esa-sceva/satcom-chunk-collection huggingface dataset:
# Graphene-to-Phoneme Conversion with Convolutional Neural Networks
## 6 Conclusions  
In this paper, convolutional neural networks for grapheme-to-phoneme conversion are introduced. Five different models for the G2P task are described, and the results are compared to previously reported state-of-the-art research. Our models are based on the seq2 seq architecture, and in the fourth and fifth models, we applied CNNs with residual connections. The fifth model, which uses convolutional layers with residual connections as encoder and Bi-LSTM as decoder outperformed most the previous solutions on the CMUDict and NetTalk datasets in terms of PER. Furthermore, the fourth model, which contains convolutional layers only, is significantly faster than other models and still has competitive accuracy. Our solution achieved these results without explicit alignments. The experiments were conducted on a test set corresponding to 9.8% and 24.9% of the whole CMUDict and NetTalk databases, respectively. The same test set was used in all cases, so we consider the results to be comparable. To draw conclusions on whether one model is better than another, the goal must be defined. If inference time is crucial, then smaller model sizes are favorable (e.g., Model 4), but if lower WER and PER are the main factors, then Model 5 outperforms the others.  
The results presented in this paper can be applied in TTS systems; however, because of the rapid development of deep learning further aspects will be investigated, like dilated convolutional networks and neural architecture search. These are possible further extensions of the current research.  
All authors have read and approved the final manuscript.  
**Acknowledgments:** The research presented in this paper has been supported by the European Union, co-financed by the European Social Fund (EFOP-3.6.2-16-2017-00013), by the BME-Artificial Intelligence FIKP grant of Ministry of Human Resources (BME FIKP-MI/SC), by Doctoral Research Scholarship of Ministry of Human Resources (UNKP-18-4-BME-394) in the scope of New National Excellence Program, by Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences, by the VUK project (AAL 2014-183), and the DANSPLAT project (Eureka 9944). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.  
**Conflicts of Interest:** The authors declare that they have no competing interests.
## 2 Related Work  
### The Acquisition of Raw Data  
Relying on the Changchun Artificial Satellite Observation Station of the National Astronomical Observatories, Chinese Academy of Sciences, we conducted a series of continuous acquisition and tracking experiments for the Starlette satellite, which operates in an orbit with an altitude of approximately 813-821 km and an orbital period of about 105 min [23]. As a low Earth orbit (LEO) satellite, Starlette was probably affected by different perturbations such as the non-spherical gravity from the Earth, tides, three-bodyperturbation, solar radiation pressure, and atmospheric drag on its orbit resulting in long-term accumulation errors and short-term random errors [24]. At present, the long-term accumulated error of the track prediction system through the ground station has been calibrated, but the short-term random error still presents a challenge to the robustness of the tracking system.  
As shown in Figure 1, the on-orbit target tracking system of the ground station was composed of track forecasting system, laser transmitting system, echo receiving system, servo control system, and computer processing system. The laser transmitting system was equipped with a nanosecond laser whose wavelength and power were, respectively, 532 nm, 30 W as the transmitting source and a refracting optical antenna with a diameter of 20 cm. The receiving system adopted an optical antenna with aperture of 1 m, and the optical signal received by the optical antenna was divided into two beams through the spectroscope, which were, respectively, received by the single photon detector and the low-light-level camera. The control system was driven by independent azimuth and pitch direct drive motors with a maximum rotation rate of 5 degrees/s. The pointing accuracy was 1 arcsec, and the pointing accuracy after stellar calibration was about 5 arcsec.  
Firstly, with the telescope guided by the ground station tracking the satellite according to the satellite forecast, a directional high-energy laser beam was emitted into space by the laser emitting system, which was reflected back to the ground station by the angle reflector of the satellite as the echo light signal. Then, through the 1 m aperture optical antenna, the echo light signal was effectively focused on the photosurface of the low-light-level camera. The tracking image data of the satellite target was obtained after the above process.  
The original image data of the on-orbit target continuous tracking image was integrated to construct a dataset. Specifically, the original image data consisted of more than 3000 images, among which 1000 continuous tracking image data of different time periods were selected as the dataset (some images were shown in Figure 2). The dataset covers a variety of features in the actual satellite target tracking, including target acquisition, target deviation, target miss, and so on. We used 500 consecutive images as the training set to train the F-GCN predictive tracking model, and another 500 images as the test set for model validation.  
Figure 1: Satellite tracking system diagram.
# Ground Simulation Tests in Two-Dimensional Dynamic Acceleration Environment
## 1 Introduction
In reality, the live-fire test usually requires high cost and a long cycle, which can hardly be repeated. The semi-physical simulation test can establish a similar environment in the laboratory to simulate an application environment of the aircraft. The existing AE simulation test methods generally adopt impact and flight overload simulation separately, which cannot realize the synchronous controllable loading of 2-D dynamic overload and cannot compare the real simulated impact and flight overload environment. Therefore, the objective of this paper is to propose a feasible 2-D dynamic AE simulation test method. Specifically, we focus on the key problems of loading in the impact overload environment, i.e., orderly transition from impact overload to steady-state overload and synchronous loading of 2-D dynamic AE. A specialized simulation test platform using an air cannon to start the compound centrifuge turntable is developed. Using the air cannon as the load propulsion system, the centrifuge provides a continuous AE. With vector distributions obtained by the single-axis turntable, synchronous loading of 2-D dynamic AE is implemented. This study contributes to research and development of extreme impact simulation tests on the structures of crystal oscillator circuits, acceleration sensors, and related instruments in dynamic AE. The rest of the paper is organized as follows. In Section 2, the principle of the air-cannon-started compound centrifugal turntable simulation test method, the structure of the simulation device, and the simulation test process are introduced. In Section 3, the theoretical calculation, simulation, and testing of the simulation process in the impact overload environment are described. By controlling the pressure of the air cannon chamber, engaging the electromagnetic clutch, and adjusting the speed of the centrifuge, an orderly transition from the impact overload to the steady-state overload is achieved. In Section 4, by implementing a feed-forward proportional-integral-derivative (PID) control algorithm and the position-velocity-time (PVT) motion mode, controlling the angular displacement of the unit during testing, and applying a vector distribution of 2-D accelerations, synchronous loading of 2-Ddynamic accelerations is achieved. In Section 5, a simulation test system is created to simulate 2-D AE. Finally, Section 6 gives concluding remarks.
## 3 Experimental Results
### Fourier-Transform Infrared Spectroscopy (FTIR)  
#### 3.3.1 Precursors, Solvents and MAPbI\({}_{3}\)  
PbI\({}_{2}\), CH\({}_{3}\)NH\({}_{3}\)I as precursor of perovskite and DMF, DMSO as the solvents used for the solution, together with MAPbI\({}_{3}\) samples, were analyzed by Fourier-transform infrared spectroscopy in order to compare their characteristic functional groups.  
The FTIR spectra for DMF and DMSO solvents (Figure 7A) show that the C=O bond strength are situated at 1671 cm\({}^{-1}\) in DMF [70; 71], and that the stretching vibration of S=O is located at 1045 cm\({}^{-1}\) in DMSO [71; 72]. Furthermore, the DMF spectrum shows a symmetrical and asymmetrical C-H bending vibrations related to the two bands located at 1457 cm\({}^{-1}\) and 1388 cm\({}^{-1}\), respectively [70]. As can be seen, all these bands disappear from the FTIR spectra obtained from the MAPbI\({}_{3}\) sample.  
Figure 6: Absorbance curves of ETL and ETL + MAPbI\({}_{3}\) before degradation process and the absorbance curve of ETL + MAPbI\({}_{3}\) and ETL + MAPbI\({}_{3}\) + EVA after 70 min under UV irradiation lamp.  
The PbI\({}_{2}\), CH\({}_{3}\)NH\({}_{3}\)I and MAPbI\({}_{3}\) FTIR spectra are shown in Figure 7B. The 1480 cm\({}^{-1}\) (symmetric NH\({}_{3}\)\({}^{+}\) bend), the 906 cm\({}^{-1}\) (NH\({}_{3}\)\({}^{+}\)/CH\({}_{3}\) rock) and the 3081 cm\({}^{-1}\) (N-H stretch) correspond to CH\({}_{3}\)NH\({}_{3}\)I [29; 73]. The peaks located at 755 cm\({}^{-1}\) and 613 cm\({}^{-1}\) could be attributed to PbI2 vibration modes [74].  
As can be seen, the characteristic peaks identified for the precursors contribute to define the ones seen in the perovskite FTIR spectra.
# Carrier-to-Noise-Threshold Filtering on Off-Shore Wind Lidar Measurements
## 2 Site and Instrumentation
### Wind Doppler Lidar  
During the period 29 August 2013 to 4 October 2014 10 min averaged measurements were performed with a wind lidar (WLS70 made by Leosphere, a Vaisala Company, Saclay, France) installed at the platform 24.6 m above mean sea level. The wind lidar measured the radial wind velocity in four azimuth angles separated by 90\({}^{\circ}\) at a zenith angle of 15\({}^{\circ}\). Starting from 100 m up to 2 km with a 50 m vertical resolution, horizontal wind velocities were determined. Details on the wind lidar and how it was operated during the campaign are provided by [22].  
In a coherent-detection lidar, such as the one used in this study, the backscattered signal is mixed with a signal from a local oscillator beam in the lidar before being sensed by a photodetector. For each of the range gates, the resulting signal is processed in real time, and radial-velocity spectra are computed and averaged. The peak frequency in the velocity spectra is a measure of the Doppler shift, providing the radial Doppler velocities from which the wind speed is calculated.  
The measurements from wind lidars rely on the presence of small airborne particles, such as aerosols, to act as tracers of the wind and provide a backscattering signal. The four most important atmospheric factors influencing the wind lidar performance are aerosol backscatter, relative humidity, precipitation, and atmospheric refractive turbulence [28].  
Lidar-data quality is specified in terms of the strength of the heterodyne signal relative to the level of noise--the so-called Carrier-to-Noise-Ratio (CNR), details are given in [18; 29; 30]. The CNR is linearly proportional to the backscatter and inversely proportional to the square of the propagation distance. Therefore, data availability decreases with range when using a constant CNR value to filter the measurements. When the heterodyne signal is very weak, velocity estimates are dominated by noise and, thus, subject to estimation errors. If the CNR falls below a predetermined threshold, [PERSON] [20] suggests that the uncertainty in the velocity is too large for the data to be useful. Therefore, it has become common practice to filter out measurements for this type of lidar when CNR <-22 dB, [28].  
Some of the consequences of filtering the data with a prescribed CNR threshold value are discussed in [31] and are further elaborated on in this study. The wind lidar profiles used in this study were analyzed up to \(\sim\)600 m above the wind lidar to ensure a sufficiently large dataset for the analysis. A full profile is identified when the CNR of concurrent measurements at all levels from 126 m to 626 m is above a threshold value. Some analysis is also performed for CNR threshold values at 126 m and 626 m only. The monthly data coverage is illustrated in Figure 2.
## 2 System Model
### Dual-LEO-Satellite System Model
At the receiver, the received signals from antenna 1 and antenna 2 are forwarded to the OFDM demodulation module. The data separation scheme is realized by channel estimation and signal detection. The linear-minimum-mean-square-error (LMMSE) estimation algorithm is used for channel estimation [29]. The signal detection scheme in this paper adopts the maximum likelihood (ML) method, and the ML detector can be expressed as [30]  
\[(\hat{x}_{1}^{g},\hat{x}_{2}^{g})=\arg\min_{(x_{1}^{g},x_{2}^{g})}\sum_{r=1}^{2 }\left\|y_{r}^{g}-\sum_{t=1}^{2}diag(x_{t}^{g})\hat{h}_{r,t}^{g}\right\|^{2} \tag{3}\]  
Splitting the received OFDM block into \(G\) sub-blocks, where \(g\in\{1,\ldots,G\}\) denotes the \(g\)-th sub-block of the received OFDM signal. \(x_{t}^{g},t\in\{1,2\}\) represents the modulation symbol of the \(g\)-th sub block transmitted by the \(t\)-th satellite. \(y_{r}^{g},r\in\{1,2\}\) denotes the received signal of the \(g\)-th sub-block received by the \(r\)-th receiving antenna. \(\hat{h}_{r,t}^{g},r\in\{1,2\},t\in\{1,2\}\) represents the estimated channel response vector. \(\hat{x}_{1}^{g},\hat{x}_{2}^{g}\) represent the estimated modulation symbols transmitted by satellite 1 and satellite 2, respectively. After that, the transmission symbols of satellite 1 and satellite 2 are demodulated and decoded, respectively. Finally, the communication rate of the system can be calculated by counting BER and bit block error rate (BLER).
# Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks
## 5 ANN Model Development  
The architecture of an ANN model is determined based on the characteristics of the problem and knowledge of ANN. In this study, the feed-forward back propagation technique is used, and the nonlinear sigmoid function is chosen as the activation function. ANN models are usually developed following four different steps. In the first step, a database is divided into three different classes namely training, testing, and validation. Training sets include around 50% of the total data and are selected randomly, including minimum and maximum values of the input data. Testing and validation sets are also selected randomly and contain about 25% of the data in each set. In the second step, the optimum number of hidden nodes and iteration of the network is determined by training and testing the network. The three best-performing networks are chosen based on their statistics for comparison. In the third step, the three best networks are validated using a validation data set. For the final step, the selected three networks are re-trained using all the data to increase the prediction accuracy on the network structure that was determined in the previous step. The performance of the selected three networks is evaluated based on Mean Absolute Relative Error (MARE), Coefficient of Determination (R\({}^{2}\)), and normalized Average Squared Error (ASE) and calculated using the following equations.  
\[\text{MARE}=\frac{\sum_{i=1}^{\text{N}}\left|\text{ }X_{i}^{\text{P}}-X_{i}^{\text{A}}\right| \right)/X_{i}^{\text{A}}}{\text{N}}*100 \tag{6}\]  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{\text{N}}\left(X_{i}^{\text{A}}-X_{i}^{\text {P}}\right)^{2}}{\sum_{i=1}^{\text{N}}\left(X_{i}^{\text{A}}-\overline{X_{i}} \right)^{2}} \tag{7}\]  
\[\text{ASE}=\frac{\sum_{i=1}^{\text{N}}\left(X_{i}^{\text{A}}-X_{i}^{\text{P}} \right)^{2}}{\text{N}} \tag{8}\]  
where, \(X_{i}^{\text{A}}\) = Actual value, \(X_{i}^{\text{P}}\) = Predicted value, \(\overline{X_{i}}\) = Mean of \(X_{i}^{A}\), N = Total number of data.
## 4 Applications
### Applications in the Steel Industry
#### 4.2.2 Heavy Plate Testing
It could be shown that, in highly texturized steel grades, adding relative differences of the US TOFs to the calibration functions could reduce the measuring error. This is shown in Table 9.  
The calibrated 3 MA + US combination system makes it possible to determine the cold ends before cutting by precisely detecting the positions of the acceptance lines, which are separating OK and NOK quality material at the head and the root of the plate. Figure 17 shows the corresponding measuring procedure. In a first step, the value of the assessing measuring quantity (here: tensile strength, Rm) is measured in the reference point in the middle of the plate. Subsequently, the 3 MA + US system is moved stepwise from the edge to the middle until the value of the measuring quantity falls below a preset threshold level, which is the value at the reference point plus a specified margin (here: \(\Delta\text{Rm}=15\) MPa). Afterwards, the acceptance line has been reached and it can be drawn on the plate. Figure 16: Avoiding pseudo-scrap by precise detection of the extension of cold ends. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters in** & **RMSE of Rm** & **RMSE of Rp0.2** & **RMSE of Hardness** \\
**Calibration Functions** & **[MPa]** & **[MPa]** & **[HB]** \\ \hline Only 3 MA parameters & 12 & 23 & 5 \\
3 MA and US parameters & 10 & 11 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of different types of calibration—without and with ultrasound (US) parameters.
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 4 The Proposed Scheme  
Mesh models are a representation of 3D models that typically contain vertex data, texture coordinate data, normal data, face data, and other data that together form the basic geometric shape and texture of the mesh model. As shown in Figure 1, different encryption methods are used for vertex coordinate data, texture coordinate data, and texture images in this article. The next section further introduces the principles and implementation details of related encryption algorithms.
## 3 Results and Discussion
### Numerical Simulations of Supercritical Debinding
This is due to the diffusion flux, which was high at the beginning of the debinding and decreased rapidly during the extraction process, according to Figure 7. However, to improvements in the debinding time, the component's position can be changed in the enclosure during supercritical debinding to reduce the impermeable surface in order to increase the diffusion flux. Figure 8 compares the remaining PEG during the numerical simulation of the supercritical debinding and the experimental results at different debinding times. The model correctly reproduces the experimental conditions because the experimental results are in good agreement and correspond with the mean value of the three tests performed at one and two hours under optimal conditions. The PEG extraction rate increased rapidly at the beginning of the debinding, showing an exponential extraction. At the final stage of the experiment, the remaining PEG was equal to 100%. The numerical simulation predicted 95% of PEG to remain at the final stage. It was necessary to add a half hour to remove all of the binder due to the low diffusion flux norm, numerically. The numerical simulation  
Figure 6: Numerical results according to a multi-slice view: (**a**) 3D model meshed by FEM; (**b**) initial stage (\(t=0\)); and (**c**) final stage (\(t=4\) h) of the PEG binder distribution during debinding by supercritical CO\({}_{2}\) under 40 MPa and 423.15 K.  
Figure 7: Diffusion flux norm from numerical simulation results vs. debinding time during supercritical CO\({}_{2}\) under 40 MPa and 423.15 K.  
results showed an excellent agreement with the experimental database. This minor discrepancy is probably due to the assumption of an impermeable surface on the cylindrical component.
# Multivariable Adaptive Super-Twisting Guidance Law Based on Barrier Function
## 5 Conclusions  
This paper presents a novel multivariable barrier function-based super-twisting guidance law, which has the following distinguished properties:  
1. Under the simulation condition given in Section 4.1, missiles using the proposed guidance law can achieve high guidance accuracy (ZEMs are smaller than 0.7 m) and high terminal angle accuracy (angle errors are smaller than \(1^{\circ}\)) in a short flight time;
2. The guidance law can maintain its distinguished performance in different scenarios of various target accelerations;
3. With the uncertainty of the missile dynamics and the measurement, the guidance law has a remarkable property of robustness.  
Moreover, compared with other sliding mode and super-twisting guidance laws, the one proposed in this paper does not require the information of the disturbance derivatives. The chattering of the acceleration commands is eliminated. Furthermore, the designed super-twisting gains are smaller, which lead to smaller super-twisting state variables and smaller acceleration commands. Following the increasing or decreasing of the disturbances, the gains are also sufficient to offset the disturbances and keep on the sliding mode during the control progress, which demonstrates a better property of robustness.  
Conceptualization, Y.L. and G.H.; methodology, Y.L.; software, Y.L.; validation, G.H., Y.Z. and Y.D.; formal analysis, Y.L., Y.Z. and Y.D.; investigation, Y.D.; resources, G.H.; data curation, G.H.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L., G.H., Y.Z., Y.D. and Z.Q.; visualization, Y.L.; supervision, G.H.; project administration, G.H.; funding acquisition, G.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.