Insights into tunnelling rays: outperforming guided rays in fiber-optic sensing device
Bibliographic record
Abstract
We report that, for an evanescent-wave (EW) based fiber-optic sensor, the less skewed tunnelling rays and a small number of very steep guided rays experience total internal reflection (TIR) at one end-face of the air-clad fiber segment. As a result, they demonstrate nine times greater efficiency in collecting EW-power than rays that do not exhibit end-face- TIR. This tunneling ray group originates from a launching ring that is seven times wider than that of end-face-TIR capable guided rays, enabling it to dominate the overall detected EW-power level. Strongly supporting this conclusion is the counter-intuitive experimental observation that the detected power at one end of the fiber abruptly drops 91% when the back reflection at the opposite end is eliminated, while an isotropic light source is placed at the core-cladding interface between these two ends. The mechanism of non-propagating ray excitation accounts for this high efficiency. We also indicate that many highly skewed tunnelling rays are naturally isolated from the external world because, outside the fiber core, they are accessible only via their EW fields even at the fiber end-faces. The findings reported here have significant implications for the design of future high-performance EW-based fiber sensing devices for the analysis of surface-event.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".