CubeSat Atmospheric Intelligence Prototype

Technical Project Report

• Date: May 2026
• Project Leads: Ali Ahmed Aga, Kritin Gupta, Amaan Syed
• Status: Prototype Phase V1

1. Introduction

The CubeSat Atmospheric Intelligence project describes the design and development of a 1U miniature satellite prototype (10x10x10 cm) optimized for data collection in Low-Earth Orbit (LEO). Unlike conventional weather satellites that function at geostationary altitudes (35,786 km), this prototype is designed to function in the Thermosphere and provide localized telemetry of hazardous gases at high resolution. The mission is to build a “transparent data pipeline”—a clear, traceable path from raw chemical sensing in the atmosphere to processed environmental intelligence for ground stations. The project aims to show how space-based atmospheric research can be made accessible, modular and cost effective for environmental agencies and aviation safety boards, standardizing the unit to 1U. 

2. Problem Statement & Mission Context

The most significant drawback of modern atmospheric monitoring is spatial resolution. Geostationary satellites have a broad view, but they don’t give the detail that today’s Flight Management Systems (FMS) need. The mission is to detect specific gases that pose environmental and biological threats. Carbon Monoxide (CO) is another important target as a byproduct of incomplete combustion. CO is chemically “silent” in that it is colorless and odorless, but it displaces oxygen in human hemoglobin, leading to dizziness and impaired cognition. Similarly, CH4 (Methane) is an important concern both as a potent greenhouse gas and as an asphyxiant in high concentrations. With a low cost LEO solution, we can close the gap between ground based sensors and distant GEO monitors and provide the high frequency data updates required for real time aviation safety and climate modeling. 

3. System Architecture

The CubeSat architecture is built upon a modular hierarchy to ensure reliability during simulated orbital sequences. The system integrates sensing, processing, and communication into a unified 1U framework.

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4. Final Design

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5. Wiring

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6. Videos of our work

VID-20260509-WA0019.mp4

VID-20260509-WA0017.mp4

VID-20260509-WA0018.mp4

7. Presentation

CubeSat_Prese​ntation

8. Payload & Technical Specifications

The sensor suite was selected to give a multi-dimensional profile of the environment around the vehicle. The MQ-4 Methane sensor contains a tin dioxide (SnO2) semiconductor layer, when combustible gases are present the conductivity increases and the Arduino interprets this as a change in voltage. The MQ-7 Carbon Monoxide sensor is more complicated, and requires a specific dual-heating cycle (5V to clear the sensor, and 1.4V to take measurements) to ensure the highest accuracy. The DHT-22 sensor provides digital temperature and humidity readings that help put these gas readings into context. This is important because humidity can often cause "noise" in the gas sensor readings. Finally, the inertial path of the satellite is measured by the MPU-6050, which utilizes its internal accelerometer and gyroscope to record the satellite’s response to atmospheric drag and turbulence. 

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9. Mission Logic and Software Pipeline

The software architecture is realized as a continuous, high speed data-acquisition loop. Once deployed, the system runs a “pre-flight” sequence that calibrates the IMU for local gravity and electrical interference. When stable, the Arduino enters its main loop, polling the MQ sensors and IMU registers simultaneously using the I2C and Analog interfaces. A key feature of the code is the "Mapping Engine", which takes raw 10-bit electrical signals (0-1023) and converts them to a user-friendly 0-100 ppm-index. This processed data is then transmitted to the ground-station telemetry bridge and local LCD, while a parallel safety interrupt constantly checks for threshold breaches indicative of a dangerous pollution hotspot. 

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10. Advantages and Limitations

• Advantage: Cost Efficiency – Standardized 1U design reduces mission costs significantly compared to traditional satellites.
• Advantage: High-Resolution Data – LEO operation allows for more precise monitoring of specific aviation corridors.
• Advantage: Orbital Decay Profiling – As the satellite loses altitude, it naturally collects data at different atmospheric layers.
• Advantage: Zero Space Debris – Mission ends with total atmospheric burn-up, preventing long-term orbital clutter.
• Limitation: Short Operational Life – Atmospheric drag in the thermosphere limits mission duration to under one year.
• Limitation: Approximation Calibration – MQ sensors provide reliable trends and ppm-indices rather than absolute lab-grade precision.
• Limitation: Single-Unit Coverage – A single unit has limited revisit times; a constellation is needed for 24/7 global tracking.

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11. Testing and Evaluation

• Drift Calibration: Verified the `calcOffsets()` routine successfully averages 200 readings to zero out gyroscopic noise.
• Threshold Validation: Confirmed that LED/Buzzer alerts trigger accurately at 35 ppm (CO) and 10 ppm (CH4).
• Loop Frequency: Confirmed the system maintains high-speed polling with under 500ms latency.
• Power Stability: Verified that the Arduino successfully handles the MQ-7's dual-voltage heating cycles without crashing.

12. Potential Improvements

• Radiation Hardening: Upgrading to space-grade microcontrollers to withstand solar radiation in orbit.
• Micro-Propulsion: Adding micro-thrusters to counteract drag and maintain a stable altitude.
• Advanced Sensor Calibration: Integrating Machine Learning to dynamically filter humidity noise from gas data.
• Ground Station Mesh: Developing a distributed antenna network for continuous telemetry downloads.

13. Conclusion

The CubeSat Atmospheric Intelligence prototype demonstrates a viable, low-cost path for real-time environmental monitoring. By streamlining the sensor-to-data pipeline, the project provides a foundational model for future LEO missions aimed at enhancing aviation safety and global atmospheric research. The mission successfully proves that standardized 1U components can deliver complex, high-value telemetry in a sustainable and scalable package.

14. Personal Statements

Ali - I truly believe that this project helped me improve a lot on my collaboration and research skills, I was able to learn brand new things from the mentors and my teammates, such as breadboard wiring, using AI efficiently, learning computer aided design (CAD), using specific tools,technical components and sensors, and I wish we had more time on this project to refine/enhance it more,by using better materials,input more efficient code for testing and by actually having a chance to launch it into the atmosphere.