Interrogation of a long-period-grating-based fiber sensor based on linear Gaussian function curve fitting
Bibliographic record
Abstract
An intensity interrogation system for a long-period-grating (LPG) -based fiber sensor using a linear combination of two Gaussian functions as the curve fitting function is proposed and demonstrated. The selected resonant dip of the LPG transmission spectrum can be reconstructed with the curve fitting function based on the measured intensities at different wavelengths. Thus, the center wavelength and the minimum transmission value of the resonant dip can be interrogated simultaneously. The center wavelength is obtained by calculating the first-order derivative of the fitting function, which is 1562.74 nm compared to 1562.3 nm directly measured using an optical spectrum analyzer (OSA). The minimum transmission value is obtained directly from the fitting curve, which is -35.6 dBm is compared to -34.2 dBm directly measured from an OSA. An arrayed waveguide grating (AWG) is supposed to be adopted for the intensity measurement. However, the experiment is carried out using a tunable optical filter to approve the concept due to the unavailability of a suitable AWG at the time of experiment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".