Ferroelectric ceramics BaTiO₃:x%Eu (x = 0, 0.1, 1, 2, 3) were synthesized by a conventional method. Structural investigation confirmed that all ceramics possessed tetragonal (P4mm) symmetries at room temperature for the undoped ceramics as well as for the doped ceramics. Furthermore, a slight downshifting of the Curie temperature (T_C) with an increasing Eu³⁺ doping amount has been noted. The Raman spectra unveiled the existence of new modes for higher-doped BaTiO₃:x%Eu (BTEx) which are related to local disorders and defects. The ferroelectric properties were found to depend on both doping and the microstructure. The electrocaloric effect was also studied for those ceramics. It was observed that ΔT decreases with doping; however, the temperature range of its occurrence widens considerably.

The rapid integration of Unmanned Aerial Vehicles (UAVs) into smart city infrastructures necessitates advanced security measures to ensure their safe and sustainable operation. However, existing Automatic Dependent Surveillance–Broadcast (ADS-B) systems are highly vulnerable to spoofing, data falsification, and cyber threats, which compromises air traffic management and poses significant challenges to UAV security. This paper presents an innovative approach to improving UAV security by introducing a novel steganographic method for ADS-B data protection. The proposed method leverages Fourier transformation to embed UAV identifiers into ADS-B signals, ensuring a high level of concealment and robustness against signal distortions. A key feature of the approach is the dynamic parameter management system, which adapts to varying transmission conditions to minimize distortions and enhance resilience. Experimental validation demonstrates that the method achieves a tenfold reduction in Mean Squared Error (MSE) and Normalized Mean Squared Error (NMSE) compared to existing techniques such as mp3stego while also improving the Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR) compared to s-tools. The proposed solution ensures compliance with existing ADS-B standards, maintaining seamless integration with air traffic management systems while enhancing cybersecurity measures. By safeguarding UAV communications, the method contributes to the sustainable development of smart cities and supports critical applications such as logistics, environmental monitoring, and emergency response operations. These findings confirm the practical feasibility of the proposed approach and its potential to strengthen UAV security and ADS-B data protection, ultimately contributing to the resilience and sustainability of urban airspace infrastructure.

This paper examines traditional machine learning algorithms, neural networks, and the benefits of utilizing ensemble models. Data preprocessing methods for improving the quality of classification models are considered. To balance the classes, Undersampling, Oversampling, and their combination (Over + Undersampling) algorithms are explored. A procedure for reducing feature correlation is proposed. Classification models based on meta-algorithms such as SVM, KNN Naive Bayes, Perceptron, Bagging, Random Forest, AdaBoost, and Gradient Boosting have been thoroughly investigated. The settings of the base classifiers and meta-algorithm parameters have been optimized. The best result was obtained by using an ensemble classifier based on the Random Forest algorithm. Thus, an intrusion detection method based on the preprocessing of highly correlated and imbalanced data has been proposed. The scientific novelty of the obtained results lies in the integrated use of the developed procedure for reducing feature correlation, the application of the SMOTEENN data balancing method, the selection of an appropriate classifier, and the fine tuning of its parameters. The integration of these procedures and methods resulted in a higher F1 score, reduced training time, and faster recognition speed for the model. This allows us to recommend this method for practical use to improve the quality of network intrusion detection.

In this paper, we studied some ferroelectric properties of archetypal oxide uniaxial ferroelectric single crystals of Pb₅Ge₃O₁₁ modified by Ba ions. They include dielectric, DSC, ferroelectric polarization, and electrocaloric effect (ECE) measurements. The measurements show that increasing Ba doping considerably influences all the measured parameters, mainly by lowering the Curie temperature, gradually diffusing the phase transition, and decreasing values of polarization as well as the coercive field. The decrease in overall ECE is influenced by decreasing polarization. Compared with the pure PGO single crystals, this decrease is from 1.2 K to 0.2 K. However, the effect of diffusing the phase transition increases the range of its occurrence (up to 30 K), which might be beneficial in applications.