esa-sceva/satcom-chunk-collection huggingface dataset:
# Named Entity Correction in Neural Machine Translation Using the Attention Alignment Map
## 4 Experiments
### Dataset: The Annals of the Joseon Dynasty  
The _Annals_ were written by the Joseon Dynasty of Korea in 1413-1865 and are listed in UNESCO's Memory of the World Registry. The _Annals_ have been digitalized by the government of Korea since 2006 and are available on the website ([[http://sillok.history.go.kr/main/main.do](http://sillok.history.go.kr/main/main.do)]([http://sillok.history.go.kr/main/main.do](http://sillok.history.go.kr/main/main.do)), last access date: 1 July 2021) with the Korean translations and the original texts in Chinese. We used this parallel corpus to train our NMT models. To simulate real-world situations, we split the records according to the time they were written. Records from 1413 to 1623 were the training corpus, and records from 1623 to 1865 were the evaluation corpus. The training and evaluation corpus contained 230 K and 148 K parallel articles, respectively. We only used articles with Chinese and Korean tokens less than 200 in length, because the articles have an extremely variable length of letters. Figure 8 shows histograms for the sequence length of the Korean-Chinese parallel corpus. The Chinese-Korean pair sequences with the top 5% length were ignored in histogram Figure 8. For all Chinese sentences, the mean sequence length was 112.87 and the median was 54. For all Korean sentences, the mean was 124.56 and the median was 56. In Chinese (input), no tokenization was used. We simply split each Chinese sentence into a sequence of characters, because each Chinese character has its own meaning. In Korean (output), meanwhile, we used an explicit segmentation method [49] to split each Korean sentence into a sequence of tokens. Thus, the number of articles for training was 168 K and for evaluation was 113 K.  
For the NER model, we also used the same corpus: the _Annals of the Joseon Dynasty_. The annotation of the Chinese named entities for this corpus is publicly available ([[https://www.data.go.kr/dataset/3071310/fileData.do](https://www.data.go.kr/dataset/3071310/fileData.do)]([https://www.data.go.kr/dataset/3071310/fileData.do](https://www.data.go.kr/dataset/3071310/fileData.do)), last access date: 1 July 2021). Additionally, Table 6 shows an analysis of the Chinese NER corpus. Approximately 7.5% of the characters belong to named entities, and the most frequently named entity type is _Person_.  
Figure 7: Named entity correction using the look-up table. The named entity, “o\(|\)\(|\)\(|\)([PERSON])” is corrected to “o\(|
## 3 Microwave NDT Techniques
### Microwave Imaging Method
The paper employs a delay-and-sum beamformer algorithm with full-view mounted antennas for image reconstruction. This study successfully demonstrated the technique's ability to detect cracks as small as 5 mm with a resolution of \(\lambda/4\). The results indicate the technique's potential for early crack detection in cement-based materials. Another area where microwave imaging is employed practically is in security applications [93]. A solution provided by Rohde and Schwarz is the QPS full-body security scanner, which utilizes low-power electromagnetic millimeter waves to detect hidden items underneath clothing noninvasively [94; 95]. A similar solution is the ProVision system provided by EAS Envirmet [96; 97]. This system uses millimeter microwave imaging technology along with SAR to detect on-body concealed objects. In comparison to other techniques, microwave imaging has the advantage of creating electrical profiles of the objects under test. Nevertheless, this comes at the expense of solving the associated inverse problem, which could be computationally expensive.
# Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation
## 3 Antenna Selection Schemes for FGSM  
TAS for FGSM is schematically depicted in Figure 1. TAS drives up the transmit diversity in traditional MIMO and SM, as described in the literature review section. Let \(\boldsymbol{H}_{T}\)\(=\)\([\boldsymbol{h}_{1},\boldsymbol{h}_{2},\ldots\boldsymbol{h}_{N_{T}}]\)\(\sim\)\(\mathcal{CN}(0,I_{N_{m}\times N_{T}})\) be the channel matrix for a MIMO. Here, \(\boldsymbol{h}_{1},\boldsymbol{h}_{2},\ldots\boldsymbol{h}_{N_{T}}\) are the column vectors of \(\boldsymbol{H}_{T}\). The TAS scheme is employed to select a subset from the candidate set \(S\)\(=\)\(\{S_{1},S_{2},\ldots,S_{l},\ldots,S_{N_{k}}\}\). \(N_{tx}\) antennas are picked from \(N_{T}\) antennas when the CQI is perfectly known to the user equipment (UE). There are \(N_{s}\) possible antenna subsets, each with \(N_{tx}\) antennas, as given below:  
\[\begin{split} S_{1}=\{1,2,\ldots,N_{tx}\},\\ S_{2}=\{1,2,\ldots,N_{tx}-1,N_{tx}+1\},\\ \vdots\\ S_{N_{s}}=\{N_{T}-N_{tx}+1,\ldots,N_{T}\},\end{split} \tag{12}\]  
where \(S_{l}\) represents the \(l\)th possible transmit antenna subset. A low-rate feedback channel communicates the chosen subset antenna index to the base station (BS). The CQI feedback latency is assumed to be insignificant in this case. If \(S_{l}\) is selected, then the signal received is expressed as follows:  
\[\mathbf{y}=\mathbf{H}_{T}^{l}\mathbf{\chi}+\mathbf{\eta}, \tag{13}\]  
where \(\mathbf{H}_{T}^{l}\) is the channel matrix corresponding to the selected set \(S_{l}\). An example of the TAS candidate set mapping is illustrated in Table 3 for \(N_{T}=4,N_{tx}=3,\& N_{rx}=4\).
# Adaptive Learning Approach for Human Activity Recognition Using Data from Smartphone Sensors
## 3 Results and Discussion
### Experimental Setup  
In this section, we validate the performance of the RHMM parameter estimation algorithm using the Activity Recognition System Dataset. Firstly, feature extraction was performed (no other additional preprocessing was conducted on the data). Observation vectors of 28 dimensions were extracted for the experiments from the dataset using a window size of 30 milliseconds with a 50% overlap. The features extracted from the dataset for each activity were  
* A mean value of accelerometer signals XYZ;
* Root mean square XYZ of accelerometer signals XYZ;
* Standard deviation XYZ of accelerometer signals XYZ;
* Signal vector magnitude of accelerometer signals;
* Signal magnitude area (FFT calculated from magnitude signal).  
The following activities--modeled with a separate HMM state--were chosen for modeling and recognition: walking, walking upstairs, walking downstairs, running, standing, lying on the floor, sitting, and falling.  
The online algorithm, using a fixed-size set of training observations, was implemented for initial HMM parameter evaluation, starting with randomly chosen initial parameters of mean and covariance.  
The accuracy of the algorithm was explored using ten-fold cross-validation. In this procedure, the data were randomly sorted and divided into 10 folds and 10 rounds of cross-validation were run. In each round, one of the folds for validation was used, and the remaining folds were used for training. After training the model, its accuracy on the validation data was measured, and a final cross-validation accuracy was computed by obtaining the average accuracy over the 10 rounds.
# Activation Process of ONU in EPON/GPON/XG-PON/NG-PON2 Networks
## 6 Conclusions  
In this paper, we introduced an activation process for IEEE and ITU PONs. Although both networks are standards for passive optical networks, they use completely different encapsulation methods, frame durations and frame structures. Our results demonstrate that EPONs have a faster activation process (with a maximum split ratio 1:32) and that GPONs operate with a split ratio of up to 1:128. We chose the most commonly-used split ratio, 1:32 or 1:64, with 20 km, because a higher distance does not follow either standard. However, GPON supports a higher split ratio, and there are some issues with the timing and managing of the time slots for the upstream direction. The main reason for the shorter activation time is the different frame durations of 16.384 us and 750 us for EPONs and GPONs, respectively.  
XG-PON and NG-PON2 use approximately the same activation process and provide almost identical results as do GPONs. However, NG-PON2 supports wavelength tuning during transmission, but it has to be initialized by the OLT. Due to this fact, it is not a part of the activation process. An activation time of 256 end units requires approximately 820 ms, but the OLT can operate with eight or more NG-PON2 ports, which leads to a multiplication of this time. During a blackout scenario, the last end user will have to wait multiples of minutes.  
Our optimization accelerates the activation process for 64 ONUs from 220 ms to 145 ms. In a real network with 512 ONUs, the operator can save approximately 40 ms. Note that our solution does not require a new PLOAM message or TC layer changes.  
In future work, we would like to implement a collision scheme into our simulation models.  
Figure 11: Visualization of the optimized GPON activation process.  
Figure 10: Graph of activation time for GPON (**left**) and optimized GPON (**right**) 32/64 units and 20-km ODN.  
**Author Contributions:** Conceptualization, T.H. and P.M. Methodology, T.H. and P.M. Software, V.O. and J.V. Validation, T.H. and P.M. Formal analysis, J.V. Investigation, T.H. Resources, P.M. Writing, original draft preparation, T.H., P.M., V.O. and J.V. Writing, review and editing, T.H. Visualization, V.O. Project administration, P.M. Funding acquisition, P.M. and T.H.  
**Funding:** The presented research has been supported by projects of the Ministry of the Interior under Grant No. VI20172019072 registration, the National Sustainability Program under Grant No. LO1401, and E-infrastructure CESNET - modernization, registration number CZ.02.1.01/0.0/0.0/16_013/0001797.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Electroencephalogram Emotion Recognition via AUC Maximization
## 3 Dataset
### Feature Extraction
#### 3.2.3 Frequency/Time-Domain Features  
Instead of directly converting the signal from the time domain to the frequency domain, it is also promising to consider the frequency/time domain features. Specifically, given  
Figure 2: An example of a DEAP EEG channel sliding window segmentation. The channel is 63 s long, with a 3 s pre-trial period denoted by the grey area. The red and purple rectangles represent a segment with a length denoted by circle 2, while the sliding window size is indicated by circle 1.  
the channel in a time-series manner, we adopted a Butterworth filter to extract the theta, alpha, beta, and gamma constituents of different frequencies while staying in the time domain. The Butterworth filter features a smooth frequency response in the passband and a seamless transition to the stopband. Mathematically, the magnitude response of an \(n\)-th order Butterworth filter is given by  
\[|H(\omega)|^{2}=\frac{1}{1+\left(\frac{\omega}{\omega_{c}}\right)^{2n}}, \tag{2}\]  
where \(\omega_{c}\) is the cutoff frequency and \(n\) is the order of the filter. When the cutoff frequency is normalized to \(\omega=1\) radian per second, the expression simplifies to:  
\[|H(\omega)|^{2}=\frac{1}{1+\omega^{2n}}. \tag{3}\]  
Within each sliding window segment, the differential entropy (DE) features from each frequency band were extracted, as DE features have been proved to be the most stable feature for emotion recognition [55; 56]. Moreover, the statistical features were also extracted for each constituent.
# Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen
## 3 Evaluation of Frost Heave Ratio
This is because frost heave behavior is mainly determined by the propagation rate of the freezing front and the water supply in the frozen zone. Whereas the particle thermal conductivity affects the propagation rate of the freezing front, the initial hydraulic conductivity is concerned with the inflow of pore water in the unfrozen zone. Thus, we used the numerical simulation model to obtain and mutually compare frost heave ratio values according to a total of 251 influencing parameter combinations. As shown in Figure 6, the amount of heave tends to decrease as the particle thermal conductivity increases. This is because the freezing rate becomes too high if freezing is accelerated due to the high thermal conductivity of the particles, resulting in thermal conditions that prevent a sufficient inflow of water from external sources. On the other hand, the frost heave ratio has a positive correlation with initial hydraulic conductivity: as the initial hydraulic conductivity increases, the frost heave ratio tends to increase. In other words, if the thermal conditions are kept the same and the hydraulic conditions are altered, a higher soil hydraulic conductivity would result in a higher frost heave ratio. However, it should be noted that these phenomena are only valid for silty soil, which is potentially subject to frost heave action. If the soil specimen is closer to sandy soil, no capillary action occurs, resulting in insignificant amounts of heave, regardless of the permeability. Meanwhile, in order to investigate the sensitivity of both the thermal and hydraulic conductivities of frozen soils on the frost heave ratio, a correlation analysis and regression analysis were conducted. Table 2 shows the results of the Pearson correlation analysis for each variable [48]. The analysis illustrates that both the thermal conductivity and initial hydraulic conductivity of a frozen soil can significantly affect the heave ratio, as the \(p\)-value  
Figure 6: Effect of geotechnical properties on the frost heave ratio. of each parameter was less than 0.05 (Table 2). Furthermore, this study also conducted a regression analysis, as shown in Table 3. According to the regression analysis, the \(p\)-values of the coefficients for thermal conductivity and initial hydraulic conductivity were also lower than 0.05, which indicates that these two variables can significantly affect the heave ratio [49]. Although a significant correlation was confirmed between the independent and dependent variables, it was confirmed that an auto-correlation exists in the dependent variable. Therefore, in this study, it was judged that it would be more beneficial to propose a predictive model for frost behavior using an artificial neural network instead of deriving a regression equation.
# Comparing EEG-Based Epilepsy Diagnosis Using Neural Networks and Wavelet Transform
## 3 Methods and Materials
### Classification
#### 3.7.1 Multilayer Perceptron (MLP)  
The Multilayer Perceptron neural network was trained using the backpropagation algorithm and a variable learning rate to diagnose the disease. The input layer had the same number of nodes as the length of the input vector for each time interval, and the output layer had one node, which represented the possibility of only two classes being classified. The number of neurons in the hidden layer was selected to achieve an optimal architecture. It is important to note that the training process was only performed on the training data to ensure that the network did not use the testing data when selecting the optimal structure, which allowed for effective generalization of the network. The network was tested using the testing data once the training error reached a minimum. A linear transfer and a sigmoid function were used as the output node and the hidden layer, respectively. The network training continued until the mean value was much less than 0.01 or the range of training iterations reached 1000.
# Human Activity Recognition Based on Point Clouds from Millimeter-Wave Radar
## 3 Proposed HAR System
### Pillar Feature Encoder  
There are several methods to encode point-cloud data. The voxel feature encoding (VFE) layer of VoxelNet [25] uses voxels to encode in three dimensions; this has the disadvantage of increasing the network complexity using 3D convolution. In addition, the multi-view (MV)CNN [26] uses as its input the 2D images that are projected from multiple directions of 3D point-cloud data; this has the disadvantage of overlapping points occurring during the process of projecting points, resulting in information loss. However, the PFE uses pillars to convert 3D point-cloud data into 2D pseudo images to enable image-based inference. Therefore, the PFE can be used to convert 3D point-cloud data into 2D images without loss of information, and as it outputs a 2D image it has the advantage of lowering the complexity of the network by utilizing 2D convolutions instead of 3D convolutions in the classifier.  
The PFE consists of three major steps: grouping, handcraft feature extraction, and scattering. Grouping is the process of grouping points into pillars with an infinitely extended z-axis. The xy plane was divided into uniformly spaced regions, and each region was treated as a pillar. Points within a uniform area were grouped into the internal set of pillars of the region; if no points were inside a pillar, the pillar was excluded from the grouping. Finally, the remaining pillar contained the pillar index, which indicated its position on the xy plane, the number of points inside the pillar, and the \((x,y,z,p)\) channel information of the internal point. In the handcraft feature extraction process, the point data consisting of \((x,y,z,p)\) 4 channels were expanded to a total of \((x,y,z,p,x_{c},y_{c},z_{c},x_{p},y_{p})\) 9 channels by adding the center point coordinates \((x_{c},y_{c},z_{c})\) of the points inside the pillar and the center coordinates \((x_{p},y_{p})\) of the pillar. Subsequently, the point data of the 9 channels were expanded to 64 channels by passing them through point-wise convolution, batch normalization, and the ReLU activation function. In the scattering process, the pillar index information saved in the grouping process was used to place each pillar and the point data of the 64 channels within the pillar at the existing location on the xy plane.
## 4 Findings and Discussion
### Discussion
#### 4.4.4 Reliability  
Reliability analysis determines a system's level of assurance in terms of probability density [133]. They utilized the XGBoost to calibrate the resistance reduction factors in reaching the stipulated objective system. The suggested ML-based models are proven to efficiently determine the shear resistance of fabric-reinforced cementitious matrix-strengthened reinforced concrete beams, yielding the most consistent, precise, and reliable predictions while meeting the specified target reliability of the system. In another study outlining some intriguing potential prospects for utilizing machine learning to increase safety and reliability concerns, the authors of [134] examined the use of machine learning in quality engineering and security solutions. Since the LIME is based on Gaussian assumptions, the reliability of the output becomes doubtful once the assumptions do not fit into any target system. The challenge of overfitting also depletes the reliability of XAI models as some researchers do not scrutinize the potential flaws of \"explainers\" [135].  
One reliability case of concern to researchers is the effect of adversarial perturbations (APs) on deep learning. APs are the procedures intended to mislead a target model by injecting imperceptible noise (attacks). [PERSON] et al. [PERSON] et al. (2016) proposed the use of the disc similarity coefficient (DSC) and correlation coefficient (CC) as measures for the reliability of XAI methods. The reliability of deep learning models was found to be affected by the huge number of pixels in the target image, and AP. However, the number of classes used in datasets had no significant effect on the reliability of XAI. However, most of the existing reliability experiments were conducted using datasets too little in comparison to real ICV expectations.
# Application of MMC-RPC in High-Speed Railway Traction Power Supply System Based on Energy Storage
## 3 VSG Control
### VSG Model
The excitation controller can adjust the reactive output of the generator according to the change of the generator terminal voltage; it can maintain the stability of the generator terminal voltage by controlling the excitation current and changing the induced electromotive force. When the voltage amplitude of the traction network changes, the reactive power compensation of the MMC-RPC system is realized by adjusting the excitation of its virtual synchronous motor; that is, changing the bridge arm voltage firstly, and then changing the output reactive power. As shown in Figure 5, the output electrical angle and potential of VSG are obtained through torque adjustment, rotor motion equation and excitation controller. Finally, the d-axis and q-axis components of VSG output current are used as the inputs of the stator electrical equation to obtain the expected value of current output. Then, through the dq axis current decoupling control, the voltage of each bridge leg of the MMC-RPC is constructed to obtain the start sequence of each submodule. Since the \(\alpha\beta\) axis component obtained by the controller through dq-\(\alpha\beta\) inverse transformation is a virtual quantity, it can be ignored.
# Isotropic IoT-Based Magnetic Flux Density Meter Implementation for ELF Field Measurements +
## 5 IoT Application with Arduino UNO Wi-Fi Rev.2 board for Real-Time ELF Field Monitoring  
The value of the measured magnetic field can be displayed in real-time on any mobile device with Wi-Fi connectivity via the Blynk application [34] supported by Android and iOS platforms. An Arduino UNO Wi-Fi Rev.2 board with the embedded Wi-Fi Nina [33] module is used to provide three analogue inputs (ADC's) for measurements in the \(X\), \(Y\), and \(Z\) axis, respectively to transfer data from the meter to the cloud as a complete EMF IoT solution [37], as shown in Figure 8. The Arduino Uno WiFi uses an 8-bit microprocessor and an onboard inertial measurement unit (IMU). Its Analog to Digital converter (\(ADC\)) has a 10-bit resolution, mapping input voltages between 0 and 5 V into integer values between 0 and 1023. This results in readings of 4.9 mV per unit (5 Volts/1024) on the Arduino UNO board.  
Figure 8: (**a**) Measuring the magnetic field exposure of an electrical toothbrush by the proposed IoT-based meter. (**b**) The isotropic ELF field strength values (in \(A/m\) and \(\mu\)_Tesla_) are displayed on the Blynk application supported by the Android platform in a mobile phone with Wi-Fi connectivity, as a complete IoT solution.  
Figure 7: (**a**) The frequency response validation via Helmholtz coils utilization feeding by a signal generator to provide a homogeneous magnetic field in the ELF band. (**b**) The frequency response curve of the meter.  
The Blynk user interface can be displayed on the mobile terminal or the web portal and both the magnetic field strength \(H\) (A/m) and the corresponding flux density \(B\) (\(\mu\)Tesla) values at the same time, using the following transformation formula:  
\[B(Tesla)=\mu_{r}\mu_{o}H(A/m) \tag{29}\]  
where \(\mu_{r}\) is the relative and \(\mu_{0}\) is the free space magnetic permeability. Magnetic field levels up to 300 nT are considered within safe limits in most countries of the world. Several studies have shown that the field is regarded as potentially harmful from 400 nT to 1 \(\mu\)T, and dangerous from 1 \(\mu\)T and above [2; 23]. Furthermore, the percentage magnetic field contribution (%) of each probe is available to the user to identify the plane direction (X, Y, or Z) of the incident magnetic field.