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Record W2885254019 · doi:10.1049/iet-opt.2018.5029

Enhanced gain saturation model of non‐linear semiconductor optical amplifiers

2018· article· en· W2885254019 on OpenAlexaff
Osayd M. Kharraz, Abu Sahmah M. Supa’at, Ahmad Atieh, A. K. Zamzuri, Mohd Adzir Mahdi

Bibliographic record

VenueIET Optoelectronics · 2018
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsOptiwave Systems (Canada)
FundersUniversiti Teknologi Malaysia
KeywordsSaturation (graph theory)Optical amplifierSemiconductorMaterials scienceAmplifierOptoelectronicsElectronic engineeringOpticsPhysicsMathematicsEngineeringLaserCMOS

Abstract

fetched live from OpenAlex

This study proposes an enhanced gain saturation model of non‐linear semiconductor optical amplifiers (SOAs) by incorporating material‐dependent gain compression factor. The rate equations are utilised with the extra gain compression term for Indium–Gallium–Arsenide material‐based SOA to account for the steep relaxation oscillations behaviour of non‐linear SOAs. The proposed gain saturation model is verified with experimental results that showed very good agreements with a mean square error of 0.094.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.012
GPT teacher head0.242
Teacher spread0.230 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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