esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 SDOF Systems with Nonlinear Eddy Current Dampers
### Response to Seismic Excitations
#### 4.3.2 Displacement and Acceleration Responses to Real Earthqu
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Seismic Wave**} & \multirow{2}{*}{**Without** **Damper (m/s\({}^{2}\))**} & \multicolumn{2}{c}{**SDOF-FVD**} & \multicolumn{2}{c}{**SDOF-FVD**} \\ \cline{3-6}  & & \(\alpha\) & **PAR (m/s\({}^{2}\))** & \(\rho\) & **PAR (m/s\({}^{2}\))** & \(\rho\) \\ \hline \multirow{4}{*}{Electro} & & 0.2 & 2.57 & 13.60\% & 2.55 & 14.43\% \\  & & 0.4 & 2.69 & 9.58\% & 2.59 & 13.09\% \\  & & 0.6 & 2.72 & 8.68\% & 2.75 & 7.54\% \\  & & 0.8 & 2.88 & 3.13\% & 2.81 & 5.54\% \\  & & 1 & 2.93 & 1.59\% & 2.86 & 3.99\% \\ \hline \multirow{4}{*}{Irpinia} & & 0.2 & 2.96 & 3.92\% & 3.05 & 0.89\% \\  & & 0.4 & 2.84 & 7.80\% & 2.84 & 7.77\% \\  & & 0.6 & 2.98 & 3.29\% & 2.92 & 5.16\% \\  & & 0.8 & 2.98 & 3.12\% & 2.94 & 4.40\% \\  & & 1 & 3.01 & 2.20\% & 2.97 & 3.39\% \\ \hline \multirow{4}{*}{ChiChi} & & 0.2 & 2.57 & 21.14\% & 2.56 & 21.54\% \\  & & 0.4 & 2.78 & 14.67\% & 2.74 & 15.78\% \\  & & 0.6 & 2.91 & 10.83\% & 2.85 & 12.70\% \\  & & 0.8 & 2.97 & 8.72\% & 2.92 & 10.51\% \\  & & 1 & 3.05 & 6.49\% & 2.95 & 9.47\% \\ \hline \multirow{4}{*}{Kobe} & & 0.2 & 2.54 & 18.77\% & 2.54 & 18.71\% \\  & & 0.4 & 2.81 & 10.21\% & 2.78 & 11.26\% \\  & & 0.6 & 3.02 & 3.48\% & 2.92 & 6.75\% \\  & & 0.8 & 3.11 & 0.71\% & 3.02 & 3.45\% \\  & & 1 & 3.12 & 0.42\% & 3.08 & 1.70\% \\ \hline \multirow{4}{*}{Northridge} & & 0.2 & 2.55 & 14.65\% & 2.51 & 15.96\% \\  & & 0.4 & 2.71 & 9.43\% & 2.66 & 11.00\% \\  & 2.99 & 0.6 & 2.85 & 4.72\% & 2.75 & 7.91\% \\  & & 0.8 & 2.92 & 2.14\% & 2.87 & 4.07\% \\  & & 1 & 2.98 & 0.26\% & 2.90 & 2.88\% \\ \hline \multirow{4}{*}{Nigat} & & 0.2 & 2.79 & 10.10\% & 2.78 & 10.52\% \\  & & 0.4 & 2.94 & 5.22\% & 2.88 & 6.9
## 2 Networking Architectures
### UAV Ad-hoc Network  
In a UAV ad-hoc network, each UAV contributes to the data forwarding process for all the other UAV of the network, as shown in Figure 1(b). The backbone UAV serves as a gateway between the GS and the member UAV in this specific architecture. The backbone UAV is normally equipped with two radios: low power short-range and high power long-range. Low power short-range radio is used for communication between the UAVs, and high power long-range radio is required to communicate with the GS. Since only one backbone UAV is connected with the GS in the UAV ad-hoc networking architecture, the coverage area by the network is substantially extended. Furthermore, as the distance between the UAV is relatively small, the transceiver mounted on the UAV can be cost-effective and lightweight, which makes it extremely attractive for small-sized UAVs. However, to make the network more persistent, the characteristics such as mobility patterns, speeds, altitudes, and heading directions of all the connected UAVs need to be similar. Therefore, such networking architecture is best suited for surveillance, monitoring operation, and other mission(s) where a small number of homogeneous UAVs are required for deployment.
## 2 Experiment and Measurements
### Correlation Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Correlation Coefficient** & **X-Axis** & **Y-Axis** & **Z-Axis** \\ \hline \multirow{8}{*}{Spindle} & \multirow{4}{*}{Vibration} & 0.45 (RMS) & 0.33(RMS) & \multirow{4}{*}{0.28(RMS)} \\  & & 0.45(STD) & 0.33(STD) & \\  & & 0.45(STD) & \(-\)0.27(Kurtosis) & 0.28(RMS) \\  & & 0.27(Skewness) & 0.23(Peak-to-peak) & 0.28(STD) \\  & & 0.37(Peak-to-peak) & \(-\)0.23(CF) & \\ \cline{2-4}  & \multirow{2}{*}{Envelope} & 0.48(RMS) & 0.36(RMS) & 0.33(RMS) \\  & & 0.44(STD) & 0.33(STD) & 0.30(STD) \\  & & 0.27(Peak-to-peak) & & \\ \hline \multirow{4}{*}{Vise} & \multirow{4}{*}{Vibration} & 0.20(RMS) & 0.31(RMS) & 0.20(RMS) \\  & & 0.20(STD) & 0.31(STD) & 0.20(STD) \\  & & \(-\)0.24(Kurtosis) & \(-\)0.26(Kurtosis) & \(-\)0.20(Kurtosis) \\  & & \(-\)0.22(CF) & 0.27(Peak-to-peak) & \(-\)0.21(CF) \\ \cline{2-4}  & \multirow{2}{*}{Envelope} & 0.21(RMS) & & \(-\)0.22(CF) \\ \hline \multirow{4}{*}{Relative vibration} & \multirow{4}{*}{Vibration} & 0.35(RMS) & \multirow{4}{*}{0.27(RMS)} \\  & & 0.39(RMS) & 0.35(STD) & 0.27(RMS) \\ \cline{1-1}  & & 0.39(STD) & \(-\)0.28(Kurtosis) & 0.27(STD) \\ \cline{1-1}  & & 0.35(Peak-to-peak) & 0.25(Peak-to-peak) & 0.22(Peak-to-peak) \\ \cline{1-1}  & & \(-\)0.21(CF) & \\ \cline{1-1} \cline{2-4}  & \multirow{2}{*}{Envelope} & 0.43(RMS) & \multirow{2}{*}{0.36(RMS)} \\ \cline{1-1}  & & 0.39(STD) & 0.31(STD) & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pearson correlation coefficients (absolute value \(\geq\) 0.2) between the vibration statistical features and the Ra values. \begin{table}
\begin{tabular}{c c} \hline \hline
**Spindle Current Features** & **Correlation Coefficient** \\ \hline RMS & 0.55 \\ STD & 0.64 \\ Skewness & \(-\)0.1 \\ Kurtosis & 0.38 \\ Peak-to-peak & 0.6 \\ CF & 0.48 \\ CV & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Pearson correlation coefficients between the statistical features of spindle current and the Ra values.
## 2 Data and Methods
### Methods
#### 2.3.4 Correlation Analysis  
Pearson correlation is regarded as a standard method to analyze the relationship between two variables. To examine the response of SWUE variations to vegetation activity, land surface temperature, and precipitation dynamics, the correlation coefficients between eight-day time series SWUE and NDVI, LST, and precipitation were calculated for each pixel, respectively. To investigate the role of photosynthetic phenological factors affecting SWUE, we analyzed the Spearman partial correlations between the accumulated SWUE in the growing season and the SOS and EOS. The equations of the correlation coefficient and partial correlation coefficient were as follows:  
\[\mathrm{r_{xy}}=\frac{\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})(y_{i}- \overline{y})}}{\sqrt{\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})^{2}\sqrt{\sum \limits_{i=1}^{n}{(y_{i}-\overline{y})^{2}}}}}}, \tag{4}\]  
\[\overline{x}=\frac{1}{n}\sum\limits_{i=1}^{n}{x_{i}},\ \overline{y}=\frac{1}{n} \sum\limits_{i=1}^{n}{y_{i}}, \tag{5}\]  
\[\mathrm{r_{xy,z}}=\frac{\mathrm{r_{xy}}-\mathrm{r_{xz}}\mathrm{r_{yz}}}{\sqrt {1-\mathrm{r_{xz}^{2}}}\sqrt{1-\mathrm{r_{yz}^{2}}}}, \tag{6}\]  
where \(\mathrm{r_{xy}}\) represents the correlation coefficient between \(x\) and \(y\), ranging from \(-1\) to \(1\), and \(\mathrm{r_{xy,z}}\) means the partial correlation coefficient between \(x\) and \(y\) when we controlled \(z\) values. If \(\mathrm{r<0}\), \(x\) is negatively correlated with \(y\). If \(\mathrm{r>0}\), there is a positive correlation between \(x\) and \(y\). Furthermore, \(\overline{x}\), \(\overline{y}\) represent the mean values of \(x_{\mathrm{i}}\) and \(y_{\mathrm{i}}\), respectively. The absolute value of \(\mathrm{r}\) indicates the relevance of two variables. The greater the value of \(\mathrm{\mid r\mid\ }\mathrm{is}\), the closer the two variables are and vice versa. In generally, \(0<\mathrm{\mid r\mid\ }<0.3\) indicates a weak correlation between two variables, while \(0.3\leq\mathrm{\mid r\mid\ }<0.6\) means moderate correlation, and strong correlation exists in two variables when \(0.6\leq\mathrm{\mid r\mid\ }<1\). The significance of the results was examined using a \(t\)-test.
## 2 Materials and Methods
### Analysis of Photosynthetic Parameters and Chlorophyll Fluorescence  
Photosynthetic parameters and chlorophyll fluorescence were measured according to the manufacturer's instructions. Briefly, photosynthetic parameters, including net photosynthetic rate (Pn), intercellular CO\({}_{2}\) concentration (Ci), stomatal conductance (Gs), and transpiration (Tr), were measured using a portable photosynthesis system (LI-6400, LI-COR Lincoln, NE, USA). The white light intensity was set to 1000 \(\upmu\)mol photons m\({}^{-2}\) s\({}^{-1}\) inside the leaf chamber for all measurements. Chlorophyll fluorescence was measured using a portable fluorometer (PAM 2500; Walz, Eftelrich, Germany). Before determining Fev/Fm, the plants were exposed to darkness for 30 min.
# Storytelling with Image Data: A Systematic Review and Comparative Analysis of Methods and Tools
## 2 Literature Selection
### Literature Selection  
We broke down our research questions into individual facets and conducted an extensive literature search on electronic sources such as Google Scholar ([[https://scholar.google.com](https://scholar.google.com)]([https://scholar.google.com](https://scholar.google.com)), accessed on 1 December 2022) and arXiv ([[https://arxiv.org/](https://arxiv.org/)]([https://arxiv.org/](https://arxiv.org/)), accessed on 1 December 2022) based on sophisticated search queries and keywords. We did our best to have an unbiased search strategy. The following search string uses AND and OR operators to represent our \"storytelling with image data\" generic search query:  
([\"Image\" AND \"Captioning\"] OR [\"Image\" AND \"Caption\" AND \"Generation\"]) AND ([\"Visual\" AND \"Question\" AND \"Answering\"] OR [\"VQA\"]) AND ([\"Visual\" AND\"Question\" AND \"Generation\"] OR [\"VQG\"]) AND (\"Visual\" AND \"Dialog\") AND ([\"Visual\" AND \"Storytelling\"] OR [\"Storytelling\" AND \"Image\" AND \"Data\"] OR [\"Multi-Image\" AND \"Story\" AND \"Generation\"]).  
We included all possible search strings related to each part and did our best to make sure no potential study is missed in our results. Moreover, the word cloud representation for the search keywords is demonstrated in Figure 2(b). In addition, we included articles published since 1980, and the yearwise distribution of the selected articles after the paper quality assessment phase is represented in Figure 4. These selected articles were included in our study if they met the inclusion criteria explained in the following.  
* English language studies.
* Articles dated between 1980 and 2022.
* Articles related to at least one aspect of our research questions.
* Reference list from relevant studies and review articles.  
On the other hand, we excluded some articles based on our exclusion criteria, which include the following items:  
* Informal literature surveys (no defined research questions, no search process, no defined data extraction or data analysis process).
* Articles that are not published (except for preprints and arXiv).
* Non-English articles.
* Studies not related to at least one aspect of our research questions.  
Ultimately, the quality of the selected studies is assessed based on the following list:  
* Frequency of citations.
* Are the study's research challenges/contributions/experiments clearly explained?
* Is there a clear statement of the objectives of the research?
* Are the experiments realized to evaluate the ideas presented in the study?
## 5 Simulation Results  
In this section, we present simulation results to show the efficiency of the proposed scheme. In the simulation, the proposed Scheme 1 in which multiple games are used, the proposed scheme 2 in which a single game is used, and a random scheme are provided for comparison. The multiple-game scheme is the proposed scheme utilizing the proposed Algorithm 1 and the proposed Algorithm 2 in all figures and is denoted as \"PPScheme 1: multiple games\" in all figures. The single game scheme is the scheme that only uses the proposed Algorithm 1 to select the channel \(a_{m}^{pre}\) and is denoted as \"PP Scheme 2: single games\". The random scheme does not consider the operation of the eavesdroppers and other SUs that randomly choose a channel in each time slot and is denoted as the \"random scheme\". In addition, the EVs in all simulations randomly chose a channel for eavesdropping. For performance comparison among the considered schemes, the secrecy rate was used.  
Figure 4 shows the secrecy rate of the considered schemes according to the number of SUs in the network. When the number of SUs increased, the multiuser diversity of the network improved, and the secrecy rate was also improved. However, more SUs created stronger interference in the network, so the secrecy rate improved very slightly. The proposed scheme with multiple games achieved the best performance, while the random scheme provided a limited performance. The reason is as follows: the proposed scheme with multiple games can estimate the immediate state of the CR system. On the other hand, the scheme with a single game utilized the estimated average state of the system only, so it provided slightly lower performance than the proposed scheme with multiple games.  
Figure 5 illustrates the secrecy rate according to the number of EVs in the network. It was observed that a higher number of EVs made the eavesdropping attack more serious, and then, the secrecy rate dropped. However, in all cases, the proposed scheme with multiple games provided the best defense to the network.  
The effect of the number of channels on the performance of the considered schemes is shown in Figure 6. Because more channels made it more difficult for the EVs to capture data from CR communications, all the schemes achieved a higher secrecy rate with an increase in the number of channels.  
In summary, the simulation results shown in all the figures prove that the proposed scheme can protect CR communications against eavesdropping attacks. By using game theory, the proposed scheme can choose the best channel for the SU. In addition, the multiple-game scheme shows a greater advantage than the single-game scheme.  
Figure 4: Secrecy rate of the CR system versus the number SUs when the number of EVs is six and the number of channels is 10.  
Figure 5: Secrecy rate of the CR system versus the number EVs when the number of SUs is five and number of channels is 10.
# Simulation Study of Microwave Ablation of Porous Lung Tissue
## 2 Materials and Methods
### Model Geometry  
The geometry of the model is shown in Figure 1a. It consists of ideal cylindrical lung tissue with a radius of 30 mm and a height of 80 mm, which includes a spherical tumor (the diameter of the tumor is 1.4 cm, 2.0 cm, and 2.6 cm, respectively). Also, it is assumed that both the lung and the tumor are isotropic and homogeneous tissues. In microwave ablation, a microwave antenna is inserted into the tumor to release energy into the tumor tissue (insertion depth of 70.5 mm). A single slot antenna with a diameter of 1.79 mm was built with three parts: inner conductor, dielectric and outer conductor, encapsulated in a catheter made of polytetrafluoroethylene (PTFE). Figure 1b shows the microwave antenna structure. Since the ablation area in lung tissue is axisymmetric, the geometry of this model is simplified to a two-dimensional axisymmetric model to reduce the computational time.
# A Shearlets-Based Method for Rain Removal from Single Images
## 6 Some Discussion for the Proposed Method
### Simple Discussion of Regularization Terms  
In order to understand the effect of three sparse terms, some tests are implemented to reveal their contribution for rain removal. We choose real rainy image \"gentlemen\" as the testing image (see Figure (a)a). The visual results after discarding sparse terms \(Reg_{1},Reg_{2},Reg_{3}\) are listed in Figure (c)c-e. From the recovery, we find that the regularizers \(Reg_{1},Reg_{2}\) have a remarkable effect on the rain removal, since \(Reg_{1},Reg_{2}\) describe the sparsity of the Shearlets transform of rainy images in the rain drops' direction and the perpendicular direction respectively.
## 2 Materials and Methods
### Fault Diagnosis Based on CASTSR  
Accurate fault feature extraction is the key to fault diagnosis. As most fault signals collected in practical engineering are in strong noise, their characteristic signals are weak and polluted by the strong noise. Their signal-to-noise ratio is extremely low, and they cannot be identified directly. Therefore, the cascaded adaptive second-order tristable stochastic resonance method (CASTSR) is used to extract the features of weak signals to realize the fault diagnosis of rolling bearings. The CASTSR method uses the principle of stochastic resonance to continuously transfer noise energy in the signal to be measured from the high-frequency region to the low-frequency region. Thus, the weak signal characteristics of the low-frequency region need to be enhanced. The output SNR of the adaptive second-order tristable stochastic resonance method for each stage was taken as the objective function, and the optimal parameter combination for the maximum output SNR of each stage was calculated by using the u algorithm. The optimal results of each stage were obtained by inputting the SNR of each stage into the stochastic resonance system.  
Figure 1: The flow of adaptive STSR method.  
Figure 2: Block of CASTSR system.  
The detailed fault diagnosis process based on CASTSR for rolling bearings is shown in Figure 3.
## 4 Performance Evaluation
### Experiments with Uniform Distribution Data Set (Data Set 1)  
In our first experiments, we execute 40 client processes in 8 client nodes that send 100,000,000 (uniform distribution) insertion workloads to Geomesa and our proposed spatiotemporal data management system with varying the number of server nodes from 3 to 9. While performing experiments, we measure the number of completed insertion operations in each server node and the total execution time. Figure 10 shows the experimental results, i.e., ingestion throughput of Geomesa and the proposed method as nodes increase. As shown in the figure, the ingestion throughput of our proposed method scales up well as nodes increase while that of Geomesa does not increase well when the number of nodes is greater than 6. Also, the throughput of the proposed method is about 4.5 times higher than that of Geomesa.  
In our second experiments, we also execute 40 client processes in 8 client nodes that send 5,000,000 range query (uniform distribution) workloads to both systems with varying number of server nodes from 3 to 9. While performing experiments, we measure the number of completed range queries and their results in each server node and the total execution time. The results of range queries of both systems are used to compare the accuracy of range queries. In Figure 11, the experimental results are shown. As shown in the figure, the throughput difference between the proposed method and Geomesa is small. When the number of nodes is 6, the range query throughput of both methods are almost the same, and when the number of nodes is 3 and 9, the throughput of the proposed method is about 1.3 times higher. In terms of scalability, the range query throughput of both systems scale up well as node increases.  
Figure 11: Range query throughput of Geomesa and the proposed method (Range query throughput: number of range queries per second).  
Figure 10: Ingestion throughput of Geomesa and the proposed method (Ingestion throughput: number of insertion operations per second).
## 4 Interruption Performance of HB- vs FB-MMC DC Grids  
### Comparison of Fault Current Blocking Performance (Pole-to-Pole Fault)  
Prior to the analysis of fault handling performance with protection devices, it is important to analyze the fault current blocking ability of the two types of MMCs. We simulated a pole-to-pole fault at 0.2 s on the DC side of the rectifier to consider the worst-case fault.  
Figure 6a shows that the peak current magnitude is about 30 kA for both topologies. In the case of the HB-MMC system, a fault current of about 5 kA is sustained due to the presence of freewheeling diodes.  
However, in the case of the FB-MMC system, all IGBTs are blocked when a fault is detected, and a reverse voltage is generated, which forces the fault current to zero.  
The DC side voltage during fault transient is shown in Figure 6b. In the case of the HB-MMC system, the voltage in the transient state suddenly reverses the polarity, and then approaches zero in about 20 ms. However, in the case of the FB-MMC system, the voltage oscillations are sustained on the DC side for about 15 ms.  
Figure 5: Case-2: DC fault handling solution composed of an FB-MMC with a residual circuit breaker (RCB): (**a**) configuration of Case-2; (**b**) operation process for DC fault handling under a DC short-circuit fault.
## 2 Theory
### Reinforcement Learning  
RL is a Markov decision process (MDP) in which the agent interacts with an environment to maximize a long-term return from the states [29]. At time step \(t\), the agent receives a state \(s\in S\), and selects action \(a\in A\), following a policy \(\pi\), where \(\pi\) is a mapping from state \(s_{t}\) to action \(a_{t}\). Transitioning from the current state, \(s_{t}\), to the next state, \(s_{t+1}\), has an associated transition probability \(P(s_{t+1}|s_{t},a_{t})\), for taking the action \(a_{t}\) and an immediate reward \(r_{t}=R_{a_{t}}(\pi(s_{t}))\). In order to favor the immediate reward over future rewards, the expected cumulative reward \(\sum_{k}^{\infty}\gamma^{k}r_{t+k}\) is discounted with a discount factor \(\gamma\in[0,1]\). The goal is to maximize the cumulative long-term reward.  
The state value \(V^{\pi}\) is the expected return for following policy \(\pi\) from state s defined as in Equation (6):  
\[V^{\pi}(s)=E\Bigg{\{}\sum_{k}^{\infty}\gamma^{k}r_{t+k+1}|\;\pi,\;s_{t}=s \Bigg{\}} \tag{6}\]  
As depicted in Equation (7), the \(Q\)-value \(Q(s,a)\) stores the action value for taking action a in state s and then following policy \(\pi\). The \(Q\)-value is updated iteratively:  
\[Q^{\pi}(s,a)=V^{\pi}(s)=R_{s}(\pi(a))+\gamma\Sigma_{s^{*}}P_{s,s^{*}}(\pi(s))V^ {\pi}(s^{*}) \tag{7}\]  
The optimal \(Q\)-value \(Q^{\pi^{*}}\) determines the best action for a given state by following the optimal policy \(\pi^{*}\) that gives maximum returns. This is achieved by the recursive Bellman optimality equation [30], which is define bellow in Equation (8):  
\[Q^{\pi^{*}}(s,a)=R_{s}(\pi(a))+\gamma\Sigma_{s^{*}}P_{s,s^{*}}(\pi(s))\underset {a}{max}Q^{*}(s^{*},a) \tag{8}\]  
Iterative algorithms are used approximate optimal \(Q\)-values. A \(Q\)-table stores the estimated values and \(Q\)-values are updated as in Equation (9):  
\[Q(s_{t},a_{t})\gets Q(s_{t},a_{t})+\alpha\cdot\Big{(}r_{t}+\gamma\cdot \underset{a}{max}Q(s_{t+1},a)-Q(s_{t},a_{t})\Big{)} \tag{9}\]  
where \(\alpha\) is the learning rate.
# Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection
## 2 Study Area and Data
### Data  
Figure 2 shows an overhead image of the Jialing River Bridge in Chongqing. Figure 2 was chosen as the data for this study. The image was taken by an unmanned aerial vehicle (UAV) and appropriately shows the characteristics of the environment around the Jialing River Bridge. The content of the image is relatively feature-focused, with clear contours and a relatively homogeneous background in the middle part, which facilitates the removal of interference and is beneficial for subsequent edge detection and other related processing.