A Sequential RFID System for Robust Communication with Underground Carbon Steel Pipes in Oil and Gas Applications
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.
Bibliographic record
Abstract
The world’s oil and gas is transported using a network of steel pipelines most of which lie underground. The length of this network in the US/Canada alone is 3.5 million kilometers. Keeping track of pipes in such a network for pipeline-health monitoring, maintenance, and logistics is an acute problem faced by pipeline-operators. Recently, radio-frequency-identification tags (RFIDs) have been proposed for tracking pipelines and even for monitoring pipeline health with additional built-in sensors. Low-cost RFID tags are wirelessly powered and battery-less. However, RFIDs do not function optimally in the presence of magnetic carbon steel pipes that are prevalent in the industry. High-frequency wireless signals also attenuate rapidly through wet soils. In this research, the use of passive RFID sensor platforms for interrogating buried pipes up to 1.25 m deep in the LF bands is proposed. Using magnetic-induction-based communication, a test-comparison between conventional full/half duplex (FDX/HDX) and sequential (SEQ) RFID schemes is detailed. Wireless measurements in the presence of an industry-standard ASTM A-53 carbon-steel pipe show a SEQ RFID offering better immunity against magnetic proximity effects of the pipe’s wall with an 8.3 dB (x6.8) improvement over a FDX/HDX RFID operating under similar conditions over a distance of 80–125 cm at which pipes are typically buried.
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 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 | 0.000 | 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 | 0.000 |
| 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 it