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Real-time monitoring and data acquisition using LoRa for a remote solar powered oil well

2023· article· en· W4390077873 on OpenAlex
Onyinyechukwu Chidolue, M. Tariq Iqbal

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

VenueInternational Journal of Applied Power Engineering (IJAPE) · 2023
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPhotovoltaic systemAlternating currentData acquisitionBattery (electricity)Node (physics)MicrocontrollerElectrical engineeringWirelessEmbedded systemFlash (photography)Computer scienceReal-time computingVoltageComputer hardwareEngineeringTelecommunicationsPower (physics)

Abstract

fetched live from OpenAlex

Real-time monitoring is essential for solar-powered systems as they can be affected by sudden environmental changes, which may occur unpredictably, especially in isolated regions. This study proposes a wireless communication-based approach that allows for data acquisition and system monitoring of the entire solar system of a remote oil well. The proposed instrumentation method offers an affordable solution for monitoring the battery voltage, photovoltaic (PV) current, the converter's alternating current (AC), and oil well management. A wireless communication tool for a long-range called LoRa is used, with the TTGO LoRa32 SX1276 organic light-emitting diode (OLED) as the sender node and Heltec long range (LoRa) ESP 32 as the transmitter node. These I.C.s are ESP32 development boards with an integrated LoRa chip and an SSD1306 flash memory. System design and some test results are included in the paper.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.019
GPT teacher head0.283
Teacher spread0.264 · 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