Using an electrical arc for fiber components implementation
Bibliographic record
Abstract
In the present paper, we take a closer look at the design of long period fiber gratings (LPFG) which present several advantages compared to similar components. In fact, the LPFG has stronger wavelength stability when opposed to temperature variations and a better resistance against mechanical strength. These qualities offer them more importance in the industry. We use an electric arc to change the fiber characteristics and to create a fringe which presents a very thin section of the fiber that has a different refractive index. We use a computer based setup which allow us the control of the arc intensity and the discharge time and the gap between two neighbor fringes for the LPFG varies in the range of 400 to 700μm. A various optical fiber components designs and implementations are possible using electrical arc. In this paper we will expose the use of electrical arc and micro deformations for writing the Long Period Fiber Gratings (LPFG) on a single mode fiber. We take a closer look at the design of this component which present several advantages compared to similar components.
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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) | 0.000 | 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 | 0.000 | 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 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".