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Record W2413969509 · doi:10.1063/1.4953421

Instability of surface electron cyclotron TM-modes influenced by non-monochromatic alternating electric field

2016· article· en· W2413969509 on OpenAlexaff
Igor O. Girka, V. O. Girka, R. D. Sydora, M. Thumm

Bibliographic record

VenuePhysics of Plasmas · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhysicsHarmonicsElectric fieldInstabilityMagnetic fieldElectron cyclotron resonanceOptical fieldElectronPlasmaQuantum electrodynamicsComputational physicsQuantum mechanicsVoltage

Abstract

fetched live from OpenAlex

The influence of non-monochromaticity of an external alternating electric field on excitation of TM eigenmodes at harmonics of the electron cyclotron frequency is considered here. These TM-modes propagate along the plasma interface in a metal waveguide. An external static constant magnetic field is oriented perpendicularly to the plasma interface. The problem is solved theoretically using the kinetic Vlasov-Boltzmann equation for description of plasma particles motion and the Maxwell equations for description of the electromagnetic mode fields. The external alternating electric field is supposed to be a superposition of two waves, whose amplitudes are different and their frequencies correlate as 2:1. An infinite set of equations for electric field harmonics of these modes is derived with the aid of nonlinear boundary conditions. This set is solved using the wave packet approach consisting of the main harmonic frequency and two nearest satellite temporal harmonics. Analytical studies of the obtained set of equations allow one to find two different regimes of parametric instability, namely, enhancement and suppression of the instability. Numerical analysis of the instability is carried out for the three first electron cyclotron harmonics.

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 categoriesInsufficient payload (model declined to judge)
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 score0.067
Threshold uncertainty score0.999

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)0.0020.000

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 head0.007
GPT teacher head0.259
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 teacher head, not a consensus.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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