Mission Instrumentation

MB3 — Weather Satellite (Level 3: Interpretation & Trade-offs)

Mission Goal

Design a micro “weather satellite” payload that collects environmental data and explains what the data means, including limitations and trade-offs.

Why This Matters

Earth-observing satellites are only useful if their data is interpretable. Level 3 is about turning measurements into meaning, while acknowledging uncertainty and constraints.

What Data You Collect

• Temperature (°C)
• Light level (relative)
• Optional: external sensor (humidity, pressure) if your kit supports it
• Timestamp or sample index

Hardware / Software Needed

• 1 × micro:bit + optional external sensors (if available)
• MakeCode / MicroPython
• Method to export data: serial CSV, radio to base station, or on-device summary
• Optional: simple weather shelter (paper cup shade, ventilated enclosure)

Inputs From Other Teams

• Data: Agree units, sampling interval, and how to store/plot data.
• Launch: Advise on mounting/location to reduce direct sunlight bias.
• Command & Control: Define mission question (e.g., “How does temperature change across locations?”).

What You Must Produce (Deliverables)

• A data collection program with a clear sampling plan.
• A short “interpretation note” explaining what the data suggests and what could mislead you.
• A simple result: chart, table, or summary comparing at least 2 conditions/locations.

Step-by-Step Build

1. Pick a mission question:
   • “Does temperature differ between sun/shade?”
   • “Does indoor vs outdoor light change predictably through a session?”
2. Choose a sampling interval (e.g., 10 seconds) and duration (e.g., 10–15 minutes).
3. Implement logging: output CSV lines with all sensor fields.
4. Control bias:
   • Shade the sensor from direct sunlight if measuring ambient temperature.
   • Keep airflow similar between tests.
5. Collect two datasets under different conditions.
6. Compare results and write an interpretation note with at least 2 caveats.

Data Format / Output

• t_ms,temp_c,light,event (event can mark location change)

Analysis Ideas

• Plot temperature vs time for each condition.
• Compute average, min, max, and rate of change.
• Correlate light changes with temperature changes and discuss causality vs coincidence.

Success Criteria

• Two comparable datasets collected with a consistent plan.
• Interpretation includes uncertainty and limitations.
• Team can explain at least one trade-off (sampling rate vs battery/output, placement vs accuracy).

Evidence Checklist

• Photo of payload placement/setup
• Two data logs (CSV or screenshots)
• Chart/table comparing conditions
• Interpretation note (including caveats)

Safety & Privacy

• Do not place devices where they can fall or cause tripping.
• No recording of people; focus on environmental data only.
• Keep equipment dry and away from spills.

Common Failure Modes

• Temperature readings biased by hand warmth or direct sunlight.
• Sampling interval inconsistent or not recorded.
• Comparing datasets collected under different uncontrolled conditions.
• Concluding “causes” without acknowledging confounders.

Stretch Goals

• Add a simple calibration check (ice water ~0°C, warm hand ~30–35°C) and note errors.
• Build a small ventilated radiation shield.
• Compute confidence intervals or variability bands across repeated runs.