Distributed phase-matching measurement enabled dynamic temperature–strain discrimination using single chirped pulse probe
Bibliographic record
Abstract
Distributed optical fiber sensors with the capability of dynamic temperature and strain discrimination can be used for various applications, including perimeter security, structural health monitoring, and seismic sensing, as they can access the tolerance of the structures and sites to natural hazards, such as earthquakes, fires, and overflows. Here, we propose a hybrid Brillouin/Rayleigh sensing system that combines distributed phase matching measurement via Brillouin dynamic grating and inhomogeneity-induced Rayleigh scatting in polarization-maintaining fiber. Due to the high birefringence of polarization-maintaining fibers imposed by the stress-rods, the detection of Brillouin dynamic gratings results in opposite signs of spectral shifts under the changed temperature and strain, giving a high discrimination accuracy. In addition, the usage of a single chirped probe pulse signal allows a single end detection system for Rayleigh of the probe wave and idler wave from stimulated Brillouin scattering enhanced four-wave mixing, which simplified the sensing system significantly. Driven by the high spatial resolution ability of distributed phase matching measurement without phonon lifetime limitation, an intensity-based analysis approach for Rayleigh traces is carried out to resolve the external perturbation applied in a short section that is smaller than the spatial distinctness associated pulse width. In the proof-of-concept experiments, a simultaneous strain and temperature variation within a 40 cm fiber section are successfully discriminated with noise equivalent discrimination errors for strain and temperature of 112.2 nɛ and 10.9 m °C. The spatial resolution here is 2 m, and the maximum system’s sampling rate is up to 100 kHz without average, corresponding to the sensing distance of 1 km.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".