Wave Height Estimation From X-Band Radar Data Using Variational Mode Decomposition
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Bibliographic record
Abstract
In the paper, a variational mode decomposition (VMD)-based method is proposed to estimate significant wave heights (<i>H<sub>s</sub></i>) 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 estimate <i>H<sub>s</sub></i> by using the sum of the amplitude modulation (AM) components extracted from the 6<sup><i>th</i></sup> to 9<sup><i>th</i></sup> IMFs. The radar data were collected from a ship at sea around 300 km from Halifax, NS, Canada. The real-time <i>H<sub>s</sub></i> data were obtained by drifting Triaxys buoys around the moving vessel. Experiment results show that the proposed VMD-based linear fitting method generates improvement in the <i>H<sub>s</sub></i> measurements, 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it