esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Consumption Comparison of GPU Linear Solvers for Cellular Potts Model Simulations
## 3 Parallel Approach
In contrast, computing the \(\Delta H_{s}\) component involves accessing the previously computed displacement \(u\) and force vectors \(f\), which can be more demanding on a thread. To address this, the dynamic parallelism capabilities of modern GPUs to significantly accelerate this computation is exploited. This parallel approach engages only two threads, each independently computing updates to \(\Delta H_{s}\) asynchronously. These threads retrieve displacement data from \(u\) and perform the necessary operations to determine the strain characteristics. Finally, one thread updates the value of \(\Delta H_{s}\). The previous discussions allow us to conclude the section with Algorithm 2, which summarizes the main parallel operations. ```
Input :\(N,N_{\tau},M,N_{\tau}\), \(\tau\) Output :\(\{L^{\tau}\}_{\tau=1,\ldots,N_{\tau}}\) Initialize : Parallel setup of \(L^{0}\), \(K\) for\(\tau=1\) to \(N_{\tau}\) in paralleldo  Compute forces and update \(f\)  choose solver among the GPU-solvers: [PERSON], LU, Gauss-Seidel, Custom CG  solve \(Ku=f\) any means of solver for\(l=1,2,\ldots,M\)do  Compute: \(\Delta H\)  Update \(L^{\tau}\) asynchronously  end for  end for
```  
**Algorithm 2** Parallel CPM method through different linear solvers
# CoMP-Aware BBU Placements for 5G Radio Access Networks over Optical Aggregation Networks
## 4 CoMP Aware BBU Placement Algorithm
7:\(L_{1}\) : Number of the new activated fiber links needed if we use \(path1\). 8:\(L_{2}\) : Number of the new activated fiber links needed if we use \(path2\). 9:\(D\) : Max allowable latency. 10:\(D_{1}\) : Total latency experienced in path 1. 11:
12:\(D_{2}\) : Total latency experienced in path 2. 13:
14: For each RRH: find max \(U_{Inter}\) in each edge of cell, do
15: Find \(BBU_{a}\) and \(BBU_{b}\)
16: If \(D_{1}<D\) and \(D_{2}<D\), do
17: If \(BBU_{CapA}>BBU_{CapB}\), do
18:\(Finalselection_{1^{st}case}\) = BBU pool a
19: Else if \(BBU_{CapA}<BBU_{CapB}\), do
20:\(Finalselection_{1^{st}case}\) = BBU pool b
21: Else if \(L_{1}<L_{2}\), do
22:\(Finalselection_{1^{st}case}\) = BBU pool b
23: Else if \(L_{1}>L_{2}\), do
24:\(Finalselection_{1^{st}case}\) = BBU pool a
25: Else
26:\(Finalselection_{1^{st}case}\) = BBU pool b
27: End if
28: Else if \(D_{1}<D\), do
29:\(Finalselection_{1^{st}case}\) = BBU pool b
30: Else if \(D_{1}<D\), do
31:\(Finalselection_{1^{st}case}\) = BBU pool b
32: Else if \(D_{2}<D\), do
33:\(Finalselection_{1^{st}case}\) = BBU pool a
34: Else
35: Else
36:\(Finalselection_{1^{st}case}\) = BBU pool a
37: End if
38: Else if \(D_{1}<D\), do
39:\(Finalselection_{1^{st}case}\) = BBU pool b
40: Else if \(D_{2}<D\), do
41:\(Finalselection_{1^{st}case}\) = BBU pool a
42: Else
43:\(Finalselection_{1^{st}case}\) = BBU pool a
44: Else
45:\(Finalselection_{1^{st}case}\) = BBU pool a
46: Else
47:\(Finalselection_{1^{st}case}\) = BBU pool a
48: Else
49:\(Finalselection_{1^{st}case}\) = BBU pool b
50: Else if \(D_{2}<D\), do
51:\(Finalselection_{1^{st}case}\) = BBU pool a
52: Else
53:\(\mathbf{No}\) path available
54:\(\mathbf{End}\) if
55:End For
```  
**Algorithm 1**Pseudo Code for case 1  
All other cases are as explained in case 1 except for the order of conditions. All different cases are summarized in Table 3.
###### Abstract
[11] conducted a simulation study on the multi-axial forging process of 7075-type aluminum alloy, optimizing multiple process parameters using Taguchi design of experiment and determining the best process parameters through signal-to-noise ratio, variance, and GRD analysis. [PERSON] et al. [12] addressed the wear issue in reciprocating engines by conducting sliding wear tests based on Taguchi orthogonal arrays, determining the optimal parameters for the minimum sliding abrasion rate and coefficient of friction through GRA and finally observing the microstructure of grey cast iron (GCI) under an optical microscope to verify the minimum wear at the optimal temperature and best lubrication conditions. [PERSON] et al. [13] implemented the grey Taguchi method to optimize the design of the plastic injection molding process parameters in the creation of bottle preforms and verified the high consistency between predicted and experimental values through experiments. This paper focuses on disc forgings, taking the upsetting and spinning processes as examples, exploring the influence of various process parameters on forging quality. The BBD experimental method was employed, with the friction coefficient, initial temperature, and downward pressing speed as design variables and the forming load, equivalent stress, and damage value as response targets. A GRD analysis method based on entropy weight was proposed, transforming the MPO into a GRD problem, determining the weights of each optimization target through the entropy weight method, and using the GRD calculated by the GRA algorithm as the objective function. A quadratic polynomial regression equation was constructed using the RSM, and its accuracy was tested. PSO was employed to hunt for the most favorable process parameters, and the reliability and effectiveness of the results were verified through simulation. The purpose of this paper is to develop a multi-objective optimization method for the forming process parameters of disc forgings, such as temperature, stress-strain, strain rate, etc., which have important effects on the shape and quality of forged parts. Employing the proposed method, the optimal combination of forming process parameters can be obtained, by which the product quality is improved and production costs can be reduced. Cumbersome and time-consuming traditional methods relying on artificial experience can be avoided. The process parameter prediction model of our method show that the forming load, equivalent stress, and damage value were all reduced, which provides an effective reference for enhancing forging quality and diminishing energy utilization in actual production.
## 5 IoT-Defined Network/Routing Protocols
### Cognitive-Opportunistic Routing over Low-Power and Lossy Network (CORPL)  
The DODAG is built the same way as RP in CORPL [31]. The main difference is an intelligent mechanism on node selection and utilization of node power. Multicasting is implemented using a forwarder set. In addition, coordination between nodes is used to select the best next hop to utilize the total network up-power efficiently. Each node possesses a forwarder set instead of the knowledge about the nodes from which it receives the packet for forwarding, as in RPL. A DODAG information object (DIO) is exchanged in a timely manner with the connected nodes to update the changes taking place in the parent node. Based on this update information, each neighbor updates the forwarder set, as well as prioritizes the information regarding the next hop utilization. It is notable that CORPL is a node-based cognition and sensing technique, and it does not guarantee channel utilization.
## 5 Inversion Analysis of Experimental Data  
The experimental data of 300 nm unimodal particles and 100 nm/450 nm bimodal particles were obtained from Brookhaven instruments. The experiment system is shown in Figure 9, which mainly involves vertically polarized He-Ne laser, sample pool, detector, and correlator. In the experiment, the temperature is 298.15 K wavelength is 632.8 nm, the scattered angle is 90\({}^{\circ}\), and the dispersion medium is the pure water, respectively. In the inversion range [1, 1200] and [1, 1500], the experimental data of unimodal particles and bimodal particles were inverted by the original WRIM from IDS 1 to 7, respectively. In order to compare the inversion results of the different IDSs, the inversion PSD of the different IDSs are uniformly converted to particle size step standard of IDS 7. The inversion results are shown in Figure 10. From Figure 10, the inversions PSDs of 300 nm unimodal particles have many sub-peaks on IDS 1, 2, and 3. The recovered PSDs of other IDSs are single peak. The corresponding of IDS 3 is minimum 0.2541. For 100 nm/450 nm bimodal particles, the inversion PSDs of IDS 1 are not obvious double peaks and have small sub-peaks. The results of IDS 7 are obviously distorted. Conversely, the results of other scales are double peaks. The corresponding of IDS 6 has minimum of 0.1223. Therefore, for above two kinds of particles, the practice optimal IDSs are 3 and 6, respectively. The inversion PSDs of the improved WRIM are the results of IDS 3 and 6.  
Figure 8: Inversion data of the improved WRIM, WRIM-RE, and IDS 1. (**a**) 10 nm\(\sim\)150 nm, (**b**) 100 nm\(\sim\)600 nm, (**c**) 90 nm\(\sim\)800 nm, and (**d**) 50 nm\(\sim\)500 nm.  
Moreover, to verify the inversion effect of the improved WRIM, above experimental data also was retrieved by CONTIN method widely used by various instruments. Figure 11 is the comparison of the PSDs recovered by the improved WRIM, CONTIN, and the traditional regularization method (IDS 1). In order to compare the three methods, the inversion results of other two methods are unified as the standard of CONTIN in the Brookhaven instrument. From Figure 11 and Table 4, for the unimodal and bimodal particles, the results of the improved WRIM are the standard single peak and double peaks. Their peak value errors are 4.81% and 9.06%/1.23%. Compared with the CONTIN method, although the first peak value error of bimodal particles increases by 3.12%, the second peak value error decreases by 15.15%. Therefore, in general, we can believe that the inversion PSDs of the improved WRIM are much better than the CONTIN method. Compared with the traditional method, the improved WRIM has a more obvious advantage. Thus, the experimental data also confirm the conclusion of the simulation data.  
Figure 10: Inversion PSDs of the experimental particles on the different IDS. (**a**) 300 nm unimodal particles and (**b**) 100 nm/450 nm bimodal particles.  
Figure 9: Experiment system.
# Scanning Electron Microscopy Techniques in the Analysis of Gunshot Residues: A Literature Review
## 2 Materials and Methods  
To conduct this narrative review, a structured search was performed using Google Scholar and PubMed NCBI. The search terms included \"Scanning Electron Microscopy\", \"SEM and Forensic Science\", \"SEM and Gunshot Residues\", and \"SEM and Ballistics Analysis\". Articles published between 2009 and 2024 were considered, focusing on the role of SEM in forensic investigations. The selection criteria included studies that explicitly addressed SEM's application in GSR detection, the effectiveness of SEM-EDS (energy-dispersive spectroscopy) in forensic casework, and comparative analyses between SEM and other microscopic techniques. Studies primarily discussing environmental contamination, without forensic implications, were excluded. A total of 65 articles were included, ensuring a comprehensive overview of current methodologies and advancements in SEM-based GSR analysis. The reference lists of selected studies were also reviewed to identify the additional relevant literature.
## 1 Introduction
In addition, [PERSON] and [PERSON] only compared GOSIF data with OCO-2 coarse-resolution SIF data (1\({}^{\circ}\)) in seasonal cycles [18]. Consequently, there is an urgent need to evaluate those products comprehensively. Moreover, as introduced above, the three products are based on very different techniques. It is thus speculated that GPP estimates based on these products would demonstrate different characteristics. Therefore, it is necessary to conduct inter-comparisons to explore the performances of different products when estimating GPP at an ecological scale. In this study, we utilized in-situ measurements of EC flux towers as the truth value of GPP and established relationships between truth values of GPP and three products of SIF/GPP to evaluate their performances. Inter-comparison experiments were carried out from two perspectives, namely, the site and the vegetation type, in this study. The location of an EC flux site determines the geographic coordinates and meteorological parameters. Performances of satellite products are determined by the original signal quality, which is related to the observation geometry and the sun's position. Further, meteorological parameters exert significant influences on generations of GPP [15]. This explains why it is important to evaluate the performance of satellite-observation-derived products in terms of the site. In addition, GPP and its seasonal cycle vary with the vegetation type. The response of SIF to GPP could also vary with the vegetation type. Hence, it is necessary to evaluate the performance of SIF-GPP models from the perspective of vegetation types. Except for the above stated issues, there are another two topics to be discussed. Firstly, the extraction range of SIF data around the flux tower has always been a problem in correlating remotely sensed data and in-situ measurements. With a small extraction range, one often cannot obtain adequate SIF data to correlate with GPP. With a large extraction range, the spatial mismatch introduces additional errors, resulting in a reduced accuracy of GPP estimates [23]. Secondly, few studies have been dedicated to investigating the effects of different observation modes and bands of OCO-2 on the accuracy of estimating GPP. The rest of this paper is organized as follows. Section 2 introduces the materials and methods. Section 3 introduces the primary results. Section 4 presents our discussion. Finally, the whole study is concluded in Section 5.
## 3 Structure, Scope, and Methodology in Expert System Design for FSGS
### Algorithms Used in Classification  
Several machine learning and artificial intelligence algorithms were implemented in this system, ranging from classical models like decision trees and logistic regression to more complex ones such as gradient boosting and ensemble techniques. These algorithms provide a variety of approaches to solving classification problems, allowing for better flexibility and improved performance across different types of data.  
These algorithms (see Table 2) are designed to provide a comprehensive set of tools for classification tasks, each bringing its own strengths and trade-offs to the process. Depending on the characteristics of the dataset, some algorithms may outperform others, but together they offer a wide range of options for data analysis and modeling, as well as finding the optimal solution.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{227.6 pt}} \hline \hline
**Model** & **Description** \\ \hline K-nearest neighbors (KNN) & A classifier that assigns a class based on the majority of neighboring points. \\ \hline Decision tree (DT) & An algorithm that splits data based on attributes to create a tree structure for classification or regression. \\ \hline Random forest (RF) & A collection of decision trees where results are aggregated to improve accuracy and reduce overfitting. \\ \hline Support vector machine (SVM) & A classifier that seeks to find a hyperplane that maximizes the margin between classes. \\ \hline Gradient boosting (GB) & A boosting method that creates models sequentially, optimizing each based on the errors of previous ones. \\ \hline Adaptive boosting (AdaBoost) & A boosting algorithm that iteratively adjusts the weights of samples to improve classification of harder cases. \\ \hline Extra Trees (ET) & A random forest-based method that builds many trees to increase model stability and accuracy. \\ \hline Bagging & A method that aggregates many classifiers (e.g., decision trees) to reduce variance and improve stability. \\ \hline XGBoost & An efficient gradient boosting algorithm widely used for classification tasks. \\ \hline LightGBM & A fast gradient boosting algorithm that performs well on large datasets. \\ \hline CatBoost & A gradient boosting algorithm that automatically handles categorical data and is robust to overfitting. \\ \hline Linear SVC & A version of SVM that optimizes a linear hyperplane to separate classes in data. \\ \hline Logistic regression & A regression model that predicts binary outcomes using a logistic function. \\ \hline Naive Bayes & A probabilistic classifier based on [PERSON] theorem, assuming independence between features. \\ \hline MLP classifier & A neural network classifier that consists of multiple layers and is used for complex pattern recognition. \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of machine learning models used with brief descriptions.  
The next section of the paper describes the procedure for designing the system and addresses technical issues.
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 2 Literature Review of AAS
### Literature Review Summary  
In summary, the reviewed studies collectively advance the refinement of diagnostic and predictive models for AAS, demonstrating progress across diverse methodologies. The emphasis on the AUC in comparing diagnostic performance underscores the effectiveness of notable models like XGBoost and random forest, indicating their accuracy in predicting and classifying cases. These findings contribute to a comprehensive understanding of factors influencing outcomes and risk estimation in Acute Aortic Syndrome and its subsets. The survey's notable discovery was the impressive AUC of 0.99 achieved using the CNN in predicting the risk of Acute Aortic Dissection rupture [16]. The dataset was strategically divided, with 70% designated for training and 30% for testing, enhancing the robustness of the model evaluation. Specifically, for predicting AAS, the highest AUC recorded was 0.974, accompanied by a notable 91.8% sensitivity, utilizing the multivariate logistic regression [19]. In the domain of AAS research, the study with the smallest patient cohort, as indicated in Table 1, is associated with [PERSON] et al. [20], conducted in 2023. This investigation focused on the identification and analysis of 128 cases of AAS evaluated in two emergency departments. On the contrary, the study with the largest patient cohort encountered in this research on AAS is [PERSON] et al. 's [21], conducted in 2023. Initially centered on the identification and analysis of cases in three UK emergency departments, this study expanded to include 5548 patients and considered 44 variables, providing a comprehensive exploration of the subject.
## 2 Methodology
### Mechanical Feasibility Study
#### 2.10.1 Boundary Conditions and Acting Forces  
The prosthesis operates based on the tenodesis effect, which serves as its primary functional principle. The pre-tension in the prosthesis traction cables, activated by wrist flexion and extension, generates a cylindrical pinch movement induced by finger flexion. Passive extension is achieved through orthodontic elastics or nylon threads located on the dorsal region of the fingers, with fixation on the palmar region of the prosthesis.  
The fixation mechanism is established by stabilizers modeled on the residual limb and adjusted with directional tensioning ratchets, ensuring stability and proper tensioning of the prosthesis cables, which function as flexor and extensor tendons. This design allows the prosthesis to replicate natural grasping and releasing motions effectively.  
The main acting forces and constraints were established based on a review of the scientific literature on upper limb prostheses, also considering the typical functional use characteristics (Figure 16).  
Gripping Force: The force applied at the fingertips to simulate the action of gripping an object was defined as 50 N, distributed uniformly along the contact surfaces of the fingers. This value aligns with studies indicating that the average gripping force in daily activities ranges between 40 N and 60 N for adults using upper limb prostheses [33]. The choice of this value represents an intermediate condition, suitable for light tasks, such as holding a cup or other household items.  
Friction Forces: A static friction coefficient of 0.4 was adopted for the contact surfaces of the fingers, aiming to simulate interaction with objects made of materials such as glass and lightweight plastic. This value was based on studies on polymer surfaces [34] and ensures that the prosthesis is capable of securely and efficiently holding and manipulating objects.
## 3 Numerical Study
### Convergence Between Mesh Size and Cracking  
Figure 11 shows a comparison between some FE models with different mesh sizes. The maximum load-carrying capacity of the FE model was compared to the maximum failure load observed in the experimental test. To verify the selected meshing size for the suggested FE models, a convergence study was conducted on the CB and BCSP beams using the FE model. The FE model, with a mesh size of 25, was adopted for this study. This model satisfied the failure load with a reasonable \(\mathrm{P_{Peak.Exp}}\)/\(\mathrm{P_{Peak.FE}}\) percentage value. Furthermore, its curve behavior was better than that of the experimental model. The crack patterns were observed with high clarity and accuracy, where the inclined crack formation in the beam under each damage level can be observed, as shown in Table 9.  
Figure 10: Mesh discretization for all the over-reinforced specimens.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Mass Density** & **Young’s Modulus** & **Poisson Ratio** & **\(f\)cu (MPa)** & **\(f\)ct (MPa)** \\
**Concrete** & **(kg/mm3)** & **(MPa)** & **Poisson Ratio** & **\(f\)cu (MPa)** & **\(f\)ct (MPa)** \\ \hline
**G40** & \(2.4\times 10^{6}\) & 26,541.48 & 0.2 & 36.08 & 3.68 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Material properties of the concrete used in the FEM.