esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Shoulder Line Extraction Method Fusing Edge Detection and Regional Growing Algorithm
###### Abstract  
Shoulder lines can best depict the morphological characteristics of the Loess Plateau. Moreover, a shoulder line depicts the external appearance of spatial differentiation of loess landforms and the internal mechanism of loess landform evolution. The efficient and accurate extraction of shoulder lines can help to deepen the re-understanding of the morphological structure and differentiation of loess landforms. However, the problem of shoulder line continuity in the extraction process has not been effectively solved. Therefore, based on high-resolution satellite images and digital elevation model (DEM) data, this study introduced the regional growing algorithm to further correct edge detection results, thereby achieving complementary advantages and improving the accuracy and continuity of shoulder line extraction. First, based on satellite images, the edge detection method was used to extract the original shoulder lines. Subsequently, by introducing the regional growing algorithm, the peaks and the outlet point extracted with the DEM were used as the growth points of the positive and negative (P-N) terrains to grow in four-neighborhood fields until they reached a P-N terrain boundary or a slope threshold. Finally, the P-N terrains extracted by the regional growing method were used to correct the edge detection results, and the \"burr\" was removed using a morphological image-processing method to obtain the shoulder lines. The experimental results showed that the method proposed in this paper can accurately and effectively complete the extraction of shoulder lines. Furthermore, the applicability of this method is better and opens new ideas for quantitative research on loess landforms.  
satellite images; shoulder line; edge detection; regional growing algorithm; positive and negative (P-N) terrain +
Footnote †: journal: Applied Sciences
# The Circadian Effect Versus Mesopic Vision Effect in Road Lighting Applications
## 3 Results
### \(CL_{A}\) and CS as a Function of the CCT and S/P Ratio  
Figures 4 and 5 show \(\mathrm{CL_{A}}\) and CS as functions of the CCT and S/P ratio, respectively, at 1, 3, 10, 30, and 100 lx. At each illuminance level, both \(\mathrm{CL_{A}}\) and CS increase with the CCT and S/P ratio. However, the \(\mathrm{CL_{A}}\) and CS scatters are divided into two clusters. Both \(\mathrm{CL_{A}}\) and CS increase with illuminance levels. However, despite presenting a similar trend, they are not simple linear relations.  
Figure 3: (**a**) Equivalent melanopic lux (EML) plot against correlated color temperature (CCT) at an illuminance value of 1 lx. In the graph, x represents CCT, y represents EML, and k is the adjusting coefficient equal to the illuminance level, e.g., 1 at a 1 lx level. (**b**) EML plot against the S/P ratio at an illuminance of 1 lx.  
Table 1 shows the EML, \(\rm CL_{A}\), and CS of HPS, with low CCT LED and high CCT LED at various photonic adaptation illuminance levels. HPS, with a minimum CCT of 2238 K and an S/P ratio equal to \(0.8417<1\), shows a minimum EML, \(\rm CL_{A}\) and CS. The low CCT LED shows a moderate level of EML,  
Figure 4: Circadian light (\(\rm CL_{A}\)) plot against the (**a**) CCT and (**b**) S/P ratio.  
Figure 5: Circadian stimulus (CS) plot against the (**a**) CCT and (**b**) S/P ratio.  
CL\({}_{\rm A}\), and CS, which are about 1.5 times that of HPS. The high CCT LED has a maximum EML, CLA, and CS, which are about 2 to 3 times the values of HPS.
# An Information-Theoretic Approach for Detecting Community Structure Based on Network Representation
## 1 Introduction
Compared with the existing methods, the AMI-NRL does not measure the quality of static community structure but judges the stability through the community structure evolution, which gains more accurate results. In addition, by introducing network representation learning for preprocessing, the search space is greatly reduced in terms of revealing community structures at multiple granularities, which brings about higher scalability. The main contributions of this paper are as follows:  
* An average mutual-information-based community evaluation index is proposed, which is applicable to top-down community evolution processes and bottom-up community evolution processes. By calculating the average mutual information and information entropy of adjacent states, the AMI-based community evaluation index can measure the stability of each state of a community evolution process. * An information-theoretic approach based on network representation named AMI-NRL is proposed. This approach combines network representation and the AMI-based community evaluation index to achieve stable and accurate community detection. * Experiments were conducted to verify the accuracy and stability of the approach, in comparison with typical community detection algorithms on real-world and synthetic datasets. The rest of this paper is organized as follows: Section 2 summarizes the current research on community detection. Section 3 introduces and explains in detail the AMI-NRL approach. Section 4 shows the experiments conducted to verify the effectiveness of the algorithm. Section 5 concludes the article and provides a prospect of our research.
# Reactor Temperature Prediction Method Based on CPSO-RBF-BP Neural Network
###### Abstract  
A neural network model based on a chaotic particle swarm optimization (CPSO) radial basis function-back propagation (RBF-BP) neural network was suggested to improve the accuracy of reactor temperature prediction. The training efficiency of the RBF-BP neural network is influenced to some degree by the large randomness of the initial weight and threshold. To address the impact of initial weight and threshold uncertainty on the training efficiency of the RBF-BP combined neural network, this paper proposes using a chaotic particle swarm optimization algorithm to correct the RBF-BP neural network's initial weight and threshold, as well as to optimize the RBF-BP neural network to speed up the algorithm and improve prediction accuracy. The measured temperature of the reactor acquired by on-site enterprises was confirmed and compared to the predicted results of the BP, RBF-BP, and PSO-RBF-BP neural network models. Finally, Matlab simulation tests were performed, and the experimental data revealed that the CPSO-RBF-BP combined neural network model suggested in this paper had a root-mean-square error of 17.3%, an average absolute error of 11.4%, and a fitting value of 99.791%. Prediction accuracy and efficiency were superior to those of the BP, RBF-BP, and PSO-RBF-BP models. The suggested model's validity and feasibility were established. The study findings may provide some reference values for the reactor's temperature prediction.  
BP; RBF-BP; temperature prediction; CPSO-RBF-BP Article
## 6 The 6 GCloHE System Architecture
### Processing Layer  
Cloud computing is a computing model that bestows a pool of configurable resources that can be accessed ubiquitously through the 6G enabled Tactile Internet to provide cloud services such as Software-as-a-Service, Platform-as-a-Service, Infrastructure-as-a-Service, and Database-as-a-Service. Cloud computing is one of the enabling technologies for IoHE  
Figure 14: The 6 GCIoHE system architecture.  
and offers significant benefits to healthcare organizations such as rapid elasticity, self-healing, and self-configuration. It is employed at the processing layer, also called the middleware layer, in the five-layer 6 GCIoHE architecture. In the IoHE, the multitude of sensors generates a massive amount of data that can be stored and analyzed with the help of cloud infrastructure. Medical professionals can monitor a patient's health, whose information is collected through various sensors and is stored in the cloud. Even though the cloud provides many advantages, one of the key challenges is latency. The process of data collection and analysis by the sensors is done through the Internet and needs to reach physicians. This process takes a significant amount of time, which might not be suitable for emergency healthcare services. The ideal solution to reduce time delay is the latency offered by 6G enabled Tactile Internet, or 6G Wi-Fi, or 6G FAW networks.
# Experimental Study on Vibration Control of
## 3 Modal Analysis of the Piping System  
A suspended piping system that is used to transport water was selected as the experiment structure to test the effectiveness of the designed PTMDs. The piping system consists of a galvanized steel pipe, an aluminum beam, two steel bars, and fixtures, as shown in Figure 3a. Two threaded steel bars connect the pipe to the beam by fixtures. The beam in the experiment has the same rigidity as the ceiling in the actual engineering, and there is no deformation on the beam during the movement of the pipe. The length of the pipe is 1133 mm, and the length of the thread steel bars is 195 mm. The total mass of the pipe, bars, and fixtures is 9.3 kg. The detailed dimensions of the piping system are shown in Table 1. In order to understand the dynamic characteristics of the piping system, the finite element (FE) model was developed by ABAQUS, as shown in Figure 3b. According to the results of FE analysis, the first mode of the piping system is lateral movement, and the second mode is longitudinal movement, as shown in Figure 3c.d. It should be noted that due to the softness of the suspension bars  
Figure 2: Schematic of (**a**) tuned mass damper (TMD); (**b**) Impact damper; (**c**) Double-sided bounding tuned mass damper (PTMD); (**d**) Single-sided PTMD.  
compared to the pipe, all of the deformation occurred at the suspension bars, and the pipe did not experience deformation.  
The natural frequencies of the first two modes are 2.54 Hz and 8.14 Hz, respectively. The first mode had low frequency and large displacement; thus, the PTMDs in this paper were designed to control the first vibration mode of the piping system. It should be noted that in practice, FE analysis will help guide the design of the PTMD. In the absence of FE analysis, experimentally-based modal analysis is a viable option. The free vibration of the piping system was tested by applying an initial displacement to the middle of the pipe. The experimental result showed that the frequency of the first mode was 2.55 Hz, and the structural equivalent viscous damping ratio was 0.58% (amplitude from 15 mm to 10 mm) using the logarithmic decrement method. Figure 4a,b show the free vibration history and the fast Fourier transform (FFT) amplitude in the frequency domain, respectively.  
Figure 4: Displacement response and its fast Fourier transform (FFT) amplitude of the pipe (measured by the laser displacement sensor near the middle of the pipe): (**a**) Free vibration response; (**b**) FFT amplitude.  
Figure 3: Piping system model and its finite element model: (**a**) Suspended piping system; (**b**) Finite element (FE) model; (**c**) First vibration mode shape; (**d**) Second vibration mode shape.
# Geolocation and Tracking by TDOA Measurements Based on Space-Air-Ground Integrated Network
## 2 Theoretical Performance
### The Effect of System Errors  
System errors are familiar in the scenario of passive geolocation based on SAGI networks, where accurate synchronization among observers is an impractical assumption. The system errors may include clock synchronization error, position bias of the observers,elevation bias of the target and non-horizontal velocity of the target. In this section, we analyze the effects of different system errors for the emitter modeled in the ECEF coordinate system.  
Introducing Lagrange multiplier \(\mathbf{\lambda}\) to analyze the effect, the cost function is  
\[\xi=(\ddagger-\ddagger^{\varphi}(\theta_{1}))^{\mathrm{T}}C^{-1}(\ddagger- \ddagger^{\varphi}(\theta_{1}))+\mathbf{\lambda}^{\mathrm{T}}G(\theta_{1}) \tag{43}\]  
By using the perturbation method, substituting \(\theta_{1}\) with \(\theta_{1}+\Delta\theta_{1}\) in (43) yields  
\[\xi=(\mathbf{n}-\mathbf{J}_{1}\Delta\theta_{1})^{\mathrm{T}}C^{-1}(\mathbf{n}-\mathbf{J}_{1} \Delta\theta_{1})+\mathbf{\lambda}^{\mathrm{T}}\Big{(}G(\theta_{1})+\mathbf{F}^{ \mathrm{T}}\Delta\theta_{1}\Big{)} \tag{44}\]  
Applying a partial derivative to (44) with respect to \(\Delta\theta_{1}\) and \(\lambda\), we can obtain  
\[\begin{bmatrix}2\mathbf{J}_{1}^{T}C^{-1}\mathbf{J}_{1}&F\\ F^{\mathrm{T}}&0\end{bmatrix}\begin{bmatrix}\Delta\theta_{1}\\ \lambda\end{bmatrix}=\begin{bmatrix}2\mathbf{J}_{1}^{T}C^{-1}\mathbf{n}\\ -G(\theta_{1})\end{bmatrix} \tag{45}\]  
Solving (45) yields  
\[\begin{bmatrix}\Delta\theta_{1}\\ \theta\end{bmatrix}=\begin{bmatrix}2\mathbf{A}_{11}\mathbf{J}_{1}^{T}C^{-1}\mathbf{n}-\mathbf{ A}_{12}G(\theta)\\ 2\mathbf{A}_{21}\mathbf{J}_{1}^{T}C^{-1}\mathbf{n}-\mathbf{A}_{22}G(\theta)\end{bmatrix} \tag{46}\]  
where \(\mathbf{A}_{11}\) and \(\mathbf{A}_{12}\) are defined as  
\[\begin{bmatrix}\mathbf{A}_{11}&\mathbf{A}_{12}\\ \mathbf{A}_{21}&\mathbf{A}_{22}\end{bmatrix}=\begin{bmatrix}2\mathbf{J}_{1}^{T}C^{-1}\mathbf{ J}_{1}&F\\ F^{\mathrm{T}}&0\end{bmatrix}^{-1} \tag{47}\]  
Hence, the corrected estimate of \(\Delta\theta\) is  
\[\Delta\theta_{1}=2\mathbf{A}_{11}\mathbf{J}_{1}^{T}C^{-1}\mathbf{n}-\mathbf{A}_{12}G(\theta_{1}) \tag{48}\]  
If there are no system errors, \(E(\mathbf{n})=0\), \(G(\theta_{1})=0\). The localization accuracy in (48) is  
\[\mathrm{MSE}(\theta_{1})=4\mathbf{A}_{11}\mathbf{J}_{1}^{T}C^{-1}\mathbf{J}_{1}\mathbf{A}_{11 }^{\mathrm{T}} \tag{49}\]  
It can be shown that (49) is equivalent to (25) when \(\mathbf{I}_{1}=\mathbf{J}_{1}^{T}C^{-1}\mathbf{J}_{1}\) is reversible. If system errors are non-negligible, \(E(\mathbf{n})\
eq 0\), \(G(\theta_{1})\
eq 0\), the mean of \(\Delta\theta_{1}\) is biased. The localization accuracy will be degraded by the system errors.
# Resilient Formation Reconfiguration for Leader-Follower Multi-UAVs
###### Abstract  
Among existing studies on formation reconfiguration for multiple unmanned aerial vehicles (multi-UAVs), the majority are conducted on the assumption that the swarm scale is stationary. In fact, because of emergencies, such as communication malfunctions, physical destruction, and mission alteration, the scale of the multi-UAVs can fluctuate. In these cases, the achievements of formation reconfiguration for fixed-scale multi-UAVs are no longer applicable. As such, in this article, the formation reconfiguration problem of leader-follower multi-UAVs is investigated with a variable swarm scale taken into consideration. First, a streamlined topological structure is designed on the basis of the parity of the vertex numbers. Then, three formation reconfiguration strategies corresponding to the scenarios covering leader disengagement, follower detachment, and new member additions are developed with the aim of reducing the frequency of connection changes. Moreover, in terms of the leader election link of the leader disengagement scenario, a knowledge-based performance assessment model for UAVs is constructed with the help of the hierarchical belief rule base (BRB). Finally, the proposed formation reconfiguration strategies for leader disengagement, new member addition, and follower disengagement are demonstrated through simulations. The connection retention rate (CRR) for swarm communication topology under the three formation reconfiguration strategies can reach 67%, 90%, and 100%, respectively.  
leader-follower multi-UAVs; formation reconfiguration; performance assessment; hierarchical belief rule base +
Footnote †: journal: Applied  
doi:10.3390/app1317385
## 4 Deployment Scenario and Prototype
### Application Layer  
The application layer is developed using a TV viewing platform. The execution platform is based on the Android operating system running the application on an external device and generating an output that is redirected to the television. The application is developed by taking into account the characteristics of the display device, which has features such as: large screen size, horizontal orientation, and limited interaction devices from a remote or small keyboard with a pointing device. For this, the design of the user interface has two vertical menus, placed on the left and right. The application layer performs various data processing tasks in order to generate information that may be displayed to the user. As an example, we could include the use of a paddle court. The motion sensor placed in the court records information on motion detected continuously, and the application uses this information to determine whether the track is occupied, if motion has been recorded in the last five minutes. The SmartResidents interface can be seen in Figure 7.  
As can be seen in Figure 7, the application displays a satellite map located geographically where each sensor appears represented by an identifying icon, next to the last value obtained. This map allows the user to view a visual representation of the location of each sensor. The same information is shown in two vertical navigable menus on the left and right of the map. By processing the data supplied by the logic layer, the application layer interprets the data in accordance with the user interface, in order to display it in a more attractive way. For example, the state of the paddle tennis court is represented by an icon containing a red light if it is busy and green if it is not. Another example is the air quality icon showing different colors, which reflect the data collected: green for excellent values, yellow for good values, orange for warning, and finally red for dangerous values.  
Through vertical options from menus and icons on the map, you can access a second screen as shown in Figure 8, through which the application provides more specific statistical data on each of the measurements. Through the form, you can set filter parameters such as date range and time scale. The figure shows a graph with the evolution of temperatures on 16 October 2016, with a time scale of 10 min.  
Figure 7: SmartResidents interface.  
As Android is a platform available on many mobile devices, including smartphones and tablets, the application may have been adapted to the particularities of these device interfaces, while the application logic is the same as for a television.
## 4 Results and Analysis
### Future Work and Potential Improvements  
Although the status-byte-assisted RDMA transmission mechanism demonstrates significant advantages over the existing methods in optimizing multi-task video streaming, there are several aspects that warrant further exploration and improvement.  
One area for future research is optimizing status byte management. While the status-byte-assisted RDMA transmission mechanism effectively manages memory blocks and improves transmission efficiency, the handling and determination of status bytes might increase system overhead. Therefore, future research could explore more efficient algorithms for managing status bytes to reduce memory and computational resource consumption. For instance, investigating methods to dynamically adjust the frequency of status byte updates could optimize the system's performance. Additionally, integrating advanced technologies such as machine learning to predict and optimize status byte behavior may lead to the development of more intelligent and autonomous systems. This integration could not only enhance performance but also minimize the need for manual intervention in data transmission management.
## 3 Simulation Examples
### Residual STFT-Based Mode Shape Extraction
#### 3.2.5 Effect of Noisy Disturbances  
As shown in the definition of Equation (39), three white noises with levels of 1%, 10%, and 20% are added to the acceleration responses of the three trailers' contact points to investigate the effect of noisy disturbances.  
\[\ddot{u}_{n}=\ddot{u}_{s}+N\mathit{l}\sigma_{s}R_{N} \tag{39}\]  
where the noisy acceleration \(\ddot{u}_{n}\) is constructed from acceleration \(\ddot{u}_{s}\) added to the uniformly distributed vector \(R_{N}\) with the noise level \(N_{l}\) and standard deviations \(\sigma_{s}\) of \(\ddot{u}_{s}\).  
With a velocity of 5 m/s and a Class A road profile, the undamped beam case from Section 3.2 is used. As shown in Figure 12, it is clear that the deviations of the mode shapes are boosted by the increasing noise level. Under 1% noise, the mode shapes are close to the undamped case in Figure 7 and could match the analytical ones. When the noise level increases to 20%, the corresponding MAC values could still be larger than 0.9. For the noise levels of 10% and 20%, the large derivations from the analytical mode shapes appear mainly at the peaks.  
Figure 11: The first three beam mode shapes using residual STFT-based extraction under different beam damping properties with traffic flow are compared to the analytical first mode shape (**a**), the second mode shape (**b**), and the third mode shape (**c**), as well as the MAC values provided in brackets.
## 2 Materials and Methods
### Plasma Diagnostic Methods
#### 2.2.3 Electron Density  
The electron density (n\({}_{\mathrm{e}}\)) is the primary plasma parameter describing the discharge operation regime (pulse corona 10\({}^{8}\)-10\({}^{11}\) cm\({}^{-3}\) > transient glow 10\({}^{12}\)-10\({}^{13}\) cm\({}^{-3}\) > streamer \(10^{14}\)-\(10^{15}\) cm\({}^{-3}\) > spark \(10^{16}\)-\(10^{18}\) cm\({}^{-3}\)) [35; 36]. In this work, n\({}_{\rm e}\) was determined using the current-voltage method utilizing the measured electrical characteristics and plasma geometry as described elsewhere [37]. For this, the electrical field strength (E/N) was estimated using the measured voltage and the discharge gap, while the neutral particles density (n) was calculated, considering T\({}_{\rm gas}\) measured via OES as follows:  
\[\rm n=\frac{P}{kT_{gas}}, \tag{2}\]  
In Equation (2), P is the gas pressure, and k is the Boltzmann constant. Accordingly, n\({}_{\rm e}\) was estimated as  
\[\rm n_{e}=\frac{J}{ev_{e}\frac{E}{N}}=\frac{J}{eE\mu_{e}},\ J=\frac{I}{S} \tag{3}\]  
where J is the current density in A.m\({}^{-2}\), S is the cross-section of the discharge in m\({}^{2}\), e is the elementary charge in C, E is the electrical field in V\(\cdot\)m\({}^{-1}\) (calculated experimentally), v\({}_{\rm e}\) is the electron drift velocity in m s\({}^{-1}\), and \(\mu_{\rm e}\) is the electron mobility in m\({}^{2}\cdot\)Vs\({}^{-1}\). J was calculated, considering a streamer diameter of 150 \(\upmu\)m [38], while the electron mobility was estimated using a BOLSIG+ solver [39].