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Record W3190839900 · doi:10.1121/10.0005738

Arrival time and angle fluctuations of sea-surface forward scattering

2021· article· en· W3190839900 on OpenAlexaff
Seyed Mohammad Reza Mousavi, Mahmood Karimi

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2021
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsRay tracing (physics)Surface (topology)ScatteringAngle of arrivalElevation (ballistics)PhysicsGeologyOpticsProbability density functionElevation angleComputational physicsGeodesyGeometryMathematicsComputer scienceAzimuthStatistics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.161

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.220
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicUnderwater Vehicles and Communication SystemsFrench-language works237,207