esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Magnetorheological Damper with Self-Powered Ability for Washing Machines
## 5 Experimental Validation  
Figures 11 and 12 show the prototype of the optimized self-powered MR damper and experimental setup, respectively, for verifying the optimal design results and evaluating the performance of the damper. In this test system, a servo motor (MSMD022S1T, Panasonic) with gearbox of ratio 15:1 is employed to provide a constant rotary speed for the crank shaft. The rotary motion of the motor is converted to the linear motion of the damper shaft by a crank-slider mechanism. The damping force is measured by a bidirectional force sensor produced by Forsentek Co., Limited, Shenzhen, China (FFG-200N, maximum measured force: 200 N) while the instantaneous velocity and displacement are measured by a linear variable differential transformer (LVDT) produced by RPD Electrosense (ACT1000A, measurement range: +/-25 mm). In order compare the self-power MR damper with the MR damper using external power, a programmable power supply (PPW-8011, TWINTEX) is employed to supply power to the coils of the damping part. The power supply is controlled by the computer through the DAQ (myRIO 1900, National Instruments) to supply desired currents. It is noted that signals from the force sensor and LVDT are also transferred to the computer through the DAQ.  
Figure 10: Finite element analysis of the optimized MR damping part: (**a**) magnetic flux line and (**b**) magnetic flux density.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter (mm)** & **Value** & **Parameter (mm)** & **Value** \\ \hline Coil height \(h_{c}\) & 8.57 & Thin wall thickness \(t_{w}\) & 0.8 \\ Coil width \(w_{c}\) & 13.81 & Housing thickness \(t_{o}\) & 3.58 \\ Coil height chamfer \(h_{ch}\) & 3.05 & Shaft radius \(r_{s}\) & 8.25 \\ Coil width chamfer \(w_{ch}\) & 5.9 & Outer radius \(R\) & 22 \\ Pole length \(l_{p}\) & 6.88 & Max. current intensity \(I\) (A) & 0.34 \\ MR fluid gap length \(L\) & 55.13 & Max. damping force \(F_{d}\) (N) & 80.2 \\ MR fluid gap thickness \(t_{g}\) & 0.8 & Zero-field friction force \(F_{o}\) (N) & 18.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal parameters of the MR damping part.
## 4 Discussion  
This review analyzes the main applications and effects of VR technology-assisted cognitive rehabilitation in patients with common neurological disorders. VR has been proven to be an effective adjunctive treatment method, aiding in the repair of damaged neural tissue and enhancing cognitive functions such as resolution, orientation, and memory. Neurological disorders are complex conditions, with varying degrees of severity among patients. Consequently, postoperative rehabilitation for these patients is challenging and requires specialized medical and rehabilitation personnel to develop targeted rehabilitation measures. Patient acceptance and compliance are particularly crucial, as conventional cognitive rehabilitation therapy is often lengthy, costly, and boring, leading to low patient acceptance and compliance [10]. In contrast, VR offers good immersion and social interaction, and cognitive rehabilitation therapy combined with VR can avoid the drawbacks of traditional cognitive rehabilitation. Many studies also indicate that VR-based treatment methods are widely welcomed by patients and can produce effective therapeutic effects. VR technology not only brings new hope to patients with cognitive impairments but also greatly reduces the burden on healthcare professionals [52].  
By summarizing the therapeutic effects of VR-assisted cognitive rehabilitation technology in the treatment of stroke, Parkinson's disease (PD), traumatic brain injury (TBI), multiple sclerosis (MS), Alzheimer's disease (AD), and autism spectrum disorder (ASD), we can observe that different diseases exhibit varying degrees of sensitivity to the dimensions of immersion and social interaction. For instance, for patients who require improvement in social skills and interactive abilities, such as those with ASD, multiplayer applications may be more suitable as they provide opportunities for communication and collaboration with others. Conversely, for patients with movement limitations that prevent them from engaging in full-body activities in the real world, such as PD patients, immersive VR technology can provide a safe environment for them to engage in activities in the virtual world, thereby facilitating the recovery of motor skills. Furthermore, immersion is crucial for enhancing patient engagement and motivation, particularly in the rehabilitation of stroke and TBI patients, where a highly immersive VR experience can help patients focus better on training tasks, thus improving treatment outcomes. For patients with Alzheimer's disease (AD) whose cognitive functions are gradually deteriorating, non-immersive or semi-immersive VR technologies may be more appropriate, as they have lower cognitive load requirements while still providing beneficial cognitive stimulation.
## 3 Applications with J-KAREN Laser System
### Prospects of High Field Science Experiments
Each of the experimental setupsis characterized by its own set of extreme field limits, which mark the subsequent onset of the classical radiation reaction regime, the regime when quantum recoil becomes important, and the electron-positron pair production from vacuum. When a high energy electron beam interacts with an extremely intense laser pulse the electrons undergo a cascade-type process, emitting multiple photons. As a result there is a massive production of electrons, positrons, and photons. The beginning of the cascade studies was marked by the E144 experiment at SLAC (_a_\(\approx\) 0.6, \(\chi_{e}\) = 0.3, and \(\chi_{s}\) =0.15) [108, 109]. The modern day laser systems can produce _a_\(\approx\) 100, and GeV-level electron beams, which not only will boost the classical and quantum nonlinearities, but also significantly reduce the characteristic scale of the process, resulting in a cascade-type multistage process in the interaction of an electron beam with an intense laser pulse. In Table 1 we summarize the parameters of the proposed experiment on the ultrarelativistic electron beam interaction with an intense PW-class laser pulse (see [107, 109]). We note that the PW class laser (upgraded J-KAREN) has a potential for accelerating electrons up to 10 GeV energy. The interaction of a 10 GeV e-beam with an intense laser pulse is characterized by \(\chi_{e}\approx 2.5\) and \(\chi_{s}\approx\)1. Interaction of the laser light with the energetic electrons lowers the intensity threshold at which the radiation friction effects come into play. Such experimental conditions will make it possible the study of the high intensity particle physics effects in the near term.
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 2 Related Work
### Network Slicing  
Slicing technology is proposed to enhance the differentiated service capability of the network, which is mainly carried by the cellular mobile communication network. In recent years, the concept of satellite slicing has been proposed, so we introduce it from the following two parts: cellular network slicing and satellite network slicing.  
_In Cellular Scenarios_, emphasis is usually placed on improving slice efficiency and increasing service types while reducing inter-slice interference. In [20], the author introduced a novel cooperative multi-agent reinforcement learning (RL) algorithm for RAN slicing, designed to adapt to variable slice numbers and effectively scale as they grow. In [21], the author formulated the slice-based service function chain embedding (SBSFCE) problem as an integer linear programming (ILP) that aims to fulfill differentiated requirements of flows. In [21], the proposed architecture leveraged new SDN extended federation modules in compliance with the ETSI requirements for inter-MEC system coordination. In [22], the author presented an innovative actor-critic Reinforcement Learning (RL) model named Slice Isolation based on RL (SIRL) to ensure the isolation between different slices while maximizing the user requests. In [23], the author proposed a QoS and security-oriented slicing resource allocation scheme in a multi-cell and multi-slice scenario in order to minimize the slice interference.  
In Satellite Scenarios, emphasis is usually placed on improving resource usage efficiency and communication coverage. In [24], the author proposed a trust-based satellite Internet resource-slicing access authentication scheme to solve the efficient and secure access requirements in satellite communication. In [25], the author proposed a two-layer dynamic reconfigurable RAN slicing architecture for the ultra-dense low earth orbit satellite network (UD-LSN) to improve the slices' efficiency. In [26], an architecture of IoT supportable satellite edge computing (SatEC) enabled by LEO satellite was proposed for global coverage extension and 3-D mobility enhancement. In [27], the author proposed an optimization satellite slicing framework able to exploit the available resources allocated to the defined network slices in order to meet the diverse QoS/QoE requirements exposed by the network actors. In [28], a hierarchical resource slicing framework was proposed for dynamic allocation of multidimensional resources to solve the problem of resource slicing and scheduling of joint 3C resources in a RAN edge scenario assisted by LEO content caching.  
In summary, previous research on network slicing technology has regarded satellite and cellular networks as two independent individuals, and ignored the possibility of combining the two systems. This circumstance in the future wide coverage and diversified services of the new generation of mobile communication scenarios will lead to the problem of resource utilization.
## 2 Related Work
### Semantic Annotation  
Semantic annotation tools are crucial for enriching documents with entities, classes, topics, or facts based on existing ontologies or knowledge bases. These tools typically fall into three main categories: manual, automatic, and semi-automatic approaches.  
In the manual annotation approach, users are responsible for the semantic annotation of content. For example, Omeka S ([[https://omeka.org/s/](https://omeka.org/s/)]([https://omeka.org/s/](https://omeka.org/s/)), accessed on 8 September 2023) and SenTag [25] offer platforms for manual tagging of documents, although this approach may introduce errors due to human limitations and variations in criteria.  
Automatic annotation methods leverage machine learning and natural language processing to extract semantic information with minimal user intervention. AnnoTag [26] provides automatic content annotations using entity-level analytics, while the ARCA [27; 28; 29] system associates unstructured content with concepts in a knowledge graph.  
Semi-automatic approaches combine machine automation with human expertise. Tools like tagtog [30] and Recogito [31] facilitate collaborative text annotation, while GoNTogle [32] offers ontology-based semantic annotations for various document formats.  
When comparing these annotation tools with SKATEBOARD, it is important to highlight SKATEBOARD's integration with GraphBRAIN and its collaborative validation of automatic Knowledge Extraction. SKATEBOARD also manages the endpoint in the GraphBRAIN connected system, providing enhanced capabilities for semantic annotations. These integrations and features make SKATEBOARD a comprehensive solution for semantic annotation tasks.
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
## 2 Background
### Security and Privacy Requirements
#### 2.1.2 Long-Term Unlink-Ability  
Long-term link-ability is a fundamental location privacy requirement for vehicular networks. The intruder would not be able to recognize the vehicle by connecting messages received by a single vehicle to attributes such as vehicle position, vehicle model and applications [9]. In order to shield users from the link-ability of two or more consecutive locations, it is important to enforce tracking security. In mobile networks, the internet service provider (ISP) should not be able to link multiple successive user positions.
## 4 Application of Methodology
### UNSW-NB15 Dataset  
The raw network packets of the UNSW-NB15 dataset were created at the Australian Cyber Security Centre's (ACCS) Cyber Range Laboratory using the IXIA PerfectStorm tool to obtain a mixture of real normal activities and artificial attack behaviors. The Tcpdump tool was employed to capture 100 GB of raw traffic (e.g., Pcap files). This dataset includes nine types of attacks, namely Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcode, and Worms. It comprises a total of 49 features.  
As applied in the KDD'99 dataset, the UNSW-NB15 dataset was also grouped according to attack types and results (Figure 4). Feature selection was performed with the proposed approach.  
In summary, Table 5 displays the selected features that will be used in our model, as determined via the proposed feature selection approach. With the proposed feature selection algorithms, 13 features were selected from among 49 features.  
Figure 4: (a) Grouping result of “_ct_ftp_cmd_” feature; (b) grouping result of “_spkts_” feature.  
By using the remaining features after the feature selection algorithm from the basic features of the UNSW-NB15 dataset, rules were derived for the signature-based model that could directly obtain an attack result. During the creation of the anomaly-based model, traffic behavior analysis was performed using content features, flow features, time-dependent features, and selected additional features. After that, rules were derived. The list of extracted signatures and rules is given in Table 6.
## 4 Simulation Results
### Performance Comparison with Other Heuristics  
Because the performance of EC heuristics depends--often dramatically--on parameter settings, empirical performance comparison of SJaya with other heuristics may not mean much unless either those competing heuristics require no other parameters except the population size and the number of generations, or the comparative study is based on runs with a very large number of parameter setting combinations. Most stochastic heuristics in the EC family, however, employ additional parameters (probability of crossover [27], probability of mutation [28], strategy parameters [1, 29], to name a few). A proper head-to-head comparison of SJaya with non-Jaya methods is therefore difficult. Table 14 presents the results of a brief comparative study of SJaya with four well-known EC algorithms, namely genetic algorithm (GA) [1], particle swarm optimization (PSO) [30], differential evolution (DE) [29] and artificial bee colony algorithm (ABC) [31]. The metrics used for comparison are the mean and the standard deviation of the best-of-run solutions of 30 independent runs for each problem, with each run executed for 500,000 evaluations (population size = 50 and number of generations = 10,000). (The population size and the number of generations used to produce the data in Table 14 are different from those used earlier in this paper; and, for [PERSON] and Rosenbrock, the bounds are not the same as the corresponding ones in Table 1.) The non-SJaya results in this table are taken from [31]. These results show that SJaya is competitive with the other methods.
# Design and Dynamic Simulation Verification of an On-Orbit Operation-Based Modular Space Robot
## 2 Configuration Design of Reconfigurable Modular Space Robot
### Space Mission Description
#### 2.1.1 System Module Addition and Removal  
As shown in Figure 1, each module is simplified and replaced by a cube. There are joint connections between each module or within the module itself, so that the relative changes in position and attitude between modules can be achieved. It is noted that every robot system is composed of four modules, i.e., it has at least four degrees of freedom. The robot can connect new modules through standardized docking surfaces to deal with many conditions such as lack of degrees of freedom, system upgrades, and end-load switching. The addition of module mission processes is as follows. The numbers on the diagram represent the names of the modules, same as Figures 2-4.
# Enhanced Path Planning by Repositioning the Starting Point +
## 4 Experimental Evaluation
### Computational Efficiency Analysis  
While the comprehensive analysis of drone and station variations provides valuable insights into optimization effectiveness, practical implementation requires careful consideration of computational requirements. The time complexity differences between approaches become particularly significant when evaluating real-world applicability, as operational scenarios often demand rapid decision-making capabilities.  
To thoroughly evaluate computational performance, an additional 130,000 tests were conducted across the 65,000 scenarios, comparing the geometric median approach against brute force's timings. This extensive testing was necessary to establish statistically reliable performance benchmarks and validate the efficiency claims across diverse operational configurations. The results are depicted in in Figure 17.  
Examining the computational performance data presented in Figure 17, the brute-force methodology demonstrates exponentially increasing processing demands, requiring approximately 12.6 s for configurations with 5 stations and extending beyond 92 s when handling 100 stations. These extended computation periods present significant limitations for time-critical applications where rapid deployment decisions are essential. In contrast, the geometric median approach maintains consistently efficient performance, completing optimization calculations within one second across all tested scenario complexities. This dramatic efficiency advantage occurs without compromising solution quality, as the geometric median consistently identifies identical optimal positions to those discovered through exhaustive search methods.
## 2 Materials and Methods
### Postural Balance Parameters
#### 2.5.3 Root Mean Square Accelerations and Angular Velocities  
Apart from the linear accelerations that the IMUs provided, this study also evaluated the postural balance based on the angular velocities (\(\omega\)), which refer to the rate of change of an object's rotational position with respect to time, measured around a specific axis. That is, \(\omega\) quantifies how fast a sensor or a body part rotated and in which direction. For example, angular velocity along the X-axis represents side-to-side tilting, along the Y-axis represents forward and backward rotation, and along the Z-axis represents twisting of the trunk from the original standing posture.  
Root Mean Square (RMS) values quantify the average magnitude of a fluctuating signal. From the collected and filtered signals of tri-axial linear accelerations and tri-axial angular velocities obtained from the IMUs, the RMS magnitudes of the acceleration and angular velocity were calculated as shown in Equations (5) and (6), respectively.  
\[RMS_{Acceleration}=\sqrt{\text{Mean}((a_{X})^{2}+(a_{Y})^{2}+(a_{Z})^{2})} \tag{5}\]  
\[RMS_{AngularVelocity}=\sqrt{\text{Mean}((\omega_{X})^{2}+(\omega_{Y})^{2}+( \omega_{Z})^{2})} \tag{6}\]  
where \(a_{X}\), \(a_{Y}\), and \(a_{Z}\) are accelerations in the X, Y, and Z axes, respectively, and \(\omega_{X}\), \(\omega_{Y}\), and \(\omega_{Z}\) are angular velocities in the X, Y, and Z axes, respectively. Higher RMS values provide insight into the overall intensity of movement and the amount of energy spent during a task.
# Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
## 4 Deep Learning for Detecting Geological Features
A confusion matrix, also known as an error matrix, is a specific table layout that allows visualization of the performance of an algorithm [45]. There are four basic terms in a confusion matrix:  
1. True Positives (_TP_): positive outcomes are predicted, and they are true;
2. True Negatives (_TN_): negative outcomes are predicted, and they are true;
3. False Positives (_FP_): positive outcomes are predicted, but they are false;
4. False Negatives (_FN_): negative outcomes are predicted, but they are false. From the information above, we can derive the following equations:  
\[precision=\frac{TP}{TP+FP} \tag{4}\]  
\[recall=\frac{TP}{TP+FN} \tag{5}\]  
\[accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{6}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Selection** \\ \hline Architecture & U-Net \\ Encoder Weights & Resnet50 \\ Number of Channels & 3 (1 stacked into 3) \\ Number of Output & Two (geological features and background) \\ Activation & Sigmoid \\ Patch Size & 64 \(\times\) 64 \(\times\) 64 \\ Learning Rate & 0.0001 \\ Optimization & Adam \\ Metrics & IOU Score \\ Loss & Binary Focal Dice Loss \\ Batch Size & 16 \\ Epochs & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Selected hyperparameters. The _IoU_ metric, also known as the Jaccard index, is the most commonly used metric for comparing the similarity between two arbitrary shapes [46]. _IoU_ is formulated as:  
\[IoU=\frac{prediction\ \cap\ ground\ truth}{prediction\ \cup\ ground\ truth}=\frac{TP}{FP+TP+ FN} \tag{7}\]
## 2 Materials and Methods
### Data Processing
#### 2.3.5 Depth Estimation with the Phase  
Defect characterization, i.e., the determination of the defect characteristics such as the depth, its thermo-physical properties, or its size, constitutes the ultimate purpose in IRT NDT since it provides a quantitative means to assess the impact of the presence of an anomaly. Nevertheless, it is not always necessary to implement defect characterization steps, many times the sole presence of a defect would be enough to determine if a given component needs to be repaired or replaced.  
In the case of porosity and impact damage, it may be important to assess the severity and location of damage in the decision-making process. Pulsed phase thermography can be used to estimate the depth of defects by knowing the thermal diffusivity of the materials being inspected, and the defect's blind frequency:  
\[z=C_{1}\sqrt{\frac{\alpha}{\pi f_{b}}} \tag{7}\]where \(C_{1}=1.8\) for the phase [13; 31].  
The blind frequency \(f_{b}\) is defined as the maximum frequency at which a defect at a particular depth presents enough phase contrast to be detected. Phase contrast equals zero, \(\Delta\phi=0\), at frequencies higher than \(f_{b}\); whilst \(\Delta\phi\) will gradually increase for decreasing frequencies until reaching a maximum \(\Delta\phi_{\max}\) at \(f_{\max}\) (which will depend on the depth of the defect) and then go down until reaching zero at \(f=0\) Hz.