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Record W2897444702 · doi:10.1088/1612-202x/aad991

Nonlinear spectrum broadening and its impact on performance of Rayleigh-scattering-based distributed strain/temperature fiber optic sensors

2018· article· en· W2897444702 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

VenueLaser Physics Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsRayleigh scatteringStrain (injury)Nonlinear systemSpectrum (functional analysis)Materials scienceDistributed acoustic sensingOptical fiberFiberOpticsFiber optic sensorPhysicsComposite materialQuantum mechanicsMedicine

Abstract

fetched live from OpenAlex

Abstract A Rayleigh-scattering-based distributed strain/temperature fiber optic sensor using low-coherence light is experimentally investigated at probe pulse powers well above the nonlinear effect threshold. OTDR technology is used for special channel interrogation. It is established that for an SMF-28e+ fiber this threshold is about 100 mW. Exceeding this power leads to the degradation of sensor performance and is explained by nonlinear spectrum broadening. The evolution of the probe pulse spectrum is investigated using a tunable MEMS filter. A novel version of the arrangement for the sensor is proposed, which partially overcomes the limitations associated with said effect. As low as 2.2 µε , the RMS noise level for strain is demonstrated at a distance of 25 km. The spatial resolution is estimated as 1.5 m, the data collection time is 20 min, and the average power in the fiber is about 0.2 mW, which allows the sensor to be used for infrastructure monitoring under explosive conditions.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score1.000

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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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