esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Study of Energy Savings for Various Control Strategies in the Tunnel Lighting System
## 3 Materials and Methods
### Hardware Structure of Tunnel Lighting Control System  
The tunnel facilities were installed with fully automated and centralized electromechanical services using SCADA (Supervisor Control and Data Acquisition) technology to implement intelligent control and monitor the tunnel lighting system. The advantages of the SCADA tunnel lighting system are briefly described below:  
Control and operate lighting system remotely: Connect to the remote SCADA monitoring system to provide even greater control and visibility. Optimize lighting throughout your tunnel: Decrease the black hole effect to maximize safety with L20 tunnel controls explicitly built for LED lighting technology. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Tunnel** & **Length (m)** & **Tunnel Type** \\ \hline
1 & 720 & Medium-long \\
2 & 660 & Medium-long \\
3 & 1018 & Long \\
4 & 1270 & Long \\
5 & 1557 & Long \\
6 & 1770 & Long \\
7 & 2024 & Long \\
8 & 2250 & Long \\
9 & 2520 & Long \\
10 & 2910 & Long \\
11 & 3215 & Extra-long \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Basic characteristics of the eleven examined tunnels. Figure 5: Lighting zones of a road tunnel. Figure 6 depicts the hardware schematic of the SCADA tunnel lighting system. As shown in this figure, a SCADA tunnel lighting system involves LED luminaires, LED dimming controllers, communication infrastructure, remote terminal units, servers, surveillance of roadways, real-time collection of outside luminance data, and monitoring of air quality and visibility levels inside and outside the tunnel. Traffic management equipment consists of microwave detectors, infrared detectors, and surveillance cameras. Microwave and infrared detectors are used to monitor the tunnel's traffic flow, which helps regulate lighting. Surveillance cameras are used to maintain a continuous flow of traffic information, which helps in decision-making during emergencies, such as accidents or car stopping. The surveillance cameras are installed every 150 m in the tunnel. Environmental monitoring equipment includes a luminance meter and visibility and CO/VI detectors. The daylight sensor is used to measure the ambient luminance to determine the luminance level of tunnel sections. The visibility and CO/VI detectors monitor the atmospheric visibility inside and outside the tunnel. the programmable logic controllers (PLCs) collect traffic and environmental sensor data and perform a diagnostic of the lighting system.
# Reinforcement Learning Based Query Routing Approach for P2P Systems
## 3 RLQR Approach  
In this section, we introduce a new machine learning approach for query routing in unstructured P2P systems. We first formalize the query routing issue in unstructured P2P systems as a reinforcement learning problem then we thoroughly describe our approach.
# Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network
## 2 Principle and Methods
### Fatigue Crack Growth Theory
According to crack growth Equation (1), the theoretical formula for predicting residual life is as follows:  
\[N=\int_{a_{0}}^{a_{c}}\frac{da}{C(\Delta K)^{m}} \tag{3}\]  
[PERSON] considered the influence of stress amplitude on crack propagation and incorporated the stress ratio \(R\) into the Paris' law, referred to as the Walker law [27], as follows:  
\[\frac{da}{dN}=\text{C}\big{(}(1-R)^{\alpha}\Delta K\big{)}^{m} \tag{4}\]However, these laws have some limitations, and the accuracy is different under different crack conditions. For the complex crack conditions in practice, which law can obtain more accurate results is a problem to be solved.
# Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map
## 1 Introduction
If straight propagation waves are classified from measured waveforms, AE source localizations do not require consideration of the diffraction and refraction waves. The self-organizing map (SOM) [20] is one of the unsupervised learning methods, and relations of measured data are visualized. In the previous study, classified artificial AE signals by the SOM were used in the source localizations based on ray tracing [21] and improved the accuracy of localized sources generated in the heterogeneous velocity distributions [22]. However, the source localizations based on ray tracing select candidates of AE sources, wherein they are set in an analytical model. Since the TDOA method does not require the use of the candidates, it is expected that the TDOA method localizes sources more accurately in comparison with the application of ray tracing in the use of straight propagation. In the use of classified waves, used sensors which measure straight propagations are selected, and it is implied that the number and the location of used sensors are not same as each localization. It is expected that the limited sensors contribute to a divergence of the computation in the TDOA method, and the application of ray tracing localizes a larger number of sources than results of the TDOA method. In this study, classified waves were applied to two of the source localizations, which are the TDOA method and the application of ray tracing, and the model tests based on pencil-lead breaks (PLBs), which are widely used in the study of the source localization to generate artificial AE sources [23; 24; 25; 26], were conducted to validate the performance of the source localizations with classified waves. Furthermore, the results of the validation discuss the localization accuracy, the number of visualized sources to evaluate, and which method is preferred for applying to classified waves. Moreover, results of the SOM rely on a selection of the used data, and it is supposed that the classification accuracy depends on the AE signals used in the SOM. Thus, the selection of the used AEs in the SOM is validated to evaluate the localization accuracy.
# Landslide Displacement Prediction Based on Variational Mode Decomposition and GA-Elman Model
## 4 Application Research and Method Comparison
### Monitoring Data Processing
#### 4.1.2 Selection and Decomposition of Influencing Factors  
The trend displacement of the landslide is related to the internal factors of the landslide. The rock and soil composition of the landslide, changes in the internal stress, and changes in  
Figure 6: Decomposition results of the displacement data after processing.  
geometric shape, along with other factors, influence the trend displacement of the landslide; moreover, these factors often change over time. Therefore, the main factor influencing the trend displacement of the landslide is the monitoring time. It can be seen from Figure 5 that the cumulative displacement increased to varying degrees with the amount of precipitation. A large increase in cumulative displacement occurred when precipitation was abundant, and displacement slowed down when precipitation was relatively low, which shows that the cumulative displacement increased more obviously in the rainy season. In addition, according to related research [47], the reservoir water level also has obvious periodicity. When the water level in the reservoir area tends to fall, the water pressure applied to the surface of the slope decreases and the support capacity of the slope is weakened, accelerating the deformation of the landslide; at these times, the accumulated displacement of the landslide appears to rise rapidly. When the water level in the reservoir area rises, the water pressure acting on the surface of the slope is greater than the force of the slope sliding outwards, inhibiting landslide deformation and slowing down the increase in cumulative displacement. The above analysis demonstrates that precipitation and reservoir levels are important external factors that influence landslide deformation.  
On the basis of previous research [38], four factors were selected as those influencing periodic displacement: the displacement increment last week (P1), the cumulative precipitation this week (P2), the average elevation of the reservoir water level this week (P3), and the variation range of the reservoir water level this week (P4). For the VMD decomposition of influencing factor data, the decomposition mode number \(K\) was set first. Considering that there was no possibility of trend items being present in the list of influencing factors, K = 2 was set during the decomposition. The decomposed component with a large proportion and low frequency was taken as the periodic term component of the influencing factors. The decomposed component with a relatively small proportion and high frequency was taken as the random term component of the influencing factors. The effect of decomposing the influencing factors is shown in Figure 7.  
Figure 7: _Cont._
# A Hardware-Efficient Vector Quantizer Based on Self-Organizing Map for High-Speed Image Compression
## 4 Experimental Results and Discussion
### Comparisons  
Performance comparisons between the proposed vector quantizer and the previous VQ-based compression circuits implemented in FPGA are conducted in this Section. Table 2 summarizes the comparison results. To make the data comparable, all of the parameters in Table 2 are calculated under the same condition. Particularly, the grayscale Lena with \(512\times 512\)-pixel is employed as the test image.  
As listed in Table 2, the values of PSNR are approximately equal for [11] and this work, and the higher value of PSNR for [13] is mainly due to the lower compression ratio of [13]. Besides, our proposed circuit seems to consume more LUTs than [11] when the parameter \(k\) is set as 32. However, this is merely caused by the large degree of block parallelism. Once we modulate the number of sub-blocks to 16, the proposed quantizer realizes a reduction of \(8\%\) in hardware usage even though its compression speed is \(33\%\) higher than that of [11]. In this way, a lower rate of hardware usage than [11] can be easily reached despite a higher compression speed than that of [11] being kept. The higher hardware efficiency of this work principally stems from the re-usage of arithmetic units in SDU and RCBAT. Moreover, Table 2 reveals that our vector quantizer achieves a higher compression speed than [13], even though [13] requires fewer clock cycles than our design for the minimum distance searching. This condition partially caused the high latency that introduced by the indispensable register file and other complicated modules in [13], but primarily arises from the high parallelism of our design. Finally, owing to the utilization of PVCS, the compression ratio of our proposed circuit is adjustable while those of the previous works are fixed.
## 4 Dynamics Simulation and Configuration Optimization Method
### Vibration Suppression Effects of Different Positions  
As indicated in Figure 10, the movement of the AGV in the \(x_{0}\)\(x_{0}\)\(y_{0}\) allows the robot to clamp the same workpiece in different configurations. In addition to the movement along the direction of \(y_{0}\) in Figure 10, it can also move along the direction of \(x_{0}\), which provides great freedom for the choice of the clamping robot configuration. Although the positions of the coordinate system \(o_{0}\)\(x_{0}\)\(y_{0}\)\(z_{0}\) and \(o_{\text{end}}\)\(x_{\text{end}}\)\(y_{\text{end}}\)\(z_{\text{end}}\) are constant, the \(x_{\text{end}}\) and \(y_{\text{end}}\) of the clamping robot changes. Moreover, the height of the end \(z_{\text{end}}\) changes when facing the workpiece at different positions in space. Therefore, it is necessary to comprehensively analyze the vibration suppression effect of different \(x_{\text{end}}\)\(y_{\text{end}}\)\(z_{\text{end}}\).  
Similarly, based on the conditions in Section 4.1, a simulation is conducted to analyze the influence of the configuration changes caused by \(x_{\text{end}}\)\(y_{\text{end}}\)\(z_{\text{end}}\) on vibration responses, and the results are depicted in Figure 11.  
Similar to the presentation in Figure 9, the three axes are \(x_{\text{end}}\), \(y_{\text{end}}\), and \(z_{\text{end}}\), and the figure is composed of six faces too. However, the difference is that the vibrational response caused by the \(x_{\text{end}}\)\(y_{\text{end}}\)\(z_{\text{end}}\) changes more regularly. The overall response showed a decreasing trend from the boundary of \(x\) = 0.5 m and \(z\) = 0.4 m to the boundary of \(x\) = 1.5 m and \(z\) = 1.4 m. It can be intuitively seen from Figure 11 that as the configuration of clamping robot changes, the overall responses of the workpiece vary from 46 m/s\({}^{2}\) to 60 m/s\({}^{2}\), and the change range reaches 14 m/s\({}^{2}\). Compared with \(RxRyRz\), the change in \(x_{\text{end}}\)\(y_{\text{end}}\)\(z_{\text{end}}\) has a more obvious effect on the vibration suppression of the clamping robot. In addition, the responses of the workpiece decrease with the increase in \(z_{\text{end}}\), which is consistent with the result shown in Figure 9. Additionally, the effect of \(y_{\text{end}}\) change on vibration responses is not as obvious as that of \(x_{\text{end}}\) and \(z_{\text{end}}\). This is because the robot is  
Figure 11: Overall responses of the workpiece under different \(x_{\text{end}}\)\(y_{\text{end}}\)\(z_{\text{end}}\).  
Figure 10: Configuration change in the clamping robot under different AGV positions.  
approximately symmetric along \(y_{\text{end}}=0\), and the configuration change caused by the of \(y_{\text{end}}\) is not as obvious as that of \(x_{\text{end}}\) and \(y_{\text{end}}\). In short, the law in Figure 11 shows that the closer the workpiece is to \(o_{B}x_{B}y_{B}z_{B}\), the worse the vibration suppression effect of the corresponding configuration of the clamping robot is.
## 3 Results  
No systematic error was found. The random error for the linear measurements ranged from 0.08 mm (F) to 0.26 mm (E) and for the angular measurements ranged from 0.29\({}^{\circ}\) (element 64) to 0.43\({}^{\circ}\) (element 53). As reported in Table 1, the analysis of the starting forms showed no statistically significant differences at T1 between the groups for all measurements. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**CAG**} & \multicolumn{2}{c}{**RG**} & \multirow{2}{*}{**Vai**} & \multirow{2}{*}{**Vai**} & \multirow{2}{*}{**Vai**} & \multirow{2}{*}{**Vai**} \\  & \multicolumn{2}{c}{**(\(\textbf{{n}}\) = 15; 8 F; 7 M)**} & & & & & & \\ \hline  & **Mean** & **SD** & **Mean** & **SD** & **Diff** & **SD** & **95\% CI** & \(\textbf{\emph{p}}\) Value** \\ \hline \multicolumn{10}{l}{**Linear measurements (mm)**} \\ \hline Inter-canine width (III-III) & 31.6 & 1.2 & 30.5 & 2.8 & 1.1 & 0.8 & \(-\)0.494 to 2.694 & NS \\ \hline First inter-deciduous molar width (IV-IV) & 37.7 & 1.3 & 37.6 & 2.5 & 0.1 & 0.7 & \(-\)1.369 to 1.569 & NS \\ \hline Second inter-deciduous molar width (V-V) & 43.6 & 2.3 & 43.6 & 2.7 & 0.0 & 0.9 & \(-\)1.824 to 1.824 & NS \\ \hline First inter-molar mesial width (6-6 mesial cusps) & 49.6 & 3.2 & 49 & 2.3 & 0.6 & 1 & \(-\)1.394 to 2.594 & NS \\ \hline First inter-molar distal width (6-6 distal cusps) & 52 & 2.8 & 51.5 & 2.6 & 0.5 & 1 & \(-\)1.450 to 2.450 & NS \\ \hline First inter-molar transpalatal width (6-6 transpalatal) & 34.7 & 2.9 & 33.3 & 3.3 & 1.4 & 1.1 & \(-\)0.857 to 3.657 & NS \\ \hline Inter-canine transpalatal width (III-III) & 25.2 & 1.1 & 24.2 & 2.1 & 1 & 0.6 & \(-\)0.236 to 2.236 & NS \\ \hline First inter-deciduous molar transpalatal width (IV-IV) & 27.3 & 1.4 & 26.3 & 2.3 & 1 & 0.7 & \(-\)0.398 to 2.398 & NS \\ \hline Second inter-deciduous molar transpalatal width (V-V) & 31.1 & 2.1 & 30.2 & 2.5 & 0.9 & 0.8 & \(-\)0.780 to 2.580 & NS \\ \hline \end{tabular}
\end{table} 
Table 1: Starting forms for linear and angular measurements between Clear aligners and RME groups. Figure 4: Crown angular measurements: For both left and right sectors, a para-coronal plane (A) was obtained perpendicularly to the occlusal plane (B). A curve passing through the first molar long axis was drawn and a best-fit line was set using the most occlusal and the most gingival points of the curve as reference (C). Tooth inclination was obtained from the angle formed between the best fit line of each tooth and the para-coronal plane [14]. Transverse discrepancy at the start of the treatment resulted mild of 5.1 \(\pm\) 0.6 mm in RG group and of 4.6 \(\pm\) 0.3 mm in CAG group. The pre-treatment value of the transpalatal arch width was of 34.7 mm for the CAG and of 33.3 mm for the RG and therefore within normal limits for both groups with a dento-alveolar origin of the transversal constriction. Tables 2 and 3 report inter-group T2-T1 linear and angular differences.
## 3 Finite Element Simulation Method  
In the TPS method, the double helical nickel strip embedded within the Kapton layer functions as both the core component in the actual experimental testing method and the key element in the simulation construction. The position of the sensor in the main body of the testing device is depicted in Figure 1b. The complete test system is illustrated in Figure 1a, which includes a stable adjustable power supply, a constant current output source, a voltage collector, a pressure sensor, a detector body, and a computer. The stable adjustable power supply is responsible for converting alternating current (AC) into direct current (DC) voltage, ensuring a consistent power supply. Meanwhile, the constant current output source can deliver a stable current ranging from 0 to 1 A. The voltage acquisition system is designed to collect the electric potential signals resulting from changes in the sensor's resistance due to temperature variations. These signals are then used to produce the temperature change data using Ohm's law and Equation (4). The pressure sensor measures the pressure within the enclosed chamber, providing crucial data for the analysis. The computer processes the collected data and performs fitting calculations to derive meaningful results. The sensor is strategically positioned between two identical samples, forming a sandwich structure, as shown in Figure 1b. In the numerical simulation, the same experimental parameters are applied, allowing for a precise replication of the physical setup. This approach ensures that the finite element method accurately models the sensor's behavior under test conditions, providing valuable insights into the sensor's performance and enabling the prediction of its response in various scenarios. By analyzing the actual device, we can validate the simulation results, thereby enhancing the reliability and applicability of the finite element model in studying the thermal properties of different materials.
## 4 Recent Advancements in Applications
### Adaptability of Different MMSs in Various Applications
Future developments should prioritize modular, scalable architectures and robust real-time processing capabilities to address the unique demands of each application--from high-accuracy urban mapping to rapid disaster assessments. By balancing cost, precision, and operational constraints, MMSs can continue to evolve as indispensable tools for geospatial innovation.
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 1 Introduction
Current research has shown that, while theoretical models for network integration exist, practical applications, especially those involving UAVs, often encounter issues with efficient handover mechanisms and robust link budget management [24; 25]. Our manuscript extends this discussion by analyzing the link budget and handover and mobility for UAVs within a 6G satellite-terrestrial framework. This study leverages recent advancements and identifies shortcomings in existing systems to enhance both the theoretical understanding and practical implementation of integrated networks. The communication system for UAVs and the data links are visualized in Figure 1 below. The introduction of satellite-terrestrial networks could enhance the range of operations of UAVs, especially in remote regions. [PERSON] et al. [26] carried out link budget analysis for 5G communication systems, considering only ground base stations. This paper further extends their study by introducing satellite networks. The contributions of this paper are as follows:  
* A detailed link budget analysis for UAV communication within satellite-terrestrial networks. This analysis considers unique factors such as UAV altitude adjustments and atmospheric conditions, which are crucial for ensuring reliable and robust communication links. * The paper proposes a handover strategy that facilitates seamless network transitions for UAVs. * Utilizing theoretical models and simulations, the study evaluates the performance of satellite network under various weather scenarios. Choosing link budget and handover performance analysis for 6G satellite-terrestrial networks targeting UAV operations addresses crucial connectivity needs. Link budget analysis ensures that communication links between UAVs and satellites have adequate signal strength for effective communication, which is crucial for UAVs operating at various altitudes and speeds. Handover performance analysis maintains continuous service as UAVs travel through their designated path, which is crucial for minimizing disruptions during transitions. These areas are interlinked, as a robust link budget underpins stable connections essential for smooth handovers, thus improving network reliability and service continuity for UAVs in dynamic environments.
# Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
## 3 RPL Parent Selection Background  
This section aims to provide the necessary background on RPL, as our design builds upon and enhances the RPL protocol. It lays the groundwork for understanding the enhancements introduced by our RL-based approach. RPL is a distance vector routing protocol that is adapted to a variety of Low-Power and Lossy Networks (LLNs). The protocol constructs a destination-oriented directed acyclic graph (DODAG) [8], which is a multi-hop routing tree rooted at single root node, that in the context of AMI is usually named a data aggregation point (DAP) or simply collector. Figure 1 shows the routing hierarchy within RPL. The root node initiates the network by transmitting a control message, called a DODAG Information Object (DIO), which includes rank information. Each node in the network, upon receiving the DIO, computes its ranking and selects a parent to form a loop-free topology. This makes RPL a highly scalable protocol that can accommodate the large and growing number of nodes in an AMI communication network. The selection of parent nodes within the DODAG is governed by an Objective Function (OF). The OF is responsible for defining the criteria used to evaluate potential parent nodes, thereby influencing the route optimization and network dynamics. Two standardized OFs