esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 3 Intelligent Navigation Marker System  
The intelligent navigation marker system retains the existing navigational assistance functions of traditional markers while enhancing their capabilities through the integration of navigation information devices within perception systems. This enhancement aims to meet the service requirements of digital waterways and intelligent shipping conditions.  
In our experiment, we deployed a series of intelligent navigation markers in the navigation area near Wugong Railway Bridge #4 in the Wuhu section of the Yangtze River. These markers are equipped with advanced sensors and communication devices, allowing for the real-time monitoring of waterway conditions and the collection of traffic data.
# Estimation of the Measurement Accuracy of Wireless Passive Resonance Sensors
## 4 Calculating the Natural Frequency of a Decaying Oscillator
### Coarse Search for the Carrier Frequency  
The coarse position of the carrier frequency is usually determined using the Fourier transformation. In Appendix C, it was found that a sampling over approximately \(\
icefrac{{0}}{{f}}\) oscillations with a subsequent new start of the measurement and subsequent averaging of the complex samples in the time domain promises optimal resolution for a given  
Figure 9: Relative frequency error \(\sigma_{f_{b}}/f_{0}\) for one readout cycle as a function of the quality factor of a resonator in selected ISM or SRD bands. The read distance \(R\) is set to \(R=1\lambda\). The diagrams start with the quality factor at which the resonance bandwidth fits into the assigned band. The resonator was stimulated and recorded for the time of \(Q\) oscillations. For quality factors where the resonator bandwidth became smaller than one fifth of the assigned bandwidth, the sampled bandwidth was limited to five times the resonator bandwidth. All other parameters correspond to those in Figure 3. The approximation in Equation (46) is also shown in the dotted lines.  
measurement time. In this case, without zero padding, there is a step size of \(\
icefrac{{f}}{{\lozenge}}\) in the frequency domain, i.e., a fairly coarse spectral resolution.  
Since always \(\
icefrac{{\mathcal{O}}}{{f}}\) oscillations of a decaying exponential function with noise are sampled, the waveforms are similar in the time domain. Therefore, the main lobes are also similar in the frequency domain. Figure 10 shows on the left the distribution of the sampling points around the maximum as small blue circles when calculating the Fourier transform without zero padding. The red line was calculated with a zero padding of 10.  
If the measurement signal is demodulated with the frequency of the maximum of the sampling points, which were calculated without zero padding via the Fourier transformation, a residual oscillation of up to \(\pm\
icefrac{{1}}{{2}}\) period remains. Figure 10 shows on the right the measured values in magnitude and phase that were demodulated with this coarse value for the carrier frequency. Since the actual frequency is about a quarter step size away from the maximum, a phase error of approximately \(\
icefrac{{2n}}{{4}}=90^{\circ}\) remains. The noise increases significantly when the signal from the decaying resonator slowly disappears into the noise.
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 4 Hardware Implementation
### Hardware Architecture  
As a target platform, we choose the Ultra96-V2, as it is low-power, low-cost, and features the Xilinx ZU3 EG with 16 nm FinFET technology along with an ARM-Cortex A53 CPU. In the following, we refer to the CPU as processing system (PS) and the ZU3 EG FPGA as programmable logic (PL). We use the processing system (PS) to create inputs for the receiver ANN by generating random bit vectors which are mapped to a complex symbol that is determined by the constellation of the trained mapper and propagated over a channel model. Additionally, the PS provides labels for training and calculates the achieved BER and MI. The data are transferred between PS and PL by using the shared  
Figure 5: MI of autoencoder constellations compared to channel capacity and 16-QAM.  
dynamic random access memory (DRAM). The hardware design consists of two separate modules for inference and training, respectively. The inference module takes the noisy input message and predicts a corresponding output message, whereas the training module additionally gets the label as input and adjusts the weights using backpropagation and gradient descent. In practice, the labels for retraining are either provided as pilot symbols or by using an outer error correction code (ECC) [36].
# The Contribution of Near-Surface Geophysics for the Site Characterization of Seismological Stations
## 4 Determination of Elastic Moduli  
In the context of site characterization of the selected seismological stations of the Athenet seismological network, the elastic moduli presented in Table 3 were calculated, i.e., the Poisson's ratio (\(\sigma\)), the shear modulus (\(G\)), the Young's modulus (\(E\)), and the bulk modulus (\(K\)). The Poisson's ratio (\(\sigma\)) specifies the ratio of the deformation perpendicular to the deformation parallel to the applied stress. It is a non-dimensional elastic modulus, with values ranging between 0.1 and 0.5, corresponding to the compact and loose formations, respectively. The Young's modulus (\(E\)) is the ratio between the longitudinal stress to the longitudinal strain, referring to an elastic solid exposed under uniaxial compression or extension. For the calculation of the shear modulus (\(G\)), the Vs and density values are required as an input to the relationship \(G=\rho\)Vs\({}^{2}\) (Vs in m/s and \(\rho\) in g/cm\({}^{3}\)). Finally, the bulk modulus (\(K\)) describes the deformation of the volume of a medium which is under pressure. The calculation approach of the elastic moduli with the contribution of geophysical and density measurements has been successfully applied in previous studies [58; 75; 76; 77; 78].  
The density values used for the calculation of the above elastic moduli are presented in Table 4, while the P- and S-wave velocities were determined by the seismic profiles of the SRT and MASW techniques, at each seismological station. Additionally, in Table 4, the median values of all the above-mentioned parameters for every geological formation, at each location, are presented.  
Figure 20: SRT and MASW (**top**) and ERT (**bottom**) sections at site of the seismological station _EPID_.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Elastic Modulus** & **Equation** & **Reference** \\ \hline [PERSON]’s ratio (\(\sigma\)) & \(\sigma=\frac{1}{2}\left[1-\frac{1}{(V_{p}/V_{5})^{2}-1}\right]\) & [80] \\ [PERSON]’s modulus (\(E\)) & \(E=\rho\frac{3V_{p}^{2}-4V_{p}^{2}}{(V_{p}/V_{5})^{2}-1}\) & [80] \\ Shear modulus (\(G\)) & \(G=\frac{2}{2(1+\sigma)}\) & [81] \\ Bulk modulus (\(K\)) & \(K=\frac{3}{3(1-2\sigma)}\) & [81] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Elastic moduli equations.
## 4 Results and Discussion  
### Data Description and Preprocessing  
During the epidemic period of the past two years, the university's online course platform has been fully improved, and students' requirements on the quality of online courses and online interaction between teachers and students have also been put forward. The source of the text sentiment database adopted in this paper is the relevant evaluation and corresponding scoring of the students at our school on the online teaching content of teachers. The data set of student evaluation texts and scores are exported through the school's online course platform, which can truly reflect the school's online course teaching effect. Each piece of data consists of the following two parts: (1) students' specific evaluation of the quality of the online course and (2) the student's score for the online course is divided into five grades.  
Before applying the data to the training model, the reliability and accuracy of the data should be guaranteed. Therefore, first of all, it is necessary to screen and preprocess data, including simplified and traditional style conversion, deletion of too long and too short data, deletion of repetitive text content, deletion of symbols without special functions, etc. In addition, in order to ensure the balance of the sample, it is necessary to equalize the collected data. Table 1 shows the specific construction of the emotional intonation library after final pretreatment and screening:  
After screening and preprocessing, a total of 20,240 tones containing emotion labeling were obtained. Due to sample equilibrium processing, the data volume of each emotion category is about 4000, among which the data volume of emotion is the largest, and the data volume of emotion is very disappointing in the least. In order to fully train the model and establish the classification model with excellent performance, it is necessary to divide the data set into the training set, verification set, and test set. This paper selects 100 samples from each category of data as test sets to evaluate the performance of the model. In addition, 200 samples from each category of data were selected as validation sets to train the parameters related to integrated learning and prevent the model from falling into over-fitting. Finally, the remaining data were used as a training set to train the four base models proposed in Section 2.
# Effects of Sinusoidal Model on Non-Parallel Voice Conversion with Adversarial Learning
## 5 Evaluation and Discussion
### Qualitative Evaluations  
A perceptual listening test was created to assess the performance of our proposed model on a non-parallel many-to-many VC task. We conducted a speaker similarity test to compare the dissimilarity and quality of the converted speech to a natural source speaker. The listeners should give a score for each stimulus, from 0 (highly similar to the source speaker) to 100 (highly similar to the sound of the target speaker). The listeners only have a reference example from both source and target speakers in order to distinguish and know the target speaker. Then, listeners have to evaluate the heard sentences based on the target speaker information (gender, accent, volume, etc.). Different sentences were randomly selected from each conversion pair and given in a randomized order. Thus, 81 utterances were involved in the listening test (3 types x 27 sentences) and randomly presented to the participants. Fifteen participants (seven males and eight females) participated in the experiment. The listening test took roughly 12 min. The audio samples can be found online ([[https://malradhi.github.io/contSM-VC](https://malradhi.github.io/contSM-VC)]([https://malradhi.github.io/contSM-VC](https://malradhi.github.io/contSM-VC)) accessed on 14 June 2021).  
Figure 7 shows the scores of the similarity test. The error bars represent the 95% confidence interval. As the results show, the developed and reference systems accomplish the same performance to the target voice. In other words, the proposed model has acceptably transformed the source voice to the target voice in the same-gender and cross-gender cases. It can also be found that both systems have equal performance considering similarity to the target speaker. In other words, there is no statistically significant difference between the proposed and StarGAN systems. The proposed framework validates the effectiveness of the sinusoidal model with continuous fundamental frequency in the conversion pipeline. From this perceptual test, we can conclude that our model is able to produce a voice like that of the target speaker in comparison to the system with more complicated discontinuous F0.
## 1 Introduction  
Black-box optimization refers to optimizing an objective function in the absence of prior knowledge of the function. The only knowledge of the objective function is the observed outputs of the given inputs. Metaheuristics such as evolutionary computation (EC) techniques and evolutionary algorithms (EAs) are widely employed for black-box optimization of various fields across science and engineering [1; 2; 3] due to their certain primary advantages over the traditional techniques (such as [PERSON]'s method and gradient descent) including high robustness in solving complex real-world problems, and domain knowledge and regularity (e.g., convexity, continuity, differentiability) of the functions to be optimized are generally not required [4].  
However, these algorithms often require a large number of function evaluations to evolve or update the candidate solutions in order for errors reduced to a satisfactory level [5]. For many real-world problems, evaluating the objective function is often costly. These function evaluations may involve conducting computational intensive numerical simulations or expensive physical experiments [6; 7; 8; 9] When only a limited number of objective function evaluations is affordable, EC's trial and error approach becomes unattractive.  
Moreover, EC techniques are commonly recognized as heuristic search algorithms because their theoretical analysis often lags behind the development of the algorithms [4; 10]. Their performance is often sensitive to the problem-dependent hyper-parameters [11]. Various approaches have been suggested to overcome these drawbacks. For example, building an inexpensive surrogate model for evaluation so as to reduce the number of objective function evaluations [5; 12], and automatic or self-adaptive tuning of the hyperparameters [11; 13; 14].  
In this paper, we propose two algorithms for expensive black-box optimization. The two algorithms are modified from Pure Random Orthogonal Search (PROS) [15]. PROS is a \((1+1)\) Evolutionary Strategy (\((1+1)\) ES), a kind of EA, that has only one parent in the population and generates one child in each generation that competes with its parent. The finite-time performance of PROS is promising and outperforms several existing EC techniques on most benchmark functions [15]. Unlike those well known EAs, PROS is free of parameters and thus the tuning of parameters is not required. To the best of our knowledge, there were no mathematical analyses on the behaviour and performance of the algorithm reported in the literature. In this paper, we aim to fill this gap. The major contributions of this research work are summarized as follows:  
1. The behavior and performance analysis of PROS, which are not available in [15], are provided here.
2. Two effective novel \((1+1)\) ES based on PROS, namely TROS and QROS, are proposed and they outperform PROS on a set of benchmark problems.
3. The performance of TROS and QROS are found competitive with three well-known optimization algorithms (GA, PSO and DE) on a set of benchmark problems.
# A 60-GHz Ultra-Thin and Flexible Metasurface for Frequency-Selective Wireless Applications
## 4 Conclusions  
This paper shows the potential utilization of flexible MMW metasurface as wallpapers or screens, in order to effectively improve signal communication and to maximize the power transmission in the indoor environments. The proposed screen-printed metasurface has been designed for the 60 GHz highly attenuated band, and its performance is evaluated by using the state-of-the-art testing facilities. The measurement of the flexible metasurface printed on Melinex and Kapton films is carried out on a quasi-optic bench, in order to validate the transmission characteristics of the simulations. The measured results depict an operating band of 60-GHz as the transmission is fully blocked perpendicular to the metasurface. This implies that the signal propagates on the surface of the designed metasurface and it acts like a waveguide medium to transfer the signal between two points with lesser loss than the wireless signal propagation. The proposed research is instrumental for conformal implements with desired flexibility to mount on irregular or non-planar surfaces.  
O.P.F., S.F.J. and A.A. performed conceptualization and methodology; O.P.F. carried out software and formal analysis; prototype fabrication is done by T.W. and P.R.; experimental investigation, validation, and data processing is done by S.F.J.; writing--review and editing is done by S.F.J. and O.P.F.; and the project administration, funding acquisition and supervision is done by A.A.  
This research was funded by the grants 132216 AMMETEX--Advanced Materials and Metamaterial Structures for MetaTextiles, and EP/N029666/1 Enabling High-Speed Microwave and Millimetre Wave Links (MiMiWaveS).  
Figure 10: Measured results of the transmission coefficient of the 60-GHz metasurface: (**a**) Melinex based metasurface, (**b**) Kapton based metasurface.  
**Acknowledgments:** Special thanks to Intrinsiq UK for the fabrication facilities and the Queen Mary University of London for the testing facilities.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform
## 4 Experimental Design  
### Benchmarks and Hyperparameter Settings  
In order to verify the performances of the DWT_LSTM method proposed in this paper, five algorithms including DWT_RNN, DWT_BP, LSTM, RNN, and BP were selected as benchmarks. The proposed forecasting method DWT_LSTM and the five benchmarks were classified into two groups: group 1 includes DWT_LSTM, DWT_RNN, and DWT_BP; group 2 includes LSTM, RNN, and BP. Obviously, all the forecasting algorithms of group 1 contain the discrete wavelet transform operation, while the members of group 2 contain only the corresponding neural networks of group 1. Group 1 shows the influence of discrete wavelet transform on prediction accuracy, and group 2 was used to find the most suitable neural network of LSTM, RNN, and BP for the time series prediction problem.  
The hyperparameters were set as follows: the number of input layer nodes and the number of the hidden layer nodes in the neural network of the six methods mentioned above were all set to 10, based on repeated experiments. The wind power generation prediction belongs to the regression problem, so the output layer of the neural network of the six methods does not have to set the activation function. The iteration period (epoch) was uniformly set to 100.  
To make the experiment more persuasive, for each of the six methods, experiments with the prediction step size from one to five were carried out.
# UAV Detection Using Thrust Engine Electromagnetic Spectra
### Advantages of UAV Thrust Power Plant Detection  
The energy source and the thrust power plant are the two primary components that are obviously necessary for UAV flight operation when the development of small UAV structures is considered historically and from a prospective viewpoint. Numerous additional parts used in UAV operations are rapidly modernizing and becoming obsolete. A tiny UAV that is produced in large quantities often uses an electrically powered BLDC motor as its thrust source. Due to their excellent motor efficiency and dense energy storage capacities, electrically driven UAVs can be built in small sizes and light weights, making them difficult to identify using standard UAV detection techniques.  
UAVs already have the ability to fly completely autonomously without the aid of radio communication or GPS. They can be readily programmed to fly over any active radar detection system that emits RF and to carry out the preprogrammed mission in complete radio silence.  
UAV manufacturers are competing to increase the maximum payload mass of the UAV with an increase in thrust motor power as a result of the rapidly growing demand for payload and the advances of lightweight battery energy storage technologies. The signature of electromagnetic interference emitted by electrically driven UAVs increases as the thrust power of electric motors increases, requiring increasingly strong switching capabilities for the management of electric motors [34; 35]. The increase in the maximum payload or cargo of electrically powered UAVs will also cause a detectable increase in the signature of the electromagnetic interference.
## 4 Results
### Second Phase of the Study
#### 4.2.3 Results
The relationships among variables are ultimately described and reflected through the regression equation, enabling an accurate understanding of the extent to which one variable is influenced by others. This, in turn, provides a reliable basis for prediction [61]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Item**} & \multicolumn{8}{c}{**Component**} \\ \cline{3-10}  & & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \multirow{3}{*}{Innovation} & Q6 & & & & & & & & 0.793 \\  & Q7 & & & & & & & & 0.772 \\  & Q8 & & & & & & & & 0.734 \\ \hline \multirow{3}{*}{Market Demand} & Q9 & & & 0.787 & & & & & & \\  & Q10 & & & 0.801 & & & & & \\  & Q11 & & & 0.781 & & & & & \\ \hline \multirow{3}{*}{Technological Maturity} & Q12 & & 0.805 & & & & & & & \\  & Q13 & & 0.803 & & & & & & \\  & Q14 & & 0.807 & & & & & & \\ \hline \multirow{3}{*}{Cross-Disciplinary Collaboration} & Q15 & 0.816 & & & & & & & & \\  & Q16 & 0.819 & & & & & & & \\  & Q17 & 0.795 & & & & & & & \\ \hline \multirow{3}{*}{Ethics and Privacy} & Q18 & & & & & & & & 0.714 \\  & Q19 & & & & & & & 0.826 \\  & Q20 & & & & & & & 0.765 \\ \hline \multirow{3}{*}{Public Acceptance} & Q21 & & & 0.796 & & & & & \\  & Q22 & & & 0.811 & & & & & \\  & Q23 & & & 0.776 & & & & & \\ \hline \multirow{3}{*}{Data Security and Copyright} & Q24 & & & & 0.799 & & & & \\  & Q25 & & & & & 0.766 & & & \\  & Q26 & & & & & 0.803 & & \\ \hline \multirow{3}{*}{Global Perspective and Localization} & Q27 & & & & 0.788 & & & & \\  & Q28 & & & & 0.787 & & & & \\  & Q29 & & & & 0.776 & & & & \\ \hline \multirow{3}{*}{Willingness to Adopt} & Q30 & & & & & & & 0.761 & & \\  & Q31 & & & & & & & 0.757 & & \\  & Q32 & & & & & & & 0.788 & & \\ \hline \multirow{3}{*}{Total \% of Variance} & & 9.316 & 1.634 & 1.499 & 1.47 & 1.435 & 1.361 & 1.219 & 1.17 & 1.103 \\  & & 8.658 & 8.587 & 8.518 & 8.383 & 8.306 & 8.303 & 8.141 & 8.067 & 7.88 \\ \cline{1-1}  & & 8.658 & 17.245 & 25.762 & 34.145 & 42.451 & 50.755 & 58.895 & 66.962 & 74.842 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Factor rotation matrix. The Table 9 results indicate that a regression model was constructed with innovation, market demand, technological maturity, cross-disciplinary collaboration, ethics and privacy, public acceptance, data security and copyright, and global perspective and localization as independent variables, and willingness to adopt as the dependent variable. In the model's F-test, an F value of 33.596 was obtained, with a significance value of 0.000 \(<\) 0.05, indicating that the model is effective. As indicated in the Table 10 above, the collinearity statistics VIF (Variance Inflation Factor) in the model are all less than 3.3, suggesting a low degree of collinearity among the independent variables, making the model reasonable.