Nonintrusive Spatiotemporal <i>Smart Debris</i> Tracking in Turbulent Flows with Application to Debris-Laden Tsunami Inundation
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Bibliographic record
Abstract
Flood disasters such as dam breaks and surges from extreme hurricanes or tsunamis entrain and transport substantial amounts of submerged or floating debris. Understanding of motion and spatiotemporal distribution of debris entrained by a flood is thus of great importance to hydraulic, coastal, and structural engineers; the displacement of debris to a location where it may eventually impact critical infrastructure requires scientific attention at the laboratory scale first. In this context, the design and application of a novel smart debris system utilizing off-the-shelf components is presented and discussed. The system tracks the spatial location and orientation of a multitude of debris specimens and it proposes an accurate tool to assess their individual trajectory, velocity, and momentum in a laboratory environment. Contrary to the traditional camera-based approach of video tracking, which often fails once objects are submerged, the proposed smart debris system delivers six-degree-of-freedom (6DOF) data in a reliable, timely manner. Miniaturized inertial measurement units (IMU), commonly called motion sensors, which are used for attitude heading reference systems are deployed to output time series of spatial orientation along with filtered 3D acceleration readings. A Bluetooth low-energy (BLE) tracking system is applied along with the motion sensor to track the 3D debris positions. A detailed investigation in controlled laboratory conditions reveals the detailed individual performance of the tested spatial orientations and positions. As an application, debris transport tests were conducted in a newly built tsunami wave basin at Waseda University in Tokyo, Japan. For this test series, a typical harbor layout with a vertical quay wall adjacent to a horizontal container-stacking platform was constructed. The advection by a broken tsunamilike bore of multiple down-scaled shipping containers in basic arrangements was then tracked from their initial position. The performance of the innovative smart debris system is qualitatively tested in order to provide guidance for their future application in hydraulic and coastal engineering as well as to provide a solid basis for its application in field studies.
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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 | 0.000 | 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 | 0.001 |
| 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 it