esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models
## 4 Proposed Work  
A number of research studies have been performed for COVID-19 detection from chest X-ray and CT-scan images to date. CT scans provide low false-positive rates than X-rays [51], which forms the backbone of our proposed work. The main issue was to train a model to give the desired results. To solve this issue, we have used transfer learning. It focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. The new learning task may belong to a different feature space or distribution. In this study, we have a classification task in one domain of interest (chest CT images), while we have considered sufficient training data from a different domain than of the images from ImageNet. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning of the new task. It also reduces the training time and helps to avoid overfitting of the data. In the current experiment, we have utilized three pre-trained models, namely VGG-16, ResNet50, and Xception, which are initially trained on the ImageNet dataset consisting of 1.2 million high-resolution images for the ImageNet LSVRC contest to classify into 1000 different classes. When using TL on a new dissimilar dataset, preliminary layers are frozen as they extract generic features, but the later layers in the network extract specific and complex features.  
Though many precautions, such as Dropout- [52], Batch Normalization- [53] etc., can be considered to avoid overfitting and to get the desired performance from the DL models, we found that there is room for improvement. Hence, to build an efficient COVID-19 prediction model, we used the concept of ensemble learning. After analyzing different ensemble strategies and their uses, we applied a stacking ensemble most appropriate for this task [32; 54]. The flowchart in Figure 2 describes the complete pipeline used in the present work.  
Figure 2: Flowchart of the proposed model.
## 5 Simulation Results and Discussions
### Performance Evaluation
It is immediately seen that the performance of the proposed detection method is almost the same independent of the CP type. Similar to the results in Figures 4 and 5, the proposed and conventional schemes have experienced some performance degradations as well,  
Figure 4: Performance of the proposed and conventional detectors versus SNR: (**a**) EPA (**b**) EVA. Figure 5: Performance of the proposed and conventional detectors versus signal-to-noise ratio (SNR): (**a**) extended typical urban (ETU) (**b**) extended delay spread (EDS). however the performance of the conventional scheme is worse because it is affected by the channel variation in the frequency domain. As expected, it is evident that the performance of the proposed sequential detection scheme using two symbol averaging is dramatically improved with increase in SNR, compared to that of the RCJD scheme. The superiority of the proposed scheme over existing methods is still observed in the case when residual STO is present. More specifically, the SNR gain of the proposed scheme over the RCJD scheme is more than 5 dB for a target estimation error of 0.1% or less, regardless of the channel model. Such observation allows us to conclude that the proposed joint detection method is better suited for situations where there is an unknown STO or high frequency selectivity than the conventional detection methods.
## 4 Experiment Arrangement and Results
### Experiment I: Comparison of Different Benchmark Models  
In Experiment I, the prediction performance of RBFNN and BPNN is compared by combining R\({}^{2}\), MAPE, RMSE, MAE, and the U1 and U2 evaluation indexes and it is found that the values of the above six indexes are 0.876, 9.52, 6635, 4787, 0.057, and 0.243 for the BPNN and 0.851, 10.2, 7402, 5842, 0.072, and 0.0258 for the RBFNN, respectively. Taking the two dimensionless metrics of MAPE and R\({}^{2}\) as an example, Figure 4 visualizes the prediction performance of the two models, from which it is obvious that the BPNN model can obtain a smaller MAPE as well as a larger R\({}^{2}\). It is found that the BPNN has the best prediction performance and that the RBFNN has a slightly poorer prediction performance than that of the BPNN. Therefore, based on the comprehensive evaluation, the BPNN is selected as the benchmark model for further development due to its superior prediction performance.
# Two-Layer Matrix Factorization and Multi-Layer Perceptron for Online Service Recommendation
###### Abstract  
Service recommendation is key to improving users' online experience. The development of the Internet has accelerated the creation of many services, and whether users can obtain good experiences among the massive number of services mainly depends on the quality of service recommendation. It is commonly believed that deep learning has excellent nonlinear fitting ability in capturing the complex interactions between users and items. The advantage in learning intricacy relationships enables deep learning to become an important technology for present service recommendation. Recently, it is noticed that linear models can perform almost as well as the state-of-the-art deep learning models, suggesting that capturing linear relationships between users and items is also very important for recommender systems. Therefore, numerous deep learning systems combined with linear models have been proposed. However, existing models are incapable of considering the size of the embedding. When the embedding dimension is too large, it leads to overfitting and thus influences the model's ability to capture linear relationships. In this paper, a neural network based on two-layer matrix factorization and multi-layer perceptron--Two-layer **M**atrix factorization and **M**ulti-layer perceptron **N**eural **N**etwork (TMMNN)--is proposed. To solve the problem of overfitting caused by an oversized embedding dimension, multi-size embedding technology has been integrated into the model. Matrix factorization and the multi-layer perceptron are placed in the upper and lower layers respectively, and they both receive embedding vectors dynamically adjusted for dimensions. In the upper layer, the matrix factorization is responsible for receiving the embedding of users and items, capturing linear relationships, and then yielding the generated new vectors as input to the multi-layer perceptron in the lower layer. Compared to other previously proposed models, the experimental results on the standard datasets MovieLens 20M and MovieLens Latest show that the TMMNN model is evidently better in terms of prediction accuracy.  
recommendation system; deep learning; matrix factorization; linear model; multi-size embedding
## 3 Identification Method of Hydrodynamic Coefficients  
According to the Morison equation [1; 2], the hydrodynamic force in the IL direction can be decomposed into two components; that is, the drag force and the inertial force that are respectively proportional to the square of the velocity and the acceleration. The similar equation was also employed  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Case No.** & **KC** & **A\({}_{\text{m}}\) (m)** & **\(T\)(s)** & \(\beta\) & **Fr** & **Re** \\ \hline
1–3 & 6.3 & 0.25 & 5.5:3:11.5 & 5435–11,364 & 0.12–0.26 & \(3.4\times 10^{4}\)–\(7.1\times 10^{4}\) \\
4–6 & 18.8 & 0.75 & 5.5:3:11.5 & 5435–11,364 & 0.37–0.77 & \(1.0\times 10^{5}\)–\(2.1\times 10^{5}\) \\
7–9 & 31.4 & 1.25 & 5.5:3:11.5 & 5435–11,364 & 0.62–1.29 & \(1.7\times 10^{5}\)–\(3.6\times 10^{5}\) \\
10–12 & 37.7 & 1.5 & 5.5:3:11.5 & 5435–11,364 & 0.74–1.55 & \(2.0\times 10^{5}\)–\(4.3\times 10^{5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test Matrix.  
to describe the hydrodynamic force on floating cylinder in the horizontal direction [18]. Thus, the total force can be expressed as,  
\[F_{XP}=\frac{1}{2}C_{D}\rho DLU(t)\big{|}U(t)\big{|}+C_{m}\rho\frac{\pi D^{2}}{4 }L\dot{U}(t) \tag{8}\]  
For convenience, it is assumed that,  
\[\begin{split} C_{1}(t)&=\frac{1}{2}\rho DLU(t) \big{|}U(t)\big{|}\\ C_{2}(t)&=\rho\frac{\pi D^{2}L}{4}\dot{U}(t)\end{split} \tag{9}\]  
Then, Equation (8) can be simplified as,  
\[F_{XP}=C_{1}(t)\cdot C_{D}+C_{2}(t)\cdot C_{m} \tag{10}\]  
The least squares method is used to estimate the drag coefficient \(C_{D}\) and added mass coefficients \(C_{m}\) in the IL direction, which minimizes the sum of the squared difference \(J(C_{D},\,C_{m})\) between the predicted and measured force, which can be written as,  
\[J(C_{D},C_{m})=\sum_{i=1}^{n}\left[F_{X}(t)-F_{XP}(t)\right]^{2} \tag{11}\]  
where \(F_{X}(t)\) is the measured force and \(n\) is the sample number.  
In order to minimize the difference between the identified and predicted values of the hydrodynamic force, Equation (11) should be satisfied,  
\[\frac{\partial J(C_{D},C_{m})}{\partial C_{D}}=0,\,\frac{\partial J(C_{D},C_{m })}{\partial C_{m}}=0 \tag{12}\]  
Transforming Equation (12) into matrix form, we have  
\[\mathbf{\Lambda\Lambda^{\mathrm{T}}}\left[\begin{array}{c}C_{D}\\ C_{m}\end{array}\right]=\mathbf{\Lambda\mathrm{F_{X}}} \tag{13}\]  
where,  
\[\mathbf{\Lambda}=\left[\begin{array}{ccc}C_{1}(t_{1})&C_{1}(t_{2})&\cdots&C_{1} (t_{n})\\ C_{2}(t_{1})&C_{2}(t_{2})&\cdots&C_{2}(t_{n})\end{array}\right]\text{, }\mathbf{F_{X}}=\left[ \begin{array}{c}F_{X}(t_{1})\\ F_{X}(t_{2})\\ \vdots\\ F_{X}(t_{n})\end{array}\right] \tag{14}\]  
The drag and added mass coefficients can be expressed as,  
\[\left[\begin{array}{c}C_{D}\\ C_{m}\end{array}\right]=\left(\mathbf{\Lambda\Lambda^{\mathrm{T}}}\right)^{-1} \mathbf{\Lambda\mathrm{F_{X}}} \tag{15}\]  
However, there is no mature empirical equation to describe the hydrodynamic force in the CF direction. The empirical prediction of the lift force will be further studied in Section 4.3 according to the features observed by our experiments in this paper.
## 4 Bigram and Traditional Results
### Forecasting Water Levels  
Forecasting water levels is crucial for efficient water resource management. Various methods, including hydrological models, hydraulic models, remote sensing, statistical models, machine learning, and ensemble forecasting, can be used to predict water levels. These forecasts have numerous applications, such as flood risk management, drought management, irrigation planning, hydropower generation, navigation, and ecosystem management.
## 3 Problem Formulation
### Parameterized Deep Q Network
The weights \(\theta\) and \(\omega\) are updated according to  
\[\theta \leftarrow\theta-\alpha_{a,p}\
abla_{\theta}L^{x}(\theta) \tag{9}\] \[\omega \leftarrow\omega-\alpha_{a}\
abla_{\omega}L^{Q}(\omega), \tag{10}\]  
where \(\alpha_{a,p}\) and \(\alpha_{a}\) are the learning rate for the weights in the actor-parameter and actor network, respectively. At a given state \(s\), the actor-parameter network produces the continuous parameters, which maximize the average \(Q(s,c,x_{c};\omega)\) for each discrete action \(c\). Then, the actor network determines the action \((c,x_{c})=\arg\max_{(c,x_{c})}Q(s,c,x_{c};\omega)\) after the action-value \(Q\)-function has been evaluated with the aid of the target network. In the training phase, the off-policy scheme is implemented where the agent selects the action based on \(\epsilon\)-greedy policy and generates the \(Q\)-target using the greedy policy for exploration. The algorithm of the proposed P-DQN with the target networks is summarized in Algorithm 1. Figure 2: Illustration of the primary network in P-DQN. At each time slot, the actor-parameter \(x_{c}(s;\theta)\) decides the continuous parameter \(\mathbf{x}(s;\theta)=\left[x_{c_{1}}(s;\theta),\dots,x_{c_{i},c_{i}}(s;\theta) \right]^{T}\) based on the current state \(s\). Then actor \(Q(s,c,x_{c};\omega)\) takes the action according to the current state \(s\) and the continuous parameter \(\mathbf{x}(s;\theta)\). ```
Input: Learning rates \(\{\alpha_{a},\alpha_{a,p}\}\), exploration parameter \(\epsilon\), mini-batch size \(B\), a probability distribution \(\xi\). Initialize network weights \(\omega\), \(\omega^{-}\), \(\theta\), and \(\theta^{-}\). for\(t=1,2,\ldots,T\)do  Determine the action parameters \(x_{c}(s_{t};\theta_{t})\) by the actor-parameter network. Select action \(a_{t}=(c_{t},x_{c_{t}})\) according to the \(\epsilon\)-greedy policy: \[a_{t}=\left\{ \begin{array}{ll}\text{a sample from the distribution }\xi,& \text{with probability }\epsilon\\ (c_{t},x_{c_{t}}):\ c_{t}=\arg\max_{c\in\mathcal{A}_{d}}Q(s_{t},c,x_{c};\omega) \text{by the actor network,}&\text{with probability }1-\epsilon.\end{array} \right.\]  Take action \(a_{t}\), observe reward \(r_{t}\) and the next state \(s_{t+1}\). Store the experience \((s_{t},a_{t},r_{t},s_{t+1})\) into \(\mathcal{D}\). Draw \(N\) samples of experience \((s_{i},a_{i},r_{i},s_{i+1})\) randomly from \(\mathcal{D}\). Define the target \(y_{i}\) by \[y_{i}=\left\{ \begin{array}{ll}r_{i},&\text{if }s_{i+1}\text{ is the terminal state,}\\ r_{i}+\gamma\max_{c^{\prime}\in\mathcal{A}_{d}}Q\Big{(}s_{i+1},c^{\prime},x_{c^ {\prime}}(s_{i+1};\theta^{-});\omega^{-}\Big{)},&\text{otherwise.}\end{array} \right.\]  Use \((y_{i},s_{i},a_{i})\) to compute the gradient \(\
abla_{\omega}L^{Q}(\omega)\) and \(\
abla_{\theta}L^{x}(\theta)\). Update the parameters \(\omega\), \(\omega^{-}\), \(\theta\), \(\theta^{-}\). endfor
```  
**Algorithm 1** Parameterized Deep Q-Network (P-DQN) Algorithm with the quasi-static target networks.
## 3 Simulation Model  
### Power System Model and Simulation Conditions  
Figure 4 illustrates the power system model [13] used for the simulation analysis. It consists of the power sources of the VSWG and three conventional SGs (SG1, SG2 and SG3). Simulation analysis using the power system model with the PV system installed instead of SG3 was also carried out. The line impedances (\(R\) + j\(X\) (jB/2)) and the initial power flows are described in per unit values on the system base (1000 MVA). The static load models with the constant impedance characteristic were used as the power demand load. The parameters of each SG are listed in Table 1. Figure 5 illustrates the models of the automatic voltage regulator (AVR) [14] and governor system, which are included in each SG model.  
Figure 4: Power system model.  
Figure 3: Reactive power control model (voltage–reactive power characteristic).  
We assumed that a symmetrical three-line-to-ground (3 LG) fault occurs at each fault location (F1, F2 and F3) in the grid model. After the 3 LG fault occurs at 0.1s, the circuit breakers on the faulted lines are opened at 0.17 s and then reclosed at 1.17 s.  
In these simulations, the effects of three control methods shown below on the transient stability enhancement are demonstrated.  
* Case 1: Active power control of the VSWG;
* Case 2: Active and reactive power control of the VSWG;
* Case 3: Active and reactive power control of the VSWG and reactive power control of the PV system.
# AESOP: Adjustable Exhaustive Search for One-Pixel Attacks in Deep Neural Networks
## 3 One-Pixel Attacks  
An _adversarial example_ is the input of a neural network that includes a small perturbation from the original input but confounds the neural network by inducing it to classify into an incorrect class. The more fatal such adversarial examples are, the more imperceptible the perturbation is to the human eye. However, many studies have not specifically investigated the closeness between an adversarial example and the original input. [PERSON] et al. [7] first proposed _one-pixel attacks_ in the image classification field. These are input images that differ from the original input by _only_ with one pixel.  
Now, we formalize the notion of one-pixel attacks we study in this paper. A _neural network_\(f\), as an image classifier, takes an input image \(\mathbf{x}=(x_{1},\ldots,x_{n})\) that is a vector where each scalar element \(x_{i}\) represents a pixel, and classifies \(\mathbf{x}\) into a class \(t_{\mathbf{x}}\). An additive adversarial perturbation for the input \(\mathbf{x}\) is a vector \(\mathbf{e}=(e_{1},\ldots,e_{n})\) with the same size with \(\mathbf{x}\). Given a neural network \(f\) and its input \(\mathbf{x}\), an _adversarial example_ with regard to \(f\) and \(\mathbf{x}\) is an input \(\mathbf{x}^{\prime}=\mathbf{x}+\mathbf{e}\) such that \(f\) classifies \(\mathbf{x}^{\prime}\) into the class \(t_{\mathbf{x}^{\prime}}\), \(t_{\mathbf{x}}\
eq t_{\mathbf{x}^{\prime}}\), and \(||\,\mathbf{e}\,||\leq\delta\) for some distance threshold \(\delta\). As a special case of an adversarial example, a _one-pixel attack_ is an input \(\mathbf{x}^{\prime}=\mathbf{x}+\mathbf{e}\) such that \(f\) classifies \(\mathbf{x}^{\prime}\) into the class \(t_{\mathbf{x}^{\prime}}\), \(t_{\mathbf{x}}\
eq t_{\mathbf{x}^{\prime}}\), and \(||\,\mathbf{e}\,||_{0}\leq 1\). Finally, given a neural network \(f\) and its input \(\mathbf{x}\), the _one-pixel attack problem_ we study in this paper is to determine whether there exists a perturbation \(\mathbf{e}\) to produce a one-pixel attack \(\mathbf{x}^{\prime}\) with regard to \(f\) and \(\mathbf{x}\). In the one-pixel attack problem, the perturbation \(\mathbf{e}\) can be described simply by the index and value of the element of \(\mathbf{e}\) which is nonzero.  
Figure 1 shows an example of a one-pixel attack on a well-trained network for the MNIST data set. Figure 1 shows the original input classified as 7, and Figure 1 shows a one-pixel attack classified incorrectly as 9.
## 4 Experimental Results and Analysis
### Experimental Data  
To ensure the reliability of the experimental data, open dataset rolling bearing experimental data from the Electrical Engineering Laboratory of Case Western Reserve University, USA were used [38]. The experimental platform consists of an electric motor, a torque sensor/converter, a power test meter, and an electronic controller. The experimental platform equipment [38] is shown in Figure 7.  
Collected bearing failure data for the experiments were single-point failure data manufactured by EDM single-point machining, and a total of 10 data categories under 0 HP functional model are used here. Bearing type SKF6205 drive end data are used for the test data with sampling frequency of 12 KHz, the sample uses damage diameter of 0.1178 mm, 0.3556 mm, a 0.5334 mm inner ring, outer ring, and roller body failure data, and a class of normal data. According to the resampling method [37], the original data of ten states are divided. Each sample has a length of 1024 [35,36] and a sampling step size of 512. In this way, a total of 4500 data samples are obtained. A total of 4500 samples were generated from 10 types of data, 90% for the training set and 10% for the test set. The specific sample  
Figure 7: Equipment of experimental platform.  
Figure 8: Time and frequency domain waveforms of bearing operating conditions.
## 4 Results and Discussion
### Comparison  
According to the Euro Code, the estimated VIV amplitude of the 3D prototype prism (equal to 0.037) is very close to the 3D prototype prism test result (equal to 0.036), even though the measured wind tunnel time histories are not steady. Modifications of the 3D effects, which are different from our traditionally 2D plane and 3D solid body differences [28; 29], mainly including the effects of modal shape, spanwise correlation, and aerodynamic forces, are indispensable effects that can be considered in the evaluation of the maximum amplitude of the VIV for prototype structures, as observed in the Euro Code's empirical formula. However, the 3D effects interact and couple with each other during the actual oscillations. Their contributions to the maximum amplitude are not easily extracted. The coupled effects may be varied by choosing different structural cross-sections, boundary conditions, and vibration amplitudes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Lj/D**} & & & & & & \multicolumn{2}{c}{**A/D**} & \\ \cline{4-7}
**(Correlation Length)** & **St** & **K** & **KW** & **Clat** & **Estimated Result** & **3D Prototype Prism** & **2D Section Model Measurement** \\ \hline
6 & 0.12 & 0.11 & 0.19 & 1.10 & 0.037 & 0.036 & 0.030 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of vortex-induced vibrations (VIV) amplitude between predicted and experimental results.  
Figure 9: Heaving mode amplitude and frequency response of the 2D section model.  
The present study eliminates the geometric scale effects by adopting the same cross-section, Reynolds number, structural mass, and damping. The comparison of the maximum VIV amplitudes between the 3D prototype prism and the 1:1 2D section model is shown in Figure 10. The 3D effects can be directly identified. The expected agreements of the VIV lock-in between them are observed. These include onset velocities, initial branches, and lower branches. However, the maximum VIV amplitude of the 3D prototype prism (at the middle point of the prism) attains a value of 0.036. It is slightly higher than 0.030, which was obtained by the 1:1 2D section model at the ratio of 1.2. This observation may explain the fact that the full-scale VIV amplitudes measured in actual structures are often larger than those from section model tests.
# What is 5G? Emerging 5G Mobile Services and Network Requirements
## 4 5G Mobile Services
### Immersive 5G Services
#### 4.1.2 Massive Contents Streaming  
The development of digital and telecommunication technologies increases the demand for multimedia content. Such demand has been rapidly expanding over the past years in the form of broadcasting, movies, Internet and personal media. The continuous development of display technologies means that 5G technologies should be able to stream 4-K and 8-K ultra-high-definition (UHD) contents. It is anticipated that 5G will support expanding multimedia  
Figure 4: Real-time mixed reality with AR and VR services.  
services such as multi-view interactive 3D, personal multimedia broadcasting, massive content sharing and 3D holograms as shown in Figure 5. Multidimensional realistic media offers information that can be seen, heard and felt through the five senses, and hence, it overcomes spatial and time constraints. 5G is required to support such multidimensional realistic media services. For example, the user can enjoy a big sports event through a real-time hyper-realistic mobile hologram table. Moreover, holographic short message service (SMS) information can be transferred through 5G in conjunction with hologram technology. Though the current technology offers HD video call services, user experience is still far away from face-to-face conversation. Hence, this gap in the user's experience is expected to decrease significantly with 5G massive content streaming services. To achieve realistic services for users, hologram generation and delivery is the most desirable feature of 5G technology. 4G offers a 1-Gbps transmission rate, but it is still not able to provide holographic telecommunication services. Thus, it is important to ensure that 5G wireless technology and core networks are able to provide real-time hyper-realistic mobile holographic telecommunication services. However, hologram services require a bandwidth of several terabytes per second, and it is not possible to process holograms even with the 5G bandwidth. Still, restrictive hologram services with the minimum rate are possible. Future 5G research should concentrate on improving compression efficiency and decoding algorithms.