esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
This Special Issue covers a wide range of scientific tasks carried out in the fields of geophysics, climatology and astronomy. The study of various atmospheric layers, their mutual influence on each other and the effect the surface has on them is one of these key tasks. Atmospheric layers are often considered independent atmospheric structures. Nevertheless, these layers are in fact connected by common physics. The influence of physical processes on remote atmospheric volumes has been observed. For example, the results of research indicate the presence of a connection between strong gravitational waves and the appearance of a sporadic layer E in the ionosphere. Evidence has been obtained that daytime gravitational waves and a sporadic E layer are often observed together on the eastern slopes of the Tibetan Plateau in summer [1]. Strong gravity waves generated by volcanic eruptions may have effects on the mesophere/lower thermosphere. For example, a Hunga Tonga-Hunga Ha\({}^{\prime}\)apai volcanic eruption generated perturbations in the mesosphere/lower thermosphere which were associated with the dissipation of primary gravity waves and the propagation of secondary waves [2]. Moreover, in addition to the relief and the volcanic eruption, gravity waves propagating into the stratosphere and mesosphere can be formed by mid-latitude frontal zones or solar activity [3].  
One of the most important tasks is to study the physics of processes and phenomena within the different layers of the atmosphere. In particular, the relevance of such research is determined, on the one hand, by the need to search for fundamental physics in the lower, middle and upper atmosphere, the occurrence of atmospheric processes in individual atmospheric layers and their propagation and impact on distant layers. The study of the atmosphere and its underlying surface as a system is relevant for the development of our understanding of climate change and the impact of these changes on various system components [4]. On the other hand, another key task is to find new regions and sites with the required atmospheric characteristics and best atmospheric conditions for placing new optical and millimeter telescopes [5, 6].  
In addition to these different prospective scientific directions, we can also highlight two further key points. The first is the development of new methods for measuring atmospheric characteristics [7]. The second is the development of models of atmospheric processes with resolutions that cover the smallest scales and energy-carrying atmospheric heterogeneities.
## 2 Structural Planes in Digital Borehole Images  
By placing a panoramic camera probe into the borehole at a uniform speed through a cable, digital panoramic borehole camera technology is a probing technology that obtains image information of the inner wall of a borehole. Figure 1 shows the schematic diagram of digital panoramic borehole imaging. The borehole camera can quickly obtain the two dimensional unfolding map of the borehole wall and restore the actual situation of the borehole wall. After the borehole cuts the structural plane of the rock, it forms circular or elliptical traces in the borehole wall. After the image of the borehole wall is expanded in a particular direction, the traces are expressed as sine curves in the plane image. The production and width of the joint fractures can be calculated according to the coordinate system. The hole wall's original orientation and depth information are recorded in the borehole image.  
The structure plane production shape includes two parameters of dip direction and dip angle, and in the plane expansion diagram, the plane fissure is presented as a sinusoidal curve shape. As shown in Figure 2, the orientation of the lowest point of the curve on the figure is the dip direction of the fissure. The structural plane yield includes two parameters of inclination and dip angle, and the plane fracture appears as a sinusoidal curve in the plane expansion diagram. As shown in Figure 2, the orientation of the curve's lowest point is the fissure's dip direction. The dip angle of the structural plane can be obtained from the ratio of the vertical distance from the peak to the trough of the sine curve of the structural plane to the diameter of the borehole. As in Figure 2, let the vertical distance from the crest to the trough of the sinusoidal curve be \(h\), and the diameter of the borehole be \(D\). Then, the inclination angle \(\beta\) of the structural plane can be solved by Equation (1).  
\[\beta=\arctan\left(\frac{h}{D}\right) \tag{1}\]  
Figure 1: Digital panoramic borehole camera imaging principle.
###### Abstract  
With the increasing demand for high-resolution images, handling high-dimensional image data has become a key aspect of intelligence algorithms. One effective approach is to preserve the high-dimensional manifold structure of the data and find the accurate mappings in a lower-dimensional space. However, various non-sparse, high-energy occlusions in real-world images can lead to erroneous calculations of sample relationships, invalidating the existing distance-based manifold dimensionality reduction techniques. Many types of noise are difficult to capture and filter in the original domain but can be effectively separated in the frequency domain. Inspired by this idea, a novel approach is proposed in this paper, which obtains the high-dimensional manifold structure according to the correlationships between data points in the frequency domain and accurately maps it to a lower-dimensional space, named Frequency domain-based Manifold Dimensionality Reduction (FMDR). In FMDR, samples are first transformed into frequency domains. Then, interference is filtered based on the distribution in the frequency domain, thereby emphasizing discriminative features. Subsequently, an innovative kernel function is proposed for measuring the similarities between samples according to the correlationships in the frequency domain. With the assistance of these correlationships, a graph structure can be constructed and utilized to find the mapping in a low-dimensional space. To further demonstrate the effectiveness of the proposed algorithm, FMDR is employed for the semi-supervised classification problems in this paper. Experiments using public image datasets indicate that, compared to baseline algorithms and state-of-the-art methods, our approach achieves superior recognition performance. Even with very few labeled data, the advantages of FMDR are still maintained. The effectiveness of FMDR in dimensionality reduction and feature extraction of images makes it widely applicable in fields such as image processing and image recognition.  
Manifold Learning; Dimensionality Reduction; Frequency Domain Correlationship; Semi-supervised Classification +
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Article
# RMRRASL: Robust Matrix Factorization with Robust Adaptive Structure Learning for Feature Selection
## 5 Experiments and Analysis
### Description of Experimental Data  
In this experiment, we performed classification and clustering tasks [37] on five publicly available datasets, including four publicly available face datasets (AR, CMU PIE, Extended YaleB, and ORL) and one object dataset (COIL20). The specific details of the five datasets are shown in Table 5, where _Tr_ and _Te_ represent the number of training samples and testing samples selected from each class.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Objective Function** & \(\textbf{L_{21}}\)**-Norm** \\ \hline LSFS [19] & \(\frac{\sum_{j}{(z_{n}-z_{n})^{2}}W_{ij}}{Var(z_{n})}\) & No \\ (2005) & \(||(I_{n}-\Delta^{T})ZS||_{1}+\frac{\alpha}{2}||S^{T}S-I_{m}||_{2}^{2}+tr(\beta S ^{T})\) & No \\ USFS [21] & \(\sum_{i=1}^{n}\sum_{j=1}^{c}||W^{T}z_{i}-o_{j}||_{2}^{2}y_{ij}+\alpha||ZS-Y||_{ 2}^{2}+\gamma||S||_{2,1}+\) & No \\ (2021) & \(\beta||Y||_{2}^{2}\) & No \\ CNAFS [22] & \(\left\{\begin{array}{ll}||Z-ZSA||_{2}^{2}+||C_{n}(Z^{T}W-Y_{p})||_{2}^{2}+ \lambda||W||_{2,1}+\alpha tr(Y_{p}^{T}LY_{p})&\text{No}\\ +\beta\sum_{ij}g_{ij}\log g_{ij}+\gamma tr(ALA^{T})+\epsilon tr(A^{T}QA)\\ (2022)&\left\{\begin{array}{ll}||Z^{T}W-Y||_{2}^{2}+\alpha_{1}||W||_{1}+ \alpha_{2}Tr(W^{T}L^{W}W)&\text{No}\\ +2\lambda((Tr(Y^{T}L^{S}Y)+\beta\sum_{ij}g_{ij}\log g_{ij})\\ (2015)&||Z-ZS||_{2,1}+\lambda||S||_{2,1}\\ \text{SPNFSR [34]}&||Z-ZS||_{2,1}+\alpha tr(S^{T}MS)+\beta||S||_{2,1}\\ \text{RMFRASL}&||Z-ZSA||_{2,1}+\alpha tr(S^{T}Z^{T}LZS)+\beta||Z^{T}-Z^{T}W||_{ 2,1}\\ \text{(2022)}&||Z-ZSA||_{2,1}+\alpha tr(S^{T}Z^{T}LZS)+\beta||Z^{T}-Z^{T}W||_{ 2,1}\\ \hline \hline \end{tabular}
\end{table}
Table 4: The summarization of compared methods in this paper.  
The AR [38] database consists of 4000 images corresponding to face images of 70 males and 56 females. The photos have different facial expressions, lighting conditions, and occlusions. There are 26 images for each object. Some examples of the AR database are shown in Figure 3a.  
The CMU PIE [39] database consists of 41,368 multi-pose, illumination, and expression face images, including face images with different pose conditions, illumination conditions, and expressions for each object. Some instances of the CMU PIE database are shown in Figure 3b.  
The Extended YaleB [40] database consists of 2414 images containing 38 objects with 64 photographs taken for each object, with strict control over pose, lighting, and the shooting angle. Some face images of the Extended YaleB database are shown in Figure 3c.  
The ORL [41] dataset consists of 400 face images containing 40 subjects taken at different times of day, under different lighting, and with different facial expressions. Some examples of the ORL dataset are shown in Figure 3d.  
The COIL20 [42] dataset contains 1440 images, taken from 20 objects at different angles, with 72 images per object. Some images the COIL20 dataset are shown in Figure 3e.
# Multilayered Network Model for Mobile Network Infrastructure Disruption
## 6 Simulations and Results
### Simulation Scenarios
#### 6.1.4 Results of Simulation Scenario 1
The reason is the fact that in addition the highest transmission speeds, the nodes of the MANET network provide also a higher radio range. The third result (Table 4) shows the average values of the data rate that was achieved on the individual optimal paths in the case of 100 Kb urgent data delivery. Those values were achieved by averaging the data rates achieved on the individual parts of the optimal paths. Table 4 shows that the data rates in the case of urgent data transmission via the WSN network are many times lower than in the case of urgent data transmission via the MANET network. In the WSN network, the average data rate ranges from 26 to 28 Kbps, while in the case of the WSN-MANET network data rate ranges from 45 to 82 Mbps. It is also possible to observe an increasing trend of the average data rate in the case of the WSN-MANET network. This phenomenon is caused by the fact that transmission of the data with increasing distance between the source WSN node and AP takes place in an increasing part of the MANET network. This fact is supported by the second result in Figure 20, where the number of hops in MANET networks is highest in the case of AP Position 3. A higher number of hops in the MANET part of WSN-MANET network resulted in a higher average data rate.
###### Abstract  
There have been few empirical investigations of how individual differences influence the perception of the sonic environment. The present study included the Big Five traits and _noise sensitivity_ as personality factors in two listening experiments (\(n=43\), \(n=45\)). Recordings of urban and restaurant soundscapes that had been selected based on their type were rated for _Pleasantness_ and _Eventfulness_ using the Swedish Soundscape Quality Protocol. Multivariate multiple regression analysis showed that ratings depended on the _type_ and _loudness_ of both kinds of sonic environments and that the personality factors made a small yet significant contribution. Univariate models explained 48% (cross-validated adjusted \(R^{2}\)) of the variation in _Pleasantness_ ratings of urban soundscapes, and 35% of _Eventfulness_. For restaurant soundscapes the percentages explained were 22% and 21%, respectively. _Emotional stability_ and _noise sensitivity_ were notable predictors whose contribution to explaining the variation in quality ratings was between one-tenth and nearly half of the soundscape indicators, as measured by squared semipartial correlation. Further analysis revealed that 36% of _noise sensitivity_ could be predicted by broad personality dimensions, replicating previous research. Our study lends empirical support to the hypothesis that personality traits have a significant though comparatively small influence on the perceived quality of sonic environments.  
soundscape; environment; perception; personality; psychoacoustics; _noise sensitivity_; Big Five
## 4 Conclusions  
In this study, a work toward creating a cheap and portable nondestructive testing (NDT) method known as the electromechanical impedance (EMI) technique was carried out. Since the EMI technique requires the brittle PZT patch to be permanently attached to the structure for damage detection analysis, the idea of making the attachment temporarily was tested using a double-sided tape. Such an approach made the PZT-metal transducer very easy to be attached and detached. In addition, regardless of the temporary attachment approach, debonding damage of a glass fiber epoxy composite could be  
Figure 6: FEM model for smaller PZT sizes: (**a**) 0.1 mm PZT; (**b**) 1 mm PZT; (**c**) 5 mm PZT; (**d**) 5 mm PZT with thicker metal plate.  
Figure 7: Simulation results for the impedance signatures of the four FEM models.  
detected by monitoring the changes in the impedance signatures. However, this change was not as significant as the change subjected to debonding that occurred right below the PZT-metal transducer. This study experimentally showed that the debonding damage is best detected when it happens under the PZT patch. Therefore, to seek the possibility of detecting smaller debonding size, the size of the PZT patch had to be smaller than the conventional size of \(10\) mm \(\times\) 10 mm with 0.5 mm thickness. Thus, the finite element analysis tool known as ANSYS Workbench was used to conduct simulations with smaller PZT patches as small as 0.1 mm \(\times\) 0.1 mm with 0.01 mm thickness, virtually impossible to conduct experiment. Four different models were created with small PZT patches to find out that the size of the PZT was not the most important factor as the 1 mm square sized PZT patch showed resonance with highest peak amplitude.  
Overall from this work, we found that the EMI technique can be made into a portable system, where the PZT transducer can be attached simply using a double-sided tape. Regardless of the damping effect, which may cause the impedance signatures to be less sensitive when subjected to damage, the results from this study have demonstrated its possibilities. Furthermore by conducting simulation studies, the PZT size can be further reduced for a successful debonding detection of composite structures.  
[PERSON] contributed in planning experiments and mainly writing the manuscript; [PERSON]-T.P. contributed in conducting experiments and analyzing data.  
The research was supported by a grant from \"Development of Safety Evaluation Techniques for Infrastructures by Micro-Macro Hybrid Monitoring Data (20190201-001)\" funded by the Korea Research Institute of Standards and Science (KRISS).  
The authors declare no conflict of interest.
## 2 MT Forward Modeling and Parameter Setting
### Finite Element Modeling
\begin{array}{l}F(u)=\int\biggl{[}\frac{1}{2}\tau(\
abla u)^{2}-\frac{1}{2} u^{2}\biggr{]}d\Omega\\ u\big{|}_{AB}=1\\ \delta F(u)=0\end{array}  \tag{8}\]  
The Finite Element Method (FEM) involves dividing the model region \(\Omega\) into a grid of rectangular elements, with bilinear interpolation applied to approximate the field variables using shape functions. This interpolation method strikes a good balance between computational efficiency and accuracy, which is crucial for capturing the smoothness and continuity of the electromagnetic fields. The principles and algorithms of FEM are extensively covered in the literature [17; 33; 34], and will not be elaborated here. The system of equations is then assembled, incorporating boundary conditions, and solved for field values \(u\) at each node. After obtaining these values, the vertical partial derivative \(\frac{\partial u}{\partial z}\) is computed, which in the TM mode corresponds to \(\frac{\partial H_{x}}{\partial z}\). This result is used to calculate the apparent resistivity and impedance phase:  
\[ \begin{array}{l}Z_{yx}=-\frac{1}{\sigma}\frac{\partial H_{x}}{\partial z} /H_{x}\\ \rho_{yx}=\frac{1}{\alpha\mu}\big{|}Z_{yx}\big{|}^{2}\\ \phi_{yx}=\arctan\frac{\text{Im}[Z_{yx}]}{\text{Re}[Z_{yx}]}\end{array}  \tag{9}\]  
Here, \(Z_{yx}\) represents the impedance, \(\rho_{yx}\) represents the apparent resistivity, and \(\phi_{yx}\) represents the impedance phase in the TM polarization mode. In the case of TM polar ization, \(\frac{2\mu}{dz}\) corresponds to \(\frac{2\rho_{x}}{dz}\). Substituting this into Equation (10) yields the apparent resistivity and impedance phase. \[ \begin{split} Z_{xy}&=\left.E_{x}\middle/\left(\frac{1}{ \lambda\mu\mu}\frac{\partial E_{x}}{\partial z}\right)\right.\\ \rho_{xy}&=\frac{1}{\omega\mu}\big{|}Z_{xy}\big{|}^{ 2}\\ \phi_{xy}&=\arctan\frac{\text{Im}\big{[}Z_{xy}\big{]} }{\text{Re}\big{[}Z_{xy}\big{]}}\end{split}  \tag{10}\]  
Here, \(Z_{xy}\) represents the impedance, \(\rho_{xy}\) represents the apparent resistivity, and \(\phi_{xy}\) represents the impedance phase in the TE polarization mode.
# A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
###### Abstract  
The intelligent satellite, iSAT, is a concept based on software-defined satellites. Earth observation is one of the important applications of intelligent satellites. With the increasing demand for rapid satellite response and observation tasks, intelligent satellite in-orbit task planning has become an inevitable trend. In this paper, a mixed integer programming model for observation tasks is established, and a heuristic search algorithm based on a symmetric recurrent neural network is proposed. The configurable probability of the observation task is obtained by constructing a structural symmetric recurrent neural network, and finally, the optimal task planning scheme is obtained. The experimental results are compared with several typical heuristic search algorithms, which have certain advantages, and the validity of the paper is verified. Finally, future application prospects of the method are discussed.  
Keywords:in-orbit observation; task planning; recurrent neural network; heuristic algorithm +
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## 2 Related Work  
### Graph Theoretic Clustering Methods  
Graph-theoretic techniques for clustering are important not only when the data is expressed in a network structure, but also due to the established effectiveness of graph partitioning techniques when varying types of input data are converted to a graph representation [20]. Graph-theoretic clustering algorithms based on spectral methods are amongst the most rigorously studied from a theoretical perspective [3; 4]. A well-known optimization framework for graph partitioning involves finding _sparse_ cuts either to \(k\)-partition the vertices with multi-way cuts [3; 4], or, more commonly, to recursively _bi-partition_ the graph using the combinatorial measure _conductance_ to quantify the sparsity of the cuts [21]. However, the hardness of the underlying combinatorial optimization [22] makes these methods less practical to apply without heuristics.  
Commonly used methods for community detection as well as the challenges related to these methods are discussed in [1; 23; 24]. The Girvan-Newman algorithm is a well-known method in which a network's highest betweenness edges are greedily removed to obtain a top-down hierarchical clustering [2]. This paper's NBR-Clust framework also uses betweenness centrality calculations as a subroutine towards heuristically computing resilience measures. However, the two approaches are otherwise completely divergent as this is only a heuristic rather than an inherent aspect of the NBR-Clust framework. This study also uses _vertex_ betweenness centralities rather than the _edge_ betweenness centralities used in Girvan-Newman. The Girvan-Newman algorithm is not based on any global resilience computations, neither edge-based nor node-based resilience measures.  
[PERSON] [25] and many others have developed heuristics to approximate a solution to the NP-complete problem of modularity optimization, which may provide a stopping condition for Girvan-Newman community detection. These modularity-based clustering approaches start with some partitioning of the vertex set and modify the groupings in a manner that provides a maximum increase in modularity from one iteration to the next. This class of methods is similar to internal validation-based methods for traditional clustering. While such methods may be used in post-processing any existing clustering, NBR-Clust shares little in common with this class of techniques.
## 7 Discussion
### Computational Efficiency and Its Impact  
The computational efficiency of STLF models has significant implications for their deployment in smart grids. Models exhibiting rapid computation times are of paramount importance for real-time analytics and immediate load adjustment, which are central to the dynamic demand-response mechanisms in smart grids. The analysis indicates that while there is a trade-off between speed and complexity, the employment of efficient algorithms can mitigate this, enabling fast and reliable forecasting.