A Comparative Study of SBAS Systems for Navigation in Geostationary Orbit
Bibliographic record
Abstract
Real data has been collected from space demonstrating the CanX-2 receiver’s ability to track the WAAS, EGNOS, GAGAN, and MSAS systems. Two types of analysis were performed, in order to assess the suitability of SBAS ranging measurements as a source of positioning information for users in geostationary and other higher orbits, in which SBAS satellites may be permanently in view while GPS visibility is severely limited by the shape of the transmit gain patterns. The first analysis, of the transmit gain patterns of the EGNOS, WAAS, MSAS and GAGAN systems, revealed that all the SBAS satellites transmit enough power to be tracked over the earth’s limb. It was revealed that GAGAN has a narrow gain pattern than the other SBAS systems, WAAS and EGNOS appear to have similar gain patterns but WAAS has a higher transmit power by 2-4 dB, and MSAS appears to transmit lower signal power than the other systems but uses an antenna design providing more even global coverage, which results in and stronger power transmitted towards the edge of the earth. The second study determined that the SBAS ranging capability was useable in space, provided that the fast correction data transmitted by the SBAS satellites was applied in addition to the MT9 broadcast ephemeris. The SBAS ranging accuracy is lower than it is for the standard GPS MEO signals, but in most cases the errors are within +/- 10 m for WAAS and +/- 20 m for MSAS and GAGAN. EGNOS does not support ranging. Provided the lower accuracy compared to GPS is taken into account, the SBAS systems could be used to provide positioning and timing information to users in GEO or other orbits above the MEO GNSS constellations
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".