Arrival time and angle fluctuations of sea-surface forward scattering
Bibliographic record
Abstract
The properties of the sound field, which is scattered from the sea surface, must be considered in any system that uses surface-reflected acoustic waves. In this paper, the probability density functions (PDFs) for time and the angle of arrival of the acoustic wave scattered from the sea surface are proposed. The trajectories of the emitted rays that reach the receiver are obtained by using the sea surface elevation and slope and the positions of the source and receiver. In this approach, the phenomenon of shadowing, which plays an important role in small grazing angles, is taken into account. The shadowing effect causes some parts of the sea surface to be shadowed by other parts. To show the validity of the proposed approach, the results are compared with experimental data and the arrival time and angle fluctuations obtained from a ray-tracing model with realistic one-dimensional sea surface boundaries. Also, it is shown that the standard deviation of the received angle fluctuations in this model is in close agreement with the experimental results reported in the literature. The importance of this model is that the PDFs of the arrival angle and time are presented theoretically, considering the shadowing effect.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".