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Record W2592611947 · doi:10.1109/joe.2017.2658738

Echo, Reverberation, and Echo-to-Reverberation Ratio for a Short Pulse in a Range-Dependent Pekeris Waveguide

2017· article· en· W2592611947 on OpenAlexaff
Michael A. Ainslie, Dale D. Ellis

Bibliographic record

VenueIEEE Journal of Oceanic Engineering · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsMount Allison UniversityDalhousie University
FundersOffice of Naval Research
KeywordsReverberationEcho (communications protocol)BathymetryAcousticsSonarScatteringWaves and shallow waterSeabedMarine mammals and sonarRange (aeronautics)Dispersion (optics)PhysicsOpticsGeologyComputational physicsComputer scienceMaterials science

Abstract

fetched live from OpenAlex

In shallow water, active sonar performance is typically limited by reverberation, making the prediction of target echo, reverberation, and echo-to-reverberation ratio an important part of sonar performance prediction. In range-dependent shallow-water environments, the echo is often calculated without considering the effect of dispersion, and reverberation predictions are often limited to Lambert's rule for seabed scattering. In this paper, analytical formulae are derived for the echo, including the effect of time dispersion, and for reverberation for non-Lambert scattering laws. More specifically, the method for calculating the short-pulse echo intensity developed by Harrison and Nielsen is extended to a range-dependent bathymetry. For reverberation, the Zhou-Harrison method is also generalized to cover an arbitrary power law for the seabed scattering coefficient, combined with a range-dependent bathymetry. These formulae are applied to range-dependent test cases from an international workshop, and compared with predictions using a normal mode sum. Neglect of time dispersion is found to result in an error of up to 16 dB. Results for a cylindrically symmetric bathymetry differ by up to 14 dB from the corresponding results with a Cartesian symmetry.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.520
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.274
Teacher spread0.248 · 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 designSimulation or modeling
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

Citations7
Published2017
Admission routes1
Has abstractyes

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