Bibliographic record
Abstract
Electric field (E-field) sensors were introduced by Stellar Incorporated in 1975 and approved for DOE use by Sandia in 1977. Since then there have been numerous product changes and adaptations to utilize this technology to address a wide range of applications. All such products and product variants rely on the basic concept of measuring changes in capacitance between a "field" wire and a "sense" wire due to the motion of an intruder in the electric field of the wires. In all cases the biggest challenge is to differentiate between changes due to intruders and those due to environmental effects. This has lead to a number of insulator designs to try to minimize the effects of rain, snow, fog, salt spray etc. and various transformer-based balancing schemes to minimize the effects of the environment of the ground plane beneath the array of wires. Recently Senstar-Stellar engineers have embarked upon a complete rework of this technology taking advantage of field programmable gate array (FPGA) devices with high speed sigma delta analog to digital converters to extract more information from the array of wires and thereby optimize the detection process. Several features of this next generation of E-field technology described in this paper include: the ability to discern between capacitive and resistive changes, simultaneous independent measurement of the capacitance between each field and sense wire (for 4 and 8 wire arrays), notch filters to remove 50 and 60 Hz power grid noise, digital signal processing (DSP) to minimize the environmental effects on the ground plane and to differentiate between intruders approaching the sensor and those penetrating between the wires. Along with these electronically generated features the next generation product has a new insulator and support hardware design to further reduce the environmental effects and insect issues. The paper concludes with a discussion of how this technology can be utilized to address the post 9/11 threat at high security sites. While all perimeter security sensors have for years been designed to detect and delay intruders more and more emphasis is now being placed on the delay function. The stacked 8-wire barrier produced by this new design can go as high as 18 feet (5.5 meters) which certainly creates a significant intruder delay if the intruder is to penetrate the sensor undetected.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.105 | 0.092 |
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".