esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Fabrication of Chitosan/Sodium Phytate/Al(OH)\({}_{3}\) Coatings on the Wood Surface  
Cationic chitosan solution was prepared by dissolving 1 g chitosan powder in 100 mL distilled water with a constant stirring speed until fully dissolved. The pH of the solution was adjusted to 2-3 by the dropwise addition of dilute hydrochloric acid. Anionic sodium phytate solution was obtained by dissolving 1 g phytate solution powder in 100 mL distilled water with a constant stirring speed until fully dissolved. The pH of the solution was also adjusted to 2-3 with dilute hydrochloric acid. In the same manner, a cationic aluminum hydroxide solution was prepared by dissolving 1 g aluminum hydroxide powder in 100 mL distilled water with a constant stirring speed until fully dissolved. The pH of the solution was also adjusted to 2-3 with dilute hydrochloric acid. The solutions were automatically sprayed using a self-made spraying machine onto the wood surface. The specific spraying process is shown in a schematic diagram (Figure 1). First, the configured chitosan solution was evenly sprayed on the wood specimen surface for 60 s. Next, the specimen was transferred into the oven and dried at 80 \({}^{\circ}\)C for 1 h. We named the wood processed in this step as CH wood. Second, the configured sodium phytate solution was evenly sprayed on the CH wood surface for 60 s. The specimen was then transferred into the oven and dried at 80 \({}^{\circ}\)C for 1 h. The wood processed in this step was named CH/SP wood. Third, the configured aluminum hydroxide solution was evenly sprayed on the wood surface for 60 s. The specimen was then transferred into the oven and dried at 80 \({}^{\circ}\)C for 1 h. The wood processed in this step was designated as CH/SP/Al(OH)\({}_{3}\) wood. Next, the sodium phytate solution was sprayed and then followed by the spraying of the aluminum hydroxide solution, and this cycle was repeated alternately. To ensure complete coverage of the wood substrate, 10 deposition cycles were used for each sample. Meanwhile, untreated wood specimens were used for comparative purposes.
# Deep Learning Methods in Image Matting: A Survey
## 8 Conclusions  
In conclusion, image matting is a fundamental problem in computer vision and has garnered significant attention from the research community. Over the years, numerous approaches have been proposed to solve this problem, including traditional methods based on manual and low-level features as well as more advanced deep learning-based techniques. In this paper, we have provided an extensive survey of image matting methods based on deep learning. We first introduced well-known datasets and traditional methods. Subsequently, we concentrated on summarizing notable image matting methods based on deep learning and analyzing their innovations. Additionally, we discussed typical evaluation indicators for image matting quality, along with several applications of the technique. Finally, we explored the primary challenges and future directions of image matting. Despite significant progress, image matting remains a vibrant research topic, and we believe that future work in this area will undoubtedly drive advancements in computer vision and related fields.  
Conceptualization, L.H. and X.L.; methodology, L.H., X.L. and B.T.; software, L.H.; validation, L.H., X.L. and B.T.; formal analysis, L.H., X.L. and B.T.; resources, B.T.; writing--original draft preparation, L.H. and X.L.; writing--review and editing, B.T., X.W. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangxi Science and Technology Major Project under Grant No. AA22068057 and the Youth Science Fund Project of Guangxi Natural Science Foundation under Grant No. 2021 GXNSFBA220039.  
Not applicable.  
The authors thank everyone that provided suggestions and assistance for this paper.  
The authors declare no conflict of interest.  
Figure 6: The first row displays the model’s output alpha mattes for a motion-blurred image, while the second row displays the model’s output alpha mattes for different scales in the foreground image, where (**a**) represents the output of the PPM [35] model, (**b**) represents the output of the AIM [29] model, (**c**) represents the output of the P3M [30] model, and (**d**) represents the output of the RVM [34] model. As shown in the figure, the methods perform poorly in capturing the motion-blurred objects indicated by the red rectangles in the first row. Additionally, when objects with different scales, as indicated by the red rectangles in the second row, appear in the image, these methods generally fail to extract them accurately and tend to only capture the most prominent objects.
## 4 Aircraft Tracking and Activity Recognition
### NCC-Based Template Matching for Aircraft Tracking
As outlined in Figure 7, the aircraft template image is updated using the tracking result every 50 tracking cycles. Based on the combination of rotated image matching and a neighborhood search, the tracking process can achieve good performance. On the one hand, based on the design of rotated image matching, the algorithm enables stable tracking of aircraft with translational and turning motions. During the aircraft tracking procedure of our algorithm, the aircraft search area is only twice the size of the currently tracked aircraft. Usually, there is a certain safety distance between aircraft targets in their working state. Therefore, based on the neighborhood search and the safe working distance requirement of aircrafts, nearby aircraft do not enter the search area of the currently tracked aircraft. The algorithm can effectively prevent false tracking of nearby aircraft.
## 2 Experimental and Numerical Methods
### Numerical Calculation Model and Effective Verification  
#### 2.3.1 Numerical Model  
The water tank model is simplified as shown in Figure 4. The models are built with SolidWorks, and Hypermesh is used to generate grids. The solid structures such as the projectile and target plate use Lagrangian solid elements; the side walls of the water tank do not involve damage and deformation; therefore, Lagrangian shell elements are used for them. The water domain is constructed in the water tank, the air domain is established outside the water tank; Eulerian elements are adopted for the water and air domains, and the common node approach is applied, so as to realize the flow of water and air.  
The projectile is divided into 14 equal parts in the radial direction, 16 equal parts in the circumferential direction, and 1.2 mm grids along the length direction. The grid of the water and air domains is 4 mm in size, and the grids are refined in the central area with a size of 2 mm. The grids of the front and rear plates of the water tank are divided into 1 mm cube elements. Because side walls are thick and can be regarded as rigid walls, their grid size is 5 mm.
## 3 Results and Discussion
### Energy Consumption Analysis  
A breakdown of the site's monthly energy consumption has been conducted, taking into account, electricity consumption from the grid before and after solar integration and load usage. The detailed analysis of Figure 12 provides a clearer picture of how solar power impacts overall electricity costs. The monthly energy consumption data reveals the proportion of electricity supplied by solar power versus grid electricity. Analysis of yearly trends indicates that energy consumption from grid before and after solar projection are 54, 196 kWh and 23,781.1 kWh respectively. It signifies that 57% of the site's total energy demand can be fulfilled using solar power system. The remaining 43% must still be sourced from the grid. This breakdown demonstrates the substantial impact of solar power on reducing reliance on grid electricity while acknowledging that some level of grid support is still necessary.  
Figure 11: Weekdays profile of grid consumption at (a) Summer and (b) Winter.  
Figure 12: Energy Mix Contribution—Energy consumption before and after the installed solar power system in kWh, load conducted using solar power system in %.  
Figure 10: Project’s carbon emission profile over time.
## 2 Formulation
### Equivalent Ionospheric Current-Density Model
\[\overline{j}^{n+1/2}=M_{1}\overline{j}^{n-1/2}+b\varepsilon_{0}\omega_{p}  
values surrounding that grid location. For example, \(J_{\theta}(i,j+1/2)\) and \(J_{\varphi}(i,j+1/2)\) can be expressed as averages of the values around the \((i,j+1/2)\) grid location.  
\[J_{\theta,i,j+1/2}^{n-1/2}=\frac{1}{4}\left[J_{\theta,i-1/2,j}^{n-1/2}+J_{ \theta,i+1/2,j+1}^{n-1/2}+J_{\theta,i-1/2,j+1}^{n-1/2}+J_{\theta,i+1/2,j+1}^{n -1/2}\right] \tag{12}\]  
\[J_{\varphi,i,j+1/2}^{n-1/2}=\frac{1}{2}\left[J_{\varphi,i,j}^{n-1/2}+J_{ \varphi,i,j+1}^{n-1/2}\right] \tag{13}\]  
The expansion of the \(J_{\theta}\) at the grid location \((i+1/2,j)\) can be obtained. \[J_{\theta+1/2,j}^{n+1/2}=(A^{-1}B)_{21}J_{r,i+1/2,j}^{n-1/2}+(A^{- 1}B)_{22}J_{\theta,i+1/2,j}^{n-1/2}+(A^{-1}B)_{23}J_{\varphi,i+1/2,j}^{n-1/2}\] \[+c(A^{-1})_{21}E_{r,i+1/2,j}^{n}+c(A^{-1})_{22}E_{\theta,i+1/2,j}^ {n}+c(A^{-1})_{23}E_{\varphi,i+1/2,j}^{n} \tag{14}\]  
In the above equation, the components \(J_{r}\) and \(J_{\varphi}\), as well as \(E_{r}\) and \(E_{\varphi}\), do not exist at the grid location \((i+1/2,j)\). Therefore, they are replaced by averages of two or four values surrounding that grid location. For instance, \(J_{r}(i+1/2,j)\) and \(J_{\varphi}(i+1/2,j)\) can be expressed as the average of values around the \((i+1/2,j)\) grid location. \[J_{r,i+1/2,j}^{n-1/2}=\frac{1}{4}\left[J_{r,i,j-1/2}^{n-1/2}+J_{r,i,j+1/2}^{n- 1/2}+J_{r,i+1,j-1/2}^{n-1/2}+J_{r,i+1,j+1/2}^{n-1/2}\right] \tag{15}\]  
\[J_{\varphi,i+1/2,j}^{n-1/2}=\frac{1}{2}\left[J_{\varphi,i,j}^{n-1/2}+J_{ \varphi,i+1/2}^{n-1/2}\right] \tag{16}\]  
The expansion of the \(J\) at the grid location \((i,j)\) can be obtained. \[J_{\varphi,i,j}^{n+1/2}=(A^{-1}B)_{31}J_{r,j,j}^{n-1/2}+(A^{-1}B)_{32}J_{ \theta,i,j}^{n-1/2}+(A^{-1}B)_{33}J_{\varphi,i,j}^{n-1/2}\]  
\[+c(A^{-1})_{31}E_{r,i,j}^{n-1}+c(A^{-1})_{32}E_{\theta,i,j}^{n-1}+c(A^{-1})_{3 3}E_{\varphi,i,j}^{n-1} \tag{17}\]  
In the above equation, the components \(J_{r}\) and \(J_{\theta}\), as well as \(E_{r}\) and \(E_{\theta}\), do not exist at the grid location \((i,j)\). Therefore, they are replaced by averages of two or four values surrounding that grid location. For instance, \(J_{r}(i,j)\) and \(J_{\theta}(i,j)\) can be expressed as the average of values around the \((i+1/2,j)\) grid location. \[J_{r,i,j}^{n-1/2}=\left[J_{r,i,j-1/2}^{n-1/2}+J_{r,i,j-1/2}^{n-1/2}\right]/2 \tag{18}\]  
\[J_{\theta,i,j}^{n-1/2}=\left[J_{\theta,i-1/2,j}^{n-1/2}+J_{\theta,i+1/2,j}^{n- 1/2}\right]/2 \tag{19}\]  
By combining the current density model with [PERSON]'s equations, iterative formulas for electric-field intensity, magnetic-field intensity, and current density over time can be derived. The detailed derivation process can be found in Section 2.3.
# UAV Swarm Real-Time Rerouting by Edge Computing D* Lie Algorithm
###### Abstract
In 2015, the United States Federal Aviation Administration (FAA) and National Aeronautics and Space Administration (NASA) jointly proposed an operational concept for a remotely controlled drone flight management system--Unmanned aircraft system Traffic Management (UTM)--to improve low-altitude flight traffic safety [10]. This conceptual approach is divided into four technical stages: the message architecture of the data exchange, position sensing and avoidance, position tracking, and communication and navigation. Among these, the development of the third stage began in 2018, with a particular focus on drone applications using avoidance technology to enable UAVs to safely fly in designated areas without line of sight to the operator. The development of the fourth phase began in 2019, focusing on UAVs flying outside the line of sight in densely populated cities, requiring not only a communication network architecture and monitoring system but also the drone system's ability to process large-scale incidents. Based on the above research, autonomous task performance ability can be developed as a necessary condition for future development tasks. In this approach, ground control still plays an important role; flight task execution for multiple UAVs should not rely exclusively on the ground station. Rather, edge computing approaches should be integrated to improve the autonomous computing capabilities of each drone to adapt to changing conditions in real time. Therefore, the present study sought to develop a long-distance intelligent multi-aircraft dispatch system to realize an autonomous multi-rotor path and multi-waypoint task dispatch, along with the automatic execution of large-area tasks. A route planning algorithm was developed to identify the shortest path while considering the mission area as multiple navigation areas subject to restricted navigation. Dynamic planning was conducted using the D\({}^{\star}\) Lite algorithm, such that each drone could achieve real-time obstacle avoidance and route re-planning [11]. The rest of the paper is organized as follows: Section 2 presents the methods and materials adopted for this research. The simulation and flight test validation are presented in Section 3, and the conclusion is given in Section 4.
# 3D Point Cloud Dataset of Heavy Construction Equipment
###### Abstract
As a result, the 3D point cloud data collected in the field tends to be large in size and high in point density. Consequently, preprocessing tasks such as noise removal and adjustment pose significant challenges when working with collected 3D point cloud data. Furthermore, when conducting 3D scanning of heavy construction equipment such as dump trucks and excavators using equipment such as UAVs and Terrestrial Laser Scanners (TLS) on a construction site, obtaining complete and undistorted point cloud data of heavy construction equipment can be challenging due to their movements, rotations, and back-and-forth motions during operation. Data collected in construction sites targeting heavy construction equipment in operation often results in point cloud data that overlaps, creating data artifacts akin to residual images in photographs. When using this point cloud data to train ML and DL models, it can potentially degrade the performance of the heavy construction equipment prediction model. For these reasons, obtaining high-quality 3D point cloud-based heavy construction equipment object data is crucial in enhancing the performance of prediction models in 3D point cloud data-based ML and DL research. As discussed, it is clear that for each research institution to build high-performing machine learning and deep learning models, individually constructing 3D point cloud data for construction sites requires a significant amount of time and effort. This challenge is not limited to research in the construction field but is a common experience for research institutions across various areas that conduct ML and DL research based on 2D images and 3D point cloud data. Consequently, in autonomous driving research, the representative dataset known as the KITTI dataset [28] has been developed to facilitate efficient execution of machine learning and deep learning research, and active research is being conducted using the KITTI dataset. Therefore, to enhance the productivity and efficiency of 3D point cloud data-based ML and DL research in the construction industry, this research has developed the 3D-ConHE (3D Point Clouds of Heavy construction Equipment) dataset, targeting heavy construction equipment.
## 3 Hybrid Computing Architecture of MSD Algorithm
### Multi-Stream Design for MSD Algorithm  
The circuits for correlation calculation based on SC require substantial computation time to ensure the accuracy of the results, which consequently leads to a notably slower update rate for these outcomes. Meanwhile, the symbol decision based on binary computing incorporates a pipeline design to serially search for the maximum complex modulus among all correlation results, yielding a similarly low update rate for its outcomes. While the computation circuit for the entire MSD algorithm can significantly reduce hardware  
Figure 8: The pipeline for symbol decision.  
resource consumption, it suffers from severe limitations in terms of its real-time computation performance. Therefore, in the design of the overall MSD algorithm, a balanced consideration must be given to the trade-off between computation time and hardware resource overhead.  
Figure 9 illustrates the multi-stream processing framework of the MSD algorithm. SC-based correlation calculations require hundreds of random bits to maintain acceptable error margins. Even in the 7-symbol MSD algorithm, its symbol decision only involves 128 complex numbers. The required computational time is fully accommodated within the processing time of the SC-based correlation calculation. Therefore, as shown in Figure 9, the symbol decision can be seamlessly embedded within the SC timeline without incurring extra time overhead. The inherently low data update rate of SC enables binary-based symbol decision to be implemented using pipeline processing architectures.  
As the symbol decision computation time can be overlapped with the correlation computation period, the MSD algorithm enables parallel processing of these two operations. Specifically, as the correlation computation for one received signal block concludes, processing of the subsequent block immediately initiates while the symbol decision module generates output data based on the previous correlation results. However, the inherent computational latency of SC imposes limitations--even with seamless pipelining of consecutive correlation operations, the system may struggle to meet the requirements of real-time processing.  
Given the low resource overhead of SC-based correlation calculations and serialized symbol decision, multi-stream parallel data processing becomes feasible. By instantiating multiple data streams, the hybrid SC-based architecture can achieve high throughput, where concurrent processing of independent data streams ensures compliance with real-time requirements.
## 4 Core Insights and Critical Reflections
### Challenges Facing UAV-RS
#### 4.3.1 Platform Performance Limitations
Overall, limited payload capacity remains one of the key challenges for UAV-RS systems, especially in complex monitoring scenarios. Nonetheless, with ongoing technological advancements and improvements in UAV design, this limitation is expected to be progressively mitigated. 3. Sensor Performance Bottlenecks. The resolution, sensitivity, field of view, and spectral range of sensors directly determine the quality of remote sensing data. High-resolution sensors can capture individual plants or subtle thermal anomalies, but they often require higher sampling rates and larger data bandwidths, posing challenges for UAV endurance and onboard storage capacity [179; 180; 181; 182]. Infrared and thermal imaging are indispensable for applications like wildfire monitoring, but typically have lower spatial resolution than visible-spectrum imaging and are more susceptible to interference from clouds and fog [183; 184]. Moreover, the trade-off between field of view and resolution makes it difficult for operators to balance coverage area with detail acquisition, increasing the complexity of flight path design and operational costs. While current technologies face limitations in resolution, viewing angle, and sensitivity, ongoing advances in sensor development and UAV payload capacity are expected to enable future remote sensing missions to collect multi-dimensional environmental data with greater efficiency and precision. 4. Limited Data Transmission Rates. In applications such as real-time monitoring and emergency response, UAVs are required to transmit large volumes of high-definition imagery, multispectral data, and video streams back to ground stations or cloud platforms promptly. While current Wi-Fi and 4G networks can support basic data transfer in urban areas, they often fail to meet bandwidth requirements in remote or low-signal regions [185; 186], forcing UAVs to store data onboard and upload it only after returning, resulting in significant latency. Although satellite communication and enhanced wireless transmission technologies can offer broader coverage in harsh environments [187], their high costs and limited accessibility hinder widespread adoption. However, the advancement of 5G and next-generation satellite networks, along with improvements in onboard AI edge computing, is expected to enable real-time preprocessing and compression on UAV platforms, thereby alleviating the burden of large-scale data transmission in the future.
# Investigation into the Flexural Toughness and Methods of Evaluating Ductile Concrete
## 5 Conclusions  
Based on the results obtained in this research, the following conclusions could be drawn:  
1. The results indicated that the flexural failure modes, ultimate strength, and toughness of HDC could be improved with an increase in PVA fiber content, especially for deformation. The ultimate flexural strength of HDC with 2% PVA fibers of about 15.32 MPa showed an increase of up to 221%. The deformation and flexural toughness ratios were 23 times and 1.43 times higher than the specimens without fibers, respectively.
2. The post-cracking ductility of conventional concrete was significantly improved due to the stress redistribution effect of the fibers. With an increase in PVA fibers, the HDC specimens exhibited better deflection-hardening behavior, characterized by multiple cracks and crack width gradually decreasing. However, the water-binder ratio and age had little influence on the flexural behavior of HDC, with a variation range between 2% and 13%.
3. Existing approaches failed to evaluate the flexural toughness of HDC because of their dependence on an unstable initial cracking performance, insufficient deflection limit, and difficulty in reflecting the properties of different tests corresponding with different force stages. Based on JSCE-SF4 and CECS 13: 2009, the equivalent flexural strength and flexural toughness ratios calculated by the peak load of the specimen were proposed to assess HDC flexural toughness, providing a more comprehensive and accurate evaluation by selecting characteristic points evenly distributed throughout the loading process.
4. Future recommendations are that the parameter \(n\) of bending toughness based on the peak load should be modified with more data, and the applicability should be proven with more ductile materials.  
Conceptualization, Y.D.; Data Curation, Y.L.; Formal Analysis, Y.L.; Funding Acquisition, Y.D.; Investigation, Y.L.; Methodology, Y.L. and J.D.; Project Administration, Y.L. and X.Z.; Supervision, Y.D.; Writing--Original Draft, Y.L. and X.Z.; Writing--Review and Editing, Y.L. and H.X. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Henan Key Laboratory of Grain and Oil Storage Facility & Safety (200 KFB04), and the application research plan of key scientific research projects for universities in Henan Province (21A560006).  
Not applicable.  
Figure 13: Effect of fiber content on \(R_{\text{n}}\): (**a**) 56 d; (**b**) 28 d.  
Data Availability Statement: The corresponding author will provide the datasets created and analyzed during this study upon reasonable request.  
The authors declare that they have no conflicting financial interests or personal relationships that may have influenced the work presented in this study.