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Vegetal oils are considered a suitable substitute for mineral oil that is widely used in power transformers as insulation and cooling medium. Due to the fact that more and more power transformers containing this alternative insulating fluid are being constructed a mathematical ageing models is needed to help predict transformer failures. The continued performance of power transformers depends on the condition of its paper insulation mainly. In this sense, this paper analyses Kraft paper degradation through the loss of its mechanical strength. Accelerated thermal ageing test of the paper in two different vegetal oils were carried out at three temperatures during diverse periods of time, in order to obtain information on the kinetics of the ageing degradation of the paper. The evolution of the mechanical properties of paper failure are analysed as a function of temperature and ageing time. In addition, the results obtained are compared with the traditional method of degradation analysis, based on the measurement of the degree of polymerisation. The authors are grateful for the funding received to carry out this work from the State Scientific and Technical Research and Innovation Plan under the DPI2013-43897-P grant agreement, financed by the Government of Spain.

Blockchain technology has become almost as famous for incidents involving security breaches as for its innovative potential. We shed light on the prevalence and nature of these incidents through a database structured using the STIX format. Apart from OPSEC-related incidents, we find that the nature of many incidents is specific to blockchain technology. Two categories stand out: smart contracts, and techno-economic protocol incentives. For smart contracts, we propose to use recent advances in software testing to find flaws before deployment. For protocols, we propose the PRESTO framework that allows us to compare different protocols within a five-dimensional framework., 8 pages, 1 figure, accepted for presentation as a regular paper at IEEE Blockchain 2018 (Halifax, Canada)

Polyvinylidene difluoride (PVDF) piezoelectric sensors were manufactured on a paper-based substrate using Aerosol Jet Printing to print silver interdigitated electrodes and the photopolymer NOA 81 to obtain restraining walls for PVDF. Silver traces were sintered thanks to Flash Lamp Annealing, a low-thermal exposure process to avoid damages to the paper substrate. The samples were printed on a 3D cellulose-based substrate. Electrical capacitance measurements showed a mean value of 2.58 pF after Ag printing, to a mean value of 4.07 pF after PVDF deposition and poling. Different masses were employed, ranging from 50 to 500 g, during tests assessing a linear behavior of the sensors when loaded. A maximum voltage peak value of 11.9 V was reached at a mass of 500 g. These preliminary results appear to be promising for the realization of a complete paper-based impact/force sensor to be applied in packaging.

2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2020 -- 26 June 2020 through 27 June 2020 -- -- 162106 2-s2.0-85089705154 Recent progress in software and hardware has allowed different data measurements in a variety of fields to be captured. These measures are produced continuously at very fluctuating data rates. For example, network sensors, web logs and computer network traffic. Computer-intensive activities are the collection, query and removal of these data sets. Mining data sources include the recovery of knowledge systems incorporated into templates and trends on streams of non-stop information. Because of the importance of its applications and the increasing generation of data stream research, information. Information. Information. Analysis applications for data streams can range from critical scientific and astronomical applications to major business and financial applications. Algorithms, processes and structures Streaming challenges have been developed over the last three years. We present the newest in this increasingly important field in this review paper. © 2020 IEEE.

This paper presents the design of a novel chipless harmonic RFID sensor in paper substrate based on a variable attenuator implemented as a π resistive network, which drives a single Schottky diode frequency doubler and a system of nested tapered annular slot antennas. The passive tag is interrogated by a signal at f0 = 1.2 GHz and the signal transmitted back to the reader is converted to 2f0 = 2.4 GHz in order for the system to be immune to clutter returns. The sensor information is encoded in the magnitude of the re-transmitted signal and a dynamic range around 20 dB is experimentally demonstrated.

Assistive service and companion robots are versatile and dexterous actuators that operate in our daily living environment. These robots are able to manipulate physical objects, to displace themselves and to engage in conversations. Human behavior is dynamic and oftentimes unpredictable, therefore it is crucial for such robotic systems to be assisted by a cloud-backend which: i) analyzes data from sensor and wearables ii) determines which robotic tasks need to be executed and iii) provides the necessary support for the execution of these tasks in our daily living environment. In this paper, we present our Internet-of-Robotic-Things system architecture design for a case study on personal interactions by a companion robot to alleviate behavioral disturbances of people with dementia.

The list of significant papers from the first 25 years of the Field-Programmable Logic and Applications conference (FPL) is presented in this paper. These 27 papers represent those which have most strongly influenced theory and practice in the field.

Metallic materials are in a peculiar position for realization of electromagnetic shielding and antennas. Implanted devices, sometimes also responsible for vital functions of the hosting human body, should present reduced response to external electromagnetic interferences. In case transmission of data between the device and external environment is additionally required, the device should also be equipped with an antenna. The biocompatibility of such metallic parts is of fundamental importance: being them positioned on the external part of the device, they are the most exposed to be in close contact with the body environment. Investigations on the timely interaction between the metal based materials and the simulated hosting body by standardized physiological solution is described and quantified.

Software Defined Network (SDN) is considered as the key paradigm for the development of future networks, but there is still a lack of operational solutions, especially for an Internet Service Provider environment. In this work we propose the idea of an advanced SDN device, referred to as Smart SDN Forwarding Device (SSFD), able to implement Network Functions (NFs) on top of the Data Plane: in this way, it will be possible to realize hybrid SDN/IP networks and to reduce the load on the SDN Controller. We also provide a prototype, realized by means of open source software, that represents a first step toward the deployment of an operational device. The performance evaluation realized connecting the SSFD prototype to the experimental LISP network, shows that its performance are very close to the ones of a legacy device: the delay introduced for the control level operations is limited and negligible from an user perspective.

A microwave sensor for glue on paper detection for industrial application has been developed. It uses a resonant cavity to detect and measure the amount of glue placed on a dielectric layer, for example a sheet of paper. It works in the ISM band, at 2.45 GHz. It can be used for the control of the industrial gluing for the mailing and packaging sector.

Collaborative tagging has grown on the Internet as a new paradigm for web information discovering, filtering and retrieval. In the physical world, we use visual tags: labels readable by smartphones with cameras. While visual tags are usually related to a web site address, collaborative tagging, instead, provides updated, recommended information contributed and shared by users. In this paper we investigate the combination of collaborative tagging systems with visual tags. We present a prototype of a semiautomatic system generating visual tags which gather information from collaborative tagging. The user can interact with a list of relevant tags (built by clustering closely related tags) that can be further encoded in a visual tag, according to user's preferences. The user experience is enriched by retrieving multimedia content linked to the selected tags, present on the web. We finally show a case study illustrating our approach.

A key issue for the success of IoT, industry 4.0 and precision agriculture is the development of sensors based on new materials and technologies. In particular, sensors which are environmentally friendly and low cost are of interest. Cellulose is one of the most abundant material on the earth. Eventually, it is green, cheap and presents several intriguing mechanical and electrical properties. In this paper the sensing properties of an all-polymeric composite, realized by using bacterial cellulose, organic conductors and ionic liquids are investigated. More specifically, it is shown that this novel composite is capable of sensing flexural deformations and producing a corresponding electrical signal. The obtained results pave the road to the possibility of realizing a new class of deformation sensors that fits the requirements of more sustainable sensor technologies. The sensor has been conceived, realized and an experimental campaign with the characterization has been performed obtaining very intriguing results.

A cache-aided $K$-user Gaussian broadcast channel (BC) is studied. The transmitter has a library of $N$ files, from which each user requests one. The users are equipped with caches of different sizes, which are filled without the knowledge of the user requests in a centralized manner. Differently from the literature, it is assumed that each file can be delivered to different users at different rates, which may correspond to different quality representations of the underlying content, e.g., scalable coded video segments. Accordingly, instead of a single achievable rate, the system performance is characterized by a rate tuple, which corresponds to the vector of rates users' requests can be delivered at. The goal is to characterize the set of all achievable rate tuples for a given total cache capacity by designing joint cache and channel coding schemes together with cache allocation across users. Assuming that the users are ordered in increasing channel quality, each file is coded into $K$ layers, and only the first $k$ layers of the requested file are delivered to user $k$, $k=1,...,K$. Three different coding schemes are proposed, which differ in the way they deliver the coded contents over the BC; in particular, time-division, superposition, and dirty paper coding schemes are studied. Corresponding achievable rate regions are characterized, and compared with a novel outer bound. To the best of our knowledge, this is the first work studying the delivery of files at different rates over a cache-aided noisy BC., Comment: Part of this work was presented at the IEEE International Symposium on Information Theory, Colorado, USA, June 2018

In this paper, we consider the detection of suspiciously high correlation between malicious Internet users that are collaborating in order to cause a Denial of Service (DoS) Attack. The main goal is to recognise cyber incidents (more specifically, collaboration/dependency between different users) in order to ultimately isolate their behavior and avoid the consequenses of the DoS attack. The method relies on the analysis of the data traffic across the concerned network (with both incoming and outgoing traffic) in an effort to identify correlations between different users. The paper models normal and malicious behaviour via hidden Markov models, and analyses the performance of the method using both mathematical reasoning and simulations. © 2016 IEEE.

In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this position paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS we propose to combine the learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared to past and current research efforts in this area, the technical approach depicted in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication networks.

This paper presents a 2.4 GHz vector modulator fabricated on mechanically flexible, low-cost, environmentally friendly, celluose-based substrate. This circuit can be adopted either as a digitally controlled target for radars' testing, or, more in general, as an I/Q modulator in RF systems. The system is composed by a directional coupler, two mixers and a Wilkinson divider, all of them designed in microstrip technology and resulting in a (64 × 119) mm2 planar device. The modulator functionality is demonstrated by showing the results of a wired test in which the modulator is connected to a signal generator (input), to a spectrum analyzer (output) and digitally controlled by means of an Arduino board.

Handling the continuity of learning experience across different activities and contexts is a key challenge for seamless learning. Current context and activity recognition techniques work well in fixed environments where sensors deployment and data are known but are not adaptable to dynamic and changing situations when, for instance, a learner moves from dense to rare sensor environments. Moreover, even if we are able to recognize with more or less precision activities, it still remains the issue of understanding if there are useful and interesting educational concepts related to the activities. In this short paper we discuss our ideas and preliminary results on the definition of an opportunistic approach to recognize activities and contents that leverages on the characterization of the environments in terms of sensor richness and knowledge expressiveness. The basic idea is to recognize the kind of environment in which a learner is involved and then to adapt the most suitable techniques taking advantage of the specific features of the environment. Next, we discuss two measures allowing us to understand i) the cohesion degree of the set of (informal, not formal, formal) activities a learner is involved in, and ii) if the the learner is able and in a proper disposition to acquire new knowledge or develop a new competence from the execution of activities. We propose the adoption of the first measure in the fitness function of a swarm intelligence algorithm to optimise the search of cohesive activities.

IEEE North West Russia Section Young Researchers in Electrical and Electronic Engineering Conference, EIConRusNW 2016 -- 2 February 2016 through 3 February 2016 -- -- 121142 Ecological textiles or eco textiles means a recyclable product or a product that can be recycled into an environmentally harmless product which has been produced by paying regard to environment during all process steps from, which does not harm the user. Natural dyeing, which is of great importance in the textiles industry in terms of being economical and applicable, has a number of advantages thanks to its features such as health, ecological cycle and providing biological availability. With the use of natural mordant as a binder during dyeing both dyeing and mordant become important for environmentally friendly processes. This study examines the usability of natural dye extract obtained from Helichrysum Arenarium, which has a wide range around the world, and Quercus Aegilops natural mordant in dyeing of Karabuk Eflani cloth, a regional weaving. Eflani cloth is made from 100% cotton staple. In this study Eflani cloth woven from 100% cotton yarn with Helichrysum Arenarium was dyed with 5 different concentrations of natural mordant. L? - a? - b? - values of the samples of dyed cotton cloth were measured and compared using CIE L? - a? - b? - colour space system. Also, lightfastness and washing features of dyeing were examined. Colour scale was darker and more vivid in dyeing made with Quercus Aegilops natural mordant compared to dyeing made without mordant. © 2016 IEEE.

This article consists of a collection of slides from the authors' conference presentation.

Current software engineering practices rely on highly heterogeneous and distributed teams working in a collaborative setting. Between 2013-2020, the publication output in the field of collaborative Model-Driven Software Engineering (MDSE) has significantly increased. However, the only systematic mapping study available is limited to studies published until 2015. In this paper, we provide an update on that study for the complementing 2016-2020 period, and report the latest results, challenges, and trends. Our analysis led to selecting 29 clusters of 54 new peer-reviewed publications on collaborative MDSE. Based on the novel developments in the field, we have extended and improved the original classification framework, making it applicable to recent and future research contributions on collaborative MDSE. The insights in this paper relate to the changing trends in the field and present new relevant information.

Cyber-physical systems (CPS) and IoT systems are nowadays commonly designed as self-adaptive, endowing them with the ability to dynamically reconfigure to reflect their changing environment. This adaptation concerns also the security, as one of the most important properties of these systems. Though the state of the art on adaptivity in terms of security related to these systems can often deal well with fully anticipated situations in the environment, it becomes a challenge to deal with situations that are not or only partially anticipated. This uncertainty is however omnipresent in these systems due to humans in the loop, open-endedness and only partial understanding of the processes happening in the environment. In this paper, we partially address this challenge by featuring an approach for tackling access control in face of partially unanticipated situations. We base our solution on special kind of aspects that build on existing access control system and create a second level of adaptation that addresses the partially unanticipated situations by modifying access control rules. The approach is based on our previous work where we have analyzed and classified uncertainty in security and trust in such systems and have outlined the idea of access-control related situational patterns. The aspects that we present in this paper serve as means for application-specific specialization of the situational patterns. We showcase our approach on a simplified but real-life example in the domain of Industry 4.0 that comes from one of our industrial projects.

In recent years, the proliferation of intelligent embedded technologies is opening venues to new service and computing models, providing diverse socio-economic benefits. These intelligent technologies are giving rise to a wide range of public and private applications by sharing and analysing generated data. This includes smart home, smart health, smart city, autonomous vehicles, smart grid and smart manufacturing etc. However, where this sharing of data brings benefits and opportunities, it simultaneously presents security risks and challenges. The realisation and prototyping of such technologies require a computing hardware widely available in the form of an embedded platform. The security perimeter and the attack surface of these platforms rely on their supported security and defence mechanisms. This paper aims to build a body-of-knowledge in this area for the security research community. It present the state-of-the-art security frameworks and architectures, discuss architectural shortcomings and root-causes of leading security technologies rather than discussing vulnerabilities and attacks. The paper concludes advocating secure-by-design platform approach and classifying platform security methods to realise robust embedded platform security architecture.

This paper reports a novel modular multilevel converter with interleaved sub-modules (ISM-MMC). The ISM-MMC exhibit a higher scalability in current rating then conventional MMC structures with parallel devices. It can employ low-cost, low-current power switches rather than their bulky and expensive counterparts normally designed in classical MMCs. Another remarkable feature is that the number of the output voltage levels is synthetically multiplied by the number of interleaved SMs. The ISM-MMC is capable of bringing the known advantages of MMC to low voltage - high power applications making it a good candidate for the sector of ultra-fast chargers for electrical vehicles where typical power rating in excess of 1 MW is required for the low voltage supply. A proper modulation scheme is implemented and explained in this paper. A comparison with a classical MMC topology is also provided in terms of number of voltage levels, output voltage harmonic content, and number of components by fixing the number of SMs. Simulation results are given to demonstrate the feasibility of the proposed topology and the implemented modulation scheme. Despite this paper is dealing with a single-phase configuration, the extension to a three-phase scheme can be obtained in a straightforward manner.

Gas radon monitoring is a very important activity that is subjected to a growing interest in recent years. The reason of this interest is due to two main aspects: (i) human health and (ii) Earth's status monitoring. To this aim many research institutes in different countries are developing Radon monitoring systems. In this paper, a first implementation of an automated and web-accessible system able to deliver Radon gas concentration data from a low-cost sensor node to the Cloud, by giving access for processing and visualization, is presented. This system can be seen as an empowering function, following principles and constraints of an IoT application, of the existing Italian Radon mOnitoring Network, namely IRON, already deployed in a large part of Italy as a research infrastructure of the Italian National Institute of Geophysics and Volcanology (INGV). Preliminary results of this activity, presented in this paper, show the feasibility of this approach.

The interest about Zero Touch Network and Service Management (ZSM) is rapidly emerging. As defined by ETSI, the ZSM architecture is based on a closed-loop/feedback control of the network and the services. Such closed-loop control can be based on the Boyd’s Observe Orient Decide and Act (OODA) loop that matches some specific management functions such as Data Collection, Data Analytics, Intelligence, Orchestration and Control. An efficient implementation of such control loop allows the network to timely adapt to changes and maintain the required quality of service.Many solutions for collecting network parameters (i.e., implementing ZSM data collection) are proposed that fall under the broad umbrella of network telemetry. An example is the Google gRPC, that represented one of the first solutions to provide a framework for data collection. Since then, the number of available frameworks is proliferating. In this paper we propose the utilisation of Apache Kafka as a framework for collecting optical network parameters. Then, the paper goes beyond that by proposing and showing how Apache Kafka can be effective for supporting data exchange and management of whole ZSM closed-loop.Experimental evaluation results show that, even when a large number of data are collected, the solution is scalable and the time to disseminate the parameter values is short. Indeed, the difference between the reception time and the generation time of data is, on average, 40-50ms when about four thousand messages are generated.

Crop type mapping represents one of the most challenging problems in remote sensing. Spatial, spectral, and temporal information are required in order to obtain a unambiguous distinction among the types of crop. This paper presents a multi-sensor approach, where labelled high-resolution images from drones, limited to small areas, are used to enhance the classification ability of machine learning models based on Sentinel 2 time series. The project described in this paper is organized into three major activities. The first part focused on the exploitation of RGB drone images by using transfer learning and convolutional networks, and it has already been described in a previous work by the team. The second part deals with preliminary analysis of multi-spectral Sentinel 2 time-series using the labelled data from the drones campaign and trees-based machine learning algorithms. Finally, the third ongoing part deals with the combination of drones and satellite data in order to show how drones data can help the Sentinel 2 classification by reducing the effort needed to collect reference crop type information.

Forest fires cost the world an estimated value of 200 Billion dollars annually in damages. Furthermore, the main concerns are not only monetary as the vanishment of the carbon-dioxide soaking forests further exacerbates climate change. This paper presents a predictor system based on deep convolutional neural network to predict the risk level of wildfire from satellite data. The proposed Neural Network has an encoder-decoder architecture that allows to provide emergency operators with a pixel-wise fire risk prediction of a given area, allowing precise preventive interventions. The dataset utilised for the training has been generated from publicly available sources as a set of raster images, including several of the most significant satellite products. The paper also proposes a customised loss function for the training of the network and several statistical metrics to establish its performances and validate the reliability of the system. A proof of concept demonstration is discussed for two different case studies: the island of Sicily and an area in California.

Cloud Computing, a key enabling technology for speedy wide-area networks are one of the emerging technologies in Computer Science. One of the pressing challenges in the ongoing research in this particular domain is data confidentiality. There is an immense need to preserve important data and confidentiality in cloud computing, which means a more critical requirement for robust mechanisms to protect it from attackers or intruders. This paper proposes a broad and thorough probing multiple simulation-based software that examines and evaluates the execution, extensibility, strength, and intricacy of Cloud data center. This paper analyzes different Cloud services using Cloud-Sim, Cloud Analyst, and OPNET simulation tool. Services, cloud management, load-distributing, cloud strength, and expand-ability are the main scope of this paper. Hence, results are compared in the real-time framework to identify the performance and extensibility, as well as consider the experiment as challenges for further discussion.

This paper presents the analysis and design of on-Silicon dielectric resonator antennas (DRAs) for energy harvesting applications. The proposed antennas, operating at 24 GHz and excited through a simple microstrip line, are built on a high-resistivity silicon substrate 0.525 mm-thick. The design of cylindrical and rectangular resonators using sapphire as dielectric material is first described, leading to 80% of maximum radiation efficiency and gain equal to 5.15 dBi. Given the increasing attention to system-on-a-chip (SoC) circuits, the second study proposed in this paper aims to a complete integrated solution, describing all-Silicon DRAs, both in cylindrical and rectangular shapes. Very promising performances are obtained in this case, too, from the twofold point of view of compactness and efficiency (75% radiation efficiency and gain equal to 4.72 dBi), if compared to standard solutions on Silicon. As a last step of the proposed study, an investigation on the miniaturization of DRAs operating at millimeter waves is described, exploiting high permittivity materials. In particular, a resonator with dielectric permittivity of 50, is analyzed.

This JOCN special issue includes extended versions of selected papers that were presented at the 24th International Conference on Optical Network Design and Modeling (ONDM2020), which took place virtually on May 18–21, 2020. The topics covered by the papers represent clear trends in current optical networking research including filterless optical networks and their applicability in metropolitan scenarios; programmable, software-defined-networking-enabled sliceable bandwidth variable transceivers supporting multi-dimensionality; and two applications of machine learning—the cognitive reconfiguration of data-center networks in support of high-performance computing, and quality of transmission estimation for reduced margins.

This paper proposes an innovative receiver coil structure for wireless power charging suitable for portable devices with thickness constraint. Litz wires represent the best solution for high-efficiency wireless power transfer coil but cannot be implemented in a thin structure. In this paper will be presented and optimized planar Litz structures with narrow profile. The performances of the coils were tested with the measurements in a wireless power transfer system in alignment and misalignment conditions.

Localization in indoor environments is a hard task because of several constraints, both in terms of multiple reflections due to wall and people moving around the localization domain. This is especially due to the communication technologies adopted to perform both ranging and positioning. In this paper, a Ultra Wide Band technology is adopted to perform localization in indoor environment, considering a square domain, by means of three fixed anchors and one mobile node. In specific applications, the object to localize is covered by a metallic shield, e.g, robots, and the localization sensor must be embedded inside the object. In this scenario, the paper proposes to solve three issues: assess nominal performance of UWB localization technology, find a way to approach it in harsh conditions, as the one above specified and a further investigation on optimal anchors' positions in the localization domain to verify sensitivity to specific set-up arrangement. First task is accomplished through an extensive measurement campaign without any obstacle; second one is solved by means of a double calibration procedure estimating anchor-to-anchor distance and online correcting the acquired data. A simulated approach that, starting from on-field measurements, verifies the role of different anchors' positions on the positioning error, completes the investigation.

Many control vehicle systems are based on the knowledge of the current load. This trend has recently emerged also in heavy-duty and agricultural vehicles. Knowing the mass has in fact benefits both on safety and driving comfort. This paper proposes a mass estimation algorithm, based on already available sensors for an agricultural tractor equipped with a Continuously Variable Transmission. To this aim, the transmission model is derived, as the key ingredient of the mass estimate is the preliminary knowledge of the wheel torque. The latter, however, may be useful also for other control purposes, concerning, for example, longitudinal dynamics control. In the paper, the estimators proposed are applied on real world data.

Augmented Reality (AR) and Virtual Reality (VR) applications has been investigated in several domains. As such, their application in educational settings has witnessed to a growing interest by the research community. Teachers can be assisted by AR/VR, in a way that students can strength the learning outcomes, gained during the classroom lecture. However, despite their potential has been widely assessed, there still exists a bottleneck preventing a widespread adoption in the education domain: the lack of easy to use platforms enabling teachers and students to become producers of AR/VR experiences. This paper fills this gap, by proposing a novel platform named ScoolAR, developed for didactic purposes. ScoolAR allows to create AR/VR applications without any programming skills. Up to now, there is no evidence in the state-of-art of a didactic tool that allows to create AR/VR applications without programming skills. From such premises, ScoolAR has been developed to overcome these limitations and to enable an autonomous content creation system and thus boosting more engagement and awareness in the exploitation of AR and VR applications in everyday educational scenarios. Beside describing the architectural framework of the proposed platform, this paper presents the results of experiments conducted in a real didactic scenario. Considering two group of students, the first group was assisted with the ScoolAR framework, the second one conducted the study phase with frontal lecture. The test performed proved that the first group outperformed the second one on all metrics of evaluation. Thus, the combined effort between common didactic activities and technological innovation permits to achieve superior results in terms of both knowledge and competences, especially for those disciplines (e.g. Cultural Heritage and History of Architecture and more) where the transversal learning is fundamental.

This paper analyses the possibility to black-start a remote area using an existing Line Commutated Converter HVDC link.To do this, a relatively small size Voltage Sourced Converter must be installed at the link receiving-end. The black start takes place using the existing receiving end network and line commutated converters, and the DC line. The paper shows that the black start operation can take place fast and effectively.

In this paper, liquid-liquid droplet formation in a cross-junction between stadium-shaped cross-section micro-channels has been studied by means of a CFD OpenFOAM approach. The particularity of the micro-junction considered in this paper is that within the junction the micro-channels have a restriction in the cross-section, while the shape remains stadium-shaped. The junction has the function to focus the dispersed flow and the droplets formed in certain regimes have dimensions that can be related to the dimensions of the junction. The object of this study is to characterise the dimensions of the droplets formed in this micro-junction. The evolution of the droplets are obtained for several ratios of the volume flow rates of the two fluids. A correlation between the dimensionless length of the droplet and the ratio of the volume flow rates are obtained and compared with other correlations available in the literature for different junctions.

A framework for simulating wide area measurement system (WAMS) of electrical transmission networks to test measurement algorithms is proposed in the paper. The framework has been implemented by a joint team involving the Italian transmission system operator (TSO), Terna S.p.A, and the University of Naples Federico II, and is essential to assess the performance of algorithms estimating network variables before making them operational on the real WAMS. On the other hand, the algorithm performance must be evaluated on a system that reproduces as faithfully as possible the problems characterizing an actual monitoring system. The authors propose a mixed solution including open-source software for managing timestamped data streams and Matlab script for both emulating PMUs and measurement algorithm. In the paper also preliminary tests carried out in order to assess the framework reliability are described.

Data transmission from the underground can be a relevant task for a wide range of Internet of Things (IoT) applications (e.g., from Smart Agriculture to environmental monitoring) thus paving the way to the recent paradigm of the Internet of Underground Things (IoUT). In this context, Low Power Wide Area Network (LPWAN) technologies proved to be reliable solutions for underground to aboveground (UG2AG) data transmission: Long Range (LoRa) modulation and LoRa Wide Area Network (LoRaWAN) protocol in particular, demonstrated to be able to transmit from depths down to 1.5 m. Nevertheless, the quality of the transmission channel may significantly vary due to several factors: ranging from soil composition to presence of water. Specifically, soil moisture may have an attenuating effect on the transmitted signal. The aim of this paper is to preliminary analyze the impact of different Volumetric Water Contents (VWCs) in soil on the LoRa transmission channel. Radio parameters are collected by burying a LoRaWAN transmitter inside a plastic case filled up with sand. The underground attenuation is measured reproducing different VWCs by progressively adding different quantities of water: this methodology allows to re-create in laboratory well-defined VWCs unlike a real environment where the actual VWC is due to a plethora of different factors. Results achieved in this paper demonstrate the correlation among VWC and losses though showing the potential feasibility of LoRa links also in case of elevate VWC levels.

Improving the economic, social, and environmental sustainability of agri-food supply chains (AFSCs) is of paramount importance, given the impact of AFSC on the survival of mankind and of the planet. Data analytics, and the digital technologies supporting collection and analysis of the (big) data produced within and outside AFSCs, represent potentially powerful tools to improve such sustainability. However, how these new technologies can support AFSCs in reaching the Sustainable Development Goals (SDGs) defined by the United Nations is still under-explored. This paper aims, through a systematic literature review, to provide an answer to such question. To this goal, 50 papers were analysed. Results suggest that digital technologies can support ASFCs in reaching SDGs by supporting real time monitoring, sharing of data across the supply chains, improving decision making by performing scenario analyses and by enabling automation of some activities. These contributions call for the building of a Decision Support System for AFSCs. Finally, directions for future research are presented.

Public transport and electric vehicles, two-wheeled especially, are expected to constitute a major part of future sustainable transport systems in smart cities. The focus of this paper is to analyze different paradigms of mobility on a fixed route in the center of Milan. The chosen path connects Milan Central station to Milano Bovisa station, a very traffic way. The first part of the paper is based on the analysis of the two most common means of urban transport in Milan: the train and the trolley bus. The comparison is performed in terms of energy consumption, emissions, and costs. In the second part, instead, the focus has been shifted to other means of transport: electric light micro-mobility. Given the increased interest these days in both electric scooters and electric bikes, a preliminary analysis of these two means of transport is carried out. From the results, it is possible to conclude that these types of vehicles represent a sustainable alternative for getting around the city.

This paper faces a challenge in power education: how to teach the students the impact of different transmission lines on the power system at power frequency. More specifically, the paper exploits the very effective and elegant Ossanna’s theorem in order to make clear which are the allowable electrical regimes of different transmission line typologies in terms of power losses, voltage drops, transmissible powers and the conditions for the voltage collapse. Moreover, the formulation of the complex power at the sending-end of the power transmission line inferred from the Ossanna’s theorem, which has never been shown in technical literature, is clearly presented. It is worth noting that although the paper is focused on high and extra-high voltage transmission lines, the presented didactical approach can be indifferently applied to any voltage level.

This paper provides the results of a survey of the grey literature concerning the use of artificial intelligence to improve test automation practices. We surveyed more than 1,200 sources of grey literature (e.g., blogs, white-papers, user manuals, StackOverflow posts) looking for highlights by professionals on how AI is adopted to aid the development and evolution of test code. Ultimately, we filtered 136 relevant documents from which we extracted a taxonomy of problems that AI aims to tackle, along with a taxonomy of AI-enabled solutions to such problems. Manual code development and automated test generation are the most cited problem and solution, respectively. The paper concludes by distilling the six most prevalent tools on the market, along with think-aloud reflections about the current and future status of artificial intelligence for test automation.

In this paper the sequence impedances of an existing HVAC insulated cable, protected by a steel reinforced concrete structure, are estimated. The paper aims at investigating if the presence of metallic protective structures significantly affects the sequence impedances of the line. The present analyses are carried out by applying both the multiconductor cell analysis and the finite element method in order to model the HVAC line with and without protective structure. The results arising from the use of the different modelling approaches are compared.

Droop control is a common method to share power among Distributed Energy Resources (DERs) in dc microgrids. In order to reduce the dc bus voltage variations during the load transient conditions, a resistive-capacitive output impedance is needed. This can be done by implementing a fast voltage controller for the converters or using a bulky output capacitance. This paper analyses an oversampled hysteresis controller for the droop controller of boost converters. Due to the higher controller bandwidth, a resistive-capacitive output impedance is obtained with a small output capacitor. The paper proposes a design approach for hysteresis droop-controlled converters, including the design of frequency-dependent droop coefficients. Experimental results on a 3kW-380V boost prototype verify the validity of the proposed solution.

The paper provides an overview of the measurement issues affecting sensing systems based on QCM, operating in liquid exploiting impedance measurement. The paper analyses the effect of the presence of a liquid and its viscosity at the interface with the QCM sensors by analytical models and experimental test. The derived model takes into account the parasitic effects due to the measurement setup and provides a model valid for applications involving a wide range of viscous media.

Electric motors with relatively small dimensions are widely used, among others, in household appliances. For this type of motors, the market imposes requirements for low electricity consumption and high operational reliability. Their final performance depend, inter alia, on the correct design of the magnetic cores taking into account the used production process and selecting the appropriate cutting technology. The presented paper deals with the problem to quantify the negative effects in terms of material characteristic degradation in magnetic laminations resulting from both punching and laser cutting. For this purpose, simple engineering measurements using a professional Single Sheet Tester system have been considered to develop accurate mathematical models. The proposed models are suitable for quick assessments of the material degradation for any width of the strip (or the motor core part) and, consequently, of the properties of the motors’ cores. In the paper, the proposed analytical models are used to quantify the changes of the relative magnetic permeability vs. distance from the cut edge. The simulation results agree very well with the results of the measurements carried out according to the authors’ concept.

This paper proposes a multi-input multioutput (MIMO) digital control strategy for a bidirectional interleaved boost converter with coupled inductors, designed to operate as an interface between an ultra-capacitor and a high-voltage dc bus in a hybrid nano-grid. The control strategy keeps the high-voltage side dc-bus of the nano-grid stable when the voltage at the low-voltage side, connected to the ultra-capacitor, changes in a wide range. Bidirectional energy flow control with current protection is allowed too. The high-voltage regulation loop is closed around an inner, low voltage side current control loop. In parallel to these, a third, clamp voltage regulator is set to keep tight control over switch voltage stress. The paper discusses the controller organization, the design criteria, and shows the subsequent implementation on a general purpose digital control platform. The achievable performance is experimentally verified on a 1.5-kW converter prototype.

The aim of this paper is to demonstrate the usability of the Long Range Wide Area Network (LoRaWAN) transmission technology for the implementation of a quasi-real time Video Surveillance Unit (VSU) to be employed for remote monitoring within the safety and security application domains. The VSU is provided with motion detection features that allow for actual transmission only of significant images: once that motion is detected within the camera field of view, image is acquired, processed, compressed and subdivided to fit within the maximum LoRaWAN payload size. The paper presents software and hardware architecture of the VSU as well as a set of preliminary tests carried out in an indoor environment, with different dynamic activities within the camera field of view. Image transmission by means of LoRaWAN connectivity is achieved keeping the average overall transmission timing in the order ten minutes, fulfilling all the requirements and limitations foreseen by the LoRaWAN specification: the system proved to be able to satisfy the quasi-real time requirement for the aforementioned application contexts.

Size and absolute concentration of suspensions of nanoparticles are important information for the study and development of new materials and products in different industrial applications spanning from biotechnology and pharmaceutics to food preparation and conservation. Laser Transmission Spectroscopy (LTS) is the only methodology able to measure nanoparticle size and concentration by performing a single measurement. In this paper we report on a new variable gain calibration procedure for LTS-based instruments allowing to decrease of an order of magnitude the experimental indetermination of the particle size respect to the conventional LTS based on the double ratio technique. The variable gain calibration procedure makes use of a specifically designed tunable-gain, dual-channel, dual-phase Lock-In Amplifier (LIA) whose input voltage signals are those ones generated by two Si photodiodes that measure the laser beam intensities passing through the sample containing the nanoparticles and a reference optical path. The LTS variable gain calibration procedure has been validated by firstly using a suspension of NIST standard polystyrene nanoparticles even 36 hours after the calibration procedure was accomplished. The paper reports in detail the LIA implementation describing the design methodologies and the electronic circuits. As a case example of the characterization of biological nanostructures, we demonstrate that a single LTS measurement allowed to determine size density distribution of a population of extracellular vesicles extracted from orange juice (25 nm in size) with the presence of their aggregates having a size of 340 nm and a concentration smaller than 3 orders of magnitude.

Autonomous vehicles(AVs) occupy a crucial part of the emerging mobility. Currently, the potential impacts of self-driving fleets are of great interest to public bodies and authorities. The impact is studied in pilots, but these still have a limited scope, therefore also simulation studies are necessary. This study aims to offer a comprehensive review of recent macro/mesoscopic agent-based models for AVs. Through keyword search, we extracted twenty-nine papers from the Web of Science database. These studies analysed AVs’ impact on the land-use, travel behaviour, environment and cost. Therefore, we summarise the similarities and particularities of modelling specifications and outcomes, meanwhile analysing the reasons for conflicting results. In general, the results from analysed papers are distinct from each other, and some even hold opposite results. There are several effective methods for increasing AVs’ customer acceptance. When implemented, it will bring significant amounts of benefit, including much lower energy consumptions, emission, operational costs and parking space demands. However, a more congested network and urban sprawl are the most unignorable problems. Finally, this research offers several suggestions and research directions regarding AV future development, which will be referential for both researchers and urban planners.