Optical Components Based on PCF Fibers
Bibliographic record
Abstract
The requirements in flow and band-width does not cease growing and the monomode optical fibre used currently in the systems of telecommunications starts particularly to show many limitations for the very big flows (>40Gb/s) in spite of the extraordinary performances of which it made proof in the past. The photonic crystal fibers are considered as a key solution for perfect optical communications. This new generation of optical fibers is also regarded as the future data carrier for the systems of telecommunications. In these fibers, the cladding is distinguished from the core only by the presence of air-hollow what gives it an index of refraction average lower than that of the core. This work is devoted to the realization and the characterization of the optical components containing this type of fibre. These components are dedicated to applications of telecommunications. We use techniques of heating and stretching in order to adjust or to locally modify the geometrical and physical parameters of a PCF fiber such as the diameter of the core, the shape and the distribution of the holes and the index of refraction. These adjustments make it possible to obtain certain performances very requested in the systems of telecommunications at very high flows in order to solve the problems of attenuation and dispersion. The principle consists in changing the internal structure of fiber in order to reduce to the maximum the losses what makes it possible to have an excellent quality control of the optical transmission. Our goal is to conceive and to design of new optical components which allow the optical telecommunication systems to have more capacity and performances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".