esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Coupled Bending-Torsional Flutter Mechanism for Ideal Plate
###### Abstract  
In order to explore the inducing mechanism of negative damping of bending-torsional coupling flutter, an ideal plate with a width of 0.45 m was taken as the research object. The changes in frequency, critical wind speed, aerodynamic stiffness, and aerodynamic damping were systematically analyzed by using the \"incentive-feedback\" mechanism theory. The source of modal damping and the inducing mechanism of bending-torsional coupling flutter were identified. The research results show that the torsional modal damping of the ideal plate mainly comes from the aerodynamic positive damping of the torsional velocity self-excitation (\(A_{2}^{*}\)) and the aerodynamic negative damping of the torsional displacement incentive feedback (\(A_{1}^{*}H_{3}^{*}\)). Among them, the aerodynamic negative damping of the item (\(A_{1}^{*}H_{3}^{*}\)) causes the torsional mode damping to be negative, and the ideal plate undergoes bending-torsion-coupled flutter under the drive of the torsional mode aerodynamic negative damping. The reason why the aerodynamic damping of the item (\(A_{1}^{*}H_{3}^{*}\)) is negative depends on two aspects: one is that the flutter derivatives \(A_{1}^{*}\) and \(H_{3}^{*}\) have opposite signs; the second is that the torsional displacement self-excited lift excites the vertical vibration to produce negative stiffness \(-m_{h}\omega_{sa}^{2}\). This results in the phase difference between the torsional displacement self-excited lift and the vertical displacement response in the range of (90-180\({}^{\circ}\)).  
ideal plate; coupled bending-torsional; flutter mechanism; incentive feedback; torsional mode; aerodynamic negative damping; negative stiffness +
Footnote †: journal: Applied sciences
# Joint Iterative Decoding Design of Cooperative Downlink SCMA Systems
## 4 Complexity Analysis  
In this section, the computational cost of the proposed joint iterative decoding MPA detector of the cooperative SCMA system is analyzed. The computational cost is measured in terms of the number of real multiplications, additions and comparisons involved in the iterations. Note that each complex multiplication consists of four real multiplications and two additions, while each complex addition consists of two real additions. In this sense, the total number of real multiplications is \(4 Kd_{c}+JMd_{v}^{2}\) in the direct transmission, while it becomes \(8 Kd_{c}+4 JMd_{v}^{2}\) in the joint detection. The total number of additions and comparisons is \(7K\) and \(Md_{c}K(M^{d_{c}-1}-1)+MJ-J\) for the direct transmission and \(14K\) and \(2 Md_{c}K(M^{d_{c}-1}-1)+MJ-J\) in the joint detection. This difference stems from the fact that the number of virtual resource nodes for joint detection is two times that of the direct transmission, or equivalently, the column weight of the indicator matrix \(\mathbf{F}\) is two times that of \(\mathbf{F}^{\prime}\).
## 3 Design Methodology Based on the Common Framework
### Performance Index  
The proposed integral resonant control (IRC) strategy aims to optimize the performance of the control system by minimizing the peak values of the FRFs associated with \(H_{11}\), \(H_{12}\), \(H_{21}\), and \(H_{22}\) within the frequency range of 0 to 15 Hz. Essentially, the IRC controller prioritizes enhancing the damping characteristics of the primary vibration modes.  
Ensuring stability entails rigorous consideration of both the actuator dynamics and the high-frequency model of the system, guaranteeing robustness against spillover effects. The Proportional-Integral (PI) term is determined as the maximum value of the FRFs representing the relationship between sensor acceleration and actuator force, as illustrated in Figure 7, but within the closed-loop system. Given the sensitivity of the structure's dynamics to actuator placement, leveraging FE modeling aids in identifying optimal actuator locations to effectively suppress critical vibration modes. Accordingly, the actuators were positioned at nodes N16 and N22.  
The IRC controller is tailored to minimize the peak response of the controlled system within the frequency band of 0 Hz to 15 Hz, thereby aiming to maximize damping primarily in the first two vibration modes illustrated in Figure 4. These modes hold particular significance concerning human-induced excitation and perception of vibration, as highlighted in references such as [44]. Consequently, a diagonal controller matrix of \(q\times q\) with \(q=2\) was employed, representing a MIMO AVC system utilizing two actuators to address the primary vibration modes.
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 4 Discussion
When there is a delay between sending RRER packets and new RREQ packets, node 0 tries to improve the route by sending RREQ packets, but because the destination of node 3 is still beyond the reach of all member nodes, the routing table is not formed, resulting in a delay in sending RREQ packets because the RREQ packet delivery limit has been reached according to the RREQ_RETRIES and MAX_RREQ_TIMEOUT values. Table 5 shows the time at which the routing table was formed at node 0, according to the RREQ_RETRIES and MAX_RREQ_TIMEOUT values. Table 4 shows that reducing RREQ_RETRIES and MAX_RREQ_TIMEOUT values can speed up the delivery of RREQ packets to speed up route formation. The faster the route is formed, the faster data packets can be sent to increase PDR, throughout, and packet loss. In the case of delay, the effects of the RREQ_RETRIES and MAX_RREQ_TIMEOUT parameters do not have a pattern. This is because of the generated delay value from when the packet was sent after the route was formed. The average energy consumption shows that an increase in the value of MAX_RREQ_TIMEOUT reduces energy consumption. This is because when there is a higher MAX_RREQ_TIMEOUT value, there is a long delay in packet delivery caused by route changes. Hence, the number of sent packets decreases. The lowest average consumption is generated by the MAX_RREQ_TIMEOUT value of 11 s. It decreases energy consumption by 0.399%. The routing overhead shows that changes of MAX_RREQ_TIMEOUT do not affect performance. This is because MAX_RREQ_TIMEOUT only delays sending RREQ packets and does not reduce the number of RREQ packets sent. What affects routing overhead is the value of RREQ_RETRIES, which can reduce the number of packets sent and increase the number of packets that can be sent. The optimal RREQ_RETRIES parameter obtained from this research is 2, while the optimal MAX_RREQ_RETRIES value is 5 s. The results are in good agreement with studies conducted by [25; 26], which show that the default parameter values of the AODV protocol do not always produce the best results.
# [PERSON]: Extreme Plastic Pollution Pathways in Riparian Waterways
## 4 Findings  
The raw telemetry data collected from the plastic tracker, including GPS coordinates, timestamps, accelerometer, and compass readings, were processed and analyzed using the MATLAB version R2024a software environment, leveraging the UAV Toolbox plugin. Data filtering and alignment were critical steps to remove irrelevant segments, synchronize timestamps at one-second intervals, and exclude noisy accelerometer and compass data.  
Key metrics such as speed, distance traveled, acceleration, and turning angles were derived using mathematical models. For example, the Haversine formula was applied to calculate distance between consecutive points, while trigonometric functions were used to determine bearings and headings. These derived metrics provided an understanding of the tracker's spatial and temporal dynamics. As this the first study we are aware of to track the movement of plastic pollution in this waterway, we did not have original data for comparative analysis testing.  
To further analyze movement patterns, states of resting and transit were identified and segmented. The application of hidden Markov models (HMMs) allowed for the modeling of transitions between these states, offering insights into how tidal dynamics and external forces influenced the tracker's behavior. Visualizations, including time-series plots and trajectory maps, were generated to explore trends, anomalies, and spatial patterns, ultimately informing the study's findings on plastic transport behaviors in urban tidal waterways.
## 5 Discussion
### Analysis of Algorithm Applicability  
In the proposed watermarking algorithm, watermark information is embedded based on the mobility analysis, with movable elements subjected to small-scale perturbations, and immovable elements carry invisible characters. This method can be used for all the elements in the model, whether the main elements, such as walls, or elements such as doors and windows. As a result, the proposed method can be applied to any type of BIM data, as demonstrated in the building model, office building, and structural model presented in Section 4. In conclusion, the method has an extremely wide range of applications.  
It is worth noting that the correct mapping of watermark bits may be affected when BIM data are rotated and the \(X\) and \(Y\) coordinates of all the elements in the model are changed, particularly for BIM data with a high proportion of movable elements. Therefore, while this method is widely used in practice, there are still some directions that need further research in the future, including rotation robustness enhancement, redundancy reduction, and combination with other watermarking algorithms.
# Precipitation-Use Efficiency and Its Conversion with Climate Types in Mainland China
## 4 Discussion
### Complexity of PUE Change in Transition Zone  
Figure 15 shows a schematic diagram of the controlling factors of PUE in different climatic regions in China, which shows the opposite change of PUE in arid and humid regions and the complexity of PUE change in transition zones.  
Figure 14: Relationship among ET\({}_{a}\)-Pre-ET\({}_{0}\) in different climatic regions of China. The ratio of mean annual ET\({}_{a}\) to Pre as a function of the index of dryness (ET\({}_{0}\)/Pre) for different values of plant-available water coefficient \(\omega\).  
Figure 15: Schematic diagram that illustrates the control factors of the PUE in different climatic regions of China.  
Warming and humidification in Northwest China [86] have a great impact on the increased ET\({}_{\text{a}}\), NPP, and PUE in arid areas. Warming and humidification increase the amount of water that can evaporate, thereby increasing ET\({}_{\text{a}}\). Temperature increase results in earlier onset of the greening period while delaying the yellowing period, enhancing vegetation activities, then increasing NPP. Multiple research based on empirical observations and process-based models have also confirmed that aboveground production in arid ecosystems has exhibited an increasing trend [87]. Decreased ET\({}_{\text{a}}\) in the humid region is related to the decrease in ET\({}_{0}\), which is consistent with the evaporation paradox [88]. In the southern humid region with sufficient water supply, changes in ET\({}_{\text{a}}\) and ET\({}_{0}\) are consistent [89]. Thus, when the ET\({}_{0}\) decreases, ET\({}_{\text{a}}\) also decreases. Rainfall is abundant in humid areas. Vegetation productivity is positively correlated with air temperature and negatively correlated with precipitation. In recent decades, the increase in precipitation in humid regions has reduced photosynthetically active radiation. Enhanced radiation restrictions reduce vegetation NPP.  
Factors influencing ET\({}_{\text{a}}\), NPP, and PUE in the transition zone are more complex because this region is a competitive zone for water and energy [90]. In the highland region in the western part of the transition zone, NPP increases along with warming and humidification, and a significant decrease in precipitation in the eastern region of the transition zone and the warming and drying caused by temperature increases are the main reasons for the NPP decrease [91]. At the same time, the region is affected by the interannual fluctuation of the intensity of the East Asian summer monsoon, so the interannual and interdecadal precipitation fluctuations in the region are large [92].
# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 3 Experimental Results and Discussion
### Field Test and Data Analysis  
In the field test, a typical intersection, located in Fushi Road, Shijingshan District, Beijing City, is selected as the experimental intersection. There are four lanes in one direction from east to west, and the rightmost lane is the dedicated right-turn lane, which is not controlled by traffic lights, as shown in Figure 9a. In addition, we design a mobile intelligent roadside perception and computing platform for multiple test scenarios. The intelligent roadside platform consists of the LiDAR sensor, HD camera, switch, intelligent RSU, GPS, MEC server and monitor screen, as shown in Figure 9b.  
Figure 8: The structure of the soft-attention mechanism.  
At the experimental platform, the resolution of the camera is 1080 P (1920 \(\times\) 1080) with a sampling rate of 25 Hz. After testing, the resolution can detect and recognize targets about 200 m. The LiDAR sensors with 32 lines can detect surrounding environment about 300 m. The camera is responsible for detecting traffic signs and vehicles, while the LiDAR sensor is for exploring blind spots and long-distance targets in complex traffic environment.  
In the experiment, we intercepted test data in 30 min, which is collected by the roadside perception platform containing a series of vehicles, pedestrians, and buildings. After data preprocessing, about 22,000 pieces of vehicular data are obtained, which are shown in Tables 2 and 3.
# An Optimization Strategy to Position CHIEF Points in Boundary-Element Acoustic Problems
###### Abstract  
The use of boundary elements in the analysis of exterior acoustic problems poses challenges at specific frequencies, since fictitious eigenfrequencies may arise at the internal resonances of cavities, leading to inaccurate results or even unstable behavior. To filter out these fictitious eigenfrequencies, a scheme based on the combined Helmholtz integral equation formulation (CHIEF) can be used to prevent the so-called non-uniqueness problem, although it requires additional equations and points. The BEM formulation final accuracy will, however, depend on the correct choice of these points. Here, a strategy to help in defining good approximations for the position and number of such points is proposed, based on an optimization process which maximizes the system matrix's smallest singular value. The accuracy of the method for exterior radiation problems is investigated using different examples. With low computational cost and simple implementation, the two proposed algorithms automatically circumvent the non-uniqueness problem, aiding the implementation of more stable BEM codes.  
boundary elements; CHIEF points; smallest singular value; exterior acoustics 2023
# A Hybrid Control Approach for the Swing Free Transportation of a Double Pendulum with a Quadrotor
## 2 Dynamic Modeling of the System
### Linearization and Discretization of the Model  
The system nonlinear model must be linearized around the equilibrium/hover conditions in order to to solve the equations that provide the optimal parameter values of the linear controllers. For simplification, we keep constant \(\psi=0\). Using the Linear Time Invariant discretization model, we can assume that the quadrotor and double pendulum dynamics can be simplified. We can calculate the first order Taylor series around the equilibrium point (\(x_{eq}\), \(u_{eq}\)) of the nonlinear dynamics of the quadrotor obtaining [45] the following equation:  
\[\begin{array}{rcl}x_{k+1}&=&Ax_{k}+Bu_{k},\\ y_{k}&=&Cx_{k},\end{array} \tag{5}\]where the \(x_{k}\), \(u_{k}\) and \(y_{k}\) vectors are the state space, input and output vectors, respectively, defined as follows:  
\[x_{k}=[\begin{array}{cccccccc}x_{Q}&y_{Q}&\theta_{L1}&\theta_{L2}&\hat{x}_{Q}& \hat{y}_{Q}&\hat{\theta}_{L1}&\theta_{L2}\end{array}]^{T}, \tag{6}\]  
\[u_{k}=[\begin{array}{cccccccc}f&\phi_{ref}&\theta_{ref}\end{array}]^{T}, \tag{7}\]  
\[y_{k} = [x_{Q}&y_{Q}&\theta_{L1}&\theta_{L2}&\theta&\phi]^{T}. \tag{8}\]  
The linearization is developed around an equilibrium point at which the quadrotor and the slung load system can hover on a specific position \((x,y,z)\). Thus, \(f=mg+(m_{1}+m_{2})g\), where \(m_{1}\) and \(m_{2}\) is the mass of the pendulum particles as shown in Figure 1. We have that the equilibrium conditions translate into:  
\[\phi_{ref}=\theta_{ref}=0, \tag{9}\]  
and the equilibrium vectors result in:  
\[x_{eq}=[\begin{array}{cccccccc}x_{Q}&y_{Q}&0&0&0&0&0&0\end{array}]^{T}, \tag{10}\]  
\[u_{eq}=[\begin{array}{cccccccc}mg+(m_{1}+m_{2})g&0&0\end{array}]^{T}. \tag{11}\]
## 4 Modeling and Implementation
### Numerical Implementation  
#### 4.2.1 Proposed Bayesian Network Configuration for Random Variable Characterization  
The proposed configuration of the Bayesian network will depend on the loading conditions of the tests. In this case, we have two configurations depending on whether the beam is loaded or unloaded (Figure 4). The input parameters to be determined \(\mathrm{C}_{\mathrm{s}}\), \(\mathrm{D}\) and \(\alpha\) are modeled as parent nodes of the Bayesian networks according to the loading case. The child nodes represent the chloride profiles at the four measurement depths of \(\mathrm{C}(\mathrm{x}_{\mathrm{i}},\mathrm{t}))\) and are the same for all loading conditions: \(\mathrm{x}_{\mathrm{i}}=5\) mm, \(15\) mm, \(25\) mm and \(35\) mm) in both cases. All inspections are undertaken at the same time after exposure t = 388 days. For tests without load, we observe in Figure 4 that we have six nodes including two parent nodes (\(\mathrm{C}_{\mathrm{s}}\) and \(\mathrm{D}\)) and four child nodes (\(\mathrm{C}(\mathrm{x}_{1},\mathrm{t})\), \(\mathrm{C}(\mathrm{x}_{2},\mathrm{t})\), \(\mathrm{C}(\mathrm{x}_{3},\mathrm{t})\) and \(\mathrm{C}(\mathrm{x}_{4},\mathrm{t})\)). For tests with load (static or cyclic), the parameter \(\alpha\) is added as a parent node giving seven nodes for this network (Figure 4).  
Figure 4: Bayesian networks setup for (i) unloaded and (ii) loaded conditions.  
Figure 3: A simple Bayesian network.  
We consider that all the nodes are discrete and independent. All nodes are divided into a number of states in a range whose boundaries were chosen large enough to cover most possible values of the different nodes. As the observed data are limited, the number of states has been adjusted to get more accurate results. The details of the discretization and the prior information of the different nodes are summarized in Table 3; Table 4 for unloaded and loaded conditions, respectively. The a priori distributions of parent nodes were modeled as uniform distributions for the Bayesian network under unloaded conditions and the acceleration factor \(\alpha\) for the Bayesian network under loaded conditions. With the uniform distribution, there is any assumption on the a priori shape of these parameters in order to obtain not skewed results after the characterization process. The a posteriori outputs (histograms) of \(\mathrm{C_{s}}\) and \(\mathrm{D}\) for the Bayesian network under unloaded conditions are used as a priori distributions for these parameters in the Bayesian network under loaded conditions (Table 4). This procedure is justified in Section 4.2.2.
# A Survey of Deep Learning for Data Caching in Edge Network
## 5 Research Challenges and Future Directions
### The Cost of Deep Learning  
Though the application of DL brings performance efficiency for caching policy, additional cost introduced by DL is unneglected, since training and deploying the DL model require not only network resources but also time duration. Naturally, there is a trade-off between the cost which DL-assisted caching policy saved and the consumption which supports DL itself running, which indicates the trading with DL results in either profit, loss, or break even. Therefore, where and when to apply DL should be carefully investigated. This is especially the case for many deep learning frameworks that utilize specialized processing cores in the form of Graphics processing units (GPUs) and for some special scenarios, like DL on maritime IoT [105] and terrestrial satellite systems [106]. Moreover, not only the DL architecture but also the implementation approach (i.e., centralized or distributed deployment) would affect the balance between the cost saving and consuming, where the distributed method can be supported by end user devices equipped with DL processing chip [107]. Besides, for the purpose of reducing resource consumption and accelerating training process, some knowledge transfer methods, like transfer learning [108], can be utilized, which can transform the knowledge already learnt from the source domain to a relevant target domain.
# Motor Imagery and Sport Performance: A Systematic Review on the PETTLEP Model
## 3 Results  
### Study Selection and Characteristics  
The PRISMA flow diagram (Figure 1) shows the study selection process. The initial search strategy provided an initial pool of 48 potential studies. After removing duplicates, the abstracts of 42 studies were screened for further evaluation. Following the application of exclusion and inclusion criteria, a total of 23 articles were retrieved, and a full-text assessment was made. Thus, the current review is based upon data provided from 12 studies that met the inclusion criteria. The included studies were published over a period spanning from 2007 to 2018. Sample sizes ranged from 8 to 102 participants, totaling a final sample of 332 participants performing the PETTLEP imagery protocol while playing individual or team sports. One-third of the samples included only male participants [44; 50; 51; 54], one-third was composed only of females [35; 45; 46; 47], and one-third included both males and females [48; 49; 52; 53]. The mean age of participants among included studies ranged from 10 to 22 years; most of them were underage boys and girls. All the included participants were healthy.  
Figure 1: Flow chart of the review performed in accordance with PRISMA statement.  
The majority of the 12 studies included in the present review applied imaging protocols during team sports such as soccer, netball, volleyball, field hockey, or cricket [35; 44; 46; 47; 49; 50; 52; 53], while only 5 studies out of 12 involved individual sports such as gymnastic or table tennis [45; 47; 51; 53; 54]. Most of the included motor imagery interventions took place over a period of 4 to 6 weeks [35; 44; 45; 50; 51; 53; 54]. Most of the included studies adopted imagery sessions consisting of participants either hearing a motor imagery script read by someone or reading the script themselves [44; 46; 49; 50; 51; 52; 53]. Detailed instructions regarding imagery training were used in four studies [35; 47; 48; 54], while in only one study the mental training protocol was provided by video-guided motor imagery 48. In all the reported studies, the PETTLEP imagery protocol was effective in enhancing sport performance, especially when combined with physical practice, even when compared to traditional imagery interventions.