esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Restoration Using a Fixed-Point Method for a TVL2 Regularization Problem
## 7 Numerical Results  
In this section, we provide numerical results for four test images that are listed in Tables 1-4 and Figure 1. In Tables 1-4, \"Alg\" represents the algorithm number to be used, \"\(\text{P}_{0}\)\" represents the PSNR values for the blurred and noisy image \(f\), \"PSNR\" represents the PSNR values for the restored image, \"Iter\" denotes the number of iterations required for Algorithms 1-5, the values in parentheses under the \"Iter\" column refer to the average number of iterations for CGLS, and \"\(\alpha,\ \beta,\ \gamma,\ \lambda\)\" and \"\(\delta\)\" denote parameters that are chosen by numerical tries. Notice that, according to Theorem 1, the parameters \(\alpha,\ \beta,\ \gamma,\ \lambda\) should be chosen appropriately for good performance of the fixed-point methods. As can be seen in Tables 1-4, Algorithm 3 restores the true image better than Algorithms 1 and 2. This means that the fixed-point-like method for TVL2 problem (4) restores the true image better than the fixed-point methods for TVL2 problems (3) and (4). The linear system in Algorithm 3 that is obtained by computing \(a^{k+1}\) after the solution step of finding \(u^{k+1}\) is well-conditioned, while the linear system in Algorithm 2 is ill-conditioned. This is the reason why Algorithm 3 restores the true image significantly better than Algorithm 2. Since PSNR values of Algorithm 2 are about 0.3 to 1.0 smaller than those of Algorithm 3 for all test images, numerical results of Algorithm 2 are not provided for House and Boat images in Tables 3 and 4. Figure 1 shows the restored images by the fixed-point method, called Algorithm 1, for TVL2 problem (3) and the fixed-point-like method, called Algorithm 3, for TVL2 problem (4). The fixed-point method (Algorithm 1) for the TVL2 model (3) performs worse than the corresponding split Bregman method (Algorithm 4), while the fixed-point-like method (Algorithm 3) for the TVL2 model (3) performs almost as well as the corresponding split Bregman method (Algorithm 5). Note that both the split Bregman methods for the TVL2 models (3) and (4) perform the same for almost all cases. When considering the number of iterations required for the CGLS, the total number of iterations for Algorithm 3 is less than that of Algorithm 1. Each iteration of Algorithms 1-3 requires one linear system solver GLS and two matrix-times-vector operations that are the main time-consuming kernels, and each iteration of CGLS requires two matrix-times-vector operations. In addition, there are some additional vector-update operations that can be negligible as compared with matrix-times-vector operation. This means that the execution time of Algorithm 3 for the new TVL2 problem (4) is less than that of Algorithm 1 for TVL2 problem (3). For example, for the Cameraman image with Gaussian blur, the CPU times for Algorithms 1 and 3 are about 68 and 42 seconds, respectively.
## 4 Proposed Solution  
This section details the proposed solution along with basic assumptions and presents concrete examples for better understanding.  
One _naive_ solution might be to create a replica of the original server, migrate one connection at a time, and check whether that connection is problematic. That approach requires the maximum amount of time translated to the processes of checkpointing the connection status, spawning the replica server, migrating the connection parameters over the network, resuming the connection, and finally, checking the health status of the replica server. Empirically, out of all these processes, the last step is particularly time-consuming. Therefore, this approach is very time-intensive, albeit it relies upon the creation of only one replica server, and thus very cost-efficient. _Another naive solution_ might be to spawn many replicas, precisely as many as the active connections supported by the server, and migrate each connection to its own replica server in parallel. Then, for each replica, the effects of the connection to the underlying, say, Web application would be studied in isolation. That would ensure that all steps would be performed in parallel, but the maximum number of replicas (and therefore resources) are required.
## 3 Results and Discussion
### Statistical Assessment of Measurement Results  
[PERSON]'s test and Student's paired _t_-test were used to assess the significance of the difference in variability and mean values of the measurement results. For the [PERSON]'s test, we assume that the difference in variances of measurement results at the beginning and after the implementation of the respective stage is statistically insignificant (\(\sigma_{1}{}^{2}-\sigma_{2}{}^{2}=0\)). For the Student's paired _t_-test, we assume that the difference in the mean values of the measurement results at the beginning and after the implementation of the respective stage is statistically insignificant (\(\sigma_{1}{}^{2}-\sigma_{2}{}^{2}=0\)). The results of the tests conducted at the selected significant level \(\alpha=0.05\), which are presented in Table 5, show that the variability in the measurement results after the individual stages is comparable to the variability in the initial measurements (the difference is statistically insignificant). When considering the differences in the mean values of the measurement results, we came to the conclusion that the statistically significant difference in the mean values of the measurement results has already been apparent after the implementation of the third stage. A reduction in noise below the set legislative limits (50 dB) was achieved only when the fourth stage has been implemented, and at the same time the difference in the mean values of the measurement results at the beginning and after the fourth stage was statistically significant.
# BrushGaussian: Brushstroke-Based Stylization for 3D Gaussian Splatting
## 2 Related Works
### Three-Dimensional Representation and Style Transfer
[70] further extended NeRF-based techniques by incorporating stroke-based rendering, simulating the progressive painting process using vector strokes. Although NeRF-based representations outperform all previous approaches in rendering quality, they are computationally expensive, requiring extensive sampling via deep neural networks. The slow rendering speed presents a major bottleneck for real-time applications, prompting researchers to explore more efficient alternatives. To overcome NeRF's computational bottlenecks, researchers have turned to 3D Gaussian Splatting [71], which provides both high-speed and high-quality rendering. Unlike NeRFs, which requires volumetric sampling, 3D Gaussian Splatting represents a scene as a collection of 3D Gaussians, allowing for efficient real-time rendering. As a result, many recent 3D style transfer approaches have adopted 3D Gaussian primitives as their underlying representation [72; 73; 74; 75]. For example, [PERSON] et al. introduced GaussianEditor [75], a method for editing 3D scenes via 3D Gaussians with text-based instructions, enabling intuitive content creation and stylization. Our work is most closely related to that of [PERSON] et al. [70] and [PERSON] et al. [76]. While [PERSON] et al. introduced stroke-based rendering into NeRFs with a limited set of predefinedstroke shapes requiring manual design, our approach enables arbitrary stroke shapes without additional intervention. [PERSON] et al. aim to improve photorealistic rendering quality by enriching each Gaussian with learnable texture maps (e.g., RGB or alpha), focusing on the faithful reconstruction of real-world appearances. In contrast, our work targets non-photorealistic rendering (NPR), with a strong emphasis on artistic stylization. We introduce a brushstroke-aware texture mapping method that enables not only color transfer but also shape-aware deformation, enabling visually expressive, painterly renderings. Moreover, we incorporate unsupervised clustering and Gaussian pruning mechanisms to enable stylization at different abstraction levels, which is outside the scope of [PERSON] et al. Our pipeline is explicitly designed to support artistic control rather than photorealistic fidelity. While both approaches enhance 3D Gaussian primitives through texture-based representations, the motivations and technical focuses are fundamentally different.
## 4 Heuristic Algorithms for UiL Content Delivery Systems
### Recommendation of Content Lists  
Once the capacity is assigned for each user, the problem of content recommendation can be formulated as  
Figure 4: Proposed heuristic algorithm for capacity allocation with multiple users.  
\[\begin{split}\text{max}&\sum_{u=1}^{U}\sum_{f=1}^{F}r_{uf}x_{uf },\\ \text{s.t.}&\forall u,t,P(\frac{q_{u}^{t}}{R_{u}^{t}} \leq\tau_{u})>\xi,\\ &\sum_{f=1}^{F}x_{uf}=N,\\ & x_{uf}\in\{0,1\}.\end{split} \tag{22}\]  
This problem is still a nonlinear integer programming problem. Therefore, a novel heuristic algorithm is proposed to solve the problem in four steps: (1) Sort the user's interest profile in a descending order, then select the top \(N\) contents of each user as the initial solution, and calculate the total interest; (2) If each user's transmission delay constraint is satisfied, exit the algorithm. If not, continue to the next step; (3) For the users whose transmission delay constraint is not satisfied, discard the content with the largest file size in the recommendation list, and select the file whose size is smaller than the content just discarded and whose interest level is next in the ranking list; (4) Finally, recalculate the total interest and go back to step 2 to continue the calculation. The flowchart of the algorithm is shown in Figure 5. It can be shown that the algorithm has a polynomial time complexity given by \(O(UFN+UF^{2})\).  
Figure 5: Proposed heuristic algorithm for content recommendation.
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 3 Vehicle Motion Estimation Using an Upward-Facing Camera
### Motion Estimation Process
The visual odometry steps [55] are depicted in Figure 2.  
```
Input :\(f_{p_{1}}=\{p_{0},p_{2},...,p_{M-1}\}\) and \(f_{p_{2}}=\{p^{\prime}_{0^{\prime}},p^{\prime}_{2},...,p^{\prime}_{M-1}\}\) Output :\(\mathbf{r}_{c}\) and \(\mathbf{t}_{c}\) // initialization \(\mathbf{r}_{c}=\mathbf{I}_{3\times 3}\) \(\mathbf{t}_{c}=\mathbf{0}_{3\times 1}\) // iterate to the total number of feature points for\(j=0,1,2,\ldots,M-1\)do // compute the weighted centroids for both inputs \(f_{p_{1}}\) and \(f_{p_{2}}\) \(\bar{p}=\dfrac{\sum_{j}w_{j}p_{j}}{\sum_{j}w_{j}}\) and \(\bar{p}^{\prime}=\dfrac{\sum_{j}w_{j}p^{\prime}_{j}}{\sum_{j}w_{j}}\) // compute the centred vectors \(c_{j}=p_{j}-\bar{p}\) and \(c^{\prime}_{j}=p^{\prime}_{j}-\bar{p}^{\prime}\) end // compute the covariance matrix \(\mathsf{Cov}=\mathsf{C}\ \times\ diag(w_{0},w_{1},...,w_{M-1})\ \times\ \mathsf{C}^{\mathsf{T}}\), \(\mathsf{C}\) and \(\mathsf{C}^{\prime}\) = matrices with \(c_{j}\) and \(c^{\prime}_{j}\) as their columns // determine the SVD of \(\mathsf{Cov}\) \(\mathsf{Cov}=U\Sigma V^{\mathsf{T}}\) // compute the rotation \(\mathbf{r}_{c}=V \begin{bmatrix}1&&&&\\ &1&&&&\\ &&\ddots&&\\ &&1&&\\ &&&\mathsf{det}(VU^{\mathsf{T}})\end{bmatrix} U^{\mathsf{T}}\) // compute the translation \(\mathbf{t}_{c}=\bar{p}^{\prime}-\mathbf{r}_{c}\bar{p}\) return :\(\mathbf{r}_{c}\ \,\ \mathbf{t}_{c}\)
```  
**Algorithm 1**Rotation and Translation Computation Algorithm.
## 3 Methodology
### Rf  
Random forest (RF) is a real-time ensemble classification algorithm. It is composed of a set of trees, with each tree depending on random variables. The vector \(\mathbf{X}=(\mathbf{X}_{1},\)\(\mathbf{X}_{2}\ldots,\)\(\mathbf{X}_{n})^{\mathsf{T}}\) represents the input value, the random variable \(\mathbf{Y}\) represents the response or  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Year** & **Dataset** & **Algorithm** & **Accuracy** & **Advantages/Disadvantages** \\ \hline \multirow{6}{*}{[PERSON] et al. [21]} & \multirow{6}{*}{2021} & \multirow{6}{*}{Heart Disease} & \multirow{6}{*}{DT, KNN} & DT has 81\% accuracy and the KNN algorithm has an accuracy rate or level of 67\%. We assume that the Random Forest algorithm is more efficient with the proposed work. \\  & & & & & DT has the highest accuracy value. The DT model consistently has higher accuracy than NB and SVM models. \\ \hline \multirow{6}{*}{[PERSON] et al. [24]} & \multirow{6}{*}{2021} & \multirow{6}{*}{Acute Bronchiolitis} & \multirow{6}{*}{SB, SVM, DT} & \multirow{6}{*}{90\%} & The XGB has the highest prediction accuracy. Reduction data implementation is important to enhance the accuracy value of the prediction. \\  & & & & & \\ \hline \multirow{6}{*}{[PERSON] et al. [15]} & \multirow{6}{*}{2021} & \multirow{6}{*}{UCI Heart Disease} & \multirow{6}{*}{RF, LM} & \multirow{6}{*}{88.4\%} & We suggest the use of suitable data preprocessing steps and a reduction algorithm as the CFS to enhance the accuracy value. \\  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
prediction values, and the used joint distribution is PXY (X, Y). The goal is predicting Y from a prediction function f(X) [28].  
The class prediction is conducted by the majority of votes, which is defined as the most common class prediction between trees. Therefore, the voting occurs on the class probability level. The predictions select the class with the highest class probability as shown in Figure 2.  
Figure 2 shows how the ensemble classification done. It gathers the results from different trees and votes the results then it chose the result with the highest voting. The following parameters are used to enhance the classification accuracy:  
* The n_estimators' parameter represents the number of trees that we need to build prior to voting. Better accuracy results from the highest number of trees, but it results in high-performance time. We use 50 tree numbers for an efficient classification.
* The max_depth parameter represents the maximum length of the trees. If we do not assign a value for max_depth, it may lead to infinite nodes. We assign it as 20.
## 1 Introduction
For example, [PERSON] _et al._ (2008) [24] evaluated MODIS GPP by using estimated GPP from EC flux measurements over an alpine meadow on the Tibetan Plateau. Their results showed that the mean annual MODIS GPP accounted for 1/2-2/3 of the flux-based GPP in the study region. [PERSON] _et al._ (2015) [25] reported that MODIS GPP performed poorly for evergreen forests but provided accurate estimates for grassland and mixed forests. However, these studies explored error sources mainly focused on one or two aspects, while comprehensive and detailed error analyses are still needed. Thus, we not only methodically evaluated the eight-day, seasonal and annual MODIS GPP against tower GPP, but also carefully analyzed each input. In this paper, we evaluated MOD17A2 GPP using EC flux tower data at eight sites in five various biome types across China (Figure 1 and Table 1). The objectives of our study were to evaluate the performance of MOD17A2 in China through comparisons with GPP measured at EC flux towers and to examine the potential error contributions of all input variables (meteorology, LAI/FPAR, land cover and LUE) used in the algorithm. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sites** & **Lat** & **Lon** & **Biome** & **MAP** & **MAT** & **Data** & **References** \\
**(Abbreviation)** & **(\({}^{\circ}\)N)** & **(\({}^{\circ}\)E)** & **Type** & **(mm)** & **(\({}^{\circ}\)C)** & **Range** & & \\ \hline Changbaishan & 42.40 & 128.10 & MF & 713 & 3.6 & 2003–2005 & [PERSON] _et al._ (2006) [26] \\ \hline Qianyanzhou forest & 26.74 & 115.06 & ENF & 1542 & 17.9 & 2003–2005 & [PERSON] _et al._ (2006) [27] \\ \hline Dinghushan forest & 23.17 & 112.53 & EBF & 1956 & 20.9 & 2003–2005 & [PERSON] _et al._ (2006) [28] \\ \hline Xishuangbanna forest site (XSBN) & 21.95 & 101.20 & EBF & 1493 & 21.8 & 2003–2005 & [PERSON] _et al._ (2006) [29] \\ \hline Yucheng cropland site (YC) & 36.83 & 116.57 & Crop & 582 & 13.1 & 2003–2005 & [PERSON] _et al._ (2006) [30] \\ \hline Haibei grassland site (HB) & 37.67 & 101.33 & Grass & 580 & \(-1.7\) & 2003–2005 & [PERSON] _et al._ (2006) [31] \\ \hline Inner Mongolia grassland site (NMG) & 43.55 & 116.68 & Grass & 338 & 0.9 & 2004–2005 & [PERSON] _et al._ (2006) [31] \\ \hline Dangxiong grassland site (DX) & 30.50 & 91.07 & Grass & 450 & 1.3 & 2004–2005 & [PERSON] _et al._ (2006) [29] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Site descriptions including name (abbreviation), latitude and longitude (lat/long, decimal degrees), general biome type, mean annual long-term precipitation (MAP, mm), mean annual temperature (MAT, \({}^{\circ}\)C), years of measurements and references. MF: Mixed forest; ENF: Evergreen needleleaf forests; EBF: Evergreen broadleaf forests; Crop: Croplands; Grass: grasslands. Figure 1: Locations of the eight flux tower sites used in this study. MF: Mixed forest; ENF: Evergreen needleleaf forests; EBF: Evergreen broadleaf forests; Crop: Croplands; Grass: grasslands.
## 2 Methods
### Deep-Learning Based Methods  
#### 2.2.1 Dsen2  
The Dsen2 network was developed in [10] is a fully convolutional neural network, with ResNet inspired skip connections, and can handle images of arbitrary size. Two forms of the network were trained on \(32\times 32\) and \(96\times 96\) pixel patches, respectively, for \(2\times\) and \(6\times\) super-resolution. Inputs are upsampled to the target resolution using simple bi-linear interpolation before they are fed into the network. The first network is trained using the 10 and 20 m bands, while the second network is trained using all bands. They are otherwise the same. The network loss function is the mean absolute error between the network output and the training target. Training is performed using [PERSON]'s protocol. Image bands are downsampled, \(10\to 20\) m and \(20\to 40\) m to train the \(20\to 10\) m network and \(10\to 60\) m, \(20\to 120\) m, and \(60\to 360\) m to train the \(60\to 10\) m network.  
Bilinear interpolation is used, so all bands have the same pixel counts before inputting them as training data. The \(20\to 10\) m network is trained with \(\mathbf{Y}_{20m}\) as the target and \(\mathbf{Y}_{10m}^{20m}\) and \(\mathbf{Y}_{20m}^{40m}\) as the input. The \(60\to 10\) m network with \(\mathbf{Y}_{60m}\) as the target and \(\mathbf{Y}_{10m}^{60m}\), \(\mathbf{Y}_{20m}^{120m}\) and \(\mathbf{Y}_{60m}^{360m}\) as the input. Scale-invariance is assumed to assure the network's usefulness for the actual \(20\to 10\) m and \(60\to 10\) m task.  
The network has been trained on 60 varied scenes from across the globe. Actual S2 data tiles were gathered from the Copernicus Services Data Hub, picked randomly, aiming for a roughly even distribution over the globe and for variety in terms of climate zone, land-cover, and biome type. In regard to the generation of training data [10] directly references the applicability of the S2 PSF, but in practice, the code supplied by the authors does not utilize it. Instead training data is generated by simply filtering the bands with a Gaussian filter with \(\sigma=1/s\) and an \(s\times s\) boxcar filter followed by downsampling by \(s\), where \(s\) is either 2 or 6.
# A Gap Waveguide-Based Compact Rectangular Waveguide to a Packaged Microstrip Inline Transition
## 2 Transition Concepts
### Ridge and Groove Gap Waveguide-Based Transition  
The second proposed configuration is shown in Figure 1b. Formally, it is simpler than the previous transition, since it consists just of a Chebyshev transformer, implemented in groove gap waveguidesections. It presents two advantages with respect to the previous configuration: no microstrip line taper is required, and since the microstrip substrate does not enter the bed of nails area, the design of the Chebyshev transformer is simplified.
# Equivalent Modeling and Verification of a High-Steepness and Lightweight Elliptical Aluminum Mirror
## 3 Equivalent Design Theory of a High-Steepness Mirror
### Area-Mass Density  
In order to evaluate the quality characteristics of lightweight mirrors, the area-mass density is introduced. Therefore, lightweight mirrors are measured in terms of area-mass density or mass per unit area [21]. The general form for estimating the mass per unit area of the mirror is shown in Equation (7):  
\[\frac{m}{A}=\rho\left(t_{f}+\eta h_{c}\right) \tag{7}\]  
In the formula, \(m/A\) is the area-mass density, \(\rho\) is the material density, \(t_{f}\) is the mirror face-sheet thicknesses, \(\eta\) is the solidness ratio, and \(h_{c}\) is the isogrid cell depth.  
The solidness ratio can be calculated using Equation (8) as follows:  
\[\eta=\frac{(2B+t_{W})t_{W}}{(B+t_{W})^{2}} \tag{8}\]  
where \(B\) is inscribed circle diameter, and \(t_{W}\) is shear core rib thickness.  
For commonly used lightweight structures, the expressed parameters are shown in Figure 5:
# Research on Geometric Design Standards for Freeways under a Fully Autonomous Driving Environment
## 4 Geometric Design Standard
### Horizontal Alignment
#### 4.1.2 Minimum Tangent Length  
If the length of the tangent between two curves of identical direction is too short, the driver will be misled visually. This does not meet the principle of \"alignment design should be continuous\". In this respect, in a fully autonomous driving environment, the on-board camera will not produce the same misleading visual as the driver. The tangent length limit between two curves of identical direction is irrelevant.  
Whether in a manual driving environment or autonomous driving environment, there are superelevation and widening transition requirements between two reverse curves. When an autonomous vehicle is driving on the reverse curve section of a freeway at high speed, it is necessary to avoid the instability caused by too short of a tangent between the reverse curves. At the same time, it is necessary to ensure that passengers adapt to changes of centrifugal force, so as to avoid an uncomfortable driving experience such as dizziness or shaking. Therefore, in an autonomous driving environment, it is suggested to limit the tangent length between reverse curves according to the current specification.  
According to the driving characteristics of autonomous vehicles and referring to the current standard, in an autonomous driving environment, the minimum tangent length in the horizontal alignment design of freeways is limited, and its length (suggested value) is shown in Table 7.