Bibliographic record
Abstract
This study presents a series of cost-effect strategies for calibrating star trackers for microsatellite missions. We examine three such strategies that focus on the calibration of the image detector, geometric calibration of the lab setup used for ground testing, and an optical calibration due to lens aberrations. Procedures are developed for each of these strategies that emphasize speed of implementation and accuracy, while trying to minimize manual labour. For the detector calibration, an existing calibration technique was adapted and implemented to reduce fixed pattern noise and dark current. Preliminary results show reduced variations in pixel sensitivity by approximately 21%, averaged across each pixel color given the use of a color imager. Although not substantial, this reduction in pixel variation will help preserve the Gaussian illumination pattern of imaged stars, aiding in correct centroid location. Results pertaining to the lab calibration show accurate star placement, in angular terms to 0.0073º across most of the field of view. This provides an accurate low-cost, variable solution for characterizing sensor performance; specifically pattern matching techniques. Finally, we present some initial results for lens aberration characterization. Using a Gaussian model of star image shape gives trends consistence with astigmatism and field curvature aberrations. Together, these calibrations represent tools that aim to improve both development and manufacture of modern microsatellite star trackers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".