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Record W2953832339 · doi:10.1109/access.2019.2925357

Open Cavity Hybrid Raman-Erbium Random Fiber Laser With Common Pump

2019· article· en· W2953832339 on OpenAlexfundno aff
N.H. Zainol Abidin, M. H. Abu Bakar, Yasmin Mustapha Kamil, Ahmad Fauzi Abas, Mohammed T. Alresheedi, Mohd Adzir Mahdi

Bibliographic record

VenueIEEE Access · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRandom lasers and scattering media
Canadian institutionsnot available
FundersInstitute of Population and Public HealthKing Saud University
KeywordsRaman amplificationMaterials scienceErbiumFiber laserOpticsLaserRaman spectroscopyOptoelectronicsRaman laserOptical amplifierRaman scatteringPower (physics)WavelengthPhysics

Abstract

fetched live from OpenAlex

A symmetrical 80-km open cavity erbium-integrated hybrid random distributed feedback fiber laser (HRFL) was proposed and experimentally demonstrated. A variation of pumping schemes and cavity lengths was first investigated prior to the integration of the EDF. The impact of Raman and EDF hybrid amplification was then investigated through EDF length variation. The proposed scheme used a single common pump to incite both Raman and erbium gain to produce a single peak at a 1567-nm wavelength with maximum OSNR of 62.37 dB. A maximum total output power generation of 1420 mW was achieved with high-slope efficiency of 38%. The proposed hybrid setup has shown improved performance despite using open-ended cavity sustained by only a single pump in contrast to previous more complex hybrid schemes. Prolonged chaotic regime manifesting spontaneous pulse burst was also observed before the stable regime. The simple operation with the high performance of the proposed configuration offers a great potential for long distance or remote access applications such as heavy metals sensing or even for biological hazard sensing.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.014
GPT teacher head0.278
Teacher spread0.264 · 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 designBench or experimental
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

Citations6
Published2019
Admission routes1
Has abstractyes

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