deformation
tactile-deformation-response
Tactile Deformation Response Dataset
Dataset Summary
This dataset contains tactile array time-series samples collected from a 32 × 32 tactile sensor. Each sample is represented by a fixed-length sequence of base-corrected tactile response frames and paired with a JSON metadata file. The primary task is binary deformation response classification: rigid versus deformable.
The dataset is intended for research on tactile perception, contact response modeling, material… See the full description on the dataset page: https://huggingface.co/datasets/Tachintech/tactile-deformation-response.Car_Accidents_and_deformation_dataset
🚗 Car Accidents and Deformation Dataset (Annotated)
Author: Muhammad ArslanLicense: CC BY-NC 4.0Source: Kaggle
📘 Overview
The Car Accidents and Deformation Dataset is a high-quality, manually curated collection of real-world car accident images, fully collected and annotated by the author. The dataset is intended to support machine learning tasks focused on:
Vehicle damage classification
Deformation severity estimation
Intelligent transportation systems
Insurance… See the full description on the dataset page: https://huggingface.co/datasets/M-ArslanArshad/Car_Accidents_and_deformation_dataset.rwth-jellyroll-deformation-2021-raw
RWTH jelly-roll deformation low-SOC cycling and CT dataset raw mirror
BSEBench status: raw_mirror_pending_validation
This repository is a raw mirror of the RWTH Aachen University Publications research-data record Raw cycle data and CT images of the development of jelly roll deformation in 18650 lithium-ion batteries at low state of charge.
Source record: https://publications.rwth-aachen.de/record/818660
DOI: https://doi.org/10.18154/RWTH-2021-04558
Institution: RWTH Aachen… See the full description on the dataset page: https://huggingface.co/datasets/bsebench-org/rwth-jellyroll-deformation-2021-raw.Car_Accidents_and_deformation_dataset
🚗 Car Accidents and Deformation Dataset (Annotated)
Author: Muhammad ArslanLicense: CC BY-NC 4.0Source: Kaggle
📘 Overview
The Car Accidents and Deformation Dataset is a high-quality, manually curated collection of real-world car accident images, fully collected and annotated by the author. The dataset is intended to support machine learning tasks focused on:
Vehicle damage classification
Deformation severity estimation
Intelligent transportation systems… See the full description on the dataset page: https://huggingface.co/datasets/ParthG09/Car_Accidents_and_deformation_dataset.euv-projection-optics-deformation-overlay-coherence-drift-detection-v0.1
What this dataset tests
When thermo-mechanical deformation stops predicting overlay error cleanly.
This is the second layer of projection optics thermo-mechanical decoupling:
deformation modesoverlay error growthcause-axis attribution
Task
Given deformation modes and overlay state, predict:
overlay_drift_score (0..1)drift_flag (0/1)dominant_cause_axis
Output format:
float,int,string
Example:
0.58,1,cooling
Fields
Inputs
thermal_mode1_nm
thermal_mode2_nm… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/euv-projection-optics-deformation-overlay-coherence-drift-detection-v0.1.euv-projection-optics-absorption-deformation-coherence-baseline-mapping-v0.1
What this dataset tests
Baseline coherence between:
absorbed EUV power per projection mirrorcooling and thermal environmentresulting thermo-mechanical deformation modes
This is the first layer needed to forecast overlay drift.
Task
Given power and environment inputs, predict:
coherence_score
A scalar 0..1 describing how predictable deformation is from absorbed power and thermal conditions.
Inputs
m1_absorbed_power_w
m2_absorbed_power_w
m3_absorbed_power_w… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/euv-projection-optics-absorption-deformation-coherence-baseline-mapping-v0.1.