esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background and Related Work
### Theory of Intergenerational Technology Application  
Intergenerational interaction research has experienced significant theoretical development over the past two decades. In their groundbreaking research, [PERSON] and [PERSON] [9] pointed out that intergenerational relationships are influenced by multiple factors, including sociocultural backgrounds, family structures, and personal experiences. They established a life course perspective framework within intergenerational interaction theory, emphasizing the dynamic and complex nature of intergenerational relationships. This theoretical framework provides an important foundation for an understanding of intergenerational relationships in modern society.  
[PERSON] et al. [14] further expanded this theoretical framework, particularly focusing on intergenerational relationships in the 21 st century digital age. Their research found that while technological development presents new challenges for intergenerational relationships it also creates new opportunities. From one perspective, digital divides may deepen generational gaps; from another, appropriately designed technological products can become effective mediators of intergenerational connections.  
[PERSON] and [PERSON] [34] focused their research on intergenerational technology learning patterns. They found that through appropriate technological mediation, intergenerational learning can not only promote elderly digital literacy but also strengthen intergenerational emotional connections. This was particularly evident in gamified interaction scenarios, where different generations could establish more natural and lasting interactive relationships.  
[PERSON] and [PERSON]'s [35] recent research further confirmed that good intergenerational communication can effectively reduce elderly users' fear of technology. Their proposed \"Intergenerational Technology Acceptance Model\" explains how intergenerational interaction influences elderly users' technology-related motivation and behavior, providing theoretical guidance for the design of technological products that promote intergenerational interaction.
# Resolving Gas Bubbles Ascending in Liquid Metal from Low-SNR Neutron Radiography Images
## 4 Performance Analysis  
### First Segment Estimates  
Figure 6 shows the effect of subsequent operations that the global filtering routine (Algorithm 3) performs for a pre-processed image.  
The difference between Figure 5(a),b is that the noise with low wavelengths (sharply localized luminance maxima and minima left over from pre-processing) have been eliminated. Next, the TV filter (Figure 5(c)) consolidates the high luminance values within the bubble regions (white dashed circles), increasing the SNR for the bubbles. Furthermore, noise is significantly damped and the wavelength of its features is increased even more. However, the TV filter does not remove the sparse larger-scale luminance maxima still seen in Figure 5(c) in the background between the bubbles as efficiently as it is desired without degrading the CNR for the bubbles. This function is performed by SSCF (Figure 5(c)) which specifically diffuses the leftover intensity maxima, increasing bubble SNR (and CNR due to reduced noise in the background) further. In the case of Figure 6 one has \(\gamma\rightarrow\infty\) for (5). A close-up view of one of the bubble neighborhood's transformations is shown in Figure 7 where one can visually notice that bubble CNR is increased by SSCF (d) and that intensity maxima are indeed eliminated from the background. A more detailed analysis of how how noise filtering stages affect the image and the bubbles can be seen in Figure 8 where a vertical strip containing both bubbles is taken from the images in Figure 6 and their relief plots (a) and mean luminance profiles (b and c) are shown.  
With SNR increased by the sequence of PM, TV and SSCF filters, SCTMM is now applied to increase the CNR--this is especially clearly seen in Figure 8c where one can see that SCTMM significantly flattens the background while preserving bubble signal intensity, as intended. This enables the gradient filter to produce bubble edge halos with an even greater CNR (note the high depth of the halo \"wells\" in Figure 8(a6), which extend well below the binarization threshold, down to background luminance levels), enabling clean segmentation, as seen in Figure 6g.  
Figure 6: First segment estimation stages (Algorithm 3): (**a**) original pre-processed (Algorithm 2) image; (**b**) PM-filtered image; (**c**) Poisson TV-filtered image; (**d**) SSCF-filtered image; (**e**) SCTMM output; (**f**) gradient filter output (edge halos); (**g**) hysteresis-segmented edge halos (prior to erosion); (**h**) bubble masks obtained after erosion, thinning, filling, small-radius mean filtering, Otsu binarization and border component removal. The color scheme is as in Figure 1.  
Figure 7: Effects of global filtering (Algorithm 3) on the luminance map of the neighborhood of the lowermost bubble in Figure 7 indicated by a white dashed circle: (**a**) neighborhood projection of the original pre-processed image; (**b**) PM-filtered; (**c**) Poisson TV-filtered; (**d**) SSCF-filtered; (**e**) SCTMM output; (**f**) gradient filter output (edge halos).
# CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention
## 3 Method
### Channel Fusion Attention Mechanism (CFAM)
#### 3.3.2 The Middle: A Channel Attention Block on the Fused Mode  
Before the two feature maps are further processed by fully connected layers, the attention mechanism is added. Attention mechanisms are widely used in various computer vision tasks, such as image classification, segmentation, and object detection. The benefits of such a mechanism have been shown for those tasks. Generally, an attention mechanism biases the allocation of available processing resources toward the most informative components of the input. [PERSON] (2018) proposed a squeeze-and-excitation (SE) block to enhance the representational power of basic modules throughout the network. Inspired by the attention model, we use the channel attention block in the latter part of the convolutional layers. The dimensions of features from C are \(4\times 4\times 256\). The feature maps \(F_{k}\) are first passed through a squeeze operation. Global average pooling is used to aggregate the feature maps across a \(4\times 4\) spatial dimension to produce a channel descriptor. A statistic \(L_{k}\) is generated by shrinking \(F_{k}\) through a \(4\times 4\) spatial dimension, where the \(i\)th (\(i\in[1,256]\)) element of \(L_{k}\) is calculated using:  
\[l_{ki}=CH_{s}(f_{ki})=\frac{1}{4\times 4}\sum_{m=1}^{4}\sum_{n=1}^{4}f_{ki}(m,n). \tag{5}\]  
This descriptor embeds the global distribution of channelwise feature responses, enabling information from the global receptive field of the network to be leveraged by its lower layers. Then, an excitation operation acts on the descriptor. The operation is given by:  
\[R_{k}=CH_{e}(L_{k},U)=\sigma(g(L_{k},U))=\sigma(U_{2}\delta(U_{1}L_{k})), \tag{6}\]  
where \(\delta\) refers to the ReLU function, \(U_{1}\in\mathbb{R}^{\frac{256}{h}\times 256}\) and \(U_{2}\in\mathbb{R}^{256\times\frac{256}{h}}\). To limit model complexity and aid generalization, we first feed the descriptor \(L_{k}\) to a fully connected layer \(U_{1}\) around the dimensionality reduction layer with a reduction ratio \(h\), followed by a ReLU. Then, a fully connected layer \(U_{2}\) is used to increase the dimensionality, followed by sigmoid activation. After the excitation operation, \(R_{k}\) is obtained to describe the weight of each feature map from \(F_{k}\). Finally, the feature maps \(F_{k}\) from C are reweighted using channelwise multiplication (represented by) between \(F_{k}\) and \(R_{k}\) to generate the output of the channel attention block \(O_{k}\). The activation is given by:  
\[O_{k}=F_{k}\cdot R_{k}. \tag{7}\]  
The frontend and the middle constitute our CFAM.
## 3 Results
### Mixing Time
#### 3.2.2 Effect of Reynolds Number on Mixing Number  
As shown in Figure 8, at the high Reynolds number of turbulent regime, the mixing number remains the independence of the Reynolds number nor dependent on proximity to the reactor wall since flow is turbulent [26; 52]. [PERSON] et al. [26] found that mixing number of the deionized water and viscous PVP solution were kept at approximately 20 and 10 at shaking frequencies of 100 to 350 rpm in a 100-, 250- and 500-mL standard unbaffled narrow-neck Erlenmeyer flasks using water and shaking diameters of 25 mm and 50 mm using 10%-filling nominal flask volume. The Reynolds number ranged at 10\({}^{4}\)-10\({}^{5}\)(turbulent region) and 10\({}^{2}\)-10\({}^{3}\)(turbulent to transition region), respectively. This evidence also shows that the mixing (homogeneous) number increases according to the liquid filling volume and the volume ratio of glycerin. Homogeneous number increased from 100.26 to 308.33, from 216.2 to 344.67, from 272.07 to 355.21 and from 298.47 to 366.17 for water, 5 vol% glycerin, 10 vol% glycerin and 20 vol% glycerin at four different filling volumes (from 4 to 10 L), respectively.  
Figure 8: _Cont._  
Figure 7: Effect of different shaking frequencies and filling volumes on mixing time. (**a**) Water; (**b**) 5 vol% glycerin; (**c**) 10 vol% glycerin; (**d**) 20 vol% glycerin.
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 3 Related Work
### Current Feature Reconfiguration Approaches for K8s in Industry  
There are plenty of feature reconfiguration approaches in the industry. Some of them are proprietary to cluster vendors. For example, when building a GKE cluster, GKE provides many customization options for the cluster. As stated before, such proprietary reconfiguration approaches are incompatible with other vendors. On the other hand, there are vendor-agnostic reconfiguration tactics that are able to install specific plugin components or replacing deprecated, yet popular APIs in a portable and unified manner. For example, the command-line tool of Cilium can install the CNI plugin cilium in almost every Kubernetes vendor [29]. Another example is when the PodPreset API was deprecated, RedHat redeveloped this API by means of extending the Kubernetes API with a custom resource definition (CRD) that mimics the PodPreset API object and an admission webhook around the API server that validates and processes requests to create or update PodPreset resources. These reconfiguration methods appear imperative however and therefore are not in line with the declarative configuration management approach of Kubernetes that comes with a control loop that continuously monitors for differences between desired and actual cluster state. The lack of such declarative configuration management approach puts the burden of the monitoring on the shoulders of the cluster administrator, therefore reducing her productivity especially when multiple reconfiguration have been performed in an imperative way. In opposition, in this paper, we present a fully declarative configuration management approach. In line with the Operator pattern [30], the declarative configuration management approach is implemented by extending the Kubernetes API with a set of custom resource definitions to declare a desired K8s feature, an admission web hook to validate feature reconfiguration requests and a controller that continuously bring the actual state of the member clusters in line with the declarative configuration of desired features.  
The aforementioned OCM cluster federation framework [20] has more in common with our approach. It supports a rich policy language for ensuring compliance with various kinds of policies and cluster configurations uniformly across multiple Kubernetes vendors [31]. The framework is also used within hybrid cloud providers such as RedHat OpenShift. At its core, OCM applies the Operator pattern as our approach does but in a decentralized manner where each member cluster takes care of its own policy enforcement and reconfiguration. A more distinct difference is that OCM aims to extend Kubernetes with new functionality and properties that build on top of the Kubernetes API, whereas our work studies the feasibility of the Operator pattern for reconfiguring the internals of Kubernetes.
## 5 Stability Analysis of High Slope of Carbonaceous Mudstone Based on Numerical Simulation
### Stability Analysis of Slope Excavation
#### 5.3.3 Effective Plastic Strain Analysis of Slope  
Natural State  
As shown in Figure 13, the plastic zone of the slope under natural working conditions is concentrated at the foot of the slope and exhibits an upward trend of arc-shaped expansion. By analyzing the color gradient changes in the plastic contour map, it can be found that the plastic deformation at the foot of the slope is most significant, but a continuous deformation zone has not yet formed. This feature indicates that in the natural state, the overall stability of the slope can be guaranteed and there will be no overall sliding failure. Therefore, the slope can maintain basic stability under natural working conditions.  
As shown in Figure 14, the potential sliding surface of the slope under saturated conditions is approximately arc-shaped, mainly distributed in the clay layer, strongly weathered mudstone layer, and moderately weathered mudstone layer, forming a composite arc-shaped sliding surface composed of three layers of rock and soil, concentrated in the middle and foot of the slope, consistent with the shape of the potential sliding surface of the slope under natural conditions. Secondly, the slope experiences shear failure, forming a situation where it runs from the foot of the slope to the top, and the range of plastic deformation also increases significantly. The shear outlet at the front edge of the slope slider is located at the foot of the slope, and there is a clear shear strain concentration zone, indicating that the instability of the slope will cause compression deformation at the foot of the slope, showing a trend of upward tilting at the foot of the slope, and finally sliding out along the front edge of the slope. From the contour map results, it can be seen that under the ultimate limit state of the slope, the obvious interface inside the slope will serve as the sliding surface for downward sliding.  
Figure 13: Plastic strain contour map of section BK1+020 in the natural state after excavation.
## 3 Results and Discussion
### Effect of Patch width Variation on S-Parameters  
In order to identify the optimum patch width (W5), at which the performance of the antenna is in its best condition, a variation in W5 was performed and the S-Parameter (S11) was measured accordingly. It was observed that the antenna performed the best return loss when the patch width was fixed at 6.5 mm, as shown in Figure 7. The design dimensions shown in Table 1 were kept constant, while the width of the patch antenna was varied from 5 to 6.5 mm in steps of 0.5 mm. It can be seen from Figure 8 that there was no vibration in the position of the resonance peak with the increase of antenna width, while there was a significant change in the shape of the resonance frequency and increased return loss to about -48 dB at the optimum width of 6.5 mm.
## 2 Numerical Simulation
### Computational Domain  
In this paper, the CFD module in COMSOL Multiphysics was used to conduct the CFD simulation of the flow channel of the differential motion assembly. According to the structure of the differential motion assembly, a 3D computational domain was built. Because of the symmetry of the fluid domain of the motion assembly and considering the cost of computational time, \(1/4\) of the fluid domain of the differential motion assembly was selected as the computational domain. Figure 2 shows the mesh and boundary conditions of the flow domain of the differential motion assembly. Hexahedral grids with high accuracy were built using sweep algorithms. The density of the drilling fluid was set as 1000 kg/m\({}^{3}\). To study the effect of the fluid viscosity, fluid viscosities of 1000, 100, 10, and 1 mPa\(\cdot\)s were employed. The mass flow rate of the particles was set to 0.5 kg/s, and the density and the diameter of the sand particles were set as 2850 kg/m\({}^{3}\) and \(6\times 10^{-5}\) m, respectively. According to the real working conditions of the differential motion assembly, the boundary conditions were set as follows:  
Inlet: The boundary condition of the inlet was set as \"fully developed flow\", and flow rates of 0.01, 0.02, 0.03, and 0.04 m\({}^{3}\)/s were set at the inlet to study the flow characteristics.  
Wall: The inner surface of the differential motion assembly was set as the wall, no slip was selected for the wall, and the wall roughness parameter and the roughness height were set as 0.26 and 3.2 \(\upmu\)m, respectively. In addition, the wall boundaries were set as \"bounce\" for the particles.  
Outlet: The boundary condition of the outlet was set as the pressure condition, and according to the pump pressure in the field, the value of the pressure at the outlet was set as 30 MPa. In addition, the wall condition of the outlet for the particles was set as \"Freeze\".  
The velocity distributions along the radial direction at a line 150 mm away from the inlet under a flow rate of 0.04 m\({}^{3}\)/s and fluid viscosity of 100 mPa\(\cdot\)s were used to analyze the grid independency. The results of the grid independence study are shown in Figure 3; it shows that when the number of grids is more than 108,134, the solution is grid independent.
## 3 Results
### Research Area P1  
Research area P1, located along Provincial Road No. 858, was characterized by a significant variation in equivalent sound level depending on the distance from the thoroughfare and the terrain (Table 2).  
At the measuring points located directly by the road, the highest sound intensity was recorded, averaging 67.3 dB, due to the heavy traffic, including trucks. The analyzed traffic route connects the region's key cities: Bilgoraj, Zwierzyniec, and Zamosc. In addition to tourist traffic, there is also transit traffic, which further increases noise emissions in the area. As the measuring points moved further from the road, the noise level decreased significantly. The most considerable decrease, averaging 23.9 dB, was noted between the points closest to the road and the middle points (located 250 m away). This relationship aligns with the theory of acoustic physics, which states that sound intensity diminishes with increasing distance from its source, following the principles of sound wave dispersion in space [61]. The reduction in sound intensity may be slightly non-linear, as indicated by the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Profile**} & **Distance from** & \(L_{AeqT}\) & \(L_{Aeq(mean)}\) & **Height** & **Dominant** \\  & **Road (m)** & **(dB)** & **(dB)** & **a.s.l. (m)** & **Sounds** \\ \hline A & 5 & 70.4 & & 270.6 & \\ B & 5 & 64.2 & 67.3 \(\pm\) 3.6 & 251.5 & Anthropogenic \\ C & 5 & 64.0 & & 234.6 & \\ \hline A & 250 & 47.0 & & 292.4 & \\ B & 250 & 39.7 & 43.4 \(\pm\) 4.8 & 256.9 & Natural and anthropogenic \\ C & 250 & 38.1 & & 246.3 & \\ \hline A & 500 & 35.3 & & 290.6 & \\ B & 500 & 43.5 & 41.5 \(\pm\) 4.4 & 274.0 & Natural \\ C & 500 & 42.0 & & 276.8 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of equivalent sound level measurements in research area P1.  
values, but the trend is clearly downward. In profiles B and C, higher sound intensity values were observed at points furthest from the road, which can be explained by differences in terrain height that modify the propagation of acoustic waves.  
The audio recordings reveal the differences between the components of the audio-sphere in various parts of the research area. The anthropogenic sounds, mainly traffic noise, were the most dominant nearest to the road. At a distance of 250 m from the road, anthropogenic sounds blended with natural sounds; the road noise became much less noticeable here, while the singing of birds and the rustling of trees became much more audible. At a distance of 500 m from the traffic route, no anthropogenic sounds were recorded; only natural sounds, particularly the singing of birds, were audible.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 2 State of the Art
### Research Insights and Challenges
Effective integration will require network resource management and satellite operations to work cooperatively rather than merely reacting to each other as external factors. Current literature primarily offers theoretical models for integrated networks, lacking practical implementations since many 5G technologies essential for NTN integration are still unstandardized. Proposed architectures mostly assume near-real-time connectivity yet require scheduled and predictive resource management to optimize control and allocation. Concepts like network slicing, resource allocation, SDN control, and VNF deployment can theoretically be mapped to schedulable tasks. However, MANO-based VNF deployment lacks autonomy and scheduling, remaining a manual process. Even in highly connected architectures, efficient network operation can benefit from resource prediction and pre-allocation through task scheduling, where scheduled operations run in tandem with near-real-time solvers to optimize performance. These insights from the literature analysis reveal fundamental gaps and challenges that must be addressed for effective satellite-terrestrial network integration. The existing solutions and their limitations highlight that potential management approaches in the literature typically fall into two categories: sporadic connectivity constellation operations and persistent connectivity constellation operations. Each category comes with specific limitations that directly translate into technical and operational challenges, as outlined in Table 4, which describes how the CMS addresses these identified gaps. Building upon these insights, Table 5 systematically categorizes the challenges that emerge from the literature analysis, spanning five critical areas: infrastructure and architecture, interoperability and standardization, integrated management, operational dynamics, and technology maturation and deployment. These challenges form the foundation for the design requirements and capabilities of the proposed CMS framework.