Opto-Mechanical Interactions in Multi-Core Optical Fibers and Their Applications
Bibliographic record
Abstract
Optical fibers containing multiple cores are being developed towards capacity enhancement in space-division multiplexed optical communication networks. In many cases, the fibers are designed for negligible direct coupling of optical power among the cores. The cores remain, however, embedded in a single, mechanically-unified cladding. Elastic (or acoustic) modes supported by the fiber cladding geometry are in overlap with multiple cores. Acoustic waves may be stimulated by light in any core through electrostriction. Once excited, the acoustic waves may induce photo-elastic perturbations to optical waves in other cores as well. Such opto-mechanical coupling gives rise to inter-core cross-phase modulation effects, even when direct optical crosstalk is very weak. The cross-phase modulation spectrum reaches hundreds of megahertz frequencies. It may consist of discrete and narrow peaks, or may become quasi-continuous, depending on the geometric layout. The magnitude of the effect at the resonance frequencies is comparable with that of intra-core cross-phase modulation due to Kerr nonlinearity. Two potential applications are demonstrated: single-frequency opto-electronic oscillators that do not require radio-frequency electrical filters, and point-sensing of liquids outside the cladding of multi-core fibers, where light cannot reach.
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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 | 0.000 |
| 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".