Miracle-dz/UniFAR-FTUs huggingface dataset:
Revision of target uncertainty for PMU-based distributed measurement systems in MV networks

The need for accurate measurements is daily increasing and recognized by system operators. Whether this aspect may be less significant for transmission networks (TNs), the same is not true for the distribution one (DN). In fact, the investments dedicated to TNs are far higher compared to those for DNs. Therefore, this work deals with the accuracy requirements needed in DNs to achieve significant measurement results. In particular, the study starts from the hypothesis that measurements are collected from typical current and voltage sensors and computed via phasor measurement units. The work first describes the typical characteristics of the DN. Second, the sources of uncertainty that contribute to the overall uncertainty of the output quantities (module and phase of currents and voltages) are detailed and analyzed. Finally, the Monte Carlo method has been applied to quantify the uncertainty of those quantities in a variety of realistic DN configurations. From the results, it emerges the significance of the measurement chain design due to its significant impact on the accuracy of the measured quantities.
A Simple Deformation Approach for Facial Expression

This paper presents an automatic approach for facial expression deformation. In order to guarantee the expression deformation is dealt with high quality and conveniently, the approach uses two key technologies: the PSO detection and the affine transformation. The simulation results show that this method can produce natural smile expression, without marking the spots manually, especially works well for some shy smile expressions.
Recognition of P300 Wave and SSVEP using a Capsule Neural Network

P300 wave and Steady-State Evoked Potentials are viable alternatives in the development of Brain-Computer Interfaces based on electroencephalography since the selection of different external stimuli can be used to interpret the intention of a subject to perform a specific task for control or communication purpose. However, there are many challenges related to learning, comfortability, and adaptability to the environment. A possible solution for these challenges is to create classification models trained with a set of subjects rather than per subject. This paper proposes the implementation of a Capsule Neural Network for P300 and SSVEP classification named CapsN2. CapsN2 architecture is based on capsules, which are groups of neurons that find features invariant to translation, rotation, and scale. Also, CapsN2 generates less variance between subjects and requires significantly fewer samples during the training than convolutional neural networks. This is because the model is trained with all subjects and not per subject. Results report that CapsN2 requires a few samples to learn the P300 wave detection and SSVEP classification with competitive performance compared with other popular methods in the literature. In addition, CapsN2 acquired the EEG signal with just one active electrode channel. Then, CapsN2 is a feasible option to process evoked potentials in comfortable, adaptable BCI embedded systems.
Memory Efficient Stream Processing for IoT Devices

Stream processors working on mass-produced IoT devices will get more important when more developers start to make applications on such devices with small memory spaces. In this study, we propose a novel task scheduling algorithm in stream processing to achieve a small and controlled memory footprint. To adhere to memory upper limit given from configurations keeping scheduling fairness, we developed two task schedulers: one is Flow-Efficient Scheduler for fairness and sufficiently low memory utilization, and the other is Memory-Reducing Scheduler for for reducing internal memory at the cost of scheduling fairness. The former is the default scheduler, and the latter is used to recover from higher memory pressure. We devised Memory State Machine to track the memory consumption and switch schedulers. We conducted experiments that imitated an in-vehicle sensor data processing. The experimental results show that our proposed methods help resilient recovery from high memory pressure caused by burst stream data. This study also aims to introduce SpringQL, our stream processor implementation for middle to high-end IoT devices. This OSS stream processor is used in the evaluation to implement the proposed schedulers.
Improving detection of malicious samples by using state-of-the-art adversarial machine learning algorithms

Cyber-security landscape has always been domi-nated by the constant arms race between security vendors and malware creators. With the increased usage of artificial intel-ligence in cyber-security products, malware writers had to use similar approaches to overcome various detection mechanisms. This paper focuses on using various adversarial machine learning algorithms to validate the effectiveness of machine learning models designed for malware detection, trained on 1 million binary files (both benign and malicious) with more than 24.000 features. We also evaluate a second approach that enhances training databases with samples obtained from generative methods in order to boost detection resilience.
Criteria to consider in a decision model for collaborative robot (cobot) adoption: A literature review

Collaborative robots are being increasingly used by manufacturing companies due to their potential to help companies cope with market volatility. Before introducing this technology, companies face the decision phase where they determine the investment feasibility. Decision models for cobot adoption can assist decision-makers in this task, but they require previous identification of decision criteria. Since existing literature overlooked this issue, this study aims to provide a list of decision criteria that can be considered in the cobot adoption decision process. These criteria were identified by a literature review of the benefits, advantages, and disadvantages of cobot adoption. Results show that flexibility, competitiveness, ergonomics, quality, safety, space, mobility, ease of programming, technical features, human-robot collaboration, and productivity are important aspects to consider when deciding whether to invest in cobots. The findings of this study provide a better understanding of the decision process for cobot adoption by listing decision criteria along with some indicators, which is an important input for the design of a decision-making process.
Effects of high-dose Mn implantation into ZnO grown on sapphire

ZnO films grown by pulsed-laser deposition on c-plane Al2O3 substrates were annealed at temperatures up to 600 °C to produce n-type carrier concentrations in the range 7.5×1015–1.5×1020 cm−3. After high-dose (3×1016 cm−2) Mn implantation and subsequent annealing at 600 °C, all the films show n-type carrier concentrations in the range 2–5×1020 cm−3 and room temperature hysteresis in magnetization loops. The saturation magnetization and coercivity of the implanted single-phase films were both strong functions of the initial anneal temperature, suggesting that carrier concentration alone cannot account for the magnetic properties of ZnO:Mn, and that factors such as crystalline quality and residual defects play a role.
Determination of efficient points in multiple-objective location problems

This article is devoted to an MCDM problem connected with locational analysis. The MCDM problem can be formulated so as to minimize the distance between a facility and a given set of points. The efficient points of this problem are candidates for optimal solutions to many location problems. We propose an algorithm to find all efficient points when distance is measured by any polyhedral norm
Evaluating P4-based Virtual Delivery Trees for Content-based Publish/Subscribe

With content-based publish/subscribe, publishers issue notifications that are matched against subscriptions issued by subscribers to which only the matching notifications are delivered in turn. Due to the expressiveness of content-based filtering, each notification requires an individual delivery tree. This fact and because subscriptions potentially change over time make it hard to preinstall delivery trees into the switch infrastructure. In this paper, we present novel notification distribution strategies implemented in P4 that install virtual trees (VTs) in the switch infrastructure. For deriving VTs, we use topological information as well as pub/sub-specific information such as the distribution of publishers and subscribers and their interests. A greedy algorithm constructs a notification-specific delivery tree at runtime by combining VTs and encoding additional routing information into the notification headers to extent, connect, and prune VTs. We present a detailed evaluation investigating the performance of the strategies in real-world topologies and under changing subscriptions. The results confirm that our strategies construct efficient distribution trees requiring only little additional routing information in the notification headers. They also show that the size of this routing information grows when subscriptions change and that the increase depends on to what extend the respective strategy tailors the delivery trees to the active subscriptions.
Augmented Reality Support for Performance and Decision-Making in Collaborative Time-Critical Tasks

This research explores augmented reality (AR) as a tool for supporting performance and decision-making in a collaborative time-critical task. To this end, we conducted a user study comparing traditional $2\mathrm{D}$ displays to traditional 2D displays supplemented with AR. The study results indicate that AR is a promising tool for supporting collaborative time-critical tasks. The results revealed that AR enhanced primary task performance and indicates that traditional 2D displays supplemented with AR can be used as an alternative to standard 2D displays for collaborative time-critical tasks.
Physical aspects of divertor Thomson scattering implementation on ITER

This paper describes the challenges of Thomson Scattering implementation in the ITER divertor and evaluates the capability to satisfy project requirements related to the range of the measured electron temperature and density. A number of aspects of data interpretation are also discussed. Although this assessment and the proposed solutions are considered in terms of ITER compatibility, they may also be of some use in currently operating magnetic confinement devices.
An Architecture to Negotiate and Monitor Energy Exchanges in the Smart Microgrid

Traditional electrical power sources and their long distribution networks can no longer cope with the ever-increasing need for energy. Distributed energy generation in close geographical proximity to the consumption point is an alternative approach to energy provisioning. The complexities introduced by this approach require an advanced management system. The smart microgrid addresses this need. A second need identified in developing regions is for a management system that is both affordable and non-proprietary. This paper presents a smart microgrid architecture, based on open-source platforms, that addresses these needs. The architecture is explained by means of a use case. A database design is given with tables to reflect the contracts and associated energy exchanged between producers, consumers, and energy store devices.
Virtual Network Embedding using a Federated DRL Approach

This paper deals with allocating resources for virtual network requests in the underlying substrate of an infrastructure provider (INP). This is called the virtual network embedding problem. A client’s virtual network request (VNR) is accepted if the INP’s Virtual Network Embedding (VNE) agent successfully maps all the virtual nodes and links to suitable substrate nodes and links satisfying the CPU and bandwidth requirements of nodes and links. We consider a scenario where multiple INPs exist, catering to the VNR requests of their respective clients. In this paper, we have used a Federated Deep Reinforcement Learning (Federated DRL) approach, where the INPs individually train their agents based on DRL. During the training, the DRL agent learns to map the virtual nodes to substrate nodes. Subsequently, the INPs operators learn collaboratively by sharing only the respective trained model information, without revealing any detailed vital information to each other. The federated reinforcement learning model is implemented using TensorFlow; the VNRs arrival is modeled using SimPy package, and the network graphs for virtual and substrate networks are created using the NetworkX package. The proposed solution accepts around 5% more VNRs, accumulates around 7% more revenue, and performs around 10% better in terms of the revenue to embedding cost ratio compared to existing algorithms.
An Intelligent Area Localization Framework for Rotating Machine Vision Vibration Measurement

Vision-based vibration measurement technology has received extensive attention due to its advantages of non-contact, high spatial resolution, and no-load effect. However, with the complexity of measurement objects and measurement tasks, the existing visual measurement technology is gradually showing greater limitations. Specifically, due to the uncertainty of actual working conditions, not all pixels in the field of view can measure vibration. Therefore, the selection of measurement points needs to rely on prior structural information and artificial experience. Frequent manual point selection tests bring a lot of resource consumption, which greatly reduces the automation degree of visual vibration measurement. This paper focuses on an intelligent area localization method for vibration measurement of rotating machine vision and designs a deep learning-based vibration measurement area localization framework to directly feedback all reliable measurement pixels from image data, which is called the VMAL framework. Firstly, the sub-pixel physical feature information associated with vibration in the data is analyzed through an unsupervised image decomposition network, and then a regularized regional localization network is used to cluster and output reliable regional pixels. Experimental results on a medium-sized single-span rotor platform verify the effectiveness of the proposed method.
Optical, dielectric and polarization studies of the electric field-induced phase transition in Pb(Mg1/3Nb2/3)O3 [PMN]

Abstract Single crystals of PMN have been studied between 10 and 300 K by means of dielectric, polarization and poling/depoling current measurements, associated with optical domain observations under bias field. Evidence for a first order electric field-induced phase transition, from the mean cubic phase to a macroscopically polar phase, has been disclosed by studying, as a function of temperature, the dielectric anomalies, the establishment of induced polarization, the onset of poling current and the appearance of the birefringence and phase boundary. By means of combined optical and domain switching analysis, the symmetry of the induced phase has been deduced to be trigonal 3m. The induced macropolarization Pind can be switched by a field of opposite polarity. The thermal depoling under a bias field takes place at a temperature depending on the field strength, whereas the zero-field depoling of an induced state occurs always at T do = 213 K, independently of the initial poling field. An electric field/t.
Estimation of In vivo Cancellous Bone Elasticity

The effect of decreasing bone density (a symptom of osteoporosis) is greater for cancellous bone than for dense cortical bone, because cancellous bone is metabolically more active. Therefore, the bone density or bone mineral density of cancellous bone is generally used to estimate the onset of osteoporosis. Elasticity or elastic constant is a fundamental mechanical parameter and is directly related to the mechanical strength of bone. Accordingly, elasticity is a preferable parameter for assessing fracture risk. A novel ultrasonic bone densitometer LD-100 has been developed to determine the mass density and elasticity of cancellous bone with a spatial resolution comparable to that of peripheral quantitative computed tomography. Bone density and bone elasticity are evaluated using ultrasonic parameters based on fast and slow waves in cancellous bone by modeling the ultrasonic wave propagation path. Elasticity is deduced from the measured bone density and the propagation speed of the fast wave. Thus, the elasticity of cancellous bone is approximately expressed by a cubic equation of bone density.
Optimization of transit network layout and headway with a combined genetic algorithm and simulated annealing method

This article proposes a methodology for optimizing transit networks, including both route structures and headways. Given information on transit demand, transit fleet size and street network in the transit service area, the methodology seeks to minimize transfers and total user cost while maximizing service coverage. The goal is to provide an effective mathematical solution procedure with minimal reliance on heuristics to solve large-scale transit network optimization problems. This article describes the representation of the transit route network and the associated network search spaces, the representation of route network headways and the associated search spaces, the total user cost objective functions, and a stochastic global search scheme based on a combined genetic algorithm and simulated annealing search method. The methodology has been tested with published benchmark problems and applied to a large-scale realistic network optimization problem. The results show that the methodology is capable of producing improved solutions to large-scale transit network design problems.
Energy Efficient Design of Coarse-Grained Reconfigurable Architectures: Insights, Trends and Challenges

Coarse-Grained Reconfigurable Architectures (CGRAs) are promising solutions to achieve more performance with the end of Moore's law. Thanks to word-level programmability, they are more energy-efficient compared to FPGAs. Although ASICs can minimize energy, they suffer from high Non-Recurring Engineering (NRE) costs and inflexibility. CGRAs provide near ASIC energy efficiency and are deployed in the literature to accelerate low-power and high-performance applications. However, focusing on low-power CGRAs is crucial as a high volume of data should be processed on a resource-constrained device by the development of IoT and Machine Learning applications. This survey has reviewed and categorized CGRA architectures from processing elements, interconnect networks, and memory points of view and derived guidelines for energy-efficient CGRA design.
CO clouds in the dwarf irregular galaxy IC 10

Les observations 12 CO (J=1−0) sont rapportees pour plusieurs regions de la galaxie irreguliere naine IC 10. L'emission Co est detectee dans les regions de formation stellaire, dont l'une correspond au plus grand pic H I, et egalement au voisinage d'un pic H I sans contrepartie d'emission du continuum radio, optique et IR. Dans les regions de formation stellaire, les nuages Co sont associes avec une ligne sombre, une source maser, des sources ponctuelles IRAS et des pics de continuum radio, mais pas dans les regions H II. Les proprietes des nuages Co dans IC 10 sont comparees a celles du Grand Nuage de Magellan et de la galaxie M 31
Comparative analysis among Steinmetz-based analytical models applied to inductors using iron powder material considering DC bias

In this work, core loss in inductors with DC bias is analyzed, with different flux densities, frequencies and duty cycles, while comparing Steinmetz-based models (OSE, MSE, IGSE and WSE) to experimental results. The losses estimated on a finite element analysis (FEA) software simulation are included in the comparison. Experimental results indicate the most accurate model according to waveform using iron powder (High Flux) material. As contribution, two main insights are given. The first is that the influence of DC Bias in High Flux material has been found to be negligible, which has not been shown. This means that Steinmetz-based models which do not consider DC Bias may be used to estimate losses in the material. The second insight is in regard to the usage of different Steinmetz-based analytical models. It is demonstrated that the actual Steinmetz coefficients that are used influence the results greatly, much more so than the difference among the models themselves. To demonstrate this fact, it is shown that the original Steinmetz equation, the simplest analytical model, is sufficient to describe core losses, given proper coefficient adjustment.
Evaluation of the Effectiveness of the Expert Dialogue Based on Markov Models

Models of paired interaction of experts in the form of a dialogue are investigated. An approach is proposed in which dialog models are developed in the form of symmetric oriented interaction graphs. Such graphs are considered as Markov models with discrete states and continuous time. Two variants of dialogue structures are investigated. For them, the final expressions of the achievability of the dialog goal are obtained. The scope of the effectiveness of the dialog interaction procedure is evaluated by the criterion of the probability of reaching consensus.
Inertial Measurement Unit based Human Action Recognition Dataset for Cyclic Overhead Car Assembly and Disassembly

Motion datasets in industrial environments are essential for the research on human-robot interaction and new exoskeleton control. Currently, a lot of Activities of Daily Living (ADL) datasets are available for researchers, but only a few target an industrial context. This paper presents a dataset for a semi-industrial Overhead Car Assembly (OCA) task consisting of synchronized video and 9-Degrees of Freedom (DOF) Inertial Measurement Unit (IMU) data. The dataset was recorded with a soft-robotic exoskeleton equipped with 4 IMUs covering the upper body. It has a minimum sampling rate of 20 Hz, lasts approximately 360 minutes and comprises of 282 cycles of a realistic industrial assembly task. The annotations consist of 6 mid-level actions and an additional Null class. Five different test subjects performed the task without specific instructions on how to assemble the used car shielding. In this paper, we describe the dataset, set guidelines for using the data in supervised learning approaches, and analyze the labeling error caused by the labeler onto the dataset. We also compare different state-of-the-art neural networks to set the first benchmark and achieve a weighted F1 score of 0.717.
Natural convection flow on a sphere through porous medium in presence of heat source/sink near a stagnation point

Abstract Steady natural convection boundary‐layer flow in the neighbourhood of lower stagnation point of a heated sphere embedded in a saturated porous medium in presence of heat source/sink is considered. The dimensionless governing equations for this investigation are solved numerically by shooting method. Then attention is focused on finding the effects of porosity parameter, heat source/sink parameter and the Prandtl number on velocity and temperature distributions. With the increase of permeability parameter of the porous medium, the fluid velocity decreases but the temperature increases at a particular point of the surface. Due to increasing values of heat source/sink parameter, fluid velocity is found to increase. It is noticed that the velocity boundary layer is suppressed with the increasing Prandtl number and the temperature decreases in this case. Prandtl number has an important effect in increasing the rate of heat transfer from the surface of the sphere.