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Open Access
A comparative analysis of hybrid sensor fusion schemes for visual–inertial navigation
(Institute of Electrical and Electronics Engineers (IEEE), 2025-12-31) Tabassum, Tarafder Elmi; Petrunin, Ivan; Rana, Zeeshan A.
Visual Inertial Odometry (VIO) has been extensively studied for navigation in GNSS-denied environments, but its performance can be heavily impacted by the complexity of the navigation environments such as weather conditions, illumination variation, flight dynamics, and environmental structure. Hybrid fusion approaches integrating Neural Networks (NN), especially Gated Recurrent units (GRU) with the Kalman filters (KF), such as Error-State Kalman Filter (ESKF) have shown promising results mitigating system nonlinearities due to challenging environmental conditions data issues, there is a lack of systematic studies quantitively analysing and comparing performance differences unhand. To address this gap and enable robust navigation in complex conditions, this study proposes and systematically analyses the performance of three hybrid fusion schemes for VIO-based navigation of Unmanned Aerial Vehicles (UAV). These three hybrid VIO schemes include Visual Odometry (VO) error compensation using NN, KF error compensation using NN, and prediction of Kalman gain using NN. The comparative analysis is performed using data generated in MATLAB incorporating the Unreal Engine involving diverse challenging environmental conditions: fog, rain, illumination level variability and variability in the number of features available for extraction during the UAV flight in the urban environment. The results demonstrate the performance improvement achieved by hybrid VIO fusion schemes compared to ESKF-based traditional fusion methods in the presence of multiple visual failure modes. Comparative analysis reveals notable improvement achieved by method 1 with enhancements of 93% in sunny, 91% in foggy and 90% in rainy conditions than the other two hybrid VIO architectures.
Open Access
A comprehensive review and future challenges of energy-aware path planning for small unmanned aerial vehicles with hydrogen-powered hybrid propulsion
(Cambridge University Press (CUP), 2025) Çinar, Hasan; Ignatyev, Dmitry; Zolotas, Argyrios
Unmanned aerial vehicles (UAVs) with fully electric propulsion systems mainly use lithium-based batteries. However, using fuel cells, hybrid propulsion systems are created to improve the flight time and payload capacity of the UAVs. Energy management and energy-aware path planning are important aspects to be explored in hybrid-propulsion powered UAV configurations. These facilitate optimal power distribution among energy sources and motion planning considering energy consumption, respectively. In the literature, although there are many studies on the energy management of hybrid-powered UAVs and path planning of only battery-powered UAVs, there are research gaps in energy-aware path planning of hybrid-powered UAVs. Additionally, the energy management of hybrid-powered UAVs is usually considered independent of path planning in the literature. This paper thoroughly reviews recent energy-aware path planning for small UAVs to address the listed critical challenges above, providing a new perspective and recommendations for further research. Firstly, this study evaluates the recent status of path planning, hydrogen-based UAVs, and energy management algorithms and identifies some challenges. Later, the applications of hydrogen-powered UAVs are summarised. In addition, hydrogen-based hybrid power system topologies are defined for small UAVs. Then, the path-planning algorithms are classified, and existing studies are discussed. Finally, this paper provides a comprehensive and critical assessment of the status of energy-aware path planning of UAVs, as well as detailed future work recommendations for researchers.
Open Access
A review of diffuse interface-capturing methods for compressible multiphase flows
(MDPI, 2025-04-03) Adebayo, Ebenezer Mayowa; Tsoutsanis, Panagiotis; Jenkins, Karl W.
This paper discusses in detail the classification, historical development, and application of diffuse interface-capturing models (DIMs) for compressible multiphase flows. The work begins with an overview of the development of DIMs, highlighting important contributions and key moments from classical studies to contemporary advances. The theoretical foundations and computational methods of the diffuse interface method are outlined for the full models and the reduced models or sub-models. Some of the difficulties encountered when using DIMs for multiphase flow modelling are also discussed.
Open Access
An advanced performance-based method for soft and abrupt fault diagnosis of industrial gas turbines
(Elsevier, 2025-04-15) Chen, Yu-Zhi; Zhang, Wei-Gang; Tsoutsanis, Elias; Zhao, Junjie; Tam, Ivan C. K.; Gou, Lin-Feng
Integrating gas turbines with intermittent renewable energy must operate for prolonged periods under transient conditions. Existing research on fault diagnosis in such systems has concentrated on the primary rotating components in steady-state conditions. There is a gap in investigating the interplay between shaft bearing failure and performance metrics, as well as fault identification under transient conditions. This study aims to identify faults not only in the main rotating components but also in the shaft bearings under transient conditions. Firstly, the performance model and fault propagation model of gas turbines are established, and the influence of bearing fault on the whole engine performance is analysed. Then, the fault diagnosis method is determined and the dynamic effects are compensated in fault identification at each time interval. Finally, the steady-state and transient fault diagnosis are carried out considering the constant and sudden faults for the main rotating components and bearings. The average run time and maximum error during the engine life cycle are 0.1064 s and 0.0086 %. For the proposed dynamic effects compensation method, the average computation time and peak error at every moment are 0.1152 s and 0.0143 %, clearly superior to the benchmark method. These results provide evidence that the proposed method can correctly diagnose the fault of the main rotating components and shaft bearings under transient conditions. Therefore, the findings mark an advancement in real-time fault diagnostic techniques, ultimately enhancing engine availability while upholding secure and affordable energy production.
Open Access
Damping identification sensitivity in flutter speed estimation
(MDPI , 2025-06-01) Dessena, Gabriele; Pontillo, Alessandro; Civera, Marco; Ignatyev, Dmitry I.; Whidborne, James F.; Zanotti Fragonara, Luca
Predicting flutter remains a key challenge in aeroelastic research, with certain models relying on modal parameters, such as natural frequencies and damping ratios. These models are particularly useful in early design stages or for the development of small Unmanned Aerial Vehicles (maximum take-off mass below 7 kg). This study evaluates two frequency-domain system identification methods, Fast Relaxed Vector Fitting (FRVF) and the Loewner Framework (LF), for predicting the flutter onset speed of a flexible wing model. Both methods are applied to extract modal parameters from Ground Vibration Testing data, which are subsequently used to develop a reduced-order model with two degrees of freedom. The results indicate that FRVF- and LF-informed models provide reliable flutter speed, with predictions deviating by no more than 3% (FRVF) and 5% (LF) from the N4SID-informed benchmark. The findings highlight the sensitivity of flutter speed predictions to damping ratio identification accuracy and demonstrate the potential of these methods as computationally efficient alternatives for preliminary aeroelastic assessments.
Open Access
Deep learning based secure transmissions for the UAV-RIS assisted networks: trajectory and phase shift optimization
(IEEE, 2024-12-08) Li, Jiawei; Wang, Dawei; Zhang, Jiankang; Alfarraj, Osama; He, Yixin; Al-Rubaye, Saba; Yu, Keping; Mumtaz, Shahid
This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS's phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV's trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.
Open Access
Distributed Spaceborne SAR: a review of systems, applications, and the road ahead
(IEEE, 2025-12-31) Hu, Cheng; Li, Yuanhao; Chen, Zhiyang; Liu, Feifeng; Zhang, Qingjun; Monti-Guarnieri, Andrea V.; Hobbs, Stephen E.; Anghel, Andrei; Datcu, Mihai
As a crucial sensor for wide-area Earth observation, spaceborne synthetic aperture radar (SAR) plays a pivotal role in large-scale terrain mapping, ocean observation, disaster monitoring, and so forth. Driven by the increasing demands for diverse applications, enhanced performance, and the continuous advancement of satellite and radar technologies, the distributed configuration has emerged as a key developmental trend for spaceborne SAR. This review comprehensively summarizes the systems and typical applications of distributed spaceborne SAR. The system configurations encompass homogenous distributed SAR, formed by multiple identical or similar platforms, and heterogeneous distributed SAR, characterized by significant differences between the transmitting and receiving platforms. Typical applications of distributed SAR include intelligent target recognition, terrain mapping, deformation retrieval, atmosphere measurement, and ocean observation, among others. Finally, the review offers a prospective outlook on the future development of distributed spaceborne SAR.
Open Access
Dual-Chamber microbial fuel cell for Azo-Dye degradation and electricity generation in Textile wastewater treatment
(Elsevier, 2025-09) Ndive, Julius Nnamdi; Eze, Simeon Okechukwu; Nnabuife, Somtochukwu Godfrey; Kuang, Boyu; Rana, Zeeshan A.
Textile wastewater, particularly azo dyes, poses significant environmental challenges due to its poor biodegradability and toxicity. This study explores a dual-chamber microbial fuel cell (MFC) for simultaneous wastewater treatment and electricity generation. The MFC consists of an anaerobic anode chamber and an aerobic cathode chamber, separated by a proton exchange membrane (PEM). Electroactive microorganisms in the anode chamber metabolize organic substrates, including azo dye contaminants, breaking them down into simpler by-products. Electrons released during this process flow through an external circuit to generate current, while protons migrate across the PEM to the cathode chamber for oxygen reduction. Electrochemically active microbes were isolated from azo-dye-contaminated soil, and their degradation abilities validated through assays. Optimized carbon-based electrodes and a Nafion 117 PEM were used to enhance conductivity and microbial activity. UV–Vis spectroscopy tracked dye degradation, with the absorbance peak of reactive yellow dye at 410 nm decreasing from 2.9 to 0.4, indicating effective azo-bond cleavage. The MFC achieved peak voltage and current outputs of 0.20 mV and 0.16 mA, respectively, demonstrating its dual functionality. Adding NaCl as a supporting electrolyte further improved ionic conductivity and performance. This study demonstrates MFC technology as a sustainable solution for industrial wastewater challenges, integrating microbial degradation with bioelectricity generation. Future work should address scalability, operational stability, and advanced electrode designs to enhance its practical applications.
Open Access
Explainable reinforcement and causal learning for improving trust to 6G stakeholders
(IEEE, 2025-06-01) Arana-Catania, Miguel; Sonee, Amir; Khan, Abdul-Manan; Fatehi, Kavan; Tang, Yun; Jin, Bailu; Soligo, Anna; Boyle, David; Calinescu, Radu; Yadav, Poonam; Ahmadi, Hamed; Tsourdos, Antonios; Guo, Weisi; Russo, Alessandra
Future telecommunications will increasingly integrate AI capabilities into network infrastructures to deliver seamless and harmonized services closer to end-users. However, this progress also raises significant trust and safety concerns. The machine learning systems orchestrating these advanced services will widely rely on deep reinforcement learning (DRL) to process multi-modal requirements datasets and make semantically modulated decisions, introducing three major challenges: (1) First, we acknowledge that most explainable AI research is stakeholder-agnostic while, in reality, the explanations must cater for diverse telecommunications stakeholders, including network service providers, legal authorities, and end users, each with unique goals and operational practices; (2) Second, DRL lacks prior models or established frameworks to guide the creation of meaningful long-term explanations of the agent's behaviour in a goal-oriented RL task, and we introduce state-of-the-art approaches such as reward machine and sub-goal automata that can be universally represented and easily manipulated by logic programs and verifiably learned by inductive logic programming of answer set programs; (3) Third, most explainability approaches focus on correlation rather than causation, and we emphasise that understanding causal learning can further enhance 6G network optimisation. Together, in our judgement they form crucial enabling technologies for trustworthy services in 6G. This review offers a timely resource for academic researchers and industry practitioners by highlighting the methodological advancements needed for explainable DRL (X-DRL) in 6G. It identifies key stakeholder groups, maps their needs to X-DRL solutions, and presents case studies showcasing practical applications. By identifying and analysing these challenges in the context of 6G case studies, this work aims to inform future research, transform industry practices, and highlight unresolved gaps in this rapidly evolving field.
Open Access
On the application of trapped vortices in motorsport application for improved aerodynamic performance using passive and active flow controls
(SAE International, 2025-04-15) Ng, Ming Kin; Teschner, Tom-Robin
New regulations introduced by the Fédération Internationale de l’Automobile (FIA) for the 2026 Formula 1 season mark the first instance of active flow control methods being endorsed in Formula 1 competition. While active methods have demonstrated significant success in airfoil development, their broader application to grounded vehicle aerodynamics remains unexplored. This research investigates the effectiveness of trapped vortex cavity (TVC) technology in both active and passive flow controls, applied to a NACA0012 airfoil and an inverted three-element airfoil from a Formula 1 model. The investigation is conducted using numerical methods to evaluate the aerodynamic performance and potential of TVC in this paper. In the single-airfoil case, a circular cavity is placed along the trailing edge (TE) on the suction surface; for the three-element airfoils, the cavity is positioned on each airfoil to determine the optimum location. The results show that the presence of a cavity, particularly with active flow control, significantly improves the lift-to-drag ratio (CL/CD) for both the single airfoil and the three-element airfoils. A maximum enhancement of 1160% was recorded for the single airfoil, while the three-element airfoils saw an improvement of 313% compared to their original configurations. However, when the TVC was placed in positions other than the TE of the mid-airfoil, a performance reduction was observed, even with active blowing applied. The passive flow control approach, which requires no additional energy input, yielded a modest improvement of 3.52% for the NACA0012 airfoil. However, passive control underperformed due to unstable vortex interactions with each airfoil element for the inverted three-element airfoil case. Even with optimal placement and geometrical modifications, the maximum CL/CD ratio for passive control was only 96% of the original CL/CD of the unmodified three-element airfoils, suggesting that passive flow control is less effective here compared to active flow control.
Open Access
Personalizing driver agent using large language models for driving safety and smarter human–machine interactions
(IEEE, 2025-12-31) Xu, Zixuan; Chen, Tiantian; Huang, Zilin; Xing, Yang; Chen, Sikai
Driver assistance systems have been shown to reduce crashes by providing real-time warnings or assistance, with their effectiveness depending on communication with driver. Due to their unique characteristics, human drivers possess varying hazard perception skills and interaction preferences, making personalized assistance crucial to improving the user experience and system acceptance. However, how to leverage multimodal interfaces that dynamically adapt to warning contents and driver characteristics remains an open question. At the same time, large language models (LLMs) have demonstrated advanced capabilities in knowledge acquisition, planning, and human–machine collaboration, offering potential solutions for existing warning systems. Thus, we develop an LLM-based personalized driver agent (PDA), which provides personalized warnings through multimodal interactions (visual, voice, and tactile). The agent’s architecture mimics human cognitive processes via four core modules: memory, perception, control, and action. Results from our experiments indicate that the LLM-PDA effectively customizes warning contents for different drivers in various situations, providing enhanced safety and driver support. This article pioneers the integration of LLMs into automotive human–vehicle interaction and offers novel insights into personalized human–machine interaction in intelligent vehicles.
Open Access
Signal-to-interference-noise-ratio density distribution for UAV-carried IRS-to-6G ground communication
(IEEE, 2025) Nnamani, Christantus Obinna; Anioke, Chidera Linda; Al-Rubaye, Saba; Tsourdos, Antonios
This paper investigates the probability distribution of the signal-to-interference noise ratio (SINR) for a 6G communication system comprising a multi-antenna transmitter, an intelligent reflecting surface (IRS) and a remote receiver station. A common assumption in the literature is that the density distribution function for SINR and signal-to-noise ratio (SNR) of an IRS-to-ground communication follows a Rayleigh and Rician distribution. This assumption is essential as it influences the derivation of the properties of the communication system such as the physical layer security models and the designs of IRS controller units. Therefore, in this paper, we present an analytical derivation for the density distribution functions of the SINR for an IRS-to-6G ground communication ameliorating the typical assumptions in the literature. We demonstrated that the SINR density function of an IRS-to-6G ground communication contains a hypergeometric function. We further applied the derived density distribution function to determine the average secrecy rate for passive eavesdropping.
Open Access
Sustainable 6G-NTN for seamless air mobility: exploring channel propagation characteristics
(IEEE, 2025-04-08) Chen, Yang; Bocciarelli, Hugo; Al-Rubaye, Saba; Tsourdos, Antonios
The air transportation vision for sustainable sixthgeneration (6 G) wireless communications networks revolves around ensuring ubiquitous coverage and spectral efficiency with enhanced network intelligence in the diverse communication scenarios. This vision extends beyond terrestrial networks to include non-terrestrial networks (NTN) by incorporating GX Inmarsat satellites and aircraft networks. In the context of 6G GX satellite scenarios, aircraft seamless transportation plays a crucial role as a densely populated intermediate network layer between ground networks and space-based ones. The paper proposes a new sustainable mechanism with mathematical model to improve channel propagation, which has been validated by crucial analysis of propagation channel modeling within the framework of 6 G technology. It highlights the significance of such modeling with the guarantee of dependable communications, maximizing availability, and establishing system parameters like antenna layout and relay deployment. It explores industry trends and ongoing field trial initiatives, offering valuable insights into the progress and outcomes that will shape the future of 6G NTN.

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