Altimetry precision of 1 cm over a pond using the wide-lane carrier phase of GPS reflected signals
Bibliographic record
Abstract
The capability of global navigation satellite system (GNSS) reflected signals for ocean altimetry can be enhanced by the use of carrier-phase measurements. The reflected signals have a Doppler spread inherent to the geometry of the measurement system, in our case of interest, a satellite in low Earth orbit flying over the rough sea surface illuminated by a constellation of GNSS transmitters. Carrier-phase measurements are thus difficult because of the short coherence time of the signal and their not obvious relation to the average water surface because of the roughness-induced fluctuations. In the near future, however, the global positioning system (GPS) and the European Galileo GNSS system will transmit several civilian signals on at least three different carriers. Using wide-lane processing, that is, the combination of the phases from the different carriers, a more coherent observable is obtained which is quite powerful for ocean remote sensing. The paper presents the first step in our research on carrier-phase processing of GNSS-reflected signals, consisting of an experiment over the smooth water surface of a pond. In addition to altimetry, the measurement of GNSS multi-carrier reflected signals can potentially provide accurate wind speed estimations based on the analysis of the statistical fluctuations of the carrier phases.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".