esa-sceva/satcom-chunk-collection huggingface dataset:
# An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction
## 3 Methodology
### Proposed Degradation Indicator Set
1:for\(f_{i}\in F\)do,
2: Calculate maximum information coefficient \(MIC(f_{i},f_{j})\), obtaining the \(MIC-FF\) matrix,
3:for Every value in every column of the \(MIC-FF\) matrix do,
4: Find the minimum values and obtain the set \(min_{FF}=\{min_{FF0},min_{FF1},\cdots,min_{FF24}\}\),
5:endfor
6:endfor
7:for Every \(min_{FFi}\) in set \(min_{FF}\)do,
8: Find the maximum values in in set \(min_{FF}\) as the \(FF-threshold\),
9:endfor
10:for Every column in \(MIC-FF\) matrix do,
11: Court the number of elements in each column that are less than the \(FF-threshold\), obtain the set \(Num_{FF}=\{Num_{FF0},Num_{FF1},\cdots,Num_{FF24}\}\),
12:endfor
13:for\(Num_{FFi}\) in set \(Num_{FF}\)do,
14: Find the median number \(Num_{med}\) in set \(Num_{FF}\),
15:if\(Num_{FFi}>Num_{med}\)then,
16: Select the features corresponding to the feature columns and form the feature subset \(F_{F-F}\),
17:endif
18:endfor
19:for\(f_{i}\in F\)do
20: Calculate maximum information coefficient \(MIC(f_{i},R)\), obtaining the \(MIC-FR\) matrix,
21:for Every value in every row of the \(MIC-FR\) matrix do
22: Rank the values and find the median value \(FR_{med}\) as the \(FR-threshold\),
23:if\(MIC(f_{i},R)>FR_{med}\)then,
24: Select the features to form a subset \(F_{F-R}\). 25:endif
26:endfor
27:endfor
```  
**Algorithm 2** MIC feature selection method. The third stage is called the feature subsets fusion method. The bearing degradation indicator subset \(F^{*}\) constructed in the first stage based on FCBF-AMB, and the bearing degradation indicator subset \(F_{F-F}\) and subset \(F_{F-R}\) constructed in the second stage, are cross-operated to construct the optimal indicator set \(F_{opt}\), which characterizes the bearings degradation state. In the above stages, three subsets of bearing degradation indicators are obtained. Subset \(F^{*}\) is an initial subset of degradation indicator with strong correlation and low redundancy. Subset \(F_{F-F}\) is a strongly uncorrelated subset composed of features with low redundancy. Subset \(F_{F-R}\) is a strongly correlated subset that consists of features that have strong correlations with failure modes.
# Measurement of Bridge Vibration by UAVs Combined with CNN and KLT Optical-Flow Method
## 5 Discussion and Conclusions  
### Discussion  
#### 5.1.1 Discussion of the Proposed Method  
The experimental results show that the displacement of the bridge model measured by a UAV combined with the CNN and KLT optical-flow method is reliable. The displacement signals obtained by the KLT optical-flow method under fixed camera and UAV are more stable than those of DIC, and the extracted structural natural frequency of the KLT optical-flow method is basically consistent with that extracted by DIC, which shows the feasibility of replacing DIC with the KLT optical-flow method in bridge vibration measurement. The difference of the tracking effect between the two methods is mainly caused by the image pyramid of the KLT optical-flow method, which tracks target points at different levels of resolutions of an image to improve tracking accuracy. Under the KLT optical-flow method, the displacement curve measured by UAV combined with CNN is very close to that of the fixed camera, and the natural frequencies obtained by the two methods are basically the same, which shows the feasibility of the method of correcting displacements measured by UAV with CNN proposed in this paper. In the experiment, the 10 reference points are from two different planes (a cardboard and a wall), which shows that the reference points from the same plane are not required for the correction method of this paper, unlike other correction methods. That is one of the highlights of the method proposed in this paper. Moreover, in order to ensure the assumption of constant brightness of the KLT optical-flow method, the measurement can be carried out on cloudy days or when the light condition is stable. Since the measurement duration of the proposed method is about 10 min, the measurement time needs to be determined according to the actual situation.
# The Next Generation Platform as A Service: Composition and Deployment of Platforms and Services
## 3 The Next Generation PaaS: Architecture, Concepts, Processes, and Workflows
### The NGPaaS Architecture  
NGPaaS consist of a multi-layer architecture, with each layer being responsible for a specific functionality. In total, six layers are defined as: (1) The Business Registration Layer, (2) The Business as a Service (BaaS) Layer 3) The Business and Operation Support System (BSS/OSS) Layer, (4) The Dev-for-operations Layer, (5) The Platform as a Service (PaaS) Layer and finally (6) The Infrastructure as a Service (IaaS) Layer. The details and scope of each of these layers can be found in Table 1, while Figure 1 provides a simplified view of the NGPaaS architecture and also of the cross-layer interactions.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**NGPaaS Layer**} & \multicolumn{1}{c}{**Description**} \\ \hline Business Registration & Registers all stakeholders that participate in NGPaaS (VSPs, Vendors, etc.). It \\  & also includes the resolution of access and execution rights. \\ \hline BaaS & A customisable catalogue from which to order service or platform workloads. \\ \hline BSS/OSS & Responsible for inventory registration, global supervision, and deployment of services and platforms on their execution environments. In our prototype, the BSS/OSS role [26] is fulfilled by the RDCL 3D tool. \\ \hline Dev-for-Operations & An environment to support the innovative NGPaaS Dev-for-Operations \\  & model—an evolution of the existing DevOps model to facilitate new types of interaction and development methods between multiple stakeholders. This environment is where staging and development are performed to bring new PaaS or service components into NGPaaS [27]. \\ \hline PaaS & PaaS components are deployed on the available (declared) infrastructure. \\  & These PaaS components form the framework to manage the VNFs included in the requested services. More than one PaaS instances can be active and \\  & managed at the same time by the OSS/BSS. \\ \hline IaaS & This layer relates to the cloud infrastructure available to the NGPaaS operator. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Layers of the NGPaaS architecture.
###### Abstract  
In photonic crystal fibers, light guidance can be achieved by a central defect of a periodic structure of air holes in a silica glass matrix and the dispersion can be adjusted over a wide spectral range to enhance nonlinear effects. By coupling short pulse laser radiation into the core with tight confinement and utilizing the nonlinear properties of glass, this radiation can be converted to a broad spectral distribution. The tight confinement puts limits on the maximum pulse fluence propagating in the core due to the damage threshold of the glass. Therefore, when higher power spectral densities are desired, it is favorable to spread the generation of light over a much larger area to prevent fiber damage. We present here a method for generating a supercontinuum using the cladding modes of an endlessly single-mode fiber. These modes generate a supercontinuum utilizing a multimodal quasi-continuum of states, for which dispersion is governed by the guiding properties of the material between the air-filled holes in the cladding. The system also provides experimental access to unique phenomena in nonlinear optics. Simulations of the propagation properties of the core mode and cladding modes were compared with measurements of the group-velocity dispersion in a modified white-light Mach-Zehnder interferometer. The coupling of similar laser parameters into the cladding of the photonic crystal fiber enables a significant increase in conversion efficiency in the visible spectral range compared with the core-pumped case.  
photonic crystal fiber; supercontinuum; cladding modes; endlessly single-mode fiber +
Footnote †: journal: Applied Physics Letters
## 4 Results
### Pairwise Comparisons  
The results of the pairwise comparisons are shown in Tables 3 and 4 for stations with a scheduled dwell time of 42 and 120 s, respectively. All but one of the observed differences are statistically significant for delays of up to 90 s. Although the delay frequencies seem to follow the unconditional distribution when delays become larger than 90 s in Figure 4a, the pairwise comparison shows this is not always the case, as we still find statistically significant differences. Passenger volumes thus also affect the frequency of these larger delays.  
For stations with a scheduled dwell time of 120 s, as shown in Table 4, delays of up to 15 s are found to be statistically significantly different from the unconditional frequency distribution up to a total volume of 50 boarding and alighting passengers. Larger delays, however, do not differ significantly from the unconditional frequency distribution, which is in line with the tails in Figure 4b. This further indicates that although the frequency of dwell time delays increases for stations with a scheduled dwell time of 120 s, the delay size is more or less stable across different volumes of boarding and alighting passengers.
# Contribution of Etalon Observation to Earth Rotation Parameters under a New Observation Scenario
###### Abstract  
The standard products of the International Laser Ranging Service (ILRS) are mainly based on the two laser geodynamics satellites (LAGEOS) due to the sparse observations of the Etalon satellites. With improvements in the ability to track high-altitude satellites, ILRS conducted a 3-month Etalon tracking campaign. In this paper, we study the contribution of more Etalon observations in the new observation scenario to weekly ILRS products, such as station coordinates, Earth rotation parameters (ERPs) and satellite orbit. We compare the ILRS products estimated from LAGEOS-only solutions and LAGEOS+Etalon solutions. In the new observation scenario of 2019, the numbers of observations of Etalon satellites are 1.4 and 1.7 times larger than those in 2018. It is shown that the quality of station coordinates, and the satellite orbit of LAGEOS satellites are only slightly affected by the increase in Etalon observations of the campaign. However, for station 1868, which is dedicated to high-altitude satellites, the root mean square (RMS) values of the residuals in the N, E, and U components are improved by 3.1 cm, 2.1 cm and 2.3 cm, respectively. The internal precision of orbit for Etalon-1/2 satellites in tangle and normal directions are improved by 1.5 cm and 2.9 cm, respectively. Most remarkably, the standard deviations for Xp, Yp and LOD can be improved by 6.9%, 14.3% and 5.1%, respectively, compared with the International Earth Rotation System (IERS)-14-C04 series. With our research, the ILRS could increase efforts on Etalon satellite tracking without affecting the routine observations of LAGEOS satellites.  
satellite laser ranging (SLR); Etalon; LAGEOS; earth rotation parameters +
Footnote †: journal: Applied  
0000-0001-9900-3300][PERSON]  
0000-0002-3300-3300][PERSON]  
4 School of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, China
# Determination of Axial Force in Tie Rods of Historical Buildings Using the Model-Updating Technique
## 4 Case Study Using the Proposed Methodology
### Numerical Simulation
#### 4.3.1 Initial Model  
The initial numerical model was constructed to determine the deviation of the real state of the structure from the ideal boundary conditions (clamp and hinge) and to evaluate which of those boundary conditions better described the real state. The finite element (FE) distribution of the numerical model corresponded to the arrangement of the measuring points shown in Figure 8. For each initial model, the first two natural frequencies and mode shapes were determined (Figure 11).  
To avoid subjective assessment of the boundary conditions, mode shapes from numerical simulation and experiments were compared using normalized RMSE (Equation (9)). For each initial numerical model and its corresponding mode shapes, the RMSE values were determined. Based on the RMSE values (Table 3), we observed that the initial model with hinged boundary conditions better correlated (values approaching 0) with the on-site measurements. Based on the results, hinged boundary conditions were identified as a good selection for the initial model and were further updated by adapting the spring stiffness on the boundary.  
Figure 11: Comparison of the experimental mode shapes of the reference tie rod 6B-C and the mode shapes associated initial numerical models for the (**a**) first and (**b**) second mode shapes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**RMSE Values**} \\ \hline
**Mode Number** & **Hinge–Hinge** & **Clamp–Clamp** \\ \hline
1 & 7.06 & 9.15 \\
2 & 8.50 & 10.66 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Root-mean-square error (RMSE) values for initial numerical models for two mode shapes.
# Code Obfuscation: A Comprehensive Approach to Detection, Classification, and Ethical Challenges
## 1 Introduction
### The Importance of Code Obfuscation in Modern Software Development Process  
The primary objective of code obfuscation is to make the code difficult, if not impossible, to read and understand. By applying various transformations, obfuscation alters the code's physical appearance while preserving its black-box specifications--meaningthe program's functional behavior and input-output interactions remain unchanged. This technique is vital for enhancing security and protecting intellectual property in software development [1]. Another key goal of obfuscation is to prevent reverse engineering and unauthorized access to the underlying logic and algorithms. By code transforming, obfuscation ensures that, while the code retains its functional behavior, it becomes much more challenging to interpret and understand. This practice helps deter malicious actors from exploiting the code and safeguards proprietary algorithms and implementations [1; 2; 6]. Common obfuscation techniques include renaming variables and functions to meaningless identifiers, restructuring the control flow, and inserting redundant or dead code. These transformations increase the code's complexity and reduce its readability, making it harder for attackers to analyze or manipulate the software. By obscuring the code's logic, obfuscation not only protects the software's intellectual property but also enhances its security [2; 6]. Consequently, code obfuscation is essential for safeguarding proprietary algorithms, business logic, and unique software functionalities. By making it challenging for competitors or attackers to decipher these elements, obfuscation helps maintain the software owner's competitive advantage and market position [9]. In industries governed by strict regulations, such as finance and healthcare, code obfuscation plays a crucial role in ensuring compliance with data protection laws and intellectual property regulations. By safeguarding software integrity and preventing unauthorized modification, obfuscation helps firms meet legal requirements related to data security and intellectual property rights [3]. Obfuscation techniques protect sensitive information, such as encryption keys and personal data, embedded within the software. By obscuring data handling processes and storage mechanisms, developers can significantly reduce the risk of data breaches and unauthorized access to critical information. This, in turn, strengthens overall data privacy measures [6; 10]. Obfuscated code resists tampering and unauthorized modifications throughout the software lifecycle, including deployment. Techniques like code obfuscation make it difficult for attackers to alter the software's behavior or functionality without detection. This helps ensure that the software operates as intended, preserving its integrity and reliability [2; 5].
# Evaluating the Capacity Coordination in the Urban Multimodal Transport Network
## 3 Evaluation Functions
### Gini Coefficient of Multimodal Transport Network  
The Gini coefficient was introduced in 1912 as a measure of the inequality of the income distribution in some nation or other society [17]. Its value ranges from 0 to 1. The coefficient approaches 0 as all income differences vanish. It approaches 1 when one person earns all the money, leaving nothing to the remaining inhabitants.  
Travel demand in an urban transport network is continuously increasing, and many components of a multimodal transport network are highly congested. The Gini coefficient has long and widely been used in various fields. Specifically, we propose the Gini coefficient, which measures how evenly the passenger flows are distributed across different sub-networks, to provide a key mathematical criterion for evaluating the disparity in service level. The Gini coefficient of a multimodal transport network indicates that a larger value denotes a greater degree of distribution imbalance. This will also help policymakers to deal with the complexity of urban mobility and undertake more sustainable transport network planning.  
For the urban multimodal transport network, we denote two cumulative frequencies as \(x_{i}\) and \(y_{i}\), respectively. Clearly, \(0\leq y_{i}\leq x_{i}\leq 1\). The two proportions can be linked by calculating both \(x_{i}\) and \(y_{i}\) for several different LOS values. Plotting \(y_{i}\) as the ordinate against \(x_{i}\) for all these points and connecting these points results in the Lorenz curve (see Figure 2), which plays an important role in the equilibrium of passenger distribution on different links.  
The Lorenz curve is the diagonal of the graph in the case of perfect equality. The Gini index is the fraction of the area between the actual Lorenz curve and the one belonging to perfect equality (see the shaded area A in Figure 2) and the area between the two extrema (that is, A + B). Mathematically, the Gini index is G = A/(A + B); hence, the resulting Gini index is 0 under perfect equality and 1 in the case of perfect inequality.  
\[x_{i}=\sum\limits_{j}^{i}\frac{n_{j}}{N^{\prime}} \tag{21}\]  
\[y_{i}=\sum\limits_{j}^{i}\frac{LOS_{j}}{\sum\limits_{j}^{i}LOS_{j}}, \tag{22}\]  
where \(x_{i}\) is the cumulative share of numbers of links whose \(LOS\) is equal to \(LOS_{j}\), \(n_{j}\) is the number of links whose \(LOS\) is equal to \(LOS_{j}\), \(N\) is the number of links on the multimodal transport network, and \(y_{i}\) is the cumulative share of numbers of links whose LOS is equal to \(LOS_{j}\).  
The Gini coefficient of the multimodal transport network is described by the following equation:  
\[G_{ini_{LOS}}=A/(A+B)=1-2\int_{0}^{1}f(x_{i})d(x_{i}), \tag{23}\]  
where \(G_{ini_{LOS}}\) is the Gini coefficient of the link LOS, \(A\) is the graphic area enclosed by the perfect distribution line and Lorenz curve, and \(B\) is the graphic area under the Lorenz curve.
# Dynamic Graph Attention Network for Skeleton-Based Action Recognition
###### Abstract  
Skeleton-based human action recognition has garnered significant attention for its robustness to background noise and illumination variations. However, existing methods relying on Graph Convolutional Networks (GCNs) and Transformers exhibit inherent limitations: GCNs struggle to model interactions between non-adjacent joints due to predefined skeletal topology, while Transformers accumulate noise through unrestricted global dependency modeling. To address these challenges, we propose a Dynamic Graph Attention Network (DGAN) that dynamically integrates local structural features and global spatiotemporal dependencies. DGAN employs a masked attention mechanism to adaptively adjust node connectivity, forming a dynamic adjacency matrix that extends beyond physical skeletal constraints by selectively incorporating highly correlated joints. Additionally, a node-partition bias strategy is introduced to prioritize attention on collaboratively moving body parts, thereby enhancing discriminative feature extraction. Extensive experiments on the NTU RGB+D 60 and NTU RGB+D 120 datasets validate the effectiveness of DGAN, which outperforms state-of-the-art methods by achieving a balance between local topology preservation and global interaction modeling. Our approach provides a robust framework for skeleton-driven action recognition, demonstrating superior generalization across diverse scenarios.  
Keywords:human action recognition; skeleton-based action recognition; graph convolution networks; transformer; graph attention network
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
## 5 Integration WLAN with 5G TSN  
In Sections 3 and 4, we have looked at the technologies that 5 GS and WLAN are researching to provide TSN service as wireless communication systems, respectively. Based on these technologies, it is possible for either the 5 GS or WLAN to deploy an independent TSN network. However, the deployment architecture of the wireless communication system for TSN service may be different according to various types of service requirements in industrial automation environment. If necessary, it will also be possible to deploy a TSN network in which 5 GS and WLAN are configured together. Currently, 5 GS also has a network architecture that can accommodate different access networks (e.g., 5G NR and WiFi access) at the same time to provide various types of services. In this section, we introduce two possible architectures of wireless TSN when 5 GS and WLAN are integrated together. The first case is when WLAN is used as an interface to connect to the TSN node, and the second case is when WLAN is used with 5G RAN within 5 GS. Then, we discuss technical challenges for wireless TSN in the proposed architectures.  
Figure 8: TWT with OFDMA operation.
## 2 Design and Methods
### Structural Analysis Using ANSYS Finite Element Analysis
#### 2.2.1 FEA Model Description  
The objective of the FEA is to identify worst-case stresses and deflections in each design by evaluating multiple angles throughout the device's motion. To simplify the model, the RWTD and patient are represented as point loads with the correct moment of inertia. Revolute joints mimic the actual pivots and rotations of a pin for replicating the linkages' motion, while motor-driven joints are portrayed as fixed joints. Fixed support constraints are applied to the wheels of each device to firmly anchor the model to the ground. Figure 2 outlines the constraints used to evaluate each of the 3 design concepts. These constraints are used as the applied loading and boundary conditions in the FEA, including gravitational acceleration, fixed supports, point loads, and simulated joints, which were used as the applied loading and boundary conditions in the FEA.  
Figure 2: _Cont._  
Figure 2: FEA constraints. Top: constraints for Concept 1—rotational lift; middle: constraints for Concept 2—scissor lift; bottom: constraints for Concept 3—double pivot lift. FEA constraint letters and colorization are listed below: