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The ROPAS project (Roll-to-roll paper sensors) combines high end electronics and wireless sensors with low cost paper substrates and processing techniques that can be applied on a large scale. Paper is the next step in the printed electronics roadmap of utilising cheaper substrate materials as a replacement of silicon and PET and is generally referred to as paper electronics. The creation of highly conductive structures applied at high speed on (temperature unstable) paper is extremely important to enable paper electronics. Hereto, recent advantages in nanotechnology such as (encapsulation) printing, surface modification of fiber based products, sensor and battery development, and electronic design (wireless communication) are integrated for large scale roll to roll and sheet to sheet paper applications. The possibilities of paper electronics are demonstrated in high impact (security) and logistics applications, which are demonstrated in security tags and smart labels for anti-counterfeiting purposes by e.g. integration of near field communication (NFC) in paper and finally track-and-trace post pieces such as envelopes. The paper explains the project and its impact, including implications of using a rough, coarse, porous and composite, paper substrate of limited high temperature stability on the design and fabrication of demonstrators and building blocks. Important topics herein are: fast curing of conductive inks on paper using state of the art printing techniques such as inkjet, screen and flexogravure. In the talk, synthesis of printable (humidity) sensor materials, placing of electrical components on paper surfaces including electronic design of the wireless communication and environmental and recycling constraints of the products are discussed as well.

During the New Space era small countries are becoming important players in the space business. While space activities are rapidly increasing globally, it is important to make operations in a sustainable and safe way in order to preserve satellite services for future generations. Unfortunately, the sustainability aspect has been largely overlooked in the existing surveys on space technologies. As a result, in this survey paper, we discuss the multi-layer networking approaches in the 6G era specifically from the sustainability perspective. Moreover, our comprehensive survey includes aspects of some interesting industrial, proprietary, and standardization views. We review the most important regulations and international guidelines and revisit a three-dimensional architecture vision to support the sustainability target for a variety of application areas. We then classify and discuss space safety paradigms that are important sustainability enablers of future satellite communications. These include space traffic management, debris detection, environmental impacts, spectrum sharing, and cyber security aspects. The paper also discusses advances towards a planned European connectivity constellation that could become a third flagship infrastructure along with the Galileo and Copernicus systems. Finally, we define potential research directions into the 2030s.

Infections encompass a set of medical conditions of very diverse kinds that can pose a significant risk to health, and even death. As with many other diseases, early diagnosis can help to provide patients with proper care to minimize the damage produced by the disease, or to isolate them to avoid the risk of spread. In this context, computational intelligence can be useful to predict the risk of infection in patients, raising early alarms that can aid medical teams to respond as quick as possible. In this paper, we survey the state of the art on infection prediction using computer science by means of a systematic literature review. The objective is to find papers where computational intelligence is used to predict infections in patients using physiological data as features. We have posed one major research question along with nine specific subquestions. The whole review process is thoroughly described, and eight databases are considered which index most of the literature published in different scholarly formats. A total of 101 relevant documents have been found in the period comprised between 2003 and 2019, and a detailed study of these documents is carried out to classify the works and answer the research questions posed, resulting to our best knowledge in the most comprehensive study of its kind. We conclude that the most widely addressed infection is by far sepsis, followed by Clostridium difficile infection and surgical site infections. Most works use machine learning techniques, from which logistic regression, support vector machines, random forest and naive Bayes are the most common. Some machine learning works provide some ideas on the problems of small data and class imbalance, which can be of interest. The current systematic literature review shows that automatic diagnosis of infectious diseases using computational intelligence is well documented in the medical literature.

Model checking methods have been proven to be a valuable asset for identifying undesired behaviour of safety-critical Instrumentation and Control (I&C) logics. Their application in the nuclear domain has been very successful and has triggered significant interest from the safety community. Creating formal models from the diagrams found on paper or from digital formats without the needed semantics is one bottleneck that hinders the adoption of model checking due to costs in time and may introduce errors. This paper proposes a methodology for the creation of formal models from I&C diagrams drawn in generic modelling tools (lacking specific I&C semantics). The generic I&C logic diagram is transformed into an intermediate UML model that in turn can be transformed to other target formats like IEC 61131 PLCopen XML I&C software or NuSMV formal model code. This methodology is demonstrated with a typical example of a trip signal generator application logic. This application logic is drawn in MS Visio, it is transformed to an I&C model in UML with the needed properties for model checking, then to IEC 61131 PLCopen XML and to an input file for the NuSMV model checker.

In this paper, our goal is to improve angular resolution of 77 GHz radar with eight receiver channel by using a multi-radar system and high-resolution direction-of-arrival (DoA) estimation algorithm. The theoretical and experimental results show that angular resolution can be improved 42.6 % with multi-radar system compared to a single radar system when two targets have exact the same distances from the radar. In this paper, we also show experimental studies for forward backward spatial smoothing (FBSS) multiple signal classification (MUSIC) DoA estimation algorithm. The algorithm is demonstrated in a real world indoor environment and the results of the estimated DoAs showed considerably good agreement with the predicted angles. By using this algorithm, angular resolution can be improved 40.8% compared to fast Fourier transform (FFT) beamforming (BF) algorithm.

Condition monitoring of rolling element bearings is of vital importance in order to keep the industrial wheels running. In wind industry this is especially important due to the challenges in practical maintenance. The paper presents an attempt to improve the capability of prediction of remaining useful life of rolling bearings. The approach is based on the understanding of the wear of bearings i.e. wear modelling is briefly discussed. A simulation model has been built to produce vibration data of the monitoring of rolling bearings taking into account typical vibration excitations in addition to the wear. The simulation model is used to develop signal analysis methods and means of prognosis of the remaining useful life. One complete example of the above described process is shown and discussed in the paper.

This paper proposes a scalable substrate-integrated waveguide (SIW) module accommodating an in-line vector modulator monolithic millimeter integrated circuit (MMIC). The SIW module is realized with low-temperature co-fired ceramic (LTCC) technology, and it can be inserted in a dielectric-filled waveguide (DFWG). The module combines λg/4-transformer-based E-plane tapering and SIWs on LTCC with the wire-bonded vector modulator. The proposed active LTCC module and two passive test structures (i.e., a constant-height-SIW module and a SIW module with E-plane taperings) are manufactured and tested as in-line modules in a DFWG. The passive test structures with the waveguide-to-DFWG and DFWG-to-SIW transitions measure 3.1 dB and 4.6 dB of insertion loss on average, respectively, at the 71–81 GHz frequency range. The active LTCC module measurements demonstrate a dielectric-filled waveguide with phase and amplitude tuning capability and gain up to 17.6 dB within the same frequency range. A four-channel mock-up module with λ0/2 channel spacing is designed and manufactured to demonstrate the scalability of the design.

5G and beyond wireless networks are the upcoming evolution for the current cellular networks to provide the essential requirement of future demands such as high data rate, low energy consumption, and low latency to provide seamless communication for the emerging applications. Heterogeneous cloud radio access network (H-CRAN) is envisioned as a new trend of 5G that uses the advantages of heterogeneous and cloud radio access networks to enhance both spectral and energy efficiency. In this paper, building on the notion of effective capacity (EC), we propose a framework in orthogonal frequency division multiple access (OFDMA)- non-orthogonal multiple access (NOMA) based H-CRAN to meet these demands simultaneously. Our proposed approach is to maximize the effective energy efficiency (EEE) while considering spectrum and power cooperation between a macro base station (MBS) and radio remote heads (RRHs). To solve the formulated problem and to make it more tractable, we transform the original problem into an equivalent subtractive form via Dinkelbach algorithm. Afterward, the combinational framework of distributed stable matching and successive convex algorithm (SCA) is then adopted to obtain the solution of the equivalent problem. Hereby, we propose an efficient resource allocation scheme to maximize energy efficiency while maintaining the delay quality of service (QoS) requirements for all users. The simulation results show that the proposed algorithm can provide a non-trivial trade-off between delay and energy efficiency in OFDMA-NOMA based H-CRAN systems in terms of EC and EEE and the spectrum and power cooperation improves EEE of the proposed network. Moreover, our proposed solution complexity is much lower than the optimal solution and it suffers a very limited gap compared to the optimal method.

Publisher Copyright: Author In this paper, we describe how the up-to-date state of a digital twin, and its corresponding simulation model, can be used as a fitness function of an evolutionary algorithm for optimizing a large-scale industrial process. An ICT architecture is presented for solving the computational challenges that arise when the fitness function evaluation takes considerable amount of time. Parallel computation of the fitness function in a cloud computing environment is proposed and the evolutionary algorithm is connected to the computational environment using the Function-as-a-Service approach. A case-study was conducted on the district heating network of Espoo, the second largest city in Finland. The study shows that the architecture is suited for optimizing the operating costs of the large district heating network, with over 800 km of water pipes and over 14 heat producers, reaching a cost-saving of an average of 2%, and up-to 4%, over the current industrial state-of-the-art method in use at the city of Espoo.

Recent transition from mobile telephony to daily consumption of mobile data presents the challenge of adequate spectrum availability. Besides regulators and operators, this challenge faces both public safety authorities and military as well. This paper investigates how selected spectrum sharing concepts (namely, European Licensed Shared Access (LSA) and US Citizens Broadband Service (CBRS)) support the military interests either as a Primary User (PU) or as a Secondary User (SU) and whether these concepts facilitate temporal adjustments of national defence from peace time mode of operations to hybrid warfare or to large scale homeland defence. In this paper we have shown that military spectrum needs vary from one scenario to another and that Shared Spectrum Access may support expected needs of spectrum in times of most dire stress. Both of the concepts reviewed have built in dynamism, which however does not support PU-SU role changes across tiers.

The millimeter wave bands are being increasingly considered for wireless communication to unmanned aerial vehicles (UAVs). Critical to this undertaking are statistical channel models that describe the distribution of constituent parameters in scenarios of interest. This paper presents a general modeling methodology based on data-training a generative neural network. The proposed generative model has a two-stage structure that first predicts the link state (line-of-sight, non-line-of-sight, or outage), and subsequently feeds this state into a conditional variational autoencoder (VAE) that generates the path losses, delays, and angles of arrival and departure for all the propagation paths. The methodology is demonstrated for 28 GHz air-to-ground channels between UAVs and a cellular system in representative urban environments, with training datasets produced through ray tracing. The demonstration extends to both standard base stations (installed at street level and downtilted) as well as dedicated base stations (mounted on rooftops and uptilted). The proposed approach is able to capture complex statistical relations in the data and it significantly outperforms standard 3GPP models, even after refitting the parameters of those models to the data., Comment: 28 pages one column, submitted to Transactions in Wireless Communications Journal. arXiv admin note: text overlap with arXiv:2008.11006

This paper presents the results of the dielectric properties and partial discharge endurance measurements conducted on polypropylene (PP)-silica nanocomposite. The material compounds were analyzed with micro-Raman spectroscopy, X-ray tomography and transmission electron microscopy (TEM). ac and dc breakdown strength of the materials was measured. Dielectric response, capacitance and loss factor of the film samples were measured as a function of temperature and frequency. Partial discharge (PD) endurance of the reference PP and PP Silica nanocomposite was studied as a function of ac voltage. Material surfaces were analyzed after PD stress with optical microscopy. All dielectric measurements were done for oriented thin films with a thickness of 11-23 μm. The results were analyzed statistically to determine the effects of the additive on the properties of PP. The paper discusses the potential of PP Silica nanocomposite with a view to high voltage applications, especially power capacitors.

In this paper, attenuation of flexural rotor vibration in electrical machines is considered. In order to generate force on the machine rotor, an electromagnetic actuator based on self-bearing machine working principle is examined. A control method for attenuating harmonic rotor vibration components is applied in a 30 kW two-pole cage induction machine. The machine is equipped with a four-pole supplementary winding for generation of lateral force on the rotor. Experimental results for the two-pole induction motor are presented. The main contribution of this paper is to apply a control method, specially designed for compensating harmonic excitations, by using a built-in electromagnetic actuator in an induction machine.

This paper presents the results of the dielectric measurements conducted on polymer nanocompounds consisting of polypropylene (PP) and polyhedral oligomeric silsesquioxane (POSS). The material compounds were analyzed with a scanning electron microscope (SEM) and Raman-atomic force microscope (Raman-AFM). Ac and lightning impulse (LI) breakdown strength of the material compounds were measured. Relative permittivity, loss factor and volume resistivity measurements were also conducted on the material samples. Two types of POSS, octamethyl and isooctyl, were used in different quantities. The thickness of the samples was approximately 600 mum. Statistical analysis was applied to the results to determine the effects of the additive type and amount on the breakdown strength of polypropylene. The paper discusses the possibilities and restrictions in order to achieve advantages in high voltage applications using polyhedral oligomeric silsesquioxanes.

The increasing amount and resolution of satellite sensors demand new techniques for browsing remote sensing image archives. Content-based querying allows an efficient retrieval of images based on the information they contain, rather than their acquisition date or geographical extent. Self-organizing maps (SOMs) have been successfully applied in the PicSOM system to content-based image retrieval in databases of conventional images. In this paper, we investigate and extend the potential of PicSOM for the analysis of remote sensing data. We propose methods for detecting man-made structures, as well as supervised and unsupervised change detection, based on the same framework. In this paper, a database was artificially created by splitting each satellite image to be analyzed into small images. After training the PicSOM on this imagelet database, both interactive and off-line queries were made to detect man-made structures, as well as changes between two very high resolution images from different years. Experimental results were both evaluated quantitatively and discussed qualitatively, and suggest that this new approach is suitable for analyzing very high resolution optical satellite imagery. Possible applications of this work include interactive detection of man-made structures or supervised monitoring of sensitive sites.

ESA Forest Carbon Monitoring project (FCM) is developing Earth Observation based, user-centric approaches for forest carbon monitoring. Forest carbon accounting based on forest inventory requires precise and timely estimation of forest variables at various spatial levels accompanied by verifiable uncertainty information. In this paper, we present the algorithm trade-off and selection approach and preliminary results of the algorithm intercomparison exercise in the FCM project. The studies were performed over 7 European test sites located in Finland, Ireland, Romania, Spain and Switzerland, and one tropical forest site in Peru. EO datasets were represented by Sentinel-1, Sentinel-2, TanDEM-X and ALOS-2 PALSAR-2 imagery. Examined approaches include popular parametric and SAR/InSAR scattering physics based approaches, and nonparametric and machine learning approaches such as k-NN, random forests, support vector regression.

This paper investigates key stakeholders' incentives for the hybrid use of the Ultra High Frequency (UHF) spectrum for broadcasting and mobile broadband using novel Supplemental Downlink (SDL) Carrier Aggregation (CA) technique from the dynamic capabilities perspective of strategic management. So far broadcaster (BC) community has lacked true incentives for sharing the spectrum they currently use and have seen sharing only as unilateral repurposing act from regulatory bodies and Mobile Network Operators (MNO). In this paper we focus on key stakeholders capability to deal with combined internal and external resources and capabilities in doing business, refered as Dynamic Capability (DC). Spectrum sharing introducing a rapid change in the stakeholders business environments requires dynamic capabilities of hybrid use from both spectrum offering and spectrum utilization perspectives. We focus on identifying and discussing broadcaster incumbents' and MNO licensees' key antecedents, processes, and outcomes related to hybrid use of UHF spectrum using the supplementary downlink (SDL) carrier aggregation (CA) concept. Based on our DC analysis, we found incentives that could motivate both the incumbents and licensees to consider the hybrid use of UHF spectrum as a co-creation opportunity, and provide viewponts for the regulatory bodies to be considered when developing new regulatory models for accessing UHF spectrum.

Effective, long-lasting Industrial IoT (IIoT) solutions start with short-term gains and progressively mature with added capabilities and value. The heterogeneous nature of IIoT devices and services suggests frequent changes in resource requirements for different services, applications, and use cases. With such unpredictability, resource orchestration can be quite complicated even in basic use cases and almost impossible to handle in some extensively dynamic use cases. In this paper, we propose SDRM; an SDN-enabled Resource Management scheme. This novel orchestration methodology automatically computes the optimal resource allocation for different IIoT network models and dynamically adjust assigned resources based on predefined constraints to ensure Service Level Agreement (SLA). The proposed approach models resource allocation as a Constraint Satisfaction Problem (CSP) where optimality is based on the solution of a predefined Satisfiability (SAT) problem. This model supports centralized management of all resources using a software defined approach. Such resources include memory, power, bandwidth, and edge-cloud resources. SDRM aims at accelerating efficient resource orchestration through dynamic workload balancing and edge-cloud resource utilization, thereby reducing the cost of IIoT system deployment and improving the overall ROI for adopting IIoT solutions. We model our resource allocation approach on SAVILE ROW using ESSENSE PRIME modeling language, we then implement the network model on CloudSimSDN and PureEdgeSim. We present a detailed analysis of the system architecture and the key technologies of the model. We finally demonstrate the efficiency of the model by presenting experimental results from a prototype system. Our test results show an extremely low solver time ranging from 0.47 ms to 0.5 ms for nodes ranging from 100 to 500 nodes. With edge-cloud collaboration, our results show about 4 percent improvement in overall task success rates.

The purpose of a Beyond 4G (B4G) radio access technology, is to cope with the expected exponential increase of mobile data traffic in local area (LA). Therequirements related to physical layer control signaling latencies and to hybrid ARQ (HARQ) round trip time (RTT) are in the order of ~1ms. In this paper, we propose a flexible orthogonal frequency division multiplexing (OFDM) based time division duplex (TDD) physical subframe structure optimized for B4G LA environment. We show that the proposed optimizations allow very frequent link direction switching, thus reaching the tight B4G HARQ RTT requirement and significant control signaling latency reductions compared to existing LTE-Advanced and WiMAX technologies. The purpose of a Beyond 4G (B4G) radio access technology, is to cope with the expected exponential increase of mobile data traffic in local area (LA). The requirements related to physical layer control signaling latencies and to hybrid ARQ (HARQ) round trip time (RTT) are in the order of ~1ms. In this paper, we propose a flexible orthogonal frequency division multiplexing (OFDM) based time division duplex (TDD) physical subframe structure optimized for B4G LA environment. We show that the proposed optimizations allow very frequent link direction switching, thus reaching the tight B4G HARQ RTT requirement and significant control signaling latency reductions compared to existing LTE-Advanced and WiMAX technologies.

This paper will look at research results so far realised from the ETICA EU co-funded project that has been looking at identifying emerging technologies and their potential related ethical issues. In keeping with the thematic priorities of the conference, the paper will focus on Cloud Computing; one of the projects identified technologies, in relation to its potential benefits and potential ethical issues for would be users, especially in the area of business. The paper will look at benefits and ethical issues of such an emerging technology basing the results on a more general level. However, the paper will also pay particular attention to Africa by looking at implications of Cloud Computing on Africa. This is in order to understand potential commonalities and cross-overs that may result from the adoption and use of Cloud Computing between the two geographical spheres as well as to understand, share and learn of potential ethical consequences which may to a certain extent be different due to different circumstances between the more developed EU and the still developing African continent. (19 refs.)

Computer game processing requirements for the CPU and graphics performance are growing all the time higher. At the same time many low cost and modest performance CE devices are gaining popularity. People are already used to mobile life style inside home and on the go and they want to enjoy entertainment everywhere. This paper describes an accelerated video streaming method used in a gaming system, called Games@Large, which enables heavy PC game playing on low cost CE devices without the need of game software modification. The key innovations of the Games@Large system are game execution distribution, game streaming, including graphics/video and audio streaming and game control decentralization as well as network quality of service management. This paper concentrates on the advanced video streaming techniques used in Games@Large.

Optimal management of demand-side flexibility in buildings is important for properly integrating large amounts of intermittent generation from windmills and photovoltaics. This paper proposes a novel Energy Management Agent (EMA) concept that can optimize building’s energy costs with respect to external prices while at the same time allow building’s flexibility to be used via explicit demand response. The EMA combines Artificial Neural Networks (ANN) and model predictive control for modelling and optimization of building’s flexibility. It continuously manages building’s flexibility with respect to external prices and provides forecasts of the load and available flexibility for a defined time window. A proof-of-concept (PoC) of the EMA is implemented for controlling a heat pump in an apartment, located in Oulu, Finland. Two ANN-based models were implemented for modelling the energy consumption of the heat pump and the indoor temperature of the apartment. Monte Carlo Tree Search based planning and control was implemented for finding optimal control policies with the ANNs. The EMA PoC was evaluated in 16-week period between 11 November 2019 - 1 March, 2020. When compared to a fixed setpoint control strategy, the EMA achieved 14.8 % lower costs under Nord Pool spot prices for Finland. At the same time, it was also able to accurately follow the 24h load plans (NRMSE was 0.050) and activate the offered flexibilities (NRMSE was 0.074).

The communication infrastructure is most likely to be damaged after a major disaster occurred, which would lead to further chaos in the disaster stricken area. Modern rescue activities heavily rely on the wireless communications, such as safety status report, disrupted area monitoring, evacuation instruction, rescue coordination, etc. Large amount of data generated from victims, sensors and responders must be delivered and processed in a fast and reliable way, even when the normal communication infrastructure is degraded or destroyed. To this end, reconstructing the post-disaster network by deploying MDRU (Movable and Deployable Resource Unit) and relay unit at edge is a very promising solution. However, the optimal wireless access control in this heterogeneous hastily formed network is extremely challenging, due to the frequent varying environment and the lack of statistics information in advance in post-disaster scenarios. In this paper, we propose a learning based wireless access control approach for edge-aided disaster response network. More specifically, we model the wireless access control procedure as a discrete-time single agent Markov decision process, and solve the problem by exploiting deep reinforcement learning technique. By extensive simulation results, we show that the proposed mechanism significantly outperforms the baseline schemes in terms of delay and packet drop rate.

Economic load dispatch (ELD) in power system problems involves scheduling the power generating units to minimize cost and satisfy system constraints. Although previous works propose solutions to reduce CO2 emission and production cost, an optimal allocation needs to be considered on both cost and emission—leading to combined economic and emission dispatch (CEED). Metaheuristic optimization algorithms perform relatively well on ELD problems. The gradient-based optimizer (GBO) is a new metaheuristic algorithm inspired by Newton’s method that integrates both the gradient search rule and local escaping operator. The GBO maintains a good balance between exploration and exploitation. Also, the possibility of the GBO getting stuck in local optima and premature convergence is rare. This paper tests the performance of GBO in solving ELD and CEED problems. We test the performance of GBO on ELD for various scenarios such as ELD with transmission losses, CEED and CEED with valve point effect. The experimental results revealed that GBO has been obtained better results compared to eight other metaheuristic algorithms such as Slime mould algorithm (SMA), Elephant herding optimization (EHO), Monarch butterfly optimization (MBO), Moth search algorithm (MSA), Earthworm optimization algorithm (EWA), Artificial Bee Colony (ABC) Algorithm, Tunicate Swarm Algorithm (TSA) and Chimp Optimization Algorithm (ChOA). Therefore, the simulation results showed the competitive performance of GBO as compared to other benchmark algorithms.

The influence of organisational factors on nuclear safety has been identified in several studies. In spite of their importance no integrated approach to address them seems to have emerged. The paper starts with issues identified in the LearnSafe project to discuss how they have been addressed in research and development within Finland and Sweden and discussed their importance in a larger frame. This discussion is used to suggest a research agenda for what could be called a midfield approach, where utility and regulatory needs could be combined. The paper serves at an introduction to the discussion part of the special session on Organisational learning and learning organisations - the LearnSafe project three years after.

The paper gives a brief overview of the LearnSafe project that was running between fall 2001 and spring 2004. Five research organisations, twelve nuclear sites and one international organisation participated in the LearnSafe project, which was funded by the EURATOM part within the fifth Framework Programme of European Union. The paper focuses especially on the issues discussed in the LearnSafe project and on lessons learned. The paper serves at an introduction to the special session on Organisational learning and learning organisations – the LearnSafe project three years after.

Management systems can be seen as the software of an organisation. Present views on managements systems have emerged from several sources and have now been integrated into the guiding documents produced by IAEA. A discussion of the new documents has in Sweden been initiated within the Vattenfall Nordic Production Safety Management Institute (NSMI), which recently has been established to support the production units in Sweden. The paper starts with a brief description of NSMI and its main tasks. Management systems are then discussed in more detail to suggest research needs within management systems and safety managements. A conclusion of the paper is that a better understanding of the structure and management systems is needed to remove present difficulties in the application of management systems at the nuclear power plants.

Vehicular communication applications require an efficient communication architecture for timely information delivery. Centralized, cloud-based infrastructures present latencies too high to satisfy the requirements of emergency information processing and transmission, while Vehicle-to-Vehicle communication is too variable for reliable in-time information transmission. In this paper, we present EAVVE, a novel Vehicle-to-Everything system, consisting of vehicles with and without comprehensive data processing capabilities, facilitated by edge servers co-located with roadside units. Adding computation capabilities at the edge of the network allows reducing the overall latency compared to vehicle-to-cloud and makes up for scenarios in which in-vehicle computational power is not sufficient to satisfy the service demand. To improve the offloading efficiency, we propose a decentralized algorithm for real-time task scheduling and a client/server algorithm for information filtering. We demonstrate the practical applications of EAVVE with a bandwidth-hungry, latency constrained real-life prototype system that connects vehicular vision through Augmented Reality vision. We evaluate this prototype system with real-life road tests. We complement this practical evaluation with extensive simulations based on real-world base station and vehicular traffic data to demonstrate the scalability of EAVVE and its performance in citywide scenarios. EAVVE decreases the latency by 42.6% and 78.7% compared to local and remote cloud solutions while relaxing congestion at the bottleneck by 99% with reasonable infrastructure expenditure.

This paper studies issues concerning the application of user configurable cooperative information agents for monitoring tasks in process automation. Within this application area the amount of information gathered from the processes has been growing vastly and the supervising personnel has been minimized in the production plants. As this trend seems to keep going further, the end users need more effective information handling tools. However, the information overflow problem has also shown up in other application domains, and it is useful to discuss the similarities and differences with solutions used in these areas. This paper proposes an agent-based architecture to support active monitoring of the changes in process related data situated in various heterogeneous information sources. This approach is based on a BDI agent model, where individual user-configurable information processing modules are flexibly linked. The approach is demonstrated with an industrially inspired test scenario.

The 5G networks are still being deployed in many countries creating new business opportunities. 5G networks allow us to include new features and deliver new levels of system capacity and efficiency such as higher speed connectivity, ultra low latency connectivity, improved security, distributed networks, virtualizednetworks and so on. They enable us to have new use cases and scenarios such as automated vehicles, smart city, eHealth, and so on. In this paper, 5G vertical use cases and large scale trials of transportation undertaken at the EU 5G-HEART project trial sites across Europe are introduced. Four representative transport use cases are validated in the 5G-HEART project. They are as follows: (1) Platooning that drives a group of vehicles together, (2) Autonomous driving that avoids collision and achieves safer driving and better traffic efficiency, (3) Remote driving support that allows an user or cloud software to control vehicles remotely, and (4) Vehicle data services that provides us with a better vehicle services byinterconnecting various third-party data to autonomous vehicles using5G networks. User requirements and KPIs are analyzed for 5G transportation use cases. The selected results of 5G-HEART transportation vertical trials are presented.

5G millimeter-wave (mmWave) cellular networks are in the early phase of commercial deployments and present a unique opportunity for robust, high-data-rate communication to unmanned aerial vehicles (UAVs). A fundamental question is whether and how mmWave networks designed for terrestrial users should be modified to serve UAVs. The paper invokes realistic cell layouts, antenna patterns, and channel models trained from extensive ray tracing data to assess the performance of various network alternatives. Importantly, the study considers the addition of dedicated uptilted rooftop-mounted cells for aerial coverage, as well as novel spectrum sharing modes between terrestrial and aerial network operators. The effect of power control and of multiuser multiple-input multiple-output are also studied.

Funding Information: *Research supported by Academy of Finland Project 331149 and 328239. 1Authors are with the Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, 00076, Aalto. Finland. 2Author is with the Department of Applied Physics, School of Science, Aalto University, 00076, Aalto. Finland. All email addresses: firstname.lastname@aalto.fi. Publisher Copyright: © 2022 IEEE. Magnetic manipulation of particles at close vicinity is a challenging task. In this paper, we propose simultaneous and independent manipulation of two identical particles at close vicinity using two mobile robotic electromagnetic needles. We developed a neural network that can predict the magnetic flux density gradient for any given needle positions. Using the neural network, we developed a control algorithm to solve the optimal needle positions that generate the forces in the required directions while keeping a safe distance between the two needles and particles. We applied our method in five typical cases of simultaneous and independent microparticle manipulation, with the closest particle separation of 30 μm.

In this paper we study the problem of comparing a set of data between two parties in a peer-To-peer network to determine the number of common friends. Several protocols for private set intersection are presented in the literature. When the sets are large these tend to be too slow for many purposes. We consider the problem of two parties finding out how many common friends they have in a privacy preserving way. This problem has arisen in designing a peer-To-peer platform called HELIOS. We present our solution for the problem that is more efficient than older protocols but still sufficiently privacy-friendly for our purposes. The solution is based on iteratively revealing information about the hash values of friends' identities in small increments.

Printed flexible supercapacitor (SC) is seen as an attractive alternative to replace batteries as energy storage unit in energy autonomous sensors. This paper assesses a cyclic bending test method for printed flexible SC. The test is evaluated using four material stacks for printed SC in five different bending radii. The measurement system analysis (MSA) found that the calculated bending radii under all test conditions exhibit variation within a range of 0.3 mm, and the variation takes up less than 8% of bending radius. The measurement system is subjected to 9% total variation, which is within acceptable range. The variation was mainly caused by the uneven thickness distribution across the SC due to its structure. Thus, the variation caused by the test method and measurement is even smaller. In addition, the SC's bending radius subjected to even smaller variation in cyclic bending test. These indicate that this test method is highly reliable and repeatable for evaluating flexible SC. acceptedVersion

This paper addresses networking in terms of tunnels and overlays establishment, naming, addressing and routing for Personal Networks (PN). A number of concepts to achieve PN networking and management are presented. A PN Agent is introduced in the PN architecture. The Agent supplies information about clusters constituting the PN to enable networking within the PN. The paper also explores the use of edge routers in the overall framework to support PN networking via virtual private network paradigms and PN services. The virtual router concept, well established for layer 3 VPN solutions in networks, is advocated to resolve scalability and latency requirements in PN networking. A hybrid solution involving both end users and provider provisioned VPNs to establish personal network overlays is suggested to find a compromise between performance and security requirements. Proactive and reactive routing along with flat and subnet based addressing for PN are also covered by this contribution.

This paper presents the design, manufacturing, and characterization of a wide-band cavity-backed aperture-coupled patch antenna and a 16-element antenna array on multilayer printed circuit board (PCB) targeted for D-band applications. Microstrip line and grounded coplanar waveguide (GCPW) transmission lines are also designed and tested to investigate line losses at D-band. The test structures are manufactured using printed circuit board technology with semi-additive processing (mSAP) of conductors on a multilayered substrate. The measurement results indicate an insertion loss of 1.9 dB/cm for the microstrip line and 1.8 dB/cm for the coplanar waveguide at 150 GHz. The measured maximum gains for single antenna and 16-element array are respectively 7 dBi and 14 dBi at 143 GHz. The measured antenna input matching bandwidth is 20 GHz. The results show the viability of advanced printed circuit technology for D-band transmission lines, antennas, and antenna arrays.

Point cloud video provides 6 degrees of freedom (6DoF) viewing experiences to allow users to freely select the viewing angles of 3D scenes and is expected to be the next-generation video. This paper studies the point cloud video streaming and proposes a fuzzy logic-based point cloud video streaming scheme to solve the inherent technical issues. In particular, a point cloud video is first partitioned into smaller tiles, along with a low-quality base layer covering the entire video. Each tile is encoded into different quality levels, and both the compressed and uncompressed (i.e., decoded) versions of each tile are prepared for selection. Then, based on the user’s viewing angle and predicted future network bandwidth condition, fuzzy logic empowered quality level selection, with properly defined novel fuzzification, fuzzy rules, and defuzzification, is conducted to maximize the received point cloud video quality under the communication resource, computational resource and quality requirements constraints. Extensive simulations based on real point cloud video sequences and network traces are conducted, and the results reveal the superiority of the proposed scheme over the baseline scheme. To the best of our knowledge, this is the first work studying point cloud video streaming using fuzzy logic.

Industry 4.0 encompasses a promise of a new industrial revolution in terms of providing secure, intelligent, autonomous and self-adaptive industrial IoT (IIoT) networks. Key industrial applications and systems will be significantly more complex due to the involvement of the vast number of different devices and diverse nature of various stakeholders and service providers. These complex industrial processes, services and applications also have strict requirements in terms of performance — latency in particular — and resource-efficiency, together with high standards for security and trust. In this context, Blockchain and Edge Computing emerge as prominent technologies to address the mentioned essential requirements and to further strengthen the rise of the new era of digitization. The Edge computing paradigm ensures low latency services for IIoT applications while optimizing the network usage, whereas Blockchain provides a decentralized way for ensuring data integrity, trust and security. In this paper, we propose a “BlockEdge” framework that combines these two enabling technologies to address some of the critical issues faced by the current IIoT networks. We verify the feasibility of our approach by evaluating the performance and resource-efficiency of BlockEdge in terms of latency, power consumption and network usage, through simulations against non-Blockchain solution.

Echocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion abnormalities (RWMA) in order to diagnose myocardial infarction (MI) or commonly known as heart attack. This paper proposes a novel approach, Active Polynomials, which can accurately and robustly estimate the global motion of the Left Ventricular (LV) wall from any echo in a robust and accurate way. The proposed algorithm quantifies the true wall motion occurring in LV wall segments so as to assist cardiologists diagnose early signs of an acute MI. It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their 'maximum motion displacement' plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF). The outputs of the method can further help echo-technicians to assess and improve the quality of the echocardiogram recording. A major contribution of this study is the first public echo database collection composed by physicians at the Hamad Medical Corporation Hospital in Qatar. The so-called HMC-QU database will serve as the benchmark for the forthcoming relevant studies. The results over HMC-QU dataset show that the proposed approach can achieve 87.94% accuracy, 92.86% sensitivity and 87.64% precision in MI detection even though the echo quality is quite poor and the temporal resolution is low.

Energy communities combine assets of different community members and can create financial value from energy trade and from providing flexibility. Sharing this value among the community members can be done in various ways depending on the main objectives of the energy community and selecting the most suitable value sharing method for a particular energy community is not always straightforward. There are several, partly contradictory, requirements to be fulfilled that include fairness, stability, understandability, computational feasibility and ability to incentivize individual members to act in a way that benefits the whole energy community. In this paper, alternative energy community value sharing methods are compared from different viewpoints. The most suitable value sharing method for a particular energy community can be selected by first defining what are the main requirements for value sharing and then proceeding to a more detailed analysis of the value sharing method options that fulfill those requirements. acceptedVersion

Nuclear power plants have many different instrumentation and control (I&C) systems. Together, these systems (and their various dependencies) form the overall I&C architecture, which needs to fulfill the principle of defence-in-depth. The safety systems need to be sufficiently independent from the normal operation systems to avoid common cause failure.Semantic Web technologies use formal conceptual models— ontologies—to associate meaning with unstructured data. The knowledge base is built on named graphs, allowing complex queries with reasoning. The results are based on more than just statistical patterns.In this paper, we demonstrate the use of an OWL ontology to represent engineering knowledge about overall nuclear I&C architectures. We show how, using flexible SPARQL queries, we can then analyse the different dependencies between the I&C systems. We have built a public case study based on a proposed pressurised water reactor type. We detected several potential design issues, which suggests the approach could improve nuclear safety and support design work.

In this paper we evaluate the performance of fifth generation new radio (5G NR) based positioning under realistic conditions model for cooperative connected automated mobility (CCAM) scenarios. We benchmark the performance using 3GPP release 16 proposed new positioning reference signal (PRS), of positioning in 5G NR mobile networks. Time difference of arrival (TDoA) positioning methods is one of the widely used method which is used for localization. Simulation results are showing that under line-of-sight (LOS) conditions, the desired positioning accuracy is achievable for various CCAM use-cases. In best case scenarios precision is less than 1m in 80% of cases. In more realistic cases, when there is no line of sight (NLOS) between user terminal (UT) and network nodes, then accuracy decreases significantly. Methods of automatic classification of LOS/NLOS channels are thus needed. TDoA positioning method suffers degradation of performance, when different network nodes are out of sync with each other. Thus, other methods, less sensitive to synchronization error, such as round-trip time (RTT) or angle-based measurements might be worth considering.

For over a decade, model checking has been successfully used to formally verify the instrumentation and control (I&C) logic design in Finnish nuclear power plant projects. One of the practical challenges is that the model checker NuSMV forces the user to abstract the way analog signals are processed in the model, which causes extra manual work, and could mask actual design issues. In this paper, we experiment with the newer tool nuXmv, which supports infinite-state modelling. Using actual models from practical industrial projects, we show that after changing the analog signal processing to be based on real number math, the analysis times are still manageable. The disadvantage is that certain useful types of formal properties are not supported by the infinite-state algorithms. We also discuss the nuclear industry specific features of I&C programming languages, which cause significant constraints on domain-specific formal verification method and tool development.

Software Product Management (SPM) is a relatively young research area which aims to understand how to productise a software product or a service as well as how to align it with the organisation's strategy. While the research of an academic discipline of SPM started to emerge as yearly as 1990s, the most impactful works have been published during 2006–2007. To understand how this young field has emerged and developed, this paper presents a bibliometric study on SPM publications found from Scopus ( n=142 ). The identified studies have been produced by a small set of authors and institutions, which are mainly located in Europe. By using Bibliographic Coupling and Co-Citation metrics, the study shows that Software Product Management literature is drawing from several different related fields. Furthermore, the studied literature is tightly interconnected. The study also shows that the SPM field might be lacking a coherent intellectual background and new openings due to scattered research foci. To prevent this development, this work calls for a formation of a shared research agenda for the Software Product Management field. acceptedVersion

This paper introduces the practical evaluation setup of the low latency video streaming system for extensive performance measurements using 5G test network. The results gathered with the latest 5G new radio indoor equipment enriched with the optimized streaming solutions and accurate measurement system reveal the bottlenecks in the mobile transmission path especially in uplink channel, and help to identify the places of improvement. These places of optimization are essential in the live low latency streaming scenarios of the different industries moving to wireless technologies in vehicle-based connectivity, such as streaming vital information from an ambulance to a receiving hospital, transmitting highly prioritized traffic or road information to other drivers, or streaming through congested mobile network in need of rapid adaptation. Furthermore, the experimental knowledge gained during the extensive measurements in real 5G test environment have demonstrated the application-based processing delay to have substantial effect on the total end-to-end latency. The ongoing 5G standardization and its actualization by the operators already enables enhanced mobile broadband and ultra-reliable low-latency communication for extreme high throughput and low latency streaming, but practical field research and evaluation is needed especially for sending time critical data to uplink direction from moving vehicles in the harsh signal environment. For better understanding of the requirements in low latency video streaming and planning the actual vehicle to network scenarios over 5G, it is essential to identify the current performance and possible restrictions before shifting to more complex system development.

In this paper, we present a highly linear balanced IQ resistive mixer implemented in a 100-nm GaN HEMT on a silicon substrate that covers the whole Ka-band. The mixer achieves an input third-order intercept point (IIP3) of 30 dBm with only 10.5 dBm of LO input power at 35 GHz which means a record linear efficiency of 20 dB. The Measured 1-dB input compression point was 15 dBm at 34 GHz. The mixer shows a better than 10 dB conversion loss over the entire Ka-band.

Automated driving has attracted enthusiasm worldwide with its potential to transform mobility and realize transport, economical and societal benefits. Gaining perspectives from previous V2X trials in 5G-Drive project, this paper is motivated to focus on two challenges encountered with LTE-V2X enabled automated driving: the impact of infrastructure antenna height in C-V2X supported automated driving and the influence of C-V2X in vehicle positioning, especially when satellite signals is unavailable. Two trials have been designed and performed in Finland, as an attempt to continue and examine the two LTE-V2X enabled automated driving use cases in previous V2X trials. The outcome shows C-V2X latency is affected when antenna height is low. Optimizing and configuring the antenna height is crucial in C-V2X enabled automated driving tests. Important lessons on RTK, Inertial and C-V2X enabled automated vehicle positioning are drawn from the trials, where the enhancement of C-V2X could be augmented by testing and strengthening the GNSS signals.

Automated driving has attracted enthusiasm worldwide with its potential to transform mobility and realize transport, economical and societal benefits. Gaining perspectives from previous V2X trials in 5G-Drive project, this paper is motivated to focus on two challenges encountered with LTE-V2X enabled automated driving: the impact of infrastructure antenna height in C-V2X supported automated driving and the influence of C-V2X in vehicle positioning, especially when satellite signals is unavailable. Two trials have been designed and performed in Finland, as an attempt to continue and examine the two LTE-V2X enabled automated driving use cases in previous V2X trials. The outcome shows C-V2X latency is affected when antenna height is low. Optimizing and configuring the antenna height is crucial in C-V2X enabled automated driving tests. Important lessons on RTK, Inertial and C-V2X enabled automated vehicle positioning are drawn from the trials, where the enhancement of C-V2X could be augmented by testing and strengthening the GNSS signals.

Digitalization is strongly entering new application domains around the world, thus also entering people’s everyday lives. Digitalization enables creating new kinds of businesses, but it also changes the way services, applications and data are provided and consumed. We carried out interviews with company representatives acting in the energy domain to receive up-to-date information about the companies’ awareness of digitalization, and the required changes they have identified in their future business scenarios. The interviewed companies were aware of the change pressure caused by digitalization, and especially the data exchange with other domain actors, predictions using data, new data-based services and data-based decision making were identified as key issues. However, currently there is a lack of digital infrastructure enabling the actors in the energy domain to perform data-based co-operation. The purpose of this paper is to identify the requirements for an energy data ecosystem that enables the companies in the energy domain to reach their business goals and to co-operate in data-based business. The initial goal is to identify the future tasks and goals of energy domain actors and define the type of co-operation environment, including knowledge management models and supporting services, required to support them. The research was implemented with industry actors in the energy domain that represent several different roles in energy markets, and the results of the research were also validated among the industry.

This paper studies the problem of non-cooperative radio resource scheduling in a vehicle-to-vehicle communication network. The technical challenges lie in high vehicle mobility and data traffic variations. Over the discrete scheduling slots, each vehicle user equipment (VUE)-pair competes with other VUE-pairs in the coverage of a road side unit (RSU) for the limited frequency to transmit queued packets. The frequency allocation at the beginning of each slot by the RSU is regulated following a sealed second-price auction. Each VUE-pair aims to optimize the expected long-term performance. Such interactions among VUE-pairs are modelled as a stochastic game with a semi-continuous global network state space. By defining a partitioned control policy, we transform the stochastic game into an equivalent game with a global queue state space of finite size. We adopt an oblivious equilibrium (OE) to approximate the Markov perfect equilibrium (MPE), which characterizes the optimal solution to the equivalent game. The OE solution is theoretically proven to be with an asymptotic Markov equilibrium property. Due to the lack of a priori knowledge of network dynamics, we derive an online algorithm to learn the OE policies. Numerical simulations validate the theoretical analysis and show the effectiveness of the proposed online learning algorithm.