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Record W4296831502 · doi:10.3390/s22197145

Coaxial Mach–Zehnder Digital Strain Sensor Made from a Tapered Depressed Cladding Fiber

2022· article· en· W4296831502 on OpenAlexaff
Sergio Celaschi, Nicolas Grégoire, Younès Messaddeq, Claudecir R. Biazoli, Gilliard N. Malheiros‐Silveira

Bibliographic record

VenueSensors · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversité Laval
FundersFinanciadora de Estudos e ProjetosConselho Nacional de Desenvolvimento Científico e TecnológicoMinistério da Ciência, Tecnologia e InovaçãoFundo para o Desenvolvimento Tecnológico das TelecomunicaçõesFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsMaterials scienceOptical fiberOpticsExtinction ratioCladding (metalworking)Free spectral rangeMach–Zehnder interferometerInterferometryFiber optic sensorInsertion lossCoaxialOptoelectronicsElectrical engineeringPhysicsWavelengthEngineeringComposite material

Abstract

fetched live from OpenAlex

An in-line digital optical sensor was proposed. It was built from a tapered depressed-cladding single-mode fiber and modeled as a coaxial Mach–Zehnder interferometer. The principle of operation of the optical digital sensor is based on the computation of the number of optical power transfer turning points (PTTP) from the transmission data of the component. Biconic tapers with high values of PTTP, high spectral resolution, high extinction ratio, and low insertion loss were modeled, fabricated, and characterized. As a proof of concept, an in-line digital strain sensor was fabricated and characterized. It presents a free spectral range of 1.3 nm, and produced 96 PTTP, at λ0 = 1.55 μm, under stretch of ΔL = 707 µm, therefore producing a digital resolution of 7.4 µm/PTTP. The sensor also produced a quasi-symmetric response to stretch and compression.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.299
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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.

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

Citations3
Published2022
Admission routes1
Has abstractyes

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