Effect of pulsewidth on strain measurement accuracy in Brillouin-scattering-based fiber optic sensors
Bibliographic record
Abstract
Distributed Sensors based on Brillouin scattering are attractive candidates to monitor structural health of mechanical structures. Currently physical limitations of time resolved techniques do not allow extracting accurate strain when pulsewidth is larger than strained length. The purpose of this study was to quantify the errors in such a case and derive a decision threshold making possible the discrimination of strained section from unstrained contribution. We achieved these goals by solving the coupled intensity equations for various strained lengths, pulsewidths, strain strengths, Brillouin linewidths, Fibre lengths, probe and pump powers. We observed two dominating behaviours principally functions of strained length and strain strength. In the first regime, a single peak broader than Brillouin natural gain/loss peak was detected. In the second case, we could distinguish two peaks. From these behaviours we then derived curves relating Brillouin peak contrast as well strain accuracy to the ratio between strained section length and pulsewidth. These curves also show a strong dependence on strain amplitude. This led us to find a strain induced frequency shift threshold characterising the transition from the single peak regime to the double peak regime. Finally we defined a Rayleigh equivalent criterion that unambiguously separates strained from unstrained contributions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".