Monitoring Cathodic Protection System Assets Using Secure Cloud-Based Communication Architecture
Bibliographic record
Abstract
Abstract Web-based monitoring is widely used in the oil and gas pipeline industry for cathodic protection data acquisition and rectifier interruption control. This technology has evolved from monitoring companies using a few servers at the back of the office to more secure, cloud-based data environments. A major concern facing many international pipeline companies is the desire to store and access the sensitive data within the operating company's national boundaries. This paper looks at the services available through major cloud-based data companies enabling sensitive cloud-based data to reside in the operating companies’ home country. In addition to focusing on the innate data security this approach provides; attention is also given to the operational benefits this type of data network affords the user. This approach enables the company providing the monitoring to supply the end user with the screens, tools, and full functionality of the monitoring system and still maintain the security provided through housing the data locally. This approach is in contrast to SCADA based data acquisition which requires a great deal of customization of the host system to provide a similar "feature rich" user experience. Finally, this paper will also look at the pluses and minuses of each data acquisition approach, providing the consumer with the necessary knowledge to make an informed decision as to the best approach for their circumstances.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".