Assessment of Galileo High Accuracy Service (HAS) test signals and preliminary positioning performance
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Bibliographic record
Abstract
The Galileo High Accuracy Service (HAS) is a GNSS augmentation that provides precise satellite corrections to users worldwide for free directly through Galileo's E6 signal. The HAS service provides free PPP corrections from the Galileo constellation and the Internet, with targeted real-time 95% positioning performance of better than 20 cm horizontal and 40 cm vertical error after 5 min of convergence time globally and shorter in Europe. The HAS initial service, under validation at the time of writing, provides these capabilities with a reduced performance (based on the current Galileo stations network). Live HAS test signals broadcasted from the Galileo satellites during summer 2022 have been decoded and analyzed. Corrections include Galileo and GPS orbit, clock, and code bias corrections, with SISRE of 10.6 cm and 11.8 cm for Galileo and GPS, respectively. Code bias corrections showed good performance as well, with rms of 0.28 ns, 0.26 ns, and 0.22 ns for Galileo C1C-C5Q, C1C-C7Q, and C1C-C6C, respectively, and 0.20 ns for GPS C1C-C2L. Float PPP positioning performance results show that the combined Galileo and GPS solution can already achieve the HAS full service accuracy performance target and is close in terms of convergence time, with 95% rms of 13.1 cm and 18.6 cm horizontally and vertically, respectively, in kinematic mode, and with a 95% convergence time of 7.5 min. The latter is expected to be improved with the inclusion of satellite phase bias and local atmospheric corrections. With these early Galileo HAS test signals, this preliminary analysis indicates that the HAS full service targets are attainable. Finally, a correction latency analysis is performed, showing that even with latency of up to 60 s, positioning can remain within the targeted HAS accuracy performance.
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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