esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Total Effect—Direct**} & \multicolumn{3}{c}{**Direct Effect (c-Path)**} & \multicolumn{3}{c}{**Indirect Effect (b-Path)**} \\
**Test Variables** & \multicolumn{3}{c}{**and Indirect (c + a \(\times\) b)**} & \multicolumn{3}{c}{**Direct Effect (c-Path)**} & \multicolumn{3}{c}{**Indirect Effect (b-Path)**} \\
**B** & **SE** & \(p\)**-Value** & **B** & **SE** & \(p\)**-Value** & **B** & **SE** & \(p\)**-Value** \\ \hline Pain \(\times\) JPS \(-\)10\({}^{\circ}\) of DF \(\times\) A-P sway (mm) & 0.42 & 0.12 & 0.001 & 0.13 & 0.02 & \(<\)0.001 & 0.05 & 0.01 & 0.002 \\ Pain \(\times\) JPS \(-\)15\({}^{\circ}\) of DF \(\times\) A-P sway (mm) & 0.43 & 0.14 & 0.001 & 0.20 & 0.01 & \(<\)0.001 & 0.04 & 0.01 & 0.002 \\ Pain \(\times\) JPS \(-\)10\({}^{\circ}\) of PF \(\times\) A-P sway (mm) & 0.58 & 0.11 & 0.011 & 0.16 & 0.01 & \(<\)0.001 & 0.09 & 0.02 & 0.003 \\ Pain \(\times\) JPS \(-\)15\({}^{\circ}\) of PF \(\times\) A-P sway (mm) & 0.46 & 0.12 & 0.012 & 0.14 & 0.01 & \(<\)0.001 & 0.11 & 0.02 & 0.004 \\ Pain \(\times\) JPS \(-\)10\({}^{\circ}\) of DF \(\times\) M-L sway (mm) & 0.42 & 0.14 & 0.001 & 0.31 & 0.02 & \(<\)0.001 & 0.05 & 0.01 & 0.002 \\ Pain \(\times\) JPS \(-\)15\({}^{\circ}\) of DF \(\times\) M-L sway (mm) & 0.53 & 0.13 & 0.001 & 0.30 & 0.01 & \(<\)0.001 & 0.04 & 0.01 & 0.002 \\ Pain \(\times\) JPS \(-\)10\({}^{\circ}\) of PF \(\times\) M-L sway (mm) & 0.48 & 0.12 & 0.011 & 0.26 & 0.01 & \(<\)0.001 & 0.09 & 0.02 & 0.003 \\ Pain \(\times\) JPS \(-\)15\({}^{\circ}\) of PF \(\times\) M-L sway (mm) & 0.46 & 0.13 & 0.012 & 0.24 & 0.01 & \(<\)0.001 & 0.11 & 0.02 & 0.004 \\ Pain \(\times\) JPS \(-\)10\({}^{\circ}\) of DF \(\times\) COP Ellipse area (mm\({}^{2}\)) & 0.48 & 0.09 & 0.011 & 0.16 & 0.01 & \(<\)0.001 & 0.09 & 0.02 & \(<\)0.001 \\ Pain \(\times\) JPS \(-\)15\({}^{\circ}\) of DF \(\times\) COP Ellipse area (mm\({}^{2}\)) & 0.46 & 0.08 & 0.012 & 0.14 & 0.01 & \(<\)0.001 & 0.11 & 0.02 & \(<\)0.001 \\ Pain \(\times\) JPS \(-\)10\({}^{\circ}\) of PF \(\times\) COP Ellipse area (mm\({}^{2}\)) & 0.42 & 0.08 & 0.001 & 0.31 & 0.02 & \(<\)0.001 & 0.05 & 0.01 & \(<\)0.001 \\ Pain \(\times\) JPS \(-\)15\({}^{\circ}\) of PF \(\times\) COP Ellipse area (mm\({}^{2}\)) & 0.53 & 0.07 & 0.001 & 0.30 & 0.01 & \(<\)0.001 & 0.04 & 0.01 & \(<\)0.001 \\ \hline \hline \end{tabular}  
* JPS = joint position sense, DF = dorsiflexion, PF = plantar flexion, A-P sway = anterior to posterior sway, M-L sway = medial to lateral sway, B = unstandardized coefficients, and SE= standard error.  
\end{table} 
Table 3: Mediation analysis using pain as mediation between ankle joint position sense and balance variables.
## 1 Introduction
The radial depression creates unique cavities in the particles, enhancing deformability and providing specific space for drug loading. In addition, the sagging surface of the bowl-shaped particles can adhere tightly to the surface of an object, similar to a suction cup, after the air in the cavity has been squeezed out by an external force. Although they are promising, the number of bowl-shaped particles developed is small and the preparation methods are generally limited. The preparation methods currently available fall into two broad categories. One is based on templates combined with hierarchical self-assembly, but this method is very complex [20; 21; 22]. The other is the electrostatic spraying method combined with solvent evaporation or solvent diffusion. However, the second method also suffers from poor control of the morphology of the formed microparticles and inhomogeneous particle size [23; 24]. Chitosan (CS) is a cationic, weakly basic polysaccharide of natural polysaccharides containing free amino groups that can be protonated by H+ to form polycationic polymers, which are naturally broad-spectrum antimicrobial, safe and non-toxic, biocompatible and degradable, and can be used as a drug carrier material to control drug release, prolong drug efficacy and reduce toxicity and side effects [25; 26; 27]. Polycaprolactone (PCL) is a polymer with good biodegradability, biocompatibility, and physico-mechanical properties, which is widely used in biomedical materials, drug-controlled release systems, and tissue engineering scaffolds [28; 29]. Recently, we successfully prepared CS/PCL bowl-shaped particles (CS/PCL MPs) using the electrostatic spraying method, which had good morphological stability, drug-loading properties, and acid-triggered solubilisation properties. These microparticles can be used as drug carriers for drug loading, while their acid-triggered solubilisation property can enable the drug to achieve a local release effect at the lesion site and in the specific human microenvironment, which is promising for AS therapy and even tumour therapy. The aim of this study was to prepare chitosan/polycaprolactone bowl-shaped microparticles with stable morphology and to realise their drug-carrying and drug-releasing functions for the natural anticoagulant drug hirudin (rH). The morphology of the microparticles was controlled by controlling the concentration of the precursor solution and the solute ratio in the electrostatic spraying process to obtain microparticles with uniform and stable size and morphology. The method of preparing CS/PCL bowl-shaped microparticles proposed in this paper is simple and effective, which can meet the requirements of drug-carrying and drug-releasing functions, and provides an efficient, low-toxicity, simple, and economical idea for the treatment of AS.
# Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL
## 3 OpenCL on HARPv2  
OpenCL [11] is a framework, developed by Khronos Group, for programming heterogeneous computing architectures. Moreover, it provides an API through which either task-based or data-based parallelism can be defined. Even though OpenCL was originally designed for GPU-programming, it is available for all types of accelerators. OpenCL facilitates the development of software for heterogeneous computing architectures on two different levels. On the one hand, it provides an API through which the accelerator device can be controlled. On the other hand, a C-based programming language is defined that is used to program the accelerator itself [12].  
An OpenCL application consists of two parts: the host and the kernel. Host code is executed on the CPU, while kernel code is executed on a selected accelerator. Both components have a different function in the OpenCL framework. The host can be considered to be the executive part of an OpenCL application as it manages the kernel. It is responsible for providing data to the kernel, invoking the kernel etc. The kernel, on the other hand, is a function executed on the accelerator. Therefore, it typically consists of critical code that is executed more efficiently on the accelerator architecture. A host can call multiple kernels on different accelerator devices.  
An OpenCL kernel is defined as a function that is executed on the accelerator. An OpenCL kernel definition has the following structure: __kernel void <kernel name> (<input parameters>). A kernel function is indicated by the __kernel keyword. Because an OpenCL kernel cannot directly return a value, it is always accompanied by the void keyword. The <kernel name> keyword indicates the name of the kernel, and the <input parameters> indicates that a kernel function can be passed a variable number of parameters. Kernel parameters can be passed either by value or by reference.
This tool supports the earthquake hazard determination for 21 attenuation models by default and these are taken from [PERSON] and [PERSON] [12], [PERSON] and [PERSON] [13], [PERSON] [14], [PERSON] [15], [PERSON] et al. [16], [PERSON] [17], [PERSON] [18], [PERSON] [19], [PERSON] and [PERSON] [20], [PERSON] and [PERSON] [21], [PERSON] et al. [22], [PERSON] [23], [PERSON] [24], [PERSON] [25], [PERSON] and [PERSON] [26], [PERSON] and [PERSON] [27], [PERSON] [28], [PERSON] and [PERSON] [29], [PERSON] et al. [30], [PERSON] and [PERSON] [31], [PERSON] [32], [PERSON] and [PERSON] [33] and [PERSON] and [PERSON] [34]. There are more than 500 attenuation models in the past literature [35]. One of the prime advantages of this tool is the users can implement any attenuation model without difficulty. In addition to these features, another addition of this tool articulated in this study is the failure risk assessment (FR-assessment) of any infrastructure. Failure risk is measured via the mean annual frequency of collapse (\(\lambda c\)), which combines the seismic hazard and the probability of failure of structures conditioned on GM intensity [36]. The extension of the PSHA curve as named by the deaggregation plot for a specific IM can be computed to know the largest contribution to the hazard. For mitigating the uncertainties in GM, the target earthquake level can also be obtained through PSHA analysis. For the failure risk assessment, the fragility parameters (mean and standard deviation) need to be input for the failure intensity measure of each GM. This assessment can be done for different performance levels or different damage states. Analytical result verification using existing well-known software makes the PSHRisk-tool more acceptable. The efficiency has been textualized through the case study for the Korean Peninsula. This tool can be specialized from both the IT perspective and a seismic hazard as well as failure risk perspective. The source code is available on request, is fully portable (platform-independent) for the user and capable of high-range computations as well. Therefore, the authors believe that although the features of this first version may look similar to the general aspects of other PSHA software, the incorporation of the structural failure risk assessment makes it different from others.
## 4 Discussion
### Discussion of LSTM and Informer Results  
The understanding of time-series prediction is not challenging due to the dependency of the prediction at time t on the output of the previous moment, t - 1. However, as the length of the time series increases, the speed of prediction decreases and convergence becomes more difficult. The reason for this difficulty is that LSTM uses a backpropagation algorithm to optimize the sequence by calculating the loss function. With longer sequences, finding the gradient becomes more challenging, resulting in slower convergence and poorer performance.  
The Informer model is built upon the Transformer architecture, which incorporates an attention mechanism to determine the temporal proximity between the current and previous time points. The parallel nature of the system has resulted in a significant improvement in speed.
## 2 Background and Related Work
### Overview on TSN Related Surveys
However, the adaptive version of TAS may increase control traffic overhead. While study [6] focuses on shaping and scheduling, our work has a broader scope, covering the general case of shaping and scheduling, explaining basic concepts, and surveying and classifying a wider range of works. In [3], the integration of TSN in automotive embedded systems is explored. The authors provide an overview of TSN amendments and their relevance to automotive applications such as assisted driving and fully autonomous driving. They identify relevant \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Topics** & [2] & [4] & [5] & [8] & [7] & [6] & [3] \\ \hline Basic concepts & & ✓ & & ✓ & & \\ \hline TSN core functions & ✓ & ✓ & ✓ & & & \\ \hline Forwarding management & ✓ & ✓ & & & & \\ \hline Application fields & & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline Coordinating technologies & & ✓ & & & & \\ \hline Tools and projects & ✓ & & & & & \\ \hline Shaping and scheduling & & & & & & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An overview of the presented TSN-related surveys. standards for in-vehicle networks and discuss the challenges and opportunities of integrating TSN into the in-vehicle model-based development process. The focus in our survey lies specifically on the scheduling problem within TSN, defined as finding the optimal path and shaper configurations for all TSN elements (e.g., in terms of transmission slots) to provide the required QoS to all scheduled streams [8]. We analyze various approaches and consider TAS scheduling as it has garnered significant attention and is particularly challenging. Moreover, we extend our coverage to network-wide scheduling issues and offer suggestions for future research. While our survey does not attempt to cover the entirety of TSN research, Figure 1 provides an overview of our paper's structure and its place within the broader TSN landscape. We acknowledge the importance of addressing other crucial aspects related to scheduling, including control plane design and associated challenges, configuration workflow and overhead, and cross-domain configuration challenges. Although beyond the scope of this survey, these areas warrant attention and further exploration.
# A Medical Image Segmentation Network with Multi-Scale and Dual-Branch Attention
## 4 Experiments and Results
### Results
#### 4.3.3 ISIC 2018 Dataset Comparison Results  
The ISIC 2018 dataset results are shown in Table 3, and the qualitative comparative results of the six models are shown in Figure 6. Dice is 0.913, IoU is 0.857, Precision is 0.937, Recall is 0.917, and Acc is 0.962. Compared to other models, MSDA-Net was improved in defined metrics. Compared with MultiResUNet, MSDA-Net obtained an improvement of 0.4% Acc, 4% IoU, 2.5% Dice, 2.6% Precision, and 2% Recall on the ISIC 2018 dataset. Infer-T reduced the time by 1.07 s compared to UCTransNet. In general, our proposed model outperformed the compared models in most evaluation metrics.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Acc** & **Dice** & **Precision** & **IoU** & **Recall** & **Infer-T** \\ \hline U-Net [11] & 0.982 & 0.903 & 0.904 & 0.839 & 0.916 & **2.61 s** \\ U-Net++ [25] & 0.985 & 0.922 & 0.925 & 0.866 & 0.924 & 6.20 s \\ MultiResUNet [45] & 0.982 & 0.900 & 0.886 & 0.835 & 0.923 & 2.10 s \\ AttentionU-Net [12] & 0.984 & 0.915 & 0.902 & 0.858 & 0.932 & 3.12 s \\ TransU-Net [46] & 0.984 & **0.923** & **0.933** & 0.864 & 0.922 & 5.12 s \\ UCTransNet [47] & 0.985 & 0.916 & 0.903 & 0.860 & 0.931 & 4.47 s \\ Ours & **0.992** & 0.917 & 0.911 & **0.870** & **0.934** & 3.45 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative comparisons with various models on the CVC-ClinicDB dataset.  
Figure 5: Visual comparison of image segmentation results of the CVC-ClinicDB dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Acc** & **Dice** & **Precision** & **IoU** & **Recall** & **Infer-T** \\ \hline U-Net [11] & 0.950 & 0.862 & 0.870 & 0.782 & 0.901 & **81.02 s** \\ U-Net++ [25] & 0.954 & 0.880 & 0.917 & 0.808 & 0.881 & 116.82 s \\ MultiResUNet [45] & 0.958 & 0.888 & 0.911 & 0.817 & 0.897 & 102.60 s \\ AttentionU-Net [12] & 0.958 & 0.883 & 0.899 & 0.812 & **0.934** & 90.57 s \\ TransU-Net [46] & 0.955 & 0.887 & 0.896 & 0.814 & 0.901 & 91.25 s \\ UCTransNet [47] & 0.953 & 0.869 & 0.875 & 0.794 & 0.904 & 101.48 s \\ Ours & **0.962** & **0.913** & **0.937** & **0.857** & 0.917 & 100.41 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative comparisons with various models on the ISIC 2018 dataset.
## 1 Introduction
In response to the concerns identified in the literature, a hybrid approach to node slice provisioning was devised. The physical network architecture was also designed to make the network slice more user-friendly. The PROMETHEE and SLE algorithms were used in this research to allocate nodes and create connections for the logical networks inside the  
Figure 1: Architecture of 5G mobile network with slicing. physical network. This project was completed in two independent stages. First, a node importance ranking array (NIRA) was constructed using PROMETHEE-II and various node attributes, and then nodes were assigned according to the NIRA. This was performed in the following step, while the SLE algorithm establishes a link between the targeted nodes. The following are the primary contributions of the planned work:  
* A mathematical model is introduced for network slicing that takes operational constraints and security considerations into account when assigning nodes and connecting them. * The prepared node importance rank array (NIRA) was used to allocate NSR nodes in physical and logical networks, and the SLE approach was utilized to connect the network slice request (NSR) nodes. NIRA preparation takes into account node information such as the node capacity, nearby line bandwidth, degree of the node, and the node's proximity centrality. * A PROMETHEE-II multi-criteria approach is suggested for NIRA preparation, and an SLE algorithm is provided for NSR node link creation. The SLE approach generates the shortest path array (SPA) for NSR nodes, guaranteeing that all possible connections are installed and increasing the acceptance percentage. * The proposed PROMETHEE-SLE approach was evaluated on a small-world network to confirm that the infrastructure is slicing-friendly, and the results were also compared to those obtained from a scale-free physical infrastructure.
# An Ensemble Extreme Learning Machine for Data Stream Classification
## 2 Background Knowledge
### Data Stream Classification  
Let \(\{\cdots,\mathbf{d}_{t-1},\mathbf{d}_{t},\mathbf{d}_{t+1},\cdots\}\) be a data stream generated by a system, and \(\mathbf{d}_{t}\) a datum at \(t\) moment; \(\mathbf{d}_{t}=\{x_{t1},x_{t2},\cdots,x_{tm},y_{t}\}\), where \(m\) is the features number of \(\mathbf{d}_{t}\) and \(y_{t}\) is the class label. Data stream classification generally adopts a sliding window mechanism, and several data make up a dataset called data block and denoted \(\mathbf{B}_{i}\), where \(\mathbf{B}_{i}=\left\{d_{(1)},d_{(2)},\cdots,d_{(n)}\right\}\) and \(n\) is the size of data block. At every moment, only one or several data blocks are allowed to enter sliding window. After one data block is processed, a new data block can be loaded to sliding window.  
Suppose that in \(\Delta t\) time, if the error rate of classifier system is at a low level in the sliding window, it is said that the concept of data stream is stable in this period and \(P\left(\mathit{error-best}\leq\varepsilon\right)\geq 1-\alpha\), where \(\mathit{error}\) is the current error rate of classifier system, \(\mathit{best}\) is the classification error rate of optimal performance classifier for data stream and \(\alpha\) is a significance level. Let the classification model of data stream be \(M\), which is trained by the data blocks in sliding window at \(t\) moment; after \(\Delta t\) time, the classification model changes to \(N\). If \(M\
eq N\), it means concept drift has happened in data stream. If \(\Delta t\) is a short time, the concept drift is called abrupt concept drift; otherwise, it is called as gradual concept drift [14].
## 1 Introduction
### The Proposed Methodology for On-Fault Displacement Evaluation
\[P(D\geq D_{0})|_{\lambda}=\left.\left[\sum_{i=0}^{l=i_{u}}P(sr\
eq 0|m_{i})P(D\geq D _{0}|m_{i})w_{i}\right]\right|_{\lambda}. \tag{8}\]  
Here and below the upper indexes \(i=i_{u}\) and \(j=j_{u}\) corresponds to the maximum magnitude and distance up-to those the disaggregation is performed. Applying Equation (8) the expected value of the on-fault displacement \(\overline{D}\big{|}_{\lambda}\) can be calculated for the worst-case since all earthquakes at the fault are centered to the site (\(l/L=0.5\) and \(x^{*}=1\)) and all earthquakes are accounted for in the PGD evaluation independent from the distance from the site. Equation (2) establishes the correlation between magnitude and \(D\) that can be used in the further analysis. Thus, writing Equation (2) as:  
\[D=exp(b+a\cdot m+c), \tag{9}\]  
and the expected value of displacement, \(\overline{D}\big{|}_{\lambda}\) will be:  
\[\overline{D}\big{|}_{\lambda}=\left.\sum_{i=0}^{i=i_{u}}w_{i}\cdot P(sr\
eq 0| m_{i})\cdot exp(b+a\cdot m_{i}+c)\right|_{\lambda} \tag{10}\]  
Due to the assumptions made above, the \(\overline{D}\big{|}_{\lambda}\) is obviously over-conservative, since the on-fault displacement is an is a proximal phenomenon. Therefore, the magnitude distribution for the proximal to the site distance bin should be accounted for in the calculation. Let's denote the weight distribution for magnitude in the closest bin to the site in the disaggregation as \((w_{i}\ m_{r=0,i})\). For this case, the Equation (10) can be rewritten as follows:  
\[\overline{D}\big{|}_{\lambda}=\left.\sum_{i=0}^{i=i_{u}}w_{i}\cdot P(sr\
eq 0 |\ m_{r=0,i})\cdot exp(b+a\cdot m_{r=0,i}+c)\right|_{\lambda} \tag{11}\]  
Thus, considering the standard deviation for the Equation (2), for each hazard level \(\lambda\), a mean, a mean minus standard deviation and a mean plus standard deviation values can be obtained. Thus, three estimations of the hazard curve can be created for \(\overline{D}\). The averaging means here that the displacement is averaged for all possible magnitudes that contributed to the seismic hazard at hazard level \(\lambda\). In this procedure, the epistemic and aleatory uncertainties on the seismotectonic modeling are accounted for in the PSHA via logic-tree technique.