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Considerations for resonant slot arrays for microwave food drying and heating

2017· article· en· W2765538118 on OpenAlexaff
Maryam Razmhosseini, Ying Chen, Rodney G. Vaughan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemistry
TopicMicrowave-Assisted Synthesis and Applications
Canadian institutionsSierra Wireless (Canada)Simon Fraser University
Fundersnot available
KeywordsSlot antennaMicrowaveAntenna (radio)Breakdown voltageElectrical engineeringSlotted waveguideVoltageDirectional antennaComputer sciencePhysicsElectronic engineeringOptoelectronicsEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Antennas for microwave heating radiate high levels of power relative to communications and most radar applications. The problem of voltage breakdown becomes a dominant factor in the antenna design. The breakdown problem is compounded when the heating is in a partial vacuum, which can be worse than the full vacuum situation of space-borne antennas. Resonant slot array antennas are ideal for microwave heating because of their variable aperture design and high efficiency except for the slot voltage breakdown problem. This paper presents new design results for antenna array design for microwave heating, based on maximum electric-field considerations. As an example design, a seven-slot waveguide array with a slot width of λ0/4 is found to be suitable for 1 kilowatt (a typical magnetron), based on a criteria of keeping the electric field below 3% of free-space breakdown.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.075
GPT teacher head0.293
Teacher spread0.218 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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