQYMGhq+vrxEbG2u8+OKLRkFBgWEYhpGammrcd999RmxsrOHv728EBwcb3bt3N7744ouz1um6HomLizOuuuoqw8/Pz4iIiDAmTZpk2O32EuPfe+89o3Pnzoavr68RGBhoXHjhhcZjjz1mHDhwwD0mMjLSuOaaa8r8Xo0cOfKM71VCQkKx97Ss12OzZs0yYmNjDS8vLyMiIsIYO3ascfTo0RLjfv75Z+PKK680AgMDDX9/f+Oiiy4yZs6cWeI9OtWkSZMM/YotUj4Ww9A8RBGp34qKimjatCnXXXcd//znP8v0nLlz5zJq1CjWrVtXpp3vzLRnzx6ioqJ47bXXeOSRR8wuR0RERGqwO+64g6+++oqsrCyzSzmr2nQ9JiKlU08pEan3Fi1aREpKSrHm6SIiIiIiIlK11FNKROqt3377jT/++IPnn3+eTp060bdvX7NLEhERERERqTc0U0pE6q133nmHsWPHEh4ezscff2x2OSIiIiIiIvWKekqJiIiIiIiIiEi100wpERERERERERGpdgqlRERERERERESk2qnReQU5HA4OHDhAYGAgFovF7HJERESkmhiGQWZmJk2bNsVq1ed7Z6LrJRERkfqprNdLCqUq6MCBA7Ro0cLsMkRERMQk+/bto3nz5maXUaPpeklERKR+O9v1kkKpCgoMDAScb3BQUJDJ1YiIiEh1ycjIoEWLFu5rATk9XS+JiIjUT2W9XlIoVUGuKehBQUG6yBIREamHtBzt7HS9JCIiUr+d7XpJjRBERERERERERKTaKZQSEREREREREZFqp1BKRERERERERESqnXpKiYiIiIiIiEiVs9vtFBYWml2GVAIvLy88PDzO+TwKpURERERERESkyhiGwaFDhzh27JjZpUglatCgAY0bNz6nzV8USomIiIiIiIhIlXEFUuHh4fj5+WkH21rOMAxycnJITk4GoEmTJhU+l0IpEREREREREakSdrvdHUg1atTI7HKkkvj6+gKQnJxMeHh4hZfyqdG5iIiIiIiIiFQJVw8pPz8/kyuRyub6Mz2XPmEKpURERERERESkSmnJXt1TGX+mCqVERERERERERKTaKZQSEREREREREakGrVq1YsaMGWUev3LlSiwWS53duVChlIiIiIiIiIjISSwWyxlvkydPrtB5161bx5gxY8o8vmfPnhw8eJDg4OAK/byaTrvviYiIiIiIiIic5ODBg+77n3/+Oc8++yw7d+50HwsICHDfNwwDu92Op+fZI5awsLBy1WGz2WjcuHG5nlObaKaUiIiIiIiIiMhJGjdu7L4FBwdjsVjcj3fs2EFgYCDfffcdnTt3xtvbm59//pm4uDhuuOEGIiIiCAgIoGvXrixbtqzYeU9dvmexWPjggw8YMmQIfn5+tGnThm+++cb9/VOX782dO5cGDRqwZMkS2rVrR0BAAAMHDiwWohUVFfHAAw/QoEEDGjVqxOOPP87IkSMZPHhwVb5lFaJQSkRERERERESqjWEY5BQUmXIzDKPSXscTTzzByy+/zPbt27nooovIyspi0KBBLF++nE2bNjFw4ECuu+46EhMTz3ie5557jltuuYU//viDQYMGcdttt5GWlnba8Tk5Obz++uvMmzePH3/8kcTERB555BH391955RXmz5/PnDlz+OWXX8jIyGDRokWV9bIrlZbviYiIiIiIiEi1yS200/7ZJab87G1TBuBnq5woZMqUKVx55ZXuxw0bNqRDhw7ux88//zwLFy7km2++Ydy4cac9zx133MHw4cMBeOmll3jrrbdYu3YtAwcOLHV8YWEhs2fPJiYmBoBx48YxZcoU9/dnzpzJxIkTGTJkCACzZs3if//7X8VfaBXSTCkRERERERERkXLq0qVLscdZWVk88sgjtGvXjgYNGhAQEMD27dvPOlPqoosuct/39/cnKCiI5OTk04738/NzB1IATZo0cY9PT0/n8OHDdOvWzf19Dw8POnfuXK7XVl00U0pEREREREREqo2vlwfbpgww7WdXFn9//2KPH3nkEZYuXcrrr79O69at8fX15aabbqKgoOCM5/Hy8ir22GKx4HA4yjW+MpclVieFUiIiIiIiIiJSbSwWS6UtoatJfvnlF+644w73srmsrCz27NlTrTUEBwcTERHBunXr6NOnDwB2u52NGzfSsWPHaq2lLOre3wIREREpriAHco5ATqrza/aR449POpaTBhYr+AQfvzVwfvVtcPpjXn5gsVTLS7A7DPIK7eQW2sktsJNXaCev0OF8XOyY3X2sWQNfbujYrFrqk+r34rfb+OmvVJ6+pj292oSaXY6IiAht2rTh66+/5rrrrsNisfDMM8+cccZTVbn//vuZOnUqrVu3JjY2lpkzZ3L06FEs1XTdVh4KpURERGoThwNyj54SMqWeFDKV8rgwp2pqsXph+ARjeAdj9w6myBZIkVcQ+V5BFHgEkOsZSJ5HANnWALIIJMviRwYBZODHMbsf2XYLeYWOYmFTbuEp4VKBM3wqsJf/gq53m1CFUnXYvrRcdhzK5K/kTIVSIiJSI0yfPp0777yTnj17EhoayuOPP05GRka11/H4449z6NAhRowYgYeHB2PGjGHAgAF4eFTe0sXKYjFq68JDk2VkZBAcHEx6ejpBQUFmlyMiIrXVaWcxnRw4pZ14nHsUjPIHNIbVC7tvIwq9G1LgHUKeVwNyPRuQ5dmATGsQGZYgCorsWPPT8SzIwKswA1thBraiTHzsWfjaM/FzZOHvyCKAbDw590/9sg1vMvAn3fAnAz/31wzDnwz8yTD8SD/+1T3O8CPPKxDD0x9vmxe+Xh74eHnga/M4ft/qfOzlQdvGgdzVO/qc6zyVrgHKrirfq1cX7+AfK+O4/ZJInh98QaWeW0REKk9eXh4JCQlERUXh4+Njdjn1ksPhoF27dtxyyy08//zzlXbeM/3ZlvUaQDOlREREKovDDrnHqmUWU55HINmewWRag8mwBnGUYI4SQKojiFS7PweLAjlc5EdSoT9HjUCy8IWcypqybeBHPsFkE2TJPv41h1DPXBp55BLikUuIJZtgSw7BlmwCycbfyMbfkYWfIwsfh/M1+1vy8SefJpa0CtRgBUsQeDUovrTw5OWFDdsAlR9KSc0QFepsLpuQmm1yJSIiIjXL3r17+f777+nbty/5+fnMmjWLhIQE/va3v5ldWgkKpURERMorPxNj3T+xJ67Fke0MoKy5aXjkH8NSgVlMhXiSZgSSZgRyxAjkKIEcMYI4agRyBOfXNAKPjwniKAEUlfOfcIsF/G2e+Nk8CPD2xM/bAz+bJ/42D/y8j3+1ebpnGfnanDOO3I+Pz0Y6eSbSidlJHnh7Wsvep8BeBPkZkHfMGeLlpTvv56U7b2c7Zi9wzhbLO+a8nU50Pzh/cLneJ6k9osMUSomIiJTGarUyd+5cHnnkEQzD4IILLmDZsmW0a9fO7NJKUCglIiICGIZBVn4RR7IKOJKdf/xrAWnZBaRmOR9nZ6bTM+1rhuYtoAFZp/1HNN3wI+2UcCmNINKMgONfneGSK2jKwhc4EehYXQGSt4f7q5/Nk2benrSxeZT4XoC3Z6kBkyt88rd54uNVjtCoqnl4gl9D5628DAOK8k4KqU4Kr4qFWccgtG2lli01S3RoAABJx3LJLbDja6t5fTJERETM0KJFC3755RezyygThVIiIlJn5RQUucOlI1n5x78676dlF5CaXUCaK4DKKjhtM20f8rnNYxnPev6HUIuzWWWcowmf2q/gMA3J8WxAvlcIed4h2L1D8Pb2xt/7pFlJNk/8vT0IsXnS3NsVGHm4jzsDpRoaINU0Fgt4+TpvQU3MrkZMFOJvo4GfF8dyCtlzJJt2TdTfS0REpLZRKCUiIrVGXqGdtFNmLznDpRP3Tw6fcgvt5f4Z/jYPGgbYaOTvTYQvDCpczBWp8wkoPAJAbkBLjnR5CP8Ot/CIv68CJBETRYX6synxGPEpCqVERERqI4VSIiJimkK7g6PZBaS6AqXs/OP3nSGT+352AWlZBWTmF5X7Z9g8rYQFeNPQ30ajABsN/W2EBnjTyP+k+8ePN/L3di4BKiqATR/Dj9Mg84DzRMEtoe9j+Ha4leYeXpX8TohIRUSHBrAp8RgJqVlmlyIiIiIVoFBKREQqzOEwyC4oIiu/iMw85y0rv4isvCKy8gtLHEvPLXT2azo+kyk9t7DcP9PTaqHR8ZlMzq82Gh6/Hxpw0n1/bxoG2PC3eZSjAXchbPgEfnwN0vc5jwU1gz6PQMf/A09buesVkarjanYer2bnIiIitZJCKZHySv0LUncd72ni5/zq6XvKYx+wWs2uVOS0XGGSKzA6OTjKzCsseex4wHQicDrx/XNlteCepXTyTCbXzKaTw6dGAd4E+XhW/nI5exFs+QJWvQJH9ziPBTSG3g9D55Hg6V25P09EKkVU6PFQKkWhlIiISG2kUEqkrBx2+PF1WPWycyvys/H0BS+fE0HVmUKsYje/kgHXqce8/Jzn9vRV+FXPOBwGWQUlg6HMvMLSj+WfEjy5gqVKCJNO5mm1EOjjSaCPFwHengT4eBLo+urjSYC31/Hve5YImRr4emG1mtSTyWGHrQtg5cuQFuc85h8GvSZAl1HO/95EpMZyz5RKycIwDPV3ExERqWUUSomUReYhWHAX7PnJ+TjiQufXwhwozD3x1Z5/4jlFuc5b7tGqrc3T50RQ5XmaEOx0oZdrfEgkhLcH74CqrVXOKiOvkO0HMth2MIM/D2QQn5JFRhWGSV4elhNB0vEQKcjnxP2Tw6STxwQePx5w/Li3Zy1r9u1wwLZFzjAqdafzmG9D6PUgdL0LbP5mViciZdSqkfO/1Yy8Io7mFNLQX0tsRUSk5ujXrx8dO3ZkxowZALRq1YoHH3yQBx988LTPsVgsLFy4kMGDB5/Tz66s81Q100Opt99+m9dee41Dhw7RoUMHZs6cSbdu3UodW1hYyNSpU/noo49ISkqibdu2vPLKKwwcONA9JjMzk2eeeYaFCxeSnJxMp06dePPNN+natat7TFZWFk888QSLFi3iyJEjREVF8cADD3DvvfdW+euVWmj3Mvj6HshJBS9/uHY6dLi19LEOuzOcKsorGVgV5kBhXinHjn8tOt33SjlXsfArz3k75/DLAg2jofGF0PgCaHwRRFwAQU2dW7BLpTIMg0MZeWw74Ayfth0PohLTcsr0/JPDJFdgFHhSmHTq95z3vYqNCfD2xMfLo4pfaQ1jGLDjv7BiKiT/6Tzm0wB63g/d7wHvQFPLE5Hy8fHyoFkDX5KO5RKfkkVD/4ZmlyQiInXEddddR2FhIYsXLy7xvZ9++ok+ffrw+++/c9FFF5X5nOvWrcPfv3I//Jw8eTKLFi1i8+bNxY4fPHiQkJCQSv1ZVcHUUOrzzz9nwoQJzJ49m+7duzNjxgwGDBjAzp07CQ8PLzH+6aef5pNPPuH9998nNjaWJUuWMGTIEFavXk2nTp0AuOuuu9i6dSvz5s2jadOmfPLJJ/Tv359t27bRrFkzACZMmMAPP/zAJ598QqtWrfj+++/5+9//TtOmTbn++uur9T2QGsxeCD+8AL/McD6OuBBungOhbU7/HKuHc7ZRVc84Omv4dZowq7TwKz8LjuyGrEPO5Utpcc4ZJC6+DU+EVI0vdAZVYW1Bu4+VWZHdQUJqtjN8OngigErLLih1fLMGvrRrEsT5TYNo2ziQED9b8eDJxxNvz3oWJp0rw4BdS2DFi3DoD+cx7yDocR9cMhZ8gs2tT0QqLDrM3xlKpWbTpZVCKRERqRyjR49m6NCh7N+/n+bNmxf73pw5c+jSpUu5AimAsLCwyizxjBo3blxtP+tcmBpKTZ8+nbvvvptRo0YBMHv2bL799ls+/PBDnnjiiRLj582bx1NPPcWgQYMAGDt2LMuWLWPatGl88skn5ObmsmDBAv7973/Tp08fwJka/uc//+Gdd97hhRdeAGD16tWMHDmSfv36ATBmzBjeffdd1q5dq1BKnI4lwlejYf9a5+Oud8FVLzr7ONUEVRF+ZaXA4S1waAsc2ur8mroLctMg4UfnzcXD5gymXLOpXLOrfGt+El/VcgqK2H4w80T4dCCdHYcyyS8q2YfMw2qhdVgA5zcNon3TINo3cX5t4KflJ5XGMCBuOax4CZI2OI/ZApxBVI/79HdW6pTyzD7/888/efbZZ9mwYQN79+7ljTfeKHUpQXnOaZaoUH9++iuVBO3AJyIilejaa68lLCyMuXPn8vTTT7uPZ2Vl8eWXX/LEE08wfPhwfvzxR44ePUpMTAxPPvkkw4cPP+05T12+99dffzF69GjWrl1LdHQ0b775ZonnPP744yxcuJD9+/fTuHFjbrvtNp599lm8vLyYO3cuzz33HIC7lcacOXO44447Sizf27JlC+PHj2fNmjX4+fkxdOhQpk+fTkCA83fKO+64g2PHjtGrVy+mTZtGQUEBt956KzNmzMDLq+omJJgWShUUFLBhwwYmTpzoPma1Wunfvz9r1qwp9Tn5+fn4+BQPBXx9ffn5558BKCoqwm63n3EMQM+ePfnmm2+48847adq0KStXrmTXrl288cYbp603Pz+f/PwTS6YyMjLK/mKldtn+H/j3fZCXDt7BcMNMaH+D2VVVvYAwCLgcYi4/cawwD1K2Fw+qDm+F/Izjx7YUP0dwixOzqVxBVYNWdbYZe0pmvjt8+vNAOtsOZpCQmo1hlBzrb/OgXZMT4dP5TYNpExFQ/5bPVRfDgIRVzjBq32/OY15+0G0M9HwA/BuZW59IJSvv7POcnByio6O5+eabeeihhyrlnGaJDj3R7FxERGoJw3Cu3DCDl1+Z2pN4enoyYsQI5s6dy1NPPeUOfb788kvsdjv/93//x5dffsnjjz9OUFAQ3377LbfffjsxMTFl+gDH4XBw4403EhERwW+//UZ6enqpHxAFBgYyd+5cmjZtypYtW7j77rsJDAzkscceY9iwYWzdupXFixezbNkyAIKDS64AyM7OZsCAAfTo0YN169aRnJzMXXfdxbhx45g7d6573IoVK2jSpAkrVqxg9+7dDBs2jI4dO3L33Xef9fVUlGmhVGpqKna7nYiIiGLHIyIi2LFjR6nPGTBgANOnT6dPnz7ExMSwfPlyvv76a+x2O+D8w+rRowfPP/887dq1IyIign/961+sWbOG1q1bu88zc+ZMxowZQ/PmzfH09MRqtfL++++7Z1eVZurUqe4EUuqowjxY+gysfc/5uFkXuOmfENLK1LJM5eUDTTs5by6GAcf2Fg+pDv3hnF2Wvs952/m/E+NtgRBx/vGQ6nhQFd6+Vu1q5nAY7E3LKRY+bTuQQXJmfqnjwwO9i4VP7ZsGEdnQz7wd5uqbPb84w6i9xz+M8PRxzna8dDwE1JxfpEUqU3lnn3ft2tXdb7O071fknGaJCnN+wquZUiIitUhhDrzU1Jyf/eSBMm9qc+edd/Laa6+xatUq90qrOXPmMHToUCIjI3nkkUfcY++//36WLFnCF198UaZQatmyZezYsYMlS5bQtKnzvXjppZe4+uqri407eZZWq1ateOSRR/jss8947LHH8PX1JSAgAE9PzzMu1/v000/Jy8vj448/dve0mjVrFtdddx2vvPKKO5cJCQlh1qxZeHh4EBsbyzXXXMPy5cvrZihVEW+++SZ33303sbGxWCwWYmJiGDVqFB9++KF7zLx587jzzjtp1qwZHh4eXHzxxQwfPpwNGza4x8ycOZNff/2Vb775hsjISH788Ufuu+8+mjZtSv/+/Uv92RMnTmTChAnuxxkZGbRo0aLqXqxUr9Td8NUdJ2b+9HwArnhWfZNKY7E4g7qQVtDu2hPHc4/B4T+PB1XHZ1Elb4eCTNj3q/PmPocVGrU5qan6hc6eXYERmC2v0M5fh7OKhU/bD2aQXWAvMdZicS4bOTl8at8kiLBA77L9MHuRs0F9zhHnLTftxP2cNOetKBeadICWPZ3hoKeW9p3WvrXOnlHxK52PPWzQeRT0egiCmphamkhVqsjs86o6pxkzy10zpfYcycHuMPDQBwAiIlJJYmNj6dmzJx9++CH9+vVj9+7d/PTTT0yZMgW73c5LL73EF198QVJSEgUFBeTn5+Pn51emc2/fvp0WLVq4AymAHj16lBj3+eef89ZbbxEXF0dWVhZFRUUEBQWV63Vs376dDh06FGuyfumll+JwONi5c6c7lDr//PPx8DixkqNJkyZs2bKlxPkqk2mhVGhoKB4eHhw+fLjY8cOHD5824QsLC2PRokXk5eVx5MgRmjZtyhNPPEF0dLR7TExMDKtWrSI7O5uMjAyaNGnCsGHD3GNyc3N58sknWbhwIddccw0AF110EZs3b+b1118/bSjl7e2Nt3cZf9GU2uWPL+C/D0FBFvg1giHvQpsrza6q9vFtAK0udd5c7IWQ+lfxoOrQFmfokrrTedv61Ynx/uEld/9r1Bo8quZ/VcdyCtxNx1274O1OycLuKLn+ztvTSmzjQNqfFD61axKIn+14bfai46FSAhw5NWQ6HjCdGj7lpZet0D8XOr96+kLzLhDZE1r2gBbdyvwpT52WtME5M2q3c8oyVi+4+Hbo/TAENz/zc0XqgIrMPq+qc5oxs7xpA19snlYKihwcOJZLi4Zl+2VARERM5OXnnLFk1s8uh9GjR3P//ffz9ttvM2fOHGJiYujbty+vvPIKb775JjNmzODCCy/E39+fBx98kIKC0jczqog1a9Zw22238dxzzzFgwACCg4P57LPPmDZtWqX9jJOd2jvKYrHgcJTsjVuZTAulbDYbnTt3Zvny5e7GWw6Hg+XLlzNu3LgzPtfHx4dmzZpRWFjIggULuOWWW0qM8ff3x9/fn6NHj7JkyRJeffVVAAoLCyksLMR6So8bDw+PKn+zpYYpyIb/PQabP3E+juwFQ9+HIJOmkdZFHl4Q0d55Y5jzmGFA5qETy/5c/aqO7IbsZGdT6rjlJ87h6QPh7U7Mpmp8oXM5oE/ZPx0wDIP9R3OLhU/bD2aQdCy31PFhvha6NrbSMdRBu6BCYgLyiPDMwSNvmzNcOpgGcUeKh0xlDZhKsDgDPb9Gzp0O/Rodv4U4v2KB/esgcY3zZ+35yXkDsHo6Z1FF9nTOpGp5CfjVo52nDv4OK6bCru+cjy0e0Ok26P0IhESaW5tIPWXGzHIPq4VWjfzYdTiL+NRshVIiIrWBxVJrPly95ZZbGD9+PJ9++ikff/wxY8eOxWKx8Msvv3DDDTfwf//3f4Azz9i1axft27cv03nbtWvHvn37OHjwIE2aOGf1//rrr8XGrF69msjISJ566in3sb179xYbY7PZ3C2NzvSz5s6dS3Z2tnu21C+//ILVaqVt27ZlqreqmLp8b8KECYwcOZIuXbrQrVs3ZsyYQXZ2trt3wYgRI2jWrBlTp04F4LfffiMpKYmOHTuSlJTE5MmTcTgcPPbYY+5zLlmyBMMwaNu2Lbt37+bRRx8lNjbWfc6goCD69u3Lo48+iq+vL5GRkaxatYqPP/6Y6dOnV/+bIOY4vA2+vMM5UwcL9H0c+j7m3NVOqpbF4lxKFdSk+Iy0gmzncr9Df5zUr+pPKMyGA5uct5OFtCoeVDW+AIJbUOgw2J2c5Q6fdh44QtLBA3jlHyOETEIsmYRZMjmPLEI8M2nhnUtT71zCPbIIMjLwKUzHmp8OB3HeyvfiTgRMxUKmhsdvpx5v5Bxflr93huHcDXHvL7B3DexdDRn7nbOEkjbA6pnOceHtT8ykiuxZN0PWw3/CyqnOTQnAuRz0oluh76PQMPrMzxWpgyoy+7yqzmnWzPKoUH92Hc4iISWLvudV33bbIiJS9wUEBDBs2DAmTpxIRkYGd9xxBwBt2rThq6++YvXq1YSEhDB9+nQOHz5c5lCqf//+nHfeeYwcOZLXXnuNjIyMYuGT62ckJiby2Wef0bVrV7799lsWLlxYbEyrVq1ISEhg8+bNNG/enMDAwBL/Ft92221MmjSJkSNHMnnyZFJSUrj//vu5/fbbS8yKrm6mhlLDhg0jJSWFZ599lkOHDtGxY0cWL17sflMSExOLzWjKy8vj6aefJj4+noCAAAYNGsS8efNo0KCBe0x6ejoTJ05k//79NGzYkKFDh/Liiy8Wm4b22WefMXHiRG677TbS0tKIjIzkxRdf5N5776221y4mMQzY+BF89zgU5UFAY+fsqKjTN7mXamLzdy5Na97lxDGHA44mnBJUbYWMJDi6x3lzBRNAtiWA7Y5meBh2OpNJf0smwZbju3qc7nckO1Dqxh9VGDBVhMUCYW2dty53Oo8dS3SGU67bkb8geZvztu4D55iQVhB56YmQqmF0mXYbqZFSdjrDKNdyRixw4U3OUDm0jamliZjpXGafV+c5q1J0WABwmHg1OxcRkSowevRo/vnPfzJo0CB3DyhXNjFgwAD8/PwYM2YMgwcPJj29bCsorFYrCxcuZPTo0XTr1o1WrVrx1ltvMXDgQPeY66+/noceeohx48aRn5/PNddcwzPPPMPkyZPdY4YOHcrXX3/NZZddxrFjx5gzZ447OHPx8/NjyZIljB8/nq5du+Ln58fQoUNrxMQci2GUtnm5nE1GRgbBwcGkp6eXu8mYmCQvA/4zHv782vm4dX8YPBsC9IlqbZOXnsL2zatJ3rUODm+hZUE8rS1JeFlKn7ZqYAHfECwlwqSTg6ZTQqaqDJiqSlayc5nf3jXOGVWHt4JxyrLkgIgTy/0iezpnVp2ynLnGORIHK1+GLV8Cx//Jaj8Y+j3hXNopUs1q4jXA559/zsiRI3n33Xfds8+/+OILduzYQURERInZ5wUFBWzbtg2AQYMGcdttt3HbbbcREBDg3rH4bOcsi+p6r75Yv4/HvvqD3m1CmTe6e5X9HBERKb+8vDwSEhKIiorCx8fH7HKkEp3pz7as1wC1avc9kQpL2ghfjXLOrLF6OnfW63F/zf9lXNz2pGazcmcyK3elsCbuCPlFnkAPoAdeHhZ6RAZwQ7MsegYfoXGjECz+oe6QyVIbA6aKCAiH9jc4b+Dsc7Vv7YmZVAc2QtZh50wj12wjn2DnLKqWPZwzqpp2rDm7TqYlwI+vwe+fgXE8cIy9FvpNdC7XFBG38s4+P3DgAJ06dXI/fv3113n99dfp27cvK1euLNM5axLXDnzxKZopJSIiUptoplQF1cRPSaUUhgG/vgNLnwVHIQS3hJs+hBZdza5MziKv0M6v8UdYuTOFVbtSSDhlSUbTYB/6tg2nX9swLm0dSoC3MvazKsx19p9yzaTat9bZs+tknr7O/z5cM6madwVbNTcNPpYIP74Om+eDo8h57LyBzplRTTud+bki1UDXAGVXXe9VWnYBFz+/FIAdzw/Ex6sefBAhIlJLaKZU3aWZUiJnkpMG/74Pdv7P+Tj2WrhhFviGmFuXnFbikRxW7kpmxY5k1sQfIa/wxNIzT6uFrq0a0q9tGP3ahnNeRACW2tobySxevtCql/PGo2AvgkO/n2icnrjGuZNgwo/OGzhnFjbtdGImVcvuVfffUMYB+GkabPjIGSIDxFwBlz1ZvNeYiMgpQvy8CPb1Ij23kD1HsoltrLBQRESkNlAoJXXT3jWwYLSzIbaHDQa8BF3vqr0NnuuovEI7axPSWLkzhZU7k0s0qG0c5MNlsWH0PS+cS1s3ItCnhiwrqys8PKFZZ+et5zhnY/nUnScCqr2rnf8N7V/nvK1+C7BAxPnFd/gLrNjuXm6Zh+Hn6bB+Dtjzncei+kC/JyGyxzm/TBGp+ywWC1Gh/mzed4z4FIVSIiIitYVCKalbHA7nL7crXnL2oGkYAzfPgSYdzK5MjtuXluPsDbUzhdVxR8gtPNGc3NNqoXNkCJfFOpfltY0I1Gyo6mS1OhuHh7eDrqOdy19dO/wlunb42+1soH54K6x9z/m8htHFm6eHtCpbAJydCj+/Aev+CUW5zmMtezpnRkX1rrKXKSJ1U3SYM5Q6dbm3iIiI1FwKpaTuyDwMC8dA/Ern4wtvgWung3egqWXVd/lFdtYlHGXFzmRW7kwm7pQmtBFB3vQ7L5zLYsPo2TqUIM2GqjksFgiJdN46Dncey0o+aSbVL3BoK6TFO2+bPnGOCWxy0kyqSyEstvimAjlpzllXv713oqdV865w2VMQ3U8zGkWkQtTsXESkZnM4HGcfJLVKZfyZKpSSuiFuBXw9BrKTwcsPBr0GHW/TL7cm2ZeWw8pdKazamczquCPkFJyYDeVxfDZUv7Zh9DsvnHZNNBuqVgkIh/MHO28AucecDdNdM6mSNkLmQdi6wHkD8GlwIqTKz4BfZ0NBpvN7TTs5w6jW/fXfq4ick6jQAADiU7NMrkRERE5ms9mwWq0cOHCAsLAwbDabrv9rOcMwKCgoICUlBavVis1mq/C5FEpJ7WYvgpVTnc2RMSC8Pdw0B8Jjza6sXskvsrN+z1FW7kxmxc4UdicX/4UgPNDb3aD80tahBPtqNlSd4dsAzrvKeQPnDn/715+YSbVvHeQdc2444Np0ACDiQucyvbZXK4wSkUoRHeacKaXleyIiNYvVaiUqKoqDBw9y4MABs8uRSuTn50fLli2xnrwqopwUSkntlb4fFtzl/OUXoPMdMPBl5w5jUuWSjuW6e0P9sju1xGyoi1s2oF9bZ2+o9k2C9GlIfeHl6+wH5eoJZS+Eg3+cmElVkO3sVxV7XfElfSIi56hVI2codSynkKPZBYT4V/xTWxERqVw2m42WLVtSVFSE3W4/+xOkxvPw8MDT0/Ocf89TKCW1087vYNFYyD0KtkC4/k24YKjZVdVpBUUO1u9JY+Uu5055uw4Xnw0VFuhN3/PCuKxtOL1ahxLsp9lQAnh4QfPOzlvP+82uRkTqMF+bB80a+JJ0LJf41Cw6+zc0uyQRETmJxWLBy8sLLy/9niAnKJSS2qWoAJZNgl//4XzcpKNzd72G0aaWVVcdTM9l5c4UVuxI5pfdqWSfNBvKaoGLW4a4l+W1bxKE1arZUCIiYp6oUH9nKJWSTedIhVIiIiI1nUIpqT3S4uGrO+HAJufjS/4O/SeDp7epZdUlhXaHszfUrmRW7khh5+HMYt8PDbDR9zznkrzebUJp4KelESIiUnNEhfrz8+5U9ZUSERGpJRRKSe2wdQF8M965Y5dvCAx+x9kgWc7Z4Yw8Vuxw9ob6eXcqWflF7u9ZLdCxRQMuaxtOv7bhnN9Us6FERKTmcjU7j09RKCUiIlIbKJSSmq0wFxY/ARvmOh+3uARu+icENze1rLrgr8OZvL1iN9/8fgCHceJ4I38bfc8Lo19sOL1bh6pRrIiI1BpRodqBT0REpDZRKCU1V/IO+GoUJG8DLND7Yeg3ETz01/ZcbE1KZ9YPu1n85yH3MddsqMtiw7igabBmQ4mISK0UHRoAQMKRbOwOAw/9eyYiIlKj6bd7qXkMAzbPh/89CoU54B8ON74HMZeZXVmttm5PGrN+2M2qXSnuYwPPb8x9l7XmwubBJlYmIiJSOZqF+GLzsFJQ5ODAsVxaNPQzuyQRERE5A4VSUrPkZ8J/J8CWL5yPo/vBkPcgMMLUsmorwzD4eXcqs37YzW8JaQB4WC1c36Epf+8XQ5uIQJMrFBERqTweVguRjfz4KzmLhNRshVIiIiI1nEIpqTkO/gFf3gFpcWDxgMuehF4TwGo1u7Jax+EwWLb9MG+v2M3v+9MB8PKwcFPn5tzbN4bIRv4mVygiIlI1okL9+Ss5i/iULPqcF2Z2OSIiInIGCqXEfIYB6z6AJU+CvQCCmsHQf0JkD7Mrq3XsDoNvtxzkHyt2s+NQJgA+XlaGd2vJmD7RNAn2NblCERGRqhUdFgAcVrNzERGRWkChlJgr9yj8exzs+K/z8XlXw+B/gF9Dc+uqZQqKHCzalMQ7q+LcF+EB3p7c3iOS0b2iCA3wNrlCERGR6hF9fAe+eIVSIiIiNZ5CKTHPvnXw1Z2QnghWL7hyClwyFizaKaes8grtfLF+H++uiifpWC4ADfy8uPPSKEb2aEWwn5fJFYqIiFSvqLDjoVSKQikREZGaTqGUVD+HA1a/BT88D44iCGkFN82BZhebXVmtkZVfxPxf9/L+TwmkZuUDEBbozZje0fyte0v8vfWftoiI1E+umVIH0nPJK7Tj4+VhckUiIiJyOvrNVapXdiosvAd2L3M+Pv9GuG4G+ASbWlZtkZ5TyNzVe/jwlwTScwsBaNbAl3v7RnNzlxa68BYRkXqvob+NIB9PMvKK2Hskh7aNtdOsiIhITaVQSqpPwk+w4C7IOgSePnD1K3DxSC3XK4OUzHz++XMCn/y6l6z8IsC5u9Df+8UwuFMzvDy0Q6GIiAiAxWIhOiyAzfuOEZ+SpVBKRESkBlMoJVXPYYdVr8KPr4LhgNC2cPMciDjf7MpqvIPpuby7Kp5/rU0kv8gBQGzjQO67rDWDLmyCh1WBnoiIyKmiQ/2doZSanYuIiNRoCqWk6n01Crb923m/0//B1a+Czd/cmmq4vUeyeWdlHAs27qfQbgDQoUUD7r+sNVe0C8ei2WUiIiKnFXW8r1SCQikREZEaTaGUVK24Fc5AyuoFN7wNHYaZXVGNtutwJm+v2M1/fj+Aw5lFcUl0Q8Zd1oZLWzdSGCUiIlIG0WEBAMSnZJlciYiIiJyJQimpOg4HLJvsvN91tAKpM9iyP51ZK/5iyZ+H3ccuaxvGuMtb0zmyoYmViYiI1D6aKSUiIlI7KJSSqrNtIRzcDLYA6P2I2dXUSGsT0pi1Yjc/7koBnD3fB57fmPsua80FzbQjoYiISEW0CvUD4GhOIUezCwjxt5lckYiIiJRGoZRUDXsh/PCC837PByAgzNx6ahDDMPjpr1RmrdjN2oQ0ADysFm7o0JS/XxZD63DtEiQiInIu/GyeNA324UB6HvGp2XRWKCUiIlIjKZSSqrHxI0iLB/8w6HGf2dXUCA6HwdLth3l7xW7+2J8OgM3DytDOzRnbN4aWjfxMrlBERKTuiArz50B6Hgmp2XSODDG7HBERESmFQimpfPlZsPIV5/0+j4F3gLn1mMzuMPjvHwf4x4o4dh7OBMDHy8rfukUypk80jYN9TK5QRESk7okK9eeX3UfU7FxERKQGUyglle/XdyA7GUJaQec7zK7GNAVFDhZu2s87K+PYcyQHgEBvT27vEcmdvaIIDfA2uUIREZG6KzrU+aGYmp2LiIjUXAqlpHJlH4Ff3nTev/wZ8Kx/PRzyCu18vm4f766K40B6HgAhfl7ceWkUI3q2ItjXy+QKRURE6r6oMO3AJyIiUtMplJLK9dPrUJAJjS+C8280u5pqlZVfxCe/7uWDnxJIzcoHICzQm3v6RDO8W0v8vfWfm4iISHWJDj0RSjkcBlarxeSKRERE5FT6LVkqz7FEWPeB837/yWC1mlpOdTmWU8Dc1XuY88se0nMLAWjWwJd7+8Vwc+fm+Hh5mFyhiIhI/dM8xA8vDwv5RQ4OpOfSPEQbioiIiNQ0CqWk8qx4CewFENUHYi43u5oql5KZzwc/x/PJmr1kF9gB56eyf7+sNTd0bIqXR/0I5URERGoiD6uFyEb+7E7OIiE1W6GUiIhIDaRQSirHoa3w+2fO+/0ng6XuTpE/mJ7Lu6vi+dfaRPKLHADENg5k3OWtufqCJnhoeYCIiEiNEB3qDKXiU7Lp3SbM7HJERETkFAqlpHIsnwIY0H4wNOtsdjVVZndyJjf+YzUZeUUAdGzRgPsvb83lseFY6nAQJyIiUhup2bmIiEjNplBKzt2eX+CvJWDxcO64V0cdzS5g9Efrycgron2TIJ66ph09YxopjBIREamhXM3O4xVKiYiI1EgKpeTcGAYsm+S8f/EICG1tbj1VpNDu4O/zN7L3SA7NQ3yZN7objQK8zS5LREREziA6LACA+JQskysRERGR0qgTs5ybHd/C/nXg5Qf9njC7mirz3H/+ZE38EfxtHnwwsosCKRERkVog6vhMqaRjueQV2k2uRkRERE6lUEoqzl50vJcUcMlYCGxsbj1V5OM1e/jk10QsFnjz1k7ENg4yuyQREREpg0b+NgJ9PDEM2Hskx+xyRERE5BQKpaTifv8UUneCbwhcOt7saqrEz3+l8tx/tgHw+MBY+rePMLkiERERKSuLxeJewpeQqiV8IiIiNY1CKamYwlxYMdV5v/cj4BNsbj1VICE1m7/P34DdYXBjp2bc0yfa7JJERESknNTsXEREpOZSKCUVs/Y9yDwAQc2h611mV1Pp0nMLGf3ROjLyiujUsgEv3XihdtkTERGphVx9peJTFEqJiIjUNAqlpPxyj8JP05z3L3sSvHzMraeSFdkdjPt0I/Ep2TQN9uHd2zvj4+VhdlkiIiJSAdFhzlAqQTOlREREahyFUlJ+P8+AvHQIawcdbjW7mkr34v+289Nfqfh6efDeiC6EB9at0E1ERKQ+cc2UUiglIiJS8yiUkvLJOAC/zXbe7z8JrHVrBtG/1iYy55c9ALwxrAMXNKt7vbJERETqE1colZZdwLGcApOrERERkZMplJLyWTkVivKgxSVw3kCzq6lUv8Yf4ZlFWwF4+MrzGHhBE5MrEhERkXPlZ/OkSbBz1rOanYuIiNQsCqWk7FJ2waZPnPevfA7qUOPvxCM5jP1kA0UOg+s6NGXc5a3NLklEREQqiXsJn5qdi4iI1CgKpaTsfpgChgPaDoKWl5hdTaXJzHPutHc0p5CLmgfz2k0Xaac9ERGROsS9A19qlsmViIiIyMkUSknZ7FsH2/8DFitc8azZ1VQau8Ng/Geb+Ss5i4ggb94f0UU77YmIiNQx0WEBgJqdi4iI1DQKpeTsDAOWTXbe7/A3CG9najmV6dXFO/hhRzLenlbeu70LEUHaaU9ERKSuiXbNlNLyPRERkRpFoZSc3e5lsPdn8PCGyyaaXU2l+XL9Pt79MR6A127uQIcWDcwtSERERKpEdJgzlNpzJBuHwzC5GhEREXFRKCVn5nCcmCXV7W4Ibm5qOZVl/Z40nlro3Gnvgctbc32HpiZXJCIiIlWlWQNfvDws5BU6OJiRZ3Y5IiIicpxCKTmzLV/C4a3gHQy9Hza7mkqx/2gO936ygQK7g4HnN+bB/ueZXZKIiIhUIU8PKy0b+gEQn6Jm5yIiIjWFQik5vaJ8WPGC836v8eDX0Nx6KkF2fhF3fbSe1KwC2jcJYvqwDlit2mlPRESkrlOzcxERkZpHoZSc3voP4VgiBDSG7mPNruacORwGD32+mR2HMgkN8Ob9kV3ws3maXZaIiIhUAzU7FxERqXkUSknp8jLgx9ec9/s9ATY/c+upBNOX7uL7bYexeVh59/bONGvga3ZJIiIiUk2iXKGUZkqJiIjUGAqlpHRrZkHOEWjUGjrdbnY15+zfm5OYtWI3AC8PvZDOkSEmVyQiIiLV6cTyPfWUEhERqSkUSklJWcmwepbz/uXPgEftXuK2ed8xHv3qDwDu7RvDjRfXjR0ERUREpOxcM6X2H80lv8hucjUiIiICCqWkNKtehcJsaNYZ2t9gdjXn5GB6Lnd/vJ6CIgf924Xz6IC2ZpckIiIiJggNsBHo7YlhwN4jOWaXIyIiIiiUklOlxcOGOc77/SeDpfbuTJdbYGfMxxtIycynbUQgM27thId22hMREamXLBYL0WFqdi4iIlKTKJSS4n54ARxFEHMFRPUxu5oKMwyDR778nS1J6TT0t/HByC4EeNfuZYgiIiJyblxL+BLU7FxERKRGUCglJxzYDFsXOO/3n2xmJefsreW7+XbLQbw8LLxz28W0aFj7dw8UERGRcxMV6mx2Hp+iZuciIiI1gUIpOWH5c86vF94MTS4yt5Zz8O0fB3lj2S4AXhh8Ad2jG5lckYiIiNQEruV7miklIiJSMyiUEqf4lRD3A1i94LKnzK6mwrYmpfPwl5sBGN0rimFdW5pbkIiIiNQYWr4nIiJSsyiUEjAMWDbZeb/LndAwytRyKio5I4+7P15PXqGDvueFMfHqWLNLEhERkRrEFUodyS4gPafQ5GpERETE9FDq7bffplWrVvj4+NC9e3fWrl172rGFhYVMmTKFmJgYfHx86NChA4sXLy42JjMzkwcffJDIyEh8fX3p2bMn69atK3Gu7du3c/311xMcHIy/vz9du3YlMTGx0l9frbBtERzYBLYA6POo2dVUSF6hnTHzNnAwPY+YMH9m/q0Tnh6m//UWERGRGsTf25PGQT4AxKeqr5SIiIjZTP2t/fPPP2fChAlMmjSJjRs30qFDBwYMGEBycnKp459++mneffddZs6cybZt27j33nsZMmQImzZtco+56667WLp0KfPmzWPLli1cddVV9O/fn6SkJPeYuLg4evXqRWxsLCtXruSPP/7gmWeewcfHp8pfc41jL4TlU5z3e4yDgDBz66kAwzB4YsEfbN53jGBfL/45sitBPl5mlyUiIiI1kJbwiYiI1BwWwzAMs3549+7d6dq1K7NmzQLA4XDQokUL7r//fp544okS45s2bcpTTz3Ffffd5z42dOhQfH19+eSTT8jNzSUwMJB///vfXHPNNe4xnTt35uqrr+aFF14A4NZbb8XLy4t58+ZVuPaMjAyCg4NJT08nKCiowucx3bp/wrcTwC8Uxm8G70CzKyq3f6zczauLd+JhtTDvzm70bB1qdkkiIlKH1ZlrgGpQE9+rpxZuYf5viYy7rDWPDGhrdjkiIiJ1UlmvAUybKVVQUMCGDRvo37//iWKsVvr378+aNWtKfU5+fn6J2Uy+vr78/PPPABQVFWG32884xuFw8O2333LeeecxYMAAwsPD6d69O4sWLarEV1dLFGTDqlec9/s+VisDqe//PMRrS3YCMPn68xVIiYiIyBlpppSIiEjNYVoolZqait1uJyIiotjxiIgIDh06VOpzBgwYwPTp0/nrr79wOBwsXbqUr7/+moMHDwIQGBhIjx49eP755zlw4AB2u51PPvmENWvWuMckJyeTlZXFyy+/zMCBA/n+++8ZMmQIN954I6tWrTptvfn5+WRkZBS71Xq/vgNZh6FBJHQeZXY15bb9YAYPfr4Zw4ARPSK5/ZJIs0sSERGRGi46zBlKxaWop5SIiIjZalUn6DfffJM2bdoQGxuLzWZj3LhxjBo1Cqv1xMuYN28ehmHQrFkzvL29eeuttxg+fLh7jMPhAOCGG27goYceomPHjjzxxBNce+21zJ49+7Q/e+rUqQQHB7tvLVq0qNoXW9Vy0uCXN533L38GPG3m1lNOqVn53PXRenIK7FzauhHPXNve7JJERESkFogODQBgz5FsHA7TuliIiIgIJoZSoaGheHh4cPjw4WLHDx8+TOPGjUt9TlhYGIsWLSI7O5u9e/eyY8cOAgICiI6Odo+JiYlh1apVZGVlsW/fPtauXUthYaF7TGhoKJ6enrRvXzzEaNeu3Rl335s4cSLp6enu2759+yr60muGn6ZBfgY0vhAuGGp2NeWSX2Tn3nkbSDqWS1SoP//4W2e8tNOeiIiIlEHzEF88rRbyCh0cysgzuxwREZF6zbTf5G02G507d2b58uXuYw6Hg+XLl9OjR48zPtfHx4dmzZpRVFTEggULuOGGG0qM8ff3p0mTJhw9epQlS5a4x9hsNrp27crOnTuLjd+1axeRkadf/uXt7U1QUFCxW611LBHWvue8f8VksNaeQMcwDJ5euJX1e48S6OPJ+yO6EOynnfZERESkbDw9rLRs5AdAfIr6SomIiJjJ08wfPmHCBEaOHEmXLl3o1q0bM2bMIDs7m1GjnP2NRowYQbNmzZg6dSoAv/32G0lJSXTs2JGkpCQmT56Mw+Hgsccec59zyZIlGIZB27Zt2b17N48++iixsbHucwI8+uijDBs2jD59+nDZZZexePFi/vOf/7By5cpqff2mWTEV7AXQqje0vsLsasrlnz8n8OWG/Vgt8PbfLqZ1eIDZJYmIiEgtEx0aQHxKNgmpWfRqo01SREREzGJqKDVs2DBSUlJ49tlnOXToEB07dmTx4sXu5ueJiYnF+kXl5eXx9NNPEx8fT0BAAIMGDWLevHk0aNDAPSY9PZ2JEyeyf/9+GjZsyNChQ3nxxRfx8joxm2bIkCHMnj2bqVOn8sADD9C2bVsWLFhAr169qu21m+bwNvj9X877/Z8Di8XcesphxY5kXvrfdgCeubY9fc4LM7kiERERqY2iw/xhO8RrBz4RERFTWQzDUIfHCsjIyCA4OJj09PTatZTv01th13fQ7noYNs/sasrsr8OZDPnHarLyixjerQUvDbkQSy0K1EREpO6otdcAJqip79W/1iYy8est9D0vjI/u7GZ2OSIiInVOWa8Bak8zITl3e9c4AymLB1zxrNnVlNnR7AJGf7SerPwiukc15LnrL1AgJSIiIhUWHeoPQIJmSomIiJhKoVR9YRiwbJLz/sW3Q2gbc+spo4IiB2PnbyAxLYcWDX155/86Y/PUX1sRERGpuKgwZyi1/2gO+UV2k6sRERGpv/TbfX2x8zvY9xt4+kLfJ8yupkwMw2DSN3/ya3wa/jYP/jmyKw39bWaXJSIiIrVcWIA3gd6eOAxIPJJjdjkiIiL1lkKp+sBhh+XPOe9fMhaCmphbTxl9vGYv/1qbiMUCbw3vxHkRgWaXJCIiInWAxWJxz5ZSs3MRERHzKJSqD37/F6TsAJ8GcOl4s6spk5/+SmHKf7cBMPHqWK5oF2FyRSIiIlKXRKmvlIiIiOkUStV1hbmw4iXn/d4Pg28DU8spi7iULP4+fyN2h8HQi5tzd+9os0sSERGROiY6NACA+JQskysRERGpvxRK1XVr34eMJAhqDt3GmF3NWaXnFHL3R+vJzCuic2QIL92onfZERESk8rmW72mmlIiIiHkUStVlucfgp2nO+5dNBC8fU8s5myK7g/s+3Uh8ajbNGvgy+/864+3pYXZZIiIiUgdFH1++F5+iUEpERMQsCqXqsl/ehLxjEBYLHYabXc1ZvfDtdn7enYqvlwfvj+hCWKC32SWJiIhIHeXqKXUku4D0nEKTqxEREamfFErVVRkH4dd3nPevmATWmj3jaP5ve5m7eg8AbwzrSPumQeYWJCIiInWav7cnEUHOD8ASjmi2lIiIiBkUStVVq16GolxocQm0vdrsas5odVwqk/79JwCPDmjLwAsam1yRiIiI1AdR7iV8anYuIiJiBoVSdVHqX7BxnvN+/8lQgxuF7z2Szd/nb6TIYXB9h6b8vV+M2SWJiIhIPREd5tyBT83ORUREzKFQqi5aPgUMO5x3NUT2MLua08rMK2T0R+s5llNIh+bBvHrTRdppT0RERKqNu9m5QikRERFTKJSqa/ZvgO3fABa44lmzqzktu8PggX9tYndyFo2DfHh/RBd8vGp23ysRERGpW6K0A5+IiIipFErVJYYByyY573cYDhHtza3nDF7+bjsrdqbg42Xl/RFdCA/yMbskERERqWdcy/f2pGbjcBgmVyMiIlL/KJSqS3Yvhz0/gYc3XPak2dWc1hfr9/H+TwkAvH5zBy5sHmxyRSIiIlIfNQ/xxdNqIbfQzuHMPLPLERERqXcUStUVDgcsm+y83+1uaNDC1HJOZ92eNJ5auAWA8Ve04dqLmppckYiIiNRXXh5WWjb0A7SET0RExAwKpeqKrQvg8BbwDoLeD5tdTan2peVw77wNFNoNBl3YmPFXtDG7JBEREannosPU7FxERMQsCqXqgqIC+OF55/1Lx4NfQ3PrKUV2fhF3f7yeI9kFnN80iNdv7oDVqp32REREKsvbb79Nq1at8PHxoXv37qxdu/aM47/88ktiY2Px8fHhwgsv5H//+1+x72dlZTFu3DiaN2+Or68v7du3Z/bs2VX5EkzhanaeoJlSIiIi1U6hVF2wYQ4c2wsBEXDJWLOrKcHhMHjw883sOJRJWKA3H4zsgp/N0+yyRERE6ozPP/+cCRMmMGnSJDZu3EiHDh0YMGAAycnJpY5fvXo1w4cPZ/To0WzatInBgwczePBgtm7d6h4zYcIEFi9ezCeffML27dt58MEHGTduHN988011vaxq4Wp2Hp+aZXIlIiIi9Y9CqdouPxNWveq83/dxsPmbW08p3lz+F0u3HcbmaeW92zvTJNjX7JJERETqlOnTp3P33XczatQo94wmPz8/Pvzww1LHv/nmmwwcOJBHH32Udu3a8fzzz3PxxRcza9Ys95jVq1czcuRI+vXrR6tWrRgzZgwdOnQ46wys2sY9U0rL90RERKqdQqnabvUsyEmFhjFw8QizqynBMAw+WrMHgBcGX0CnliHmFiQiIlLHFBQUsGHDBvr37+8+ZrVa6d+/P2vWrCn1OWvWrCk2HmDAgAHFxvfs2ZNvvvmGpKQkDMNgxYoV7Nq1i6uuuqpqXohJoo+HUvvScsgvsptcjYiISP2iNVS1WVYKrDn+ieYVz4CHl7n1lCItu4BjOYVYLHCddtoTERGpdKmpqdjtdiIiIoodj4iIYMeOHaU+59ChQ6WOP3TokPvxzJkzGTNmDM2bN8fT0xOr1cr7779Pnz59TltLfn4++fn57scZGRkVeUnVKizQmwBvT7Lyi9iXlkPr8ECzSxIREak3NFOqNvvxNSjIgqadoP1gs6spVdzxpqHNGvjia/MwuRoREREpq5kzZ/Lrr7/yzTffsGHDBqZNm8Z9993HsmXLTvucqVOnEhwc7L61aNGiGiuuGIvF4l7CF69m5yIiItVKM6Vqq7QEWH+8T0T/58BSM3eyi0txNg11NREVERGRyhUaGoqHhweHDx8udvzw4cM0bty41Oc0btz4jONzc3N58sknWbhwIddccw0AF110EZs3b+b1118vsfTPZeLEiUyYMMH9OCMjo1YEU1Gh/mxJSidefaVERESqlWZK1VYrXgRHIcRcDtF9za7mtOKPh1IxYTWvAbuIiEhdYLPZ6Ny5M8uXL3cfczgcLF++nB49epT6nB49ehQbD7B06VL3+MLCQgoLC7Fai18qenh44HA4TluLt7c3QUFBxW61QfTx65QEzZQSERGpVpopVRsd/B22fOm833+yqaWcjWv5XoxmSomIiFSZCRMmMHLkSLp06UK3bt2YMWMG2dnZjBo1CoARI0bQrFkzpk6dCsD48ePp27cv06ZN45prruGzzz5j/fr1vPfeewAEBQXRt29fHn30UXx9fYmMjGTVqlV8/PHHTJ8+3bTXWVW0A5+IiIg5FErVRsuec3694CZo0sHcWs7ixPI9zZQSERGpKsOGDSMlJYVnn32WQ4cO0bFjRxYvXuxuZp6YmFhs1lPPnj359NNPefrpp3nyySdp06YNixYt4oILLnCP+eyzz5g4cSK33XYbaWlpREZG8uKLL3LvvfdW++uratGhzg/P4lOzTK5ERESkfrEYhmGYXURtlJGRQXBwMOnp6dU7NT3hR/joOrB6wrh10DC6+n52OeUX2Wn3zGIcBqx98grCg3zMLklEROScmXYNUAvVlvcqK7+ICyYtAeD3SVcR7FvzdjQWERGpTcp6DaCeUrWJYcDSSc77nUfV6EAKYO+RHBwGBHp7EhbobXY5IiIiIqUK8PYk/Pi1yh4t4RMREak2CqVqk23/hgMbwcsf+j5mdjVnFZd8YumepYbuDigiIiICJ/pKaQmfiIhI9VEoVVvYi+CH5533e46DgHBz6ykD17bKanIuIiIiNV308esV7cAnIiJSfRRK1Rab5sGR3eDXCHqMM7uaMjl5ppSIiIhITRbtnimlUEpERKS6KJSqDQpyYOXLzvt9HgOfmtso9GRxmiklIiIitYTrQ7R4zZQSERGpNgqlaoPf3oGsQ9CgJXQZZXY1ZWIYBvHHZ0rFhCuUEhERkZrN1VMqITUbbU4tIiJSPRRK1XQ5afDzm877lz0NnrVjF7uUzHwy84uwWiCykZ/Z5YiIiIicUYuGfnhaLeQW2jmUkWd2OSIiIvWCQqma7qdpkJ8OERfChTebXU2ZxR2f+t6ioR/enh4mVyMiIiJyZl4eVlo2dH6QpmbnIiIi1UOhVE12bB+sfd95v/8ksNaeP664lONL99RPSkRERGqJKDU7FxERqVa1J+Woj1a+DPZ8iOwFrfubXU25uEIp1042IiIiIjWdO5TSTCkREZFqoVCqpkreDr9/6rx/5XNgsZhbTzm5LubU5FxERERqi+jjM7wTUrNMrkRERKR+UChVUy2fAoYD2l0HzbuYXU25afmeiIiI1DYn78AnIiIiVU+hVE2U+Cvs/B9YrHD5s2ZXU255hXaSjuUCEB2m5XsiIiJSO7iuW/YdzaWgyGFyNSIiInWfQqmaxjBg6STn/U63Q9h55tZTAQmp2RgGBPt60cjfZnY5IiIiImUSHuiNv80Du8MgMS3H7HJERETqPIVSNY1hwMUjoFEb6PeE2dVUyImle/5YalkvLBEREam/LBYLUWFawiciIlJdFErVNFYrdLoNxq2DoKZmV1MhccnOi7ho9ZMSERGRWiYq1Hn9Ep+iZuciIiJVTaFUTVWLZxjFp6rJuYiIiNRO0Wp2LiIiUm0USkmlO3n5noiIiEht4mp2Hq9QSkREpMoplJJKZRgG8SlaviciIiK1U7R7+Z5CKRERkaqmUEoq1aGMPHIK7HhaLUQ28jO7HBEREZFyaRXqvH5JzconI6/Q5GpERETqNoVSUqlcTc5bNvLDy0N/vURERKR2CfTxIizQG4AEzZYSERGpUkoNpFK5+km5pr6LiIiI1DZqdi4iIlI9FEpJpXJtnxwTribnIiIiUjup2bmIiEj1UCgllSru+DT3GDU5FxERkVoq6vhMKdeHbSIiIlI1FEpJpXIt34sJ00wpERERqZ1cbQi0fE9ERKRqKZSSSpOdX8TB9DxAPaVERESk9ooKO9FTyjAMk6sRERGpuxRKSaVxfZrYyN9GiL/N5GpEREREKqZFiB8eVgs5BXYOZ+SbXY6IiEidpVBKKo175z0t3RMREZFazOZppWVDPwDiU9VXSkREpKoolJJKoybnIiIiUle4mp2rr5SIiEjVUSglleZEk3OFUiIiIlK7ndiBT6GUiIhIVVEoJZUmLlnL90RERKRuiA7TTCkREZGqplBKKoXDYbgv2jRTSkRERGo7Ld8TERGpegqlpFIkHcslv8iBzcNK8xBfs8sREREROSfRoc4P2RLTcigocphcjYiISN2kUEoqRfzxTxEjG/nh6aG/ViIiIlK7RQR542fzwO4w2Hc0x+xyRERE6iSlB1IpXP2ktHRPRERE6gKLxaJm5yIiIlVMoZRUCtfOe2pyLiIiInVF9PEP2xJSs0yuREREpG5SKCWVwvUJomZKiYiISF2hZuciIiJVS6GUVArXTKmYcIVSIiIiUjdEHw+l4rR8T0REpEoolJJzlplXSHJmPqDleyIiIlJ3uK5rNFNKRESkaiiUknPmWroXFuhNkI+XydWIiIiIVI5Wx2dKpWTmk5lXaHI1IiIidY9CKTln7qV7miUlIiIidUiQjxehAd6AZkuJiIhUhRoRSr399tu0atUKHx8funfvztq1a087trCwkClTphATE4OPjw8dOnRg8eLFxcZkZmby4IMPEhkZia+vLz179mTdunWnPee9996LxWJhxowZlfWS6pUTO++pn5SIiIjULVrCJyIiUnVMD6U+//xzJkyYwKRJk9i4cSMdOnRgwIABJCcnlzr+6aef5t1332XmzJls27aNe++9lyFDhrBp0yb3mLvuuoulS5cyb948tmzZwlVXXUX//v1JSkoqcb6FCxfy66+/0rRp0yp7jXWddt4TERGRusrV7Dxezc5FREQqnemh1PTp07n77rsZNWoU7du3Z/bs2fj5+fHhhx+WOn7evHk8+eSTDBo0iOjoaMaOHcugQYOYNm0aALm5uSxYsIBXX32VPn360Lp1ayZPnkzr1q155513ip0rKSmJ+++/n/nz5+PlpV5IFaXleyIiIlJXRblCKc2UEhERqXSmhlIFBQVs2LCB/v37u49ZrVb69+/PmjVrSn1Ofn4+Pj4+xY75+vry888/A1BUVITdbj/jGACHw8Htt9/Oo48+yvnnn19ZL6nesTsM9qTmAJopJSIiInWPqz1BQmqWyZWIiIjUPaaGUqmpqdjtdiIiIoodj4iI4NChQ6U+Z8CAAUyfPp2//voLh8PB0qVL+frrrzl48CAAgYGB9OjRg+eff54DBw5gt9v55JNPWLNmjXsMwCuvvIKnpycPPPBAmWrNz88nIyOj2E1g/9EcCuwOvD2tNG3ga3Y5IiIiIpXKNVMqISUbwzBMrkZERKRuMX35Xnm9+eabtGnThtjYWGw2G+PGjWPUqFFYrSdeyrx58zAMg2bNmuHt7c1bb73F8OHD3WM2bNjAm2++ydy5c7FYLGX6uVOnTiU4ONh9a9GiRZW8vtrGtXQvKtQfD2vZ3ksRERGR2qJlQz88rBayC+wkZ+abXY6IiEidYmooFRoaioeHB4cPHy52/PDhwzRu3LjU54SFhbFo0SKys7PZu3cvO3bsICAggOjoaPeYmJgYVq1aRVZWFvv27WPt2rUUFha6x/z0008kJyfTsmVLPD098fT0ZO/evTz88MO0atWq1J87ceJE0tPT3bd9+/ZVzptQy8Ulq8m5iIiI1F02TystQpyzwdXsXEREpHKZGkrZbDY6d+7M8uXL3cccDgfLly+nR48eZ3yuj48PzZo1o6ioiAULFnDDDTeUGOPv70+TJk04evQoS5YscY+5/fbb+eOPP9i8ebP71rRpUx599FGWLFlS6s/z9vYmKCio2E0gPlVNzkVERKRucy/hU7NzERGRSuVpdgETJkxg5MiRdOnShW7dujFjxgyys7MZNWoUACNGjKBZs2ZMnToVgN9++42kpCQ6duxIUlISkydPxuFw8Nhjj7nPuWTJEgzDoG3btuzevZtHH32U2NhY9zkbNWpEo0aNitXh5eVF48aNadu2bTW98rrBPVMqXDOlREREpG6KDgtgxc4U4lPU7FxERKQymR5KDRs2jJSUFJ599lkOHTpEx44dWbx4sbv5eWJiYrF+UXl5eTz99NPEx8cTEBDAoEGDmDdvHg0aNHCPSU9PZ+LEiezfv5+GDRsydOhQXnzxRby8vKr75dV5rp5S0aEKpURERKRu0kwpERGRqmExtI1IhWRkZBAcHEx6enq9Xcp3LKeAjlOWAvDncwPw9zY94xQREalyugYou7ryXq3encrfPviNqFB/VjzSz+xyREREaryyXgPUut33pOaIO97ss0mwjwIpERERqbOij2/okpiWQ6HdYXI1IiIidYdCKakw99I9NTkXERGROiwiyBtfLw/sDoN9aTlmlyMiIlJnKJSSCnNtixwTpn5SIiIiUndZLBZ3XynX9Y+IiIicO4VSUmGumVIKpURERKSuc80MV7NzERGRyqNQSipMy/dERESkvoh2zZRSKCUiIlJpFEpJhRTaHSQecfZU0EwpERERqeuiwlzL97JMrkRERKTuUCglFZKYlkORw8DP5kHjIB+zyxERERGpUtGhzg/htHxPRESk8iiUkgqJS3Z+ShgV6o/VajG5GhEREZGq1er48r3kzHyy8otMrkZERKRuUCglFeLqp6CleyIiIlIfBPt6ERpgAyBBO/CJiIhUCoVSUiGumVIKpURERKS+cC3hi09VXykREZHKoFBKKsQ1U0o774mIiEh9EXV8CZ/6SomIiFQOhVJSboZhsFszpURERKSeiXbvwKdQSkREpDIolJJyS8suID23EIvlxCeGIiIiInWdZkqJiIhULoVSUm6upXtNg33xtXmYXI2IiIhI9TgxUyoLwzBMrkZERKT2Uygl5eZuch6upXsiIiJSf7Rs6I/VAtkFdlIy880uR0REpNZTKCXlFpfiDKWitXRPRERE6hGbp5UWDf2AEzPHRUREpOLKHUq1atWKKVOmkJiYWBX1SC3gau6pmVIiIiJS37j6SqnZuYiIyLkrdyj14IMP8vXXXxMdHc2VV17JZ599Rn6+pi/XJ66ZUjFhmiklIiIi9Ut0qPNDuYTULJMrERERqf0qFEpt3ryZtWvX0q5dO+6//36aNGnCuHHj2LhxY1XUKDVIfpGdxLQcAGLCNFNKRERE6peoMO3AJyIiUlkq3FPq4osv5q233uLAgQNMmjSJDz74gK5du9KxY0c+/PBD7UhSRyUeycFhQIC3J+GB3maXIyIiIlKtorV8T0REpNJ4VvSJhYWFLFy4kDlz5rB06VIuueQSRo8ezf79+3nyySdZtmwZn376aWXWKjXAyUv3LBaLydWIiIiIVK/o4zOlEtNyKLQ78PLQvkEiIiIVVe5QauPGjcyZM4d//etfWK1WRowYwRtvvEFsbKx7zJAhQ+jatWulFio1Q9zxTwWjtXRPRERE6qGIQB98vTzILbSz/2iuu/G5iIiIlF+5Q6muXbty5ZVX8s477zB48GC8vLxKjImKiuLWW2+tlAKlZlGTcxEREanPrFYLrUL92X4wg/iULIVSIiIi56DcoVR8fDyRkZFnHOPv78+cOXMqXJTUXK6ZUmpyLiIiIvVVdJgzlFKzcxERkXNT7kXwycnJ/PbbbyWO//bbb6xfv75SipKayTAM4pOdM6W0fE9ERETqK3ezc4VSIiIi56TcodR9993Hvn37ShxPSkrivvvuq5SipGZKyconM78IqwUiG/mZXY6IiIiIKaLcO/BlmVyJiIhI7VbuUGrbtm1cfPHFJY536tSJbdu2VUpRUjPFJTs/DWzR0A8fLw+TqxERERExh2vGuJbviYiInJtyh1Le3t4cPny4xPGDBw/i6VnuFlVSi7ianEeroaeIiIjUY66ZUocz8snKLzK5GhERkdqr3KHUVVddxcSJE0lPT3cfO3bsGE8++SRXXnllpRYnNUu8mpyLiIiIEOzrRWiADYA9mi0lIiJSYeWe2vT666/Tp08fIiMj6dSpEwCbN28mIiKCefPmVXqBUnO4ZkrFhCuUEhERkfotKtSf1KwC4lOzuaBZsNnliIiI1ErlDqWaNWvGH3/8wfz58/n999/x9fVl1KhRDB8+HC8vr6qoUWoILd8TERERcYoK9WfdnqNqdi4iInIOKtQEyt/fnzFjxlR2LVKD5RXaSTqWC2imlIiIiIianYuIiJy7Cncm37ZtG4mJiRQUFBQ7fv31159zUVLzJKRmYxjOHgqN/G1mlyMiIiJiKlezc4VSIiIiFVfuUCo+Pp4hQ4awZcsWLBYLhmEAYLFYALDb7ZVbodQI7qV7Yf7uP2sRERGR+srVziA+JRvDMHR9JCIiUgHl3n1v/PjxREVFkZycjJ+fH3/++Sc//vgjXbp0YeXKlVVQotQE2nlPRESk8u3bt4/9+/e7H69du5YHH3yQ9957z8SqpCxaNvLDaoGs/CJSsvLNLkdERKRWKncotWbNGqZMmUJoaChWqxWr1UqvXr2YOnUqDzzwQFXUKDWAe+c9hVIiIiKV5m9/+xsrVqwA4NChQ1x55ZWsXbuWp556iilTpphcnZyJt6cHzUP8AEhI0RI+ERGRiih3KGW32wkMDAQgNDSUAwcOABAZGcnOnTsrtzqpMU5eviciIiKVY+vWrXTr1g2AL774ggsuuIDVq1czf/585s6da25xclauvlLx6islIiJSIeXuKXXBBRfw+++/ExUVRffu3Xn11Vex2Wy89957REdHV0WNYjLDMLR8T0REpAoUFhbi7e0NwLJly9wbxsTGxnLw4EEzS5MyiA7zZ9WuFDU7FxERqaByz5R6+umncTgcAEyZMoWEhAR69+7N//73P956661KL1DMdygjj5wCO55WC5GN/MwuR0REpM44//zzmT17Nj/99BNLly5l4MCBABw4cIBGjRqZXJ2czcnNzkVERKT8yj1TasCAAe77rVu3ZseOHaSlpRESEqJdR+oo14VWy4Z+eHmUO8cUERGR03jllVcYMmQIr732GiNHjqRDhw4AfPPNN+5lfVJzRYU6Z5DHp2aZXImIiEjtVK5QqrCwEF9fXzZv3swFF1zgPt6wYcNKL0xqjhP9pLR0T0REpDL169eP1NRUMjIyCAkJcR8fM2YMfn6anVzTuXptJh7JocjuwFMf3omIiJRLuf7l9PLyomXLltjt9qqqR2qguOTjO++Fq8m5iIhIZcrNzSU/P98dSO3du5cZM2awc+dOwsPDTa5OzqZxkA8+XlaKHAb7juaaXY6IiEitU+6Pc5566imefPJJ0tLSqqIeqYFcO8rEhGqmlIiISGW64YYb+PjjjwE4duwY3bt3Z9q0aQwePJh33nnH5OrkbKxWi3sJX4KW8ImIiJRbuUOpWbNm8eOPP9K0aVPatm3LxRdfXOwmdY9mSomIiFSNjRs30rt3bwC++uorIiIi2Lt3Lx9//LE2kKkl1OxcRESk4srd6Hzw4MFVUIbUVDkFRRxIzwMgWjOlREREKlVOTg6BgYEAfP/999x4441YrVYuueQS9u7dW65zvf3227z22mscOnSIDh06MHPmzDM2S//yyy955pln2LNnD23atOGVV15h0KBBxcZs376dxx9/nFWrVlFUVET79u1ZsGABLVu2LP+LraOiXKFUqkIpERGR8ip3KDVp0qSqqENqKNenfg39bYT420yuRkREpG5p3bo1ixYtYsiQISxZsoSHHnoIgOTkZIKCgsp8ns8//5wJEyYwe/ZsunfvzowZMxgwYMBpe1OtXr2a4cOHM3XqVK699lo+/fRTBg8ezMaNG92b2cTFxdGrVy9Gjx7Nc889R1BQEH/++Sc+Pj6V8+LrCFez8wTNlBIRESk3bREiZ+TaeS8mTEv3REREKtuzzz7LI488QqtWrejWrRs9evQAnLOmOnXqVObzTJ8+nbvvvptRo0bRvn17Zs+ejZ+fHx9++GGp4998800GDhzIo48+Srt27Xj++ee5+OKLmTVrlnvMU089xaBBg3j11Vfp1KkTMTExXH/99WrAfgrXTKkEzZQSEREpt3KHUlarFQ8Pj9PepG6JO/6pn5buiYiIVL6bbrqJxMRE1q9fz5IlS9zHr7jiCt54440ynaOgoIANGzbQv39/9zGr1Ur//v1Zs2ZNqc9Zs2ZNsfEAAwYMcI93OBx8++23nHfeeQwYMIDw8HC6d+/OokWLyvkK6z7XNdKhjDyy84tMrkZERKR2KffyvYULFxZ7XFhYyKZNm/joo4947rnnKq0wqRniU9TkXEREpCo1btyYxo0bs3//fgCaN29+xl5Qp0pNTcVutxMREVHseEREBDt27Cj1OYcOHSp1/KFDhwDn8sGsrCxefvllXnjhBV555RUWL17MjTfeyIoVK+jbt2+p583Pzyc/P9/9OCMjo8yvo7YK9vOikb+NI9kFJKRmc0GzYLNLEhERqTXKHUrdcMMNJY7ddNNNnH/++Xz++eeMHj26UgqTmsE1UyomTDOlREREKpvD4eCFF15g2rRpZGU5PwgKDAzk4Ycf5qmnnsJqNafTgsPhAJzXfa4+Vx07dmT16tXMnj37tKHU1KlT6+WHlFGh/gqlREREKqDSrnQuueQSli9fXlmnkxrA4TBISHVeIEcrlBIREal0Tz31FLNmzeLll19m06ZNbNq0iZdeeomZM2fyzDPPlOkcoaGheHh4cPjw4WLHDx8+TOPGjUt9TuPGjc84PjQ0FE9PT9q3b19sTLt27UhMTDxtLRMnTiQ9Pd1927dvX5leQ23n3oFPzc5FRETKpVJCqdzcXN566y2aNWtWGaeTGuJAei55hQ68PCy0CPE1uxwREZE656OPPuKDDz5g7NixXHTRRVx00UX8/e9/5/3332fu3LllOofNZqNz587FPhx0OBwsX77c3Tj9VD169CjxYeLSpUvd4202G127dmXnzp3FxuzatYvIyMjT1uLt7U1QUFCxW33g+vDO9WGeiIiIlE25l++FhIRgsVjcjw3DIDMzEz8/Pz755JNKLU7M5Vq616qRP54e2qhRRESksqWlpREbG1vieGxsLGlpaWU+z4QJExg5ciRdunShW7duzJgxg+zsbEaNGgXAiBEjaNasGVOnTgVg/Pjx9O3bl2nTpnHNNdfw2WefsX79et577z33OR999FGGDRtGnz59uOyyy1i8eDH/+c9/WLly5bm96DpIO/CJiIhUTLlDqTfeeKNYKGW1WgkLC6N79+6EhIRUanFirrhk19I9NTkXERGpCh06dGDWrFm89dZbxY7PmjWLiy66qMznGTZsGCkpKTz77LMcOnSIjh07snjxYncz88TExGL9qXr27Mmnn37K008/zZNPPkmbNm1YtGgRF1xwgXvMkCFDmD17NlOnTuWBBx6gbdu2LFiwgF69ep3jq657XNdK8SnZGIZR7FpZRERETs9iGIZhdhG1UUZGBsHBwaSnp9fZqelPL9rCJ78m8vd+MTw2sOSnuCIiIvVRZV4DrFq1imuuuYaWLVu6l86tWbOGffv28b///Y/evXtXRsmmqQ/XSwD5RXZin1mMYcC6p/oTFuhtdkkiIiKmKus1QLnXZM2ZM4cvv/yyxPEvv/ySjz76qLynkxosLlk774mIiFSlvn37smvXLoYMGcKxY8c4duwYN954I3/++Sfz5s0zuzwpI29PD5of778Zn6K+UiIiImVV7lBq6tSphIaGljgeHh7OSy+9VClFSc0Ql6LleyIiIlWtadOmvPjiiyxYsIAFCxbwwgsvcPToUf75z3+aXZqUQ3Soq9m5+kqJiIiUVblDqcTERKKiokocj4yMPOMWwVK7ZOYVkpyZD5zYUUZERERESqdm5yIiIuVX7lAqPDycP/74o8Tx33//nUaNGlVKUWK++OM774UFehPs62VyNSIiIiI1m2tmuWv3YhERETm7codSw4cP54EHHmDFihXY7Xbsdjs//PAD48eP59Zbb62KGsUE7qV7oVq6JyIiInI2J5bvqaeUiIhIWXmW9wnPP/88e/bs4YorrsDT0/l0h8PBiBEj1FOqDnHNlIoJ19I9ERGRynbjjTee8fvHjh2rnkKk0kQdnymVmJZDkd2Bp0e5P/sVERGpd8odStlsNj7//HNeeOEFNm/ejK+vLxdeeCGRkZFVUZ+YxDVTSjvviYiIVL7g4OCzfn/EiBHVVI1UhiZBPvh4WckrdLD/aC6tNNtcRETkrModSrm0adOGNm3aVGYtUoNo5z0REZGqM2fOHLNLkEpmtVpo1cifHYcySUjNViglIiJSBuWeVzx06FBeeeWVEsdfffVVbr755kopSsxldxjsSc0BoLVmSomIiIiUievDvHjtwCciIlIm5Q6lfvzxRwYNGlTi+NVXX82PP/5YKUWJufYfzaHA7sDb00rTBr5mlyMiIiJSK0Qdnx0Vn6Jm5yIiImVR7lAqKysLm81W4riXlxcZGRmVUpSYy7V0LyrUHw+rxeRqRERERGqHEzvwaaaUiIhIWZQ7lLrwwgv5/PPPSxz/7LPPaN++faUUJeZy77ynpXsiIiIiZebagU+hlIiISNmUu9H5M888w4033khcXByXX345AMuXL+fTTz/lq6++qvQCpfqd2HlPDTpFREREyir6+PK9g+l55BQU4Wer8J5CIiIi9UK5/6W87rrrWLRoES+99BJfffUVvr6+dOjQgR9++IGGDRtWRY1SzeKSnZ/uRWumlIiIiEiZNfCz0dDfRlp2AQmp2ZzfNNjskkRERGq0ci/fA7jmmmv45ZdfyM7OJj4+nltuuYVHHnmEDh06VHZ9YoL4VNdMKYVSIiIiIuVxotm5lvCJiIicTYVCKXDuwjdy5EiaNm3KtGnTuPzyy/n1118rdK63336bVq1a4ePjQ/fu3Vm7du1pxxYWFjJlyhRiYmLw8fGhQ4cOLF68uNiYzMxMHnzwQSIjI/H19aVnz56sW7eu2Dkef/xxLrzwQvz9/WnatCkjRozgwIEDFaq/LknPKSQ1qwA4sa2xiIiIiJSNK5RSXykREZGzK1codejQIV5++WXatGnDzTffTFBQEPn5+SxatIiXX36Zrl27lruAzz//nAkTJjBp0iQ2btxIhw4dGDBgAMnJyaWOf/rpp3n33XeZOXMm27Zt495772XIkCFs2rTJPeauu+5i6dKlzJs3jy1btnDVVVfRv39/kpKSAMjJyWHjxo0888wzbNy4ka+//pqdO3dy/fXXl7v+uibu+CypxkE++HurD4KIiIhIeUSr2bmIiEiZlTmUuu6662jbti1//PEHM2bM4MCBA8ycOfOcC5g+fTp33303o0aNon379syePRs/Pz8+/PDDUsfPmzePJ598kkGDBhEdHc3YsWMZNGgQ06ZNAyA3N5cFCxbw6quv0qdPH1q3bs3kyZNp3bo177zzDgDBwcEsXbqUW265hbZt23LJJZcwa9YsNmzYQGJi4jm/ptosLvn40r1wzZISERERKa9o9/K9LJMrERERqfnKPBXmu+++44EHHmDs2LG0adOmUn54QUEBGzZsYOLEie5jVquV/v37s2bNmlKfk5+fj4+PT7Fjvr6+/PzzzwAUFRVht9vPOKY06enpWCwWGjRocNqfm5+f736ckZFxxtdWW8Ud73+gflIiIiIi5efaKCY+NRvDMLBYLCZXJCIiUnOVeabUzz//TGZmJp07d6Z79+7MmjWL1NTUc/rhqamp2O12IiIiih2PiIjg0KFDpT5nwIABTJ8+nb/++guHw8HSpUv5+uuvOXjwIACBgYH06NGD559/ngMHDmC32/nkk09Ys2aNe8yp8vLyePzxxxk+fDhBQUGljpk6dSrBwcHuW4sWLc7hlddcrk/1XJ/yiYiIiEjZtWzoh8UCmXlFHMkuMLscERGRGq3ModQll1zC+++/z8GDB7nnnnv47LPPaNq0qTsYyszMrMo63d58803atGlDbGwsNpuNcePGMWrUKKzWEy9l3rx5GIZBs2bN8Pb25q233mL48OHFxrgUFhZyyy23YBiGe3lfaSZOnEh6err7tm/fvip5fWaLS3Et39NMKREREZHy8vHyoFkDX0A78ImIiJxNuXff8/f358477+Tnn39my5YtPPzww7z88suEh4eXu1F4aGgoHh4eHD58uNjxw4cP07hx41KfExYWxqJFi8jOzmbv3r3s2LGDgIAAoqOj3WNiYmJYtWoVWVlZ7Nu3j7Vr11JYWFhsDJwIpPbu3cvSpUtPO0sKwNvbm6CgoGK3uqbQ7mDvkRxAy/dEREREKsq1hC8hVX2lREREzqTcodTJ2rZty6uvvsr+/fv517/+Ve7n22w2OnfuzPLly93HHA4Hy5cvp0ePHmd8ro+PD82aNaOoqIgFCxZwww03lBjj7+9PkyZNOHr0KEuWLCk2xhVI/fXXXyxbtoxGjRqVu/66Zl9aDkUOA18vDxoH+Zz9CSIiIiJSgrvZuXbgExEROaMyNzo/Ew8PDwYPHszgwYPL/dwJEyYwcuRIunTpQrdu3ZgxYwbZ2dmMGjUKgBEjRtCsWTOmTp0KwG+//UZSUhIdO3YkKSmJyZMn43A4eOyxx9znXLJkCYZh0LZtW3bv3s2jjz5KbGys+5yFhYXcdNNNbNy4kf/+97/Y7XZ3D6uGDRtis9nO8R2pnVxNzqPD/LFa1ZRTREREpCKi3DvwKZQSERE5k0oJpc7FsGHDSElJ4dlnn+XQoUN07NiRxYsXu5ufJyYmFusFlZeXx9NPP018fDwBAQEMGjSIefPmFds1Lz09nYkTJ7J//34aNmzI0KFDefHFF/Hy8gIgKSmJb775BoCOHTsWq2fFihX069evSl9zTeXqJxWtpXsiIiIiFRYd5gylEjRTSkRE5IxMD6UAxo0bx7hx40r93sqVK4s97tu3L9u2bTvj+W655RZuueWW036/VatWGIZR7jrrOtfOezFh2nlPREREpKJcM6X2HsnG7jDw0Ax0ERGRUp1TTympW1zL99TkXERERKTimgb74u1ppdBusP9ojtnliIiI1FgKpcTtxPI9zZQSERERqSir1XKir5SW8ImIiJyWQikBIC27gGM5hQBEh2qmlIiIiMi5cIVSCWp2LiIicloKpQQ4MUuqWQNffG0eJlcjIiIiUrudmCmVZXIlIiIiNZdCKQEgLllL90REREQqi2s3Y+3AJyIicnoKpQQ40e9ATc5FREREzp17ppSW74mIiJyWQikBTsyUiglXKCUiIiJyrmKOzz4/mJ5HTkGRydWIiIjUTAqlBDjRUyomVMv3RERERM5VAz8bIX5eAOxJzTG5GhERkZpJoZSQX2Rn39FcQDOlRERERCqLmp2LiIicmUIpIfFIDnaHQYC3J+GB3maXIyIiIlInuJudq6+UiIhIqRRKiXvpXnSYPxaLxeRqREREROoG10wp7cAnIiJSOoVSQlyKdt4TERERqWzRx0OpOIVSIiIipVIoJSeanIepybmIiIhIZTmxfC8LwzBMrkZERKTmUSgl7plS0ZopJSIiIlJpIhv5YbFARl4RadkFZpcjIiJS4yiUqucMwyDePVNKoZSIiIhIZfHx8qBpsC8A8VrCJyIiUoJCqXouJSufzLwirBbnp3kiIiIiUnmij7dH0A58IiIiJSmUqufikp0XSM1D/PDx8jC5GhEREZG6xdXsXDOlRERESlIoVc/Fp6rJuYiIiEhViXKFUsfbJYiIiMgJCqXqOddMKfWTEhEREal87h34NFNKRESkBIVS9ZxrppR23hMRERGpfK6ZUnuP5GB3GCZXIyIiUrMolKrn4lK0fE9ERESkqjRr4IvN00qB3UHS0VyzyxEREalRFErVY3mFdvYfvziKCddMKREREZHKZrVaiGrkanauvlIiIiInUyhVj+05ko1hQJCPJ438bWaXIyIiIlInnWh2rr5SIiIiJ1MoVY+5m5yHB2CxWEyuRkRERKRuij7eJkHNzkVERIpTKFWPnegnpaV7IiIiIlXFNVNKoZSIiEhxCqXqsfgU1857anIuIiIiUlVc11quay8RERFxUihVj8Ud72ugmVIiIiIiVSc61HmtdSA9j9wCu8nViIiI1BwKpeopwzDcn9YplBIRERGpOiH+Nhr4eQHOjWZERETESaFUPXU4I5/sAjseVgstG/qZXY6IiIhInaYd+EREREpSKFVPuZqcRzb0w+apvwYiIiIiVcm1hC8hVX2lREREXJRG1FNxanIuIiIiUm3czc61A5+IiIibQql6Kl5NzkVERESqjZbviYiIlKRQqp6KU5NzERERkWrjnimVkoVhGCZXIyIiUjMolKqn4pK1fE9ERESkurRq5LzmysgrIi27wORqREREagaFUvVQTkERB9LzAM2UEhEREakOPl4eNGvgC0CC+kqJiIgACqXqJVcvg4b+NkL8bSZXIyIiIlIBBdmwfg7UoqVwanYuIiJSnEKpesi9816olu6JiIhILWQvhI+ug/8+CKtnml1NmanZuYiISHEKpeoh7bwnIiIitZqHF5x/o/P+0mdg27/NraeMXB8IJqRmmVyJiIhIzaBQqh5y77wXrplSIiIiUkv1uA+63uW8//UY2L/e3HrKIOr4B4LqKSUiIuKkUKoeijs+Uyo6VDOlREREpJayWGDgK9DmKijKg0+HwdE9Zld1Rq6ZUnuO5GB31J5eWCIiIlVFoVQ943AY7injMeEKpURERKQW8/CEm+ZA4/9v777Do6zyNo5/ZyZt0ia9QSAkIF1AmoBrWVkRKy4qIipiWxVQlrWAAnZZLCwqLqy+K6iIICrKWkDETleKoDRJaCEhCek9mZn3j8kMBAIESDKT5P5c13Nl8sxTzpNRONw553e6QnEWvHcDlOS4u1UnFBdixsfLSHmljYO5Je5ujoiIiNsplGpmDuaVUFphw9tkID7U7O7miIiIiJwd30C4+QMIioOsnbDwVqgsd3eramQyGkgI9we0Ap+IiAgolGp2nFP3WocH4GXSxy8iIiJNQHAcjPgAfAJhz4/wvwfA7pnT446swKdi5yIiIkolmhlnBygpUkXORUREpAmJ6Qo3vA0GE2x+H75/wd0tqlGiip2LiIi4KJRqZlwr70WqnpSIiIg0Me0GwpUvOV5/9zxsXuje9tTAOVJKoZSIiIhCqWZnd0bVynsKpURERKQp6nUH9H/A8frT0bDnJ/e25xiJrul7CqVEREQUSjUzyVmaviciIiJN3MCnoNO1YKuABSMga5e7W+Ti/MVgam4JpRVWN7dGRETEvRRKNSMFpRUcyi8DNFJKREREmjCjEa77D7TsDaW58N71UJTl7lYBEOrvjcXsDWgKn4iIiEKpZsTZ8YkI9HV1hkRERESaJG8z3PQ+hLSGnD3w/k1QUeLuVmEwGFRXSkREpIpCqWZkt1beExERkeYkMBJGfAh+IXBgPSz+G9hs7m4ViZEKpUREREChVLPiLHKeFKWpeyIiItJMRJ4DN70HRm/4/VNY8aS7W+Qqdu78haGIiEhzpVCqGXEWOXd2hERERESahYQL4NrXHa9XvgI/z3Frc5y1PTVSSkREmjuFUs2IRkqJiIhIs9VtGFw80fH683/Arq/d1hTVlBIREXFQKNVMWG12Ug47Oj5ttfKeiIiINEcXPQrdhoPdCotuh/StbmlGQrgjlMotriC7qNwtbRAREfEECqWaidScEsorbfh4GYkLMbu7OSIiIiINz2CAq1+FhD9BeQHMvxHy0xq8GWYfEy2q+mMpWaorJSIizZdCqWbCWUgzMSIAk9Hg5taIiIiIuImXDwx7FyLOgfxURzBV1vDBkHMKX3KmpvCJiEjzpVCqmXCGUkmauiciIiLNnTkUbv4A/CMg/Vf48A6wVjZoE1yhlOpKiYhIM6ZQqpnYXfVbuMRIrbwnIiIiQlgbuHkhePnBrmWwdALY7Q12e2efLEUjpUREpBlTKNVMaKSUiIiIyDFa9oK/vgEYYP2bsObfDXZrrcAnIiKiUKrZSFYoJSIiInK8TtfCX552vF72OGz7X4PcNjHC0SdLOVyE1dZwI7REREQ8iUKpZiCvuIKsQsdyw200fU9ERESkuv5jodcdgB0+uhsO/FLvt2wRasbHZKS80sbB3JJ6v5+IiIgnUijVDOyuWmo4JtiPQF8vN7dGRERExMMYDDD4RWj7F6gsgfeHQc7eer2lyWigdbg/oGLnIiLSfCmUagZ2ZzhCKRU5FxERETkBkxfcMAeiu0JRJrx3A5Tk1ustXXWlqsosiIiINDcKpZoB52/fVE9KRERE5CR8gxwr8gXFQtYO+OBWqCyvt9slVvXNVOxcRESaK4VSzYBzpFSSRkqJiIiInJylBdz8AfgEQsoP8Nk4sNdPIfLEqpFSmr4nIiLNlUKpZmB3pnP6nkZKiYiIiJxS7Llw/RwwGGHTe/DDS/VyG2dpheRMhVIiItI8KZRq4iqsNvZlFwOQFKVQSkRERKRWzrkMrnjR8frbZ+HXRXV+C2dNqYN5JZRWWOv8+iIiIp5OoVQTtz+7mAqrHbO3idhgP3c3R0RERKTx6H0X9BvjeP3p/bB3VZ1ePizAh2A/L+x22HNYo6VERKT5USjVxO2uGg7eJiIAo9Hg5taIiIiINDJ/eQY6Xg3WclhwM2T9UWeXNhgMR4qdawqfiIg0QwqlmrjkqnpSmronIiIicgaMRrjuDWjRE0py4L3roSirzi6vYuciItKceUQo9frrr5OQkICfnx99+/Zl3bp1Jzy2oqKCp59+mqSkJPz8/OjWrRtLly6tdkxBQQHjxo2jdevWmM1m+vfvz/r166sdY7fbmTJlCrGxsZjNZgYOHMiuXbvq5fncyVnkXCvviYiINH2n06cCWLRoER06dMDPz4+uXbvyxRdfnPDYe++9F4PBwIwZM+q41Y2Ajz8MXwAhrSAnxTFiqqK0Ti7trCulYuciItIcuT2UWrhwIePHj+eJJ55gw4YNdOvWjUGDBpGRkVHj8ZMmTeI///kPr732Gr///jv33nsv1113HRs3bnQdc9ddd7F8+XLeffddtmzZwmWXXcbAgQNJTU11HfPCCy/w6quvMnv2bNauXUtAQACDBg2itLRuOhiewjl9TyvviYiING2n26datWoVw4cP584772Tjxo0MGTKEIUOGsHXr1uOOXbx4MWvWrCEuLq6+H8NzBUbBiA/BzwL718In94LNdtaXdU3fyyo862uJiIg0Nga73W53ZwP69u1L7969mTlzJgA2m434+HjGjh3LhAkTjjs+Li6Oxx9/nNGjR7v2DR06FLPZzLx58ygpKSEoKIhPP/2UK6+80nVMz549GTx4MM8++yx2u524uDj+8Y9/8NBDDwGQl5dHdHQ0c+fO5aabbjplu/Pz87FYLOTl5REcHHy2P4Z60+Ppr8gpruDzBy6gc5zF3c0RERFp9Dy1D3C6faphw4ZRVFTEZ5995tp3/vnn0717d2bPnu3al5qaSt++fVm2bBlXXnkl48aNY9y4cbVqk6f+rM5Kyg/w7l/BVgEX/B0GPnlWl/v9YD5XvPojof7ebJxyWd20UURExM1q2wdw60ip8vJyfvnlFwYOHOjaZzQaGThwIKtXr67xnLKyMvz8qq8iZzab+emnnwCorKzEarWe9JiUlBTS09Or3ddisdC3b9+T3jc/P7/a5umyi8rJKa4AIDFCI6VERESaqjPpU61evbra8QCDBg2qdrzNZuPWW2/l4YcfpnPnzvXT+MamzYVwzauO1z/9C36Ze1aXS4jwByCnuIKcovKzbJyIiEjj4tZQKisrC6vVSnR0dLX90dHRpKen13jOoEGDmD59Ort27cJms7F8+XI+/vhj0tLSAAgKCqJfv34888wzHDx4EKvVyrx581i9erXrGOe1T+e+U6dOxWKxuLb4+PizevaG4Kwn1SLEjNnH5ObWiIiISH05kz5Venr6KY+fNm0aXl5ePPDAA7VqR2P8Jd4Z6X4zXPSo4/Vn4+GPFWd8KX8fL+Isjl+mqti5iIg0N26vKXW6XnnlFdq1a0eHDh3w8fFhzJgxjBo1CqPxyKO8++672O12WrRoga+vL6+++irDhw+vdszpmjhxInl5ea5t//79dfE49cq58l6iipyLiIjIafrll1945ZVXmDt3LgaDoVbnNMZf4p2xiyfCucPAboUPRsKh3874Um2q+mopCqVERKSZcWsoFRERgclk4tChQ9X2Hzp0iJiYmBrPiYyM5JNPPqGoqIi9e/eyfft2AgMDSUxMdB2TlJTE999/T2FhIfv372fdunVUVFS4jnFe+3Tu6+vrS3BwcLXN0zmLnCepyLmIiEiTdiZ9qpiYmJMe/+OPP5KRkUGrVq3w8vLCy8uLvXv38o9//IOEhIQar9kYf4l3xgwGuOY1aD0AygvgvRshP+2MLnVkBT4VOxcRkebFraGUj48PPXv2ZMWKI0OebTYbK1asoF+/fic918/PjxYtWlBZWclHH33Etddee9wxAQEBxMbGkpOTw7Jly1zHtGnThpiYmGr3zc/PZ+3atae8b2Pi7NgkaaSUiIhIk3Ymfap+/fpVOx5g+fLlruNvvfVWfv31VzZt2uTa4uLiePjhh1m2bFmN12yMv8Q7K16+MGwehLeD/APw/jAoO/1gyVn7UyOlRESkufFydwPGjx/PyJEj6dWrF3369GHGjBkUFRUxatQoAG677TZatGjB1KlTAVi7di2pqal0796d1NRUnnzySWw2G4888ojrmsuWLcNut9O+fXv++OMPHn74YTp06OC6psFgYNy4cTz77LO0a9eONm3aMHnyZOLi4hgyZEiD/wzqi0ZKiYiINB+n26d68MEHueiii3j55Ze58sorWbBgAT///DNvvPEGAOHh4YSHh1e7h7e3NzExMbRv375hH86T+YfBiA/g/wZC2mb46C646T0w1r6ep3P6XnKmQikREWle3B5KDRs2jMzMTKZMmUJ6ejrdu3dn6dKlrsKb+/btq1YLqrS0lEmTJpGcnExgYCBXXHEF7777LiEhIa5j8vLymDhxIgcOHCAsLIyhQ4fy3HPP4e3t7TrmkUceoaioiHvuuYfc3FwuuOACli5detyqfY1VeaWNfdnFACRFKZQSERFp6k63T9W/f3/mz5/PpEmTeOyxx2jXrh2ffPIJXbp0cdcjNF5hiTB8Acy9CnZ+CUsnwhUv1Pr0xKrpeymHi7DZ7BiNtavhJSIi0tgZ7Ha73d2NaIzy8/OxWCzk5eV55ND0PzIKGDj9BwJ8TGx9alCtC5SKiIjIyXl6H8CTNLuf1W+LYdHtjteX/xPOv69Wp1ltdjpM/pIKq52P7+/Pea1C66+NIiIiDaC2fYBGt/qe1M4fGVVT96ICFUiJiIiINITO18HApxyvl06E7Z/X6jST0UC/pAgARr61jjXJh+urhSIiIh5FoVQTtdtV5FxT90REREQazIAHoeftgN1RXyp1Q61Oe/Wm7vRqHUpBaSW3/XcdX2w5s5X8REREGhOFUk2Us1Cms0aBiIiIiDQAgwGueBmSLoWKYpg/DHL3nfK0EH8f5t3Vl8s6RVNutTF6/gbeXrWn/tsrIiLiRgqlmijXSCkVORcRERFpWCYvuGEuRHWGogx470YozTvlaX7eJmbd0pNbzm+F3Q5PLPmNaUu3oxKwIiLSVCmUaoLsdrum74mIiIi4k18wjPgAgmIhcxt8cBtYK055mslo4Jlru/DQZecAMOu73fxj0WYqrLb6brGIiEiDUyjVBGUVllNQWonBAK3D/d3dHBEREZHmydISbl4I3gGQ/B18Ng5qMerJYDAw5s/teGHouZiMBj7ekMpdb/9MUVllvTdZRESkISmUaoKco6TiQ/3x8za5uTUiIiIizVhsN7hhDhiMsHEe/PhyrU+9sXc8b97WEz9vI9/vzGT4m2vIKiyrx8aKiIg0LIVSTdCRqXsqci4iIiLiducMgsEvOF5/8wxs+bDWp/65QzTv330+of7e/Hogj6GzVrH3cFE9NVRERKRhKZRqglwr76melIiIiIhn6HM39BvjeP3JfbB3da1P7dEqlI/u60/LUDN7DxczdNYqthw4deF0ERERT6dQqglSkXMRERERD/SXZ6DDVWAthwXD4fDuWp+aGBnIx/f3p3NcMFmF5Qx7YzXf78ysx8aKiIjUP4VSTZCm74mIiIh4IKMR/vomtOgJJTnw3vVQdLjWp0cF+bHgnvMZ0Dac4nIrd85dz8cbDtRjg0VEROqXQqkmprTCyoGcEkDT90REREQ8jo8/DF8AIa0gOxkW3AwVpbU+PcjPmzm39+Ha7nFU2uyM/2Azs77bjb0Wq/qJiIh4GoVSTcyew0XY7RDs50VEoI+7myMiIiIixwqMgpsXga8F9q+BT+8Hm63Wp/t4GfnXjd25+09tAJi2dDtP/e93rDYFUyIi0rgolGpidmccKXJuMBjc3BoRERERqVFUBxj2Lhi9YOtH8O2zp3W60Wjg8Ss7MenKjgDMXbWHB97fSGmFtT5aKyIiUi8USjUxySpyLiIiItI4JF4E17zmeP3jy/DL26d9ibv+lMirw3vgbTLw+ZY0Rr61jrySijpuqIiISP1QKNXEuIqcR6nIuYiIiIjH634zXPiI4/X/HoBXz4Olj0HKD2CtXbh0Tbc43h7Vh0BfL9amZDPsP6tJz6t9nSoRERF3USjVxOzOrJq+F6GRUiIiIiKNwiWPQd/7wOgN2bthzevw9tXwQhIsGgWbF0Jx9kkv0b9tBAv/dj6RQb5sTy/gr/9eyR8ZBQ30ACIiImdGoVQTYrfbXdP32mqklIiIiEjjYDDA4H/CI8lw4zvQ7WbwD4eyPPjtY1h8D7yYBG9dDj/9CzK2Qw2r7XWOs/Dxff1JjAzgYF4pQ2et5uc9Jw+zRERE3EmhVBNyKL+MonIrJqOBVmEKpUREREQaFb9g6HQtXDcLHtoFd34Nf/oHRHcBuw32rYavn4R/94VXusGXj8Lub6Cy3HWJ+DB/Pry3Pz1ahZBXUsGI/1vLV7+lu++ZRERETkKhVBPirCfVKswfHy99tCIiIiKNltEE8b3h0ilw30oYtwWueAnaDgSTD+TuhbWz4d3r4IVEWHgrbJoPhZmEBfgw/67zubRDFGWVNu6d9wvvrd3r7icSERE5jpe7GyB158jKexolJSIiItKkhLSCPnc7trJCSP4Odi6FncugKAO2LXFsGKBlb8znDOI/l13GpMCWLPj5AI8v3sqhvFL+/pdzMBgM7n4aERERQKFUk+Iscp4UqSLnIiIiIk2WbyB0vMqx2WyQttERTu34EtJ/hQPr4MA6vL55hqmWltyUcD7/2pfEf74p51B+Gc9d1wUvk0bVi4iI+ymUakKc0/cSNVJKREREpHkwGqFFT8d2yWOQlwq7ljlCquTvMOQdoHveh7ztA0V2X37a3JV30y7iphF3YQ6Lc3frRUSkmVMo1YQka6SUiIiISPNmaQG97nBs5cWQ8gPs/BJ2LiOgII1Bpp/h8M/w6stUxvTAq+MVcM4giDnXsQqgiIhIA1Io1UQUl1eSmlsCKJQSEREREcDHH9pf7tjsdkjbzMH1n5CzcQmd2Y1X+kZI3wjfPgdBcY5w6pzLIfEi8Da7u/UiItIMKJRqIlKyHKOkQv29CQ3wcXNrRERERMSjGAwQ1524a7tT3O8fXPPfZXQsXMNgn01caNqKseAg/DLHsXmZHcHUOZc7gqpgTfMTEZH6oVCqiVCRcxERERGpjbZRQbxx/1XcPieKhemXEOZr490/l9G5cDXsWAr5B6pW9lvqOCG2W1VAdTnEdnfUsRIREakD+hulidid4ShyrlBKRERERE4lxuLHwr/14/zEMLLLjAz5yp8lLf8Bf98K966EP0+Clr0BA6Rthu+nwZuXwPQOsGQsbP8cyovc/RgiItLIaaRUE5FcNX1PK++JiIiISG1YzN68fUcfxn+wmc9/TeOB9zeSkV/KXX/qAjFd4MKHoTADdi13FEvf/S0UHoIN7zg2ky+0ufBILaqQeHc/koiINDIKpZoIjZQSERERkdPl62XitZt6EBXky5yVe3j2822k55Xy2BUdMRoNEBgFPUY4tsoy2PMT7FzmCKly98Efyx3bFw9BdJcj0/xanAdGk7sfT0REPJxCqSbAZrOTnFUVSkUplBIRERGR2jMaDUy5qhMxwX5M/XI7//dTChkFZbx0Qzd8vI6q9uHlC20vdWyDp0HmdkfdqR1L4cA6OLTVsf34EvhHQOLFjnAq7jyIPRd8NKJfRESqUyjVBKTll1JaYcPbZCA+VMv3ioiIiMjpMRgM/O2iJCKDfHnkw19Zsvkg2UXlzLrlPIL8vGs6AaI6OrYL/g5Fhx0jpnYuhT9WQHEWbP3QsQEYjBDZEVr0cIRULc6DqM7gpVWjRUSaM4VSTYBz6l7r8AC8TKpdLyIiIiJn5q/ntSQi0Jd75/3CT39kMew/a5h7R2+igvxOfmJAOHS7ybFVlsO+1bB/HRzcAKkboDAdMn5zbBvnOc4x+TpqVzlDqrjzIKKdpv2JiDQjCqWagN2ZznpSGhItIiIiImfnwnMiWXhPP0bNXcfvafn89d+reOeOPiTWtnaplw8kXuTYnPIPOsIpZ0h1cCOU5kLqL45tfdVxPoEQ2w3iehwJqkITHCOzRESkyVEo1QQkZzpX3lM9KRERERE5e11bWvjovv6MfGsdew4XM3TWKt66vTc9WoWe2QWD4xxbx6sc39vtkJ3sCKecYVXaZigvhL0rHZuTOax6SNXiPAiKOfuHFBERt1Mo1QQcGSmlUEpERERE6kbr8AA+vK8/d85dz+YDedz85lpeH9GDP3eIPvuLGwwQnuTYul7v2GethKwd1YOq9K1Qkg27Vzg2p6C4qpCqx5Gv5jMMzERExG0USjUBmr4nIiIiIvUhItCX+Xefz/3vbeD7nZnc/c4vTL2uKzf2jq/7m5m8ILqzY+txi2NfZZljRT/nlL/UDY5V/woOwvaDsP2zI+eHtqk+miq2m1b8ExHxcAqlGrnCskoO5ZcBmr4nIiIiInUvwNeL/xvZiwkfbeGjDQd45KNfOZRfypg/t8VQ37WevHyhRU/H5lRW6Jjq56pPtQFy9kBOimPb+pHjOIMRIjtUhVRVq/5Fd9GKfyIiHkShVCOXXDVKKiLQF4u5huV6RURERETOkrfJyEs3nEuMxZfXv93Ny8t3kp5fytPXdsFkbOAi5L6BkDDAsTkVZztGUh3cAKlVXwvSION3x7bJueKfjyOYco6oiusBke214p+IiJsolGrkNHVPRERERBqCwWDg4UEdiA7244klv/He2n1kFpTx6vAe+Hm7OdTxD4O2lzo2p/y06qOpUjc4Vvw7WPW9k3eAY6rf0TWqQttoxT8RkQagUKqR08p7IiIiItKQbuuXQGSgLw8u3MRXvx/ilv9by/+N7EWIv4dNiwuOheArocOVju/tdsf0vqPrU6Vthooi2LfKsTmZQx0BlbM+Vdx5EBjlmBKosEpEpM4olGrkNFJKRERERBra4K6xhAX4cNc7P/Pz3hyun72at+/oQ4sQs7ubdmIGA4QlOjbnin82K2TuqBo9VRVUHdoKJTmw+xvHVuO1TFUBVdVmNB0JrAzGE7xvOGrfse8bT/Le6Zx7zPsnes/oBUExEJoAoa0dX/0sDfVJiIi4KJRq5HZnOEZKJUVppJSIiIiINJy+ieF8eG9/Rr61jj8yChn671XMvaM3HWKC3d202jOaILqTY6u24t9v1etTZW4Hu+3IeXarY2tKzKEQUhVQHR1WhSaAJR5Mql8rInVPoVQjZrXZSTlcFUpFKJQSERERkYbVPiaIj+93BFO7Mgq5YfZq3rytF+cnhru7aWfOy9cxZa/FedC7al9FiWOz2xyjq+y2ozbn9/Z6er+G7VTHnOp9aznkpVatWrgHirMco8NKciBt0/E/E4MRglseFVS1dtTdCk1wBFkBEZrWKCJnRKFUI5aaU0J5pQ0fLyMtQj14qLSIiIiINFlxIWY+vLc/d7/zM+v2ZHPbf9cx46buXNE11t1NqzveZsfWVJUVQu7eqpBq75GwKmePY39lKeTtc2x7fjz+fO+A40dXOUddhbQCH/+GexYRaVQUSjViznpSbcIDGn4pXhERERGRKhZ/b965sw8PLtjIst8OMXr+Bh66rD2jBiTg76N/cng830CI7uzYjmW3Q+Gh4wMrZ4iVf9BRLD7jN8dWk8CYmgOr0AQIinXUvhKRZkl/QzRiriLnUSpyLiIiIiLu5edt4t8jevLkkt94d81eXly2gzd+SOamPvHc1i/Bs4ugy4kZDI6i6EEx0Or849+vLIPc/VVhVUr1wCpnL5TlQ2G6Y9u/9vjzTT6O0VTHhlUqwC7SLCiUasR2Z1bVk4pUPSkRERERcT+T0cDT13amU1wws7/fzd7Dxfzn+2Te/CGZy7vEMGpAG3q1DsWg+kNNh5cvRLR1bMey2x11qo6dDuh8nXfAUd/q8B+OrSbHFWBPOBJY1XcBdltVzS+b9ZivdbX/BMcaDOAT6BjB5hN4zOsA1e+SJkWhVCPmHCmVGKmRUiIiIiLiGQwGA8P7tOLGXvF8uz2DOatSWPnHYb7Yks4XW9Lp0iKYUf3bcFW3WHy9TO5urtQngwH8wxxbi/OOf99aCfmpx4dVzlFWtS3AbmkBGGoIfmy1CIgqTxwaeSTDkXDKGVT5Bp14X03Blm+Q41iFXOIBFEo1YskaKSUiIiIiHspkNDCwUzQDO0WzI72AuatS+HhDKltT8/nHos1M/XI7I/q2YsT5rYgK8nN3c8UdTF5Vo55a1/x+WYEjnKoWWO2tuQC7OxiMYDCB0XTUV+Mx35scNbOMXsfvO+7cqv12O5QXOgrQH/0Vu2MrL3BshXXyEEcFVgE1BFo17asp5HIeq5BLTo/Bbrfb3d2Ixig/Px+LxUJeXh7BwcENfv+84gq6Pf0VAFufGkSgr/JFERGRhuDuPkBjop+VHCu7qJz31+3j3dV7Sc8vBcDbZODqc+MYNaANXVuqfpDUks3mKMCeu9dRbN1gOEVA5HWKQKimMOkU+xsyfLHboaL4qKCqwPG1vOjIa+d7NQVaJwy56tqxUw8DIDAKIs5xbJHtHV/9w+rh3uJJatsHUJLRSO3OcsTi0cG+CqREREREpFEIC/Bh9CVtuefCRJZuTWfOyhQ27Mvl442pfLwxlV6tQxk1oA2DOkfjZdKKbHISRiMExzq25sBgODISieizv16NIVdR9cCrrCr0Ki84jZDrqJFcR9v1VfXv/SOOBFQR50DkORDRHiwtNdKqmVGa0Uhp6p6IiIiINFbeJiNXd4vj6m5xbN6fy5yVKXy+JY2f9+bw894c4ix+3NovgeF94gnx93F3c0WanoYMufJTIXMnZO1wfM0/4KgXtjcL9q6sfh3vgKrC+e2rgqqqsCosEbz0Z0FTpFCqkXIWOVcoJSIiIiKNWbf4EGbc1IPHrujIvDV7eW/tPg7mlTJt6XZeWbGT63q0ZNSABM6JDnJ3U0XkRE4n5CorhMO7qoKqo8Kq7N1QUQRpmx1bteubHMHU0aOqIs6BiHbgp+nhjZlCqUYqWSvviYiIiEgTEhXsx/jL2nP/JW353+aDzFm5h9/T8nl/3T7eX7ePC9pGMGpAApe0j8Jo1PQekUbLNxDieji2o1krHEXsM3c4gqqsXVWvdzpGXB3e5dh2fF79vKA4RzhVbTpgewiM1lTARkChVCO1W9P3RERERKQJ8vM2cUOveK7v2ZJ1KdnMWbmHr35P56c/svjpjywSwv0Z2T+BG3rFq7aqSFNi8naESxHtgKuO7LfbHcXss6pGVjmDqqydjmL3BQcdW8r31a/nazlqCuBRRdZDWjtWfhSPoE+iEaq02th7uCqUilIoJSIiIiJNj8FgoG9iOH0Tw9mfXcy7a/ayYN0+9hwu5qn//c7LX+3khl4tub1/Aq3DNXtApMkyGMDSwrElXVL9vZIcx4iqY8OqnD1QlgcH1ju2o5l8ILxtVQDW/khYFd4WfPwb7LHEwWC32+tjHcgmz51LHKdkFXHJS9/h523k96cu1/BlERGRBuTOPkBjo5+V1LWisko+3pjK3JUprpkDBgNc2iGKUQPa0D8pHIOm64hIRamjRlVm1TRAZ92qw7ugsvQEJxkgJP5IvSpn7arI9uAf1qDNbwpq2wfQSKlGaHdGVT2piEAFUiIiIiLSbAT4enHr+a0Z0acVP+zKZM7KPXy/M5Ovt2Xw9bYM2kcHcfuABK7r0QI/b5O7mysi7uLtB9GdHdvRbDbI23d8kfWsHY5RV7n7HNsfy6uf5x9+ZEVA/3Awejk2g7HqtamW+5z7TY7i7cfuM3odtb+2+7zAaGy4n20dUyjVCLlW3tPUPRERERFphoxGAxe3j+Li9lH8kVHI26v28NGGA+w4VMDEj7cwbel2hvdpxW39WhNrMbu7uSLiKYxGCE1wbOdcdmS/3Q7Fh2susp633/HevlWOzVPVFFS5QjFT9UDr6H3xfeCKF93WbIVSjVBy1VDlxAjNnRcRERGR5q1tVCDPDOnCQ4Pa88H6/by9eg8HckqY9d1u3vghmcu7xHDHgATOaxWqqX0iUjODAQIiHFvCgOrvlRXC4T+O1KsqzQdbJditjq82W9VX5z7ndrr7nNexHnPMUfs4SfUlWyVQCday03t2N09NVCjVCGmklIiIiIhIdRazN3dfmMgdF7Rh+e+HmLMyhbUp2Xz+axqf/5rGuS0tjBqQwJVd4/DxarxTXUSkgfkGQlx3x+ZuNttRYZj1mDDrDPeZQ936SAqlGiFXKBWpkVIiIiIiIkczGQ1c3iWGy7vE8NvBPOau3MOnmw/y64E8/r5wM89/sZ1bz2/NzX1bERHo6+7miojUntEIGMHk7e6W1Bn9iqCRyS4qJ6e4AoA2mr4nIiIiInJCneMsvHhDN1ZP+DP/+Ms5RAX5kllQxvTlO+k/9RseWrSZ3w7mubuZIiLNlkZKNTLJVaOkWoSY8ffRxyciIiIicirhgb6MvbQdf7soiS+3pvHWyj1s3p/Lh78c4MNfDtCnTRh3DEhgYMdovEz6vb2ISENRqtHIOKfuJWrqnoiIiIjIafHxMnJt9xZc270FG/blMGflHr7Yksa6lGzWpWTTIsTMyP6tGdarFRb/pjM9RkTEU+nXAI2Mc+W9pEgVORcREREROVPntQrlteE9+OnRS7j/4iRC/b1JzS3h+S+2c/7UFUz6ZAt/ZBS6u5kiIk2aQqlGRkXORURERETqTqzFzCOXd2D1xEv551+70j46iJIKK/PW7GPg9O+57a11fLsjA5vtJEuxi4jIGdH0vUZmt0ZKiYiIiIjUOT9vEzf1acWw3vGs3n2Yt1buYcX2Q/ywM5MfdmYSFuBDn4Qw+iaG0bdNOB1igjAaDe5utohIo+b2kVKvv/46CQkJ+Pn50bdvX9atW3fCYysqKnj66adJSkrCz8+Pbt26sXTp0mrHWK1WJk+eTJs2bTCbzSQlJfHMM89gtx/5zUZhYSFjxoyhZcuWmM1mOnXqxOzZs+vtGetKeaWNfdnFACQqlBIRERERqXMGg4H+bSP4v5G9+O6hi7ljQBuCfL3ILipn6W/pPPW/37ni1R/p/vRX3PX2et78IZlfD+RSabW5u+kiIo2OW0dKLVy4kPHjxzN79mz69u3LjBkzGDRoEDt27CAqKuq44ydNmsS8efN488036dChA8uWLeO6665j1apV9OjRA4Bp06Yxa9Ys3n77bTp37szPP//MqFGjsFgsPPDAAwCMHz+eb775hnnz5pGQkMBXX33F/fffT1xcHNdcc02D/gxOx77sIqw2OwE+JqKDfd3dHBERERGRJq11eABTru7EhMEd2JKay5rkbNamZPPLnmzySyv5elsGX2/LACDQ14teCaH0bRNO38Qwuraw4K2V/ERETspgP3oIUQPr27cvvXv3ZubMmQDYbDbi4+MZO3YsEyZMOO74uLg4Hn/8cUaPHu3aN3ToUMxmM/PmzQPgqquuIjo6mv/+978nPKZLly4MGzaMyZMnu47p2bMngwcP5tlnn61V2/Pz87FYLOTl5REcHHz6D38Glm5N5955v3BuSwtLxlzQIPcUERGR6tzRB2is9LOSpqrSamPrwXzWJh9mbUo261OyKSirrHaMv4+Jnq1D6dsmjL6J4Zzb0oKvl8lNLRYRaVi17QO4baRUeXk5v/zyCxMnTnTtMxqNDBw4kNWrV9d4TllZGX5+ftX2mc1mfvrpJ9f3/fv354033mDnzp2cc845bN68mZ9++onp06dXO2bJkiXccccdxMXF8d1337Fz507+9a9/1fFT1q3kLEeR88QIFTkXEREREXEXL5OR7vEhdI8P4W8XJWG12dmWls/alGzWJh9m3Z5scosr+HFXFj/uygLA18tIj1YhrpFU57UKxc9bIZWING9uC6WysrKwWq1ER0dX2x8dHc327dtrPGfQoEFMnz6dCy+8kKSkJFasWMHHH3+M1Wp1HTNhwgTy8/Pp0KEDJpMJq9XKc889x4gRI1zHvPbaa9xzzz20bNkSLy8vjEYjb775JhdeeOEJ21tWVkZZWZnr+/z8/DN99DO2O0NFzkVEREREPI3JaKBLCwtdWli484I22Gx2dmYUsDY5m7Uph1mbnM3honLWJGezJjkbVoCPyUi3eIsrpOrZOhR/H61DJSLNS6P6U++VV17h7rvvpkOHDhgMBpKSkhg1ahRvvfWW65gPPviA9957j/nz59O5c2c2bdrEuHHjiIuLY+TIkYAjlFqzZg1LliyhdevW/PDDD4wePZq4uDgGDhxY472nTp3KU0891SDPeSK7Mx0jpZKiFEqJiIiIiHgqo9FAh5hgOsQEM7J/Ana7nd2Zha6aVGuTD5NRUMb6PTms35PDzG/By2iga8sjIVWv1qEE+Xm7+1FEROqV22pKlZeX4+/vz4cffsiQIUNc+0eOHElubi6ffvrpCc8tLS3l8OHDxMXFMWHCBD777DN+++03AOLj45kwYUK1ulPPPvss8+bNY/v27ZSUlGCxWFi8eDFXXnml65i77rqLAwcOHLean1NNI6Xi4+MbrEaC3W6n21NfkV9aydJxf6JDjOoyiIiIuIPqJNWeflYiNbPb7ew9XOwaRbU2JZvU3JJqxxgN0DnO4qpJ1SchDIu/QioRaRw8vqaUj48PPXv2ZMWKFa5QymazsWLFCsaMGXPSc/38/GjRogUVFRV89NFH3Hjjja73iouLMRqrr3JhMpmw2RxLtFZUVFBRUXHSY2ri6+uLr6/7VrzLKiwnv7QSgwESwlVTSkRERESksTIYDCREBJAQEcCw3q0A2J9d7BpFtTYlm33ZxWxJzWNLah7/91MKBgN0iAmmb5swzk8Mo0+bcMICfNz8JCIiZ8et0/fGjx/PyJEj6dWrF3369GHGjBkUFRUxatQoAG677TZatGjB1KlTAVi7di2pqal0796d1NRUnnzySWw2G4888ojrmldffTXPPfccrVq1onPnzmzcuJHp06dzxx13ABAcHMxFF13Eww8/jNlspnXr1nz//fe888471Yqhexrn1L2WoWYVRBQRERERaWLiw/yJD/Pn+p4tAUjLK3GNolqbcpjkzCK2peWzLS2fuav2AHBOdKBrul+fNmFEBfmd5A4iIp7HraHUsGHDyMzMZMqUKaSnp9O9e3eWLl3qKn6+b9++aiOaSktLmTRpEsnJyQQGBnLFFVfw7rvvEhIS4jrmtddeY/Lkydx///1kZGQQFxfH3/72N6ZMmeI6ZsGCBUycOJERI0aQnZ1N69atee6557j33nsb7NlPV3KmipyLiIiIiDQXsRYzQ3q0YEiPFgBkFJSyLiXbVTx956FC1/bumr2AY5XuvolhrqAq1mJ25yOIiJyS22pKNXYNXSPhmc9+578/pXDnBW2YfFWner+fiIiI1Ex1kmpPPyuR+nO4sIz1e7JdxdO3p+dz7L/sWoX5u2pS9W0TRnyYv3saKyLNjsfXlJLT45y+lxipelIiIiIiIs1deKAvl3eJ5fIusQDkFVewfo9jFNXalGy2puaxL7uYfdnFLPrlAAAtQsz0beOY6tctPoS2UYF4m4wnu42ISL1SKNVIaPqeiIiIiIiciMXfm4GdohnYyVEKpaC0gp/35rim+205kEdqbgkfb0zl442pAPh4GWkfHUTnuGA6xQXTOS6YjrHB+Pvon4ki0jD0p00jUFphZX9OMaBQSkTEU1mtVioqKtzdDKkD3t7emExaVEREGrcgP28uaR/FJe2jACgur+SXvTmsS8lmXUo2vx/Mp6Cs0rXCn5PBAG0iAugcZ6FzVVDVKTaY8ED3rUQuIk2XQqlGYM/hIux2CPLzIiJQy76KiHgSu91Oeno6ubm57m6K1KGQkBBiYmIwGAzuboqISJ3w9/HiT+0i+VO7SABsNjsHckr47WAevx3Md33NKCgjObOI5Mwi/rf5oOv8mGC/IyFVVWDVMtSsPydF5KwolGoEjp66pz/0RUQ8izOQioqKwt/fX39ON3J2u53i4mIyMjIAiI2NdXOLRETqh9FooFW4P63C/Rnc9cifdZkFZfx2MI/f0/L57WA+vx/MJyWriPT8UtLzS1mxPcN1bLCfV9W0P+eoKgtJkQF4qU6ViNSSQqlGYHeGo8i5pu6JiHgWq9XqCqTCw8Pd3RypI2azYwn1jIwMoqKiNJVPRJqVyCBfLm4fxcVV0/4ACssq2ZaWz2+pjtFUv6fls/NQAfmllaxJdqwA6OTrZaRDTBCd4ixH6lTFBGP20Z+lInI8hVKNQHKWY6SUVt4TEfEszhpS/v5aYrupcX6mFRUVCqVEpNkL9PWid0IYvRPCXPvKK23syihwjab67WAevx/Mp6jcyuYDeWw+cKROldEAiZGBrul/neMsdIoNJjRApUlEmjuFUo3A7kyNlBIR8WSastf06DMVETk5Hy9j1bQ9i2ufzWZnX3ZxtRpVvx3MJ6uwjD8yCvkjo5BPNx2pUxVn8XPVp+ocF0znFhbiLH76M1ikGVEo5eHsdrtr+l7bKI2UEhERz5WQkMC4ceMYN25crY7/7rvvuOSSS8jJySEkJKRe2yYiIvXPaDSQEBFAQkQAV557pE5VRn6pa9qfM6zae7iYg3mlHMwr5etth1zHhvh70yn2yIiqznHBJEYGYjIqqBJpihRKebiMgjKKyq2YjAZahSmUEhGRs3eq30A/8cQTPPnkk6d93fXr1xMQUPu/q/r3709aWhoWi+XUB4uISKMVFexHVLAfl3Q4Uqcqv7SCbVUjqZyB1a5DBeQWV7Bq92FW7T7sOtbP20iHmGBXjarOcRY6xATh563p1SKNnUIpD+ccJdUqzB8fL61iISIiZy8tLc31euHChUyZMoUdO3a49gUGHpkubrfbsVqteHmdussQGRl5Wu3w8fEhJibmtM4REZGmIdjPm76J4fRNPLJQSFmllV2HCqtN/duWlk9xuZVN+3PZtD/XdazJaCApMsBVnyoxMoA2EQHEh/njrdX/RBoNhVIe7kg9KY2SEhGRunF0EGSxWDAYDK59zil1X3zxBZMmTWLLli189dVXxMfHM378eNasWUNRUREdO3Zk6tSpDBw40HWtY6fvGQwG3nzzTT7//HOWLVtGixYtePnll7nmmmuq3cs5fW/u3LmMGzeOhQsXMm7cOPbv388FF1zAnDlziI11TAOprKxk/PjxvPPOO5hMJu666y7S09PJy8vjk08+aZgfoIiI1AtfLxNdWljo0uLICFqrzc7ew0WukMpZUP1wUTk7DxWy81Ahizemuo43GQ3Eh5pJiHCEVM4tITyAuBCzpgGKeBiFUh5ud6Zz5T0VORcRaQzsdjslFVa33Nvsbaqz4rATJkzgpZdeIjExkdDQUPbv388VV1zBc889h6+vL++88w5XX301O3bsoFWrVie8zlNPPcULL7zAiy++yGuvvcaIESPYu3cvYWFhNR5fXFzMSy+9xLvvvovRaOSWW27hoYce4r333gNg2rRpvPfee8yZM4eOHTvyyiuv8Mknn3DJJZfUyXOLiIhnMRkNJEYGkhgZyNXd4gDH37WH8sv4PS2P31Lz2Z5eQHJWEXuyiiipsLLncDF7Dhfz3Y7Matfy8TLSOsy/elgVEUBiRACRQb4qsC7iBgqlPJxGSomINC4lFVY6TVnmlnv//vQg/H3q5q/2p59+mr/85S+u78PCwujWrZvr+2eeeYbFixezZMkSxowZc8Lr3H777QwfPhyA559/nldffZV169Zx+eWX13h8RUUFs2fPJikpCYAxY8bw9NNPu95/7bXXmDhxItdddx0AM2fO5IsvvjjzBxURkUbHYDAQY/EjxuLHnztEu/Y7w6qUrCJSsorYc7iI5EzH172HiyivtLEro5BdVSVSjhbgY3IVaU+sGlnVJtLxOsTfpyEfT6RZUSjl4ZKrRkolaaSUiIg0oF69elX7vrCwkCeffJLPP/+ctLQ0KisrKSkpYd++fSe9zrnnnut6HRAQQHBwMBkZGSc83t/f3xVIAcTGxrqOz8vL49ChQ/Tp08f1vslkomfPnthsttN6PhERaXqODqv6JYVXe89qs3Mwt8Q1oirlqO1ATjFF5VbXFMFjhfh7O0ZWhQdUmxaYEBFAoK/+SS1yNvR/kAcrKbeSmlsCaPqeiEhjYfY28fvTg9x277py7Cp6Dz30EMuXL+ell16ibdu2mM1mrr/+esrLy096HW9v72rfGwyGkwZINR1vt9tPs/UiIiLVmYwG4sP8iQ/z56Jzqi/MUV5pY1928ZGw6nARKVUjrNLySsktrmDjvlw27ss97rqRQb60cY6uOiqwahXmr9UBRWpBoZQHS85yDCsN9fcmLEBDRkVEGgODwVBnU+g8ycqVK7n99ttd0+YKCwvZs2dPg7bBYrEQHR3N+vXrufDCCwGwWq1s2LCB7t27N2hbPNXrr7/Oiy++SHp6Ot26deO1116rNrLsWIsWLWLy5Mns2bOHdu3aMW3aNK644grAMZVy0qRJfPHFFyQnJ2OxWBg4cCD//Oc/iYuLa6hHEhGpdz5eRtpGBdI26viBAMXllew9XFxtZJUzvDpcVE5mQRmZBWWsS8mudp7BAHEWM4mRVVMBjwqsWoaa8dIKgSKAQimPtltT90RExEO0a9eOjz/+mKuvvhqDwcDkyZPdMmVu7NixTJ06lbZt29KhQwdee+01cnJyVJwWWLhwIePHj2f27Nn07duXGTNmMGjQIHbs2EFUVNRxx69atYrhw4czdepUrrrqKubPn8+QIUPYsGEDXbp0obi4mA0bNjB58mS6detGTk4ODz74INdccw0///yzG55QRKTh+ft40TE2mI6xwce9l1dSwZ5jalelZDlGWRWUVZKaW0Jqbgk/7sqqdp6X0UCrMH/XyKqEo0ZaxQb7YdQKgdKMKJTyYMlVRc4TVeRcRETcbPr06dxxxx3079+fiIgIHn30UfLzj6+7Ud8effRR0tPTue222zCZTNxzzz0MGjQIk0lTJKZPn87dd9/NqFGjAJg9ezaff/45b731FhMmTDju+FdeeYXLL7+chx9+GHAUr1++fDkzZ85k9uzZWCwWli9fXu2cmTNn0qdPH/bt23fSVRdFRJoDi9mbbvEhdIsPqbbfbrdzuKi8xtFVew4XUVphIzmriOSsouOu6etlpFWYP7EhZuIsfsRazMRa/IgN8XN8tZgJUB0raUIMdhVqOCP5+flYLBby8vIIDj4+Na8LY9/fyP82H2Ti4A787aKkU58gIiINqrS0lJSUFNq0aYOfn5+7m9Ms2Ww2OnbsyI033sgzzzxTZ9c92WfbEH2A01VeXo6/vz8ffvghQ4YMce0fOXIkubm5fPrpp8ed06pVK8aPH8+4ceNc+5544gk++eQTNm/eXON9vv76ay677DJyc3Nr9eye+LMSEXEnm81Oen4pe6pCqaPrWO07XEyl7dT/PA/y8yLOYibG4kdciB8xwWZiQ/yq7WuKpQSkcaltH0D/pXqw3VVLlWr6noiIiMPevXv56quvuOiiiygrK2PmzJmkpKRw8803u7tpbpWVlYXVaiU6Orra/ujoaLZv317jOenp6TUen56eXuPxpaWlPProowwfPvyEncuysjLKyspc37tjNJ2IiCczGg3EhZiJCzHTv21EtfcqrTZSc0vYl11MWm4paXmlpOWVVPtaUFpJQWklO0oL2HGo4IT3CfbzIi7EEVK5Rls5X1eNulJwJZ5A/xV6KJvNTkrVcE5N3xMREXEwGo3MnTuXhx56CLvdTpcuXfj666/p2LGju5vWpFVUVHDjjTdit9uZNWvWCY+bOnUqTz31VAO2TESk6fAyGWkdHkDr8BP/+6+wrJL0vBIO5h4VWOWWkpZfSlpuCel5pRSUVZJfWkl+egHb008cXFnM3kfCqhAzscFVX48KsMw+mh4v9UuhlIdKyy+lpMKKt8mxdKmIiIhAfHw8K1eudHczPE5ERAQmk4lDhw5V23/o0CFiYmJqPCcmJqZWxzsDqb179/LNN9+cdAj+xIkTGT9+vOv7/Px84uPjT/dxRETkBAJ9vWgbFUTbqKATHlNQWkF6XikH8xxBVfURV459ReVW8koqyCupOGlwFeLvTUywn2vUVfU6V46vft4KruTMKZTyUM6pe63DA/DWcqEiIiJyEj4+PvTs2ZMVK1a4akrZbDZWrFjBmDFjajynX79+rFixolpNqeXLl9OvXz/X985AateuXXz77beEh4eftB2+vr74+vqe9fOIiMiZC/LzJsjPm3bRJw6u8p3BVdXoKmeAlZ7v2JeWV0pxuZXc4gpyi08eXIX6exNjcRRmd9S0MhMT7OeqcxUd7KcRV3JCCqU8lGvlvQhN3RMREZFTGz9+PCNHjqRXr1706dOHGTNmUFRU5FqN77bbbqNFixZMnToVgAcffJCLLrqIl19+mSuvvJIFCxbw888/88YbbwCOQOr6669nw4YNfPbZZ1itVle9qbCwMHx8fNzzoCIictaC/bwJ9vPmnBMEV3a7nfzSyqrAyhFcHRl15diXluuY3ZNTXEFOcQXb0k5cRzDAx0RkkC8RgVVbkA+RgX5EBPm49kVVva8Aq3lRKOWhdmc66kklRanIuYiIiJzasGHDyMzMZMqUKaSnp9O9e3eWLl3qKma+b98+jMYjo6/79+/P/PnzmTRpEo899hjt2rXjk08+oUuXLgCkpqayZMkSALp3717tXt9++y0XX3xxgzyXiIg0PIPBgMXsjcXsTfuYkwRXJZWk5ZecsDC7M7gqKrdSdLiYPYeLT3nvAB8TEUG+RB4VYEUE+lYLtSKr9qtYe+OnT9BD7c7UynsiIiJyesaMGXPC6XrffffdcftuuOEGbrjhhhqPT0hIwG4/9dLkIiLSPBkMBiz+3lj8vekQU3O9QbvdTkFZJVkFZWQVlpNVWEZmQRlZhWVVr8vJLCyrer+MskqbK8DaexoB1tFBlSu4ClKA1RjoU/FQyZlaeU9EREREREQaL4PB4JoqmBh58mPtdjuFZZVVoVX5UcHVkQDr6O/PJsCKCPSpPp2wKsRSgNXw9JP2QIVllaTnlwKQFKGRUiIiIiIiItK0GQwGV5H22gZYWYXl1UZeZRWUkXlUgOUMsU43wPKvVgOr+vTBUH8fgvy8CDZ7E+Tn5Xjt561VCM+QQikP5CxyHhHog8Xf282tEREROd7FF19M9+7dmTFjBuCY6jVu3LhqK7kdy2AwsHjxYtfqcGeqrq4jIiIijdPRAVabUywOdnSAVW36YEEZmceGWoVllFbYKC63sreWAZaTj8lIsNmrql2OoOror0F+3q73g/2OHGcxH3nfZDSc7Y+m0VEo5YGOTN3TKCkREal7V199NRUVFSxduvS493788UcuvPBCNm/ezLnnnlvra65fv56AgLqdcv7kk0/yySefsGnTpmr709LSCA0NrdN7iYiISNN0ugFWUbm1WnDlDLIyq0KtvOIK8ksrKCitJL+0gsKySux2KLfaqoKv8jNua4CP6agRWEfCqxrDLvNR71ft8/cxYTA0rmBLoZQHUpFzERGpT3feeSdDhw7lwIEDtGzZstp7c+bMoVevXqcVSAFERp5inH0diomJabB7iYiISPNhMBgI9PUi0NfrlAGWk81mp7C80hFSlVQc+VpWUX1faQX5pUcf5/y+gtIKG4BjimG5lbS8M2u/yWioNqXwyNfqQdbRo7eig31pF13zCosNQaGUBzoSSqnIuYiI1L2rrrqKyMhI5s6dy6RJk1z7CwsLWbRoERMmTGD48OH88MMP5OTkkJSUxGOPPcbw4cNPeM1jp+/t2rWLO++8k3Xr1pGYmMgrr7xy3DmPPvooixcv5sCBA8TExDBixAimTJmCt7c3c+fO5amnngJw/cZvzpw53H777cdN39uyZQsPPvggq1evxt/fn6FDhzJ9+nQCAx2/3Ln99tvJzc3lggsu4OWXX6a8vJybbrqJGTNm4O2tafIiIiJy5ozGI8XcW4SYz+ga5ZU2Co4afVVzmFVBfknl8cdVfbXa7FhtdnKLK8gtrgBKanXvfonhvH/P+WfU7rqgUMoDhQf40jLUTFKURkqJiDQ6djtU1L7+QJ3y9odaDNn28vLitttuY+7cuTz++OOu0GfRokVYrVZuueUWFi1axKOPPkpwcDCff/45t956K0lJSfTp0+eU17fZbPz1r38lOjqatWvXkpeXV2OtqaCgIObOnUtcXBxbtmzh7rvvJigoiEceeYRhw4axdetWli5dytdffw2AxWI57hpFRUUMGjSIfv36sX79ejIyMrjrrrsYM2YMc+fOdR337bffEhsby7fffssff/zBsGHD6N69O3ffffcpn0dERESkPvl4GQkP9CU80PeMzrfb7RSXWylwhldVQZYz2DoSXh0fbLUO96/jpzk9CqU80DNDuri7CSIicqYqiuH5OPfc+7GD4FO7UbZ33HEHL774It9//z0XX3wx4BiJNHToUFq3bs1DDz3kOnbs2LEsW7aMDz74oFah1Ndff8327dtZtmwZcXGOn8Xzzz/P4MGDqx139CithIQEHnroIRYsWMAjjzyC2WwmMDAQLy+vk07Xmz9/PqWlpbzzzjuumlYzZ87k6quvZtq0aURHRwMQGhrKzJkzMZlMdOjQgSuvvJIVK1YolBIREZFGz2AwEODrRYCvFzEWP3c357QY3d0AERERaXgdOnSgf//+vPXWWwD88ccf/Pjjj9x5551YrVaeeeYZunbtSlhYGIGBgSxbtox9+/bV6trbtm0jPj7eFUgB9OvX77jjFi5cyIABA4iJiSEwMJBJkybV+h5H36tbt27ViqwPGDAAm83Gjh07XPs6d+6MyXRkqebY2FgyMjJO614iIiIiUrc0UkpERKQuefs7Riy5696n4c4772Ts2LG8/vrrzJkzh6SkJC666CKmTZvGK6+8wowZM+jatSsBAQGMGzeO8vIzX03mWKtXr2bEiBE89dRTDBo0CIvFwoIFC3j55Zfr7B5HO7Z2lMFgwGaz1cu9RERERKR2FEqJiIjUJYOh1lPo3O3GG2/kwQcfZP78+bzzzjvcd999GAwGVq5cybXXXsstt9wCOGpE7dy5k06dOtXquh07dmT//v2kpaURGxsLwJo1a6ods2rVKlq3bs3jjz/u2rd3795qx/j4+GC1Wk95r7lz51JUVOQaLbVy5UqMRiPt27evVXtFRERExD00fU9ERKSZCgwMZNiwYUycOJG0tDRuv/12ANq1a8fy5ctZtWoV27Zt429/+xuHDh2q9XUHDhzIOeecw8iRI9m8eTM//vhjtfDJeY99+/axYMECdu/ezauvvsrixYurHZOQkEBKSgqbNm0iKyuLsrKy4+41YsQI/Pz8GDlyJFu3buXbb79l7Nix3Hrrra56UiIiIiLimRRKiYiINGN33nknOTk5DBo0yFUDatKkSZx33nkMGjSIiy++mJiYGIYMGVLraxqNRhYvXkxJSQl9+vThrrvu4rnnnqt2zDXXXMPf//53xowZQ/fu3Vm1ahWTJ0+udszQoUO5/PLLueSSS4iMjOT9998/7l7+/v4sW7aM7OxsevfuzfXXX8+ll17KzJkzT/+HISIiIiINymC32+3ubkRjlJ+fj8ViIS8vj+DgYHc3R0RE3KC0tJSUlBTatGmDn1/jWulETu5kn636ALWnn5WIiEjzVNs+gEZKiYiIiIiIiIhIg1MoJSIiIiIiIiIiDU6hlIiIiIiIiIiINDiFUiIiIiIiIiIi0uAUSomIiIiIiIiISINTKCUiInKWtJBt06PPVERERKT+KZQSERE5Q97e3gAUFxe7uSVS15yfqfMzFhEREZG65+XuBoiIiDRWJpOJkJAQMjIyAPD398dgMLi5VXI27HY7xcXFZGRkEBISgslkcneTRERERJoshVIiIiJnISYmBsAVTEnTEBIS4vpsRURERKR+KJQSERE5CwaDgdjYWKKioqioqHB3c6QOeHt7a4SUiIiISANQKCUiIlIHTCaTggwRERERkdOgQuciIiIiIiIiItLgFEqJiIiIiIiIiEiDUyglIiIiIiIiIiINTjWlzpDdbgcgPz/fzS0RERGRhuT8u9/ZF5ATU39JRESkeaptf0mh1BkqKCgAID4+3s0tEREREXcoKCjAYrG4uxkeTf0lERGR5u1U/SWDXb/mOyM2m42DBw8SFBSEwWCo02vn5+cTHx/P/v37CQ4OrtNry9nT5+PZ9Pl4Ln02nk2fT+3Z7XYKCgqIi4vDaFQlhJNRf6n50ufj2fT5eC59Np5Nn0/t1ba/pJFSZ8hoNNKyZct6vUdwcLD+Q/dg+nw8mz4fz6XPxrPp86kdjZCqHfWXRJ+PZ9Pn47n02Xg2fT61U5v+kn69JyIiIiIiIiIiDU6hlIiIiIiIiIiINDiFUh7I19eXJ554Al9fX3c3RWqgz8ez6fPxXPpsPJs+H2ls9N+sZ9Pn49n0+XgufTaeTZ9P3VOhcxERERERERERaXAaKSUiIiIiIiIiIg1OoZSIiIiIiIiIiDQ4hVIiIiIiIiIiItLgFEp5oNdff52EhAT8/Pzo27cv69atc3eTBJg6dSq9e/cmKCiIqKgohgwZwo4dO9zdLKnBP//5TwwGA+PGjXN3U6RKamoqt9xyC+Hh4ZjNZrp27crPP//s7mY1e1arlcmTJ9OmTRvMZjNJSUk888wzqNykNAbqL3km9ZcaD/WXPI/6S55J/aX6pVDKwyxcuJDx48fzxBNPsGHDBrp168agQYPIyMhwd9Oave+//57Ro0ezZs0ali9fTkVFBZdddhlFRUXubpocZf369fznP//h3HPPdXdTpEpOTg4DBgzA29ubL7/8kt9//52XX36Z0NBQdzet2Zs2bRqzZs1i5syZbNu2jWnTpvHCCy/w2muvubtpIiel/pLnUn+pcVB/yfOov+S51F+qX1p9z8P07duX3r17M3PmTABsNhvx8fGMHTuWCRMmuLl1crTMzEyioqL4/vvvufDCC93dHAEKCws577zz+Pe//82zzz5L9+7dmTFjhrub1exNmDCBlStX8uOPP7q7KXKMq666iujoaP773/+69g0dOhSz2cy8efPc2DKRk1N/qfFQf8nzqL/kmdRf8lzqL9UvjZTyIOXl5fzyyy8MHDjQtc9oNDJw4EBWr17txpZJTfLy8gAICwtzc0vEafTo0Vx55ZXV/h8S91uyZAm9evXihhtuICoqih49evDmm2+6u1kC9O/fnxUrVrBz504ANm/ezE8//cTgwYPd3DKRE1N/qXFRf8nzqL/kmdRf8lzqL9UvL3c3QI7IysrCarUSHR1dbX90dDTbt293U6ukJjabjXHjxjFgwAC6dOni7uYIsGDBAjZs2MD69evd3RQ5RnJyMrNmzWL8+PE89thjrF+/ngceeAAfHx9Gjhzp7uY1axMmTCA/P58OHTpgMpmwWq0899xzjBgxwt1NEzkh9ZcaD/WXPI/6S55L/SXPpf5S/VIoJXIGRo8ezdatW/npp5/c3RQB9u/fz4MPPsjy5cvx8/Nzd3PkGDabjV69evH8888D0KNHD7Zu3crs2bPVyXKzDz74gPfee4/58+fTuXNnNm3axLhx44iLi9NnIyJnTf0lz6L+kmdTf8lzqb9UvxRKeZCIiAhMJhOHDh2qtv/QoUPExMS4qVVyrDFjxvDZZ5/xww8/0LJlS3c3R4BffvmFjIwMzjvvPNc+q9XKDz/8wMyZMykrK8NkMrmxhc1bbGwsnTp1qravY8eOfPTRR25qkTg9/PDDTJgwgZtuugmArl27snfvXqZOnapOlngs9ZcaB/WXPI/6S55N/SXPpf5S/VJNKQ/i4+NDz549WbFihWufzWZjxYoV9OvXz40tEwC73c6YMWNYvHgx33zzDW3atHF3k6TKpZdeypYtW9i0aZNr69WrFyNGjGDTpk3qYLnZgAEDjlsOfOfOnbRu3dpNLRKn4uJijMbqXQGTyYTNZnNTi0ROTf0lz6b+kudSf8mzqb/kudRfql8aKeVhxo8fz8iRI+nVqxd9+vRhxowZFBUVMWrUKHc3rdkbPXo08+fP59NPPyUoKIj09HQALBYLZrPZza1r3oKCgo6rVREQEEB4eLhqWHiAv//97/Tv35/nn3+eG2+8kXXr1vHGG2/wxhtvuLtpzd7VV1/Nc889R6tWrejcuTMbN25k+vTp3HHHHe5umshJqb/kudRf8lzqL3k29Zc8l/pL9ctgt9vt7m6EVDdz5kxefPFF0tPT6d69O6+++ip9+/Z1d7OaPYPBUOP+OXPmcPvttzdsY+SULr74Yi1x7EE+++wzJk6cyK5du2jTpg3jx4/n7rvvdnezmr2CggImT57M4sWLycjIIC4ujuHDhzNlyhR8fHzc3TyRk1J/yTOpv9S4qL/kWdRf8kzqL9UvhVIiIiIiIiIiItLgVFNKREREREREREQanEIpERERERERERFpcAqlRERERERERESkwSmUEhERERERERGRBqdQSkREREREREREGpxCKRERERERERERaXAKpUREREREREREpMEplBIRERERERERkQanUEpEpB4ZDAY++eQTdzdDRERExGOpvyTSfCmUEpEm6/bbb8dgMBy3XX755e5umoiIiIhHUH9JRNzJy90NEBGpT5dffjlz5sypts/X19dNrRERERHxPOoviYi7aKSUiDRpvr6+xMTEVNtCQ0MBx1DxWbNmMXjwYMxmM4mJiXz44YfVzt+yZQt//vOfMZvNhIeHc88991BYWFjtmLfeeovOnTvj6+tLbGwsY8aMqfZ+VlYW1113Hf7+/rRr144lS5a43svJyWHEiBFERkZiNptp167dcZ1CERERkfqk/pKIuItCKRFp1iZPnszQoUPZvHkzI0aM4KabbmLbtm0AFBUVMWjQIEJDQ1m/fj2LFi3i66+/rtaJmjVrFqNHj+aee+5hy5YtLFmyhLZt21a7x1NPPcWNN97Ir7/+yhVXXMGIESPIzs523f/333/nyy+/ZNu2bcyaNYuIiIiG+wGIiIiInIL6SyJSb+wiIk3UyJEj7SaTyR4QEFBte+655+x2u90O2O+9995q5/Tt29d+33332e12u/2NN96wh4aG2gsLC13vf/7553aj0WhPT0+32+12e1xcnP3xxx8/YRsA+6RJk1zfFxYW2gH7l19+abfb7farr77aPmrUqLp5YBEREZHTpP6SiLiTakqJSJN2ySWXMGvWrGr7wsLCXK/79etX7b1+/fqxadMmALZt20a3bt0ICAhwvT9gwABsNhs7duzAYDBw8OBBLr300pO24dxzz3W9DggIIDg4mIyMDADuu+8+hg4dyoYNG7jssssYMmQI/fv3P6NnFRERETkT6i+JiLsolBKRJi0gIOC44eF1xWw21+o4b2/vat8bDAZsNhsAgwcPZu/evXzxxRcsX76cSy+9lNGjR/PSSy/VeXtFREREaqL+koi4i2pKiUiztmbNmuO+79ixIwAdO3Zk8+bNFBUVud5fuXIlRqOR9u3bExQUREJCAitWrDirNkRGRjJy5EjmzZvHjBkzeOONN87qeiIiIiJ1Sf0lEakvGiklIk1aWVkZ6enp1fZ5eXm5imMuWrSIXr16ccEFF/Dee++xbt06/vvf/wIwYsQInnjiCUaOHMmTTz5JZmYmY8eO5dZbbyU6OhqAJ598knvvvZeoqCgGDx5MQUEBK1euZOzYsbVq35QpU+jZsyedO3emrKyMzz77zNXJExEREWkI6i+JiLsolBKRJm3p0qXExsZW29e+fXu2b98OOFZ6WbBgAffffz+xsbG8//77dOrUCQB/f3+WLVvGgw8+SO/evfH392fo0KFMnz7dda2RI0dSWlrKv/71Lx566CEiIiK4/vrra90+Hx8fJk6cyJ49ezCbzfzpT39iwYIFdfDkIiIiIrWj/pKIuIvBbrfb3d0IERF3MBgMLF68mCFDhri7KSIiIiIeSf0lEalPqiklIiIiIiIiIiINTqGUiIiIiIiIiIg0OE3fExERERERERGRBqeRUiIiIiIiIiIi0uAUSomIiIiIiIiISINTKCUiIiIiIiIiIg1OoZSIiIiIiIiIiDQ4hVIiIiIiIiIiItLgFEqJiIiIiIiIiEiDUyglIiIiIiIiIiINTqGUiIiIiIiIiIg0OIVSIiIiIiIiIiLS4P4f/+4qo3mSNwcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test = y_test_combined\n",
        "\n",
        "#Matrix Confusion\n",
        "\n",
        "y_pred = model.predict(X_test_combined)\n",
        "y_pred_classes = (y_pred > 0.5).astype(int)\n",
        "\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred_classes)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[\"Benign\", \"Malign\"])\n",
        "disp.plot(cmap='Blues')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "HUj0QGgmign7",
        "outputId": "645d8e86-9589-4364-bcce-a459854a72e6"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m7600/7600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 836us/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x78076b997810>"
            ]
          },
          "metadata": {},
          "execution_count": 120
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "\n",
        "# Calculate the ROC curve\n",
        "fpr, tpr, thresholds = roc_curve(y_test_combined, y_pred)\n",
        "\n",
        "# Calculate the AUC\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# Plot the ROC curve\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC Curve (AUC = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')  # Diagonal line (random chance)\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('FPR (False Positive Rate)')\n",
        "plt.ylabel('TPR (True Positive Rate)')\n",
        "plt.title('ROC Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "_9Jn5mqcilQL",
        "outputId": "1d2435d4-9c83-42d1-df13-f6f00777b772"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "gpuType": "V28",
      "provenance": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}