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.
A rapid growth of wireless communication networks, particularly in 5G Non-Standalone (NSA) deployments, has necessitated advanced multiple access techniques to enhance spectral efficiency, interference management, and energy optimization [1-3]. Rate-Splitting Multiple Access (RSMA) has arisen as a strong candidate to replace conventional Non-Orthogonal Multiple Access (NOMA) by efficiently splitting user data into common and private components. [1-2].
The CyberAlert-25 Dataset is a comprehensive collection of curated cyber threat data, developed to support advanced research in vulnerability detection, classification, and threat intelligence. Aggregated from authoritative sources such as the National Critical Information Infrastructure Protection Center (NCIIPC) and the MITRE Corporation, the dataset focuses on Common Vulnerabilities and Exposures (CVEs), encompassing a total of 29,650 entries.
<p>This dataset contains simulation results generated in OptiSystem for an 18‐tupling optical communication system. The parameters include optical source settings, modulator configurations, and a range of power and signal quality metrics. Key performance indicators—such as total power penalty (TPP), signal power penalty (SPP), and RMS jitter—are provided for each set of simulation inputs. The data are intended to facilitate reproducible research and to enable further analysis of high‐order frequency multiplication in optical networks.
At-sea testing of underwater acoustic communication systems requires resources unavailable to the wider research community, and researchers often resort to simplified channel models to test new protocols. The present dataset comprises in-situ hydrophone recordings of communications and channel probing waveforms, featuring an assortment of popular modulation formats. The waveforms were transmitted in three frequency bands (4-8 kHz, 9-14 kHz, and 24-32 kHz) during an overnight experiment in an enclosed fjord environment, and were recorded on two hydrophone receivers.
Astronomical instrumentation and related fields have seen remarkable evolution in recent decades, driving the need for advanced signal acquisition and processing techniques. Current experiments demand readout capabilities beyond traditional approaches, leading to the adoption of a wideband instrumentation system architecture for high-speed Radio Frequency (RF) measurements.
We introduce BS-Breath, the first open dataset for respiration sensing using a cell-free massive MIMO system. Collected from a 64-antenna MIMO testbed, this dataset provides uplink Channel State Information (CSI) at 3.51 GHz, captured from 10 subjects performing controlled breathing. Ground truth respiration data is synchronized using a Motion Capture (MoCap) system, enabling precise validation.
This paper explores the cryptanalysis of the ASCON algorithm, a lightweight cryptographic method designed for applications like the Internet of Things (IoT). We utilize deep learning techniques to identify potential vulnerabilities within ASCON's structure. First, we provide an overview of how ASCON operates, including key generation and encryption processes.
This paper explores the cryptanalysis of the ASCON algorithm, a lightweight cryptographic method designed for applications like the Internet of Things (IoT). We utilize deep learning techniques to identify potential vulnerabilities within ASCON's structure. First, we provide an overview of how ASCON operates, including key generation and encryption processes.
