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Penterep: Comprehensive Penetration Testing with Adaptable Interactive Checklists
(Elsevier, 2025-03-17) Lazarov, Willi; Šeda, Pavel; Martinásek, Zdeněk; Kümmel, Roman
In the contemporary landscape of cybersecurity, the importance of effective penetration testing is underscored by NIS2, emphasizing the need to assess and demonstrate cyber resilience. This paper introduces an innovative approach to penetration testing that employs interactive checklists, supporting both manual and automated tests, as demonstrated within the Penterep environment. These checklists, functioning as a quantifiable measure of test completeness, guide pentesters through methodological testing, addressing the inherent challenges of the security testing domain. While some may perceive a limitation in the dependency on predefined checklists, the results from a presented case study underscore the criticality of methodological testing. The study reveals that relying solely on fully automated tools would be inadequate to identify all vulnerabilities and flaws without the inclusion of manual tests. Our innovative approach complements established methodologies, such as PTES, OWASP, and NIST, providing crucial support to penetration testers and ensuring a comprehensive testing process. Implemented within the Penterep environment, our approach is designed with deployment flexibility (both on-premises and cloud-based), setting it apart through an overview comparison with existing tools aligned with state-of-the-art penetration testing approaches. This flexible and scalable approach effectively bridges the gap between manual and automated testing, meeting the increasing demands for effectiveness and adaptability in penetration testing.
COMPARISON OF GRAND PIANOS ANT. PETROF AND MISTRAL
(Journal Akustika, 2024-11-24) Jirásek, Ondřej; Peloušek, Tomáš
This paper describes the measurement of two Petrof grand pianos: Ant. Petrof and Mistral. The goal was to compare their parameters, i.e. harmonic spectra, decay time, relative sound level, spectral centroids, and cumulative line spectra (CLS). The complete range of 88 tones (keys) was measured in the anechoic chamber of Petrof, where we used a play-bench for precision. The obtained data were analysed and put into comparison. This analysis was supplemented by a comparison of selected samples (keys A2 and A4 played in forte and piano dynamics) to samples of three competing brands (Shigeru Kawai, Yamaha, Steinway).
Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning
(IEEE, 2024-11-07) Alvarez Justo, Jon; Ghiţă, Alexandru; Kováč, Daniel; L. Garrett, Joseph; Georgescu, Mariana-Iuliana; Gonzalez-Llorente, Jesus; Tudor Ionescu, Radu; Arne Johansen, Tor
Satellites are increasingly adopting onboard AI to optimize operations and increase autonomy through in-orbit inference. The use of deep learning (DL) models for segmentation in hyperspectral imagery offers advantages for remote sensing applications. In this work, we train and test 20 models for multiclass segmentation in hyperspectral imagery, selected for their potential in future space deployment. These models include 1-D and 2-D convolutional neural networks (CNNs) and the latest vision transformers (ViTs). We propose a lightweight 1D-CNN model, 1D-Justo-LiuNet, which outperforms state-of-the-art models in the hypespectral domain. 1D-Justo-LiuNet exceeds the performance of 2D-CNN UNets and outperforms Apple's lightweight vision transformers designed for mobile inference. 1D-Justo-LiuNet achieves the highest accuracy (0.93) with the smallest model size (4563 parameters) among all tested models, while maintaining fast inference. Unlike 2D-CNNs and ViTs, which encode both spectral and spatial information, 1D-Justo-LiuNet focuses solely on the rich spectral features in hyperspectral data, benefitting from the high-dimensional feature space. Our findings are validated across various satellite datasets, with the HYPSO-1 mission serving as the primary case study for sea, land, and cloud segmentation. We further confirm our conclusions through generalization tests on other hyperspectral missions, such as NASA's EO-1. Based on its superior performance and compact size, we conclude that 1D-Justo-LiuNet is highly suitable for in-orbit deployment, providing an effective solution for optimizing and automating satellite operations at edge
The Problem of Integrating Digital Twins into Electro-Energetic Control Systems
(MDPI, 2024-09-18) Bohačík, Antonín; Fujdiak, Radek
The use of digital twins (DTs) in the electric power industry and other industries is a hot topic of research, especially concerning the potential of DTs to improve processes and management. This paper aims to present approaches to the creation of DTs and models in general. It also examines the key parameters of these models and presents the challenges that need to be addressed in the future development of this field. Our analysis of the DTs and models discussed in this paper is carried out on the basis of identified key characteristics, which serve as criteria for an evaluation and comparison that sets the basis for further investigation. A discussion of the findings shows the potential of DTs and models in different sectors. The proposed recommendations are based on this analysis, and aim to support the further development and use of DTs. Research into DTs represents a promising sector with high potential. However, several key issues and challenges need to be addressed in order to fully realize their benefits in practice.
Computer-Aided Diagnosis of Graphomotor Difficulties Utilizing Direction-Based Fractional Order Derivatives
(Springer, 2024-11-27) Gavenčiak, Michal; Mucha, Ján; Mekyska, Jiří; Galáž, Zoltán; Šafárová, Katarína; Faúndez Zanuy, Marcos
Children who do not sufficiently develop graphomotor skills essential for handwriting often develop graphomotor disabilities (GD), impacting the self-esteem and academic performance of the individual. Current examination methods of GD consist of scales and questionaries, which lack objectivity, rely on the perceptual abilities of the examiner, and may lead to inadequately targeted remediation. Nowadays, one way to address the factor of subjectivity is to incorporate supportive machine learning (ML) based assessment. However, even with the increasing popularity of decision-support systems facilitating the diagnosis and assessment of GD, this field still lacks an understanding of deficient kinematics concerning the direction of pen movement. This study aims to explore the impact of movement direction on the manifestations of graphomotor difficulties in school-aged. We introduced a new fractional-order derivative-based approach enabling quantification of kinematic aspects of handwriting concerning the direction of movement using polar plot representation. We validated the novel features in a barrage of machine learning scenarios, testing various training methods based on extreme gradient boosting trees (XGBboost), Bayesian, and random search hyperparameter tuning methods. Results show that our novel features outperformed the baseline and provided a balanced accuracy of 87 % (sensitivity = 82 %, specificity = 92 %), performing binary classification (children with/without graphomotor difficulties). The final model peaked when using only 43 out of 250 novel features, showing that XGBoost can benefit from feature selection methods. Proposed features provide additional information to an automated classifier with the potential of human interpretability thanks to the possibility of easy visualization using polar plots.

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