Leakage Prevention and Real-Time Internal Detection in Pipelines Using a Built-In Wireless Information and Communication Network
Bibliographic record
Abstract
Summary A series of recent pipeline leakage incidents created severe societal concerns to a point of impeding, or even completely preventing, building new pipelines in North America. Various systems have been proposed to identify and locate leakages. However, despite the fact that pipelines remain the safest means of oil and gas transportation, incidents still persist and pipeline acceptance from the public has become compromised. In order to address the need for early leakage detection, while providing comprehensive leakage prevention, a novel pipeline system is proposed. This concept builds on the already existing pipe-in-pipe design by segmenting the pipeline system with segmentation rings and embedding a linear wireless network in the annular airgap between the two pipe layers. Presence of fluid in the case of a leakage into the interpipe space causes degradation of the wireless network to a point of interrupting the communication in a particular pipeline segment well before any external leak occurs. Thus, the internal leak is detected in real time by an external central unit connected to the linear wireless network, as demonstrated with a 6 ft 8 in. experimental pipeline setup.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".