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The objective of this study was to characterize hemodynamic changes during trans-vascular stimulation of the renal nerve and their dependence on stimulation parameters. We employed a stimulation catheter inserted in the right renal artery under fluoroscopic guidance, in pigs. Systolic, diastolic and pulse blood pressure and heart rate were recorded during stimulations delivered at different intravascular sites along the renal artery or while varying stimulation parameters (amplitude, frequency, and pulse width). Blood pressure changes during stimulation displayed a pattern more complex than previously described in literature, with a series of negative and positive peaks over the first two minutes, followed by a steady state elevation during the remainder of the stimulation. Pulse pressure and heart rate only showed transient responses, then they returned to baseline values despite constant stimulation. The amplitude of the evoked hemodynamic response was roughly linearly correlated with stimulation amplitude, frequency, and pulse width.

Technological innovation is functional in various areas, from the digitization of Public Administration to citizen services and welfare. Today the “Science of Where” applies data-driven approach that uses geography to unlock understanding of our world, through Geographical Information System (GIS). GIS is thus linked to the social context, and the adoption of GIS as a decision-support tool is becoming increasingly widespread. In fact, this technology can help explore and visualize large volumes of data - dubbed “the new oil” by Clive Humby in 2006 - combining different aspects (geographic and non-geographic) for better management of activities and resources on Earth to maintain economic viability, environmental quality and smart living. Accordingly, this study presents a novel cloud-edge GIS architecture designed to support the Special Office for the Reconstruction of the Municipalities of the Seismic Crater (USRC), operating in Central Italy for the 2009 post-earthquake reconstruction process, to decentralize power at the edge of the network and create an advanced distributed system that improves operational efficiency, increases performance and ensures data security. Proposed solution will be useful for further work, such as urban regeneration, sub-service planning for smart communities, and analysis of (cyber-)security scenarios.

Geographical Information System (GIS), as an Information System (IS), helps public/private organizations represent changes in natural/human context and discover patterns/relationships for social and economic progress through the examination of spatial/non-spatial data. In GIS applications, like smart communities services, rapid updating of great amount of data and expenditure of resources (hardware, human, financial) are significant, making traditional analysis on a single computer impractical due to computing limitations. Over the years, GIS has transformed from a tool that merely visualizes data to one that can model future scenarios. So, at first Cloud Computing (CC) emerged as an Internet-based paradigm offering dynamic and scalable solutions. Then, a new ecosystem has been defined Multi-Access Edge Computing (MEC), enabling CC capabilities and Information Technology (IT) services environment to the edge of the network, running applications, and performing related processing tasks closer to the customers. In this way network congestion is reduced and applications perform better. Therefore, a transition from CC to MEC is recommended for GIS. In this survey we provide a clear and structured overview of GIS applications proposed to date, who is using GIS and for what, to take advantage of its capabilities from integration with emerging technologies, CC first and MEC later, critically considering the problems, strengths and challenges inherent in these approaches, to support intelligent resource management and to fully grasp the opportunity offered by fifth-generation (5G) connectivity. The survey concludes by presenting some recommendations and future research directions.

The need for enhancing image spatial resolution has motivated the researchers to propose numerous super-resolution (SR) techniques, including those developed specifically for hyperspectral data. Despite significant advancements in this field attributed to deep learning, little attention has been given to evaluating the practical value of super-resolved images in specific applications. Most methods are validated in application-independent scenarios, often using simulated low-resolution images, resulting in overly optimistic conclusions. In this article, we propose task-based evaluation strategies for hyperspectral image SR and we present results obtained with various approaches that include pansharpening, multispectral–hyperspectral data fusion, and single-image SR. We demonstrate that the proposed framework allows us to highlight both benefits and limitations of each method and can, therefore, guide the development of SR techniques suitable for real-world applications.

Vision and Robotic technologies are progressively becoming ubiquitous for automating and digitizing quality control in the food industry. This paper examines the crucial role of advanced automation technologies, including versatile or dedicated robotic systems, specialized end-effectors, machine vision, and efficient material handling systems, which collectively enhance food processing efficiency. This paper aspires to provide a high-level technical review on the crucial role of advanced automation technologies, including versatile or dedicated robotic systems, specialized end-effectors, machine vision, and efficient material handling systems, which collectively enhance food processing efficiency. While the manuscript aims to document the various automation sub-systems utilized generally in food processing, it places a particular emphasis on the primary processing phase of food production. Most food products in the primary processing phase exhibit a plethora of complex physical properties and manipulation conditions, making it difficult to reliably automate the various processes. This research aims to outline the contemporary advances and requirements for integrating various automation technologies, to enhance the efficiency and precision of primary food processing. Furthermore, it aspires to serve as a valuable, up-to-date survey and analysis of the latest advances in automation and vision technologies and their capability to automate a food processing line.

While it is a widespread understanding that the sustainability of the global economy requires a transition to a circular economy paradigm where a growing share of the raw materials resources used for the manufacturing of the products are recycled when products reach their end-of-life, still this much-needed transition faces organizational and technical challenges. The key technical and economic bottlenecks are in the automation of disassembly. In this article, we propose a viable functional framework for the systematic analysis, design, and implementation of disassembly cells. This framework consists of two main parts: a systematic categorization of disassembly tasks and a modular and flexible hardware (HW)/software (SW) architecture of a disassembly cell able to implement the disassembly tasks. We analyze and categorize human manipulation when disassembling a common object of daily working activities as a new companion concept to the more common concept of daily life activities. We tested and validated our methodology on the disassembly of a car suspension.

In real-world multi-objective optimization, dealing with many objectives and a large number of solutions is a common challenge that complicates data visualization and analysis. This study aims to simplify decision-making by analyzing tools to better explore Pareto optimal solutions in many-objective scenarios, integrating clustering, filtering, and ranking with existing graphics techniques. The dynamic combination of these tools should reduce complexity and highlight significant patterns in the data set, allowing decision-makers to tailor the visualization to their specific needs and preferences. Central to the approach presented in this work is the innovative application of shape-based clustering to organize the solution set and the use of this clustering to define distinct types of filters. Additionally, ranking methods originally proposed to enhance search in many-objective evolutionary algorithms are used here to identify the best solutions based on predefined criteria in combination with other techniques. The efficacy of the proposed integrated approach was evaluated using an application developed with this aim and considering a five-objective problem as a case study. The analysis suggests that using these combined strategies aids interactive visual exploration, effectively reducing solution volume and improving data understanding, potentially facilitating decision-making tasks.

Commissioned in 2017 by the Italian Space Agency (ASI), the Sardinia Deep Space Antenna (SDSA) is currently able to operate in the X-band, supporting deep space and near Earth telemetry missions and scientific experiments. While sharing its infrastructure with the Sardinia Radio Telescope (SRT), the SDSA boasts its own dedicated control center and specific equipment. This paper outlines a new optical design and the radio frequency upgrades to enhance the SDSA’s capabilities for current and future tracking of deep space and near Earth missions. The proposed design aims to establish the SDSA as a state-of-the-art antenna in the field of space activities by capitalizing on its exceptional versatility. This includes its ability to accommodate new equipment and leverage the inherent characteristics of the SRT antenna, such as its active surface and frequency agility. A collaborative effort with ESA has resulted in a newly designed optical configuration enabling reception and transmission in the X, K, and Ka frequency bands. Specifically, the X and K frequency bands are allocated to near Earth radio communication (within a 2 million kilometer range). Additionally, the X- and Ka- bands configurations are designed for deep space operations, including the well-known triple-link technique for radio science missions beyond 2 million kilometers. To characterize the performance of the upgraded antenna, we present the electromagnetic simulations of the new optical design. We analyze the antenna radiation pattern across various downlink and uplink frequency bands and evaluate key parameters like gain-to-noise system temperature ( $G/T_{sys}$ ) and effective isotropic radiated power (EIRP). The simulations were conducted using the dedicated commercial software GRASP and CHAMP 3D by TICRA. The results of this study provide valuable information on the capability of the upgraded SDSA to enable a wide range of space communication and space science applications, making the SRT in its SDSA configuration one of the most equipped and technologically advanced ground segment facility.

Test flakiness arises when a test case exhibits inconsistent behavior by alternating between passing and failing states when executed against the same code. Previous research showed the significance of the problem in practice, proposing empirical studies into the nature of flakiness and automated techniques for its detection. Machine learning models emerged as a promising approach for flaky test prediction. However, existing research has predominantly focused on within-project scenarios, where models are trained and tested using data from a single project. On the contrary, little is known about how flaky test prediction models may be adapted to software projects lacking sufficient historical data for effective prediction. In this paper, we address this gap by proposing a large-scale assessment of flaky test prediction in cross-project scenarios, i.e., in situations where predictive models are trained using data coming from external projects. Leveraging a dataset of 1,385 flaky tests from 29 open-source projects, we examine static test flakiness prediction models and evaluate feature- and instance-based filtering methods for cross-project predictions. Our study underscores the difficulties in utilizing cross-project flaky test data and underscores the significance of filtering methods in enhancing prediction accuracy. Notably, we find that the TrAdaBoost filtering method significantly reduces data heterogeneity, leading to an F-Measure of 70%.

This paper introduces a novel control approach for an LCC-S Wireless Power Transfer (WPT) system. The system's output voltage regulation is achieved through a variable inductor, leveraging magnetic core saturation. A comprehensive design methodology for the variable inductor tailored to the desired control characteristics is presented. Addressing a significant gap in the current literature, the paper addresses the non-trivial challenge of developing a small signal model that correlates output voltage variations with changes in inductance. To fill this gap, the proposed approach pioneers a transfer function, providing an accurate description of this dynamic. Additionally, a closed-loop control system is proposed for prompt adjustment of the output voltage. The efficacy of this control system is demonstrated even in the face of rapid load variations or misalignment, ensuring reliable regulation. The robustness and effectiveness of the proposed approach are substantiated through extensive experimental measurements, validating the theoretical and simulation results.

This work presents an advanced cognitive architecture for networked aerial robots to implement autonomous swarm systems effectively. It focuses on designing, implementing, and evaluating an architecture that enables unmanned aerial vehicles (UAVs) to coordinate and cooperate for complex tasks, with or without human intervention. Inspired by artificial intelligence, cognitive science, and robotics, the architecture integrates perception, planning, decision-making, and adaptive learning to optimize swarm behavior in dynamic environments. The architecture uses a distributed processing model based on the “edge-fog-cloud” (EFC) concept. Edge-level robots handle real-time data collection, local decision-making, and environmental perception. Fog-level vehicles manage intermediate processing and supervision of the groups, while cloud servers perform comprehensive data analysis and long-term storage, being the higher-level hierarchy of the framework. This structure allows efficient distribution of computational tasks, with critical decisions made at the robot level and complex analysis done in the fog or cloud. The implementation of ARCog-NET involves deploying a multi-agent simulation system using the Robot Operating System (ROS) and Gazebo simulator, facilitating the integration of sensors, communication protocols, and data processing algorithms. The performance evaluation demonstrates the architecture’s effectiveness in a wind farm inspection scenario, where the UAV swarm exhibits improved trajectory planning, collision avoidance, and data processing efficiency. Simulation results show that ARCog-NET reduces latency, increases data throughput, and enhances operational effectiveness, providing a robust platform for future developers to focus on applications and direct robot control methods.

This article deals with a comprehensive analysis and performance evaluation of a fully integrated two-phase cooling system for power converters. A suitable test bed has been properly manufactured to perform the experimental campaign for the evaluation of the benefits and to deploy a dedicated management procedure of the two-phase cooling. The system is tested under real operating conditions, in which the system is employed for cooling a 1200-V 100-A insulated-gate bipolar transistor (IGBT) power module of an inverter controlling a permanent magnet synchronous machine. The experimental results show the inverter temperature behavior in start-up for different flow rates of coolant and different temperatures of the fluid on the secondary system. In the steady-state operation, using lower coolant flow rates in the primary circuit achieved equal or even better cooling for the IGBT module compared with higher flow rates. With respect to traditional cooling approaches, the proposed arrangement allows a greater extraction of the heat at a very low flow rate of the cooling fluid, even with standard industrial grade heat sinks, which motivates the use of this cooling technology for the next generation of power electronics converters.

This study focuses on optimizing the efficient operation of standalone direct-current (DC) microgrids with photovoltaic (PV) sources using semi-definite programming (SDP) optimization. The PV source operation model is formulated as a nonlinear programming (NLP) problem with the objective of minimizing daily energy losses and reducing CO2 emissions compared to diesel generators. Transforming the NLP model into convex optimization involves a linear matrix model that combines positive semi-definite matrices with an affine space. This approach enhances robustness by incorporating uncertainties in demand and PV source power. The robust SDP model employs a min–max strategy for worst-case scenario energy management dispatch (EMD). Evaluating a 27-bus standalone DC microgrid, the SDP model outperforms random-based algorithms by achieving global optima in both objectives. Under uncertainties, the energy loss objective increases by 21.6706% with demand uncertainty, 0.3997% with PV source uncertainty, and 22.2009% with both. Meanwhile, the CO2 emissions objective increases by 11.9184%, 1.8237%, and 14.0045%, respectively. Additional simulations on an 85-node DC network confirm the efficacy of SDP in worst-case scenario EMD. All simulations utilized MATLAB’s Yalmip tool with the Mosek solver.

This paper presents an extensive characterization of the low-frequency noise (LFN) at room temperature (RT) and cryogenic temperature (4.2K) of 40-nm bulk-CMOS transistors. The noise is measured over a wide range of bias conditions and geometries to generate a comprehensive overview of LFN in this technology. While the RT results are in-line with the literature and the foundry models, the cryogenic behavior diverges in many aspects. These deviations include changes with respect to RT in magnitude and bias dependence that are conditional on transistor type and geometry, and even an additional systematic Lorentzian feature that is common among individual devices. Furthermore, we find the scaling of the average LFN with the area and its variability to be similar between RT and 4.2K, with the cryogenic scaling reported systematically for the first time. The findings suggest that, as no consistent decrease of LFN at lower temperatures is observed while the white noise is reduced, the impact of LFN for precision analog design at cryogenic temperatures gains a more predominant role.

Energy efficiency has become a significant challenge for manufacturing companies. Although it is possible to improve efficiency by applying new and more efficient machines, decision makers tend to look for less expensive alternatives. Furthermore, the current reality of manufacturing companies, brought about by Industry 4.0, requires more flexibility of production systems and increase the complexity off machine rescheduling without compromising sustainable requirements. This study contributes to the subject by applying machine learning techniques in a job shop to reduce the makespan and estimate the total energy consumption. First, an artificial neural network (ANN) was trained to estimate the total electrical energy consumption in the system. A new input variable for the network was defined to assist in energy estimation. This variable is called the Priority Factor (PF) and helps capture the different patterns in the job shop. Second, as the ANN was trained, a Genetic Algorithm (GA) was used to reduce the makespan. Therefore, it is possible to reduce the makespan and know in advance the total electricity consumption in production. This solution supports a more sustainable manufacturing process, and is completely developed in a digital manufacturing environment.

The research on Reconfigurable Intelligent Surfaces (RISs) has dominantly been focused on physical-layer aspects and analyses of the achievable adaptation of the wireless propagation environment. Compared to that, questions related to system-level integration of RISs have received less attention. We address this research gap by analyzing the control signaling operations needed to integrate the RIS as a new wireless infrastructure element. As the main contribution of the paper, we build a systematic procedure for evaluating the impact of control operations on communication performance along two dimensions: i) the rate selection for the data channel (multiplexing or diversity), and ii) the allocated bandwidth of the control channels (in-band and out-of-band). Specifically, the first dimension results in two generic transmission paradigms: one focuses on optimizing RIS setting according to the propagation environment, labeled as optimization based on channel estimation (OPT-CE); the other is based on sweeping through predefined RIS phase configurations, labeled as codebook-based beam sweeping (CB-BSW). We analyze the communication performance in multiple setups built along these two dimensions. While necessarily simplified, our analysis reveals the basic trade-offs in RIS-assisted communications and the associated control operations: CB-BSW is better suited for high mobility scenarios since its operation is not conditional on performing channel estimation within the coherence time; OPT-CE performs significantly better when the channel coherence time is sufficiently long, but it requires exchanging more control data, necessitating higher control reliability and profiting more from out-of-band control channel design. Our systematic procedure can be easily adapted to include more complex systems and transmission modes.

Object recognition is a quite critical task that involves the identification and potential localization of a target inside a monitored area. Target detection usually relies on material, shape and size identification in order to infer higher level information, and it can be employed in many different frameworks and applications. In this connection, we carry out a performance evaluation campaign on WiFi Channel State Information (CSI)-based object recognition techniques used to automatically identify material, category and specific objects among daily life items. The main contribution of this work is to provide a thorough comparison of the performance of different machine learning algorithms on recognition using a real-life experimental testbed. The performance study shows that the Random Forest (RF) classifier proves very accurate in terms of correct target recognition, independently of the considered task. The employed deep learning algorithm, Long Short-Term Memory (LSTM), is similarly able to attain very good results. In particular, accuracy values reach 94.8% in material identification, 97.1% in category differentiation and 98.1% in object recognition tasks.

Reaching the European climate target is a complex and multifaceted challenge that involves different sectors and requires coordinated efforts at various levels. Increasing the share of renewable energy sources in the energy mix and the electrification of final energy uses in all sectors represent viable solutions for the energy transition. Distribution networks are expected to be strongly influenced by and influence such transformation while withstanding progressively increasing climate impacts, and this will require a revolution at the Power System level, starting from the planning phase. The future roadmap of a power distribution system shall include not exclusively network upgrades but also non-network solutions focusing on operation strategies exploiting the flexibility gathered from distributed energy resources. To design new transition-ready planning tools for distribution systems able to consider these aspects, the role of flexibility has been analyzed on a real-world, large-scale test case characterized by a high number of connection requests and an expected high yearly electrification rate. Stress has been put on correctly assessing the value of flexibility in planning the distribution system development. One of the most important findings is that flexibility can be a valid option in helping grid management but, most importantly, an opportunity to reconsider planning by applying a new revolutionary risk-oriented approach that may lead to modify the way distribution grids have been planned and operated so far. This emerges as the disruptive value of local flexibility utilization, besides the engagement of all players, that is necessary for the completion of the energy transition.

Quantum processors based on color centers in diamond are promising candidates for future large-scale quantum computers thanks to their flexible optical interface, (relatively) high operating temperature, and high-fidelity operation. Similar to other quantum computing platforms, the electrical interface required to control and read out such qubits may limit both the performance of the whole system and its scalability. To address this challenge, this work analyzes the requirements of the electrical interface and investigates how to efficiently implement the electronic controller in a scalable architecture comprising a large number of identical unit cells. Among the different discussed functionalities, a specific focus is devoted to the generation of the static and dynamic magnetic fields driving the electron and nuclear spins, because of their major impact on fidelity and scalability. Following the derived requirements, different system architectures, such as a qubit frequency-multiplexing scheme, are considered to identify the most power efficient approach, especially in the presence of inhomogeneity of the qubit Larmor frequency across the processor. As a result, a non-frequency-multiplexed 1-$\,\mathrm{m}\mathrm{m}^{2}$ unit-cell architecture is proposed as the optimal solution, able to address up to one electron-spin qubit and nine nuclear-spin qubits within a 3-mW average power consumption, thus establishing the baseline for the scalable electrical interface for future large-scale color-center quantum computers.

Unmanned Aerial Vehicles (UAVs) play a crucial role in exploring unpredictable terrains, such as accident sites and search zones. These robots require autonomous navigation and precise path planning to operate safely and efficiently in dynamic environments. UAVs must balance navigation accuracy and energy efficiency constraints during autonomous operations. This paper introduces an innovative boundary-driven mapping strategy for UAV exploration in unknown environments. The study proposes a novel approach for boundary extraction using deep learning and presents a decision-making methodology to enable the UAV to select the most optimal frontier for exploration based on deep learning-derived information. The primary objective is to efficiently expand the unknown map. The research demonstrates a significant reduction in exploration time within a simulated environment, achieving better performance compared to other methodologies documented in the literature. The results highlight the effectiveness and efficiency of the proposed strategy in terms of mapping exploration.

Advanced traffic navigation systems, which provide routing recommendations to drivers based on real-time congestion information, are nowadays widely adopted by roadway transportation users. Yet, the emerging effects on the traffic dynamics originating from the widespread adoption of these technologies have remained largely unexplored until now. In this paper, we propose a dynamic model where drivers imitate the path preferences of previous drivers, and we study the properties of its equilibrium points. Our model is a dynamic generalization of the classical traffic assignment framework, and extends it by accounting for dynamics both in the path decision process and in the network's traffic flows. We show that, when travelers learn shortest paths by imitating other travelers, the overall traffic system benefits from this mechanism and transfers the maximum admissible amount of traffic demand. On the other hand, we demonstrate that, when the travel delay functions are not sufficiently steep or the rates at which drivers imitate previous travelers are not adequately chosen, the trajectories of the traffic system may fail to converge to an equilibrium point, thus compromising asymptotic stability. Illustrative numerical simulations combined with empirical data from highway sensors illustrate our findings.

This paper conducts a thorough examination of the 12th Video Browser Showdown (VBS) competition, a well-established international benchmarking campaign for interactive video search systems. The annual VBS competition has witnessed a steep rise in the popularity of multimodal embedding-based approaches in interactive video retrieval. Most of the thirteen systems participating in VBS 2023 utilized a CLIP-based cross-modal search model, allowing the specification of free-form text queries to search visual content. This shared emphasis on joint embedding models contributed to balanced performance across various teams. However, the distinguishing factors of the top-performing teams included the adept combination of multiple models and search modes, along with the capabilities of interactive interfaces to facilitate and refine the search process. Our work provides an overview of the state-of-the-art approaches employed by the participating systems and conducts a thorough analysis of their search logs, which record user interactions and results of their queries for each task. Our comprehensive examination of the VBS competition offers assessments of the effectiveness of the retrieval models, browsing efficiency, and user query patterns. Additionally, it provides valuable insights into the evolving landscape of interactive video retrieval and its future challenges.

In this paper, a strategy for the identification of models based on supercapacitors equivalent circuits is proposed. The approach is based on an innovative and efficient encoding procedure, suitable for a generic impedance network. The latter, in combination with an optimization algorithm, is used to investigate the components and topology of the best-fitting network for a given dataset. To demonstrate the generality of the proposed approach, equivalent circuits involving both integer and fractional order elements have been derived. The identification of each model is performed using a cascade optimization algorithm for maximum robustness against local minimum entrapment. The approach is applied to the identification of a 4000 F hybrid supercapacitor, for which data is acquired from an extensive experimental spectroscopy campaign at different states of charge and temperatures. Two models are selected, one for each set, both in excellent agreement with the experimental measurements.

Towards 6G, a key challenge lies in the placement of virtual network functions on physical resources. This becomes complex due to the dynamic nature of mobile environments, making the design a major point of research. We propose a framework that sees this challenge as a complex and dynamic collective process, presenting a novel perspective which encompasses transport network and wireless segment aspects. The framework is built around an analytical modeling and algorithmic tools that rely on complex systems’ paradigm as multiplex networks and evolutionary game theory. The multiplex network enables capturing the layered and heterogeneous nature of the environment. Evolutionary game theory models the dynamical behavior of the system as a collective social process, where each decision on functions influences the overall outcome. Our model allows us to achieve a placement scheme that optimizes 6G functions deployment and minimizes the number of active computational nodes. Compared to traditional transport network centric approach, it effectively reduces interference, ensuring the network’s effective operation and performance. Results show the efficacy of the strategy, enabling the dynamic distribution of functions as the outcome of a social dilemma, and highlight the potential applicability of this approach to tackle the network function placement problem in 6G networks.

In semiconductor spin quantum bits (qubits), the radio-frequency (RF) gate-based readout is a promising solution for future large-scale integration, as it allows for a fast, frequency-multiplexed readout architecture, enabling multiple qubits to be read out simultaneously. This article introduces a theoretical framework to evaluate the effect of various parameters, such as the readout probe power, readout chain's noise performance, and integration time on the intrinsic readout signal-to-noise ratio, and thus readout fidelity of RF gate-based readout systems. By analyzing the underlying physics of spin qubits during readout, this work proposes a qubit readout model that takes into account the qubit's quantum mechanical properties, providing a way to evaluate the tradeoffs among the aforementioned parameters. The validity of the proposed model is evaluated by comparing the simulation and experimental results. The proposed analytical approach, the developed model, and the experimental results enable designers to optimize the entire readout chain effectively, thus leading to a faster, lower power readout system with integrated cryogenic electronics.

The unique geographical and topographical features of Madeira International Airport in Portugal significantly influence flight safety, primarily due to variable wind patterns. In this study, a machine learning approach is developed to predict runway operational statuses at Madeira International Airport, focusing on addressing wind-related challenges. To tackle this issue, a Deep Learning model is utilized. This model undergoes a particle swarm optimization process, resulting in one optimized model for each timestep, to provide minute-resolution predictions within a 20-minute timeframe. The training, validation, and testing phases for the optimized models were conducted using high-frequency wind data from Madeira International Airport. The main objective is to accurately predict the runway operational statuses, specifically whether the airport is open or closed for landing, take-off, or both. The models exhibit high performance, particularly in identifying operational conditions, reaching 99.93% precision, and a top accuracy of 94.35% predicting all runway status, underscoring their potential to enhance decision-making processes and operational efficiency under challenging weather conditions.

Coherent pluggable optics are rapidly evolving and are introducing advanced optical capabilities not yet supported in current standard packet host systems. A management paradigm is presented, that simplifies the integration of smart optical pluggables into hosts, accelerating the deployment of these advanced optical capabilities into disaggregated networks and a wide set of host devices. It relies on a host-independent management channel which integrates into the optical transport control system, maintaining the separation between packet and optical domains.

Unmanned aerial vehicle (UAV) remote sensing images used for semantic segmentation possess distinct features compared to urban street scene images, including high resolution and a complex background. Spatial information plays a pivotal role in enhancing the performance of semantic segmentation for high-resolution images. The dual-branch architecture for semantic segmentation incorporates supplementary branches to capture spatial information. However, prior research on dual-branch semantic segmentation neglected the interaction between the contextual and spatial branches, leading to suboptimal model performance. In this discourse, the article introduces a dual-branch semantic segmentation framework. This design advances the system's understanding of spatial information while facilitating inter-branch learning through two key modules. Initially, the spatial calibration feature extraction module employs frequency domain processing and learning tactics distinct from the contextual approach to generate image features under varied noise conditions. Calibration is achieved by generating features from diverse angles. Subsequently, the spatially-guided loss function directs the acquisition of spatial information for the spatial branch by condensing the deep image characteristics for the context branch. To assess the generalization capacity of the proposed method, experiments will be conducted on three different datasets. The proposed method's modules will be integrated into three representative dual-branch networks, allowing assessment of the generalization capacity of the key DBCG components. Empirical evidence demonstrates that this approach is highly effective, significantly surpassing the performance of the baseline network.

Software quality is critical, as low quality, or “Code smell,” increases technical debt and maintenance costs. There is a timely need for a collaborative model that detects and manages code smells by learning from diverse and distributed data sources while respecting privacy and providing a scalable solution for continuously integrating new patterns and practices in code quality management. However, the current literature is still missing such capabilities. This paper addresses the previous challenges by proposing a Federated Learning Code Smell Detection (FedCSD) approach, specifically targeting “God Class,” to enable organizations to train distributed ML models while safeguarding data privacy collaboratively. We conduct experiments using manually validated datasets to detect and analyze code smell scenarios to validate our approach. Experiment 1, a centralized training experiment, revealed varying accuracies across datasets, with dataset two achieving the lowest accuracy (92.30%) and datasets one and three achieving the highest (98.90% and 99.5%, respectively). Experiment 2, focusing on cross-evaluation, showed a significant drop in accuracy (lowest: 63.80%) when fewer smells were present in the training dataset, reflecting technical debt. Experiment 3 involved splitting the dataset across 10 companies, resulting in a global model accuracy of 98.34%, comparable to the centralized model’s highest accuracy. The application of federated ML techniques demonstrates promising performance improvements in code-smell detection, benefiting both software developers and researchers.

Autonomous navigation in unstructured vegetated environments remains an open challenge. To successfully operate in these settings, autonomous ground vehicles (AGVs) must assess the environment and determine which vegetation is pliable enough to safely traverse. In this paper, we propose ForestTrav (Forest Traversability): a novel lidar-only (geometric), online traversability estimation (TE) method that can accurately generate a per-voxel traversability estimate for densely vegetated environments, demonstrated in dense subtropical forests. The method leverages a salient, probabilistic 3D voxel representation, continuously fusing incoming lidar measurements to maintain multiple, per-voxel ray statistics, in combination with the structural context and compactness of sparse convolutional neural networks (SCNNs) to perform accurate TE in densely vegetated environments. The proposed method is real-time capable and is shown to outperform state-of-the-art volumetric and 2.5D TE methods by a significant margin (0.62 vs. 0.41 Matthews correlation coefficient (MCC) score at qty 0.1 m voxel resolution) in challenging scenes and to generalize to unseen environments. ForestTrav demonstrates that lidar-only (geometric) methods can provide accurate, online TE in complex, densely-vegetated environments. This capability has not been previously demonstrated in the literature in such complex environments. Further, we analyze the response of the TE methods to the temporal and spatial evolution of the probabilistic map as a function of information accumulated over time during scene exploration. It shows that our method performs well even with limited information in the early stages of exploration, and this provides an additional tool to assess the expected performance during deployment. Finally, to train and assess TE methods in highly-vegetated environments, we collected and labeled a novel, real-world data set and provide it to the community as an open-source resource.

Due to the high level of magnetic stray field around high power electromagnetic systems, the human exposure needs to be properly assessed in order to check the compliance with international standards and guidelines. Such analyses are usually made in two steps: first a proper map of the magnetic field in the vicinity area is computed, where, in a second time a human model is used to compute induced dosimetric quantities. Unfortunately, such high power systems have a high computational cost in addition to the complexity of 3D human models. Thus, this paper shows the useful combination of stochastic tools with numerical solvers in order to build accurate predictors at a low computation cost in the case of human exposure for various high power systems. These surrogate models can be used to accurately analyze the sensitivity of the exposure problem regarding various input parameters at a low computation cost. A dosimetric methodology for assessing the safety of a human body around an inductive power transfer system for automotive applications, using an adaptive metamodelling algorithm coupled with a voxelized 3D human model, has been developed. Such analysis has been successfully extended to a system where human exposure assessment are crucially needed: medium-frequency direct-current welding guns, treating the case of human exposure to a pulsed magnetic field. This methodology manages to reduce the computation time by more than 99.9% compared to a classical analysis for both exposure problems.

This paper proposes the Set-Membership sign-NLMS (SM-sign-NLMS) adaptive filter, which combines the ability for data censoring (offered by Set-Membership schemes) with robustness against impulsive noise (provided by signed schemes). The algorithm can present a much lower steady-state probability of update than the standard SM-NLMS algorithm when impulsive noise is present in the system. It is derived from a local deterministic optimization problem modulated by a minimum disturbance cost function combined with a bounded error criterion. Several stochastic models are proposed in order to extract insights and a time-variant step size extension of the algorithm. The first of them, based on energy conservation arguments, leads to a fixed-point analytic equation whose solution predicts the asymptotic performance of the algorithm. Further, a transient analysis based on a statistical decoupling of the radial and (discrete) angular distributions of the input vector is derived. Based on such an analysis, an efficient time-variant step-size version of the algorithm is proposed. Additionally, such an analysis is also utilized to obtain a fixed-point formula whose solution describes the asymptotic performance when the unknown plant that the filter intends to match varies according to a first-order Markovian model. Lastly, a novel stochastic model is advanced for the description of the algorithm learning behavior under a deficient-length scenario for a white input signal, which provides some insights about the asymptotic performance of the algorithm. The findings are confirmed by extensive simulations.

In a pandemic, the availability of health indicators among at-risk populations, such as the elderly, is crucial due to the disease’s rapid transmission and the need to act quickly to contain its evolution. Traditional clinical methods for monitoring vital signs typically involve contact-based sensors that must be precisely attached by a skilled healthcare professional. However, these methods are less practical for repeatable measurements and are unsuitable for long-term monitoring. On the other hand, contactless vital signs monitoring, employing radar-based techniques or IR-thermal imaging, eliminates the need for physical electrodes and proves beneficial for remote health monitoring. This not only reduces physical contact between subjects and healthcare professionals, ensuring social distancing, especially in nursing homes, but also shields the elderly population from external factors that may increase the likelihood of infection. This article presents three key contributions: (i) vital signs characterization in the elderly population; (ii) a state-of-the-art review of the most prominent techniques and methods for Contactless Health Monitoring (CHM); (iii) the design, specification, and evaluation of a low-cost proof-of-concept CHM system for nursing homes, incorporating an IoT Edge Device. This facilitates real-time monitoring of vital signs (cardio-respiratory rates and elevated body temperature) using a multimodal approach based on Doppler radar and IR thermal imaging sensors, generating health indicators without any form of contact or invasiveness. Direct comparisons with reference instruments have revealed an error rate below 10%, in 74%, 52%, and 96% of cases for Heart Rate, Respiratory Rate, and Body Temperature measurements, respectively.

Electromagnetic compatibility (EMC) is crucial when designing and operating wireless power transfer (WPT) systems for charging Electric Vehicles (EVs). WPT technology allows the transmission of electrical energy from a power source to a device without requiring physical contact. However, it can generate electromagnetic interference (EMI) that could disturb nearby electronic devices or harm people nearby. Shielding techniques are commonly employed to mitigate EMI in WPT systems. However, implementing effective shielding techniques can be complex, involving trade-offs between effectiveness, cost, and shielding weight. In this article, different types of existing shielding techniques for wireless charging systems of EVs are compared. The main concepts regarding the functioning of electromagnetic shields and the theory of the functioning of WPT systems have been also briefly introduced.

This paper considers the tasks of detecting and recognizing bearing faults in electric motors from the signals collected from supply currents, using machine learning techniques. In particular, following recent trends in AI, the main point of interest was focused towards interpretable solutions that provide explanations on the decisions taken by the classifiers. For this reason, decision trees were chosen, since they represent a classic machine learning approach which inductively learns tree structures from a collection of observations. Paths along the learnt trees can be easily interpreted as plain classification rules. An extensive experimental comparison shows the strong generalization capabilities of such a classifier. In particular, the present work reports results obtained in a highly challenging scenario, usually overlooked in the literature, where the system is tested on configurations of radial and torsional loads that have not been observed during training. The approach achieves over 90% of accuracy even on this cross-load generalization setting.

In recent years, the use of HVDC cables has grown exponentially. One of the main challenges that remains concerns the space charge accumulation inside the insulating materials. A better understanding of the mechanisms governing this phenomenon is essential to improve the performance of HVDC systems. Numerical simulations are often employed to achieve this goal. For this reason, it is important to perform them in an efficient way. In this work, we test several numerical techniques, aiming to assess which one is the best to use for fast and reliable simulations. We consider a well-known bipolar dynamic model from the literature for our simulations. The model considers a single level of deep traps and is implemented in a one-dimensional Cartesian coordinate system, considering a thin specimen of polymeric material. We compare three different time discretization methods: a fully explicit, a semi-implicit, and a fully implicit approaches. For the advective flux discretization, we compare the first-order upwind scheme (FOU) with a second-order upwind scheme coupled with the Koren flux limiter (SOU/KL). Regarding the computation of the polarization current, we introduce a simple approach using Sato’s equation and compare it with the well-established approach based on the total current density.

This paper presents a theoretical circuit analysis for the non-isolated PWM DC-DC Zeta converter, operated at CCM (Continuous Conduction Mode) in steady-state conditions. The analysis derives the voltage and current waveforms for both current and voltages in the converter, and determines the DC voltage conversion ratio. The boundary between CCM and DCM modes is derived, as well as the expressions to determine the values of the four reactive components of the converter. The expression resulting in the maximum voltages across the MOSFET and diode are determined. The expressions giving the maximum, average and rms values of the currents associated to all the converter devices were also derived as well as the expressions of power losses due to parasitic elements in the converter circuit, and, therefore, the converter efficiency, which also included the switching losses. To validate the proposed theoretical analysis, the converter is studied both using a time-domain circuit simulation software, and building an experimental circuit utilized for practical measurements. Theoretical results were in good agreement with simulation and experimental results.

Spiking neural network models characterize the emergent collective dynamics of circuits of biological neurons and help engineer neuro-inspired solutions across fields. Most dynamical systems’ models of spiking neural networks typically exhibit one of two major types of interactions: First, the response of a neuron’s state variable to incoming pulse signals (spikes) may be additive and independent of its current state. Second, the response may depend on the current neuron’s state and multiply a function of the state variable. Here we reveal that deterministic spiking neural network models with additive coupling are equivalent to models with multiplicative coupling for simultaneously modified intrinsic neuron time evolution. As a consequence, the same collective dynamics can be attained by state-dependent multiplicative and constant (state-independent) additive coupling. Such a mapping enables the transfer of theoretical results between spiking neural network models with different types of interaction mechanisms and at the same time extends the option space for hardware implementation or modeling. By allowing to choose the coupling type or neuron type that is the simplest one to implement in a given practical situation where a specific dynamic or functionality is required, it potentially allows simpler or more effective engineering applications.

Wireless chargers for electric vehicles are usually based on a magnetic field between two coils to transfer power without cables. Metallic objects in the volume comprised among these coils affect the magnetic field, reduce the power transferred to the load, and also result in hazards. Therefore, the implementation of techniques suitable to detect foreign objects is mandatory. The accurate modelling of the objects can help in the precision of the methods used for their detection. In this paper, we accurately identify the equivalent electrical model of a specific foreign object. The main advantage of using this model consists of the possibility of evaluating the performance of the whole charging system by performing time-domain simulations instead of complex finite element analysis. This is of interest for techniques using sensing coils and for self-sensing approaches. In particular, we have modelled a beverage can, which is an object expected in typical EV wireless chargers. The model is validated by comparing simulations based on this model and experimental results. From this analysis, some hot areas where the object causes a higher impact are identified. The procedure can be extended to other objects.

This work presents the application of Direct-Sequence Code-Division-Multiple-Access (DS-CDMA) to tackle the primary problem of simultaneous multi-access of a shared light channel in an indirect Time-of-Flight measurement system based on on-off-keyed (OOK) lasers and highly sensitive single-photon avalanche diodes (SPADs). A theoretical model of the generalized sensor system, possibly suffering from multi-path propagation, is devised for the general application of DS-CDMA. The particular choice of Gold codes is argued to not only enable multi-access but even allow for multi-path resolution down to the sampling time of the utilized digital correlator. It also increases the unambiguously detectable range by three orders of magnitude in comparison to trivial continuous square-wave modulation. Simulations and successive experimental data reveal the proposed improvements compared to trivial square-wave modulation, whilst showing an expected degradation in precision. However, still a precision below 1.5 mm is reached. A novel interleaving procedure of the codes is postulated and verified to recover the precision originally experienced upon trivial modulation.

Cluster synchronization is of great importance for the normal functioning of numerous technological and natural systems. Deviations from normal cluster synchronization patterns are closely associated with various malfunctions, such as neurological disorders in the brain. Therefore, it is crucial to restore normal system functions by stabilizing the appropriate cluster synchronization patterns. Most existing studies focus on designing controllers based on state measurements to achieve system stabilization. However, in many real-world scenarios, measuring system states in real time, such as neuronal activity in the brain, poses significant challenges, rendering the stabilization of such systems difficult. To overcome this challenge, in this article, we employ an open-loop control strategy, vibrational control, which does not require any state measurements. We establish some sufficient conditions under which vibrational inputs stabilize cluster synchronization. Further, we provide a tractable approach to design vibrational control. Finally, numerical experiments are conducted to demonstrate our theoretical findings.

Wine is a relevant part of the diet in many countries, showing significant nutritional properties, providing health benefits to consumers, and having a significant weight in economy. Also, wine plays an important role in many cultures as a part of their social relationships, feasts, or religion where some of them may become a sign of luxury and distinction. For those reasons, objective and subjective quality of wines is an important issue in their production and marketing. To improve wine excellence, some production methods try to relate its physicochemical properties to the quality as it is perceived by humans. Then, modern data prescriptive analysis can be applied to measure the importance (the influence) of each wine attribute. This paper examines and compare several metrics of the attribute importance and its application to the quality-aware design and production of wines. Moreover, for the cases where the perceived quality is characterized using a discrete value, a novel importance metric, based on the Jensen-Shannon Divergence (JSD) is introduced and compared to the existing ones. The results show that JSD clearly overperforms other metrics previously proposed in the literature. Also, it can be asserted that JSD properly reflects the importance of discrete multivalued functions. The results, using this metric in an importance performance analysis of a public wine dataset, show that the main physicochemical attributes of a red wine are citric acidity, alcohol, sulphates and fixed acidity. As for the white wine case, the main attributes are alcohol, free sulfure dioxide and pH.

Future collider accelerators will rely on high-temperature superconductors reaching high field up to 20 T and above. Among the existing high-temperature superconducting materials, the Rare-earth Barium Copper Oxide (ReBCO) tapes arranged according to the Conductor-on-Round-Core (CORC®) concept could be a viable solution to wound accelerator magnets such as Cosine Canted Theta (CCT) magnets. Dedicated experimental characterization of the critical current to quantify the degradation due to the winding process and operating conditions should proceed in parallel to the development of numerical models capable to reproduce and, in perspective, predict the cable performance. This paper presents the development of a new multi-physics model for a CORC® wound with ReBCO tapes together with its validation. The $T-A$ formulation has been used leveraging the high aspect ratio of tapes, suitably coupled with a conduction thermal model which for the first time properly accounts for the cable convective cooling. The model developed in this work can accurately simulate the thermal, electric and magnetic behaviors and the current sharing among tapes by using a set of self-consistent boundary conditions adopted for the first time in this kind of simulations. The model is verified and benchmarked against other well-established formulations on a set of test cases. The comparison of the computed $V-I$ characteristic of the straight cable to available experimental data shows that the main physics features of the cable are well captured by the model, including performance degradation due to cable tapering at the terminations.

The beam self-cleaning effect in graded-index multimode optical fibers has several interesting potential applications, such as high-resolution nonlinear imaging and mode-locked fiber lasers. Most experimental and theoretical studies of beam self-cleaning have neglected the role of the state of polarization of light. In this work, we fill this gap by reporting extensive experimental investigations of beam self-cleaning in multimode fibers, for beams with different input states of light polarization. We found that the state of polarization undergoes a complex evolution, which may lead either to full conservation of the input state of polarization, or to nearly complete light depolarization at the fiber output. The former outcome is compelling for applications based on the beam self-cleaning effect, such as multimode mode-locked fiber lasers for micromachining, and multimode devices for microscopy and endoscopy. The latter result, instead, permits to test the limits of validity of current purely scalar theoretical approaches for describing nonlinear propagation in multimode fibers.

DC microgrids (DCMGs), typically envisioned with high bandwidth communications, may well expand their application range by considering additional autonomous control strategies, especially to increase resiliency in contingency situation. These strategies are based on the droop control methods, enabling voltage regulation and proportional load sharing based only on local measurements. Control challenges arise when coordinating the output of multiple DCMGs, each composed of several distributed energy resources through bus-to-bus transactional converters. This paper proposes an autonomous control strategy for transactional converters when multiple DCMGs are connected through a common bus. This control strategy seeks to match the external bus voltage with the internal bus voltage, balancing power. Three case scenarios are considered: standalone operation of each DCMG, excess generation on one DCMG, and generation deficit in one DCMG. Results using Sandia National Laboratories’ Secure Scalable Microgrid Simulink library, and models developed in MATLAB are compared. These results demonstrated that the proposed control strategy supported the advantage of seamless operation with and without local generation-load mismatches and expanded the range of autonomous operation between DCMGs.

Over the past decade, significant progress in quantum technologies has been made, and hence, engineering of these systems has become an important research area. Many researchers have become interested in studying ways in which classical integrated circuits can be used to complement quantum mechanical systems, enabling more compact, performant, and/or extensible systems than would be otherwise feasible. In this article—written by a consortium of early contributors to the field—we provide a review of some of the early integrated circuits for the quantum information sciences. Complementary metal--oxide semiconductor (CMOS) and bipolar CMOS (BiCMOS) integrated circuits for nuclear magnetic resonance, nitrogen-vacancy-based magnetometry, trapped-ion-based quantum computing, superconductor-based quantum computing, and quantum-dot-based quantum computing are described. In each case, the basic technological requirements are presented before describing proof-of-concept integrated circuits. We conclude by summarizing some of the many open research areas in the quantum information sciences for CMOS designers.

System dynamic models for hundreds of inverter-based generators are necessary to represent the whole behavior and to perform stability analysis at the transmission and distribution level in the electric power system. Aggregated models such as DER_A and PVD1 have been proposed, however, they have many parameters and there is currently no methodology to parameterize them. Many of the parameterization methods for this type of model require only smooth functions. Because these models have non-linear blocks, such as saturation and dead-band functions, this paper proposes a smooth mathematical representation of the DER_A aggregated model. The proposed smooth mathematical representation uses a novel smooth function proposed to represent the saturation function and further uses a proposed method to obtain a smooth dead-band function from the proposed smooth saturation function. Finally, the impact of the use of smooth functions on the dynamics of the DER_A model states is experimentally presented and it is also demonstrated by means of electrical signals that the proposed smooth mathematical representation can represent the dynamics of the aggregated DER_A model, paving a way for future research on parameter identification and stability assessment of the aggregated inverter-based generation models.

Radio frequency (RF) signals transmitted by Global Navigation Satellite Systems (GNSSs) are exploited as signals of opportunity in many scientific activities, ranging from sensing waterways and humidity of the terrain to the monitoring of the ionosphere. The latter can be pursued by processing the GNSS signals through dedicated ground-based monitoring equipment, such as the GNSS Ionospheric Scintillation and Total Electron Content Monitoring (GISTM) receivers. Nonetheless, GNSS signals are susceptible to intentional or unintentional RF interferences (RFIs), which may alter the calculation of the scintillation indices, thus compromising the quality of the scientific data and the reliability of the derived space weather monitoring products. Upon the observation of anomalous scintillation indices computed by a GISTM receiver in the Mediterranean area, the study presents the results of the analysis and characterization of a deliberate, unclassified interferer acting on the L1/E1 GNSS signal bands, observed and captured through an experimental, software-defined radio setup. This article also highlights the adverse impacts of the interferer on the amplitude scintillation indices employed in scientific investigations, and presents a methodology to discriminate among regular and corrupted scintillation data. To support further investigations, a dataset of baseband signals samples affected by the RFI is available at IEEE DataPort.

The growing amount of data collected by Earth Observation (EO) satellites requires new processing procedures able to manage huge quantity of information. Artificial intelligence (AI) and deep learning (DL) can provide advanced information also because of their ability to extract valuable information from complex data. Thanks to specific hardware platforms, these algorithms can be used also in space, opening the possibility for new procedures for intelligent data processing. The European Space Agency Φ-Sat-2 mission was designed with the purpose of demonstrating the benefits of using AI in space by running AI-based applications on-board a CubeSat. We present here the convolutional autoencoder-based algorithm developed for on-board lossy image compression of the Φ-Sat-2 mission and provide a first benchmark addressing a real space mission and a new image compression end-to-end architecture based on AI. Image compression is a crucial application that allows to save transmission bandwidth and storage. In fact, images acquired by the sensor can be compressed on-board and sent to the ground where they are reconstructed. DL algorithms have already been successfully applied for image compression however performance degradation may occur in the context of a representative on-board environment. Therefore, besides analyzing the results for the local hardware environment, this article investigates the performance variation for the on-board setting. An additional piece of innovation is the introduction of an applicative metric for the evaluation of the compression to assess the applicability of the reconstructed images for other tasks. Such metric completes those more traditional based on the original-reconstructed image similarity.

Alzheimer’s disease is a hereditary neuro-degenerative disorder whose typical form causes amnestic cognitive impairment and less common variants cause non-amnestic cognitive impairment. The aim of the paper is to classify and localize magnetic resonance images of patients with Alzheimer’s disease, Mild Cognitive Impairment, and Cognitively Normal. We propose a method for the automatic detection and localization of Alzheimer’s disease, by proposing a neural network designed by authors i.e., TriAD. One of the distinctive features of the proposed network is its ability to process three simultaneous input MRI images of the brain corresponding to the three reference planes (axial, coronal, and sagittal). The assembly of these three classifications has led to excellent quantitative results (95% in accuracy, precision, and recall) and punctual heatmaps for the localization of the region of interest in the images. Moreover, we show how visual explainability can increase the trustworthiness and reliability in the adoption of deep learning by the medical staff. The main novelty, the TriaAD network, guarantees good performances from a quantitative and qualitative point of view, providing a very precise classification and localization of Alzheimer’s disease images.