esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 E-Sail Experiment Design for Solar Wind in Lunar Orbit
### E-Sail Tether Deflection  
Upon its deployment, the E-sail tether lags behind the normal of the spacecraft side panel, and after deployment, it will oscillate around the side-panel normal. As discussed in Section 3.2, the maximum lagging angle is a design parameter for tether deployment. The oscillations with respect to the nominal tether deployment direction (side-panel normal) must be limited to \(\pm\)45\({}^{\circ}\) to provide a safe limit in order to avoid the tether wrapping around the spacecraft. Upon charging the tether and in addition to the post-deployment oscillations, the tether will enter a **coning motion** of 2\({}^{\circ}\) under the 1 mN thrust, assuming reasonable 3 cN tether tension. The amplitude of coning motion is an estimation based on the previous simulation experience and on the given design parameters. By coning motion, we mean that the tether describes a slightly conical path as the solar wind force keeps it deflected from the spin plane into the anti-sunward direction. In the actual mission, the tether's motion will be much more complex and a subject of characterisation. We propose a \(\pm\)45\({}^{\circ}\) field-of-view (FoV) camera for monitoring the endmass and characterising the tether motion due to the deployment oscillations and the thrust coning. As the design baseline, we use the Crystalspace \"Suupistri\" monitoring camera [45].  
In order to monitor the tether deployment and motion with the camera, it is essential that at least one of the sail components is bright enough to be captured. There are three options to achieve this: making the endmass reflective, treating the surface of the tether itself so that it becomes reflective, or adding a bright light-emitting diode (LED) at the endmass. The second option would require finding a material and a coating process that turns the tether reflective without altering its electrical properties. The LED \"beacon\" method would require the development of a standalone power circuit inside the endmass with its own solar panel, adding complexity to the setup. The option of making the endmass reflective and using sunlight for detection is preferred because of its simplicity.
# Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things
## 4 Simulation Settings
### Simulation and Performance Analysis
#### 4.2.2 Performance Analysis
Compared with data packets based on the IP address, the transmitted data packets based on the ICN-HMcH naming mechanism can reduce the transmission delay of the LEO mega satellites for the IoT by 53.97%. This result occurs because the proposed method does not need to consider the content address of the interest packet sent by ground station #0 so that it can quickly obtain a response from ground station #1. Results show that the transmission delay based on the proposed hierarchical structure of the ICN-HMcH naming mechanism decreases for different types of IoT services. Figure 16: Delay variation for two structured packets. Figure 15: Throughput variation for two structured packets. (3) Multi-source data retrieval performance  
Baseline represents to the scenario of sending one interest for each producer (retrieve one data packet). In Figure 17, the number of interest messages grows linearly when the number of producers increases, whereas ICN-HMcH allows only one interest to be sent and provide in-network data aggregation; therefore, only one interest is used even when the number of producers increases. This results in improved bandwidth utilisation and energy efficiency. ICN-HMcH needs to receive all response data packets before performing in-network aggregation, whereas baseline performs the same thing but in the application layer. Figure 18 shows that the data collection time for ICN-HMcH is rapid compared to the baseline scenario, especially when the number of producers increases.
# Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
## 6 Stages of the Quantification of QoE in CIAs
### Output Stage  
Below, we present a classification of the proposals for evaluating QoE in CIAs based on the metrics they contribute to the output stage. We group the proposals into five broad groups. The aim of this section is to discern which research directly assesses the human perceptions outlined in Section 2 and actually provides QoE-related metrics. Table 7 summarises the proposals we discussed in Section 5 and groups them by their output metrics, where more than one can be used simultaneously. In this section, we briefly review these metrics:  
* Video metrics: Metrics that quantify the human perception of visual quality. * Audio metrics: Metrics that quantify the human perception of sound quality. * Time metrics: Metrics that quantify the human perception of interactivity or responsiveness. * QoE metrics: Metrics that directly summarise the user experience. * Other QoS metrics: Quality of service metrics that do not relate to human perceptions or QoE. Video metrics assess the quality of the graphical content consumed by the CIA user. It was common to provide measures related to the number of frames per second perceived by the user, the number of frames lost or skipped by the architecture following its delivery on the cloud server, or the percentage change of a frame from one screen update to the next. Other researchers proposed output metrics directly related to their processing stage. Some output metrics were proprietary to each work, whereas others were well known (e.g., PSNR or SSIM) or endorsed by institutions such as ITU-T (e.g., PEVQ). Audio metrics evaluate the quality of the audio content consumed by the CIA user. In the output stage, some proposals utilised well-known sound-quality metrics such as WSS,LLR, or ViSQOL, as discussed above. As in the case of video metrics, some authors also used ITU-T-endorsed metrics such as POLQA. Among the proposals that utilised output time metrics, a large number of researchers tried to extract interactivity time: the elapsed time perceived by the user from the moment they interact with the CIA until the representation of the associated response. Meanwhile, other works used approximations (synthetic interactivity time). Some proposals also provided a measure of the time it takes for users or CIA applications to complete a sequence of tasks. Others opted to provide visual load times, often for interactive web applications (e.g., speed index, first paint, page load time, or time for full visible rendering). These proposals did not directly measure interactivity time but indirectly measured the human perception of responsiveness. Through ACR/MOS scale values, QoE metrics often provided a real quantification of user satisfaction. These proposals obtained these values directly by asking CIA users or
# Voting-Based Document Image Skew Detection
## 2 Skew Detection Algorithms
### Frequency Domain
#### 2.1.2 Improvements  
The FFT image presents the challenge of detecting the dominant lines which represent the document orientation. It is noisy because of the high-frequency signals generated by text, small symbols spread across the whole document, images, etc. The FFT image also lacks contrast. These two shortcomings make it difficult for the Hough transform to identify the dominant lines which give the document orientation.  
We solved these issues by stretching the contrast of the FFT image and then equalizing its histogram, before Otsu binarization and then feeding it to the Hough transform procedure. These operations help the binarization to emphasize dominant lines.  
We have experimented with various methods of emphasizing the correct dominant lines by processing both the input image and the FFT image. Empirically, we have come to the conclusion that there is no need to process the input image in the spatial domain, as its FFT already contains all the orientation information, visually. Altering it only decays the dominant lines in the frequency domain. So, we only processed the FFT image.  
Due to the noise in the FFT image, despite losing information in the input image, we initially tried blurring in the spatial domain in order to filter out high frequencies, but quickly discarded it as it produces fainter dominant lines in the frequency domain. Dilation [24] of the page content also produces incomplete lines on the FFT. These make the binarization produce gaps in the dominant lines and make it impossible to detect them using Hough transform.  
Then we experimented with blurs in the frequency domain (namely Gaussian blur, box blur, bilateral filter, and median filter [25]), before binarization, and they partially worked, but none turned out to be nearly as feasible as contrast stretching followed by histogram equalization, and even when used together with these two, blurs do not improve accuracy.  
In Figure 2, we present the result of contrast stretching and histogram equalization.  
Figure 2: From left to right, top to down: Input, FFT of Input, Otsu on FFT of Input, processed FFT (with contrast stretching and histogram equalization), Otsu on the processed FFT, skew angle detection performed on the latter, using the Hough transform.
## 6 The Effect of Mesh Element Density  
Figure 9a shows the experimental data and equivalent permeability of austenite-ferrite in the RCPS-VM microstructure when the model is meshed into 0.19 M, 0.46 M, 0.82 M and 1.23 M elements when the background field is a unit-uniform magnetic H-field vertically penetrating the steel sample from top to bottom.  
It can be concluded from Figure 9a that the equivalent permeability of austenite-ferrite in the RCPS-VM microstructure calculated by the refined mesh (1.23 M elements) is much smaller and more accurate than that calculated by the coarse mesh (0.19 M elements) when compared with the measured data. In addition, further refining the mesh would barely affect the equivalent permeability curve.  
Figure 9b illustrates the 2D (40 k elements) and 3D (8 M elements) modelling results using COMSOL Multiphysics for the effective relative permeability variation with ferrite fraction in the ferrite-austenite steel sample. The ferrite and austenite phases are randomly distributed in 2D and 3D models. Since the magnetic flux have one more degree of freedom to penetrate the microstructures, it is obvious that the 3D FE modelling predictions present higher relative permeability values than that for the 2D FE modelling. Previous experimental data for EM sensor measurements of DP and CP steels have displayed an almost linear relationship between inductance (which is correlated to permeability) and ferrite fraction (specimens with a 24%-86% second phase) [9], which are more similar to the 3D model predictions. Moreover, the 3D modelling predictions demonstrate better agreement with the measured magnetic permeability values over a full range of 0-100% second phase in ferrite-austenite microstructures reported in [2]. Therefore, it can be concluded that, the 3D modelling results shows  
Figure 8: Chromatogram of the magnetic H-field when the volume fraction of ferrite grains is 70%.  
Figure 7: Chromatogram of the magnetic H-field when the volume fraction of ferrite grains is 50%.  
a closer fit with the experimental data for the ferrite-austenite microstructure. In addition, when the relative permeability variation with the ferrite fraction results in Figure 9a,b are compared, the equivalent permeability-ferrite-fraction curve trend of the 1.23 M element austenite-ferrite RCPS-VM microstructure shows a closer fit to that of the 3D EM model using COMSOL Multiphysics (version 5.2).
## 2 Materials and Methods
### Classification  
Since there is no uniform and efficient classification method for all applications, in general, it may be useful to test and compare multiple classification methods [14]. In the present work, we explored multiple classification algorithms (K nearest neighbor (KNN), support vector machine (SVM), logistic regression (LR)) to classify EEG signals, and these classification algorithms were carried out using MATLAB R2018b. The sample entropy of different channels was fed as input to the multiple classification algorithms, and the target variables were fatigue state and normal state. For fine KNN and medium KNN, the distance was computed using Euclidean distance and distance weight was equal. For cubic KNN, the distance was computed using Minkowski distance and distance weight was equal. We obtained the highest accuracy when the number of neighbors was 1 for fine KNN and the number of neighbors was 10 for medium KNN and cubic KNN. Additionally, SVMs with three kinds of kernel functions (linear, quadratic, and cubic) were explored and compared, and the kernel scale was automatic.  
The performance of the classifiers considered in the present work was evaluated in terms of sensitivity (SEN), specificity (SPE), precision (PRE), and accuracy (ACC), using the following equations:  
\[Sensitivity=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100\% \tag{7}\]  
\[Specificity=\frac{\text{TN}}{\text{TN}+\text{FP}}\times 100\% \tag{8}\]  
\[Precision=\frac{\text{TP}}{\text{TP}+\text{FP}}\times 100\% \tag{9}\]  
\[Accuracy=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}} \times 100\% \tag{10}\]  
where TP = true positive, representing the correctly detected fatigue signals; FN = false negative, representing the undetected fatigue signals; FP = false positive, representing the wrong detections; and TN = the number of normal signals.  
Furthermore, a receiver operating characteristic (ROC) curve was applied to evaluate the performance of these classification algorithms. The area under the ROC curve (AUC) was also computed to evaluate the classification performance quantitatively.
## 4 Experiment Results
### Experiment Parameter Settings
This strategy helps prevent the network from overfitting by discontinuing the learning process once it has captured the essential statistical properties of the training data, thereby avoiding unnecessary refinement on individual features. Figure 12: Delay plot of channel model at 800 m distance. Figure 13: Delay plot of channel model at 3000 m distance. In actual UWA communication application scenarios, the SNR tends to fluctuate with time, so it is not feasible to train a weight model under a single SNR. In order to make the trained network applicable to a variety of application scenarios, the neural network is trained by a series of signal-to-noise ratio data instead of data under a single SNR. Therefore, channel samples of [0-20] dB are mixed in the training process dataset.
## 4 Implementation of Autonomous Task Planning
### Principle of DDPG  
In the intelligent algorithm level, in order to test the effectiveness of the overall simulation framework, this paper chooses the widely used deep deterministic strategy gradient algorithm (DDPG). The DDPG algorithm is an advanced reinforcement learning technology that is specially designed to deal with complex problems with continuous action space. The DDPG algorithm combines deep learning and the deterministic strategy gradient method, and solves the limitation of traditional reinforcement learning methods in continuous action space by using neural network to approximate strategy and value function.  
The core principle of the DDPG algorithm is based on the strategy gradient method in reinforcement learning, which optimizes the strategy by directly increasing the gradient of the strategy function. Different from traditional value-based methods, the strategy gradient method directly models the decision-making process of actions, allowing effective exploration and utilization in continuous action space. The DDPG algorithm uses empirical playback mechanism and stores historical interaction data in playback buffer. The learning process randomly selects samples from this buffer, which helps the algorithm learn more efficiently from experience and reduces the time series dependence of data.  
The DDPG algorithm adopts a deterministic policy, which means that in a given state, the policy will produce a deterministic action instead of a random action. This simplifies the strategy optimization process and allows more precise action control. The DDPG algorithm includes two neural networks: actor network and critic network. The actor network is responsible for generating strategies, that is, selecting actions. The critic network evaluates the quality of the current strategy and provides guidance on the direction of strategy improvement.  
The DDPG algorithm, based on the Actor-Critic framework, is classified as an off-policy algorithm. The actions in the DDPG algorithm are deterministic, which is more conducive to decision-making in continuous action spaces. The DDPG algorithm features a replay buffer to store samples generated from the agent's interactions with the environment. In the experiment, the size of the buffer is set to \(10^{6}\), the corresponding discount factor is set to 0.95, and the network model will begin to randomly sample and update when the number of interactions between the agent and the environment reaches \(10^{3}\). The policy and value networks in the DDPG algorithm correspond to the actor and critic, respectively. In the experiment, both the policy and value networks consist of hidden layers with five fully connected layers, each with 128 neurons, and the activation function is ReLU. The learning rate for network training is set to \(3\times 10^{-4}\), and the batch size for stochastic gradient descent is 1024. When setting the hyperparameters, the target network update interval is \(d=1\) s, and the soft update rate \(\tau=0.5\) s.
## 3 Methodology  
### Data Preparation and Partitioning  
This study primarily focused on parameters that significantly influence particle settling velocity in both Newtonian and non-Newtonian fluids. The input data included particle characteristics (like size, density, and shape), fluid properties (such as density and viscosity), and flow dynamics parameters. These types of data were selected based on their recognized impact on settling behavior, as documented in the existing fluid dynamics literature. The criteria for selecting specific input variables were based on several factors. (1) Each variable  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Particle** & **Fluid** & **Data Point Number** \\ \hline Spherical & Newtonian & 102 \\ Non-spherical & Newtonian & 276 \\ Spherical & Power-law & 98 \\ Non-spherical & Power-law & 455 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data point numbers for different particles in different fluids of this study.  
Figure 2: Input parameter density distribution and box plot for predicting particle settling velocity in Newtonian and power-law fluids.  
was chosen for its direct or indirect impact on particle settling velocity, as supported by previous research findings. (2) The variables selected were measurable and available within the scope of experimental capabilities. (3) To ensure the model's applicability across different scenarios, a diverse range of variables representing various particle types and fluid conditions was selected. (4) The selection was also influenced by the feasibility and reliability of accurately measuring each variable under laboratory conditions.  
The dataset of particle settling velocity is partitioned into two sets: a train set used to adjust the weights and biases in the ANN model and a test set used to independently evaluate the performance of the model. Following previous research, 80% of the dataset is allocated to the train set, while the remaining 20% is assigned to the test set. As shown in Table 4, the statistical properties of the train and test sets are similar.
## 5 Results and ANN Model Validation
### Optimization for Various Scenarios
In addition, the increment of the cumulative mass was 33.94% (74 kton), when the injection period was increased from 10 years to 20 years, while it was increased only 13.7% (40 kton) if the period was extended from 20 years to 30 years. This is because the injection process was unexpectedly terminated at 22 years by the CO\({}_{2}\) breakthrough phenomenon and was not performed for the designed period of time. Therefore, it was concluded that a safe injection process is crucial for total storage capacity. Figure 16: The top view of the geological model with the property of vertically averaged permeability and the optimized location of a relief well for the injection periods of 10, 20, and 30 years. Moreover, the storage efficiency for the monitoring phase was investigated with the trapping mechanisms. As shown in Figure 18, the residual and solubility trapping indices (RTI and STI) were decreased during the injection period because the injected CO\({}_{2}\) diffuses via the pressure gradient. However, after the end of the injection, not only was the injected CO\({}_{2}\) trapped through pore spaces by gravitational, viscous, and capillary effects, but it also dissolved in brine, which eventually increased the residual and solubility trapping indices [49, 68].
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**RCA**} \\ \hline  & **Exe1** & **Exe2** & **Exe3** & **Exe4** & **Exe5** & **Exe6** & **Rec1** & **Rec2** & **Rec3** & **Rec4** & **Rec5** \\ \hline Rel1 & 0.508 & 0.169 & **0.017** & **0.013** & **0.013** & **0.017** & 0.114 & 0.445 & 0.508 & 0.285 & 0.285 \\ Rel2 & 0.508 & 0.114 & **0.022** & 0.017 & **0.013** & **0.025** & 0.114 & 0.445 & 0.575 & 0.333 & 0.508 \\ Rel3 & 0.445 & 0.139 & **0.022** & 0.017 & **0.013** & **0.025** & 0.114 & 0.575 & 0.445 & 0.386 & 0.445 \\ Rel4 & 0.441 & 0.093 & **0.022** & **0.017** & **0.005** & **0.012** & 0.059 & 0.445 & 0.721 & 0.386 & 0.086 \\ Rel5 & 0.767 & 0.139 & **0.038** & **0.028** & **0.011** & **0.028** & 0.086 & 0.767 & 0.859 & 0.515 & 0.594 \\ \hline Exe1 & & **0.005** & **0.005** & **0.005** & **0.007** & **0.012** & 0.285 & 0.646 & 0.799 & 0.333 & 0.333 \\ Exe2 & & & **0.005** & **0.005** & **0.009** & **0.012** & 0.575 & 0.285 & 0.059 & **0.013** & **0.017** \\ Exe3 & & & & **0.017** & **0.025** & **0.012** & 0.285 & **0.028** & **0.009** & **0.007** & **0.007** \\ Exe4 & & & & & **0.047** & **0.050** & 0.203 & **0.009** & **0.007** & **0.007** & **0.005** \\ Exe5 & & & & & & **0.050** & **0.009** & **0.007** & **0.005** & **0.005** & **0.005** \\ Exe6 & & & & & & & **0.012** & **0.012** & **0.012** & **0.012** & **0.012** \\ \hline Rec1 & & & & & & & & **0.007** & **0.005** & **0.005** & **0.005** \\ Rec2 & & & & & & & & & **0.009** & **0.005** & **0.005** \\ Rec3 & & & & & & & & & & **0.022** & **0.022** \\ Rec4 & & & & & & & & & & & & 0.959 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistical significance of differences between the respiratory cycle amplitude (RCA) values calculated from 3 min segments in different conditions: Relaxation (Rel), Exercise (Exe), and Recovery (Rec). Statistically significant values are in bold. Figure 4: Relationship between the heart rate and the breathing frequency in different phases of the experiment. Then, we applied the sample entropy method to calculate the regularity of the RR intervals and respiratory signal time series, as well as the cross-sample entropy, to calculate their synchrony. The SampEnRR values were the highest during relaxation and the lowest during exercise (Figure 6). There were no statistically significant differences between the SampEnResp values in the three different conditions. Finally, asynchrony between the rhythms was the highest in relaxation and the lowest during exercise. The Pearson correlation coefficients between the subjects' fat mass and calculated cardio-respiratory parameters are given in Table 7. During the recovery period, we found strong negative correlations between the fat mass and parasympathetic activity, and a positive correlation of the fat mass with BF and HR at the end of the recovery.