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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".