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Record W2162518979 · doi:10.1364/ol.33.002707

Using pulse with a dark base to achieve high spatial and frequency resolution for the distributed Brillouin sensor

2008· article· en· W2162518979 on OpenAlexafffund
Feng Wang, Xiaoyi Bao, Liang Chen, Yun Li, Jeffrey Snoddy, Xuping Zhang

Bibliographic record

VenueOptics Letters · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsBrillouin scatteringOpticsBrillouin zonePulse (music)Materials scienceWavelengthPhysicsOptical fiberDetector

Abstract

fetched live from OpenAlex

A stimulated Brillouin scattering based distributed optical fiber sensor using a probe pulse (<5 ns) of finite extinction ratio (ER) (>20 dB) followed by a dark base of a finite length is proposed to achieve higher spatial and frequency resolution for the first time. The basic mechanism for detecting the small stress or temperature section is to reduce the peak height of the Brillouin spectrum contributed by dc at the stress point so that the Brillouin frequency shift (BFS) of small stress or temperature section can show up at a much lower peak height of the Brillouin spectrum. The finite ER of the positive pulse is used as prepumping of the phonon field to achieve higher contrast for the Brillouin spectrum. The length of dark base is determined by the balanced contribution of the normal base in fiber and the dark base after the pulse to the Brillouin gain spectrum. The theoretical simulation and the experimental results both demonstrate that the proposed novel pulse shape can be used to measure a centimeter stress or temperature section with small uncertainty for the BFS. For two 5 cm stress sections of 15 MHz equivalent strains in BFS with a 30 cm separation, the measured frequency uncertainty is 0.9 MHz.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.592

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 head0.018
GPT teacher head0.219
Teacher spread0.200 · 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

Citations21
Published2008
Admission routes2
Has abstractyes

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