esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Performance of an Aircraft Landing
## 3 Results of Dynamic and Static Analyses
### Results of the Dynamic Model  
In this section, the results of the dynamic model of the lock mechanism based on the Lagrange equation, which are shown as light gray lines in Figure 5, are compared with those of the static bifurcation analysis. The dynamic calculation process is described as follows. During locking, a locking moment \(M\) is applied to the hook. After stabilization of the mechanism, the stable angle \(\theta_{1}\) of linkage 1 is obtained. Then, the magnitude of \(M\) is changed at specific steps to continue the computation, thereby obtaining the variation curve of \(\theta_{1}\) with respect to M. Similarly, the variation curve of \(\theta_{1}\) with respect to the unlocking force \(F_{Uunlock}\) can be obtained during unlocking.  
It can be seen from Figure 5 that the stable equilibrium points of the bifurcation analysis model agree well with the results of the dynamic calculations, however there are no evident unstable equilibrium points (dotted line) in the dynamic results. Notably, the results of the dynamic analysis show that linkage 1 jumps at a certain angle, but the dynamic model cannot explain the underlying principle of this phenomenon. According to the static bifurcation model, the lock bifurcates at the unstable equilibrium point, which causes the lock to jump from the unstable state to the stable state.
## Sluge++: A Secure Over-the-Air Programming Scheme in Wireless Sensor Networks  
**[PERSON], [PERSON] * and [PERSON]**  
Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 JB, Malaysia;  
E-Mails: [EMAIL_ADDRESS] (F.D.); [EMAIL_ADDRESS] (I.F.I.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +6-075-532-370.  
_Received: 12 December 2013; in revised form: 26 January 2014 / Accepted: 28 January 2014 / Published: 11 March 2014_  
**Abstract:** Over-the-air dissemination of code updates in wireless sensor networks have been researchers' point of interest in the last few years, and, more importantly, security challenges toward the remote propagation of code updating have occupied the majority of efforts in this context. Many security models have been proposed to establish a balance between the energy consumption and security strength, having their concentration on the constrained nature of wireless sensor network (WSN) nodes. For authentication purposes, most of them have used a Merkle hash tree to avoid using multiple public cryptography operations. These models mostly have assumed an environment in which security has to be at a standard level. Therefore, they have not investigated the tree structure for mission-critical situations in which security has to be at the maximum possible level (e.g., military applications, healthcare). Considering this, we investigate existing security models used in over-the-air dissemination of code updates for possible vulnerabilities, and then, we provide a set of countermeasures, correspondingly named Security Model Requirements. Based on the investigation, we concentrate on Seltge, one of the existing over-the-air programming schemes, and we propose an improved version of it, named Seltge++, which complies with the Security Model Requirements and replaces the use of the inefficient Merkle tree with a novel method. Analytical and simulation results show the improvements in Seltge++ compared to Seltge.  
**Keywords:** wireless sensor networks; Seltge; Merkle tree; over-the-air programming; security
## 2 Results
### Spatial and Temporal Dynamic Distribution of Grassland AGB in Xinjiang
#### 2.2.3 Spatial Distribution of Grassland AGB in Xinjiang from 2000 to 2022  
The spatial distribution of grassland AGB in Xinjiang from 2000 to 2022 is shown in Figure 4a. The high AGB values in the northwestern Yili region, due to its warm climate and abundant rainfall, were suitable for alpine meadows, montane meadows, and temperate meadows. High AGB values were also distributed in mountainous regions such as the Altai and western Tianshan Mountains. The Junggar Basin was dominated by desert grasslands, and its climate was arid and water-scarce with low AGB. The Tarim Basin, which has a large lowland meadow area, had a high AGB. Furthermore, a high AGB was observed in northern Xinjiang. However, AGB was lower in southeastern Xinjiang. In summary, AGB in Xinjiang showed a spatial distribution of high in the northwest and low in the southeast. Figure 4b shows that high CV values were mainly distributed in the Yili River Valley region in the northwest, the Tarim Basin peripheral area, and the northern part of Xinjiang, indicating that grasslands in regions with higher AGB had greater volatility.  
The change in grassland AGB in Xinjiang from 2000 to 2022 is shown in Figure 5. The overall trend of AGB had an increasing trend. The growing trend of grassland AGB accounted for 61.19%, of which the significant growth accounted for 16.15%, mainly in the Tarim Basin's peripheral area and southeastern Xinjiang. The area of the decreasing trend in grassland AGB was 38.81%, which is located in the northwest of Xinjiang. Figure 5c shows that the trend of non-significant increase accounts for the highest proportion, ranging from 40.29% to 58.51%. Among the different grassland types, the lowland meadow had the highest proportion of increasing trends (79.50%), while the montane meadow accounted for the smallest proportion (46.21%). The proportion of increasing trends in most grassland types exceeded 50%.  
Figure 4: Spatial distribution of the mean (**a**) and coefficient of variation (**b**) of grassland AGB from 2000 to 2022.  
Figure 5: (**a,b**) Spatial distribution of grassland AGB trends and significance tests in Xinjiang from 2000 to 2022. (**c**) Statistical data on AGB trends for different grassland types.
# Dual Clustering-Based Method for Geospatial Knowledge Graph Partitioning
## 2 Methods
### Geographic Node Partitioning Based on Density Peak Clustering
#### 2.1.2 Cluster Center Selection and Node Agglomeration  
To determine the cluster centers, the DPC algorithm typically involves manually selecting the cluster centers according to a decision diagram, as shown in Figure 3. However, this manual selection reduces the efficiency of DPC and introduces subjectivity into the results. Therefore, the strategy proposed by [PERSON] et al. [54] is adopted for determining the cluster centers. First, nodes where both the local density \(\rho\) and the relative distance \(\delta\) are greater than the remaining 80% are selected as candidate nodes. Then, the candidate nodes are ranked in descending order based on the product value of \(\delta\) and \(\rho\) (\(\gamma=\delta\times\rho\)). A linear fit line is drawn according to the \(\gamma\) values, and the fitted value \(\gamma^{\prime}\) is calculated. The difference between \(\gamma\) and \(\gamma^{\prime}\) is obtained for each node (\(\Delta\gamma=\gamma-\gamma^{\prime}\)). The nodes with \(\Delta\gamma\) greater than the average \(\Delta\gamma\) are treated as candidates. Finally, cluster centers are consecutively selected from the front to the back of the ranked candidate sequence.  
After the centers of clusters are determined, non-cluster center nodes are directly assigned to the cluster of the nearest node with a higher density than themselves. To ensure the spatial correlation of geographic nodes, the nodes within each cluster are merged into a super-node. The weight of the super-node is set as the sum of the weights of the nodes contained within the cluster. The edges between a cluster are merged into a single edge, with the edge weight set as the sum of the weights of the edges. For instance, nodes A, B, C, and D are merged into the orange super-node, and node I, J, and K are merged into the purple super-node (Figure 4). The weights of the orange super-node and purple super-node are 4 and 3, respectively. For example, the weight of the edge between the orange super-node and node G is set as the sum of the weights of BG and DG.
## 3 Experimental Results
### Recommending Interface Quality Parameters  
Events data collected with ECPM contained dozens of parameters regarding an observed event and the related HTML element. Factors regarding user behavior in recommendation interfaces as well as their features and product page attributes were extracted. Collected rows contained the following fields: layout type of the recommendation interface (horizontal/vertical), _rc_layout_; text length in the recommended products section, _recommended_length_; text length inside all text elements visible on the page, _document_length_; time between webpage load and unload events, _page_time_; total time when browser tab enclosing the monitored page was active, _tab_active_time_; total time when user was interacting actively with the webpage (registered on the basis of generated mouse and/or keyboard events), _user_active_time_; time when mouse pointer was located inside recommending interfaces, _prod_recommended_time_; time when the pointer was placed over recommending interfaces in relation to text length inside recommendation. _rel_recommended_time_recommended_length_; time when the pointer was placed over recommending interfaces in relation to text length contained inside all text areas in the page, _rel_recommended_time_document_length_; time when the pointer was placed over recommending interfaces in relation to time between webpage load and unload events, _rel_recommended_time_page_time_; time when mouse pointer was placed over recommending interfaces in relation to time when browser tab enclosing the monitored page was active, _rel_recommended_time_tab_active_; time when mouse pointer was placed over recommending interfaces in relation to time when user was interacting actively with the webpage (registered on the basis of generated mouse and/or keyboard events), _rel_recommended_time_user_active_.  
Simplified layouts of all studied e-commerce websites are presented in Figure 1. Komputronik.pl e-store was not taken into account in further analyses as it contained recommending interfaces only in vertical layout.  
\begin{table}
\begin{tabular}{c c} \hline
**Star Rating** & **Number of Ratings** \\ \hline ******** & 415 \\ \hline **** & 346 \\ \hline **** & 325 \\ \hline **** & 180 \\ \hline **** & 130 \\ \hline \end{tabular}
\end{table}
Table 1: Product ratings distribution.
## 3 Materials and Methods
### Strategies for Combining Optimization Algorithms
#### 3.3.5 Mutation Operator  
The mutation operator in all population generation procedures consists of modifying individuals' genes using a Gaussian random number. The mutation we use has a certain chance of altering an individual's genes, thereby improving the diversity of the population. The mutation operator proposed in this paper is described in the following Algorithm 1:  
```
1. Mutate-individual
2. inputs: x: the individual's genes
3. mutProb: the individual's chance to be mutated
4. mutGain: control factor for genes mutation chance
5. \(\sigma^{2}\); variance
6. outputs: x*: the resulted individual
7.
8. // check if the individual should be mutated or not
9. if mutProb < rand() then
10. x* \(\leftarrow\) x // no mutation performed
11. else
12. // check which genes should be mutated
13. geneMutProb \(\leftarrow\) mutGain/noGenes
14. foreach gene in x
15. if geneMutProb > rand() then
16. x*[gene] \(\leftarrow\) x[gene] + Gaussian(gene, \(\sigma^{2}\))
17. return x*
```  
**Algorithm A1**The mutation operator.  
The terms used in Pseudocode 1 are described as follows:  
* _mutProb_ represents the probability that the mutation operator is applied to the given individual;
* _mutGain_ represents a factor that controls the mutation probability of each gene;
* _Gaussian(gene, \(\sigma^{2}\))_ is a Gaussian random number with a mean equal to the gene value and variance \(\sigma^{2}\);
* _rand()_ is a uniformly distributed random number in the range [0, 1). The parameter values we use in the mutation operator are _mutProb_ = 0.5, _mutGain_ = 2 and \(\sigma\) = 1.
# Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation
## 5 Results and Analysis  
In this section, the proposed WFQ continual-DRL as the adaptive algorithm is evaluated in terms of optimizing bandwidth in the WFQ system. The experiments were implemented in Python 3.10 using PyTorch version: 2.6.0, a library for numerical computation in Python. TensorFlow is an open-source library facility for the computation and training of deep learning models. The network scheduling algorithm, represented as WFQ, manages the flow of packets across multi-queue systems. In this paper, the effectiveness of the proposed approach is evaluated through experiments conducted in an environment with the first eight and then twelve queues. A summary of the key parameters used in the implementation is given in Table 1.  
The holding cost is \(h_{i}=\frac{1}{n}\), and the penalty for packet loss is \(c_{i}=1\), where \(i=1,2,\ldots n\), and \(n\) is the number of queues. The reward function is represented as:  
\[r(q,a)=-\sum_{i=1}^{8}\left(\frac{1}{n}\ q_{i}+l_{i}\right)\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Learning rate \(\eta\) & \(\eta_{\pi}=10^{-4}\), \(\eta_{\mathbb{Q}_{1}}=10^{-3}\), \(\eta_{\mathbb{Q}_{2}}=10^{-3}\) \\ \hline Update rate \(\tau\) & \(10^{-2}\) \\ \hline Discount factor \(\gamma\) & 0.99 \\ \hline Entropy coefficient \(\alpha\) & 0.2 \\ \hline Regulation coefficient \(\Lambda_{\text{EWC}}\) & \(\lambda_{\text{EWC}}\) for eight queues = 2271, \(\lambda_{\text{EWC}}\) for twelve queues = 2275 \\ \hline Reply buffer & eight queues = 10,000, twelve queues = 30,000 \\ \hline Batch size & eight queues = 128, twelve queues = 512 \\ \hline Optimizer & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key parameters of experimentally implemented.
# Learning Accurate Pseudo-Labels via Feature Similarity in the Presence of Label Noise
## 4 Experiments and Discussion
### Results and Discussion
#### 4.2.1 Results on Simulated Noise Datasets
\begin{table}
\begin{tabular}{l l c c c c c c c} \hline \multicolumn{2}{c}{**Noise Type**} & \multicolumn{2}{c}{**Symmetric**} & \multicolumn{2}{c}{**Pair flip**} & \multicolumn{2}{c}{**Tridiagonal**} & \multicolumn{2}{c}{**Instance**} \\ \hline \multicolumn{2}{c}{**Setting**} & \multicolumn{1}{c}{**20\%**} & \multicolumn{1}{c}{**40\%**} & \multicolumn{1}{c}{**20\%**} & \multicolumn{1}{c}{**40\%**} & \multicolumn{1}{c}{**20\%**} & \multicolumn{1}{c}{**40\%**} & \multicolumn{1}{c}{**20\%**} & \multicolumn{1}{c}{**40\%**} \\ \hline \multirow{6}{*}{MNIST} & CE & 94.66 \(\pm\) 0.12 & 79.15 \(\pm\) 0.30 & 89.07 \(\pm\) 0.20 & 64.96 \(\pm\) 0.29 & 91.67 \(\pm\) 0.14 & 70.32 \(\pm\) 0.58 & 90.15 \(\pm\) 0.25 & 69.24 \(\pm\) 0.41 \\  & GCE & 98.99 \(\pm\) 0.14 & 98.46 \(\pm\) 0.12 & 99.16 \(\pm\) 0.11 & 98.75 \(\pm\) 0.13 & 99.12 \(\pm\) 0.09 & 99.03 \(\pm\) 0.14 & 98.28 \(\pm\) 0.17 & 97.97 \(\pm\) 0.21 \\  & APL & 99.54 \(\pm\) 0.03 & 99.21 \(\pm\) 0.04 & 99.16 \(\pm\) 0.04 & 84.27 \(\pm\) 0.28 & 99.48 \(\pm\) 0.03 & 97.14 \(\pm\) 0.06 & 97.63 \(\pm\) 0.73 & 87.90 \(\pm\) 1.94 \\  & ELR & 99.22 \(\pm\) 0.13 & 98.97 \(\pm\) 0.16 & 99.09 \(\pm\) 0.1 & 98.99 \(\pm\) 0.14 & 99.09 \(\pm\) 0.09 & 99.06 \(\pm\) 0.15 & 99.05 \(\pm\) 0.11 & 98.94 \(\pm\) 0.17 \\  & MSLC & 99.07 \(\pm\) 0.11 & 98.85 \(\pm\) 0.14 & 99.57 \(\pm\) 0.10 & 99.33 \(\pm\) 0.10 & 99.64 \(\pm\) 0.12 & 99.35 \(\pm\) 0.14 & 99.58 \(\pm\) 0.14 & 99.41 \(\pm\) 0.16 \\  & CDR & 94.27 \(\pm\) 0.35 & 75.76 \(\pm\) 0.94 & 87.40 \(\pm\) 0.94 & 62.83 \(\pm\) 1.57 & 91.40 \(\pm\) 0.45 & 70.13 \(\pm\) 1.16 & 89.62 \(\pm\) 0.53 & 67.46 \(\pm\) 1.59 \\  & PES & 99.57 \(\pm\) 0.04 & 99.49 \(\pm\) 0.05 & 99.62 \(\pm\) 0.02 & 99.58 \(\pm\) 0.03 & 99.69 \(\pm\) 0.01 & 99.54 \(\pm\) 0.03 & 99.51 \(\pm\) 0.02 & 99.42 \(\pm\) 0.04 \\  & MLC & 98.80 \(\pm\) 0.04 & 98.47 \(\pm\) 0.07 & 98.94 \(\pm\) 0.05 & 97.36 \(\pm\) 0.06 & 98.91 \(\pm\) 0.01 & 98.54 \(\pm\) 0.03 & 98.94 \(\pm\) 0.03 & 98.42 \(\pm\) 0.05 \\  & CTRR & 98.96 \(\pm\) 0.24 & 98.16 \(\pm\) 0.34 & 99.29 \(\pm\) 0.52 & 99.06 \(\pm\) 0.47 & 99.01 \(\pm\) 0.35 & 98.94 \(\pm\) 0.26 & 98.83 \(\pm\) 0.28 & 98.14 \(\pm\) 0.32 \\  & FPL & **99.71 \(\pm\) 0.01** & **99.71 \(\pm\) 0.02** & **99.72 \(\pm\) 0.02** & **99.70 \(\pm\) 0.02** & **99.73 \(\pm\) 0.02** & **99.70 \(\pm\) 0.02** & **99.6 \(\pm\) 0.01** & **99.52 \(\pm\) 0.02** \\ \hline \end{tabular}
\end{table} 
Table 4: Average test accuracy (%) on four simulated noise datasets over the last 10 epochs. The best mean results are in bold. \begin{table}
\begin{tabular}{l l c c c} \hline  & **Parameter** & \(\kappa\) & \(\beta\) & \(\gamma\) \\ \hline \multirow{3}{*}{im-MNIST} & Symmetric & & & \\  & Pair flip & & \\  & Tridiagonal & & \\  & Instance & & \\ \hline \multirow{3}{*}{im-SVHN} & Symmetric & & \\  & Pair flip & & \\  & Tridiagonal & & \\  & Instance & & \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._
## 5 Conclusions  
The present work reviews the LB-MPC technique and its application in the field of mobile platforms for path tracking control. The LB-MPC and its two components, namely, MPC and ML, are summarized. According to the relevant literature and research results, the application of the LB-MPC in path tracking is classified, and the characters and advantages of the application is explained. Under uncertain environmental disturbances, the data-driven prediction models obtained by LB-MPC can better adapt to complex situations. In controller design, the parameterized version of the LB-MPC problems and offline training samples are introduced to ensure control precision and real-time response. Moreover, combined with the security control framework, the controller output can be optimized in path tracking control. This work also highlights the current research challenges of prediction model interpretability and accuracy, controller design, and output optimization in LB-MPC. It will provide a reference for the research and application of LB-MPC in path tracking control in mobile platforms.  
Conceptualization, K.Z., J.W. and X.X.; data curation, X.L.; funding acquisition, J.W.; methodology, K.Z., J.W. and J.H.; software, C.S. and W.K.; supervision, X.L.; project administration, X.X. and X.L.; writing--original draft preparation, K.Z., J.W. and X.X.; writing--review and editing, K.Z., J.W. and X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51875239.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors would like to thank all anonymous reviewers and editors for their helpful suggestions for the improvement of this paper.  
The authors declare no conflict of interest.
## 2 Importance of Thermal Condition Monitoring
### Attributes of Thermal Condition Monitoring
Again, the speed of the wind and ambient temperature as a cooling medium impacts the absorption of solar irradiance on the modules [13]. The band gap of the solar cell is also affected if the temperature of the PV module increases. It increases the short-circuit current (SC) at about 0.1%/\({}^{\circ}\)C and causes a reduction in open-circuit voltage (OC) of about 2 mV/\({}^{\circ}\)C [11]. These assertions seek to confirm that the PV modules are strongly affected by the incident solar temperature. Thus, the temperature of the module is a critical measured variable in order to predict the performance of the PV system for DERs integrated grid [11; 25]. Our proposed design concept has unique composite functions for temperature conditions monitoring, data acquisition, and wireless data transmission with data storage. It further explores the potentials of ZigBee/GSM and WSNs communication links for abnormal condition alert and notifications for control solutions.
## 3 Experimental Evidence of Synchronization between Rossler Systems
### Adaptive Generalized Synchronization between Circuit and Computer Model
#### 3.3.2 Circuit Operation
It is interesting to note that the first-order symplectic Euler-Cromer method presents an equally accurate synchronization as the second-order Heun's method. This result is significant as it allows the use of an algebraically simpler simulation algorithm without loss of accuracy. This is of great importance in embedded applications. A rough explanation of this phenomenon can be that the Rossler system phase space is approximately isomorphic to the harmonic oscillator phase space in regions in which \(|z|<\varepsilon\), and \(\varepsilon>0\) is a small real number. However, this needs to be investigated further. Figure 10 shows the accuracy of the master-slave and slav-auxiliary systems' synchronization achieved in our experiment. It can be compared with Figure 4, which shows that GS is preserved in the cases of analog and numeric chaotic systems. Figure 8: (**a**) Synchronization error between circuit and computer model. (**b**) Evaluation of parameters for the slave system. Figure 9: Synchronization error between master and slave systems for different integration methods, smoothed with a median filter; the window size is 200 samples. Figure 11 illustrates one again the proposition that the rate of convergence between slave systems weakly depends on the integration method, and this can be compared with Figure 5. In the case of heterogeneous chaotic systems, the slave-auxiliary synchronization rate is approximately similar for all investigated methods. It is of interest that the averaged rate of all methods except for Euler and Trapezoidal methods is slightly superlinear. Figure 11: Synchronization error between slave systems for different integration methods, smoothed with a median filter; the window size is 200 samples. Figure 10: Synchronization error between computer models simulated by [PERSON]’s method. Time sequences are smoothed with a median filter; the window size is 200 samples. Summarizing the results, we can conclude that, even though the master and slave systems are heterogeneous, GS is experimentally verified, as well as CS. It is shown that the synchronization error in dynamics actually depends on the numerical method. Among all investigated methods, [PERSON]'s and Euler-Cromer methods look preferable.
# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 3 Optimization Problem Analysis  
With the node model and energy model defined, we can proceed to formulate and analyze the optimization problem for data transmission within the HWSNs. The objective is to optimize the network's performance by selecting the best routes for data transmission, thereby extending the overall network lifetime and minimizing energy consumption.