esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Cuda Implementation  
In order to implement Algorithm 1 in a GPU with CUDA, several kernels and mathematical operations were designed. First, the application of the Sensitivity Maps operator \(C_{c}\) and its Hermitian transpose \(C_{c}^{H}\) required the implementation of an _element-wise_ complex multiplication, where each thread processes one data point, performing concurrently the real and imaginary parts of the complex multiplication for all the elements. Another _element-wise_ product kernel was designed to apply the sampling mask operator \(S_{t}^{H}S_{t}\). The kernels for the displacement operator \(T_{t}\) and its Hermitian transpose \(T_{t}^{H}\) assign threads to individual voxels and compute displacement fields based on rigid or non-rigid motion parameters, warping the image using linear interpolation. In the C++ implementation, the non-zero elements of \(T_{t}\) were precomputed before the CG solver was run and stored in CPU memory. In the Matlab-GPU implementation, due to the GPU memory limitation, the precomputed elements had to be transferred from the CPU to the GPU memory at the beginning of the outer loop (before line 3 of Algorithm 1). To avoid this repeated and costly transfer, in the native CUDA implementation, the non-zero elements of \(T_{t}\) were computed dynamically within the kernel from the motion model definition given in Equation 1. This resulted in a higher computational complexity; however, all the required problem variables were stored in GPU memory before running the CG solver. The Fast Fourier Transform  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & \(\mathbf{D_{1}}\) & \(\mathbf{D_{2}}\) & \(\mathbf{D_{3}}\) & \(\mathbf{D_{4}}\) & \(\mathbf{D_{5}}\) & \(\mathbf{D_{6}}\) \\ \hline \(N_{x}\) & 320 & 320 & 320 & 256 & 256 & 256 \\  
_FFT_ algorithm was implemented using the GPU-optimized library cuFFT [20]. Moreover, and in order to avoid race conditions, _atomicAdd_ was explicitly employed at multiple locations, mainly in the reduction process (line 8 in Algorithm 1), to safely accumulate partial results into the shared memory location. Finally, the CG solver was implemented using the cuBLAS library [21].
# Resilient Formation Reconfiguration for Leader-Follower Multi-UAVs
## 7 Conclusions  
The majority of the existing formation reconfiguration studies are carried out under the assumption that the swarm scale is fixed. By contrast, in the article, the problems of formation reconfiguration for leader-follower multi-UAVs under three conditions of dynamic swarm scale are investigated with the corresponding formation reconfiguration strategies developed. In terms of the formation reconfiguration under leader disengagement, the hierarchical BRB performance assessment model for UAVs is constructed with four designed pivotal performance metrics for each UAV as the inputs and the ultimate performance assessment result for each UAV as the output. Furthermore, connection retention rate (CRR) is introduced to quantitatively analyze the influence of the proposed formation reconfiguration strategies for changing swarm scale on the stability of the swarm communication topology. In simulation, CRR for the swarm communication topology under the formation reconfiguration strategies for leader disengagement, new member addition and follower disengagement can reach 67%, 90% and 100%, respectively.  
In the article, it is assumed that there is only one leader for an UAV swarm and only two communication methods are permitted, that is, leader-to-follower and follower-to-follower. The control architecture involving multiple leaders and how to design the communication mechanism under this architecture, including leader-to-leader, leader-to-follower and follower-to-follower, will be further studied. Moreover, in the article, the main focus was put on how to reconfigure the formation when the scale of the multi-UAVs is altered and how to fuse the multiple sources of information of followers to elect a leader for a leaderless swarm, where inter-vehicle collision avoidance during formation reconfiguration is not incorporated. This challenge needs to be considered in our future works.  
Conceptualization, H.Z. and R.Y.; methodology, H.Z. and W.H.; writing--original draft preparation, H.Z.; writing--review and editing, H.Z., R.Y.; visualization, H.Z.; supervision, R.Y., Z.F. and W.H.; funding acquisition, G.Z., R.Y. and Z.F. All authors have read and agreed to the published version of the manuscript.  
This study was funded partly by National Natural Science Foundation of China under Grant No. 62203365. and No. 62203461, partly by the Natural Science Basic Research Program of Shaanxi under Grant No. 2022 JQ-580, partly by the Young Talent Promotion Program of Shaanxi Association for Science and Technology under Grant No. 20220121.  
Not applicable.  
The authors declare no conflicts 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.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 2 Characteristics of the Underwater Acoustic Channel  
### The Speed of Sound in Water  
The speed of sound in oceans depends on three main factors namely; temperature, salinity and depth. Equation (1) presents the empirical formula given by [PERSON] to estimate the speed of sound in water.  
\[\begin{array}{ll}c=&1449+4.591T-5.304\times 10^{-2}T^{2}+2.374\times 10^{-4}T^{3 }+1.34(S-35)\\ &+1.63\times 10^{-2}D+1.675\times 10^{-7}D^{2}\\ &+1.025\times 10^{-2}T(S-35)-7.139\times 10^{-3}TD^{3}\end{array} \tag{1}\]
## 2 Methods
### Sensitivity Analysis
Then the maximum displacement in time domain can be determined by the following expression:  
\[q_{\max}=\max_{\tau=(0,2\pi)}\left(\mathbf{Q}_{0}+\sum_{j=1}^{n}\left[\mathbf{ Q}_{cj}\cos\left(m_{j}\tau\right)+\mathbf{Q}_{sj}\sin\left(m_{j}\tau\right) \right]\right)=\left.\left.\mathbf{\Psi}\right|_{\tau_{\max}}\mathbf{Q} \tag{29}\]  
Substituting Equation (29) into Equation (16) and Equation (20) yields:  
\[s_{1}=\frac{\partial q_{\max}}{\partial p}=\left.\mathbf{\Psi}\right|_{\tau_ {\max}}\frac{\partial\mathbf{Q}}{\partial p} \tag{30}\]  
\[s_{2}=\frac{\partial^{2}q_{\max}}{\partial p^{2}}=\left.\mathbf{\Psi}\right|_ {\tau_{\max}}\frac{\partial^{2}\mathbf{Q}}{\partial p^{2}}+\left(2\left.\mathbf{ \Psi}\right|_{\tau_{\max}}\frac{\partial\mathbf{Q}}{\partial p}+\ddot{q}( \tau_{\max})\frac{\partial\tau_{\max}}{\partial p}\right)\frac{\partial\tau_{ \max}}{\partial p} \tag{31}\]  
The extra term \(\frac{\partial\tau_{\max}}{\partial p}\) is introduced in consideration that the non-dimensional time instant for maximum displacement is dependent on the parameter \(p\). Thus the first-order sensitivity \(s_{1}\) and the second-order sensitivity \(s_{2}\) are obtained. For different parameters which possess different units, the results may be misunderstanding. So Equations (30) and (31) can be refined as:  
\[{s_{1}}^{*}=s_{1}\left(p_{0}^{+}-p_{0}\right),s_{2}^{*}=\frac{1}{2}s_{2}\left( p_{0}^{+}-p_{0}\right)^{2} \tag{32}\]  
In this cases, the sum of \(s_{1}^{*}\) and \(s_{2}^{*}\) can be expressed as the variation of maximum amplitudes under slight change of specific parameters. When \(p_{0}^{+}=(1+1\%)p_{0}\), terms in Equation (32) describe the variation of maximum amplitudes under \(1\) percent change of the investigated parameters, which is the main focus in the following discussion.
## 5 Cutting Path Planning Based on Clustering Algorithm  
Figure 6 shows the flowchart of the agglomerative hierarchical clustering algorithm. The original data of the clustering algorithm include the floor series and its number \(m\), and  
the predefined cluster number \(p=m-1\). According to the piecewise linear representation method proposed in reference [17], the floor series are transformed into the PLR series, and then the PLR series are aligned with each other with linear interpolation. Take each individual PLR series as a cluster, calculate the distances between each of the clusters. The pair of clusters with the minimum distance is merged into one cluster, and then update the cluster center and cluster number. Repeat steps 2 to 4 until the cluster number, K, is equal to the predefined cluster number, \(p\). Finally calculate the clustering validity index and cluster results. Steps 1 to 5 are recursively repeated until the predefined cluster number, \(p\), is equal to 1. The clustering validity and dendrogram were recorded during the recursive process. It should be noted that the implementations of the agglomerative hierarchical clustering based on morphology similarity and coplanarity are the same, but the distances between clusters must be calculated by Equations (1) and (4), respectively.  
According to the recorded clustering validity and dendrogram, the morphology-based and conplanarity-based cluster centers and their cover zones were determined. The coal seam floor cutting path can be obtained through reconstructing the floor with the morphology-based and conplanarity-based cluster centers and their cover zones. The floor reconstructing process includes: (1) aligning the morphology-based and conplanarity-based clusters, so that the cover zones of the morphology-based cluster and conplanarity-based cluster are coincident; (2) taking the morphology-based cluster center as a generating line; (3) taking the gradient in the face direction obtained from conplanarity-based clustering as the stretching angle; and (4) moving the generating line at the stretching angle along the face direction in a cover zone.  
The proposed cutting path planning method based on multiple clustering algorithms aims to guide the longwall shearer cutting in the coal seam by considering the geometry of the coal seam. So it is inapplicable to a low-thickness coal seam, in which undercut rock is  
Figure 6: The flowchart of the agglomerative hierarchical clustering algorithm.  
unavoidable because the extracting thickness of longwall mining machines is larger than the thickness of the coal seam [38,39].
## 3 Materials and Methods
### Performance Evaluation Metrics  
The performance of the proposed methodology was quantitatively evaluated using the standard classification metrics of precision, recall, and F1-score[32, 37]. The calculation of these metrics relies on a comparison against a reliable ground truth, which for this study was established through a rigorous manual segmentation process. This procedure involved independent labeling of the point clouds by several researchers using CloudCompare v2.14 software, with on-site photographs providing an essential visual reference to ensure accuracy. Following the initial labeling, a joint review was conducted to resolve any discrepancies until a unanimous consensus was reached for the final reference data. These metrics are derived from the four components of the confusion matrix, which compares the model's predictions against this established ground truth: true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). Precision, which measures the exactness of the classifier, is calculated using Equation (8); recall, representing a measure of completeness, is calculated using Equation (9); and the F1-score, which provides the harmonic mean of precision and recall, is determined via Equation (10).  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
\[F_{\text{1-score}}=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{10}\]
# Efficient Parallel FDTD Method Based on Non-Uniform Conformal Mesh
## 5 Recommendation  
In this paper, a parallel FDTD method based on non-uniform conformal mesh is proposed, which can greatly reduce the computational burden and solve large-scale problems effectively, but there are still some aspects worth further exploration.  
In the process of region decomposition, we try our best to ensure the uniform distribution of computation in each subregion and reduce the amount of data transmission, and perform load balancing to improve the parallel performance. By designing a more efficient load balancing strategy, such as one highly associated with CPU and other hardware, realizing the dynamic scheduling of processors, and using a parallel coloring method, the processor performance can be fully utilized, and the parallel efficiency can be further improved. In addition, the use of a hybrid parallel strategy, such as combining GPU parallel methods, so that it can be applied to large-scale simulation scenarios, including large-scale phased array antennas, system-level electromagnetic compatibility, and simulation applications based on realistic scenarios, is a feasible direction for future work.  
Figure 12: (**a**) The parallel scalability and efficiency of the 100-period folded waveguide on a multi-node; (**b**) the parallel scalability and efficiency of the 16 \(\times\) 16 phased array antenna on a multi-node.  
**Author Contributions:** Conceptualization, K.L.; methodology, K.L.; software, K.L., G.L., L.H., W.C. and C.F.; data curation, K.L.; validation, K.L., G.L. and L.H.; data curation, K.L.; writing--original draft preparation, K.L.; writing--review and editing, X.J.; project administration, T.H., L.Z. and D.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
Thanks to Chengdu Huangfeng Technology Co., Ltd. for providing the electromagnetic field simulation software 'BUMBLEBEE v1.0'.  
The authors declare no conflicts of interest.
## 4 Results and Discussion
### Performance between Multiple Datasets
After analysis, it is believed that the reason for this is that the model performance has not yet achieved accurate predictions for all joints; however, there is severe occlusion of some joints in the input images, which affects the prediction accuracy. Partial experimental results for the CMU Panoptic dataset are shown in Figure 8. The prediction results obtained using this dataset were more accurate. Among them, there are fewer action occlusions, the accuracy of the prediction results is relatively high, and the prediction of some joint points is not very accurate. However, the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Joint Point** & **Base** & **2D-3D Pose** & \
\begin{tabular}{c} **Base +** \\ **Anchor Pose** \\ \end{tabular} &
\begin{tabular}{c} **Base + Anchor** \\ **3D Pose** \\ \end{tabular} & **Our Method** \\ \hline Right shoulder & 12.6 & 63.5 & 58.2 & 57.5 & 23.1 \\ Left elbow & 11.8 & 22.1 & 17.1 & 57.5 & 22.5 \\ Right elbow & 7.6 & 20.5 & 18.5 & 17.8 & 12.2 \\ Left wrist & 8.1 & 9.0 & 8.9 & 18.5 & 14.1 \\ Right wrist & 11.9 & 9.8 & 8.9 & 8.9 & 50.5 \\ Left hip & 7.6 & 49.0 & 41.8 & 8.9 & 41.9 \\ Right hip & 7.5 & 44.6 & 46.6 & 44.5 & 39.9 \\ Left knee & 22.6 & 33.5 & 40.4 & 41.8 & 31.2 \\ Right knee & 26.5 & 49.6 & 38.3 & 39.1 & 45.9 \\ Left ankle & 22.1 & 10.2 & 38.6 & 41.7 & 46.1 \\ Right ankle & 19.2 & 6.3 & 42.3 & 41.6 & 46 \\ Average & 25.6 & 39.0 & 40.1 & 41.7 & 46.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 7: Positive attitude estimation for multi-views based on the Microsoft COCO 2014 dataset. overall prediction trend is accurate. In summary, the FPNet can effectively predict human frontal posture, but the performance still has room for improvement.
## 4 Experimental Results
### Experimental Results and Analysis
When the system environment is abnormal, such as abnormally high CPU utilization, the requested risk assessment value will increase. To protect the system and resource security, virtual machine \(M_{2}\) will reject requests with high-risk values. At the 500 th second (zone C in Figure 6), illegal requests are added, and both access controls can detect illegal requests, so the rejection percentages go up. Figure 7 shows the response times of \(M_{1}\) and \(M_{2}\) at the 300 th second. In Figure 7, because virtual machine \(M_{1}\) continues to receive and respond to requests, it continuously occupies already tense resources and increases the system burden. Therefore, the request-response time is about 1100 ms for a long time. In the local area network, this delay is unbearable, and to protect the security of the system, virtual machine \(M_{2}\) rejected the request with a high-risk assessment value, which guaranteed the response time and prevented the system from facing the risk of crashing due to continuing to receive requests, although this would cause a certain degree of misjudgment. However, compared with the losses caused by system crashes, it is acceptable to sacrifice a certain accuracy rate to ensure that the system and resources are always in a relatively safe state and avoid causing greater losses. In the recovery phase, since the risk assessment of virtual machine \(M_{2}\) protects the system resources from being overused, it can recover from the anomaly faster than virtual machine \(M_{1}\). Figure 6: Comparison of request interception rates of the virtual machines \(M_{1}\) and \(M_{2}\). Through the above experiments, we can see that the risk-based access control strategy proposed in this paper can perform a risk assessment on requests according to the dynamic changes of subjects, resources, and environments and make corresponding decisions, which dynamically protects the system and resources from a relatively secure state, to avoid system crash and cause greater losses. The evaluation method is simple and efficient and has great feasibility. The proposed access control method is ideal for ensuring the security of cloud-based dynamic medical data storage, processing, and access.
## 3 Parametric Study of Sub-Rectangular Tunnels in Quasi-Static Conditions
### Effect of the Soil Young's Modulus (\(E\))  
The soil Young's modulus values are assumed to vary in the range from 10 to 350 MPa while keeping \(K_{0}\) equal to 0.5 and \(a_{\text{H}}\) of 0.5 g. The other parameters based on the reference case are assumed (Table 1). The following can be seen from Figure 11.  
For low \(E\) values smaller than 50 MPa, an increase in \(E\) induces an increase in the absolute extreme incremental bending moments. When the \(E\) value is greater than 50 MPa, the increase in \(E\) causes a decrease in the absolute extreme incremental bending moments (Figure 11a). It should be noted that the dependency of the absolute extreme incremental bending moments in the sub-rectangular tunnels on the \(E\) value is insignificant compared to the circular-shaped tunnels (Figure 11a). It is also worth highlighting that while the absolute extreme incremental bending moments of the circular tunnel for the no-slip condition are smaller than the corresponding full-slip ones [9; 25], the absolute extreme incremental bending moments of the sub-rectangular tunnel for the no-slip condition are greater than the corresponding full-slip ones. The behavior of sub-rectangular tunnels is different from the circular-shaped tunnels. The same conclusion was also obtained when considering the horizontal seismic acceleration \(a_{H}\).  
Figure 10: Effect of the \(a_{\text{H}}\) value on the internal forces of circular and sub-rectangular tunnel linings. (**a**) Incremental bending moments; (**b**) incremental normal forces.  
Figure 11a also shows greater absolute extreme incremental bending moments induced in sub-rectangular tunnels for the no-slip condition compared with circular tunnels with the same utilization space area. However, in the full-slip condition, absolute extreme incremental bending moments in the circular tunnel are greater than the sub-rectangular ones for \(E\) values smaller than approximately 150 MPa. When \(E\) values are larger than 150 MPa, absolute extreme incremental bending moments developed in circular tunnels are smaller than in sub-rectangular tunnels.  
Figure 11b indicates that an increase in \(E\) value causes a significant corresponding increase in the absolute extreme normal forces in both sub-rectangular and circular tunnels for the no-slip condition. However, it induces an insignificant change in absolute extreme incremental normal forces for the full-slip condition. Absolute extreme incremental normal forces in the sub-rectangular tunnels are generally 10% smaller than for the circular tunnels.
# Piezoelectric Electro-Mechanical Impedance (EMI) Based Structural Crack Monitoring
## 2 Principle of Piezoelectric Electro-Mechanical Impedance Method
While the local stiffness of the host structure decreases, the peak frequency in the piezoelectric admittance signature also decreases [59]. The piezoelectric patch bonded near the crack can transform this stiffness variation into peak frequency changes in the piezoelectric admittance admittance signature. By analyzing the peak frequency changes in the piezoelectric admittance signature, the structural crack can be detected and thus it provides a theoretical basis for monitoring the crack status of a host structure with the peak frequency changes of the piezoelectric impedance.
## 2 Main Disadvantage of an SDN Controller
### Variability in Programming Interfaces  
Some of the most popular SDN controllers (such as OpendayLight, Ryu, or ONOS [18; 41]) use REST API interfaces as a communication mechanism. The RESTful API is a device communication interface to secure information exchange over the hypertext transfer protocol. However, each controller exposes its APIs in a different way, forcing users to modify the request syntax or the programming language according to the specific conditions [16]. This lack of standardization turns the applications into systems that depend on a particular SDN controller.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Monolithic** & **Microservices** \\ \hline \multirow{4}{*}{Resiliency} & The whole system can be affected by a bug, communication or device failure, or security issues. & Other services are not affected by a failure in a particular microservice. \\ \hline \multirow{4}{*}{Deployment} & Simple and fast deployment architecture. & Orchestrating the deployment becomes complex due to communication and hardware. restrictions. \\ \hline \multirow{4}{*}{Scalability} & Redeploying the entire system to manage new changes make it difficult to manage and maintain. & You can scale each element independently without experiencing any downtime. \\ \hline \multirow{4}{*}{Compatibility} & Adopting new technology languages or frameworks is impossible due to the lack of flexibility. & Multiple integration and standardization. \\ \hline \multirow{4}{*}{Security} & Communication within a single unit secure data processing. & The use of APIs to communicate different services produces some security threats. \\ \hline \multirow{4}{*}{Development} & The huge indivisible database makes distributing the team’s efforts impossible. & Each component can be independently operated by a team of developers. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of monolithic centralized SDN controller vs. microservices controller.