Design of an Attitude Control System for a High-Altitude Balloon Payload
Bibliographic record
Abstract
Physicists seek to uncover expansion history of the universe by examining polarization patterns in the cosmic microwave background (CMB). Observatories in the South Pole are used to examine these polarization patterns, but no sucient celestial source exists to properly calibrate these observatories. A method of calibrating these observatories would be to point a tuned microwave source to the ground-based observatory from a far distance. The purpose of this paper is to outline how the McGill High-Altitude Balloon (McHAB) team intends to use a high-altitude balloon with a reaction wheel actuator to slew and point the payload at altitudes of 15km to 20km to within an accuracy of 2. A complementary lter with an inertial measurement unit (IMU) will be used to estimate the attitude of the payload. A proportional-derivative controller will be used to control the reaction wheel, and hence point the payload to a desired angle. The main processor will be a Raspberry Pi, a single-board computer using a single-core 700MHz ARMv6 CPU. The team has previously own a payload, McHAB-1, containing the Raspberry Pi and an IMU to test the attitude estimator and collect attitude data. The team has own a complete prototype of the system, McHAB-2, on April 28th, 2013. Data from this flight is presented showing that the reaction wheel can better point the system as the platform reaches higher altitudes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".