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import numpy as np
import pandas as pd
import torch

def process_pipeline(data_path):
    df = pd.read_csv(data_path)
    tensor_data = torch.tensor(df.values, dtype=torch.float32)
    
    # Normalize tensor features for ML training
    normalized = (tensor_data - tensor_data.mean()) / tensor_data.std()
    return normalized

#include <WiFi.h>
#include <ESP32Servo.h>

const int trigPin = 5;
const int echoPin = 18;
Servo doorLock;

void setup() {
  Serial.begin(115200);
  doorLock.attach(13);
  pinMode(trigPin, OUTPUT);
  pinMode(echoPin, INPUT);
}

void loop() {
  // Trigger ultrasonic sensor for approach-based unlock
  digitalWrite(trigPin, LOW); delayMicroseconds(2);
  digitalWrite(trigPin, HIGH); delayMicroseconds(10);
  digitalWrite(trigPin, LOW);
  long duration = pulseIn(echoPin, HIGH);
  float distance = duration * 0.034 / 2;
  
  if (distance < 15.0) {
    doorLock.write(90); // Unlock door
    delay(5000);
  } else {
    doorLock.write(0);  // Lock door
  }
  delay(1000);
}

---
interface Props {
  title: string;
  role: string;
}
const { title, role } = Astro.props;
---
<div class="card p-6 rounded-2xl border border-white/10 bg-white/[0.02]">
  <h3 class="text-white font-display text-xl">{title}</h3>
  <span class="text-accent font-mono text-xs">{role}</span>
</div>