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Data, Volume 9, Issue 5

May 2024 - 13 articles

Cover Story: In the rapidly evolving landscape of the IoT, the security of connected devices is paramount, making the unique identification of devices within these ecosystems increasingly critical. In the context of emergent AI-driven identification systems, we present a novel dataset obtained from the on-chip sensors of twenty STM32L microcontrollers stimulated under five different workloads in various operational conditions, introducing a groundbreaking approach to real-world device authentication. This dataset comprises the unique responses of devices as a result of exploiting the intrinsic physical variations introduced into the on-chip sensors and other hardware components during manufacturing, which were elicited through the stimulation of electronic activity. View this paper

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Article

7 Citations

2,291 Views

15 Pages

Detailed Landslide Traces Database of Hancheng County, China, Based on High-Resolution Satellite Images Available on the Google Earth Platform

Junlei Zhao,
Chong Xu and
Xinwu Huang

Data2024, 9(5), 63;https://doi.org/10.3390/data9050063

29 April 2024

Hancheng is located in the eastern part of China’s Shaanxi Province, near the west bank of the Yellow River. It is located at the junction of the active geological structure area. The rock layer is relatively fragmented, and landslide disasters...

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Data Descriptor

2 Citations

2,245 Views

16 Pages

Stimulated Microcontroller Dataset for New IoT Device Identification Schemes through On-Chip Sensor Monitoring

Alberto Ramos,
Honorio Martín,
Carmen Cámara and
Pedro Peris-Lopez

Data2024, 9(5), 62;https://doi.org/10.3390/data9050062

28 April 2024

Legitimate identification of devices is crucial to ensure the security of present and future IoT ecosystems. In this regard, AI-based systems that exploit intrinsic hardware variations have gained notable relevance. Within this context, on-chip senso...

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Data Descriptor

7 Citations

4,239 Views

10 Pages

Training Datasets for Epilepsy Analysis: Preprocessing and Feature Extraction from Electroencephalography Time Series

Christian Riccio,
Angelo Martone,
Gaetano Zazzaro and
Luigi Pavone

Data2024, 9(5), 61;https://doi.org/10.3390/data9050061

26 April 2024

We describe 20 datasets derived through signal filtering and feature extraction steps applied to the raw time series EEG data of 20 epileptic patients, as well as the methods we used to derive them. Background: Epilepsy is a complex neurological diso...

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Data - ISSN 2306-5729|Creative Common CC BY license

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