esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods  
### FDTD Simulations  
We performed fully-dispersive three-dimensional FDTD simulations using our home-made simulator NANOCPP [24; 25; 26; 27; 28; 29]. In our simulations, the computational domain was organized as follows: the z-aligned nanodisks were placed at the centre of a \(2\,\upmu\mathrm{m}\times 2\,\upmu\mathrm{m}\times 1\,\upmu\mathrm{m}\) box, with uniaxial perfectly matched layer (UPML) boundary conditions emulating an open system [30]. The numerical resolution was set as \(\Delta x=2\,\mathrm{nm}\), corresponding to 81 points per internal wavelength at the anapole frequency \(\lambda_{\mathrm{an}}\). The system was illuminated by plane wave, implemented according to the total-field scattered-field (TFSF) formalism [31]. The scattering cross-section (Figure 1b) was computed by integrating the Poynting vector along a three-dimensional surface surrounding the objects and entirely placed in the scattered-field region of the TFSF. In order to characterize the anapole coupling among distinct nanoparticles, we included one of the resonators in the total-field region of the TFSF, while the other resonators were placed in the scattered-field region.  
The following are available online at [[http://www.mdpi.com/2076-3417/7/6/542/s1](http://www.mdpi.com/2076-3417/7/6/542/s1)]([http://www.mdpi.com/2076-3417/7/6/542/s1](http://www.mdpi.com/2076-3417/7/6/542/s1)), Figure S1: Scattered field from an isolated anapole state.  
This research is supported by KAUST (Award No. OSR-2016-CRG5-2995). For the computer time, we have used the resources of the KAUST Supercomputing Laboratory and the Redragon cluster of the PRIMALIGHT group.  
[PERSON] and [PERSON] performed first principle parallel simulations and analytical modelling. [PERSON] supervised the research. All authors contributed to the analysis of data. All authors contributed equally to manuscript preparation.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
FDTD Finite-Differences Time-Domain DOS Density Of States UPML Uniaxial Perfectly Matched Layer TFSF Total-Field Scattered-field TM Transverse Magnetic TE Transverse Electric PEC Perfect Electric Conductor PMC Perfect Magnetic Conductor NSO Near-field scanning optical microscopy
# Hydrochemical Characteristics and Suitability Assessment of Groundwater Quality for Irrigation
## 2 Materials and Methods
### Collection of Water Samples and Data Analysis  
Data on physical and chemical parameters were obtained from the Hydrological Yearbook of water quality by the Serbian Environmental Protection Agency (SEPA) [18] for the period from 2011-2020. According to this publication, sampling and testing of groundwater quality, is performed throughout each year. The existing network of monitoring locations was used, as previously defined by SEPA. The study area and the existing three monitoring locations were selected because of their importance in future projects that plan to expand irrigation in this region.  
The basic parameters of water quality such as total dissolved salts, electrical conductivity, as well as concentrations of cations and anions were the basis for statistical analysis of the physical and chemical parameters that were performed.  
In order to apply certain classifications for assessing the quality of groundwater for irrigation, it is necessary to carry out a detailed analysis of the required parameters. Parameters included in this research are electrical conductivity (EC), total dissolved salts (TDS), pH values, concentrations of cations-sodium (Na\({}^{+}\)), calcium (Ca\({}^{2+}\)), magnesium (Mg\({}^{2+}\)) and potassium (K\({}^{+}\)) and anions-bicarbonate (HCO\({}_{3}\)\({}^{-}\)), chloride (Cl\({}^{-}\)), sulfate (SO\({}_{4}\)\({}^{2-}\)), and nitrate (NO\({}_{3}\)\({}^{-}\)). To determine the reliability of the major cations and anions, it is usual to calculate the ion balance error (IBE). It is proposed that this value should be less than \(\pm\)5% to consider an analysis valid [19]. The standard methods used are shown in Table 1.  
Figure 1: The location of Serbia, the Srem region, and groundwater monitoring wells.  
Differences between parameters of irrigation groundwater, locality, years, and their mutual interactions were determined using Factorial ANOVA and \(t\)-test. After that, the main components that separate the examined years in relation to the water parameters were further separated by PCA analysis. In order to determine the connection between the parameters of irrigation groundwater, a cluster analysis was performed. All statistical analyses were performed using STATISTICA 13.2 software (Dell(tm) Statistic(tm) 13.2 University License).
## 2 Proposed Methods
### 3D Convolutional Neural Network for Spatiotemporal Encoding  
Typically, two-dimensional (2D) CNNs use 2D convolution blocks to process static images [22]. Similarly, the 3D convolution (Conv3D) block applies 3D filters to 3D EEG cuboid representations of each frequency band, and these filters move in the \(x\), \(y\), and \(z\) directions to compute low-level feature representations of output shapes as 3D volume spaces. The 3 DConv block directly extracts spatiotemporal relationships among the multi-channel EEG feature sequences from a 3D cuboid representation (\(C^{b}\)), comprising PSD (\(X^{b}\)) in each specific frequency band, where \(b\in\{\theta\), \(\alpha\), \(\beta\), \(\gamma\}\) denotes one of the four frequency bands. In other words, 3 DConvB can encode 3D spatiotemporal information for all multi-channel electrodes in each frequency band \(b\) as \(F^{b}=3\text{DConvB}\left(C^{b}\right)\) to reflect spatiotemporal electrode variations in different brain regions, while considering the strong correlations between different electrodes. Here, \(F^{b}\in R^{p\times d^{b}}\), where \(p\) is the number of convolutional channels, and \(d^{b}\) represents the dimension of the EEG feature vector in the frequency band \(b\), which matches the number of electrodes.  
The proposed method includes a 3 DConv block with three Conv3D layers and three scaled exponential linear units (SELUs) [23] for EEG-based stress recognition. Each Conv3D layer comprises a Conv3D filter with a kernel size of \(3\times 3\times 3\), which exhibits significant potential to detect stress features in spatiotemporal information. The first layer includes 64 kernels, and the number of kernels is doubled in the subsequent layer. After the convolution operation, the SELU function is used as the activation function instead of the rectified linear unit (ReLU) function. When ReLU is applied to the hidden layer, if the value of the neuron is 0 or negative, the neuron does not fire; thus, only a few neurons are activated. This results in a dying ReLU problem, because these neuron weights and biases are not updated during backpropagation. By contrast, SELU induces self-normalization properties like variance stabilization to avoid exploding and vanishing gradients, resulting in better performance than that achieved by applying ReLU [24]. In Figure 4, the shapes of SELU and ReLU functions are compared.
# An Elliptical-Shaped Dual-Band UWB Notch Antenna for Wireless Applications
## 2 UWB Antenna Design Approach  
The evolution of the UWB antenna is depicted in Figure 1, while the S\({}_{11}\) characteristics are depicted in Figure 2. In the first stage, an ellipse radiating patch and a rectangle ground plane are created. However, the S\({}_{11}\) value is much higher than \(-10\) dB.  
As a result, the ground plane was further modified in stage 2 to become a partly rectangular ground plane. The S\({}_{11}\) value decreases as a result. In stage 3, a partially rectangular ground plane is slotted to improve S\({}_{11}\). The rectangular slot on the ground plane significantly brings the S\({}_{11}\) value below \(-10\) dB, but it does not cover the UWB range. As a result, in stage 4 of Figure 1, more modifications are made to obtain UWB characteristics in the antenna. Two corner cuts are etched on the rectangular slot on the ground plane. It gives the impedance bandwidth from 2.5-11 GHz, which covers the UWB range.
## 3 Experimental Setup
### Assessment Parameters
#### 3.8.1 Useful Heat Gain  
As mentioned above, the part of the absorbed solar irradiance that is not converted into electricity by the \(PV\) system is completely dissipated as heat (\(Q_{th}\)). But not all of this heat is used to increase the performance of the system, as there are heat losses.  
The useful heat, \(Q_{u}\), can be written as follows [59]:  
\[Q_{u}=m\cdot C_{p}\cdot(T_{out}-T_{in}) \tag{12}\]  
where m is the fluid mass flow rate (kg/s), \(Cp\) is the heat capacity of the water (J/kg \({}^{\circ}\)C) evaluated at the average temperature, \(T_{in}\) is the temperature of the inlet fluid to the secondary system (\({}^{\circ}\)C), and \(T_{out}\) is the temperature of the outlet fluid to the secondary system (\({}^{\circ}\)C).  
This study evaluates the useful heat gain (\(UHG\)) due to the longitudinal tilt configuration (\(C_{S}\)) of the secondary system with respect to the base configuration (\(C_{B}\)). To do so, the difference between the useful heat with the configuration studied and the base configuration must be calculated. These values are % useful heat loss, as concerns the base configuration, i.e.,  
\[UHG=\frac{Q_{uC_{S}}-Q_{uC_{B}}}{Q_{uC_{B}}}\cdot 100 \tag{13}\]
# Experimental Approaches in Delineating mTOR Signaling
## 1 Introduction  
As one of the most abundant gene families in human, protein kinases tightly control cell signaling and cell function through protein phosphorylation. There are 634 protein kinases encoded in human genome to date, and \(\sim\)85% kinases are observed dysregulated in human disorders [1]. However, only 49 small molecule kinase inhibitors have been approved for treating human diseases [2], urging for more in-depth investigations on the rest 77% possibly druggable kinome. Among all protein kinases, mTOR (mechanistic target of rapamycin, previously known as mammalian target of rapamycin) has drawn extensive attention. This is due to its indispensable roles in regulating key cell function such as proliferation, metabolism, autophagy, ageing, and others, and its ability to sense environmental changes including nutrients and growth factors to adjust cell physiology. Dysregulation of mTOR signaling is observed in solid tumors, epilepsy, obesity, and diabetes. Hyperactivation of mTOR signaling is believed to drive tumorigenesis supported by both clinical data and rodent models [3; 4; 5; 6]. From cloning of TOR genes by the [PERSON] group in 1993 [7] to currently advanced understanding of mTOR biology, multiple experimental techniques/approaches have been applied to identify key mTORpathway components to delineate mTOR signaling. In this review, we focus on major and distinct experimental approaches used in early work that led to identification and characterization of key mTOR signaling pathway members. The journey of mTOR research starts from isolation of rapamycin as an immune suppressant and mTOR1 inhibitor, and is greatly expanded by traditional genetic approaches and biochemical protein purification-mediated studies. Due to advances of technology, more recent studies tend to rely on large-scale nonbiased screening approaches for discoveries. In addition, immunofluorescence, bioinformatic, and hypothesis-driven approaches or a combination of different techniques are indispensable to advance our understanding of mTOR biology, regulation, and function. We hope that these experimental approaches and inspiring logics behind in this review will benefit future research related to mTOR and other protein kinases.
###### Abstract  
The need to modernize existing district heating systems is due to increased requirements for their flexibility, energy efficiency, and environmental friendliness. The technical policy on district heating pursued in different countries centers on the listed goals and takes account of historical, climatic, and regional features of the resource, technology, and economic availability of various thermal energy sources. This study aims to analyze methods designed to improve the flexibility, energy efficiency, and environmental friendliness of district heating systems. The focus of the study is district heating system, which provides heating and hot water supply to consumers and consists of various types of thermal energy sources. The work shows the possibility for the heating system to transition from the third generation to the fourth one, which differ in their level of intellectualization. The establishment of an intelligent control system will ensure the interaction of various heat sources, but this is a separate strand of research. In this study, a model and a methodology were developed to optimize the structure of thermal energy sources and their operating conditions when covering the heat load curve of a territory with a predominance of household consumers. Gas-reciprocating and gas-turbine cogeneration plants are considered as the main thermal energy sources, whose efficiency is boosted through their joint operation with electric boilers, thermal energy storage systems, low-grade heat sources, and absorption chillers. The primary emphasis of the study is on the assessment of the environmental benefit to be gained by using cogeneration plants as a factor of enhancing the investment appeal of the district heating systems. The findings suggest that the transition of district heating systems to the next generation is impossible without changing the institutional environment, strengthening the role of active consumers, and introducing intelligent control for district heating systems.  
district heating system; flexibility; energy efficiency; environmental friendliness; cogeneration plant; residential consumer; electric boiler; heat pump; thermal energy storage; intelligent control system +
Footnote †: journal: Applied sciences
# From Modeling to Failure Prognosis of Permanent Magnet Synchronous Machine
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**SPMM Parameters** & **Values** \\ \hline Machine active length \(L_{\mathrm{S}}\) & 204.79 mm \\ Stator core thickness \(e_{\mathrm{e}s}\) & 7.22 mm \\ Stator outer radius & 117.78 mm \\ Stator inner radius \(R_{\mathrm{s}}\) & 61.421 mm \\ Rotor outer radius & 54.92 mm \\ Rotor inner radius & 47.71 mm \\ Nb of slots \(N_{\mathrm{s}}\) & 36 \\ Slot opening \(\theta_{\mathrm{s}}\) & 4\({}^{\circ}\) \\ Radial tooth length \(h_{\mathrm{f}}\) & 49.16 mm \\ Area of slot & 281.05 mm\({}^{2}\) \\ Slot filling factor & 0.3 \\ Area of winding turn & 3.666 mm\({}^{2}\) \\ Nb of turns per phase & 13 \\ Air-gap thickness g & 2 mm \\ Radial PM length \(e_{m}\) & 4.5 mm \\ Nb of pole pairs p & 6 \\ Magnet opening angle \(\theta_{PM}\) & 20\({}^{\circ}\)/30\({}^{\circ}\) \\ Magnet remanence \(B_{r}\) & 1.2 T \\ Magnet coercive force Hc & 955 kA/m \\ PM relative permeability \(\mu_{r}\) & 1.05 \\ Material properties & Values \\ Young modulus of steel & 210 GN/m\({}^{2}\) \\ Mass density of steel & 7650 kg/m\({}^{3}\) \\ Poisson ratio of steel & 0.3 \\ Young modulus of copper & 9.4 GN/m\({}^{2}\) \\ Mass density of copper & 8953 kg/m\({}^{3}\) \\ Poisson ratio of copper & 0.35 \\ Conductivity of copper & 4.257 (\(\Omega\).m)\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Machine’s specifications and parameters. Electromagnetic, thermal and vibration aspects are all important to accurately model the electrical machine. Hence, an electromagnetic, thermal and vibration finite element model is built at normal operation of the machine and when different types of faults are integrated. The measured collected data will be torque, temperature and vibration at normal operation and in the case of fault. This will be presented in Section 2 where those data signals will be illustrated, and the corresponding feature that will help for fault detection will be mentioned. Those selected data features will be inputs of the HMM. In Section 3, the prognostic strategy using HMM and the technical concept will be illustrated. In Section 4, additional information about the permanent magnet machine's aging is presented. In Section 5, a strategy for the calculation of the remaining useful life of the machine in the case of turn-to-turn short circuit in one slot is developed.
## 4 Experimental Results and Analysis  
The proposed hybrid approach was compared against conventional GPU [8; 9] and hardware [20] architectures. The hardware implementation of the syndrome generator was synthesized for 28-nm technology, and it achieved an operational frequency of 1 GHz. The setup used for the GPU implementation is given in Table 1. In our experiments, we analyzed the performance and the area consumed for different BCH code sizes. We have used the finite field dimension of \(m=12,13,14,15\) in our comparison, which corresponds to block sizes of \(256,512,1024,2048\) bytes. Furthermore, we have analyzed the results for different bit errors \((t=2,...,40)\) in our experiments.
# Performance Evaluation of VANET Routing Protocols in Madinah City
## 4 Simulations  
### Simulation Parameters  
Table 2 shows the simulation parameters used in our OMNet++ simulation. For radio medium, we select IEEE 802.11 Dimensional Radio with a radio band of 5.9 GHz. The number of channels for each node (vehicle) is three, which represents the number of channels within the band. The transmission power for the vehicles is set to 20 mW, while the bandwidth is 10 MHz. As mentioned earlier, we selected AODV, DYMO, and DSDV as the three routing protocols in our simulations. The simulation can be visualized on OMNeT++, as seen in Figure 5.  
Table 3 details the list of parameters and their respective values for the traffic model simulations. Four traffic densities were considered with their respective arrival rates departing from a simulation time equal to 5 s. The fringe factor refers to the probability of the routes generated starting or ending at the edges of the network given to the SUMO route generator. All vehicles were of passenger class with human-driven vehicles implementing the IDM car-following model. Each traffic model was generated three times per distribution with fixed seeds, respectively, and repeated for each of the five distributions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline \hline \
\begin{tabular}{c} Traffic Densities \\ Traffic Distributions \\ Fringe Factor \\ Vehicle Class \\ Car-Following Model \\ Custom Trips \\ Custom Trip Lengths \\ \end{tabular} &
\begin{tabular}{c} 500, 1000, 2500, 5000 \\ 0:100, 25:75, 50:50, 75:25, 100:0 \\ 100 \\ Passenger \\ 100 \\ 100 \\ 100 \\ 100 \\ 100 \\ 100 \\ 100  
Drivers in SUMO may react to environmental changes at specific intervals measured in simulation time, for example, by changing the velocity of the vehicle or changing lanes. The reaction time of the driver depends on the car-following model prescribed to the vehicles in the traffic distribution configuration. The frequency of the drivers' decision-making can be decoupled from the simulation time using the _actionStepLength_ parameter, as presented in Table 4.  
By assigning 1 s, the drivers evaluate their surroundings and make a decision every simulation time of 1 s. The parameter _minGap_ defines the distance maintained by the driver at a full stop or'standing' situations, such as congestions and traffic light stops. The table further describes the way the IDM car-following model works through the use of parameters such as the acceleration, deceleration, and emergency deceleration rates, predefined for passenger vehicles in this instance. _Tau_ and _delta_ are tuning parameters. The former is the minimum gap measured in the time unit that the driver will try to maintain while behind another vehicle, and the latter is the recommended value of the acceleration exponent. The acceleration exponent delta is part of the IDM differential equations to calculate the approaching rate for a given vehicle. Finally, the _stepping_ parameter defines the internal step length in seconds for the calculation of the following speed.
## 3 Results and Discussions
### Results and Analysis of the Grape Segmentation Test
#### 3.1.2 Training Results  
Applying the trained model to the validation set, it can be seen from the actual segmentation effect that the model could effectively identify the target, and the boundary segmentation was accurate, distinguishing the grape surface from the other parts (as shown in Figure 9).  
Figure 9: Instance segmentation effect. **(a)** Original image. **(b)** Segmentation result.
# Multispectral Fusion Approach for Traffic Target Detection in Bad Weather
## 4 Procedure and Results
### Implementation Details  
Our experiments are based on the open source framework of Mxnet [25] with a Python interface, and the model introduced in Section 3. Fast RCNN and RPN modules are fine-tuned end-to-end with Pascal Voc_2007 and Pascal Voc_2013 [26]. In our experiment, the threshold of the intersection-over-union is set to 0.7 and the learning rate is set to 0.005 for the first 40 k iterations and 0.001 for the last 40 k iterations. In addition, the weight decay and momentum are set to 0.0005 and 0.9, respectively. Two pairs of infrared and visible images make up a batch. Other network hyper-parameters of our approach are the same as those in traditional faster RCNN. The infrared and visible images in our dataset have the same perspective, and there is no need for image registration. If two images have different perspectives, they should be registered before training. Our experiments were performed on a 64 bit Ubuntu 16.04 computer with CPU Intel(R) Core (TM) i7-6700K CPU@ 4.00 GHz and NVIDIA GeForce GTX 1050. During the training process, we use the feature map connection approach, as introduced in Section 3.