MétaCan
Menu
Back to cohort
Record W2669987074 · doi:10.1364/josaa.34.001187

Modeling turbulence in underwater wireless optical communications based on Monte Carlo simulation

2017· article· en· W2669987074 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the Optical Society of America A · 2017
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTurbulenceMonte Carlo methodScintillationPhysicsProbability density functionLog-normal distributionComputational physicsStatistical physicsUnderwaterComputer scienceOpticsMechanicsMathematicsGeologyStatistics

Abstract

fetched live from OpenAlex

Turbulence affects the performance of underwater wireless optical communications (UWOC). Although multiple scattering and absorption have been previously investigated by means of physical simulation models, still a physical simulation model is needed for UWOC with turbulence. In this paper, we propose a Monte Carlo simulation model for UWOC in turbulent oceanic clear water, which is far less computationally intensive than approaches based on computational fluid dynamics. The model is based on the variation of refractive index in a horizontal link. Results show that the proposed simulation model correctly reproduces lognormal probability density function of the received intensity for weak and moderate turbulence regimes. Results presented match well with experimental data reported for weak turbulence. Furthermore, scintillation index and turbulence-induced power loss versus link span are exhibited for different refractive index variations.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.410

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.284
Teacher spread0.251 · 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