esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
allowed us to analyze the influence of the Poisson coefficient on the stress concentration from the results included in Figure 15b. Results show a linear variation of the \(\eta\) radial, tangential, and von Mises with the Young modulus. In such a way, it can be concluded that the lower the Young modulus of the material of the contact ring, the lower the stress concentration at the edge of the hub. This is valid for radial, tangential, or von Mises stress. Regarding the variation with the contact ring Young modulus for the two values of the thickness ratio \(\gamma\) considered, the same decreasing trend is obtained with minimal variations for the tangential and von Mises components and slight variations for the radial component being higher for low values of the contact ring Young modulus. Finally, according to the results obtained, the variations of the \(\eta\) parameter with the Poisson coefficient (see Figure 15b) for the cases studied are minimal, resulting in an almost plane curve for all the stress components. In addition, the variation with the contact ring thickness ratio \(\gamma\) is minimal, and, therefore, the influence of this parameter can be considered negligible. Figure 14: Variation of the parameter \(\eta\) radial, tangential, and von Mises as a function of the radial interference (blank symbols, \(\gamma\) = 0.125 and full symbols, \(\gamma\) = 0.250). Figure 15: Variation of the parameter \(\eta\) radial, tangential, and von Mises (**a**) as a function of the material Young modulus of the contact ring, and (**b**) against the Poisson coefficient of the material of the contact ring (blank symbols, \(\gamma\) = 0.125 and full symbols, \(\gamma\) = 0.250). Design equations can be derived from the obtained results in terms of the linear interpolation of the curves shown in Figure 15a. \[\eta_{r}(E)=0.00598\ E+0.83372,\quad R^{2}=0.9826,\ \ \gamma=0.125 \tag{6}\]  
\[\eta_{r}(E)=0.00736\ E+0.55252,\quad R^{2}=0.9896,\quad\gamma=0.250 \tag{7}\]  
Figure 12:0  
Figure 12:1  
According to the results, a correlation coefficient higher than 0.98 is obtained, so these equations can be useful for estimating the degree of reduction in terms of the Young modulus of the material used in the contact ring within the interval analyzed.
## 2 Background and Problem Statement  
Consider a dynamic network, in which interconnection is represented by \(G(V,E)\), an undirected graph with components' set \(V\) and links' set \(E\), consisting of \(N=|V|\) nodes, let us denote by \(\mathcal{N}_{i}=\{j\in V:(i,j)\in E\}\) the set of neighbors of node \(i\) and \(d_{i}=|\mathcal{N}_{i}|\) its degree. Interactions between nodes can be captured by the Laplacian matrix \(\mathbf{L}\in\Re^{N}\) with entries \(l_{ii}=d_{i}\), \(l_{ij}=-1\) if \(j\in\mathcal{N}_{i}\) and \(l_{ij}=0\) elsewhere. Denote the Laplacian spectrum by \(sp(\mathbf{L})=\{\lambda_{1}^{m_{1}},\lambda_{2}^{m_{2}},\ldots,\lambda_{D+1}^ {m_{D+1}}\}\), where the different Laplacian eigenvalues are in increasing order \(0=\lambda_{1}<\lambda_{2}<\ldots<\lambda_{D+1}\) and superscripts stand for multiplicities \(m_{i}=m(\lambda_{i})\), while \(\mathbf{S}_{2}=\{\lambda_{2},\ldots,\lambda_{D+1}\}\) stands for the set of the non-zero distinct Laplacian eigenvalues.
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## 6 Experiment Analysis  
In this section, our scheme has been experimentally compared with that of the RAAC, to test the operational efficiency of our PCAC scheme. The experiments were implemented in an environment of Windows 10 (in a PC with Core i7-7500U and 2.7 GHz) and programmed with JPBC [20]. The algorithm in the RAAC scheme was programmed by the description in the reference [5]. As the RAAC scheme was the first to propose the audit and tracking phase based on the CP-ABE, we chose it for the comparison experiments. Therefore, the computational efficiency of our Act_Audit phase could be tested as a comparison with the RAAC scheme.
## 5 Conclusions  
The objective of this paper is to address the issue of real-time interference mitigation for reliable target detection with FMCW radar in interference environments. The RIMformer network is improved to be lightweight by integrating the linear attention mechanism and the depthwise separable convolution, which reduce computational complexity and network size while maintaining interference mitigation performance. Additionally, we implement a two-stage KD process to further optimize the network by training an intermediate-sized assistant teacher model before training the final student model. Compared to the original RIMformer, the improved framework significantly reduces the network size and inference time with a slight SINR loss, achieving a good balance between performance and processing delay. Both simulations and experiments demonstrate the promising performance and inference time of the lightweight RIMformer. The proposed framework provides an efficient solution for real-time FMCW radar applications with strict latency requirements and limited computing resources.  
Conceptualization, Y.W. and G.C.; methodology, Z.Z.; software, Z.Z.; validation, Y.W., Z.Z. and J.C.; resources, G.C.; data collection, Y.W., Z.Z., Y.Z., and J.C.; experimental analysis, Y.W., Z.Z., H.S., and Y.Z.; writing--original draft preparation, Y.W. and Z.Z.; writing--review and editing, Y.W. and H.S.; supervision, G.C.; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by the National Natural Science Foundation of China under Grant 62101020, Grant 62141405 and Grant 62293495, and Special Scientific Research Project of Civil Aircraft under Grant MJZ5-2N22.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 4 Discussion
### Issues with Yield Estimation across Different Years
For the all-year model, the variable \"year\" captured temporal variability only, by assuming a constant variation of yields throughout the whole period, yet this might not be true. The average EVI2 at peak growth stages captured spatial variability as well as temporal variability if inter-annual variability of growth conditions showed up during the peak growth stages. A limit for the model in this study was that yield variability induced by abnormal growth conditions after the peak growth stage was not captured. Figure 9: Comparison of yield estimation error, that is, mean relative absolute error (MRAE, %), using 250 m MODIS EVI2 using year-specific models and an all-year model. EVI2 was extracted using a general cropland mask and crop-specific mask for 2003–2016. The circles show the results of the years when there is a large difference between the all-year model and the year-specific model using the general cropland mask.
## 1 Introduction
To address this limitation, out-of-memory graph processing systems have been proposed [7]. For example, EGraph [7] integrates Subway [31], a GPU-based static graph processing system, with GPMA's dynamic graph update mechanism, enabling the processing of graphs that exceed GPU memory capacity. Despite the rapid parallel processing capabilities of GPUs, their memory constraints remain a significant challenge, particularly for real-time dynamic graph processing. Recent advancements in GPU-based dynamic graph processing have focused on both hardware and software optimizations to enhance performance. GPU-accelerated graph libraries such as cuGraph and RAPIDS have optimized large-scale graph analytics, while new frameworks have been developed to support real-time data streaming and dynamic graph updates. In this paper, we propose an efficient scheme for processing large-scale dynamic graphs based on subgraph scheduling and operation reduction. First, the structure and dynamic change patterns of the graph are analyzed on the host to determine the optimal partition loading order using priority scores. These scores consider not only active vertices within a partition but also those that may potentially become active, optimizing data processing on GPUs and maximizing memory utilization. Additionally, since the structure of dynamic graphs evolves over time, snapshots are generated at specific intervals to capture the graph's state and collect relevant information. These snapshots help identify instances where identical vertices or edges are repeatedly inserted and deleted, enabling an operation reduction method to minimize unnecessary computations. This approach effectively reduces resource usage and enhances graph processing speed. This study experimentally verifies that GPU-based dynamic graph processing techniques play a crucial role in large-scale data transmission optimization and real-time data analytics in big data environments. In particular, performance evaluations using graph algorithms in various big data applications, such as social network analysis, financial transaction monitoring, cybersecurity threat detection, and large-scale IoT network management, confirm that the proposed method significantly enhances computational efficiency in these environments. The structure of this paper is as follows. Section 2 analyzes existing research on large-scale graph processing using GPUs and examines the associated challenges. Section 3 presents the proposed scheduling and operation reduction methods for efficient dynamic graph processing on GPUs. Section 4 evaluates the effectiveness of the proposed method through performance assessments using various graph algorithms. Finally, Section 5 concludes the paper and outlines potential directions for future research.
# Modelling Pollutant Dispersion in Urban Canyons to Enhance Air Quality and Urban Planning
## 2 Materials and Methods
### Indicators  
In order to obtain valuable information from the cases studied, it is necessary to define parameters of interest to fully characterise the dispersion of pollutants in urban canyons as well as the dynamics of the flow within them.  
Firstly, the airflow pattern inside the street will determine the ventilation. Other studies have shown that for a low AR, only one vortex is formed whereas for a high \(AR\), a multi-vortex regime is found [53; 54]. Furthermore, for a fixed AR, the creation of one or more vortices is determined by the Reynolds number and thus by the wind speed and the height of the building. For low \(Re\) values, more vortices tend to appear [55]. That is why it is vital to obtain the airflow pattern, the number of vortices, and the position of their centres for a range of \(Re\) which resembles typical street morphology.  
Secondly, to numerically characterise street ventilation, the \"purging flow rate\" (PFR), in kg/s, is used as suggested by the literature [13; 56]. This defines the amount of air entering the street from the exterior, which implies air renovation, dispersing pollutants in the zone. For street canyons in the multi-vortex regime, each recirculation can be considered isothermal and with a pollutant concentration that is practically uniform, which will be later proved. This means that there is not a high temperature or concentration gradient in a vortex, which leads to a segregation of the canyon street by attending to the frontiers between vortices forming the same number of subvolumes, because each one will have a different temperature and pollutant concentration.  
With this innovative approach, the PFR is not only calculated in the roof level of the street canyon but also on the interfaces of each vortex. This is essential to characterise since, for people who are spending time in a street where a bottom vortex is formed, the pollutant dispersion is determined by the PFR of that bottom recirculation. The PFR is defined according to Equation (9):  
\[\text{PFR}_{i}=\frac{\text{Vol}_{i}\cdot S_{p}\cdot\rho}{c} \tag{9}\]  
where i is the number of the subvolume, \(\text{Vol}_{i}\) is the volume of the subvolume \(i\) considered (m\({}^{3}\)), \(c\) is the mean pollutant concentration (kg/m\({}^{3}\)), \(\rho\) is the density of the air (kg/m\({}^{3}\)), and \(S_{p}\) is the pollutant source rate (kg/s\(\cdot\)m\({}^{3}\)). The PFR gives a numerical value of the real air mass flow coming from the exterior to the street as well as the air mass flow through each subvolume, which helps in understanding the ventilation efficiency of the bottom layers of a street canyon.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 4 Results
### Hierarchical Decision Tree
#### 4.1.1 The Decision Tree for Distinguishing Vegetation and Non-Vegetation  
Figure 6 shows a typical decision tree for distinguishing vegetation and non-vegetation. Generally, the differences of NDVI between vegetation and non-vegetation in the leaf-on season are obvious. Therefore, the mean value of NDVI derived from the HJ image in leaf-on season was used to distinguish between non-vegetation and vegetation. Then the MNDWI derived from the same image was used to identify open waters misclassified as vegetation, because it is sensitive to open waters.  
Figure 6: The decision tree for distinguishing vegetation and non-vegetation. The HJ-CCD image acquired on 24 July 2010. The ellipsis represents the land cover class needed for further division.
## 1 Introduction
### Contributions of This Paper  
This paper addresses existing problems in mangrove remote sensing monitoring, including data resource scarcity, insufficient feature extraction, inadequate multi-scale adaptability, and low boundary segmentation accuracy. The main contributions are as follows:  
(1) The construction of the Mangrove Remote Sensing Image Semantic Segmentation Dataset in Beihai of Guangxi (MRSDBG). This dataset is built upon multi-temporal high-resolution remote sensing images, integrating multiple satellite data sources, and features multi-temporal, multi-scale, and high generalization characteristics. The dataset provides fine-grained, pixel-level annotations, offering crucial support for the development and validation of mangrove remote sensing monitoring algorithms. The construction of this dataset fills the void of high-quality standard datasets in the field of mangrove remote sensing monitoring, providing a reliable data foundation for related research.  
(2) The design of an innovative MFA-UperNet network architecture. This network achieves high-precision semantic segmentation of mangrove remote sensing images through the organic combination of three core modules: feature encoder, Auxiliary Edge Neck, and semantic decoder: The feature encoder, based on the ConvNeXt [35] feature extraction backbone network, performs multi-level semantic feature extraction, generating feature maps at different scales, fully utilizing feature information from different levels to establish a solid feature foundation for subsequent precise segmentation. The Auxiliary Edge Neck specifically designs an edge-aware branch, enhancing segmentation accuracy in mangrove boundary regions through fine-grained boundary feature learning. The semantic decoder introduces Cascade Pyramid Fusion Modules and Multi-Scale Selective Kernel Attention Modules, innovatively combining cascade pyramid structure with selective kernel attention mechanisms to achieve efficient multi-scale feature fusion. This not only enhances the model's adaptability to mangrove multi-scale variations but also significantly improves the recognition accuracy of mangrove targets in complex backgrounds.  
(3) The systematic experimental validation of MFA-UperNet architecture's superior performance in mangrove remote sensing image semantic segmentation tasks. Experimental results demonstrate significant improvements in mangrove segmentation tasks. These improvements provide new technical solutions for enhancing the accuracy and efficiency of mangrove coastal wetland ecological monitoring, offering important practical value for advancing mangrove protection and management.
## 3 Results
### Voltage Productions and Losses in Stacked Systems  
The theoretical and actual voltage productions were compared according to the electrode connection method at each phase (Table 3). When the electrode was connected in series and parallel, the actual voltage production was lower than the theoretical voltage  
Figure 5: Polarization (dot line) and power curves (solid line) of S- (red), M- (blue), and L- (green) MFCs in a stacked MFC system at phases I (**a**), II (**b**), and III (**c**).  
production regardless of the electrode connection method. In addition, voltage losses (28-74%) in a parallel electrode connection were lower than those (32-94%) in the electrode series connection, which is a similar trend in previous studies. The lower energy losses in a parallel connection generally occur because of the voltage reversal in the series connection and lower internal resistance in the parallel connection [18; 30; 31].  
Interestingly, the lowest voltage losses (28% for a parallel connection and 30% for a series connection) were observed when connected to M- and L-MFCs, producing similar voltage at phases II (M- and L-MFCs stack) (Figure 5). In contrast, the other stacks showed higher voltage losses (69-94% for series connection and 43-74% for parallel connection). This suggests that the simple hydraulically series connected stack system with different volume MFCs can minimize the voltage loss and avoid the voltage imbalance.
# Aerial Tele-Manipulation with Passive Tool via Parallel Position/Force Control +
## 3 Control System
### Position Control  
The position control of the mechanically under-actuated quadrotor is implemented using a two layer cascade controller. The orientation is controlled using PID in the inner loop, and the outer loop provides the inner loop with reference roll and pitch (\(\phi_{d},\theta_{d}\)) to control the robot planar motion using a gravity-compensated-PD. The yaw motion is controlled independently.  
\[\begin{cases}u_{z}=\frac{m}{\cos(\phi)\cos(\theta)}(g-k_{pz}\tilde{z}-k_{dz}z) \\ u_{x}=-k_{px}\tilde{x}-k_{dz}\tilde{x}\\ u_{y}=-k_{py}\tilde{y}-k_{dy}\tilde{y}\\ \begin{bmatrix}\sin(\phi_{d})\\ \sin(\theta_{d})\end{bmatrix}=\frac{m}{u_{z}}\begin{bmatrix}\sin(\psi)&-\cos( \psi)\\ \frac{\cos(\psi)}{\cos(\phi)}&\frac{\sin(\psi)}{\cos(\phi)}\end{bmatrix} \begin{bmatrix}u_{y}\\ u_{x}\end{bmatrix}\\ \mathbf{u}_{\eta}=-K_{D}\tilde{\eta}-K_{P}\tilde{\eta}-K_{I}\int_{0}^{t}\tilde{ \eta}(s)ds,\end{cases} \tag{5}\]  
where \(\mathbf{p}-\mathbf{p}_{d}=[\tilde{x}\ \tilde{y}\ \tilde{z}]^{\top}\) is the position error with \(\mathbf{p}_{d}\in\mathbb{R}^{3}\) being the desired position, \(k_{px},k_{dz},k_{py},k_{dy},k_{pz},k_{dz},\in\mathbb{R}^{+}\) are proportional and derivative gains, \(\tilde{\eta}=\eta-\eta_{d}\) is the  
Figure 4: The bilateral teleoperation scheme with a parallel position/force controller in the slave side to control an aerial manipulator.  
Figure 5: Parallel position/force controller for an aerial manipulator, in the remote side of the teleoperation scheme.  
orientation error with \(\eta_{d}=[\phi_{d},\theta_{d},\psi_{d}]^{\top}\) being the desired orientation, and \(K_{P},\ K_{D},\ K_{I}\) are the proportional, derivative, and integral diagonal gain matrices to regulate the attitude.
## 4 Experimental Verification
In terms of head, the experimental results and numerical simulation results show that the head increases with the increase in speed at different speeds, with a maximum error of 2.7%, a minimum error of 1.04%, and an average error of 1.89%. In terms of efficiency, the efficiency increases with the increase in rotational speed. The maximum error between experimental results and numerical simulation results is 6.46%, the minimum error is 5.65%, and the average error is 6.01%. In terms of shaft power, the shaft power increases with the increase in rotational speed. The maximum error between experimental results and numerical simulation results is 9.67%, the minimum error is 6.45%, and the average error is 7.85%. These experimental results showed good consistency with the simulation results,  
Figure 11: Comparison of simulation and experiment results: (**a**) head of vortex pump, (**b**) efficiency of vortex pump, and (**c**) shaft power of vortex pump. and the error range between the simulation results and the experimental results is within 10%, which verifies the reliability of the numerical simulation results and provides strong support for further performance optimization of the vortex pump.
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 8 Evaluation
### Memory Overhead  
In conjunction with computation overhead, we measure the memory usage associated with DPTracer's logging operations. Figure 8 illustrates the memory footprints for storing both the Process Tree and interval proofs.  
The memory usage for a Process Tree linearly increases with the number of nodes. When the Process Tree supports from 100 nodes to 990 nodes, the memory consumption grows from 47 KBytes to 503 KBytes. Similar to the computation overhead, memory usage for generating proofs is primarily influenced by the height of the Process Tree. As depicted in Figure 8, the memory usage for proof generation increases logarithmically: ranging from 7.2 KBytes to 9.6 KBytes as the number of nodes expands from 100 to 990.
# Design of SBS-Modified Bitumen Stabilizer Powder Based on the Vulcanization Mechanism
## 7 Conclusions  
In this paper, S, TMTD, ZnO, BHT, Al\({}_{2}\)O\({}_{3}\):2 SiO\({}_{2}\):2H\({}_{2}\)O and SiO\({}_{2}\) were used as raw materials to obtain SBS-modified bitumen stabilizer powder. The following conclusions can be drawn on the basis of the results and analysis.  
1. The vulcanization reaction was the main mechanism by which stabilizers improved the thermal storage stability of SBS-modified bitumen. Sulfur, vulcanization accelerator, vulcanization activator, antioxidant, other active ingredients was the basic design idea, and the basic components of the stabilizer were S:TMTD:ZnO:BHT = 60:16:4:20. The presence of the basic components of the stabilizer improves the storage stability and aging resistance of SBS-modified bitumen significantly. It can also improve the softening point and viscosity and reduce the penetration.  
Figure 11: Fluorescence microscope test (stabilizer was S:TMTD:ZnO:BHT:Al\({}_{2}\)O\({}_{3}\):2 SiO\({}_{2}\):2H\({}_{2}\)O = 60:16:4:20:1250). (**a**) Base bitumen; (**b**) top portions (SBS bitumen); (**c**) top portions (SBS bitumen); (**d**) base bitumen; (**e**) bottom portions (SBS bitumen); (**f**) bottom portions (SBS bitumen).  
Al2O3:2 SiO2:2H2O and SiO2 can further improve the storage stability of SBS-modified bitumen significantly. However, they also reduce the low temperature property at the same time. The proper formula of SBS-modified bitumen stabilizer was S:TMTD:ZnO:BHT: Al2O3:2 SiO2:2H2O = 60:16:4:20:1250 or S:TMTD:ZnO:BHT:SiO2 = 60:16:4:20:625; the optimum content was 0.9% (bitumen mass percentage, expressed as S content).
3. The stabilizers developed in this paper were a dry powder, which can be directly put into bitumen easily, and the performance of SBS-modified bitumen with these stabilizers can meet the requirement of \"Technical Specification of Construction of Highway Asphalt Pavement\" (JTG F40-2004, China) [20].  
This work was supported by the Natural Science Foundation of Shandong Province, China (Grant No. BS2015 SF016) and the National Natural Science Foundation of China (Grant No. 51408044)  
[PERSON] conceived of and designed the experiments. [PERSON] performed the experiments. [PERSON] analyzed the data. [PERSON] contributed reagents/materials/analysis tools. [PERSON] wrote the paper.  
The authors declare no conflict of interest.