esa-sceva/satcom-chunk-collection huggingface dataset:
# Coupled Vibration Analysis of Submerged Floating Tunnel System in Wave and Current
## 2 Modeling of the SFT System
### Hydrodynamic Forces on SFT  
It has been realized that the periodic vortex shedding in the near wake of the cable induces the oscillating lift and drag forces. The lift forces on the \(i^{\#}\) pair of cables in the CF direction and the vortex-shedding frequency are respectively given as [29; 30]:  
\[F_{iL}=\frac{1}{2}\rho_{w}d_{i}C_{L}(v_{0}\sin\alpha_{i})^{2}\cos(2\pi f_{vi}t +\theta_{i}) \tag{3}\]  
\[f_{vi}=\text{St}\frac{v_{0}}{d_{i}} \tag{4}\]  
where \(\rho_{w}\) is the fluid density; St is the Strouhal number, and St = 0.2, here [30]; \(d_{i}\) is the diameter of the cable; \(C_{L}\) is the lift force coefficient, and \(C_{L}\) = 0.7 [19]; \(v_{0}\) is the uniform flow velocity; \(\theta_{i}\) is arbitrary phase angle, and \(\theta_{i}\) = 0 here.  
The added mass and drag force on the \(i^{\#}\) anchor-cables in the CF direction and the wave force acting on the tube in the \(Z\) direction are expressed via [PERSON]'s equation [10]:  
\[F_{iD}=\frac{1}{4}\pi d_{i}^{2}\rho_{w}C_{mi}\tilde{v}_{i}+\frac{1}{2}\rho_{w} d_{i}C_{Di}\dot{v}_{i}|\dot{v}_{i}| \tag{5}\]  
\[F_{iW}=\frac{1}{4}\pi D^{2}\rho_{w}\dot{w}_{z}+\frac{1}{4}\pi D^{2}\rho_{w}C_{ m}\big{(}\dot{w}_{z}-\ddot{z}\big{)}+\frac{1}{2}\rho_{w}DC_{D}\big{(}w_{z}- \dot{z}\big{)}\big{|}w_{z}-\dot{z}\big{|} \tag{6}\]  
where \(C_{m}\) and \(C_{mi}\) are the added mass coefficients, taken as 1.0 [24]; \(C_{D}\) and \(C_{Di}\) are the drag force coefficients, equal to 0.65 here [10].  
According to the linear wave theory, \(w_{z}\) is defined as [10]:  
\[w_{z}=\frac{\pi H}{T}\bigg{(}\frac{\sinh k(z_{0}+d)}{\sinh kd}\bigg{)}\sin \omega_{w}t \tag{7}\]  
in which \(H\) is the wave height; \(T\) is the wave period; \(k\) is the wave number; \(\omega_{w}\) is the angular frequency (\(\omega_{w}\) = 2\(\pi\)/\(T\)); \(z_{0}\) is the installation depth of the center line of SFT tube; \(d\) is the water depth.
## 3 From Discrete Multiphysics to Deep (Discrete) Multiphysics
### The Particle-Neuron Duality  
As mentioned above, discrete models, such SPH, LSM and DEM, work by exchanging forces among computational particles (Figure 4a). These forces change the velocity and the position of the particles and, by doing that, the model achieves a discrete representation of the mechanics.  
Besides velocity and position, computational particles can have other properties. In heat transfer problems, for instance, particles require a new property called 'temperature'. If we want this property to behave like the physical temperature, we must link it to the heat transfer equation. In this case, the particles do not exchange forces, but rather more general interactions that simulate the physical process of heat transfer from one computational particle to another (Figure 4b). This can be generalized to any conservation law: we assign a new property to the particles, and the corresponding transfer equation calculates the interactions that dynamically evolve this property with time.  
Interactions are more general than forces. If we only account for forces, we have a 'discrete mechanics' tool that is limited to momentum-conservation problems. If we include interactions, we have a 'discrete multiphysics' tool that applies to a wider variety of conservation problems. Discrete multiphysics, therefore, is a generalization of discrete mechanics (Figure 5).  
Figure 4: (**a**): Solid and fluid particles exchange mechanical forces; (**b**): Hot and cold particles exchange heat; (**c**): Layers of neurons exchange information.  
The next question, at this point, would be: can we generalize this idea even further? In addition, to achieve this, what concept, more general than 'interaction', can we use?  
Information is the most general exchange of'something' we can think of. Whenever two computational particles exchange forces, interactions or any other property, they exchange information.  
The exchange of information is exactly what happens in ANNs, where information is transferred from one neuron to another (Figure 4c). In the case of ANNs, this information has no direct connection with any physical property. However, 'physical interactions' or 'physical forces' can be seen as a form of information transfer and, as such, a subset of the more general concept of 'information' (Figure 5).  
This idea constitutes the basis of the particle-neuron duality we use to establish the general framework of deep discrete Multiphysics (or simply deep multiphysics). The elemental building block of deep multiphysics is a particle-neuron hybrid: it behaves like a DMP particle when it carries out physical interactions and like a neuron when it exchanges non-physical information, and, like a neuron, it can be trained.  
To illustrate and clarify this point, the rest of the article focuses on a case study where practical implementation of deep multiphysics is presented.
## 4 Fault-Tolerant Control of Three-Level T-Type Inverter
### Fault-Tolerant Control Analysis  
The proposed fault-tolerant control system deactivates the \(a\)-phase half-bridge switches (\(S_{a1}{}^{+}\) and \(S_{a2}{}^{-}\)) when an open-circuit fault occurs in \(S_{a1}{}^{+}\) or \(S_{a2}{}^{-}\). The \(b\)-phase, \(c\)-phase, and neutral-point switches still operate normally, and switch \(T_{a}\) in the three three-pole ac semiconductor switches are activated to replace the faulty \(a\)-phase leg with the spare leg. The spare leg adopts the switching mode of the original \(a\)-phase leg to maintain the three three-pole ac semiconductor switches are activated to replace the faulty \(a\)-phase leg with the spare leg. The spare leg adopts the switching mode of the original \(a\)-phase leg to maintain the three-phase balanced output voltage. If an open-circuit fault occurs in \(S_{b1}{}^{+}\) or \(S_{b2}{}^{-}\), the \(b\)-phase half-bridge switches (\(S_{b1}{}^{+}\) and \(S_{b2}{}^{-}\)) are deactivated. The \(a\)-phase, \(c\)-phase, and neutral-point switches still operate normally, and switch \(T_{b}\) in the three three-pole ac semiconductor switches are activated to replace the faulty \(b\)-phase leg with the spare leg. The spare leg adopts the switching mode of the original \(b\)-phase leg to maintain the three-phase balanced output voltage. Similarly, if an open-circuit fault occurs in \(S_{c1}{}^{+}\) or \(S_{c2}{}^{-}\), the \(c\)-phase half-bridge switches (\(S_{c1}{}^{+}\) and \(S_{c2}{}^{-}\)) are deactivated. The \(a\)-phase, \(b\)-phase, and neutral-point switches still operate normally, and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fault Category**} & \multirow{2}{*}{**Error Percentage**} & \multicolumn{4}{c}{**Output Weight Value**} & \multirow{2}{*}{**Detection Result**} \\ \cline{3-3} \cline{5-6}  & & \(\mathbf{F_{7}}\) & \(\mathbf{F_{8}}\) & \(\mathbf{F_{9}}\) & \\ \hline F\({}_{7}\) & \(5\%\) & \(\mathbf{0.281603}\) & \(-0.581739\) & \(-0.598714\) & F\({}_{7}\) \\ F\({}_{7}\) & \(-5\%\) & \(\mathbf{0.51428}\) & \(-0.517926\) & \(-0.602419\) & F\({}_{7}\) \\ \hline F\({}_{8}\) & \(5\%\) & \(-0.551061\) & \(\mathbf{0.455688}\) & \(-0.617525\) & F\({}_{8}\) \\ F\({}_{8}\) & \(-5\%\) & \(-0.522304\) & \(\mathbf{0.586427}\) & \(-0.654039\) & F\({}_{8}\) \\ \hline F\({}_{9}\) & \(5\%\) & \(-0.17552\) & \(-0.367097\) & \(\mathbf{0.50828}\) & F\({}_{9}\) \\ F\({}_{9}\) & \(-5\%\) & \(-0.330461\) & \(-0.336816\) & \(\mathbf{0.514661}\) & F\({}_{9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Fault detection results under the failure of different switches in \(c\)-phase and addition of \(\pm 5\%\) erroneous data (inverter frequency = 60 Hz).  
Figure 9: The architecture of proposed three-level T-type inverter.
## 5 Results and Analysis
### Results  
#### 5.2.1 Results after Feature Selection  
Table 3 presents the number of attributes after selection for all schemes. Based on the parameters we set in this paper, the _Gini_ scheme selected 13 and 12 attributes for model DM000 and NM0007, respectively. Thus, to compare this scheme with the others, for _Pearson_, _Spearman_, and _J-Index_, we selected the top 13 and 12 attributes with the highest coefficient value for disk model DM000 and NM0007, respectively. For _Entropy_, the importance threshold was the same as that of _Gini_, although the number of attributes was larger than _Gini_ in NM0007. For the last two rows of this table, the number of selected attributes based on methods in the first group was further reduced using Pearson's correlation coefficient and Spearman's correlation coefficient, respectively.
## 4 Solution Proposal for UAV Applications in Greenhouses
To provide an example of SLAM solutions with different sensors, Table 3 describes SLAM, VSLAM and VISLAM studies. ORB-SLAM, large-scale direct SLAM (LSD-SLAM), semi-direct visual odometry (SVO) and visual-inertial system (VINS) mentioned in Table 3 are frequently used methods in the field of autonomous mobile robotics. Numerous indoor SLAM studies can be added to Table 3.  
In Table 3, LIDAR was used for SLAM in the first two studies. While this sensor makes the application easier, it has a cost disadvantage. Today, the most popular studies in the field of SLAM employ low-cost methods. These studies aim to realize SLAM applications using only cameras. Popular examples of these studies, called V-SLAM or monocular SLAM, are listed in rows 3-5 in Table 3. Although VSLAM methods are quite successful, the main weakness of these applications is the inability to provide scale information. To solve this scale uncertainty problem, VIO/VISLAM studies have been developed that combine an inertial sensor (IMU) with a monocular SLAM system to provide scale information. The prominent studies on VIO/VISLAM are included in rows 6-8 in Table 3. Detailed information on these SLAM studies is shared in Table 3.  
Although UAVs have applications such as spraying, imaging, mapping and fertilization in open lands (see Table 1), in indoor environments such as greenhouses, agricultural practices with UAVs have almost never been applied due to localization and mapping difficulties (see Table 2). The UAVs used in previous applications were manually controlled, and measurements were made with the sensors on them. The studies shared in Table 3 are successful studies aimed at self-localization and recognizing the environment of a UAV in indoor environments. In these studies, methodological recommendations for SLAM were presented, and prediction errors in UAV poses were compared with previous studies. Indoor SLAM applications are often an important requirement in the robotics industry, such as search and rescue or defense in environments without GPS access, or in the entertainment industry, such as virtual reality [101; 102]. However, as we know, there are no agricultural applications of such advanced SLAM methods in indoor environments such as greenhouses. The need for SLAM in indoor environments arises from the need to perform autonomous tasks only with the sensors on the robot in a GPS-denied environment. At the same time, since smart greenhouses are indoor environments where autonomous tasks will be performed by the UAV, SLAM solutions can be directly applied in greenhouses. Figure 7: Our previous work on UAV positioning in an indoor environment [100].
# Stochastic Analysis of an Industrial System with Preparation Time Repair under Warranty Policy
## 5 Numerical Results
### Numerical Results for \(R(t)\), MTSF, and \(Av(\infty)\)
Figure 3 shows that the rate of warranty expiration \(\alpha\) increases from 0.2 to 0.9 in interval \(\lambda_{2}\in[0,0.5]\), leading to an increase in the mean time to failure of the MTSF system when \(\lambda_{1}=0.5\). In contrast, in interval \(\lambda_{2}\in[0.5,1]\), when the rate of warranty expiration decreases from 0.2 to 0.9, the mean time to failure of the MTSF increases in this interval. Finally, we track the change in steady state availability with the parameters that affect it through Tables 4 and 5 as follows:  
Table 4 explains the generation of steady state availability values when the out-of-warranty unit failure rate changes from 0.1 to 0.9 for three different cases of repair rates. As we note, steady state availability decreases with increasing values of \(\lambda_{2}\) in the following cases: \(\mu_{3}>\mu_{4}\), \(\mu_{3}=\mu_{4}\), and \(\mu_{3}<\mu_{4}\). Comparing the three states of repair rates with the steady state availability results in the same table; we find that the highest steady state availability values can be obtained when \(\mu_{3}>\mu_{4}\). Table 5 shows the variety of steady-state availability values with increasing values of the preparation rate before repair of the failed unit \(\kappa_{2}\) and different repair rates (\(\mu_{3}>\mu_{4}\), \(\mu_{3}=\mu_{4}\), \(\mu_{3}<\mu_{4}\)); we can observe the following:  
1. The higher the preparation rate before repairing the failed unit \(\kappa_{2}\), the higher the steady state availability values. 2. The steady state availability value increases when \(\mu_{3}>\mu_{4}\) and starts to decrease gradually in the following cases: \(\mu_{3}=\mu_{4}\) and \(\mu_{3}<\mu_{4}\), respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Failure Rate (\(\lambda_{2}\))** & \(\mu_{3}>\mu_{4}\) & \(\mu_{5}<\mu_{4}\) & \(\mu_{3}=\mu_{4}\) \\ \hline
0.1 & 0.559851 & 0.547588 & 0.542495 \\
0.2 & 0.388745 & 0.37702 & 0.372208 \\
0.3 & 0.297746 & 0.28747 & 0.283286 \\
0.4 & 0.241268 & 0.232301 & 0.228659 \\
0.5 & 0.202801 & 0.194896 & 0.191693 \\
0.6 & 0.174913 & 0.167866 & 0.165017 \\
0.7 & 0.153767 & 0.14742 & 0.144858 \\
0.8 & 0.137183 & 0.131414 & 0.129088 \\
0.9 & 0.123828 & 0.118544 & 0.116414 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The steady-state availability \(Av(\infty)\) for different values of \(\lambda_{2}\) at \(\kappa_{2}=0.4,\ \gamma_{2}=0.3,\ \theta_{3}=0.7\). Figure 3: Mean time to system failure versus failure rate \(\lambda_{2}\) and rate of completion of warranty \(\alpha\). Figure 2: Mean time to system failure versus failure rate \(\lambda_{1}\) and rate of completion of warranty \(\alpha\).
## 2 Problem Description  
The uncertainty variables are divided into aleatoric uncertainty variables \(\mathbf{x}\) and epistemic uncertainty variables \(\mathbf{y}\). In this paper, the distribution type and distribution parameters for \(\mathbf{x}\) are deterministic, but the probabilistic distributions of \(\mathbf{y}\) cannot be given, as the available data of \(\mathbf{y}\) are always insufficient and the knowledge is limited. However, in many conditions, \(\mathbf{y}\) can be represented with determinate distribution type \(\theta\) and uncertain distribution parameters \(\xi\). Taking a part in a mechanical system as an example, its dimension parameters (length, width, etc) are uncertainty variables due to manufacturing tolerance, whose distributions follow a Normal distribution because of the size randomness. Furthermore, the distribution parameters (mean and standard deviation) are also uncertain when there is only a small amount of sampling data.  
Although, for the uncertain distribution parameters \(\xi\) of \(\mathbf{y}\), the available uncertainty information is limited, the data acquired from multiple sources can be used to decrease the uncertainty of \(\xi\) under some circumstances. For example, information from two sensors, theoretical calculation or simulation analysis may be available. However, the uncertainty information from two sources is not identical; how to fuse these data to obtain accurate uncertainty information for \(\xi\) is the key problem.  
Here, it is assumed that the available uncertainty information of distribution parameters \(\xi\) includes insufficient data from Source 1 and evidence theory information from Source 2 at the same time. The insufficient data from Source 1 contains point data and/or interval data simultaneously. The available interval information for every distribution parameter \(\xi\) from Source 2 is \(\kappa\) sub-intervals \(A_{i}(i=1,\cdots,\kappa)\) with corresponding basic probability assignment (BPA) \(\Theta(A_{i})\). A relationship within these available uncertainty data is established firstly, then the fusion representation model for \(\mathbf{y}\) is constructed and the reliability index considering aleatoric uncertainties and epistemic uncertainties is calculated. Three problems that need to be addressed are shown as follows:  
1. How to fuse the uncertainty information from two sources and construct the representation function for distribution parameters \(\xi\).
2. How to get the probabilistic representation of epistemic uncertainty variables \(\mathbf{y}\) considering uncertain distribution parameters \(\xi\).
3. How to calculate the reliability index considering aleatoric uncertainty variables \(\mathbf{x}\) and epistemic uncertainty variables \(\mathbf{y}\) simultaneously.
# Electric Load Forecasting Based on Deep Ensemble Learning
## 2 Materials and Methods
### DCC-EL Forecasting Framework
#### 2.3.3 DCC-EL Ensemble Model
As holidays progress, data trends may decrease; fluctuations might be observed pre- or post-holiday. The day is segmented into seven intervals: morning, noon, afternoon, evening, night, late night, and early morning. Finally, these engineered features are merged with the crossover features from ensemble learning to compile the complete feature set. (3) DCC training: A three-layer structure is predominantly employed within the DCC module. The activation function is set to Relu. The neuron count in each layer is adjusted according to tower variations to facilitate accurate future load data predictions.
## 4 Design and Results of the Experiments
### Results of the Experiments
#### 4.3.3 Experiment 3
The comparison of test frame number and accuracy rate of various expressions are shown in Table 7 and Figure 7, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subject** & **Total Frames** & **Adjustment 1** & **Correctly Recognized** & **Accuracy** & **Absolute Increase** & **Relative Increase** \\ \hline A & 2647 & \(\times\) & 1722 & 65.05\% & 18.55\% & 28.52\% \\  & & \(\checkmark\) & 2213 & **83.60\%** & & \\ B & 2650 & \(\times\) & 1901 & 71.74\% & 10.37\% & 14.46\% \\  & & \(\checkmark\) & 2176 & **82.11\%** & & \\ C & 2289 & \(\times\) & 1575 & 68.81\% & 14.11\% & 20.51\% \\  & & \(\checkmark\) & 1898 & **82.92\%** & & \\ \hline Average & - & - & - & - & 14.34\% & 21.16\% \\ \hline \hline \end{tabular}  
* In the column ’Adjustment’, to the training set, \(\times\) means the noise adjustment hasn’t been made and \(\checkmark\) means the noise adjustment has been made.  
\end{table} 
Table 6: Tests of recognition system. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emotion** & **Subject A** & **Subject B** & **Subject C** \\ \hline Angry & 230 & 466 & 583 \\ Disgust & 292 & 221 & 415 \\ Scared & 383 & 459 & 444 \\ Happy & 387 & 361 & 210 \\ Sad & 540 & 394 & 286 \\ Surprised & 512 & 550 & 231 \\ Neutral & 303 & 199 & 120 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Numbers of test frames for various expressions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Dataset** & **Noise Adjustment 1** & **Accuracy** & **Standard Deviation** \\ \hline RAF-DB & \(\times\) & 59.00\% & 0.17 \\  & \(\checkmark\) & 59.50\% & 0.14 \\  & \(\times\) & 32.60\% & 0.31 \\ CK+ & \(\checkmark\) & 33.70\% & 0.28 \\ KDEF & \(\times\) & 24.00\% & 0.25 \\  & \(\checkmark\) & 29.10\% & 0.22 \\ FER2013 & \(\times\) & **67.50\%** & **0.12** \\ \hline \hline \end{tabular}  
* In the column ’Noise Adjustment’, to the training set, \(\times\) means the noise adjustment hasn’t been made and \(\checkmark\) means the noise adjustment has been made.  
\end{table} 
Table 5: Effect of models in Experiment 2. It can be judged from Figure 7 that the accuracies of neutral and happy have high recognition using the model before optimization and were not significantly improved after optimization. However, the model greatly improved in the categories of anger, disgust, fear, and sadness. The system interface displays the optimized model recognition results in real-time. Some recognition frames and the corresponding expression probability outputs of subject A are shown in Figure 8.  
Figure 8: Expressions of sadness and disgust of Subject A.  
Figure 7: Comparison of various expression recognition tests. It can be seen that the adjusted model also has a higher probability output of correct expression when recognizing the correct label at the same time.
# Cooperation Based Proactive Caching in Multi-Tier Cellular Networks
## 6 User Satisfaction Ratio
### Intra-Tier and Cross-Tier Cooperation (ITCTC)
#### 6.1.1 Intra-Tier Cooperation  
This cooperation can be further divided into two types:  
1. Intra-cluster cooperation and
2. Cross-cluster cooperation  
Intra-Cluster Cooperation (ICC)  
Clusters are formed in each tier and contents are cached accordingly. Figure 1 depicts clusters formation in tier 1 and tier 2. The user request may be fulfilled by the nearby associated base station or the contents may be fetched from other BSs within the cluster. The cluster comprises of similar contents on the BS, but probably some BSs have different contents within the cluster. The user will send a request to the local base station and if it is not satisfied then the request will be forwarded to the other BSs within the cluster.  
Figure 1: Cluster formation in each tier using K-mean clustering.  
Cross-Cluster Cooperation (CCC)  
If a user does not find its requested contents in the associated cluster then the request is forwarded to the other clusters of the same tier. This is cross-cluster cooperation.
# Sample Reduction Strategies for Protein Secondary Structure Prediction
## 4 Results
### Average Accuracies  
Table 3 summarizes the average and standard deviation of the accuracies obtained from the 7 folds of cross-validation experiment on CB513. In this table the first two rows include the results obtained for the sample reduction strategies and the last row represents the case where all samples are used to train the SVM classifier. Having low standard deviation values demonstrates that the accuracy evaluations are robust and models are trained with sufficiently large samples. To assess whether the difference between the accuracy values of sample reduction methods and the method that uses all samples is statistically significant, a two-tailed Z-test is performed with a confidence value of 95%. Based on this test, the accuracy difference between sample reduction by stratified random selection and the method that uses all samples is not found to be statistically significant with a Z-score of \(-\)0.0217 and a \(p\)-value of 0.98404. On the other hand, the accuracy difference between sample reduction by clustering and the method that uses all samples is statistically significant with a Z-score of \(-\)4.8713 and a \(p\)-value \(<\) 1 \(\times 10^{-5}\). Based on these results, it can be concluded that sample reduction by stratified random sampling is more effective than sample reduction by hierarchical clustering approach for protein secondary structure prediction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CV-Fold** & \(k\) & \(N_{c}\) & \(N_{tr}\) & \(Acc_{v}\)(\%) & \(Acc_{t}\)(\%) \\ \hline
1 & 13 & 1500 & 48,928 & 83.5473 & 81.9567 \\
1 & & all & 65,903 & 83.8700 & 81.7186 \\ \hline
2 & 17 & 1500 & 55,458 & 81.3475 & 82.1839 \\
2 & & all & 65,212 & 81.1100 & 82.1839 \\ \hline
3 & 17 & 1100 & 47,607 & 84.0580 & 82.9848 \\
3 & & all & 64,833 & 83.8733 & 83.0583 \\ \hline
4 & 17 & 1400 & 54,249 & 83.3821 & 82.6808 \\
4 & & all & 65,901 & 83.3983 & 82.5315 \\ \hline
5 & 17 & 1500 & 55,628 & 84.1406 & 81.8397 \\
5 & & all & 65,048 & 84.2652 & 82.0593 \\ \hline
6 & 17 & 1500 & 54,810 & 81.9583 & 82.4427 \\
6 & & all & 63428 & 82.1063 & 82.6733 \\ \hline
7 & 17 & 1500 & 54,330 & 83.0679 & 84.8011 \\
7 & & all & 62,517 & 83.0439 & 84.7257 \\ \hline \end{tabular}
\end{table}
Table 2: Results for sample reduction by [PERSON]’s hierarchical clustering. A seven-fold cross-validation experiment is performed on CB513 benchmark.