esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Magnetic storms represent major signatures of variability in the Sun-Earth interaction. Such events appear as magnetic disturbances caused by bursts of radiation and charged particles emitted from the Sun in the form of coronal mass ejections (CMEs), solar flares, co-rotating interaction regions (CIRs), etc. [1,2]. These nonlinear and multiscale processes involve a vast set of plasma regions in the mutually interacting magnetosphere and ionosphere.  
The terrestrial magnetosphere is under the permanent action of the solar wind. An increase in the solar wind dynamic pressure and a southward direction of the interplanetary magnetic field (IMF) are considered among the fundamental factors in magnetic storm development [3,4]. Under the solar wind driver, global changes occur in the magnetosphere following two principal dynamic triggers: magnetic reconnection at the dayside magnetopause [5] and viscous-like interactions causing magnetospheric convection [6]. One major consequence is the change in the fluxes of charged particles that constitute the magnetospheric ring current [7]. In cascade, the magnetosphere, which is mapped to the upper ionosphere through a system of field-aligned currents, can exchange momentum, energy, and particles with the latter by means of a variety of interactions [8,9]. For example, the transport of plasma between the plasmasphere and ionosphere is severely impacted by altered geomagnetic activity, leading to convection-driven erosion and refilling of the plasmasphere [10] or depletion due to reduced upward flux from the perturbed ionosphere [11].  
Discerning physical phenomena that mark the solar-terrestrial environment is not the sole goal of the investigation of storm phenomena, since currently, geomagnetic storms and substorms can severely impact infrastructures at the ground level and in space, also posing a hazard to human health [12, 13, 14, 15, 16, 17].  
On 25 August 2018, the China Seismo-Electromagnetic Satellite (CSES-01) encountered the first strong magnetic storm since its launch on 2 February 2018. In this paper, after a sketch of the CSES-01 mission and the High-Energy Particle Detector (HEPD) in Section 2, a description of the major solar and geomagnetic characteristics of the storm is reported (Section 3). The magnetospheric disturbance was strong enough to trigger a response in the HEPD instrument; this is presented on the basis of HEPD trigger rate variations observed in the MeV energy range as a function of time and the McIlwain L-shell parameter (Section 4). Observations of this storm in a lower energy interval--from other particle detectors on board CSES-01--were previously presented in [18]. We discuss our results and draw our conclusions in Sections 5 and 6, respectively.
## 5 Discussion  
As emphasized in this study, deep learning's ability to detect anomalies highlights the evolving landscape of security measures. Our results align with a broader shift in the technology sector, where machine learning, intense learning, has gained prominence due to its potential to discern patterns and anomalies in large, complex datasets.  
One of the main contributions of this work lies in demonstrating that autoencoders, including VAEs, can be significantly improved through targeted modifications to their cost functions. The introduction of the ELBO function and its variant, the modified M-ELBO function, has proven particularly effective. These modifications allowed us to create models capable of reconstructing normal data while still handling anomalous data, a key requirement in real-world network anomaly detection. By incorporating these adjustments iteratively, we showcased how fine-tuning the latent space and cost functions can boost both the sensitivity and specificity of the models.  
The proposed architecture and modified cost function demonstrate practical utility in real cybersecurity scenarios by leveraging normal and limited anomalous data. This dual approach allows the model to detect anomalies without requiring balanced datasets, a standard limitation of traditional methods.  
For instance, these models could address advanced threats like APTs or zero-day exploits, which often bypass signature-based detection. The model enhances its latent space representations by integrating limited anomalous data into training, effectively identifying deviations even in complex, evolving attack patterns. This adaptability makes the approach scalable and efficient for dynamic cybersecurity environments.  
Despite these improvements, this study highlights several challenges. Deep learning models remain computationally intensive, mainly when applied to high-volume, real-time data. This is a key limitation in network systems where latency is critical. Moreover, the need for large, labeled datasets continues to pose a problem, especially in anomaly detection, where anomalous events are rare. A semi-supervised approach offers a middle ground, effectively detecting previously unknown anomalies while minimizing the need for exhaustive data labeling.  
Looking forward, anomaly detection can benefit from methods that reduce the reliance on labeled datasets while maintaining the efficiency of supervised techniques. Our exploration into the potential of unsupervised learning shows great promise, as it could revolutionize network anomaly detection by combining the best of both supervised and unsupervised methods.  
In summary, while traditional methods like signature-based and rule-based detection are still helpful, they lack the flexibility and adaptability of deep learning approaches. This study reinforces the growing importance of machine learning in network security, demonstrating that with the proper architectural adjustments, autoencoders and VAEs can effectively address the complexity and unpredictability of modern network traffic.
## 5 Implementation of the Proposed Model
### Implementation Phase
#### 5.3.2 Tests  
The virtual environment was tested under various interaction control inputs.  
Figure 14: Scene diagram of the virtual environment user interface.  
Figure 15: (a) Setting user interface. (b) Scene of user interface.  
* Keyboard and joystic input
* Spatial relationships, this test consisted of validating the control inputs with the video game coordinate space in the x, y, and z axes (so that the controls would make the correct movement in the video game).  
In addition, performance tests were executed to ensure the proper functioning of the virtual environment, these tests were divided into volume tests, game elements tests, reliability tests, and usability tests. Regarding usability, the tests were performed at various times to find errors during the interaction, all the errors that were found were related to the game elements when the kart collided with objects. Finally, the virtual environment was built to be compatible with several operating systems such as Windows and Mac OS X, in order to perform compatibility tests.
## 3 Methods
### Data Analysis Methods  
Exploratory factor analysis (EFA) [41] was conducted on the items in section D of the questionnaire in order to explore the underlying factors affecting users' satisfaction regarding the choice A and choice B. This formal measurement model is useful when both observed and latent variables are assumed to be measured at the interval level. A characteristic of EFA is that the observed variables are first standardized (mean of zero and standard deviation of 1). EFA is executed on the correlation matrix between the items. In EFA, a latent variable is called a factor and the correlations between latent and observed variables are called factor loadings. Factor loadings are standardized regression weights.  
Principal axis factoring was used as the factor extraction method because maximum likelihood and principal axis factoring are found to provide better results depending on the normality of the data [42]. Orthogonal Varimax rotation was used to obtain interpretable underlying factors.  
For further statistical analyses, a numerical value, known as factor score, is required for each participant to represent its relative standing on the extracted underlying factors. There are two common methods of computing factors scores: non-refined and refined methods. In this study, a non-refined factor computation method, average sum scores, was used to compute the factor scores because they are simpler and easier to interpret [43]. The factor scores were not normally distributed. Therefore, the effect of the demographic variables on the factor scores was evaluated using non-parametric tests such as Mann-Whitney U test and Kruskal Wallis test, Spearman's correlation. Ordinal regression was conducted to model the relationship between overall satisfaction and the explanatory variables. The statistical software package SPSS v. 20 was used for all the statistical analyses.
## 4 Masked Addressing
### Performance Metrics  
An effect of this addressing scheme is that the maximum number of chips that can be addressed is reduced from the theoretical maximum of \(2^{N}\). If only chips that have a number of unstable bits less than or equal to some maximum value, \(U\), are selected for use, than the maximum number of chips that can be uniquely addressed is  
\[S=2^{N-U} \tag{15}\]  
For a chip with \(U\) unreliable bits, a false negative can only occur if there is an error among one of the \(N-U\) reliable bits. If the probability of an error in one of the reliable bits is \(\epsilon\), then the false negative rate is  
\[p_{FN}=1-(1-\epsilon)^{N-U} \tag{16}\]  
Errors among reliable bits could also cause false positive identifications. Calculating the combined probability of any false positive event is difficult, so we will restrict the calculation to single bit errors, since the probability of \(E\) bit errors is \(\epsilon^{E}\), which decreases rapidly for \(E>1\). For \(E=1\), only chips with codes within Hamming distance \(1\) of the target chip code can cause false positives. In the worst case, for an \(N\) bit code with \(U\) unreliable bits, there are \((N-U)2^{U}\) possible chip codes within a Hamming distance of \(1\). However, this number is usually much larger than the maximum number of uniquely addressable chips, \(S\), given by Equation (15). In actual practice, therefore, there are at most \(S-1\) other chips that could cause false positive errors. The probability of any of these chips causing a false positive is then  
\[p_{FP}=1-(1-\epsilon)^{2^{N-U}-1} \tag{17}\]  
The total error rate, \(p_{E}\) can then be found  
\[p_{E}=1-(1-p_{FN})(1-p_{FP}) \tag{18}\]  
\[p_{E}=1-(1-\epsilon)^{(N-U)(2^{N-U}-1)} \tag{19}\]  
Restricting the use of chips to those that have a number of unreliable bits less than or equal to \(U\) reduces the yield. If the probability that a bit is unreliable is \(p_{U}\), then the yield is given by  
\[Y=\sum_{u=0}^{U}\binom{N}{u}\,p_{U}^{u}(1-p_{U})^{N-u} \tag{20}\]
# Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks
## 3 Network of Networks in Virtualized Networks
### Model of Network of Networks with Network Slicing
#### 3.2.2 Availability States of Interfaces
Combining the characteristics of type-SD and type-UD, the availability of V-interfaces for type-DD is described as  
\[\sigma_{r,s}^{V_{k}}(t) = \Big{\{}1-\big{(}1-R_{r,s}^{V_{k}}(t)\big{)}\big{(}1-R_{r,s}^{V}(t )\big{)}\Big{\}}\big{(}1-n_{r,s}^{P}(t)\big{)}. \tag{12}\]  
The first factor is 1 if the traffic condition satisfies either \(R_{r,s}^{V_{k}}(t)=1\) or \(R_{r,s}^{V}(t)=1\). This behavior expresses the _logical OR_-like interdependence that \(i_{r,s}^{V_{k}}\) is available when either the allocated resources on VN \(k\) or the whole resources on the PN have room for packet processing.
## 4 Integrated Reflectance and Transmittance Imaging  
Light reflectance and transmittance are two common sensing modes in VIS/NIR spectroscopy and hyperspectral imaging. The former measures the light that is diffusely reflected primarily from the upper or superficial region of a sample [4; 77], while the latter measures the light that passes through a sample, which allows for detecting internal quality or defects in foods [78; 79; 80; 81]. Most research on hyperspectral imaging has focused on the reflectance measurement, which is appropriate for evaluating surface or subsurface quality characteristics but not for internal defects in food products. Our group proposed a new concept of integrating reflectance and transmittance measurements in one hyperspectral imaging system for detecting both external quality attributes (e.g., color and size) and internal defects [82; 83; 84; 85; 86; 87; 28]. The concept is based on the fact that the visible light in the region of 400-675 nm, which is relatively poor in penetrating biological tissues, is more suitable for assessing surface features of the product, while the red and NIR light in the region of 675-1000 nm has better penetration capabilities and can thus be used for internal quality assessment in transmittance mode. Since most CCD-based hyperspectral imaging systems cover the visible and NIR region of 400-1000 nm, it is technically feasible and also advantageous to acquire hyperspectral reflectance (400-675 nm) and transmittance (675-1000 nm) images simultaneously using one imaging system. The following sections give an overview of the instrumental configuration of the integrated hyperspectral reflectance and transmittance imaging system and its applications for quality assessment of pickling cucumbers.
# Application of a Real-Time Visualization Method of AUVs in Underwater Visual Localization
## 3 Results
### Experiment Setup  
Nvidia Jetson TX2 was chosen as the platform, with an Ubuntu 16.04 system and ROS kinetic. The embedded development board was located in the sealing bin of the AUV. The ZED stereo camera (forward-looking) was located in the head of the AUV. The two parts were connected through the USB (Universal Serial Bus) 3.0 interface (see Figure 12(a)). The length of the AUV was approximately 730 mm and the diameter of the cabin was approximately 220 mm. Because of the limited experimental conditions, the experiment was only carried out in an artificial pool with a diameter of 4 m and a depth of 1.5 m. Scenes were placed in the pool to simulate the natural environment of the shallow water area (see Figure 12(b)). In the experiment, the speed of the AUV was set to 0.6 m/s.  
Figure 11: Comparison of the pose measurement experiments with the AR-marker.  
Figure 12: Analysis and contrast diagram of the experimental results. The blue scatter points Pb (Xb, Yb, Zb) represent the PL-SLAM positions. The red scatter points Pr (Xr, Yr, Zr) represent the AR-marker positions.  
Figure 13: The developed shark AUV. (**a**) The appearance of the AUV; (**b**) Experiment in an artificial pool.  
Due to the large distortion of the underwater scene, the stereo camera should have been calibrated first. However, camera calibration based on rigorous underwater camera calibration models is time-consuming and cannot achieve real-time performance. Thus, the traditional pinhole model in air was selected to calibrate the AUV. The calibration experiment shows that the refraction effect was considered to be absorbed by the focal length and radial distortion, and the conclusion was that when the camera is in an underwater environment, the focal length is approximately 1.33 times of that in air.
# A Hierarchical Fusion SAR Image Change-Detection Method Based on HF-CRF Model
## 3 Experiment and Analysis
### Parameter Setting and Evaluation Indexes  
D-DRUNet parameter settings: the number of guide mask regions \(m=2\); the learning rate is set to 0.001; the batch size is set to 8. The image block size is \(256\times 256\); the overlap step is 4; and the number of training iterations is 200.  
Boundary entropy dynamic fusion CRF parameter setting: \(\omega^{1}=20\); \(\omega^{2}=20\); \(\theta_{\alpha}=12\), \(\theta_{\beta}=12\), \(\theta_{\gamma}=2\); the number of iterations is 3; the boundary entropy sliding window size is \(25\times 25\) and the step size is 1.  
Simulation of experimental environment: Core (TM) i7-7820X CPU @ 3.60 GHz, Nvidia RTX 2080 Ti \(\times\) 2, Ubuntu18.04, python3.7, pytorch1.4, cuda10.0.  
The number of false alarms, FP; the number of missed detection, FN; the correct rate, PCC; and the Kappa coefficient, which measures the classification accuracy, are used as the evaluation metrics for the experiments in this paper, which could formulated as follows.  
\[PCC=\frac{TP+TN}{TP+FP+TN+FN}\times 100\%,\]  
\[Kappa=\frac{PR_{0}-PR_{C}}{1-PR_{C}}.\]
## 3 Results
### Range of Motion  
The ROM of the lumbar spine in which the spinal implant was placed was evaluated at different porosities. ROM is the measurement of the rotation angle before and after FE analysis of the center of the top of each segment (lumbar 3-5) based on the top of the sacrum. The measurement points of each segment were set through the remote point, and the angle was measured using a flexible rotation probe, a function provided by ANSYS workbench [41]. The trend of the ROM increased as the porosity increased. Figures 6 and 7 show the ROM and deformation behavior of the lumbar spine, respectively.  
Figure 6: The range of motion (ROM) of the lumbar spine at different porosities (\(p\)) of the spinal implants in each spinal motion: (**a**) flexion; (**b**) extension; (**c**) lateral bending; and (**d**) rotation.
# On the Effect of Friction on Tibiofemoral Joint Kinematics
## 8 Results and Discussion
### Coulomb and Stribeck Friction Models
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Friction Co.**} & \multicolumn{1}{c}{\begin{tabular}{c} **Medial Linear Wear** \\ **Rate (mm/mc)** \\ \end{tabular} } & \multicolumn{1}{c}{\
\begin{tabular}{c} **Lateral Linear Wear** \\ **Rate (mm/mc)** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **Volumetric Wear** \\ **Rate (mm3/mc)** \\ \end{tabular} } \\ \hline \(\mu_{c}=0.2\) & 0.031 & 0.023 & 6.07 \\ \(\mu_{c}=0.1\) & 0.060 & 0.052 & 6.76 \\ \(\mu_{c}=0.05\) & 0.049 & 0.050 & 7.01 \\ \(\mu_{c}=0\) & 0.037 & 0.031 & 6.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effects of static friction coefficient in Coulomb law on wear rates. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(\xi\)} & \multirow{2}{*}{\(v_{0}\)} & \multirow{2}{*}{\(\mu_{s}\)/\(\mu_{c}\)} & \multicolumn{1}{c}{ \begin{tabular}{c} **Linear Wear** \\ **Rate (Medial)** \\ **(mm/mc)** \\ \end{tabular}  } & \multicolumn{1}{c}{\ \begin{tabular}{c} **Linear Wear** \\ **Rate (Lateral)** \\ **(mm/mc)** \\ \end{tabular}  } & \multicolumn{1}{c}{ \begin{tabular}{c} **Volumetric** \\ **Wear Rate** \\ **(mm3/mc)** \\ \end{tabular}  } \\ \hline
10 & 0.005 & 0.2/0.13 & 0.059 & 0.03 & 7.74 \\
10 & 0.005 & 0.1/0.065 & 0.0466 & 0.044 & 6.81 \\
10 & 0.005 & 0.05/0.0325 & 0.04 & 0.046 & 7.3 \\
5 & 0.005 & 0.1/0.065 & 0.052 & 0.046 & 6.79 \\
15 & 0.005 & 0. Figure 9b,d. The damaged area on the medial condyle with high damage increases in the case study with \(\mu_{c}=0.2\), as seen in Figure 9d. The same study is performed to assess the effects of both static and dynamic friction coefficients used in the Stribeck friction model on the damage of tiboferomal joint five-year post-operation, illustrated in Figure 10. It can be observed that increasing friction coefficients leads to a decrease in damage magnitude on both condyle, while a greater area of lateral condyle gets engaged in wear occurrence along the anterior-posterior direction, in particular. The highest damage magnitudes also happen in the case of the frictionless joint as can be seen in Figure 10a. Figure 8: Effects of friction coefficient in Coulomb friction law (left-hand side) and both static and dynamic friction coefficients of Stribeck friction model on the trajectory of TKAs. Figure 9: Effect of friction coefficient on the damage of TKA after five million cycles: (a) \(\mu_{c}=0\); (b) \(\mu_{c}=0.05\); (c) \(\mu_{c}=0.1\); (d) \(\mu_{c}=0.2\).
# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
###### Abstract  
High-performance coding solutions are urgently needed for the storage and transmission of 3D point clouds due to the development of 3D data acquisition facilities and the increasing scale of acquired point clouds. Video-based point cloud compression (V-PCC) is the most advanced international standard for compressing dynamic point clouds. However, it still has serious issues of time consumption and the large size of the occupancy map. Considering the aforementioned issues, based on V-PCC, we propose the Vovel Selection-based Refining Segmentation (V-RS), which is used to accelerate the refining segmentation process of the point cloud. Furthermore, the data-adaptive patch packing (DAPP) is proposed to reduce the size of the occupancy map. In order to specify the effect of the improvement, we also designed novel evaluation indicators. Experimental results show that the proposed method achieves a Bjontegaard Delta rate (BD-rate) gain of \(-1.58\%\) in the V-PCC benchmark. Additionally, it reduces encoding time by up to 31.86% and reduces the size of the occupancy map by up to 20.14%.  
video-based point cloud compression; refining segmentation; projection uniformity index; expanded projection uniformity index; patch packing; sorting metric; positioning metric +
Footnote †: journal: Applied sciences