Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomass of autotrophic picoplankton in subtropical coastal lagoons: Is it relevant?

Biomass of autotrophic picoplankton in subtropical coastal lagoons: Is it relevant? The importance of the autotrophic picoplankton (APP, <2 μm) to the primary productivity of coastal lagoons has been recently recognized. However, the role of these organisms in highly dynamic systems such as coastal lagoons is not well understood. The Atlantic coast of Uruguay (34 ◦ 20 � -34 ◦ 54 � S; 53 ◦ 55 � -55 ◦ 00 � W) is characterized by the presence of a series of subtropical coastal lagoons (depth: 0.2-2.0 m) that differ in the dynamics of the interaction with the ocean, their salinity, morphometry, and anthropogenic impact. The relative contribution of eukaryotic and prokaryotic APP to the total phytoplankton biomass in five coastal lagoons was determined and its relation to the abiotic factors was analyzed. Phytoplankton was identified and quantified using flow cytometry and optical microscopy. Chlorophyll a (Chl-a) concentration was determined through seasonal samplings. The conductivity varied from limnic (< 0.3 mS/cm) to brackish conditions (36.6 mS/cm). The concentration of dissolved inorganic nitrogen and the DIN/SRP atomic ratio were generally low (ranging from undetectable to 107.2 μg/l, and from < 1 to 52.3 μg/l, respectively). The total Chl-a ranged from 0.7 to 18.3 μg/l from which the picoplanktonic fraction represented from 4.2 to 96.6 %. In general, the Chl-a picoplanktonic fraction was negatively correlated with the total concentration of Chl-a. The cytometric analysis of the picoplankton allowed the identification of six ataxonomic groups, indicating that picoeukaryote groups were dominant throughout the study, although picocyanobacteria were occasionally important. It was found that suspended solids and dissolved inorganic nitrogen were the most important factors related to the APP Chl-a contribution.
A Model-Based Fault Detection, Diagnosis and Repair for Autonomous Robotics systems

Autonomous robots comprise of several complex software and hardware components which interact with the environment to fulfill a certain task. Due to the non-determinism, inherent of the environment and complexity of the components one cannot expect that the robot will never show a fault. Instead one needs to deal with the occurrence of faults in the robotics system. As we focus on autonomous robots the robot should deal with faults in an automated fashion. In this paper, we present a model-based fault detection and diagnosis method with a simple but powerful method to repair faults. Using this method, the robot can detect and react to faults in a timely manner. Furthermore, no human intervention is necessary thus allowing the robot to be autonomous. As not every repair can be performed by the robot itself the system allows the robot also to inform the maintenance staff which repairs are necessary. Thus, this approach reduces the time for fault localization of the maintenance staff.
Blockchain: A Novel Approach for the Consensus Algorithm Using Condorcet Voting Procedure

The blockchain technology allows interested parties to access a common register, the update, and integrity of which are collectively managed in a decentralized manner by a network of actors. It is the consensus protocol that ensures a common and unambiguous update of transactions by creating blocks of transactions for which integrity, veracity, and consistency are guaranteed through geographically distributed nodes. Bitcoin, the first popular blockchain concept, introduced the Proof of Work consensus based on work validation, and has been extended to thousands of participants. Despite its success and its large use in other crypto-currencies, Proof of Work's disadvantages are a high latency, a low transaction rate, and a high energy expenditure, making it a less-than-perfect choice for many applications. In addition the validation of transactions is not carried out with a definite temporality. For Bitcoin, it takes on average 10 minutes to create a block. However, for certain use cases such as auctions or the exchange of energy, there is a need for this temporality. The purpose of this article is to propose a new type of consensus that is faster, less energy-consuming and that can be synchronized with a time reference. The core of the reflection is the use of the Condorcet voting mechanism to determine the miner.
Passive Autofocusing System for a Thermal Camera

Most of the Thermal (Infrared) cameras nowadays are equipped with a motorized lens for focusing a scene manually. The subjective nature of manual focusing makes it an inefficient and cumbersome process. In contrast, Autofocusing (AF) obtains the best focused image based on a quantitative measure with the benefits of convenience and intelligence. Various AF systems for visual cameras have been developed, but relatively less amount of work has been done for thermal imaging systems. This paper presents a Vision and Control based Autofocusing System (VCAFS) comprising: (1) an uncooled thermal camera with motorized lens, (2) a passive contrast-based focus measure, (3) a smoothing operator to avoid local extrema, and (4) two different lens motion controllers. Experimental results show the efficacy of the proposed system on live videos even when the scene and its depth are continuously changing.
Successive-Interference-Cancellation-Free NOMA for Indoor VLC: A Generalized Spatial Modulation Based Approach

Recently, visible light communication (VLC) has been regarded as one promising candidate to complement the conventional radio frequency communication in the future communication systems. In this paper, a novel successive-interference-cancellation-free non-orthogonal multiple access (NOMA) scheme is proposed for indoor VLC systems, based on the generalized spatial modulation technique. Different from the conventional NOMA schemes, information bits for users with diverse quality of service requirements are conveyed through different domains of the transmitted signals. Therefore, the intra-group interference among users is canceled and the successive interference cancellation is avoided to reduce the computational complexity at the receiver side. Theoretical analysis and simulation results are further provided to validate the superior performance of the proposed scheme over conventional orthogonal and non-orthogonal multiple access schemes.
Analysis of Non-Conformity in Flexible Pipe through Data Mining from Image Bank

To make possible the exploration of oil and natural gas in maritime waters, it is necessary to develop high technology to support the projects of drilling and exploration of submarine wells. One of the equipment that enables offshore exploration is the flexible pipes. Considering the high cost and complexity of the flexible pipe manufacturing process, the objective of this work is to analyze the effect of the occurrence of whitening stress identified as bleaching observed in the polymeric material known as Polyfluoride of Vinylidene (PVDF), which is used in the manufacture of inner liner layers in flexible pipes. The proposed methodology is to develop a prediction model based on classification, which evaluates the whitening bleaching by extracting knowledge from an image database. Through the proposed methodology, the most relevant parameters associated with the study problem that can define classification rules areidentified. As a result, some relationships between some characters, fruits of image data extraction and product characteristics were identified through decision trees. The conclusion is that these relationships provides support for decision making when peformed by a domain expert.
A Robust Performance Evaluation Metric for Extracted Building Boundaries From Remote Sensing Data

Various methods for automatic building extraction from remote sensing data including light detection and ranging (LiDAR) data have been proposed over the last two decades but a standard metric for evaluation of the extracted building boundary has not been found yet. An extracted building boundary from LiDAR data usually has a zigzag pattern with missing detail, which makes it hard to compare the boundary with its reference. The existing metrics do not consider the significant point (e.g., corner) correspondences, therefore, cannot identify individual extralap and underlap areas in the extracted boundary. This article proposes an evaluation metric for the extracted boundary based on a newly proposed robust corner correspondence algorithm that finds one-to-one true corner correspondences between the reference and extracted boundaries. Assuming a building has a rectilinear shape, corners and lines are first detected for the extracted boundary. Then, corner correspondences are obtained between the extracted and reference boundaries. Each corner has two corresponding lines on its two sides that ideally are perpendicular to each other. The corner correspondences are finally ranked based on their distance, angle, and parallelism of corresponding lines. The metric is defined as the average minimum distance $d_{\mathrm{avg}}$ from the extracted boundary points to their corresponding reference lines. Extralap and underlap areas are identified by comparing the point distances with $d_{\mathrm{avg}}$. In experiments, the proposed metric performs more realistic than the existing metrics and finds the individual extralap and underlap areas effectively.
Hardware Impairment-Aware Data Collection and Wireless Power Transfer using a MIMO Full-Duplex UAV

In this paper, we study a hardware impairment-aware data collection and wireless power transfer (WPT) framework using a multiple-input multiple-output (MIMO) full-duplex (FD) unmanned aerial vehicle (UAV). To enable high energy saving of ground users, we allow a MIMO FD UAV charge battery life time limited ground users while at the same time collect data from them in a flying-and-hovering mode. With the aim of practical implementation, we formulate an energy minimization problem by taking the UAV hardware impairments, its flying-and-hovering time, the number of uploaded data bits, and the energy harvesting causality into account. Due to non-convexity of the problem in terms of UAV trajectory and transmit beamforming for WPT, tracking the global optimality is quite challenging. Alternatively, we exploit a local optimal solution by implementing the proposed fixed-point search combining with successive convex approximation algorithm. In comparison to benchmark schemes, our results illustrate superior performances especially when the hardware impairment effects are taken into account. In addition, the proposed iterative algorithm exhibits fast convergence and fairly low computational complexity.
Effects of Resistance and Endurance Training on HIF-1α and VEGF in Heart Tissues of Pregnant Rats with Cadmium Toxicity

Background: Exposure to cadmium can cause cardiovascular disorders, including possible risks of angiogenesis abnormalities associated with Hypoxia Inducible Factor (HIF), in pregnancy period. Objectives: This study aimed to investigate the effects of resistance and endurance training on HIF-1α and vascular endothelial growth factor (VEGF) levels in heart tissue of pregnant rats with cadmium toxicity. Methods: In this study, 48 female rats were selected and divided into 6 groups of 8 rats, including (1) cadmium, (2) resistance training, (3) resistance training with cadmium consumption, (4) endurance training, (5) endurance training with cadmium consumption, and (6) control. Groups 2, 3, 4, and 5 performed three weeks of resistance and endurance training, five sessions per week. Also, groups 1, 3, and 5 received cadmium as cadmium chloride dissolved in water as 400 mg per liter through drinking water. To analyze the data, Kolmogorov-Smirnov, One-way ANOVA, and Tukey’s post hoc tests were used and P ≤ 0.05 was considered statistically significant. Results: Cadmium had a significant effect on the increase of HIF1-α and VEGF (P = 0.001); resistance training had a significant effect on the increase of HIF1-α (P = 0.03), resistance training (P = 0.02) and endurance training (P = 0.001) had a significant effect on the increase of VEGF. Also, resistance training with cadmium consumption as well as endurance training with cadmium consumption significantly decreased HIF1-α (P = 0.001) and VEGF (P = 0.001), compared with cadmium consumption. Conclusions: Cadmium appears to lead to an abnormal increase in HIF-1α and VEGF in the heart tissues of pregnant rats, which can stimulate abnormal angiogenesis of tumors; however, resistance and endurance training can reduce the severity of cadmium effects on abnormal angiogenesis of the heart tissue of pregnant rats.
Automatic Shape Control of Deformable Wires Based on Model-Free Visual Servoing

In this letter, we propose a novel approach to automatically control the 3D shape of deformable wires using robots. Our approach proposes a novel visual feature along with a novel shape servoing method to enable dual arm manipulation of deformable wires. The visual feature relies on a geometric B-spline model and the use of Sequential Importance Resampling (SIR) particle filtering to track the 3D deformed shape of a wire over time. The shape servoing method is an adaptive model-free method that iteratively updates the deformation Jacobian matrix using weighted least-squares minimization with sliding window and an eigenvalue-based confidence criterion. We performed several experiments on wires with different mechanical properties. The results show that our approach succeeded to control the 3D shape of various wires for many different desired deformations, while working at an interactive time. It has also been shown that the shape servoing method can be used to handle large deformations by subdividing the task in successive intermediary targets to reach. These promising results pave the way for automatic control of the 3D shapes of deformable wires in many fields such as catheter insertion in medicine or wire manipulation in industry.
Image Projection onto Flat LiDAR Point Cloud Surfaces to Create Dense and Smooth 3D Color Maps

This paper proposes an area-wise method to build aesthetically pleasing RGB-D data by projecting camera images onto LiDAR point clouds corrected by Graph SLAM. In particular, the focus is on projecting images to corresponding flat surfaces, extracted as plane equations by RANSAC. The newly created data boasts a camera-like view even in 3D due to its dense, yet smooth flat point clouds. However, since this method is only limited to planar surfaces, other 3D data points that could not be separated as planes had to suffer poor quality due to sparse and rough LiDAR point clouds.
Power Quality in Microgrids Including Supraharmonics: Issues, Standards, and Mitigations

A microgrid (MG) is a small-scale power system with a cluster of loads and distributed generators operating together through energy management software and devices that act as a single controllable entity with respect to the grid. MG has become a key research element in smart grid and distribution power systems. MG mainly contains different renewable energy sources (RESs) that use various technological advancements, such as power electronics-based technologies. However, it has an unstable output, thereby causing different types of power quality (PQ) events. As a result, standards and mitigation methods have been developed in recent years. To mitigate PQ issues due to MG integration, various methods and standards have been proposed over the last years. Although these individual methods are well documented, a comparative overview had not been introduced so far. Thus, this study aims to fill the gap by reviewing and comparing the prior-art PQ issues, solutions, and standards in MGs. We compare the main issues related to voltage sag, voltage swell, voltage and current harmonics, system unbalances, and fluctuations to ensure high-quality MG output power. The new technologies associated with MGs generate harmonics emission in the range of 2–150 kHz, thereby causing a new phenomenon, namely, supraharmonics (SH) emission, which is not sufficiently covered in the literature. Therefore, the characteristics, causes, consequences, and measurements of SH are highlighted and analyzed. The mitigation strategies, control, and devices of PQ issues are also discussed. Moreover, a comparison is conducted between the most popular devices used to mitigate the PQ issues in MG in terms of cost, rating, and different aspects of performance. This review study can strengthen the efforts toward the mitigation and standards development of PQ issues in MG applications, especially SH. Finally, some recommendations and suggestions to improve PQ of MG, including SH, are highlighted.
A Model-Driven Deep Learning Method for Normalized Min-Sum LDPC Decoding

With the applications of deep learning networks booming in physical layer communication, deep-learning-based channel decoding methods have become a research hotspot. However, the high complexity hinders the application of deep neural network (DNN) on long code. In this paper, we propose a model-driven deep learning method for normalized min-sum (NMS) low-density parity-check (LDPC) decoding. First, we propose a neural normalized min-sum (NNMS) LDPC decoding network. By unfolding the iterative decoding progress between checking nodes (CNs) and variable nodes (VNs) into a feedforward propagation network, we can make use of the advantages of both model-driven deep learning methods and conventional normalized min-sum (CNMS) LDPC decoding method. Second, considering that the NNMS decoder needs large number of multipliers, we propose a shared neural normalized min-sum (SNNMS) decoding network to reduce the number of correction factors. Experimental results show that the BER performance of the proposed NNMS decoder is 1.5dB better than the conventional LDPC decoder, using fewer iterations. Furthermore, the proposed SNNMS decoder outperforms the proposed NNMS decoder and reduces the computation complexity.
An address-matching algorithm for household-scale databases to enhance electricity demand characterization

This paper participates in the challenging data science opportunity offered by the growing number of databases made available to public institutions. It presents an innovative method to match household-scale databases using address information. The developed algorithm authorizes different matching qualities, depending on the reliability of the link between the paired elements. This work was carried out in collaboration with the French DSO Enedis, which provided valuable customer information that was matched with a national database describing dwellings. The matching algorithm performances are analyzed, and adjustments are proposed to improve the matching quality in urban, suburban and rural contexts. Lastly, two basic characterization analyses were made to highlight the potential of these consolidated databases.
Cooperative Detection for mmWave Radar-Communication Systems

Spectral co-existence of radar and communication (RadCom) systems causes mutual interference between the two systems, compromising both the data exchange and radar sensing capabilities. Focusing on the radars' detection performance, in this paper, we propose a novel cooperative detection technique in the context of millimeter-wave (mmWave) RadCom systems, under a constant false alarm constraint. Existing detection techniques assign a single base station (BS) operating in radar mode (RM) for the detection of a target. In contrast, our proposed technique performs joint target detection through multiple cooperative RM-enabled BSs (RBSs). Specifically, our proposed technique exploits the fusion of individual sensing information by a set of pre-selected cooperative RBSs, aiming at enhancing the detection performance. We consider three pre-selection policies based on: 1) the Euclidean distance, 2) the received signal power, and 3) a random selection. In addition, for the fusion of the sensing information, we consider three hard-decision combining rules, namely OR, Majority and AND rule. By using stochastic geometry tools, analytical expressions for the detection performance are expressed for each pre-selection policy and combining rule. Our results reveal that the proposed technique significantly improves the detection performance of the mmWave RadCom systems.
Information-Theoretic Evaluation of Orbital Angular Momentum Transmission

The capacity of wireless communications with Orbital Angular Momentum (OAM) based on maximum mutual information is investigated in this paper. The wireless communications with OAM can be divided into the OAM multiplexing and the OAM index modulation. In this paper, the system architecture of wireless communications with OAM is established. Afterwards, the capacity of wireless communications with OAM is analyzed based on the propagation scheme. Additionally, the capacity bound of wireless communications with OAM is also presented. Then, the mutual information results for the OAM multiplexing and the OAM index modulation are derived. Since the OAM index modulation is a specific modulation of wireless communications with OAM, the mutual information of the OAM index modulation is much lower than the capacity bound. It is demonstrated by numerical results that the maximum capacity is achieved when the OAM multiplexing technique is adopted. Moreover, it is also concluded that the OAM modes and bandwidth are symmetry for the channel capacity.
A Community Detection Method Based on Local Optimization in Social Networks

Social networks are a kind of communal structure composed of many nodes according to different kinds of relationships. Community detection can help people understand the topology of a network and identify meaningful clusters. A large variety of community detection algorithms have been proposed. However, many of these algorithms are suitable for unweighted networks since they ignore the social properties of the links between nodes. Meanwhile, their discovery results may lead to the emergence of huge communities. Therefore, a weighted method and local community stability for social networks based on LFM is proposed in this article. This WSLFM algorithm uses a weighted method based on the social attributes of the link and the degree of common neighboring nodes to update the fitness function. It also introduces the concept of stability to control the expansion of local communities. The proposed WSLFM algorithm was tested and proved to be effective on a synthetic network and a real-world network for finding smaller and more meaningful clusters.
Enhanced kernels for nonparametric identification of a class of nonlinear systems

This paper deals with nonparametric nonlinear system identification via Gaussian process regression. We show that, when the system has a particular structure, the kernel recently proposed in [1] for nonlinear system identification can be enhanced to improve the overall modeling performance. More specifically, we modify the definition of the kernel by allowing different orders for the exogenous and the autoregressive parts of the model. We also show that all the hyperparameters can be estimated by means of marginal likelihood optimization. Numerical results on two benchmark simulation examples illustrate the effectiveness of the proposed approach.