Satellite-to-LTE System Architecture

Complete Data Flow

Satellite (orbit) → UHF/S-band (~10 min contact) → Ground Station (dish + SDR) → Fiber/Internet → Processing Server (AWS) → REST API → Mobile/Web App (LTE)

  ① Satellite collects data in orbit
  ② Ground station receives signal during pass
  ③ Processing server decodes & runs ML
  ④ API delivers to users via LTE

CubeSat Specifications

// satellite_app.py (runs on CubeSat)
import camera, radio, gps
while True:
    location = gps.get_location()
    if is_target_area(location):
        image = camera.capture()
        storage.write(f"image_{now()}.jpg")
    if ground_station_in_range():
        for file in storage.list():
            radio.transmit(file)
            storage.delete(file)

Ground Station Setup

# Ground Station Stack
- OS: Linux (Ubuntu 20.04 LTS)
- SDR: GNU Radio or CubeSatRadio
- Tracking: Gpredict (orbital prediction)
- Decoding: gr-satellites
- Storage: PostgreSQL + timeseries
- Upload: AWS S3 API (boto3)
- Dashboard: Grafana (monitor contacts)

Data Processing Pipeline

# AWS Lambda: Process satellite data on arrival
import boto3, rasterio, numpy as np

def lambda_handler(event, context):
    # 1. Receive raw image from ground station
    bucket = event['bucket']; key = event['key']
    
    # 2. Download & decompress
    response = s3.get_object(Bucket=bucket, Key=key)
    image_data = response['Body'].read()
    
    # 3. Georeference (map pixels to lat/lon)
    with rasterio.open(image_data) as src:
        data = src.read(); bounds = src.bounds
    
    # 4. Run ML: detect deforestation
    deforestation = detect_forest_loss(data)
    
    # 5. Generate products (GeoTIFF, PNG tiles, GeoJSON)
    # 6. Update searchable database
    db.put_item(TableName='SatelliteProducts', Item={
        'timestamp': now(), 'product_type': 'deforestation',
        'location': str(bounds), 'alert_level': alert
    })
    
    return {'statusCode': 200, 'body': 'Data processed'}

REST API & LTE Integration

# FastAPI: Serve satellite data over HTTP/LTE
from fastapi import FastAPI, Query

app = FastAPI()

@app.get("/api/v1/satellite/imagery")
async def get_imagery(lat: float, lon: float, product: str = "deforestation"):
    """
    curl "https://api.example.com/api/v1/imagery?lat=40.7&lon=-74.0"
    Returns GeoJSON + imagery tiles
    """
    products = db.query_spatial(bbox=(lat-0.1, lon-0.1, lat+0.1, lon+0.1))
    return {"type": "FeatureCollection", "features": [...]}

# WebSocket: Real-time alerts to mobile app
@app.websocket("/ws/alerts")
async def websocket_alerts(websocket):
    await websocket.accept()
    async for msg in db.stream_alerts(product="deforestation"):
        await websocket.send_json({
            "type": "alert", "severity": msg.alert_level,
            "location": msg.location, "area_km2": msg.area
        })

Typical Contact Window (10 minutes)

Complete Cost Breakdown (Year 1)