esa-sceva/satcom-chunk-collection huggingface dataset:
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
###### Abstract  
In the processing of remotely sensed data, classification may be preceded by feature extraction, which helps in making the most informative parts of the data emerge. Effective feature extraction may boost the efficiency and accuracy of the following classification, and hence various methods have been proposed to perform it. Recently, Singular Spectrum Analysis (SSA) and its 2-D variation (2D-SSA) have emerged as popular, cutting-edge technologies for effective feature extraction in Hyperspectral Images (HSI). Using 2D-SSA, each band image of an HSI is initially decomposed into various components, and then the image is reconstructed using the most significant eigen-tuples relative to their eigen-values, which represent strong spatial features for the classification task. However, instead of performing reconstruction on the whole image, it may be more effective to apply reconstruction to object-specific spatial regions, which is the proposed objective of this research. As an HSI may cover a large area, multiple objects are generally present within a single scene. Hence, spatial information can be highlighted accurately by specializing the reconstruction based on the local context. The local context may be defined by the so-called superpixels, i.e., finite sets of pixels that constitute a homogeneous set. Each superpixel may undergo tailored reconstruction, with a process expected to perform better than non-spatially-adaptive approaches. In this paper, a Superpixel-based SSA (SP-SSA) method is proposed where the image is first segmented into multiple regions using a superpixel segmentation approach. Next, each segment is individually reconstructed using 2D-SSA. In doing so, the spatial contextual information is preserved, leading to better classifier performance. The performance of the reconstructed features is evaluated using an SVM classifier. Experiments on four popular benchmark datasets reveal that, in terms of the classification accuracy, the proposed approach overperforms the standard SSA technique and various common spatio-spectral classification methods.  
hyperspectral image; superpixel segmentation; evaluation; 2D-singular spectrum analysis (2D-SSA); feature extraction +
Footnote †: journal: Applied Sciences
## 3 Standard Firebrand Accumulation Temperature Curve  
In this section, the firebrand accumulation temperature curve proposed by [38] is reviewed, using the previous measured temperatures of the firebrand experimental campaign. It is worth noting that this standard design curve is used to simulate the effect of firebrand accumulation on a plane surface, showing the fire growth and cooling phase.  
In order to develop a standard design curve, the 95% characteristic value (\(T_{k,95\%}^{max}\)) in Annex D \"Design Assisted by Testing\" of Eurocode 0 [50] for the maximum temperature was used, given using  
\[T_{k,95\%}^{max}=T_{a}^{max}+k_{n}\cdot S_{x} \tag{1}\]  
using the average peak temperature of all experiments (\(T_{a}^{max}\)). The values of \(k_{n}\), which depend on the size of the sample, can be consulted in Annex D of [50], and the value of 1.72 is used. The standard deviation coefficient (\(S_{x}\)) adopted is 96.68, in which this value is almost 1/3 of the average peak temperature of all experiments (see Figure 4), showing that the variability of the results is high. This observation depends mainly on the type of wood analysed, and for this reason, a conservative value of \(T_{k,95\%}^{max}\) is used in this work (\(T_{k,95\%}^{max}=430\)\({}^{\circ}\)C).  
The experimental campaign showed that if there is an influence of wind, the firebrand temperature tended to stabilize at a maximum temperature between 15 to 30 min after the test started. This fact guides the idea that the proposed design standard firebrand curve must maintain its maximum temperature after a few minutes. In a wildfire situation, the engineer must always take into account the worst possible scenario, which is with wind exposure, and the design curve should show endurances for 60 min without cooling. The proposed standard firebrand accumulation curve (Figure 5) is divided in two phases, heating and cooling. The heating phase has a duration of 60 min (simple function based in exponential growth until it reaches the maximum temperature (\(T_{max}\))), and the cooling phase admits a linear variation for another 60 min until it reaches the ambient temperature (\(T_{amb}\)) (see Equation (2)). The value of 0.003685 was determined for \(a_{1}\)--a parameter that impacts the flashover of the curve. More information about how to calculate this parameter can be consulted in [38]. The ambient temperature in the presence of a nearby fire must be taken as a higher value, therefore the value of 50 \({}^{\circ}\)C was adopted [9]. As mention before, with the \(T_{k,95\%}^{max}=430\)\({}^{\circ}\)C and \(T_{amb}=50\)\({}^{\circ}\)C, the maximum temperature (\(T_{max}\)) due to firebrand accumulation stabilizes at 480 \({}^{\circ}\)C, which is in accordance with the observed maximum values provided by [27].  
\[T_{\rm S}[^{\circ}\mathrm{C}]=\left\{\begin{array}{ll}T_{k,95\%}^{max}(1- \exp(-a_{1}t))+T_{amb}&if\ t\leq 3600\ \mathrm{s}\\ -\frac{T_{k,95\%}^{max}}{3600}t+2T_{k,95\%}^{max}+T_{amb}&if\ t>3600\ \mathrm{s}\end{array}\right. \tag{2}\]
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 8 Conclusions  
In this survey, we discussed the possibilities provided by advanced V2X networks for safe and secure future autonomous driving. We presented state-of-the-art V2X through a comprehensive literature survey in the related research area. We particularly highlighted evolution towards future V2X based on the 802.11 bd and 5G NR. We discussed the issues and challenges in existing V2X based on 5G and edge computing. Apparently, blockchain has emerged as a promising technology to solve the majority of issues and challenges related to privacy, security and networking faced by the existing and next-generation V2X technologies. We examined and explored, in detail, the convergence of blockchain in next-generation V2X communications that opens up new opportunities to empower advanced V2X networks, capabilities, applications, and services. We then investigated the integration of blockchain in 5G-based MEC vehicular networks for security, privacy protection, and content caching. Lastly, we mentioned numerous open research challenges and prospective paths for future research direction on the integrated emerging technologies, such as big data, machine learning, and beyond 5G.  
This research was supported in part by Basic Science Research Program through National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2020 RIA2C1010366, 2015R1 DIA1A01058595). This research was supported in part by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2020-2016-0-00313) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation).  
The authors declare no conflict of interest.
## 4 ERPCAWF-Based OMA Method for LTV Structures
### Performance Analysis of ERPCAWF-Based OMA and Its Advantages  
Obtaining the eigenvectors and PCs via recursive method directly places lesser computational and memory requirements on ERPCAWF-based OMA. In other words, the algorithm is more suitable for integration into embedded devices. For PCAWF, we must update all PCA models, then calculate the autocorrelation matrix, and finally compute the PCs and eigenvectors by decomposing the autocorrelation matrix, resulting in high overall time and space complexities. ARPCAWF-based OMA recursively updates the autocorrelation matrices rather than all the PCA models. This means that ARPCAWF-based OMA has lower time and space complexities than PCAWF-based OMA, but is still more computationally expensive than ERPCAWF-based OMA.  
As matrix decomposition is only performed at the start of the calculation process, ERPCAWF-based OMA faces few problems with ill-posed matrices. However, both PCAWF- and ARPCAWF-based OMA perform matrix decomposition \(T\) times, so there is potential for many ill-posed problems to occur. Thus, ERPCAWF-based OMA may achieve higher accuracy than PCAWF- and ARPCAWF-based OMA.  
There are many different adaptive PCA-based OMA methods that use only nonstationary vibration response signals from LTV structures under unmeasured stationary ambient loads. Table 1 compares several such methods in terms of their time complexity, space complexity, modal exchange, number of matrix decompositions, accuracy, and applicability. \(L\) denotes the length of the moving  
Figure 5: Procedure of ERPCAWF-based OMA for LTV structures.  
window, \(r\) denotes the number of iterations, and \(M\) is the number of principal components to be extracted.
# Fault Diagnosis of Vehicle Gerboxes Based on Adaptive Wavelet Threshold and LT-PCA-NGO-SVM
## 2 Principle of the Method
### Fault Classification and Recognition Research  
Support vector machine (SVM) serves as a linear classifier rooted in a supervised learning approach. It transforms the problem into solving convex quadratic programming. The core concept involves mapping sample data into a high-dimensional feature space through a kernel function. The objective is to identify an optimal hyperplane in this space, maximizing the classification interval between different categories and achieving effective linear classification [29]. The key is to find a hyperplane that can better distinguish between different categories and ensure its superior performance in the high-dimensional space, to improve the accuracy of classification. The principle of SVM is shown in Figure 8, where the red circles and green asterisks indicate two categories of samples. \(L\) is the optimal classification surface, and \(L_{1}\)\(L_{2}\) represents the hyperplane of the nearest two categories of samples from the optimal surface, parallel to it. These hyperplanes serve as the positive and negative boundaries of SVM. The distance between \(L_{1}\) and \(L_{2}\) constitutes the classification interval, and the points located on the positive and negative boundaries are referred to as support vectors.  
Utilizing SVM allows training to identify and classify feature vectors after dimensionality reduction, and the machine can be optimized using intelligent algorithms to enhance identification accuracy. One such optimization approach is Northern Goshawk Optimization (NGO), a heuristic optimization algorithm inspired by the hunting behavior of hawks in nature [30]. NGO serves as a meta-heuristic optimization algorithm applicable to solving various optimization problems. Consequently, the parameters of SVM can be effectively optimized using this algorithm.
## Materials and Methods
### Image Analysis  
The fMRI data were analyzed using SPM 8 (Wellcome Department of Cognitive Neurology, London, UK). All functional images were aligned with the anatomical images obtained during the study using transformation routines that were built into SPM 8. The realigned scans were co-registered to each participant's anatomical images obtained within each session and normalized to the template image obtained by SPM 8, which used the space defined by the Montreal Neurologic Institute. Motion correction was implemented using Sinc interpolation. Time-series data were filtered with a 240-s high-pass filter to remove artifacts associated with cardiorespiratory and other cyclical influences. The functional map was subsequently smoothed with a 3-mm isotropic Gaussian kernel prior to statistical analysis. Statistical analysis was done both individually and as a group using a general linear model and the Gaussian random field theory, which were implemented in SPM 8 [21]. Statistical parametric maps were computed using the t-statistic. Comparisons made within individual subjects were considered as significant at a threshold of \(p<0.05\) and were corrected for multiple comparisons. We used random effect analysis to compute group activation.  
Region of interest (ROI) analysis was applied to specifically observe the activated regions of the S1 and BA3 in the left hemisphere of the activated area of the brain. The PICKATLAS Tool of the Wake Forest University PickAtlas 3.0 S/W was used for ROI masking of each region [22]. The subtraction method (stimulation phase-rest phase) was used to estimate the area of activation of the S1 and BA3 regions after administering the vibration stimulation in each finger and phalange [23]. Among the SPM analysis tools, the number of activated voxels for each finger and joint was extracted for each subject using small volume correction (SVC). In addition, images acquired for each experimental phase were averaged to increase the spatial resolution. The area of activation was calculated by counting the number of activated voxels and multiplying it by the in-plane resolution of \(1.5\times 1.5\) mm\({}^{2}\).  
Figure 2: Experimental design.
## 2 Large-Eddy Simulation Turbulence Model and Numerical Method  
### Large-Eddy Simulation Turbulence Model  
LES decomposes variables into sublattice and solvable scale quantities by filtering operations. The LES filtering is more similar to the spatial average than it is to the RANS average. The filtering is performed by the convolution operation, and the instantaneous variable \(f\) is filtered to obtain \(\overline{f}\):  
\[\overline{f}(x)=\int f(y)\overline{G}(x-y,\Delta)dy \tag{1}\]  
\(\overline{G}\) is the filtering function, and \(\Delta\) is the filtering scale.  
Commonly used filter functions are the box, Gaussian, and spectral truncation filters. If the filtration and derivative processes are exchanged, the RANS equation is filtered to obtain the following [11]:  
\[\frac{\partial\overline{u_{i}}}{\partial t}+\frac{\partial\overline{u_{i}u_{j }}}{\partial x_{j}}=-\frac{1}{\rho}\frac{\partial\overline{P}}{\partial x_{i }}+\mu\frac{\partial^{2}\overline{u_{i}}}{\partial x_{j}\partial x_{j}} \tag{2}\]  
\[\frac{\partial\overline{u_{i}}}{\partial x_{i}}=0 \tag{3}\]  
Let \(u_{i}u_{j}\) be written in the form of \(\overline{u_{i}u_{j}}+\left(\overline{u_{i}u_{j}}-\overline{u_{i}u_{j}}\right)\) in which \(-\left(\overline{u_{i}u_{j}}-\overline{u_{i}u_{j}}\right)\) is the sub-grid scale model and can be expressed as  
\[\frac{\partial\overline{u_{i}}}{\partial t}+\frac{\partial\overline{u_{i}u_{ j}}}{\partial x_{j}}=-\frac{1}{\rho}\frac{\partial\overline{P}}{\partial x_{i }}+\mu\frac{\partial^{2}\overline{u_{i}}}{\partial x_{j}\partial x_{j}}-\frac {\partial\left(\overline{\tau}_{ij}\right)}{\partial x_{j}} \tag{4}\]  
\[\overline{\tau}_{ij}=\left(\overline{u_{i}u_{j}}-\overline{u_{i}u_{j}}\right) \tag{5}\]  
\(x_{1}\) and \(x_{2}\) (\(i,j=1,2,3\)) are cartesian coordinates; \(\overline{u_{i}}\) and \(\overline{u_{j}}\) (\(i,j=1,2,3\)) are the upper velocities filtered by the filtering function; and \(\overline{P}\) is the pressure filtered by the filtering function.  
\[\overline{\tau}_{ij}-\frac{1}{3}\delta_{ij}\overline{\tau}_{kk}=-2\
u_{T} \overline{S_{ij}} \tag{6}\]  
\[v_{T}=\rho L_{s}^{2}|\overline{S}| \tag{7}\]  
\[\left|\overline{S}\right|=\sqrt{2\overline{S_{ij}}\overline{S_{ij}}} \tag{8}\]  
\[L_{s}=\min(xd,c_{s}V^{\frac{1}{3}}) \tag{9}\]  
\(\
u_{T}\) is the sublattice vortex viscosity coefficient and sublattice scale mixing length; \(\rho\) is the fluid density; \(L_{s}\) is the sublattice scale mixing length; \(\overline{S_{ij}}\) is the strain rate tensor; \(x\) is the Von Karman coefficient; \(d\) is the distance between the grid and the nearest wall; \(c_{s}\) is the Smagorinsky coefficient; and \(V\) is the volume of the grid cells.  
The used turbulence model is the SubGrid-Scale model, which can be used to accurately resolve the boundary layer detachment and the formation of an internal shear layer at the inner bank as well as the outer-bank cell of secondary flow [4]. In addition, the effectiveness of the used turbulence model is further illustrated by the comparison of water level, flow velocity, and Reynolds stress between the test and the simulation.
## 4 Implementation  
The application was implemented in a modular way using ROS for several reasons. On the one hand, it enabled us to modularise the application and to parallelise processes. On the other hand, Gazebo is perfectly integrated with ROS and offers facilities to control the simulation using topics, services and actions.  
OpenAI Gym is a toolkit to do research on DRL algorithms and to do benchmarks between DRL algorithms. This library includes some simulated environments that are used to test the quality of new algorithms and its use is widespread in the DRL community. Gym offers simple functions to interact with the environments, which are mostly modelled in the Mujoco [38] simulator. Due to the simplicity of the interface that Gym offers, it has become a standard way to interact with environments in DRL algorithms. Gym-gazebo is an extension of Gym that enables the user to create robotic environments in Gazebo and offers the same simple interface to the algorithm to be able to interact with the environment. All the environments we modelled were integrated with Gym-gazebo, which enabled us to straightforwardly use OpenAI Baselines DRL library, which is designed to work with Gym.  
Gym-gazebo wraps the used DRL algorithms in ROS nodes and enables interaction with the environment. Nevertheless, another ROS node has been developed that is in charge with controlling all the elements of the simulation and works as bridge between the Gym-gazebo node and the simulator. To be able to control the simulation physic updates and to compute them as fast as possible, we developed a Gazebo plugin, which in turn is a ROS node. Thus, using ROS communication methods, we could control each simulated discrete time-step and we simulated those steps faster than real time.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Test** & **Robot Initial Pose** & **Box Initial Pose** & **Objective** \\ \hline Test 1 & Variable & Variable & The arm to be able to plan a trajectory up to the box \\ Test 2 & Variable & Constant & To plan a trajectory with an obstacle in the table \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test setup summary.  
Figure 2: Simulated world in Gazebo.  
Specifically, this node takes care of all time-steps being of the same length, executing a fixed number of physic updates at each step.  
The _tf broadcaster_ node uses the _tf_ tool that ROS offers to keep track of all the transformations between frames. We used this node to publish some transformations such as the transformation between the _object_ and _world_ frames. Consequently, the robot is always aware of where the object is in order to be able to navigate up to it. The implemented architecture is shown in Figure 3.
## 1 Introduction
### Related Work
#### 1.2.2 Machine Leaning Based Approaches  
[PERSON] et al. [14] proposed an evolutionary neural network to predict host CPU utilization using several different recurrent neural networks. The authors used a dataset that used PlanetLab files and three optimization algorithms to optimize weights for RNN, namely: optimization of a particle swarm (PSO), differential evolution (DE) and evolutionary adaptation strategy of the covariance matrix (CMA-ES). [PERSON] et al. [15] used a machine learning based approach for forecasting CPU utilization. They utilized a recurrent neural network trained using the algorithm known as backpropagation over time to predict host CPU utilization in the Google Cluster trace dataset. [PERSON] et al. [16] developed a novel model for forecasting the workload of cloud data centers using LSTM networks. They tested the model using three benchmark datasets of HTTP traces of the NASA server, Saskatchewan server, and Calgary server. The proposed model achieved significant results. Their proposed model can perform a 60-min prediction of the workload of big datacenters.  
Two methods for predicting the resource consumption were presented by [PERSON] et al. [17]. The first method predicts the resource usage for a specific node and uses both CPU and memory usage for its performance predictions. The second method focuses on a large number of nodes and the workload running on them, which is likely caused by a smaller number of jobs. [PERSON] et al. [17] proposed an approach based on PCA to predict processor and memory utilization in the commercial data center.  
[PERSON] et al. [18] proposed a method that uses machine learning algorithms that predict the 24-h availability of resources at the host level. Their predicting method is based on the employment of quantile regression to ensure an elastic composition between the potential amount of resources to reclaim and to define unused resources. Hardware components like the central processor, memory, storage, and network metrics have been predicted to ensure exhaustive availability is guaranteed. [PERSON] et al. [19] have developed basic guidelines for developing an appropriate system for predicting the availability of CPUs for such computing infrastructures. They proposed a process for constructing a predictor that automatically checks the assumptions of vector autoregressive models in a time series. The authors performed three different past analyses, that is, the last hours, the same hours of the previous days, and the same weekly hours of the previous weeks. The suggested prediction model uses a multi-state prediction method to select a corresponding predictor.