Polish Air Force University

The European Geostationary Navigation Overlay Service (EGNOS) augmentation system can be used in air, sea, and road transport. In the case of the latter, an important aspect is its application in the field of car navigation. The main objective of this work is to develop an algorithm and computational strategy for determining the accuracy of a car's position based on a multi-receiver GPS solution with EGNOS corrections in navigation. In particular, the article proposes determining the coordinates of a car based on a mathematical equation of the center of the segment connecting two Global Navigation Satellite System (GNSS) receivers placed on the roof of the car while driving. A total of four GNSS receivers were used for this purpose, two for each geometric segment. Thus, the center coordinates for two independent segments that intersected at a single point were finally determined, where a fifth GNSS receiver was installed to verify the coordinates obtained from the GPS/EGNOS solution. This made it possible to determine the center coordinates of the car while driving. The study used actual GPS/ EGNOS kinematic data recorded by on-board GNSS receivers. The obtained results of the center coordinates of the geometric section were compared with the reference position of the movement for the purpose of calculating accuracy. Based on the tests performed, it was found that the average positioning accuracy is higher than 0.80 m for all BLh geodetic coordinates. The Kalman filter was also used in the study, reducing positioning errors by up to 40% for all BLh components. The developed methodology and calculation strategy can be used, for example, in air navigation for aircraft positioning.

Emergency Medical Services (EMS) trainees are exposed to multiple occupational hazards during their training; however, the extent to which their awareness translates into safe professional behaviours remains unclear. This study aimed to assess the level of occupational hazard awareness and safety-related knowledge among EMS students in the Makkah region, Saudi Arabia. A cross-sectional study was conducted among 96 EMS students from three academic institutions. Data were collected using a structured questionnaire adapted from previously published instruments assessing demographic characteristics, awareness of occupational hazards, safety-related measures, and sources of information. Descriptive statistics and inferential analyses using non-parametric tests (Mann-Whitney U, Kruskal-Wallis), Chi-square/Fisher's exact tests, and Spearman correlation analysis were applied. Overall, students demonstrated moderate to high awareness of occupational hazards, particularly regarding hand hygiene and personal protective equipment (PPE). Lower awareness was observed for ergonomic risks and procedures for reporting occupational injuries. Physical (83.3%) and chemical (81.3%) hazards were most frequently recognized, while ergonomic hazards were least identified (26.0%). A total of 63.5% of participants reported encountering aggressive patients at least once per year. No statistically significant association was observed between academic progression and awareness (p = 0.14). A weak, non-significant correlation was found between awareness and self-reported safety-related measures (r = 0.182, p = 0.076), suggesting a limited association between knowledge and reported safety behaviours. Although EMS students demonstrate generally high awareness of occupational hazards, this may be insufficient to ensure safe behaviour, as reflected by self-reported safety-related measures. These findings highlight the need for educational strategies that better support the application of knowledge in practice.

INTRODUCTION:The COVID-19 pandemic exposed structural weaknesses in European public health systems while simultaneously accelerating institutional and digital reforms at the European Union (EU) level. This study examines how the EU has evolved from reactive crisis management toward a governance paradigm conceptualized as transformative resilience, understood as the institutional capacity to anticipate, adapt, and structurally reconfigure health governance in response to systemic shocks. METHODS:This study employs a structured qualitative policy analysis based on a purposive corpus of key EU legislative and strategic documents (2020-2025), complemented by a contextual review of selected EU-level indicators. The analysis focuses on reforms associated with the European Health Union, including the establishment of the Health Emergency Preparedness and Response Authority (HERA), the development of the European Health Data Space (EHDS), and the adoption of the Artificial Intelligence Act. RESULTS:The findings indicate progressive consolidation of supranational coordination mechanisms, deeper integration of digital infrastructure into health governance, and strategic incorporation of health security into the EU's broader security architecture. Rather than assessing policy effectiveness, the analysis documents a structural and regulatory shift toward anticipatory and embedded preparedness. Persistent challenges remain, including uneven implementation capacity across member states, disparities in digital maturity, and tensions between innovation and data protection. CONCLUSIONS:The EU's post-pandemic trajectory reflects a distinctive governance model in which health security, digital sovereignty, and democratic safeguards are framed as mutually reinforcing dimensions of resilience within an increasingly complex risk environment.

This paper presents an intelligent framework for real-time hand gesture recognition using Frequency-Modulated Continuous-Wave (FMCW) mmWave radar and deep learning. Unlike traditional radar-based recognition methods that rely on Discrete Fourier Transform (DFT) signal representations and focus primarily on classifier optimization, the proposed system introduces a novel pre-processing stage based on the Continuous Wavelet Transform (CWT). The CWT enables the extraction of discriminative time-frequency features directly from raw radar signals, improving the interpretability and robustness of the learned representations. A lightweight convolutional neural network architecture is then designed to process the CWT maps for efficient classification on edge IoT devices. Experimental validation with data collected from 20 participants performing five standardized gestures demonstrates that the proposed framework achieves an accuracy of up to 99.87% using the Morlet wavelet, with strong generalization to unseen users (82-84% accuracy). The results confirm that the integration of CWT-based radar signal processing with deep learning forms a computationally efficient and accurate intelligent system for human-computer interaction in real-time IoT environments.

Dangerous goods transport (DGT) is of strategic importance for any economy, and the structure of the fuel and energy industry includes a number of systems and facilities qualified as “critical infrastructure” (CI). Given the current geopolitical situation, sabotage, hybrid or even terrorist activities in the area of logistics and transport pose an increasing threat. At the same time, next to the economic sector, liquid fuels are of great importance to citizens, which is why the transport of this group of goods should be given special importance, ensuring appropriate efficiency and safety parameters, taking into account the risk of intentional, destructive human interference. A significant source of data in the road transport of dangerous goods is the spatial data infrastructure (SDI); digital interactive platforms (DIP) are important here. This scientific research work concerns the application of DIP and related information technologies (IT) in road transport—smart mobility (SM). The main objective of the scientific research work is to develop proposals for effective tools to minimize the overall risk, using publicly available digital interactive platforms. In the implementation of the topic, the following methods were integrated: OKR (Objectives and Key Results), SMART (Specific, Measurable, Achievable, Relevant, Time-bound) and CS (Case Study). The main problem was identified and the main goal of the work was achieved. The results made it possible to present effective risk minimization tools in DGT using DIP. The elaboration was prepared under the research subvention of the AGH University of Krakow, No. 16.16.150.545 in 2026.