High-Sensitivity Microbend Sensor Based on Light Cones in Coreless Fiber
Bibliographic record
Abstract
Abstract Eigenmode expansion (EME) is a widely used method for modeling the electromagnetic wave propagation in multimode waveguides, where it breaks down signals into local eigenmodes and calculates them independently. Nevertheless, this methodology may challenge the causality mandated by the theory of special relativity, thus potentially disrupting the cause-and-effect relationship. This study experimentally explored light transmission in the multimode coreless fiber and found discrepancies between the EME method and measurement. To reconcile these inconsistencies, we introduced a light cone model, providing an alternative interpretation guided by the principles of special relativity. Remarkably, this innovative model did not merely resolve the observed discrepancies between the theory and experiments, but also presented a pioneering technique for designing microbend sensors. Through experimentation, we achieved the remarkable sensitivity of 500 dB/m −1 at a bending curvature of 0 m −1 . Our research advances the understanding of multimode systems and paves the way for innovative sensing and communications applications in compact devices.
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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) | 0.001 | 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 | 0.001 | 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".