Wave Height Estimation From X-Band Radar Data Using Variational Mode Decomposition
Bibliographic record
Abstract
In the paper, a variational mode decomposition (VMD)-based method is proposed to estimate significant wave heights (Hs) from X-band marine radar images. Firstly, 10 intrinsic mode functions (IMFs) are decomposed from the selected radar sub-images with VMD. Then, a linear fitting method is conducted to estimateHsby using the sum of the amplitude modulation (AM) components extracted from the 6thto 9thIMFs. The radar data were collected from a ship at sea around 300 km from Halifax, NS, Canada. The real-timeHsdata were obtained by drifting Triaxys buoys around the moving vessel. Experiment results show that the proposed VMD-based linear fitting method generates improvement in theHsmeasurements, compared to the typical ensemble empirical mode decomposition (EEMD)-based linear fitting method, by reducing the root-mean-square error (RMSE) from 0.34 m to 0.32 m and increasing the correlation coefficient (CC) from 0.90 to 0.92 after using the moving average.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".