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Electroplating chemicals are the silent architects behind the shiny, corrosion-resistant surfaces we often take for granted. From the gleaming chrome on car parts to the delicate gold plating on jewelry, these chemicals are the backbone of a process that blends chemistry with craftsmanship. At its core, electroplating is the method of depositing a thin layer of metal onto the surface of another material using electric current, and the chemicals involved make all the difference. But this isn’t just science—it’s also art.

Electroplating chemicals are the silent architects behind the shiny, corrosion-resistant surfaces we often take for granted. From the gleaming chrome on car parts to the delicate gold plating on jewelry, these chemicals are the backbone of a process that blends chemistry with craftsmanship. At its core, electroplating is the method of depositing a thin layer of metal onto the surface of another material using electric current, and the chemicals involved make all the difference. But this isn’t just science—it’s also art.

This dataset presents the distribution networks developed in our unpublished paper, "US Representative Feeder Sets for Distribution Grid Economics and Policy Applications." In the paper, we generate feeder sets that reproduce the economic characteristics of local distribution grids by starting from prototypical feeders that represent the electric installations in a specific location.

In order to accelerate the CU partitioning in VVC-SCC, we have established a specialized dataset for screen content. Firstly, we collect a wide variety of screen content videos and images in YUV format. Secondly, we encode these sequences under four different Quantization Parameters (QPs) to obtain comprehensive information about CU partitioning in VVC-SCC. This includes the length, width, position, mode labels, and RDcost values corresponding to each mode. This dataset will provide valuable data support for optimizing the CU partitioning process in screen content coding.

This is the dataset for paper VI-BEVSEG training and testing. For more details about the dataset structure, please refer to Nuscense or V2X-Sim. We only use the number 1 vehicle in the V2X-Sim dataset. Replace the folder in this dataset, and only keep the vehicle 1 six onboard cameras information, infrastructure camera information and their semantic camera information in 'sweeps' and 'samples' folder. Then replace the 'v1.0-trainval' folder with ours and put the h5 maps under V2X-Sim folder.

This is the dataset for paper VI-BEVSEG training and testing. This dataset mostly inherits from V2X-Sim dataset. Follow the same dataset structure as the V2X-Sim dataset. Follow the V2X-Sim instruction to use. The only change we made is adding some 'h5' ground truth semantic maps. And we only keep the vehicle 1 camera information and infrastructure camera information.

This dataset presents longitudinal measurements of plant growth characteristics under varying lighting conditions. Collected for 30 individual plants, the data spans multiple time points and includes key variables such as plant fresh weight (g), plant height (mm), plant width (mm), and number of leaves. Each measurement is associated with a specific plant, date (DD/MM/YYYY), experimental group, and a lighting recipe defined by the percentage composition of Red, Green, and Blue light.

Introduction

Welcome to the new challenging event-based multi-object tracking dataset (DSEC-MOT) repository. Our goal is to provide a challenging and diverse event-based MOT dataset with various real-world scenarios to facilitate the objective and comphrehensive evaluation of event-based multi-object tracking algorithms. This dataset, built upon DSEC, contains a variety of traffic entities and complex scenarios, aiming to address the current lack of event-based MOT datasets.

The dataset can be used to deal with the optimization design of feeder-bus network related to urban rail transit. The research on the optimization design of feeder-bus network related to urban rail transit is helpful to improve passengers' travel satisfaction and convenience, and solve the connection problem between rail transit station and bus stop. The dataset contains a 4 km by 5 km area, providing the coordinates of 80 bus stops and 4 rail transit stations.

Distributed Denial of Service (DDoS) attacks,

particularly those executed by bots, significantly impair the

Quality of Service (QoS) for legitimate users. While network-level

DDoS attacks have been largely mitigated through decades of

research, application-level DDoS attacks remain a challenge due

to the difficulty in distinguishing malicious from legitimate traffic.

Traditional approaches have relied on statistical models analyzing

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This dataset is a curated and processed version of the ISIC2019 skin lesion dataset, specifically prepared for research on lightweight skin disease classification and knowledge distillation. The dataset includes:

A subset of dermoscopic images from ISIC2019, formatted and resized for training and evaluation.

Corresponding metadata tables containing patient information (e.g., age, sex, lesion location).

Pre-processed CSV files that map image names to diagnostic labels.

Split files (train/val/test) for reproducibility.

In the attached dataset, several continuous records of chirp-sequence FMCW radar bursts are presented. The data consist of the direct ADC output of an AWR1443BOOST radar board from Texas Instruments. In most of the measurements, the FMCW radar was interfered by a PMCW one operating in the same frequency band. Different bandwidths have been considered, as well as different phase codes for the interfering radar. All measurements were conducted inside an anechoic chamber, with some static items and two moving targets consisting of a flying drone and a walking pedestrian.

When artificial intelligence (AI) systems take actions or make decisions, the issue of accountability for the outcomes or decisions made by AI systems comes into question. The rapid advancement of AI technology poses a greater challenge, as current legal and ethical frameworks struggle to keep up with innovation, resulting in a lack of standardization in existing policies that comprehensively address accountability in AI.

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Ultra Wideband (UWB) signals offer high spatio-temporal resolution, penetrability, and low cost, which facilitates accurate characterization of limb features through micro-Doppler (mD) analysis, even with micro random body movements, during the wireless contactless sensing. Due to challenges introduced by arm motions, which may be perpendicular to the radar, we introduce a dual radar arm motion recognition system with light-weighted feature extraction and appropriate data fusion.

Ultra Wideband (UWB) signals offer high spatio-temporal resolution, penetrability, and low cost, which facilitates accurate characterization of limb features through micro-Doppler (mD) analysis, even with micro random body movements, during the wireless contactless sensing. Due to challenges introduced by arm motions, which may be perpendicular to the radar, we introduce a dual radar arm motion recognition system with light-weighted feature extraction and appropriate data fusion.

This dataset is a unified compilation of the Electricity Transformer Temperature (ETT) datasets: ETTh1, ETTh2, ETTm1, and ETTm2. It includes both hourly and minute-level temperature and load data collected from power transformers, which are vital for developing and benchmarking time-series forecasting models. The dataset contains features such as high and medium voltage transformer temperatures (HUFL, HULL, MUFL, MULL) and the operational temperature (OT), which serves as the primary prediction target.

The integration of artificial intelligence (AI) in the teaching of English as a Foreign Language (EFL) is on the rise alongside technological progress. This implementation is founded on various contemporary theories that have become central in academia, particularly regarding non-native speakers. These theories encompass sociocultural approaches, connectivism, and adaptive learning, which work in conjunction with AI’s capacity to tailor learning experiences and enhance language engagement.

The integration of artificial intelligence (AI) in the teaching of English as a Foreign Language (EFL) is on the rise alongside technological progress. This implementation is founded on various contemporary theories that have become central in academia, particularly regarding non-native speakers. These theories encompass sociocultural approaches, connectivism, and adaptive learning, which work in conjunction with AI’s capacity to tailor learning experiences and enhance language engagement.

To support research on multimodal speech emotion recognition (SER), we developed a dual-channel emotional speech database featuring synchronized recordings of bone-conducted (BC) and air-conducted (AC) speech. The recordings were conducted in a professionally treated anechoic chamber with 100 gender-balanced volunteers. AC speech was captured via a digital microphone on the left channel, while BC speech was recorded from an in-ear BC microphone on the right channel, both at a 44.1 kHz sampling rate to ensure high-fidelity audio. 

The LibriSpeech corpus, a publicly available English speech dataset derived from audiobook recordings. The corpus contains approximately 1,000 hours of 16 kHz read speech from over 2,400 speakers, encompassing diverse speaking styles, rates, and regional accents. For the purpose of contrastive learning, a subset of 100 speakers was sampled, with 20 utterances per speaker ranging from 3 to 10 seconds. The dataset provides clean, labeled speech suitable for tasks involving speaker representation, acoustic modeling, and multi-style synthesis.