esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy-Based Multi-Modal Query-Forwarding in Mini-Datacenters
## 4 Modeling the Query Flow Within the Mini-Datacenter
### Modeling Fine-Grain Forwarding  
Fine-grain forwarding approach selects multiple gateways to forward any query \(q_{i}\) redundantly from the command center (CC); the selection of the number of gateways is restricted by the gateways' buffer occupancy and the backlog of incoming queries waiting to be serviced at the CC. In the following subsections, we define and derive the fitness factor and the gamma factor to determine the number of gateways to be selected as redundant gateways, ensuring reliability while minimizing overflow at the gateways' buffers.
## 4 Methodology Implementation Procedure
### Parameter Optimization  
The SVM parameters are optimized by means of both PSO and five-fold cross validation. The value ranges of two parameters \(C\) and \(\gamma\) were set as \([0.1,100]\) and \([0.01,100]\), respectively. When using PSO for parameter optimization, the population size of the particle swarm was 20, the maximum number of iterations was 100, and two learning factors, \(c_{1}\) and \(c_{2}\), were separately set as 1.5 and 1.7 [48]. A group of initial particles was randomly generated in the range of parameters, and the mean square error (MSE) of cross validation [49] was selected as the fitness function. The iterative optimization process of the PSO algorithm is illustrated in Figure 2, and the fitness curve obtained can be seen in Figure 4. When the iteration number is 79, the fitness value reaches the minimum of 0.008, and the optimal parameters, \(C_{best}=16.474\) and \(\gamma_{best}=0.129\), are acquired by model training. The PSVM model with optimal parameters needs to be verified through the test set.
###### Abstract
In addition, the importance of predictive maintenance and real-time monitoring in bearing fault detection is also emphasized. At the same time, the importance of predictive maintenance and real-time monitoring in bearing fault detection is emphasized. This paper uses the Google Scholar and IEEE Xplore databases to search the literature from 2020 to 2024 to select the latest research related to bearing fault detection techniques, especially studies that include vibration analysis, thermography, AI, or multimodal methods. During the search, the main keywords used were \"bearing fault detection\" and auxiliary words included \"AI-based diagnostics\", \"vibration analysis\", \"multi-modal technologies\", etc. When conducting research, we selected the literature that was most recently published, highly relevant to the technology, and of good quality, excluding studies that deviated from the topic, were in non-English languages, or had already been discussed in other reviews. Extant reviews lack a comprehensive perspective of interdisciplinary integration; for instance, they may primarily focus on one aspect while neglecting the synergistic effects between different detection methods. There is a lack of coverage of the latest technologies. Insufficient consideration has been given to practical application scenarios, and the existing reviews lack a comprehensive discussion of factors such as cost and the interpretability of deep learning models in industrial applications. There is inadequate systematic summarization and comparison. This paper conducts a comprehensive analysis from a multidisciplinary perspective, integrating various methods such as vibration, acoustics, current, electromagnetic, infrared, vision, and intelligent detection, systematically exploring their advantages, disadvantages, and potential for integration in practical scenarios. It comprehensively covers the latest research findings from the past 3 years and systematically organizes the development process over nearly 125 years. It focuses on discussing practical application cases in the industrial sector in recent years, showcasing the implementation effects of cutting-edge technologies. It provides an intuitive table that systematically compares the advantages and disadvantages of different technologies through performance parameters. It summarizes applicable scenarios and method selection strategies, offering practical references for engineers and researchers. This paper summarizes various methods for bearing fault detection, aiming to systematically compare and analyze current research methods and technologies in this field, addressing the shortcomings of existing reviews in terms of comprehensiveness, timeliness, and practicality, thereby providing references and guidance for future studies.
# DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
## 4 Model Training and Experimental Results
### Social Distancing Evaluations
In cases where a breach occurs between two neighbouring couple, or between a couple and an individual, all of the involved people will turn to red status, regardless of being a couple or not. The flexibility of our algorithm in considering different types of scenarios enables the policymakers and health authorities to proceed with different types of investigations and evaluations for the spread of the infection. For example, Figure 15 from the Oxford Town Centre dataset, provides a basic statistics about the number of people in each frame, the number of people who do not observe the distancing, the number of social distancing violations without counting the coupled groups as violations. Figure 14: Social distancing violation detection for coupled people and individuals. (**a**) Examples of coupled people detections-orange bounding boxes; (**b**) Three types of detections: safe, violations, coupled. Figure 15: A 2D recording of the number of detected people in 900 frames from the OTC Dataset, as well as the number of violations and number of couples with no violations. Regarding the coupled group we reached to an accuracy and recall rate of 98.7% and 23.9 \(fps\), respectively which is slightly lower than our results of normal human detection as per the Table 3. This was expected due to added complexity in tracking two side by side people and possibly more complex occlusion scenarios.
# Joint Channel Estimation Algorithm Based on DFT and DWT
## 3 DFT-Based Channel Estimation Algorithm
### Traditional DFT-Based Channel Estimation Algorithm
\begin{split}\widetilde{\text{MSE}}_{\text{V-W-DFT}}[k]\approx \sum_{n=0,n\in\mathcal{O}}^{N-1}E\Big{[}\left|h_{\text{V}}[n]\right|^{2}\Big{]} +\sum_{n=0}^{N-1}E\Big{[}\left|\tilde{w}[n]\right|^{2}\Big{]}-\sum_{n=0,n\in \mathcal{O}}^{N-1}E\Big{[}\left|\tilde{w}[n]\right|^{2}\Big{]}\\ =\sum_{n=0,n\in\mathcal{O}}^{N-1}E\Big{[}\left|\hat{h}_{\text{V}}[n ]\right|^{2}\Big{]}+\sum_{n=0}^{N-1}E\Big{[}\left|\tilde{w}[n]\right|^{2} \Big{]}-2\sum_{n=0,n\in\mathcal{O}}^{N-1}E\Big{[}\left|\tilde{w}[n]\right|^{2} \Big{]}.\end{split}  \tag{17}\]  
It can be seen from Equation (13) that the introduction of virtual sub-carriers leads to the CIR obtained by IFFT, resulting in channel multipath power spread. Consequently, if all samples beyond the CP length range are still forced to be set to zero, the MSE performance of channel estimation will deteriorate. In addition, it is not mandatory to set a certain area beyond the CP length range to zero, but it is necessary to use the uniform threshold for the entire SNR range given by [13] to judge all the samples, which can alleviate the estimation performance floor under high SNR conditions. However, the estimation performance under medium and low SNR conditions is difficult to guarantee. This is because, at medium and low SNR, the power of the channel component in the samples beyond the CP length is generally less than the power of the noise component. If the instantaneous large noise component causes the overall power of the sample to exceed the threshold, it will inevitably lead to misjudgment of the effective channel samples, thereby reducing the channel estimation performance.
## 2 Materials and Methods  
### Investigated Samples  
The samples used in this work were circular blanks made of fused silica (Spectrosil 2000 from Heraeus) with a diameter of 25 mm and a thickness of 5 mm. The samples were produced by classical optical manufacturing techniques. For polishing, a plane polishing tool with a polishing pad made of polyurethane and a premixed water-based polishing suspension (Hastilite PO from Universal Photonics, Inc., New York, NY, USA) were used. The surface accuracy was \(\lambda/10\), corresponding to 0.2 Newton fringes, while the surface roughness was Rq < 0.5 nm as determined via an interferometer over a spatial bandwidth of 1 \(\upmu\)m-300 \(\upmu\)m.  
First the bonding process was tried out with pairs of uncoated samples. For the following investigation, an uncoated blank and a coated one were bonded where two different coatings were tested: first, a metal-containing non-polarising beam splitter (NPBS) coating consisting of silicon dioxide (SiO\({}_{2}\)), niobium pentoxide (NbO\({}_{5}\)) and silver (Ag) and second, a pure dielectric polarising beam splitter (PBS) coating made of niobium pentoxide and silicon dioxide. The outermost protection layer of both coatings is made of silicon dioxide where the layer thickness is higher in the case of the NPBS coating.
## 4 Results
### Single Electric Vehicle Connection
#### 4.2.3 Responses of DGs  
Figure 8 shows the behavior of the power supplied by each DG connected to the microgrid after the six events tested for single EV connection. Figure 8a shows the active power supply, and Figure 8b shows the response of reactive power supply with the strategy control applied with variable virtual impedance. This test allows identifying the distribution of the active and reactive power supplied among generators in the microgrid.  
Figure 8 shows that active power increases with the connection of each EV to the different nodes, and each generator stabilizes as quickly as possible to deliver adequate reactive power to the various connections of EVs. In the two first events (10 s and 20 s), it is observed that DG 1 is the one that responds more sharply to the change of load, because it is closer to the node where the respective load is located. After 30 s, the connection occurs at Node 3, where DG 2 is the one that responds most to this change during the first few seconds as it is the generator closest to the load, whereas the control stabilizes the three generators to a same active power value. From 40 s, an EV is connected to Node 4, and DG 3 responds more sharply to this type of load change because it is the generator closest to the node where the EV connection occurs.  
Figure 7: Voltages in the six nodes (Nodes 1–6) considering control strategies based on variable and fixed virtual impedances for six power variations: (**a**) Event 1, (**b**) Event 2, (**c**) Event 3, (**d**) Event 4, (**e**) Event 5, and (**f**) Event 6.  
From 50 s, the load of an EV is connected to Node 5, and now DG 3 increases its active power because this is the closest generator to the load. From 60 s, the load of an EV is connected to Node 6, and DG 3 changes the active power generation because this is the generator that is closest to the load; however, control strategy support is achieved by stabilizing the output power to the same value of the other two DGs.  
Figure 9 shows the output frequency of the three DGs and their variation after the different load changes in the nodes that constitute the microgrid. This result considers the control strategy applied with the variable virtual impedance. The frequency of the three generators always stabilizes in a single value a few seconds after the load changes in each of the different nodes. This figure shows that some events create more variations in the frequency, which is related to the magnitude of power changes and the distance to DGs. However, the controller with the virtual impedance maintains the frequency value close to 50 Hz, which is the desired value.
# Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network
## 1 Introduction
How to make the network nodes in different orbits cooperate closely and collaborate in-depth to enable complex or large-scale tasks to be processed efficiently, is an urgent problem should be solved at present. At present, computation offloading technology [5] is widely used in Internet of things and onboard network. It has become an important means of real-time processing and lasting endurance of terminal business, mainly to solve the problems of lack of computing capacity and limited storage resources of computing nodes. Computation offloading is the key technology in Mobile Edge Computing (MEC), which can be executed by offloading the business to other network sites with plentiful computational resources and powerful computing capabilities, and then after the tasks are completed, the results are passed back to the offloading terminal devices, thus saving the cost of the offloading terminal devices and reducing the limitation of limited resources in the mobile system [6]. Therefore, in the Space-Air-Ground Integrated Network, the computation offloading technology can be used to decompose the compute-intensive tasks. Selecting any computing node in the network that can take on the task offloading as the offloading site to perform tasks can effectively alleviate the computational storage pressure of network nodes and improve the efficiency of huge and complex tasks. The general process for task computation offloading in the Space-Air-Ground Integrated Network is [7]: (1) Analysis whether any terminal located in the space-based backbone network, the edge access network or the ground-based backbone network requires task computation offloading. (2) Get the resources of the network. (3) Analyze the state of the wireless channel, and determine whether the task is executed locally or needs to be offloaded to other service devices for execution by the computation offloading decision. (4) According to the decision instructions, the tasks that need to be offloaded are split into several separate subtasks, the subtasks that do not support offload are executed by the local terminal, and the subtasks that can be offloaded are offloaded to other network nodes in the Space-Air-Ground Integrated Network. The design of computation offloading mainly includes offloading decisions and optimization of algorithms [8]. We consider the different types of computing nodes capabilities of the space-based backbone network, the edge access network and the ground-based backbone network in the Space-Air-Ground Integrated Network, and in-depth study of computation offloading decisions and algorithms for optimization decisions that are suitable for the integrated network environment, in order to improve the execution efficiency of huge and complex tasks in complex network environments.
# A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning
## 5 Discussion
### Contributions and Limitations of This Review  
This systematic literature review offers several significant contributions to the understanding of federated machine learning in cybersecurity. By rigorously defining and categorizing FML models, it provides a clearer taxonomy of hybrid approaches, moving beyond ambiguous \"Hybrid AI\" definitions. The comprehensive analysis across five key performance metrics (accuracy, privacy, scalability, explainability, robustness) offers a structured comparative overview, identifying both strengths and persistent challenges. Critically, this review synthesizes emerging trends and identifies specific research gaps, directly informing future research directions in this rapidly evolving field.  
However, this review is subject to certain limitations. Its scope was limited to peer-reviewed journal articles published between 2015 and 2024, written exclusively in English. The conclusions drawn are based on the reported metrics and findings within the selected literature, and thus may reflect the experimental setups and datasets used in the original studies. While efforts were made to critically evaluate the literature, inherent biases or limitations in the original research might persist.
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 2 Materials and Methods
### Processing Chain
#### 2.3.1 Delineating Flood-Prone Areas  
Flood-prone areas are delineated in order to limit processing only on such zones. It also avoids false positive flood detection. The HAND model enables a pixel's flood-proneness to be determined: the greater the difference in elevation between the pixel and the thalweg network, the less likely it is to be flooded [29]. By applying a threshold to HAND values, we can identify flood-prone areas and mask all pixels outside of them. This threshold must be chosen to maintain a balance between accuracy and performance. If the threshold value is set too high, non-flood-prone zones will be included and thus needlessly slow down processing; too low, and zones genuinely at risk of flooding will be excluded.  
In the specific case of Madagascar, a HAND threshold value of 5 m and a FAT value of 100 were chosen to include zones already known to have flooded previously. These values were determined on the basis of data on previous floods and local terrain features.
## 1 Introduction
This paper builds on experience in CFRP defect detection and addresses the shortcomings of the traditional matrix acid etching method for detecting carbon fiber areal density, such as its complex process, highly variable results, and operational hazards. By analyzing the eddy current conductivity and numerical model of CFRP prepreg, the \"8\"-shaped structure probe is improved. Through finite element numerical analysis of the quadrupolar coil group, the impact of different coil parameters and excitation frequencies on impedance is investigated. Models of unidirectional strip and braided carbon fibers are established. The distribution laws of conduction current density in the x, y, and z directions are simulated, and the relationship between the areal density of different structural carbon fibers and coil impedance variation is verified. The organization of this paper is as follows: Section 2 introduces the numerical model of CFRP prepregs, analyzes the material's electromagnetic properties in detail, and establishes the electromagnetic relationship between the probe and the material. Section 3 further validates the effectiveness of the numerical model through finite element simulations and optimizes the geometric parameters of the probe to improve detection sensitivity and accuracy. Section 4 presents the detection results of the surface density of CFRP prepregs and discusses the performance differences between woven and unidirectional carbon fiber structures. Finally, Section 5 summarizes the main contributions of this paper and suggests directions for future research.
# Extended Reality: Types and Applications
## 5 Software in XR
### Types of Software by Implemented Platform
An overall comparison of the most popular 3D engines is shown in Table 2.  
The provided comparison of the 3D engines and analysis of their capabilities and implementation offers some highlights. First, the Unreal graphical engine, being open source and open for modification, is much more suitable for further modernization and can be explicitly implemented into the robotic system using ROS or by direct programing. In other words, the Unity 3D engine is ready for the final user, but the customization of this \begin{table}
\begin{tabular}{c c c} \hline \hline
**Criteria** & **Unity 3D** & **Unreal Engine** \\ \hline Productivity and speed & Allows one to quickly develop and prototype applications, also mobile and offline devices; suitable for applications with medium or high graphics, lighter apps & More productive but requires more resources and time to set up graphics and simulations; provides high graphical detail, better suited for projects requiring photorealistic graphics \\ \hline XR support & Native support using Unity XR Plugin, support for ARCore, ARKit, HoloLens, Oculus, and Magic Leap & Unreal supports OpenXR, ARCore, ARKit, HoloLens, Oculus, Magic Leap, and other VR/AR devices \\ \hline Ease of development & Lower entry threshold, clear interface, large library of documentation, training resources and developer community & Higher complexity, requires C++ knowledge for complex tasks, need understanding of Unreal Blueprints for creating logics \\ \hline Community and support & Large community for finding solutions in different areas, many free and paid assets, regular updates & Active community, considerable number of assets, and focus on high-quality gaming projects \\ \hline Integration with robots & Excellent integration with ROS; convenient for simulation, testing, and robot control & Robot Operating System (ROS) support is possible via plugins and C++; better for simulations requiring complex physics and visualizations \\ \hline Use cases in mobile robots & Suitable for controlling and simulating robots with support for XR interfaces & Suitable for high-quality simulations and XR visualizations \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification of 3D engines for creating XR software. device is hardly possible. As an outcome for comparison, it can be said that both graphical engines have their own place in the field and do not replace each other.
###### Abstract  
Rotor is a widely used and easily defected mechanical component. Thus, it is significant to develop effective techniques for rotor fault diagnosis. Fault signature extraction and state classification of the extracted signatures are two key steps for diagnosing rotor faults. To complete the accurate recognition of rotor states, a novel evaluation index named characteristic frequency band energy entropy (CFBEE) was proposed to extract the defective features of rotors, and support vector machine (SVM) was employed to automatically identify the rotor fault types. Specifically, the raw vibration signal of rotor was first analyzed by a joint time-frequency method based on improved singular spectrum decomposition (ISSD) and Hilbert transform (HT) to derive its time-frequency spectrum (TFS), which is named ISSD-HT TFS in this paper. Then, the CFBEE of the ISSD-HT TFS was calculated as the fault feature vector. Finally, SVM was used to complete the automatic identification of rotor faults. Simulated processing results indicate that ISSD improves the end effects of singular spectrum decomposition (SSD) and is superior to empirical mode decomposition (EMD) in extracting the sub-components of rotor vibration signal. The ISSD-HT TFS can more accurately reflect the time-frequency information compared to the EMD-HT TFS. Experimental verification demonstrates that the proposed method can accurately identify rotor defect types and outperform some other methods.  
Keywords:improved singular spectrum decomposition; characteristic frequency band energy entropy; support vector machine; rotor; fault diagnosis +
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