esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Time-Variant Reliability Analysis Method with Mixed Uncertainties
## 3 Analysis Method of Structural Time-Variant Reliability Model with Random and Interval Variables  
### Time-Variant Reliability Model of Structures with Random and Interval Variables  
Studies show that structural models in the majority of practical engineering problems contain random and interval variables. Accordingly, the time-variant reliability model of structural mixed variables can be changed into the following expression:  
\[g(R(t,\mathbf{Y}),S(t,\mathbf{Y}))=R(t,\mathbf{Y})-S(t,\mathbf{Y}) \tag{7}\]  
\[\mathbf{Y}\in\left[\mathbf{Y}^{L},\mathbf{Y}^{U}\right],Y_{j}\in\left[Y_{j}^{ L},Y_{j}^{U}\right],j=1,2,\ldots,n \tag{8}\]  
where \(\mathbf{Y}\) is an \(n\)-dimensional interval variable.  
Under the generalized stress intensity model, the limit state function is expressed as  
\[g(R(t,\mathbf{Y}),Q(t,\mathbf{Y}),\mathbf{A})=R(t,\mathbf{Y})-\mathbf{A}-Q(t, \mathbf{Y}) \tag{9}\]  
within the design service period \(T\), instantaneous reliability and failure probability of the structure are as follows:  
\[P_{s}(t)=P\{g(R(t,\mathbf{Y}),Q(t,\mathbf{Y}),\mathbf{A})>0,t\in[0,T]\} \tag{10}\]  
\[P_{f}(t)=1-P_{s}(t)=1-P\{g(R(t,\mathbf{Y}),Q(t,\mathbf{Y}),\mathbf{A})>0,t\in[ 0,T]\} \tag{11}\]
# Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity
## 4 Results
### Seasonal Decomposition  
Individual decompositions of the data were performed, focusing on their trend, seasonality, and residual components. Previous analysis of the charts shows that the series do not vary too much over time, but even so they may contain some seasonal components, with additives being more probable than multiplicatives, considering that the average and standard deviation do not suffer large changes over time. Therefore, seasonal decomposition was performed using the additive model. In terms of the period parameter, different values were tried. The value 2016 (\(288\times 7\)) was used to produce the charts shown in Figure 2, taking into account the number of intervals present weekly, in order to understand the weekly pattern. As the figure shows, the variable VCP 1 exhibits an absence of significant trends in the time series; there are no clear upward or downward movements. Additionally, it is worth noting the presence of reduced seasonality, as the seasonal component is fluctuating between the values of \(-0.02\) and \(0.03\).  
Figure 3 shows the seasonal decomposition of variable VCP 2. Again, despite the presence of numerous oscillations in the trend, crystal clear long-term growth and declining movements are not visible. The oscillations show there is more noise in this signal than in VCP 1, but the noise does not show clear patterns that could be identified at this point. Despite the noise and small amplitude of most mid-term trend variations, the chart seems to show that there is a growing trend until mid-April, followed by a sudden decline. And then, after this decline, there is a trend of slow growth until late September, which is again followed by a decline, this time with a period of instability. These faint variations over time may be important for further analysis. As for the seasonality, it exhibits similarities to the behaviour observed previously in variable VCP 1, so there are no clear seasonal components of significant amplitude.  
Regarding variable VCP 3, its seasonal decomposition is shown in Figure 4. The charts show the peculiar characteristics of this signal, which is different from VCP 1 and 2. Again, the seasonal component is negligible. The trend is variable in the first months and more stable after April. Until May, a quite pronounced but unstable trend is observed, which denotes problems in the equipment's normal operation. At the end of April, something changed and the trend decreased suddenly, before slowly increasing in subsequent months.
## 4 Discussion  
On the basis of the results presented in the previous chapter, the proposed water recovery system may be the real alternative to tap water for hydroponic lettuce cultivation located in Wroclaw in a cold season. In January, February, March, and December, the system covered 100% of the water demand in the analyzed period of 2012-2019. Moreover, high efficiency was achieved in April (89.2%), October (83.6%), and especially in November (99.6%). It should be emphasized that the obtained results are highly repeatable in years. Due to the very high efficiency of the system in the winter months (daily water recovery greater than demand) and very low system efficiency in the summer months (practically daily water recovery less than the demand), a large volume tank for water collection is not required. In this study a tank with a capacity of only 2 m\({}^{3}\) is proposed. The use of a larger reservoir would only slightly increase the efficiency of the system. It would happen only in the spring season (around April), when there was a sudden drop in the efficiency of the system and the accumulated water could replace tap water for a few days. Despite the clear seasonality, the water recovery system from the exhaust air covers an average of 67.1% of the water demand for hydroponic lettuce cultivation located in Wroclaw (from 61.1% in 2018 to 69.5% in 2012). Regarding the cold half-year when there is almost complete coverage of the water demand, this is a very good result.  
In order to fully cover the water demand, the proposed system should be equipped with alternative solutions, which are effective in the warm half-year. Due to the characteristics of rainfall in Poland, where statistically 2/3 of the annual rainfall falls in the warm half-year, rainwater harvesting is a promising supplement to the proposed system.
# Calculation of Displacement-Dependent Active Earth Pressure for Deep Excavations in Soft Soil
## 2 Materials and Methods
### Test Results and Analysis  
The radial stress-strain curves corresponding to the 15% axial strain range are plotted in Figure 8. It can be seen from the figure that the nonlinear relationships of the three curves are hyperbolic representations; therefore, the radial stress-strain relationship can be described on the basis of the Duncan-Chang model.  
The hyperbolic model of the soft soil is proposed in Equation (2), as follows:  
\[\frac{\varepsilon_{\mathrm{r}}}{\sigma_{\mathrm{r0}}-\sigma_{\mathrm{r}}}=a+b \varepsilon_{\mathrm{r}} \tag{2}\]  
where \(\sigma_{\mathrm{r0}}\) is the initial radial stress, \(\sigma_{\mathrm{r}}\) is the radial stress, and \(\varepsilon_{\mathrm{r}}\) is the radial strain. Figure 9a-c shows the linear relationship of the test results processed by Equation (2). The coefficients of determination R\({}^{2}\) are both larger than 0.99.  
The tangential modulus of the nonlinear curve can be obtained as follows:  
\[E_{\rm t}=\frac{\rm d(\sigma_{\rm r0}-\sigma_{\rm r})}{\rm d\varepsilon_{\rm r}}= \frac{a}{\left(a+b\varepsilon_{\rm r}\right)^{2}} \tag{3}\]  
When \(\varepsilon_{\rm r}\) approaches 0, \(a\) is a reciprocal of the initial tangential modulus \(E_{\rm i}\). The intercepts of the line vary under different initial radial stress values, as shown in Figure 9a-c. According to the suggestion by [PERSON] [48], \(E_{\rm i}\) can be described as follows:  
\[E_{\rm i}=Kp_{\rm a}(\frac{\sigma_{\rm r0}}{p_{\rm a}})^{n} \tag{4}\]  
When \(\varepsilon_{\rm r}\)\(\rightarrow\)\(\infty\), \(b\) is a reciprocal of the asymptotic value of \(\sigma_{\rm r}-\sigma_{\rm r}\). If the failure ratio \(R_{\rm sf}\) is defined as  
\[R_{\rm sf}=\frac{\left(\sigma_{\rm r0}-\sigma_{\rm r}\right)_{\rm f}}{\left( \sigma_{\rm r0}-\sigma_{\rm r}\right)_{\rm ult}} \tag{5}\]  
then  
\[b=\frac{R_{\rm sf}}{\left(\sigma_{\rm r0}-\sigma_{\rm r}\right)_{\rm f}} \tag{6}\]  
In this case, the initial radial stress is the static earth pressure \(\sigma_{\rm r0}=\)\(\epsilon_{\rm 0}=\)\(K_{\rm 0}\)\(\gamma\)\(\gamma\), while the radial stress at failure is the active earth pressure (\(\sigma_{\rm r}\))\({}_{\rm f}=\)\(\varepsilon_{\rm a}\). Thus \((\sigma_{\rm r0}-\sigma_{\rm r})_{\rm f}=\)\(\epsilon_{\rm 0}-\varepsilon_{\rm a}\).  
The expression for the displacement-dependent active earth pressure can be obtained as follows:  
\[e_{\delta}=\sigma_{\rm r}=e_{0}-\frac{(e_{0}-e_{\rm a})\varepsilon_{\rm r}E_{ \rm i}}{(e_{0}-e_{\rm a})+R_{\rm sf}\varepsilon_{\rm r}E_{\rm i}} \tag{7}\]
## 2 Technical Enablers
### Deploying Low-Power Long-Range Networks
Existing designs dictate that IoT devices and concentrators should only transmit at about 1% of their total uptime to maintain a low power consumption while supporting high deployment numbers. Moreover, since the low-power operating mode is essential for IoT nodes, being constantly attached to a communication channel for down-link messages is not an option and is carried out only occasionally. In order to accommodate all the needs of the future, very dense, LPWAN deployments specifically designed as extensions need to be proposed and implemented [26; 81; 82]. The suitability of LPWAN technologies to interconnect smart water metering devices with cloud-based services has been evaluated in real-world deployments [20]. The evaluation of the LPWAN technology was based on a pilot deployment of different sensors deployed at various rural locations near to the Bay of Bengal to measure the quality of water by generating real-time data. The system enabled bi-directional communication to accommodate both sensing and control of the flow of water in a timely manner. In the system presented in this paper, although using similar LPWAN technologies, the data collected from the sensors and the decisions to control the flow of water do not take place at the cloud part of the system. In contrast to [20], the system presented here follows the fog computing paradigm, and in this way, the data are analysed at the edges of the network. For a comparative evaluation of the leading low-power, wide area network technologies useful for developing IoT networks in terms of a battery lifetime, cost, network coverage, latency, range and security, the reader is pointed to [83].
## 4 Technical Solution
During the initialization of each communication channel, the Broker provides the constraints that must be applied to the transactions and maintains an up-to-date register of the available EXIC endpoints so that, at any moment, a participant of the circular network is capable of discovering and connecting to a required data source. In this way, the Broker supports the third fundamental aspect of data management represented by Data Consumption. If the Broker and EXIC are the key enablers of the sovereignty principle, the Shared Vocabularies represent the foundations for semantic data exchange, since they contain the common format and data structures that each supplier must use to publish information and consume data. The whole set of information flowing onto the EXIC connections, in fact, must be expressed using neutral and, whenever possible, standard formats, maximizing  
Figure 4: Overview of the proposed platform architecture. the interoperability among the suppliers. As previously stated, the component of the architecture devoted to trusted data translation is the Data Apps deployed in the INIC; the management of the Vocabularies is another complementary natural responsibility of the AppStore. The following potential risks are standing related to the deployment of such platform [23]:  
* Being able to understand the requirements of users in terms of content, data encodings, and semantics and granularity is critically important. * The governance of data in an increasingly complex setting is challenging. How to make the most out of the scattered resources while retaining sovereignty from big software platforms is to be addressed. * Regulatory aspects that would ensure that all involved actors, such as the private sector and citizens, can contribute, but also benefit from the emerging data spaces should be clarified. Finally, it is important to highlight that multiple data spaces can occur and co-exist at different territorial levels, which, in turn, would require the elaboration of organizational and technological approaches for coupling the different data spaces in a loose and flexible manner.
## 2 Proposed Method
### Model Training Details  
To train the parameters in the proposed network, a proper loss function is needed. For a training set of synchronized \"load-response\" data, the loads at a certain time stamp \(t_{i}\) can be decomposed into 8 high-frequency components and \(1\) low-frequency components using the procedures in Section 2.2.1. Let \(v^{i}_{truth}\in\mathbb{R}^{9\times 1}\) be the vector of the ground truth load at time \(t_{i}\). The loss is defined as the mean square error (MSE) between the predicted load \(v^{i}_{out}\) at time \(t_{i}\) and \(v^{i}_{truth}\),  
\[L_{i}=||v^{i}_{out}-v^{i}_{truth}||_{2}, \tag{7}\]  
where \(||\cdot||\) is the L2 norm. The mini-batch gradient descent using Adam optimization is applied for network parameter learning. For a training batch that containing \(B\) load samples, the total loss is \(L=\sum_{i=1}^{B}L_{i}\). The learning rate is \(1.0\times 10^{-4}\), and the batch size is set to 1024. To reduce the model over-fitting, during the training of the three conventional layers, i.e., Conv1D #1 to Conv1D #3, a dropout [39] is applied with a dropout probability of 0.2.
# Control Chart Concurrent Pattern Classification Using Multi-Label Convolutional Neural Networks
## 2 Methods
### Traditional Machine Learning Algorithms  
#### 2.3.1 Support Vector Machines  
Support vector machines (SVMs) are a set of supervised learning methods that can be used for solving both classification and regression problems [47]. The algorithm used for classification is usually referred to as SVC and that used for regression is called SVR. In this section, we introduce the principles of SVC, which is relevant to this research. SVC is based on the margin maximization principle. It performs structural risk minimization, which improves the complexity of the classifier with the aim of achieving excellent generalization performance. The basic objective of this technique is to project nonlinear separable samples onto another higher dimensional space by using different types of kernel functions. The SVC algorithm accomplishes the classification task by constructing an optimal hyperplane that separates the data into categories in a higher dimensional space. SVC was first designed for binary classification. Nevertheless, it can be adapted for multi-class classification using the one-vs-one or one-vs-rest (OVR) method [48]. OVR is a heuristic approach for using binary classification algorithms in performing multi-class classification. It involves splitting the multi-class dataset into multiple binary classification problems. The one-vs-one strategy splits a multi-class classification into one binary classification problem per each pair of classes. To optimize classification performance, the SVC classifier parameters, kernel width \(\gamma\), and regularization constant \(C\) must be properly chosen.
## 3 Numerical Results  
In order to evaluate the performance of the proposed Q-learning-based PV GMPPT method, a model of the PV system has been developed in the MATLABTM/Simulink software platform (Figure 5). The PV system under study consists of the following components: (a) a PV array connected according to the series-parallel topology, (b) a DC/DC boost-type power converter [29], (c) a GMPPT control unit which produces the duty cycle of the power converter PWM control signal, and (d) a battery that is connected to the power converter output terminals.  
Table 1 presents the operational characteristics of the PV modules comprising the PV array. Table 2 displays the values of the operational parameters of the proposed Q-learning-based GMPPT method, while Table 3 presents the action-space that was used for the Q-learning-based method. For each state, the action of \(\Delta D\) \"increment\" or \"decrement\" with the highest Q-value is performed, in order to ensure the detection of the GMPP with the minimum number of time-steps. There are three categories of duty cycle change (i.e., \"low\", \"medium\" and \"high\"), which may be selected depending on how close the agent is to the GMPP. The action of \"no change\" of \(\Delta D\) is used such that the agent is able to realize that the position of GMPP has now been detected. The operation of the PV system with the proposed Q-learning-based GMPPT method was simulated for 9 different shading patterns of the PV array. Regarding the state-space, the duty cycle was quantized in 18 equal steps, the PV array output power was quantized in 30 equal steps and the duty cycle value of the previous time-step was quantized in 9 equal steps. For comparison with the proposed method, the PSO-based GMPPT method [30] was also implemented and simulated as analyzed next. Table 4 displays the values of the operational parameters of the PSO-based GMPPT method. The values of the operational parameters of the proposed and PSO-based GMPPT algorithms (i.e., Tables 2-4) were selected, such that they converge as close as possible to the global MPP and with the minimum number of search steps for the partial shading patterns under consideration, which are presented in the following section.  
\begin{table}
\begin{tabular}{c c} Maximum Power (\(W\)) & 11.6 \\ Open-circuit voltage, \(\mathrm{V_{oc}}\) (\(\mathrm{V}\)) & 7.25 \\ Voltage at maximum power point \(\mathrm{V_{MP}}\) (\(\mathrm{V}\)) & 5.75 \\ Temperature coefficient of \(\mathrm{V_{oc}}\) (\%/deg\(\cdot\)\(\cdot\)\(\mathrm{{}^{\circ}C}\)) & \(-\)0.322 \\ Cells per module (\(\mathrm{N_{cell}}\)) & 24 \\ Short-circuit current \(\mathrm{I_{sc}}\) (\(\mathrm{A}\)) & 2.204 \\ Current at maximum power point \(\mathrm{I_{mp}}\) (\(\mathrm{A}\)) & 2.016 \\ Temperature coefficient of \(\mathrm{I_{sc}}\) (\%/deg\(\cdot\)\(\cdot\)\(\mathrm{{}^{\circ}C}\)) & 0.071996 \\ \end{tabular}
\end{table}
Table 1: Operational characteristics of each PV module.  
Figure 5: The model of the PV system under study in MATLABTM/Simulink.
# Intra-Mode Decision Based on Lagrange Optimization Regarding Chroma Coding
## 5 Experimental Results
### Coding Efficiency  
The proposed chroma intra coding is compared with the original scheme using Bjontegaard delta bitrate (\(\Delta\)bitrate) with the aid of YCbCr PSNR based on the RD curves [31]. A negative \(\Delta\)bitrate value indicates a reduction in the coding bitrate compared to the anchor scheme. The results of coding efficiency are summarized in Table 2, showing an approximate bitrate reduction of 1.23% in the intra-only configuration. Correspondingly, a bitrate saving of 1.03% is observed in the Random-Access configuration, as shown in Table 3, where the period of intra frame is 32. Based on the data, it can be seen that coding efficiency can be promoted with better results by using the modified intra-mode decision-based chroma RDO method. Furthermore, the RD curves in Figure 8 demonstrate that  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class/Resolution/Bit-Depth** & **Frame Rate** & **Sequence** \\ \hline \multirow{3}{*}{Class B/1920 \(\times\) 1080/8} & 50 & Cactus \\ \cline{2-3}  & 50 & BasketballDrive \\ \cline{2-3}  & 60 & BQTerrace \\ \hline \multirow{3}{*}{Class C/832 \(\times\) 480/8} & 30 & RaceHorses \\ \cline{2-3}  & 60 & BQMall \\ \cline{2-3}  & 50 & PartyScene \\ \cline{2-3}  & 50 & BasketballDrill \\ \hline \multirow{3}{*}{Class D/416 \(\times\) 240/8} & 30 & RaceHorses \\ \cline{2-3}  & 60 & BQSquare \\ \cline{2-3}  & 50 & BlowingBubbles \\ \cline{2-3}  & 50 & BasketballPass \\ \hline \multirow{3}{*}{Class E/1280 \(\times\) 720/8} & 60 & FourPeople \\ \cline{2-3}  & 60 & Johnny \\ \cline{2-3}  & 60 & KristenAndSara \\ \hline \hline \end{tabular}
\end{table}
Table 1: The test sequences used for verifying the proposed method.  
Figure 7: The framework related to the modified scheme.  
the proposed scheme achieves better RD performance compared to conventional VVC. In conclusion, the proposed algorithm contributes to enhancing chroma coding efficiency.
# Text Normalization Using Encoder-Decoder Networks Based on the Causal Feature Extractor
## 3 Experimental Results
### Third Experiment  
Unlike the other experiments, the purpose of dataset E3 is not to compare the different encoders, but to train the final architecture of the proposed CFE method with a more complete and complex input, in order to compare the obtained results with other state-of-the-art works reported in the literature. Therefore, the training set contains 1 million samples, and the test and validation sets have 200,000 samples each.  
The architecture is identical to the one used in the previous experiments, with the hyperparameters presented in Table 2, and five repetitions. Figure 11 depicts the evolution of the training and validation errors during the training phase, and Table 8 shows the accuracy measures obtained for the test set.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(p\)-Value & LSTM & FCNN & FE & CFE \\ \hline
**LSTM** & & 0.0001 & 0.0001 & 0.0001 \\
**FCNN** & 0.0001 & & 0.0001 & 0.0001 \\
**FE** & 0.0001 & 0.0001 & & 0.0003 \\
**CFE** & 0.0001 & 0.0001 & 0.0003 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: \(p\)-Values of the second experiment (E2), indicating the probability of the null hypothesis, i.e., the results of the models are not distinguishable.  
Figure 10: Evolution of the validation error (NLLLoss) during training of the four models on E2.  
The accuracy measures achieved for E3 are between those obtained for E1 and E2, with a mean accuracy of 92.74% and a CER of 5.44. This indicates that although the test cases are more complex and varied than E2, having a larger training set is beneficial for the system. On the other hand, Figure 11 seems to indicate that convergence was reached long before the 22 h duration of the experiment.