esa-sceva/satcom-chunk-collection huggingface dataset:
# Marine UAV-USV Marsupial Platform: System and Recovery Technic Verification
## 3 Main Method
### Dynamic Analysis of Arresting System  
The recovery process of the UAV crashing cable is a nonlinear transient dynamic process, and the recovery mechanism needs to be modeled in the design process. The system's dynamics simulation can be used to judge the realization of the recovery function, as well as the dynamics and kinematic response characteristics of the UAV during the recovery process.  
Based on Lagrange's equation, a dynamic model consisting of an arresting cable, a damper, a variable stiffness compliance system, and a UAV is established. The dynamics simulation of the UAV from the hook to the recovery process with zero relative speed to the recovery platform is performed. Suppose that the UAV has completed the hook in the initial stage, and the UAV is reduced to a mass point (the lower abdomen hook is designed with a rotational degree of freedom, and the resulting rotational bending moment is small and can be ignored) to simplify the three-dimensional feature.  
According to the state of the UAV, the kinetic energy (\(T\)), system potential energy (\(V\)), and the dissipated energy (\(D\)) can be obtained first. According to the Lagrangian variational principle, dynamic differential equations, including design parameters and state parameters, such as the initial state and posture when the UAV hits the cable, the inertia (\(J\)) of the blocking mechanism, the damping coefficient (\(K\)), and the length of the arresting cable (\(L\)) can be solved. Then, the fourth order Runge-Kutta method is used to track its dynamics and kinematics. It can analyze the impact of different parameters on UAV recovery performance, such as maximum acceleration. Take the maximum acceleration of the UAV recovery process as an example. According to the Lagrangian variational principle, the total kinetic energy of the UAV is as follows:  
\[T=0.5 mv^{2}+0.5J\big{(}\frac{v\sin\theta}{t}\big{)}^{2} \tag{11}\]  
Figure 10: DCM attitude solution program flowchart.  
Figure 9: DCM attitude solution program flowchart.  
The total dissipated energy of the system is calculated as follows:  
\[D=0.5 Kw^{3}=0.5K(\frac{v\sin\theta}{r})^{3} \tag{12}\]  
The total potential energy of the system is calculated as follows:  
\[V=0.5\frac{\left(Ku^{2}+Ju^{\prime}r\right)^{2}}{kr^{6}}, \tag{13}\]  
where  
\[\sin\theta=\frac{x}{\sqrt{x^{2}+l_{0}^{2}}}. \tag{14}\]  
This is derivable based on the variational principle:  
\[ma+J(\frac{a\sin\theta^{2}}{r^{2}})+J(\frac{2v\sin\theta}{r^{2}})\sin\theta^{ \prime}-J(\frac{v^{2}\sin\theta}{r^{2}})\frac{\partial\sin\theta}{\partial x }+1.5K(\frac{\sin\theta}{r})^{3}v^{2}=0 \tag{15}\]  
The maximum acceleration of the UAV recovery process is  
\[a=-\frac{2(Ku^{2}+Jr\sin\theta^{\prime})\sin\theta}{mr^{3}+2 Jr\sin\theta^{2}} \tag{16}\]
## 4 Conclusions  
This study proposes a clustering correction method to improve power prediction for heavy-duty gas turbines. By combining Support Vector Regression (SVR) with clustering techniques, the approach effectively addresses nonlinear system characteristics and adapts to varying operating conditions. The method leverages data-driven modeling and condition-specific refinements to achieve enhanced prediction accuracy and reliability. The main conclusions of this study are as follows:  
Figure 11: Comparison of predicted performance between the basic model and the combined model: (**a**) power predicted by both models along with the actual power values; (**b**) distribution of absolute percentage error of the basic model and combined model.  
1. The integration of SVR and clustering introduces a composite framework that adapts dynamically to diverse operating conditions. By leveraging clustering to classify operational states and tailoring correction models for each category, the framework ensures predictions remain precise and responsive to varying system dynamics.
2. The composite model achieves a 32.66% reduction in MAPE and increases R\({}^{2}\) to 0.9982 compared to single-model methods. These results demonstrate the model's ability to handle the complex nonlinear behavior of gas turbines and provide reliable predictions across different conditions.
3. The analysis shows that using 70% of the annual dataset for training is sufficient to achieve optimal performance. This demonstrates that the proposed method can effectively utilize limited data while ensuring high prediction accuracy.
4. The composite prediction framework significantly reduces high-error predictions, with 75% of predictions achieving errors below 0.2946%. This refined error distribution validates the adaptability and robustness of the proposed model across diverse operating conditions.  
Conceptualization, J.K. and H.Z.; Methodology, J.K., W.Y., J.C. and H.Z.; Software, J.K. and J.C.; Validation, W.Y.; Formal analysis, J.C. and H.Z.; Writing--original draft preparation, J.K. and J.C.; Writing--review and editing, W.Y., J.C. and H.Z.; Supervision, W.Y. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Key Research and Development Program of Zhejiang Province grant number 2022C01206.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to commercial restrictions.  
Authors [PERSON] and [PERSON] were employed by the company \"Huadian Electric Power Research Institute Co., Ltd.\". The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 9 A Complete Pattern: WS-Trust
### Known Uses  
* DataPower's XS40 XML Security Gateway [33] is a device for securing web services that provides web services access control, message filtering and field-level encryption. It centralizes policy enforcement, supporting standards such as WS-Security, WS-Trust, WS-Policy and XACML.
* SecureSpan(tm) XML Firewall [34] enforces WS* and WS-I standards to centralize security and access requirements in policies that can be run as a shared service in front of applications.
* Vordel Security Token Service [35] is used to issue security tokens and to convert security tokens from one format to another. The security tokens created by an STS are bound to the messages travelling between web services..
* PingTrust, a standalone WS-Trust Security Token Server [36] creates and validates security tokens that are bound into SOAP messages according to the Web Services Security (WSS) standard.
## 6 Discussion  
The presented results of the measurement campaign reveal the applicability of the proposed radar concept. The introduced and utilized measurement equipment was capable to detect and measure all targets within the described scenarios. Furthermore, it was shown that all time-gated targets carried the corresponding notch filter transfer functions of their sub-channel, leading to a successful direction alignment of every single target. As this observation is valid for center beam and overlap beam targets, the enhancement of the cross-range resolution was proved as well. Moreover, it was further observed that recorded clutter lead to an unpredicted frequency behavior. Regarding the presented signal processing methods, both single target scenarios were classified with high accuracy of 85.5% and 100% for the center beam and overlap beam scenario, respectively. In the future, these results can be further improved by considering additional training data. In contrast, the unsupervised method already revealed lower classification accuracies with only 38.89% and 22.73% for the mentioned scenarios. The lower accuracies are the result of the already mentioned additional clutter, which caused interference effects in the frequency domain leading to confusion with the notches of the intended frequency coding. Here, different coding filters with improved coding design abilities might improve the classification accuracy of the unsupervised method. In terms of the multi-target scenarios, a further classification accuracy decrease to 60.6% was observed for the supervised method when the responses of two targets located in a center and in an overlap beam are superimposed. This is the result of further interference effects but can be tackled by considering additional training data in the future. As predicted in the theory section, the overlap of two identical targets in identical LOS distances located in two main beam areas lead to the confusion of the result classes so this scenario was not classified at all. However, depending on the radar application this worst-case scenario will not appear very often or for a long time when targets move independently. In order to overcome the drawback anyway, one might consider modulating the beam distributions by activating and de-activating corresponding sub-channels. The unsupervised classification method failed for both multi-target scenarios, which clearly reveals the advantage of the supervised method. In future work, the classification methods must be optimized by considering further result classes and more divergent training data. Furthermore, the coding filter design can be improved for avoiding confusion with interference effects. Finally, calibration methods should be implemented for reducing the system's frequency behavior.
# Evaluation of a Reputation Management Technique for Autonomous Vehicles
## 5 Experimental Design
### Model Operations  
The model runs for a given number of turns, which represents the time component of the simulation. Figure 4 illustrates the lifecycle of a turn. For each turn, a randomly generated floating point value is used to stochastically determine if a maneuver-event or sensing-based report will occur. Once this first determination is made as to whether such an event will occur or not during the turn, subsequent determinations are made, regarding what type of vehicle initiated the event. The recipients of the transaction are randomly selected from the remaining vehicles; the number of recipients is constrained by fixed lower and upper bounds. A proportion of these recipients are randomly chosen as witnesses to validate the transaction. Along with this, an RSU is selected to store the transaction and witness scores.  
The expected value of a transaction is based on the status of the sender: malicious operating and complete malicious vehicles are expected to broadcast non-credible transactions; normal and malicious reporting vehicles are expected to broadcast credible transactions. Conversely, the status of the witness determines how it scores the transaction, where normal and malicious operating vehicles are expected to score the transaction accurately, and malicious reporting and complete malicious vehicles are expected to score the transaction inaccurately. However, the expected behavior of a vehicle is inverted for a small proportion of transactions, where malicious vehicles will operate and report credibly while normal vehicles will operate and report incredibly. This accounts for real-life noise that introduces inaccuracies in vehicle behavior.  
Each witness then assigns a score to the transaction, which is either 0 or 1, to denote a non-credible or credible transaction. Alternatively, a quartile system can be used, in which either 0, 0.25, 0.5, 0.75, or 1.0 denotes the credibility of a transaction with varying levels of confidence. Both were tested and are discussed herein.  
Figure 4: The experimental design of a single turn.  
After the transaction is scored by all of the witnesses, it is then stored by the RSU in a running list of transactions. The RSU then updates the operating reputation of the sender and the reporting reputation of each witness using weighted averages.
## 1 Introduction
### Description of the Optimization Problem  
The optimization goal was to minimize the total cost of the power network, and a large number of equations and inequality constraints were met in the optimization process, such as the current of any cable segment was less than the allowable current carrying capacity of the cable, the voltage drop of each branch was less than the allowable voltage drop, the number of loops in the entire network was 0). The minimization is shown in Equation (10).  
\[\min\ C_{Total} \tag{10}\]  
satisfying:  
\[\left\{\begin{array}{l}I_{i}<I_{p,i},\ \ \ \ \ \forall i=1,2,\ldots,N_{C}\\ \Delta U_{F,j}<\Delta U_{\max},\ \ \ \ \ \forall j=1,2,\ldots,N_{Fender}\\ N_{Loop}=0\end{array}\right.\]  
where \(I_{p,i}\) is the allowable current carrying capacity of cable segment \(I\), \(\Delta U_{\max}\) is the allowable voltage drop of the branch, and \(N_{Fender}\) is the number of branches in the topology.
# An Efficient Opportunistic Routing Based on Prediction for Nautical Wireless Ad Hoc Networks
## 2 Related Work
The results showed that the performance was greatly improved regarding transmission delay, the energy consumption and the communication throughput compared with other algorithms. In [13], reliable delegation geographic routing was designed for disruption-/delay-tolerant networks. Delegation forwarding utilizes the time to intercept (TTI) to compare the currently encountered node and a historical node, and takes lifetime and node mobility information into account to overcome the limitation of local maximization, which solves the problem of the message relay decisions of traditional geographic metrics. The method was found to improve routing reliability and transmission efficiency with low transmission overhead. The algorithms mentioned above mainly focus on optimizing the selection of local relay nodes via controlling local broadcasting to reduce the amount of replicated information to improve packet delivery. However, for partitioned OppNets, only optimizing local relays is not enough, because it is not known whether the currently selected relay node is capable of further forwarding, and this may lead to unnecessary packet requests and resource consumption. Therefore, it is essential to take into account not only the relay selection between the current node and its relay nodes but also the relay advancement prediction.
## 2 Materials and Methods
### Retrieval of FVC  
This study evaluated the impact of the urban structure on FVC estimates using the NDVI and selected three methods for this purpose.  
A classical FVC calculation method was proposed by [PERSON] et al. (1998) (Equation (3)):  
\[FVC\_1=\frac{NDVI_{mean}-NDVI_{min}}{NDVI_{max}-NDVI_{min}} \tag{3}\]  
In this study, we focused on calculating the FVCs for mixed scenes, with each scene comprising a specific number of cells (Table 1)[PERSON] To calculate the FVC for a specific mixed scene, \(NDVI_{mean}\) represents the mean NDVI for all the cells within the mixed scene, [PERSON]NDVI_{max}\) represents the maximum NDVI for a vegetation-only scene cell, while \(NDVI_{min}\) represents the minimum NDVI for a building-only scene cell. Figure 2 shows a scene schematic of the three types of scenes (i.e., mixed scene, vegetation-only scene, and building-only scene)[PERSON]  
Additionally, two semi-empirical relationships proposed by [PERSON] et al. (1998) (Equation (4)) and [PERSON] et al. (1999) (Equation (5)) were applied:  
[PERSON]FVC\_2=1-\left(\frac{NDVI_{max}-NDVI_{mean}}{NDVI_{max}-NDVI_{min}}\right)^{0.6 175} \tag{4}\]  
\[FVC\_3=\left(\frac{NDVI_{mean}-NDVI_{min}}{NDVI_{max}-NDVI_{min}}\right)^{2} \tag{5}\]  
The accuracy of FVC estimates using these three methods was evaluated using the difference (\(\mathrm{dif}_{\mathrm{FVC}}\)) between the FVC estimates and the \(\mathrm{FVC}_{\mathrm{true}}\) defined by the scene design (Table 2):  
\[{{{\it dif}_{FVC}}}=FVC-FVC_{true} \tag{6}\]  
\(FVC\) is calculated using Equations (3)-(5). The derived \(\mathrm{dif}_{\mathrm{FVC}}\) (\(\mathrm{dif}_{\mathrm{FVC}}\_1\), \(\mathrm{dif}_{\mathrm{FVC}}\_2\), \(\mathrm{dif}_{\mathrm{FVC}}\_3\)) are represented by the absolute values of the corresponding \(\mathrm{dif}_{\mathrm{FVC}}\)s, which are designated as \(\left|\mathrm{dif}_{\mathrm{FVC}}\_1\right|\), \(\left|\mathrm{dif}_{\mathrm{FVC}}\_2\right|\), and \(\left|\mathrm{dif}_{\mathrm{FVC}}\_31\right|\) (Section 3.3).  
The root mean square error (RMSE) and bias (BIAS) were also used to assess the FVC estimation accuracy.  
\[\mathrm{RMSE}=\sqrt{\frac{1}{N}{\sum\limits_{i=1}^{N}{(FVC_{true}-FVC_{i})}^{ 2}}} \tag{7}\]  
\[BIAS=\frac{1}{N}{\sum\limits_{i=1}^{N}{(FVC_{i}-FVC_{true})}} \tag{8}\]
## 6 JCS Sidelink Performance Evaluation Using CCM-SPS
### JCS Sidelike Performances with Dynamic Packet Sizes  
#### 6.3.1 Conflicting Impacts of Packet Sizes on JCS Performance Metrics  
Secondly, in this section, we investigate the conflicting impacts of packet sizes on JCS performance metrics in terms of root CRLB range and PRR in different vehicle density scenarios by using a traditional SB-SPS scheme. Then, we evaluate the optimization performance results of a Q-learning-based CCM-SPS scheme, including sensing and communication metrics in different vehicle density scenarios.  
Figure 10 illustrates the empirical CDF of root CRLB range estimation with different packet sizes and vehicle densities. With a given pack size, the empirical CDF of the root CRLB Range decreases rapidly with an increase in vehicle density. As the packet size increases, the CDF curve of the root CRLB Range shifts toward the left with a small value of CRLB. This result indicates the enhanced sensing accuracy performance.  
Figure 11 illustrates the communication reliability metric PRR vs. distance under different packet sizes and vehicle densities. With a given packet size, the communication reliability PRR decreases with the transmission distance. As the packet size or the vehicle density increases, the PRR curves decline fast.  
Figure 10: Empirical CDF of root CRLB for range estimation.  
Based on the above discussion of the results of both Figures 10 and 11 of the JCS system with the traditional SB-SPS scheme, it can be seen that with increasing vehicle density, both sensing and communication performances decrease. This is because the worse channel quality of the sidelink in a high-density vehicle network yields more consecutive collisions over the shared resource pool with traditional SB-SPS. Moreover, it also can be seen that with increasing packet size, the sensing accuracy increases with a smaller root CRLB range value, while the communication reliability declines with a smaller PRR value. In the case of a big packet size, the required bandwidth for transmission increases when more subcarriers are allocated. An echo radio with a large subcarrier number N can produce a small CRLB value range, according to (8). At the same time, there is a high probability that different vehicles occupy the same spectrum resource, resulting in serious consecutive collisions due to the resource competition over the sidelink, which would deny successful access and produce a small PRR.  
In short, the traditional sidelink resource allocation scheme is less effective for a sidelink JCS system to support various services in highly dense dynamic vehicular networks. In order to overcome the conflicting requirement on the spectrum resource allocation between sensing and communication, this paper has proposed a novel CCM-SPS scheme to realize an effective sidelink resource allocation for enhancing JCS performances. Next, in the following section, JCS performances will be evaluated.
# A Low-Cost CPW-Fed Multiband Frequency Reconfigurable Antenna for Wireless Applications
## 4 Comparison with Other State of the Art Designs  
The proposed antenna was compared with the recent state of the art to highlight the novelty of this work. The comparison is summarized in Table 9. It can be seen that the proposed antenna is advantageous in terms of maximum passbands, its wide reconfigurable behavior, low-cost substrate, and overall size.
# Intelligent Total Transportation Management System for Future Smart Cities
## 3 Results--Examples of Sub-Model Developments
### Managing Drones as a Formation to Avoid Obstacles in Smart Cities
#### 3.2.3 Formation Control Strategy  
1. The Centralized Control Strategy  
This approach is primitively proposed, where a central processor exists (see Figure 8). The central processor is responsible for collecting data of subsystems and return decisions to them. This control strategy is usually considered a simple, easy to implement, and efficient approach, but it has weak robustness for the central processor's fault. This weakness may cause severe problems in large-scale practical systems. The standard leader-follower formation configuration is intuitively considered a centralized method, where the leader is independent of the followers.  
2. Decentralized Control Strategy  
Considering the complexities and difficulties of the research on the overall system, there is more interest in the approach that can divide the analysis and synthesis of the global system into independent or almost independent subsystems (see Figure 9a). The idea is that each subsystem in the whole system has its processing unit and makes its own decisions based on its measurements. Decentralization allows the overall system to take advantage of the division of labor by sharing the subsystems' decision-making load. In a decentralized control system, the whole system is no longer controlled by a single controller but by several independent controllers, which altogether consist of decentralized controllers implemented on each module. In general, decentralized control is used in a large-scale system, which has a couple of subsystems.  
In a drone formation, if each drone has its controller and moves according to its measurement (detection or sensing), the formation control strategy is decentralized.  
Distributed Control Strategy  
This approach evolves from decentralized control with sharing local information (see Figure 9b). The distributed control is promising to be superior to centralized control when data delays are present. Distributed control is related to the areas of decentralized control and large-scale systems. Distributed control strategies are usually related to a decentralized control design framework with communication issues. Such extensions of decentralized control contain communication among subsystems, local controllers, and communication in the feedback loop.  
In the drone formation, if each drone communicates with all the other drones in this formation, the control strategy is not distributed. Therefore, in a multi-drone system, if the communication issues are considered in the decentralized control design framework, the formation control strategy is distributed. The behavior-based formation configuration is regarded as a decentralized method or a distributed method depending on the communications or interactions between drones.