High-sensitivity temperature sensing using higher-order Stokes stimulated Brillouin scattering in optical fiber
Bibliographic record
Abstract
In an effort to reduce the cost of sensing systems and make them more compact and flexible, Brillouin scattering has been demonstrated as a useful tool, especially for distributed temperature and strain sensing (DTSS), with a resolution of a few centimeters over several tens of kilometers of fiber. However, sensing is limited by the Brillouin frequency shift's sensitivity to these parameters, which are of the order of ~1.3 MHz/°C and of ~0.05 MHz/με for standard fiber. In this Letter, we demonstrate a new and simple technique for enhancing the sensitivity of sensing by using higher-orders Stokes shifts with stimulated Brillouin scattering (SBS). By this method, we multiply the sensitivity of the sensor by the number of the Stokes order used, enhanced by six-fold, therefore reaching a sensitivity of ~7 MHz/°C, and potentially ~0.30 MHz/με. To do this, we place the test fiber within a cavity to produce a frequency comb. Based on a reference multiorder SBS source for heterodyning, this system should provide a new distributed sensing technology with significantly better resolution at a potentially lower cost than currently available DTSS systems.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".