WaveTransNet: A Transformer-Based Network for Global Significant Wave Height Retrieval From Spaceborne GNSS-R Data
Bibliographic record
Abstract
Global Navigation Satellite System Reflectometry (GNSS-R) is a novel remote sensing technique for global significant wave heights (SWHs) observation. Previous studies have illustrated the efficacy of deep learning methods in SWH retrieval from GNSS-R data. However, most of these methods rely on convolutional layers to extract features from delay Doppler maps (DDMs), facing the limitations imposed by the fixed receptive field. To address this issue, in this study, a transformer-based network called WaveTransNet is proposed for SWH retrieval from GNSS-R data. Specifically, the transformer encoder block is exploited to capture long-range dependencies from DDMs. In addition, an attention mechanism-aided ancillary parameters feature extraction branch is devised to extract discriminative features from ancillary parameters, including geometry-related and map-related parameters. The developed model is evaluated on the Cyclone Global Navigation Satellite System (CYGNSS) dataset, and the experimental results demonstrate its improved performance. Compared with the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, it achieves a root mean square difference (RMSD) of 0.443 and 0.444 m when National Data Buoy Center (NDBC) buoy data are used for evaluation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".