esa-sceva/satcom-chunk-collection huggingface dataset:
# Interval Uncertainty Optimization Method for Electromagnetic Orbital Launcher
## 4 Process of Interval Uncertainty Optimization Method
### Algorithm Implementation Process  
When using the interval-based uncertainty optimization method, the specific implementation process is as follows.  
1. Sampling in the mixed space (design space and uncertainty domain) to obtain multiple sets of samples consisting of design variables \(X\) and uncertainty variables \(U\).
2. For any sample \((X,U)\), the uncertain objective function value \(f(X,U)\) is calculated by the finite element simulation model, which constitutes the sample set of the inner layer proxy model.
3. The design variables and uncertainty variables are used as inputs, and the uncertain objective function values are used as outputs to build an inner layer high-precision proxy model.
4. For each sample \(X\), two inner-level optimizations of \(\min_{U\in U^{l}}f(X,U)\) and \(\max_{U\in U^{l}}f(X,U)\) are performed to find the lower bound \(f^{L}(X)\) and upper bound \(f^{R}(X)\) of the objective function, respectively. The optimization solution process is shown in Figure 5.
5. An outer proxy model is established, with the design vector \(X\) as input and the lower bound \(f^{L}(X)\) and the upper bound \(f^{R}(X)\) of the uncertain objective function at \(X\) as output, to realize the nonlinear mapping between the input and output.
6. The trained outer proxy model is combined with the optimization algorithm to solve the transformed Equation (18) problem, as shown in Figure 6. For the design vector individuals generated by the PSO optimization algorithm, the interval of the objective function is obtained directly by the outer proxy model, and then the optimal solution or optimal solution set is selected by calculating the fitness value based on the multi-objective evaluation function.  
Figure 4: Flowchart of two-layer optimization system.  
Figure 5: The boundary of the objective function.  
Figure 6: Flowchart of optimization solution process.
# Improvement of Learning Stability of Generative Adversarial Network Using Variational Learning
## 3 Improving the Learning Stability of Generative Adversarial Networks Using Variational Learning  
### Problem Statement  
As indicated previously, it was proved that there was a unique solution of the minimax game of Equation (1) if the optimal discriminator could always be obtained for the generator during the learning of GAN [1]. However, in the learning process of GAN, if the discriminator approaches the local optimum, the gradient of the generator can vanish without reaching a unique solution. The generator deals with the challenging problem of generating high-dimensional data of excellent quality that is difficult to be distinguished from real data. Conversely, given that the discriminator deals with the relatively easy problems of discriminating between real and low-quality data generated by the generator early in the learning process, the learning of the discriminator often converges much faster than that of the generator. In this case, the discriminator can be close to the local optimum. This results in a vanishing gradient that occurs in the generator. As the weights of the generator are updated by the backpropagation algorithm during the learning of GAN, the generator generates fake data that are closer to real data. Given that the generated fake data reduce the accuracy of the learning of the discriminator up to that point, the learning of the discriminator continues. The discriminator again provides a new learning standard for the generator, and the learning of GAN thus continues. However, when the gradient of the generator vanishes owing to the reason described above, the quality of the fake data generated by the generator no longer improve. Consequently, the learning of the discriminator also halts and, in the end, the learning of GAN fails. Figure 1 shows an example of the vanishing gradient of the generator during the learning process of GAN for the CelebA dataset [33].  
We trained GAN using a pretrained discriminator to artificially create a situation where the discriminator performed better than the generator, and observed that the gradient vanished from the generator, as shown in Figure 1.  
To solve the vanishing gradient problem, [PERSON] et al. [1] used Equation (11) in the learning of the generator by slightly modifying the original objective function of Equation (3).  
\[E_{z\sim P(z)}[-\log P_{\psi}(y|P_{\theta}(x|z))] \tag{11}\]  
However, [PERSON] et al. [38] indicated that the modification of the objective function contributed to the generation of good-quality data and facilitated early learning, yet caused mode collapse and destabilized learning. Moreover, the vanishing gradient problem was still observed when these alternative objective functions were applied to real problems [11].
## 1 Introduction  
### Background on Multivariate Time Series Clustering  
Many daily events around us, such as physical activities tracked by sensors, stock market fluctuations, and changes in nature, can be represented as time series, which can be broadly divided into univariate and multivariate time series. A univariate time series, recorded over time \(t=1,2,...,L\), is defined as a row vector \(\mathbf{y}=[y(t),...,y(L)]\in\mathbb{R}^{1\times L}\)[1, 2, 3, 4, 5, 6]. In contrast, a multivariate time series (MTS), which is a record of \(p>1\) dimensional data, is defined as a matrix \(\mathbf{Y}=[\mathbf{y}(1),...,\mathbf{y}(L)]\in\mathbb{R}^{p\times L}\). During the last few decades, several analysis techniques such as clustering [7, 8], forecasting [9], and classification [10] have been explored to understand the relationships among the \(p\) variables and their temporal dynamics in MTS. Among these techniques, MTS clustering has attracted significant research interests due to its capability in unveiling temporal characteristics and the underlying relationships among the variables [3, 7, 11, 12].  
To effectively capture the characteristics of MTS data, various representation techniques have been extensively investigated in the literature, including dynamical models [13, 14], linear subspaces [2, 15], and variants of principal component analysis [16, 17].  
The autoregressive moving average (ARMA) model, in particular, has been a popular choice for dynamical modeling due to its ability to effectively capture MTS characteristics [14, 18, 19]. However, since MTS data represented by ARMA models do not reside in a Euclidean vector space, calculating similarities between MTS samples for clustering becomes challenging. To address this issue, the observability matrix derived from ARMA model parameters has been introduced to represent MTS in a linear subspace [20, 21]. Note here that such linear subspace representations have been primarily investigated in computer vision problems [22].
# Access Control and Pilot Allocation for Machine-Type Communications in Crowded Massive MIMO Systems
## 3 Dynamic Access Control and Pilot Allocation Algorithm
### Optimization Problem and Algorithm  
The proposed DACB-SUCR method is shown in Figure 3, it consists of access control by the ACB scheme and pilot allocation by SUCRe protocol. Let \(P_{0}\) denote the set of mutually orthogonal pilots, \(P_{0}=\{\xi_{1},\xi_{2},\xi_{3},...,\xi_{\tau_{p}}\}\). Moreover, we have \(\tau_{p}\ll N\) because of coherence limitation. We assume that each active MTCD chooses a pilot of the \(\tau_{p}\) pilot sequences at random in each time slot. When MTCD \(k\) attempts to access, it transmits a pilot with a non-zero power \(\rho_{k}>0\), otherwise \(\rho_{k}=0\). Let \(\mathbf{h}_{k}\in\mathbb{C}^{M}\) denote the channel vector between MTCD \(k\) and the BS. Assume that the propagation model has channels satisfying two conditions:  
\[\lim_{M\rightarrow\infty}\frac{||\mathbf{h}_{k}||^{2}}{M}=\beta_{k},\quad \forall k,\]  
\[\lim_{M\rightarrow\infty}\frac{\mathbf{h}_{k}^{H}\mathbf{h}_{i}}{M}=0,\quad \forall k,i,\;k\
eq i,\]  
where \(\beta_{k}\) is the strictly positive and is known to MTCD \(k\). The study in [27] showed that such channels offer channel hardening and asymptotic favorable propagation. The BS can know the number of pilot collisions and successes, but it cannot know the number of MTCDs that are requesting pilots. To realize the dynamic access control mechanism, the BS needs to estimate the number of MTCDs which are contending for pilots. We present a dynamic access control algorithm by using the estimation mechanism in order to find the optimal ACB factor \(p_{\mathrm{ACB},i+1}\) for the next time slot. According to [28], the number of active MTCDs is determined by three factors: the number of new arrivals \(X_{i}\) in the \(i\)th time slot; the number of MTCDs backlogged by the pilot random access failures, which need to re-access the network in \(i\)th time slot, denoted as \(Y_{i}\); and the number of MTCDs backlogged by the ACB scheme in some previous slots, denoted as \(Z_{i}\). Let \(K_{i}\) denote the number of active MTCDs during \(i\)th slot. Thus, \(K_{i}=X_{i}+Y_{i}+Z_{i}\). Let \(K_{C,i}\) denote the number of MTCDs passes through the ACB scheme. During the \(i\)th time slot, there are \(K_{C,i}\) MTCDs applying for pilot allocation by SUCRe protocol after ACB scheme and in each time slot the number of available pilots is \(\tau_{p}\). The proposed method consists of two phases: the Estimation phase and Access control phase.
## 1 Introduction
The results from comparison experiments demonstrate the efficiency of the proposed ECPSO-MTP. The structure of this paper is organized as follows: Section 2 provides a brief introduction to the basic framework of TPs with evolutional symport/antiport and promoters and MTPs with evolutional symport and promoters. Section 3 details the evolutionary mechanism of PSO based on environmental factors and crossover operators. Section 4 presents a detailed depiction of the proposed ECPSO-MTP, including its general framework, evolution rules, communication rules, and complexity analysis. Experimental results and discussions of the proposed ECPSO-MTP, which were conducted on eight clustering datasets and compared with five existing methods, are discussed and analyzed in Section 5. Section 6 illustrates the performance of the proposed ECPSO-MTP based on the comparison results of three clustering approaches on eight segmentation images. Finally, the summarization and conclusions for this paper and recommendations for future works are outlined in Section 7.
## 3 Results and Discussion
### Importance of Wavelengths
The dominant effect of calcium carbonate over PCBs appears evident in Figure 5.  
The bar plot of the regression b-coefficient for EOX (Figure 7b) basically reflects that for PCBs. In this plot, more significant wavelengths appear around 1410 nm, 2060 nm, and 2280 nm. As previously discussed, kaolinite and illide adsorb strongly around 1410 nm, and this absorption is reduced by the interference of PCBs (Figure 5) and, eventually, by that of other contaminants linked with EOX. The bar plot of the b-coefficient of the PLSR calibration model for Penta-CB (Figure 7c) shows important wavelengths in the visible range, with the highest, negative value at 530 nm, near the absorption band of hematite. In the NIR, high, positive b-coefficients occur around 1390, 1890, 2180, 2360, and 2360 nm, while negative b-coefficients occur around 1440, 1940, 2230, and 2260 nm. Most of these wavelengths are close to the absorption bands of soil properties, having direct spectra response in the NIR, such as clay minerals, carbonates, and organic matter [64]. The PLSR regression coefficient, b, spectrum for Hepta-CB shows several important wavelengths in the visible range and in the first part of the NIR, around 970 nm, close to the position of the absorption bands of iron-oxides, along with a high number of important wavelengths in the rest of the NIR, close to the absorption bands of clay, carbonates, and organic carbon. It must be observed that much of the important NIR wavelengths have a negative b-coefficient. Therefore, the interference of Hepta-CB with other spectrally active soil properties is negligible.
# Influence of Load Dynamics on Converter-Dominated Isolated Power Systems
## 3 Case Study  
The power system of a medium-size island is considered in this paper, as shown in Figure 6, comprising a transmission infrastructure operated at 30 kV. This case study is based on a real isolated power system from a medium-size geographical island located in the European Atlantic Ocean, which was also used in previous works [30]. The data of this study case can be obtained upon request. The generation system integrates a 9 MW solar power plant (SPP) and two wind farms (WF) with a nominal power capacity of 9 (WF1) and 5.5 MW (WF2), respectively. Since the purpose of this work is to study operational scenarios with 100% converter-based generation, no thermal power plant was considered. Consequently, the local system operator assumes the integration of a BESS, whose power converter is operated as a grid-forming unit, to provide grid regulation services (for voltage and frequency). The BESS must have a power capacity which assures the system stability following large voltage disturbances (energy capacity sizing is out of the scope of this paper).  
The considered network operational scenario is presented in Table 1, where CB represents capacitor banks. The admissible frequency for the system is within the range of 48.5 to 53 Hz. For frequency values below 48.5 Hz, load shedding protection takes place.  
A 100 ms three-phase symmetrical short circuit occurring at t = 0 s of the simulation time in the transmission line interconnecting buses \(B_{7}\) and \(B_{8}\) (see Figure 6) is considered, leading to the disconnection of the line after the fault clearance. In this paper, it is intended to further develop the work presented in [32], which led to the definition of a 12 MVA BESS, due to the need for assuring N-1 generation unit tripping for the presented network operational scenarios. Based on this assumption, it is considered that the initial BESS sizing should be of at least 12 MVA. The criterion followed to define the BESS power sizing is based on the assumption that all the network elements must recover to their pre-fault state after the fault clearance.  
The default grid-forming control parameters are provided in Table 2 (in Appendix A), with a maximum admissible value of the grid-forming converter interface current of 2 p.u.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Operational Scenario**} \\ \hline WF1 & 7 MW & 0 MVAR \\ WF2 & 5 MW & 0 MVAR \\ SPP & 8 MW & 0 MVAR \\ BESS & 0 MW & 1.3 MVAR \\ CB & 0 MW & 8.7 MVAR \\ \hline Load & 20 MW & 10 MVAR \\ \hline \hline \end{tabular}
\end{table}
Table 1: Studied operational scenario.  
Figure 6: Single-line diagram of the studied isolated power system.  
having been assumed, in line with some of the currently available commercial solutions [4]. Since there are no SMs operating the network, it is considered that the network frequency corresponds to the frequency synthetized by the grid-forming BESS.
# Review of Vibration-Based Structural Health Monitoring Using Deep Learning
## 2 Early Health Monitoring Using Vibration Responses
The inserted Lamb wave was diffracted by an object-caused time delay between the main burst and specific outgoing mode. Two types of modes (SH0 and A0) showed good results. SH0 ensured more reliability and robustness. The localization extent fitted well to the actual damage extent. [PERSON] et al. [65] diagnosed the impulsive vibration applied to a transformer tank by a sudden short circuit in a winding machine. Because the impulse signal applied to the transformer has a large nonlinearity, they applied the Hilbert-Huang transform (HHT) [66] to the analysis in the time-frequency domain. The fault occurred in the winding machine when the response in the 800-1000 Hz band became large. The impact vibration from a fault can be used for diagnosis. The impulse response has been analyzed to determine the natural frequency response of a system, which is advantageous for comparing normal and abnormal conditions in the frequency domain. The noise due to vibration generated by a machine has also been used for health monitoring. [PERSON] and [PERSON] [67] used empirical mode decomposition (EMD) to remove the aliasing effect to find the condition of an on-load tap changer (OLTC). The energy spectral similarity of the Lorentz information measure (LIM) was used for the fault diagnosis. LIM compares the energy distribution of the vibration response with the state of the machine. With their proposed method, they further confirmed that a certain state abnormality occurs depending on the degree of similarity and distinction between the normal and fault states. The results were applicable to the OLTC, and the model is actually used in industry guidelines. Because the training process for deep learning requires a huge amount of data, various attempts have been made to apply it in fields where securing a sufficiently large dataset is difficult [68; 69; 70; 71]. Simulations have been used to accumulate large amounts of training data to discriminate actual data.
# An Intelligent Time-Series Model for Forecasting Bus Passengers Based on Smartcard Data
## 2 Literature Review
### Time Series Forecasting  
Forecasting passenger flow is a time-series research field because bus data points (including smartcard and meteorology data) are indexed in time order and are therefore time-series data. A time series is a sequence of discrete time data, and the use of a time series model can help organizations understand the underlying causes of trends and systemic patterns over time. As such, the following section briefly introduces relevant knowledge about time series. Time series are data arranged in time order, and the first time series model to be developed was the linear autoregressive moving average model (ARIMA), which was proposed by [PERSON] and [PERSON] in 1970. The ARIMA model [24] consists of three components, and each component helps model different types of patterns. The autoregressive (AR) component attempts to explain the patterns between any time period and previous lag periods; the moving average (MA) component can adapt the new forecasts to previous forecast errors (error feedback term); and the integrated (I) component indicates trends or other integrative processes in the data.  
Traffic flow data are time series of periodic and irregular fluctuations, and many studies have used time-series methods to predict traffic flow. [PERSON] et al. [25] combinedARIMA with a wavelet neural network to overcome the limitations of using ARIMA for short-term forecasting of traffic flow. [PERSON] et al. [9] used seasonal differences to eliminate nonstationary seasonal ARIMA and combined these data with support vector regression to predict the demand of the aviation industry. [PERSON] et al. [26] used wavelet analysis to detect abnormal passenger flow to estimate the sudden traffic peak and then used a multiple regression model to estimate the peak time. Finally, they used seasonal ARIMA to estimate passenger flow.  
In terms of an intelligent time-series model, AR neural network (ARNN) is a classic intelligent time-series model that uses a neural network to learn AR coefficients [27]. Further, we can collect many influential variables and lag periods of the dependent variable, and then we use MLP, SVR, RBF neural network, and LSTM network to train their parameters for building intelligent time series models. From the practical viewpoint, it is critical to properly handle weights in time series, and weighted time-series models include weighting on recent observations, important variables, and the better forecasting methods. For example, [PERSON] and [PERSON] [28] proposed a weighted sequential hybrid model to calculate each model weight to construct a final hybrid output for time series forecasting; [PERSON] et al. [29] proposed a multifactor fuzzy time-series fitting model to weight the three significant variables; [PERSON] et al. [30] presented a weighted time-series forecasting model to weight recent observations.
## 2 Experiment Setup and Model Configuration
### Experimental Setup for Aerodynamic Force Measurements  
A six-component force balance (Model Gamma SI-600-60, ATI Industrial Automation) was employed to obtain the values for all aerodynamic forces (i.e., lift, drag and moment of the test models, Figure 3). The force balance was mounted on a frame outside the wind tunnel using a threaded iron rod. The sampling frequency and time of the force balance were set to 1000 Hz and 90 s, respectively.  
The lift, drag, and moment coefficients (i.e.,\(C_{d}\), \(C_{l}\) and \(C_{m}\) can be obtained by applying aerodynamic forces to the test models at different wind attack angles, as follows:  
\[C_{l}=\frac{F_{l}}{\frac{1}{2}\rho U^{2}B},\ C_{d}=\frac{F_{d}}{\frac{1}{2} \rho U^{2}B},\ C_{m}=\frac{F_{m}}{\frac{1}{2}\rho U^{2}B^{2}} \tag{2}\]  
where \(U=12\) m/s is the wind speed of the incoming airflow, which is calculated by the static pressure and total pressure tested by a Pitot tube; \(B\) is the width of the test model; \(\rho\) is the air density, which was chosen as 1.225 kg/m\({}^{3}\); and \(F_{d}\), \(F_{l}\) and \(F_{m}\) are the drag, lift, and moment per unit length, respectively.