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Applications of Reflectarray Technology for Radio Astronomy Interference Mitigation

2025· article· en· W4410584524 on OpenAlexafffund
Fatemeh Sadr, Nan-Rong Hui, Jordan Budhu, Sean V. Hum, Steven W. Ellingson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsRadio astronomyInterference (communication)Electromagnetic interferenceComputer scienceTelecommunicationsAstronomyPhysicsRemote sensingGeology

Abstract

fetched live from OpenAlex

High-gain reflector antennas commonly used to make observations in radio astronomy are especially susceptible to the reception of undesirable radiation through their sidelobes. One source of this radiation is megaconstellations associated with satellite based communications systems. In this paper, we present some recent advances in the application of reflectarrays for mitigation of interference from these megaconstellations. By placing a reflectarray along the rim of a reflector and thereby repurposing reflections to generate destructive interference to unwanted incoming radiation, interference can be nulled. The first example is a dual band reflectarray capable of producing a null to track interference throughout the sidelobe envelope in two frequency bands associated with Iridium and Starlink satellite systems. The second example is a proof-of-concept demonstration utilizing a C-band planar reflectarray of which the elements along its perimeter are made reconfigurable. Both examples point to the possibility of utilizing reflectarrays for radio astronomy interference mitigation.

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: Methods · Consensus signal: none
Teacher disagreement score0.832
Threshold uncertainty score0.156

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 head0.005
GPT teacher head0.227
Teacher spread0.222 · 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
GenreMethods

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

Citations0
Published2025
Admission routes2
Has abstractyes

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