esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Hot Jet Detonation with Different Ignition Positions
## 2 Numerical Model and Methods
### Grid-Independent and Model Validation  
According to the geometric characteristics of the physical model, the quadrangular structured meshes are chosen in this paper. Selecting different mesh sizes (ranging from 0.5 mm to 2 mm). Figure 2 shows mesh size independence analysis. By comparing the variations of pressure with time at the same monitor point (P shows in Figure 1a), the monitoring pressure no longer changes significantly with decreasing mesh size when mesh size is reduced to 1 mm. Therefore, the parameters of 1 mm for mesh size is chosen in the following numerical simulation, which meets the requirements of independence.  
Experiments have been done to verify the validity of the numerical simulation. The experimental equipment schematic is shown in Figure 3. The PCB 113B24 pressure sensor is selected. Figure 4 compares the pressure curve of the experiment and numerical simulation at the same positions (P\({}_{2}\) and P\({}_{3}\) are located at 1035 mm and 1285 mm away from the left wall of detonation chamber), the pressure variation trends and peaks of them are very similar. According to Figure 4, we can confirm the experimental and simulated results are both the deflagration wave by the shape of the wave before the P\({}_{2}\), and there are the detonation waves at P\({}_{3}\). So DDT is completed between P\({}_{2}\) and P\({}_{3}\). In addition, the high detonation waves prove that the detonation initiation point is after P\({}_{2}\)[8; 32]. The DDT time and distance of numerical simulation have good similarity with the experiment. Therefore, the validity of the numerical simulation used in this paper is proved.  
Figure 3: Schematic of the experience model.  
Figure 2: Mesh size independence analysis.  
Table 1 shows the comparison of theoretical and numerical detonation wave parameters. The theoretical parameters of detonation waves are obtained by chemical equilibrium with applications (NASA CEA) [41]. The value of the detonation parameters of this simulation method used in this paper is slightly larger than CEA, and the maximum deviation is only 3.4%. The comparison further verifies the validity of the numerical simulation method used in this paper.
###### Abstract  
We reviewed the recent progress in coherent manipulation on light fields based on the electromagnetically-induced transparency (EIT) effect in Pr\({}^{3+}\)-doped Y\({}_{2}\)SiO\({}_{5}\) crystal. The results show that, on one hand, the atomic coherence grating, formed when the light pulse is stored in Pr\({}^{3+}\):Y\({}_{2}\)SiO\({}_{5}\) crystal under the EIT condition has similar properties to the traditional holographic grating. On the other hand, the atomic coherence grating has its own unique characteristics that are different from those of traditional holographic grating. The EIT-induced nonlinearity and atomic coherence gratings can be used to manipulate the amplitude, the phase and the polarization state of light fields; therefore, they are of important applications for optical signal processing, quantum information processing and imaging processing.  
electromagnetically-induced transparency; optical signal processing; imaging 1
## 5 Simulation Results and Discussion  
### Experimental Setup  
Our server uses a Geforce GTX 2080 Ti GPU to perform calculations. Firstly, the original signal with dimensions of (4096, 2) is converted into time-frequency images with dimensions of (64, 64) on MATLAB. The time-frequency images are fed into Python for subsequent processing. The environment in Python is the torch 1.4.0 deep learning framework. We use sklearn 0.24.2 tool to test the model. The dataset we use contains 20,000 time-frequency images with twenty modulation schemes. Each modulation scheme includes 1000 time-frequency images and the dimensions of each time-frequency image are 64 \(\times\) 64 \(\times\) 3. We set the proportion of labeled samples to 1%, 2%, 3%, 4%, and 5%, respectively. The specific parameter settings are shown in Table 1.
# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 3 Proposed Solution
### Pitfalls  
No truly foolproof approach exists; as such, it is to be expected that the proposed solution has its downsides, one of which may be self-evident even in a planning phase. This problem derives from its core concept, the use of interim features to represent combinations of the original ones, which is, in essence, a combinatorial problem.  
As stated in [67], a combination refers to an unordered arrangement of a set of objects. Repetitions may or not be allowed. The number of \(r\)-combinations of a set with \(n\) distinct elements may be expressed as seen in Equation (1) [68].  
\[C(n,r)\equiv\,_{n}C_{r}\equiv\binom{n}{r}\equiv\frac{n!}{r!(n-r)!} \tag{1}\]  
By fixing a value for \(r\) (as 5 in this case) and then plotting the previous function, Figure 2 may be drawn:As depicted in the plot (Figure 2), the number of combinations that can be created from a given group increases exponentially with the original sample size. For the proposed solution, it would become exponentially more expensive to use it for datasets with a large amount of features.  
There are some ways to address this problem. One possibility would be to increase the number of features included in each combination group (\(r\)) in proportion to the increase in the total number of features (\(n\)). However, that would mean that for larger sets, each combination group would be equally broad, meaning that the effects of singular features and/or feature interactions would be greatly diluted.  
A more permissible option would be to reduce the original feature set to a more manageable size. This can be accomplished by applying feature selection to the original set, and since the method should not be computationally expensive, a filter method is preferred.
# Interpretable Variational Graph Autoencoder with Noninformative Prior
## 5 Experimental Results and Analysis
### Link Prediction  
#### 5.3.1 Experimental Setup  
Link prediction is one of the most common downstream tasks in network representation learning. It can recover missing links or predict possible links in the future by analyzing the given network structure. Consistent with VGAE, we also make use of 5% and 10% links as the validation set and test set, respectively, and randomly generate the same number of non-existent links. The purpose of using the validation set is to adjust the hyperparameters, and the remaining 85% of the links are employed for the training set. In the training process, we add \(\boldsymbol{\Pi}\) as a neural network parameter to the objective function and update it through backpropagation. Due to the relatively small scale of Cora and Citeseer datasets, we set weight_decay = 0.01 to prevent overfitting. We iterate 800 times to train sufficiently for the large scale of the Pubmed dataset. The number of hidden layer units and iteration times of three datasets of our model are described in Table 2. We take advantage of Adam optimizer to optimize the parameters with a learning rate of 0.01.  
We take the same experimental setup as our model for GAE and VGAE. As for ARGE, ARVGE, and EVGAE, we adopt the same setting as the original papers. We exploit the area under the ROC curve (AUC) and average precision (AP) to evaluate the performance of our model and other baselines. These are two evaluation indexes commonly employed in the link prediction task. The more the values of AUC and AP approach 1, the better the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **\# Nodes** & **\# Links** & **\# Features** & **\# Lables** \\ \hline Cora & 2708 & 5429 & 1433 & 7 \\ Citeseer & 3327 & 4732 & 3703 & 6 \\ Pubmed & 19717 & 44338 & 500 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the datasets utilized in the experiments.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Datasets** & **The Number of Neurons per Layer** & **The Number of Iterations** \\ \hline Cora & 64-16 & 200 \\ Citeseer & 128-32 & 200 \\ Pubmed & 64-16 & 800 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental settings for training.  
experimental results are. We ran each dataset five times and reported the average value to ensure the accuracy of the algorithm.
## 3 Results
### Economic Analysis of the Crop Production
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Control**} & \multicolumn{4}{c}{**C100**} & \multicolumn{4}{c}{**Wheat**} & \multicolumn{4}{c}{**PM + C50**} & \multicolumn{4}{c}{**GM + C50**} \\ \hline  & Exp.\({}^{1}\) & Earn.\({}^{2}\) & Profit\({}^{3}\) & BCR \({}^{4}\) & Exp. & Earn. Profit & BCR & Exp. & Earn. & Profit & BCR & Exp. & Earn. & Profit & BCR & Exp. & Earn. & Profit & BCR \\ \hline Crop-1 & 219.02 & 347.24 & 128.21 & 1.59 & 352.73 & 485.31 & 132.57 & 1.38 & 301.91 & 535.05 & 233.99 & 1.77 & 315.36 & 497.85 & 182.50 & 1.58 & 296.56 & 485.30 & 188.74 & 1.64 \\ \hline Crop-2 & 225.90 & 455.80 & 229.90 & 2.02 & 362.51 & 638.11 & 227.51 & 1.76 & 319.56 & 688.76 & 369.20 & 2.16 & 320.53 & 663.44 & 342.91 & 2.07 & 303.55 & 643.18 & 339.63 & 2.12 \\ Crop-3 & 230.49 & 502.04 & 271.54 & 2.18 & 371.79 & 713.42 & 341.63 & 1.92 & 327.89 & 760.98 & 433.10 & 2.32 & 333.23 & 729.28 & 396.05 & 2.19 & 317.23 & 708.13 & 390.91 & 2.23 \\ \hline \multicolumn{11}{c}{**Malize**} \\ \hline  & \multicolumn{4}{c}{**Control**} & \multicolumn{4}{c}{**C100**} & \multicolumn{4}{c}{**FM + C50**} & \multicolumn{4}{c}{**FM + C50**} & \multicolumn{4}{c}{**GM + C50**} \\ \hline  & Exp.\({}^{1}\) & Earn.\({}^{2}\) & Profit & BCR & Exp. & Earn. Profit & BCR & Exp. & Earn. & Profit & BCR & Exp. & Earn. & Profit & BCR \\ \hline Crop-1 & 230.43 & 324.58 & 94.15 & 1.41 & 364.14 & 430.49 & 66.35 & 1.18 & 313.32 & 443.24 & 129.91 & 1.41 & 336.76 & 436.37 & 109.61 & 1.34 & 307.93 & 448.93 & 150.99 & 1.49 \\ \hline Crop-2 & 232.26 & 376.55 & 144.30 & 1.62 & 371.54 & 478.18 & 106.64 & 1.29 & 328.59 & 496.37 & 167.78 & 1.51 & 328.67 & 470.69 & 142.03 & 1.43 & 312.52 & 525.25 & 212.73 & 1.68 \\ \hline \multicolumn{11}{c}{\({}^{1}\) total epeness, \({}^{2}\); total earnings, \({}^{3}\); net profit/loss, \({}^{4}\); benefit cost ratio.} & & & & & & & & & & & & & & & \\ \hline \end{tabular} \({}^{1}\): total epeness, \({}^{2}\); total earnings, \({}^{3}\); net profit/loss, \({}^{4}\); benefit cost ratio.  
\end{table} 
Table 4: Cost-benefit analysis (USD ha\({}^{-1}\)) during reclamation and crop production in study area during wheat-maize rotation.
## 5 Experimental Results and Analysis
### Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Class** & **KNN** & **MultinomialNB** & **RF** & **SVM** & **ANN** & **DBN** & **MDPCA-DBN** \\ \hline Normal & 74.81 & 62.0 & 76.85 & 58.36 & 63.89 & 72.12 & **82.85** \\ Generic & 96.63 & 96.24 & 96.4 & 96.21 & 95.55 & 96.08 & **96.93** \\ Exploits & 74.48 & 42.81 & 77.63 & 75.52 & 87.74 & **91.02** & 83.51 \\ Fuzzers & 42.33 & 33.25 & 51.32 & **69.31** & 68.98 & 43.32 & 44.39 \\ DoS & 19.44 & **73.84** & 17.6 & 0.00 & 5.13 & 2.32 & 23.72 \\ Reconnaissance & 58.94 & 36.34 & **76.68** & 0.00 & 63.38 & 70.67 & **76.68** \\ Analysis & 1.48 & 0.0 & **3.7** & 0.00 & 0.00 & 0.00 & 0.00 \\ Backdoor & 2.56 & 0.0 & **5.13** & 0.00 & 0.00 & 0.00 & 0.85 \\ Shellcode & 14.47 & 0.0 & **51.32** & 0.00 & 0.00 & 0.00 & 39.47 \\ Worms & **11.11** & 0.0 & **11.11** & 0.00 & 0.00 & 0.00 & **11.11** \\ \hline
**Accuracy** & 85.38 & 76.75 & 87.56 & 79.36 & 83.29 & 86.02 & **90.21** \\
**Recall** & 94.01 & 88.78 & 96.29 & 96.49 & **99.12** & 97.36 & 96.22 \\
**Precision** & 82.05 & 74.11 & 83.6 & 73.95 & 77.08 & 81.06 & **87.3** \\
**F1-score** & 87.63 & 80.78 & 89.5 & 83.73 & 86.72 & 88.46 & **91.54** \\
**FPR** & 25.19 & 38.0 & 23.15 & 41.64 & 36.11 & 27.88 & **17.15** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison results for the NSL-KDD (KDDTest-21) dataset (%). \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Model** & **Normal** & **Probe** & **DoS** & **U2R** & **R2L** & **Accuracy** & **Recall** & **Precision** & **F1-Score** & **FPR** \\ \hline KNN & 68.49 & 59.08 & 69.81 & 3.50 & 3.56 & 55.5 & 52.62 & 88.27 & 65.93 & 31.51 \\ MultinomialNB & 83.32 & **82.81** & 38.12 & 0.5 & 22.22 & 60.08 & 54.93 & 93.69 & 69.25 & 16.68 \\ RF & **88.38** & 60.45 & 66.08 & 0.50 & 10.42 & 56.84 & 49.84 & 95.08 & 65.39 & **11.62** \\ SVM & 68.26 & 61.41 & 56.79 & 0.00 & 0.00 & 47.38 & 42.74 & 85.85 & 57.07 & 31.74 \\ ANN & 67.24 & 56.45 & 50.41 & 0.00 & 0.00 & 45.0 & 40.06 & 84.64 & 54.38 & 32.76 \\ DBN & 71.75 & 58.33 & **71.72** & 0.50 & 13.25 & 57.45 & 54.28 & 89.65 & 67.62 & 28.25 \\
**MDPCA-DBN** & 86.94 & 63.20 & 68.75 & **6.00** & **34.93** & **66.18** & **61.57** & **95.51** & **74.87** & 13.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison results for the UNSW-NB15 dataset (%). classifiers have low detection rates on the Analysis, Backdoor, and Worms classes, mainly because many contemporary attacks in the UNSW-NB15 dataset have similar behaviors, such as attack features of Analysis, Backdoor and Worms similar to those of Exploits.
## 2 Materials and Methods
### Image Preprocessing, Segmentation, and Feature Extraction  
For reducing the light source effect, darkness and reflectance images of white Teflon were also collected for use as a reference to correct the raw reflectance image of each sample. The relative reflectance image was calculated by dividing the difference in energy readings between sample and darkness by the difference in energy readings between the white Teflon and darkness. All the relative reflectance images were used in following analyses.  
In order to extract useful information for model developing and testing, segmentation of the vegetable soybeans from the hyperspectral image background is a key step. Threshold segmentation was used, because of convenient calculation and high accuracy. In this study, the reflectance image of soybean samples at 718.2 nm was selected, because of its superior contrast outline in this image (step I) [1]. Prior to image segmentation, the obtained gray image had noise removed by image filtering and enhancement (step II). Threshold segmentation was then performed to produce a binary mask by expansion, corrosion, opening, and closing of the gray image (step III). The obtained binary mask was used to segment the true soybean regions from the 94 wavelengths in the image spatial block (step IV). Finally, the mean relative reflectance of the 270 soybean samples was calculated after the automatic segmentation of the image background, and each soybean from the true image yielded large amounts of spatial and spectral information. Considering the physical state and chemical composition changes of the soybeans during the drying process, mean reflectance was applied to extract and predict the moisture content characteristics of the dried soybeans. The mean reflectance of each dried soybean was calculated in following equation:  
\[Mean\ =\ \frac{1}{M_{x}*M_{y}}\sum_{i\ =\ 1}^{M_{x}}\sum_{j\ =\ 1}^{M_{y}}f(i,j), \tag{1}\]  
where \(f(i,j)\) is the relative reflected light intensity of each pixel. \(M_{x}\) and \(M_{y}\) are the number of pixels in the horizontal and vertical directions of the sample, respectively.
## 4 Proposed Method
### Motivation
Although a simple defense that limits the number of message forwarding at each sensor seems to be helpful in alleviating PDoS attacks, it is indeed useless because the network traffic on each sensor cannot be estimated in advance. As a consequence, if some limits are applied on the number of message forwarding at each sensor, some packets such as transmitted MACs in ExCo will be dropped somewhere on its way to the destination Under the circumstance, the verification will fail and some sensor readings will be regarded as falsified ones because some of the co-authenticators cannot provide the MACs. Third, ExCo is also vulnerable to FEDoS attacks. In fact, this is a common attack if only a simply designed collaboration scheme is used. Specifically, after a sensor is compromised and is inquired by the sink to ask for the MAC used for authenticating the sensed data of the other sensors, it can always reply a random string to the sink. This implies that some data sensed by genuine sensors will be thought of as fake. This is because the MACs involved in a large number of sensors are necessary for authentication, leading to be easily vulnerable to FEDoS attacks. Simply taking the majority of the verification resultsof MACs from different co-authenticators seems to be helpful in alleviating FEDoS attacks, but it also reduces the resilience against sensor compromises. As a matter of fact, without the proper design, there always exists a dilemma of enhancing the resilience against sensor compromises or enhancing the resilience against FEDoS attacks. In addition, it has been demonstrated [4] that the expected number \(\
u(n,t)\) of sensors involved in the authentication of the sensed data of one sensor in ExCo is  
\[\
u(n,t)=n\Big{(}1-\big{(}\frac{t}{n-1}\big{)}^{2}\big{(}1-\frac{t}{n-2}\big{)} ^{t}\Big{)} \tag{1}\]  
where \(|R^{r}_{j}|=t\) is the number of co-authenticators. Figure 3 shows different settings of \(\
u(n,t)\). Here, we further define a metric \(R_{r}\), called _FEDoS ratio_ of \(r\), which denotes the ratio of the number of sensors required to be compromised to the total number of sensors in the network. Note that the parameter \(r\) means that \(r\times 100\%\) of the verifications of sensor readings are affected. FEDoS ratio is used to evaluate and quantify the resilience against FEDoS attacks. According to the \(\
u(n,t)\)'s derived as above, \(R_{0.5}\)'s in different settings are shown in Figure 4. It can be observed that when the number of co-authenticators used in ExCo is \(2\) and approximately \(10\%\) of sensors are compromised, over \(50\%\) of sensor readings will be regarded as bogus by the sink because of the bogus authentication tags provided by the compromised sensors. When \(t=4\), less than \(2\%\) of sensor compromises would result in above \(50\%\) of contaminated sensor readings, which will be a disaster in terms of the network security.
# Time to Capture a Moving Target Travelling along a Circular Trajectory
## 3 Results  
### Analysis of Target Selection Accuracy  
Among the data collected in 20 repeated trials, data that did not accurately click the target was extracted. The mean number of accurate clicks on the target was 18.27 and the standard deviation was 1.18. As shown in Figure 2, the error of target selection was larger as the target width decreased (\(p<0.001\)) and the speed of the moving target increased (\(p<0.001\)). However, the distance between the home position and the moving target on the click error was not statistically significant (\(p=0.157\)). In general, the target selection is less accurate as the difficulty of the task increases. In this experiment, as the width of the target increased, the accuracy increased, and as the speed decreased, the accuracy increased. On the other hand, although not statistically significant, the shorter the distance between the target and the home position, the greater the accuracy.  
Among the three independent variables, the interaction effect on the selection accuracy was significant only between the moving speed of the target and the target width (\(p<0.001\)), as shown in Figure 3. When the target speed was high and the width of the target was narrow, the click accuracy was very low. It was found that if the width of the target was narrow and the speed of the target was high, the difficulty of the task was greatly increased.  
Figure 3: Interaction effects between the speed of moving target and the target width on the target selection accuracy.  
Figure 2: Main effects of the speed of moving target (**left**), the target width (**center**) and the distance between home position and moving target (**right**) on the target section accuracy.
# CommC: A Multi-Purpose COMModity Hardware Cluster
## 4 Technologies and Services
### Desktop Environments  
Desktop environments offer a graphical workspace that abstracts the complexities of command-line interfaces, providing users with a visually appealing and customizable experience. They are designed to cater to diverse needs, from resource-efficient options like Xfce to more feature-rich and visually intensive alternatives. These environments support extensive customization, enabling users to personalize layouts, themes, and workflows, and they typically come with integrated tools like file managers and system settings to streamline usage.  
**Xfce** is a lightweight desktop environment known for its balance of performance, functionality, and resource efficiency. Built on the GTK toolkit, Xfce is modular in design, allowing users to replace or customize components. Its low system requirements make it ideal for older hardware or resource-constrained environments. Xfce supports extensive theming and customization, offering a user-friendly and stable desktop experience suitable for both personal and professional use.
###### Abstract
A customised, generic, reconfigurable fast Fourier transform and its inverse coprocessors in FPGA were also designed and developed for the hardware implementation of the SAR processing algorithm. The results show a reduction in hardware usage via the implementation of an FPGA-based FFT/IFFT coprocessor. The achieved processing time and power consumption also outperformed the existing implementations.
## 3 Results
### The Temporal and Spatial Variation Trends of LUCC Since 1990
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **Cropland** & **Woodland** & **Grassland** & **Water Body** & **Built-Up Land** & **Unused Land** & **Losses** \\ \hline \multirow{8}{*}{1990–2000} & Cropland & 38,936.19 & 49.77 & 3.27 & 396.47 & 542.02 & 0.01 & 991.54 \\  & Woodland & 92.23 & 104,364.36 & 119.25 & 20.78 & 53.29 & 0.00 & 285.55 \\ \cline{1-1}  & Grassland & 42.73 & 470.11 & 11,577.32 & 5.15 & 9.19 & 0.00 & 527.18 \\ \cline{1-1}  & Water body & 55.69 & 2.00 & 0.47 & 3824.41 & 43.59 & 0.09 & 101.85 \\ \cline{1-1}  & Built-up land & 0.00 & 0.00 & 0.00 & 0.00 & 3930.47 & 0.00 & 0.00 \\ \cline{1-1}  & Unused land & 0.00 & 0.00 & 0.00 & 0.03 & 0.00 & 24.48 & 0.03 \\ \cline{1-1}  & Gains & 190.66 & 521.88 & 123.00 & 422.42 & 648.09 & 0.10 & 1906.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Land-use transition possibilities, 1990–2000. Compared with last period, the TPs and transition area of most land-use types decreased; the land-use activities in this region became stronger in this period (Tables 4 and 5). During 2000-2017, woodland was still the most stable class with TPs of 0.9909. Woodland areas experienced deforestation (woodland lost) in the eastern cities (Wuzhou, Guangzhou, and Zhaoqing) during this period. Most of the lost woodland was converted into built-up land and grassland. Water bodies were the most dynamic land-use types with TPs of 0.9186, followed by cropland and grassland (Table 5). Water bodies continued to increase during this period (346 km\({}^{2}\)). Cropland continued to decrease by a total of 1600 km\({}^{2}\) (Table 4). The most obvious loss of cropland occurred in the eastern cities and supported the built-up land expansion (Figure 5c,d). Grassland changed slightly (Table 4). The most obvious loss in grassland was in the western and middle areas of the ZXEB, while most of the grassland increases occurred in the middle and eastern areas (Wuzhou and Zhaoqing) (Figure 5d). Built-up land dramatically increased in Guangzhou and Foshan, followed by Nanning and Zhaoqing. Other cities experienced different levels of built-up land expansion (Table 3 and Figure 5d). To summarize, four land-use conversion processes occurred in this region; namely, built-up land expansion, woodland loss, water body increases, and cropland decreases.