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Novel Dual-Band In-Line Filters Using Coaxial Dual-Post Resonances

2022· article· en· W4308086409 on OpenAlexaff
Uwe Rosenberg, S. Amari

Bibliographic record

Venue2022 52nd European Microwave Conference (EuMC) · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsMulti-band deviceDual (grammatical number)ResonatorFilter (signal processing)Coupling (piping)m-derived filterCoaxialPhysicsPrototype filterConstant k filterResonance (particle physics)Band-pass filterTopology (electrical circuits)Filter designElectronic engineeringComputer scienceOptoelectronicsOpticsMaterials scienceEngineeringElectrical engineeringTelecommunicationsAtomic physicsAntenna (radio)

Abstract

fetched live from OpenAlex

It is shown, that different resonances of close dual-post configurations can be utilized for dual-band filter designs. The resonances of close dual-post arrangements are no longer the resonances of the individual posts because of the strong coupling. The independent control of these dual-post resonances is one prerequisite for the design of the novel dual-band filters. The second one is the independent control of interface and inter-resonator couplings of the different resonances. Thus, possible measures that allow the individual adjustment of resonances and couplings for a dual-band filter design are introduced. The general feasibility of this novel approach is demonstrated by a dual-band filter design at 2.5GHz providing two 4-order filter characteristics.

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)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.030
GPT teacher head0.221
Teacher spread0.191 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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