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Record W2000975649 · doi:10.1002/mop.28103

A pneumatically controlled capacitive switch

2013· article· en· W2000975649 on OpenAlexaff
Billy Wu, M. Okoniewski, Chris Hayden

Bibliographic record

VenueMicrowave and Optical Technology Letters · 2013
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCapacitive sensingMicrostripMaterials scienceSolid-state relayFabricationInsertion lossElectrical engineeringCapacitive couplingMicrowaveCapacitanceRF switchOptoelectronicsEngineeringElectronic engineeringVoltageAntenna (radio)ElectrodePower (physics)TelecommunicationsPhysics

Abstract

fetched live from OpenAlex

ABSTRACT The design and fabrication process of a unique pneumatically controlled capacitive switch is described. The switch, developed for reconfigurable antennas, presents a low‐loss alternative switching mechanism, and avoids the issue of a complicated biasing layout associated with electrostatic actuation in the conducting layer. The closed state of the switch is created by a movable copper‐coated solid slug that bridges a gap in a microstrip line with capacitive coupling. The basis of the switching mechanism is pressurized air causing the physical displacement of the slug inside a confining dielectric channel. Repeatable switching between open and closed states by pneumatically controlling the position of the slug was demonstrated with the fabricated switch. The minimum pressure required to move the slug was measured. This switch was measured from 0.5 to 15 GHz using a Thru‐Reflect‐Line calibration to de‐embed the coax‐to‐microstrip transitions. Insertion loss of 0.2 dB and isolation of 25 dB have been achieved at 5 GHz. The successful fabrication and measurement of the switch demonstrates the viability of applying this technology in reconfigurable antenna designs. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:489–493, 2014

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.882

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 head0.004
GPT teacher head0.172
Teacher spread0.168 · 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 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
Published2013
Admission routes1
Has abstractyes

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