Pushing the limit of the distributed Brillouin sensors for the sensing length and the spatial resolution
Bibliographic record
Abstract
Long distance sensing based on Brillouin scattering with centimeter spatial resolution, and yet high strain or temperature resolution requires the optimization of the optical and electronic system. In optical domain the limiting factors include gain saturation of the Stokes signal and pump depletion induced the Brillouin spectrum distortion, and thus a low gain is desired that requires low pump power, which sets a limit in the signal to noise ratio (SNR). The detection system must have high gain and narrow bandwidth to reduce electronic noise. The coded pulse offers the best solution as a low power solution of long distance sensing based on BOTDA to improve the signal to noise ratio (SNR), comparing two most common used formats: non-return-to-zero (NRZ) and return-to-zero (RZ), RZ coded pulses offer minimum distortion in the spatial resolution and the Brillouin spectrum, because the signal in RZ format returns to zero in very bit, while in NRZ coded pulse the signal returns to zero after continuous "1"s, which brings the higher gain and lower bandwidth comparing that in RZ coded pulse for BOTDA system. Hence NRZ coded pulse BOTDA would introduce spatial broadening and lower the spatial resolution. With minimum distortion of RZ signal we can use differential Brillouin gain to realize DPP-BOTDA technique for sub-meter spatial resolution. The minimum coded pulse width must be larger than the acoustic wave relaxation time to avoid the distorted Brillouin gain spectrum. Using LEAF fiber we achieved 50km sensing length and 50cm spatial resolution with the strain resolution of 8με which is equivalent to 0.7MHz Brillouin frequency shift, this is the 1st sub-meter spatial resolution for 50km sensing length combined with high strain resolution.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".