esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Open Challenges and Research Directions
### Task Segmentation and Migration  
In a highly dynamic vehicular environment, and due to quickly changing vehicle positions, completing offloaded tasks on time is vital. Thus, researchers suggest segmenting tasks and/or migrating them to other MEC hosts to be able to complete them correctly and on time.  
Segmentation entails executing parts of a task at multiple servers and then getting back the results once all parts of a task have been completed [191; 192]. This approach is particularly valuable when dealing with tasks that have stringent timing constraints, where obtaining results from each component in a matter of milliseconds is of paramount importance. This way, the seamless and timely execution of tasks in a dynamic vehicular setting is achieved, ensuring that critical tasks receive the necessary attention and deliver results with minimal latency. However, as of today there are few works addressing this issue and the research opportunities are still open in this direction.  
Migration involves moving the computation of the ongoing task to a different server, usually one physically closer to the vehicle for reduced latency, or one with more resources/capabilities [100; 193]. There are two primary categories for service migration techniques: _stateful_ and _stateless_. Stateless migration pertains to services that lack an active state and do not retain any session data. This implies that these services can be effortlessly deployed by initiating fresh instances on the target nodes. On the other hand, stateful migration presents a greater challenge because it necessitates the preservation and transfer of state information to the destination node for service resumption. Most research contributions in this field concentrate on stateful migration, as stateless migration simply involves halting the instance on the source node and initiating it anew on the destination node without preserving the current operational state.
# Aerodynamic Drag Study of the Heat Exchange Equipment with Different Fin Geometries
## 5 Results and Discussion
Analyzing the change in the coefficient of aerodynamic drag, according to the graph obtained in our simulation (Figure 8, curve 3), it can be stated that the drag coefficient at values of the Reynolds number Re > 10,000 tends to decrease. The limiting value in our study corresponds to \(C_{X}\) = 1.83 at 6 m/s, and the reduction in the drag coefficient in the interval 10,000 < Re < 30,000 is 15%. Using the simulation data, it can be assumed that at a speed of 10 m/s, the difference in the coefficients of aerodynamic drag of the fins of our geometry and those studied by [PERSON] will not exceed 25%. Such a discrepancy is quite acceptable, since in our study, the nozzle contains thirty ribs, compared to the nozzle with eight ribs in [PERSON]'s study [29]. A denser rib structure can cause an increase in the component of form resistance or friction, which is the result of a higher value of the aerodynamic drag coefficient. However, if our finned nozzle is used as a heat or mass exchange element, then in addition to the coefficient of aerodynamic drag, the developed surface area should be taken into account, which is almost four times greater than the surface of the nozzle in the study by [PERSON] [29]. The obtained values of drag coefficients can be used to carry out estimated aerodynamic calculations of heat exchange elements of a similar geometric configuration. To clarify the obtained values, it is necessary to conduct additional field studies in a wind tunnel, which will be the subject of future research.
## 3 FE Analysis  
### Modeling of Crack  
In nonlinear FE analysis, accurately representing cracks is crucial for assessing their influence on structural behavior. Each crack was modeled by disconnecting the elements along the crack path to simulate the discontinuity in the geometry. In other words, the elements along the crack line were not merged or connected to adjacent nodes, ensuring that stress redistribution and local failure mechanisms were properly captured.  
Since the accuracy of the analysis depends on the proper representation of crack-induced stress concentration, special attention was given to the mesh refinement in the crack region. In the FE analysis of a cracked structure under tensile loads, the elements around the crack zone should be finer than those in the uncracked area due to the crack propagation and stress concentration [33]. By contrast, the influence of the element size in  
Figure 1: Types of cracks studied in plate and stiffened panels.  
the crack zone on the structural behavior is minimal in structures subjected to compressive loading [10].  
A case study was conducted to Investigate the effect of the element size In the FE model of a crack zone on the structural behavior. To compare structural responses, longitudinal compression was applied to the plate containing a transverse crack extending across 50% of the plate breadth, perpendicular to loading direction. Fine elements (2.4 mm) were used around the crack zone, as shown in Figure 1(a); a coarser mesh size of 21.5 mm was used for the surrounding area where finer elements were not required.  
Figure 1(b),c show a comparison of the structural behavior and von-Mises stress distribution of plate with and without fine elements in the crack zone. Figure 2 indicates that the element size had no significant impact on structural behavior or stress distribution, including the stress concentration. Therefore, a uniform element size was sufficient for the entire structure in the ultimate compressive strength analysis.
## 5 Experiments and Results
### People Following  
In the People Following FBM, the main data collected are the position of the robot and the position of the person from which the evaluation metrics are calculated automatically. Figure 15 shows the path of the robot when following a person inside the ISRoboNet@Home testbed, while using the People Following method described in Section 3.3.  
Figure 16 shows the FBM results from seven different trials, indicating the three output metrics that are provided by the benchmarking system. The total distance covered by the robot while following and the average deviation from the desired distance indicate how well the robot has performed the following behavior. The benchmark reliability metric shows how well the benchmark trial was captured by the benchmarking system in different runs. The reliability score can easily be increased to near 100% by increasing the number of MoCap cameras and carefully positioning the cameras to cover all regions of the arena.  
Figure 17 shows the relative distance between the robot and the person as measured by the robot and the MoCap system. The desired following distance to be maintained by the robot was 2 m, and the robot was considered to be following if its relative distance was inside a threshold indicated by the yellow region. The MoCap distance of 0 symbolizes the moments when the MoCap system was unable to locate the person or the robot, and hence that data point was not considered.  
Figure 16: People Following FBM results per trial.  
Figure 15: Path of robot, where the red lines indicate the path estimated by the robot and the blue lines indicate the paths measured by the MoCap system.
## 2 Computational Approach
### GW Approximation  
The many-body perturbation theory based on the one-body Green's function is currently considered the most suitable approach to study electronic quasi-particle excitations [95]. In this approach, the nonclassical many-body effects are incorporated by the energy-dependent and nonlocal self-energy term \(\Sigma\)xc. The GW method approximates the \(\Sigma\)xc by utilizing its first-order expansion concerning the dynamically screened Coulomb interaction W and the Green's function G [95; 96]. The quasi-particle energies are calculated as a first-order correction to the single-particle eigen energies, which are often obtained by computing W and G based on the eigenstates of a reference single-particle Hamiltonian. Although the GW method has been successfully applied to calculate quasi-particle band structure properties for a wide range of materials, it suffers from convergence issues and unfavorable scaling of the computational cost with respect to the system size.
# Evaluation of 6 LoWPAN Generic Header Compression in the Context of a RPL Network
## 5 Methodology and Measurements
### Evaluation by Means of a Simulator
Computing the compression ratio of a RPL packet when using SCHC or ROHC is more difficult as these compression mechanisms are less intuitive to apply to our scenario. For SCHC, a specific rule could be created for compressing RPL packets, which might result in high compression ratios. Note that the simulation study only considers RPL control traffic, which is shown here to be compressible. Data packets will usually be less compressible as they contain data that match less with the content of the dictionary. This will negatively affect the global compression ratio.
## 3 Automated Diagnosis of COVID-19
### Pixel-Level Diagnosis: Segmentation-Based Models  
#### 3.2.1 Overview  
The pixel-level diagnosis of COVID-19 can be formalized as follows: suppose we have a dataset \(\mathcal{D}=\{(X_{i},Y_{i})\}_{i=1}^{N}\) with \(N\) samples, where \(X_{i}\) and \(Y_{i}\) are the \(i\)th input scanning image (CT/\(X\)-ray) and the corresponding binary lung lesion annotation. For each pixel of \(Y_{i}\), zero represents the background, whereas one represents a lung lesion instance. The goal here is to learn a function \(f\) that predicts \(Y_{i}\) from \(X_{i}\) accurately. Similar to classification-based models, the lung lesion detection can be written as \(\hat{Y}_{i}=f(X_{i})\), and we expect a low prediction error \(d_{i}=\hat{Y}_{i}-Y_{i}\) for each \(i\).  
We present a typical segmentation-based COVID-19 diagnosis model in Figure 7. This model uses a U-Net for segmentation, which mainly consists of two parts: an encoder network and a decoder network. The encoder network has four downsample stages with convolution and pooling layers that analyze the contextual pixel information in the image to obtain the semantic feature. In each stage, the input tensor first goes through two convolutional layers with ReLU activation. The output of convolutional layers is max-pooled with a kernel size of \(2\times 2\), therefore reducing the spatial resolution by a half. Let \(conv(\cdot)\) denote the two convolutional layers, and let \(pool(\cdot)\) represent the max-pooling layer. The \(L\) is the total number of stages. The output feature maps of each stage of the encoder \(E\) can be formulated as follows:  
\[X_{E}^{l}=\begin{cases}conv(X),&l=1\\ conv(pool(X_{en}^{l-1})),&l=2,..,L\end{cases} \tag{1}\]  
The decoder network consists of four upsample stages. It recovers the same resolution of the given input image. In each stage, a skip connection is built with the corresponding stage in the encoder. The output tensor of the former stage of the decoder is upsampled and concatenated along the channel axis with the output tensor of the same stage of the encoder. The upsampling is performed by conducting nearest interpolation. Then, two  
Figure 7: A typical UNet-segmentation-based pixel-level COVID-19 diagnosis model.  
\(3\times 3\) convolutions with ReLU activation and the same padding is applied. Let \(upsample(\cdot)\) denote the upsampling operation and let the \(\oplus\) represent the concatenate operation; the output tensor of each stage of the decoder \(D\) can be formulated as follows:  
\[X_{D}^{l}=\begin{cases}X_{E}^{l},&l=L\\ conv(X_{E}^{l}\oplus upsample(X_{D}^{l+1})),&l=1,..,L-1\end{cases} \tag{2}\]
## 2 The Proposed Method  
Figure 1 shows the overall framework of the proposed method, and the following subsections provide details of TFCF and RTFANet. As can be seen from Figure 1, the input of the RTFANet model is a TFCF dual-channel time-frequency image, and the output is the probability of this image belonging to bearing health, inner race fault and outer race fault. RTFANet first carries out the first convolution operation on the input image. After each convolution operation, the nonlinear expression ability of the model is improved by the ReLU activation function, and the parameters of the feature graph are reduced to 1/2 of the original by maximum pooling. Then, the residual time-frequency mixed attention (RTFA) is used to enhance the vital information of feature mapping. After RTFA, the second convolution operation is carried out, and the tensor dimension is reconstructed. A fully connected layer (FC) is input, and the probability of each failure is output by the softmax classifier. The details of TFCF and RTFA in Figure 1 are described in subsequent sections.
## 2 Modeling of Hybrid-Propellant Launch Vehicle
### Trajectory Model  
The trajectory model plays a crucial role in the simulation of launch vehicles as it encompasses all transformation functions between frames and translational and angular equations of motion. As it encompasses interactions between subsystems and serves as a bridge among them, it is essential to ensure the accuracy of the trajectory model to avoid irreversible situations. In this study, the 6 DOF trajectory model was implemented using MATLAB 2017b in the optimization process [47]. The inclusion of the Coriolis term enables the calculation of angular velocities in the fixed-body coordinate system. The equation of angular motion is presented in Equation (6) [48]:  
\[{}^{b}\dot{\omega}_{b/i}^{bf}=\Big{(}J^{bf}\Big{)}^{-1}\Big{[}\mathbf{M}^{bf }-\omega_{b/i}^{bf}\times J^{bf}\omega_{b/i}^{bf}\Big{]} \tag{6}\]where \(\omega_{b/i}\) represents the angular velocity of the body with respect to the inertial frame, \(\dot{\omega_{b/i}}\) represents the time derivative of \(\omega_{b/i}J\) represents the inertia matrix, and \(\mathbf{M}\) represents the moment vector. \(J\) remains zero if the center of \(m\) of the body does not change during the motion. However, due to propellant consumption and stage separation, \(J\) changes during the flight. Therefore, \(J\) variations are also included in the model. To determine the state parameters of translational motion, [PERSON]'s second law of motion is applied. The position and velocity (\(V\)) of the launch vehicles are considered state parameters, considering the effects of propulsion, aerodynamics, atmosphere, and gravity. The equation for the translational acceleration vector of the body is given by Equation (7) [48]:  
\[{}^{e}\dot{\mathbf{v}}_{b/e}=\frac{1}{m}\mathbf{F}+\mathbf{G}-\omega_{e/i} \times(\omega_{e/i}\times\mathbf{P}_{cm/O})-2\omega_{e/i}\times\mathbf{v}_{b/e} \tag{7}\]  
where \(\mathbf{v}_{b/e}\) represents the velocity vector in the body frame with respect to ECEF, \(\mathbf{F}\) represents the sum of the force vector, \(\mathbf{G}\) represents the gravitation vector of Earth, \(\mathbf{P}_{cm/O}\) represents the position vector of the body's center of mass in reference to the Earth's origin, and the \(e/i\) subscript represents ECEF concerning the inertial frame.  
To solve the equations of motion, including centripetal and Coriolis accelerations due to the moving frame and rotating Earth, the fourth-order Runge-Kutta integration method is employed [49]. By solving the equations of motion, state parameters such as the position, velocity, angular orientation, and angular velocity of the launch vehicle are obtained, and the assessment is made as to whether it fulfills the objectives aimed at putting the payload into the targeted orbit.
# Influence of Synthesis Parameters in Obtaining KIT-6 Mesoporous Material
## 2 Materials and Methods
### Modifications in the Standard Synthesis  
The changes in the parameters of the synthesis of KIT-6 were carried out starting from the synthesis adopted as standard [9], altering only one parameter at a time, keeping the others unchanged.  
The molar ratio of organic template P123 was modified by changing the standard ratio of 0.017 to 0.012 and 0.022. Samples obtained through this procedure were coded as K0.012 and K0.022, respectively. The dissolution time of the organic template was systematically modified, altering the initial time from 6 h to 8, 4 and 2 h, coded by KDT8, KDT4 and KDT2, respectively. Another modified parameter was the standard alcohol (butanol), which was replaced by the following alcohols: methanol (Synth 99.4%), ethanol (Synth 99.5%), 1-propanol (99.5% Vetec) and 1-pentanol (99% Sigma Aldrich).  
These samples were coded as Kmt, KEt, KPr and KPe, respectively. The aging time of the synthesis gel was also modified, varying the standard time from 24 h to 6, 12, 18 and 30, with KAT6, KAT12, KAT18 and KAT30 samples, respectively. The last modified parameter was the time of heat treatment, changing the initial time from 24 to 12, 18 and 30 h, which were coded as KHT 12, KHT 18 and KHT 30, respectively.  
Figure 1 shows a representative scheme with a summary of all modified parameters in the KIT-6 synthesis, and the respective sample codes for each parameter.
# Efficient Parallel Algorithms for 3D Laplacian Smoothing on the GPU
## 4 Results
### The Running Time and Speedup of Form A in Standard Laplacian smoothing  
Iteration Form A uses updated coordinates that have been calculated in current pass. As a result, it must exchange data during the iterating process. Tables 2-4 show the results of implementing Form A in different data layouts.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Specifications** & **Details** \\ \hline CPU & Intel Xeon Gold 5118 \\ CPU Frequency (GHz) & 2.30 \\ CPU RAM (GB) & 128 \\ CPU core & 48 \\ GPU & NVIDIA Quadro P6000 \\ GPU memory (GB) & 24 \\ CUDA cores & 3840 \\ OS & Windows 10 Professional \\ Compiler & VS2015 Community \\ CUDA version & v9.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the workstation employed for performing experimental tests.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Size**} & \multirow{2}{*}{**Elements**} & \multicolumn{3}{c}{**Running Times (ms)**} & \multicolumn{3}{c}{**Speed Up**} \\ \cline{3-6}  & & **CPU** & **Single** & **Multiple** & **Single** & **Multiple** \\ \cline{3-6}  & & **Block** & **Blocks** & **Block** & **Block** & **Blocks** \\ \hline
5k & 33k & 200.6 & 106.2 & 58.4 & 1.89 & 3.43 \\
10k & 66k & 677.1 & 311.6 & 96.2 & 2.17 & 7.04 \\
50k & 391k & 5660.4 & 1358.6 & 361.8 & 4.17 & 15.65 \\
77k & 388k & 8232.6 & 3696 & 473 & 2.23 & 17.41 \\
100k & 727k & 22,365.4 & 3723.2 & 1661.2 & 6.01 & 13.46 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Running time and speedup when using the SoA data layout.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Size**} & \multirow{2}{*}{**Elements**} & \multicolumn{3}{c}{**Running Times (ms)**} & \multicolumn{3}{c}{**Speed Up**} \\ \cline{3-6}  & & **CPU** & **Single** & **Multiple** & **Block** & **Block** & **Blocks** \\ \hline
5k & 33k & 200.6 & 175 & 94 & 1.15 & 2.13 \\
10k & 66k & 677.1 & 569.6 & 228.6 & 1.19 & 2.96 \\
50k & 391k & 5660.4 & 2159.8 &  
This significantly improves the efficiency of implementation; see Figure 6.
## 2 Experimental Investigation
### Mechanical Characterization  
The mechanical properties of both alloys were investigated by means of a tensile test, a three-point bending test, and nanoindentation experiments. Figure 3 and Table 2 show the geometry and dimensions of the specimens used for the bending test and tensile test. For the nanoindentation, we used samples in the form of disks with a thickness of 1 mm and a diameter of 10 mm (Figure 4). Milling of the specimens was performed initially with a T26-3.0 I 6.0 mm milling cutter (Art. No. 526030 3006) and finished with a T64-1.5 I 6.0 mm milling cutter (Art. No. 52028 1506) (Times-icore GmbH).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Manufacturing Parameter** & **Value** & **Unit** \\ \hline Laser power & 140 & W \\ Layer thickness & 30 & \(\upmu\)m \\ Laser spot & 50 & \(\upmu\)m \\ Scan speed & 125 & mm/s \\ Hatch spacing & 80 & \(\upmu\)m \\ Oxygen level & \(<\)0.3 & \(\%\) \\ Build platform heating & 200 & \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Manufacturing parameters of the 3D-printed Ti-6 Al-4V sample.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen Type—5B** & \multicolumn{2}{c}{**Dimension in mm**} \\ \hline l\({}_{3}\) & Overall length & \(\geq\)35 \\ b\({}_{2}\) & Width at ends & 6 \(\pm\) 0.5 \\ l\({}_{1}\) & Length of narrow parallel—sided portion & 12 \(\pm\) 0.5 \\ b\({}_{1}\) & Width at narrow portion & 2 \(\pm\) 0.1 \\ r\({}_{1}\) & Small radius & 3 \(\pm\) 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dimensions of the specimens used for bending test and tensile test.  
The tensile test was made according to the ISO 6892-1:2019 [27] standard, using the electromechanical machine Instron 1185 (High Wycombe, Buckinghamshire, UK) at an absolute deformation rate of 0.2 mm/min. The three-point bending test was made according to the ISO 7438:2020 [28] standard using the electromechanical machine Mecmesin 2, 5-1 (Slinfold, West Sussex, UK) (Figure 2e) at a speed of absolute deformation of 1 mm/min.  
The nanoindentation experiments were made according to the ISO 14577 standard [29] using a Nanoindenter G200 (KLA Corporation, Milpitas, CA, USA) (Figure 2f) with a Berkovich tip. The indentation method with five loading-unloading cycles and load control was used in order to check if there were changes in the properties of the samples in depth. The maximum load was 30 gf (-294 mN), the peak hold time at the maximum load was 10s, and the time to load was 15s. Twenty-five indentations of each sample were made at a distance of 50 m between them.
# Research on Construction Sequencing and Deformation Control for Foundation Pit Groups
## 2 Materials and Methods
### Numerical Simulation Framework  
#### 2.3.1 Finite Element Modeling  
Indoor model tests simulate real-world excavation by scaling down structural dimensions, while they are still able to provide controlled, tangible insights into deformation behaviors under different construction sequences. However, these tests are limited by factors such as experimental equipment and conditions, resulting in a limited number of  
Figure 8: Backfilling of the diaphragm wall installation.  
Figure 7: Test excavation process.  
Figure 9: Excavation stages.  
simulated working conditions. On the other hand, numerical simulations extended these insights across a broader set of conditions--such as varied spacing configurations--that were impractical to physically test. This combination ensures both the rigor of controlled experimentation and the flexibility of comprehensive parametric analyses. Therefore, this section will use numerical simulation to replicate the model tests and investigate the mutual influences of construction under more working conditions. Numerical simulations were performed using PLAXIS 3D (Version 22.03) employing 10-node tetrahedral elements (TET10) with three translational degrees of freedom per node (\(u_{x},u_{y},u_{z}\)). The mesh was refined to element sizes of 20-40 mm in critical zones (retaining walls) and coarsened to 60-100 mm in far-field regions, resulting in a total of 25,382 elements and 38,573 nodes. This discretization generated a system of 115,719 equations solved via Newton-Raphson iteration. According to the theory of earth pressure calculation for finite-width soil, the width of the soil between adjacent foundation pits affects the magnitude and distribution of active earth pressure acting on the diaphragm walls, thereby influencing the deformation of the retaining structures [27; 28; 29; 30; 31; 32]. Theoretically, if the width of the separation zone is sufficiently large, the mutual influence of adjacent pit excavations can be neglected. However, an excessively large separation zone width (B) can lead to issues such as limited site space and increased project costs.