The non-uniformity and dispersion in SBS-based fiber sensors
Bibliographic record
Abstract
For a fiber with complex index profile, its density fluctuations change with position and it also introduces birefringence, hence the Brillouin frequency changes with position due to variation of modal index, and sound velocity has a range instead of being a constant. As a result, the single mode fibers support multiple Brillouin resonances varying in position, even with polarization scramblers (PS) of the pump and probe waves. For a Brillouin optical time domain analysis (BOTDA), at a specific location, because of the spatial resolution, the measured Brillouin frequency still gives a range, although PS can help to reduce this fluctuation, as the spatial resolution is much smaller than the beat length of single mode fiber (SMF). The measured Brillouin frequency variations in one position and its location dependence reflect the non-uniformity of the optical fiber, rather than the systematic error of the sensor detection system. When a fiber supports elliptical birefringence, then four Brillouin resonances can be found for an acoustically uniform fiber based on theoretical calculation of their eigenmodes. The beat of different Brillouin peak frequencies and their magnitudes change with temperature and strain, which can be used to measure temperature and strain simultaneously in LEAF fiber without the need of the sweeping Brillouin spectrum.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".