esa-sceva/satcom-chunk-collection huggingface dataset:
# Offloading of Atomic Tasks in Satellite Networks:
## 3 Task Offloading Model  
Future space-based network systems will face many resource request tasks. During the mission planning process, all missions are decomposed into indivisible atomic missions, which means that satellites will receive many atomic mission requests. As a result, a single satellite receives more resource requests for atomic tasks than it can provide. Most atomic missions would need to wait for other atomic missions to be executed, which causes high latency and low timeliness. High orbiting satellites have a wide coverage area and the use of inter-layer link technology allows high orbiting satellites to exchange data with targets within their coverage area. Therefore, this paper proposes to transfer individual satellite missions to high-orbiting satellites and use the powerful computing and storage capabilities of high-orbiting satellites to help individual satellites accomplish coordinated resource allocation to solve the problem of inefficient traditional mission execution.  
Therefore, the critical problem to be solved in this paper is how to quickly offload the tasks to high-orbit satellites when the satellite receives multiple tasks. Although the resources and capabilities of high-orbit satellites are higher than those of medium-orbit and low-orbit satellites, the number of high-orbit satellites is relatively rare. If the number of missions exceeds the upper limit that the high-orbit satellites can accept, the high orbit satellites need to offload the mission to the medium or low-orbit satellites with free resources.  
Satellite tasks can be decomposed into atomic tasks. The so-called atomic tasks are the smallest executable tasks that cannot be decomposed any further. Atomic tasks include resource requirements and task execution time. If a high-orbiting satellite has already offloaded the corresponding atomic task, then there is no need for a low-orbiting or medium-orbiting satellite to repeatedly offload the corresponding atomic task.
###### Abstract  
The current study hypothesized that omega-3 fatty acids \(\omega\)-3 FAs consumed with various tea extracts, which included green tea extract comprising 35% and 65% catechin (GTE35 and GTE65), naturally fermented green tea (Heukcha) extract (NFGT), and a complex of heat-treated green tea and enzymatically-modified isoquercitrin (1:1, _w:w_) (Adiphenon(tm)) would enhance the digestive stability and intestinal absorption of \(\omega\)-3 FAs. The digestive stability of ciocasopentaeonic acid (EPA) and docosahexaenoicenoic acid (DHA) co-digested with GTE65 using an in vitro digestion model system was improved by up to 20.76% and 23.22%, respectively, compared to \(\omega\)-3 FAs. The oxidative stability, measured using peroxide values, was the lowest, whereas the DPPH radical scavenging capacity during digestion was the highest in Adiphenon(tm), showing 1.03 \(\pm\) 0.25 med O\({}_{2}\)/kg oil and 1251.96 \(\pm\) 26.03 \(\mu\)mol TE/g. The deviation in zeta potential was reduced when \(\omega\)-3 FAs were co-treated with various tea extracts, indicating that the micelle of \(\omega\)-3 FAs is stable. The intestinal absorption in Caco-2 cells increased by up to 34.53% for EPA and 60.23% for DHA with various tea extracts compared to \(\omega\)-3 FAs alone. The co-treatment with GTE35 and Adiphenon(tm) did not alter the expression of P-glycoprotein (P-gp) compared to \(\omega\)-3 FAs alone, which implies the efflux of tea polyphenols, such as catechins, could be limited due to the suppression of P-gp by \(\omega\)-3 FA. The results from the current study suggest that the co-intake of \(\omega\)-3 FAs with various tea extracts could increase the bioavailability of \(\omega\)-3 FAs by preventing oxidation, stabilizing micelle structures, and minimizing intestinal efflux.  
Keywords:omega-3 fatty acids; tea extracts; digestive stability; micelle stabilization; intestinal absorption; P-glycoprotein
## 3 The State-of-the-Art Review in Tunnel Blasting Evaluation
### Monitoring and Assessment Technologies
The integration of laser scanning, remote sensing, 3D reconstruction, and advanced data analytics enables comprehensive assessments of tunnel conditions. These methodologies not only enhance safety and operational efficiency but also support proactive maintenance and informed decision-making throughout the tunnel construction process. As these technologies continue to evolve, they will play an increasingly vital role in ensuring the structural integrity and safety of tunnel infrastructures.
# Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control
## 3 Methodology
### Hand Tracking for Hand Pose Estimation  
Accurate hand gesture recognition requires a highly reliable hand tracker to detect the hand position and estimate its pose accurately. The hand model is defined by a set of joint coordinates. Additionally, its real-time feasibility on edge devices must be considered. Since real-time hand pose estimation is a challenging problem that was beyond the scope of our study, we employed Google MediaPipe [29], a reliable two-stage hand tracking module, to simplify our implementation. MediaPipe has been validated in various real-time applications, making it suitable for edge device deployment. This enabled us to focus on developing an intuitive hand gesture vocabulary and a classification model. MediaPipe consists of two pretrained models: a palm detector and a hand pose estimator. The palm detector first takes an RGB image as input and locates the hand by estimating the palm bounding box. The hand pose estimator then identifies 21 keypoints from the detected hand region. We implemented it using the Python v 3.10 package mediapipe, which provides a simple API for the integration of the palm detection and hand pose estimation models. For the keypoint nodes of a single hand, a list of the node sequence \(S\) can be expressed as follows:  
\[S=\{v_{i}\in\mathbb{R}^{2}|\;i=0,\cdots,K-1\}, \tag{1}\]  
where \(v_{i}\) is the \(i\)-th keypoint in the Cartesian coordinate \((x,y)\) of a landmark, and the number of keypoints \(K\) is set to 21. Then, the obtained keypoint coordinates for the hand are converted into a relative distance with regard to the wrist:  
\[\widetilde{v}_{i}\gets v_{i}-v_{0}, \tag{2}\]  
where \(v_{0}\) represents the coordinate of the wrist and \(\widetilde{v}_{i}\) is the updated relative coordinate of the \(i\)-th keypoint. Finally, the pose information is flattened into a one-dimensional tensor and normalized to the maximum absolute value so that it is prepared for input to the posture-based HGR model.
## 3 Results and Discussion
### Spatial Distribution of BEPS-Simulated Crop GPP and AGB  
BEPS-simulated GPP for the whole growing season is shown in Figure 9. The lower-right corner is excluded from analysis due to biases in LAI retrievals. In the study area, the crop's total GPP in the whole growing season ranges from 600 to 2000 g C m\({}^{-2}\) yr\({}^{-1}\). Lower GPP values in the upper-right corner are associated with a region of heavy clay soil; for this  
Figure 8: The relationship between crop accumulated LAI and ground-measured above-ground biomass for each crop type for SMAPVEX16-MB.  
clay area, the matric water potential can be very low even when the soil water content is relatively high, limiting available water to the crops (Figure S1). The area with center-pivot irrigation (circles; potatoes according to Figure 1) is characterized with high GPP values up to 2000 g C m\({}^{-2}\) yr\({}^{-1}\). The variation of GPP is generally small within each field, while considerably larger among different fields, reflecting individual farmer's management on irrigation and fertilizer use.  
The regressions in Figures 3 and 4 are used to derive an AGB map for the study area (Figure 10). For the six target crops, their explicit GPP-AGB relationships are applied; for the rest of crop types, the regression coefficients averaged for all crop types in Figure 3 are applied. In Figure 10, distinct variations among fields are revealed. In the study, the total AGB ranges from 200 to 2000 g m\({}^{-2}\). The peas and beans are characterized with low AGB values (blue area), e.g., 5.4 tons ha\({}^{-1}\) for soybean (Figure 11 and Table S1), while potato field has the largest AGB (16.6 tons ha\({}^{-1}\)). Compared to Figure 9, more variations  
Figure 9: Annual crop GPP in 2016 for the SMAPVEX16-MB study area simulated by BEPS. The used Sentinel-2 images are shown in Table 1. shows the averaged AGB by crop type in 2016 derived for the SMAPVEX16-MB study area. It is found that the average AGB among crop-type is clear while the one-standard deviation of AGB for each crop type is relatively small; we suggest that the small deviation is due to the small study area with the similar climate condition. Two sample \(t\)-test results suggest that the AGB average values among crop types are significantly different (\(p<0.05\)) except for Oats vs. Triticale, Triticale vs. Spring Wheat, and Winter Wheat vs. Hemp.  
among fields are observed since the crop-specific calibration from Figure 4 is applied. This suggests that although the accumulative GPP correlates well to the observed AGB, early crop identification/inventory within the crop growing season is critically important for crop growth monitoring and yield prediction.  
Figure 10: Annual crop above-ground biomass (AGB) in 2016 derived from simulated crop GPP. For the six target crops, their explicit GPP-AGB relationships are applied; for the other crop types, the regression coefficients averaged for all crop types in Figure 3 are applied. The peas and beans are characterized with low AGB values (blue); while potatoes within center-pivot irrigation have high AGB values (red).
## 2 Literature Review
### Infrastructure, Connectivity, and Technological Convergence  
The adoption of the commercial internet in the 1990s radically restructured the digital infrastructure, paving the way for the development and consolidation of digital services such as the sharing economy, social media, e-commerce, and online advertising services. These digital services continue to grow and become more important in GDP. Indicatively, in 2017, e-retail in the US reached $545 billion, a 65% increase since 2012, while online advertising exceeded $105 billion, an increase of 250% [26]. Information and communication technologies (ICTs) continue to transform industries such as healthcare and energy through applications such as remote diagnostics and smart grids. Therefore, as businesses turn to digital transformation, the integration of IT and ICT infrastructure is required [27].  
The European Union is investing in the deployment of high-speed broadband Internet with the aim of boosting growth and employment, especially in rural areas. However, the socio-economic benefits depend on the adoption of services and vary depending on the region and the specialization of the workforce [28]. At the same time, the adoption of 5G and the expansion of spectrum bring significant improvements in connectivity. In the Netherlands, for example, the integration of 5G spectrum can increase capacity per capita by 40% compared to LTE [29]. A 5G private network (5G-NPN) allows enterprises to deploy dedicated infrastructures with high reliability and speed, transforming production and adapting services to the increasing demands of the digital age [30].
## 3 Results and Discussion
### PSF Calculation for Different Effects  
According to fast calculation using the convolution we proposed in Equation (6), we first calculates \(PSF_{Jitter}\), \(PSF_{turbulence}\), and \(PSF_{p+TB}\), for the single effect. The \(PSF\) under different jitter conditions, \(PSF_{Jitter}\), is calculated according to Equation (9). Figure 2 shows the results for \(jitter=5\)\(\mu\)\(rad\) and \(jitter=10\)\(\mu\)\(rad\), which are recorded as \(PSF_{jitter=5\mu}\)\(rad\) and \(PSF_{jitter=10\mu}\)\(rad\), respectively, in the article.  
The \(PSF\) under different turbulence conditions, \(PSF_{turbulence}\), is calculated according to Equation (11). Figure 3 shows the results for \(D/r_{0}=4.0,D/r_{0}=6.13\) and \(D/r_{0}=16.1\), which are recorded as \(PSF_{D/r_{0}=4.0}\), \(PSF_{D/r_{0}=6.13}\), and \(PSF_{D/r_{0}=16.1}\), respectively.  
Figure 4 shows the corresponding \(PSF\) of the transmitting system without and with aberration, which are recorded as \(PSF_{\theta_{0}=1}\) and \(PSF_{\theta_{0}=5}\), respectively.  
Figure 5 shows the \(PSF\) of the system without and with aberration under different thermal distortion parameters. The PSF is calculated by a 4D wave optics simulation program, and is recorded as \(PSF_{\theta_{0}=1,N_{D}=25.5}\), \(PSF_{\theta_{0}=1,N_{D}=127.3}\), \(PSF_{\theta_{0}=1,N_{D}=2543}\), \(PSF_{\beta_{0}=5,N_{D}=25.5}\), \(PSF_{\beta_{0}=5,N_{D}=127.3}\), and \(PSF_{\beta_{0}=5,N_{D}=254.3}\), respectively.  
Figure 2: The \(PSF\) results for (**a**) \(jitter=5\)\(\mu\)\(rad\) and (**b**) \(jitter=10\)\(\mu\)\(rad\).  
Figure 4: The _PSF_ results for (**a**) \(\beta_{0}=1\) and (**b**) \(\beta_{0}=5\).  
Figure 3: The _PSF_ results for (**a**) \(D/r_{0}=4.0\), (**b**) \(D/r_{0}=6.13\), and (**c**) \(D/r_{0}=16.1\).
# Flow-Induced Vibration of Cantilever Type Elastic Material in Straight Tricylinder
## 1 Introduction
[PERSON] [13] used three-dimensional numerical simulations to investigate the flow around three cylinders in this arrangement at the super-critical Reynolds number Re = 3 \(\times\) 10\({}^{6}\), concentrating on the influence of the spacing ratio among cylinders. When L/D is greater than 3.5, the separation point of the cylinder in the upper stream is close to that of a single cylinder, indicating that the minimum L/D for negligible interaction among the cylinders is 3.5. Learning about flexible slender risers, [PERSON] [14] studied the VIV of a vertical riser subject to uniform and linearly sheared currents. A standing wave response is observed for the single-mode IL and CF vibrations. Dual resonance is found to occur at most of the locations along the riser. [PERSON] [15] developed a bidirectional fluid-structure coupling method. By comparing and analyzing the vortex forms of riser wake under different flow rates, it is found that the riser has a slight 3D effect at low flow rates, while the riser has a strong 3D effect at high flow rates. [PERSON] [16] used a Q3D FSI numerical method that couples the strip theory-based DVM and the FEM of structural dynamics developed to simulate the FIV of the flexible cylinder. The study answered two questions: What is the difference between the flexible cylinder FIV response with an upstream wake interference and the isolated flexible cylinder VIV response? How does the difference between the FIV response with the upstream wake interference and the isolated cylinder VIV response come about? [PERSON] [17] used numerical investigations into the VIV of a flexible cylinder with different length-to-diameter ratios, named the aspect ratio. Comparisons among simulations with different aspect ratios were analyzed in detail. The maximum vibration amplitude, the dominant vibration mode, and the non-dimensional dominant vibration frequency in both directions were apparently enlarged with the increase in the cylinder aspect ratio. The typical vibration trajectory changes from the butterfly type to the stripe type with L/D increases. [PERSON] [18] also used the two-dimensional method to study the flexible slender rod and made a comparison with the experiment to prove the feasibility of this method, but these methods were not a true three-dimensional simulation. This study focuses on the numerical development and analysis of the VIV response of cantilever flexible cylindrical triangular arrays at subcritical Reynolds numbers. To enhance the investigation into the VIV characteristics of such arrays, we analyze the distribution of flexible cylindrical structures with triangular rigid connections using a combination of the SST-IDDES model and the fluid-structure coupling model.
# Syn2 Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images
###### Abstract  
Object detection in computer vision requires a sufficient amount of training data to produce an accurate and general model. However, aerial images are difficult to acquire, so the collection of aerial image datasets is a priority issue. Building on the existing research on image generation, the goal of this work is to create synthetic aerial image datasets that can be used to solve the problem of insufficient data. We generated three independent datasets for ship detection using engine and generative model. These synthetic datasets are rich in virtual scenes, ship categories, weather conditions, and other features. Moreover, we implemented domain-adaptive algorithms to address the issue of domain shift from synthetic data to real data. To investigate the application of synthetic datasets, we validated the synthetic data using six different object detection algorithms and three existing real-world, ship detection datasets. The experimental results demonstrate that the methods for generating synthetic aerial image datasets can complete the insufficient data in aerial remote sensing. Additionally, domain-adaptive algorithms could further mitigate the discrepancy from synthetic data to real data, highlighting the potential and value of synthetic data in aerial image recognition and comprehension tasks in the real world.  
Spr2 Real; synthetic dataset; ship detection; aerial images +
Footnote †: journal: Applied Sciences
## 4 Methodology
### Diffusion Augmentation Module
First, we can define the parameter matrix of the new graph \(\mathcal{G}\) generated by the diffusion of the undirected graph \(G\) as follows:  
\[\mathcal{G}=\sum_{k=0}^{\infty}\theta_{k}T^{k}, \tag{1}\]  
where \(k\in\mathcal{R}\), the transition matrix is denoted as \(T\), and \(\theta_{k}\) is the weighted coefficient. The principle of selecting \(T\) and \(\theta\) is to ensure the series convergence of Equation (1). The relatedwork has demonstrated that the choice of coefficients defined by Personalized PageRank (PPR) and the heat kernel yields strong experimental performance [40; 41; 42; 43; 44]. PPR constructs the transfer matrix \(T_{rw}\) by walking randomly, which can be written as Equation (2). The random walk starts from the starting node, selects the next node according to the probability distribution, and then continues to walk until the specified number of walking steps or the termination condition is reached. In each step, the transition probability of the node can be adjusted according to the structure of the graph or personalized preferences. \(\theta_{k}^{PPR}=\alpha(1\times\alpha)^{k}\). \(\alpha\in(0,1)\) represents the propagation probability while the heat kernel is defined by \(T=T_{rw},\theta_{k}^{HK}=e^{-k}\frac{k}{k! }\), where \(t\in[1,10]\) is the diffusion time. And, as in [45], the symmetrical version of \(T_{sym}\) is selected as the transition matrix, which can be written as Equation (3). \[T_{rw}=AD^{-1} \tag{2}\]  
\[T_{sym}=D^{-1/2}AD^{-1/2} \tag{3}\]  
\[T_{gat}=softmax(A+I) \tag{4}\]  
The matrix \(I\) is the unit matrix with dimensions matching those of the graph \(G\), and the matrix \(D\) represents the symmetric matrix of the node degrees within the graph \(G\), so that  
\[D_{ii}=\sum_{j=1}^{N}A_{ij}. \tag{5}\]  
The previous work found that \(T_{sym}\) outperforms \(T_{rw}\) in most cases [45], and this advantage relies on the insensitivity when introducing self-loops of the node's own information, although self-loops introduce the dissemination of the node's proprietary information, in the GCN approach, because the weights of the self-loop are usually learned and it only involves the node's own information and not interactions with other nodes. Therefore, the self-loop is a relatively small change to the model. In the case of self-loops, we observe that the GAT model outperforms the GCN approach, as reported in prior work [45]; the self-loop can be viewed as a regularization mechanism. It imposes a constraint on the learning of the attention weights by ensuring that the node's own information is taken into account in the attention computation. This measure can aid in mitigating the overfitting of the model during the training process. Therefore, in this study, we used the transfer matrix \(T_{gat}\) for the GAT method, which can be expressed as Equation (4), and \(\theta_{k}=\alpha(1-\alpha)^{k}\), \(\alpha\in(0,1)\) for the PPR method. So, one can write Equation (1) in the following form:  
\[\mathcal{G}=\sum_{k=0}^{\infty}\alpha(1-\alpha)^{k}\cdot softmax(A+I)^{k}. \tag{6}\]
## 6 Conclusions  
Based on the structure diagram of the DFVSPS unit and the mathematical models of each part, the switching process of the DFVSPS unit under generating mode and the pump mode were simulated. The power output model of each stage of DFVSPS unit was simplified in order to make the DFVSPS unit able to be applied to power system simulation, the simplified model was applied to the power system simulation, and the following conclusions are drawn.  
After the power priority control strategy is adopted for the DFVSPS unit, and the electromagnetic torque and the mechanical torque of DFIM are respectively controlled by the power control closed loop and the speed control closed loop, so two closed loops are coupled together through the rotating shaft. The power control closed loop is an independent control closed loop, and the speed control closed loop is adjusted by speed, so the control characteristic of the speed control closed loop is affected by the power control closed loop.  
The electromagnetic transient simulation includes the change of all parameters in the whole switching process of DFVSPS unit. Due to the switching frequency of the converter, the simulation step is very short, so the simulation speed is slow and the data volume is large, which is not suitable for studying the influence of switching process of DFVSPS unit on the power system. The simplified model can well characterize the power output characteristics of the DFVSPS unit, greatly shortened the simulation time, and it can replace the electromagnetic transient model for power system simulation.  
The DFVSPS unit has a good compensation ability for the power fluctuation of a power system.  
For the compensation speed and compensation range for the power fluctuation of DFVSPS unit, after analysis, it is found that the larger the compensation range, the faster the compensation speed, and the higher the requirements for converter capacity and DFIM stator and rotor voltage and current levels. Therefore, the compensation range and compensation speed are constrained by parameters such as converter capacity.  
Conceptualization, G.Z.; methodology, G.Z. and J.R.; software, J.R.; validation, G.Z. and J.R.; formal analysis, J.R.; investigation and resources, J.R.; data curation, J.R.; writing--original draft preparation, G.Z., J.R.; writing--review and editing, J.R.; supervision, G.Z.; project administration, G.Z.  
This research was funded by the Fundamental Research Funds for the Central Universities (2018 MS008).  
The authors declare no conflict of interest.