This dataset contains 535 recordings of heart and lung sounds captured using a digital stethoscope from a clinical manikin, including both individual and mixed recordings of heart and lung sounds; 50 heart sounds, 50 lung sounds, and 145 mixed sounds. For each mixed sound, the corresponding source heart sound (145 recordings) and source lung sound (145 recordings) were also recorded. It includes recordings from different anatomical chest locations, with normal and abnormal sounds.
Social media has become a significant platform for information dissemination. This study uses X (formerly known as Twitter) to analyze the key characteristics of information that goes viral on social media. A viral tweet is defined as one that receives 1,000 or more retweets. Data were collected from three Malaysian news channels (501Awani, BernamaTV, and fmtoday) between January and October 2024.
This dataset consists of four datasets. LWNN train and LWNN test data sets consist of synthetically generated 64-OFDM carrier-wise QAM signals. Each signal consists of 1000 I-Q samples. RNN-BC-train and RNN-BC-test datasets consist of modulation (QAM64, QAM32, QAM16, QAM8, QAM4) in each carrier allocated by a bit loading algorithm based on a synthetically generated multipath channel response.
This dataset, denoted as š¾_data, represents a synthetic yet structurally authentic warehouse management dataset comprising 4,132 records and 11 well-defined attributes. It was generated using the Gretel.ai platform, following the structural standards provided by the TI Supply Chain APIāStorage Locations specification. The dataset encapsulates essential operational and spatial parameters of warehouses, including unique identifiers, geospatial coordinates, storage capacities, and categorical capacity statuses.
The main goal of this research is to propose a realistic benchmark dataset to enable the development and evaluation of Internet of Medical Things (IoMT) security solutions. To accomplish this, 18 attacks were executed against an IoMT testbed composed of 40 IoMT devices (25 real devices and 15 simulated devices), considering the plurality of protocols used in healthcare (e.g., Wi-Fi, MQTT and Bluetooth).
This is the CSI data between the UAV and the base station in the ISAC system in an urban environment. It is generated by the raytracing module of sionna. It is used for UAV navigation.
Wi-Fi sensing has emerged as a promising non-intrusive approach for human activity recognition (HAR), leveraging the widespread availability of Wi-Fi-enabled devices. While machine learning tools help extract meaningful features from complex wireless propagation patterns, developing robust and domain-adaptive models remains challenging without diverse datasets.
Molecular Communications (MC), transferring information via chemical signals, holds promise for transformative healthcare applications within the Internet of Bio-Nano Things (IoBNT) framework. Despite promising advances toward practical MC systems, progress has been constrained by experimental testbeds that are costly, difficult to customize, and require labor-intensive fabrication.
This dataset comprises the time cost of ten cryptographic operations on our testing platform, as well as the required number of operations and corresponding time consumption at each phase of the authentication schemes from six selected literature sources. The data is utilized for comparative analysis and evaluation of the operational efficiency among various identity authentication schemes.
The source data files and code files of the paper: optical chaos shift keying communication system via neural network-based signal reconstruction. The following data is included:
1. Source figure file in the paper;
2. Source code of the proposed scheme, include the simulation code for communication, secure analysis and parameter mismatch range.
3. The source Simulink module is included for time-delayed chaotic signal generation.
