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"Rodriguez, Jonathan"
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Uncle Scrooge. The bodacious butterfly trail
Scrooge, Donald, and the nephews are off on more adventures in these tales from around the world. When Brigitta MacBridge and Huey, Dewey, and Louie find a centuries-old insect with a built-in Columbus-era treasure map, how can Scrooge McDuck not try to cash in? Then it's Scrooge and John D. Rockerduck vs. the Beagle Boys in \"The Villainous Vase Case
Book
Fundamentals of 5G Mobile Networks
Fundamentals of 5G Mobile Networks provides an overview of the key features of the 5th Generation (5G) mobile networks, discussing the motivation for 5G and the main challenges in developing this new technology. This book provides an insight into the key areas of research that will define this new system technology paving the path towards future research and development. The book is multi-disciplinary in nature, and aims to cover a whole host of intertwined subjects that will predominantly influence the 5G landscape, including the future Internet, cloud computing, small cells and self-organizing networks (SONs), cooperative communications, dynamic spectrum management and cognitive radio, Broadcast-Broadband convergence , 5G security challenge, and green RF. This book aims to be the first of its kind towards painting a holistic perspective on 5G Mobile, allowing 5G stakeholders to capture key technology trends on different layering domains and to identify potential inter-disciplinary design aspects that need to be solved in order to deliver a 5G Mobile system that operates seamlessly.
eBook
Blockchain-Based Security Mechanisms for IoMT Edge Networks in IoMT-Based Healthcare Monitoring Systems
Despite the significant benefits that the rise of Internet of Medical Things (IoMT) can bring into citizens’ quality of life by enabling IoMT-based healthcare monitoring systems, there is an urgent need for novel security mechanisms to address the pressing security challenges of IoMT edge networks in an effective and efficient manner before they gain the trust of all involved stakeholders and reach their full potential in the market of next generation IoMT-based healthcare monitoring systems. In this context, blockchain technology has been foreseen by the industry and research community as a disruptive technology that can be integrated into novel security solutions for IoMT edge networks, as it can play a significant role in securing IoMT devices and resisting unauthorized access during data transmission (i.e., tamper-proof transmission of medical data). However, despite the fact that several blockchain-based security mechanisms have already been proposed in the literature for different types of IoT edge networks, there is a lack of blockchain-based security mechanisms for IoMT edge networks, and thus more effort is required to be put on the design and development of security mechanisms relying on blockchain technology for such networks. Towards this direction, the first step is the comprehensive understanding of the following two types of blockchain-based security mechanisms: (a) the very few existing ones specifically designed for IoMT edge networks, and (b) those designed for other types of IoT networks but could be possibly adopted in IoMT edge networks due to similar capabilities and technical characteristics. Therefore, in this paper, we review the state-of-the-art of the above two types of blockchain-based security mechanisms in order to provide a foundation for organizing research efforts towards the design and development of reliable blockchain-based countermeasures, addressing the pressing security challenges of IoMT edge networks in an effective and efficient manner.
Journal Article
Implementing Anomaly-Based Intrusion Detection for Resource-Constrained Devices in IoMT Networks
Internet of Medical Things (IoMT) technology has emerged from the introduction of the Internet of Things in the healthcare sector. However, the resource-constrained characteristics and heterogeneity of IoMT networks make these networks susceptible to various types of threats. Thus, it is necessary to develop novel security solutions (e.g., efficient and accurate Anomaly-based Intrusion Detection Systems), considering the inherent limitations of IoMT networks, before these networks reach their full potential in the market. In this paper, we propose an AIDS specifically designed for resource-constrained devices within IoMT networks. The proposed lightweight AIDS leverages novelty detection and outlier detection algorithms instead of conventional classification algorithms to achieve (a) enhanced detection performance against both known and unknown attack patterns and (b) minimal computational costs.
Journal Article
Site-dependent reactivity of MoS2 nanoparticles in hydrodesulfurization of thiophene
The catalytically active site for the removal of S from organosulfur compounds in catalytic hydrodesulfurization has been attributed to a generic site at an S-vacancy on the edge of MoS
2
particles. However, steric constraints in adsorption and variations in S-coordination means that not all S-vacancy sites should be considered equally active. Here, we use a combination of atom-resolved scanning probe microscopy and density functional theory to reveal how the generation of S-vacancies within MoS
2
nanoparticles and the subsequent adsorption of thiophene (C
4
H
4
S) depends strongly on the location on the edge of MoS
2
. Thiophene adsorbs directly at open corner vacancy sites, however, we find that its adsorption at S-vacancy sites away from the MoS
2
particle corners leads to an activated and concerted displacement of neighboring edge S. This mechanism allows the reactant to self-generate a double CUS site that reduces steric effects in more constrained sites along the edge.
MoS
2
nanoparticles catalyze the extraction of heteroatom S in hydrocarbons by adsorption onto S vacancies. Here, the authors show that S vacancy properties are highly site sensitive and that adsorption of thiophene leads to self-generation of a more open double vacancy site.
Journal Article
Projections of changes in extreme storm surges for European coasts using statistical downscaling
Understanding future changes in extreme storm surges (ESSs) is critical for coastal risk assessment and adaptation. However, existing projections in Europe are often based on computationally expensive dynamical models, limiting ensemble sizes and thus confidence in projected changes. In this study, we develop a cost-effective statistical downscaling model (SDM) trained to replicate dynamically downscaled storm surges, enabling the generation of a pan-European ensemble of ESS projections based on 17 global climate models (GCMs) – substantially expanding previous efforts. The SDM is trained on a storm surge hindcast and demonstrates stable skill across historical and future climates, broadly capturing projected changes in the 10-year return level given by dynamical simulations. Skill degrades for higher extremes and hence ensemble projections focus on the 10-year return level. Results also show overall lower skill for the eastern Mediterranean and Baltic Seas. Ensemble projections reveal robust multi-model mean changes in the 10-year return level of ESSs by 2100. Negative multi-model mean changes are identified in the Mediterranean Sea (−7 %), Moroccan Atlantic coast (−10 %), and Danish Straits (−6 %), while positive changes of around +6 % are projected for the Celtic and Irish Seas, western Denmark, and the Gulf of Finland. Despite these robust signals, inter-model spread is substantial, with likely ranges (17th–83rd percentiles) extending from −25 % to +17 % across Europe, and changes of up to ±35 % in individual models. The southern North Sea and northern Baltic Sea emerge as low-confidence regions, marked by particularly strong inter-model spread. Our results underscore the importance of extended ensembles in projecting ESSs in Europe and demonstrate the value of cost-effective statistical models to complement dynamical downscaling in applications that demand extensive simulations, such as large-ensemble projections. They also reveal that more sophisticated, extreme-targeted statistical methods are required to project ESSs in the eastern Mediterranean and Baltic Sea, and overall for higher return periods.
Journal Article
Optimization of Mixed Numerology Profiles for 5G Wireless Communication Scenarios
The management of 5G resources is a demanding task, requiring proper planning of operating numerology indexes and spectrum allocation according to current traffic needs. In addition, any reconfigurations to adapt to the current traffic pattern should be minimized to reduce signaling overhead. In this article, the pre-planning of numerology profiles is proposed to address this problem, and a mathematical optimization model for their planning is developed. The idea is to explore requirements and impairments usually present in a given wireless communication scenario to build numerology profiles and then adopt one of the profiles according to the current users/traffic pattern. The model allows the optimization of mixed numerologies in future 5G systems under any wireless communication scenario, with specific service requirements and impairments, and under any traffic scenario. Results show that, depending on the granularity of the profiles, the proposed optimization model is able to provide satisfaction levels of 60–100%, whereas a non-optimized approach provides 40–65%, while minimizing the total number of numerology indexes in operation.
Journal Article
Generating Datasets for Anomaly-Based Intrusion Detection Systems in IoT and Industrial IoT Networks
Over the past few years, we have witnessed the emergence of Internet of Things (IoT) and Industrial IoT networks that bring significant benefits to citizens, society, and industry. However, their heterogeneous and resource-constrained nature makes them vulnerable to a wide range of threats. Therefore, there is an urgent need for novel security mechanisms such as accurate and efficient anomaly-based intrusion detection systems (AIDSs) to be developed before these networks reach their full potential. Nevertheless, there is a lack of up-to-date, representative, and well-structured IoT/IIoT-specific datasets which are publicly available and constitute benchmark datasets for training and evaluating machine learning models used in AIDSs for IoT/IIoT networks. Contribution to filling this research gap is the main target of our recent research work and thus, we focus on the generation of new labelled IoT/IIoT-specific datasets by utilising the Cooja simulator. To the best of our knowledge, this is the first time that the Cooja simulator is used, in a systematic way, to generate comprehensive IoT/IIoT datasets. In this paper, we present the approach that we followed to generate an initial set of benign and malicious IoT/IIoT datasets. The generated IIoT-specific information was captured from the Contiki plugin “powertrace” and the Cooja tool “Radio messages”.
Journal Article
Visualizing hydrogen-induced reshaping and edge activation in MoS2 and Co-promoted MoS2 catalyst clusters
Hydrodesulfurization catalysis ensures upgrading and purification of fossil fuels to comply with increasingly strict regulations on S emissions. The future shift toward more diverse and lower-quality crude oil supplies, high in S content, requires attention to improvements of the complex sulfided CoMo catalyst based on a fundamental understanding of its working principles. In this study, we use scanning tunneling microscopy to directly visualize and quantify how reducing conditions transforms both cluster shapes and edge terminations in MoS
2
and promoted CoMoS-type hydrodesulfurization catalysts. The reduced catalyst clusters are shown to be terminated with a fractional coverage of sulfur, representative of the catalyst in its active state. By adsorption of a proton-accepting molecular marker, we can furthermore directly evidence the presence of catalytically relevant S–H groups on the Co-promoted edge. The experimentally observed cluster structure is predicted by theory to be identical to the structure present under catalytic working conditions.
Rational design of a hydrodesulfurization catalyst relies on a fundamental understanding of its working principles. Here, the authors use scanning tunneling microscopy to directly visualize and quantify hydrogen-induced reshaping and edge activation in MoS
2
and Co-promoted MoS
2
catalyst clusters.
Journal Article
Prototyping a Secure and Usable User Authentication Mechanism for Mobile Passenger ID Devices for Land/Sea Border Control
As the number of European Union (EU) visitors grows, implementing novel border control solutions, such as mobile devices for passenger identification for land and sea border control, becomes paramount to ensure the convenience and safety of passengers and officers. However, these devices, handling sensitive personal data, become attractive targets for malicious actors seeking to misuse or steal such data. Therefore, to increase the level of security of such devices without interrupting border control activities, robust user authentication mechanisms are essential. Toward this direction, we propose a risk-based adaptive user authentication mechanism for mobile passenger identification devices for land and sea border control, aiming to enhance device security without hindering usability. In this work, we present a comprehensive assessment of novelty and outlier detection algorithms and discern OneClassSVM, Local Outlier Factor (LOF), and Bayesian_GaussianMixtureModel (B_GMM) novelty detection algorithms as the most effective ones for risk estimation in the proposed mechanism. Furthermore, in this work, we develop the proposed risk-based adaptive user authentication mechanism as an application on a Raspberry Pi 4 Model B device (i.e., playing the role of the mobile device for passenger identification), where we evaluate the detection performance of the three best performing novelty detection algorithms (i.e., OneClassSVM, LOF, and B_GMM), with B_GMM surpassing the others in performance when deployed on the Raspberry Pi 4 device. Finally, we evaluate the risk estimation overhead of the proposed mechanism when the best performing B_GMM novelty detection algorithm is used for risk estimation, indicating efficient operation with minimal additional latency.
Journal Article