esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Applications of Natural Fibers Reinforced Epoxy-Based Composites
### Automobile Applications
Depending upon the component and place of application (properties of composites needed such as impact, tensile, and compressive strength), NFRCs are used as components in automobiles. The hemp and kenaf fiber composites significantly increase the Young's modulus and tensile strength [177]. Therefore, hybrid composites of hemp and kenaf can be used as components subjected to high tensile strength and lesser impact strength. Examples are furniture, boardings, or holders for grinding discs. In contrast, the composites of the cotton fibers show high-impact strength but low-impact strength [172]. These composites can be used for the component subjected to high-impact stress but low-tensile strength, e.g., the car's interior parts and safety helmets. In a few cases, components with high-tensile and impact strength are required; the composites can be manufactured by hybridizing different natural fibers, such as the bast and cotton fiber. [PERSON] et al. [178] performed a comparative study between glass and hemp fiber-reinforced epoxy composites. They concluded that the hemp fiber epoxy composites could be used as the automobile component where high-impact strength and hardness are required. Automobile industries such as BMW, Mercedes, Toyota, Daimler, Audi, and Ford have shown interest in natural fiber composites [179]. They have used NFRCs for components such as door lining and panels in car interiors. Since 1991, Daimler-Benz has strived to replace glass fiber-reinforced composites with NFRCs [180]. Interior door linings, panels, upholstery, and seatback cushions in BMW 7 series automobiles use NFRCs for soundproofing [82]. In several Ford cars, components have been made of soya, rice, and wood composites [170]. According to Motive Industries of Calgary, an electric automobile with components made from hemp fiber at a peak speed of 90 km/h is touted as the world's most environmentally friendly car.
## 2 Modeling of Electric Field Concentration by Conducting Nanoparticles  
Electric field analysis is important when evaluating laser-induced nanoparticle fragmentation and growth. We modeled the behavior of multiple conducting nanoparticles exposed to an electric field. Variations in electric field strength caused by localized surface plasmon resonance (LSPR) coupling effect were analyzed according to the sizes of, particles and the distances between them. The simulation data aided interpretation of the experimental results. The electromagnetic field distribution caused by a laser in water was analyzed. The metallic nanoparticles were highly conductive and thus exhibited high current densities. Electromagnetic wave reduced its wave energy within nanoparticles and the electric field became concentrated around the conductors.  
The distribution of the electric field by the incident laser beam can be approximated by a plane wave when comparing the sizes of the nanoparticles and the Gaussian beam. A Y-directed incident electric field can be described as follows:  
\[E=E_{0}\hat{y} \tag{1}\]  
Here, \(E_{0}\) is the magnitude of the incident field and \(\hat{y}\) is the unit vector in the \(y\) direction. The electric field distribution \(E\) can be expressed as a function of the electric potential (\(\varphi\)) as:  
\[E=-\
abla\varphi \tag{2}\]  
To obtain this distribution, an analysis of the potential is required; this can be done using the following Laplace equation:  
\[\
abla^{2}\varphi=0 \tag{3}\]  
For nanoparticles smaller than the wavelength of the laser, a quasi-static approximation is acceptable. When an electric field irradiates a nanoparticle, the electron clouds of atoms oscillate against the field because of Coulomb attraction by the atomic nucleus. The vibrating plasmon frequency is governed by the electron density clouds and a shape of nanoparticles. This plasmon frequency can be determined from the metal dielectric constant. We used the Lorenz-Drude model to approximate the dielectric function [43]:  
\[\varepsilon=\varepsilon_{0}+\sum_{j=1}^{M}\frac{G_{j}\omega_{p}^{2}}{\omega_{ 0j}^{2}-\omega^{2}+i\Gamma_{j}\omega^{\prime}},\sigma=0 \tag{4}\]  
where \(\varepsilon\) is the dielectric constant, \(\varepsilon_{0}\) is the permittivity at infinite frequency, \(\omega\) is the frequency of the incident electric field, \(\omega_{p}\) is the plasmon frequency of the bulk metal, \(G_{j}\) is the oscillator strength, and \(\Gamma_{j}\) is the damping coefficient. To determine electric field distribution, we set boundary conditions for the governing equation above. The potential and electric displacement were continuous at the boundary of the particle [44]. In general,the electric field distribution of the above equation, which can be easily solved, is affected by the dipole field, including the particle dielectric function \(\varepsilon\).
## 4 Numerical Results and Discussion
### Temperature and Magnetic Field Dependence of the Specific Heat in CBCO  
In the context of Figure 3, curve 1 depicts the specific heat \(C_{p}\) of CBCO as a function of temperature \(T\) for zero magnetic field, \(h=0\), determined through the derivative \(C_{p}=d\langle H\rangle/dT\), where \(H\) represents the comprehensive Hamiltonian. Notably, a discernible  
Figure 2: Temperature dependence of the magnetization, \(M\), of CBCO for different magnetic field, \(h\), values: (1) 0, (2) 1 kOe.  
anomaly appears in the curve near the critical temperature (\(T_{C}\)) = 70 K, a phenomenon consistently observed in studies by [PERSON] et al. [3], [PERSON] et al. [18], and [PERSON] et al. [6]. Upon the imposition of an external magnetic field, \(h\), the specific heat, \(C_{p}\), increases. Moreover, for weak magnetic fields, the peak becomes smaller, wider, and shifts to higher temperature values, whereas for strong magnetic fields, it disappears, as depicted in Figure 3, curves 2 and 3.
## 2 Materials and Methods
### Monte Carlo Simulations  
All \(MC\) simulations of radiation transport were performed using the GATE/Geant4 code, version 8.1 [29], on a desktop computer equipped with an Intel Core i7 Extreme Edition 8 Core Processor and 16 GB of Random Access Memory (RAM). The simulations used the nested sampling algorithm for volume parametrization and the emstandart-opt3 physics model, which simulates all electromagnetic processes, including bremsstrahlung. The range cut was defined as a production threshold for secondary particles, with an R_cut of 0.1 mm. For \(LS\
eq 0\), \(10^{9}\) primaries were used, whereas \(10^{10}\) primaries were used for \(LS=0\%\).  
The simulation input file for the Reference phantom included activity and density map distributions in MetaImage format, a file with tissue composition information, and \"macro\" files with all the simulation parameters (e.g., geometry definitions, output file creations, number of primaries, decay information, etc.). The density map for the lungs was obtained through the calibration curve of the CT image, whereas the ICRU lung tissue composition [21; 22] was assigned to all lung voxels based on user-defined HU ranges to identify the tissue class. Dosimetric calculations using \(MC\) simulations were performed for all the activity maps defined for the Reference phantom (see Section 2.1).  
A dedicated \(MC\) simulation was also performed to calculate a new VSV kernel for the lungs, assuming the ICRU lung tissue [21; 22; 28] (density of 0.296 g/cm\({}^{3}\)) as a medium. The simulation involved a uniform cube with a 600 mm side, a source voxel at its center, and 2.21 mm cubic voxels. The VSV kernel was validated by comparing the \(\overline{AD}\) to lungs obtained by convolution with the value obtained by a direct \(MC\) simulation. For this comparison, the reference phantom had activity only in the lungs, \(LS=10\%\), and a uniform lung density of 0.296 g/cm\({}^{3}\).
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 5 Results and Discussion
### Performance of Proposed Approach Using Real-Time Dataset
#### 5.5.1 Dataset Description
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Feature** & **Attribute** & **Domain** & **Data Type** & **Mean** & **STD** & **Missing Values (\%)** \\ \hline Age & age & Age in years 29-77 & Real & 54 & 9 & 0.00 \\ \hline Sex & sex & Male = 1, Female = 0 & Binary & & & 0.00 \\ \hline  & & & 1 = typical angina & & & \\ \multirow{4}{*}{Chest pain type} & \multirow{4}{*}{cp} & 2 = atypical angina & \multirow{4}{*}{Nominal} & \multirow{4}{*}{0.00} \\  & & 3 = non-anginal pain & & & \\  & & 4 = asymptomatic & & & \\ \hline Resting Blood Pressure in mm/Hg & \multicolumn{1}{l}{trestps} & 94-200 & Real & 131.344 & 17.862 & 6.41 \\ \hline Serum Cholesterol in mg/dL & chol & 126-564 & Real & 249.659 & 51.866 & 3.26 \\ \hline Fasting blood sugar \textgreater{} 120 mg/dL & \multicolumn{1}{l}{fbs} & 1 = yes, 0 = no & Binary & & 9.78 \\ \hline  & & 0 = normal & & & \\  & & 1 = having ST-T wave abnormality (T wave inversions) & & & \\ Resting ECG observations & \multicolumn{1}{l}{resseq} & 1 = having ST-T wave abnormality (T wave inversions) & & & \\  & & & and/or ST elevation or depression of \textgreater{}0.05 mV) & \multirow{4}{*}{Nominal} & \multirow{4}{*}{0.22} \\ Resting ECG observations & \multicolumn{1}{l}{resseq} & 2 = showing probable or definite left ventricular hypertrophy by ETS’ criteria & & & \\ \hline Maximum heart rate achieved & \multicolumn{1}{l}{thalach} & 71-202 & Real & 149.678 & 23.166 & 5.98 \\ \hline Exercise-induced angina & \multicolumn{1}{l}{c}{cang} & 1 = yes, 0 = no & Binary & & 5.98 \\ \hline ST depression induced by angina relative to rest & \multicolumn{1}{l}{oldpeak} & 0-6.2 & Real & 1.05 & 1.145 & 6.74 \\ \hline  & & 1 = upsploping & & & & \\ Slope of the peak exercise ST segment & \multicolumn{1}{l}{slope} & 2 = flat & \multirow{4}{*}{Ordered} & \multirow{4}{*}{33.58} \\ \cline{3-3} \cline{5-6}  & & 3 = downsploping & & & \\ \hline Number of major vessels colored by fluoroscopy & \multicolumn{1}{l}{ca} & Number of vessels: 0, 1, 2, 3 & Real & & 66.43 \\ \hline Thallium stress test result & \multicolumn{1}{l}{that} & 3 = normal; 6 = fixed defect & \multirow{2}{*}{Nominal} & \multirow{4}{*}{52.83} \\ \hline \end{tabular}
\end{table} 
Table 14: Real-time cardiovascular disease prediction dataset.
## 3 Results and Discussion
### Determination of FAs in Vegetable Oils
These were grouped into saturated (SFAs), monounsaturated (MUFAs), and polyunsaturated saturated (PUFAs) fatty acids, and their respective calculation formulae were:  
\[ \begin{split}\text{SFAs}=\text{C4:0}+\text{C6:0}+\text{C8:0}+\text{ C10:0}+\text{C11:0}+\text{C12:0}+\text{C13:0}+\text{C14:0}+\text{C15:0}+ \text{C16:0}+\text{C17:0}+\text{C18:0}+\text{C20:0}+\text{C21:0}+\text{C22:0}+ \text{C23:0}+\text{C24:0}\end{split}  \tag{1}\]  
\[\text{MUFAs}=\text{C14:1}+\text{C15:1}+\text{C16:1}+\text{C17:1}+\text{C18:1n9 }+\text{C20:1n9c}+\text{C22:1}+\text{C24:1} \tag{2}\]  
\[\text{PUFAs}=\text{C18:2n6c}+\text{C18:3n6c}+\text{C18:3n3c}+\text{C20:2}+\text{ C20:3}+\text{C20:4}+\text{C22:2}+\text{C20:5}+\text{C22:6} \tag{3}\]  
Figure 3 shows the SFA, MUFA and PUFA compounds of the different oils analyzed. In all oils, palmitic acid C16:0 was always the main SFA, where the percentages were between 5.59% and 22.24%, followed by stearic acid C18:0 (2.34-6.34%). The content of other SFAs, such as C14:0, C20:0, C22:0 and C24:0, was too small, being less than 1% in total. Both MUFA and PUFA compounds showed variations depending on the VOs and varied from 15.67% to 66.32% (MUFAs) and from 25.56% to 74.39% (PUFAs) of the total FAs. Oleic acid C18:1n9c was always the main MUFA, with a content between 15.78% and 66.31%. Rapeseed oil had C18:1n9c (a MUFA) as its main oleic acid (66.31%). Except in linseed oil, linoleic acid C18:2n6c was the major PUFA in all of the VOs (18.62-74.19%). Linseed oil was predominantly the PUFA \(\alpha\)-linolenic acid C18:3n3c (55.67%). Cottonseed oil had the highest SFA content (25.8%), of which palmitic acid C16:0 was the main SFA (21.66%). Soybean oil had the highest content of stearic acid (the second main SFA) at 6.34%. From the data shown in Figure 3, it was found that SFAs predominated in cottonseed oil, and USFAs predominated in safflower oil. Therefore, safflower is a valuable oil-containing crop, and the oil does not belong to the VO with high oleic acid. Rapeseed oils had a higher proportion of MUFAs than the other VOs researched. The ratio of the content of PUFAs to the content of SFAs: in safflower oil was 7.58%; in sunflower oil was 5.02%; in maize oil was 4.89%; in linseed oil was 7.01%; in cottonseed oil was 2.20%; in soybean oil was 3.07%; and in rapeseed oil was 3.50%. These levels were optimal for these types of VOs, thus indicating their high nutritional quality. The values of the PUFA/SFA ratios are recommended to be greater than 0.45 [40; 41].
###### Abstract  
This paper proposes, for the first time, a two-source asymmetric turbo-coded-cooperative spatial modulation (SM) scheme over the slow Rayleigh fading channel. As in any coded cooperative communication, the interleaver plays a vital role in mitigating the harsh effect of the wireless channel. Therefore, the code matched interleaver (CMI) is effectively used in the proposed design. The simulated results reveal that the bit error rate (BER) performance of the proposed coded cooperative communication system outperforms the asymmetric turbo-coded non-cooperative scheme under identical conditions. This prominent performance improvement has been made possible due to the joint asymmetric turbo decoding at the destination node. Furthermore, to check the effectiveness of the proposed scheme, we have also developed a two-source asymmetric turbo-coded cooperative scheme based on the vertical bell labs layered space-time (VBLAST), incorporating the CMI as the suitable benchmark. It is observed that the proposed scheme employing SM has a better BER performance than the VBLAST scheme under identical conditions.  
s +
Footnote †: journal: Electronics Electronics  
17 December 2019; accepted: 12 January 2020; Published: 16 January 2020
## 4 Results and Discussion
### Impact on Energy Production for Farm to Farm Interaction Case  
The velocity at hub height for case ws in the grid with two idealized wind farms can be seen in Figure 12a for the streamwise direction and in Figure 12b for the spanwise direction. The principle differences in a wake flow between the four different configurations (Table 1) are more easily seen behind the row of turbines, Figures 5 and 7, therefore only case ws is presented here.  
Figure 11: The percental change in production in comparison to case ws.  
Figure 10: The relative production \(P_{rel}\) (Production (\(P\)) normalized with \(P\) for the first turbine in the row of turbines (\(P_{1}\)) for the different cases).  
The relative production (mean for the four turbines for each line) for both the wind farms is shown in Figure 13. The impact of using the different configurations for the first wind farm is relatively small. For the second wind farm larger differences can be seen, the main impact coming from the inclusion of wind veer which leads to a higher production.  
Looking at the relative production in more detail for the second wind farm compared to the first wind farm, see Figure 14a, it can be seen that with wind veer the relative loss in production for the second wind farm is less than 2% compared to around 7% for case ws. The impact from the considered Coriolis correction is smaller but in combination with wind veer it decreases the losses. The change in relative production for both wind farms compared to case ws can be seen in Figure 14b. When including the wind veer and Coriolis correction a total increase of around 3% can be seen compared to case ws for this full wake direction.  
Figure 12: Velocity (see colorbar) at hub height for two wind farms using case ws, for: (**a**) Streamwise direction (W) (**b**) Spanwise direction (U).
## 4 Experimental Design
### Performance Testing Scenarios
#### 4.2.1 Scenario 1: Application on Monolithic Architecture over Kvm  
Figure 9 illustrates the monolithic scenario, which is up to the present day a traditional version used in technological projects of different sizes and purposes. In such a scenario, KVM contains the necessary applications and libraries for the application to run, exposing two ports that allow calls from the host. The databases, both MySQL and MongoDB, start their services when the virtual platform is turned on. These databases respond to requests that are sent from workers. To take full advantage of the resources configured in KVM, we use a cluster managed by a NodeJS library. With this, a worker can be generated for each existing core. A worker is a process that runs on a logical core, providing the connection service to interact with the application. In this experiment, there are four logic cores. Therefore, there are four workers who not only process connection requests but also allow general stability to the application. Hence, availability is guaranteed if a worker fails due to an error in the processing of requests. Each worker has established a connection with the databases and the endpoints that support, specifying the service to which the request is sent from the client.  
The cluster operates as a load balancer and assigns the request processing to the workers to finally send a response to the client. The port 3000, which is by default for NodeJS servers, is the same used to send requests from the host. In this case, using the JMeter Server Agent service, located inside the KVM, exposes port 4444. Then, it runs a daemon that receives instructions sent from JMeter (on the host) when running the stress tests in order to start the collection of data related to the performance of the resources running these tests. Furthermore, a NewRelic Server Agent, which, similar to the JMeter Server Agent, collects performance data with the difference that this data is general. Therefore, it collects data from the moment it starts as a service when the Operating System starts. The data collected is stored in log files on the host and synchronized in real time with its server within the Cloud (NewRelic Server). It analyzes, interprets, and displays the results with interactive graphs, being very useful for quick analysis and understanding. This service allows us to observe the server's behavior in general and effectively see the performance of the resources when executing the stress tests. The purpose of using this service in coordination with JMeter was to corroborate the results and their analysis to demonstrate the feasibility of the present investigation. On the host side is JMeter, an application based on Java technology that allows editing of the scripts to create stress tests and collect the results when they run. Likewise, it allows the collection of logs with data from the processes executed and the resources' performance.
## 3 Patulin Control Methods
### Biological control  
Biological methods of patulin control result largely from the observation that patulin is almost always completely degrade during years of fermentation. Besides being successful, this method is much better understood compared with other decontamination methods. Approximately 96% of patulin can be removed during years fermentation [242]. [PERSON] _et al._[243] examined and found that out of eight yeast strains tested six reduced patulin levels to below detectable levels, while all eight strains resulted in a 99% or better decrease in total patulin content. However, a control, stored for an equal amount of time (two weeks had only a 10% reduction. In another study by [PERSON] _et al._[244] observed that yeast formation reduced patulin levels completely after two weeks. They also noticed that patulin level failed to decrease significantly in juices that had been yeast fermented then filter sterilized to remove years, suggesting that active yeast and not their by-products, were required for the reduction. [PERSON] and [PERSON] [245] observed reduction of patulin levels during fermentative growth but not aerobic growth by the strains of _Saccharomyces cerevisiae_. This reduction resulted in the production of two major products: _E_-ascladiol, patulin's immediate biosynthetic precursor, and its isomer _Z_-ascladiol. These two products were also seen in the treatment of patulin with the reducing agent sodium borohydrate. _E_-ascladiol is itself a mycotoxin [246], which has reduced toxicity compared with patulin and also reacts with sulfhydryl-containing compounds [247]. While effective, biological control with yeast is limited to products that can be fermented. Furthermore, yeast is itself sensitive to patulin and at concentrations greater than 200 \(\mu\)g/mL, yeast has been completely inhibited, preventing fermentative detoxification [248]. No research has been done to examine the potential use of other fermenting microbes, such as lactic acid bacteria, in decreasing patulin content within juices. Similar reducing enzymes and environments produced by these bacteria may very well be able to degrade patulin. Finally, no research has investigated the direct enzymatic degradation of patulin. Reducing enzymes such as those involved in yeast fermentation, as well as lactone degrading enzymes such as -lactamase, may well be able to degrade patulin alone.
# Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data +
## 3 Interpolation Methods
### Extended Linear-Spline Interpolation  
Linear spline function for complex-valued data interpolation \(p_{\rm l}(\tau)\) is a linear polynomial function on the interval \([\tau_{0},\tau_{1}]\), \(\tau\in\mathbb{R}\), where the samples \(\tau_{0}<\tau_{1}\) are the knot points. The linear spline \(p_{\rm l}\) is uniquely defined by  
\[p_{\rm l}(\tau)=a+b(\tau-\tau_{0}),\qquad\tau_{0}\leq\tau\leq\tau_{1}, \tag{8}\]  
with the corresponding derivative  
\[p_{\rm l}^{\prime}(\tau)=b,\qquad\tau_{0}\leq\tau\leq\tau_{1}, \tag{9}\]  
and where \(a,b\in\mathbb{C}\) are the polynomial coefficients. Furthermore, the spline \(p_{\rm l}\) satisfies the following spline conditions  
\[\left\{\begin{array}{ll}p_{\rm l}(\tau_{0})=\tilde{y}_{0},\\ p_{\rm l}(\tau_{1})=\tilde{y}_{1},\end{array}\right. \tag{10}\]  
where \(\tilde{y}_{i}\), \(i=0,1\), are the data values, the phase of which is controlled through the procedure (6). It should be noted that the phase controlled in \(\tilde{y}_{i}\) may vary with a factor of \(\pi\) due to the corresponding location of complex data parameters either in the right \(\mathbb{C}_{+}\) or in the left complex half-plane \(\mathbb{C}_{-}\).  
Assume that the range time \(\tau_{\rm p}\) defined in (4) satisfies the condition \(\tau_{0}<\tau_{\rm p}<\tau_{1}\). Then, by employing the spline conditions (10), the following system of equations can be constructed  
\[\mathbf{Ac}=\mathbf{y}, \tag{11}\]  
where  
\[\mathbf{A}=\left[\begin{array}{cc}1&0\\ 1&\tau_{1}-\tau_{0}\end{array}\right], \tag{12}\]  
\(\mathbf{c}=[a\ b]^{\rm T}\), and \(\mathbf{y}=[\tilde{y}_{0}\ \tilde{y}_{1}]^{\rm T}\). The resulting filter that provides complex-valued data estimation at range time \(\tau_{\rm p}\) based on linear-spline interpolation with phase control is given by  
\[p_{\rm l}(\tau_{\rm p})=a+b(\tau_{\rm p}-\tau_{0}). \tag{13}\]  
Here, the polynomial coefficients \(a=\tilde{y}_{0}\) and \(b=(\tilde{y}_{1}-\tilde{y}_{0})/(\tau_{1}-\tau_{0})\) are determined from the system of equations (11).