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Record W3196886767 · doi:10.1109/jlt.2021.3110505

Analytical Expressions for Power Coupling Coefficients Into Graded-Index Fibers With Generalized Beam Launch Conditions

2021· article· en· W3196886767 on OpenAlexafffund
Shanglin Li, Mohammadreza Sanadgol Nezami, Odile Liboiron-Ladouceur

Bibliographic record

VenueJournal of Lightwave Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of British ColumbiaMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOffset (computer science)ComputationOpticsMulti-mode optical fiberMathematical analysisCoupling coefficient of resonatorsGaussianMathematicsPhysicsCoupling (piping)Optical fiberMaterials scienceComputer scienceAlgorithmResonator

Abstract

fetched live from OpenAlex

In this paper, we derive the closed-form analytical expressions of the power coupling coefficients for a Laguerre-Gaussian beam coupled into a graded-index fiber with either central, offset, or tilted launch. We further generalize to an arbitrary launching condition with the radial offset and the three-dimension angular tilt simultaneously occurring. The analytical results are in good agreement with the numerical ones and can be applied to manufactured graded-index multimode fibers with an index exponent parameter from 1.8 to 2.2. Using the derived analytical expressions, the computation time shortens by at least a four-order of magnitude compared to conventional numerical methods.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.262
Teacher spread0.252 · 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 designTheoretical or conceptual
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
Published2021
Admission routes2
Has abstractyes

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