esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Driver Acceptance Evaluation Model
### Parameter Selection
#### 5.1.2 Acceptance in Relation to the Control Commands  
The representative parameters of the control commands in relation to subjective acceptance were analyzed by their statistical significances and acceptance sensitivities. The parameter sets of the control commands are defined by Equations (14) and (15): \(\mathbf{X}\) for the accepted cases and \(\mathbf{Y}\) for the rejected cases.  
\[\mathbf{X}_{Control} =\left[\begin{array}{ccc}TPS^{Accepted}\\ SWA^{Accepted}\\ SWV^{Accepted}\end{array}\right] \tag{14}\] \[=\left[\begin{array}{ccc}\min\Big{[}TPS^{Accepted}\Big{]}&& &\text{SD}\Big{[}TPS^{Accepted}\Big{]}\\ \sim n(\mu_{\min[TPS^{Accepted}]},\sigma_{\min[TPS^{Accepted}]}^{2})&& \sim n(\mu_{\text{SD}[TPS^{Accepted}]},\sigma_{\text{SD}[TPS^{Accepted}]} ^{2})\\ \vdots&\ddots&\vdots\\ \min\Big{[}SWV^{Accepted}\Big{]}&&\text{SD}\Big{[}SWV^{Accepted}\Big{]}\\ \sim n(\mu_{\min[SWV^{Accepted}]},\sigma_{\min[SWV^{Accepted}]}^{2})&&\sim n (\mu_{\text{SD}[SWV^{Accepted}]},\sigma_{\text{SD}[SWV^{Accepted}]}^{2}) \end{array}\right]\]  
\[\mathbf{Y}_{Control} =\left[\begin{array}{ccc}TPS^{Rejected}\\ SWA^{Rejected}\\ SWV^{Rejected}\end{array}\right] \tag{15}\] \[=\left[\begin{array}{ccc}\min\Big{[}TPS^{Rejected}\Big{]}&& &\text{SD}\big{[}TPS^{Rejected}\big{]}\\ \sim n(\mu_{\min[TPS^{Rejected}]},\sigma_{\min[TPS^{Rejected}]}^{2})&&\cdots& \sim n(\mu_{\text{SD}[TPS^{Rejected}]},\sigma_{\text{SD}[TPS^{Rejected}]}^{2} )\\ \vdots&\ddots&\vdots\\ \min\Big{[}SWV^{Accepted}\Big{]}&&\cdots&\text{SD}\big{[}SWV^{Rejected}\big{]} \\ \sim n(\mu_{\min[SWV^{Accepted}]},\sigma_{\min[SWV^{Accepted}]}^{2})&&\cdots& \sim n(\mu_{\text{SD}[SWV^{Rejected}]},\sigma_{\text{SD}[SWV^{Rejected}]}^{2} )\end{array}\right]\]  
Each set of parameters comprises the following descriptive statistical values: the minimum value, maximum value, median value, mean value, and standard deviation. A significance test and acceptance sensitivity analysis were performed for each parameter. Parameters for which the significance level was below 5% (\(p\)-value \(<0.05\)) or the acceptance sensitivity was over 30% are presented in Table 2. As seen in the results, three parameters met the acceptance acceptance sensitivity criterion while no parameters met the statistical significance criterion. Additionally, Table 2 shows that no parameters were selected according to the historical effects on the acceptance in both the statistical significance test and the acceptance sensitivity analysis. Hence, the three parameters for the control commands selected from the acceptance sensitivity analysis are throttle open (\(TPS\)), steering wheel angle \((\delta)\), and steering wheel angular velocity \(\Big{(}\dot{\delta}\Big{)}\).
# Real-Time Detection of DDoS Attacks Based on Random Forest in SDN
###### Abstract  
With the development of the Internet of Things, a huge number of devices are connected to the network, network traffic is exhibiting massive and low latency characteristics. At the same time, it is becoming cheaper and cheaper to launch DDoS attacks, and the attack traffic is becoming larger and larger. Software-defined networking SDN is proposed as a new network architecture. However, the controller as the core of SDN is vulnerable to DDoS attacks and causes a single point of failure in the network. This paper combines the ideas of distributed and edge computing, firstly, a DDoS attack detection algorithm using heterogeneous integrated feature selection and random forest algorithm is proposed. Then, this DDoS attack detection algorithm is distributed and deployed on the edge equipment switches of SDN to perform distributed edge parallel computing using the residual computing power of the switches for fast and accurate detection of DDoS attacks. Finally, simulation experiments are conducted in the SDN environment using the CIC-DDoS2019 dataset to evaluate the effectiveness and feasibility of the proposed scheme. The experimental results show that the performance evaluation metrics of this solution: accuracy, precision, recall and F-value all reach 99.99%, while the prediction time is only 0.4 s, all metrics are better than other DDoS attack detection methods in the same category. Therefore, this solution is able to detect DDoS attacks in a timely and accurate manner.  
SDN; DDoS attacks; machine learning; edge computing; distributed computing; network security +
Footnote †: journal: Medical  
attack detection techniques available in the SDN, it is difficult to detect DDoS attacks under today's low-latency, large-scale network traffic [3]. Therefore, it is essential to find a method that can handle large-scale traffic and perform real-time DDoS attack detection in the SDN. In this paper, we propose an EDRFS (Edge-Distributed-Random Forest-Feature Selection) framework, where EDRFS incorporates the ideas of edge computing and distributed computing. Firstly, the distributed edge device switches in SDN are used to divert large-scale network traffic. Then, feature selection methods are used to remove redundant features and reduce the subsequent algorithm training and prediction time. Finally, the optimized random forest RF model is deployed in the edge device switches of SDN to perform real-time parallel computation on the data after the switch has been shunted to detect DDoS attacks in a fast and timely manner, relieving the pressure on the server side to detect DDoS attacks. EDRFS can handle the massive network traffic generated in the era of the Internet of Everything while providing fast and accurate detection of DDoS attacks in real time.
# In Vitro Corrosion Behavior of Zn3 Mg0.7Y Biodegradable Alloy in Simulated Body Fluid (SBF)
## 3 Results
### Electro-Corrosion Behavior of Zn, ZnMg and ZnMgY Materials in SBF Electrolyte
In all cases, the immersion tests present a decrease in samples mass with corrosion compounds that pass from the material surface to electrolyte solution, values are gib = ven in Table 9. Using the follow densities: [g/cm\({}^{2}\)]: 7.13 for Zn, 6.52 for ZnMg and 6.45 for Zn3 Mg0.7\(\chi\) we calculate the corrosion rate of the material in SBF electrolyte based on formula: CR = CR = (8.76*10,000*mass loss)/(Total area*time*density) [mm/year] [39]. The differences between the cleaned and uncleaned sample are given by the instable compounds formed on the surface during immersion and that pass to solution after ultrasonication of the samples. The macro and micro aspects of the compounds passed from the alloy surface into the electrolytic solution are shown in Figures 14a and 14b, respectively. Generally, small and large parts of the material can be observed. The larger parts are usually agglomerations of small round oxides, Figure 14b. At microscale, the minimum diameter measured using VegaTC software was around 75 \(\upmu\)m and the maximum one at 1600 \(\upmu\)m, with an average of 480 \(\upmu\)m. At micro scale (analyze of the parts from Figure 14b) the minimum value measured was of 2 \(\upmu\)m and the maximum of 5.07 \(\upmu\)m and an average value of 3.29 \(\upmu\)m (50 determinations) and a standard deviation of \(\pm\)0.74. As marked in Figure 14a, the chemical composition on different areas was analyzed and the quantitative results are given in Table 10.  
All the products present a high amount of oxygen, mainly oxides passing from the material surface to electrolyte solution, and also chlorine and carbon (due to the formation of carbonates) based on the compounds identified. Figure 14: SEM images of compounds passed from Zn3 Mg0.7\(\chi\) in electrolyte solution after 18 days 25\(\times\) in (**a**) and 1000\(\times\) in (**b**). \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Material/Alloy**} & **Initial Mass** & **After Immersion** & **After Ultrasound** & **Loss Mass During** & **Corrosion Rate** \\  & **[g]** & **(18 Days) [g]** & **Cleaning [g]** & **Imersion** & **(CR)** \\  & & & & **[g]** & **[mm/Year]** \\ \hline Zn & 3.7153 & 3.7012 & 3.7001 & 0.0011 & 0.13 \\ Zn3 Mg & 4.2286 & 4.2104 & 4.2095 & 0.0009 & 0.23 \\ Zn3 Mg0.7\(\chi\) & 2.5994 & 2.5883 & 2.5879 & 0.0004 & 0.12 \\ \hline \end{tabular}
\end{table} 
Table 9: Mass loss after 18 days of immersion in SBF electrolyte.
# Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification
## 2 Few-Shot Classification Problem
Based on the prototypical networks, [PERSON] et al. [14] use a concatenation of foreground and background vector representations as feature vectors. [PERSON] et al. [15] introduced the variational autoencoder (VAE) structure [31] into the embedding module for training the prototype images. These works focused on obtaining a good embedding network and there has not been much interest in the classification module. This is primarily due to the limitations of the few-shot classification task. Even though it is known that other alternatives such as Mahalanobis distance [25] can be adopted instead of the simple deterministic distance, it is difficult to apply the statistical distance, because it needs distribution information of samples which are not sufficiently given in the few-shot classification tasks. As an attempt to overcome these difficulties, [PERSON] [22] tried confidence region estimation in the embedding space in the form of a Gaussian covariance matrix and used it to construct metrics. [PERSON] et al. [24] proposed to use of a new metric learning formula based on Mahalanobis distance [25] to avoid the tendency to overfit the training class. However, these methods still have difficulty in estimating the covariance matrix of each class under the few-shot setting. [PERSON] et al. [23] proposed a method relatively free from this problem by defining a local covariance that is obtained from local features in the embedding modules. Though this method shows the efficiency of the second-order statistics, it needs a more complex classifier with specially designed metrics. In this paper, we try to find some possibility of improving the classifier by using a probabilistic similarity based on the intra-class statistics, which can be estimated robustly especially when the number of classes increases. Unlike [20], our proposed method does not depend on the structure of the embedding module, and it can be combined with the original prototypical network as well as other sophisticated models.
# Cloud-Assisted UAV Data Collection for Multiple Emerging Events in Distributed WSNs
## 3 Mathematical Model and Algorithm Analysis
### Acquisition Sequence Design  
For an event-driven UAV and WSN, every flight to collect cluster data must consider the flying distance, correlation and the event priority. In other words, we should strike a balance between these factors. The goal of this work is to maximize \(Fs\) of the WSN and can be formulated as:  
\[\begin{array}{c}\text{Maximize }Fs\\ \text{subject }\alpha_{a}(j),\beta_{a}(j),D_{f}>0\end{array} \tag{8}\]  
We formulate the problem of UAV collecting data from WSN for emerging events as a maximization problem. In this optimization process, the event correlation matrix plays a critical role. We divide the proposal into two stages. First, according to the distance, we obtain the hierarchal relation between the node and the cluster. Next, based on the total flying score, we select the correlation clusters for the UAV. A step-wise greedy strategy is used to deal with the problem. Algorithm 1 describes the rules for designing the path of an UAV for collecting a sequence of clusters from multiple emerging events. Algorithm 1 works as follows: Initially, \(C_{N}\), \(U_{m}\), \(W_{u}\), \(R_{S}\) and \(R_{u}\) are empty. Then, parameters values are inserted according to the sensors, clusters and the emerging events. It iteratively computes the node event correlation \(\varphi(s,i,j)\) and cluster event correlation \(\Phi(i,j)\) using Equations (1)-(3) (see Steps 1-11). Then, from the Steps 13-18, the Total Flying Score \(Fs\)_for different solutions_ are found according the Equations (4)-(7). Finally, the value of \(Fs\) is ordered from Max to Min.  
```
0: Cluster contains the Sensor \(C_{N}\), the emerging event \(U_{M}\), and the event priority \(W_{u}\), \(R_{s}\), \(R_{u}\).
0: Set of ordering correlation clusters \(G=\{g_{1},g_{2},g_{3},\ldots,g_{k}\}\).
1:Begin
2:for\(j\gets 1\) to \(m\)do
3:for\(i\gets 1\) to \(n\)do
4:for\(S\gets 1\) to \(C_{i}\) do \(r\)
5:if\(D(s,i,j)<R_{s}+R_{u}\)
6:\(\varphi(s,i,j)=1\), \(\Phi(i,j)=1\)
7:\(\alpha_{i}(j)\leftarrow\alpha_{i}(j)+1\)
8:endif
9:endfor
10:endfor
11:endfor
12:for\(i\gets 1\) to \(n\)do
13:if\(\Phi(i,j)>1\)
14:\(\sum\limits_{\alpha\in j\in M}\alpha_{a}(j):w_{j}\)
15:endif
16:endfor
17: compute \(F_{s}\) using Equation (7).
18: Ordering the \(F_{s}\) from max to min
19:end
```  
**Algorithm 1** Based on the correlation of the flying sequence for multiple emerging events.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 2 Data
### Data Format  
The data levels for ASTRI project [10] were originally defined in compliance with those of the Cherenkov Telescope Array. ASTRI data levels of interest for the work presented here are:  
* Level 2a (DL2a): array-wise merge data. For each event, they contain the single-telescope image parameters plus the stereoscopic shower event parameters;
* Level 2b (DL2b): array-wise fully reconstructed data. These also include the estimated event energy and its gammaness, i.e., the likelihood that the primary is a gamma event instead of a hadronic event.  
The FITS data format [11] has been adopted for all ASTRI data levels. For our tests, we use FITS files in the official ASTRI format (level DL2a) as input data. In the output, we produce FITS files in the official ASTRI format (level DL2b).  
In the ASTRI data reduction and scientific analysis software (A-SciSoft) package [10], which we use as a benchmark, data at level DL2a are reduced to level DL2b. A-SciSoft uses Random Forest methods both for gamma/hadron discrimination and for energy estimation.
## 1 Introduction
Optical remote sensing data offer an enormous amount of spectral information. However, microwave data offer a certain penetration of ground objects that have high surface roughness, complex permittivity, and body structure. Furthermore, it is reflected that the fusion of optical images and SAR images can effectively improve the accuracy of LULC classification [16; 17; 18]. At present, remote-sensing image fusion has developed into the following three levels: pixel-level fusion, image fusion, and feature-level fusion [19]. Compared with the pixel-level fusion, feature-level fusion considers various factors such as the feature information and correlation contained in the image itself, and it helps obtain more macro-level feature-level information than that obtained using pixel-level fusion. In this study, we intend to conduct the feature-level fusion of Landsat 8 OLI data and Advanced Land Observing Satellite-2 (ALOS 2) data, following which we analyze and compare the classification results and classification accuracy of different remote-sensing images and classification methods; subsequently, we select suitable methods for extracting the land information in cloudy, foggy mountain areas under different cloud covers. Taking Chongqing Zhongliang Mountain as a study area, the fusion classification method of optical and SAR images is verified, providing the theoretical guidance for LULC classification in cloudy mountainous areas.
# Reinforcement Learning of Bipedal Walking Using a Simple Reference Motion
## 4 Numerical Experiments  
### Simulation Setups  
In this study, we used a 3D robot simulator, Webots [36], to perform the simulations required for reinforcement learning and Bayesian optimization. The Webots simulator performed the physical simulations at 50 Hz.  
The target robot is a small humanoid robot, KHR-3 HV (ver. 2). This robot has 22 degrees of freedom. To simplify the learning of walking, we controlled 10 joints of the legs. The angles of the other joints were fixed. Each joint angle was controlled by the PID controller of a servo motor, and the PID parameters were set to \(K_{\text{p}}=18.5\), \(K_{\text{I}}=0\), and \(K_{\text{D}}=0.009\). Webots has two parameters regarding the joints, which are dampingConstant and staticFriction, and we set them to 0.002 and 0.025, respectively.  
The values for \(u_{t}^{\text{target}}\), \(a_{t}^{\text{IK}}\), and \(a_{t}^{\text{RL}}\) in Equation (5) were calculated at 25 Hz, i.e., every 40 ms. We set the walking cycle \(T\) to 50 steps. Thus, one period of the reference motion was \(40\times 50=2000\) ms.  
For Bayesian optimization to determine the four parameters of the reference motion, as mentioned in Section 3.6, we used the TPE algorithm [32] implemented in Optuna [33].  
We used the algorithm with its default parameter values. We set \(H\) to 8. We set the search space of each reference motion parameter to be integers within \(10\leq w\leq 20\) [mm], \(0\leq\Delta w\leq 10\) [mm], \(20\leq h\leq 30\) [mm], and \(0\leq\Delta h\leq 10\) [mm].  
To implement the environment of the reinforcement learning, we used OpenAI Gym [37]. To implement the neural network and PPO algorithm, we used Stable-Baselines3 [38].  
The neural network was a fully connected four-layer feedforward network consisting of an input layer of 84 neurons, two hidden layers of 64 neurons each, and an output layer of 20 neurons, with tanh activation functions. The outputs of the neural network were sampled from Gaussian distributions whose means and standard deviations were determined by the activation values of the neurons of the output layer. Sampled values below \(-1\) or above 1 were clipped to \(-1\) or \(1\), respectively.  
Table 2 lists the values for the PPO hyperparameters. The parameter \(\lambda\) in Table 2 is a parameter that is used to calculate the estimated advantage \(\hat{A}_{t}\) in Equation (12) with the Generalized Advantage Estimation (GAE) method (see [34] for details).  
The weights to balance each term of the reward function were set to \(w_{1}=0.1,w_{2}=0.75\), \(w_{3}=0.1\), and \(w_{4}=0.05\). These values were empirically determined through several experiments.  
We used a personal computer with an Intel Core i7-11700K CPU and an NVIDIA GeForce GTX1650 GPU for the simulation experiments. The simulation took approximately one hour per \(10^{6}\) steps.
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 2 Materials and Methods
### Proposed Algorithm  
1. **Uplink training phase**: The received signal is mapped in the time-frequency plane; see Figure 5A. Then, the pilots are sampled. After taking \(k\) samples of pilots \(\phi_{k}\), \(R\) is computed as shown in (11), where each column of \(\Phi_{k}\) matrix is given by \(\phi_{n}=\phi_{1},n_{1};\phi_{1},n_{2};\phi_{1},n_{3};\ldots;\phi_{1},n_{k}\), as shown in Figure 5B. Then, each column is used as shown in (12), and then DMD is applied to reduce the system dimension while preserving the main spatial and temporal characteristics. Subsequently, PiDMD and mpeDMD are applied. PiDMD is employed to ensure that the matrix takes the form of a Toeplitz matrix taking advantage of previous knowledge of the system. The mpeDMD technique is also capable of revealing the underlying Toeplitz structure, as will be demonstrated later. The corresponding approach to estimate the \(M\times M\) correlation matrix R is to form the sample correlation matrix (11). As illustrated in Figure 5, the correlation matrix will be formed by the pilot's samples (11): \[\hat{R}_{sample}=\frac{\tau_{p}}{N}\sum_{s=1}^{S}\phi_{k}\,\phi_{k}^{H}.\] (11) The structure of \(\phi\) matrix is shown on (12). Each element of \(\hat{R}_{\text{sample}}\) asymptotically converges to the corresponding element of \(R\)[11] by the law of large numbers. However, obtaining a sample correlation matrix with eigenvalues and eigenvectors that are well aligned with those of \(R\) is more challenging, as estimation errors in all \(M^{2}\) elements of \(\hat{R}_{\text{sample}}\) affect the eigenstructure. This is where PiDMD/mpeDMD comes into play. PiDMD/mpeDMD can accurately identify the eigenstructure of the data [9].
2. **Use PiDMD/mpeDMD**: The purpose of this step is twofold; the first one is to keep only the most relevant components of the system and the second one is to achieve a better approximation of eigenvalues and eigenvectors of a reduced version of the system. \[\Phi_{k}=\left[\begin{array}{cccc}\big{|}&\big{|}&\big{|}&\big{|}\\ \phi_{1}&\phi_{2}&\ldots&\phi_{k}\\ \big{|}&\big{|}&\big{|}&\big{|}\end{array}\right].\] (12)
3. **Channel Estimation**: Once the correlation matrix is known in the whole network, it is possible to compute the Channel Estimation for every link.
4. **Design of Downlink precoders**: With a good Channel Estimation, it is possible to use well-known downlink precoder design, locally at the Access Points or centrally at the CPU.
5. **Spectral Efficiency**: Finally, the Spectral Efficiency can be computed and analyzed.
# Density Functional Theory Study on the Adsorption of Co(II) in Aqueous Solution by Graphene Oxide
## 1 Introduction  
When nuclear power plants use light water as a moderator and coolant, high-purity water will decompose H\({}_{2}\)O\({}_{2}\) and O\({}_{2}\) under the action of radiation, resulting in corrosion of fuel surfaces and core structural materials. Corrosion materials become corrosion products after neutron activation and are important radioactive sources in nuclear facility wastewater. Compared with other nuclides in radioactive wastewater, \({}^{60}\)Co has a longer half-life (5.27a) and a higher content. Once it enters the environment and human body, it is bound to cause serious harm, and it is the object that needs to be focused on and removed in wastewater treatment.  
As a new type of carbon material, graphene oxide (GO) has the characteristics of a large specific surface area and many oxygen-containing functional groups (including hydroxyl, carboxyl and epoxide groups) and can be further modified. It can be combined with radionuclides through coordination, electrostatic interaction and hydrogen bonding [1] and is expected to become an ideal adsorption material for removing radioactivity in water. In the process of the adsorption of metal ions by GO and GO-modified functional materials, experimental methods are usually adopted to explore the adsorption kinetics, thermodynamics and influencing factors of water environments; the study of the mechanism of interaction between GO and metal ions at the microscopic level is still in the initial stage. The research on the modification direction of increasing adsorption sites to improve adsorption performance lacks theoretical guidance. Especially in the process of treating radioactive wastewater, blind experiments also increase the radiation dose of experimental personnel. In this paper, GO is used to remove radioactive cobalt ions from water. Based on density functional theory (DFT), the quantum chemistry calculation software Gaussian 16 is used to calculate the adsorption mechanism of Co\({}^{2+}\) by the material, analyze the factors affecting the adsorption effect of the material structure, and validate the calculation resultsthrough experiments so as to provide more effective theoretical guidance for the functional modification of GO and more efficient adsorption experiments.