esa-sceva/satcom-chunk-collection huggingface dataset:
# A Polymer Inclusion Membrane for Sensing Metal Complexation in Natural Waters
## 3 Results
### Accumulation of Zn and Cu: Individual Studies  
The accumulation of both Zn and Cu was investigated using the PIM device at different times. As can be seen in Figure 3, even though both metals were effectively transported, the accumulation of Cu was faster than Zn at the initial times. Moreover, the system seems to approach equilibrium at t > 100 h bringing about a considerable depletion of the feed solution (46% for Zn and 60% for Cu). As this specific study is aimed at providing data on the possibility of using the PIM system as a tool for the rough estimation of the two main metal fractions in the water sample, avoiding long depletion times and using reasonable volumes of the feed solution (to be measured in the laboratory), we chose 24 h as the experimental time for further experiments.  
For both Zn and Cu, the effect of the initial metal concentration (in the range 0.001-0.03 mM for Zn and 0.008-0.03 mM for Cu) on their accumulation in the receiving phase was studied. The results are presented in Figure 4, where it can be seen that the relationship between the metal accumulated in the receiving phase and the metal initially present in the feed phase follows a linear trend, with the accumulation of Cu being higher than Zn, under the same conditions.  
Taking into account that--according to MINTEQ calculations--Zn and Cu are present as free ions at the studied pH and KNO\({}_{3}\) medium, the obtained relationship can be used as a calibration curve for the estimation of free metal ions by measuring the metal accumulated in the receiving solution.
## 3 Results  
### Spatial and Temporal Patterns of \(NDVI_{\text{max}}\) and GPP\({}_{\text{max}}\)  
The spatial patterns and trends of NDVI\({}_{\text{max}}\) from 2001 to 2022 are shown in Figure 2. Spatially, the high NDVI\({}_{\text{max}}\) values were dispersed in the southeastern region, while the low values were dispersed in the northwestern region. The areas with NDVI\({}_{\text{max}}\) values ranging from 0.1 to 0.4 comprised 43.08% of the total area (Figure 2a). In the results of the NDVI\({}_{\text{max}}\) standard deviation calculation, the vast majority of the area (88.63%) comprised values ranging from 0 to 0.06, while only a few regions exhibited high values, which were mostly in the northeastern and southern areas (Figure 2b). Based on the Theil-Sen slope estimation and the Mann-Kendall trend test, NDVI\({}_{\text{max}}\) demonstrated an increasing trend from 2001 to 2022 (Figure 2c). Approximately 16.35% of the pixel area showed a significant upward trend. The area with a significant increase in pixels was more than five times greater than the area with a significant decrease (Figure 2c).  
GPP\({}_{\text{max}}\) displayed a spatial pattern similar to NDVI\({}_{\text{max}}\), with the high GPP\({}_{\text{max}}\) values primarily scattered across the eastern TP. Conversely, the GPP\({}_{\text{max}}\) values exhibited in the western regions were generally low: the overall average was 0.52 g C/m\({}^{2}\) (Figure 2d). The spatial distribution of the GPP\({}_{\text{max}}\) standard deviation mirrored that of the mean, with the elevated values observed in the eastern regions and the lower values in the western areas. The regions with values ranging from 0 to 0.1 g C/m\({}^{2}\) covered 83.60% of the area (Figure 2e). From 2001 to 2022, GPP\({}_{\text{max}}\) showed an increasing trend (Figure 2f). Roughly 16.68% of the pixels demonstrated a notable upward trend, while 0.86% showed a significant downward trend (Figure 2f).
## 3 Implementation
### Feature Pair Selection  
In order to obtain the projection matrix \(H\), the selection of more trustful and accurate matching point pairs of images plays a key role. To perform trustful matching, the feature descriptor of points in images should be representative and stable. Matching pairs selection includes feature detection, feature matching, and mismatched elimination.  
1. Feature detection: [PERSON]'s SIFT method [PERSON] (1993) is used to realize feature selection and description. It uses a 128-element-long feature vector descriptor to characterize the  
Figure 3: 3D Inter-Vehicle Projection Model.  
gradient pattern in a properly oriented neighborhood surrounding a SIFT feature. The features are invariant to incidental environmental changes in lighting, viewpoint, and scale.
2. Feature matching: By searching the most similar descriptors, SIFT features in front and back images are matched. Brute-force algorithm [23] is adopted here to match feature pairs. The Euclidean distance, used as the matching score, was computed between feature vectors. The selected matching point pairs (also named feature pairs in the following) need to satisfy Equation (1). \[ratio=\frac{\max(dis(f_{a},f_{b}))}{\max\_\_\sec(dis(f_{a},f_{b}))}>0.8\] (2) \((p_{a},p_{b})\) is a pair of corresponding points in image A and image B. \(f_{a}\) and \(f_{b}\) represent feature descriptor of \(p_{a}\in P_{A}\) and \(p_{b}\in P_{B}.\max(dis(f_{a},f_{b}))\) means the best matching pair and \(\max\_\_\sec(dis(f_{a},f_{b}))\) is the second best one. Figure 7a in experiment part displays the matching result, and it is obvious that error matching pairs exist only based on similarity.
3. Mismatched elimination: To achieve more accurate feature pairs, we use the RANSAC algorithm [24] to eliminate mismatched feature pairs. Randomly selected \(n\) small subsets \"seed\" (\(n\) pairs of matching points), and the calculation of fundamental matrix \(F\) is repeated n times. The value of \(|p_{a}Fp_{b}|\) calls the residual error, which is ideally supposed to be zero. \(F\) will be computed by those outlier-free seeds and will produce small residual errors in \(|p_{a}Fp_{b}|\) for mostly inlier matching pairs. We preserve those seeds that produce the minimum median \(|p_{a}Fp_{b}|\) residual errors, so that error pairs are filtered. Figure 7b in experiment part displays the result of features after the RANSAC procedure, and most error feature pairs are eliminated.
## 3 Results
### Multivariate Analysis  
We performed a multivariate analysis of the items that were significant in the univariate analysis. For the respiratory symptoms, R2 was not much higher than 0.2, and we did not obtain a prediction formula for respiratory symptoms. However, we did obtain a formula for pbPEFR, pbPEFRm, and pbPEFRe (Table 3); the results of pbPEFRe are shown in This Table and the respective prediction formula is shown below;  
\[\text{pbPEFRe}=0.8303-0.0054\text{NO}_{3}{}^{-}+0.0242\text{Na}^{+}+0.0336\text {K}^{+}-0.5473\text{Mg}^{2+}\]  
Figure 1: Scatter plot of the relationship between NO3 – and respiratory symptoms or ratio of daily Peak Expiratory Flow Rate to personal best Peak Expiratory Flow Rate(pbPEFR), in daily mean(pbPEFR), in morning(pbPEFRm), and in the evening(pbPEFRe). \begin{table}
\begin{tabular}{l l l l l l} \hline \multicolumn{5}{l}{(1) pbPEFR (mean)} \\ \hline Residuals: & Min & 1Q & Median & 3Q & Max \\ \hline  & \(-0.034975\) & \(-0.007397\) & \(0.001305\) & \(0.007532\) & \(0.026191\) \\ \hline Coefficients: & Estimate & Std.Error & t value & P (\(>\)1t1) & \\ \hline (Intercept) & \(0.820187\) & \(0.003365\) & \(243.755\) & \(0.00000000000002\) *** \\ \hline NO3 – & \(-0.003430\) & \(0.001267\) & \(-2.708\) & \(0.007784\) ** & \\ \hline Na+ & \(0.021338\) & \(0.006101\) & \(3.497\) & \(0.000667\) *** & \\ \hline K+ & \(0.025214\) & \(0.009322\) & \(2.705\) & \(0.007866\) ** & \\ \hline Mg2+ & \(-0.552945\) & \(0.214320\) & \(-2.580\) & \(0.011128\) & \\ \hline \multicolumn{5}{l}{Residual standard error: 0.01142 on 116 degrees of freedom} \\ \multicolumn{5}{l}{Multiple R-squared: 0.2609, Adjusted R-squared: 0.2355} \\ \multicolumn{5}{l}{F-statistic: 10.24 on 4 and 116 DF, \(p\)-value: 0.0000003899} \\ \hline \end{tabular}
\end{table} 
Table 3: Results of multivariate analysis of mean evening personal best peak expiratory flow rate. Figure 2 shows the relationship between the values calculated by this prediction formula and the measured values and indicates that the formula showed a good correlation (n = 121; rs = 0.626, \(p\) =0.00000000000017 [two tailed]). Figure 2: Correlation between calculated and measured ratio of Peak Expiratory Flow Rate to personal best peak expiratory flow rates in the evening (pbPEFRe). The relationship was statistically significant (n = 121, rs = 0.626, \(p\) = 0.000000000000017 [two tailed]). The correlation results indicated that peak expiratory flow rate depends to some extent on the concentration of ionic components.
## 2 Methods
### Initial Path Planning of Parallel Parking
#### 2.4.2 Initial Path Planning of Arc-Line-Arc Based on Idea of Simulating Human to Drive  
Based on the idea of simulating a human driver [15; 16] and the parallel parking process of experienced drivers, the path planning is carried out. The vehicle path is composed of two arcs. When the vehicle moves to middle position, the steered wheels are turned from the right limit position to the left limit position. Vehicle pivot steering will aggravate tire wear and driving with the minimum steering radius for a long time will cause a certain burden on the steering system. In order to reduce the vehicle loss, a straight line transition is introduced between the two arcs, so the parking path is planned based on the arc-line-arc three segment combined curve.  
As shown in Figure 10, the rectangular coordinate system is established to plan the parking path, the right rear corner \(O(c)\) of the parking space is taken as the coordinate origin, the direction \(Ob\) parallel to the road is \(x\) axis, and the direction \(Od\) perpendicular to the road is \(y\) axis with the length unit of m and the angle unit of rad. \(G_{5}\) is the starting point of parking, \(G_{1}\) is the end point of parking. The planning path consists of arc section \(G_{5}G_{4}\), straight line section \(G_{4}G_{2}\) and arc section \(G_{2}G_{1}\). \(G_{4}G_{2}\) is tangent to \(G_{5}G_{4}\) and \(G_{2}G_{1}\). \(G_{3}\) is the midpoint of the parking path. The point \(O_{2}\) is the center of the trajectory circle where \(G_{5}\) is located, and \(O_{1}\) is the center of the circle in which \(G_{1}\) is located. The steering wheel is cancelled in the driverless vehicle with by-wire chassis and the steered wheels are turned by the steering motor assembly directly in steering -by-wire system. The parking process includes three stages: the steered wheels are turned to the right to the limit position at \(G_{5}\), reverse the steering to point \(G_{4}\) with the minimum turning radius \(R_{m}\); return to the normal direction, reverse to point \(G_{2}\) in straight line; the steered wheels are turned to the limit position to the left, reverse steering to point \(G_{1}\), and return to the right position to complete parking.  
The coordinates of \(G_{1}\) and \(G_{5}\) are set respectively as (\(x_{1}\), \(y_{1}\)) and (\(x_{5}\), \(y_{5}\)  
\[\theta_{2}=\arccos\frac{R_{m}}{\sqrt{\left(x_{3}-x_{1}\right)^{2}+\left(y_{1}+R_{m}- y_{3}\right)^{2}}} \tag{7}\]  
\[\theta_{3}=\theta_{1}-\theta_{2} \tag{8}\]  
Through coordinates of point \(G_{1},G_{5}\) and value of \(\theta_{3}\), coordinates of point \(G_{2}\) (Formula (9)) and coordinates of point \(G_{4}\) (Formula (10)) are obtained:  
\[[x_{2},y_{2}]=[x_{1}+R_{\rm m}{\rm sin}\theta_{3},y_{1}+R_{\rm m}(1-{\rm cos} \theta_{3})] \tag{9}\]  
\[[x_{4},y_{4}]=[x_{5}-R_{\rm m}{\rm sin}\theta_{3},y_{5}-R_{\rm m}(1-{\rm cos} \theta_{3})] \tag{10}\]
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 2 JSC Network Configurations  
There exist three primary setups for JSC systems, namely, monostatic, bistatic, and multistatic deployments (as depicted in Figure 2) that depend on the arrangement of transmitters and receivers. This section presents the distinct characteristics of these deployments and outlines their fundamental challenges.
## 3 Results  
### Effects of Freezing Temperature on Density and P-Wave Velocity  
The relationship between the density and freezing temperature under the same number of freezing-thawing cycles is shown in Figure 3. The density slightly decreases with decreasing temperature from 20 to \(-60\)\({}^{\circ}\)C, and the decreasing extent of density approximately reaches 1.0% at \(-60\)\({}^{\circ}\)C.  
Figure 4 shows the effects of freezing temperature on P-wave velocity. The P-wave velocity first slightly decreases with the decrease in freezing temperature, and then the rate of decrease in P-wave velocity rapidly increases below \(-20\)\({}^{\circ}\)C. Compared with the P-wave velocity at 20\({}^{\circ}\)C, the P-wave velocity at the freezing temperature of \(-40\), \(-50\), and \(-60\)\({}^{\circ}\)C decreases by 7.7%, 12.8%, and 14.5%, respectively.
# A Dynamic Multi-Layer Steganography Approach Based on Arabic Letters' Diacritics and Image Layers
## 2 Related Work
### Image Steganography Methods
These fractal images, generated through unique mathematical equations and characterized by specific parameters, provide a complex framework for hiding information. Furthermore, by incorporating a combination of techniques such as SVD and DWT or SVD and IWT, the algorithm addresses the limitations of using SVD alone, ensuring enhanced security and robustness. Furthermore, this hybrid approach offers various benefits, including the ability to resist noise interference, maintain image quality even with minor changes to singular values, achieve robust concealment using the DWT-SVD combination, enable simultaneous compression from loss to lossless with IWT-SVD, facilitate performance comparison for optimal technique selection, and preserve color features in image steganography. Overall, this algorithm introduces an effective and versatile solution for secure and efficient information hiding within fractal cover images.
## 5 Numerical Analysis
### Computational Domain and Grid System  
In this study, the total number of grid elements was 1,350,000. To investigate the change in the result value according to the number of grids, the grid elements were gradually increased from a small number. In the case of 551,000 grid systems, changes in flow distribution trends and internal velocity values in the fan and condenser were reduced (grid number 1,350,000), and changes in numerical calculation values, such as flow and velocity, became constant in more than 2,239,250 grid elements. Therefore, 1,350,000 grid systems were adopted in this study, as shown in Figure 5. Figure 5(a),b shows the front and rear view of a computational grid system of the MCR. From Figure 5(a),b, the arrangements of the compressor, condenser, and the CBM can be visualized. The internal velocity was measured between the compressor and condenser, as shown in Figure 5(a).  
The governing equations were a type of continuously composed governing equations and were analyzed using the commercial CFD software ANSYS-CFX 16.2 [26; 27]. The elements used in the calculation were a type of hexahedral elements, about 1,350,000, as shown in Figure 6. Moreover, a type of non-staggered grid system that determines pressure and velocity at different points was used to perform a linked calculation in pressure and velocity [5]. The CFX integrated coupled solver (pressure and velocity) was used as the solution method in this study.  
Figure 5: Grid dependency of flow at MCR.  
Figure 6: Computational grid system.
## 3 Results and Discussion
### Optimization Parameters by Response Surface Methodology  
In order to further investigate the interactions between the variables, including spiramycin concentration, the volume ratio of antisolvent to solvent and the dripping speed of solvent adding into antisolvent, RSM with BBD was applied in this study. As can be seen in Table 1, the model with an \(F\)-value of 113.00 and \(p\)-value of less than 0.0001 implied the model term was significant and appeared to reasonably represent the data, with only a \(<\)0.01% chance that a value of this size was due to noise. A \(p\)-value less than 0.05 generally indicates that a model term is significant, and non-significant otherwise. In this case, eight terms of \(X_{1}\), \(X_{2}\), \(X_{3}\), \(X_{1}X_{2}\), \(X_{2}X_{3}\), \(X_{1}{}^{2}\), \(X_{2}{}^{2}\), and \(X_{3}{}^{2}\) performed significantly, whereas \(X_{1}X_{3}\) with \(p\)-value greater than 0.10 indicated that the model term was not significant. The \"Lack of fit\" with \(F\)-value of 1.00 and \(p\)-value of 0.3970 indicated that the lack of fit was not significant relative to the pure error. A 39.70% chance for a \"lack of fit\" this large could occur due to noise. Non-significant lack of fit is desired, which implied that the model could fit the data adequately. The \"Pred \(R^{2}\)\" of 0.9410 was in reasonable agreement with the \"Adj R\({}^{2}\)\" of 0.9843. \"Adequacy precision\" was used to measure the signal to noise ratio. A ratio of 31.94, greater than 4, implied an adequate signal. This model can be used to navigate the design space.  
The final MPS of spiramycin microparticle (\(Y\)) was given by:  
\[\begin{array}{c}Y=1497.84-7.83X_{1}-70.88X_{2}-43.28X_{3}-0.69X_{1}X_{2}-0. 6X_{1}X_{3}+\\ 0.76X_{2}X_{3}+0.38X_{1}{}^{2}+3.61X_{2}{}^{2}+0.48X_{3}{}^{2}\end{array} \tag{2}\]  
The mutual influences of the three independent variables and their interactions on MPS of spiramycin were visualized and studied by three-dimensional response surfaces, which are shown in showed in Figure 4. Figure 4a-c presented the interaction of spiramycin concentration and volume ratio of antisolvent to solvent, spiramycin concentration and dripping speed of solvent adding into antisolvent, and the volume ratio of antisolvent to solvent and dripping speed of solvent adding into antisolvent, respectively.  
The optimum conditions predicted by software were: a spiramycin concentration of 20 mg/mL, a volume ratio of antisolvent to solvent of 7 mL/mL, and a dripping speed of solvent adding into antisolvent of 44 mL/h. Under these conditions of point prediction, the MPS of spiramycin microparticles can reach 224.37 nm.  
The verification tests were conducted three times under the optimum conditions mentioned above. The actual MPS was 228.36 nm with an error about 3.99 nm.
# An ADMM Based Parallel Approach for Fund of Fund Construction
## 4 Acceleration Approaches
### Parallelization  
In the \(k\)-th iteration for updating \(z^{k+1}\), we solve the nonlinear optimization problems (13). The procedure for solving each variable \(z_{i}^{k+1}(i=1,\dots,n+m)\) is independent. We parallelize the steps for updating \(z^{k+1}\) by  
\[z_{i}^{k+1} = \arg\min_{l_{i}\leq z_{i}\leq u_{i}}f_{i}(\tilde{z}_{i})+\frac{ \tau_{k}}{2}||x_{i}^{k+1}-v_{i}^{k}||^{2},i=1,\dots,n. \tag{27}\] \[z_{i}^{k+1} = \arg\min_{z_{i}\geq 0}f_{i}(\tilde{z}_{i})+\frac{\tau_{k}}{2}||x_{i} ^{k+1}-v_{i}^{k}||^{2},i=n+1,\dots,n+m.\]  
To avoid solving the constrained problems, we solve the unconstrained problems since the objective function is convex. \(z_{i}^{k+1}=\arg\min_{z_{i}}f_{i}(\tilde{z}_{i})+\frac{\tau_{k}}{2}||x_{i}^{k+ 1}-v_{i}^{k}||^{2}\) And \(z_{i}^{k+1}\) is updated by  
\[z_{i}^{k+1} =\Pi_{[l_{i},u_{i}]}(z_{i}^{k+1}),i=1,\dots,n\] \[z_{i}^{k+1} =\max\{\tilde{z}_{i}^{k+1},0\},i=n+1,\dots,n+m. \tag{28}\]Listing 1 shows an overview of how we designed the kernel in the device on GPU. Each thread computes the new variables \(z_{i}^{k+1}\) by (27) and (28) in parallel. To make the program more extendable, we created a base class named Cost to implement the different types of the cost function by the subclass in the device and pass the point to launch the kernel.  
```
classCost { public: _device_ virtualdoublecost(double*z); _device_ virtualdoublegrad(double*z); }; classFoF_cost:publicCost { public: _device_ virtualdoublecost(double*z)override{ //computethetransactioncost } } _device_ virtualdoublegrad(double*z)override{ //computethegradient } }; } _global_ voidupdate_z(double*z,{ParameterList}){ itid=blockIdx.x * blockDim.x + threadIdx.x; if(tid >=n + m) return; dfp_solver(&cost[tid],&z[tid]); z[tid] = min(ub[tid],max(z[tid],lb[tid]); return; } }
```  
In our implementation, we apply the quasi-Newton method for minimizing (27), w which is a second-order method that helps to quickly find the optimal value (see Listing 2).  
```
__device__voiddfp_solver(Cost*cost,double*z){ doubleHk=1.0;doublegk=0.0;doubledk=0.0; intk=0; while((++k)<MAXITER){ gk=cost->grad(z); if((gk*gk)<epsilon) break; dk=-Hk*gk; intm=0;doublestep=1.0; while((++m)<INENRITER){ doublecost=cost->cost(z+step*dk); doublecost_new=cost->cost(z)+step*gk*dk; if(cost<cost_new){break;} else[step*=rho;]; } doubsk=step*dk; *z+=sk; doubylek=cost->grad(z)-gk; Hk=sk/(yk); } }
```  
**Algorithm 1** Computing the variables \(z_{i}^{k+1}\) in parallel.  
**Remark 1**.: _When the function \(f_{i}(x),i=1,\ldots,n\) is a linear or quadratic function, it is achievable to perform the matrix equilibration to transfer the matrix \(\bar{P}\) into diagonal matrix. After that, we do not have any term like \(\bar{x}_{i}\bar{x}_{j}\), which will reduce the number of iterations. However, for more complicated functions, such preconditioning will make updates of \(z^{k+1}\) difficult to be parallelized._
# Laser Ablation Manipulator Coverage Path Planning Method Based on an Improved Ant Colony Algorithm
## 2 Point Cloud Pre-Processing
### Point Cloud Denoising and Simplification
As the spot size of a laser is variable and the manipulator must remain at each position for at least 2 s during the processing to meet the ablation requirements, a dynamic mesh generation method is used. A larger spot size can be used to improve the efficiency at a position with a small curvature change. In contrast, a smaller spot size must be used to meet the machining requirements at a position with a large curvature change. First, it is necessary to denoise the point cloud data. Methods such as median filter, mean filter, and Gaussian filter change the original point cloud data and reduce the accuracy. Therefore, a point cloud filtering method based on the \(k\)-nearest neighbours (\(k\)-NN) algorithm is proposed. The \(k\)-NN algorithm can determine the \(k\) closest points of a point. Then, the distances between this point and its \(k\) closest points are calculated. Once all the distances become significantly large, this point is considered as an error point. Then, the point cloud data are transformed into structured point cloud data for path point extraction by projecting all the points onto the \(X\)-\(Y\) plane. A 2D mesh (grid) of points is prepared by computing extents (minimum and maximum) of the projected coordinates of the \(X\)-\(Y\) plane. The grid sizes are computed using a recursive method. First, the mesh is divided using a larger side length. Then, a plane fit is performed in each mesh, and its residual is calculated. If the residual is lesser than the accuracy requirement, meshing is performed at this location. If it is greater than the accuracy requirement, the mesh is subdivided with a smaller side length until all the grid points meet the accuracy requirement. Each grid point is a path point. Thereby, the fitted value and the grid point normal vector are obtained. The grid size is the spot size. The size of the grid is related to the accuracy requirement and is influenced by the processing methods and machining accuracy, requiring specific analysis. If there are other constraints, restrictions on the normal vector and grid size are added.