Attitude Sensing and Control of a Stratospheric Ballon Platform
Bibliographic record
Abstract
A fundamental question yet to be answered is how fast the universe is expanding. In order to answer this exciting question physicists must be able to accurately determine the magnitude of various astronomical objects, such as stars and supernovae. In order to precisely calibrate the magnitudes of such objects, ground-based astronomical observatories are relied on. However, the observatories must be calibrated before use. To calibrate aground-based observatory, a known microwave source is pointed at the observatory from far away. The purpose of this paper is to outline how the McGill University High Altitude Balloon (McHAB) team intend to use a high-altitude balloon to carry the required light source to altitudes of 15km to 20km. The main challenge, and the novelty of this work, is the design, construction, and implementation of a low-cost attitude control and estimation system that will enable pointing of the microwave source at ground-based observatories. The McHAB team has designed an attitude control system to point a payload to within an accuracy of ±1◦. This platform will be actuated using a single reaction wheel that will control the platform yaw angle. A reaction wheel is chosen due to its simplicity. A PID controller is used to control the reaction wheel, and hence point the scientific payload. A difficult challenge is accurately estimating the attitude of the platform. A rotation-matrix-based complementary filter will be used to fuse accelerometer, gyroscope, and magnetometer measurements from an onboard inertial measurement unit. Hardware consists of an inertial measurement unit containing a 16MHz ATmega328 microcontroller for data acquisition and a single-board computer, the Raspberry Pi, containing a 700MHz ARMv6 processor for data processing and control. The first flight of the system is documented in this paper.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".