esa-sceva/satcom-chunk-collection huggingface dataset:
# Parameter Design and Optimization of Grass Aerial Seeding Tower Based Computational Fluid Dynamics
## 2 Structure and Research Methods
### Modeling of the Basic Nozzle Unit  
Based on the actual situation, the Laval nozzle is simplified into a converging tube (shown in Figure 1), in which the \(\beta\) is the cross-sectional inclination angle.  
Using Maya for modeling, a basic nozzle is obtained. Fluent Meshing is then used to generate the mesh of the basic nozzle, as shown in Figure 2. The standard k-\(\varepsilon\) turbulence model [24] is employed to solve the flow field. Air is considered as a compressible ideal gas. The wall of the nozzle is simulated using a no-slip wall boundary condition, with standard wall functions applied near the wall, and the \(y+\) adaptive technique is utilized to refine the near-wall mesh, controlling the \(y+\) value between 30 and 120. The computational domain is set as a cube, with the nozzle positioned at the center and all walls of the computational domain, except for the inlet direction, are set as pressure outlet boundary conditions. For the inlet direction, the pressure far-field boundary condition is applied to the computational domain wall, with the Mach number used to define the inflow velocity.  
Figure 1: Schematic diagram of the Laval nozzle.  
In order to control the airflow velocity, a box is set on the outside of the nozzle. When airflow enters the box with a velocity of 5 m\(\cdot\)s\({}^{-1}\), the inlet airflow velocity of the nozzle is 0.5 m\(\cdot\)s\({}^{-1}\). The simulation results, as shown in Figure 3, indicate that the basic nozzle achieves the desired effect of accelerating the airflow. In this operating condition, with an inlet airflow velocity of 0.5 m\(\cdot\)s\({}^{-1}\), the outlet velocity reaches 4.545 m\(\cdot\)s\({}^{-1}\), achieving a more than 9-times increase in velocity. This indicates that the simplified basic nozzle model can well represent the realistic Laval nozzle.  
Figure 4 shows the velocity profile along the center line. It is apparent from the figure that air flows towards the nozzle with an initial velocity of 5 m\(\cdot\)s\({}^{-1}\). As the air gradually approaches the nozzle entrance, its velocity slowly decreases. Upon nearing the nozzle entrance, the velocity sharply declines, reaching approximately 0.5 m\(\cdot\)s\({}^{-1}\) upon arrival at the nozzle entrance. Subsequently, the air enters the nozzle, where the acceleration effect of the nozzle causes its velocity to swiftly increase, peaking at around 4.5 m\(\cdot\)s\({}^{-1}\) at the nozzle exit. Even after leaving the nozzle, the air's velocity continues to increase gradually. This elucidates the pronounced acceleration effect of the structure, achieving the intended outcome.  
Figure 3: Phenomenon of contraction nozzle acceleration.  
Figure 2: Model of basic nozzle unit.
## 4 Target Model and Frequency Offset Extraction Method  
### Model Establishment  
#### 4.1.1 The Bistatic Radar Model  
Utilizing 5G signals for rotating target detection essentially employs bistatic radar principles for target identification. The transmitting antenna and receiving antenna of the bistatic radar are separated from each other, and their structural model is shown in Figure 3[15]. Where \(v\) is the velocity of the target, \(L\) is the distance between the receiving and transmitting antennas, \(L_{T}\) is the distance between the target and the transmitting  
Figure 1: The schematic diagram of BWP.  
Figure 2: A CSI-RS RE mapping.  
antenna, \(L_{R}\) is the distance between the target and the receiving antenna, \(\beta\) is the double base angle, and \(\delta\) is the angle of the target at the velocity direction relative to the bisector of the double base angle.  
Assuming the wavelength of the signal is \(\lambda\), the Doppler frequency shift generated by the moving target can be expressed as [16]  
\[f_{d}=\frac{v}{\lambda}\bigg{[}\cos\bigg{(}\delta+\frac{\beta}{2}\bigg{)}+\cos \bigg{[}\delta-\frac{\beta}{2}\bigg{]}\bigg{]}=\frac{2v}{\lambda}\cos(\delta) \cos\bigg{(}\frac{\beta}{2}\bigg{)} \tag{4}\]
## Materials and Methods
### Determination of Microbial Diversity  
Total genomic DNA from the Xuanwei ham was extracted using the CTAB/SDS method [39] and monitored using 1% agarose gels. For bacteria, 16S rRNA, the front primer was using 27F(5\({}^{\prime}\)-AGAGTTTGATCMTGGCTCAG-3\({}^{\prime}\)) and the reverse primer was used 1492R(5\({}^{\prime}\)-ACCTTGTTACGACTT-3\({}^{\prime}\)). For fungi, ITS rRNA was amplified. The front primer, ITS1F(5\({}^{\prime}\)-CTTGCTATTAGAGAATAA-3\({}^{\prime}\)), and the reverse primer, ITS2 (5\({}^{\prime}\)-CCTGCGTCTTTCCATGC-3\({}^{\prime}\)) were used. PCR products were detected on a 2% agarose gel and further purified using the QIAquick@ Gel Extraction Kit (QIAGEN, Hilden, Germany). Sequencing libraries were prepared for the 16S rRNA gene and the ITS rRNA gene using the SMRTbellTTM Template Preparation Kit (PacBio, Menlo Park, CA, USA). Both libraries were sequenced on the PacBio Sequel platform, and the raw sequences obtained were multiplexed and quality-filtered through the PacBio SMRT portal. Potential chimeras were removed using the UCHIME algorithm, resulting in clean sequences. These sequences were then assigned to operational taxonomic units (OTUs) using UPARSE with a 97% identity threshold. The classification of each representative sequence was assigned using the SSUrRNA database from the Silva Database.
# Satellite Edge Computing for the Internet of Things in Aerospace
## 3 The Architecture and Application Scenarios of Satellite Edge Computing  
We propose the architecture of the space edge computing system and its application scenarios, as shown in Figure 1. Several iSats form a space edge computing system, which can acquire, store, process, and forward data from the end-users (such as vehicles, airplanes, ships, buoys, and sensors). For small sensors, data can be relayed to the satellite via the base station. In the face of different end-users, the space edge computing system can flexibly change the applications on each iSat to satisfy different mission requirements. Additionally, iSats can virtualize on-board resources and form a resource pool. Depending on the task, the space edge computing system can allocate resources on demand. The detailed structure and application scenarios are illustrated as follows.  
Figure 1: The architecture and application of satellite edge computing.
## 3 Methods
### Estimation of the Initial 3D Box
#### 3.1.3 Fusion of Different Modal Information  
The value at each pixel position of the 2D region of interest represents the RGB value of the color image and the value at each position of the regional pseudo-lidar represents the physical position of the pixel in the 3D space. It is difficult to achieve a good experimental performance by adding together these two-modal data for 3D object detection. AM3D [17] uses the attention map generated by the pseudo-lidar to selectively incorporate useful RGB image information into the position information of the pseudo-lidar. Considering that the pseudo-lidar generated by the 2D image has a certain error, the process of utilizing the pseudo-lidar to generate the attention map will amplify this error. Therefore, we design the following attention mechanism shown in Figure 3. Primarily, we feed the region of interest to a two-layer CNN to generate an attention map and perform a dot product between the attention map and the region pseudo-lidar to obtain a weighted region pseudo-lidar. Then, we superimpose the weighted region pseudo-lidar and the 2D region of interest on the channel to complete the fusion of two different modal information. The operation of the point product between the attention map and the regional pseudo-lidar promotes a competitive mechanism between the values of the region pseudo-lidar, encouraging the values at important positions and suppressing the values at unimportant positions, which can highlight the more important position. The mechanism helps the two-modal information to be more fully fused so as to better perform subsequent initial 3D object detection. Compared with AM3D [17], our attention map is derived from the more accurate 2D region of interest rather than the pseudo-lidar with a large error, which reduces the source of error and is beneficial to improving the precision of the 3D object detection. Considering the subsequent feature extraction problem of pseudo-lidar, AM3D [17], Frustum Points [41], Pseudo-Lidar [19], etc., directly use the existing lidar-based detection framework for 3D object detection. Most of these algorithms have high memory requirements and time-consuming training. In this paper, we use a lightweight two-dimensional convolutional neural network to process the pseudo-lidar fused with image information in the image mode. The convolutional neural network has a simple structure and short training time. The computing power and storage requirements are relatively low, which greatly improves the detection speed without reducing the detection accuracy.  
Figure 3: Information fusion process with attention mechanism of 2D region of interest and region pseudo-lidar.
# Optimal Energy Consumption Tasks Scheduling Strategy for Multi-Radio WSNs
## 6 Conclusions  
In order to fully take advantage of the multi-radio technology in WSNs while reduce the energy consumption, this paper analyzed the two types of energy dissipation processed first, i.e., transmission  
Figure 7: Task extensibility analysis.  
process and idle listening process, then combined the two types of processes together and obtained the accurate energy consumption model of the multi-radio node. Moreover, the optimization energy consumption problem was converted into a multi-task scheduling problem. Inspired by the PSO algorithm, this paper proposed OETS based on the PSO to search for the best solution of such optimization problems, and then provided the detailed steps of the proposed OETS. Both the experiment and the simulation results were given. Those results showed that the proposed OETS based on the PSO algorithm could effectively improve the energy consumption of a multi-radio node and prolong the network lifetime. Furthermore, the task extensibility of the proposed OETS was better, especially when the number of tasks was less than 400.  
In future work, we will deploy more experiments by taking the influence of different topologies into consideration and explore more potential application scenarios. Furthermore, the proposed strategy based on the PSO algorithm will apply in the base station for reducing the energy consumption and prolonging the lifetime of the network. For this application, the position and velocity can respond to the energy consumption of the base station and the remote nodes, respectively.  
Thanks to all the authors for their contribution to this paper. Q.Y. provide the main idea and conduct the experiments and simulations, interpret the results and writ the manuscript. W.P. and G.Z. offer help with funding. All authors have read and agreed to the publish version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant NO.61473176, NO.61573225, and NO.61903226), the Taishan Scholar Project of Shandong Province (Grant NO.TSQN201812092), Youth Innovation Technology Project of Higher School in Shandong Province (Grant NO.2019 KJN005) and the Key Research and Development Program of Shandong Province (Grant NO.2019 GX101072). The authors would like to thank the anonymous reviewers for their insightful suggestions that will help us to improve the quality of this paper. The authors declare no conflicts interest.
## 3 Case Study  
### Project Overview  
The aforementioned method was implemented in the Shanghai Xujiahui Center (SXC) super-high-rise construction project. The SXC, comprising 70 floors, features a concrete core tube structure that reaches a height of 350.10 m. The concrete core structure was built using an HBM, equipped with two tower cranes, two placing booms, and a construction elevator. The HBM had 46 hydraulic cylinders, \(l_{c}=3\) m, and \(L_{max}=29.35\) m. A triaxial accelerometer was installed on the belly plate of the steel beam. This location was recognized as the main force plane of the construction equipment. Real-time vibration data were collected, as depicted in Figure 6. In the feature extraction and training step, the sensor operated at a sampling frequency of f = 125 Hz, and \(\Delta t\) was set to 30 s. Consequently, a total of \(m=3750\) sampling points were generated in a single direction over each \(\Delta t\) period.  
Figure 5: Conditional output step for state recognition based on the actual working condition of the HBM.
# Digital Forensics Readiness in Big Data Networks:
## 5 Proposed Framework
### Research Question 1 Insights
This verifies the resilience and reliability of the forensic setup. 10. Reconfigure Apache Spark to extend its capabilities from BI analytics to also support forensics analytics, ensuring that Spark can handle both BI and forensic data processing requirements effectively. 11. Implement end-to-end data integrity checks, including checksum validations and consistency checks across the HDFS. This step ensures that forensic data can be accurately retrieved and analysed without any discrepancies. 12. Implement MFA (multifactor authentication) for SSH access to the forensic nodes, adding an extra layer of security to prevent unauthorised access. This should be integrated into the network's existing security protocols. 13. Configure RBAC (Role-Based Access Control) to restrict access to forensic data and tools based on user roles, ensuring that only authorised personnel can access sensitive forensic data. 14. Set up a continuous monitoring solution using tools like Prometheus and Grafana to track the performance and health of the forensic nodes, as well as the BI segment. This ensures that any issues are quickly identified and resolved. 15. Finally, verify the integration by ensuring that the forensic nodes are recognised by the NameNode in the BI segment and can communicate efficiently with it and the cloud-based standby NameNode. Test data retrieval and forensic tool operations to confirm the setup functions as intended. To optimise the implementation, a phased strategy is recommended for smooth integration. Firstly, the DFR node is set up to collect data from a subset of the network while monitoring and validating the overall configuration and performance. Gradual expansion to cover the entire network then ensues. Next, the establishment of the BI compliance node and subsequent migration of all relevant data and policies is conducted. Finally, the integration of BI and FI processes using Apache Spark to ensure seamless operation is performed. Thorough testing and validation are essential. Functional testing ensures the new nodes operate correctly without negatively impacting the existing BI Hadoop cluster, while usability testing ensures the system meets the needs of both BI and FI teams. However, these tests are beyond the scope of this research. Lastly, adequate training programmes for IT and security personnel, alongside comprehensive documentation, will support the effective use and maintenance of the new nodes.
## 4 The Results
### Classification of the Infrastructure Assessment of Bike-Sharing System
#### 4.3.1 Key Positive Relationships  
One of the most notable findings is the positive spillover effect of application ease-of-use (I1) on several perceived benefits. Efficient app usability significantly correlates with time savings (B4 = 0.185), physical activity (B5 = 0.166), health promotion (B6 = 0.206), and availability of bicycles (B9 = 0.237). These results highlight the critical role of a user-friendly mobile application--not only in facilitating rentals, but also in integrating bike-sharing into users' daily routines for health and mobility.  
Similarly, the absence of technical errors during the rental process (I2) is strongly linked to perceptions of economic efficiency (B3 = 0.264) and bicycle availability (B9 = 0.346). These relationships underscore the importance of seamless digital processes in reducing user frustration, enhancing trust, and promoting the view of bike-sharing as an accessible, affordable public transport alternative.  
Another key variable, access to real-time information on bike availability (I4), is positively associated with a range of benefits: avoiding traffic congestion (B2 = 0.279), low usage costs (B3 = 0.226), time savings (B4 = 0.255), health promotion (B6 = 0.173), and multimodal integration (B11 = 0.218). These results suggest that reliable, real-time data enhances system efficiency and user confidence, encouraging regular use and positioning bike-sharing as a dependable component of daily mobility.  
Integration with public transportation (I11) also demonstrates strong associations with intermodal connectivity (B11 = 0.284) and reduced concerns about theft (B12 = 0.391). These findings reflect the added value of a well-connected transport network, which offers safer, more convenient transitions between cycling and other modes of transit.  
Finally, the availability of diverse bike types (I15) is significantly related to health promotion (B6 = 0.660) and the practical ability to carry shopping items (B13 = 0.251). This diversity--from cargo bikes to e-bikes--broadens the appeal and functional scope of the system, supporting both fitness-related and utilitarian use cases.
# Women's Singles Tennis Match Analysis and Probability of Winning a Point
###### Abstract  
(1) Background: The analysis of women's tennis performance has not been extensively explored by the scientific community, necessitating further research to understand the tactical actions occurring in matches. This research aimed to examine the chance of winning a point in professional women's tennis based on the key variables that influence performance in the sport. (2) Methods: Data from 3239 points were examined across three distinct court surfaces, sourced from the final rounds (starting with the quarterfinals) of three Grand Slam tournaments in the 2021 season. An observational methodology was employed, using various analysis techniques: descriptive and chi-square analyses, with a significance level of \(p<0.05\). (3) Results: The probability of winning a point on the first serve was 61% on clay, 70% on grass, and 69% on hard courts. For second serves, the probability of winning the point varied between 55% and 57%, depending on the court surface. Additionally, the majority of points, ranging from 70% to 71%, concluded with short rallies, involving one to four shots. On clay courts, the server won up to 65% of points with a first serve and a short rally, while the success rate increased to 75% on both grass and hard courts. For medium-length rallies (5-8 shots), the probability of winning the point dropped to 55-57%. The point outcome (winner, forced error, and unforced error) varied according to court surface, serve type, and rally length. (4) Conclusions: Descriptive data from this research on the probability of winning a point could assist coaches and players in developing match strategies.  
performance analysis; soccer; success rates; key performance indicators; match analysis +
Footnote †: journal: Applied
# Research on 2D Laser Automatic Navigation Control for Standardized Orchard
## 2 Materials and Methods
### Navigation Controller Design  
The navigation controller of this experiment was mainly composed of 2D laser acquisition and processing system, path-tracking control system and automatic steering control system. We collect the fruit tree information in real time from the 2D laser and send it to the main controller PC which processes the acquired laser data. The system recognizes the navigation path and calculates the front wheel angle of the vehicle target and sends it to the Stm32F103. The embedded development platform uses PID control to drive the stepper motor to rotate and adjust the front wheel angle to achieve path-tracking. The working principle of the control system is shown in Figure 9.
## 4 Case Study
### Scenario Generation  
#### 4.4.1 Scenario 1: \(P_{v}\): 15 kw and ES: 50 Kwh  
The outcomes of scenario 1 are presented in Table 5. It is considered that vehicles' availability is 100%. Put otherwise, all EV owners respect the agreement. The total yearly benefit is USD 188,089.803.  
\begin{table}
\begin{tabular}{c c} Bus number & 6 & 8 \\ Optimum number of EVs & 249 & 302 \\ Benefit of loss reduction (USD) & USD 62,266.4 \\ Benefit of providing peak power (USD) & USD 125,823.403 \\ Total benefit (USD) & USD 188,089.803 \\ \end{tabular}
\end{table}
Table 5: Simulation results of scenario 1.  
Figure 5 displays the voltage profile of load locations during peak hours, when parking lots provide electricity to the distribution system. The algorithm converges at a total of six iterations with a tolerance of \(10^{-5}\) p.u.  
There is a voltage decrease on buses 6 and 8, as seen in Figure 5 and Table 6 (without V2G). Since the voltage on buses 6 and 8 is less than 0.9 p.u., the optimization constraint is not in the required range in this scenario. Nonetheless, when V2G power is present, the voltage profile of the buses improves. It is important to remember that the voltage in every bus is the same as the voltage in every node determined by the load flow technique (after convergence).
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 5 Conclusions
### Challenges and Opportunities  
In addition to the stacked sheets reducing the storage performance, some of the other challenges that MXenes still face in supercapacitor applications include the following:  
Scalability and cost: While the properties of MXenes make them promising for supercapacitor development, challenges related to large-scale production and cost-effective synthesis still need to be addressed. Finding scalable methods that can produce high-quality MXene materials at a reasonable cost is essential for practical commercialization.  
Stability: Ensuring long-term stability and robust performance under various operating conditions remains a significant challenge. MXene materials need to maintain their electrochemical properties over numerous charge/discharge cycles and under different environmental conditions to be reliable for commercial supercapacitor use.  
High areal capacitance: Although MXenes exhibit high specific capacitance values, increasing the areal capacitance--how much charge a supercapacitor can store per unit of surface area--remains an ongoing challenge. Enhancing the areal capacitance while maintaining other desirable properties is critical for practical device implementation.  
Electrode processing: Developing effective, scalable methods for integrating MXenes into electrode structures with a controlled morphology is crucial. Ensuring efficient charge transfer and accessibility of active sites within the MXene material is essential for optimizing supercapacitor performance.  
Compatibility with electrolytes: MXenes must be compatible with a wide range of electrolytes to maximize their potential in various supercapacitor designs. Solving challenges related to maintaining stability and performance across different electrolyte chemistries is key for broadening the application scope of MXene-based supercapacitors.  
Binder and separator compatibility: Ensuring that MXene-based electrodes are compatible with a range of binders and separators presents a challenge. The interactions between MXene, binders, and separators can significantly impact overall device performance, requiring careful optimization.