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Record W1613670359 · doi:10.1109/iccs.1994.474118

Diffraction loss prediction for land mobile radio communication

2002· article· en· W1613670359 on OpenAlexaff
S. Le-Ngoc, Kai Gu, T. Banerjee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsTerrainDiffractionComputer scienceInterpolation (computer graphics)TransmitterUniform theory of diffractionRadio propagationPath lossQuadtreeRadio propagation modelRemote sensingTelecommunicationsAlgorithmGeographyArtificial intelligenceImage (mathematics)OpticsPhysicsCartographyWireless

Abstract

fetched live from OpenAlex

It is well known in land mobile communications that almost 95% of the radio links between the transmitter and receiver is non-line-of-sight. The diffraction loss plays a major role in the total propagation loss prediction. Hence, this paper examines and improves the diffraction loss predication in two main aspects. First, the diffraction loss is calculated based on the multiple bridged knife edge model proposed by J.H. Whitteker (see Radio Science, vol.28, no.4, p.487-500, 1993). Secondly, a higher resolution and more accurate digital terrain database is investigated by using various interpolation techniques. To cope with a huge amount of terrain data, a spatial database structure called a quadtree is also introduced.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score0.251

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 head0.024
GPT teacher head0.218
Teacher spread0.194 · 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 designSimulation or modeling
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

Citations0
Published2002
Admission routes1
Has abstractyes

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