esa-sceva/satcom-chunk-collection huggingface dataset:
# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
## 7 Conclusions  
This paper presents a multi-submap fusion mapping approach utilizing semantic ground fitting to construct an occupancy grid map for robot navigation within a global coordinate system. The key contributions of this work are as follows. First, the method  
Figure 14: Visual interactive interface for navigation systems.  
Figure 15: Route planning using constructed maps. The green area represents the navigable region, the black area represents the non-navigable region, and the white dots indicate the planned route.  
simplifies environmental reconstruction by focusing exclusively on the road surface and obstacles, constructing a lightweight navigation map. Additionally, we introduce a novel \(3\times 3\) grid-based submap management strategy to enable real-time map storage and retrieval, balancing mapping accuracy and computational efficiency. We addressed the issue that sparse point cloud maps generated by visual SLAM algorithms cannot be used for navigation and that real-time mapping is not feasible using dense depth estimation. We evaluated our method in real-world scenarios and presented a practical application in which the constructed map is utilized for AGV navigation. Experimental results confirm that the proposed method efficiently constructs an occupancy grid map for outdoor environments while reducing computational overhead, achieving high accuracy across areas of up to 3000 square meters. While our method demonstrates significant application potential, it has some limitations, including occasional inaccuracies in semantic segmentation and an inability to handle dynamic objects. In future work, we will train the model on our own dataset tailored for specific scenarios to enhance semantic segmentation performance, as well as incorporate dynamic feature point removal and local path planning. Additionally, to improve system integration and portability, we will develop an ROS version of the system.  
Conceptualization, G.L. and C.H.; methodology, G.L.; software, C.H.; validation, H.L. and J.Y.; formal analysis, G.L.; investigation, G.L. and H.L.; resources, G.L.; data curation, C.H. and J.Y.; writing--original draft preparation, G.L. and C.H.; writing--review and editing, G.L. and C.H.; visualization, C.H.; supervision, G.L.; project administration, G.L.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the National Natural Science Foundation of China under Grant 51466001 and in part by the Natural Science Foundation of Guangxi under Grant 2017 GXNS-FAA198344 and Grant 2017 GXNSFDA 198042.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
## 2 Complete Machine Structure and Working Principle  
### Platform Structure and Details  
Aiming at the needs of small-scale aquaculture tasks, this paper develops a lightweight unmanned boat device. The hardware part mainly includes the hull, environmental data acquisition module, underwater vision system module, and hull motion control module (Table 1). Among them, the hull is made with EVA foam boards, which is not only lightweight but ensures good buoyancy performance. The environmental data acquisition module contains a NodeMCU development board, a digital water temperature sensor, a pH sensor, and a TDS turbidity sensor, which can realize accurate measurement and data transmission of various water quality parameters. The underwater vision module includes a NeZha development board and a 1080P high-definition underwater camera, which can realize real-time shooting and transmission of underwater targets. The motion control module includes a STM32 development board and a BeiDou antenna, which is dedicated to realize the precise motion of the boat. The specific parameters of the unmanned boat are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Module** & **Norm** & **Instructions** \\ \hline \multirow{6}{*}{Hull} & Width & 26 cm \\  & Length & 48.5 cm \\  & Battery Capacity & 5200 mAh \\  & Weight Capacity & 2.5–5 kg \\  & Hull Weight & 1.5 kg \\  & Endurance & 4–5 h \\ \hline \multirow{6}{*}{Environment Data Acquisition Module} & NodeMCU & Operating module ESP8266; operating voltage 5 V; input voltage 5 V; digital input and output pins 10 (all can be used as PWM pins); PWM pins 10; analog input pins 1; Wi-Fi standard 802.11 b/g/n; operating mode STA/AP/STA + AP. \\ \cline{1-1}  & DS18B20 & \\ \cline{1-1}  & Digital Water & Operating range (temperature) –55\(\leftrightarrow\)125 \({}^{\circ}\)C; Temperature accuracy \(\pm\) 0.5 \({}^{\circ}\)C \\ \cline{1-1}  & Temperature Sensor 2 & \\ \cline{1-1}  & & Detectable Concentration Range: PHO-14; Detectable Temperature Range: 0–80 \({}^{\circ}\)C; Response Time: \(\leq\)5 s; Operating Temperature: –10–50 \({}^{\circ}\)C (Standard Temperature 20 \({}^{\circ}\)C); Operating Humidity: 95\% RH (Standard Humidity 65\% RH) \\ \cline{1-1}  & TDS Turbidity Sensor 2 & Operating range (TDS) 0–1000 ppm; TDS accuracy \(\pm\) 5\% F.S. (25 \({}^{\circ}\)C) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of unmanned boat.  
Figure 1: Overall technical route.
# Aggregate Type and Concrete Age Effects on Anchor Breakout Performance: Large Database and Insights
#### Research Significance  
Current anchor design codes and anchor qualification guidelines provide quite limited guidance related to concrete mix design, curing, and storage as well as the age of testing. Regarding the coarse aggregate used, merely medium hardness is specified, leaving the range of possibly resulting concrete properties wide open. The only determined material property that enters design equations and qualification rules is concrete compressive strength. This contribution summarizes the largest data set ever created of an aggregate effect on anchor capacity, obtained in two independent studies and performed by two different research groups. The combined experimental data are analyzed and novel insights into the predictive quality of available design models for concrete cone capacity in tension and edge breakout in shear with respect to a potential aggregate effect as well as age effect are presented.
## 4 Numerical Simulation of the Influence of Vehicle Load on Civil Air Defense Construction Vibration
### Model Introduction  
According to the actual project, the calculation model is properly simplified to analyze the propagation law of driving vibration in civil air defense construction. The width of the calculation model is 20 m, the height is 13 m, and the axial length is 70 m. The soil layer is divided into three layers. The height of the first layer and the second layer is 1 m, and the height of the third layer is 11 m. The project section is shown in Figure 9. The supporting structure of civil air defense construction is reinforced concrete, with a covering thickness of 3 m, a height of 4 m, a width of 8 m, and a thickness of 0.5 m for the floor, roof and wall. SOLID45 solid element is selected for soil layer and civil air defense structure, which has many functions such as stress stiffening, expansion, creep, plasticity, large strain, large deformation, etc., so it can be used as an ideal element for simulating geotechnical materials. The numerical calculation model has 139,720 elements after meshing. The left and right boundaries are fixed with X direction displacement, the bottom boundary is fixed with Y direction displacement, and the front and rear boundaries are fixed with Z direction displacement. The components are connected by common nodes. The numerical simulation adopts transient analysis, and the time step is 0.03 s. The calculation model is shown in Figure 10.  
Figure 9: Project section.
## 3 Control Strategies for Obstacle-Aided Locomotion
### Obstacle Avoidance  
A traditional approach to dealing with obstacles consists of trying to avoid them. Collisions may make the robot unable to progress and cause mechanical stress or damage to equipment. Therefore, different studies have focused on obstacle avoidance locomotion. For instance, principles of artificial potential field (APF) theory [32] have been adopted to effectively model imaginary force fields around objects that are either repulsive or attractive on the robot. The target position emits an attractive force field while obstacles, other robots, or the robot itself emits repulsive force fields. The strength of these forces may increase as the robot gets closer. Based on these principles, a controller capable of obstacle avoidance was presented in [33]. However, the standard APF approach may cause the robot to end up trapped in a local minima. In this case, the repulsive forces from nearby obstacles may leave the robot unable to move. To escape local minima, a hybrid control methodology using APF integrated with a modified simulated annealing (SA) optimization algorithm for motion planning of a team of multi-link snake robots was proposed in [34]. An alternative methodology was developed in [35]. Central pattern generators (CPGs) were employed to allow the robot to avoid obstacles or barriers by turning the robot's body from its trajectory. A phase transition method was also presented in the same work, utilising the phase difference control parameter to realise the turning motion. This methodology also provides a way to incorporate sensory feedback into the CPG model, allowing for the detection of possible collisions.
# An Innovative Approach to Anomaly Detection in Communication Networks Using Multifractal Analysis
## 5 Multifractal Analysis of Performed Tests Results
### Simulation of Malware Infections  
For malware infections, no multifractal spectrum graphs were created, due to the fact that the tests were carried out on local machines and did not cause the spread of infections, which did not create anomalous traffic inside the tested network. The detection of malicious programs and processes was the responsibility of the Rootcheck scanner installed together with Wazuh agent software. The scanning frequency was set every five minutes. Using Diamorphine, a harmless system process was hidden, which Rootcheck then managed to detect as a potential threat in the form of kernel rootkit. With Reptile software, files were hidden in system folders. The scan was also able to find and classify one of the files as an anomaly due to incorrect file size. The results are shown in Figure 18.  
Figure 17: The multifractal spectrum during brute force attacks on the File Transfer Protocol (FTP) server.  
The VirusTotal tool integrated with software agent Wazuh was used to scan the folders. The monitored folder was the default folder for the Mozilla Firefox browser on Ubuntu. Both the malware samples and the eicar text file triggered the alarm shown in Figure 19. The activity related to the exe file downloading was reported by the NIDS system as a probable malicious file.  
The overview of the report provides information on the location of the virus, the date of infection, and the checksums. One of the detected files that triggered the alerts was downloaded using Google Rapid Response, which was used for further threat analysis. The malicious file was scanned, and essential information on size and hash code was obtained. The file system was then examined for viruses with similar properties. The system returned five results. Figure 20 shows the results of the scan of one of the infections found.  
Figure 19: Malware software detected in the monitored directory in Snort.  
Figure 18: Detected threats related to rootkit activities in Snort.  
There are no antivirus programs installed on the network's end terminals, whose main task is to eliminate the threats mentioned above. The purpose of the analysis in this section was to show the tools for centralized detection and response to network threats resulting from malware infection.
###### Abstract  
The performance and efficiency of a centrifugal compressor are usually affected by the highly complex 3-dimensional flow structures which develop in the flow field of the compressor. Several experiments and research using numerical analysis have been reported, however, there are still many unknown physical phenomena that need to be studied, in order to optimize the design and improve the efficiency of turbomachines, especially those installed on hydrogen-powered fuel cell electric vehicles (FCEVs). In this study, the 3-dimensional vortex structures were analyzed using the critical-point theory and the probabilistic definitions, for an air supply device mounted on the commercial hydrogen FCEVs. The behavior of the complex 3-dimensional vortex structures at the design flow rate and low flow rate were elucidated. A tip leakage vortex was observed to develop at the leading edge of the main blade at all flow rates, which caused interference to the splitter blade. At 60% of the design flow rate, a vortex breakdown occurred at the tip leakage vortex near the leading edge of the main blade, and a reverse flow at 50% chord length of the main blade's suction surface. The boundary layer which developed at the leading edge of the main blade's suction surface at all flow rates led to the creation of a hub separation vortex by interfering with the boundary layer developed at the hub surface as a result of the centrifugal force. In addition, the boundary layer developed at the hub and shroud surface created a horseshoe vortex as it moved downstream and interfered with the leading edge of the main blade and splitter blade. It was confirmed that the behavior of the tip leakage, hub separation, and horseshoe vortex structures determined the aerodynamic performance of the centrifugal compressor. The average pressure difference improved by 1.47% of the entire flow rate after optimizing the compressor design.  
captrightal compressor; vortex; computational fluid dynamics; optimization Article
## 3 Mechanism Reliability Modeling
### Reliability Model Validation  
In order to verify the correctness and validity of the proposed reliability models, the Monte Carlo simulation method is used in this paper. In the Monte Carlo method, the working process is simulated based on the working mechanism of the mechanisms and the stochastic characteristics of the system parameters. The proposed reliability models are not used in the simulation process. In this way, it is possible to make the simulation process independent of the proposed computational models, thus accomplishing the validation of the reliability analytical models. The specific Monte Carlo simulation process is shown in Figure 5. As shown in Figure 5, during each simulation of the system, in each TP, random loads are generated according to the random distribution characteristics of the loads and the corresponding stress responses are obtained. Through the damage caused by the stress and the maximum stress, it is judged whether the residual strength can resist the working stress. When the system can work normally in all TPs, the system is considered to be reliable, otherwise the system is considered to fail. Finally, the system reliability is obtained by counting the number of system failures.
## 3 The Proposed CCBA Model and Distillation Techniques
### Knowledge and Feature Distillation  
Knowledge distillation is a model compression technique first introduced by [PERSON] et al. [34] in 2015. Knowledge distillation transfers knowledge from a larger, more complex \"teacher\" model to a smaller \"student\" model. Knowledge distillation allows the student model to achieve performance comparable to the teacher's while being more efficient regarding computational resources. Feature distillation extends the knowledge distillation concept by focusing on transferring intermediate layer representations, or \"features\", from the teacher model to the student model. This approach allows the student model to learn rich internal representations from the teacher, resulting in better generalization and improved accuracy.
# DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles
###### Abstract  
Object detection in complex road scenes is vital for autonomous driving, facing challenges such as object occlusion, small target sizes, and irregularly shaped targets. To address these issues, this paper introduces DGSS-YOLOv8s, a model designed to enhance detection accuracy and high-FPS performance within the You Only Look Once version 8 small (YOLOv8s) framework. The key innovation lies in the synergistic integration of several architectural enhancements: the DCNv3_LKA_C2f module, leveraging Deformable Convolution v3 (DCNv3) and Large Kernel Attention (LKA) for better the capture of complex object shapes; an Optimized Feature Pyramid Network structure (Optimized-GFPN) for improved multi-scale feature fusion; the Detect_SA module, incorporating spatial Self-Attention (SA) at the detection head for broader context awareness; and an Inner-Shape Intersection over Union (IoU) loss function to improve bounding box regression accuracy. These components collectively target the aforementioned challenges in road environments. Evaluations on the Berkeley DeepDrive 100K (BDD100K) and Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) datasets demonstrate the model's effectiveness. Compared to baseline YOLOv8s, DGSS-YOLOv8s achieves mean Average Precision (mAP)@50 improvements of 2.4% (BDD100K) and 4.6% (KITTI). Significant gains were observed for challenging categories, notably 87.3% mAP@50 for cyclists on KITTI, and small object detection (AP-small) improved by up to 9.7% on KITTI. Crucially, DGSS-YOLOv8s achieved high processing speeds suitable for autonomous driving, operating at 103.1 FPS (BDD100K) and 102.5 FPS (KITTI) on an NVIDIA GeForce RTX 4090 GPU. These results highlight that DGSS-YOLOv8s effectively balances enhanced detection accuracy for complex scenarios with high processing speed, demonstrating its potential for demanding autonomous driving applications.  
YOLOv8 optimization; multi-scale feature fusion; autonomous driving; small object detection; high FPS detection 202518358  
3
## 3 Results and Discussion
### Machine Learning to Predict \(q_{u}\) and \(E_{t}\)
#### 3.4.2 Streamlined Interface for ANN Model Predictions  
The incorporation of a user-friendly interface enhances this approach by providing a streamlined, one-click prediction process. This interface simplifies the utilization of the ANN model, ensuring accessibility for users of varying technical expertise. For instance, to utilize the ANN \(q_{u}\) model, users can easily open the \"ANN \(q_{u}\) model.ipynb\" (Supplementary Materials) Python file using the Jupyter Notebook app. By simply pressing shift + enter, the interface promptly appears, facilitating a user-friendly experience, as depicted in Figure 18. This straightforward process remains consistent for the ANN \(E_{t}\) model, ensuring uniform and accessible user interaction.  
Figure 18: Illustration of user-friendly interface: (**A**) ANN \(q_{u}\) model, (**B**) ANN \(E_{t}\) model.
# On Blockchain-Based Cross-Service Communication and Resource Orchestration on Edge Clouds
## 3 Network Service Marketplace Architecture
### System Architecture
#### 3.2.1 User Layer  
The User layer provides the essential functionality required for the interaction of the users with the NSM, as it is shown in the left part of Figure 1. Through a client, portal and/or some dashboard, both service providers and consumers can register a service available for leasing, discover service features, request service lease and trigger service instantiation, and establish CSC. In addition, this layer provides to the users monitoring and billing functionalities for the leased network services. Any data storage or retrieval is performed by invoking the appropriate smart contracts through the Blockchain layer's interfaces.
# Smart Low-Cost Control System for Fish Farm Facilities
## 5 Smart Control Algorithm for Aquaculture
### Example 1: Control Algorithm Operation in Sea Cages
Thus, the use of the smart control algorithm supposes the following improvements in the performance of the aquaculture control system:  
* A reduction in the cost of feed, since 14.25 kg/m\({}^{3}\) is used with the smart control algorithm against 15 kg /m\({}^{3}\) with the predefined control algorithm. Considering that feed represents 70% of the costs of aquaculture production, saving 5% of the feed is an important economic saving. * An improvement in feed utilization, considering that fish consumed the feed in a ratio of 13.82 kg /m\({}^{3}\) when the fish farm received the information from the DB and the AI compared with the 13.18 kg /m\({}^{3}\) when it did not receive it. This difference in feed consumption supposes better fish growth, which implies a better fish size at the harvest moment resulting in a higher market price of the produced fish. * A reduction in environmental pollution due to uneaten food pellets, since 0.45 kg /m\({}^{3}\) are deposited in the seab when the system is fully operative. The value increases to 1.82 kg /m\({}^{3}\) when predefined control algorithms regulate the feeding process. Figure 18: Scenario 1 without connection to the Smart Control System. (**a**) Shows the remaining feed and the feeder velocity. (**b**) Portrays the uneaten feed and feed falling detection. When feed falling is detected, the feeder velocity is reduced from 100% to 80%, then to 60%, 40%, and 20%. Uneaten feed is considered one of the most relevant environmental impacts of the aquaculture industry. A reduction in environmental impact supposes a greener and more sustainable production, better usage of resources, and cleaner oceans. In addition, the uneaten feed might unleash several pollution problems, which can affect fish, causing a decrease in fish welfare.