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Record W4287958894 · doi:10.1002/dac.5305

Numerical investigation of elliptical core few‐mode fiber for next generation data transmission

2022· article· en· W4287958894 on OpenAlexaff
Sumaiya Akhtar Mitu, Lway Faisal Abdulrazak, Kawsar Ahmed, Youssef Trabelsi, Fahad Ahmed Al-Zahrani, M.S. Mani Rajan

Bibliographic record

VenueInternational Journal of Communication Systems · 2022
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceMultiplexingOptical fiberCrosstalkSensitivity (control systems)Optical communicationWavelength-division multiplexingCore (optical fiber)Transmission (telecommunications)OpticsWavelengthTelecommunicationsElectronic engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Summary A multi‐mode supported elliptical ring core fiber is demonstrated in the suggested work. The reported fiber structure is capable of supporting a large number of modes and influences to be favorable for high capacity, less crosstalk in the area of mode division multiplexing, and Internet of Things (IoT) compatible communications. To ensure the efficiency of the described structure, many characteristics such as V‐parameter, birefringence, sensitivity response, and confinement loss are analyzed. The investigation results are obtained from the offered structure by taking 22 distinct eigenmodes and the wavelength range from 1.54 to 1.7 μm. The evaluation of the numerical analysis shows the confinement loss of less than 2.85 × 10 −9 dB/m, the sensitivity response of 80,757.73292 nm/RIU. These are, to the best of our knowledge, the maximum responses received in this trade. IoT‐based technologies allow networked tools to communicate, sense, and interact with each other. Where the wireless networks are harsh to operate on the ecological surroundings, the optical technology as fiber optic cables delivers the best resources of handling data and transferring it from one place to another effectively.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.147
GPT teacher head0.327
Teacher spread0.180 · 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 teacher head, 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

Citations4
Published2022
Admission routes1
Has abstractyes

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