Athens International Airport

This paper explores the application of Temporal Fusion Transformers (TFTs) to target recognition and multi-horizon tracking, with a particular emphasis on enhancing Direction of Arrival (DoA) estimation in complex signal environments. By leveraging the advanced temporal modeling capabilities and dynamic feature selection mechanisms inherent to the TFT architecture, we adapt and optimize the model for improved performance in antenna signal processing, leading to a more accurate and robust estimation framework. Our analysis underscores the architectural strengths of TFTs, especially their capacity to model long-range temporal dependencies and to dynamically prioritize sensor-derived features—capabilities that are essential for precise target tracking and recognition. We train and evaluate the model under diverse conditions, including multi-horizon forecasting and varying prediction latencies across models. The proposed method is benchmarked against state-of-the-art algorithms, including recurrent neural networks and standard transformer-based models. Extensive experimental results demonstrate that the TFT-based approach consistently outperforms existing techniques in forecasting accuracy, multi-target recognition, and computational complexity, highlighting its potential to advance the field of DoA estimation.

Medicinal and aromatic plants continue to attract attention as rich sources of natural bioactive compounds with potential health benefits. Among them, Greek mountain tea (Sideritis scardica) is widely recognized for its high content of phytochemicals, which have been associated with various biological activities. In this study, Natural Deep Eutectic Solvents (NADESs) were investigated as a sustainable and efficient alternative to conventional solvents for the extraction of such compounds, aiming to the development of a more efficient extraction process. Six task-specific designed NADESs were prepared and evaluated for their extraction efficiency, based on the Total Phenolic Content (TPC) and Total Flavonoid Content (TFC) of the extract. The most promising NADES, comprising betaine and 1,3-propandeiol, was selected for process optimization using a Box–Behnken design and key extraction parameters were systematically examined to maximize TPC and TFC. The extract obtained under the proposed conditions (S/L = 20 mg/g, 240 min, 40% water as cosolvent) exhibited a TPC and TFC value of 49.2 mgGAE/g and 45.9 mgCAE/g, respectively, approximately two times higher than the values of a hydroethanolic extract, obtained under the same conditions (TPC = 26.6 mgGAE/g, TF = 19.9 mgCAE/g). The optimum extract was further analyzed using HPLC-DAD to determine its phytochemical profile and was compared with the conventional hydroethanolic extract, revealing the role of the selected media on the extracted compounds.

Insulating stand-offs are important components of an electrically insulated LPS towards preventing dangerous sparking between the components of the ILPS and metallic parts of the protected structure. In order to maintain the required separation distance, they shall be able to withstand the extremely high overvoltages arising on an electrically insulated LPS due to lightning strikes. Thus, for the safe and reliable design of an electrically insulated LPS, knowledge on the impulse surface dielectric behavior of insulating stand-offs is of great importance. The present study reports on an experimental investigation of the surface dielectric strength of insulating stand-offs subjected to standard lightning impulse voltages of both polarities. The surface dielectric strength is evaluated in terms of the 50% flashover voltage and flashover voltage-time characteristics. Contrary to that observed for 50% flashover voltage, for overvoltages the surface dielectric strength of the insulating stand-offs is lower under negative than positive impulses. Empirical expressions describing flashover gradienttime characteristics are derived. Also, flashover characteristics are well predicted by the integration method.

Digital infrastructure is vital for the economy, democracy, and everyday life, yet it is becoming increasingly vulnerable to strategic cyber-attacks. These attacks can lead to significant disruptions, resulting in widespread service outages, financial losses, and a decline in public trust. Ensuring resilience is difficult due to the infrastructure's complexity, the large volume of data involved, and the growing need for quick, coordinated responses. In the EU Horizon project CyberSecDome, we propose a multi-layered framework that provides AI-driven solutions for incident prediction and detection, automated testing, risk assessment, and rapid incident response, supporting continuity amid complex, large-scale cyber threats. Additionally, Cyber-SecDome introduces a virtual reality interface to enhance AI model explainability and provide real-time contextual awareness of ongoing attacks and defense mechanisms. It also enables privacy-aware model sharing across AI systems, fostering secure collaboration among different domes.

Consumption of sweeteners and sweetness enhancers (S&SEs) is a popular strategy to reduce sugar intake, but the role of S&SEs in body weight regulation and gut microbiota composition remains debated. Here, we show that S&SEs in a healthy diet support weight loss maintenance and beneficial gut microbiota shifts in adults with overweight or obesity. In this multi-centre, randomized, controlled trial, we included 341 adults and 38 children with overweight or obesity. Adults followed a 2-month low-energy diet for ≥5% weight loss, followed by a 10-month healthy ad libitum diet with <10% energy from sugars. One group replaced sugar-rich products with S&SE products (S&SEs group), while the other did not (sugar group). Primary outcomes included changes in body weight and gut microbiota composition at 1 year. Secondary outcomes included changes in cardiometabolic parameters. The S&SEs group, compared to the sugar group, maintained greater weight loss at 1 year (1.6 ± 0.7 kg, P = 0.029) and exhibited distinct gut microbiota shifts, with increased short-chain fatty acid and methane-producing taxa (q ≤ 0.05). No significant differences were observed in cardiometabolic markers or in children. Overall, our findings indicate that prolonged consumption of S&SEs in a healthy diet is a safe strategy for obesity management. ClinicalTrial.gov identifier: NCT04226911.

TrialsNet is a project dedicated to enhancing European urban ecosystems through a variety of innovative use cases in domains including Security and Safety, Infrastructure, and Transportation. These use cases are being implemented across different clusters in Italy, Spain, Greece, and Romania, involving real users. This paper provides an overview of the diverse use cases, and of the corresponding network solutions, which leverage advanced functionalities like dynamic slicing management, NFV, MEC, AI/ML, and more. The project aims to identify network limitations, optimize infrastructure, and define new requirements for next-generation mobile networks. Ultimately, TrialsNet seeks to improve urban livability by driving advancements across multiple domains.

To analyze the outcomes of urgent/emergent endovascular aortic repair of patients with free/contained ruptured thoracoabdominal aortic aneurysms (rTAAA). Endovascular repair of rTAAA has been scarcely described in emergent setting. An international multicenter retrospective observational study (ClinicalTrials.govID:NCT05956873) from January 2015 to January 2023 in 6 European and 1 US Vascular Surgery Centers. Primary end points were technical success, 30-day and/or in-hospital mortality, and follow-up survival. A total of 100 rTAAA patients were included (75 male; mean age 73 years). All patients (86 contained and 14 free ruptures) were symptomatic and treated within 24 hours from diagnosis: multibranched off-the-shelf devices (Zenith t-branch, Cook Medical Inc., Bjaeverskov, Denmark) in 88 patients, physician-modified endografts in 8, patient-specific device or parallel grafts in 2 patients each. Primary technical success was achieved in 89 patients, and 30-day and/or in-hospital mortality was 24%. Major adverse events occurred in 34% of patients (permanent dialysis and paraplegia in 4 and 8 patients, respectively). No statistical differences were detected in mortality rates between free and contained ruptured patients (43% vs 21%; P =0.075). Multivariate analysis revealed contained rupture favoring technical success [odds ratio (OR): 10.1; 95% CI: 3.0-33.6; P <0.001]. Major adverse events (OR: 9.4; 95% CI: 2.8-30.5; P <0.001) and pulmonary complications (OR: 11.3; 95% CI: 3.0-41.5; P <0.001) were independent risk factors for 30-day and/or in-hospital mortality. The median follow-up time was 13 months (interquartile range 5-24); 1-year survival rate was 65%. Aneurysm diameter >80mm (hazard ratio: 2.0; 95% CI: 1.0-30.5; P =0.037), technical failure (hazard ratio: 2.6; 95% CI: 1.1-6.5; P =0.045) and pulmonary complications (hazard ratio: 3.0; 95% CI: 1.2-7.9; P =0.021) were independent risk factors for follow-up mortality. Endovascular repair of rTAAA shows high technical success; the presence of free rupture alone appear not to correlate with early mortality. Effective prevention/management of postoperative complications is crucial for survival.

Abstract Radial diffusion is one of the dominant physical mechanisms driving acceleration and loss of electrons in the outer radiation belt. Therefore, the accurate estimation of radial diffusion coefficients (DLL) is crucial for detailed radiation belt modeling. In recent years several semi‐empirical (SE) models have been developed for the estimation of radial diffusion coefficients which predominantly rely on parameterizations of the Kp index. However, several studies have suggested that the estimations derived from such models can have large deviations from actual (measurement derived) DLL values. In this work we have used the extensive DLL database created in the framework of the Horizon 2020 SafeSpace project which spans 9 years of hourly DLL calculations to develop a model which uses solely solar wind parameters for the derivation of DLL values. The Electric and MagnEtic RAdiaL Diffusion (EMERALD) model is able to derive simultaneously the magnetic and electric components ( and , respectively) of the radial diffusion coefficient, and furthermore, provide realistic confidence levels on their estimation, which allows the transition from a deterministic paradigm to a robust probabilistic one. Evaluations on the performance of the EMERALD model are shown by comparing its outputs to the DLL data, and examining the reproduction of various DLL characteristics. Finally, comparisons with widely used SE models are shown and discussed.

Certification is the means to promote excellence through adherence to best practices, and ensure the appropriate security and transparency, in alignment to regulations set by the market and (various levels of) administrations. In the mobile telecommunications domain, certification for the radio equipment and user terminals has been a fundamental practice. With the 3GPP5G specifications, a novel core network (5GC) model comes to promote dynamic, open 5G services and enable network programmability through standard APIs unlocking the network intelligence to the vertical application needs. A consequence of this “openness” is the mandate for supplements in the established practices of the mobile network operators (MNOs) to include beyond device also software conformance and quality assessment certification. This study describes the work carried out by EU Horizon 2020 EVOLVED-5G project, to define the relevant certification framework for the NetApps, a novel concept introduced as an enabler for the 5G adoption from the vertical industries. The paper goes beyond the definition of the key constructs for the appropriate certification creation process, and explores the technical characteristics of the execution environment to support the certification auditing in an effective, automated and repeatable manner.

Over the past years, the physics of Kitaev's spin-1/2 honeycomb model and its extensions have attracted enormous interest, fueled by a pursuit of the fundamental understanding as well as experimental realizations of quantum spin-liquid phases. A notorious achievement in this field has been the discovery that, when applied in specific directions, a magnetic field can induce a gapped topological spin liquid in the Kitaev quantum magnet -RuCl 3 . In parallel, the search for other magnets with strong spin-orbit coupling has resulted in recent proposals of material candidates to host spin-1 and spin-3/2 analogs of the Kitaev interaction. Remarkably, all of these materials display nontrivial responses to magnetic fields, such as strongly anisotropic magnetization processes and novel field-induced states, due to the lack of spin-rotational symmetry. Given such a rich background, this dissertation aims at expanding the current knowledge of the effects of magnetic fields on extended Kitaev systems with three different contributions. First, we employ a combination of linear and nonlinear spin-wave theory to study the ordered field-induced phases of the nearestneighbor Heisenberg-Kitaev model, which is often regarded as a minimal model to describe Kitaev magnetism for different spin quantum numbers S. By developing a consistent 1/S expansion, we analyze the influence of the leading-order quantum fluctuations on physical observables and phase diagrams of the experimentally relevant cases of S = 1/2, 1 and 3/2. Second, we consider a more realistic spin model to describe the low-temperature elastic response of -RuCl 3 in an applied magnetic field and small uniaxial pressure. Our results suggest that anomalous features found in experiments are indicative of an intermediate-field quantum paramagnetic regime. Finally, we return to the Heisenberg-Kitaev model in a magnetic field, but by now applying the numerical technique of exact diagonalization for S = 1/2. Besides finding good agreement with our spin-wave calculations, we report possible evidence for a new quantum tricritical point.