Benefits of Electronically Controlled Active Electromechanical Suspension Systems (EMS) for Mast Mounted Sensor Packages on Large Off-Road Vehicles
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
<div class="section abstract"><div class="htmlview paragraph">Battlefield reconnaissance is an integral part of today's integrated battlefield management system. Current reconnaissance technology typically requires land based vehicle systems to observe while stationary or, at best, significantly limits travel speeds while collecting data. By combining current Canadian Light Armored Vehicle based reconnaissance systems with the Center for Electromechanics (CEM) electronically controlled active Electromechanical Suspension System (EMS), opportunities exist to substantially increase cross-country speeds at which useful reconnaissance data may be collected. This report documents a study performed by The University of Texas Center for Electromechanics with funding from L3-ES to use existing modeling and simulation tools to explore potential benefits provided by EMS for reconnaissance on the move.</div><div class="htmlview paragraph">In both peak performance metrics and PSD analyses, the Fully Active Electronic Suspension systems proved their superior performance over Hybrid Active systems, Semi Active systems, and Passive systems. Peak body response measurements, accelerations, forces, and torques were consistently lower over aggressive off-road terrain with typical improvements being 80 to 90% lower than the stock passive vehicle. PSD and transfer function analyses of the payload accelerations revealed orders of magnitude improvement. Given these results it may be possible for reconnaissance operations to be carried out over aggressive cross country terrains at speeds of 10-15 MPH vs. conventional stationary operation.</div></div>
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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