esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Variation in Fly Ash-Stabilized Loess on Swell Potential  
Swelling occurs when unsaturated soils absorb water by interaction and thus increase in volume [75]. Figure 4 shows the influence of the variation in FASL on swell potential at different curing times.  
The swell potential significantly increased by 88.20% at 14 days of curing time compared with that at zero days of curing time in the pure loess treatments. However, this value was higher than that at the next days, which decreased by 10.67% at 28 days (Figure 4). Thus, the effect of the increase in curing time on swell potential was insignificant. The test results indicated that the untreated loess provided a swell potential with the addition of FA, whereas FA-treated loess provided significantly decreased swell potential with the increase in FA ratio. Specifically, the swell potential in loess and FA mixtures (with 10%, 20%, and 30% FA) decreased by 64.61%-93.82%, 53.73%-85.07%, and 83.25%-94.42% at 0, 14, and 28 days of curing time, respectively, compared with that of untreated loess (Figure 4). The increment in swell potential might be due to various factors, such as specific gravity mixture, particle size and shape of mixture, and chemical reaction between FA and soil. The rapid hydration process and simultaneous cation exchange reduced the swell potential when FA was added into the soil; in these phenomena, the sodium ions in the soil were replaced by the calcium ions in the FA, the soil flocculated into larger lumps, and new pozzolanic reaction products, such as C-S-H and C-A-H, were produced [51; 52; 70]. The change in the physical properties of loess-FA mixtures might also be a reason, which was dependent on the non-expansive characteristics and particle size and shape of FA [55]. Meanwhile, the plasticity index of the soil is an important indicator of the swell potential; thus, the reduction in plasticity index in the current study (Section 3.1) decreased the swell potential [2]. Therefore, the addition of FA was very effective in reducing the swell potential of loess, especially with high FA ratio. This result was consistent with that of previous studies with the addition of FA on  
Figure 4: Swell potential at different FA ratios and curing times.  
loess [51; 52; 55; 75] or the addition of FA on clays [70]. However, [PERSON] (2016) suggested that the addition of FA did not change the swell potential at zero days of curing time, and but it significantly decreased the swell potential at seven and 28 days of curing time [46]. This result might be explained by the lower FA ratio than that in the present study or the time-consuming pozzolanic reaction.
## 3 Results and Discussion  
### Effects of OM Treatment on Microstructure of Muscle Tissue  
Since the damage of muscle fiber is correlated to the lipid and protein oxidation [27], as well as quality attributes, including the water-holding capacity and texture [28], histology was used to evaluate the effects of OM washing treatments on the microstructure of muscle tissue. The cross-sections of pork and fish tissues subjected to different washing treatments are shown in Figure 1. The cell nucleus appears blue, muscle fibers red, and the space between the muscle fibers white.  
For the CW group of the pork sample (Figure 1A), the muscle fibers looked tightened and round, and were undulated and arranged freely relative to each other. Intact myofibrillar bundles and cellular structure, as well as clear boundaries, were found. For the OM group of pork samples (Figure 1B), the border between fibers appeared clear. The pork  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Modification** & **Amino Acid Residue** & **Position** & **Formula** & **Mass Shift (Da)** \\ \hline Monooxidation & HWMRYPNKDC & any & O & 15.99 \\ Dioxidation & YWFRKPCM & any & O(2) & 31.99 \\ Trioxidation & CWYF & any & O(3) & 47.98 \\ Triyptophan oxidation to kynure & W & any & C(\(-1\))O & 3.99 \\ Oxidation to nitro & YW & any & H(\(-1\))NO(2) & 44.99 \\ Triyptophan oxidation to hydroxykynuren & W & any & C(\(-1\))O(2) & 19.99 \\ Histidine oxidation to Asparatic Acid & H & any & H(\(-1\))C(\(-2\))N(\(-1\))O & \(-22.03\) \\ Histidine oxidation to Asparagine & H & any & H(\(-2\))C(\(-2\))N(\(-2\))O(2) & \(-23.02\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The identified oxidative modifications of amino acid residues under the impact of two washing treatments.  
Figure 1: Effects of different washing treatments on the microstructure of muscle tissue. CW: conventional water-washing; OM: ozone-microbubble-washing. Pork samples treated with CM (**A**) and OM (**B**); fish treated with CW (**C**) and OM (**D**).  
samples treated with OM exhibited the same microstructure of muscle fibers as the CW group of pork samples, with round and closely arranged fibers and clearly marked boundaries. Thicker perimysium and thinner endomysium were observed in both treatment groups of the pork samples. Overall, the pork samples treated with CW and OM treatments exhibited complete cell shapes, intact myofibrillar bundles, and similar extracellular spaces (Figure 1A,B).  
For the CW group of the fish samples (Figure 1C), the muscle fibers were regularly arranged with great extracellular spaces, while the spacing between the muscle fibers from the OM group of fish samples (Figure 1D) was wide or narrow at random. Although the distance between muscle fibers was visible in both treatment groups, muscle fibers were polygonal in shape, and sarcolemma remained intact.  
The microstructure observations allow us to conclude that OM treatment did not rupture the fiber membranes or damage the muscle cells, compared with CW treatment, which indicated OM treatment might not lead to the oxidation of cell contents.
## 4 The Geometries of the Proposed Transmitters
### Octagonal Array  
As shown in Figure 10, an octagonal-shaped array transmitter is proposed with a separation \(s\) between each LED. Each element has a half FOV of \(\phi_{\frac{1}{2}}^{\prime}\). Again, each LED \(e_{i,j}\) is oriented along a vector \(\mathbf{O}_{i,j}\) crossing the location of the \(e_{i,j}\) at \(y=0\) plane and the location of \(e_{i,j}^{\prime}\) on the \(y=d_{0}\) plane. Consistently throughout all configurations, the \(y=d_{0}\) plane is defined such that the two octagonal arrays formed by the \(e_{i,j}^{\prime}\) points from the two transmitters become tangent on this plane (illustrated in Figure 11). To meet this condition, the separation between the \(e_{i,j}^{\prime}\) points should be \(D/(\sqrt{2}+1)\).  
With the naming convention of Figure 10, the locations of the elements \(e_{i,j}\) and \(e_{i,j}^{\prime}\) relative to the center element \(e_{0,0}\) are determined with  
\[\mathrm{P}(\frac{s}{2}j,0,s\frac{\sqrt{2}+1}{4}i),\quad i=\text{even}; \tag{26}\]  
\[\mathrm{P}(s\frac{\sqrt{2}+1}{2}j,d_{0},\frac{s}{2}i),\quad i=\text{odd}. \tag{27}\]  
and  
\[\mathrm{P}(\frac{D}{2(\sqrt{2}+1)}j,d_{0},\frac{D}{4}i),\quad i=\text{even}; \tag{28}\]  
\[\mathrm{P}(\frac{D}{2}j,d_{0},(\frac{D}{2(\sqrt{2}+1)}i)),\quad i=\text{odd}. \tag{29}\]  
respectively.  
Figure 10: Octagonal -shaped LED array geometry.  
Figure 9: Single hexagonal arrays formed by the points \(e_{i,j}^{\prime}\) on the \(y=d_{0}\) plane.  
Thus, the required orientation vectors \(\mathbf{O}_{i,j}\) can be calculated by  
\[\langle(\frac{D}{\sqrt{2}+1}-s)\frac{j}{2},d_{0},(\frac{D}{\sqrt{2}+1}-s)\frac{ \sqrt{2}+1}{4}i\rangle,\quad i=\text{even}; \tag{30}\]  
\[\langle(\frac{D}{\sqrt{2}+1}-s)\frac{\sqrt{2}+1}{2}j,d_{0},(\frac{D}{\sqrt{2}+ 1}-s)\frac{i}{2}\rangle,\quad i=\text{odd}. \tag{31}\]  
Similar to the considerations made for the \(3\times 3\) square array and the single hexagonal array, the power of each LED in the octagonal array transmitter is determined to be \(P_{0}/9\) since it includes a total of nine LEDs. Proportional to the power level divided by the squared tangent of the half FOV, the intensity \(I_{3}\) of each LED of the array is set as follows:  
\[I_{3}=\frac{1}{9}\frac{\tan^{2}\phi_{\frac{1}{2}}}{\tan^{2}\phi_{\frac{1}{2}}^{ \prime}}I_{0} \tag{32}\]  
The initial values of the design parameters for the octagonal array are listed in Table 2. Using Equations (2) and (3), and the values given in Table 2, the irradiance distribution in the plane \(y=1\) m is plotted using MATLAB software. The result is given in Figure 6d.
## 2 Efficient Estimation of Residual OFDM BER for Small Cells
### Optimal Sample Delay for Least BER Floor
Moreover, if we observe the \(i\)-th echo of the received OFDM symbol:  
\[\hat{r}_{i}=\sum_{m=1}^{M}s_{m}e^{i\varphi}\cdot e^{im\cdot\frac{2\pi}{M}( \overline{\tau}+\Delta\tau_{\text{opt}}-\tau_{i})};\quad i=1,2,\ldots N \tag{15}\]  
which is not time-sampled at the optimal sampling instant \(\tau=\tau_{\text{opt}}\), but with the offset of \(\Delta\tau_{\text{opt}}\) of the mean delay \(\overline{\tau}\) from \(\tau_{\text{opt}}\), it introduces the phase shift:  
\[\vartheta_{i}=m\cdot\frac{2\pi}{M\tau_{\text{s}}}\cdot\Delta\tau_{\text{opt}} \tag{16}\]  
and, consequently, the symbols' constellation rotation, as well as scattering of symbol constellation spots, since Equation (16) depends on \(m\) (i.e., the rotation angle is echo-dependent). This (rotation) outcome is similar to the carrier lock angle error [13; 14; 15] (while symbol spots scattering can be attributed to noise) being the arithmetic mean between the angles \(\theta_{1}\) and \(\theta_{\text{Q}}\) of the \(I\) and \(Q\) axis, respectively, and the cluster-fitting lines (Figure 1) [16]. The carrier lock error distorts the constellation effectively closing the eyes of \(I\) and \(Q\) components by degrading the equivalent SNR of the additive white Gaussian noise (AWGN), as well as the non-AWGN impairments abstracted by AWGN that would equally increase BER. Moreover, under our assumption of a high SNR, which is quite realistic for small cells due to their short propagation paths [12], we expect that the OFDM BER floor is affected by time dispersion just to the extent for which the echo delays exceed the standard CP of 4.69 \(\upmu\)s, which is very unlikely to occur in small cells [17]. This finally implies that the only remaining OFDM impairment with regard to BER floor is CFO, even if it was compensated (mostly in time domain), as a phase error may still reside (to be corrected, too, mostly in frequency domain).
## 3 Results  
### Convergence Analysis  
In Table 4 and Figure 6, the results of the convergence analysis are given. The maximum reaction force converged toward a value of 1662 N. The coarsest mesh, mesh number 1, had the highest percentage deviation of \(-1.9\%\). As the mesh density increased, the percentage deviation from the value of the finest mesh decreased. In general, the percentage deviation was low for all meshes. As the number of elements increased, an exponential increase in computation solving time becomes apparent.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mesh**} & **Element Size** & **Total Number** & **Total Number** & **Maximal Reaction** & **Percentage Deviation to** & **Computation** \\  & **[mm]** & **of Elements** & **of Nodes** & **Force [N]** & **the Finest Mesh [\%]** & **Time [min]** \\ \hline \hline
1 & 9 & 18,696 & 23,144 & 1624.7 & \(-1.9\) & \(19\) \\
2 & 8 & 27,272 & 33,997 & 1646.8 & \(-0.9\) & \(39\) \\
3 & 7 & 38,553 & 47,035 & 1656.4 & \(-0.3\) & \(71\) \\
4 & 6 & 51,702 & 60,775 & 1656.9 & \(-0.3\) & \(83\) \\
5 & 5 & 89,761 & 104,405 & 1662.0 & - & \(244\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the convergence analysis of the different mesh densities.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **High Point** & **Low Point** \\  & **Ground Reaction Force** & **Ground Reaction Force** \\ \hline JM—Dorsiflexion [Nmm] & 24,002.00 & 53,192.00 \\ JRF—Medio (\(-\))/Lateral (\(+\)) [N] & \(-38.20\) & 2.28 \\ JRF—Anterior (\(-\))/Posterior (\(+\)) [N] & 86.42 & \(-24.19\) \\ JRF—Proximal (\(-\))/Distal (\(+\)) [N] & 704.68 & 577.78 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Boundary conditions of the FE model; i.e., dorsiflexion moment (JM) and joint reaction forces (JRF) of the ankle at the two-time points of the first high point and low point of the vertical ground reaction force calculated in the musculoskeletal multibody simulation.  
In terms of the plantar pressure distribution, it was seen that as the number of elements increased, the plantar pressure distribution approached a specific pressure distribution pattern (Figure 7).  
Accordingly, in the convergence analysis, a suitable mesh for the FE model was found. The maximum reaction force, as the primary evaluation criterion, showed a desired converging result, with a deviation of less than three percent for all meshes. The mesh with 51,702 elements and 60,775 integration points was chosen because an even finer mesh no longer showed considerable differences in the plantar pressure distribution. The calculation times of 83 min were assumed to be acceptable, with respect to the performance of other parameter studies to adjust the stiffness of the insole.
# Adaptive Channel-Enhanced Graph Convolution for Skeleton-Based Human Action Recognition
## 2 Related Work
### Attention Mechanisms in Action Recognition  
Attention mechanisms [27] are widely used in deep learning models. In human action recognition, [PERSON] et al. [28] were the first to introduce temporal and spatial attention in order to extract spatio-temporal aspects from human action RGB data and human joint postures. [PERSON] et al. [29] introduced an attention-enhanced graph convolutional LSTM network (AGC-LSTM) for the purpose of identifying human behaviors based on skeletal data. The AGC-LSTM model utilizes an attention mechanism to amplify the input from important joints in each AGC-LSTM layer. This allows the model to effectively capture the distinguishing characteristics of spatial arrangement and temporal changes. In addition, [PERSON] et al. [30] proposed the multi-grain contextual focus module (MCF) to capture relation information related to actions from body joints and parts.  
In summary, several GCN-based human skeleton action recognition methods have been proposed, demonstrating the potential applications of GCN models in human action recognition. In the human action recognition task, in order to be able to focus on the feature information of skeletal joint points more effectively, many scholars have proposed models that use different attention mechanisms. However, current GCN methods lack attention to the optimization aspect of the channel adjacency matrix. In order to improve the feature extraction ability of GCN models in human skeleton action recognition tasks, this study proposes the ACE-GCN, which uses a channel attention mechanism to recode the adjacency matrix, thus obtaining similarity attention maps and affinity attention maps for the construction of non-shared adaptive adjacency matrices. This study solves the problem of insufficient abstraction ability of existing GCN models for graph topological features in the channel in human action recognition tasks.
###### Abstract
As a result, to guarantee the operation safety of the receiver, it is necessary to determine the dispersions of temperature and thermal stress inside the tube wall by taking the uncertainties into account. The uncertainty analysis method has been widely used for the safety assessment of engineering structures [25; 26; 27]. [PERSON] and [PERSON] [25] calculated the seismic risk of the building in servicing life under various uncertainties. Compared with the case of a deterministic model, the collapse probability of the case under uncertainty increased by 133%. [PERSON] et al. [26] established a hybrid uncertain analysis model of nanobeams to estimate the lower and upper bounds of the response deflection by considering the uncertainties of the materials and loads. [PERSON] et al. [27] also applied the uncertainty analysis method to accurately quantify the dispersions of the mechanical responses for a fiber-reinforced polymer composite truss bridge with hybrid random and interval uncertainties. Although the uncertainty analysis method has been applied in many areas, its application in the field of solar receivers is rare. Therefore, in this study, the uncertainty analysis method is introduced to obtain the dispersions of temperature and thermal stress inside the tube wall under multi-source uncertainties. The main objective of this paper is to obtain the lower and upper bounds of the maximum temperature and thermal stress inside an SPT molten salt receiver tube wall under multi-source uncertainties. For that purpose, a deterministic, coupled thermal-structural model of the receiver tube is first demonstrated by importing the inner and outer tube wall temperature distributions from the FVM simulation to the FEM calculation. Then, the uncertain input parameters are identified and assigned with appropriate probability distributions. Furthermore, an uncertainty propagation analysis method is applied to obtain the probability distributions of the maximum temperature and thermal stress inside the receiver tube wall by combining the response surface approximation model and Monte Carlo (MC) simulations. Finally, the importance ranking of uncertainty sources is evaluated by using the global sensitivity analysis method. The results are beneficial to provide additional reliability and confidence in the temperature and thermal stress evaluation process of SPT molten salt receiver tubes.
# DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
## 2 Related Works
Given the orbital periodicity of LEO satellites, SFC readjustment necessitated by satellite movement demands urgent attention, particularly in developing integrated approaches that simultaneously address deployment and redeployment challenges.
## 6 Future Work, Challenges, and Limitations  
Various ML-based algorithms have been proposed to mitigate the NLoS effects on IPS. However, the adaptation of ML-based methods for UWB indoor localization is still in its infancy, and some issues still need further investigation. The future directions can be summarized as follows.
## 2 Modeling and Finite Element Analysis
### Shell Element Discretization  
In a shell model, the midsurface of the part is represented by a series of thin elements. The formulation is based on the plane stress assumption wherein the only significant stress components are the in-plane normal and shear stresses due to bending moments and in-plane forces. The out-of-plane normal and shear stresses are typically small for a thin shell element and can be neglected. For the purpose of modeling the gyroid, it was assumed that, for low relative densities, these through-thickness effects are small enough as to be insignificant.  
The first step in modeling the gyroid is to generate a mesh of the surface using triangular shell elements. Since tools for generating such a mesh based on an implicit equation were not readily available, this needed to be done using a purpose-built script, which built up the mesh using implicit function plotting tools to locate nodes along the surface. These nodes were then triangulated into a mesh. Since the implicit plotting tools  
Figure 1: Examples of the gyroid geometry. (**a**) A 3D rendering of the gyroid minimal surface; (**b**) 3D printed gyroid.  
are optimized for properly displaying the geometry rather than for numerical analysis, the resulting mesh contained a number of elements whose shapes were unacceptable for FEA modeling. To remedy this, a series of mesh cleanup steps were performed using mesh-smoothing, and remeshing tools included in an FEA software. These cleanup tools rearrange nodes within the surface with minimal out-of-plane deviation in order to produce a mesh which matches the geometry, but whose elements are closer to the ideal equilateral triangles for FEA. The final mesh can be seen in Figure 2. The meshed gyroid was then analyzed using FEA with periodic boundary conditions (discussed in Section 2.4), and assuming a linear elastic response. Due to the nature of the periodic boundary conditions, each node on a boundary of the RVE must correspond exactly with one node on the opposite boundary. This adjustment is made by re-seeding the boundaries with nodes that correspond with each other, and re-aligning the mesh to these new bounding-nodes.  
In order to simulate different relative densities, several different shell thicknesses were modeled, and a curve of stiffness vs. relative density was generated. These showed clear deviation from expected behavior, particularly at high relative densities where the gyroid is expected to approach neat-material behavior. At these high relative densities, the structure is no longer thin-walled, which means that the shell assumptions are invalid and through-thickness stress effects are not negligible. Additionally, in some cases the thickness in the shell creates material overlap, thus double counting certain regions.
# Investigating BIM Implementation Barriers and Issues in Pakistan Using ISM Approach
## 3 Results and Discussions
### ISM Methodology
#### 3.7.5 Step 5: Barriers Classification  
In Figure 8, a MICMAC diagram can be seen which was formed by modifying the reachability matrix. A MICMAC diagram helps in evaluation of driving power and dependence power. If the value of dependence power is higher, it illustrates that the other barriers should be addressed before the barrier with the highest dependence power is eliminated. A higher driving power means that by eliminating this barrier, other barriers could be easily solved [40].  
Figure 6: Initial Diagraph.  
Figure 7: ISM-based hierarchical model for BIM barriers in Pakistan.  
Researchers classified the diagram into four groups [44],  
1. Linkage variables
2. Driver variables
3. Dependent variables
4. Autonomous variables  
Figure 8 classifies the 12 BIM barriers based on the observations of MICMAC analysis. The common discussions on the BIM barrier classifications are:  
* B9 (high initial cost of BIM) has the healthiest ability to affect other barriers and should be addressed prior to others.
* B3, B2 (BIM implementation requires organizational restructuring, Lack of conferences/seminars on new technologies by organizations) has high dependence power and the lowest driving power. Therefore, these barriers can be marked as the adverse result of other barriers.
* An autonomous variable does not have any barrier, which means that all barriers can obstruct the BIM implementation in Pakistan.
* Five BIM barriers, i.e., B1, B5, B6, B8, and B12 are classified as linkage variables. This means that any stance taken on these five barriers affects the other barriers, as they also react to themselves.
## 3 Results
### DC in Multi-User Scenarios
When UDs engage in different applications, the DC distribution is very similar to that of FTP downloading, with an average DC of 81.9%, i.e., the high-traffic applications like file transfer dominate the overall DC in multi-user scenarios. In summary, multi-user scenarios exhibit higher DC compared to single-user scenarios, but the trends and data distributions vary notably depending on the application type.