An Attention-Aided Convolutional Neural Network for Global Sea Surface Wind Speed Estimation from Gnss-R Data
Bibliographic record
Abstract
Global navigation satellite system reflectometry (GNSS-R) is an emerging remote sensing technology for sea surface wind speed measurement. GNSS-R captures the reflected signal from sea surface and generates Delay Doppler maps (DDM). Existing studies have demonstrated the effectiveness of convolutional neural network (CNN) in retrieving wind speed from GNSS-R. However, most of the existing CNN-based methods assign equal importance to each pixel in the DDM and cannot focus on more discriminative pixels. To address this issue, in this study, the attention mechanism is integrated and an attention-aided CNN (Att-CNN) is developed for sea surface wind speed estimation. The performance of Att-CNN is evaluated on the Cyclone GNSS (CYGNSS) data. Compared to conventional CNN without attention module, the proposed Att-CNN obtains a lower RMSD of 1.151 m/s with an improvement of 3.46%.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".