Generalised noise cancellation method for wave estimation by HF surface wave radar
Bibliographic record
Abstract
High frequency (HF) surface wave radar (HFSWR) has been demonstrated, in many experiments and papers, to be a powerful tool for sea‐state detection. However, the availability and accuracy of the HFSWR measurements are limited by various unwanted clutter and interferences (collectively called ‘noise’) that contaminate the radar received signals, especially for wave estimation. This study extends the image recognition, segmentation and subspace projection method for removing the radio frequency interference developed in the previous study, to the mitigation of more general types of noise. Applications of this generalised method are presented. The results show that the noise can be largely removed regardless of their correlation in Doppler or range, their size in the range‐Doppler domain and whether they are homogeneous or inhomogeneous. The effectiveness of all these approaches is validated by using data obtained with the Pisces HF radar, which is a high‐performance radar developed for long‐range wave measurement, operating in the lower half of the HF band (5–10‐MHz).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".