esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Effect of the Traffic Load on Concrete Placement and Pipes
#### 5.3.4 Effect of Traffic Load on Concrete Pavement Surface with Different Depths of Pipe  
The findings showed that the pavement surface experienced deformation, including the energy collection pipes, under traffic loads and was greater by 3.3% than the deformation of the conventional pavement surface with respect to the static load of the vehicle. The deformation increased by 0.5% when the pipe depth was reduced to 60 mm, as shown in Table 6. As the depth of the pipe was located further away from the surface of the pavement, the deformation increased. This shows that the presence and location of pipe have a direct  
Figure 11: Equivalent stress and deformation of the surface with various pipe spacing: M1:200 mm and M4:100 mm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **M1** & **M4** \\ \hline Total deformation (mm) & 0.0028 & 0.00301 \\ Equivalent elastic strain mm/mm & 3.45 & 4.19 \\ Equivalent stress (MPa) & 3.66 & 4.609 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparing the deformation and stress experienced on the pipe as the pipe spacing was reduced from 200 mm to 100 mm.  
effect on the deformations that occur in the surface of the pavement. Studies on a road pavement solar collector using 3D Computational Fluid Dynamics (CFD)-finite element model (FEM) also indicate that the incorporation of heat pipes has a notable impact on the mechanical behavior of the pavement structure as a whole [18].  
In addition, from Table 6, the presence of pipe (M1) in the concrete reduces the stress and strain experienced on the concrete surface as the traffic load was applied by 25.61% and 24.82%, respectively, compared with no pipes in the concrete (M6). It was observed that there were small reductions in stress and strain as the depth of the pipe moved from 85 mm to 60 mm. The study found that the behavior of concrete pavement was similar to that of asphalt pavement in the literature. It indicated that, under vehicle load, the pipes in the pavement helped to reduce the deformation and improve the mechanical properties of the surface layer, regardless of the type of pavement material used. A simulation study employing vehicle-temperature coupled loading on hydronic asphalt pavement demonstrates an improvement in the pavement's nutting resistance performance relative to conventional pavement [27].
# Regularized Zero-Forcing Dirty Paper Precoding in a High-Throughput Satellite Communication System
## 5 Conclusions  
This paper studies the precoding problem of the high-throughput satellite forward link. Based on the forward link model, considering the effects of free space loss, rainfall attenuation, and beam gain, the classical low-complexity zero-forcing precoding algorithm is improved in this paper. Moreover, the regularized zero-forcing precoding algorithm considering the influence of system noise is studied, and a low complexity regularized zero-forcing dirty paper precoding algorithm is proposed, based on the idea of continuous interference elimination using non-linear dirty paper coding to achieve the maximum rate. It can be seen through simulation analysis that the throughput of regularized zero-forcing dirty paper precoding is larger, and the greater the number of formed beams, the worse the throughput performance per beam. The beam edge at 3 dB width shows the best performance. When the beam overlap factor exceeds a certain extent, the regularized zero-forcing dirty paper precoding algorithm is more robust than other algorithms. It can be concluded that the proposed dirty paper regularized zero-forcing precoding, which introduces the idea of dirty paper coding, can achieve better performance than classical precoding algorithms.  
Conceptualization, M.Y.; methodology, M.Y.; software, X.S.; validation, X.S., G.X. and B.L.; investigation, B.L.; resources, G.X.; data curation, X.S.; writing--original draft preparation, G.X.; writing--review and editing, Y.S.; visualization, G.X.; supervision, M.Y.; funding acquisition, M.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number No.62071146.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Formulation Strategies to Improve Oral Bioavailability of Ellagic Acid
## 4 Formulation Strategies for Improving EA Oral Bioavailability
### Dendrimers  
Dendrimers are branched polymers characterized by a tree-like structure of globular shape of nanometric dimension endowed with inner cavities, able to host lipophilic drugs, and a peripheral shell rich in functionalized groups [80; 81]. Among various typologies of dendrimers, the well-known polyamidoamine dendrimers (PAMAM) and branched polyethyleneimine polymers (b-PEI) are in vitro well-performant, but mainly because of their extremely cationic inner framework and poor biodegradability, they can exert hemolytic effect, which hindered their clinical translation. To reduce dendrimers cytotoxicity in the study by [PERSON] et al., PAMAM dendrimers were avoided and EA has been physically encapsulated into cationic dendrimers characterized by an uncharged polyester-based hydrolysate inner core peripherally esterified with a selection of four different amino acids [58]. Histidine, lysine, N-methyl-glycine and N, N-dimethylglycine were chosen to provide the carrier with primary, secondary, tertiary protonable amino groups and with the guanidine residue that is known to promote cellular up-take. Two dendrimers, one hydrophilic and one encompassing in the structure a C-18 chain that provides an amphiphilic character, were employed to encapsulate EA. Dendrimers were dissolved in MeOH and EA was added obtaining a conspicuous suspension maintained at acidic pH under magnetic stirring at room temperature for 48 h. The non-complexed EA was removed by filtration and the final EA dendrimer nanodispersions were brought to a constant weight under vacuum. The values of DL were of 46% (hydrophilic) and 53% (amphiphilic). NPs mean dimension was of 70 nm for both compounds while EA water solubility increased up to 1.7 mg/mL in the case of the amphiphilic dendrimer and up to 4.8 mg/mL in the case of the hydrophilic one.
## 4 Methodology
### Overview of the Modified AMCL with Map Updating
The algorithm also assumes the Markov property that the current state's probability distribution depends only on the direct previous state (not any states before that). This only works if the environment is static and does not change through time. Typically, on start-up, the robot has no information on its current pose, and thus, the particles are uniformly distributed over the configuration space. Subsequently the particles move to predict the new state at each moment of robot movement. The particles are also resampled based on recursive Bayesian estimation whenever the robot sensor detects something, and it represents how well the detected data correlate with the predicted state. In other words, resampling means discarding particles that do not match the sensor observation and producing more particles that are more consistent with the sensor measurement. This is because the robot does not always behave in a predictable way; thus, there needs to be corrections to the pose based on sensor data. In the end, hopefully, most particles should converge to the actual robot pose. Therefore, the AMCL algorithm estimates the position and orientation of a robot as it moves and senses the environment in the given map.
## 2 Frequencies and Mode Shapes of the Double-Beam System
### Mathematical Formulation
#### 2.1.2 Pinned-Pinned Boundary Conditions  
The boundary stiffness is  
\[\left\{\begin{array}{c}k_{\
u,1}=k_{\
u,2}=\infty\\ k_{r,1}=k_{r,2}=0\end{array}\right. \tag{23}\]  
The boundary conditions can be written as  
\[\left\{\begin{array}{c}\varphi_{1}(0)=0\\ \varphi_{1}^{\prime\prime}(0)=0\end{array}\right.,\ \left\{\begin{array}{c} \varphi_{2}(0)=0\\ \varphi_{2}^{\prime\prime}(0)=0\\ \varphi_{2}(l)=0\\ \varphi_{2}^{\prime\prime}(l)=0\end{array}\right. \tag{24}\]  
Equation (18) becomes  
\[\mathbf{\Phi}=\left(\begin{array}{cccccccc}1&1&1&1&0&0&0&0\\ 0&0&0&0&1&1&1&1\\ 1&1&-1&-1&0&0&0&0\\ 0&0&0&0&1&1&-1&-1\\ e^{r_{1}l}&e^{-r_{1}l}&e^{ir_{1}l}&e^{-ir_{1}l}&0&0&0\\ 0&0&0&e^{r_{5}l}&e^{-r_{5}l}&e^{ir_{5}l}&e^{-ir_{5}l}\\ e^{r_{1}l}&e^{-r_{1}l}&-e^{ir_{1}l}&-e^{-ir_{1}l}&0&0&0\\ 0&0&0&0&e^{r_{5}l}&e^{-r_{5}l}&-e^{ir_{5}l}&-e^{-ir_{5}l}\end{array}\right) \tag{25}\]  
The solution for \(\det\left[\mathbf{\Phi}\right]=0\) is  
\[\left\{\begin{array}{c}r_{1}l=n\pi,\ n=1,\ 2,\ 3\ldots\\ r_{5}=0\end{array}\right. \tag{26}\]or  
\[\left\{\begin{array}{c}r_{1}=0\\ r_{S}l=n\pi,\;n=1,\;2,\;3\ldots\end{array}\right. \tag{27}\]  
It is noticed again that the solutions for \(r_{1}l\) in Equation (26) or the solutions for \(r_{S}l\) in Equation (27) are the same as the wavenumbers for a single beam with pinned-pinned boundary conditions.  
Equations (26) and (27) result in the same solution for Equation (13) as  
\[\varphi_{2,n}(x)=C\sin\Bigl{(}\frac{n\pi}{l}x\Bigr{)} \tag{28}\]  
Substituting Equation (28) into Equation (4) results in  
\[\varphi_{1,n}^{(i)}(x)=C\left[1+\frac{1-\left(\frac{\omega_{n}^{(i)}}{\omega_ {2,n}}\right)^{2}}{s_{e}}(n\pi)^{4}\right]\sin\Bigl{(}\frac{n\pi}{l}x\Bigr{)} \qquad i=1,\;2 \tag{29}\]  
where \(\omega_{n}^{(i)}\) is obtained from Equation (9) and \(\widetilde{\omega}_{2,n}\) is  
\[\widetilde{\omega}_{2,n}=\Bigl{(}\frac{n\pi}{l}\Bigr{)}^{2}\sqrt{\frac{E_{2} I_{2}}{\rho_{2}A_{2}}} \tag{30}\]
## 4 Experimental Result of IGSAE
### Effect of the Coefficients and Classifiers
#### 4.5.2 Effects of Classifier  
The classifier has a significant impact on the performance of IGSAE. We conducted an experiment using the following three representative classifiers: support vector machine (SVM), K-nearest neighbor (KNN), and random forest [49]. The experimental results are shown in Table 6. As a result, KNN shows poor performance. The random forest achieves higher accuracies than KNN and has the highest accuracy for Yale. However, it does not show optimal performance because all hyperparameters are set as default. Since SVM can be applied stably to the proposed algorithm and shows excellent accuracies in most cases, it was chosen as a final classifier for IGSAE. Since the proposed IGSAE is in the field of dimensionality reduction and feature extraction domain, further experiments on different classifiers were not carried out. However, if more suitable and optimized classifiers are used in future work, performance can be improved, and it can be extended to other tasks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Heart** & **Wine** & **USPS** & **Fashion-MNIST** & **Yale** & **COIL-100** \\ \hline SVM (\%) & 82.64 \(\pm\) 2.0 & 96.25 \(\pm\) 1.1 & 93.50 \(\pm\) 2.4 & 88.87 \(\pm\) 1.8 & 71.35 \(\pm\) 5.5 & 95.33 \(\pm\) 0.5 \\ KNN (\%) & 77.48 \(\pm\) 6.3 & 92.78 \(\pm\) 3.2 & 89.75 \(\pm\) 4.7 & 83.79 \(\pm\) 2.9 & 69.95 \(\pm\) 4.1 & 86.46 \(\pm\) 4.6 \\ Random Forest (\%) & 81.72 \(\pm\) 1.8 & 94.55 \(\pm\) 2.1 & 90.56 \(\pm\) 2.2 & 86.78 \(\pm\) 2.7 & 72.17 \(\pm\) 4.8 & 94.8 \(\pm\) 11.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification accuracies of the IGSAE under different classifiers.  
Figure 5: Effects of the coefficients for classification accuracies on the six datasets: (**a**) Heart; (**b**) Wine; (**c**) USPS; (**d**) Fashion-MNIST; (**e**) Yale; (**f**) COIL-100.
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 2 Problem Description
### Mathematical Formulation  
Define the scheduling model for MSOOSP. The model is:  
\[\text{max}\sum_{i=1}^{|S|}\left(\sum_{j=1}^{|T|}\left(\sum_{k=1}^{|W_{ij}|}x_{ ijk}r_{j}\right)\right), \tag{2}\]  
subject to:  
\[\sum_{j=1}^{|T|}\left(\sum_{k=1}^{|W_{ij}|}x_{ijk}m_{j}\right)\leq M_{i},\,\,\, \,\forall i\in S, \tag{3}\]  
\[\sum_{i=1}^{|S|}\left(\sum_{k=1}^{|W_{ij}|}x_{ijk}\right)\leq 1,\,\,\,\forall j \in T, \tag{4}\]  
\[ws_{ijk}\leq ts_{j}<te_{j}\leq we_{ijk},\,\,\,\,\,if\,\,x_{ijk}=1, \tag{5}\]  
\[te_{j}+tr_{jj}\leq ts_{j},\,\,\,if\,\,te_{j}<ts_{j}, \tag{6}\]  
\[ts_{j}+d_{j}=te_{j},\,\,\,\text{if}\sum_{i=1}^{|S|}\left(\sum_{k=1}^{|W_{ij}|} x_{ijk}\right)=1,\,\,\,\forall j\in T, \tag{7}\]  
\[x_{ijk}\in\{0,\,1\},\,\,\forall(i,j,k)\in S\times T\times W, \tag{8}\]  
Equation (2) represents the common objective function of all satellites, maximizing the sum of benefits of successfully assigned tasks. Equation (3) indicates that during the planning cycle, the storage occupied by the satellite to perform its tasks cannot reach its maximum storage capacity; Equation (4) indicates that each task is executed at most once; Equation (5) indicates that the actual execution time of a task must be within its O-VTW; Equation (6) indicates that there must be a certain attitude transition time between two adjacent observation tasks of the satellite; Equation (7) indicates that mission observations need to last for a certain amount of time; Equation (8) defines the range of values for the decision variables.
# Mechanical Properties and Freeze-thaw Durability of Basalt Fiber Reactive Powder Concrete
## 2 Materials and Methods
### Mixture Proportion and Specim Preparation  
The best mixture proportion of RPC was obtained from the response surface method in our group's previous study [32], and based on the optimal mixture proportion; basalt fiber and steel fiber were added into RPC with an external mixing method. The content of basalt fiber was 4, 8 and 12 kg/m\({}^{3}\), respectively. The content of steel fiber was 2% volume fraction [23]. Moreover, PRPC specimens without fiber were prepared as the control group to investigate the effects of basalt fiber and steel fiber on the mechanical properties and freeze-thaw durability of RPC. The mixture proportion of BFRPC and SFRPC is shown in Table 5.  
All RPC mixtures were prepared by using an ISO 679 mixer with a capacity of 5 L. The degree of dispersion of basalt fiber and steel fiber in the mixture has a significant impact on the properties of RPC. During the process of mixing, the fibers were put into the mixer step by step to ensure the dispersion of fibers. The specific of mixing were as follows. (1) Put the quartz sand and fiber into the mixer and mix for 2 min. The friction between the quartz sand particles makes the fiber evenly dispersed. (2) Put the cement, quartz powder and silica fume into the mixer and mix for 3 min. (3) Dissolve the polycarboxylic superplasticizer into the water and add the solution into the mixer in two lots, mixing for 3 min each time. After stirring, the mixture was filled into a metal mold with a size of \(40\times 40\times 160\) mm, immediately, and compressed by vibrating on the ZT-96 vibrator table. The specimens were placed in an environment of 95% humidity and \(20\,^{\circ}\)C for 24 h and then de-moulded. The specimens were cured for 48 h under the condition of \(90\,^{\circ}\)C steam curing, the heating speed was \(12\,^{\circ}\)C/h and the cooling speed was \(15\,^{\circ}\)C/h.  
Figure 1: Fiber: (**a**) Basalt fiber (**b**) Steel fiber.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mix ID**} & **Basalt** & **Steel** & **Cement** & **Quartz** & **Silica** & **Quartz** & **Water** \\  & **Fiber** & **Fiber** & **(kg/m\({}^{3}\))** & **Sand** & **Fume** & **Powder** & **(kg/m\({}^{3}\))** & **Reducer** \\  & **(kg/m\({}^{3}\))** & **(\%)** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** \\ \hline R & - & - & 834.73 & 939.03 & 208.68 & 308.85 & 166.95 & 52.17 \\ BF4 & 4 & - & 834.73 & 939.03 & 208.68 & 308.85 & 166.95 & 52.17 \\ BF8 & 8 & - & 834.73 & 939.03 & 208.68 & 308.85 & 166.95 & 52.17 \\ BF12 & 12 & - & 834.73 & 939.03 & 208.68 & 308.85 & 166.95 & 52.17 \\ SF & - & 2 & 834.73 & 939.03 & 208.68 & 308.85 & 166.95 & 52.17 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The mixture proportion of basalt fiber reactive powder concrete (BFRPC) and steel fiber reactive powder concrete (SFRPC).
# Transmission Performance Analysis of RV Reducers Influenced by Profile Modification and Load
## 4 Discussion  
To determine the optimal profile of the modification methods used for different applications, the changes in the mean value and amplitude of the TE, the contact ratio, the maximum contact force, the friction, and the transmission efficiency of the three kinds of modification methods are compared. The TPM increases the mean value of the TE linearly under the three load conditions using the proposed three modification methods. However, the mean value of the TE is at minimum when using the case 2 method as compared with that of the other two methods. In addition, the mean value of the TE is the largest when using the case 1 method. However, under large load conditions, the amplitude of the TE is the smallest when using the case 3 method. Moreover, the case 1 method will reduce the contact ratio significantly from 0.38 to 0.24 under the rated load condition, although the contact ratio changes slightly when using the case 2 and 3 methods. The maximum contact force and friction have a changing point when using the case 2 method. Moreover, the TPM has little effect on the maximum contact force and friction when using the case 3 method, although the effect is significant when using the case 1 method. Although the first two modification methods will affect the transmission efficiency significantly, the case 3 method has a slight effect on the transmission efficiency, with a change of only 92.8% to 92.1% under the rated load condition. Therefore, it can be concluded that the case 2 and 3 modification curves are better than that of the case 1 method in terms of the contact force and transmission accuracy. However, under large load conditions, such as twice the rated load, the case 3 method may be the optimal profile modification method compared with the case 2 method. This is because the case 3 method can maintain a good transmission performance with an increase in the load and modification coefficient.
# Research on Wind Speed Inversion Method for X-Band Networked SAR Satellite
## 2 Materials
### Geophysical Model Functions  
GMF is a common method for inversion of sea surface wind speed from SAR data. This model is an empirical model based on a large number of radar backscatter coefficients and statistical data of buoy data or numerical prediction models. GMF describes the relationship between radar incidence angle, sea surface wind speed, relative wind direction, and backscatter coefficient. At present, the GMF applied to X-band SAR data mainly includes XMOD1 and XMOD2. For the SAR data provided by TerraSAR-X and COSMO/SKYMED, XMOD1 and XMOD2 have two forms respectively. XMOD1, developed by [PERSON], is a model obtained by fitting the relationship between Cosmo-SkyMed SAR data and wind data obtained from the SeaWinds instrument on QuikSCAT [7]. The model was developed and tested for wind speeds in the range of 2 to 8 m/s. The XMOD2 model was obtained by [PERSON] optimizing XMOD1 [8], and is suitable for Cosmo/SkyMed X band data. It raises the upper limit of wind speed that can be extracted to 25 m/s. The XMOD1, developed by [PERSON], was tuned based on TerraSAR-X data and co-located wind fields derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-40 re-analysis data [9]. It is a linear geophysical model function developed for 6-14 m/s SAR data. Then, [PERSON] optimized XMOD1 to obtain XMOD2 [10]. This model is a non-linear geophysical model suitable for co-polarized SAR data with an incident angle of 20-45\({}^{\circ}\), and a wind speed of 2-20 m/s.  
Because the performance of XMOD2 is better than XMOD1, this paper chooses XMOD2 for the research of this paper. The XMOD2 used for TerraSAR-X data is similar in form to CMOD5, so this model is used in subsequent studies. The functional form of XMOD2 is as follows:  
\[\sigma^{0}=B_{0}(1+B_{1}\cos\phi+B_{2}\cos 2\phi)^{1.6} \tag{1}\]  
where \(\sigma^{0}\) is the backscatter coefficient, \(\phi\) is the relative wind direction angle (the angle between the wind direction and the SAR viewing direction), and \(B_{0}\), \(B_{1}\), and \(B_{2}\) are functions of the wind speed \(u\) and the incident angle \(\theta\). The transfer functions in \(B_{0}\) and \(B_{2}\) are the same as those used in CMOD5. The \(B_{0}\) term is defined as:  
\[B_{0}=10^{a_{0}+a_{1}u}f(a_{2}u,s_{0})^{\gamma} \tag{2}\]  
in which \(a_{0}\), \(a_{1}\), \(a_{2}\), \(s_{0}\), and \(\gamma\) are functions of the incident angle. The \(B_{1}\) in XMOD2 is defined in (3), which is not the same as that in CMOD5.  
\[B_{1}=\Big{(}c_{14}+c_{15}x+c_{16}x^{2}\Big{)}+\Big{(}c_{17}+c_{18}x+c_{19}x^{ 2}\Big{)}u+\Big{(}c_{20}+c_{21}x+c_{22}x^{2}\Big{)}u^{2} \tag{3}\]  
where \(x\) is related to the incidence angle. The specific function form of \(B_{2}\) is shown as:  
\[B_{2}=(-d_{1}+d_{2}v_{2})exp(-v_{2}) \tag{4}\]  
in which \(d_{1}\) and \(d_{2}\) are functions of the incident angle, and \(v_{2}\) is related to incident angle and wind speed.
# Microwave Heat Treatment Induced Changes in Forage Hay Digestibility and Cell Microstructure
###### Abstract  
To investigate the effects of microwave (MW) treatment on hay nutritive value, five types of hay (50 g) were treated with MW for 0 (control), 20, 40, 60, and 80 s (0, 440, 880, 1320, and 1760 kJ kg\({}^{-1}\) of MW energy, respectively) and analyzed for nutritive value prior to scanning by an electron microscope to observe microstructure changes. The dry matter (DM) % of hays were increased with increasing treatment time (\(p<0.001\)). The improved DM and digestible organic matter in the DM were recorded from MW treated lucerne (60 s), wheat (40 s), and canola (20 s) (\(p<0.001\)), which might have been due to the MW ruptured cell wall. The MW energy required for maximal DM digestibility improvement was linearly related to control hay crude protein content (\(r^{2}=0.79\); \(p<0.001\)). Overall, the study showed MW treatment can increase some hays' digestibility. In addition, the crude protein content from control hay may be a proxy to indicate DM digestibility improvement.  
thermal treatment; nutritional value; feed quality; SEM-IA; conserved forage +
Footnote †: journal: Applied Sciences