Accurate strain detection and localisation with the distributed Brillouin sensor based on phenomenological signal processing approach
Bibliographic record
Abstract
We introduce a phenomenological model, based on steady state analytical solution adapted to transient regime through modification of the Brillouin spectrum with the pulse spectrum. This model can accurately de-convolve the strain profiles from measured spectra. The model includes experimental parameters such as the electro-optic modulator Extinction Ratio, the pulse width, pulse and pump powers, position and sensing fibre length. The pulse base is treated as pure steady state contribution. A systematic numerical analysis has been carried out and the results are qualitatively matched with our experimental results. The experimental results have been used to validate the model and evaluate its limitations. Within this context, the approach has been applied to experimental data obtained under well-controlled laboratory conditions. The agreement is good and reflects the Brillouin frequency and then the strain distribution along the fibre. The approach is also successful when used to deconvolve the main strain contributions of a pipe subjected to a compression stress. The strength of the model lies in its simplicity of implementation because it is quasi-analytical and is not restricted to short fibre lengths.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".