Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensitivity of Grapevine Soil–Water Balance to Rainfall Spatial Variability at Local Scale Level

In rainfed agriculture systems, rainfall water management (harvesting, storage, and efficient use) is a key issue. At local scale (i.e., from 100 m to 50 km), the impact of rainfall spatial and temporal variability on crop water availability is seldom addressed. In order to accurately depict the space and time variations of rainfall at local scale, a dense rain-gauges network composed of 45 rain-gauges has been deployed over 28-km2 area, in Burgundy vineyards (North-East France). Rainfall data collected by each rain gauge from 2014 to 2016 were used as input variables in the Lebon et al. (2003) grapevine water balance model. All other climate variables, vineyard, and soil parameters were kept the same for each simulation in order to capture the impact of the sole spatial variability of rainfall on vineyard water status. As rainfall dynamics impact on the vineyard depends on the soil water content, water balance was modeled considering soils with low (50 mm) and medium (150 mm) soil water-holding capacities, representative of the soils of the area. The impact of modeled soil water availability for grapevine was assessed using the water deficit stress index (WDSI), i.e., the relative stomatal conductance. Local rainfall variability throughout the vine vegetative period leads to large variations in WDSI; it varied up to 0.3 within the study area due to because of rainfall spatial variability. Using a set of 34 weather stations at mesoscale level over Burgundy (186 km from North to South), we showed that local rainfall might contribute to change in grapevine water status as large as 50% of the simulated regional water balance spatial variability. Our results indicate that local rainfall and its impacts on agricultural production are probably not sufficiently considered in farming systems, potentially leading to inaccurate water management (cover-crop, irrigation) due to sparse rainfall network.
Double-Head Clustering for Resilient VANETs

Scalability and the highly dynamic topology of Vehicular Ad Hoc Networks (VANETs) are the biggest challenges that slow the roll-out of such a promising technology. Adopting an effective VANET clustering algorithm can tackle these issues in addition to benefiting routing, security and media access management. In this paper, we propose a general-purpose resilient double-head clustering (DHC) algorithm for VANET. Our proposed approach is a mobility-based clustering algorithm that exploits the most relevant mobility metrics such as vehicle speed, position, and direction, in addition to other metrics related to the communication link quality such as the link expiration time (LET) and the signal-to-noise ratio (SNR). The proposed algorithm has enhanced performance and stability features, especially during the cluster maintenance phase, through a set of procedures developed to achieve these objectives. An extensive evaluation methodology is followed to validate DHC and compare its performance with another algorithm using different existing and newly proposed evaluation metrics. These metrics are analyzed under various mobility scenarios, vehicle densities, and radio channel models such as log-normal shadowing and two-ray ground loss with and without Nakagami-m fading model. The proposed algorithm DHC has proven its ability to be more stable and efficient under different simulation scenarios.
Connectivity of the Frontal Cortical Oscillatory Dynamics Underlying Inhibitory Control During a Go/No-Go Task as a Predictive Biomarker in Major Depression

Background Major depressive disorder (MDD) is characterized by core functional deficits in cognitive inhibition, which is crucial for emotion regulation. To assess the response to ruminative and negative mood states, it was hypothesized that MDD patients have prolonged disparities in the oscillatory dynamics of the frontal cortical regions across the life course of the disease. Method A “go/no-go” response inhibition paradigm was tested in 31 MDD patients and 19 age-matched healthy controls after magnetoencephalography (MEG) scanning. The use of minimum norm estimates (MNE) examined the changes of inhibitory control network which included the right inferior frontal gyrus (rIFG), pre-supplementary motor area (preSMA), and left primary motor cortex (lM1). The power spectrum (PS) within each node and the functional connectivity (FC) between nodes were compared between two groups. Furthermore, Pearson correlation was calculated to estimate the relationship between altered FC and clinical features. Result PS was significantly reduced in left motor and preSMA of MDD patients in both beta (13–30 Hz) and low gamma (30–50 Hz) bands. Compared to the HC group, the MDD group demonstrated higher connectivity between lM1 and preSMA in the beta band (t = 3.214, p = 0.002, FDR corrected) and showed reduced connectivity between preSMA and rIFG in the low gamma band (t = −2.612, p = 0.012, FDR corrected). The FC between lM1 and preSMA in the beta band was positively correlated with illness duration (r = 0.475, p = 0.005, FDR corrected), while the FC between preSMA and rIFG in the low gamma band was negatively correlated with illness duration (r = −0.509, p = 0.002, FDR corrected) and retardation factor scores (r = −0.288, p = 0.022, uncorrected). Conclusion In this study, a clinical neurophysiological signature of cognitive inhibition leading to sustained negative affect as well as functional non-recovery in MDD patients is highlighted. Duration of illness (DI) plays a key role in negative emotional processing, heighten rumination, impulsivity, and disinhibition.
A Novel Cubic Convolutional Neural Network for Hyperspectral Image Classification

Recently, the hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have developed rapidly with the advance of deep learning (DL) techniques. In order to more efficiently extract spatial and spectral features, we propose an end-to-end cubic CNN (Cubic-CNN) in this article. The proposed Cubic-CNN is a supervised DL framework that significantly improves classification accuracy and shortens training time. Specifically, Cubic-CNN employs the dimension reduction method combined with principal component analysis and 1-D convolution to remove redundant information from HSIs. Then, convolutions are performed on the planes in different directions of the feature cube data to fully extract spatial and spatial–spectral features and fuse the features from different dimensions. In addition, we performed batch normalization on the data cube after each convolutional layer to improve the performance of the network. Extensive experiments and analysis on standard datasets show that the proposed algorithm can outperform the existing state-of-the-art DL-based methods.
The Use of Factorization and Multimode Parametric Spectra in Estimating Frequency and Spectral Parameters of Signal

A multiplicative autoregressive model is constructed based on the linear prediction model. The expressions describing such a model are derived. A formula for the parametric spectral estimation of a random signal multiplicative model is given. Relations are obtained for the decomposition of the multimode power spectral density into simple spectral components. Examples of the decomposition of multimode spectra of random signals into simpler components are considered. Keywords — power spectral density, multiplicative autoregressive model, parametric spectral analysis, characteristic equation.
Improving Multirotor Landing Performance on Inclined Surfaces Using Reverse Thrust

Conventional multirotors are unable to land on inclined surfaces without specialized suspensions and adhesion devices. With the development of a bidirectional rotor, landing maneuvers could benefit from rapid thrust reversal, which would increase the landing envelope without involving the addition of heavy and complex landing gears or reduction of payload capacity. This letter presents a model designed to accurately simulate quadrotor landings, the behavior of their stiff landing gear, and the limitations of bidirectional rotors. The model was validated using experimental results on both low-friction and high-friction surfaces, and was then used to test multiple landing algorithms over a wide range of touchdown velocities and slope inclinations to explore the benefits of reverse thrust. It is shown that thrust reversal can nearly double the maximum inclination on which a quadrotor can land and can also allow high vertical velocity landings.
Improving Performance and Extending Lifetime of PMSMs via Advanced End-Winding Cooling

In the automotive field, high performance and high reliable electrical machines are required and an appropriate thermal management allows to fulfil such a demand. Thermal management is also crucial for enhancing the power density, while it can also be applied to preserve the winding insulation lifetime of the electrical motor. From this prospective, an advanced cooling method is presented, whose action focuses on the end-windings, since they are commonly identified as the machine hot-spot. The effectiveness of the proposed cooling method is experimentally proven and the benefits raising from its implementation are quantified in terms of both temperature reduction, as well as insulation lifetime extension. A permanent magnet synchronous machine, employing a conventional housing waterjacket is used as study-case.
Performance Analysis of UWB Communication Receiver in Multipath Environment Based on RAKE Receiver

The paper contains the results of bit error rate (BER) performance research of the Rake receiver in multipath channel. The BER performance comparison with the non-Rake correlation receiver is presented, the method of estimation of BER performance of multipath components for the choice of optimal UWB wireless system parameters such as data rate, modulation scheme, duty cycle, pulse shape and multipath components assigned to Rake receiver fingers is proposed. Binary Pulse Position Modulation is employed as a modulation scheme.
DNA Methylation Profile in Human Cord Blood Mononuclear Leukocytes From Term Neonates: Effects of Histological Chorioamnionitis

Background: Histological chorioamnionitis (HCA) is an infection/inflammation of fetal membranes and complicates 5.2–28.5% of all live births. Exposure to HCA can have long-term consequences including abnormal neurodevelopment and an increased risk for allergic disorders and asthma later in childhood. HCA may incite epigenetic changes, which have the potential to modulate both the immune and neurological systems as well as increase the risk of related disorders later in life. However, there is limited data on the impact of HCA on epigenetics, in particular DNA methylation, and changes to immune and neurological systems in full-term human neonates. Objective: To determine differential DNA methylation in cord blood mononuclear leukocytes from neonates exposed to HCA. Methods: Cord blood was collected from 10 term neonates (5 with HCA and 5 controls without HCA) and mononuclear leukocytes were isolated. Genome-wide DNA methylation screening was performed on Genomic DNA extracted from mononuclear leukocytes. Results: Mononuclear leukocytes from cord blood of HCA-exposed neonates showed differential DNA methylation of 68 probe sets compared to the control group (44 hypermethylated, 24 hypomethylated) with a p ≤ 0.0001. Several genes involved in immune modulation and nervous system development were found to be differentially methylated. Important canonical pathways as revealed by Ingenuity Pathway Analysis (IPA) were CREB Signaling in Neurons, FcγRIIB Signaling in B Lymphocytes, Cell Cycle: G1/S Checkpoint Regulation, Interleukin-1, 2, 3, 6, 8, 10, 17, and 17A signaling, p53 signaling, dopamine degradation, and serotonin degradation. The diseases and disorders picked up by IPA were nervous system development and function, neurological disease, respiratory disease, immune cell trafficking, inflammatory response, and immunological disease. Conclusions: HCA induces differential DNA methylation in cord blood mononuclear leukocytes. The differentially methylated genes may contribute to inflammatory, immunological and neurodevelopmental disorders in neonates exposed to HCA.
Estimation of UAV to UAV Performance as a Hotspot by Proposed Friss Model on Downlink AF Co-operative NOMA

the advent of unmanned aerial vehicles (UAVs) has brought about a new paradigm of development in different sectors of technology. One such area is the communication industry. Telecommunication operators are finding ways to improve coverage, capacity, and security. One alternative that can be applied to enhance the performance of the Wi-Fi hotspot is unmanned aerial vehicles. In many situations where the distance between a base station and mobile subscribers is very long, it is sometimes difficult to attain good coverage by mobile users. To this end, UAVs can be applied to support the transmission of signals to these users. In this paper, we study the following UAV relay scenarios: fixed-gain amplify-and-forward (AF) relay co-operative, the theoretical path loss by Friss method, use of QPSK modulation and outage probability. Simulation results demonstrate improvements in performance compared to the theoretical acknowledged type.
Modified Carriers Pulse Width Modulation for Cascaded H-bridge Inverters

Cascaded H-bridges has been a topic of interest in drives and grid inverter applications, as the topology has an inherent ability to produce output power with low distortion. The multilevel output voltage waveform produced by the topology can increase the apparent switching frequency in direct proportion to the number of cascaded cells. When identical H-bridge cells are used, the inverter is assumed to load each H-bridge symmetrically. The conventionally used pulse width modulation (PWM) methods, such as phase-shifted PWM and level-shifted PWM, suffer from either inferior line-to-line voltage or asymmetric loading of the H-bridges. This paper presents a carrier based PWM method for cascaded H-bridges, that provides symmetric load sharing for the H-bridge cells, H-bridge specific DC link voltage compensation, and high-quality line-to-line voltage waveform. The applicability of the modulation method is demonstrated with simulation of a photovoltaic cascaded H-bridge inverter.
Electromagnetic Response of 3D-printing Carbon-containing Matrix Structure at Sub-THz Frequency Range

In this paper the results of a research in the sub- THz range of the electromagnetic response of matrix structures based on a carbon-containing polymer obtained by 3D printing are presented. Frequency dependences of the transmission coefficient, complex permittivities of matrix structures samples at the frequency range 48-258 GHz are shown. A comparative analysis of the electromagnetic characteristics of the matrix structures with a solid material was carried out.
Effect of Eddy Currents on the Star-Point Voltage Dynamics of Synchronous Machines for Sensorless Operation

In the field of sensorless control for Synchronous Motors (SMs), machine magnetic anisotropy based techinques which exploit the star-point voltage to estimate the rotor position have been applied successfully. Although these techniques present a mathematical model to describe the star-point voltage dynamics, the influence of the eddy currents effects is usually neglected. The inclusion of these effects into the mathematical model can be exploited to improve the measurement techniques and therefore to reduce the position estimation error. Thus, the aim of this work is to improve the mathematical model of the star-point voltage dynamics by considering the presence of eddy currents. Therefore, in the first section of this work a new mathematical model is developed. Through experimental results, this new model is validated and compared with a star-point dynamics model proposed in literature.
Fuzzy Tuned PID Controller for Vibration Control of Agricultural Manipulator

Image-based phenotyping systems have evolved over the years, and become an integral part of crop and plant science research. Phenotyping systems provide great potential to deliver critical insights, than the conventional destructive field m ethods. S table i mage a cquisition a nd p rocessing i s very important to accurately determine the characteristics in general, which further becomes very challenging and non-trivial when mounted over an motor mechanised arm. To address the near associated problems, we investigate on the possibility of applying the Proportional–Integral–Derivative (PID) control algorithm to the present manual setup with an aim to reduce vibration. This study focused towards investigating the active control and stabilization of the external camera shake, that may be induced by the driving motor. Nonetheless, very few researchers have focused on application of control algorithms for agriculture related practices. We validate the active control, and justify the need for the same.Type-2 fuzzy logic is combined with the PID control for better effectiveness. The non-linearity associated with the system is compensated by the type-2 fuzzy logic. The results shows that the active control has been achieved, and the vibration is minimized.
Peak Frequency Tracking versus Outlier Rejection for Photoplethysmographic Heart Rate Sensing during Exercise

In this paper, we experimentally compare the performances between the peak frequency tracking technique (PFT) and outlier rejection technique (OLR) in a photoplethysmographic (PPG) heart rate (HR) sensing during exercises. Experimental results involving thirteen subjects show that the PFT outperforms the OLR for the forearm position of exerciser achieving the root mean square error (RMSE) of 10.62 beats per minute (bpm), whereas the former underperforms the latter for the waist position achieving the RMSE of 42.28 bpm.
A Probabilistic Finite State Automata-based Fault Detection Method for Traction Motor

Traction motor plays an important role in the reliable operation of high-speed train, so it is necessary to detect the fault of traction motor. The traction system of high-speed train is complex and the operating conditions are variable, resulting in different fault severity and fault type, which is difficult to detect. And, most of the fault detection methods are based on the time series of traction motor, and few are based on the symbol sequence. This paper presents a probabilistic finite state automata (PFSA)-based fault detection method for traction motor of high-speed trains. Three-phase current of traction motor is first converted into a symbol sequence via symbolic aggregate approximation (SAX). PFSA model is constructed to represent the symbol sequence, and state transitions in the model are described through D-Markov machine. The state transition counting matrix of PFSA model is used to detect the occurrence of faults. The proposed method is tested on a benchmark of the traction motor of a high-speed train. Through the simulation results, it is verified the effectiveness of the proposed method.
Summary of Findings

• There are a good number of waterbodies (793 covering 1633 6 bigha area) in the study area and majority of those belong to small & medium size category • Majority of small size waterbodies belong to single owner operator whereas majority of large waterbodies are mostly with jomt owner operator A considerable proportion of large size waterbodies are also with institutional owner operator • Among all types of waterbodies, large proportion (42%) belongs to the single owner operator • 77% of the total waterbodies are under culture and 23% are remamed uncultured within which 16% waterbodies are farmable and 7% are derelict Among the cultured waterbodies, only 2% are under well managed aquaculture i e semi-intensive aquaculture and rest 76% are under very poorly managed aquaculture • Ownership has been found to be an important determinant of the status of aquaculture in the existing waterbodies Pnvate and individually owned waterbodies have much better farming systems, than jointly owned and Khas waterbodies However, m some cases, waterbodies under institutional ownership used good farming practices
Impact of grid impedance and their resonance on the stability of single-phase PV-inverters in low voltage grids

This paper studies the behavior of grid-connected single-phase photovoltaic inverters in low voltage grids. The interaction of the inverter control, the grid-side filter and the power grid can lead to harmonic instabilities. The term is used, since its origin is not depending on the active or reactive power at fundamental frequency but an interaction of the inverter control with the impedance of the power grid at frequencies in the harmonic range. In power electronics, the power grid impedance is typically assumed as an RL- equivalent so that a critical RL- combination above which the inverter becomes instable can be calculated by using the Nyquist criterion. Neglecting resonances in a power grid can result in an erroneous assessment of stability based on the Nyquist criterion. This is of importance since recent field measurements indicate that the origin of harmonic instabilities of PV-inverters is primarily related to resonances in the grid impedance. This paper studies the behavior of a single-phase PV-inverter based on a simulation model for different impedance conditions of the power grid to illustrate the impact of the grid impedance and its resonances. The results are compared with laboratory measurements of commercially available PV-inverters.
A projected gradient method for αℓ 1 − βℓ 2 sparsity regularization

The non-convex α‖⋅‖ℓ1−β‖⋅‖ℓ2(α⩾β⩾0) regularization is a new approach for sparse recovery. A minimizer of the α‖⋅‖ℓ1−β‖⋅‖ℓ2 regularized function can be computed by applying the ST-(αℓ 1 − βℓ 2) algorithm which is similar to the classical iterative soft thresholding algorithm (ISTA). It is known that ISTA converges quite slowly, and a faster alternative to ISTA is the projected gradient (PG) method. However, the conventional PG method is limited to solve problems with the classical ℓ 1 sparsity regularization. In this paper, we present two accelerated alternatives to the ST-(αℓ 1 − βℓ 2) algorithm by extending the PG method to the non-convex α‖⋅‖ℓ1−β‖⋅‖ℓ2 sparsity regularization. Moreover, we discuss a strategy to determine the radius R of the ℓ 1-ball constraint by Morozov’s discrepancy principle. Numerical results are reported to illustrate the efficiency of the proposed approach.
Data-driven Sensor Fault Estimation for the Wind Turbine Systems

As the need for early fault detection increases day by day in large industries, the importance of a reliable fault diagnosis becomes more obvious. Moreover, sensors in industrial systems are prone to faults or malfunctions due to aging or accidents. Motivated by the above, in this study, a neural network sensor fault diagnosis approach is proposed and the stability and convergence of the algorithm are proven by using the robust estimation theorem and input-to-state stability (ISS). The proposed algorithm is applied to a wind turbine benchmark with 4.8 MW rated power. 10% to 30% of the sensor performance reduction is considered to illustrate the effective performance of the addressed algorithm.