Artificial Intelligence

The composition of the natural gas components is as follows: $CH_{4}$ (97.07$\%$), $C_{2}H_{6}$ (0.17$\%$), $C_{3}H_{8}$ (0.02$\%$), $N_{2}$ (0.71$\%$), and $CO_{2}$ (2.03$\%$). The standard state conditions are a pressure of 101325 $Pa$ and a temperature of 20 $^\circ C$. The pipeline components have an inner diameter of 0.307 $m$, an outer diameter of 0.323 $m$, a total heat transfer coefficient of 0.5, and a roughness of 0.02286 $mm$. The initial temperature of both the pipeline and the surrounding medium is 15 $^\circ C$.

The composition of the natural gas components is as follows: $CH_{4}$ (97.07$\%$), $C_{2}H_{6}$ (0.17$\%$), $C_{3}H_{8}$ (0.02$\%$), $N_{2}$ (0.71$\%$), and $CO_{2}$ (2.03$\%$). The standard state conditions are a pressure of 101325 $Pa$ and a temperature of 20 $^\circ C$. The pipeline components have an inner diameter of 0.307 $m$, an outer diameter of 0.323 $m$, a total heat transfer coefficient of 0.5, and a roughness of 0.02286 $mm$. The initial temperature of both the pipeline and the surrounding medium is 15 $^\circ C$.

Transient simulation data set of natural gas pipeline under three working conditions,The composition of the natural gas components is as follows: $CH_{4}$ (97.07$\%$), $C_{2}H_{6}$ (0.17$\%$), $C_{3}H_{8}$ (0.02$\%$), $N_{2}$ (0.71$\%$), and $CO_{2}$ (2.03$\%$). The standard state conditions are a pressure of 101325 $Pa$ and a temperature of 20 $^\circ C$. The pipeline components have an inner diameter of 0.307 $m$, an outer diameter of 0.323 $m$, a total heat transfer coefficient of 0.5, and a roughness of 0.02286 $mm$.

The article Aims to compare the application effect of different machine learning algorithms in the recognition of badminton stroke types, so as to better meet the needs of quantitative analysis of badminton skills and tactics, and provide a more reasonable basis for the layout of game strategies.

This dataset comprises high-resolution 3-axis accelerometer recordings collected from human participants performing distinct hand gestures, intended for training gesture-based assistive interfaces. Each participant’s raw motion signals are individually organized, enabling both user-specific and generalizable model development. The dataset includes time-series accelerometer data, along with a feature-augmented version containing extracted statistical and temporal descriptors such as RMS, Jerk, Entropy, and SMA. 

This data provides a comprehensive collection of air quality and meteorological data from several large cities in India. With 1,410 records, it includes key characteristics like the Air Quality Index (AQI) according to both U.S. and China standards, temperature, atmospheric pressure, humidity, wind speed, wind direction, and timestamps.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder that impairs a person's ability to concentrate, manage impulses, and maintain attention. ADHD can have a wide range of repercussions, including academic and professional difficulties as well as relationship and emotional issues. Individuals with ADHD may also have handwriting impairments, such as poor fine motor coordination, legibility, and writing speed. These writing difficulties may be related to dysgraphia, a specific writing impairment that affects people with ADHD.

This dataset collection supports the research presented in the manuscript titled “Privacy-preserving and Verifiable Federated Learning for Biometric Data in Edge Computing” (submitted to IEEE Transactions on Knowledge and Data Engineering). It includes three curated biometric datasets—SigD, BIDMC, and TBME—that are used to evaluate the BPVFL framework’s performance in privacy-preserving and verifiable federated learning scenarios.

This dataset collection supports the research presented in the manuscript titled “Privacy-preserving and Verifiable Federated Learning for Biometric Data in Edge Computing” (submitted to IEEE Transactions on Knowledge and Data Engineering). It includes three curated biometric datasets—SigD, BIDMC, and TBME—that are used to evaluate the BPVFL framework’s performance in privacy-preserving and verifiable federated learning scenarios.

This dataset collection supports the research presented in the manuscript titled “Privacy-preserving and Verifiable Federated Learning for Biometric Data in Edge Computing” (submitted to IEEE Transactions on Knowledge and Data Engineering). It includes three curated biometric datasets—SigD, BIDMC, and TBME—that are used to evaluate the BPVFL framework’s performance in privacy-preserving and verifiable federated learning scenarios.