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Record W4381189194 · doi:10.24018/ejece.2023.7.3.489

Open Source SCADA System for Hybrid Power System for Francois, NL

2023· article· en· W4381189194 on OpenAlexaff
Karankumar Patel, Dharmik Kiranbhai Kachhadiya, Dhruvkumar Rinkeshbhai Kapatel

Bibliographic record

VenueEuropean Journal of Electrical Engineering and Computer Science · 2023
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsData loggerOpen sourceArduinoSCADAElectrical engineeringBattery (electricity)Energy sourcePower (physics)Computer scienceComputer hardwareGenerator (circuit theory)Embedded systemEngineeringRenewable energyOperating systemSoftwarePhysics

Abstract

fetched live from OpenAlex

A hybrid power system needs monitoring and measuring the power system parameters using a data logger. In this paper, a low-cost, open-source Arduino- based data logger is designed. The formation of voltage and current of the solar-diesel generator, along with the battery storage system that the proposed data logger records, will be analyzed and determined. The open-source platform and remote terminal unit will display the result. The design can illustrate how much energy Francois uses, and how much energy is produced by various sources.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0530.030

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 head0.008
GPT teacher head0.185
Teacher spread0.178 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2023
Admission routes1
Has abstractyes

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