Bibliographic record
Abstract
Surface scattering is a major source of interference for sidescan sonar systems operating in high traffic areas or in choppy water. This surface scattering from wakes, as a result of boat traffic or from the chop of the sea surface, obscures the bottom return. A sonar system with a multi-element array can separate surface signals from bottom signals using beamforming, thereby creating clear images of the seafloor. This multi-element array can have as few as six elements and still effectively remove surface scattering. In this paper, different beamforming techniques are applied and their impact on suppressing surface returns is shown. Comparisons between beamforming methods are made by comparing the relative path levels with and without beamforming applied. The ability of a multi-element array to successfully discriminate between bottom returns and surface returns using beamforming is then shown using both simulated and experimental data. It is concluded that sonar systems employing a multi-element array can produce clear images of the seafloor even in the presence of strong surface interference when beamforming is used to create a beam that has a broad main lobe pointed toward the bottom and low sidelobes pointed toward the surface.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".