Real-time interrogation of a linearly chirped fiber Bragg grating sensor based on chirped pulse compression using a Sagnac loop interferometer
Bibliographic record
Abstract
A novel approach to interrogating in real time a linearly chirped fiber Bragg grating (LCFBG) sensor based on chirped pulse compression using a Sagnac loop interferometer (SLI) with improved pulse compression performance is proposed and experimentally demonstrated. The proposed system consists of a mode-locked laser (MLL), a SLI incorporating an LCFBG, which makes the SLI have a spectral response with an increasing or decreasing free spectral range (FSR), a dispersive element and a photodetector. The significance of using an SLI incorporating an LCFBG is its capability of providing equal dispersion for two pulses traveling along the clockwise and counter-clockwise paths, which would effectively avoid a non-complete temporal interference, and improves the pulse compression performance. When the fiber sensor is experiencing a strain, the strain information would be conveyed to a wavelength shift caused by the Bragg wavelength change, which is further transferred to the change of the FSR. An ultra-short pulse train generated by the MLL would be spectrum shaped by the SLI, and the shaped spectrum would contain the information of the wavelength change. The demodulation is performed in the time domain by mapping the spectrally shaped waveform to the temporal domain using a dispersion compensating fiber (DCF) as the dispersive element. The generated temporal waveform is then correlated with a special reference waveform, with the location of the correlation peak indicating the wavelength change which reflects the strain or temperature change. A theoretical analysis is carried out, which is validated by an experiment. The experimental results show that the proposed system can provide an interrogation resolution as high as 0.22 με at a speed of 48.6 MHz with a correlation peak to sidelobe ratio of 2.5.
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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 | 0.000 | 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) | 0.000 | 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".