Miracle-dz/UniFAR-FTUs huggingface dataset:
Informative Bayesian type A evaluation of standard uncertainty

The non-informative (improper priors) Bayesian type A evaluation of standard uncertainty consistent with the Supplement 1 and Supplement 2 of the Guide to the expression of Uncertainty in Measurement (GUM) leads to a result that cannot be applied in case of small sample size $n$, namely when $n=2,3$. An ad hoc and non-rigorous solution to this issue, based on the use of the quantiles of the Student's $t$ probability PDF, is provided in the IEC/TR 61000-1-6 (Guide to the assessment of measurement uncertainty), a basic electromagnetic compatibility (EMC) publication. A novel informative Bayesian type A evaluation of standard uncertainty is proposed here that can be easily implemented through a spreadsheet and it is applicable also in the case of small sample size. The aim and the approach are like those in a recent paper published in Metrologia by Cox and Shirono. The apparent limitation that the variance has an upper bound, as assumed by Cox and Shirono, is here removed at the expense of introducing a best guess and a value of the variance that is unlikely to (but it can) be exceeded. In addition to a step-by-step derivation numerical examples are offered to support the viability the proposed type A evaluation. The imminent start of the maintenance of the IEC/TR 61000-1-6 stimulates a critical review and update of the type A evaluation of standard uncertainty in EMC.
Systematic design of blockchain based testbed for Peer - To- Peer energy trading

The power grids are changing rapidly and the energy generation is becoming more distributed than ever, resulting in a paradigm shift from a centralized system to a decentralized one. With the surge in Distributed Energy Resources (DER), there is a requirement for an optimal strategy to share the energy between these resources. Recently, considerable literature has grown up around the theme of decentralized energy sharing. These systems should allow the consumers to seamlessly buy and sell energy. In this paper, we discuss how these systems should be designed to efficiently support peer-to-peer energy trading. An operational distributed virtual demonstrator is built and presented to support reliable energy trading between the DER.
A Double-Layered Hybrid Approach for Network Intrusion Detection System using combined Naive Bayes and SVM

A pattern matching method (signature-based) is widely used in basic network intrusion detection systems (IDS). A more robust method is to use a machine learning classifier to detect anomalies and unseen attacks. However, a single machine learning classifier is unlikely to be able to accurately detect all types of attacks, especially uncommon attacks e.g., Remote2Local (R2L) and User2Root (U2R) due to a large difference in the patterns of attacks. Thus, a hybrid approach offers more promising performance. In this paper, we proposed a Double-Layered Hybrid Approach (DLHA) designed specifically to address the aforementioned problem. We studied common characteristics of different attack categories by creating Principal Component Analysis (PCA) variables that maximize variance from each attack type, and found that R2L and U2R attacks have similar behaviour to normal users. DLHA deploys Naive Bayes classifier as Layer 1 to detect DoS and Probe, and adopts SVM as Layer 2 to distinguish R2L and U2R from normal instances. We compared our work with other published research articles using the NSL-KDD data set. The experimental results suggest that DLHA outperforms several existing state-of-the-art IDS techniques, and is significantly better than any single machine learning classifier by large margins. DLHA also displays an outstanding performance in detecting rare attacks by obtaining a detection rate of 96.67% and 100% from R2L and U2R respectively.
Practical performance of the VEXPA estimation method in sparse regular arrays

This paper presents an investigation into the practical performance of a direction of arrival (DOA) estimation method for sparse regular arrays, called VEXPA. Its ability to estimate the number of incoming signals through cluster analysis is compared to the traditional method used by dense estimation techniques. It is seen that the usefulness of the cluster analysis method depends on the underlying estimation method used by VEXPA. The effect of quantisation errors introduced by the use of an ADC is also investigated. It is found that VEXPA performs satisfactorily with one-bit data for a maximum of one signal.
NFV-Enabled Vertical Scalability for IoT Slices

With network slicing, 5G is able to best support heterogeneous IoT applications each with a specially tailored virtual network, called IoT slice. NFV (Network Function Virtualization) is the key enabler to virtualize a 5G network into multiple IoT slices for different types of IoT applications. All 5G network slices are required to support scalability for their operations in order to deal with dynamic fluctuation of incoming IoT requests. This research focuses on designing and implementing an NFV-enabled vertical scalability system for IoT slices according to their CPU loading. Our system is tested using a traffic generator with three types of IoT services. Each type of IoT services has a different QoS requirement, and the system will forward it to a specific network slice according to its requirement. We will evaluate the performance of vertical scalability by comparing two systems: one system implements vertical scalability followed by horizontal scalability while the other system implements only horizontal scalability. Our aim is to demonstrate that the former involves lower CPU utilization and power consumption while still achieves compatible response time and throughput when compared to the latter.
Impact of Window Penetration Loss on Building Entry Loss From 3.5 to 24 GHz

We investigated the impact of window penetration loss (WinPL) on the frequency dependence of building entry loss (BEL) from 3.5 to 24 GHz. The WinPL characteristics of an ideal double-glazed glass and an actual double-glazed window were simulated and measured on-site, respectively, and both results showed almost the same oscillatory characteristics with respect to the frequency changes that occurred due to the impedance oscillation of the double glass-like multilayer dielectrics. Two BEL measurement scenarios were examined to analyze the frequency dependence of BEL in a traditional office building with double-glazed windows identical to those analyzed in the on-site WinPL measurements. The experiments included a complex propagation route (the first scenario) from the facade of the building to the corridors through windows and offices and a simple propagation route (the second scenario) through only windows lateral to the building. The two main findings are (i) BEL showed strong frequency-dependent behavior regardless of the propagation route and (ii) the WinPL characteristics of the outer double-glazed window were the main contributors to the frequency dependence of BEL.
Higher-order harmonics scattering cancellation by thin metasurfaces for dielectric cylinders

We present a closed-form expression for the impedance of a metasurface coating for a dielectric cylinder, able to cancel all m-indexed harmonic scattered by the cylinder until an arbitrary order M. Combining Mie theory with the transmission line methodology, a dielectric scatterer, seen as a generic load admittance $Y_{a}$, is covered by a mantle cloak with surface admittance function $Y_{s}(M, \phi)$, depending on the $M$ index and on the azimuthal variable $\phi$. It is demonstrated that the scattered energy is hidden inside the dielectric object, forming internal nonradiating modes supported by the azimuthally varying complex surface impedance boundary.
Deep Learning-Based Cryptocurrency Price Prediction Scheme With Inter-Dependent Relations

Blockchain technology is becoming increasingly popular because of its applications in various fields. It gives an edge over the traditional centralized methods as it provides decentralization, immutability, integrity, and anonymity. The most popular application of this technology is cryptocurrencies, which showed a massive rise in their popularity and market capitalization in recent years. Individual investors, big institutions, and corporate firms are investing heavily in it. However, the crypto market is less stable than traditional commodity markets. It can be affected by many technical, sentimental, and legal factors, so it is highly volatile, uncertain, and unpredictable. Plenty of research has been done on various cryptocurrencies to forecast accurate prices, but the majority of these approaches cannot be applied in real-time. Motivated from the aforementioned discussion, in this paper, we propose a deep-learning-based hybrid model (includes Gated Recurrent Units (GRU) and Long Short Term Memory (LSTM)) to predict the price of Litecoin and Zcash with inter-dependency of the parent coin. The proposed model can be used in real-time scenarios and it is well trained and evaluated using standard data sets. Results illustrate that the proposed model forecasts the prices with high accuracy compared to existing models.
Latency Constrained Simultaneous Wireless Information and Power Transfer

This paper studies a power splitting (PS)-based simultaneous wireless information and power transfer (SWIPT) multi-user system. Specifically, an optimization problem is formulated to minimize the average transmit power of the base station (BS) by jointly optimizing the transmit beamformer and receive PS ratios, while meeting user-specific latency and energy harvesting (EH) requirements. We employ the Lyapunov optimization framework and provide a dynamic control algorithm for the time-average problem. The coupled and non-convex constraints are handled via the Successive Convex Approximation (SCA) technique, and a low-complexity iterative algorithm, where each step is computed in closed-form, is proposed by solving a system of Karush-Kuhn-Tucker (KKT) optimality conditions. The numerical results provide insights on the robustness of the proposed design to realize a power-efficient SWIPT system while ensuring latency and EH requirements in a dynamic network.
Decentralized and Self-Sovereign Identity: Systematic Mapping Study

Self-Sovereign Identity is an emerging, user-centric, decentralized identity approach utilizing some form of decentralized technology. It provides a means for digital identification without reliance on any external authority, enabling entities to control their identity and data flow during digital interactions while enhancing security and privacy. With the rise of blockchain technology, Self-Sovereign Identity is gathering momentum in academia and industry while the number of research papers increases rapidly. However, Self-Sovereign Identity is still a young unstructured field in its early stages of research. Thus, a systematic mapping methodology was adopted to provide a coarse-grained overview of decentralized and Self-Sovereign Identity and structure the research area by identifying, analyzing, and classifying the research papers according to predefined parameters, which is to say according to their contribution, application domain, IT field, research type, research method, and place of publication. Furthermore, the nature and scope of the research were determined, while existing research topics, gained insights into trends, demographics, challenges, gaps, and opportunities for future research were also presented. The results suggest that validation research and solution proposals prevail, addressing decentralized identity in a general matter. Papers mainly propose systems/solutions, architectures, and frameworks, focusing on authentication, security, privacy, and trust, while there are hardly any studies researching usability, user experience, patterns, and good practices.
High-Efficient Electrolytic-Capacitor-Less Offline LED Driver With Reduced Power Processing

In this article, an integrated parallel buck–boost and boost converter (IPB3C) is proposed as an electrolytic-capacitor-less light-emitting diode (LED) driver. The IPB3C provides a high power factor (PF) and low total harmonic distortion (THD). The driver is composed of two converters that are connected in parallel, using just one controlled switch. The buck–boost duty is to deliver constant power to the LED, while ensuring a good PF. The boost converter is employed to cancel the low-frequency ripple at the LED. In return, this decreases the flicker effect and only a relatively small capacitance is needed to fulfill the standard requirements. The buck–boost converter handles the full power of the LED, while the boost converter handles only a portion of the LED power. Thus, better efficiency is ensured by this parallel configuration compared to conventional cascaded integrated converters. Moreover, the voltage across the switch is low, as it is the higher, whether buck–boost or boost converter, but not the addition of both. In this article, the IPB3C is analyzed, and its design methodology is presented. A universal input voltage range prototype of the proposed converter supplying an LED lamp of 108-V/ 0.35-A is presented. The prototype shows high PF, nearly equal to one, very small THD, nearly zero, output voltage ripple of 4.5%, output current ripple of 19%, and high efficiency, equal to 92.4%. Moreover, the converter requires the use of a bulk capacitance of only 68 μF, while the required output capacitance is just 1 μF.
Vehicular VLC Channel Model for a Low-Beam Headlight Transmitter

The availability of LED-based headlights and taillights allows visible light communication (VLC) to be considered as a powerful wireless access technology for vehicle-to-vehicle (V2V) communications. A crucial system design concern is modeling a realistic vehicular VLC channel which is verified through measurements. In this paper, we experimentally measure the optical channel for V2V considering a low beam headlight and propose a closed-form path loss expression through data fitting. We further calculate the achievable system capacity that can be supported with such a VLC transmitter.
A Truly NTD-Based PLL: Simple Approach of Double-Frequency Oscillation Rejection

Nonfrequency-dependent transport delay phase-locked loop (NTD-PLL) comprised of an additional transport-delay (TD) unit in the Park transformation matrix (TM), supposedly to improve the offset and double-frequency oscillatory errors (DFOEs) during the frequency deviations. This modification in the Park TM, however, does not eliminate the DFOEs from all the estimation parameters of the utility grid. It is shown in this letter that the issue can be completely alleviated from all the estimation parameters of the grid just by adding another TD unit in the Park TM of NTD-PLL. The effectiveness of the suggested approach is verified through numerical and experimental results.
Inferring Relations Among Test Programs in Microservices Applications

The emergence of the microservices-oriented architectural style calls for novel methodologies and technological frameworks that support the design, development, and main-tainance of applications structured according to this new style. In this paper, we consider the issue of designing suitable strategies for the governance and the automation of testing activities within the microservices paradigm. We focus on the problem of discovering relations between test programs that help avoiding to re-run all the available test suites each time one of its constituents evolves. We propose an analysis technique, based on symbolic execution of test programs, which is able to collect information about the invocations of local and remote APIs performed when running such programs. Symbolic execution enables the analysis of sets of executions corresponding to different input data, and hence it is also suitable for parametric test programs. The information extracted by symbolic execution is processed by a rule-based automated reasoning engine, which infers dependencies and similarities among test programs. In particular, test programs are considered similar if they involve the same microservice instance, or they connect to the same remote API, or they locally activate overlapping APIs, or they raise similar kinds of errors. We show the viability of our approach by presenting a case study within the context of a real-world microservice application that implements an open-source educational platform.
Multi-Relay Assisted Computation Offloading for Multi-Access Edge Computing Systems With Energy Harvesting

In multi-access edge computing systems with energy harvesting (MEC-EH), the mobile devices are empowered with unstable energy harvested from renewable energy sources. To prolong the life of mobile devices, as many computation-intensive tasks as possible should be offloaded to the MEC server. However, when the system states of mobile device and MEC server are unstable, e.g. poor communication channel conditions, a great number of tasks will be executed locally, leading to a long execution time. Even worse, some tasks may be dropped due to low energy levels. To address this problem, in this paper, we propose a multi-relay assisted computation offloading framework for MEC-EH systems. In this framework, a computation task can be executed by offloading to the MEC server with the help of multiple relay nodes, such as the neighboring nodes. We introduce execution cost as a performance metric to incorporate both the task execution time and task failure. We then develop a low-complexity online algorithm, namely MRACO algorithm, to minimize the average execution cost. MRACO algorithm can select the optimal execution strategy for each task from (1) executing the task locally, (2) offloading it to the MEC server directly, (3) offloading it to the MEC server with the help of the most suitable neighboring nodes, and (4) simply dropping it. Moreover, we also develop an algorithm for selecting the suitable neighboring devices to act as relays and determining the optimal task splitting ratio between them. Finally, performance evaluation shows that the proposed MRACO algorithm greatly outperforms the benchmarks in terms of both average execution time and task drop rate.
Exposure of the South Korean population to the 5th generation (5G) of mobile phone networks (3.4 to 3.8 GHz) and its respective contribution among other radio frequencies in the country

The race to install and deploy 5G has taken an important turn in industrialized countries. Certain countries have taken the lead, and 5G is already operational in these locations. South Korea is among those countries that have already deployed 5G. In this study, we conducted experimental measurements of exposure to electromagnetic fields in this country to see the contribution of 5G compared to other frequencies such as 2G, 3G and 4G. The results of the study showed that the emission of 5G is about 15% of the global level. The highest level was observed in the vicinity of the antenna and remains below the limits set by ICNIRP.
GP4: Gaussian Process Proactive Path Planning for the Stochastic on Time Arrival Problem

This paper investigates the stochastic on time arrival (SOTA) problem in Gaussian process (GP) regulated environments. We presume that the travel times of the underlying transportation network follow a multi-variate Gaussian distribution, i.e. Gaussian process, and propose a Gaussian process proactive path planner (GP4), which outputs a dynamic routing policy for maximizing the traveller's on-time-arrival probability. When given an instantiated travel time of a certain link, GP is able to analytically represent/predict the posterior travel time distribution over the entire transportation network. With that, we let the ego vehicle ‘proactively’ select the next traversal link, which maximizes the expected stochastic on time arrival probability based on the predicted posterior travel time distribution. Various extensions of GP4 to other forms of travel time distribution assumptions, such as Log-GP and Bi-GP, are proposed and extensive experimental results demonstrate the efficiency and applicability of GP4 to various transportation networks, including a real use case with real traffic data in Chengdu, China.
Dynamic Priority-Based Computation Scheduling and Offloading for Interdependent Tasks: Leveraging Parallel Transmission and Execution

Mobile edge computing (MEC) has recently emerged as an effective paradigm to enhance the computing capability of capability-limited mobile devices (MDs). In this article, we consider an MEC system consisting of a number of MEC servers and one MD which generates a series of tasks characterized by their dependency relationships. We study computation scheduling and offloading problem of the tasks. To improve the task processing performance, we first propose a parallel transmission and execution (PTE) scheme, based on which we design a computation scheduling and offloading algorithm. Considering the fairness among tasks in terms of task transmission and execution time, we formulate the computation scheduling and offloading problem as a constrained worst-case latency optimization problem which minimizes the maximum completion time of all the tasks. As the original optimization problem cannot be solved conveniently, we first categorize the tasks into high priority tasks (HPTs), medium priority tasks (MPTs) and low priority tasks (LPTs) based on their task execution status and causal relationship. Then, a dynamic priority-based computation scheduling and offloading algorithm is proposed, which designs computation scheduling and offloading strategy for dynamically-changed HPTs and MPTs, respectively. In particular, for HPTs, a multiple knapsack-based heuristic algorithm is proposed, and a task weight and data size-based computation scheduling and offloading algorithm is further proposed for MPTs. Numerical results demonstrate the effectiveness of the proposed scheme.
1.95-kV Beveled-Mesa NiO/β-Ga2O3 Heterojunction Diode With 98.5% Conversion Efficiency and Over Million-Times Overvoltage Ruggedness

The technical progress of Ga2O3 power diodes is now stuck at a critical point where a lack of performance evaluation and reliability validation at the system-level applications seriously limits their further development and even future commercialization. In this letter, by implementing beveled-mesa NiO/Ga2O3 p–n heterojunction diodes (HJDs) into a 500-W power factor correction (PFC) system circuit, high conversion efficiency of 98.5% with 100-min stable operating capability has been demonstrated. In particular, rugged reliability is validated after over 1 million times dynamic breakdown with a 1.2-kV peak overvoltage. Meanwhile, superior device performance is achieved, including a static breakdown voltage (BV) of 1.95 kV, a dynamic BV of 2.23 kV, a forward current of 20 A (2 kA/cm2 current density), and a differential specific on-resistance of 1.9 mΩ·cm2. These results indicate that Ga2O3 power HJDs are developing rapidly with their own advantages, presenting the enormous potential in high-efficiency, high-power, and high-reliability applications.