esa-sceva/satcom-chunk-collection huggingface dataset:
# An Evasion Attack against Stacked Capsule Autoencoder
## 5 Experimental Evaluation
### Experimental Method  
We evaluate our attack on both the prior k-means classifier and the posterior k-means classifier. In each experiment, we choose 5000 samples that can be recognized correctly by the target classifier. The perturbed images are computed using the above three algorithms separately. Then, these images are input into the SCAE, and the encoded results of the SCAE are fed into the corresponding classifier to test the attack success rate (the ratio of the number of samples that are misclassified by the classifier to the total number of all test samples). We compare the three algorithms to find the best algorithm for our evasion attack. The source code of our attack is available at [[https://github.com/FrostbiteXSW/SCAE_Attack](https://github.com/FrostbiteXSW/SCAE_Attack)]([https://github.com/FrostbiteXSW/SCAE_Attack](https://github.com/FrostbiteXSW/SCAE_Attack)) (accessed on 12 January 2022).  
The imperceptibility of the perturbation is a challenge on images from the MNIST dataset and the Fashion-MNIST dataset since the background of those images is black (the pixel value is zero) and white noise on the black background can be easily noticed and detected. Therefore, we place mask \(m\) on the perturbation to confine its scope to the objects and their vicinity rather than the background of the images to improve the stealthiness of the attack. The mask value \(m_{i}\) at each pixel position \(i\) is calculated using Formula (9):  
\[m_{i}=\frac{1}{9}\Big{(}x_{i}+\sum N_{\text{g}}(x_{i})\Big{)} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **MNIST** & **Fashion-MNIST** & **GTSRB** \\ \hline Prior k-means classifier & 97.82 & 63.20 & 60.11 \\ Posterior k-means classifier & 97.62 & 63.79 & 56.14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of the SCAE models trained on three datasets (in %).  
Figure 2: The 10 chosen categories of the German traffic sign recognition benchmark dataset used to train the SCAE model.  
where \(m_{i}\) is the average of the corresponding pixel \(x_{i}\) and its eight neighbors \(N_{8}\). For the perturbation value \(p_{i}\) on each pixel \(i\), let \(p_{i}^{\prime}\gets p_{i}*m_{i}\), where \(m_{i}\) is used as the weight of variable \(p_{i}\). In the background area \(p_{i}^{\prime}=0\), which is due to the fact that \(m_{i}=0\); therefore, the background area of the image is not affected by the perturbation.  
In the following subsections, we will introduce the setups for each algorithm, and explain how the mask method is applied to them separately.
## 4 Results
### Standard Testbed Productivity Comparison  
Table 2 shows the mean productivity before and after the replenishment of the vermiponics testbed. It is found that the plant length was improved by 5.04% and leaf breadth size was increased by 7.89%. Leaf length was improved by 11.00% with a stem size betterment of 26.06% than the yields without replenishment. Fish weight and fish length had an increase of 2.01% and 0.86%, respectively, showing improvement after replenishment. With respect to average plant length, average breadth, average leaf length, average stem size, average fish weight and fish length, Figure 7a shows a comparison chart between mean productivity before and after replenishment in the vermiponics testbed with the percentage of productivity difference on top of every bar. The X-axis shows the plant and fish attributes, and the Y-axis contains the sum of mean productivity. Figure 7b shows a pie chart showing the percentage productivity difference.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Attributes**} & \multicolumn{2}{c}{**Mean Productivity**} & \multirow{2}{*}{**Mean Productivity**} & \multirow{2}{*}{**\% of Productivity**} \\  & **before** & & \\  & **Replenishment** & & \\ \hline Avg plant length & 38.86 & 40.82 & 5.04 \\ Avg breadth & 14.91 & 16.09 & 7.89 \\ Avg leaf length & 19.86 & 22.05 & 11.01 \\ Avg stem size & 3.29 & 4.15 & 26.06 \\ Fish weight avg & 29.83 & 30.43 & 2.02 \\ Fish length avg & 13.20 & 13.31 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean productivity comparison with productivity difference percentage for vermiponics testbed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Attributes**} & \multicolumn{2}{c}{**Mean Productivity**} & \multirow{2}{*}{**Mean Productivity**} & \multirow{2}{*}{**\% of Productivity**} \\  & **before** & & \\  & **Refenishment** & & \\ \hline Avg plant length & 38.86 & 40.82 & 5.04 \\ Avg breadth & 14.91 & 16.09 & 7.89 \\ Avg leaf length & 19.86 & 22.05 & 11.01 \\ Avg stem size & 3.29 & 4.15 & 26.06 \\ Fish weight avg & 29.83 & 30.43 & 2.02 \\ Fish length avg & 13.20 & 13.31 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean productivity comparison with productivity difference percentage for vermiponics testbed.  
Figure 6: (**a**) Comparison chart between mean productivity before and after replenishment in the standard testbed with percentage of productivity difference on top of every bar. The X-axis shows plant and fish attributes, and the Y-axis contains the sum of mean productivity. (**b**) A pie chart showing the percentage productivity difference.
# Experimental and Numerical Study of Orifice Coefficient of Cargo Tank Design of LNG Vessels
## 2 Orifice Coefficient  
Figure 2 describes the concept of orifice plate geometry and orifice flow as described in hydrodynamics. Assuming that the fluid flow in the pipe is an incompressible fluid, the flow behind the cracks causes the fluid velocity and flow rate to change dramatically due to the crack. As a result, the pressure distribution in the pipe is also decreased from P1 to P2. The Bernoulli equation is  
derived based on the energy conservation law in fluid mechanics, and the equation for calculating the leakage rate through the crack is defined as Equation (1). In this equation, the amount of LNG flowing through the cracks is influenced by the orifice coefficient (\(C_{\mathit{orifice}}\)), which is assumed to be the leakage flow of the liquid cargo:  
\[Q_{\mathit{Leak}}\ =\ C_{\mathit{orifice}}A\sqrt{2g\bigg{(}h+\frac{p_{1}+p_{2}}{ \gamma}\bigg{)}} \tag{1}\]  
where \(Q_{\mathit{Leak}}\) is the leakage flow rate(liter/sec); \(A\) is the cross-sectional area of the crack; \(h\) is the pressure head of the fluid in the tank at the crack position; \(\gamma\) is the specific gravity of the discharged liquid; and \(p_{1}\) and \(p_{2}\) are the internal and external pressures of the tank, respectively [1; 5].  
This equation strictly holds for an incompressible steady-state flow. It describes the flow with good accuracy for laminar and turbulent flow conditions. The actual form of the fluid flow strongly depends on the geometry of the restriction (particularly for whether it is sharp-edged), and small disturbances may lead to a change from laminar to turbulent flow conditions. However, assuming that this local flow variation is negligible in this study, the equation was utilized even when the flow might have been the orifice flow, as shown in Figure 2. The experiment and the CFD analysis in this study were also assumed to be fluid flows as an incompressible fluid.
## 4 Methodology
### Data Preparation and Preprocessing  
Firstly, we combined the CSV files into one using JupyterLab and Python with the Pandas and Numpy libraries. Furthermore, we cleaned the dataset by removing positive and negative infinity and NaN values. After that, we dropped unnecessary features (\"Timestamp\", \"Flow ID\", \"Src IP\", \"Src Port\", and \"Dst IP\"). In the next step, from the Scikit-learn library, we used the random forest classifier to compute the feature importance, as illustrated in Figure 10.  
Figure 8: General methodology workflow.  
Figure 10: Feature importance ranking of CSE-CIC-IDS2018.  
Figure 9: The proposed models’ frameworks: (**a**) binary classification; (**b**) multi-class classification.  
In contrast, from the Scikit-learn library, we used the SelectFromModel class for feature selection. The outcome of this process was that 24 features were selected, as displayed in Table 7.  
Afterward, we made two copies of the dataset. The first copy was for binary classification, and the second was for multi-class classification, containing the following classes: Benign, DDoS, DoS, BruteForce, Bot, Infilteration, and Web.  
In the final step, we applied OneHotEncoder to the label column (Y) to convert the categorical data into numerical data, differentiating each class from the others. This process is essential because the neural network requires the data to be numeric. Finally, we applied StandardScaler to the other columns (X); the data had a mean value of 0 and a standard deviation of 1. In other words, after applying StandardScaler, the original distribution of the data remains the same, with the range restricted to [0, 1]. This process is helpful in classification.
## 1 Introduction
However, to best of our knowledge, there is no literature combining GRACE, GRACE-FO and Swarm models to construct a continuous long observations series to detect and characterize the regional droughts. Therefore, in our study, we focus on using Swarm model to fill the gap between GRACE and GRACE-FO models to form an uninterrupted TWSC-based drought index series in the ARB to monitor and characterize the regional droughts. We first used the generalized three-cornered hat (GTCH) and the least square method to fuse the TWSC results from six GRACE/GRACE-FO solutions to improve the reliability of TWSC results. Then, the fused TWSC results were used to construct the GRACE/GRACE-FO-based drought index (GRACE/GRACE-FO-DSI), and the Swarm-based drought index (Swarm-DSI) was used to fill the 11-month gap between GRACE-DSI and GRACE-FO-DSI to form an uninterrupted long drought index (GRACE/Swarm-DSI) series in the ARB. Finally, we used GRACE/Swarm-DSI to detect and characterize the drought events in the ARB in the past 18 years. The paper is organized as follows. We briefly introduce the study area, data, and method in Sections 2-4, respectively. Section 5 presents the analysis results of the drought causes and characteristics in the ARB based on GRACE/Swarm-DSI. Sections 6 and 7 are the discussion and conclusions, respectively.
# Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics
## 4 Materials and Methods
### One-Class Classification Algorithms
#### 4.5.1 Ocsvm  
SVM is a widely used supervised learning algorithm for multi-class classification. It was extended for the one-class classification problem in [37; 38]. OCSVM looks for a non-linear decision boundary by mapping the training samples \(x_{i}(i=1,2,...,n)\) into a higher dimension feature space using a non-linear kernel map function \(\Phi(\cdot)\). A hyperplane was then calculated to separate the mapped training samples from the origin with maximal margin, which will be used as the decision boundary. The quadratic programming minimization function used to separate the training samples from the origin is defined below, where \(\omega\) is the normal vector to the hyperplane, \(\xi\) is for penalization, \(\rho\) is the margin, and \(\
u\in(0,1)\) is a parameter to control the ratio of anomalies in the training samples.  
\[min\left(\frac{1}{2}||\omega||^{2}+\frac{1}{\
u n}\sum_{i=1}^{n} \xi_{i}-\rho\right)\] (12) subject to \[(\omega\cdot\Phi(x_{i}))\geq\rho-\xi_{i},\quad\xi_{i}\geq 0\]  
After solving the problem with \(\omega\) and \(\rho\), the decision function is defined in Equation (13). A new sample \(y\) was classified as an anomaly if \(f(y)\) was negative.  
\[f(x)=sgn((\omega\cdot\Phi(x))-\rho) \tag{13}\]  
There are several choices of the kernel function for OCSVM. In this study, the radial basis function (RBF) kernel was chosen, which produced the best results among all kernels. The RBF kernel is defined in Equation (14), where \(||x-x^{\prime}||^{2}\) is the squared Euclidean distance between two data points \(x\) and \(x^{\prime}\), and \(\sigma\) is a parameter related to the decision region. A smaller \(\sigma\) leads to a broader decision region.  
\[K(x,x^{\prime})=exp\left(-\frac{||x-x^{\prime}||^{2}}{2\sigma^{2}}\right) \tag{14}\]
# Thermogravimetric Study of Refuse Derived Fuel Produced from Municipal Solid Waste of Kazakhstan
## 1 Introduction
At present, the energy production of Kazakhstan is highly depended on thermal power plants, majority of which utilizes low-grade coal [17]. According to an International Energy Agency (IEA), the total CO2 emissions from coal combustion including electricity and district heating production were 121 Mt in 2015, representing 60% of the total Kazakhstan's CO2 emissions in recent years [20]. Therefore, efficient and sustainable development of energy generation and consumption chain is imperative, wherein WtE option can be an important contributor towards circular economy initiatives. Figure 1: Average municipal solid waste (MSW) composition of Nur-Sultan city in 2018/2019. The co-firing of RDF with coal is an optimal solution, particularly in locations where centralized district heating system is available; however, to efficiently use RDF as fuel, it is important to determine its thermal and kinetics properties [8]. The availability of kinetic parameters is crucial to design/modify reactors for conversion of RDF [21]. Extensive studies on thermogravimetric analysis of pyrolysis and combustion of RDF had been reported in literature, including its components. A concise summary of the relevant studies is reported in Table 1. Thermogravimetric analysis tests are reliable methods of getting thermal properties of various solid fuels and temperature peaks during conversion. On the other hand, several classical kinetic methods have been developed for kinetic measurements. Such methods include first-order [22], \(n\)th-order reaction [23], sequential models [24], distributed activation energy model, among which, as seen from Table 1, the Flynn-Wall-Ozawa (FWO), Friedman, and the Kissinger-Akahira-Sunose (KAS) methods have been widely applied for RDF and MSW [25]. Furthermore, it can also be seen from Table 1 that kinetic reaction rates and activation energy of MSW and RDF have large variations. These variations can be attributed to the dissimilarity of RDF materials, experimental apparatus, test conditions, and analytical methods for calculation of kinetic parameters [9]. Moreover, the difference in decomposition rates between the various studies occurred mainly due to the variation of MSW compositions, which in turn depends on location, lifestyle, waste management system, and other factors of society and demography [26]. There is no literature available for the combustion characteristics of RDF samples produced in Kazakhstan. In the following wok, we discuss the thermal properties of RDF obtained from MSW of capital city of Kazakhstan over the period of 2018-2019. Both mixed and non-mixed RDF samples were decomposed in air environment and its kinetic parameters are reported herein. The effect of heating rates was discussed, and kinetics models were evaluated by Flynn-Wall-Ozawa (FWO), Kissinger-Akahira-Sunose (KAS) and Friedman methods.
###### Abstract  
Due to its capacity for temporal and spatial coverage, remote sensing has emerged as a powerful tool for mapping inundation. Many methods have been applied effectively in remote sensing flood analysis. Generally, supervised methods can achieve better precision than unsupervised. However, human intervention makes its results subjective and difficult to obtain automatically, which is important for disaster response. In this work, we propose a novel procedure combining spatiotemporal context learning method and Modest AdaBoost classifier, which aims to extract inundation in an automatic and accurate way. First, the context model was built with images to calculate the confidence value of each pixel, which represents the probability of the pixel remaining unchanged. Then, the pixels with the highest probabilities, which we define as 'permanent pixels', were used as samples to train the Modest AdaBoost classifier. By applying the strong classifier to the target scene, an inundation map can be obtained. The proposed procedure is validated using two flood cases with different sensors, HJ-1A CCD and GF-4 PMS. Qualitative and quantitative evaluation results showed that the proposed procedure can achieve accurate and robust mapping results.  
Keywords:inundation mapping; flood; optical sensors; spatiotemporal context learning; Modest AdaBoost; HJ-1A/B CCD; GF-4 PMS +
Footnote †: journal: Remote Sensing, 2017
###### Abstract  
Coal mining induces surface subsidence, making rapid and precise monitoring of this subsidence a key area of current research. To address the limitations of D-InSAR technology in capturing large-gradient deformations in the central subsidence basin and the challenges facing UAVs in accurately monitoring small deformations at the basin's edge, we propose a method for inverting the expected parameters of surface subsidence by synergistically integrating InSAR and UAV monitoring. We determined the cumulative subsidence of monitoring points along the dip and strike observation line of the Banji 110,801 working face between 10 April 2021 and 28 June 2022, employing D-InSAR and UAV techniques. By leveraging the complementary strengths of both monitoring techniques, we fused the two types of monitoring data and verified the error of the fusion data to be within 10 cm through leveling data verification. Simulation experiments utilizing the probability integration method and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization algorithm confirmed that the 10 cm data source error remains within the required limits for probability integration parameter inversion. Finally, the BFGS algorithm was employed to invert the parameters of the probability integration method based on the fusion data results. Subsequently, these inversion parameters were used to predict the subsidence at the monitoring point and were compared with the level measured data. The results demonstrate that the use of collaborative InSAR and UAV monitoring technology for inverting the expected parameters of surface subsidence in the mining area yields superior results, aligning with the actual patterns of ground surface movement and deformation. This study addresses the global need for unmanned monitoring of mining-related subsidence. It employs InSAR and UAV technologies in a synergistic approach to monitor surface subsidence in mining regions. This approach harnesses the strengths of multiple data sources and presents a novel concept for the unmanned monitoring of surface subsidence in mining areas, contributing to environmental protection efforts.  
mining area; unmanned monitoring; InSAR; UAV; parameter inversion Article
# A Reinforcement Learning Model of Multiple UAVs for Transporting Emergency Relief Supplies
## 2 Related Works
### Potential of Using UAVs in Disaster Response  
In recent years, many studies have discussed the use of big data and information technology in disaster risk management [31; 32].  
[PERSON] et al. [33] introduced a rapid human body detection method using image processing from a UAV camera to save lives during natural disasters such as earthquakes. They showed that irrespective of distance, a camera mounted on a UAV can clearly detect a human body. [PERSON] et al. [34] proposed a path planning method for multiple UAVs to aid in 3D building damage surveys or disaster situations. They proposed a methodology that combines a fuzzy C-means method for assigning camera location points to each UAV with a route optimization algorithm for calculating the visit order of the camera location points for each UAV by solving the Multiple Traveling Salesmen Problem (MTSP). [PERSON] et al. [35] explored the potential of utilizing UAVs for crowd management during emergency evacuations. They suggested a simulation model with two UAV guidance approaches: partial guidance and full guidance. [PERSON] et al. [36] presented an intelligent solution based on RL to find the best position for multiple UAVs to be used as cellular hot spots in an emergency scenario. They maximized the number of users covered by the system, while in this case, the UAVs were limited by both backhaul and radio access network constraints. [PERSON] et al. [37] proposed a mixed-integer linear programming model for a Heterogeneous Fixed Fleet Drone Routing problem (HFFDRP) that minimizes the post-disaster inspection cost of a disaster-affected area.  
As described above, various applications of UAVs are being considered in disaster response, and there is a strong possibility that UAVs could be applied to the transportation of emergency relief supplies during a disaster. This is because UAVs have the potential to save time and cost compared to traditional means of transportation and to enable the transport of emergency relief supplies to disconnected areas (e.g., tsunami inundation areas) [18; 38].
# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 5 Experimental Results
### Motion Test of the Aerial Manipulator
During the entire motion testing experiment of the aerial manipulator, to ensure the system's safety during the grasping operation, the angles of the two servos in the manipulator and gripper were repeatedly tested and optimized. The angle values of the two servos were defined as \(\theta_{1}\) and \(\theta_{2}\), where \(\theta_{1}\) represents the pitching action of the manipulator and \(\theta_{2}\) represents the grasping action of the gripper. The corresponding output values \(\theta_{1}\) and \(\theta_{2}\) for the five poses during the operation of the flight robotic arm are shown in Table 2.  
From the analysis of the operational motion experiment, it was concluded that the aerial manipulator system was capable of autonomously positioning and navigating throughout the flight. It also performed target object grasping through visual guidance. Moreover, it accurately controlled the UAV's attitude angles while maintaining a constant spatial position, possessing excellent spatial operational capabilities.
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 7 Evaluation
### Experimental Results and Findings
#### 7.3.2 Experiment 2: Disruption Impact on Running Applications When Reconfiguring
Figure 13: Cassandra node CPU usage when reconfiguring. 3.3 Experiment 3: Time to Reconfigure Features in a Newly Created Cluster without any Application Running  
This experiment explores the reconfiguration time for each selected feature on a newly created cluster without any application running. We measure the time from when the controller starts monitoring whether the feature is supported until it deems the feature activated. Experimental Setup  
We test the reconfiguration time of each selected feature on newly created default GKE and on-premise clusters. As introduced in Section 7.2.2, our GKE clusters have two e2-highcpu-4 worker nodes and our on-premise clusters have two c4m4 worker nodes and one c4m4 control plane node. As discussed in Section 7.2, switching to the CNI plugin is only conducted on GKE. To reduce the randomness of the experimental results, we repeat our experiments five times. Experimental Results and Findings  
Figure 15 presents the average reconfiguration time for each selected feature. We find that if our reconfiguration tactics create more complex API resources on the cluster, the reconfiguration process will take longer. Additionally, the controller takes significantly less time to reconfigure the CNI network plugin than the 10+ minutes it takes to reconfigure via GKE's proprietary interface. As stated above already, this is because GKE creates new worker nodes running in CNI mode and gradually migrates workloads from existing to new nodes.