Nonlinear spectrum broadening and its impact on performance of Rayleigh-scattering-based distributed strain/temperature fiber optic sensors
Bibliographic record
Abstract
Abstract A Rayleigh-scattering-based distributed strain/temperature fiber optic sensor using low-coherence light is experimentally investigated at probe pulse powers well above the nonlinear effect threshold. OTDR technology is used for special channel interrogation. It is established that for an SMF-28e+ fiber this threshold is about 100 mW. Exceeding this power leads to the degradation of sensor performance and is explained by nonlinear spectrum broadening. The evolution of the probe pulse spectrum is investigated using a tunable MEMS filter. A novel version of the arrangement for the sensor is proposed, which partially overcomes the limitations associated with said effect. As low as 2.2 µε , the RMS noise level for strain is demonstrated at a distance of 25 km. The spatial resolution is estimated as 1.5 m, the data collection time is 20 min, and the average power in the fiber is about 0.2 mW, which allows the sensor to be used for infrastructure monitoring under explosive conditions.
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) | 0.000 | 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 | 0.001 |
| 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".