esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Feasibility of Construction Plan
### Design of Numerical Simulation  
To avoid the occurrence of joint failure and groundwater seepage, lots of the measures can be adopted during the actual project to reduce the deformation of existing rectangular pipe-jacking tunnels. Based on some previous cases, interlayer soil grouting and steel support reinforcement are the most effective methods. A total of four schemes for numerical simulation were devised. Scheme 1 entails no reinforcement measures and is abbreviated as \"No Reinforcement\". Scheme 2 involves interlayer soil grouting reinforcement, as illustrated in Figure 6a. Scheme 3 utilizes steel support reinforcement, depicted in Figure 6b. Scheme 4 combines both grouting and steel support reinforcement and is referred to as \"Comprehensive Reinforcement\". The parameters of the shield machine should be adjusted appropriately. The chamber pressure and grouting pressure of the shield machine are the most important factors, as the parameters usually change during shield tunneling. In this paper, the parameters of the shield machine are not considered.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Stata**} & \multirow{2}{*}{\begin{tabular}{c} **Thickness** \\ **(m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Paisson’s Ratio** \\ **Poisson’s Ratio** \\ \end{tabular} } & \begin{tabular}{c} **Angle of Friction** \\ **(°)** \\ \end{tabular} & \begin{tabular}{c} **Elastic Modulus** \\ **(MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Cohesion** \\ **(kPa)** \\ \end{tabular} &
\begin{tabular}{c} **Density** \\ **(kN/m3)** \\ \end{tabular} \\ \hline Plain fill & 3.51 & 0.33 & 35 & 12 & 7 & 19.2 \\ Gravel & 7.53 & 0.31 & 34 & 32 & 0 & 20.6 \\ Silty clay & 19.2 & 0.34 & 15 & 13 & 32 & 19.6 \\ Grouting & 1.5 & 0.32 & 41 & 154 & 78 & 22.5 \\ reinforcement & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculation parameters of strata.  
Figure 6: Illustrations of different reinforcement approaches. **(a)** Grouting reinforcement; **(b)** steel support reinforcement.
# Aero-Engine Remaining Useful Life Estimation Based on CAE-TCN Neural Networks
## 2 Methodology
### Complete RUL Prediction Algorithm  
To further improve the overall application efficiency, a novel process of the RUL prediction is introduced. A state detector is built for automatic data filtering and labelling. Besides, for multiple operating conditions, new features are extracted and added by clustering. The diagram of the complete RUL estimation algorithm is displayed in Figure 5.  
First, with the z-score function, data preprocessing is applied to normalize the data into 0-1. To extract the features of operating conditions, parameters are clustered with the K-means++ algorithm. Then, a state detector is built with several data points before and after the whole life. With the state detector, the first data predicted as a failure is taken as the degenerate initial point to label the RUL. Moreover, through the interpretative analysis of the modeling process, the degradation sensitivity of each parameter is obtained. The parameters with low sensitivity are removed. Finally, the efficient neural network proposed is built to achieve the learning of the RUL prediction.  
Figure 4: The RUL estimation model structure.
# Multi-WiIR: Multi-User Identity Legitimacy Authentication Based on WiFi Device
## 5 Experiment Results
### Experimental Setup  
As there was no publicly available dataset for multi-user based legitimacy authentication, we curated our own dataset for validation purposes. The experimental setup is depicted in Figure 9.  
Figure 9: Experimental environment.  
In our study, we employed a TP-Link router as the transmitter and a computer equipped with an Intel 5300 NIC as the receiver. The implementation was carried out using the Python 3.8 programming language in conjunction with the PyTorch framework. The training process took place on a server equipped with an NVIDIA 3060 GPU. Each batch during training comprised 64 samples, and the model underwent training for a total of 100 epochs. For detailed information regarding the experimental equipment and parameters, please refer to Table 1.  
The experiment involved data collection from 12 volunteers, comprising 7 males and 5 females aged between 20 and 30 years. We collected data from 5 legitimate individuals, each contributing 200 entries as single-user data. Additionally, data collection was also conducted on legitimate and illegitimate combinations of different user counts, with each group comprising 180 records, resulting in a total of 3580 records. For example, for a triple-user combination, we collected data for three scenarios: all three being legitimate users, two legitimate users with one illegitimate user, one legitimate user with two illegitimate users, and three illegitimate users. Each scenario involved the collection of 180 records. Regarding the determination of legitimacy or illegitimacy, we considered that as long as there was at least 1 single-user in the crowd who was legitimate, then it could be considered that these people, with the permission of the legitimate individual, were also legitimate. The basic information of the volunteers is provided in Table 2. During the experiment, the volunteers maintained a distance of 0.8 m and performed stepping movements with a natural posture for approximately 5 s each time.
## 3 Reference Case Study
### Numerical Model and Field Monitoring
#### 3.2.2 Field Monitoring  
A water level logger was buried between the antiseepage wall and the house foundation to measure the change in the groundwater level. The water level logger is located 6.5 m from the top of the ancient dike, and the depth is 12.46 m below the ground. It monitored the change of groundwater level behind the ancient dike. The groundwater level data was extracted from June to July 2021 and is shown in line 2 of Figure 9. The buried water level logger and the extracted water level data are shown in Figure 12. In order to get the seepage stability of the ancient dike under the action of the transient water level, the typical locations A\({}_{1}\) and A\({}_{2}\) of the numerical model were taken to study. Points A\({}_{1}\) and A\({}_{2}\) are located on both sides of the impervious core. Point A\({}_{1}\) is located 1.1 m above the foundation behind the ancient dike, that is, at 25.90 m elevation. And point A\({}_{2}\) is 2.2 m horizontally away from point A\({}_{1}\), with the same elevation at point A\({}_{1}\).
# Seismic and Geodetic Crustal Moment-Rates Comparison: New Insights on the Seismic Hazard of Egypt
## 8 Geodetic Moment-Rates  
As mentioned above in the following computation, we refer to the results recently published by [PERSON] et al. [61]. For each of the previously defined seismic source zones, the geodetic moment-rate was estimated by adopting the [PERSON] and [PERSON] [20] formulation:  
\[\hat{M}_{geod}=2\mu H_{s}A[Max(|\varepsilon_{H\max}|,|\varepsilon_{h\min}|,| \varepsilon_{H\max}+\varepsilon_{h\min}|)] \tag{15}\]  
where \(\mu\) is the shear modulus of the rocks (a constant value of 3.0 \(\times\) 10\({}^{10}\) N/m\({}^{2}\) for the crustal rocks is used for all sources), _Hs_-_A_ is the volume of the source zone with _Hs_ representing its seismogenic thickness (D90, as stated before); \(\varepsilon_{H\max}\) and \(\varepsilon_{\min}\) are the principal horizontal strain-rates (Figures 6 and 7) as defined in [PERSON] et al. [61], and _Max_ is a function returning the largest of the arguments. Strain-rates values are almost homogeneous for most of the seismogenic source zones; however, additional considerations are required for the EG-03, EG-08, and EG-10 zones. In particular, the strain-rate estimation for EG-03 source is only concentrated on the western side of the region, while no estimations are available for the eastern side. Besides, strain-rates values are not homogeneous for EG-10 either, with values increasing southward. Due to this factor, the largest obtained strain-rate value in each source has been considered in the geodetic moment-rate computation. Furthermore, the north-western corner of EG-08 is characterized by large strain-rates, which could represent a computational boundary effect (due to stations located in Cyprus), being these values larger with respect to the well-constrained ones of the southern sector of the zone. Therefore, for this source, the largest strain-rate value used in the computation has been estimated by excluding the values inferred for the north-western area.  
Keeping in mind these considerations, geodetic moment-rates for the EG-03, EG-08, and EG-10 sources should be considered with caution. Parameters and achieved results are reported in Table 3. Estimated values range in the interval 2.64 \(\times\) 10\({}^{17}\)-4.93 \(\times\) 10\({}^{18}\) N\(\cdot\)m/yr, with high values inferred for EG-05 and EG-10 seismic source zones, while low values were computed for sources EG-01 and EG-09.
## 3 Dependable Wireless System
### Sharing Method of Shortened Information  
To provide high error correction capability to the receiver and low error correction capability to the cybererrorist, the sender and the receiver need to share the shortened information securely. We propose to use distance information between integrated terminals as the shortened information. Distance information is measured by UWB which performs ranging and positioning with high resolution in the order of centimeters because it communicates over a wide band using pulses with short time intervals in the order of nanoseconds [29]. Therefore, it can be treated as confidential information that can be only known between integrated terminals in a practical wireless system.  
In June 2020, IEEE 802.15.4z, a standard of UWB with an enhanced reliability ranging method, was approved [30]. IEEE 802.15.4z specifies three ranging methods: single-sided two-way ranging (SS-TWR), double-sided two-way ranging (DS-TWR), and time difference of arrival (TDOA). Here, DS-TWR, in which distance information can be shared between the terminals, will be described using Figure 5.  
First, a message frame is sent from terminal A to terminal B. Terminal B then sends a message frame to terminal A. Terminal A measures the round-trip time, \(R_{a}\), and if the re  
Figure 4: Parity check matrix of systematic RA code. The column weight \(q\) is 3, the row weight \(r\) is 3.  
Figure 5: Operation of double-sided two-way ranging with three messages.  
Figure 3: Structure of systematic repeat-accumulate (RA) code encoder.  
\(D_{b}\), is known, Equation (6) can be used to calculate the propagation time \(\hat{T}_{f}\). The distance between terminal A and terminal B is obtained by multiplying \(\hat{T}_{f}\) by the speed of light. This is SS-TWR.  
\[\hat{T}_{f}=\frac{1}{2}(R_{a}-D_{b}) \tag{6}\]  
The disadvantage of SS-TWR is that when the reply time becomes large, the measurement accuracy degrades due to the influence of the clock error between terminals A and B. Therefore, a message frame is further sent from terminal A to terminal B, and the effect of the clock error between terminals A and B is minimized by calculating using Equation (7). This is DS-TWR.  
\[\hat{T}_{f}=\frac{R_{a}R_{b}-D_{a}D_{b}}{R_{a}+D_{a}+R_{b}+D_{b}} \tag{7}\]
## 3 Case Studies
### From Survivable to Resilient Topologies: 2-Connected Graphs
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Invariants** & **(a) Ring** & **(b) Wheel [37]** & **(c) Improved Solution (AGX)** \\ \hline Nr. of edges & 14 & 26 & 26 \\ Max. degree & 2 & 13 & 4 \\ Average degree & 2 & 3.71 & 3.71 \\ Degree variance & 0 & 6.63 & 0.20 \\ Diameter & 7 & 2 & 3 \\ Average distance & 3.77 & 1.71 & 2.04 \\ Wiener index & 343 & 156 & 186 \\ Max./Min. edge betweenness & 24.5/24.5 & 10/2 & 12/6.5 \\ Max./Min. node Wiener impact & 70/70 & 130/0 & 6/0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: 2-connected topologies (14 nodes). node pairs become unreachable after a single node removal). On the other hand, in our optimized topology, any node removal still produces a connected graph. Moreover, this topology minimizes the Wiener impact for our problem, and has very special resilience properties, because it is a twin graph, with working and backup paths of equal length for all nonadjacent node pairs [39]. This can be verified by the fact that both the maximum and minimum node Wiener impact are zero, meaning that after any node removal there is still another path of equal cost that can replace a path that contains the faulty node. The only drawback of our optimized solution is the increase of the number of edges from 16 to 22, which also affects the average degree. All other invariants are maintained or improved, including the average distance (from 2.72 to 2.49). In particular, our method heavily improves the topology resilience and provides a much more adequate topology for C-RAN requirements, which justifies the cost of adding extra edges.
# Event Knowledge Graph: A Review Based on Scientometric Analysis
## 2 Background Knowledge
### Data Acquisition  
To effectively extract events and event relationships, multiple data sources have been used in the existing studies. They can be obtained through downloading directly, web crawling, and application programming interfaces (APIs). Table 1 lists 10 public datasets that can be directly downloaded for event knowledge graph construction, including the year when they were first available, the research fields they support, the language they are in, and a brief introduction. The datasets cover a wide range of fields, including disasters, network security, and news forums. The majority of them are textual data, which involve abundant information for entity and relationship extraction to compose event knowledge graphs. Despite that, the event information, such as occurring time, occurring place, and event content, can be extracted from news websites (e.g., BBC News, Sina News, and Sohu News) by leveraging web crawling technologies. As mobile phones and Internet of Things (IoT) devices emerge, people are able to post what they witness and experience on various platforms (e.g., social media platforms like Twitter and Sina Weibo) anytime and anywhere. As such, both real-time and historical event-related information can be collected with certain criteria (e.g., setting spatial extent or keywords) through the provided APIs  
Figure 2: An overall workflow of constructing an event knowledge graph.  
either for free or at a cost. The collected data lay a solid foundation for event knowledge graph construction, providing abundant spatial, temporal, and semantic sources to extract event entities and relations.
## 4 Results
### Performance Evaluation Metrics
#### 4.1.4 Average Channel Utilization (\(\overline{U}_{j}\))  
Equation (40) could perhaps be used to obtain the average channel utilization for the \(j\)th communication channel (\(\overline{U}_{j}\)). The level of a particular communication channel's occupancy is measured by channel utilization. When the channel usage score is high, it means that the network uses the channel frequently. A lower number for channel usage is preferred.  
\[\overline{U}_{j}\ =\ \frac{100}{\mathcal{K}}\sum_{m=1}^{\mathcal{K}}\frac{ \textit{total channel utilization time per data cycle}}{\textit{total time of the data cycle}} \tag{40}\]  
The quantity \(\mathcal{K}\) in Equation (40) denotes the sum of data collection cycles.
# Aerodynamic Simulation of Helicopter Based on Polyhedron Nested Grid Technology
## 2 CFD Simulation Method
### Grid System  
In this paper, the polyhedron nested grid method is used to simulate the unsteady flow field of the helicopter. In this method, the flow field is usually divided into several regions, and each region is meshed separately. These mesh regions can overlap, nest, or overlay each other. In this way, the difficulty of mesh generation is reduced. The key technology is to build the connection relationship between the mesh regions by \"hole mapping\". When it comes to the fluid problem of the body moving with time, there is also relative motion among corresponding grid blocks, so the nested grid method should be used at each time step respectively to build the relation between different mesh regions, which can be used to transport the messages at the interface needed for the CFD simulation. There are three steps to implement the overset mesh method: hole mapping, contributing cell search, and interpolation. It should be noted that in the process of generating overlapped grids, in order to ensure the interpolation accuracy at the interface of grid blocks, sufficient overlap, almost equal mesh size, and low inclination must be ensured. At the same time, the interface of the grid blocks must avoid the large physical gradient area in the flow field.  
Figure 2: Blade motion nested mesh. (**a**) Grid section of the foreground area. (**b**) The boundary layer region of the foreground grid section. (**c**) Assembly system of the nested grid.  
In order to verify the simulation technique proposed in this paper, numerical simulations are carried out on the hovering condition of the Caradona-Tung (C-T) rotor, the forward flight condition of the AH-1G rotor, the rotor/fuselage interaction flow field of Robin, and the hover and forward flight conditions of coaxial rotor. In addition, the calculated results are compared with the test data.
## 4 Results and Discussion
### Results of Turbulence Models
#### 4.6.2 Spalart-Allmaras  
Figure 16 depicts the force coefficients error ratios and Table 18 contains the mean error ratios under different AOA ranges. The optimal results are (0.00%, 0.00%), (0.98%, 1.73%), (2.98%, 2.13%), (5.03%, 1.76%), (5.61%, 1.77%), and (6.67%, 1.55%), and the corresponding simulation configurations are 20 m+200 formula, 12 m+200 formula, 20 m+264 formula, 20 m+132 formula, and 20 m+400 formula. At the range of (0\({}^{\circ}\), 2\({}^{\circ}\)), despite the \(C_{l}\) of 12 m+NACA4 being the same as that of 12 m+200 formula, which is the smallest, the \(C_{d}\) of those configurations is somewhat large. Considering the \(C_{d}\) and \(C_{l}\) comprehensively, 12 m+200 formula is suggested. At the range of (0\({}^{\circ}\), 4\({}^{\circ}\)), similarly, the result of 20 m+264 formula is the best. At the range of (0\({}^{\circ}\), 6\({}^{\circ}\)), the result of 20 m+132 formula has the minimum \(C_{d}\) while 20 m+264 formula has the minimum \(C_{l}\). Because the \(C_{l}\) is a magnitude greater than the \(C_{d}\), 20 m+264 formula behaves best.
## 3 Results and Discussions
### Effect of Arrangement of Square Inclusions  
In addition to the effect of number, sizing, and orientation of inclusions on the band gaps generated by the phononic crystal, the effect of the arrangement of the inclusions was investigated. A phononic crystal with four-square inclusions and a filling fraction of 0.2 is considered in this section. The location of the inclusions was varied by changing the distance \(r\) from the center of the unit cell to the center of the inclusion, as shown in Figure 6a. Figure 6b,d show that the locations of the inclusions significantly affect bandwidth of the bandgap. When the \(r\) is greater or less than the 0.3535 m, corresponding to the \(r\) in Figure 6c, the bandwidth is reduced. This study indicates that the gap width among inclusions is critical for controlling the bandwidth. A similar conclusion can be found in Section 3.1 for phononic crystals with high filling fractions.
# Deep Learning Methods in Image Matting: A Survey
## 7 Challenges and Future Directions  
### Constructing Large-Scale Datasets  
Datasets play a critical role in deep learning methods, and high-quality datasets such as COCO [41] and Imagenet [75] have made a significant impact on related research fields. However, due to the laborious process of annotating alpha mattes, most publicly available matting datasets are constructed using synthetic techniques. These involve synthesizingdifferent images by combining foregrounds with different backgrounds to effectively expand the dataset. However, the limited variety of foreground types and the introduction of artefacts in the synthesized images can result in poor learning outcomes, leading to overfitting and a reduced generalization ability in neural networks.  
Furthermore, due to the expensive production costs, current hand-annotated image matting datasets focus on a single category, typically humans or animals. Natural multi-class image matting datasets are therefore rare. Some recent studies, such as GFM, have proposed methods to improve image synthesis that address issues such as resolution and noise. These studies have also constructed unique background datasets to enhance the quality of synthetic datasets.  
We believe that proposing new data augmentation and synthesis strategies to overcome the limitations of synthetic datasets in image matting is crucial. Additionally, exploring how advanced image generation algorithms can assist in constructing matting datasets is an important area for future research. In summary, building large-scale high-quality image matting datasets that include diverse foregrounds and backgrounds as well as precise alpha mattes is necessary to advance the field of image matting.
# Reliability Analysis of an Air Traffic Network: From Network Structure to Transport Function
## 3 Reliability Measurement of the Air Traffic Network  
An air traffic system is a complex system. Influenced by human, equipment, and environmental factors, the reliability of air traffic networks is constantly changing. This requires us to evaluate the reliability from a dynamic perspective. As far as we know, to date, research on traffic reliability has been carried out only from a static perspective. This work is the first to evaluate traffic reliability from a dynamic perspective. Thus, we have to study air traffic reliability not only from the perspective of the current static reliability but also from the perspective of future dynamic reliability. We construct the calculation models of the CORN, TTRN, and CARN considering both the current reliability and its future evolution trends.