Sensors

The increasing use of LiDAR sensors in autonomous
driving, drones, and intelligent transportation system highlights
their ability to provide accurate 360-degree environmental
perception. However, unfavorable weather conditions such as
fog, snow, rain, dust, low temperature introduce noise into
LiDAR point cloud data, complicating the perception and
decision-making process. Since labeling every point in point
cloud data is very hard, this paper addresses this challenge using
an energy-based model (EBM). We have proposed a LiDAR

This graphical abstract showcases a novel dual-layer haptic sensor using frustrated total internal reflection (FTIR) imaging. The design integrates a solid sensing layer that captures contact force distribution and a flexible probe layer that enables 3D surface geometry reconstruction. By combining data from both layers through contact mechanics models, the sensor quantifies tissue viscoelasticity while simultaneously mapping 3D geometry with high spatial resolution.

This data sheet provides figure-source numerical data and supporting information in the associated manuscript “Pd–PdO/SnO₂ Nanoparticle Stack as the Sensing Gate of a Si Nanobelt Device for NH₃ Detection.” The files contain processed values used for calibration curves, equivalent work-function (EWF) modulations, I–V characteristics, and self-heating mappings. 

The Data are recorded by TMNU Akhund, “Hiking gps data by user 7851343.” https://www.wikiloc.com/wikiloc/user.do?id=7851343. Accessed: May, 2025. Later these are converted to CSV format for further analysis. 

Achieving truly circular electronics requires a coordinated, multi-phase approach that addresses every stage of a device’s lifecycle—from material sourcing and design to manufacturing and end-of-life recovery and reintegration. This roadmap outlines the key steps and strategic focus areas needed to transition from today’s linear "take-make-dispose" model to a circular, sustainable, and regenerative electronics ecosystem:

This dataset contains tightly synchronized recordings from a full-scale bathroom mock-up designed to replicate residential conditions with wet, highly reflective surfaces and confined geometry.  Two privacy-preserving ambient sensors were used: (i) a millimeter-wave (mmWave) radar node that outputs frame-wise point clouds and kinematic summaries at 12.5 Hz;  and (ii) a floor-mounted triaxial vibration node sampled at 200 Hz (resampled to 100 Hz for modeling).

Simultaneous localization and mapping (SLAM) is a crucial component of unmanned systems, playing a key role in autonomous navigation. Currently, most LiDAR SLAM methods are focused on structured environments. However, highly irregular off-road terrain poses more challenges for LiDAR SLAM tasks, but these environments are not fully represented in existing datasets. To address this issue, we introduce the first dedicated LiDAR SLAM benchmark dataset for off-road environments, named Jlurobot Off-Road Dadaset (JORD).

We present a 43 μm × 269 μm tetherless neural recording microsystem.

The data originates from a high-speed rail test section, collected via axle box acceleration sensors mounted on track inspection vehicle. Axle box acceleration signals are sampled at 5 kHz (i.e., 5,000 points per second). It encompasses vibration signals under various operating conditions and operating speeds (0-90km/h).

The Data.txt file comprises a dataset of glucose measurements collected over a 100-day period from five individual patients. For each patient, five glucose sensors recorded data three times daily, corresponding to breakfast, lunch, and dinner.