esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Application of Dynamic Soaring by Unmanned Aerial Vehicle
## 5 Conclusions  
This paper mainly focuses on the new dynamic soaring trajectory determination method, the dynamic soaring flight test, and the energy harvesting mechanism verification. First, this paper has raised a new method to avoid longtime trajectory optimization by controlling certain parameters to guide soaring. The flight simulation has verified this method. Second, this paper has built a flight simulation program capable of implementing the dynamic soaring flight test. After simulation, the UAV can improve its flight distance and endurance through dynamic soaring. The flight direction has an angle to the crosswind. Meanwhile, the energy harvesting mechanism model was integrated with the flight test simulation program. The airspeed and position during the dynamic soaring flight simulation were recorded, and then the mechanical energy equation calculated the real-time energy state. The result was compared and matched with the energy change calculated by the energy harvesting mechanism model. Hence, the accuracy of the energy harvesting mechanism model was verified.  
For further research, this paper points out the indirect way to generate the dynamic soaring trajectory and proposes a new method to overcome the longtime optimization. More controlling parameters need to be studied in the future; therefore, the UAV can get a better soaring performance. The open-loop trajectory and the different shapes of all trajectories can be tested. After confirming the correction of the energy harvesting mechanism model for the mechanism study, the energy-gaining influencing factors can be determined from the model. The dynamic soaring performance will be improved by studying how these factors will affect the UAV energy extraction during the dynamic soaring.  
Figure 21: The energy variation curves calculate by the two methods (modified).  
**Author Contributions:** Conceptualization, W.W.: methodology, software, writing--original draft preparation, W.A.: writing--review and editing, B.S.: writing--review and editing, supervision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Experiments
### Missing Trajectory Completion  
To illustrate the performance of the bidirectional LSTM vehicle trajectory reconstruction model based on graph attention in this module, we selected different baseline models and applied these to the Argoverse bidirectional trajectory completion dataset. We used ADE as the evaluating indicator. Table 3 presents the results.  
In the comparison results, the model considering the interaction between vehicles (Vanilla Bi-LSTM) performs better than the model that does not consider the interaction (NAOMI). Meanwhile, the model using the graph to represent interactions (STGAT) outperforms other models (Vanilla Bi-LSTM, CS-LSTM). In this experiment, our bidirectional LSTM vehicle trajectory reconstruction model based on graph attention outperforms all mentioned models. This reveals that our model can effectively capture the spatio-temporal  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **mAP (Mean Average Precision)** \\ \hline Our model & 73.46\% \\ Original vehicle re-identification model \({}^{\star}\) & 71.96\% \\ \hline \hline \end{tabular} \({}^{\star}\) The model is taken from reference [56].  
\end{table}
Table 2: Evaluation indicators of our vehicle re-identification model fused with lane features.  
Figure 8: The CMC (cumulative matching characteristics) diagram of our vehicle re-identification model fused with lane features.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **ADE (m)** \\ \hline Our model & 0.7649 \\ Vanilla Bi-LSTM & 1.9246 \\ CS-LSTM & 1.8523 \\ NAOMI & 2.0335 \\ STGAT & 1.8139 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Imputation errors in the Argoverse bidirectional trajectory completion dataset.  
characteristics of vehicle trajectories and the vehicle-vehicle interactions, and the bidirectional trajectory completion performs better than the unidirectional trajectory completion.  
In this experiment, we applied our trained model to the vehicle trajectory completion on the Donghai Bridge. Figure 9 represents the effect of our model and some baseline models on vehicle trajectory completion on the Donghai Bridge. The result indicates that our model outperforms other models in the trajectory completion task on Donghai Bridge.
## 4 Results and Descriptive Analysis  
### Speed  
#### 4.1.1 Speed Distribution in the Work Zone  
Based on the collation and analysis of the experimental vehicle speed, it was found that the S-W normality test results for a total of 18 sets of data for different vehicles in areas A-I were 0.061-0.681 > 0.05, all of which conformed to the normal distribution. The speed distribution of different areas and vehicles is shown in Figure 6, and the statistical results are shown in Table 4.  
For freeways, the 85 th-percentile speed is usually taken as the speed limit, which can be regarded as the running speed of vehicles. The U-test results indicated that there were significant differences in vehicle speeds under different areas and vehicles at a 95% confidence level. The speed and speed variability (standard deviation) of cars were larger in most areas compared to those of trucks. Different numbers of drivers adopted speeding behavior in all areas, except for the downstream median opening (I).  
In the standard freeway section (A), the road condition was good, but the temporary speed limit was lower (80 km/h), leading to a higher vehicle running speed (98.23 km/h)--which was inconsistent with the speed limit--larger speed dispersion and a speed limit compliance rate of only 43.75%. After vehicles drove into the advance warning area (B), the speed limit was further reduced (60 km/h), while the road conditions did not change; therefore, the vehicle speed only reduced slightly, the vehicle running speed (87.39 km/h) was inconsistent with the speed limit and the speed limit compliance rate was further reduced. In the upstream transition area (C), the road conditions changed, the number of lanes decreased and the vehicle speed decreased significantly, but the speed limit compliance rate was still low (58.33%). After vehicles drove into the activity area (H) through the median opening, only one lane was available for driving, so there was no  
Figure 6: Distribution of vehicle speed for different areas and vehicles. (**a**) Passenger car; (**b**) truck; (**c**) all vehicles.  
effective separation from the vehicles in the opposite direction; the cars with a higher speed were affected by the trucks with a lower speed, which led to a reduction in the vehicle speed and speed dispersion, and the vehicle running speed (61.88 km/h) remained the same as the speed limit. The downstream median opening (I) length was only 40 m, the road conditions changed and different vehicles interacted with each other in this area, so the vehicle speeds further converged.
# A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels
## 6 Evaluation of Algorithm Performance
### Network Lifetime  
Figure 12 shows a comparison between the PCEB-MS protocol and the CNP algorithm, in terms of network lifetime with an increasing number of sinks, where the average value of the network lifetime is calculated by conducting 50 simulations using both algorithms. The figure shows that the network  
Figure 11: Cluster interface comparison.  
Figure 10: Comparison of average connectivity.  
lifetime of the two algorithms has an increasing trend with an increase in the number of sinks, but there is minimal change observed in the network lifetime when there are more than eight sinks. Using the PCEB-MS protocol, the network lifetime is approximately 3200 s with eight sinks and approximately 3300 s with 12 sinks. However, using the CNP algorithm, the network lifetime is only approximately 2800 s with eight sinks and approximately 3000 s with 12. Therefore, the network lifetime of the CNP algorithm only increases by 14%. It is thus evident that the superior energy balance of the PCEB-MS protocol helps to increase the network lifetime.
## 1 Introduction
We prove the robustness of the voting scheme compared with others in different collusion attacker probabilities. Experiments show that with the voting weight continuously increased, the probability of successful collusion attack can be reduced to less than 0.1 when the attacker ratio is 0.5. The remaining sections of this article are as follows. Section 2 provides a brief introduction of related work on the security and privacy for the SNR system, content pollution attacks in caching, and the distributed voting mechanisms. Section 3 mainly describes the system architecture of our proposed scheme. The basic definition and rules, initialization, and voting procedure of the voting algorithm is introduced in detail in Section 4. Then, in Section 5, we evaluate and analyze the security of our approach. The scenario set up and performance evaluation are demonstrated in Section 6. We conclude the paper and present some future plans in Section 7.
# Continuous Adaptation with Online Meta-Learning for Non-Stationary Target Regression Tasks
## 4 Related Work
### Sliding Windows  
The concept of sliding window is commonly used to deal with non-stationary targets [1; 18]. The most recent and relevant work is meta-learning via online change point analysis (MOCA). It employs both the sliding window idea and meta-learning and also detects a task-switching point without explicit task switch timing from outside [19]. It uses Bayesian online change point detection (BOCPD) [20] to detect a task-switch timing and combines it with an online meta-learning algorithm to learn multiple tasks without task boundary information. BOCPD computes the probability distribution of the task length. It builds a graphical model and applies a message passing algorithm to compute the posterior of the current task length (they call it \"run length\") \(p(r_{t}|x_{1:t})\), where \(r_{t}\) is the run length at time \(t\), and \(x_{1:t}\) is observed data up to time \(t\). MOCA extends it to support a supervised learning setting, computing an expectation of the predictive posterior over all possible task lengths with regard to the posterior of the task length. In other words, it takes a weighted average of the predictive posteriors for all possible task lengths with the posteriors of the task length as their weight. MOCA performs well for multi-task learning even without the task boundaries information. Because MOCA assumes and is evaluated only on a discrete (abrupt) task-switching case, it is not clear if it still works well on tasks with incremental changes over time. The optimal sliding window length for the non-stationary targets should be obtained by balancing two sources of errors--one due to a lack of data (by having short a sliding window) and the other one due to failing to track the target variation (by having a long sliding window). Although the basic idea of BOCPD should be applicable to the non-stationary target, it is worth investigating if a better method could be applied to the non-stationary target case. Another potential issue with MOCA would be its high computational complexity for calculating the predictive posterior for all possible task lengths. As a result, for the longest possible sliding window length \(n_{max}\), it has linear complexity with \(n_{max}\). Conversely, CORAL has linear complexity with the number of tasks \(\lceil n/m\rceil+1\). Typically, we use \(m=n\), so CORAL complexity stays low even for a large \(n\). This is not a very fair comparison because MOCA adaptively adjusts its sliding window size, whereas CORAL does not. However, it is true that the MOCA algorithm could not reduce its complexity even if we fixed the sliding window size. The complexity of MOCA might be fine for a relatively short task length case, but we need a computationally efficient method for a long task length case.
# Feasibility Study and Prospects of Rock Fragmentation Using Ultrasonic Vibration Excitation
## 4 Engineering Application and Progress of Ultrasonic Excitation of Coal and Rock Mass  
### Application of Power Ultrasound in Underground Mining Engineering  
At present, ultrasonic power has been successfully used in underground mines, especially in the field of outburst forecasting, coal and rock mass flaw detection, and supplementary rock breaking. The associated research outcomes mainly include the following three aspects:  
(1) Detection of damage characteristics of coal and rock mass (specimen) given its penetration and attenuation characteristics in the solid. For example, [PERSON] et al. [22] and [PERSON] et al. [23] used an ultrasonic testing system to test the crack propagation in steel fiber reinforced concrete beams, and the surrounding rock stability, fracture development within the coal mass in roadways, respectively. The results show that ultrasonic technology has advantages in rock flaw detection, which is characterized by a strong penetrating power of the ultrasonic wave and a large detectable depth; easy identification of damage flaw positions; high sensitivity and small detectable fracture scale; and fast reaction time and results visualization.  
(2) Enhance the crack propagation of coal and rock mass by ultrasonic vibration excitation. Based on theoretical analysis, [PERSON] et al. [21] studied the influence of power ultrasound on the development of fractures and the mechanical properties of coal and rock samples (see Figure 1) using CT microscopy. The work provides evidence of using power ultrasound to prompt the fracture development in the coal and rock mass, therefore changing their mechanical properties.  
(3) The use of ultrasonic vibration excitation (cavitation) and mechanical impact to optimise rock breakage. Based on the physical characteristics of ultrasonic energy coupling, [PERSON] [24] studied the sampling of extraterrestrial space ultrasonic impact drilling and preliminary design of the drilling prototype (see Figure 2). The preliminary test results are promising.  
Figure 1: Fracture development under ultrasonic vibration excitation.  
Figure 2: The drilling prototype.
# Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique
## 3 Results
### Simulation settings
Performance evaluation of proposed optimization strategies  
Adopting the ant colony optimization algorithm and further considering heuristic distance factors can improve the global search ability, avoid becoming stuck in local optimal solutions, and improve the convergence speed. Therefore, compared with traditional ant colony optimization models, the proposed model effectively solves the problem of low energy efficiency and extends the lifespan of the network. The K-means clustering method is used to determine the optimal configuration of network elements, including the number of CHs, the clustering nodes, and the link quality between the CM and CHs. In addition, it maintains a balanced use of resources and extends the lifespan of the network. The improved whale algorithm uses a BP neural network to optimize the initial population distribution, thereby accelerating global search and subsequently increasing node coverage. It effectively alleviates hot issues and improves the overall performance of wireless sensor networks. Meanwhile, monitoring the remaining energy of nodes and the direct communication between mobile aggregation nodes and CHs within a short communication range can help reduce EC. Our simulation results indicate that our proposed performance optimization strategy provides an energy-saving, robust, and scalable solution, significantly optimizing network performance by reducing average EC, improving base station throughput, and minimizing execution time compared to traditional methods. Therefore, its effectiveness in maximizing the performance of wireless sensor networks has been validated. The following section can be divided into subheadings. It will provide a concise description of the experimental results, their explanations, and the conclusions that can be drawn.
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 5 Proposed Solution
### Tools and Technologies
#### HTTP Clients  
* Java _HttpClient_ was utilized in the asynchronous application for delivering a rich interface, abstracting, and enabling communication with external services based on HTTP requests [51]. It provides direct configuration and integration with _ExecutorService_, which allows asynchronous processing of the response.
* _WebClient_ was utilized in the reactive application due to being part of the WebFlux module, providing a non-blocking implementation, advanced features and support for reactive data flows [28]. The interface is more lightweight and streamlined compared to other implementations given its tight integration within the Spring ecosystem.
# An Equilibrium Strategy-Based Routing Optimization Algorithm for Wireless Sensor Networks
## 3 ESRA Routing Algorithm
### Path Optimization Based on Edge-Cutting Strategy
The details are as follows:  
The minimum routing graph including all nodes in the network shown in Figure 2a is generated according to the link cost. Firstly, the load of first layer node A, B, and C can be calculated, at this time, it shows that node C has the smallest load, so the link FB is cut off and the loads of other nodes are updated, and Figure 2b is obtained. In Figure 2b, node B becomes the node with the smallest load in first layer, so the link EA needs to be cut off and the load of each node is updated. At this point, the edge-cutting of all the nodes in the first layer is completed, and Figure 2c is obtained. In Figure 2c, the second layer consists of four nodes, namely D, E, F, and G. The link IE is firstly cut off and the node loads are updated, then we can get the Figure 2d. Here, considering that the loads of node D, E, F, and G are the same and the smallest, according to above Case 1, node D is the only parent of its child node H and I, so the node D doesn't need to perform edge-cutting processing. Then we need to cut off the link NF since the node product of node E is the smallest and update all node loads according to the foregoing case 2. Hence, Figure 2e is obtained. Moreover, the load of node F is smaller than that of node G, so the link KG is cut off and all node loads are updated. At this point, all nodes in the second layer have completed the edge-cutting, and Figure 2f can be obtained. In Figure 2f, according to the Case 1, node H does not need to implement the edge-cutting, and the link MJ need to be cut off and all node loads are updated to obtain Figure 2g. At this point, the node H, I, and J do not need to perform the edge cutting. According to the Case 2, the node product \(LCN(K)\) is less than \(LCN(L)\) after calculation, so the link OL is cut off and all node loads are updated. Finally, the minimum routing tree with minimum link cost and maximum load balance as shown in Figure 2h is obtained. Figure 2: Example of the edge-cutting process. (Note: the value in parentheses indicates the corresponding node load, (**a**) is the minimum routing graph, (**h**) is the minimum routing tree, (**a**-**h**) show the edge-cutting process from the minimum routing graph to the minimum routing tree).
# Energy-Efficient Power Scheduling Policy for Cloud-Assisted Distributed PV System: A TD3 Approach
## 1 Introduction
present a deep RL-based scheme for microgrids to trade energy with each other and with the main power grid to increase the utility of microgrids and reduce the dependence on the main grid. Ref. [23] addresses the impact of renewable energy on the frequency stability of the power system, and proposes a load frequency coordinated control scheme based on the deep deterministic policy gradient (DDPG) algorithm for the distributed PV power network. The scheme considers the nonlinear constraints of the system, the load fluctuations, and the randomness of PV and the energy storage system (ESS), and learns the optimal control strategy through deep reinforcement learning methods, the experimental results in both the time domain and frequency domain demonstrate the superiority of the scheme in uncertain environments. However, existing studies mainly focus on applying reinforcement learning to single microgrid scenarios, and neglect the inter-microgrid energy scheduling problem. To fill this gap, we propose an efficient power scheduling policy for the multi-parks distributed PV system in this paper. Our main contributions are summarized as follows:  
* We design a cloud-assisted distributed PV system for multi-park, which enables an internal trading market to coordinate the power allocation among different parks and achieve the supply-demand balance in the system. * The optimization problem is formulated as a Markov decision process (MDP), and solved by using the TD3 algorithm, which is a state-of-the-art reinforcement learning method that can handle continuous action spaces. * We conduct extensive simulations with various parameters to assess the performance of the proposed TD3-based optimization scheme. The simulation results and numerical analysis show that our scheme can significantly reduce the electricity cost of all parks compared with other baseline schemes. The rest of this paper is organized as follows. The model of the PV system in each park and the power trading system are written in Section 2. The problem formulation and the solution with TD3 are placed in Sections 3 and 4, respectively. Then, the simulation results are discussed in Section 5. Finally, the whole paper is summarized in Section 6.