esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Path Loss Models  
For accurate design and comparison of wireless networks and for their deployment, wireless channel models are necessary, which will simulate signal propagation accurately and efficiently. In this paper, we considered the existing four path-loss models that are introduced by the four major organizations: (i) 5G channel model (5 GCM) [16], (ii) the 3 rd Generation Partnership Project (3 GPP) [17], (iii) mobile and wireless communication enablers for the twenty-twenty information society (METIS) [18], and (iv) millimeter wave
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
## 4 Applications with Numerical Examples
### Source Localization  
In contrast to the TR application presented in Section 4.1, here, we return to a more usual application, that of source localization. Here, we assume that the forward step might be held physically in the process of which response data is collected in a limited number of DOFs on specific spatial locations. Then, we may take advantage of these data since by numerically solving the backward step, we may redefine the original spatial location of the source.
## 2 Materials and Methods
### Analytical Analysis
#### 2.2.4 Calculation of Torsional Natural Frequencies  
For the free torsional vibration of the two-disc torsional rotor system (Figure 3b) with two different polar mass moments of inertia \(I_{\rm P1}\) and \(I_{\rm P2}\) in kgm\({}^{2}\), the following system of equations for the simple harmonic motion [21; 22; 25] was applied:  
\[\left[\begin{array}{cc}\left(\frac{G\;I}{L}-\omega^{2}\;I_{p1}\right)&- \frac{G\;I}{L}\\ -\frac{G\;I}{L}&\left(\frac{G\;I}{L}-\omega^{2}\;I_{p2}\right)\end{array} \right]\left\{\begin{array}{c}\varphi_{z1}\\ \varphi_{z2}\end{array}\right\}=\left\{\begin{array}{c}0\\ 0\end{array}\right\} \tag{23}\]  
The torsional stiffness of the shaft \(k_{\rm t}\) in Nm/rad can be calculated as:  
\[k_{\rm t}=\frac{G\;I}{L}, \tag{24}\]  
where \(G\) is the modulus of rigidity (shear modulus) in N/m\({}^{2}\), \(L\) is the length of the shaft and \(J\) is the polar second moment of area of the shaft cross-section calculated according to the equation:\[J=\frac{\pi}{32}D^{4} \tag{25}\]  
where \(D\) is the diameter of the shaft.  
The torsional natural frequencies can be calculated based on the nontrivial solution of the equation; hence, by calculating the determinant of the equation, the following torsional natural frequency equation is obtained:  
\[\omega^{2}\left\{\omega^{2}\ I_{p1}\ I_{p2}-\ \frac{G\ J}{L}\left(I_{p1}+I_{p2} \right)\right\}=0. \tag{26}\]  
The analytical solution provides the torsional natural frequency \(f\) [Hz], i.e., \(\left\langle f=\omega/(2\cdot\pi)\right\rangle\) for the two-disc torsional system (if the mass of the shaft is neglected and the discs are considered to be thin), according to Equation (27):  
\[f=\frac{1}{2\pi}\sqrt{\frac{\left(I_{p1}+I_{p2}\right)\!k_{t}}{I_{p1}I_{p2}}}. \tag{27}\]  
In this study of torsional natural frequency of the simplified IPM motor with the load, \(I_{\mathrm{p1}}\) and \(I_{\mathrm{p2}}\) stand for the polar mass moment of inertia of the solid rotor and the load, respectively.
## 1 Introduction
Determining the effects of high-intensity perturbations, such as those induced by the Motek GRAIL system, on kinematic, kinetic, and spatiotemporal parameters is crucial for understanding the body's response limits and gait pattern changes. Few studies have investigated perturbations during different gait cycle phases, such as Initial Contact (IC) [27; 28], Mid Stance (MS) [29; 30], or Pre-Swing (PS) [31]. However, [PERSON] and [PERSON] [4] investigated how external perturbations induced by treadmill belt acceleration and deceleration during these phases affect gait regularity in young adults. Their findings revealed that, during treadmill deceleration in the Pre-Swing phase, the center of mass (CoM) displacement displayed a consistent pattern in the anterior-posterior (AP) and vertical directions. Conversely, the least regular CoM trajectories were observed during treadmill deceleration in Mid Stance in the AP direction. However, no paper has been found that directly compares the effects of perturbations during all three phases (Initial Contact, Mid Stance, and Pre-Swing) on altered lower limb joint angles and torques compared to undisturbed gait. To address this issue, the purpose of this study was to examine the effect of the acceleration of one belt of a treadmill during three different phases of the gait cycle (Initial Contact, Mid Stance, and Pre-Swing) on kinematic and kinetic parameters and relate these changes to unperturbed gait.
# Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications
## 2 Techniques in Alzheimer's Disease Detection
### Summary & Future Directions in AD Detection Techniques  
The various techniques used to detect AD have distinct advantages and limitations. Traditional ML methods such as SVM and RF are easier to implement and interpret, as they provide reliable results with relatively low computational costs. However, it may face difficulties in handling high-dimensional data and lack the feature extraction capabilities of DL models. DL models, especially CNNs, have shown superior performance in classifying stages of AD due to their ability to learn complex patterns from data. Attention mechanisms enhance the ability of these models to focus on critical areas, thereby improving diagnostic accuracy. However, DL models are often criticized due to their \"black box\" nature, which limits their applicability in clinical decision-making without additional interpretive measures. Ensemble learning methods can improve diagnostic performance further by combining the strengths of multiple models. While this improves accuracy and robustness, ensemble approaches tend to be computationally expensive and challenging to interpret. XAI techniques such as SHAP, LIME, LAVA Score-CAM, Grad-CAM, and others, attempt to fill the interpretability gap, providing transparency into model predictions. However, they still do not completely solve the interpretability problem in DL models, and their interpretations may sometimes lack clinical nuance. While DL models have the highest diagnostic potential, the need to improve explainability remains critical.
# Hygro-Thermo-Mechanical Analysis of Brick Masonry Walls Subjected to Environmental Actions
## 5 Hygro-Thermo-Mechanical Simulations and Discussion
### Description of the Numerical Model and Boundary Conditions  
The same three-wythe brick masonry wall presented for the hygro-thermal simulations was used for the HTM analyses (see description of the model in Section 3.1). Similarly, the physical, thermal, and hygric material properties presented in Table 1 were employed for the model definition. The newly added properties needed for the mechanical field are introduced in Table 3. The numerical simulations were performed with a 2D plane strain finite element model using the software COMSOL Multiphysics [59].  
Two different scenarios were studied: namely, a gravitational load case and a combined hygro-thermo-mechanical case. For the gravitational case, body forces were considered, including a uniformly distributed load, \(L=12.5\) kN/m, applied on top of the wall to simulate the weight of a simply supported roof. The HTM scenario was built upon the gravitational case so that the body forces were included. Moreover, for the HTM case, the temperature and moisture conditions defined previously for the time-dependent hygro-thermal analysis TD2 (see Section 3.1) were imposed on the wall. It is recalled that the TD2 analysis considers an initial step for the wall to reach the steady-state equilibrium with the environment. In other words, the wall is already in a deformed configuration (initial conditions to steady-state equilibrium) before the application of the variable boundary conditions.  
Regarding the mechanical boundary conditions, the bottom of the wall is assumed to be fixed: that is, all degrees of freedom are constrained. Conversely, the uppermost edge is not restrained, and all degrees of freedom remain free. The wall is assumed to be in a non-deformed configuration before the application of the gravitational and environmental loads. Moreover, the initial temperature and relative humidity are \(\phi_{0}=13.5\)\({}^{\circ}\)C and \(\varphi_{0}=0.74\), respectively. For these conditions, the temperature- and moisture-induced strains are zero.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Property** & **Symbol** & **Value/Expression** & **Units** & **Source** \\ \hline \multirow{6}{*}{Brick} & [PERSON]’s modulus & \(E\) & 5580 & MPa & \\  & Poisson’s ratio & \(\
u\) & 0.20 & – & \\  & Compressive strength & \(f_{c}\) & 19.80 & MPa & \\  & Tensile strength & \(f_{t}\) & 2.00 & MPa & \\  & Coefficient of thermal expansion & \(\alpha_{T}\) & \(2.56\times 10^{-6}\) & \(1/^{\circ}\)C & \\  & Coefficient of hygric expansion & \(\alpha_{h}\) & \(1.40\times 10^{-4}/w_{g}^{0.72}\) & – & \\ \hline \multirow{6}{*}{Lime mortar (LM) (LM  
Assuming the applicability of [PERSON]'s principle, the following discussion on the distribution of stresses focuses on a section of the mid-portion of the wall (height around 1.20 m). In other words, the area selected for the analysis of stresses is reasonably distant from any applied load or boundary condition that could directly influence the local stress/strain distribution.
# Cortical Connectivity Response to Hyperventilation in Focal Epilepsy: A Stereo-EEG Study
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**Group 1**} & \multicolumn{2}{c}{**Group 2**} & \multicolumn{2}{c}{\(p\)**-Value**} \\  & **Label** & **Mean \(\pm\) CI** & **Label** & **Mean \(\pm\) CI** & **Greater** & **Less** \\ \hline \multirow{6}{*}{\(Med[ER]\)} & Ictal & 0.0600 \(\pm\) 0.0126 & Awake & 0.0672 \(\pm\) 0.0207 & 0.2534 & 0.7466 \\  & Ictal & 0.0600 \(\pm\) 0.0126 & N-REM & 0.0463 \(\pm\) 0.0185 & 0.0587 & 0.9413 \\  & Ictal & 0.0600 \(\pm\) 0.0126 & HV & 0.0277 \(\pm\) 0.0077 & 0.0092 & 0.9908 \\  & Awake & 0.0672 \(\pm\) 0.0207 & N-REM & 0.0463 \(\pm\) 0.0185 & 0.0034 & 0.9966 \\  & Awake & 0.0672 \(\pm\) 0.0207 & HV & 0.0277 \(\pm\) 0.0077 & \(<\)0.0001 & \(>\)0.9999 \\  & N-REM & 0.0463 \(\pm\) 0.0185 & HV & 0.0277 \(\pm\) 0.0077 & 0.0066 & 0.9934 \\ \hline \multirow{6}{*}{\(IQR[ER]\)} & Ictal & 0.0988 \(\pm\) 0.0177 & Awake & 0.0849 \(\pm\) 0.0192 & 0.0874 & 0.9126 \\  & Ictal & 0.0988 \(\pm\) 0.0177 & N-REM & 0.0556 \(\pm\) 0.0194 & 0.0185 & 0.9995 \\ \cline{1-1}  & Ictal & 0.0988 \(\pm\) 0.0177 & HV & 0.0382 \(\pm\) 0.0115 & 0.0059 & 0.9941 \\ \cline{1-1}  & Awake & 0.0849 \(\pm\) 0.0192 & N-REM & 0.0556 \(\pm\) 0.0194 & 0.0005 & 0.9995 \\ \cline{1-1}  & Awake & 0.0849 \(\pm\) 0.0192 & HV & 0.0382 \(\pm\) 0.0115 & \(<\)0.0001 & \(>\)0.9999 \\ \cline{1-1}  & N-REM & 0.0556 \(\pm\) 0.0194 & HV & 0.0382 \(\pm\) 0.0115 & 0.0181 & 0.9819 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the median and inter-quartile range of the energy ratio (ER) computed in different groups of SEEG epochs. The cortical connectivity, estimated via the PTE algorithm, enabled the most significant discrimination between all the epochs. As shown in Table 4, the PTE was minimized during the ictal transition (0.8692), took higher values during the awake status (0.9063), was even higher during the N-REM period (0.9239), and was maximized during the HV maneuver (0.9399). In this case, the statistical test led to significant results even with a significance level of \(\alpha<0.01\). Besides showing the lowest median connectivity, the ictal transition reported the highest values in terms of the interquartile range. On the other hand, the HV and the N-REM epochs presented very similar interquartile ranges (\(\approx\)0.152), lower than the ictal transition (0.1773) but higher than the awake periods (0.1305). Hence, the connectivity increment during the N-REM and HV phases was also associated with a higher variability, which goes against what was observed during the seizure onset. A visual representation of the overall results is given in Figure 2
## 3 Results and Discussion
### Applications  
Research into oil-water emulsions stabilized by plant proteins offers several practical advantages, particularly in the food industry. These emulsions are often used in products such as mayonnaise, sauces, creams, and desserts, providing texture and stability. In addition, plant-based drinks are gaining more market share as they are an excellent alternative for drinks and ingredients for home recipes for consumers who do not consume milk and dairy products. The use of plant-based proteins, such as those mentioned in this paper--peas, lentils, and Java beans--provides a healthy alternative to synthetic emulsifiers, which many consumers associate with unnatural additives. With the growing interest in clean-label products, emulsions stabilized by plant proteins can meet the demands of a public that is increasingly concerned about the quality of the food they consume. In addition, plant proteins are often more sustainable and help to reduce the environmental footprint of food production. Research in this field not only enables the innovation of new products but also the exploration of different protein sources, expanding formulation possibilities. Thus, the development of these emulsions can not only improve consumer acceptance but also increase the functionality and nutritional value of food.
# An Efficient Delay Tolerant Networks Routing Protocol for Information-Centric Networking
## 2 Related Works
In the search phase, content requests are generated in a distributed way. In the return phase, nodes are selected to forward content to requester node. In [15], the authors proposed content searching by using random regret minimization, where the decision on Interest replication is based on several measurable attribute, and the lifetime of a message is repeatedly scaled down to reduce overhead. In [16], the authors proposed a reputation-based content caching strategy to achieve a logical trade-off between the dissemination rate and the energy efficiency, where DTN stations deliver content stored in ICN stations. Since intermediate relay nodes have limited buffer storage ICN provider selects appropriate DTN stations as caching nodes based on the reputation mechanism, where a reputation of a DTN nodes is determined by buffer capacity, forward rate of content, and battery energy to deliver the content. In [17], the authors proposed opportunistic named functions which rely on both user-defined Interests and locally optimal decisions based on remaining battery lifetime and device capabilities in information-centric disruption-tolerant networks. In [18], the authors proposed a content distribution and retrieval framework in disaster networks for public protection (CIDOR) in interworked ICN and DTN network architecture. They proposed a new Data structure called CIDOR-PRIT and a new DTN forwarding mechanism is proposed. In CIDOR-PRIT table, the requester end point ID of the received Interest is stored. The requester end point ID is used as the destination end point ID. To reduce overhead, both duplicated Interest and Data packets are suppressed. For redundancy elimination, CIDOR nodes aggregates similar Interests in the CIDOR-PRIT table and it is used to return the Data to requesters efficiently.
# Experimental Investigation of Crystal Blocking in Drainage Pipes for Tunnels in the Kast Region
## 4 Results and Analysis
### Influence of Overlaying Soil  
The time history curves of the crystallization on the drainage pipes both with and without the presence of overlaying soil are shown in Figure 9a. It can be seen that the crystals increase rapidly (0-4 d) and the crystallization rate of the no-filling condition is significantly greater than that of the filling condition. The maximum difference is that the soilless working crystallization curve has a descending section (5-6 d). This is maybe because the crystals initially formed in the inner wall of the drainage pipe are not stable at high flow rates, and continue to fall off under the erosion of the water flow. The crystallization rate of the inner wall of the drainage pipe is less than the scouring rate of the solution, resulting in a decrease in crystallization. The crystals formed inside the drainage pipe tend to be stable, and their adhesion to the inner wall is stronger than the scouring force of the solution. The rate of crystal formation at this stage is greater than the scouring rate of the solution, and the crystallization continues to increase (6-8 d).  
The time history curve of the flow rate for each drainage pipe is shown in Figure 9b. The average unit flow rate of a single drainage pipe under the soilless condition is seven times larger than the filled condition due to the buffering effect of sand. The initial crystallization rate increases with the increase in ions that pass through per unit time, indicating that the water flow rate is an important factor affecting the formation of crystals. At the beginning of the test, the crystallization rate of the blockage is stronger than the scouring rate of the water, which led to the continuous deposition of the blockage and the increase in the crystallization amount, and the drainage performance decreased continuously. When the scouring rate and crystallization rate reached equilibrium, the blockage no longer increased, and the tunnel drainage performance remained stable.  
Figure 8: Crystal weight change curves at different parts of circular drainage pipes.  
The time history curve of the crystallization on the circular drainage pipes is shown in Figure 10. The crystallization started to decrease rapidly with the ratio of 26.2%, 10.0%, and 6.9%, respectively, after the crystallization amount of each part reached the peak on the fourth day (arch: 1.6 g/m; spandrel: 1.3 g/m; vault: 1.2 g/m). At this time, due to the higher flow rate in the lower part of the annular drainage pipe, the crystallization stripping rate is much larger than the crystallization formation rate, resulting in the reduction of crystallization in the lower part. With the gradual accumulation of crystals, the scouring rate of the solution is smaller than the crystallization rate, and the crystals again show a rising trend.  
Figure 10: Crystal weight variation curve of circular drainage pipes.  
Figure 9: Variation of crystallization rate and flow rate of circular drainage pipes with time. (**a**) Crystallization rate curves. (**b**) Flow rate curves.
# Investigation of the Inverse Magnus Effect on a Rotating Sphere in Hypersonic Rarefied Flow
## 4 Results and Discussion
### Overall Aerodynamic Characteristics
#### 4.3.2 Lift  
The free-molecular solution for the lift acting on the rotating sphere in hypersonic rarefied flows [18] has a simple form:  
\[C_{L}=\frac{4\kappa}{3}W, \tag{6}\]  
which indicates that \(C_{L}\) increases in proportion to \(W\). Figure 13 displays the \(C_{L}\)-\(W\) plots for both the free-molecular and the DSMC solutions. At \(Kn_{\infty}=0.51\), the relation between the lift and the rotation speed is qualitatively similar to that in Equation (6), but the slope is smaller than \(4\alpha/3\). Again, this is because the rarefaction of the simulated flow is not strong enough to satisfy the free-molecular assumption underlying Equation (6).  
The positivity of the lift \(F_{y}\) means that it has an upward direction, which is the same as \(\mathbf{\omega}\times\mathbf{V}_{\infty}\) and opposite to the direction inferred from the knowledge of the conventional Magnus effect in low-speed continuum flows. By utilizing the rotation, a linearly increasing lift can be gained without any increase in drag. Thus, the lift-to-drag ratio of the sphere can be effectively elevated in this manner.
# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 3 The Proposed Critical Action Information Extraction Network
### Feedback Attention-Guided Module (FAGM)
#### 3.2.2 Channel Attention-Guided Module (CAGM)  
As shown in Figure 6, the features \(\mathbf{F}_{l}^{T}\) and \(\mathbf{F}_{h}^{T}\) are first average-pool along the channel dimension to generate feature vectors \(V_{l}\in\mathbb{R}^{N\times C_{l}\times 1\times 1}\) and \(V_{h}\in\mathbb{R}^{N\times C_{h}\times 1\times 1}\). Next, the number of channels is reduced into \(C_{l}/\kappa\) through separate Multilayer Perceptron (MLP). The resulted vectors are then concatenated along the channel dimension and processed through another MLP to generate the visual attention-guided feature \(V_{g}\in\mathbb{R}^{N\times C_{l}\times 1\times 1}\). Finally, \(V_{g}\) is fed into a sigmoid function to produce the channel attention weight vector \(W_{c}\in\mathbb{R}^{N\times C_{l}\times 1\times 1}\). This weight vector \(W_{c}\) is used for element-wise multiplication with the low-level feature \(\mathbf{F}_{l}\) to obtain the feature \(\mathbf{F}_{l}^{T,C_{l}}\in\mathbb{R}^{N\times C_{l}\times H_{l}\times W_{l}}\). The specific formulas are as follows:  
\[V_{l}=\text{AvgPool}(\mathbf{F}_{l}^{T}) \tag{11}\]  
\[V_{h}=\text{AvgPool}(\mathbf{F}_{h}^{T}) \tag{12}\]  
\[V_{g}=\text{MLP}_{3}[\text{MLP}_{1}(V_{l});\text{MLP}_{2}(V_{h})] \tag{13}\]  
Figure 5: Temporal Attention Module (TAM). The module first expands the time dimension of the original feature with shape \(\mathbb{R}^{N\times C_{l}\times H_{l}\times W_{l}}\) and reshapes it to the feature with shape \(\mathbb{R}^{N_{1}\times T\times C_{l}\times H_{l}\times W_{l}}\). Then, average pooling is applied along the time dimension to generate attention weights for each frame, which are assigned to each frame. Finally, the result is transformed back to the original dimensions and added to the original features for subsequent computations.  
\[W_{c} =\sigma(V_{g}) \tag{14}\] \[\mathbf{F}_{l}^{T,C_{l}} =W_{c}\odot\mathbf{F}_{l} \tag{15}\]  
After processed by the CAGM, different channels of the low-level features are assigned different weights by the guidance of high-level contextual information leverage.