Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault Detection Device for PV Array using the Page-Hinkley Test

In a system of solar energy production, defects can cause a partial or total loss of energy, damage to the system, or failure to safety. Detecting defects is more than necessary to ensure reliability, productivity, safety, and system efficiency. In this paper, we propose a device appropriate for fault detection on the PV plant such as shading. The Bishop model is adapted to the defaults studied in such a system because it considers the avalanche mode of a cell. The model is used to estimate the output values of the system including current, voltage, and power based on the measured inputs such as temperature and irradiance. The model is implemented in the programmable card-based device. Afterward, the residues calculated from the difference between the estimated and the measured value are used in the detection algorithm. Then the Page-Hinkley algorithm is implemented in the card to verify any eventual mean jumps of the residues. The interest of the device is to offer a low-cost monitoring system able to detect any defects in the PV system. The device was tested on the photovoltaic system installed at the Vignola-Ajaccio site to confirm its performance.
Impact of Base Station Activation in Edge Computing-enabled Networks

In this paper, we provide the framework of edge computing (EC)-enabled coordinated multipoint (CoMP)-based network with base station (BS) activation composed of the BSs equipped with EC servers having different transmission power and computing capabilities. We consider the BS activation strategy to select the BSs with EC servers, and selected BSs shut down their transmission and do not provide the computing capabilities to users. We analyze the successful edge computing probability (SECP) defined as the probability that the edge computation is finished within a target latency. Through numerical results, we then show how the BS activation affect the SECP.
Improvement in the Post-Processing of Wave Buoy Data Driven by the Needs of a National Coast and Sea Monitoring Agency

Technological development in terms of the power requirement for data acquisition and processing opens new perspectives in the field of environmental monitoring. Near real-time data flow about the sea condition and a possible direct interface with applications and services devoted to marine weather networks would have a significant impact on several aspects, such as, for example, safety and efficiency. In this scenario, the needs of buoy networks have been analyzed, and the estimation of directional wave spectra from buoys’ data has been deeply investigated. Two methods have been implemented, namely the truncated Fourier series and the weighted truncated Fourier series, and they have been tested by both simulated and real experimental data, representative of typical Mediterranean Sea conditions. From simulation, the second method proved to be more efficient. From the application to real case studies, it emerged that it works effectively in real conditions, as confirmed by parallel meteorological observations. The estimation of the main propagation direction was possible with a small uncertainty of a few degrees, yet the method exhibits a limited directional resolution, which suggests the need for undertaking further studies, briefly addressed in the conclusions.
Throughput Fairness in Congestion Control of Multipath TCP

In order to improve network throughput and fault tolerance, multipath TCP (MPTCP) has been proposed and installed on various operating systems. In this paper, we have investigated throughput fairness between MPTCP flows using results obtained from an experimental network. We have shown that the fairness changes like a concave function for the increase of packet storing queue size, that is, as the size becomes larger, the fairness becomes smaller until the size reaches a threshold value and it reversely becomes larger when the size is beyond the threshold value, and we have shown that as round trip propagation delay time becomes larger, the minimum of the concave function becomes larger.
Impact growth structures of nanoalloys: Atomistic simulation on an immiscible Cu–Ag system

Abstractauthoren The impact deposition of Ag (or Cu) atoms over Cu (or Ag) nanoparticle with truncated octahedral structure is studied by molecular dynamics. The embedded‐atom method is used to describe interatomic interactions. The simulations are performed at incident energies of 10 to 50 eV. The incident‐energy dependence of the deposition on configurations of Cu–Ag nanoparticles is analyzed. For the deposition of Ag atoms on Cu substrate, a perfect Cu‐core/Ag‐shell nanoparticle with truncated octahedral structure is obtained at all considered incident energies. A reversed core–shell nanoparticle with Cu covering the surface is observed at lower incident energies as Cu atoms are deposited on Ag substrate. An icosahedral multi‐core nanoparticle with Ag bridging several Cu cores is also observed with increased incident energy to 50 eV. These simulation results well agree with the experimental observations. The transformation from truncated octahedron to icosahedron is attributed to the incident energy and immiscibility of Cu–Ag.
Using IoT in Smart Agriculture: Study about Practical Realizations and Testing in a Real Environment

The Smart Agriculture concept has emerged as a solution to provide a decision support system for farm management. Although extensive research has been done in this field, many solutions involve substantial costs and are not feasible for small farms. The development of communication technologies and the accessibility of internet connections facilitate the production of cheap IoT nodes for agriculture. This paper presents a method for creating cheap IoT sensors that can be realized in a hobby manner, such that the data storage and processing steps do not involve supplementary costs. We checked the feasibility and viability of the proposed solution in practice by making the sensors and exposing them to real environmental operating conditions during a period of 6 months. We identified several problems related to the operation of the IoT nodes, tested multiple solutions and constructed a feasible system for data collection and storage for smart agriculture.
Uniqueness theorems for the Boussinesq system

We address the uniqueness problem for mild solutions of the Boussinesq system in R 3. We provide several uniqueness classes on the velocity and the temperature, generalizing in this way the classical C([0, T ]; L 3 (R 3))-uniqueness result for mild solutions of the Navier-Stokes equations.
Blindly detecting the number of signals using HOS in spatially correlated noise

Blind detection of the number of sources is a key research topic in array signal processing. Most of the algorithms in the literature assume that the noise is spatially white. However, this assumption cannot be satisfied in multiple-input multiple-output systems when the channel is impaired by interference and jamming. This paper focuses on solving this problem by utilizing higher order statistics (HOS). Utilizing the diagonal structure of the cumulant matrices, the components of the unitarily transformed signals corresponding to the noise subspace follow Gaussian law. A statistic is proposed to test the Gaussianity of the signals with explicit mathematical explanation. Simulations verify the validity of our algorithm in spatially correlated noise.
K-Nearest Neighbor Based Association Data Mining in Healthcare Correlated Data Systems

Indeed, data mining techniques have been used to find hidden patterns and linkages in health-related practices, summarize data in unique ways that are both understandable to healthcare stakeholders, and predict future patterns and behaviors. Various data mining strategies and approaches have gotten a lot of attention and research. Medical information has progressed in the direction of intelligence as a result of the rapid advancement of information technology. The K-nearest neighbor classification algorithm is frequently utilized in various disciplines due to its simplicity. When the sample size is high and the feature attributes are substantial, the K - nearest neighbor algorithm classification efficiency has also grown greatly. This study demonstrates how a K-nearest neighbor- based data mining technique was used to index data and analyze a clinical data set from an outpatient facility. As a result, the experimental findings suggest that the proposed technique can significantly increase the KNN algorithm's classification efficiency when processing a huge data set. Using the K-nearest neighbor algorithm, data mining techniques may classify customer behavior depending on the prospect, responder, active, and other entities of the customer's life cycle.
Plant Disease Detection Using Quantum Image Processing

EDQRCI allows automation in order to creates, establish quantum images and it converts color pixel in well-defined ways. Our model is independent of image rectification process and algorithm, although storage and other necessary process. EDQRCI generally make use of the same idea of energy metamorphose, related to 2D mapping in order to encode values of energy to group of quantum data. Suppose, if the data given is electromagnetic, then it will be processed and stored, then the data given can be encrypted to quantum mode in order to represent color data. On the other hand, if the given data is infrared radiation, then the encoding of quantum data into color representation can be done based on the intensity of radiation. Different Quantum Image Description permits the portrayal, development and acquisition of data in individual modes in quantum mechanism for another experimental implementation.
Optimal Reactive Power Dispatch Considering Load Variation in Distribution Networks using an Improved Sine-Cosine Algorithm

The accelerated economic development during the latest decades resulted in an increased electricity demand around the world and major challenges for power systems. Moreover, the climate changes mitigation measures triggered significant changes and an increased pressure on all power systems sectors from generation to distribution and consumers. The Optimal Reactive Power Dispatch (ORPD) is a key component for reducing the active power losses and improving the bus voltage profiles in distribution networks. In this paper, an Improved Sine-Cosine Algorithm (ImSCA) is proposed for solving the ORPD for a distribution network operating under different load scenarios. The case study presents a performance comparison between the proposed ImSCA and the classical Sine-Cosine Algorithm (SCA) and the reactive power control impact upon the network operational conditions.
Design of Nanophotonic Devices using Multi Objective Optimization Method

The performance of nanophotonic devices was very sensitive and nonlinear to the structural design parameters. In this manuscript, two examples of multi-objective optimizations using the response surface method and Kriging surrogate model with the disability function for the designing of nanophotonic devices were introduced. Although reasonable optimum design parameters could be obtained using performance expectation models after the proper selection of key design factors and ranges of design factors, a machine learning method with big data could be a powerful solution for the extensive parametric analysis and optimization in the design of nanophotonic devices.
Special Right Jacobson Radicals for Right Near-rings

In this paper three more right Jacobson-type radicals, J r g , are introduced for near-rings which generalize the Jacobson radical of rings, 2 f0;1;2g. It is proved that J r g is a special radical in the class of all near-rings. Unlike the known right Jacobson semisimple near-rings, a J r g -semisimple near-ring R with DCC on right ideals is a direct sum of minimal right ideals which are right R-groups of type-g , 2 f0;1;2g. Moreover, a nite right g2-primitive near-ring R with eRe a non-ring is a near-ring of matrices over a near-eld (which is isomorphic to eRe), where e is a right g2-primitive idempotent in R.
Microcell electrochemical technique in seawater applied on dissimilar joints of aluminum alloy and carbon steel obtained by friction stir welding

Over the past twenty years, friction-welding technology, in particular the Friction Stir Welding (FSW) has become a potential industrial welding method that has been used efficiently for combining similar and dissimilar materials. The latter solution is very interesting in ship-building, where the use of aluminum in the superstructures allows lowering of the center of gravity for a better stability of the ship. In making dissimilar joints, the FSW technique is more cost and time efficient, allows to weld various sheet thickness with different joint configurations. In this work a coupled mechanical, metallurgical and electrochemical characterizations of dissimilar joints of aluminum alloy AW5083 and carbon steel S355 obtained by butt joint welding (FSW) were performed. The results showed that during the FSW process several well-defined microstructural zones are generated, that affected the mechanical performances and the corrosion behavior of the weld.
A Survey on System Configurations of Integrated Sensing and Communication (ISAC) Systems

Integrated sensing and communication (ISAC) is one of the promising technologies for future wireless communication systems. The ISAC is expected to mitigate the interference between the communication and radar systems and the scarce frequency band resources problem. By integrating two heterogeneous systems, namely a radar system and a communication system, the hardware cost can be reduced compared to implementing the individual systems. In this study, the ISAC systems are surveyed and categorized by the system configurations: ISAC, sensing target, and communication users. With the related work, each case is discussed. Further recent work on ISAC is then introduced with the current challenges.
Inception-based Deep Learning Architecture for 3D Point Cloud Completion

3D point clouds are a simple and compact data format that represents the surface geometry of 3D objects. The output of the data acquisition process often yields incomplete shapes. Hence, it is crucial to infer the missing regions of 3D objects from incomplete ones for many real-world applications. By leveraging a framework of 3D point cloud completion architectures, the proposed inception module is an intermediate layer that aims to extract the hierarchical features, recognize the fine-grained details of point clouds and avoid overfitting. We conduct comprehensive experiments on three state-of-the-art datasets: ShapeNet-55, ShapeNet-34, and PCN. The experimental results demonstrate that the enhanced architectures outperform the state-of-the-art point cloud completion methods.
Investigating the Interplay Between Self-Reported and Bio-Behavioral Measures of Stress: A Pilot Study of Civilian Job Interviews with Military Veterans

Transitioning from the military to the civilian lifestyle, especially for military veterans who decide to pursue careers in the civilian workforce, is often a difficult experience. The job interview, a task in which the interviewees meet and discuss their skills and career goals with strangers in a position of authority, is the first step of assimilation into the civilian workplace, which might cause them to experience nervousness or anxiety. This feeling of excessive stress may compromise the interviewee's performance, therefore potentially impeding their successful transition to the workforce. Intelligent interview training technologies would benefit from automated stress detection systems that could assist interviewees in better understanding causes and antecedents of stressors during their interaction with the interviewer. This paper examines self-reported and bio-behavioral measures of stress experienced during mock job interviews conducted with 24 U.S. military veterans. Self-reported measures were captured via a global measure of stress reported by the participant at the conclusion of the interview, and a continuous moment-to-moment annotation of stress resulting from the retrospective inspection of the interview video recording. Bio-behavioral indices of stress include physiological reactivity measures captured via electrodermal activity and electrocardiogram signals, as well as acoustic measures extracted from speech. Results indicate that physiological reactivity measures exhibit moderate-to-strong correlation with self-reported measures of stress, and can be thus used to estimate the self-reported stress measures. Augmenting the feature space with demographic and psychological traits can further improve the accurate detection of stress during the interviews.
Deep Reinforcement Learning Based Cooperative Retransmission in Downlink NOMA Systems

Non-orthogonal multiple access(NOMA) can be considered a candidate for next-generation communications networks due to its high spectral efficiency and ability to reduce latency by superposing signals of users in a user-pair at the same time and same frequency. NOMA has a key feature that the near user(NU) has prior information about the messages of the far user(FU) in the same user pair. It means that NU can be used for retransmission when the FU requests retransmission. However, retransmission from the NU can cause high power consumption which is not suitable for mobile stations or IoT devices. Therefore, adaptive power allocation is studied to reduce power consumption in this paper. Deep reinforcement learning(DRL) is used to decide the power for retransmission from the NU. Simulation results showed that the proposed DRL scheme can reduce power allocation and improve better reliability over the conventional retransmission scheme.
Design and Implementation of UWB MIMO Microstrip Antenna for Sub-6GHz 5G Communication

In this paper, we develop a 4-element MIMO an-tenna, which is in a position to sweeping the entire UWB spec-trum of 3.1 - 10.6 GHz. We started by designing an antenna using a singular Printed Circular Disc Monopole (PCDM) antenna element upon a 1.6 mm of thick FR-4 dielectric substrate with $\varepsilon_{\mathrm{r}}=4.4$, which is both effective and relatively low-cost substrate. We have conducted parametric analysis of inserting slots in the radiating patch along with ground plane to optimize the antenna performance. The dimensions of final designed structure is 104 x 104 mm2. We utilized a 3D electromagnetic high frequency software named ANSYS HFSS to design the structure. In this UWB frequency band, we have calculated the diversity performance parameters like ECC (Envelope Correlation Coefficient), DG (Diversity Gain) and Channel Capacity Loss (CCL), which are found to be way better than the suitable limits. After fabrication of the antenna, we have measured parameters by using VNA (Vector Network Analyzer) to verify the performance. The simulated and the measured performance parameters are found to be in agreement.
A study on deep learning-based classification for Pneumonia detection

In this paper, we investigate the various aspects of methodologies in deep learning-based pneumonia classification using chest x-ray images. As widely known, selecting appropriate hyper-parameters is essential for increasing the classification performance in convolution neural networks(CNN). We experiment with various hyper-parameters, including the number of layers, optimizer, learning rate, and momentum factor for diagnosing pneumonia using CNN. In addition, we test different CNN models and augmentation methods for chest x-ray diagnosing. Experimental results show that the proposed non-rigid transform based on augmentation increases classification accuracy by up to 5%.
Analysis of Quantum Search Algorithm for Weighted Solutions: Simple Case

Quantum search algorithm proposed by Grover(Grover's algorithm) finds target data with equal probability for all target data. Y. Seo et al. [IEEE Access, vol. 10, pp.16209-16224 (2022)] proposed the quantum search algorithm for weighted solutions to discriminate the importance of target data. In the simple case, we represent the analysis of the algorithm. The low bound of the number of applying Grover operator $t$ is described using amplitude expression. If we choose an appropriate $t$ value, we provide the maximum ratio of the amplitude of high and low-importance target data. In addition, we arrange the maximum probability of high-importance target data using the bound.
Portable Device for Measuring Lighting Characteristics in Working Spaces

Knowing the characteristics of the lighting system in a working place is important as numerous studies have shown that the quality of light can influence the mood and productivity of people working in those spaces. This research proposes the creation of a portable device that can be used to measure characteristics of lighting systems in working places. The device is based on TCS34725 light sensors and is used to measure, in an automatic way, different light parameters. The paper presents an initial prototype of the device which includes a microcontroller board, light sensors, a step motor, a step motor drive and a Bluetooth (BT) module. Using the BT connection, the device can be controlled remotely using a smartphone, the data can be transmitted and recorded on the smartphone or can be sent to the cloud for storing and processing.
Free-viewpoint based high-resolution 3D data acquisition and generation technology for the construction and diffusion of digital cultural assets

3D scanning technology and photogrammetry technology are representative technologies for generating three-dimensional data, and are widely used in various fields. However, they are still insufficient to support high-precision 3D meshes and high-resolution textures. In particular, 3D data with high-precision and high-resolution are important in fields that require minimal error from the original, such as cultural heritage preservation and restoration. Therefore, in this paper, we propose free-viewpoint based high-resolution 3D data acquisition and generation technology that can overcome the shortcomings of these technologies and provide their advantages. We also present the prototypes of the hardware acquisition equipment and guide viewer and some experiment results of this technique.
Stratified spaces and synthetic Ricci curvature bounds

We prove that a compact stratied space satises the Riemannian curvature-dimension condition RCD(K, N) if and only if its Ricci tensor is bounded below by K ∈ R on the regular set, the cone angle along the stratum of codimension two is smaller than or equal to 2π and its dimension is at most equal to N. This gives a new wide class of geometric examples of metric measure spaces satisfying the RCD(K, N) curvature-dimension condition, including for instance spherical suspensions, orbifolds, Kahler-Einstein manifolds with a divisor, Einstein manifolds with conical singularities along a curve. We also obtain new analytic and geometric results on stratied spaces, such as Bishop-Gromov volume inequality, Laplacian comparison, Levy-Gromov isoperimetric inequality.