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Record W2781429793 · doi:10.1016/j.icte.2017.11.002

A new approach to design of RF energy harvesting system to enslave wireless sensor networks

2017· article· en· W2781429793 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

VenueICT Express · 2017
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversité LavalUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsWireless sensor networkEnergy harvestingComputer scienceEnergy (signal processing)Node (physics)Computer networkWirelessHierarchyCluster analysisKey distribution in wireless sensor networksProtocol (science)Wireless networkTelecommunicationsEngineeringPhysics

Abstract

fetched live from OpenAlex

In trying to reach the goal of controlling the environment, recent years have seen the rapid emergence of Wireless Sensors Networks (WSN). Nevertheless, the lifetime of sensor nodes shows a strong dependence on battery capacity. Recently energy harvesting techniques have been considered to allow the use of WSN in the “deploy and forget” mode. This paper proposes an assessment of the performance of a WSN enslaved to an optimized Radiofrequency Energy Harvesting System (REHS). The energy budget of a sensor node in a Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is quantified and used to evaluate the performance of the WSN.

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 categoriesMeta-epidemiology (narrow)
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 score0.837
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.221
Teacher spread0.197 · 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