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Record W2548382742 · doi:10.1109/aps.2016.7696759

A Gaussian beam approach for embedding antennas into vector parabolic equation based propagation models

2016· article· en· W2548382742 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Wave Propagation Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDiscretizationGaussian beamAntenna (radio)Ray tracing (physics)GaussianEmbeddingRepresentation (politics)Beam (structure)Wave propagationComputer scienceMathematical analysisPhysicsOpticsMathematicsTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

Vector parabolic equation (VPE) offers a very efficient methodology for high fidelity modeling of wave propagation in complex environments. While the propagation environment can be discretized and represented in detail, the representation of radiating sources (such as transmitting antennas) requires the calculation, analytical if possible or numerical via another method such as ray-tracing, of the fields that the sources generate on the initial plane of the VPE model. These initial conditions are necessary for subsequently applying VPE. Addressing this significant limitation of VPE methods, we introduce a technique that allows one to directly embed antennas into a VPE mesh, via a Gaussian beam approximation of their radiated fields. Hence, the initial conditions for VPE are generated for practical antenna patterns, without invoking other techniques and with no compromise on the inherent efficiency of VPE. Comparisons of numerical results to experimental data demonstrate the validity and usefulness of the technique.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.511

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 head0.036
GPT teacher head0.254
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

Quick stats

Citations7
Published2016
Admission routes1
Has abstractyes

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