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Record W3211936911 · doi:10.1109/tap.2021.3125423

An Ultrawideband Nested Coaxial Waveguide Feed Antenna for Radio Astronomy

2021· article· en· W3211936911 on OpenAlexafffund
Xuan Du, Thomas Johnson, T. L. Landecker, B. Veidt

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaHerzberg Institute of Astrophysics
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsPhase centerChokeBeamwidthOpticsAntenna (radio)PhysicsAntenna feedWidebandAcousticsComputer scienceAntenna efficiencyRadiation patternElectrical engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

We describe a new type of ultrawideband feed antenna based on nested coaxial waveguides. The feed is for radio astronomy applications and covers two closely spaced bands: 400&#x2013;800 and 900&#x2013;1800 MHz. New design techniques are described for a three-cavity choke, a wideband iris matching network, and printed circuit board (PCB) excitation structures. The design methods were developed to improve broadband impedance matching, to control radiation patterns, to separate bands, and to suppress unwanted waveguide modes. The feed has nearly constant beamwidth over the entire 4.5:1 frequency range and a stable phase center. On an <inline-formula> <tex-math notation="LaTeX">$f/D=0.43$ </tex-math></inline-formula> reflector antenna, the aperture efficiency exceeds 70&#x0025; and the phase efficiency exceeds 95&#x0025;. Other features include low ohmic loss and a compact, lightweight, and low-cost structure. Experimental results are shown for a scaled prototype to verify the design methodology. The methods described are general and can be applied to continuous frequency coverage on reflectors of different geometries.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score0.874

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 head0.014
GPT teacher head0.244
Teacher spread0.231 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

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