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# メール認証技術（SPF・DKIM・DMARC）学習メモ

---

## 1. SPF（Sender Policy Framework）

### 概要

- 「このドメインからのメールは、これらのIPアドレスから送信される」とDNSで宣言する仕組み
- 受信サーバーが送信元IPをSPFレコードと照合し、正当性を検証する

### なぜ必要か

- SMTPプロトコルは送信元アドレスを検証する仕組みを持たない
- 任意のサーバーから任意の送信元アドレスでメール送信が可能
- SPFにより、ドメイン所有者が「正当な送信元IP」を定義できる

### 設定場所

- ドメインのDNS TXTレコード

### レコード例

```
example.com. IN TXT "v=spf1 ip4:203.0.113.1 include:_spf.google.com -all"
```

| 部分 | 意味 |
|------|------|
| `v=spf1` | SPFバージョン1 |
| `ip4:203.0.113.1` | このIPからの送信を許可 |
| `include:

.hoge{
  /*ここの詳細度は1*/
}
.hoge.hoge{
  /*ここの詳細度は2*/
}

#!/bin/sh
dconf reset -f /org/gnome/shell/extensions/dash-to-panel/

services:
  postgres:
    image: postgres:16-alpine
    container_name: n8n-postgress
    restart: always
    environment:
      - POSTGRES_USER=${POSTGRES_USER}
      - POSTGRES_PASSWORD=${POSTGRES_PASSWORD}
      - POSTGRES_DB=${POSTGRES_DB}
    volumes:
      - ./postgres_data:/var/lib/postgresql/data

  n8n:
    image: n8nio/n8n:latest
    container_name: n8n
    restart: always
    environment:
      - DB_TYPE=postgresdb
      - DB_POSTGRESDB_DATABASE=${POSTGRES_DB}
      - DB_POSTGRESDB_HO

from sqlalchemy import create_engine, Column, Integer, String, DateTime
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

# SQLite Database (local file - no configuration needed)
DATABASE_URL = "sqlite:///./complaints.db"

engine = create_engine(DATABASE_URL, connect_args={"check_same_thread": False})
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
Base = declarative_base()

class Complaint(Base):
    __tablen

/**
 * @param {number} n
 * @return {number}
 */
var numOfWays = function(n) {
    const MOD = 1_000_000_007;

    // a = number of ABC patterns for the current row
    // b = number of ABA patterns for the current row
    let a = 6;  // ABC has 6 permutations
    let b = 6;  // ABA also 6 valid patterns

    // Build row by row using the recurrence
    for (let i = 2; i <= n; i++) {
        // Compute next row counts based on transitions
        let nextA = (2 * a + 2 * b) % MOD;  // ABC -> ABC

echo $SHELL

# Filter only >90Days and >180Days
filtered_df = open_aging_df[open_aging_df['Age_Bucket'].isin(['>90Days', '>180Days'])]

# Pivot table with complaint type + age bucket
pi_data_df = pd.pivot_table(
    filtered_df,
    values='CLOSED/OPEN',        
    index=['COMPLAINT TYPE'],       
    columns=['Age_Bucket','DEPT'], 
    aggfunc='count',
    fill_value=0
)

# ➡️ Add Grand Total column (row-wise sum)
pi_data_df['Grand_Total'] = pi_data_df.sum(axis=1)

# ➡️ Add Grand Total row 

/**
 * @param {number[]} nums
 * @return {number}
 */
var repeatedNTimes = function(nums) {
    // We'll use a Set to track which numbers we've seen.
    const seen = new Set();
    
    // Walk through each number in the array
    for (let num of nums) {
        // If we've seen this number before, it must be the one
        // that is repeated n times (because only one element repeats).
        if (seen.has(num)) {
            return num;
        }
        
        // Otherwise, record it as s

sudo dnf remove nginx

dnf install nginx

dnf upgrade or dnf update

### Install OpenAI SDK across Multiple Languages

Source: https://platform.openai.com/docs/quickstart_api-mode=chat

This section provides instructions for installing the official OpenAI SDK or client libraries for various programming languages. These installations are prerequisites for making API calls to the OpenAI service.

```JavaScript
npm install openai
```

```Python
pip install openai
```

```C#
dotnet add package OpenAI
```

```Java
<dependency>
  <groupId>com.openai</groupId>
  <artifa

# Notes

import sys
import pandas as pd
import numpy as np
from flask import Flask, jsonify, request
from src.logging.logger import get_logger
from src.exceptions.exception import CustomException

logger = get_logger(__name__)

# flask_app.py

# -----------------------------------------------------------------------------
# Try to import custom modules
# -----------------------------------------------------------------------------
try:
    from src.constants.paths import dataset_path