esa-sceva/satcom-chunk-collection huggingface dataset:
# Heterogeneous Distributed Big Data Clustering on Sparse Grids
## 3 Estimating Densities on Sparse Grids
### Streaming Algorithms for the Sparse Grid Density Estimation  
A high-performance sparse grid density estimation algorithm needs to efficiently compute the two operations described above: the computations of the matrix-vector product \(\mathbf{v}^{\prime}=(B+\lambda I)\mathbf{v}\) and the right-hand side \(\mathbf{b}\) with \(b_{i}=\frac{1}{m}\sum_{j=1}^{m}\phi_{i}(\mathbf{x}_{j})\). To efficiently perform these operations, we use an implicit matrix approach. That is, components of the matrix \(B\) get re-computed whenever they are accessed. This approach might seem wasteful at first glance. However, as the size of \(B\) scales quadratically in the number of grid points, it quickly becomes infeasible to store the matrix in memory.  
The computation of the right-hand side requires the computation of \(m\) vector components. Algorithm 1 shows the loop structure of a scalar version of this operation. As the basis function evaluations in the innermost loop are independent, we can parallelize this algorithm over the outer loop, i.e., the iteration over the grid points. The evaluation of hat functions is branch-free. Therefore, this algorithm is well-suited for vectorization.  
```
for\(i=1\dots N\)do \(b_{i}\gets 0\) for\(j=1\dots m\)do \(b_{i}\mathrel{+}=\prod_{d=1}^{\dim}\phi_{i}^{(d)}(\mathbf{x}_{j}^{(d)})\) \(b_{i}\leftarrow\frac{1}{m}b_{i}\)
```  
**Algorithm 1**The streaming algorithm for computing the right-hand side \(\mathbf{b}\)  
We can formulate the matrix-vector operation as a second streaming algorithm with two nested loops over the grid points. This is shown in Algorithm 2. Similar to the hat function evaluations of the right-hand side, the \(L_{2}\) norms can be independently computed as well. Thus, we can parallelize Algorithm 2 by processing the outer loop in parallel. The two cases for computing the 1-D integral according to Equation (15) slightly complicate vectorization. Our approach is the computation of both cases in a vectorized algorithm and a single conditional move to return the correct result. Computing the \(x_{li}=x_{l^{\prime}i^{\prime}}\) case is only a single multiplication, as we can move the computation of \(h_{l}=2^{-l}\) to a precomputation step. Therefore, and because the \(x_{li}=x_{l^{\prime}i^{\prime}}\) case rarely occurs, the overhead in each integration step is low compared to an optimal algorithm that would only evaluate the correct integration case.  
Figure 4: The effect of the regularization parameter \(\lambda\) on a 2-D dataset with four data points. For smaller \(\lambda\) values, the function becomes more similar to the initial density guess of Dirac \(\delta\) functions. The function becomes smoother for higher values of \(\lambda\).  
```
for\(i=1\dots N\)do \(v^{\prime}_{i}\gets 0\) for\(j=1\dots N\)do \(v^{\prime}_{i}\leftarrow\prod_{d=1}^{\dim}\int_{0}^{1}\phi^{(d)}_{i}\phi^{(d)}_{j} dx\cdot v_{j}\) \(v^{\prime}_{i}+=\lambda\cdot v_{i}\)
```  
**Algorithm 2**The streaming algorithm for computing the matrix-vector multiplication \(\mathbf{v}^{\prime}=(B+\lambda I)\mathbf{v}\)
## 3 Results
### Relationship between the COP-COM Distance and JRF during Slope Walking  
Figure 15 shows the correlation analysis results between the COP-COM distance and JRFs at all lower-extremity joints during slope walking. It was found that the COP-COM distance in the anterior-posterior direction was positively correlated with the JRF at the knee (r = 0.505, \(p\) < 0.05) but not significantly correlated with the JRF at the ankle or hip (r = 0.270 and 0.301, respectively) during inclined walking. During declined walking, there was no significant correlation between the COP-COM distance in the anterior-posterior direction and the JRF at the ankle, knee, or hip (r = 0.328, \(-\)0.219, and \(-\)0.312, respectively).  
The COP-COM distance in the medial-lateral direction was not significantly correlated with the JRF at the ankle, knee, or hip during inclined walking (r = 0.309, 0.243, and 0.281, respectively) (Figure 16). Meanwhile, during declined walking, the COP-COM distance in the medial-lateral direction was not significantly correlated with the JRF at the ankle or  
Figure 14: Relationship between the VSI and JRF of the lower limb during slope walking. * Significant different at \(p\) < 0.05.  
Figure 15: Relationship between the COP-COM distance (anterior–posterior) and the JRF of the lower limb during slope walking. * Significant different at \(p\) < 0.05.  
knee (r = 0.266 and 0.004, respectively) but was positively correlated with the JRF at the hip (r = 0.342, \(p\) < 0.05).
## 4 Discussion  
Finite element analysis has been widely used in orthodontic research to evaluate the biomechanics of clear aligners, particularly regarding aligner geometry and attachment design. However, its application in upper molar distalization remains limited, with most studies addressing only a narrow range of trimline lengths and attachment types. Therefore, the effects of different trimline heights, attachment orientations, and aligner thicknesses on molar movement mechanics are still not fully understood [10; 12; 16; 17].  
The periodontal ligament is critical in orthodontic force transmission [22]. Traditional FEA models typically represent the PDL as a linear elastic material [14; 15], which may oversimplify its actual biomechanical behavior [21]. In this study, a second-order Ogden hyperelastic model was used to more accurately replicate PDL physiology [22]. Although [PERSON] et al. [25] argued that nonlinear modeling may be less critical during the initial phase of tooth movement, using an advanced material model enhances the realism of soft tissue response [13; 23]. Nevertheless, the lack of standardized experimental data on PDL properties continues to limit model consistency [23].  
Although von Mises stress is commonly used to assess force distribution, it cannot distinguish between tensile and compressive stresses in anisotropic tissues like the PDL. Therefore, complementary methods, such as principal or hydrostatic stress analysis, may enhance simulation accuracy [22].  
Studies have compared aligner thicknesses of 0.3 mm [15; 16], 0.5 mm [14; 17; 26], and 0.75 mm [10; 27] with 0.5 mm, showing the most efficient performance based on micro-CT data [14; 17]. Accordingly, the present study modeled the aligner with a 0.5 mm thickness. Nonetheless, clinicians should consider potential material degradation over time, which may reduce force transmission efficiency [28].  
Aligner trimline design is another factor that affects treatment outcomes [19; 20]. Scalloped and straight edges are commonly used, with trimlines ending at or 2 mm above the gingival margin. Extended trimlines generally offer improved crown engagement and mechanical control [19; 20]. [PERSON] et al. [29] reported that flat trimlines enhance translational control, while [PERSON] et al. [18] observed stronger forces with extended designs compared to gingival-level trimlines. These findings highlight the relevance of trimline configuration in clinical planning.  
Conventional rectangular attachments with uniform dimensions are commonly preferred for molar distalization [14; 16; 17]. However, the present findings suggest that variations in attachment geometry may affect tooth displacement patterns.
## 5 Development of CNN Model
### Training the Classifier  
The proposed deep PUF consists of convolution, max-pooling and fully connected layers. The summary of our network model is presented in Table 5. The activation function of all layers except the output layer is the rectified linear unit (ReLU). Classification is performed by determining the probability of different classes using the Softmax. In this classifier, we utilize categorical cross-entropy as the loss function and the Adam algorithm as the optimizer. Algorithm 1 shows the proposed scheme in form of a pseudo-code containing dataset generation, training process and testing process.  
```
Dataset generation Input: a set of challenges, including the address of PUF segment and input pattern: (C\({}_{1}\),C\({}_{2}\),..., C\({}_{N}\)) Output: collections of images corresponding to different challenges: (S\({}_{1}\), S\({}_{2}\),..., S\({}_{N}\)) Process: // build N folders: folder_1, folder_2,..., folder_N for i = 1 to N do //N: number of classes for k = 1 to M do // M: number of measurements  Write the input - pattern in the address using C\({}_{1}\) ;  Change the timing parameter (t\({}_{\text{RCD}}\));  Read the DRAM segment \(\;\rightarrow\;\text{R}_{k}\) ;  Visualize(R\({}_{k}\)) //Convert the R\({}_{k}\) to integer values and a gray-scale image  Store R\({}_{1}\), R\({}_{2}\),..., R\({}_{M}\) into the folder_i ;  end for end for Output: a dataset including N folders with M images in each folder.
```  
**Training process**  
Input: a collection of labeled images (responses): (s\({}_{1}\),s\({}_{2}\),...,sM\({}_{M}\), sM+1,..., sMN) Output: a collection of features assigned to different labels  
Process:  
for k = 1 to e do // e: number of epochs for i = 1 to M \(\times\) N do // N: number of classes, M: number of samples for each class  Get the sample with the label \(\;\rightarrow\;\text{(s}_{i}\), y\({}_{i}\));  Gain the features \(\;\rightarrow\;\text{f}_{i}\); // after applying the defined layers  Assign the features to the label (f\({}_{i}\), y\({}_{i}\));  end for for j = 1 to N do  Build collection of features for each class\(\rightarrow\)(F\({}_{j}\),Y\({}_{j}\));  end for  Build F = (F\({}_{1}\), F\({}_{2}\),..., F\({}_{N}\))  Update the features \(\;\rightarrow\;\text{F}\);  end for  Build the final collection of features Output: (F\({}_{1}\), Y\({}_{1}\)), (F\({}_{2}\), Y\({}_{2}\)),..., (F\({}_{N}\), Y\({}_{N}\)) ```  
**Testing process**  
Input: x // testing sample  
Process:  
apply the CNN  Select (x\({}^{\prime}\), y\({}^{\prime}\)), p(x\({}^{\prime}\),y\({}^{\prime}\)) = max{p1(x\({}^{\prime}\), y1), p2(x\({}^{\prime}\),y2),..., pN(x, yN)} // p: the probability vector calculated by Softmax function Assign y to the x
# YOLOv8m for Automated Pepper Variety Identification: Improving Accuracy with Data Augmentation
## 3 Materials and Methods
### YOLO Usage
The FN is a ground truth bounding box that the model failed to detect. * Mean Average Precision at 50% IoU (mAP50): Calculates the average precision across different intersection-over-union (IoU) thresholds, focusing on a 50% threshold (Equation (3)). The mAP50 is a standard metric for evaluating object detection models. It represents the average of the average precision (AP) calculated for each object class. For this metric, a predicted bounding box is considered a true positive only if its intersection over union (IoU) with a ground truth box of the same class is 0.50 or greater. The AP for each class is the area under its corresponding precision-recall curve, reflecting the trade-off between precision and recall at varying detection confidence thresholds under the 0.50 IoU criterion. \[\text{mAP}_{50}=(1/\text{N})\times\Sigma\text{ [AP}{<}{\text{sub>c}},\text{IoU}=0.50{<}/{\text{sub>}}]\text{ from c = 1 to N}\] (3) where N = total number of object classes, and AP_c, 0.50 = average precision for class c, calculated using an IoU threshold of 0.50. This metric refers to the area under the precision-recall curve for class c, where predictions are counted as true positives if their IoU with ground truth is greater than 0.50. * Mean average precision at 95% IoU (mAP95): Calculates the average precision at a stricter 95% IoU threshold (Equation (4)). \[\text{mAP}_{95}=(1/\text{N})\times\Sigma\text{ [AP}{<}{\text{sub>c}},\text{ IoU}=0.95{<}/{\text{sub>}}]\text{ from c = 1 to N}\] (4) where N = total number of object classes, and AP_c, 0.95 = average precision for class c, calculated using a fixed IoU threshold of 0.95. This metric is the area under the precision-recall curve for class c, where predictions are counted as true positives if their IoU with ground truth is greater than 0.95. The YOLOv8m model was chosen for the present study due to its proven efficiency in real-time object detection tasks, aligning with the precision and speed requirements for identifying pepper varieties. This version represents a significant advancement within the YOLO family, combining architectural enhancements and advanced training techniques, making it particularly suited for agricultural applications. According to [PERSON] and [PERSON] [11], YOLOv8m achieved superior performance in metrics such as mean average precision (mAP) compared to earlier models, including YOLOv5, and even competing approaches like Faster R-CNN. The results were compared between datasets with and without augmentation, highlighting the impact of data augmentation on the YOLOv8m model's performance in pepper detection. Figure 3 shows the workflow of the methodology.
# The Influence of Various Types of Functional Bread on Postprandial Glycemia in Healthy Adults
## 3 Results
### Nutrition Value and Glycemic Index of Tested Products According to Added Oilseeds  
Seeds (sunflower, pumpkin, and linseed) were the most common additives in the bread tested; therefore, to verify their influence on postprandial glycemic response and glycemic index value, we divided the group into two subgroups: bread without and with oilseeds. Figure 2 shows the average glycemic course over two hours for each group. Table 5 indicates characteristics of the glycemic index and average nutritional value for groups with and without oilseed addition. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Selected Relationships** & **Rye (n = 9)** & **Wheat/Spelt (n = 8)** & **Others (n = 6)** \\ \hline IAUC vs. carbohydrate content & NS & NS & \(p=0.0001\); \(r=0.4586\) \\  & & & R\({}^{2}\) = 19.77\% \\ \hline IAUC vs. protein content & \(p=0.0259\); & \(p=0.0387\); \(r=-0.2183\); & NS \\  & \(\text{R}^{2}=1.57\%\) & & \\ \hline IAUC vs. fat content & \(p=0.004\); & & \\  & \(\text{r}=-0.2221\); & & \\  & \(\text{R}^{2}=4.55\%\) & & \\ \hline IAUC vs. fiber content & \(p=0.0001\); & & \\  & \(\text{r}=-0.4586\); & NS & NS \\  & \(\text{R}^{2}=19.77\%\) & & \\ \hline IAUC vs. fiber/carbohydrates & & & \\   ratio & NS & NS & \\ \hline IAUC vs. salt content & NS & NS & NS \\ \hline GI vs. carbohydrate content & NS & \(p=0.0284\); \(r=0.2311\); & NS \\  & & \(\text{R}^{2}=4.26\%\) & & NS \\ \hline GI vs. protein content & NS & NS & \(p=0.0035\); \(r=-0.2616\); \\  & & & \(\text{R}^{2}=5.37\%\) \\ \hline GI vs. fat content & \(p=0.0131\); & & \\  & \(\text{r}=-0.2499\); & \(p=0.004\); \(r=-0.3006\); & \\  & \(\text{R}^{2}=5.27\%\) & & \\ \hline NS–not significant. & & & \\ \end{tabular}
\end{table} 
Table 4: Selected associations between IAUC, glycemic index, and nutritional value of groups divided according to dominant flour. Figure 2: The course of the mean glycemic values for each group within two hours after the test product consumption. * indicate a significant difference between groups (ANOVA, test LSD; \(p\geq 0.05\)); mean \(\pm\) SD. Significantly lower mean glycemic values at 60, 90, and 120 min were observed for breads with oilseeds added than those without oilseeds. The glycemic index of bread with
## 3 Results  
The systematic search identified a total of 218 articles, from which 64 duplicates were removed. After screening the title and abstract of the remaining 154 articles, 144 studies were excluded due to their focus on non-human models, irrelevance to GTR membranes, or lack of applicability to immunocompromised populations (Figure 1). A full-text review of the remaining 10 articles identified 5 studies that met the defined inclusion criteria. These studies focused on experimental applications of GTR membrane technologies in preclinical models and human sample-based research relevant to immunocompromised individuals. The findings from these studies highlighted advancements in the design and use of GTR membranes for tissue regeneration in environments affected by systemic conditions (Table 3). Several key themes emerged, showcasing innovations in membrane engineering, incorporation of biological agents, and applications in complex regenerative scenarios. One notable finding was the incorporation of stem cells with GTR membranes to improve regenerative outcomes. Stem cells such as oral mucosa stem/progenitor cells (OMSFCs) and dental pulp stem cells (DPSCs) showed remarkable potential when combined with engineered membranes or scaffolds. These cells demonstrated self-renewal capabilities and differentiation potential, contributing to the development of complex tissues such as bone and periodontal structures [32]. The addition of bone morphogenetic protein-2 (BMP-2) pre-treatment in combination with biomimetic scaffolds significantly enhanced osteogenesis and mineralized matrix deposition [28]. Similarly, OMSFCs cultured using the neuroscience technique regenerated cementum-like structures and ectopic bone in non-hard tissue regions [27]. These studies emphasize the synergistic effects of combining stem cell-based therapies with bioengineered membranes, particularly in overcoming limited regenerative capacities in immunocompromised patients. Figure 1: PRISMA flow diagram of article selection.
## 4 Results  
### Compare between ConWep and ALE Incident Pressure  
This section compares the spherical free air blast incident pressure between ConWep and ALE and shown in Table 7. ConWep is a collection of conventional weapons effects calculations from the equations and curves of TM 5-855-1, \"Fundamentals of Protective Design for Conventional Weapons\". ConWep is based on the empirical model described in the TMS-855 US army handbook [42]. The blast effects of an explosion are in the form of a shock wave composed of a high-pressure shock front which expands outward from the center of the detonation, with the pressure intensity decaying with distance. Figure 8 and Table 6 show the results from the ConWep and ALE simulation. Scale distance for TNT and position of wall are 0.421 m/kg\({}^{1/3}\), 0.665 m/kg\({}^{1/3}\) and 0.908 m/kg\({}^{1/3}\). Scale distance is a distance between protection wall surface and the denotation point of TNT. For ALE simulation, the peak pressure reaches faster, and the blast wave becomes zero within a short time than the ConWep, but the peak pressure is similar. At a distance of 1600 mm the error (%) is high, and for others, the error (%) is less. There is good agreement between the ConWep and ALE simulation methods for incident pressure generated from 69.7 kg of TNT after explosion.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**TNT Weight**} & **Central** & **Central** & **Central** & **Central** & **Central** & **Central** \\  & & **Displace-** & **Displace-** & **Displace-** & **Displace-** & **Displace-** \\  & **(kg)** & **ment form** & **ment from** & **ment from** & **ment from** & **ment from** & **ment from** \\  & **(kg)** & **Experiment** & **Simulation** & **Simulation** & **Simulation** & **Simulation** & **This Study** \\  & **(kg)** & **(kg)** & **(kg)** & **(kg)** & **(kg)** & **(kg)** & **(kg)** \\  & **(kg)** & **(kg)** & **(kg)** & **(kg)** & **(kg)** & **(kg)** \\ \hline \(1\) & 0.2 & 10 & 8 & 8.8 & 8.15 & 9.25 & 8.1 \\ \(2\) & 0.31 & 15 & 13 & 12.7 & 12.25 & 14.39 & 4.23 \\ \hline \end{tabular}
\end{table}
Table 6: Compare present study with previous study.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Distance (mm)**} & \multicolumn{2}{c}{**Peak Pressure (kPa)**} & \multirow{2}{*}{**Error (\%)**} \\  & & **ConWep** & **LS-DYNA** \\ \hline
1600 & 3433 & 3234.83 & 5.77 \\
2600 & 1564 & 1588.02 & 1.54 \\
3600 & 857 & 872.74 & 1.84 \\ \hline \end{tabular}
\end{table}
Table 7: Compare peak pressure between ConWep and LS-DYNA.  
Figure 7: Displacement vs time graph from simulation results.
## 2 Experimental Methods
### Characterization  
#### 2.2.1 Raman Test  
Nondestructive Raman spectroscopy tests were conducted using a Renishaw Raman imaging microscope System 2000 (Raman lab at UOIT, ON, Canada). To obtain the most intense excitation peaks, a 514 nm wavelength laser was used. Each test was conducted at 0.5 W power for 10 accumulations. Statistical analysis was performed to characterize the multilayer graphene and annealed titanium dioxide.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characterization** & **Weight Concentration** & **Type of Additive in a Base Oil (99.9\% Pure)** \\ \hline S-1 & 0.01\% _w/w_ & Graphene \\ S-2 & 0.05\% _w/w_ & Graphene \\ S-3 & 0.1\% _w/w_ & Graphene \\ S-4 & 0.01\% _w/w_ & Titanium dioxide \\ S-5 & 0.05\% _w/w_ & Titanium dioxide \\ S-6 & 0.1\% _w/w_ & Titanium dioxide \\ S-7 & 0.05\% _w/w_ & Graphene (50\%) + Titanium dioxide (50\%) \\ S-8 & 0.05\% _w/w_ & Graphene (25\%) + Titanium dioxide (75\%) \\ S-9 & 0.05\% _w/w_ & Graphene (75\%) + Titanium dioxide (25\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample characteristics of liquid base lubricants (liquid nano lubricants).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characterization** & **Weight Concentration** & **Type of Additive in Grease (Commercial Grease)** \\ \hline G-1 & 0.5\% _w/w_ & Graphene \\ G-2 & 0.5\% _w/w_ & Titanium dioxide \\ G-3 & 0.5\% _w/w_ & Graphene (50\%) + Titanium dioxide (50\%) \\ G-4 & 0.5\% _w/w_ & Graphene (25\%) + Titanium dioxide (75\%) \\ G-5 & 0.5\% _w/w_ & Graphene (75\%) + Titanium dioxide (25\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample characterization of semi-liquid lubricants (grease nano lubricants).