This article provides a discussion on the results of the author’s in-house study on the greenhouse gas (GHG) emissions generated over the life cycle of the electric buses (BEV) used in Poland’s public transport services. The life cycle assessment (LCA) method was employed to evaluate the greenhouse gas emissions. The relevant changes to the energy mix, assumed in line with the electricity generation projections for Poland by 2040, have caused a drop in the average greenhouse gas emissions from electric buses in the consecutive years. For the energy mix envisaged for the years 2025, 2030, 2035, and 2040, being the source of the electricity consumed to charge batteries of electric buses, a decline in the mean emissions by 5%, 13.5%, 30%, and 39%, respectively, can be observed in comparison with the 2022 energy mix. Additionally, the article describes an original model for assessing the greenhouse gas emissions from electric buses which enables the GHG emissions to be studied with reference to various sources of electric energy.
This paper presents the results of an aerodynamic investigation of the XGyro fuselage–empennage airframe. The main rotor was not included in the present study in order to isolate the aerodynamic behaviour of the airframe. A combined research methodology was applied, integrating computational fluid dynamics (CFD) simulations with experimental validation in a low-speed wind tunnel. Numerical analyses were performed using ANSYS Fluent with the k–ω SST turbulence model for a wide range of angles of attack and sideslip angles. Wind tunnel tests were conducted on a 3D-printed scaled model under corresponding flow conditions. Both approaches confirmed longitudinal static stability and predictable lateral - directional behavior of the analysed airframe. Within the investigated range, the minimum drag coefficient occurred at α≈0˚– 5˚, while the maximum CZ/CX ratio was obtained at β=0˚ and α=10˚. The results provide insight into the aerodynamic characteristics of the fuselage–empennage configuration and support further design and development of the overall XGyro concept.
Reliable detection and localization of cable faults are essential for the secure operation of modern distribution networks, particularly with increasing distributed generation integration. This study proposes a physics-informed spatiotemporal deep learning framework for fault detection and localization in medium-voltage cable systems. The approach embeds telegrapher-equation residual constraints and distributed-generation boundary consistency within a physics-informed neural network to incorporate physical knowledge into the learning process. Long short-term memory networks capture transient temporal features of fault signals, while a topology-aware graph convolutional network identifies the faulted cable section within the network. A physics-regularized regression stage then estimates the precise fault distance inside the identified section. The framework is validated using electromagnetic transient simulations of a 10 kV underground cable network developed in PSCAD/EMTDC under various fault locations, grounding conditions, and distributed generation penetration levels.
The article analyzes how the rotational speed of the Wabik helicopter rotor affects both the overall noise level and its spectral characteristics. Measurements were performed in one third octave bands for two configurations: the rotor hub alone and the complete rotor system with blades installed. This allowed clear separation of the hub noise from the aerodynamic noise generated by the blades and showed how both components depend on rotational speed. The results confirm that rotor speed strongly influences total noise emission and the spectral structure of the emitted sound, while at higher speeds the blades become a major noise source under piston engine operating conditions. The findings provide a basis for improving blade geometry, selecting optimal engine and rotor operating ranges, and reducing noise in the most critical frequency bands. The novelty of the work is the detailed one third octave analysis of a prototype custom designed Wabik rotor system at three rotational speeds.
In dynamic, occlusion-prone environments like football stadiums, reliable human pose estimation is essential for mobile robots, where conventional systems often fail due to partial visibility and rapid motion. This paper presents a lightweight pose estimation framework for real-time operation on resource-constrained edge platforms and the X5 robot. It employs selective knowledge distillation to transfer occlusion-robust and motion-aware features from a pre-trained teacher model to a compact student model, preserving efficiency while enhancing reliability. Three components are integrated by synthetic occlusion pattern embeddings for visibility, temporal motion cue extraction for movements and cross-modal attention alignment to focus on visible body regions. Validated on the D-Robotics mono2d_body_detection benchmark, the system achieves accuracy improvements under heavy occlusion while maintaining ≥30 FPS on the target platform. Experimental results confirm the framework balances high accuracy with low complexity, which makes it suitable for reliable deployment in football stadiums.
