esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Goal and Scope
#### 2.1.3 System Boundary and Data Sources  
The system, with regards to the vehicle, ranges from the production of the components to the use phase (system boundary scheme as in [35]). The EoL phase of the vehicles has not been included here and will be discussed in detail in a separate paper. Information about the production materials of the vehicle and energy was taken from the primary data, whereas the vehicle use phase was evaluated using an ad hoc developed backward-facing simulation approach [35] that takes the longitudinal dynamics of the case-study LDCV into account. Additional details about this approach are given in Section 2.2.2.1.  
The assessment of the NMC111 battery involves a cradle-to-graves assessment. The system definition covers the whole production chain, the use phase and the EoL (Figure 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Characteristics**} & \multicolumn{1}{c}{**Sources**} \\ \hline Available Energy (BOL) & 35 kWh & Primary data \\ No. of parallel units per battery pack & 2 & Primary data \\ No. of cells in series per each unit & 96 & Primary data \\ Nominal voltage of the cell & 3.7 V & Primary data \\ Cell weight & 0.856 kg & Secondary data 1  \\ Battery pack weight & 226 kg & Estimated 1  \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the NMC111 battery pack.  
The NMC production stage includes the raw material supply, component production, cell and pack battery assembly, transport, and infrastructure. The bills of the materials and energy required for all the LCA stages of an NMC111 battery were determined on the basis of literature references and laboratory tests, as specified in Section 2.2. The specific emissions of each material and energy source were taken from the Ecoinvent 3.6 database.
# Generative Adversarial Network for Overcoming Occlusion in Images: A Survey
## 5 GAN in Amodal Completion
### Amodal Segmentation
The model predicts the mask of the invisible region through an hourglass network structure. Figure 3: Different types of image segmentation. The local fine features and the higher-level semantic details are aggregated in the encoding stage, and they are added to each layer's feature maps in the decoding stage. The predicted amodal mask is evaluated by a Patch-GAN discriminator. To improve the amodal segmentation outcome, some typical human poses are concatenated with the feature maps as a priori information to be used in the decoding stage. Although the a priori knowledge enhances the predicted amodal masks, it restricts the application of the model to humans with specific poses. **A coarse-to-fine architecture with multiple discriminators:** In the applications such as visual surveillance and autonomous driving, path prediction, and intelligent traffic control, detecting vehicles and pedestrians is essential. However, these are often obstructed by other objects which makes the task of learning the visual representation of intended objects more challenging. The model in [67] aims to recover the amodal mask of a vehicle and the appearance of its hidden regions iteratively. To tackle both tasks, the model is composed of two parts: a segmentation completion module and an appearance recovery module. The first network, follows an initial-to-refined framework. Firstly, an initial segmentation mask is generated by taking an input image with occluded vehicles through a pre-trained segmentation network. Then, the input image is fed again into the next stage after it is concatenated with the output from the initial stage. The second part, in contrary to a standard GAN, has a generator with an encoder-decoder structure, an object discriminator, and an instance discriminator. To assist the model in producing more realistic masks, an additional 3D model pool is employed. This provides silhouette masks as adversarial samples which motivates the model to learn the defining characteristics of actual vehicle masks. The object discriminator, which uses a Stack-GAN structure [68], enforces the output mask to be similar to a real vehicle, whereas the instance discriminator with a standard GAN structure aims at producing an output mask similar to the ground-truth mask. The recovered mask is fed to the appearance recovery module to regenerate the whole foreground vehicle. Both modules are trained with reconstruction loss (i.e., \(\mathcal{L}1\) loss) and perceptual loss. Although using the 3D model pool and multiple discriminators produces better amodal masks, when the model is tested on synthetic images with different types of synthetic occlusions, it requires multiple iterations to progressively eliminate the occlusions. However, on real images with less severe occlusions, the model is unable to refine the results beyond three iterations and its performance declines.
## 2 System Model and Problem Formulation
### Caching Model  
Let \(\mathcal{D}=\{1,2,\ldots,d,\ldots,D\}\) and \(\mathcal{K}=\{1,2,\ldots,k,\ldots,K\}\) denote the set of services and the set of contents, respectively. We use \(s_{d}\) and \(s_{k}\) to represent the size of service \(d\) and content \(k\). Each UAV can cache some services that will be used by the associated vehicle to save the wireless backhaul time. Caching services at the edge is crucial for reducing latency in VEC systems [39].  
Let \(x_{u,d}(t)\in\{0,1\},u\in\mathcal{U},d\in\mathcal{D}\) (\(x_{u,k}(t)\in\{0,1\},u\in\mathcal{U},k\in\mathcal{K}\)) be the binary decision variable indicating whether the UAV \(u\) caches program \(d\) (content \(k\)). \(x_{u,d}(t)=1\) (\(x_{u,k}(t)=1\)) indicates that program \(d\) (content \(k\)) is cached by UAV \(u\); otherwise, \(x_{u,d}(t)=0\) (\(x_{u,k}(t)=0\)).  
Since the caching capacity of each UAV is limited, we have the following constraint:  
\[\sum_{d=1}^{D}x_{u,d}(t)s_{d}+\sum_{k=1}^{K}x_{u,k}(t)s_{k}\leq Q_{u},\forall u \in\mathcal{U},t\in\mathcal{T} \tag{5}\]  
where \(Q_{u}\) is the total storage space of UAV \(u\). This constraint is standard in caching problems [40].
# Similarity-Based Malware Classification Using Graph Neural Networks
## 3 Proposed Problem-Solving Mechanism  
Classification model and similarity model are widely used in security related applications. The classification model identifies new observables based on the data it was trained on. During the training, the model learns from a given dataset and then classifies the test data. For example, the model can identify whether the given sample is malware or not. The similarity model learns to output embedding, projecting items into a vector space where similar items will be close and dissimilar items will be farther away. When threat hunting, threat researchers need to select meaningful data from the massive dataset and perform some analysis on it. Therefore, the interactive use of classification and similarity model is needed. Threat researchers leverage the classification model to perform preliminary classification and filtering, and use the probability provided by the classification model to prioritize, while the similarity model helps the researcher to understand the similarity between files to reduce the review time.  
Figure 1 presents an overview of the proposed mechanism for identifying malware families. The four main stages are data collection, dataset construction, classification model and prediction, and similarity model and measurement.  
In data collection, malware samples and family signatures are downloaded from Malware Bazaar [29]. The corresponding function is then embedded into a vector as the related node feature in the call graph. For the classification and prediction, a GNN model is used to embed the graph that is extracted from the malware samples. Then, the embedding vector is input to the Softmax function to predict the probability that the malware belongs to each family. The similarity model integrates the classification model with a Siamese network [30]. Two of the same models in the Siamese network embed the given input data, and the distance between the given input data is then measured. The embedding vector should be close to that of another vector if the data are similar.
# Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events
## 5 Formal Analysis of DTLS-SRTP Combined Protocol  
The DTLS-SRTP protocol is designed for point-to-point media sessions involving only two participants, distinguished as the authenticating party and the authenticated party. In this context, the client is defined as the authenticated party, while the server is defined as the authenticating party. We operate under the premise that both communicating parties possess pre-agreed symmetric keys. Given the practical scenario in which data packets may be lost or reordered in a UDP environment, we employ an extended of Logic of Events Theory to formally prove the mutual authentication between the communicating parties. Subsequently, we demonstrate the confidentiality of the communication process to ensure the security of the protocol.
## 4 Experiments
### Ablation Experiment  
During the experiments, 20,014 images were randomly selected, and three strategies were integrated into the original algorithm to compare precision, accuracy, recall, and processing speed. The experimental results are presented in Table 2.  
1. Single Strategy Usage  
Strategy 1: By dynamically adjusting the threshold, the algorithm became more precise in distinguishing the foreground from the background, significantly reducing false negatives and enhancing precision and recall rates by 8.00% and 7.29%, respectively. Although accuracy slightly decreased, the overall performance improved significantly. Strategy 2: The center point selection method was optimized, making the algorithm more accurate and comprehensive in locating the foreground. This resulted in a 5.17% improvement in precision and a 1.18% improvement in recall, with a slight 0.57% decrease in accuracy. Strategy 3: Continuity detection was implemented, enabling the algorithm to more accurately determine the start and end points of the foreground region, thereby increasing  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & \multicolumn{2}{c}{**Magrefehome**} & \multicolumn{5}{c}{**Our Algorithm**} \\ \cline{2-9}
**Number of Image** & \
\begin{tabular}{l} **Speed** \\ **(Number/s)** \\ \end{tabular} & **Accuracy** & **Precision** & **Recall** &
\begin{tabular}{l} **Speed** \\ **(Number/s)** \\ \end{tabular} & **Accuracy** & **Precision** & **Recall** \\ \hline
423 & 48.6 & 77.17\% & 99.10\% & 69.18\% & 48.34 & 88.58\% & 96.40\% & 83.59\% \\
2098 & 48.43 & 74.74\% & 98.76\% & 68.30\% & 47.56 & 90.94\% & 98.05\% & 86  
target recognition accuracy and reducing false negatives. This strategy improved precision by 6.61%, accuracy by 1.35%, and recall by 5.71%. Overall, the average improvements in precision and recall were 6.59% and 4.73%, respectively, despite a slight 1.53% decrease in accuracy. These optimizations provided strong support for enhancing target detection accuracy and efficiency without affecting processing speed.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Pairwise Chloride Comparisons for \(\mathbf{i}_{\text{corr}}\)**} & \multicolumn{2}{c}{**Pairwise Chloride Comparisons for \(\mathbf{E}_{\text{corr}}\)**} \\
**Sample** & **Test Statistic** & **Test Statistic** & **Sig.** & **Test Statistic** & **Test Statistic** & **Sig.** \\
**1–Sample 2** & **Test Statistic** & **Std. Dev.** & **Sig.** & **Test Statistic** & **Std. Dev.** & **Sig.** \\ \hline
0–0.4\% & \(-69.965\) & \(-2.854\) & **0.004** & \(-40.795\) & \(-1.664\) & 0.096 \\
0–1.2\% & \(-175.560\) & \(-7.161\) & **0.000** & 57.805 & 2.358 & **0.000** \\
0–0.8\% & \(-188.850\) & \(-7.703\) & **0.000** & 116.285 & 4.743 & **0.000** \\
0–1.6\% & \(-212.870\) & \(-8.683\) & **0.000** & 241.060 & 9.833 & **0.000** \\
0–2\% & \(-340.025\) & \(-13.870\) & **0.000** & 116.285 & 6.079 & **0.000** \\
0–4–1.2\% & \(-105.595\) & \(-4.307\) & **0.000** & 98.600 & 4.022 & **0.000** \\
0–0.8\% & \(-118.885\) & \(-4.849\) & **0.000** & 157.080 & 6.407 & **0.000** \\
0–4–1.6\% & \(-142.905\) & \(-5.829\) & **0.000** & 281.555 & 11.497 & **0.000** \\
0–2\% & \(-270.060\) & \(-11.016\) & **0.000** & 189.820 & 7.743 & **0.000** \\
1.2–0.8\% & \(13.290\) & \(0.542\) & 0.588 & \(-58.840\) & \(-2.385\) & **0.000** \\
1.2–1.6\% & \(-37.310\) & \(-1.522\) & 0.128 & 183.255 & 7.475 & **0.000** \\
1.2–2\% & \(-164.465\) & \(-6.709\) & **0.000** & 91.220 & 3.721 & **0.000** \\
0.8–1.6\% & \(-24.020\) & \(-0.980\) & 0.327 & 14.775 & 5.090 & **0.000** \\
0.8–2\% & \(-151.175\) & \(-6.167\) & **0.000** & 32.740 & 1.335 & 0.182 \\
1.6–2\% & \(-127.155\) & \(-5.187\) & **0.000** & \(-92.035\) & \(-3.745\) & **0.000** \\ \hline \hline \end{tabular} Each row tests the null hypothesis that the distributions of Sample 1 and Sample 2 are equal. The asymptotic significances (two-tailed tests) are displayed. The significance level is 0.050; The significance values have been adjusted via the Bonferroni correction for multiple tests.  
\end{table} 
Table 9: Pairwise Chloride Comparisons \(\mathbf{i}_{\text{corr}}\). Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare no conflict of interest.
## 1 Introduction
Circular CFST columns, in particular, distribute confinement uniformly, resulting in higher strength and ductility compared to rectangular tubes [32; 33; 34]. [PERSON] et al. [35] extensively studied CFST columns with circular and square sections, confirming substantial alignment between predicted and actual results. [PERSON] et al. [36] conducted a numerical exploration into the axial behavior of CFST stub columns, verifying the model's accuracy. Similarly, [PERSON] et al. [37] examined the axial capacity of square and circular CFST stub columns under partial axial loading, effectively simulating their behavior. In general, prior research has focused on experimental investigations into the mechanical characteristics of DS-CFCST columns. However, there is a noticeable deficiency in theoretical and numerical analysis of the composite behaviors of DS-CFCST stub columns under axial compression. Due to technological limitations in testing, a comprehensive Finite Element (FE) modeling analysis is necessary to examine interactions among CFST column components during loading. Unlike the numerous advanced numerical analyses for CFST columns, FE analysis has not been applied to DS-CFCST. Understanding the difference in confinement effectiveness between DS-CFCST and concrete-filled circular steel tubular (CFCST) stub columns is crucial for informed adjustments to existing design formulas for CFCST columns. This study aims to fill the gap in theoretical and numerical analysis of the composite behavior of DS-CFCST stub columns under axial compression. Given the limitations of current testing methods, a comprehensive Finite Element (FE) modeling analysis is essential to understand the interactions among CFST column components during loading. Unlike the numerous advanced numerical analyses available for CFST columns, FE analysis has not yet been applied to DS-CFCST columns. This study seeks to elucidate the differences in confinement effectiveness between DS-CFCST and conventional concrete-filled circular steel tubular (CFCST) stub columns, which is critical for adapting existing design formulasfor CFCST columns. The primary goal is to develop a numerical model and design formula through a detailed FE model for DS-CFCST stub columns. This model will incorporate an elastoplastic steel constitutive model and a plastic-damage constitutive model for DS concrete, validated against experimental data including ultimate bearing capacity, loading-axial strain curves, and stress response. A parametric study will explore the effects of DS concrete strength, steel tube strength, steel ratio, DS replacement ratio, and confinement efficiency on axial compressive behavior. This study will also compare the confinement effects between CFCST and DS-CFCST stub columns. Finally, a practical design formula incorporating an enhancement factor will be proposed and validated against existing design codes and experimental data, with a reliability analysis to calibrate the formula.
## 3 Heterogeneous Parallel Implementation
### Multi-Node Parallel Algorithm Implementation  
In LBM simulations using MPI, the domain is typically partitioned using a domain decomposition approach. For the D2Q9 model in a 2D computational domain, two types of partitioning can be used: 1D and 2D partitioning. The schematic diagrams of these partitioning approaches are shown in Figure 3. This domain partitioning method divides the computational domain into multiple subdomains, with each subdomain assigned to a node.  
Experimental tests showed that the time overhead of one-dimensional and two-dimensional partitions is about the same. For some simple simulation problems, one  
Figure 3: The 1D and 2D partitioning of a 2D grid.  
-dimensional partitioning can handle data more efficiently. One-dimensional partitioning requires storing relatively less data, saving memory space during large-scale simulations, and reducing communication overhead between a large number of nodes.  
This paper employs a 1D partitioning approach for a 2D computational domain, where the model is divided along the x-axis to distribute the lattice evenly among multiple processes in the communication domain.  
During the flow process in the LBM method, the lattice on the boundaries of the computational domain requires information about the distribution functions of the neighboring lattice. This necessitates data communication to obtain the relevant information from neighboring lattices. Therefore, an additional layer of virtual boundaries is added outside the boundaries of each computational region involved in data communication. These virtual boundaries are used to store the relevant information about the boundary lattice from neighboring nodes, facilitating data communication.  
Figure 4 illustrates a schematic diagram of 2D communication. The shaded regions represent data buffer zones, and the arrows indicate communication. The main purpose is to transmit the distribution function values from the boundaries of each sub-grid to the buffer zones of neighboring grids. As a result, each node needs to perform four communication operations.
# DiffRS-net: A Novel Framework for Classifying Breast Cancer Subtypes on Multi-Omics Data
## 3 Results  
### Evaluation Metrics  
This section details the key metrics for evaluating the classification performance on breast cancer subtypes. The number of positive samples correctly predicted is known as the true positive (_TP_) number, while the number of negative samples incorrectly predicted as positive is called the false positive (_FP_) number. Similarly, the number of negative samples correctly predicted is referred to as the true negative (_TN_) number, and the number of positive samples incorrectly judged as negative is termed the false negative (_FN_) number.  
With these definitions, we can calculate several important performance metrics: Accuracy (ACC) = (_TP + TN_)/(_TP + TN + FP + FN_), which represents the proportion of correctly predicted samples; Precision = _TP_/(_TP + FP_), which indicates the proportion of actual positives among the predicted positives; Recall = _TP_/(_TP + FN_), which reflectsthe proportion of actual positives that were correctly identified; and the F1 score = 2 \(\times\) Precision \(\times\) Recall/(Precision + Recall), which is the harmonic mean of precision and recall. Additionally, the Receiver Operating Characteristic (ROC) curve is generated by plotting the True Positive Rate (TPR) = \(TP\)/(\(TP\) + FN) against the False Positive Rate (FPR) = \(FP\)/(\(TN\) + \(FP\)), and the Area Under the Curve (AUC), which is no greater than 1, serves as a measure of the overall performance of the classifier.  
Traditional metrics such as Precision, Recall, and F1 score are originally defined for binary classification problems. In experiments with multiclass classification, we utilize a macro-average strategy to apply these metrics. This involves independently calculating the Precision, Recall, and F1 score for each class and then taking the arithmetic mean of these metrics without considering the variation in sample size across classes. Therefore, we use macro-averaged Precision (Precision-macro), macro-averaged Recall (Recall-macro), and macro-averaged F1 score (F1 score-macro) to comprehensively evaluate the performance of each method on multi-classification problems.
## 1 Introduction
In a second study, [PERSON] et al. [20] showed that, at high frequency, the high uncertainties found, which influence the weighted indices, depend on the sound source directivity and position. The design of the sound insulation performances of a building is usually carried out according to the ISO 12354-3 standard [21]. This standard describes a calculation model to estimate the acoustic performances of buildings starting from the performances of single elements (airborne sound insulation against outdoor sound). A problem that is often encountered during the acoustic design stage of facades is correctly evaluating corner rooms. The difficulty originates from the fact that this specific case is not considered in the ISO 12354-3 calculation procedures. This work aims to study the facade acoustic insulation of corner rooms. A method for the calculation of the acoustic insulation of the entire facade of a corner room exposed to noise is proposed. The evaluation is made considering the attenuation due to the corner of the building and the attenuation due to the different distances between the sound source and the centers of the individual walls composing the facade. This type of attenuation is indicated with the here introduced symbol \(\Delta D_{\text{FL}}\) (where F indicates the facade side and L the loudspeaker position). \(\Delta D_{\text{FL}}\) is the difference between the average sound pressure level on the side of the facade exposed directly to noise and the average sound pressure level on the side indirectly exposed to noise. The article is organized as follows. Section 2 summarizes the existing measurement methods for the acoustic design of facades. In Section 3, new formulas for determining facade insulation performances of corner rooms for the different possible source arrangements are presented. In Section 4, the results of simulations performed with different positions of the source, also considering the attenuation due to the building corner, are reported. Experimental measurements and a comparison with the predictions, carried out using the proposed method, are reported in Section 5. Conclusions are drawn in Section 6.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 1 Introduction  
Non-terrestrial networks (NTN) are to provide wireless connectivity from flying objects above ground usually from space and the stratosphere. In a nutshell, NTNs involve non-terrestrial flying objects such as satellites and unmanned aerial vehicles/drones (UAVs). Figure 1 illustrates the concept of NTN. Rapid development of non-terrestrial networks (NTN) in the last decade has envisioned emerging concepts of **\"integrated space-terrestrial network (ISTN)\"**[1, 2, 3] and the **\"Internet of space things (IoST)\"**[4, 5, 6], which have the potential to leverage current mobile communication networks to deliver advanced communication services for the 6 th generation (6G) in the future. A notable example is mega-constellation satellite networks such as Starlink [7] and OneWeb [8], spurred by the recent advances in NTNs. By turning these networks into the IoST one can connect the unconnected in rural, rugged, and ocean environments. In addition, by integrating NTNs with terrestrial communications, one can form ISTN provisioning connectivity not only on the ground but also in the air and space.