Satellite augmentation systems for acceleration determination in airborne gravimetry: a comparative study
Bibliographic record
Abstract
In this paper, we investigate the computation of the kinematic acceleration for airborn gravimetry applications using three different GPS-based technologies. We present a comparison study of some experimental measurements recorded using the wide-area differential GPS OmniSTAR service, the global satellite-based augmentation system (GSBAS) StarFire, and the post-processing of the carrier-based DGPS measurements. This later is performed using a high-precision real-time kinematic (RTK) platform developed by authors. We analyze the performance of these technologies using real data collected in both static and dynamic mode (in the fly). The measurements are based on a series of flight tests conducted in the area of Toronto, Canada, by the industrial project partner, during a period between 2006 and 2007. The results are analyzed in time and frequency domains. Since the gravity sensors operate in low frequencies (below 0.05 Hz), we focus the analysis of the GPS-based acceleration computation in this frequency range. As a preliminary result, we show that in some particular cases, differential GPS can offer almost the same accuracy as RTK GPS, a commonly used approach for acceleration determination in airborne gravity survey.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".