Distributed temperature and strain sensing with high order stimulated Brillouin scattering
Bibliographic record
Abstract
Summary form only given. Stimulated Brillouin Scattering (SBS) has been extensively studied over the past few decades due to the many interesting properties and potential applications. Primarily, spontaneous Brillouin scattering (BS) has been used as a temperature and strain sensor enabling long range detection (tens of kilometres) with a relatively good spatial resolution (few meters) [1]. Such distributed temperature or strain sensors (DTSS) are capable of sensing 0.1°C temperature changes or micro-strains over long distances across large areas. However, sensitivity has remained mostly unchanged due to intrinsic properties of BS (1storder Brillouin frequency shift) leading towards a typical sensitivity of respectively ~1.2 MHz/°C and ~0.046MHz/με to temperature and strain.In 2014, we proposed to improve the temperature sensitivity (6x) of a BS sensor by using higher order SBS [2]. In this paper, we show strain and temperature sensitivity increase of 6x and 10x respectively compared to commercially available DTSS devices. With this technique, we achieved ~12 MHz/°C, and ~0.28MHz/ με) as shown in Fig.1 b) and c). We also propose a new way of making this high sensitivity sensor truly distributed. Preliminary results of high order SBS generation within short period of time are shown in Fig.1 a).
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".