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Record W2147456715 · doi:10.1109/lpt.2004.839788

Optical network control overlay using silicon VOA arrays

2005· article· en· W2147456715 on OpenAlexaff
T.E. Darcie, Peter F. Driessen, Milan Osusky, Wallace Lin

Bibliographic record

VenueIEEE Photonics Technology Letters · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceSubcarrierWavelength-division multiplexingOptical attenuatorBandwidth (computing)Control channelChannel (broadcasting)Optical performance monitoringOptical amplifierSubcarrier multiplexingOptical powerPower controlElectronic engineeringOptical fiberMaterials scienceOpticsPower (physics)Computer networkTelecommunicationsEngineeringOrthogonal frequency-division multiplexingOptoelectronicsPhysicsMulti-mode optical fiber

Abstract

fetched live from OpenAlex

A silicon-based variable optical attenuator (VOA) is used to overlay optical control data on an underlying high-speed optical data channel. We demonstrate experimentally the addition of a 10.7-MHz (5 Kb/s) control subcarrier with <1-dB penalty to a high-speed channel (500 Mb/s) and quantify the penalty versus control data rate. The modest modulation bandwidth of the VOA enables control subcarriers at frequencies sufficiently high to avoid gain saturation-induced crosstalk in erbium amplifiers, while using the same devices required for channel gain equalization or power control. At OC-192 data rates, we predict that control-channel data rates of 10 Kb/s can be tolerated with <0.5-dB penalty.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.009
GPT teacher head0.229
Teacher spread0.220 · 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

Citations5
Published2005
Admission routes1
Has abstractyes

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