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Record W3114974219 · doi:10.24018/ejece.2020.4.6.261

Design of an Ultra-Low Powered Data-Logger for Stand-Alone PV Energy Systems

2020· article· en· W3114974219 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

VenueEuropean Journal of Electrical Engineering and Computer Science · 2020
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsData loggerMicrocontrollerPhotovoltaic systemComputer scienceEmbedded systemSleep modeComputer hardwareReal-time computingMode (computer interface)Energy (signal processing)Web serverElectrical engineeringAutomotive engineeringOperating systemEngineeringThe InternetPower (physics)

Abstract

fetched live from OpenAlex

This paper presents an open-source, ultra-low powered data-logger for off-grid photovoltaic (PV) energy systems. Deep-sleep mode of ESP32-S2 microcontroller is used along with voltage, current, and light sensors for logging the data of PV energy system to an external micro SD card. A toggle switch is used to switch the operational modes of data-logger between deep-sleep and web-server modes. Real-time PV data can be monitored in a local web-portal programmed in the microcontroller. The same web-portal is also used to check and download the historical data of a PV energy system. The energy consumption of the designed system is 7.33mWh during deep-sleep mode and 425mWh during the web-server mode. The total cost of the designed data-logger is C$ 30.

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 categoriesnone
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 score0.901
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.218
Teacher spread0.193 · 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