esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of a Permanent Magnet Synchronous Motor for e-Mobility Using Metamodels
## 3 Design Optimization
### Consideration  
The number of electrical slots in the optimal design model was increased by incorporating notches in the stator teeth of the PMSM. The peak-to-peak value of cogging torque is inversely proportional to the least common multiple of poles and slots numbers [31]; this contributed toward a reduction in the cogging torque, and consequently, the torque ripple. The FEA results confirmed that the cogging torque of the optimal model was reduced by 33.3% compared with that of the initial model, as shown in Figure 8. To analyze the effects of the stator notch and flux barrier on the motor characteristics, an additional analysis was performed; the results of this analysis are summarized in Table 6. When only the notch was applied, the torque ripple reduced in the optimal model more than in the initial model, and the average torque and efficiency also  
The optimal design results after applying both the best and worst metamodels were compared, as presented in Table 7. When the best metamodels were applied, the variation between the optimal model and FEA results was minimal. However, when the worst metamodels were applied, large errors occurred between them. In particular, the largest error occurred in the torque ripple. In addition, it was confirmed that the torque ripple of the best model was 5.032%, which is significantly better compared with 6.522% of the worst metamodel. Through additional analysis, it was found that the best metamodel exhibits higher accuracy and better performance than the worst metamodel. Therefore, after comparing the RMSE test results of the different metamodel techniques, the best metamodel with the highest accuracy was applied in this study and its validity was verified.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Items**} & \multirow{2}{*}{**Unit**} & \multirow{2}{*}{**Initial**} & **Best** & **Best** & **Worst** & **Worst** \\  & & & **Metamodel** & **Metamodel** & **Metamodel** & **Metamodel** \\  & & & **(STDQAO)** & **(FEA)** & **(STDQAO)** & **(FEA)** \\ \hline Notch width (X1) & mm & 0 & 1.504 & & & 1.457 \\ Notch depth (X2) & mm & 0 & 0.398 & & 0.308 \\ Barrier length (X3) & mm & 0 & 0.072 & & 0 \\ \hline Torque ripple & \% & 6.664 & 4.835 & 5.032 & 4.529 & 6.522 \\ Average torque & N-m & 25.296 & 25.278 & 25.284 & 25.323 & 25.195 \\ Efficiency & \% & 90.108 & 90.118 & 90.120 & 90.130 & 90.083 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the optimal design results for the best and worst metamodels.  
Figure 10: Flux density distribution at the load operation: (**a**) Initial model; (**b**) Optimal model.  
Figure 9: Flux distribution at the load operation: (**a**) Initial model; (**b**) Optimal model.
## 2 Materials and Methods
### Experiment Specifications  
#### 2.3.1 Network Topology  
We used a real-world dataset to construct a trace-driven simulation for our experiment. We consider the topology of the proprietary CDN of an Italian Video Delivery operator in our experiments. Such an operator delivers Live video from content providers distributed around the globe to clients located in the Italian territory. This operator's network consists of 41 CP nodes, 16 hosting nodes, and 4 client cluster nodes. The hosting nodes and the client clusters are distributed in the Italian territory, while CP nodes are distributed worldwide. Each client cluster emits approximately \(1\times 10^{4}\) Live-Video requests per minute. The operator gave us access to the access log files concerning service from 25-29 July 2017.
# Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification
## 2 Full-Field Displacement Monitoring of a Structure Based on Natural Texture Feature Monitoring
### The Structure's Full-Field Displacement Monitoring Results under Natural Texture Conditions  
Using the method described above, the FFDV of the structure surface was calculated under each load condition and chromatographic assignment was performed based on vector size. Figures 18-20 show the FFDV of the structure surface under various operating conditions. Owing to space limitations, only two load levels, i.e., 100 and 400 kN, are shown under operating conditions D01-D03.  
The FFDV distribution presented in Figures 18-20 shows that the displacement vector of the top chord steel plate of the test beam is directed toward the mid-span. Meanwhile, the displacement vector of the bottom chord steel plate is directed toward the support, indicating that the top chord steel plate of the test beam is under pressure and that the bottom chord steel plate is under tension. This vector distribution is consistent with the stress characteristics of the test beam under load. In addition, the test beam's deformation increases with the load. Taking the undamaged condition as an example, the maximum displacement of the test beam under the load of 100 kN is 3.82 mm, and the maximum displacement under the load of 400 kN is 9.57 mm. This is consistent with the deformation law of the structure under load.  
Figure 20 shows the structural deformation characteristics under a load. The full-field displacement monitoring method significantly expands the structural deformation monitoring data and extends the measurement of structural points to full-field displacement measurement.
## 2 System Model
### Power Splitting Receiver and Sum Rate  
The received signal corresponding to each UE will be separated into two components as part of the SWIPT modelling. One component will be employed for information decoding (ID), while the other module it utilised for energy harvesting (EH). The signal intended for EH can be articulated as follows:  
\[y_{d,k}^{EH}=\sqrt{1-\rho_{d,k}}\,y_{d,k}\,, \tag{9}\]  
where \(\rho_{d,k}\in[0,1]\) denotes the PS factor associated with the \(k^{th}\) UE within the \(d^{th}\) beam. As in [17], the harvested energy can be expressed as  
\[P_{d,k}^{EH}=\eta(1-\rho_{d,k})\left(\sum_{i=1}^{D}\sum_{j=1}^{|\Xi_{i}|}\left\| \mathbf{h}_{d,k}^{H}\mathbf{f}_{i}^{BB}\right\|^{2}\!\!p_{i,j}+\sigma_{n}^{2} \right), \tag{10}\]  
where \(\mathbf{f}_{d,k}^{H}(=\mathbf{h}_{d,k}^{H}\mathbf{F}^{RF})\) represents the effective channel vector and \(0\leq\eta\leq 1\) symbolizes the energy conversion efficiency. Similar to (9), the signal corresponding to ID can be conveyed as  
\[y_{d,k}^{ID}=\sqrt{\rho_{d,k}}\,y_{d,k}+u_{d,k}\,, \tag{11}\]  
where \(u_{d,k}\sim\mathcal{CN}(0,\sigma_{u}^{2})\) denotes the noise introduced because of the PS. Based on [3], the estimated received signal for ID associated with the \(k^{th}\) UE within the \(d^{th}\) beam can be conveyed as  
\[\begin{split}\hat{y}_{d,k}^{ID}=\sqrt{\rho_{d,k}}\Bigg{(}\mathbf{ f}_{d,k}^{H}\mathbf{f}_{d}^{BB}\sqrt{p_{d,k}}\,x_{d,k}+\mathbf{h}_{d,k}^{H} \mathbf{f}_{d}^{BB}\sum_{j=1}^{k-1}\sqrt{p_{d,j}}\,x_{d,j}+\\ \mathbf{f}_{d,k}^{H}\sum_{i\
eq d}\sum_{j=1}^{|\Xi_{i}|}\mathbf{f }_{i}^{BB}\sqrt{p_{i,j}}\,x_{i,j}+n_{d,k}\Bigg{)}+u_{d,k}\,.\end{split} \tag{12}\]  
Following [3, 17], the SINR at the \(k^{th}\) UE within the \(d^{th}\) beam can be conveyed as  
\[\gamma_{d,k}=\frac{\left\|\mathbf{f}_{d,k}^{H}\mathbf{f}_{d}^{BB}\right\|^{2} \!\!p_{d,k}}{\xi_{b,k}}, \tag{13}\]  
where  
\[\begin{split}\xi_{d,k}=\left\|\mathbf{h}_{d,k}^{H}\mathbf{f}_{d }^{BB}\right\|^{2}\!\!\sum_{j=1}^{k-1}p_{d,j}+\\ \sum_{i\
eq b}\left\|\mathbf{\hat{h}}_{d,k}^{H}\mathbf{f}_{d}^{BB} \right\|^{2}\!\!\sum_{j=1}^{|\Xi_{i}|}p_{i,j}\sigma_{n}^{2}+\frac{\sigma_{u}}{ \rho_{d,k}}.\end{split} \tag{14}\]  
Thus, the achievable data transmission rate at the \(k^{th}\) UE within the \(d^{th}\) directional beam is meant to be portrayed as  
\[R_{d,k}=log_{2}(1+\gamma_{d,k}). \tag{15}\]  
The system's achievable sum rate can be obtained by \[R_{sum}=\sum_{d=1}^{D}\sum_{k=1}^{|\Xi_{d}|}R_{d,k}. \tag{16}\]
## 6 Conclusions  
A multiple-input CNN (MI-CNN) model is proposed in this study to consider the time-/frequency-domain characteristics of ground motions and attribute data of buildings. The proposed model can achieve a good prediction accuracy and significantly better computational efficiency compared to nonlinear time history analysis of the MDOF shear model. Some detailed conclusions can be drawn as follows:  
1. The proposed MI-CNN model can achieve the overall prediction accuracy of 79.7% for the test set, and more than 90% of the predicted damage states are within one level of difference to the actual damage states.
2. The prediction accuracies for cases with different PGAs, building fundamental periods, and fortification levels are also around 80%, which shows good prediction performance of the model in different simulation scenarios.
3. The prediction performance of the proposed model is compared with methods using ground motion intensity measures as the earthquake input. The prediction accuracy of the proposed MI-CNN model is 79.7%, which outperforms the simulation using PGA, PGV, \(Sa_{\text{max}}\), and \(Sa(T_{1})\) as earthquake inputs (73.7%).
4. The computation efficiency of the proposed model is significantly better than nonlinear time history analysis of MDOF shear model. The speedup ratio is 340 on a laptop platform.
5. The prediction accuracy for some individual classes is relatively low (less than 70%), which is caused by some very unique ground motions and a relatively small number of samples in the corresponding classes. This is a limitation of the proposed method, and further investigation can be conducted to improve the prediction accuracy through methods such as active learning.  
The proposed MI-CNN model can be a useful tool for the fast seismic damage assessment of regional buildings. The outcomes of this work are expected to assist in post-disaster emergency response and rapid disaster relief.  
Conceptualization, C.X.; Data curation, J.Z., R.Z. and C.C.; Funding acquisition, L.X., Y.L. and C.X.; Investigation, C.X., J.Z. and C.C.; Methodology, C.X. and J.Z.; Project administration, L.X. and C.X.; Resources, L.X., C.X. and Y.L.; Software, C.X., R.Z. and J.Z.; Supervision, L.X. and C.X.; Writing--original draft, C.X., J.Z. and C.C.; Writing--review and editing, C.X., J.Z. and C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program (Grant No. 2019 YFC1511003), the Intellectual Innovation Program of Shenzhen Science and Technology Innovation Committee (Grant No. JCYJ20180305123919731), and the National Natural Science Foundation of China (Grant No. 51708361).  
The authors declare no conflict of interest.  
Figure 20: Prediction error of the MI-CNN model: (**a**) M6.5 earthquake scenario; (**b**) M7.5 earthquake scenario.
# Variable Selection Using Deep Variational Information Bottleneck with Drop-Out-One Loss
## 2 Preliminaries
### Information Bottleneck  
The IB [19] can be viewed as a rate-distortion problem [28], with a distortion function \(KL(p(y|x)|p(y|z))\) that measures how well \(Y\) is predicted from a compressed representation \(Z\) compared to its direct prediction from \(X\). This means that specifying the features of \(X\) is required. The problem can be also formalized as finding a short code \(Z\) for \(X\) that preserves the maximum information about \(Y\). For example, we hope to extract the most useful information in the speech sounds about the words spoken. The IB principle is formulated as the maximization  
\[I(Z,Y)-\beta I(X,Z), \tag{1}\]  
where the mutual information \(I(X,Z)\) measures the dependence between random variables \(X\) and \(Z\) as follows:  
\[I(X,Z)=\iint p(x,z)\,\log\,\frac{p(x,z)}{p(x)p(z)}dxdz. \tag{2}\]  
Intuitively, \(I(Z,Y)\) encourages \(Z\) to predict \(Y\), and \(I(X,Z)\) forces \(Z\) to \"forget\" \(X\). The hyperparameter \(\beta>0\) controls the trade-off between the compression (complexity) and fitting. From another perspective, \(\beta\) can be also used to control the bias-variance trade-off in cases where the distribution function is not known and the mutual information is only estimated from a finite number of samples [29]. The complexity here is measured by the minimum description length (or rate) at which the observation is compressed.  
When \(\beta\rightarrow+\infty\), (1) leads to a trivial representation \(Z\) that is independent of \(X\), and it recovers a maximum likelihood objective while \(\beta\to 0\)[30]. In summary, IB seeks the right  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **Lasso** & **SpAM** & **Knockoff** & **GL** & **Lasso Net** & **NAM** & **DeepPINK** & **Ours** \\ \hline Coefficient-based & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ Function-based & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & \(\times\) \\ IB-based & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\surd\) \\ DNN-based & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of different methods (\(\surd\) means satisfying the given information, and \(\times\) means not).  
balance between data fit and generalization by using the mutual information as both a cost function and a regularizer, which is vital to variable selection [20].
## 1 Introduction
In this paper, we propose an efficient hardware architecture for implementing the ELR-aided K-Best detector, where we consider the design trade-off of the operating frequency, chip area and energy efficiency [20]. In [21], an adaptive hardware implementation was proposed, that tries to minimize the energy consumed by using different detection methods including zero forcing and K-Best detection. The adaptation techniques were implemented with the aid of a control unit that selects the detector based on the received Signal to Noise Ratio (SNR). This design does indeed lower the power consumption of the decoder, but it comes at the cost of additional area requirement. Additionally, in [22] the authors focused on the parallelism of the K-Best detector, which can significantly improve the throughput and latency of the hardware implementation, where the different traversal paths of a K-Best tree search can be performed simultaneously. However, this also comes at the cost of more area as there are some replicated hardware blocks, which are run in parallel. Ref. [23] presented a LR-aided detector, which showed that LR aided detectors are viable in hardware and offer an improvement in BER at the cost of higher area overhead. Therefore, the main problem with the previous implementations, which were all for two transmit antenna and two receive antenna systems, is that the optimisations mainly came at the cost of an increased area requirement. Therefore, in this paper we propose a hardware efficient implementation of the ELR-aided K-Best detector, which is capable of significantly outperforming the K-Best detector, while requiring a small increase in the area overhead and power consumption compared to a K-Best detector dispensing with ELR. Against this background the novel contributions of this paper can be summarized as follows:  
* We analyze the ELR-aided K-Best detector for MIMO systems, where we consider the performance versus complexity trade-off of the design and compare this with the optimal ML detection. We show that the proposed design has a significantly lower complexity than the ML detector, while attaining near-ML performance. * We then propose an efficient hardware architecture for implementing the ELR-aided K-Best detector, where we consider the design trade-off of the operating frequency, chip area and energy efficiency. We show that the proposed design requires a small increase in the area overhead and power consumption compared to a K-Best detector dispensing with ELR, while attaining a 2 dB performance improvement at a bit error rate of \(10^{-5}\). The rest of the paper is organized as follows. In Section 2 we present an overview of the MIMO system model used in this paper followed by Section 3, where the LR-aided MIMO detection is explained. In Section 3, we also present our proposed ELR-aided K-Best detector followed by the hardware implementation in Section 4. Finally, we present our conclusions in Section 5.
###### Abstract  
In recent years, the number of objects connected to the internet have significantly increased. Increasing the number of connected devices to the internet is transforming today's Internet of Things (IoT) into massive IoT of the future. It is predicted that, in a few years, a high communication and computation capacity will be required to meet the demands of massive IoT devices and applications requiring data sharing and processing. 5G and beyond mobile networks are expected to fulfill a part of these requirements by providing a data rate of up to terabits per second. It will be a key enabler to support massive IoT and emerging mission critical applications with strict delay constraints. On the other hand, the next generation of software-defined networking (SDN) with emerging cloud-related technologies (e.g., fog and edge computing) can play an important role in supporting and implementing the above-mentioned applications. This paper sets out the potential opportunities and important challenges that must be addressed in considering options for using SDN in hybrid cloud-fog systems to support 5G and beyond-enabled applications.  
SDN; cloud computing; 5G; 6G; fog computing; challenge; massive IoT +
Footnote †: journal: Network
## 4 Results
### Terrain Spatial Perception
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Model 1**} & \multicolumn{2}{c}{**Model 2**} & \multicolumn{2}{c}{**Model 3**} \\ \cline{2-7}
**Variable** & **Coef.** & \(p\) & **Coeff.** & \(p\) & **Coef.** & \(p\) \\ \hline con\_ & 2.808 & 0.000 *** & \(-\)2.284 & 0.036 ** & 8.366 & 0.000 *** \\
**Individual socioeconomic attributes** & & & & & & \\ Gender & \(-\)0.371 & 0.083 * & \(-\)0.308 & 0.167 & \(-\)0.401 & 0.092 * \\ Monthly income & 0.087 & 0.363 & 0.110 & 0.269 & 0.074 & 0.484 \\ Drive age & \(-\)0.453 & 0.034 ** & \(-\)0.519 & 0.020 ** & \(-\)0.593 & 0.012 ** \\ Degree of freedom in working time & \(-\)0.170 & 0.072 * & \(-\)0.167 & 0.088 * & \(-\)0.164 & 0.113 \\ The number of private cars owned & 0.513 & 0.018 ** & 0.528 & 0.019 ** & 0.498 & 0.036 ** \\ The payment method of traffic cost & \(-\)0.409 & 0.056 * & \(-\)0.386 & 0.084 * & \(-\)0.440 & 0.065 * \\ The experience of using P\&R & \(-\)0.466 & 0.030 ** & \(-\)0.479 & 0.032 ** & \(-\)0.594 & 0.012 ** \\
**Travel characteristics** & & & & & & \\ Departure time & 0.272 & 0.028 ** & 0.235 & 0.073 * & \(-\)0.326 & 0.300 \\ The time saved & 0.403 & 0.001 *** & 0.323 & 0.013 ** & \(-\)0.319 & 0.315 \\ The cost saved & 0.449 & 0.000 *** & 0.307 & 0.020 ** & \(-\)0.405 & 0.195 \\
**Objective built-environment attributes between ODs** & & & & & & \\ The distance & & & & & & \\ The role slope & & & & & & \\ The demand of river crossing & & & & & & \\ Group characteristic & & & & & & \\ Terrain spatial perception \(\times\) Group time & & & & & & \\ Terrain spatial perception \(\times\) The time saved & & & & & & \\ Terrain spatial perception \(\times\) The cost saved & & & & & & \\ N & 448.000 & 448.000 & 448.000 & 448.000 \\ Prob \(>\) chi2 & & 0.000 & 0.000 & 0.000 & 0.000 \\ AIC & & & & & & \\ Pseudo R2 & & & & & & \\ \hline \hline \end{tabular} Note: * \(p<\) 0.1, ** \(p<\) 0.05, *** \(p<\) 0.01.  
\end{table} 
Table 7: The results of three models. of river-valley-type cities, such as commute distance, the road slope, the demand of river crossing, group characteristic, and travelers' subjective terrain spatial perception, has significant positive effects on the use of P&R. Model 3 is the model to analyze the moderating effect. After Model 1 and Model 2 both prove that the main effect has a significant impact on the use of P&R, we only need to focus on the significance of the moderating effect. The results show that there is no moderating effect of the cost saved by P&R on terrain spatial perception influencing the use of P&R; the other six moderating effects exist. In order to further investigate how terrain spatial perception moderates the impact of significant influencing factors on P&R usage behavior, moderating effect figures are created based on high and low terrain spatial perception [50]. Figure 5 illustrates how the influence of different factors on P&R usage behavior is moderated by motorists' terrain spatial perception in river-valley-type cities. Figure 5: _Cont._
# Short-Term Wind Speed Prediction Based on Principal Component Analysis and LSTM
## 1 Introduction
The above approaches discussed a variety of methods to show the improvement in modeling techniques for wind speed or power prediction. Nevertheless, these approaches ignore the fact that the wind speed is highly influenced by meteorological characteristics such as temperature, air pressure, wind speed, and humidity. Therefore, it is not sufficient and realistic to formulate prediction models only using the historical data of wind speed. It has been demonstrated that using meteorological features for wind speed prediction can improve its prediction accuracy [31; 32; 33]. A nonlinear autoregressive wind speed prediction model was proposed in [31], which employed meteorological time series as inputs. [PERSON] et al. [32] put forward a wind farm prediction model based on dynamic Bayesian clustering of meteorological features and support vector regression. Factors such as wind direction, temperature, humidity, and altitude were used as model inputs. [PERSON] et al. [33] established a causal relationship between wind speed and meteorological factors to propose an LSTM network based on neighborhood gates structure; however, the hyperparameters of the LSTM networks are chosen empirically. Wind speed prediction considering meteorological features usually involves a huge amount of high-dimensional data with massive redundant information, which leads to inaccurate prediction [34; 35]. It is crucial to eliminate the redundant information in the features while reducing the complexity of prediction models. A recent study presented an effective principal component analysis (PCA)-based decision tree machine learning classification technique [36]. Following this framework, this paper proposes a hybrid wind speed prediction method based on PCA and LSTM networks. Specifically, a PCA algorithm is employed to process the original data for reducing the dimensions and redundant information. A differential evolution (DE) algorithm is presented to determine hyperparameters for LSTM networks. The main components processed by PCA and historical wind speed data are used as the input of LSTM. The main contributions of this work in comparison with the existing literature are as follows:1. A wind speed prediction algorithm considering meteorological features based on PCA and LSTM networks is presented. DE as a hyperparameter selection method is also included in the proposed method. 2. The PCA preprocessing method can effectively reduce the dimensions and retain the features in the data, which lays an important foundation for more accurate prediction with improved computational efficiency. 3. The proposed method is validated on three different cases considering real-world data, and experimental results show that the proposed method outperforms other popular forecasting methods. The rest of this paper is organized as follows. The proposed hybrid prediction approach is described in Section 2. The experimental design and numerical validation are shown in Section 3. Conclusions and future work are provided in Section 4.