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Record W2025533769 · doi:10.1063/1.1601225

Cross-phase modulation effects in surface-wave-sustained plasmas

2003· article· en· W2025533769 on OpenAlexaff
K. Marinov, H. Schlüter, A. Shivarova, L. Stoev

Bibliographic record

VenuePhysics of Plasmas · 2003
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsOptiwave Systems (Canada)
Fundersnot available
KeywordsPhysicsEnvelope (radar)Waves in plasmasAmplitudePlasmaModulation (music)Plasma oscillationLow frequencyComputational physicsPhase (matter)InstabilityPhase velocityOpticsMechanicsAcousticsQuantum mechanics

Abstract

fetched live from OpenAlex

The study extends models of the cw-regime of operation of diffusion-controlled discharges by incorporating the actual shape of the high-frequency signal producing the discharge as a narrow-band signal. The slow variations of the wave envelope determining slowly varying Joule heating of the electrons in the wave field leads to a low-frequency plasma-density response which influences the propagation properties of the high-frequency signal. Depending on the frequency shift of the spectral components from the carrier-wave frequency of the signal, the low-frequency plasma response appears as stationary or nonstationary. The derived nonlinear evolution equation for the wave envelope shows cross-phase modulation acting simultaneously through self-action and mutual action of coupled spectral components. A strong impact of the mutual action of the coupled spectral components is found. It removes the effect of self-action: The decay of the spectral components associated with a stationary low-frequency response transforms into an instability, and the monotonic variations of the amplitudes of the spectral components associated with a nonstationary low-frequency plasma response are replaced by space modulation. For verification of the origin of the effects, also the case of axially homogeneous plasma columns is treated in which obtaining analytical solutions of the evolution equations is possible.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.798

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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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

Citations5
Published2003
Admission routes1
Has abstractyes

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