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Record W3208826733 · doi:10.1364/ol.441815

Single-shot chirped pulse BOTDA for static and dynamic strain sensing

2021· article· en· W3208826733 on OpenAlexafffund
Yuan Wang, Liang Chen, Xiaoyi Bao

Bibliographic record

VenueOptics Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Ottawa
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsOpticsLaser linewidthBrillouin scatteringBrillouin zoneMaterials scienceDistributed acoustic sensingImage resolutionFiber optic sensorSIGNAL (programming language)Optical fiberFiber laserLaserPhysicsComputer science

Abstract

fetched live from OpenAlex

Driven by the strong need for distributed high-frequency dynamic strain sensing, ultra-fast Brillouin optical time-domain analysis is rapidly becoming a vital technique. Thus, in this Letter, we propose and demonstrate a novel method by using a chirped pulse as a pump signal to extract the relative Brillouin frequency shift (BFS) changes through the real-time delays between adjacent Brillouin traces; this enables static and dynamic strain measurement without time-consuming frequency sweeping process. Benefiting from single-shot measurement based on Brillouin traces, the system has a high acquisition rate that is only limited by the sensor length without averaging, and is immune to the polarization fading problem, thanks to electrical delay time measurement. The pump source is a 1 MHz linewidth laser without a phase-locking loop; the laser frequency drifting noise could be compensated by the signal from the non-disturbed fiber section. In the experiments, BFS measurement resolution of 0.42 MHz with 4.5 m spatial resolution is demonstrated over a 5 km non-uniform fiber.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.232
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations22
Published2021
Admission routes2
Has abstractyes

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