Rayleigh scatter based order of magnitude increase in distributed temperature and strain sensing by simple UV exposure of optical fibre
Bibliographic record
Abstract
We present a technique to improve signal strength, and therefore sensitivity in distributed temperature and strain sensing (DTSS) using Frequency domain Rayleigh scatter. A simple UV exposure of a hydrogen loaded standard SMF-28 fibre core is shown to enhance the Rayleigh back-scattered light dramatically by ten-fold, independent of the presence of a Bragg grating, and is therefore created by the UV exposure alone. This increase in Rayleigh back-scatter allows an order-of-magnitude increase in temperature and strain resolution for DTSS compared to un-exposed SMF-28 fibre used as a sensing element. This enhancement in sensitivity is effective for cm range or more sensor gauge length, below which is the theoretical cross-correlation limit. The detection of a 20 mK temperature rise with a spatial resolution of 2 cm is demonstrated. This gain in sensitivity for SMF-28 is compared with a high Ge doped photosensitive fibre with a characteristically high NA. For the latter, the UV enhancement is also present although of lower amplitude, and enables an even lower noise level for sensing, due to the fibre's intrinsically higher Rayleigh scatter signal.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".