esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Inpainting Based on Multi-Patch Match with Adaptive Size
## 3 Proposed Approach
### Multi-Patch Match
As explained above, the order of \(\Psi_{\hat{p}_{i}}\) may not be consistent with the descending order of their nonuniformity, but follows the order of filling priority. After generating multiple target patches, the strategy of \"subregion search\" mentioned in Section 3.3 is also involved. For each target patch \(\Psi_{\hat{p}_{i}}\), subregion \(\phi_{i}\) with the similar content is assigned by Equation (10). As shown in Figure 6, during a single iteration, the patch match between \(\Psi_{\hat{q}_{i}}\) and \(\Psi_{\hat{p}_{i}}\) can proceed in their corresponding subregion \(\phi_{i}\), respectively, and each subregion \(\phi_{i}\) will produce a best sample patch \(\Psi_{\hat{q}_{i}}\) to fill its target patch. In this way, multiple patches can be filled in one iteration. By combining the subregion search and multi-patch match strategies, the speed of algorithm can be effectively improved. It must be stressed out that step (2) in Algorithm 1 ensures that \(\forall\hat{p}_{i},\hat{p}_{j}\in\delta\Omega\ (i\
eq j)\), the relation \(\left|T(\hat{p}_{i})-T(\hat{p}_{j})\right|\geq 2\alpha\) always hold, in addition, Equation (10) guarantees that \(\forall q\in\phi_{i}\,\) there always exists that \(\left|T(q)-T(\hat{p}_{i})\right|\leq\alpha\). These two equations will avoid the overlap of the nonuniformity interval and spatial scope between any two subregions, i.e., \(T(\phi_{i})\cap T(\phi_{j})=\varnothing,\ \phi_{i}\cap\phi_{j}=\varnothing\ (i\
eq j)\), ensuring that the match process in every subregion is independent of each other. The description of our overall algorithmic steps is shown in Algorithm 2. ```
1. Initialization: compute the nonuniformity map \(T(\Phi)\) as Equation (8). 2. Determine the filling front \(\delta\Omega\), compute its priorities \(P(\delta\Omega)\) as Equation (1). 3. Generate multiple target patches \(\Psi_{\hat{p}_{i}}\) on \(\delta\Omega\), as shown in Algorithm 1. 4. Assign subregion \(\phi_{i}\) as the search scope for every \(\Psi_{\hat{p}_{i}}\) as Equation (10). 5. Match the best sample patch \(\Psi_{\hat{q}_{i}}\) for \(\Psi_{\hat{p}_{i}}\) in \(\phi_{i}\) using Equation (4). 6. Fill the unknown part of \(\Psi_{\hat{p}_{i}}\) with corresponding pixels in \(\Psi_{\hat{q}_{i}}\). 7. Update the data term for new pixels as Equation (5), confidence term and nonuniformity term are directly copied from their source pixels. 8. Update region \(\Omega\) and \(\Phi\), if \(\Omega=\varnothing\), exit the whole process. Otherwise, back to step 2. ```  
**Algorithm 2** Overall Steps of Our Algorithm.
## 2 Numerical Model Validation
### Validation Results  
Figure 5 shows the computed and measured strains in the reinforcement layers that were installed with strain gauges. The computed strain was larger at the reinforcement connection and decreased along the reinforcement length in each layer. This distribution shape of the reinforcement strains agrees with the measurements, with the exception of those for Layer 8. The measured connection strain was the smallest in Layer 8 as a result of some looseness between the facing column and the reinforcement layer, which occurred during the wall's construction [10]. However, this looseness was not captured by the numerical model, which led to a difference in the computed and measured strains at the connection in Layer 8. In general, the computed reinforcement strains agree well with the measurements.  
Figure 6 shows the measured and computed connection loads \(T_{\text{con}}\) and the maximum reinforcement loads within the backfill \(T_{\text{bmax}}\). The maximum reinforcement load beyond 1 m away from the wall facing was selected for the \(T_{\text{bmax}}\) value to avoid the influence of the down-drag force motivated by compaction near the connection. The computed values are in good agreement with the measured \(T_{\text{con}}\) and \(T_{\text{bmax}}\). Only the computed \(T_{\text{con}}\) in Layer 8 is much larger than the measurement as a result of the looseness occurring at the connection in the field wall. The shear strength and stiffness at the facing block-leveling pad interface will significantly influence wall performance [19; 20; 32]. The encouraging agreement in the reinforcement strains and loads between the numerical and field SR-18 wall indicates that our proposed interface shear stiffness calculation model is reasonable.  
Figure 5: Computed and measured reinforcement strains of SR-18 wall.  
Figure 6: Computed and measured reinforcement loads of SR-18 wall.
# Dynamic Nonprehensile Manipulation of a Moving Object Using a Batting Primitive
## 3 Method
### Ball Trajectory Estimation
#### 3.2.2 Weighted Least Square Regression  
Least square regression was applied to the ball trajectory estimation regression described in Section 3.2.1, based on the assumption that the data measured by the stereo vision sensor are accurate. However, the accuracy of the position data measured from the stereo vision sensor is inversely proportional to the distance between the stereo vision sensor and the object. This is because the closer the distance, the greater is the number of pixels representing the object. Figure 8 shows a graph of position accuracy versus distance between Bumblebee2 and the object. Therefore, it is more appropriate to apply the weighted least-squares regression than the least-squares regression considering the accuracy of the measured value according to the distance.  
Least squares regression minimizes the sum of the squares of the residuals, and the weighted least-squares regression minimizes the sums (Q) of the squares of the residuals (\(y_{i}-bx_{i}\)) multiplied by the weight (\(w_{i}\)); it is given by  
Figure 8: Depth error vs. distance. Depth accuracy of stereo vision sensor according to distance.  
Figure 7: Estimated ball trajectory according to the number of sampled data points. As the number of sampled data points increases, the trajectory estimation becomes more accurate.  
\[\text{Q}=\sum_{i=1}^{n}w_{i}(y_{i}-bx_{i})^{2},\text{ }where\ w_{i}=w_{i+1}\lambda \tag{6}\]  
The weights are calculated as a geometric sequence based on the last weight. In several tests, 0.8 was used as \(\lambda\) of the weighted least square. Figure 9 is a graph showing the RMS errors for the X, Y, and Z components obtained by applying the weighted least square and least square regressions. Although there is almost no difference in the RMS error for X and Y with and without the weighted least square, the RMS error decreases rapidly with weighted least square for the Z axis. This result is expected because the change in the trajectory of the ball on the z axis is larger than that on the x and y axes.
# A Multi-Organ Segmentation Network Based on Densely Connected RL-Unet
## 3 Experiments
### Experiment Results
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Methods** & **RV** & **Myo** & **LV** & **DSC** \\ \hline R50+AttnUnet [19] & 87.58 & 79.20 & 93.47 & 86.75 \\ R50+Unet [19] & 87.10 & 80.63 & 94.92 & 87.55 \\ Trans-Unet [19] & 86.67 & 87.27 & 95.18 & 89.71 \\ Swin-Unet [20] & 85.77 & 84.42 & 94.03 & 88.07 \\ MT-Unet [43] & 86.64 & 89.04 & 95.62 & 90.43 \\ PVTCASCADE [28] & 89.97 & 88.90 & 95.50 & 91.46 \\ RL-Unet (ours) & 88.54 & 87.65 & 95.46 & 91.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Segmentation results of different methods on ACDC dataset. As illustrated in Figure 8, the detailed annotations and consistent segmentation shapes in the ACDC dataset enable most architectures, including RL-Unet, to achieve high segmentation accuracy and visually appealing results. In certain cases, U-net exhibits partial segmentation omissions. As observed in the third line, the segmentation results produced by RL-Unet and Attention Unet are more complete and closely aligned with the Ground Truth. In contrast, the segmentation contours of Swin-Unet and U-Net show partial omissions. In the classification of benign cases, most architectures achieve satisfactory segmentation, with smooth transitions along the segmentation boundaries. However, in the classification of malignant cases, most architectures are only able to delineate the approximate contours and often lack the ability to capture detailed features accurately. RL-Unet more effectively restores details, mitigating issues of over-segmentation and under-segmentation. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & **Benign** & **Malignant** & **DSC** \\ \hline U-Net [7] & 70.49 & 63.47 & 66.98 \\ AttU-Net [10] & 73.30 & 62.95 & 68.13 \\ Swin-Unet [20] & 73.31 & 62.13 & 67.72 \\ U-Net++ [24] & 75.56 & 65.52 & 70.54 \\ U-Net3+ [9] & 75.07 & 66.19 & 70.67 \\ RL-Unet (ours) & 74.92 & 66.09 & 70.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Segmentation results of different methods on BUSI dataset. Figure 8: Visualization of segmentation results on ACDC and BUSI datasets. (**a**) Image; (**b**) Ground Truth; (**c**) RL-Unet; (**d**) Attention Unet; (**e**) Swin-Unet; (**f**) U-Net.
## 6 Simulation Results and Discussion
### Optimization Results  
#### 6.1.1 Stand-Alone Diesel System  
As depicted in Table 3, when only diesel power generation is considered the LCOE of USD 0.346/kWh, NPC of USD 235,734 and 38.3 t/yr of pollutant emission gases were the optimization results. However, since the simulation result of only diesel system is the reference for other configurations, to make it cost effective excesses energy of not more than 3% and a maximum capacity shortage of 0.09% were considered. Once the optimization result of the stand-alone diesel system was determined, its feasibility relative to the grid extension cost was determined using BGED analysis. According to BGED analysis, a stand-alone diesel system is relatively feasible for the selected site compared to grid extension cost when the electrification distance is farther than 3.11 km, as shown in Figure 10a. However, due to the high level of polluting gases emitted, the high operating  
Figure 9: (a) Nominal interest rate, (b) Inflation rate.  
fuel cost, and the tourist-attracting nature of the island, the stand-alone diesel system is not the favored solution to electrify the specified island.
# Valorization of Date Fruit (_Phoenix dactylifera_ L.) Processing Waste and By-Products: A Review
## 3 Nutritional and Biochemical Composition of Date Fruit and Date By-Products
### Protein and Amino Acids  
Proteins are large organic molecules with one or multiple lengthy sequences of amino acids. Traditionally, individuals have obtained protein by consuming animal-based sources such as meat, eggs, and milk. However, the research on alternative protein sources to replace expensive animal proteins has led researchers to consider materials previously considered wastes but rich in protein. While the protein content of date palm may not be as high as its carbohydrate content, its essential amino acids provide significant benefits to human health [29]. Recovering functional proteins from date waste can increase the value of discarded low-grade fruits and date peels, skins, and seeds.  
Date fruits have a protein range of 2.43% to 3.12%, while protein content in date seed is 4.81% to 5.84 [21; 43]. [PERSON] et al. [43] reported the presence of essential amino acids in dates, with significant proportions of glutamine, aspartic acid, glycine, proline, histidine, and valine. In their research, it was observed that all tested date varieties predominantly contained glutamine and aspartic amino acids, with varying percentages ranging from 24.23% to 38.82% for glutamine and 20.98% to 32.11% for aspartic acid. Additionally, glycine, proline, histidine, and valine were also present in notable amounts, contributing to the overall amino acid composition of dates, with concentrations ranging from 11.60% to 18.03%, 9.83% to 14.19%, 3.47% to 6.20%, and 8.23% to 12.02%, respectively. Other essential amino acids, such as glycine, proline, and valine, were also at significant levels. These amino acids play a crucial role in the formation and synthesis of proteins in the body, involving the production of neurotransmitters or the formation of collagen, as the protein is the structure that forms the connective tissues in the body, including cartilage, tendons, and ligaments [44]. Also, the isoleucine content in dates is over 800 times greater than that found in apples [45]. According to the research conducted by [PERSON] and [PERSON] [46], the quantity of lysine found in dates is remarkably high. Specifically, the lysine content in date seeds, at 4.05 g per 100 g of protein, is higher than the lysine in wheat flour by approximately 1.8 times, which is 2.46 g per 100 g of protein.
## 3 Sink-oriented Dynamic Location Service  
In this section, we describe in detail the SDLS protocol. SDLS works under the following basic network assumptions:  
1. Sensor nodes are placed in two-dimensional square field where they are randomly deployed with high density.
2. Sensor nodes communicate with each other using single-hop communications through short-range radios. Long-range data delivery is accomplished forwarding data across multiple-hops.
3. Each sensor node knows its own location, as well as the location of its 1-hop neighbor nodes using localization algorithms [25].  
4. Upon detecting an event of interest, a source node immediately reports its location information using the Eight-Direction Anchor (EDA) system.
5. One or more mobile sinks moves randomly in the deployment field. Sinks (users) query the network to collect the location of source nodes. Then, sinks wait until they receive the location of source nodes. After getting the location of the source node, the sink can dynamically send the data request query to the source node with consideration of source node's location and direction of sink's movement. Therefore, we named our proposed scheme as Sink-oriented Dynamic Location Service (SDLS) protocol.
# A New Approach for Environmental Risk Assessments of Living Modified Organisms in South Korea
## 2 Materials and Methods
### Insect Bioassays of the Vip3 Aa Protein  
Fresh recombinant Vip3A protein was prepared shortly before the insect bioassay and stored at \(-70\)\({}^{\circ}\)C for the test duration [28]. _Spodoptera litura_ eggs were provided by the National Institute of Crop Science (Wanju, Korea). For the insecticidal activity assay of Vip3 Aa protein, freshly hatched neonate _S. litura_ larvae were kept under controlled conditions in an insect-rearing room at 25 \(\pm\) 1 \({}^{\circ}\)C and 60% relative humidity for 16 and 8 h (light/dark).  
For the artificial diet-overlay bioassay, the first instar larvae were fed with two concentrations (40 and 100 ng/mg) of recombinant Vip3 Aa protein and buffer 1 (1\(\times\) PBS) in an artificial diet. An artificial diet without anything else was also maintained as a control (mock). Fresh recombinant Vip3A protein dilutions (200 uL) and control solution were dispensed uniformly onto the surface of 1 g of artificial diet and allowed to dry on a clean bench. Thereafter, the larvae were placed on each test plate using a soft brush, and the plates were covered with sterilized gauze and a perforated plastic cap. Subsequently, an artificial diet-overlay bioassay was performed in our insect-rearing room for 12 d. During the bioassay period, 200 uL of each recombinant Vip3 Aa protein dilution (or control buffer) was added every 24 h, and the number of dead larvae was scored daily.  
The insecticidal activity of Vip3 Aa was evaluated using the second- or third-day leaves after agroinfiltration. The experiment was set up on test plates, with leaves placed on moist filter paper. First, agroinfiltration transient leaves and control leaves (mock, buffer 2: infiltration buffer, and buffer 3: containing vector) were placed in a test plate. Afterward, first-instar larvae were added to each test plate, and the plates were covered with sterilized gauze and a perforated plastic cap. The plates were then kept in an insect-rearing room for 12 d under the same conditions as the artificial diet-overlay bioassay. During the bioassay period, fresh leaves were changed every 48 h, and the number of dead larvae was recorded daily. For the artificial diet-overlay bioassay and insecticidal activity of Vip3 Aa, 10 S. litura larvae were used. The treatments were performed in duplicate and repeated five times. Larvae that were motionless when gently touched with a soft brush were considered dead.  
Furthermore, in the bioassay, the mortality trend was assessed in terms of time in days until 50% lethality (LT\({}_{50}\)).
# Blind Image Deblurring Based on Local Edges Selection
###### Abstract  
The edges of images are less sparse when images become blurred. Selecting effective image edges is a vital step in image deblurring, which can help us to build image deblurring models more accurately. While global edges selection methods tend to fail in capturing dense image structures, the edges are easy to be affected by noise and blur. In this paper, we propose an image deblurring method based on local edges selection. The local edges are selected by the difference between the bright channel and the dark channel. Then a novel image deblurring model including local edges regularization term is established. The obtaining of a clear image and blurring kernel is based on alternating iterations, in which the clear image is obtained by the alternating direction method of multipliers (ADMM). In the experiments, tests are carried out on gray value images, synthetic color images and natural color images. Compared with other state-of-the-art blind image deblurring methods, the visualization results and performance verify the effectiveness of our method.  
blind image deblurring; bright channel; dark channel; local edges selection Article Blind Image Deblurring Based on Local Edges Selection
## 5 Experiments and Results
### Model Variants  
To systematically assess the impact of each component of Multi-Element Contextual Hypergraph Extractive Summarizer (MCHES), we conducted an ablation study. This study involves modifications to the base model by sequentially removing or altering specific modules, thereby evaluating the performance decrement and underscoring the contribution of each component. Below, we detail the variants considered in this study:  
* Includes: contextual hypergraph, HCA, and Extractive Read-out.
- Excludes: CHM.
* Includes: contextual hypergraph, CHM, and Extractive Read-out.
- Excludes: HCA.
* Includes: contextual hypergraph, CHM, and HCA.
- Excludes: Extractive Read-out.
* Includes: basic hypergraph, CHM, HCA, and Extractive Read-out.
- Replaces multi-element contextual hypergraph with basic hypergraph.  
Each variant was tested by using the same datasets and evaluation metrics as the full model to ensure fair comparison. The results from this ablation study provide clear insights into the functionality and necessity of each component within the MCHES framework, contributing to a deeper understanding of the model's operational dynamics.
# Risk Prediction for Winter Road Accidents on Expressways
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Est.**} & \multirow{2}{*}{**Exp(Est.)**} & \multicolumn{2}{c}{**95\% CI**} & \multirow{2}{*}{**z**} \\ \cline{3-3} \cline{5-6}  & & & **Lower** & & **Upper** \\ \hline hr & \(-\)0.089 & 0.915 & \(-\)0.129 & \(-\)0.049 & \(-\)4.371 *** \\ lon & \(-\)0.140 & 0.870 & \(-\)0.433 & 0.154 & \(-\)0.932 \\ lat & \(-\)0.986 & 0.373 & \(-\)1.244 & \(-\)0.729 & \(-\)7.500 *** \\ alt & 0.004 & 1.004 & \(-\)0.001 & 0.009 & 1.700 \\ diff\_up & 0.005 & 1.005 & \(-\)0.036 & 0.046 & 0.245 \\ diff\_down & 0.009 & 1.009 & \(-\)0.037 & 0.056 & 0.389 \\ diff\_right & \(-\)0.007 & 0.993 & \(-\)0.020 & 0.007 & \(-\)0.971 \\ diff\_left & 0.003 & 1.003 & \(-\)0.011 & 0.018 & 0.420 \\ temp & 0.038 & 1.039 & \(-\)0.006 & 0.082 & 1.692 \\ ws & 0.207 & 1.230 & \(-\)0.004 & 0.418 & 1.924 \\ prec & \(-\)0.063 & 0.938 & \(-\)0.408 & 0.281 & \(-\)0.362 \\ rh & 0.051 & 1.052 & 0.038 & 0.064 & 7.827 *** \\ hpa & \(-\)0.001 & 0.999 & \(-\)0.043 & 0.040 & \(-\)0.071 \\ min\_radius & 0.000 & 1.000 & 0.000 & 0.000 & \(-\)0.039 \\ max\_diff & \(-\)0.017 & 0.984 & \(-\)0.037 & 0.004 & \(-\)1.557 \\ cov\_right & 0.000 & 1.000 & \(-\)0.001 & 0.001 & 0.778 \\ cov\_left & 0.000 & 1.000 & \(-\)0.001 & 0.001 & \(-\)0.353 \\ tunnel & 0.398 & 1.488 & \(-\)1.701 & 2.497 & 0.371 \\ bridge & \(-\)0.031 & 0.970 & \(-\)2.078 & 2.017 & \(-\)0.029 \\ \hline \hline \end{tabular}  
* \(p<0.001\), *  
\end{table} 
Table 4: The summary of logistic regression model. Figure 2: Important variables of Random Forest and XGBoost. The random forest model showed the best prediction performance for weather-related warnings and road accidents. The result of XGBoost demonstrated similar performance to the random forest in the training data, but as the random forest shows superior performance in the test data, it can be considered that overfitting occurred in the training data of XGBoost.
## 3 Proposed System
### Design of Likelihood Functions
#### 3.3.2 Weighting Function by Distance  
When locating at a given position, the distribution of fingerprint points indicates that four fingerprint points around the particle can be used. Let \(c_{(x,y)}^{i}\) be the four surrounding fingerprint points and \(d^{i}(x,y)\) be the distance from the particle to the fingerprint points. The weight value of the particle varies depending on the distance of the fingerprints.  
The distance from the particle to each of the four surrounding fingerprint points is \(d_{i}(i=1,2,3,4)\), and the following function is used to find the distance and weighting function \(\omega^{i}(x,y)\). The function is altered from the classical sigmoid distance weighting function by adding coefficients and a logarithmic function to make the curve fall more gently and the weights are close to 1 at a distance of zero.  
\[l(a,d)=\frac{1}{(0.7)^{-1}\text{log}\{1+\exp(-ad)^{-1}\}},a\in\mathbb{N} \tag{7}\]  
The determination of \(a\) is shown in Figure 5. Here, the distance weighting function \(\omega^{i}(x,y)\) is expressed as follows:  
\[\omega^{i}(x,y)=\frac{l\big{(}a,d^{i}(x,y)\big{)}}{\sum l\big{(}a,d^{i}(x,y) \big{)}} \tag{8}\]