Double-Sensor Multiplexed Reflectometric Fiber-Optic FMCW Displacement Sensor
Bibliographic record
Abstract
Multiplexed fiber-optic sensors are very useful in practice since it enables the information to be obtained from a single sensor, which would otherwise require several sensors.1 Another important application of the multiplexed fiber-optic sensor is that one individual sensor can be used to detect the environmental effect, so that the error introduced by the change of environmental parameters (such as temperature) can be dynamically compensated, and the accuracy and long-term stability of the fiber-optic sensor can be significantly improved. Optical frequency- modulated continuous-wave (FMCW) interference generally can provide a higher accuracy and a longer dynamic range than the classical homodyne interference, because it generates a dynamic signal and thus to calibrate the fractional phase, determine the phase shift direction and count the number of the full periods is easy.2,3 This paper will introduce a practical double-sensor multiplexed fiber-optic FMCW displacement sensor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".