Influence of Brillouin Slow Light on Distributed Brillouin Fiber Sensor due to Depletion of Pump Beam
Bibliographic record
Abstract
The influence of Brillouin slow light on distributed Brillouin fiber sensors is studied. We demonstrate Brillouin slow light for a 1.2 ns pulse with peak power from 3.3 to 56.2 mW at the depletion of the pump power from 1.3 mW to 83.2 mW in conventional optical fibers (SMF-28). Our experimental results show that when the depletion of the pump power is not negligible (pump and probe powers are comparable, which is the optimized distributed Brillouin fiber sensor regime), for a given pump power the Brillouin gain and delay time of a pulse decrease when the pulse peak power increases in a long length (≥10 km) sensing fiber. The strong depletion of the pump beam resulting from the strong interaction between pump and probe in the fiber provides accurate local temperature and strain information at high spatial resolution. Our study reveals that for a nanosecond pulse at low pump power, the spatial resolution error caused by pulse delay for a distributed Brillouin fiber sensor with centimeter spatial resolution is less than 5% of the pulse length, which is within experimental uncertainty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.001 | 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".