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Record W2117656114 · doi:10.5555/1602165.1602222

Demo abstract: Application of WINTeR industrial testbed to the analysis of closed-loop control systems in wireless sensor networks

2009· article· en· W2117656114 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

VenueInformation Processing in Sensor Networks · 2009
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsCape Breton University
Fundersnot available
KeywordsTestbedComputer scienceWireless sensor networkNetwork topologyContext (archaeology)WirelessWireless networkDynamical systems theoryTopology controlInterference (communication)Distributed computingTopology (electrical circuits)Key distribution in wireless sensor networksComputer networkTelecommunicationsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

WINTeR is an open access, multi-user experimental facility which supports implementation and evaluation of wireless sensor networks for industrial applications in radio-harsh environments. An important new characteristic of the testbed is its ability to incorporate built-in and user generated dynamical models of industrial processes. This allows researchers to inexpensively and realistically evaluate wireless technologies, topologies and other variables in the context of direrent closed-loop dynamical systems. The present demo gives an overview of the testbed and introduces its real-time simulation abilities by utilizing three dynamical models of known processes with direrent response times. As two of a myriad of variables, the demonstration will show the effects of electromagnetic interference and topology in the response of such closed loop control systems.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.794
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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