Virtualized Experiential Learning Platform (VELP) for Smart Grids and Operational Technology Cybersecurity
Bibliographic record
Abstract
The advent and the rapid expansion of Industry 4.0 solutions and technologies such as IoT, Artificial Intelligence (AI), cloud computing, and digital twinning, is causing industrial companies, utilities, and other organizations to either retrain their workforce or hire experts from other industries who have the knowledge or hands-on experience working with such technologies. With the emergence of remote working routines, providing such hands-on training programs becomes difficult. As such, BCIT's Smart Microgrid Applied Research Team has developed an advanced Virtualized Experiential Learning Platform (VELP) with the support of the Future Skills Centre and its industry partner to provide trainees from industry and academia with hands-on experience in the fields of smart grids, substation automation systems and Operational Technology (OT) cybersecurity utilizing virtualization technologies such as digital twins, AI, and cloud apps. This paper aims to introduce VELP, its features, and functionalities, and explain how monitoring and control layers can be migrated into cyberspace. Moreover, this paper proposes a reliable communication network and a cybersecure topology that can be utilized in similar virtualization platforms. The results of this paper show that such technologies can be applied to other digital experiential learning programs where reliable and secure remote access to physical assets is essential to support relevant experiential learning pedagogical models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".