esa-sceva/satcom-chunk-collection huggingface dataset:
# The Vibration Isolation Design of a Re-Entrant Negative Poisson's Ratio Metamaterial
## 1 Introduction
Meanwhile, the vibration suppression bands will also move, and the vibration propagation can be suppressed by making the suppression band contain the frequency of the vibration source. The resonance frequency of the overall structure of the metamaterials can be adjusted by the mass of the particle damping, the amplitude of the resonance peak can be suppressed by the particle damping, and the damping energy consumption effect can also suppress the vibration of other frequencies. On the other hand, in the medium- and high-frequency bands, the bandgap properties of the metamaterial can be adjusted by regulating the mode of the metamaterial cells through an increase in the particle mass or metal pins. In summary, by introducing metal pins and particle damping, the structural bandgap and the overall mode frequency can be adjusted, and vibration control can be realized. The improvement of the carrying capacity and vibration suppression capacity is conducive to the application of negative Poisson's ratio metamaterials in automotive, aerospace, and other engineering fields. The design scheme presented in this paper is expected to be applied in the design of space equipment. In this paper, Section 2 introduces the structural characteristics and design principles of the re-entrant negative Poisson's ratio metamaterial. Section 3 presents the static mechanical performance under different assembly conditions. Section 4 investigates the bandgap characteristics of the metamaterial under different assembly conditions, along with methods for bandgap control. Additionally, particle damping is introduced to suppress low-frequency vibration, and the effect of particle damping on the bandgap characteristics is examined. Section 5 validates the vibration isolation performance of the metamaterial by using simulation and experimental methods, with a focus on the increased suppression of low-frequency vibration due to particle damping.
## 2 Materials and Methods
### Real-Time Radar-Gauge Merging Using Spatio-Temporal Kriging with External Drifts (CombiPrecip)  
Combining rain gauges with radar measurements through geostatistical interpolation techniques is a valid solution to obtain reliable precipitation fields [17; 18]. The merging of radar and rain gaugemeasurements is operationally performed at MeteoSwiss by CombiPrecip, a kriging-with-external-drift geostatistical method which incorporates both spatial and temporal information into the modeling and estimation technique [22]. CombiPrecip locally adjusts the radar rainfall map according to the values of the rain gauges; the main assumption is that the point rain gauge measurements are the primary, trustworthy data, while the radar data function as an external drift. The scheme includes a convection control routine, in order to reduce the negative effect on kriging of the limited representativeness of rain gauge measurements in case of convective rainfall patterns [40]. The product is based on hourly accumulations and is updated every 10 min.
## 3 Proposed Cumulative QoE Model
### Memory Effects
#### 3.2.2 Recency Effect  
The recency effect [16; 41] refers to the ability of the human memory to recall the most recent events [42]; hence, the evaluated QoE heavily depends on the recent experiences. The recency effect also can be described by an exponential curve represented by the following equation:  
\[f_{R}(t)=exp(-\alpha_{R}*(L-t)),\quad 0\leq t\leq L \tag{4}\]  
where \(\alpha_{R}\) determines the _intensity_ of recency effect.  
The primacy effect and the recency effect can be combined as the U-shaped form [16], quantifying the influenced weight of the events occurring from the beginning to the end of a video session. As shown in Figure 3, it can be observed that Equations (3) and (4) reflect the primacy and recency effect extremely well.  
Figure 2: LSTM (Long Short-term Memory)network [15] for the user’s instantaneous QoE prediction. The network is composed of two LSTM layers. The inputs to the layers are four features including STSQ, PI, NR, and TR. The outputs combine the LSTM layers’ hidden states, representing the predicted instantaneous QoE values.
# Implementation of Digital Twin for Engine Block Manufacturing Processes
## 1 Introduction  
The digital twin (DT) is becoming progressively more interesting to academia and industry. The main questions are: what technologies are needed to create DT and how can the DT improve manufacturing processes? Commonly characterized as containing a physical entity, a virtual counterpart, and the data connections in between, the DT is increasingly being examined as a way of advancing the efficiency of the physical assets. [1]. The DT is a digital description of an operating product or service that consist of its preferred features, attributes, circumstances, and ways of behaving through replicas, information, and data within a single or even across multiple life-cycle phases [2]. The DT has important pertinence in the production context as it has the capability to improve production processes, discover points of congestion in a production system, confirm settings, and simulate situations to forecast performance [3].  
Manufacturing and fully integrated DTs are suggested in the literature. However, currently operated DTs in industrial practice still need attention [4]. According to [PERSON] et al. [5] regardless of an intense ambition from small and medium-size enterprises (SMEs) to integrate DT into their everyday business, a majority of the SMEs are inexperienced with the essential technologies andtools of the DT. [PERSON] and [PERSON] [6] formulated four obstacles to the implementation of the DT: lack of human resources, organizational barriers, high expenses, and the deficiency of legislative support. Furthermore, the next limitation relates to DT implementation occurring because there is no standardized approach for development of the DT, and an application framework for DT is missing. Normalized procedures guarantee comprehension between users while ensuring information flow between each stage of DT development and implementation [4]. However, the four standards for a DT manufacturing network [7; 8; 9; 10] are currently under development. These standards define general principles of the DT framework for manufacturing and specify requirements for the reference architecture, digital representation of the manufacturing elements and information exchange.  
Although many authors proposed and described various frameworks for DT implementation in the manufacturing industry context, there is an absence of real-life implementation studies reported in the available literature. The main aim of this paper is to demonstrate the feasibility of DT implementation under real conditions of a production plant specializing in manufacturing of the aluminum components for the automotive industry.
## 3 Model Construction
### Softmax-AUC Index Weighting Method  
There are different prediction effects for different stations despite using the same model. Thus, this paper proposes the method of multi-station softmax-AUC with index weighting, which can integrate the information of each station for seismic prediction. The AUC index on the validation set can measure the prediction effects of each station. Therefore, this paper uses the softmax-AUC index weighting method to select suitable stations with AUC above 0.65. The prediction result of each station is 0 or 1. Specifically, 0 means no earthquake has occurred, and 1 means an earthquake has occurred, and the weight is normalized by \(\text{e}^{\text{AUC}_{\text{i}}}\). The threshold is set to 0.5, if the risk value (risk_value) is bigger than the threshold (threshold_value), the region is identified to have an earthquake, and vice versa. The equations are defined as follows:  
\[\text{station\_risk\_value}_{\text{i}}=\frac{\text{e}^{\text{AUC}_{\text{i}} }}{\sum_{\text{j}}\text{e}^{\text{AUC}_{\text{j}}}}*\text{pred}_{\text{i}} \tag{21}\]  
\[\text{risk\_value}=\sum_{\text{i}}\text{station\_risk\_value}_{\text{i}} \tag{22}\]  
\[\text{area\_pred}=\left\{\begin{array}{l}1\text{ if risk\_value}\ >\ \text{threshold\_value}\\ 0\text{ if risk\_value}\ \leq\ \text{threshold\_value}\end{array}\right. \tag{23}\]  
where station_risk_value\({}_{\text{i}}\) is the risk index of the station, \(\text{pred}_{\text{i}}\) is prediction result of station, threshold_value is the threshold which is set to 0.5, area_pred is the final regional prediction results.  
Figure 9: (**a**) Effect of window size for electromagnetic signals on prediction model. (**b**) Effect of time window size for geoacoustic signals on prediction model.
# Autonomous Pointing Control of a Large Satellite Antenna Subject to Parametric Uncertainty
## 3 Autonomous Controller Design
### Active Disturbance Rejection Filter
The separation however also influences the gain-phase characteristics of the system. The magnitude of the gain actually increases with the separation between zero and pole. A proper \(z_{1}\) and \(p_{1}\) should be chosen to balance the settling time and the stability of the closed-loop system._  
**Remark 4**: _If the constant errors are further considered in the closed-loop system, the controller from Equation (30) can be rewritten as:_  
\[T_{z}(s)=-(520s+10+\frac{0.02}{s})\cdot\frac{1\times 10^{6}s^{2}+200s+1}{1 \times 10^{6}s^{2}+1}\cdot F(s)\psi(s) \tag{31}\]  
_where an integrator \(\frac{0.02}{s}\) is added to the closed-loop system, and then a proportional-integral-derivative controller is achieved. The integrator and rejection filter have different effects, where the integrator is used to decrease the steady-state constant error and rejection filter can decrease periodic error of the closed-loop system. However, an integrator could also destroy dynamic performance of closed-loop system._
# Enhanced Spectral Amplitude Coding OCDMA System Utilizing a Single Photodiode Detection
## 4 Results and Discussion  
Based on the described model, the simulation was performed using Optisystem ver. 7. The obtained results are divided into four parts according to the absence or presence of DCF and EDFA as follows.
## 5 Conclusions  
This paper designed an active jet noise controller based on an explicit model predictive control. Firstly, an integrated model of a turbofan engine and jet noise was established to calculate the engine parameters and jet noise in real time. The semi-empirical model of jet noise and the component level model of the turbofan engine were adopted. Secondly, the explicit MPC controller of active jet noise control was designed, based on explicit MPC theory. Thirdly, to improve the search efficiency and the real-time performance of the explicit MPC, a binary search tree was adopted as the online search algorithm to solve the location problem. Several conclusions can be drawn.  
1. The integrated model proposed in this paper reflects the characteristics of the turbofan engine. It can be used to calculate the engine parameters and jet noise in real time simultaneously.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Mpc** & **empc\_ss** & **empc\_bst** \\ \hline Max 1/ms & 261.33 & 10.597 & **7.889** \\ Min 2/ms & 4.397 & 0.436 & **0.149** \\ Mean/ms & 20.916 & 1.671 & **1.389** \\ \hline \hline \end{tabular}  
* max—maximum, 2 min—minimum.  
\end{table}
Table 3: Statistical characteristics of calculated time consumption in each step.  
Figure 9: Comparison of time consumed between different controllers.  
2. The prediction horizon 5 is the best choice. The explicit MPC controller with the prediction horizon 5 balances the computation burden and control accuracy. If the prediction horizon is less than 5, the control effect is worse, and if the prediction horizon is more than 5, the computation burden is large.
3. From Table 3, the explicit MPC controller based on a binary search tree has better real-time performance than the MPC controller with the same parameters for the studied system, with little loss of track performance. Compared with the explicit MPC controller based on a sequential search, the mean of time consumed at all steps decreases 16.87%.  
Conceptualization, R.J.; Data curation, R.J.; Formal analysis, X.H.; Investigation, X.Z.; Methodology, R.J.; Resources, X.H.; Writing--original draft, R.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 51976089 and 51576097.  
Not applicable.  
The data presented in this paper are available on request from the corresponding author.  
The authors declare no conflict of interest.  
The data presented in this paper are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection
## 6 Conclusions  
In this paper, we propose a maximum marginal approach to EEG signal preprocessing for emotion detection. The approach selects the features that are the least similar segments between EEG signals from the detail components. In the future, we can apply the proposed approach to select and study bispectrum features from the EEG signals for diagnosing [35; 36]. The proposed approach includes a time interval division method based on the dot product between two signals. It can be used in other time-series domains, such as in financial series and meteorological series in future research. Our experiments verify the performance of different models based on the selected features for emotion detection. The experimental results show that the proposed approach performs better than other feature selection methods by 17.9% on average in terms of accuracy. The MM-based models achieve better performance than the models without feature selection by 21%, on average, in terms of accuracy. We validate the performance by using the discrete wavelet transform. The results show that the first-level of wavelet transform achieves the best performance on emotion detection tasks. As the level increases, the evaluation metrics of accuracy, precision, and F1-score exhibit a downward trend. If the wavelet transform levels are more than 6, the performance tends to be stable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline MM-based & **0.880** & **0.952** & 0.822 & **0.835** \\ Wrapper & 0.790 & 0.749 & **0.880** & 0.763 \\ Built-in & 0.640 & 0.626 & 0.724 & 0.625 \\ Filter & 0.730 & 0.694 & 0.802 & 0.697 \\ Fisher score & 0.860 & 0.835 & 0.906 & 0.819 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results under the BiLSTM model.  
Figure 7: The performance of MM-based BiLSTM.  
**Author Contributions:** Conceptualization, G.L.; methodology, G.L.; software, G.L.; validation, G.L. and J.J.J.; formal analysis, G.L.; investigation, G.L.; resources, G.L.; data curation, G.L.; writing--original draft preparation, G.L.; writing--review G.L. and J.J.J.; supervision, J.J.J.; project administration, J.J.J.; funding acquisition, J.J.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2019K1A3A1A80113259).  
**Conflicts of Interest:** The authors declare no conflict of interest
## 4 Reliability Evaluation Method of Integrated Electric-Water System  
### Reliability Indices  
The power system's reliability indices are already fairly developed, but there are currently no reliability indices that account for leakage and water supply sustainability. Inthis section, the reliability indices of the WS are proposed by illustrating the expectation and probability of a load supply shortage.  
(1) Loss of Electricity Load Probability (LOELP)  
\[LOELP=\left(\sum_{y=1}^{N_{y}}\sum_{t=1}^{8760}x_{yt}^{\text{EDS}}\right)/8760N _{y} \tag{30}\]  
where \(x_{yt}^{\text{EDS}}\) is the state variable of the electric load shedding of the system at moment \(t\) in hours. If \(x\) is 1, it means that there is load shedding, and if \(x\) is 0, it means that there is no load shedding.  
(2) Expected Energy Not Supplied (EENS)  
\[EENS=\left(\sum_{y=1}^{N_{y}}\sum_{t=1}^{8760}\sum_{i=1}^{n}\Delta D_{i,yt}^{ \text{EDS}}\right)/N_{y} \tag{31}\]  
where \(\Delta D_{i,yt}^{\text{EDS}}\) is the electrical load shedding at node \(i\) of the power system at time \(t\) in hours.  
(3) Loss of Water Load Probability (LOWLP)  
\[LOWLP=\left(\sum_{y=1}^{N_{y}}\sum_{t=1}^{8760}x_{yt}^{\text{WDS}}\right)/876 0N_{y} \tag{32}\]  
where \(x_{yt}^{\text{WDS}}\) is the state variable of water load shedding of the system at moment \(t\) in hours.  
(4) Expected Water Not Supplied (EWNS)  
\[EWNS=\left(\sum_{y=1}^{N_{y}}\sum_{t=1}^{8760}\sum_{i=1}^{\text{W}_{\pi}} \Delta D_{\text{W}_{\pi,t}}^{\text{WDS}}\right)/N_{y} \tag{33}\]  
where \(\Delta D_{\text{W}_{\pi,t}}^{\text{WDS}}\) is the water load shedding at node W\(\pi\) of the WS at time \(t\) in hours.  
(5) Probability of Water Leakage (PWL)  
\[PWL=\left(\sum_{y=1}^{N_{y}}\sum_{t=1}^{8760}x_{yt}^{\text{L}}\right)/8760N_{y} \tag{34}\]  
where \(x_{yt}^{\text{L}}\) is the state variable for the presence of pipe leakage in the WS at moment \(t\) in hours.  
(6) Expected Water Leakage (EWL)  
\[EWL=\left(\sum_{y=1}^{N_{y}}\sum_{t=1}^{8760}\sum_{i=1}^{N_{\text{W}_{\pi}}}q_ {\text{L}Wmm,yt}\right)/N_{y} \tag{35}\]  
where \(q_{\text{L}Wmm,yt}\) is the leakage at pipe \(m-n\) of the WS at time \(t\) in hours.  
(7) Comprehensive Loss of Energy Not Supply (CLENS)  
\[C_{\text{ENS}}=c_{p}\text{EENS}+c_{w}\text{EWNS}+c_{l}\text{EWL} \tag{36}\]  
where \(c_{p}\), \(c_{w}\), and \(c_{l}\) denote the penalty cost per unit of electric load shedding, water load shedding, and pipeline leakage, respectively; and EENS, EWNS, and EWL de note the expected values for electric load shedding, water load shedding, and pipeline leakage, respectively.
# A Relay Optimization Method for NOMA-Based Power Line Communication Systems
## 2 System Model
### Channel and Noise Model
For modeling impulsive noise, the Markov chain model is generally considered a good fit. The fundamental characteristic of a Markov chain is that previous states do not affect subsequent states, similar to the basic characteristics of impulsive noise. **Definition 1**. : _Let a Markov chain's state value \(X_{m+k}\) at time \(m+k\), be \(a_{j}\), and the state value \(X_{m}\) at time \(m\), be \(a_{i}\). If the state values \(X_{s_{i}}\) at times \(s_{i}\) are \(a_{s_{i}}\) and satisfy \(s_{1}<s_{2}<\cdots<s_{l}<m<m+k\), then according to the definition of the Markov chain:_  
\[ \begin{split} P\big{\{}X_{m+k}=a_{j}&\big{|}X_{s_{ 1}}=a_{s_{1}},\cdots,X_{s_{l}}=a_{s_{l}},X_{m}=a_{i}\big{\}}\\ =P&\big{\{}X_{m+k}=a_{j}\big{|}X_{m}=a_{i}\big{\}}\\ =&\ p_{ij}(m,m+k)\end{split}  \tag{6}\]  
_where \(p_{ij}(m,m+k)\) is the \(k\)-step transition probability, which is the probability that \(X_{m+k}=a_{j}\) given \(X_{m}=a_{i}\). When \(k=1\):_  
\[ \begin{split} P\big{\{}X_{m+1}=a_{j}&\big{|}X_{s_{ 1}}=a_{s_{1}},\cdots,X_{m-1}=a_{m-1},X_{m}=a_{i}\big{\}}\\ =&\ P\big{\{}X_{m+1}=a_{j}\big{|}X_{m}=a_{i}\big{\}}\\ =&\ p_{ij}(m,m+1)\end{split}  \tag{7}\]At this time, \(p_{ij}(m,m+1)\) represents the one-step transition probability, and the matrix composed of these \(p_{ij}(m,m+1)\) values is the Markov chain transition probability matrix. Let \(N\) be the total number of states in the Markov chain. The transition probability matrix is given by:  
\[P(m,n)= \begin{bmatrix}p_{11}(m,n)&p_{12}(m,n)&\cdots&p_{1N}(m,n)\\ p_{21}(m,n)&p_{22}(m,n)&\cdots&p_{2N}(m,n)\\ \vdots&\vdots&\ddots&\vdots\\ p_{N1}(m,n)&p_{N2}(m,n)&\cdots&p_{NN}(m,n)\end{bmatrix}  \tag{8}\]  
For example, the moment from low noise to high noise is defined as the state transition moment, and the state transition probability at this moment is the probability of pulse noise. According to the law of total probability, the sum of the elements in each row and column of the transition matrix equals 1. This fundamental characteristic of the Markov chain transition matrix can be used to verify the accuracy of the calculated matrix.
# Cracking and Toughening Mechanisms in Nanoscale Metallic Multilayer Films: A Brief Review
## 2 Microstructure
### Dimensionally-Induced Structural Transformations
#### 2.2.2 Interface Structure Evolution  
The degree of epitaxy, coherency, and texture of multilayers increased with decreasing \(h\) in fcc/fcc, hcp/hcp, and fcc/hcp NMMFs. For fcc/fcc Ag/Al [4] with lattice parameter mismatch is <1%, only a single peak was present in XRD results as shown in Figure 6a, indicating epitaxial character for all multilayers; for hcp/hcp Mg/Ti [32], epitaxial deposition between Mg and Ti matching the basal plane has been the prevalent growth mode as \(h\) is small enough, in spite of the relatively large lattice mismatch between Mg and Ti (Figure 6b); for fcc/hcp Cu/Co [20], interface structure evolution will experience different stages as \(h\) decreases (Figures 3 and 6c). The moderate lattice mismatch between Cu and Co indicated that the interface of the Cu/Co multilayers should be semi-coherent at best.  
Figure 5: (**a**) HRTEM of the Mg/Nb multilayer shows the metastable bcc Mg; (**b**) The structure transformation diagram of Ti/Nb multilayers [21; 26].  
At the Cu/Co interface, dis-registry of atoms could be replaced by misfit dislocations periodically. The equilibrium spacing of misfit dislocations is given as:  
\[s=b/\varepsilon \tag{3}\]  
\[h_{c}=\frac{b}{8\pi\varepsilon(1+v)}(\ln\frac{h_{c}}{b}+1) \tag{4}\]  
In general, as suggested by [PERSON] and [PERSON] [33; 34; 35; 36], the critical thickness \(h_{c}\) for epitaxial growth could be larger than the calculated value by Equation (4) since other strain relaxation factors, such as the elastic isotropy, linear elastic strain in the lattices or difficulties associated with the formation of misfit dislocations, are involved.  
Figure 6: \(Cont.\)
## 4 Discussion
### EO Derived Information on Human Settlements
When considering the least efficient LUE class (LUE > 2), built-up areas per capita in urban centers in Northern America are 10 times that in centers in Africa. Similar patterns are also found in the other LUE classes. This fact implies that substantially heterogeneous patterns of development can be captured by comparable--even the same--LUE values. By formulation, the LUE indicator does not retain a spatially explicit nature (dimensionless), yet it could be a proxy for a spatial and demographic trajectory of change. The Land Use Efficiency estimates seem to suggest that several territories encountering extensive population growth (i.e., Sub-Saharan Africa and South-East Asia) develop with LUE values that are between 0 and 1, corresponding to population densification, while negative LUE values (LUE < \(-1\)) is frequent in countries with low--or even negative--population growth rates (i.e., Eastern Europe, Japan, and some converging regions of the European Union).