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Record W2127099197 · doi:10.1109/etwcs.1999.897326

Local multipoint communications systems (LMCS) field tests in Canada

2003· article· en· W2127099197 on OpenAlexaffabout
Yiyan Wu, Bernard Caron, Alain Bergeron, B. Ledoux, Pierre Bouchard, Alex Kennedy, M. Guillet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsTransmitterAntenna (radio)RADIUSTest siteTerrainSIGNAL (programming language)Path lossRadio propagationCluster (spacecraft)Remote sensingTelecommunicationsComputer scienceElectrical engineeringElectronic engineeringGeographyEngineeringComputer network

Abstract

fetched live from OpenAlex

The Communications Research Centre (CRC) conducted field tests on a pilot local multipoint communications system (LMCS) at 28 GHz, in a major urban centre in September of 1997. The coverage of two of four operational cells was studied. This system was configured to broadcast digital video and audio programs originating from a satellite signal. Measurements were taken at 395 sites within a 5-km radius to evaluate the system's availability. The availability is highly dependent on terrain, path clearance, and transmitter/receiver antenna height. Another noteworthy point is that, for both cells, there were sites available outside the anticipated 5-km radius. Measurements were done within clusters consisting of three-by-three test sites, spaced at a distance equal to one residential house, to simulate a real LMCS implementation case. The purpose of this test was to confirm that no large variations of the signal power after down conversion (SPAD) within a small area existed where there were no major blockage. From the results it can be noted that the SPAD variations are only a few dB within a cluster and that all the measured SPAD match closely with the calculated SPAD curves assuming free space propagation.

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 score0.843
Threshold uncertainty score0.291

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 head0.015
GPT teacher head0.220
Teacher spread0.205 · 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
Published2003
Admission routes2
Has abstractyes

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