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Record W3167794075 · doi:10.1002/9781119292371.ch20

High‐Power Considerations in Microwave Filter Networks

2018· other· en· W3167794075 on OpenAlexaff
Richard J. Cameron, Chandra M. Kudsia, Raafat R. Mansour

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMicrowavePhenomenonFilter (signal processing)Cabin pressurizationElectronic engineeringMultiplexingComputer sciencePower (physics)Space (punctuation)Electrical engineeringEngineeringTelecommunicationsPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

This chapter presents an overview of high-power considerations for microwave filters and multiplexing networks in terrestrial and space applications. It reviews the phenomenon of breakdown and then describes in more detail the practical considerations in the design of highpower filter networks. The chapter also reviews the gas discharge phenomenon at microwave frequencies, and the impact on the design of high-power passive microwave components. A number of constraints are essential to initiate and sustain multipaction discharge. The violation of any of those constraints should, therefore, provide the means to suppress multipaction. Possible ways to prevent the occurrence of this phenomenon are enumerated as follows: control of the frequency-gap product; dielectric filling; pressurization; magnetic or dc bias; and reduction of the surface potential by treatment of materials. The chapter further tabulates the characteristics and relative advantages of these methods.

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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.004

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 head0.207
Teacher spread0.198 · 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
GenreOther

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

Citations2
Published2018
Admission routes1
Has abstractyes

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