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Record W3111648384 · doi:10.1109/jlt.2020.3045389

Design, Acceptance and Capacity of Subsea Open Cables

2020· article· en· W3111648384 on OpenAlexaff
Elizabeth Rivera Hartling, A. N. Pilipetskiǐ, Darwin Evans, Eduardo Mateo, M. Salsi, Pascal Pecci, P. Mehta

Bibliographic record

VenueJournal of Lightwave Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsCiena (Canada)
Fundersnot available
KeywordsSubseaComputer scienceElectronic engineeringEngineering

Abstract

fetched live from OpenAlex

This article will discuss the collaboratively formed cross-industry open cables concept for characterizing optical performance of undersea cables with the intent of assessing and understanding their capacity potential. The article proposes definitions of two critical nonlinear and linear performance metrics for open cables: GSNR (Gaussian or generalized signal to noise ratio) and SNR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ASE</sub> (Signal to noise ratio amplified spontaneous emission), including effects such as GAWBS (guided acoustic wave Brillouin scattering) and signal droop. Measurement methodologies for these metrics are proposed, with considerations for limitations and impact of the test conditions and characteristics of the transponders used. Expanded definitions are offered to enable variable symbol rate transponders to be used for measurement, with considerations for scaling of SNR values. Considerations for using these metrics for capacity assessment and applying these techniques to concatenated multi-segment systems are introduced. Recommendations on key parameters for system specification, system characterization, and proposals for SNR-based performance budgeting tables are also discussed as foundational elements to enabling accurate estimation of the capacity potential of a subsea open cable.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.040
GPT teacher head0.245
Teacher spread0.205 · 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 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

Citations52
Published2020
Admission routes1
Has abstractyes

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