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Record W4362706310 · doi:10.1016/j.dib.2023.109134

Synthetic dataset of speckle images for fiber optic temperature sensor

2023· article· en· W4362706310 on OpenAlexafffund
Juan Diego Arango, Víctor H. Aristizábal, Francisco J. Vélez, Juan Carrasquilla, Jorge A. Gómez, Jairo Quijano, Jorge Herrera-Ramírez

Bibliographic record

VenueData in Brief · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsVector InstituteUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCompute CanadaUniversidad Cooperativa de ​Colo​mb​iaInstituto Tecnológico MetropolitanoUniversidad EAFITCanadian Institute for Advanced Research
KeywordsCladding (metalworking)MultiphysicsSpeckle patternOpticsFinite element methodWavelengthOptical fiberMulti-mode optical fiberModalMaterials scienceSpeckle noiseRefractive indexPhysics

Abstract

fetched live from OpenAlex

The published data correspond to images of simulated specklegrams, which result from the calculation of the modal interference that occurs in a multimode optical fiber. These have a characteristic pattern due to the constructive or destructive interference between the light modes depending on their phase differences. The specklegram contains valuable information since the propagation of the modes varies according to the influence of some external disturbances, and therefore, the speckle pattern changes. This dataset contains specklegrams that vary according to the temperature. These data have been obtained by simulation using the finite element method (FEM) through the COMSOL multiphysics platform. In the simulation, the vector wave equation is solved, and the refractive index of the fiber is recalculated due to the temperature change. We simulated a 1490 nm wavelength laser, an optical fiber with a core diameter of 50 µm and cladding diameter of 125 µm. The dataset contains specklegrams covering the range of temperatures from 0°C to 120°C in 0.2°C steps.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.496
Threshold uncertainty score0.587

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 head0.024
GPT teacher head0.276
Teacher spread0.252 · 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 designNot applicable
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

Citations11
Published2023
Admission routes2
Has abstractyes

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