esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains
## 5 Discussion and Conclusions
The change degree of elevation, slope, aspect, and LCT can be used as an index to adaptively adjust the spatial resolution of the inversion data. (5) This study was based on the assumption that the scale of the model relationship was constant, and spatial information difference was used to reduce the impact of spatial heterogeneity. The downscaling methods presented in this study have potential applications for other elements that have downscaling requirements in complex terrains, such as precipitation, temperature, radiation, and wind speed. Conceptualization, Q.C., X.T. and F.M. ; methodology, Q.C. and X.T. ; software, B.L. and G.S. ; validation, Q.C., B.L. and Z.T. ; formal analysis, H.W. and Q.Z. ; investigation, G.S. ; resources, Z.T. and L.Y. ; data curation, H.W. and Q.Z. ; writing--original draft preparation, Q.C. ; writing--review and editing, Q.C. and X.T. ; visualization, Q.C. ; supervision, F.M. ; project administration, Q.C. ; funding acquisition, L.Y., H.W. and Q.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the Project of the Sichuan Department of Science and Technology (2022 NSFSC0216). Publicly available datasets were used in this study. This data can be found here: (1) DEM dataset can be obtained at [[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/)) (accessed on 1 November 2021). (2) SMAP dataset can be obtained at [[https://nsidc.org/data/SPL3](https://nsidc.org/data/SPL3) SMP/versions/5]([https://nsidc.org/data/SPL3](https://nsidc.org/data/SPL3) SMP/versions/5) (accessed on 1 November 2021). (3) MODIS dataset can be obtained at [[https://edftl01.cr.usgs.gov/MOLA/](https://edftl01.cr.usgs.gov/MOLA/)]([https://edftl01.cr.usgs.gov/MOLA/](https://edftl01.cr.usgs.gov/MOLA/)) (accessed on 1 November 2021). In situ SM are provided by the China Meteorological Administration and belong to non-public data. Regarding the datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. We would like to express our sincere thanks to the China Meteorological Administration, Geospatial Data Cloud Platform of Computer Network Information Center of Chinese Academy of Sciences, National Snow and Ice Data Center, and Land Processes Distributed Active Archive Center for supplying the data and the reviewers for their constructive comments and editorial suggestions that helped improve the quality of the manuscript. The authors declare no conflict of interest.
## 6 Evaluation
### Evaluation Results
#### 6.3.3 Route Conflicts Ratio at Relay Nodes Density  
We show the evaluation results in terms of the evaluation perspective (Eval. 4) in Section 6.1.4. The \"route conflicts ratio\" is calculated by (the number of conflicted routes)/(the number of all routes), and a lower value is better.  
(Eval. 4) Figure 21 shows the \"route conflicts ratio\" at each relay node density. The source and destination nodes are located in sparsely populated regions. The experimental results confirm that the \"route conflicts ratio\" increases as the relay node density decreases. When the relay node density is low, nodes are sparse and the number of adjacent nodes per node is small. Fewer adjacent nodes mean fewer route candidates. This makes it easier to construct routes that relay certain nodes. As a result, the \"route conflicts ratio\" is likely to increase.  
Figure 22 shows the \"route conflicts ratios\" at each relay node density when the source and destination nodes are located in population-dense regions. The \"route conflicts ratio\" was lower than that for route constructions between nodes in sparsely populated regions. Although there were routes that bypassed nodes in population-dense regions, many routes were constructed in small population-dense regions. Therefore, the number of relay links was low in almost routes. There were few cases where multiple routes overlapped at relay links. As a result, the \"route conflicts ratio\" was low. This suggests that \"route conflicts ratio\" can be suppressed by constructing short-distance routes in addition to increasing the relay node density.  
Figure 20: “Population-dense regions distance” in each method.  
To suppress the \"route conflicts ratio\", the relay node density greater than 5 nodes/km\({}^{2}\) is desirable, considering both routes between sparsely populated regions nodes and routes between population-dense regions nodes.  
\"Using MPR\" can reduce the number of relay nodes to nearly 70%, but since the relay node density must be greater than 5 nodes/km\({}^{2}\), it was found that the relay node density to which \"using MPR\" should be applied is \(5/0.7=7.12\) nodes/km\({}^{2}\) or greater. This appropriate relay node density value is the result for the node arrangement model based on a regional city (as shown in Figure 9 and Table 1) with a radio propagation range of 1.5 km. The appropriate relay node density will change depending on the evaluation conditions. In particular, it is thought that the value is different in large-scale urban environments, and further simulation experiments are needed.  
Routes that pass through sparsely populated regions are prioritized by the route construction algorithm, so more conflicts may occur. Therefore, it is necessary to further study on the appropriate relay node density in population-dense region and sparsely populated regions.  
Figure 21: “Route conflicts ratio” for relay node density (between sparsely populated regions nodes).  
Figure 22: “Route conflicts ratio” for relay node density (between population-dense regions nodes).
# Non-Uniform Fusion Tree Generation in a Dynamic Multi-Sensor System
## 2 Preliminaries: The Fusion Process Model  
### Two-Tier Hierarchical Fusion Process  
Figure 1 describes the two-tier hierarchical fusion system which consists of vehicle-mounted radars, local fusion nodes that are elected among radars, and a central server. Radars are divided into distinct clusters. For each cluster, one of the radars becomes a cluster head whose role is like that of a local fusion node, and the others become children of their local fusion node. In a cluster, child radars should be connected to their local fusion node via one hop. On the other hand, local fusion nodes can be connected to the central server via multiple hops. Radars form a mobile ad-hoc network to support the multi-hop connection between the local fusion nodes and the central server. This structure can be represented via a tree structure called a fusion tree. The fusion tree is defined as follows: radars are leaf nodes; local fusion nodes are internodes; and the central server is the root of the fusion tree.  
Figure 1: Two-tier hierarchical fusion process model.  
The capabilities of radars:
* Radars are equipped with an anti-air radar sensor whose detection range is a circle with a radius of \(d\) km;
* Radars have a tracker which creates local tracks with a series of measurements from the anti-air radar sensor; and
* Any radar can be elected as a local fusion node which executes local fusion processes to create the local SIAP. The capabilities of the central server:
* The central server executes the global fusion process to create global SIAP with the tracks from local fusion nodes; and
* The central server is equipped with enough computing power to process all received tracks in a fusion cycle.  
The two-tier hierarchical fusion process consists of tracking, local fusion, and global fusion processes. These processes are arranged in a hierarchical structure in the fusion tree. Radars, including local fusion nodes, execute the tracking process to create local tracks with their measurements. Local fusion nodes execute the local fusion process to create the local SIAP with the local tracks from their child radars and their own tracker. The central server executes the global fusion process to create the global SIAP with tracks from the local fusion nodes.
## 4 Fatigue Wear Test
### Analysis of Fatigue Test Results
#### 4.3.3 CV Signals  
It can be seen in Figure 16 that the CV of the copper alloy bearing was relatively high. This was due to the Seebeck coefficient of the copper alloy being positive, whereas the Seebeck coefficient of the aluminium alloy is negative. However, because the material of the main shaft was mainly steel--the Seebeck coefficient of steel is also negative--therefore, the Cu-Fe CV potential was more active than the Al-Fe CV potential, resulting in the copper alloy bearing having a higher CV potential. With the extension of the fatigue wear test time, the CV characteristic value of the aluminium alloy bearing rose, and when the aluminium alloy bearing was seriously worn out, the average values of the CV signals increased as a whole. Not only did the bearing load increase at the time when the bearing load was at its maximum, but the CV signals at other times also increased. Due to the better wear resistance of the copper alloy bearing, its CV amplitude increased less.  
Figure 14: Comparison of oil film thickness of different bearings with different times.  
Figure 15: Comparison of minimum oil film thickness for different wear test durations.  
It can be seen from Figure 17 that with the extension of the bearing wear time, the CV characteristic values show an upward trend. Due to the larger wear amount of the aluminium alloy bearing, the CV characteristic value of this bearing increased significantly, from around 2.25 mv at the beginning to around 4.1 mv. The CV characteristic value of the aluminium alloy bearing increased by 95%, whereas the CV characteristic value of the copper alloy bearing only rose by 50%.  
From Figure 18, it can be observed that with the increase in the bearing clearance, the increase in CV signal, bearing temperature and torque showed an upward trend. For the aluminium alloy bearing, when the clearance increased by 0.010 mm, the aluminium alloy bearing began to wear out. The CV characteristic value increased by 46.3%, whereas the temperature signal and torque signal only increased by 5.1% and 11%, respectively. Along with the progress of the test, the wear amount further increased because the bearing was in the boundary lubrication state. When the wear amount reached 0.20 mm, the CV characteristic value increased by 63.6%, the torque signal increased by 20%, but the temperature signal did not increase significantly. When the test reached the end, the clearance increased by 0.035 mm and the CV characteristic value of the aluminium alloy increased significantly, up to 81%, whereas the bearing temperature rose by 6.3% and the torque value increased by 32%. However, for the copper alloy bearing with better wear resistance, the CV characteristic value increased by 50%, the bearing temperature of the aluminium alloy increased by 3.8%, and the torque value rose by 16%. This shows that the  
Figure 16: CV signals of the different wear times.  
Figure 17: Comparison of CV characteristic values of different wear test durations.  
CV tensile characteristic value increase is more sensitive than the bearing temperature and the torque increase.
Based on the possible object locations detected in the first stage, the discriminative regional weight is proposed to distinguish different objects with similar parts effectively in the second stage. Finally, we test the proposed method on two image datasets and the results are compared with some other algorithms. Experimental results illustrate that the proposed method is suitable for real-time texture-less object detection, and its performance is competitive with other state-of-the-art texture-less object detectors in homogenous conditions.
## 3 Results and Discussion
### Parameters Optimization  
Response optimization involves the identification of the most beneficial combination of variables to achieve predetermined objectives, such as the reduction of wear depth or power consumption, as well as the optimization or maximization of parameters like mass flow. The application of tools such as Minitab's Response Optimizer [49] enables users to finely adjust settings to effectively reach the above goals. This iterative process requires the creation of models for each response and the assignment of appropriate weights that reflect their level of importance. Through visual representation and analytical examination,  
Figure 11: Pareto Chart illustrating the Standardized Effects of Stress (\(\alpha\) = 0.05) on the left, accompanied by a Normal Probability Plot depicting the distribution of Stress on the right.  
Figure 10: Pareto Chart illustrating the Standardized Effects of Deformation (\(\alpha\) = 0.05) on the left, accompanied by a Normal Probability Plot depicting the distribution of Deformation on the right.  
Figure 9: Pareto Chart illustrating the Standardized Effects of Shear Intensity (\(\alpha\) = 0.05) on the left, accompanied by a Normal Probability Plot depicting the distribution of Shear Intensity on the right.  
optimal solutions can be determined using the desirability function, where a desired value of (1) indicates highly desirable responses. By concentrating on the target mass flow of the screw conveyor while simultaneously minimizing the shear intensity, the optimization process was successfully applied in our research study. All desirability values met the desired criteria and were considered acceptable (D = 1).  
The prediction and simulation results presented in Table 4 indicate a close agreement for the mass flow of the screw conveyor, with only a 0.75% error observed. However, for shear intensity, there is a slightly higher discrepancy, with a 4% error between the predicted and simulated values.
# An Enhanced Deep Q Network Algorithm for Localized Obstacle Avoidance in Indoor Robot Path Planning
## 5 Experimental Analysis
### Experimental Simulation Environment  
The experimental environment used in this study is the Gazebo simulation platform, which is known for its highly realistic physical and sensor simulation capabilities. Gazebo can not only accurately simulate physical interactions in the real world, but also allow users to customize complex robot models and environment settings. Its seamless integration with ROS builds a bridge between simulation and real-world applications, and supports parallel interactive and collaborative simulation operations, providing a comprehensive and flexible testing environment for the innovation and verification of robotics technology. Compared with traditional simulation tools, Gazebo has more advanced design and functions, which promotes the rapid development of robotics in research and practical applications. To evaluate the performance of the proposed PER-D2 MQN algorithm in local path planning within typical indoor environments, we designed three simulated indoor scenarios. Figure 7 illustrates these scenarios and their layouts, with the starting coordinates of the robot and the coordinates of obstacles indicated in each assumed environment.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Hyperparameter** & **Value** & **Description** \\ \hline \(\Gamma\) & 0.99 & Discount factor \\ T & 0.03 & Temperature factor \\ \(\varepsilon_{decay}\) & 0.99 & Exploration rate of action \\ A & 0.00025 & Neural network learning rate \\ E & 1.0 & Exploration rate of action \\ \(\varepsilon_{min}\) & 0.01 & Minimum exploration rate \\ episode\_step & 6000 & One episode time step \\ target\_update & 2000 & Update rate of the target \\ Min\_batch size & 64 & The size of a set of experience \\  & & extracted \\ \(\alpha_{PER}\) & 0.6 & Constants that determine a \\  & & specific priority \\ PERB & 1,000,000 & Experience Replay Buffer \\  & & Maximum Capacity \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters.  
1. In scenario (a), the environment is static, with obstacles at fixed positions.
2. In scenario (b), based on the static environment in (a), four white obstacles are set as dynamic obstacles rotating counterclockwise, with an angular velocity of 0.35 rad/s.
3. In scenario (c), two white dynamic obstacles move in random directions.  
In each simulation environment, the task for the robot is to start from its initial position, avoid all obstacles, and successfully reach a randomly generated target location.
# DNN Beamforming for LEO Satellite Communication at Sub-THz Bands
## 4 Conclusions  
In this work, we have proposed a DNN beamformer to implement the signal processing algorithm for a massive number of antenna array receiver at the ground station. It was shown that the proposed beamformer performs with very high accuracy for a wideband signal to match the output of a true time delay beamformer as the angle of arrival at the receiver array changes due to rapid movement of the LEO satellite. Such deployment of signal processing algorithm with a DNN beamforming will remove the use of phase shifters and the nonidealities caused by its implementation thus making it suitable for wideband communication systems. The implementation will lead to a highly efficient and low cost receiver design for wideband LEO satellite applications at sub-THz bands. In future, we will address the joint beamforming of multi-satellite station and ground station networks for optimal allocation of satellite resources in order to maintain high quality of service to the users.  
Conceptualization, R.K. and S.A.; Methodology, R.K. and S.A.; Writing--original draft, R.K. and S.A.; Writing--review & editing, R.K. and S.A.; Supervision, S.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 5: Performance comparison of PSB, TTD, and DNN Beamformer for the satellite elevation angle in the range of 10\({}^{\circ}\) to 90\({}^{\circ}\) at different frequencies of the wideband signal arriving at the antenna array.  
**Acknowledgments:** The authors would like to thank Kreitman School of Advanced Graduate Studies and Ben-Gurion University of the Negev, Israel for providing fellowships to continue the research. The authors would also like to thank the anonymous reviewers for their valuable suggestions to improve the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 6 Analysis and Validation of Statistical Model for AMQP IoT Attack Detection
### Summary of the Statistical Model Performance
#### 6.5.1 Detection Accuracy Across Attack Scenarios  
The model demonstrated high detection accuracy for attack-specific traffic while maintaining robust performance for normal traffic. Key performance indicators are detailed as follows:  
* **DoS Attacks:*
* Detection Rate:
* A total of 71.50% of packets were classified as malicious.
* A total of 1.22% of packets were correctly identified as RST DoS-specific packets.
* False Negatives: 27.28% of malicious traffic misclassified as normal packets, highlighting areas for refinement in recognizing subtle attack traces.
* **MiTM Attacks:*
* Detection Rate:
* A total of 0.04% of packets were accurately classified as duplicate IP MiTM traffic.
* A total of 0.02% were categorized as general malicious traffic.
* False Positives: Minimal, with 99.95% of normal traffic correctly classified, indicating strong reliability in non-attack scenarios.
* **Brute Force Attacks:*
* Detection Rate:
* A total of 3.83% of packets were identified as malicious.
* A total of 0.40% were recognized as RST/ACK brute force packets.
* False Positives: Negligible, with 95.77% of normal traffic classified correctly, although attack detection rates suggest opportunities to enhance granularity.
## 1 Introduction
### Research Scope
The study's innovative approach provides a unique combination of classical and ensemble algorithms to analyze and predict the unconfined compression parameters of expansive soils with varying sand content, saturation levels, and dry densities. By tapping into the vast potential of these diverse models, the research provides a nuanced understanding of how different variables influence soil behavior, a task that traditional methods have found challenging. The study's novelty stems from the direct application of sophisticated machine learning techniques to experimental data, resulting in remarkably precise predictions of previously unattainable soil parameters. Unlike traditional methods, which rely on theoretical assumptions or simplified models, this novel methodology uses advanced methods to analyze raw experimental data. Through this, the study achieves remarkably precise predictions of soil parameters, outperforming previous methods. One important feature of this novel approach is its consideration of the percentage of sand in the soil, which has frequently been overlooked in previous research efforts. By incorporating this critical factor into the analysis, the predictive models developed in this study provide a more complete picture of soil behavior. The inclusion of sand percentage allows for more accurate predictions, filling a significant gap in the literature and increasing the findings' applicability to real-world scenarios. Furthermore, this study presents predictive models and equations designed to estimate the elasticity modulus (\(E_{t}\)) of expansive soil. Unlike previous studies, which may have focused solely on parameters such as unconfined compressive strength (\(q_{u}\)) or investigated different soil types, our study fills a gap by developing models specifically for expansive soils. Considering the unique properties and behaviors of expansive soils, which require tailored predictive models for precise assessment of elastic modulus and unconfined compressive strength, this study aims to contribute to the field of geotechnical engineering by addressing identified gaps in the literature and proposing innovative methodologies. The use of advanced machine learning techniques, combined with the consideration of previously overlooked factors such as sand percentage and the creation of specialized models for expansive soils, improves prediction accuracy and reliability. Finally, these advancements improve our understanding of soil behavior and have practical engineering applications, laying the groundwork for future field study.