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Record W4280603215 · doi:10.1515/joc-2021-0120

Raman pumps power distribution optimization for maximum overall gain and flatness of a hybrid SOA/EDFA/Raman optical amplifier

2022· article· en· W4280603215 on OpenAlexaff
Ajaybeer Kaur, Manjit Singh Bhamrah, Ahmad Atieh

Bibliographic record

VenueJournal of Optical Communications · 2022
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsOptiwave Systems (Canada)
Fundersnot available
KeywordsFlatness (cosmology)Optical amplifierOpticsWavelength-division multiplexingWavelengthNoise figureRaman amplificationMaterials scienceAmplifierOptoelectronicsAmplified spontaneous emissionLaserPhysics

Abstract

fetched live from OpenAlex

Abstract A hybrid optical amplifier (HOA) is designed and optimized for the transmission of 40 dense wavelength division multiplexed system (DWDM) channels modulated at 10 Gbps having 25 GHz spacing at the edge of the L and U wavelength bands over more than 250 km. Multi-parameter optimization process is used to achieve the highest gain and best gain flatness. Different power combinations distributed among four lasers of a total forward pumping power (1270 mW) and total backward pumping power of either 730 mW or 850 mW are investigated for their effect on the hybrid amplifier gain and flatness. The best power distribution among the pumps provides about 31 dB overall gain with a flatness about 0.8 dB and noise figure ∼5.7 dB. It is found that the red-shifted pumps’ wavelengths should be used in the forward direction, while the blue-shifted pumps’ wavelengths should be used in the backward direction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.251
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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