Comparison and generalization of GNSS satellite attitude models
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Bibliographic record
Abstract
<p>Global navigation satellite system (GNSS) constellations such as GPS, GLONASS, Galileo, and BeiDou and the Japanese regional system QZSS apply various satellite attitude modes during eclipse season, which is the period when the Sun is close to the orbital plane of the satellite. Due to different satellite manufacturers and technological advances over time, these modes can vary between constellations but also between different satellite types within a constellation. For some constellations, namely Galileo and QZSS, the satellite attitude law has been officially published by the satellite operator. For most other GNSS satellite types, researchers have developed attitude models, for example using reverse kinematic precise point positioning, that approximate the actual attitude behaviour.</p><p>Outside of eclipse seasons, GNSS satellites generally apply either a nominal yaw-steering or an orbit normal attitude law. While both modes point the antennas towards Earth, the former yaws the satellite around the antenna axis to point the solar panels towards the Sun, while the latter always keeps a fixed yaw angle. When a satellite applying a yaw-steering law is in eclipse season and close to the orbit noon or midnight point, it may have to yaw faster than physically possible to keep the nominal attitude. The various attitude modes used by the satellites aim to prevent this scenario by applying a modified attitude law during this period, for example by yawing at a constant rate around orbit noon/midnight or by switching to orbit normal mode.</p><p>Comparisons of attitude files generated by analysis centers of the International GNSS Service (IGS) within the scope of its 3<sup>rd</sup> reprocessing campaign show significant differences in some cases. This contribution compares all available attitude models with the aim of finding similarities that allow for generalization, which in turn simplifies the implementation of the various attitude modes into GNSS software packages. The developed functions have been implemented into the open-source software GROOPS (https://github.com/groops-devs/groops), which makes them publicly available and documented.</p>
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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 it