esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Aggregate Characterization  
Beyond porosity, initial molding strength constitutes another critical factor affecting cold patch material durability, particularly in determining the repaired area's ability to withstand vehicular loads. From a microstructural perspective, material strength is primarily controlled by its aggregate skeleton density [41]. Recent developments in CT imaging have allowed direct characterization of aggregate contact features in cross-sections, enabling quantitative evaluation of mixture skeleton density. Studies have demonstrated that coarse aggregate contact points represent reliable indicators of skeleton density [42].  
The identification of coarse aggregate contact points necessitates accurate measurement of inter-particle distances relative to a specified contact threshold. As shown in Figure 5, the contact distance is defined as the edge-to-edge separation between neighboring coarse aggregates. The analysis procedure consists of (1) randomly selecting point A on one aggregate's edge, (2) locating point B on an adjacent aggregate's edge, and (3) computing their Euclidean distance through digital image analysis. This method is systematically repeated for all aggregate pairs within the image, with contact confirmed when the calculated distance is less than the predetermined threshold.  
Existing research recommends setting the contact distance threshold at 23% of the minimum coarse aggregate particle length [43]. Traditional particle size classification methods utilize fixed thresholds (4.75 mm for mixtures with maximum nominal size > 16 mm; 2.36 mm otherwise) [44], while the Bailey method employs a dynamic threshold of 22% of the maximum nominal size [44]. However, given the heterogeneous nature of cold patch materials, this study proposes an enhanced approach combining Bailey method principles with bulk mechanics to calculate weighted average particle size and set 23% of the weighted average particle size as the contact distance threshold. This modification better accounts for the proportional distribution of various aggregate sizes within the mixture.  
The average particle size can be expressed as:  
\[\bar{\tau}=\frac{1}{2}{\sum_{i=1}^{n}{P_{i}d_{i}}} \tag{4}\]  
where \(\bar{\tau}\) is average particle size; \(P_{i}\) is the mass fraction of the aggregate in group \(i\); and \(d_{i}\) is the maximum nominal diameter of the aggregate in group \(i\).  
The contact threshold is converted to pixel distance according to the specimen diameter (101.6 mm) to digital image scale ratio. This conversion allows direct comparison with image-based inter-aggregate distances, enabling precise identification and quantification of contact points across various cold patch formulations.
## 3 Results: Network Performance  
The performance of the network will be evaluated in four aspects: throughput, end-to-end delay, queue length, and fairness. Closed-form expressions are given for throughput. In case of packet end-to-end delay, we present simulation results. Furthermore, by evaluating different initial conditions, we show that it is possible to reduce the average delay in the network; numerical results are given to prove the statement. The lengths of the queues are determined by simulations. Finally, we have calculated the Gini index to assess the fairness behavior of the network. Simulation results of the Gini index for both throughput and packet delay are shown.
## 4 Simplified Calculation Method  
### Development of the Simplified Method  
An important consideration in the design of offshore wind turbine structures is their natural frequency. In order to conveniently calculate the natural frequency, some simplified calculation methods were proposed to calculate the natural frequency of offshore wind turbine structures [43, 44]. [PERSON] [45] created a single degree of freedom system in which the wind turbine and blade is considered as a top mass to quickly obtain the natural frequency of offshore wind turbine structures. The equation is as follows.  
\[f_{1}=\frac{1}{2\pi}\sqrt{\frac{3}{\left(m_{t}+\frac{33}{140}m_{\mathrm{T}} \right)\frac{L_{\mathrm{T}}^{3}}{EI}}} \tag{11}\]  
where \(L_{\mathrm{T}}\) is the tower height (m), \(m_{\mathrm{t}}\) is the wind turbine and blade (kg), \(EI\) represents the tower structure's flexural rigidity (N\(\cdot\)m\({}^{2}\)), and \(m_{\mathrm{T}}\) denotes the tower structure's mass (kg).  
Since the above method does not consider how foundation stiffness affects the natural frequency of offshore wind turbine structures, the computed outcomes lack accuracy. Therefore, based on Equation (11), [PERSON] et al. [6, 46] found a natural frequency expression considering soil-pile interaction. Soil-pile interaction is modeled using soil springs. The lateral, rocking, and cross-coupling spring stiffness values are denoted as KL, KR, and KLR, respectively. The refined calculation method is as follows.  
\[f_{\eta}=C_{R}C_{L}f_{FB} \tag{12}\]  
where \(f_{\mathrm{FB}}\) is the fixed base natural frequency which is calculated by Equation (11). \(C_{\mathrm{R}}\) and \(C_{\mathrm{L}}\) are the influence factors of the foundation stiffness and provided by the monopile. They depend on the geometrical properties of the tower and monopile, with their expressions provided as follows.  
\[C_{R}(\eta_{L},\eta_{R},\eta_{LR})=1-\frac{1}{1+\alpha\left(\eta_{R}-\frac{ \eta_{LR}^{2}}{\eta_{L}}\right)} \tag{13}\]  
\[C_{R}(\eta_{L},\eta_{R},\eta_{LR})=1-\frac{1}{1+b\left(\eta_{R}-\frac{\eta_{ LR}^{2}}{\eta_{R}}\right)} \tag{14}\]  
\[\left\{\begin{array}{l}\eta_{L}=\frac{K_{L}L_{\mathrm{T}}^{3}}{EI_{\mathrm{ T}}}\\ \eta_{R}=\frac{K_{\mathrm{L}}L_{\mathrm{T}}^{3}}{EI_{\mathrm{T}}}\\ \eta_{LR}=\frac{K_{\mathrm{L}}L_{\mathrm{T}}^{3}}{EI_{\mathrm{T}}}\end{array}\right. \tag{15}\]  
where \(a\) and \(b\) are the empirical coefficients 0.6 and 0.5, respectively. The application of Equations (13) and (14) is conditioned by  
\[\eta_{R}>1.2\frac{\eta_{LR}^{2}}{\eta_{L}},\eta_{L}>1.2\frac{\eta_{LR}^{2}}{ \eta_{R}} \tag{16}\]  
In this study, the above method is used to calculate the natural frequency \(f_{1}\), which is applied to Equation (10). A simplified method is thus formulated to gauge the natural frequency of monopile-supported offshore wind turbine structures when subjected to long-term cyclic loading.  
\[f_{N}=(C_{R}C_{L}f_{FB}-\alpha\xi_{L})N^{C} \tag{17}\]
## 2 Materials and Methods
### Feature Extraction  
The zero-crossing rate (ZCR) is a simple and effective method of frequency domain analysis. Three standard spatial axes--the \(x\)-, \(y\)-, and \(z\)- axes--are used to describe the three-dimensional action space, as in Figure 1. Moreover, variance and ZCR have been used to quantify the variation and frequency of movement acceleration of the hand along a spatial axis and its rotation rate around a spatial axis during a period of time, respectively [23; 24]. In the present study, the variance and ZCR of the three axes (\(x,y,\) and \(z\)) were used to quantify the movements of children with ADHD at school and compare these with those of the controls.  
Let \(S\) be the matrix of signal raw values measured by the accelerometer that was installed in a participant's smart watch, which may be represented as follows:  
\[S=\left[\begin{array}{c}s_{x}\\ s_{y}\\ s_{z}\end{array}\right]=\left[\begin{array}{c}s_{x1},s_{x2},\ldots,s_{xN}\\ s_{y1},s_{y2},\ldots,s_{yN}\\ s_{z1},s_{z2},\ldots,s_{zN}\end{array}\right] \tag{1}\]  
where \(s_{x}\), \(s_{y}\), and \(s_{z}\) are the signal raw value sequences corresponding to the \(x\)-, \(y\)-, and \(z\)-axes, respectively, and \(N\) is the number of samples. To characterize the local distribution in the time domain of each signal sequence, the variance and ZCR were calculated using the sliding window approach. The sliding window approach is a well-known signal processing technique in which windows signify successive equal intervals of time for dividing the signal sequence into a set of non-overlapping subsequences. The window size was determined experimentally and was crucial for detecting local variation regarding the signal sequence more sensitively.  
A signal sequence \(\mathbf{s}=[s_{1},s_{2},\ldots,s_{N}]\) was divided into a set of \(K=N/W\) non-overlapping subsequences:  
\[\left\{s_{1}^{\prime},s_{2}^{\prime},\ldots,s_{K}^{\prime}\right\}=\left\{[s _{1},s_{2},\ldots,s_{W}],[s_{W+1},s_{W+2},\ldots,s_{2W}],\ldots,\left[s_{(K-1 )W+1},s_{(K-1)W+2},\ldots,s_{KW}\right]\right\} \tag{2}\]  
where  
where \(sgn(x)\) is the sign function and is defined as follows:  
\[sgn(x)=\begin{cases}1,&if\ x\geq 0,\\ -1,&if\ x<0.\end{cases} \tag{5}\]  
After these two features were calculated for all subsequences of the signal sequence \(\mathbf{s}\), the average values corresponding to each type of feature were calculated as follows:  
\[\overline{f^{var}}=\frac{1}{K}\sum_{k=1}^{K}f_{k}^{var},\overline{f^{scr}}= \frac{1}{K}\sum_{k=1}^{K}f_{k}^{scr} \tag{6}\]  
Therefore, two features, \(\overline{f_{x}^{var}}\) and \(\overline{f_{x}^{scr}}\), were obtained from the signal sequence \(\mathbf{s}_{x}\); two features, \(\overline{f_{y}^{var}}\) and \(\overline{f_{y}^{scr}}\), were obtained from the signal sequence \(\mathbf{s}_{y}\); and two features, \(\overline{f_{z}^{var}}\) and \(\overline{f_{z}^{scr}}\), were obtained from the signal sequence \(\mathbf{s}_{z}\). In total, 12 features were used to characterize the accelerometer and gyroscope recordings of each patient.
# Research on the Compactness of Lunar Soil Simulant Based on Static Cone Penetration Test
## 3 Results and Analysis
### The Compactness of Lunar Soil Simulant with Penetration Depth
Figure 4: The variation curves of the density of the real lunar soil and the lunar soil simulant under different overall densities with the depth. At a depth of less than 40 cm, the two curves representing the variation in real lunar soil density lie between those of the lunar soil simulant. They show an overall density ranging from 1.6 g/cm\({}^{3}\) to 1.8 g/cm\({}^{3}\), corresponding to an overall compactness from 68% to 86%. At depths exceeding 40 cm, the two curves of the real lunar soil density are on either side of that of the lunar soil simulant. They show an overall density of 1.7 g/cm\({}^{3}\), corresponding to an overall compactness of 78%, which falls within the medium compactness range. The density variation obtained using the hyperbolic relationship is close to the experimental results of the lunar soil simulant with an overall density of 1.6 g/cm\({}^{3}\), and the density variation obtained using the power function is close to the experimental results of the lunar soil simulant with an overall density of 1.7 g/cm\({}^{3}\). This demonstrates that both functions can be used to estimate the density of shallow lunar soil, and thus the compactness of shallow lunar soil can also be estimated through the relationship between density and compactness. By comparing the density change rates of the four groups of lunar soil simulant, it is found that as the overall density increases, the rate of change in density with depth shows a gradually decreasing trend, and the difference between the maximum and minimum values of the density of each group of the lunar soil simulant gradually decreases. This indicates that there is less change in the density of the lunar soil simulant with a larger overall density, and the sample is more uniform. The variation in the density of the lunar soil simulant with depth is highly consistent with that of real lunar soil, and the density values are also similar. The density of the lunar soil simulant at different depths can be preliminarily predicted. The variation curve of the real lunar soil density with depth verifies the selection of the CUG-1A lunar soil simulant and the validity of the experimental results.
## 2 Multipath Routing Algorithm for Inter-Satellite Links Based on Load Balancing  
### Inter-Satellite Network Topology  
The network model examined in this research, seen in Figure 1, is an inter-satellite network made up of N satellite nodes, expressed as \(G=\left\{\left[V_{i},V_{j}\right],\ldots\right\}\). \(V_{i}\), \(V_{j}\) are neighbor nodes. Define \(AdjMat[i][j]\) as an adjacency matrix in which the subscript i and j map the node address and if a link exists between the nodes, the element value is 1; otherwise, it is 0.  
The inter-satellite network nodes update the topology information of the entire network periodically, and calculate the current link adjacency matrix according to the link connection status between nodes in the entire network. We define \(AdjMat[i][j]\) to show the link connection status between node i and j. If there is a link between any two mobile nodes, it means that the two nodes are neighbor nodes, and the two-nodes adjacent matrix value is marked as m, otherwise the value is n.  
\[AdjMat[i][j]=\begin{bmatrix}n&m&\cdots&n&\cdots&n&n\\ m&n&\cdots&m&\cdots&n&n\\ \vdots&\vdots&\ddots&\vdots&\ddots&\vdots&\vdots\\ n&n&\cdots&m&\cdots&n&n\\ \vdots&\vdots&\ddots&\vdots&\ddots&\vdots&\vdots\\ n&n&\cdots&n&\cdots&n&m\\ n&n&\cdots&n&\cdots&m&n\end{bmatrix} \tag{1}\]  
Figure 1: Schematic diagram of inter-satellite link.
## 2 Problem Formulation  
### Voltage Unbalance Factors and Sequence Voltage  
Distribution networks are experiencing a steady increase in the number of unbalanced components, such as single-phase PV systems, EV charging stations, and loads distributed along feeders that cause voltage unbalance at the PCC. The voltage unbalance is defined by voltage unbalance factors (VUFs) [18; 19]. To calculate VUFs, sequence voltages such as positive sequence voltage \(V^{+}\), negative sequence voltage \(V^{-}\), and zero-sequence voltage \(V^{0}\), are required, which are computed by the Fortescue transformation. Equation (1)  
Figure 1: Day-ahead training in data learning center using a digital twin grid and implemented in an actual grid platform.  
shows the Fortescue transformation, where \(\alpha=e^{l2\pi/3}=1\angle 120^{\circ}\), and \(V_{A}\), \(V_{B}\), \(V_{C}\) are phase-to-neutral voltages [20].  
\[\left[\begin{array}{c}V^{0}\\ V^{+}\\ V^{-}\end{array}\right]=\left[\begin{array}{ccc}1&1&1\\ 1&\alpha^{2}&\alpha\\ 1&\alpha&\alpha^{2}\end{array}\right]^{-1}\left[\begin{array}{c}V_{A}\\ V_{B}\\ V_{C}\end{array}\right] \tag{1}\]  
The negative sequence voltage unbalance factor and the zero-sequence voltage unbalance factor are defined as \(VUF^{-}\) and \(VUF^{0}\), where \(VUF^{-}(\%)=\frac{\left|V^{-}\right|}{\left|V^{+}\right|}\times 100\) and \(VUF^{0}(\%)=\frac{\left|V^{0}\right|}{\left|V^{+}\right|}\times 100\). In this study, the aim was to minimize the total sum of \(VUF^{-}\) and \(VUF^{0}\) at the PCC during one day. The objective function defined by the VUFs is explained in Section 3.3.
## 2 Materials and Methods
### Silica-Encapsulated SPION Clusters (MNCs)  
The silica-encapsulated SPION clusters were provided by Nanos Scientificae Ltd. (Nanos SCI, Ljubljana, Slovenia). These nanoparticle clusters were synthesized in a microemulsion where a large number of maghemite nanoparticles were self-assembled into spherical clusters, followed by encapsulation of the nanoparticle clusters with a layer of silica [32; 33]. First, small maghemite nanoparticles were synthesized using precipitation from an aqueous solution, as described in Section 2.2. Then, the nanoparticles were self-assembled by using polyacrylic acid and polyvinylpyrrolidone. This approach was exclusively developed and is suitably protected by Nanos SCI. The nanoparticle cluster size was finally unified by the use of high-gradient magnetic separation, as described in our previous paper [32; 34].
# TARCS: A Topology Change Aware-Based Routing Protocol Choosing Scheme of FANETs
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multirow{2}{*}{**MANET**} & \multirow{2}{*}{**VANET**} & \multicolumn{4}{c}{**FANET**} \\ \cline{4-7}  & & & \multicolumn{2}{c}{**Small UAVs**} & \multicolumn{2}{c}{**Large UAVs**} \\ \cline{4-7}  & & & **FW * & **RW * & **FW * & **RW * \\ \hline Node Speed & Low & Medium & Medium & Medium & High & High \\ Density & High & High & & Low & & \\ Power Consumption & Low & High & Med & Med & High & High \\ Network & High & Medium & & Low & & \\ Connectivity & & & & & & \\ QoS & Medium & & & Depends on the applications & & \\ Mobility Patterns & 2D & 2D & & 3D & & \\ Mobility Degree & Low & Medium & Low-Med & Low-Med & Med-High & Med-High \\ Altitude & Close to ground & Close to ground & Low & Low & High & High \\ Static Pause & Yes & Yes & No & Yes & No & Yes \\ Topology Variation & Occasionally & Frequently & Very frequently & Very frequently & Very frequently \\ scalability & Medium & High & & Low & \\ Typical Mobility & regular & Restricted & & & \\ Models & regular & & & & Depends on the applications and need to pre-determined \\ \hline \hline \end{tabular}  
* FW: fixed-wing,
* RW: rotary wing.  
\end{table} 
Table 1: Comparison of MANET, VANET and FANET. Secondly, a heuristic routing strategy of FANETs is proposed using the measurement results of the sensing method. The policy compares the measured topology change result with the threshold reference value, determines the current node movement mode, and appropriately adjusts the routing protocol used in the network, so that the network routing protocol is more suitable for the changed mobile scenario, and finally the purpose of improving network performance in highly dynamic topology is achieved. Simulations show that compared with traditional practices, the proper use of TARCS in highly dynamic MANETs in complex scenarios can effectively improve some aspects of network performance. The content discussed in this article is based on the following assumptions:  
* The node motion is simplified to two-dimension movements. * The transmission range of each node is a circular area with the node in the center and a radius of \(r\). The transmission power of the node is constant within this area. * The number of nodes in the network is constant during the network lifetime. The remainder of this paper is organized as follows. In Section 2, the TARCS is introduced in detail. In Section 3, the TARCS strategy is validated in the form of simulation experiments. The experimental results are discussed in Section 4, and Section 5 contains the conclusion.
## 2 Related Work
[30] introduced the TESL-Net model, which uses Swin-Transformer blocks in the encoder module to effectively extract global contextual information from skin lesion images. Nevertheless, the model's ability is limited when dealing with complex lesion images. [PERSON] et al. [31] proposed the U-LSTM model, which integrates the spatial feature extraction capability of CNNs with the temporal sequence modeling ability of LSTMs. This architecture enables simultaneous capture of both static features and dynamic changes of skin lesions; however, the model is structurally complex and demands substantial computational resources. [PERSON] et al. [32] introduced the RCNN-LSTM, combining the strengths of RCNN and LSTM to extract spatial features while handling temporal information, making it suitable for complex image data. Nevertheless, its segmentation performance is limited when processing lesion boundaries in smaller images. Generative Adversarial Networks (GANs), first proposed by [PERSON] et al. [33] in 2014, pioneered a new direction in the research of deep generative models. The core concept of GAN involves constructing an adversarial game between a generator and a discriminator, allowing the generator to progressively improve the authenticity of the generated samples. [PERSON] et al. [34] introduced an adversarial learning-based method for automatic skin lesion segmentation, which enhances segmentation performance by performing data augmentation and deeply fusing features extracted through convolution operations in both the encoder and decoder. [PERSON] et al. [35] proposed the Att-DenseUnet model based on GANs, which effectively integrates multi-scale features within the encoder, thereby strengthening feature representation capabilities. Additionally, by incorporating an attention mechanism, the model suppresses irrelevant regions in the output feature maps, reducing the impact of artifacts. The introduction of adversarial loss further enhances the discriminative power of the generated features, thereby improving the segmentation accuracy of the model. [PERSON] et al. [36] proposed HEXA-GAN, which significantly enhances the accuracy of hair segmentation and enables natural reconstruction of hair-occluded regions, thereby improving the overall visual consistency of dermoscopic images. [PERSON], [PERSON] et al. [37] introduced EA-GAN, which integrates attention mechanisms and vision transformers to effectively remove artifacts and hair interference from lesion areas. This approach substantially improves the quality of skin lesion image preprocessing, thereby enhancing the accuracy and robustness of subsequent diagnostic systems.
## 6 Results
### Scenario with More Servers and Fewer Fog Nodes
#### 6.3.1 Server Load Distribution  
**Efficient resource utilization:** While some servers handle more load than others, it is advantageous that servers like 7 (144.79 units) and 2 (110.22 units) absorb the majority of the demand. This indicates that the system can direct the load to servers with higher processing capacity, ensuring that the distribution does not exceed their limits.  
**Balanced load distribution:** Servers such as 0 (90.64 units), 4 (88.21 units), and 5 (88.65 units) exhibit a balanced and sustained load distribution over time. This suggests that the load balancer is effectively utilizing available resources and maintaining efficient operation without overloading these servers.  
**Peak load absorption:** Servers with higher loads, like server 7, demonstrate the system's ability to handle load peaks (up to 150 units) without collapsing. This ensures system stability and availability even during high demand periods, providing a continuous and uninterrupted user experience.  
Activation of Fog Nodes  
**Timely fog node activation:** Despite having only three fog nodes, the system shows that these nodes activate promptly, helping to prevent total server overload. This responsiveness ensures that the system can manage high traffic volumes without compromising service quality.  
**Effectiveness during critical moments:** The fog nodes, though fewer than in previous scenarios, effectively distribute user requests when servers reach their limits. This optimizes load distribution between the edge and the fog, helping to prevent failures and improve overload tolerance.