Student Innovation-Space technology refers to the application of engineering principles to the design, development, manufacture, and operation of devices and systems for space travel and exploration.

Space Tech Innovation Dossier: Modular Nano-Satellite Payload, Software-Defined Ground Station, & AI Telemetry Analytics Architecture

1. Executive Snapshot

This dossier presents a comprehensive engineering design, software-hardware architecture, and hackathon execution strategy for Problem Statement SIH26226 under the Space Technology theme, hosted by AICTE / MIC-Student Innovation [PS]. The problem addresses the open-ended challenge of applying core engineering principles to design, manufacture, and operate hardware/software systems for space travel, exploration, and education [PS]. To convert this broad mandate into a winning, implementable project, we propose "AstraNode & GroundSight": an open-architecture 1U CanSat/CubeSat modular hardware payload paired with an automated dual-axis Software-Defined Radio (SDR) ground tracking station and an edge AI telemetry anomaly detection engine [INFERENCE]. This end-to-end space system provides hands-on flight qualification, real-time RF downlink monitoring, automated antenna azimuth-elevation tracking, and predictive fault diagnosis [INFERENCE].

Fundamental System Objective: To build an open, end-to-end hardware-in-the-loop space technology prototype consisting of a modular nano-satellite payload (sensory OBC, EPS, LoRa/UHF RF telemetry, active mechanism actuation) and an automated dual-axis SDR ground station with AI telemetry analytics to democratize satellite development and flight testing for academic institutions [INFERENCE].

2. Problem Deconstruction

The explicit problem statement provided by AICTE/MIC is open-ended [PS]:

"Student Innovation-Space technology refers to the application of engineering principles to the design, development, manufacture, and operation of devices and systems for space travel and exploration." [PS]

To make this actionable for a 4–6 student engineering team, we deconstruct the statement into core explicit requirements and derived engineering implications:

Requirement Breakdown

1. [PS-01] Domain Focus: Space Travel & Exploration Devices

   • Source Text: "application of engineering principles to the design, development, manufacture, and operation of devices and systems for space travel and exploration." [PS]
   • Interpretation: The project must physically demonstrate key space systems subsystems—such as an On-Board Computer (OBC), Electrical Power System (EPS), Attitude Sensing/Determination, Telemetry & Telecommand (TM/TC), and Payload Deployment [INFERENCE].
   • Implementation Implication: Construct a physical 1U CanSat / 1U CubeSat form-factor flight hardware stack with environmental sensors, IMU, GPS, solar battery charging, and RF transmission [ASSUMPTION].
   • Confidence: [PS] for domain; [ASSUMPTION] for CanSat/CubeSat form factor.

2. [PS-02] Category: Hardware Track

   • Source Text: Category = "Hardware" [PS]
   • Interpretation: The solution cannot be a pure software simulator or UI mockup; it must include physical electronic boards, mechanical structures, sensors, microcontrollers, and RF hardware [INFERENCE].
   • Implementation Implication: Custom PCB design/breadboarding, 3D printed mechanical chassis with deployment mechanisms (servo/shape-memory wire/solenoid), and physical ground antenna rotator hardware [ASSUMPTION].
   • Confidence: [PS] for category; [ASSUMPTION] for mechanical actuation choices.

3. [DERIVED-01] Operation & Communication Link (Ground Segment)

   • Source Text: "...operation of devices and systems..." [PS]
   • Interpretation: Operating a space device requires establishing a reliable bidirectional wireless telemetry and telecommand link with a ground station [INFERENCE].
   • Implementation Implication: Build an SDR-based (RTL-SDR / HackRF) ground terminal equipped with a 2-DOF (azimuth/elevation) rotator driven by stepper motors to auto-track flight trajectories via TLE or real-time GPS coordinates [ASSUMPTION].
   • Confidence: [INFERENCE]

4. [DERIVED-02] Scalability & AI Mission Analytics

   • Source Text: Implied by modern space technology practices [RESEARCHED].
   • Interpretation: Modern space missions rely on automated ground monitoring and predictive telemetry analysis to prevent on-orbit mission loss [KNOWN].
   • Implementation Implication: Implement an ML telemetry analysis engine (Isolation Forest / Autoencoder) running on the ground station server to detect voltage drops, tumble states, and RF degradation [ASSUMPTION].
   • Confidence: [INFERENCE]

3. Stakeholders & User Needs

4. Requirements Traceability

5. Constraints & Environment Realities

1. Radio Frequency & Regulatory Constraints:

   • In India, transmitting over unlicensed ISM bands (433 MHz / 868 MHz / 2.4 GHz) is restricted in power (typically ≤ 10mW to 100mW depending on band WPC guidelines) [RESEARCHED].
   • Amateur radio bands (144–146 MHz, 435–438 MHz) require HAM radio licenses for operations [KNOWN]. For hackathon field testing, ISM 433 MHz / 868 MHz LoRa modules operating within legal power limits are selected [ASSUMPTION].

2. Field Deployment Conditions:

   • Drop tests from high altitudes (drones, balloons, or high buildings) involve high vibration, wind drift, and potential structural impact upon landing [KNOWN].
   • The hardware structure must be mechanically shock-resistant, with reinforced 3D printed PETG/PLA+ or aluminum framing [ASSUMPTION].

3. Power & Thermal Budget:

   • Battery power is limited during flight operations [KNOWN].
   • Microcontrollers and transmitters must operate with low power consumption, utilizing power-saving sleep modes and efficient transmission cycles [INFERENCE].
   • Direct sunlight during drop/flight tests can cause sensor thermal drift [KNOWN].

4. Connectivity Realities during Field Operations:

   • Field testing sites (e.g., open grounds, launch fields) often suffer from unstable cellular data [KNOWN].
   • The ground station software must function completely offline on a local WiFi/LAN hot-spot generated by a Raspberry Pi or laptop [ASSUMPTION].

6. Research Questions

1. [RQ-01] Transceiver Selection: What is the most reliable, cost-effective RF modulation protocol (LoRa vs. FSK vs. AX.25 packet radio over SDR) for line-of-sight satellite telemetry up to 2–5 km during student drop tests?
2. [RQ-02] Mechanical Deployment Mechanics: What active payload release mechanism (micro-servo latch vs. burn-wire SMA spring vs. solenoid) offers the highest reliability under vibrational stresses?
3. [RQ-03] Ground Tracking Algorithm: How accurately can a low-cost stepper-driven pan-tilt antenna rotator track a moving aerial payload using live GPS telemetry backhauls versus TLE-based SGP4 orbital propagation algorithms?
4. [RQ-04] AI Anomaly Detection Feasibility: Can an ML model (Isolation Forest or Autoencoder) accurately distinguish between normal sensor noise during tumbling and actual power failure or structural malfunction in high-frequency flight data streams?

7. Data Requirements & Sources

8. Existing Solutions & Gap Analysis

9. Candidate Solution Approaches

We evaluate three potential solution architectures for the SIH team:

Approach 1: Pure Software Simulation & Virtual Ground Station

• Mechanism: A web-based 3D software simulator displaying a virtual satellite orbit, synthetic telemetry generation, and a mock ground station dashboard using Three.js and CesiumJS.
• Problem Fit: Low. Fails to meet the mandatory SIH Hardware Category requirement [PS-02].
• Complexity: Low (Software only).
• Cost: Nearly zero.
• Hackathon Feasibility: High, but high risk of disqualification or low judging score due to lack of physical hardware [INFERENCE].

Approach 2: Static Hardware CanSat Payload with Handheld Receiver

• Mechanism: A basic 3D printed cylinder with an Arduino Nano, IMU sensor, and 433 MHz RF module sending telemetry to a handheld LCD receiver board.
• Problem Fit: Moderate. Fulfills basic hardware requirement but represents a generic beginner project with minimal engineering differentiation [INFERENCE].
• Complexity: Low.
• Cost: ~₹3,000–₹5,000.
• Hackathon Feasibility: High, but lacks automated ground tracking, open ground station software, active mechanical actuation, and AI analytics [INFERENCE].

Approach 3: Integrated Hardware-in-the-Loop Space System (Recommended)

• Mechanism: A multi-layered architecture featuring:
  1. AstraNode (Space Segment): Standardized 1U modular stack featuring dual-core ESP32-S3/STM32 OBC, EPS with solar charging & voltage monitoring, 9-DOF IMU + Baro + GPS sensor suite, 433 MHz LoRa transceiver, and servo-activated payload deployment mechanism.
  2. GroundSight (Ground Segment): Automated 2D Pan-Tilt antenna tracking rotator driven by dual NEMA-17 steppers and Arduino, RTL-SDR receiver module, and Raspberry Pi/laptop host.
  3. AstraIQ Engine (Software & AI Layer): Local Python backend receiving telemetry, calculating SGP4/GPS pointing angles, training an Isolation Forest model for real-time anomaly detection, and serving a 3D Web Dashboard.
• Problem Fit: High. Delivers a complete hardware-software co-designed system that satisfies space engineering standards [INFERENCE].
• Complexity: Moderate-High.
• Cost: ~₹10,000–₹14,000 [ASSUMPTION].
• Hackathon Feasibility: High for a 4–6 student multidisciplinary team (electronics, software, mechanical) [INFERENCE].

10. Recommended Approach & Why

We select Approach 3: Integrated Hardware-in-the-Loop Space System ("AstraNode & GroundSight").

Why Alternatives Were Rejected:

• Approach 1 was rejected because pure software does not fulfill the mandatory SIH Hardware category requirement [PS-02].
• Approach 2 was rejected because a simple handheld RF receiver lacks the technical complexity, automated ground station tracking, and AI anomaly detection needed to stand out in a competitive national hackathon [INFERENCE].

Why Approach 3 Wins:

1. End-to-End System Demonstration: Demonstrates the complete space mission life cycle: Design -> Manufacturing -> Telemetry Transmission -> Ground Antenna Tracking -> AI Anomaly Processing [INFERENCE].
2. High Judge Impact: A live demo featuring a physical antenna rotator dynamically tracking a moving flight payload while real-time 3D orientation and ML anomaly scores stream to a web dashboard creates a compelling showcase [INFERENCE].
3. Clear Division of Labor: Allows a 4–6 student team to split cleanly into sub-teams: Mechanical/3D Printing, Firmware/PCB, RF/Ground Station, and Software/AI [INFERENCE].

11. Technical Architecture

Layered System Architecture Diagram

+-----------------------------------------------------------------------------------+
|                            SPACE SEGMENT (AstraNode)                              |
|                                                                                   |
|  +--------------------+   +--------------------+   +---------------------------+  |
|  |   Sensors Suite    |   | Electrical Power   |   |    Actuation Mechanism    |  |
|  | - 9-DOF IMU        |   | - Solar Panels     |   | - Micro-Servo Latch       |  |
|  | - BMP280 Baro/Temp |   | - TP4056 + BMS     |   | - Parachute/Drag-Sail     |  |
|  | - NEO-6M GPS       |   | - LiPo Battery     |   | - Active Deployment       |  |
|  +---------+----------+   +---------+----------+   +-------------+-------------+  |
|            |                        |                            |                |
|            +-------------------+    |    +-----------------------+                |
|                                |    |    |                                        |
|                     +----------v----v----+-----------+                            |
|                     |  On-Board Computer (OBC)       |                            |
|                     |  - ESP32-S3 / STM32F4          |                            |
|                     |  - FreeRTOS / Embedded C++     |                            |
|                     |  - SD Card Telemetry Logger    |                            |
|                     +--------------+-----------------+                            |
|                                    |                                              |
|                     +--------------v-----------------+                            |
|                     |  RF Transceiver Module         |                            |
|                     |  - SX1278 LoRa (433 MHz)       |                            |
|                     +--------------+-----------------+                            |
+------------------------------------|----------------------------------------------+
                                     |  Wireless RF Downlink
                                     v  (LoRa / AX.25 Packets)
+-----------------------------------------------------------------------------------+
|                           GROUND SEGMENT (GroundSight)                            |
|                                                                                   |
|  +-----------------------------------------------------------------------------+  |
|  |  Hardware Tracking Rotator                                                  |  |
|  |  - High-Gain Yagi/Helical Antenna                                           |  |
|  |  - 3D Printed Pan-Tilt Chassis (Azimuth & Elevation)                        |  |
|  |  - Dual NEMA-17 Stepper Motors + A4988 Drivers + Arduino Nano Controller    |  |
|  +-------------------------------------+---------------------------------------+  |
|                                        |                                          |
|  +-------------------------------------v---------------------------------------+  |
|  |  Radio Receiver & Telemetry Processing                                      |  |
|  |  - RTL-SDR Dongle / ESP32 RF Gateway Base Station                         |  |
|  |  - Raspberry Pi 4 / Laptop Local Host                                       |  |
|  +-------------------------------------+---------------------------------------+  |
|                                        |                                          |
|  +-------------------------------------v---------------------------------------+  |
|  |  Ground Backend & AstraIQ AI Engine                                         |  |
|  |  - Python FastAPI Server + WebSockets                                       |  |
|  |  - SQLite / In-Memory Time-Series DB                                        |  |
|  |  - Isolation Forest Telemetry Anomaly Detector Engine                       |  |
|  |  - SGP4 & GPS Bearing Tracking Controller                                   |  |
|  +-------------------------------------+---------------------------------------+  |
|                                        |                                          |
|  +-------------------------------------v---------------------------------------+  |
|  |  Ground Station Web Dashboard UI                                            |  |
|  |  - React.js + Tailwind CSS Dashboard                                        |  |
|  |  - Three.js 3D Real-Time Attitude Visualizer                                |  |
|  |  - Leaflet / Mapbox Live Trajectory Map                                     |  |
|  +-----------------------------------------------------------------------------+  |
+-----------------------------------------------------------------------------------+

Data Flow Narrative

1. Data Acquisition (Space Segment):

   • The OBC reads accelerometer, gyroscope, magnetometer values from the BNO055/MPU6050 via I2C at 20 Hz [ASSUMPTION].
   • Pressure, temperature, and altitude are sampled from the BMP280 via I2C at 5 Hz [ASSUMPTION].
   • Latitude, longitude, altitude, and UTC time are read from the NEO-6M GPS via UART at 1 Hz [ASSUMPTION].
   • Battery voltage and charge current are monitored via analog ADC pins [ASSUMPTION].

2. Telemetry Formatting & Local Storage:

   • Raw telemetry values are packed into a binary struct/packet (reducing bandwidth) or lightweight JSON frame [ASSUMPTION].
   • Telemetry is simultaneously logged to the on-board MicroSD card for black-box data recovery [ASSUMPTION].

3. RF Downlink Transmission:

   • The SX1278 LoRa module transmits encoded data packets over 433 MHz at 2 Hz [ASSUMPTION].

4. Ground Station Signal Reception & Rotator Tracking:

   • The ground station receiver (ESP32 Gateway or RTL-SDR) picks up the packet [ASSUMPTION].
   • The GPS coordinates extracted from the packet are passed to the Ground Sight Rotator Controller [ASSUMPTION].
   • The controller calculates the target Azimuth and Elevation angles relative to the ground station's fixed coordinates and sends pulse commands to the NEMA-17 stepper motors, pointing the high-gain antenna directly at the flying payload [ASSUMPTION].

5. Backend Ingestion & AI Processing:

   • Telemetry packets are sent via local WebSocket to the Python FastAPI backend [ASSUMPTION].
   • The AstraIQ AI Engine passes the incoming feature vector [Voltage, Temp, Gyro_X, Gyro_Y, Gyro_Z, Altitude_Delta] through an Isolation Forest anomaly detection model [ASSUMPTION].
   • An anomaly score (0 to 100%) and flag status (NORMAL / WARNING / CRITICAL) are calculated [ASSUMPTION].

6. Real-Time UI Visualization:

   • The Web Dashboard renders real-time time-series plots of altitude and power, updates the live 3D orientation model in Three.js, plots trajectory on an interactive map, and displays AI health diagnostics [ASSUMPTION].

12. Technology Stack

13. AI/ML Justification

Where AI Should NOT Be Used:

• Critical Parachute / Recovery Deployment Trigger: The mechanical release trigger must be governed strictly by deterministic, deterministic hard-coded firmware rules (e.g., if (altitude < 100m && descent_rate > 5m/s) deploy()) or physical barometric switches [ASSUMPTION]. AI must NEVER control critical flight termination or parachute release mechanisms due to non-deterministic failure risks [INFERENCE].

14. Data Architecture & Offline/Low-Network Design

Schema Design

1. Payload Telemetry Packet (Binary / JSON Frame over RF)

{
  "pkt_id": 1042,
  "timestamp_ms": 124500,
  "batt_v": 3.82,
  "temp_c": 24.5,
  "press_hpa": 985.4,
  "altitude_m": 245.2,
  "gyro": [0.02, -0.12, 0.45],
  "accel": [0.11, 0.05, 9.78],
  "gps": {"lat": 28.6139, "lng": 77.2090, "alt": 248.0, "sats": 8},
  "status_flags": {"solenoid_deployed": false, "sd_logging": true}
}

2. Ground Station Database Schema (SQLite / Time-Series Table)

CREATE TABLE telemetry_logs (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    packet_id INTEGER,
    received_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    payload_id VARCHAR(32),
    battery_voltage REAL,
    temperature REAL,
    pressure REAL,
    altitude REAL,
    gyro_x REAL, gyro_y REAL, gyro_z REAL,
    accel_x REAL, accel_y REAL, accel_z REAL,
    latitude REAL, longitude REAL, gps_alt REAL,
    ai_anomaly_score REAL,
    ai_status VARCHAR(16)
);

Offline & Low-Network Operation

• Local Hotspot Architecture: The Raspberry Pi ground station acts as its own local WiFi access point (AstraGround_Hotspot). Laptops and tablets connect locally without needing external internet access [ASSUMPTION].
• On-Board SD Logging (Black Box): In case of RF communication blockage or signal loss, the OBC logs all high-frequency data (20 Hz) to an SPI MicroSD card module [ASSUMPTION].
• Bandwidth Optimization: RF payload frames use fixed-point binary integer encoding (struct packing) rather than plain text JSON, reducing transmission size from ~220 bytes to 48 bytes per frame [ASSUMPTION].

15. Security, Privacy & Governance

1. RF Interference & Authorization:

   • Transmissions will strictly adhere to ISM 433 MHz band power constraints (≤ 10mW) to avoid interfering with local public safety radio channels [RESEARCHED].
   • The team will use unique payload callsigns/identifiers embedded in every packet header [ASSUMPTION].

2. Data Integrity & Encryption:

   • To prevent unauthorized command injection or hijacking during telecommand operations, telecommand frames utilize lightweight AES-128-CTR encryption with HMAC packet verification [ASSUMPTION].

3. Location Privacy:

   • GPS flight coordinates recorded during drop testing do not expose sensitive military or private restricted zones [INFERENCE]. Testing must be conducted in designated university grounds or open rural fields [ASSUMPTION].

4. Liability & Safety Governance:

   • Flight drop tests must include manual tether lines or safety parachutes to prevent injury or property damage [INFERENCE].
   • The SIH prototype remains an educational testing framework; real orbital integration requires formal IN-SPACe authorization and ISRO qualification standards [RESEARCHED].

16. MVP Scope (Hackathon Build)

+-----------------------------------------------------------------------------------+
|                            HACKATHON BUILD MVP SCOPE                              |
|                                                                                   |
|  +-----------------------------------------------------------------------------+  |
|  |                            IN SCOPE (MUST BUILD)                            |  |
|  |                                                                             |  |
|  | 1. Physical 1U CanSat/CubeSat Payload Stack                                 |  |
|  |    - ESP32-S3 OBC + BNO055 IMU + BMP280 Baro + NEO-6M GPS.                   |  |
|  |    - TP4056 EPS circuit with Solar Panel charging & LiPo battery.           |  |
|  |    - Micro-servo parachute deployment mechanism.                            |  |
|  |    - SX1278 433 MHz LoRa telemetry transmitter.                             |  |
|  |                                                                             |  |
|  | 2. Physical Dual-Axis Ground Tracking Rotator                               |  |
|  |    - 3D-printed Pan-Tilt structure with NEMA-17 steppers + A4988 drivers.   |  |
|  |    - Arduino Nano rotator controller receiving GPS target bearings.         |  |
|  |    - High-gain direction Yagi or Biquad antenna mounted on rotator.         |  |
|  |                                                                             |  |
|  | 3. Ground Backend & AI Engine                                               |  |
|  |    - Python FastAPI WebSocket server receiving LoRa packets.                |  |
|  |    - Isolation Forest AI model predicting real-time anomaly scores.         |  |
|  |    - SQLite telemetry database logging.                                     |  |
|  |                                                                             |  |
|  | 4. Live Web Dashboard & 3D Flight Visualizer                                |  |
|  |    - Real-time time-series charts (Altitude, Power, Temp).                  |  |
|  |    - Three.js 3D attitude visualization matching physical IMU tilt.          |  |
|  |    - Interactive Leaflet map displaying live payload coordinate marker.     |  |
|  +-----------------------------------------------------------------------------+  |
|                                                                                   |
|  +-----------------------------------------------------------------------------+  |
|  |                     DESCRIBED ONLY / OUT OF SCOPE                           |  |
|  |                                                                             |  |
|  | 1. Orbital Rocket Launch / High-Altitude Weather Balloon Flight (Simulated).  |  |
|  | 2. Space-grade radiation-hardened components (Silicon-on-Insulator).          |  |
|  | 3. Multi-band S-band / X-band satellite communication links.                  |  |
|  | 4. Full IN-SPACe environmental thermal-vacuum (TVAC) chamber testing.          |  |
|  +-----------------------------------------------------------------------------+  |
+-----------------------------------------------------------------------------------+

17. Live Demo Simulation Plan

To present a compelling, working hardware demonstration inside an evaluation hall without an active rocket or balloon launch:

1. Hardware Setup in Evaluation Hall:

   • Place the physical GroundSight Rotator on the presentation table, connected via USB to the host laptop running the dashboard [ASSUMPTION].
   • The presenter holds the physical AstraNode Payload on a portable hand rig or mounting pole [ASSUMPTION].

2. Interactive Live Demonstration Steps:

   • Step 1: Real-Time IMU 3D Mirroring: As the presenter tilts and rotates the payload in their hand, the BNO055 sensor transmits quaternions via LoRa. The 3D satellite model on the Web Dashboard mirrors the physical movements in real time [ASSUMPTION].
   • Step 2: Automated Antenna Tracking: The presenter moves the payload across the evaluation hall. The ground station pan-tilt rotator automatically rotates its NEMA-17 motors, panning and tilting the antenna to track the moving payload based on live GPS/bearing updates [ASSUMPTION].
   • Step 3: Active Mechanism Release: The team triggers an RF telecommand from the ground station web UI. The micro-servo latch on the payload opens instantly, deploying the mini chute or drag sail [ASSUMPTION].
   • Step 4: AI Anomaly Injection: The team flips a physical toggle switch on the payload simulating a "solar panel fault" (disconnecting charge rail) or tilts the board into a rapid spin. The AstraIQ ML Engine immediately flags a red alert on the dashboard display (ANOMALY DETECTED: UNUSUAL TUMBLE / POWER DROP), demonstrating real-time diagnostic capabilities [ASSUMPTION].

18. Implementation Roadmap

Target Deadline: 30 September 2026 [PS]

+-----------------------------------------------------------------------------------+
|                             IMPLEMENTATION ROADMAP                                |
+-----------------------------------------------------------------------------------+
  Phase 1: Architecture & Circuit Prototyping (Weeks 1 - 2)
  - Finalize component selection & wiring schematics.
  - Bench-test ESP32-S3, BNO055, BMP280, NEO-6M GPS over breadboard.
  - Verify LoRa SX1278 packet transmission across room distances.

  Phase 2: Hardware Fabrication & Rotator Assembly (Weeks 3 - 4)
  - 3D print 1U CanSat/CubeSat internal chassis structure in PETG.
  - Assemble Pan-Tilt rotator frame with NEMA-17 stepper motors and A4988 drivers.
  - Program Arduino Nano rotator firmware for Gcode/azimuth-elevation stepping.

  Phase 3: Software Backend, AI & Web Dashboard (Weeks 5 - 6)
  - Build FastAPI Python backend and WebSocket data pipeline.
  - Implement Three.js 3D attitude renderer and Leaflet live tracking map.
  - Train Isolation Forest model on benchtop telemetry dataset + synthetic faults.

  Phase 4: Full System Integration & Field Testing (Weeks 7 - 8)
  - Perform drone drop tests or high-structure drops to validate parachute release.
  - Test antenna rotator auto-tracking against outdoor walking/drone payload path.
  - Refine UI, fix bug edge-cases, and prepare demo narrative for SIH judging.
+-----------------------------------------------------------------------------------+

19. Testing & Validation Plan

20. Risk Register

21. Cost & Scalability

Hackathon Prototype (Hardware MVP)

Production Scale (Academic Institution / IDEA Lab Deployment)

• Per-Unit Cost at 100+ Scale: ~₹6,500 per payload unit using custom integrated 4-layer PCB manufacturing [INFERENCE].
• Ground Station Deployment: ~₹15,000 per fully automated station setup (including outdoor weatherproof enclosure and high-gain Yagi antenna) [INFERENCE].

22. Differentiation

23. Future Scope

1. IN-SPACe Payload Qualification: Refine the physical payload stack to meet IN-SPACe cube-sat dimensions (10cm x 10cm x 10cm, < 1.33 kg) and undergo vibration testing for piggyback ISRO launches [RESEARCHED].
2. SatNOGS Global Network Integration: Connect the automated SDR ground station to the global open-source SatNOGS network, allowing international satellite operators to schedule observations through the station [RESEARCHED].
3. Multi-Payload Swarm Networking: Extend the LoRa firmware to support mesh networking protocols (e.g., Meshtastic / Reticulum), allowing multiple CanSats to communicate inter-satellite link (ISL) telemetry during descent [INFERENCE].

24. Assumptions & Open Items Log

Consolidated Assumptions Log

1. [ASSUMPTION] The team will use 433 MHz LoRa modules operating under local ISM power limits (≤ 10mW) for field drop testing.
2. [ASSUMPTION] An ESP32-S3 microcontroller provides sufficient CPU speed and RAM to handle 20 Hz sensor sampling, SD logging, and LoRa packet encoding concurrently.
3. [ASSUMPTION] A 3D-printed PETG pan-tilt mechanism powered by dual NEMA-17 stepper motors provides adequate torque to hold a high-gain Yagi antenna without gear slippage.
4. [ASSUMPTION] Unsupervised Isolation Forest ML model trained on synthetic benchtop fault data is effective for baseline anomaly detection during hackathon demonstrations.
5. [ASSUMPTION] Indoor evaluation hall demos will simulate target GPS movements or use local Bluetooth/WiFi bearing fallback if indoor satellite lock is restricted.

Open Items Log (Needs Validation)

1. [NEEDS VALIDATION] WPC (Wireless Planning & Coordination) precise power limit clearance for non-interfering indoor hackathon RF transmissions in specific city locations [NEEDS VALIDATION].
2. [NEEDS VALIDATION] Maximum wind drift resistance and descent velocity stability of the chosen 3D printed drag sail/parachute during high-altitude drone drop tests [NEEDS VALIDATION].

Final Recommendation

Build "AstraNode & GroundSight"—an integrated 1U modular nano-satellite hardware payload paired with an automated dual-axis SDR ground tracking station and an AI telemetry anomaly detection engine [INFERENCE]. This directly answers AICTE's Space Technology challenge by demonstrating end-to-end space engineering principles [PS].

Recommended Stack

• Space Hardware: ESP32-S3, BNO055 9-DOF IMU, BMP280, NEO-6M GPS, SX1278 LoRa (433MHz), TP4056 EPS, Micro-Servo Latch [ASSUMPTION].
• Ground Rotator: 3D Printed PETG Chassis, 2x NEMA-17 Stepper Motors, A4988 Drivers, Arduino Nano, High-Gain Yagi Antenna, RTL-SDR Receiver [ASSUMPTION].
• Backend & AI: Python 3.11, FastAPI, WebSockets, SQLite, Scikit-Learn (Isolation Forest) [ASSUMPTION].
• Frontend UI: React.js, Tailwind CSS, Three.js (3D WebGL attitude renderer), Leaflet.js [ASSUMPTION].

Biggest Dependency

Acquisition and assembly of physical hardware components (stepper motors, RTL-SDR, IMU sensors, 3D printed structural parts) before software integration [INFERENCE].

Biggest Risk

RF signal loss or GPS lock failure during indoor demo presentations [INFERENCE].

Biggest Technical Challenge

Achieving smooth, low-latency pan-tilt antenna tracking synchronization with fast-moving aerial payload GPS coordinates [INFERENCE].

First 5 Actions

1. Order hardware components (ESP32-S3, BNO055, SX1278 LoRa modules, NEMA-17 motors, RTL-SDR) [ASSUMPTION].
2. Flash ESP32-S3 with basic I2C sensor reading firmware and benchmark BNO055 quaternion data output [ASSUMPTION].
3. 3D print the pan-tilt antenna rotator frame and test stepper motor angular precision using Arduino Nano driver scripts [ASSUMPTION].
4. Build the FastAPI WebSocket server and construct the Three.js 3D attitude visualizer [ASSUMPTION].
5. Execute an end-to-end benchtop test: transmit LoRa sensor packets from moving payload board to ground receiver and mirror rotation on 3D dashboard UI [ASSUMPTION].

Validation Before Coding

• Verify I2C address compatibility across BNO055, BMP280, and OLED hardware on the same physical bus [ASSUMPTION].
• Confirm RF line-of-sight range between SX1278 transceivers across at least 500 meters in open field conditions [ASSUMPTION].

Buildability Score /100

88/100 (Highly buildable by a multidisciplinary 4–6 engineering student team within 6–8 weeks using commercial off-the-shelf electronics and 3D printing) [INFERENCE].

Confidence

HIGH (The solution leverages proven open-source hardware standards, established Python AI libraries, and readily available microcontroller hardware) [INFERENCE].

Research Sources

1. Programmes / Student Satellite - ISRO

   • URL: https://www.isro.gov.in/Student_Program_Satellite.html [1.1]
   • Source Type: Official Government / Space Agency Portal
   • Supported Claim: Guidance on university student satellite payload development, bus integration, and ISRO launch support programs.
   • Reliability: High

2. ISRO Expanding Student Participation in Space Missions (PIB Delhi)

   • URL: https://www.pib.gov.in/PressReleasePage.aspx?PRID=2239145&reg=3&lang=2 [1.2]
   • Source Type: Official Government Press Release
   • Supported Claim: Government initiatives and IN-SPACe competitions for student rocket and satellite design.
   • Reliability: High

3. SatNOGS Open Source Global Network of Satellite Ground Stations

   • URL: https://satnogs.org/ [4.1]
   • Source Type: Open Source Organization
   • Supported Claim: Architecture standards for software-defined open satellite ground stations and tracking rotators.
   • Reliability: High

4. ESA CanSat Educational Project & Standards

   • URL: https://www.esa.int/Education/CanSat/CanSat_2025-2026_Challenge_your_students_to_build_a_can-sized_satellite [3.1]
   • Source Type: International Space Agency Portal
   • Supported Claim: CanSat volume, payload, sensor, and recovery mission specifications.
   • Reliability: High