esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Melting** & \multirow{2}{*}{**PCM**} & **Skeleton** & \multicolumn{2}{c}{**Thermal**} & \multirow{2}{*}{**Characterization**} & \multirow{2}{*}{**Ref.**} \\  & **Temperature** (\({}^{\circ}\)C) & & & & & \\ \hline
1 & 103.5 & LiNO\({}_{3}\)NaNO\({}_{3}\)-KNO\({}_{3}\)-Ca(NO\({}_{3}\))\({}_{2}\) & Calcium & Graphite & SEM, XRD, STA, Hot Plate, 1000 thermal cycles & [14] \\  & & & & silicate & 1000 thermal cycles & [21] \\
2 & 168.5–229.7 & LNO\({}_{3}\)-KCl, LiNO\({}_{3}\)-NaNO\({}_{3}\) & EG\({}^{*}\) & EG & SEM, XRD, STA, Hot Disc & [15] \\
3 & 200 & LiNO\({}_{3}\)-KCl & EG & EG & DSC, Hot Disc & [16] \\
4 & 240-255 & LiNO\({}_{3}\) & Diatomite & - & SEM, XRD, DSC, XKE, BET, 17 & [17] \\
5 & 220 & NaNO\({}_{3}\)-KNO\({}_{3}\) & Graphite & Graphite & DTA, Hot Plate & [18] \\
6 & 220-350 & NaNO\({}_{3}\)-KNO\({}_{3}\) & NaNO\({}_{3}\) & EG & EG & DSC, Hotwire & [19] \\
7 & 220 & NaNO\({}_{3}\)-KNO\({}_{3}\) & EG & EG & SEM, XRD, DSC, ETS, TEM & [20] \\
8 & 220 & NaNO\({}_{3}\)-KNO\({}_{3}\) & SiC & & SEM, DSC & [21] \\
9 & 220 & NaNO\({}_{3}\)-KNO\({}_{3}\) & Graphite & Graphite & [22] \\
10 & 300 & NaNO\({}_{3}\) & Diatomite & Graphite & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\
11 & 300 & NaNO\({}_{3}\) & Expanded & & SEM, XRD, STA, XKE, BET, 23 & [23] \\
12 & 486 & Li\({}_{3}\)CO\({}_{3}\)-K\({}_{3}\)CO\({}_{3}\) & Graphite & Graphite & DSC, LFA & [25] \\
13 & 500 & Li\({}_{3}\)CO\({}_{3}\)-Na\({}_{3}\) & MgO & Graphite & SEM, XRD, TA, IC, TO-DSC & [26; 27] \\
14 & 506 & NaCl\({}_{3}\)-Ca\({}_{3}\) & EG & EG & SEM, XRD, DSC, Hot Disc & [28] \\
15 & 880 & Na\({}_{3}\)-SO\({}_{4}\) & Diatomite & - & SEM, XRD, STA, XKE, BET & [29] \\
16 & 880 & Na\({}_{3}\)-SO\({}_{4}\) & SiC & - & XRD, SEM, TG-DSC & [30] \\ \hline \hline \end{tabular}  
* EG is short for expanded graphite.  
\end{table} 
Table 1: Overview of salt-based form-stable composite phase change materials (PCMs). * Different materials have been used as skeleton materials but many of them cannot be used at high temperatures;  
The above brief summary suggests clear gaps in the literature with regard to composite PCM research. The specific objective of this work was therefore to develop formulations of composite PCMs for high temperature applications. The work involved the syntheses of form-stable composite PCMs with a eutectic salt (K\({}_{2}\)CO\({}_{3}\)-Na\({}_{2}\)CO\({}_{3}\)), MgO and SiC. The eutectic salt (K\({}_{2}\)CO\({}_{3}\)-Na\({}_{2}\)CO\({}_{3}\)) is used as a phase change material to store latent heat. MgO and SiC have high melting points of up to 3000 \({}^{\circ}\)C [31] and 2250 \({}^{\circ}\)C [32]. In addition, SiC is highly thermally conductive [33; 34]. Therefore, MgO and SiC have been used as structural supporting materials and thermal conductivity enhancers, respectively. Various methods (XRD, SEM, STA, etc.) were used to characterize the thermophysical properties and the thermal stability of the composite PCM.
## 4 Results and Discussion  
### Training Mechanism by Using ML Methods  
In experiments, the training parameters and strategy of the simplified VGG model are listed in Table 2, where each entry is optimal for the model. An initial learning rate of 0.001 and 250 training epochs were chosen in order to achieve adequate convergence. The Adam algorithm [25] was used as the optimizer and the mean square error as the loss function. A batch size of 50 was selected, and the average training time was 232.47 s.  
In order to evaluate the performance of a trained simplified VGG model, we computed the mean absolute error (MAE) (see Equation (28)) of the predicted and actual product attributes. The MAEs of \(Y_{1}\), \(Y_{2}\), and \(Y_{3}\) are shown in Table 3, which reveals that the simplified VGG is the best, i.e., it is capable of producing strong correlations between the process parameters and the product attributes.  
\[MAE_{i}= \frac{1}{299}\sum_{l=1}^{299}{|Y_{il}-\varphi(X_{l})|i=1,2,3} \tag{28}\]  
where \(Y_{il}\) is i-th product attributes of \(l\)-th test sample.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Batch Size** & **Training Epochs** & **Trainable Variables** & **Initial Learning Rate** & **Loss Function** & **Optimizer** & **Average Training Time (s)** \\ \hline Simplified VGG Model & 50 & 250 & 360,661 & 0.001 & MSE & Adam & 232.47 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters assigned in the simplified VGG model.
## 3 Results
### Validation of the Derived Wind
#### 3.2.1 Different Quality Control Methods
Figure 10 illustrates a comparison between the wind speed derived using the traditional and the optimized methods at S1 (upstream of the wind farm) and S3 (inside the wind farm) with the 10\({}^{\circ}\) elevation angle VADs in every half hour and those measured by the meteorological tower. Compared to the optimized method, the results from the traditional method have some abnormally large or small values relative to the observations from the meteorological tower. The consistency between the optimized method and the meteorological tower observations is better, especially within the wind farm (Figure 10a). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Elevation Angle** & **Site/Height** & **MAE (m/s)** & **RMSE (m/s)** & **R\({}^{2}\)** & **Significance** \\ \hline \multirow{4}{*}{10\({}^{\circ}\)} & S1/10 m & **0.748** & **1.029** & **0.965** & \multirow{4}{*}{*} \\  & & 0.859 & 1.433 & 0.934 & \\  & & **0.927** & **1.239** & **0.932** & \\  & & 1.231 & 2.116 & 0.826 & \\  & & **0.771** & **1.102** & **0.958** & \\  & & 1.023 & 1.470 & 0.926 & \\ \hline \multirow{2}{*}{30\({}^{\circ}\)} & \multirow{2}{*}{S3/70 m} & **0.903** & **1.210** & **0.954** & \multirow{2}{*}{**\(\ast\)**} \\  & & 1.326 & 2.253 & 0.838 & \\ \hline \end{tabular}  
* The difference passed the Kolmogorov–Smirnov test with a confidence level of \(\alpha=0.01\).
* The difference passed the Kolmogorov–Smirnov test with a confidence level of \(\alpha=0.05\).  
\end{table} 
Table 1: Statistical comparison between Lidar-derived and meteorological tower-measured wind speed. Bold and plain numbers are for the optimized and the traditional methods, respectively. Figure 10: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Elevation Angle** & **Site/Height** & **MAE (\({}^{\circ}\))** & **RMSE (\({}^{\circ}\))** & **R\({}^{2}\)** & **Significance** \\ \hline \multirow{4}{*}{\(10^{\circ}\)} & S1/10 m & **11.323** & **19.168** & **0.987** & \multirow{4}{*}{*} \\  & 13.744 & 25.392 & 0.977 & & \\  & & **14.715** & **19.242** & **0.992** & \\  & & 18.272 & 27.453 & 0.978 & \\  & & **13.714** & **16.255** & **0.995** & \\  & & 16.075 & 20.840 & 0.983 & \\ \hline \multirow{4}{*}{\(30^{\circ}\)} & S3/70 m & **13.731** & **17.626** & **0.993** & \multirow{4}{*}{*} \\  & 18.419 & 26.891 & 0.978 & & \\ \hline \hline \end{tabular}  
* The difference passed the Kolmogorov–Smirnov test with a confidence level of \(\alpha=0.05\).  
\end{table} 
Table 2: Comparison between Lidar-derived and meteorological tower measured wind directions. Bold and plain numbers are for the optimized and the traditional methods, respectively. Figure 9: Same as Figure 8, but for wind direction.
# Selection Criterion of Reanalysis Methods for Plane Truss Optimization
## 4 Construction of Selection Criterion
### Method Comparison Study
#### 4.1.1 Unchanged Number of DOFs
Since the number of iteration steps of the PCG method is generally smaller than the number of basis vectors in the Epsilon method when achieving similar accuracy (Epsilon usually takes an odd number of basis vectors), it is stipulated that when using the Epsilon method, only three basis vectors should be used. Otherwise, the PCG method should be employed. In order to verify this conclusion, three examples with different DOFs (\(n=20\), 96, and 1368) are designed for validation (Figure 5). The above examples all consist of circular solid members with a cross-sectional area of 250 cm\({}^{2}\) and an elastic modulus of 210 \(\times\) 10\({}^{9}\) Pa. The following types of modifications are applied to all three examples:  
Figure 5: Initial layouts of the three structural cases based on number of DOFs: (**a**) \(n=20\); (**b**) \(n=96\); (**c**) \(n=1368\). (1) Size modification: one randomly selected member's area is changed to twice that of its original cross-sectional area. (2) Shape modification: one randomly selected node undergoes vertical displacement. (3) Topology modification: one randomly selected member undergoes a connection node transformation (see Figure 2a). Complete analysis, the Epsilon method, and the PCG method were used to perform calculations on the three examples under the three modifications. In order to ensure the reliability of the results, the calculations for Examples 1, 2, and 3 were iterated 10,000 times, 5000 times, and 5 times, respectively, with a required accuracy of 1% error limit. These examples were implemented using the MATLAB programming language and executed on a computer with Intell Corel i7-6700k CPU @ 4.00 GHz. The results are given in Table 3.  
It can be seen that under different DOFs, the Epsilon method can always complete the calculation and meet accuracy requirements with only three basis vectors when dealing with changes in structural size (modification type (1)), while the corresponding PCG method requires two iteration steps. In terms of computation time, the Epsilon method outperforms the PCG method. However, for the other two modification types, the Epsilon method requires more than three basis vectors to meet the accuracy requirements, and the computation time for the Epsilon method is longer than that for the PCG method. Therefore, the PCG method is more effective for shape and topology modifications (with unchanged DOFs). It is worth noting that when the DOFs of the structure are low, the iteration steps or the number of basis vectors required by both methods for shape modification (modification type (2)) are relatively large, resulting in significantly lower computational efficiency than complete analysis. Therefore, the complete analysis should be considered for structural calculations with low DOFs in shape modifications.
## 3 Mesoscale Equivalent Model for UHPC
### Generation of Fibers  
In a UHPC specimen, fibers are uniformly and randomly distributed. The generation of random straight round fibers inside a certain specimen volume \(V\) can be as follows. First, the number of fibers is calculated by \(N=4V_{f}/V/(\pi d/2L_{f})\) according to the fiber diameter \(d_{f}\), length \(L_{f}\) and volume content \(V_{f}\). Random points with the number of \(N\) are uniformly generated in the specimen space and assigned to be the initial point of each fiber, labeled as (\(x_{1i}\), \(y_{1i}\), \(z_{1i}\)). Then, for each fiber, the initial direction is defined by two random spatial angles \(\varphi_{i}\) and \(\theta_{i}\) in the spherical coordinate system with the initial point as the origin. The ending point of a fiber, labeled as (\(x_{2i}\), \(y_{2i}\), \(z_{2i}\)), is determined according to the initial point, random angles, and \(L_{f}\), as shown in Equation (1).  
\[\left\{\begin{array}{l}x_{2i}=x_{1i}+L_{f}\sin\theta_{i}\cos\varphi_{i}\\ y_{2i}=y_{1i}+L_{f}\sin\theta_{i}\sin\varphi_{i}\\ z_{2i}=z_{1i}+L_{f}\cos\theta_{i}\end{array}\right. \tag{1}\]  
With the initial point and ending point determined, any fiber in the given specimen space can be identified. Equation (1) is cycled \(N\) times until all fibers are generated. All generated points and fibers will be written into node keyword file and element keyword file, respectively, which will facilitate subsequent model establishment.
###### Abstract  
The brown planthopper _Nilaparuta lugens_ (BPH) is one of the most harmful insect pests in rice paddy fields, which causes considerable yield loss and consequent economic problems, particularly in the central plain of Thailand. Accurate and timely forecasting of pest population incidence would support farmers in planning effective mitigation. In this study, artificial neural network (ANN), random forest (RF) and classic linear multiple regression (MLR) analyses were applied and compared to forecast the BPH population using weather and host-plant phenology factors during the crop dry season from 2006 to 2016 in the central plain of Thailand. Data from satellite earth observation was used to monitor crop phenology factors affecting BPH population density. An ANN model with integrated ground-based meteorological variables and satellite-derived host plant variables was more accurate for short-term forecasting of the peak abundance of BPH when compared with RF and MLR, according to a reasonably validating dataset (RMSE of natural log-transformed (In) BPH light trap catches = 1.686, 1.737, and 2.015, respectively). This finding indicates that the utilization of ground meteorological observations, satellite-derived NDVI time series, and ANN have the potential to predict BPH population density in support of integrated pest management programs. We expect the results from this study can be applied in conjunction with the satellite-based rice monitoring system developed by the Geo-Informatic and Space Technology Development Agency of Thailand (GISTDA) to support an effective pest early warning system.  
brown planthopper; host-plant phenology; weather; satellite; remote sensing; multiple linear regression; artificial neural network; random forest +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Function Optimization and Parameter Performance Analysis Based on Gravitation Search Algorithm
## 2 Gravitational Search Algorithm
### Basic Principles of Gravitation Search Algorithm
#### 2.2.3 Calculate Gravitational Force  
At the moment \(t\), the calculation formula for the gravitational force of object \(j\) to object \(i\) described as follows.  
\[F_{ij}^{k}=G(t)\frac{M_{pi}(t)\cdot M_{aj}(t)}{R_{ij}(t)+\epsilon}(x_{j}^{k}(t )-x_{i}^{k}(t)) \tag{11}\]  
where, \(\epsilon\) is a very small constant, \(M_{ai}(t)\) is the inertial mass of the object itself, \(M_{pi}(t)\) is the inertial mass of an object \(i\). \(G\left(t\right)\) is the universal gravitational constant at the moment \(t\), which is determined by the age of the universe. The greater the age of the universe, the smaller \(G\left(t\right)\). The inner relationship is described as follows.  
\[G(t)=G_{0}\cdot\mathrm{e}^{-at/T} \tag{12}\]  
where \(G_{0}\) is the universal gravitational constant of the universe at the initial time \(t_{0}\). generally it is set as 100. \(\alpha\) is 20, \(T\) is the maximum number of iterations and \(R_{ij}(t)\) represented the Euclidean distance between object \(i\) and object \(j\).  
\[R_{\mathrm{ij}}=\mid\mid X_{i}(t),X_{j}(t)\mid\mid \tag{13}\]  
In GSA, the sum \(F_{i}^{k}(t)\) of the forces acting on the \(X_{i}\) in the \(K\)-th dimension is equal to the sum of all the forces acting on this object:  
\[F_{i}^{k}(t)=\sum\limits_{j=1,j\
eq i}rank_{j}F_{ij}^{k}(t) \tag{14}\]  
where \(rank_{j}\) is the random number in the range \([0,1]\), \(F_{ij}^{k}(t)\) is the gravity of the \(j\)-th object acting on the \(i\) the object in the \(k\)-th dimension space. According to [PERSON]'s Second Law, the acceleration of the \(i\)-th particle in the \(k\)-th dimension at the moment \(t\) is defined as follows:  
\[a_{i}^{k}(t)=\frac{F_{i}^{k}(t)}{M(t)} \tag{15}\]
## 1 Introduction
Therefore, an antenna length of \(\sim\)2 m was selected in the S-STEP to obtain images with a high resolution of 1 m in the stripmap mode. This study proposes a design and performance analysis for a novel SAR payload suitable for an 80 kg class SAR microsatellite. The essential SAR payload design for the flat-panel-type satellites integrated with both bus and payload mechanical structures demands high-gain antennas, high-power TRMs, and miniaturized/lightweight unit designs to enable the SAR image acquisition for the required mission performance. A lightweight SIW cavity-backed microstrip patch antenna is used. A brick-type TRM is applied with a high-power GaN-type monolithic microwave integrated chip (MMIC) rather than a GaAs MMIC. An effective heat dissipation and structural reinforcement functions are employed to TRM assembly. More specifically, the multifunctional integrated structure of the TRM assembly enables effective heat dissipation through its integrated radiators when a large amount of heat is generated from TRMs during SAR imaging [26]. The structural stiffness of the satellite structure is reinforced by mechanically integrating the TRM housing with  
Figure 1: Comparison of satellite masses vs. SAR image resolutions. the satellite body. Additionally, a transceiver capable of transmit (Tx)-band stretching by quadruple frequency multiplication was applied. Furthermore, a digital control unit based on a high-performance field-programmable gate array (FPGA) provides multiple functions of generating a wideband Tx waveform based on parallelized direct digital synthesis (PDDS), which is robust in the radiation environment in space, and digital processing of the received signal. These strategies contribute to minimize the satellite mass and facilitate the implementation of \"lighter\" SAR satellites. Moreover, a performance analysis was conducted on the S-STEP SAR payload to verify the feasibility of the proposed design. In this study, the proposal focuses more on the design and development of the SAR payload, rather than at the satellite level. The remainder of this paper is organized as follows: The mission design and satellite system of the S-STEP are explained in Section 2. The SAR payload design proposed for the innovative S-STEP satellite is presented in Section 3, and the unit design of the SAR payload is briefly described in Section 4. Thereafter, the antenna model of the SAR payload, performance analysis results, and the conclusions of this study, as well as the future scope of this study, are discussed in Sections 5-7, respectively. The mechanical and thermal design strategies of the S-STEP are described in prior studies on S-STEP missions [25; 26].
## 2 System Design
### Pre-Equalization  
The LED source is an essential device in underwater vehicles for video observation. To realize high-speed data transmission and effective compatibility with underwater lighting equipment, we employed a frequency domain pre-equalization circuit at the transmitter to improve the frequency response characteristics of the commercial off-the-shelf LEDs. Figure 4 shows the bridged-T pre-equalization circuit used in the proposed UOWC system. It comprises two RLC networks with the equivalent impedances of \(Z_{11}\) and \(Z_{22}\), respectively.  
The impedance \(Z_{11}\) consists of a resistor \(R_{1}\), a capacitor \(C_{1}\), and an inductor \(L_{1}\). The impedance \(Z_{22}\) consists of a resistor \(R_{2}\) and a two-port network in series. The two-port network consists of a capacitor \(C_{2}\) and an inductor \(L_{2}\) in parallel. The two-series resistors \(R_{0}\) are set to equal the load impedance \(R_{\text{L}}\). The bridged-T pre-equalization circuit should follow the following relationship provided by [22].  
\[Z_{11}Z_{22}=R_{0}^{2} \tag{3}\]  
where \(Z_{11}\) and \(Z_{22}\) are given by  
\[Z_{11}=\frac{1}{\frac{1}{R_{1}}+\frac{1}{j\omega L_{1}+\frac{1}{j\omega C_{1}}}} \tag{4}\]  
\[Z_{22}=R_{2}+\frac{1}{\frac{1}{j\omega L_{2}}+j\omega C_{2}} \tag{5}\]  
where \(\omega\) is the angular frequency of the transmitted signal. For easy realization and analysis, we set \(L_{1}=L_{2}\) and \(C_{1}=C_{2}\), and thus the forward transmission gain \(S_{21}\) of the pre-equalization circuit can be expressed as:  
\[S_{21}=\frac{1}{1+\frac{R_{\text{L}}}{R_{2}+\frac{j\omega L_{1}}{1-\omega^{2} C_{1}L_{1}}}} \tag{6}\]  
From (6), when (\(1-\omega^{2}C_{1}L_{1}\)) tends to zero, \(S_{21}\) is equal to 1. Therefore, the bandwidth \(f_{\text{c}}\) of the pre-equalization circuit can be calculated by:  
\[f_{\text{c}}=\frac{1}{2\pi\sqrt{L_{1}C_{1}}} \tag{7}\]  
Based on the bandwidth of the existing components in the proposed UOWC system, the parameters used for the bridged-T pre-equalization circuit are \(R_{1}=249\)\(\Omega\), \(R_{0}=49.9\)\(\Omega\), \(R_{2}=10\)\(\Omega\), \(C_{1}=C_{2}=47\) pF, and \(L_{1}=L_{2}=120\) nH. The S-parameters are measured by a  
Figure 4: Schematic of the bridged-T pre-equalization circuit.  
vector network analyzer (VNA, Keysight E5063A), operating at a frequency from 100 kHz to 18 GHz.  
Figure 5 shows the forward transmission gain \(S_{21}\) of the designed pre-equalizer, which has a frequency response range of 28.47 MHz to 67.08 MHz.
# An Adaptive Scheduling Mechanism Optimized for V2N Communications over Future Cellular Networks
## 2 Scheduling Algorithms
### Well-Established 5G Scheduling Algorithms  
Some of the well-known types of schedulers considered for wireless communications, including 5G networks, are the following:  
* Round-Robin (RR) scheduler: the scheduler distributes the available Resource Block Groups (RBGs) evenly among UEs in a cell.
* Proportional Fair (PF) scheduler: the scheduler tries to maximize the total throughput of the network while at the same time attempting to provide all users with at least a minimal level of service. To achieve this, the scheduling priority is inverse to the UE and proportional to the anticipated resource consumption for a UE. Our paper considers the ns-3 PF scheduler, where a UE is scheduled when its instantaneous channel quality is high relative to its own average channel condition.
* Maximum Rate (MR) scheduler: the scheduler aims to maximize the overall throughput for a base station. To achieve this, it allocates resource blocks to the user that can achieve the maximum achievable rate in the current Time Transmission Interval (TTI).  
Each of the above scheduling techniques comes with its strengths and shortcomings. In our paper, we use PF scheduling as the baseline scheme for comparison as it targets to provide at least a minimal level of service for all UEs. However, PF like the other scheduling schemes, shares the same principle: scheduling takes place at a granularity of each UE, meaning that the resource allocation is applied per UE and not per service. This, of course, is not suitable as different V2X services (even those related to the same family, e.g., safety) have other requirements. When scheduling takes place on the UE level, this may violate the desired requirements (e.g., throughput, delay or reliability), especially when the load of the access network increases. One of the novelties of the proposed solution (SOVANET) is to allocate resources on a traffic flow/service type basis, rather than a UE one, to cope with the fast-changing environment and diverse V2X UCs requirements.