esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
Figure 15 displays the distance between the center of the island observed by the naked eye and the center of the island detected from the algorithm taken from images acquired during sapphire growth at the center position (750, 600) of the growth furnace crucible. All the island centers were within 10 mm of the center of the growth furnace crucible. They approached the center of the crucible over time and continuously moved at distances of 2-3 mm. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Observed Positions**} & \multicolumn{2}{c}{**Measured Positions**} & \multicolumn{2}{c}{**Distance between the**} \\  & & **(Image Processing Results)** & **Coordinates/Positions (in Pixels)** \\ \hline
1 & (647, 555) & (653, 553) & 6.32 \\
2 & (663, 576) & (679, 606) & 34.00 \\
3 & (680, 585) & (701, 606) & 29.70 \\
4 & (694, 569) & (702, 576) & 10.63 \\
5 & (713, 583) & (698, 556) & 30.89 \\
6 & (717, 563) & (717, 556) & 7.00 \\
7 & (733, 571) & (728, 558) & 25.94 \\
8 & (738, 573) & (731, 573) & 7.00 \\
9 & (742, 591) & (745, 613) & 22.20 \\
10 & (728, 611) & (741, 616) & 13.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of coordinates between observed and measured positions (i.e., the results of image processing). Figure 14: Loss of stream-line data caused by the outline of the rod and seed. Figure 13: Changes of island positions at the center of the growth furnace crucible: (**a**) observed positions; (**b**) the positions resulting from image processing.
# A Scoping Review on Virtual Reality-Based Industrial Training
## 4 Discussion
### Research Questions
Because of this, it has been seen that, for industrial training functions, the most used equipment is HTC VIVE and Oculus Rift. HTC Vive allows for users to have a fully immersive experience with recognition of physical movements, also enables the user's hands to be part of the game, thus seeing their actions reflected in real-time. It also has haptic controls that improve the user experience, providing feedback on the position and turn within programmed environments. On the other hand, Oculus Rift presents an architecture that is similar to the HTC VIVE set. However, its movement precision is lower, since it has a maximum field tracking of 1.5 to 3.3 m. The Oculus Rift's tracking stations can only perceive what the user is doing from a frontal angle, so the precision in the detection of movement is less accurate. **RQ4: What are the users' benefits of applying VR in industrial training? **  
VR makes it easier for companies and industries to carry out training in various scenarios in an effective and completely real way. It provides an opportunity for employees to face adverse situations without risk while learning how to react and work under unfavorable circumstances. Among the key advantages that VR offers for the training of workers, the following can be mentioned: (i) commitment, conventional training methodologies can become boring, so workers are unlikely to pay the necessary attention. Immersive environments and 3D representations make users more interested and committed to their training, (ii) speed, due to the creative and attractive way that this digital tool offers, workers can obtain and retain information in an efficient way. VR allows for training through emotional response, the best method to not forget what has been learned [68; 69]. (iii) Measurability, because it works with computer-generated environments, researchers can easily obtain any required statistics, such as the time that it takes an operator to carry out a certain activity, the most common accidents or the procedure with less difficulty, (iv) reduction of work accidents, the use of VR offers a much safer scenario, in which users can practice obtaining the necessary skill without the need to risk their integrity, (v) personalization, scenarios can adapt to the requirements of each worker, and, (vi) reduction of costs, VR helps to reduce infrastructure, materials, time, and personnel expenses that are required by real simulations [70].
## 4 Comparison Results  
To verify the proposed prescribed performance constraint control method, some known hydraulic parameters are \(\tilde{C}_{d}=0.62\), \(w=0.024\) m, \(x_{\rm{\,}max}=7.9\) mm, \(\tilde{\beta}_{e}=7000\) bar, \(\tilde{\rho}=850\) kg/m\({}^{3}\), \(\tilde{R}=1000\) N/m, \(\tilde{b}=100\) Ns/m, \(\tilde{C}_{tl}=2.5\times 10^{-11}\) m\({}^{3}\)/(s \(\cdot\) Pa), \(K_{sv}=4.9\times 10^{-6}\) m/V, \(p_{r}=2\) bar, \(A_{p}=2.01\) cm\({}^{2}\), \(V_{t}=1.74\times 10^{-5}\) m\({}^{3}\), \(m=1.739\) kg, \(\Delta C_{d}=0.1\tilde{C}_{d}\), \(\Delta\beta_{e}=0.5\tilde{b}_{e}\), \(\Delta K=0.5\tilde{K}\), \(\Delta b=0.5\tilde{b}\), \(\Delta\rho=-0.1\tilde{\rho}\), \(\Delta C_{tl}=0.2\tilde{C}_{tl}\), \(F_{L\,\max}=500\) N. The time constants of two dynamic surfaces is \(\tau_{1}=\tau_{2}=10^{-3}\). Some control parameters are designed as \(k_{1}=100\), \(k_{2}=1200\) and \(k_{3}=6000\), \(x_{1\,\min}=-50\) mm, \(x_{1\,\max}=50\) mm, \(y_{\min}=-50\) mm, \(y_{\max}=50\) mm, \(\rho(0)=0.95\), \(\rho(\infty)=0.03\), \(\lambda=0.3\).  
In addition, to compare with the traditional control scheme, Proportional-Integral (PI) controller is also adopted in this EHS such that  
\[u=k_{p}(y_{d}-x_{1})+k_{i}\int\limits_{0}(y_{d}-x_{1})dt \tag{30}\]  
Figure 2: Block diagram of the prescribed performance constraint controller.  
where the control gains \(k_{p}=150\) and \(k_{i}=10\) have been well tuned to guarantee the fast response of the cylinder position.
###### Abstract  
Sliding-mode control (SMC) has been successfully applied to boost inverters, which solves the tracking problem of imposing sinusoidal behavior to the output voltage despite the coupled or decoupled operation of both boost cells in the converter. Most of the results reported in the literature were obtained using the conventional cascade-control structure involving outer loops that generate references for one or two sliding surfaces defined using linear combinations of inductor currents and capacitor voltages. As expected, all proposed methods share the inherent robustness and insensitivity to the uncertainties of SMC, which are the reasons why one of the few comparison criteria between them is the simplicity of their implementation that is evaluated according to the required measurements and mathematical operations. Furthermore, the slight differences between the obtained dynamic performances do not allow a clear distinction of the best solution. This study presents a new SMC approach applied to a boost inverter in which two boost cells are independently commutated. Each of these boost cells integrates an outer loop, enforcing the tracking of harmonic-enriched waveforms to the capacitor voltage. Although this approach increases by two the number of measurements and requires multiloop controllers, it allows effective alleviation of the semiconductor voltage stress by reducing the required voltage gain. A complete analytical study using harmonic balance technique allows deducing a simplified model allowing to obtain a PI controller valid into to the whole set of operation conditions. The several simulation results completely verified the potential of the control proposal and the accuracy of the employed methods.  
boost inverter; harmonic balance; sliding mode control +
Footnote †: journal: Applied Sciences
# Deterministic Coresets for \(k\)-Means of Big Sparse Data +
## 7 Comparison to Existing Approaches
### Datasets  
For our experimental results we use three well known datasets, and the English Wikipedia as follows.  
**MNIST handwritten digits [25].** The MNIST dataset consists of \(n=60,000\) grayscale images of handwritten digits. Each image is of size \(28\times 28\) pixels and was converted to a row vector row of \(d=784\) dimensions.  
**Pendigits [26].** This dataset was downloaded from the UCI repository. It consists of 250 written letters by 44 humans. These humans were asked to write 250 digits in a random order inside boxes of 500 by 500 tablet pixel resolution. The tablet sends \(x\) and \(y\) tablet coordinates and pressure level values of the pen at fixed time intervals (sampling rate) of 100 milliseconds. Digits are represented as constant length feature vectors of size \(d=16\) the number of digits in the dataset is \(n=10,992\).  
**NIPS dataset [27].** The OCR scanning of NIPS proceedings over 13 years. It has 15,000 pages and 1958 articles. For each author, there is a corresponding words counter vector, where the \(i\)th entry in the vector is the number of the times that the word used in one of the author's submissions. There are overall \(n=2865\) authors and \(d=14,036\) words in this corpus.  
**English Wikipedia [28].** Unlike previous datasets that were uploaded to memory and then compressed via streaming coresets, the English Wikipedia practically can not be uploaded completely to memory. The size of the dataset is 15 GB after converting to a term-documents matrix via gensim [29]. It has 4M vectors, each of \(10^{5}\) dimensions and an average of 200 non-zero entries, i.e., words per document.
# Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks
## 4 Theoretical Analysis and Experimental Results
### Performance Metrics  
Network lifetime: In this paper, we characterize the network lifetime by the number of transmission rounds of the source node when any relay node runs out of energy.  
Energy consumption: The energy consumption of each node can be expressed by subtracting the remaining energy of the node at the end of the simulation from the initial total energy of each node; the sum of these is the energy consumption of the network. It can be expressed as:  
\[E_{cont}=\sum\limits_{i=1}^{n}\left(E_{nodei\_initial}-E_{nodei\_re}\right) \tag{17}\]  
Here, \(E_{nodei\_initial}\) represents the initial energy of node \(i\), and \(E_{nodei\_re}\) represents the remaining energy of node \(i\) at the end of the simulation.  
The average end-to-end delay: For the end-to-end delay of data packets from the source node to the sink node, when receiving duplicate packets, only the earliest one will be considered. It can be expressed as:  
\[\bar{t}=\frac{\sum\limits_{i=1}^{N}\left(T_{i\_rec}-T_{i\_send}\right)}{N} \tag{18}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Data Rate & 10 kbps \\ Frequency & 25 kHz \\ Node Number & 100–500 \\ Deployment Region & 3D region of 1.5 \(\times\) 1.5 \(\times\) 1.5 (length \(\times\) breadth \(\times\) depth) km\({}^{3}\) \\ Communication Radius & Maximum coverage 500 m \\ Initial Position of Sink Node (take the bottom depth value as zero) & (500, 500, 1500), (1000, 500, 1500), (500, 1000, 1500), \\ Initial Position of Source Node & (750, 750, 0) \\ Initial Energy of Sink Node and Source & 1000 J \\ Node & 150 J \\ Initial Energy of Relay Node & 150 J \\ Payload of Data & 50 Bytes \\ Data Packet Interval & 40 s \\ Transmitting Power & 1–10 W \\ Energy Model & ns3:AquaSimEnergyModel (RX: 0.1 W, Idle: 1 mW) \\ Node Speed & 0–3 m/s \\ Mobility Model & ns3:RandomWalk2 dMobilityModel \\ MAC Model & ns3:AquaSimBroadcastMac \\ Physical Layer Model & ns3:AquaSimPhyCmn \\ Channel Model & ns3:AquaSimChannel \\ Propagation Model & ns3:AquaSimRangePropagation \\ Noise Model & ns3:AquaSimConstNoiseGen \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation settings.  
Here, \(N\) represents the total number of data packets received by the sink node. \(T_{i\_send}\) represents the time when the source node sends the \(i\)-th data packet, which is written into the packet header by the source node at the time of sending. \(T_{i\_rec}\) represents the time when the sink node receives the data packet.  
Packet Delivery Ratio (PDR): The ratio of the total number of data packets received by the sink node to the total number of data packets sent by the source node, considering duplicate packets only once.
## 3 Databases and Results
### Qualitative Results  
The pair-wise redundancy matrix of extracted features can help to better visualize the redundancy in the features extracted, as is shown in Figure 3. Here, we take CHB-MIT database as an example. According to the middle panel of Figure 3, it is obvious that energy-based features (mean absolute and variance) are highly correlated with each other, and the same is true for complexity-based features (MMSE). However, there is only a small amount of information redundancy between two kinds of features (energy-based and complexity-based features). According to the left panel of Figure 3, we can clearly see three bright diagonal lines. The middle diagonal line represents that each feature is highly correlated with itself. The lower left and upper right diagonal lines demonstrate the high redundancy between corresponding features extracted from two wavelet trees. According to the right panel of Figure 3, the periodic occurrence of the bright blocks indicates the high redundancy between adjacent EEG channels and between two wavelet trees. Moreover, the bright part of each block refers to the high redundancy between different entropy scales. In summary, from Figure 3, features extracted by DTDWT had high redundancy so an effective redundancy removal method was required to refine the extracted features.  
The visualization of high-dimensional features (CHB-MIT database) by t-distributed stochastic neighbor embedding (t-SNE) [40] is shown in Figure 4. Although the epileptic seizure detector was trained specifically for each patient, here, we visualize data of all patients at the same time. Figure 4a represents visualization of original 2254-dimensional features with high redundancy. Obviously, ictal and interictal data is difficult to distinguish. Figure 4b represents visualization of 50-dimensional features after redundancy removal achieved by AGRM. Compared with scatter in Figure 4a, ictal and interictal data in Figure 4b are easier to classify, proving the effectiveness of redundancy removal.  
Figure 3: Pair-wise feature redundancy matrix (CHB-MIT database).
# Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform
## 3 Proposed DWT_LSTM Forecasting Method
### Long Short-Term Memory (LSTM)  
The long short-term memory network (LSTM), which is a variant of RNN, was proposed in 1997 [36]. An LSTM unit shown in Figure 1 is composed of a cell, an input gate, an output gate, and a forget gate. The unique structure of LSTM can effectively solve the problems of gradient disappearance and gradient explosion problems in the training process of RNN.  
The plus sign in Figure 1 denotes the addition of element levels, the multiplication sign denotes element level multiplication, and con presents vector merge. \(\mathbf{f_{t}}\), \(\mathbf{i_{t}}\), \(\mathbf{g_{t}}\), \(\mathbf{o_{t}}\) present the forgotten gate, the input gate, the input node, and the output gate, respectively. The dependencies relationship between the data in the input sequence is caught by the cell. The input gate controls the extent of the values which flow into the cell. The forget gate controls the extent of the values which remain in the cell. The values in the cell are used to calculate the output activation of the LSTM; its extent is controlled by the output gate.  
The calculation formulas related to the LSTM structure in Figure 1 are:  
\[\mathbf{f_{t}} = \sigma\big{(}\mathbf{W_{f}}[\mathbf{h_{t-1}},\mathbf{x_{t}}]+\mathbf{b_{f}}\big{)} \tag{6}\] \[\mathbf{i_{t}} = \sigma\big{(}\mathbf{W_{i}}[\mathbf{h_{t-1}},\mathbf{x_{t}}]+\mathbf{b_{i}}\big{)}\] (7) \[\mathbf{g_{t}} = tanh\big{(}\mathbf{W_{g}}[\mathbf{h_{t-1}},\mathbf{x_{t}}]+\mathbf{b_{g}}\big{)}\] (8) \[\mathbf{o_{t}} = \sigma\big{(}\mathbf{W_{o}}[\mathbf{h_{t-1}},\mathbf{x_{t}}]+\mathbf{b_{o}}\big{)}\] (9) \[\mathbf{s_{t}} = \mathbf{s_{t-1}}\odot\mathbf{f_{t}}+\mathbf{g_{t}}\odot\mathbf{i_{t}}\] (10) \[\mathbf{h_{t}} = \tanh(\mathbf{s_{t}})\odot\mathbf{o_{t}} \tag{11}\]  
In Equations (6)-(11), \(\mathbf{W_{f}}\), \(\mathbf{W_{i}}\), \(\mathbf{W_{g}}\), \(\mathbf{W_{o}}\) are the corresponding weight matrix connecting the input signal \([\mathbf{h_{t-1}},\mathbf{x_{t}}]\), and \(\odot\) represents the element level multiplication. \(\sigma\) represents the sigmoid activation  
Figure 1: The structure of the long short-term memory (LSTM) unit.  
function, and tanh represents the hyperbolic tangent function. The state (\(s_{t}\)) of cell remembers previous values over arbitrary time intervals and the three gates control the flow of information into and out of the cell. Thus, the LSTM network is very suitable for prediction problems based on a time sequence. Hence, this paper uses LSTM to fit the approximation function \(\hat{f}\) mentioned in Formula (3). The schematic diagram of the LSTM network training is shown in Figure 2.
## 3 Results and Discussion
### Sensitivity to Lcing Environment Parameters
#### 3.1.2 Lwc  
Through a comparative analysis of the test results for cases 1, 4 and 5, it was determined that a designed IPS prevented ice formation on the surface when LWC varied from 0.5 to 1.5 g/m\({}^{3}\). As shown in Figure 11, the surface temperature decreased significantly when LWC increased from 0.5 to 1 g/m\({}^{3}\) but did not decrease further when LWC increased further to 1.5 g/m\({}^{3}\). The surface temperature of case 3, with LWC set to 1.3 g/m\({}^{3}\), was lower than that of case 5 with LWC set to 1.5 g/m\({}^{3}\) but revealed a temperature decrease compared to case 5. Since case 5 involved an increase in MVD to 40 \(\upmu\)m, it could be reasoned that the decrease in surface temperature resulted from the combined effects of LWC and MVD.  
Based on the results of the tests, it was inferred that more power was required for heating with the increase of LWC, but once the LWC increased to a certain critical value, the power required would plateau. However, when coupled with an increased MVD, an expansion of droplet impingement area accompanied by more liquid water would lead to a considerable increase in power requirement.
# ETAS Space-Time Modeling of Chile Triggered Seismicity Using Covariates: Some Preliminary Results
## 2 Description of the Datasets
### The GPS Measurements  
For calculating the daily accumulated displacements associated with seismic events, we use the GPS measurements (longitude, latitude and altitude above sea level, a.s.l.), downloaded by the website [[http://geodesy.unr.edu/NGLStationPages/GlobalStationList](http://geodesy.unr.edu/NGLStationPages/GlobalStationList)]([http://geodesy.unr.edu/NGLStationPages/GlobalStationList](http://geodesy.unr.edu/NGLStationPages/GlobalStationList)) (accessed on 22 February 2021).  
Figure 7 represents the daily coordinates of a GPS station (which name is EMAT) located in the Coquimbo region in the period 2005-2020. The calculation of the accumulated daily horizontal displacements is performed using the GPS measurements of both one day and five days prior to the seismic event, collected by the GPS station closest to the same event. For this calculation, the Euclidean distance, given by \(\sqrt{(x_{1}-x_{2})^{2}+(y_{1}-y_{2})^{2}}\), is used. The GPS altitude component, normally used for evaluating the vertical displacement, has not been considered in this application. As documented in various studies, this variable does not have sufficient accuracy, and it is not largely used to describe seismic events [23; 24]. Therefore, in the proposed analysis we have not included this variable as potential covariate.  
To acquire the accumulated displacements (called Mt1D and Mt5D, for one and five days, respectively) the data were pre-processed by interpolating the missing values using the R package imputTS, [25], and denoising them through the R package wmtsa [26].  
Figure 6: Log-cumulate distribution of magnitude in the Southern area (on the **left**) and in the Northern area (on the **right**).  
Figure 7: Time series of the GPS measurements in the EMAT station: longitude (**top**) and latitude (**bottom**).
# Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network
## 3 Cellular Traffic Prediction Model
### Attention Module  
The human brain receives considerable external input information at every moment. When the human brain receives this information, it consciously or unconsciously uses the attention mechanism to obtain more important information. At present, this attention mechanism has been introduced into the fields of natural language processing, object detection, semantic segmentation, etc., and has achieved good results. In our work, an attention mechanism was added to the network as a module to optimize the density map generated by the prediction.  
The statistical value of the most densely populated area is often much larger than the value of most other areas in the traffic dataset. For example, in a traffic distribution matrix, the average is 30, but the maximum is 4000. This is very disadvantageous for accurate prediction of images by convolutional neural networks. Therefore, to solve the problem that the value gap between different regions is too large in the model prediction process and improve the overall prediction performance of the model, this paper proposes a weight adjustment scheme based on an attention mechanism. The density map traffic density distribution has a strong correlation with the corresponding historical data. Therefore, we integrated the corresponding historical data as input, merged them into a two-column attention matrix through a 1 \(\times\) 1 convolution kernel, and normalized it to form a weight matrix \(W^{(h,w)}\). Then, \(W^{(h,w)}\) multiplies the matrix \(M^{(h,w)}\) generated by the prediction branch to generate a prediction matrix \(O^{(h,w)}\) for adjusting the weights. The calculation is as follows:  
\[O^{(h,w)}=W^{(h,w)}\cdot M^{(h,w)} \tag{7}\]  
In this way, higher weights can be obtained for pixels with relatively higher traffic density in historical data, and lower weights can be obtained for pixels with relatively lower density. In the case, weights are differentiated from the density map generated by the prediction module. The operation can improve the quality of the final density map.