Structural Analysis of Nano Core PCF With Fused Cladding for Supercontinuum Generation in 6G Networks
Bibliographic record
Abstract
Abstract The Sixth Generation (6G) networks have identified the use of frequency range between 95 GHz and 3 THz with a targeted data rate of 1 Terabytes/second at the access network for holographic video applications. As is demands broadening of spectrum at the core network, this paper proposes a Supercontinuum Generation (SCG) through photonic crystal fiber (PCF) as it provides excellent broadening of the optical spectrum. Discussed in the paper is supercontinuum generation at high pumping power as per the standards specified by the International Telecommunications Union. The proposed PCF is designed with silicon nanocrystal core and the cladding microstructures is arranged in a fusion approach to effectively optimize the optical parameters such as dispersion, nonlinearity, birefringence, group‐velocity dispersion, and confinement loss. The fused cladding comprises of a flower‐cladding assembly in which air‐holes arrangement is inspired from petals in a pleated structure. Such arrangement is shown here to provide high nonlinearity and negative dispersion for high power supercontinuum generation. The novel nanocore assembly with improved structural constraints delivers a non‐linearity of 6.37 × 106 W−1 km−1 and a negative dispersion of −142.1 (ps/nm‐km) at 1,550 nm. Moreover, a supercontinuum spectrum is generated using different pulse widths ranging from 350 to 650 ps with 25 kW pump power for PCF lengths of 10 and 15 mm.
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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) | 0.000 | 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".