🛰️ CosmoPulse - Multi-Planet Space Environment Tracking MVP

A unified platform that visualizes and tracks space objects, debris, and weather across multiple planets using NASA's open datasets.

🌟 Features

Input Layer

• ✅ TLE (Two-Line Element) orbit data processing
• ✅ Radar tracking data (range, velocity, cross-section)
• ✅ Optical telescope observations
• ✅ NASA Space Weather integration
• ✅ Multi-sensor data fusion

Processing Layer

• ✅ SGP4 orbit propagation engine
• ✅ Real-time position calculation
• ✅ Track association across time
• ✅ Object classification (satellites vs debris)
• ✅ Collision risk assessment
• ✅ Threat scoring algorithm

Output Layer

• ✅ 3D orbital visualization (Earth & Mars)
• ✅ Ground track mapping
• ✅ Real-time alerts dashboard
• ✅ Risk heatmaps
• ✅ Mission impact reports
• ✅ Space weather monitoring
• ✅ Data catalog browser

🚀 Quick Start

Prerequisites

• Python 3.8+
• pip

Installation

1. Clone or navigate to the project directory

cd /Users/Ayan/Desktop/Hackathon

2. Install dependencies

pip install -r requirements.txt

3. Run the application

streamlit run streamlit_app.py

The dashboard will open automatically in your browser at http://localhost:8501

📁 Project Structure

Hackathon/
├── streamlit_app.py          # Main Streamlit dashboard
├── data_processor.py          # Data loading and processing
├── orbit_engine.py            # SGP4 orbit calculations
├── risk_assessment.py         # Collision risk algorithms
├── visualization.py           # Plotly 3D visualizations
├── requirements.txt           # Python dependencies
├── app.py                     # NASA data fetcher
├── mockdata.py               # Mock data generator
└── data/                     # Data directory
    ├── mock_tle.csv          # Satellite orbital elements
    ├── mock_radar.csv        # Radar tracking data
    ├── mock_optical.csv      # Telescope observations
    ├── mock_space_weather.csv # Space weather data
    └── ... (other data files)

🎯 Key Modules

1. Data Processor (data_processor.py)

• Loads TLE, radar, optical, and space weather data
• Parses orbital elements
• Merges multi-source data
• Classifies space objects

2. Orbit Engine (orbit_engine.py)

• SGP4 satellite propagation
• Trajectory generation
• Ground track calculation
• Keplerian element computation
• Mars orbit simulation

3. Risk Assessment (risk_assessment.py)

• Collision probability calculation
• Conjunction event detection
• Space weather impact analysis
• Threat level classification
• Mission impact scoring

4. Visualization (visualization.py)

• 3D orbital plots (Plotly)
• Ground track mapping
• Altitude profiles
• Risk heatmaps
• Space weather charts

📊 Dashboard Pages

🏠 Home

• System overview with key metrics
• Active alert summary
• Quick statistics
• Real-time status indicators

🌍 3D Visualization

• Interactive 3D orbital plots
• Multi-satellite trajectory view
• Ground track mapping
• Altitude profiles
• Risk distribution heatmap

⚠️ Alerts & Risks

• Real-time conjunction warnings
• Risk assessment dashboard
• Mission impact summary
• Detailed alert log
• Conjunction timeline

📊 Data Catalog

• Complete satellite catalog
• Radar tracking data browser
• Optical observation gallery
• Raw TLE data viewer
• CSV export functionality

🌤️ Space Weather

• Current conditions monitor
• Solar flare tracking
• Geomagnetic activity (Kp index)
• Solar wind speed trends
• Historical data analysis

📈 Mission Impact

• Executive summary report
• Risk breakdown analysis
• Top risk objects list
• Alert distribution charts
• Downloadable reports

🔬 Technical Details

Orbit Propagation

• Uses SGP4 (Simplified General Perturbations) model
• Accurate for LEO, MEO, and GEO satellites
• Handles orbital perturbations
• Real-time position prediction

Risk Calculation

Collision risk score based on:

• Proximity Score (60%): Distance between objects
• Velocity Score (30%): Relative velocity
• Size Score (10%): Radar cross-section

Threat Levels:

• CRITICAL: < 2 km separation
• HIGH: 2-5 km separation
• MEDIUM: 5-10 km separation
• LOW: > 10 km separation

Space Weather Impact

Factors considered:

• Solar flare class (A, B, C, M, X)
• Kp geomagnetic index (0-9)
• Solar wind speed
• Atmospheric density (drag effect)

📈 Data Sources

• Mock TLE Data: Simulated satellite orbital elements
• NASA Space Weather: Real-time solar activity
• Radar Tracking: Simulated range/velocity measurements
• Optical Observations: NASA image database
• Space-Track.org: Real TLE data (via app.py)

🎨 Customization

Adjust Risk Thresholds

Edit risk_assessment.py:

self.collision_threshold = 5.0  # km
self.critical_threshold = 2.0   # km

Change Visualization Settings

Edit visualization.py:

self.earth_radius = 6371.0  # km
resolution = 30  # sphere detail

Modify Dashboard Layout

Edit streamlit_app.py to add/remove pages or customize UI.

🐛 Troubleshooting

Import Errors

pip install --upgrade -r requirements.txt

Port Already in Use

streamlit run streamlit_app.py --server.port 8502

Data Loading Issues

Ensure all CSV files exist in the data/ directory. Run mockdata.py to regenerate:

cd data
python ../mockdata.py

🚀 Future Enhancements

• Real-time TLE updates via Space-Track API
• Machine learning for debris classification
• Multi-planet conjunction analysis
• Historical trajectory playback
• Mobile-responsive design
• WebGL-based 3D rendering for performance
• Automated report scheduling
• Email/SMS alert notifications

📝 License

MIT License - Feel free to use for hackathons, research, or commercial projects.

👥 Contributors

Built for NASA Space Apps Challenge 2024

🙏 Acknowledgments

• NASA Open Data Portal
• Space-Track.org
• SGP4 Library
• Plotly Visualization Library
• Streamlit Framework

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⭐ Star this repo if you find it useful!

For questions or issues, please open a GitHub issue or contact the development team.