esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Work
### Sample Selection Bias Mitigation in Time Series  
There is relatively little work in SSB for function approximation algorithms or time series forecasting. In function approximation, the usual approach is to reweight examples [47], and so these are relatives of CSC. The Numeric Smote algorithm [48] instead uses stratification to correct SSB in these problems. SSB also occurs in time series data, affecting time series classification, forecasting, and anomaly detection [49]. Resampling approaches (including SMOTE) have been adapted to the time series domain in, e.g., [50; 51; 52]. More generally, resampling is also one approach for data augmentation, a collection of techniques for perturbing examples from a dataset to create new synthetic examples that help to improve deep neural network training [53]. Interestingly, there appears to be no work to date on using time series forecasting to generate individual synthetic observations, as is performed in our work. Existing work either generates a synthetic example in embedding space, thus producing a completely synthetic delay vector, or produces an entire synthetic times series using generative models (with Generalized Adversarial Networks a common choice) [49].  
The problem with the above is that delay vectors should not be thought of as independent points in a feature space, which we can interpolate between. Each delay vector is a sampled portion of a (reconstructed) state-space trajectory for the system being observed, as per [PERSON] [54]. Other sampled trajectories cannot simply be found by interpolating between two existing ones, particularly if the system's state-space attractor has a complicated geometry (time series datasets are generally not simple quadratic systems, so attractors are not limited to points or limit cycles [55]). Thus, synthetic examples are fairly likely to inject noise into the forecast model, rather than mitigating SSB. Meanwhile, creating a synthetic time series is perhaps appropriate for forecasting problems with dozens or hundreds of variates but would again be a strong noise signal when there are only a few variates being modeled. Adding a synthetic variate might still be helpful in a pure forecasting application (by again expanding the decision regions surrounding actual data points, as in OCAN), but in AD, the goal is to find unusual behaviors. It seems likely that the injected noise points themselves would be identified as outliers, which defeats the purpose of adding them to improve the detection of existing outliers in the dataset.
## 3 Aerospace Engineering Applications  
After the first application of SMA to the hydraulic tubing coupling on the F-14 fighter in 1971, aerospace engineering has long been one of the major fields of research for the use of SMA actuators. From the 1990s to the early 2000s, many SMA-based research programs were carried out such as the \"Smart Wing Project\" and the \"Smart Aircraft and Marine Propulsion System Demonstration (SAMPSON) Program\" for jet engine by DARPA, NASA, and Boeing, Hartl and Lagoudas [81]. As presented in Figure 4, the potential applications of an SMA actuator in aerospace engineering are for structural morphing, engine, landing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Control Type** & **Controller** & **Application** \\ \hline
[68] & \multirow{2}{*}{PID Control} & PWM-based nonlinear PID controller & control the radius of curvature \\ \hline
[69] & & PI controller & position control using compliant differential \\ \hline
[77] & \multirow{2}{*}{Others} & fuzzy logic controller & vibration control of civil structures \\ \hline
[71] & & stiffness and force feedback controller & stiffness control \\ \hline
[70] & \multirow{2}{*}{Passive controller} & passive controller with phase transformation & adaptive absorber for spring-mass system \\ \hline
[73] & & passive controller (multi-modal tuned mass damper) & control of natural frequency \\ \hline
[74] & \multirow{2}{*}{Passive} & passive controller (thermal control method) & vibration control of rotating machines \\ \hline
[75] & & passive controller & sound radiation control \\ \hline
[76] & & passive controller & seismic vibration control of structure \\ \hline
[54] & \multirow{2}{*}{Passive} & passive controller & \\
[54] & & (multi-level SMA/lead rubber bearing isolation) & earthquake control \\ \hline
[79] & \multirow{2}{*}{Passive} & passive controller (tuned mass damper inerter) & vibration control of oscillator \\ \hline
[80] & & passive controller & vibration control of civil structures \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 3: Control of various applications: (**a**) radius of curvature control for steerable cannula [68] Reproduced with permission from SAGE under STM Permissions Guidelines; (**b**) vibration control of civil structures subjected to seismic excitation [80] © IOP Publishing. Reproduced with permission. All rights reserved.  
gear release mechanism, and so on. In this section, the aerospace engineering applications of an SMA actuator in the last decade are described and summarized.
Graphene and metamaterial-based absorbers can be designed to give multiband responses to be applicable in solar applications [12; 21]. A perfect solar absorber is designed for the infrared spectrum using graphene material and metamaterials. The absorption spectra are also tuned to different frequencies in the near-infrared spectrum [22]. The metamaterial absorber uses a metamaterial component as a resonator, which improves the absorption of the solar absorber design [23]. Metamaterials are sometimes combined with graphene material to absorb solar energy and generate highly efficient solar absorbers [24]. Zirconium nitride is combined with metamaterials and graphene to improve solar absorber absorption in the visible and infrared spectrum [25]. The optimization of the parameters is required to improve the system's performance. In this research, the parameters are improved using parametric optimization with a nonlinear parametric optimization algorithm. Nonlinear optimization is utilized in situations in which the function does not behave linearly in relation to the variable being optimized for. Nonlinear optimization is having a function f(x), or a constraint c\({}_{\text{i}}\)(x) = 1, 2,...n or d\({}_{\text{j}}\)(x) = 0, 1,...n, are nonlinear functions of the vector of variables x [26]. Broadband absorbers absorb the majority of the energy in the visible spectrum, as discussed in the literature, and only a portion of the energy in other regions is absorbed, so there is a need for an absorber that absorbs energy from most of the spectrums. We have proposed an absorber that absorbs solar radiation in the visible and infrared spectra. Thus, the efficiency of the solar absorber is increasing. The absorber is designed on a two-layer substrate with a metamaterial resonator. The metamaterial resonator is also placed over the graphene monolayer to improve the overall absorption. The following sections discuss the design and the results of the design.
## 2 Quality Analysis of BeiDou Measurements  
In order to obtain the precise orbits and satellite clock biases of BeiDou satellites to assess the performance of precise positioning, since 2011 Wuhan University has established a continuous worldwide observation reference network, called the BeiDou Experimental Tracking Stations (BETS), which includes nine tracking stations in China and six tracking stations abroad. All the stations are equipped with UB240-CORS receivers produced by the Beijing Unicore Communication Company, which can acquire the pseudo-range and phase observations of dual frequencies and dual systems. The Unicore UA240 dual-frequency dual-system high gain antenna is used in the network. The accuracy of phase observations can reach millimeter level [6]. The GEO C02 and MEO C30 did not work and were discarded [18]. The MEO C13 and C14 are not included as they were still under in-orbit testing. The GEO C15 was also under in-orbit testing and is planned to replace C02. Hence, 11 BeiDou satellites were employed for data processing in this contribution. They were four GEOs including C01, C03, C04 and C05, five IGSOs including C06-C10 and two MEOs including C11 and C12. Figure 1 shows the distribution of BETS around the world and the tracks of satellite points of the BeiDou system. As Figure 1 shows, the ground tracks of the five BeiDou IGSOs are confined from approximately 55\({}^{\circ}\)S to 55\({}^{\circ}\)N latitude and 102\({}^{\circ}\)E to 135\({}^{\circ}\)E longitude. They describe two \"8\" shape loops with a mean longitude difference of roughly 30\({}^{\circ}\), so the eastern and western parts of China and the adjoining regions can be covered effectively. There are four GEOs distributed in the Indian and Pacific oceans over the Equator as supplements for the IGSO satellites to ensure users in Asian-Pacific regions can observe enough satellites. They are located at the 58.75\({}^{\circ}\)E, 80.0\({}^{\circ}\)E, 140.0\({}^{\circ}\)E, and 160\({}^{\circ}\)E longitudes, respectively. The inclination of the orbit of a BeiDou MEO is about 55\({}^{\circ}\), and the orbit height is between that of GPS and Galileo satellites [19]. The revolution period is about 773 minutes, leading to 1.86 revolutions per day. The ground track of the satellite repeats every seven days [4].
# Numerical Study on Redbed Slope Stability Under Multi-Factor Conditions
## 3 Study of the Deformation and Instability Modes of the Slope After Excavation and Backfilling
### Stress Response Analysis  
Figures 5 and 6 display the maximum displacement contours of the slope after excavation. In both scenarios, the principal stress and shear stress decrease as the model height increases, which is consistent with the general stress variation law of slopes under gravitational forces. The maximum principal stress and maximum shear stress both occur at the deepest part of the model's base. The stress distribution and magnitude with the contact surface applied are very similar to those without the contact surface, with both on the order of \(1\times 10^{-6}\) Pa.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Working Condition**} & \multirow{2}{*}{**Monitoring Direction**} & \multicolumn{6}{c}{**Monitoring Point Displacement (mm)**} \\ \cline{3-6}  & & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{3}{*}{Contact surface applied} & X & 0.00294 & 0.061 & 1.56 & 0.959 & 3.23 \\  & Z & 0.0013 & 0.378 & 2.04 & 4.07 & 25.53 \\ \cline{1-1} \cline{3-6}  & X & 0.00241 & 0.00537 & 0.00632 & 0.00461 & 0.0232 \\ \cline{1-1} \cline{3-6}  & Z & 0.00131 & 0.00453 & 0.00469 & 0.00458 & 2.03 \\ \cline{1-1} \cline{3-6}  & X & 0 & 1 & 0 & 4 & 11 \\ \cline{1-1} \cline{3-6}  & Z & 1 & 0 & 1 & 5 & 12 \\ \hline \end{tabular}
\end{table}
Table 4: Displacement at monitoring points.  
Figure 4: Displacement contour plot: (**a**) without contact surface; (**b**) with contact surface.  
Figures 5a and 6a show the shear strain increment contours for both conditions. The sliding instability of the soil and rock mass occurs along the regions of maximum shear strain, which can, to some extent, reflect the location of the potential slip surface. In both cases, the slip surface is located in the vicinity of the first layer's contact surface, near the exposed rock mass, and takes on an incomplete circular arc shape. This observation aligns with the previously mentioned displacement patterns of the slope.  
Figure 5: Stress response contour plot without contact surface: (**a**) maximum shear strain increment; (**b**) maximum principal stress; (**c**) maximum shear stress.
# An Energy Centric Cluster-Based Routing Protocol for Wireless Sensor Networks
## 6 Conclusions  
In this paper, an energy-centric cluster-based routing protocol for wireless sensor networks is proposed. A static clustering is adopted, so that the deployed nodes can save energy, unlike the periodic clustering throughout the network lifetime. The cluster-head election is weighted on the basis of ranks that consider the residual energy and average distance of nodes. This balances the energy consumption of nodes along with saving the energy in intra-cluster communication. Moreover, the adopted piggybacking and hand-over techniques reduce a significant number of control messages regarding the cluster-head competition in each round. Besides in routing, the ECCR consider the major factors of energy consumption, which balances the energy among the cluster heads. It also ensures an energy-efficient route towards the BS. Overall, the proposed algorithm takes into consideration that saving the energy of nodes can achieve and sustain a prolonged network lifetime.  
An extensive simulation is performed to evaluate the performance of ECCR. The selected performance metrics for this analysis is mainly the steady state and network lifetime. The simulation results validate ECCR protocol performed better for the selected metrics as compared to the existing protocols.  
[PERSON] conducted the research, performed the simulations and wrote the paper; [PERSON] supervised the work and revised the article.  
This research was funded by Ministry of Science and ICT grant number IITP-2018-2015-0-00378.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-2015-0-00378) supervised by the IITP (Institute for Information & communications Technology Promotion).  
The authors declare no conflict of interest.
## 5 Discussion
### Geologically Complex Terrain
#### 5.2.2 Topographic Characteristic of Groundwater Potential  
Revised from a previous study [64], the seven categories of terrain are defined as near roof (Rf), at ridge (Rg), steep slope (Ss), flat slope (Sf), valley or creek bottom (Vb), alluvial fan of downstream the valley (Vbf), and main riverbed deposit (Vm), as shown in Table 7. To illustrate those terrains, the TPI, TWI, and SD were implemented. As for SD, classes 1 and 2 belong to Ss, classes 3 and 4 represent Sf, Vb, and Vbf, and class 5 is Vm. Furthermore, Vb and Vbf can be extracted by the class 4 of TWI. Finally, the topography tool of ArcGIS was used to derive the TPI and delineate the geomorphic terrain from ridge to valley (Figure 12) where the areas with TPI values above 28 were defined as Rf and Rg.  
Within the data from the 72 investigated wells, less data on the Rf, Rg, and Ss terrains were available due to the difficulty of drilling and testing. The lower well yields may be located in those terrains as well as Vb. However, other terrains with higher percentage of GWPS included Vm, Vbf, and Sf, the average regolith depths of which were thicker in sequence. In addition, there was a positive correlation between regolith depth and well yield. Thus, for groundwater exploration based on topographic characteristics, the possibility of GWPS was about 44.4% to 76% (Table 7). With integration of RS and GIS, the possibility increased to 84.7% (Table 5). Therefore, another key parameter of GWPS is the characteristics of geology and remote sensing index (e.g., the proposed parameters of HGUs, LD, and VSMI in this study).
# Prediction of Friction Coefficient Based on 3D Texture Characteristics of Road Surfaces
## 2 Measurement Tools and Experimental Methods  
### Traction Watcher One  
Skid resistance was determined by measuring the longitudinal coefficient of friction (\(\mu\)) using the Traction Watcher One (TWO) shown in Figure 1. The TWO is a vehicle-mounted instrument designed to measure fixed-slip friction on roads and runways. It uses a power transmission chain to connect two wheels, creating a precise slip value of 17.8% from the forward reference wheel to the rear measuring wheel. A water pump and control valve supply a water film in front of the measuring wheel. Friction coefficients can be accurately measured at speeds ranging from 10 to 110 km/h. The entire process is managed by the TWO's computer software (version V22.01) which displays real-time data on a monitor and simultaneously saves them to a database. Continuous measurement data can be exported with intervals as small as 0.5 m.  
A smooth test tire and a water film with a thickness of 1.0 mm were used for the experiment. The water film was applied during the experiments to simulate real-world conditions wherein road surfaces are often wet, especially under adverse weather conditions. Measuring friction under wet conditions provides a more comprehensive understanding of how surface textures affect skid resistance, which is critical for safety assessments. The measuring speed for all sections was set to 40 km/h for safety reasons. Ensuring precise localization of measuring spots was crucial for accurate correlations between single-point texture measurements and continuous skid resistance measurements. Therefore, the results' interval was minimized to 0.5 m, and the exact point of the beginning of the measurement section (POB of MS) was marked using a metal sheet attached to the road (Figure 1, middle and right). Figure 2 illustrates how the metal sheet influences friction coefficient values and determines measuring point locations.
## 3 Results
### Optimization of Security Attributes  
In this section, we report optimization of all previously discussed attributes (i.e., Table 4). According to the proposed algorithm, all existing properties are optimized, and the algorithm is concerned with the presence or absence of any single attribute that could be a vulnerable point in the system's design. In order to maximize the overall attributes of a system component and maintain their efficacy against any attack propagation, this method examines each system's component. It determines which of its security propertiesare covered by the components that came before it and which properties can be overlooked. Employing attack propagation scenarios based on **Path 1** and **Path 2**, we demonstrate the effectiveness of the finally optimized attributes. Table 5 illustrates all steps, followed by the algorithm to optimize all security properties defined in our agriculture system model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Protected** & **Incoming Covered** & **Covered** & **Final** \\ \hline \multirow{2}{*}{Sensor} & Authentication, & Authentication, & Authentication, & Authentication, \\  & Authorization & & Authorization & Authorization \\ \hline \multirow{4}{*}{Sensor} & Anomaly & & Anomaly & Anomaly \\  & Detection, & Detection, & Detection, & Detection, \\  & Authentication, & Authentication, & Authentication, & Input \\ Fusion & Input validation, & Authentication & Authorization, & validation, \\  & Secure boot & & Input validation, & Secure boot \\ \hline \multirow{4}{*}{Interface1} & Authentication, & Anomaly & & Anomaly \\  & Authorization, & Detection, & Authentication, & Authentication, \\  & Authorization, & Authorization, & Encrypted, & Encrypted \\  & Encrypted & Input validation, & Input & \\  & Secure boot & & Validation, & \\ \hline \multirow{4}{*}{IoT Gateway} & & Authorization, & Authorization, & Authorization, \\  & Encrypted, & Encrypted, & & Encryption, \\  & Input & & & Output \\  & Validation, & & & Secure \\  & Secure boot, & & & Support \\  & Tapper & & & Protection \\  & Protection & & Protection & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Optimized security attributes in our proposed agriculture system model.
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
## 4 Hops-Based Back-Pressure Routing
### Distributed Routing Algorithm
#### 4.3.1 Queue Management  
Denoting the source and destination as \(s\), \(d\), respectively, when packet \(p\) arrives at the intermediate node \(n\) on link \((a,b)\), the destination hop delay \(H(p)\) between the packet and destination can be calculated. When the first packet arrives at \(n\), a virtual DHD queue with a backlog of \(\hat{Q}_{n}\) is created, and \(H(p)\) is calculated. When packet \(p\) reaches \(n\), \(\hat{Q}_{n}^{c}\) will increase by \(H(p)\). When \(p\) is sent from \(n\) to the next hop, \(\hat{Q}_{n}^{c}\) will decrease by \(H(p)\) correspondingly. The computational complexity of the above process is \(O(1)\).  
In the LSN, assume that the number of nodes is \(N\), and each node has \(R\) neighbors. Thus, DHBP stores a maximum of \(R(N-1)(N-2)\) virtual DHD queues. In the current LEO satellite networks, usually, \(R=4\), as shown in Figure 3. Therefore, each satellite needs to maintain \(O(N^{2})\) virtual DHD queues. According to the above analysis, the storage pressure is acceptable.
## 3 Methodology  
To map the forest vertical structure, a probability map of forest vertical structure was generated by applying artificial neural network to input layers from (1) Sentinel-2 optical image and (2) DTM (NGII DEM) and DSM (WorldDEM) with the training/validation dataset based on the forest vertical map through fieldwork. Probability maps are classified into single, double, and triple vertical hierarchies, taking into account the characteristics of the forest structures in study area. Finally, test data of field survey data was compared to determine the accuracy of classification through the Error Matrix. Figure 3 shows the overall flow of this study.  
Figure 2: Forest vertical structure classification map of study area constructed by the field survey: (**a**) Forest vertical structure, (**b**) dominant species, (**c**) natural and artificial forests, and (**d**) dominant species’ leaf type [35].
## 4 Simulation and Practical Application
### Practical Application  
The intersection of Linyi Road and Gonghe New Road in Zhabei District of Shanghai city is taken as an example to illustrate the practical application of the integrated waiting area. There are six lanes in the south entrance of this intersection. Lanes 1, 4 and 5 are used for the through vehicles, while lanes 2 and 3 are used for the left-turn vehicles. Lane 6 is used for right-turning vehicles.  
By adding a set of LED variable signs, some lanes are turned into \"variable waiting lanes\". As regards the control plan, it is possible to switch the \"integrated waiting area\" between through vehicles and the left-turning vehicles waiting lane. After the implementation of the integrated waiting area, lanes 2 to 4 at the south approach of the intersection are set as the three variable waiting lanes (see Figure 7(b)). Figure 9 illustrates the signal phase sequence with the integrated waiting area. Arriving vehicles can enter the integrated waiting area according to the prompt information indicated by the LED variable sign, and then pass through the intersection according to the phase of the main signal.  
Figure 7: Comparison of average vehicle delays under different volumes.  
Figure 8: Lane assignment at the approaches without and with the integrated waiting area.  
Figure 9: Operating status of peak hour with the integrated waiting area.  
By modifying the number of random seeds in the simulation menu, five simulation experiments were performed with or without the integrated waiting area, and the random seed number was taken as 42, 369, 1578, 5961, and 7171 respectively. Tables 4 and 5 depict the experimental data and results. It is observable that the design of the integrated waiting area at the signalized intersection reduces the travel time and delay spent by the vehicle passing through the intersection. It also improves the efficiency of the intersection. In particular, the improvement of the left-turning vehicle lane is apparent. In addition, the average travel time and vehicle delay decrease by about 18 s, while the improvement range is 24.9% and 31.3% respectively. Meanwhile, it is also apparent that the average travel time and delay of the through vehicles have not decreased as much as that of the left-turn vehicles.