esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup
### Full-Swivel Chair Rotational Movement Procedure  
Figure 6 shows the full-swivel chair rotational movement pattern that were followed to generate reproducible video freeze conditions. The procedure starts with selecting an initial direction in which the HMD and TPCAST receiver are pointing to in terms of one of the four azimuthal angles \(\phi\in\{0^{\circ},90^{\circ},180^{\circ},270^{\circ}\}\) (see also Figure 2). The full-swivel chair, holding the receiving HMD, is first rotated to the left by \(\Delta\phi=90^{\circ}\) with respect to the selected initial azimuthal angle \(\phi_{0}\) covering rotations within the respective quadrant from \(\phi_{0}\) to \(\phi_{l}=\phi_{0}+90^{\circ}\). Subsequently, the full-swivel chair is rotated to the right by \(\Delta\phi=-180^{\circ}\) starting at \(\phi_{l}=\phi_{0}+90^{\circ}\) and ending at \(\phi_{r}=\phi_{0}-90^{\circ}\). Finally, the full-swivel chair is  
Figure 5: Example traces produced by the monitoring: (**a**) Excerpt of the text trace obtained for a transmitter-receiver distance of \(d=7.5\,\mathrm{m}\) and \(270^{\circ}\) initial HMD direction (first column: frame number; second column: measured inter-frame time in micro-seconds (11169 \(\mu\)s relating to a nominal frame rate of 89.53 fps); third column: flow indicator (0 = freeze, 1 = no freeze), (**b**) Video trace sample of “SteamVR home screen” [24] given as screenshot of the first frozen frame, here frame number 3474 with HMD residing in Q3.  
rotated again to the left by \(\Delta\phi=90^{\circ}\) from \(\phi_{r}=\phi_{0}-90^{\circ}\) back to the initial azimuthal angle \(\phi_{0}\). The full-swivel chair rotational speed was kept approximately constant at \(\pm 60^{\circ}/\mathrm{s}\) with 40 turns performed for each of the considered four initial azimuthal angles. Accordingly, each of the four quadrants is covered by 80 forth-and-back turns. It should be noted that the selected rotational speed is consistent with the average head speeds of up to \(48^{\circ}/\mathrm{s}\) around the yaw that were reported in [25] as averages over a whole session of a subjective experiment on \(360^{\circ}\) video quality.
## 3 Results
### Fitting of the Vegetation Growth Data Curve and Extraction of the Growth Feature Time Nodes
From day 250 to 300, precipitation was frequent and irregular, and the change rate of CK increased, whereas DIFI and BIFI decreased gradually. Time Node Extraction of Vegetation Growth Characteristics  
According to Formula (3), it was concluded that the SOS of the forest was the starting point of the rising slope of the curve, and the EOS was the lowest end of the curve. The start of the forest growth season under CK, DIFI and BIFI was the same as the chronological day at which the MOE was reached. As shown in Table 4, SOS and MOS were days 83 and 111, respectively. However, the EOS days were different. Before day 83, the trees were in a period of dormancy, GCC was affected by snowfall, and the overall trend was relatively stable. Affected by the temperature rise, the forest gradually began to germinate, the GCC index increased, and the vegetation growing season began. At day 111, the MOE was realized, and the relative GCC reached a high value. Around day 265, the withering stage began. As the temperature dropped, the forest activities weakened and the GCC decreased accordingly. The growing seasons of CK, DIFI and BIFI ended at days 307, 315 and 314, respectively. At this time, the value of GCC dropped to its lowest point. In addition, the leaves of the planted forest almost withered, returning the trees to their initial stable state. The trend remained unchanged.
## 3 Optical Design and Analysis
### Optical System of GeoFWHIS  
Design results and performance of each component of GeoFWHIS have been aforementioned. In this section, the optical system of GeoFWHIS is given, and its optical path diagram is shown in Figure 10. To evaluate its imaging performance adequately, MTF curves of each spectrometer in each band are shown in Figure 11. The columns (a) to (d) correspond to spectrometer 1-4 in Figure 4. We can see that the MTF values of spectrometers 2 and 3 are slightly higher than those of spectrometers 1 and 4. This is because the image quality of the fore-optics in the central field of view is better than that of the edge field of view, and the spectrometers 1 and 4 are aligned on both sides of the slit image of the fore-optics. The lowest MTF values at Nyquist frequency of B1 to B5 bands are 0.59, 0.48, 0.59, 0.39, and 0.25, respectively. They are high enough to meet the requirements in Table 1, which are 0.17, 0.17, 0.17, 0.12, and 0.12, respectively.  
Figure 10: Optical path diagram of GeoFWHIS.  
Figure 9: **(a)** Optical-mechanical model of B1 spectrometer and **(b–d)** illuminance maps of the image plane. **(b)** shows the illuminance map of the spectrometer’s image plane, **(c)** shows the illumininance map when the slit is blocked by 10%, and **(d)** shows the illuminance map when the wavelength range is notched by 10%.
# DeCGAN: Speech Enhancement Algorithm for Air Traffic Control
## 2 Materials and Methods
### Discriminator  
There is typically a lack of direct correlation between the objective function and common assessment indicators in speech enhancement. Therefore, an assessment result is likely to fall short of expectations when dealing with an optimized objective loss. Furthermore, as non-differential metrics, metrics like the perceptual evaluation of speech quality (PESQ) [16] and short-time objective intelligibility (STOI) [17] cannot be directly used as loss functions. Therefore, a discriminator is designed here, based on the MetricGAN model. By incorporating the metrics of assessment into adversarial training, MetricGAN enhances speech, while DeCGAN further improves this approach. Specifically, the discriminator in DeCGAN is designed to model evaluation metrics, adding the evaluation score to the loss function during model training to improve speech-denoising performance.  
Figure 3 shows the structure of the DeCGAN discriminator. As the figure shows, the discriminator comprises four convolutional modules, with the number of channels set at 16, 32, 64, and 128. Each convolutional module includes convolutional layers, instance normalization layers, and a PReLU activation function. The convolutional layers extract time-frequency features, the normalization layers normalize the features, and the PReLU activation function helps alleviate the vanishing gradient problem. After these convolutional blocks, a global average pooling layer aggregates the feature maps into a fixed-length feature vector, which is crucial for handling variable-length inputs. This vector is then fed into two fully connected layers that further process the features, and finally, a sigmoid activation function produces a scalar output representing the predicted speech quality score. This discriminator plays an important role in guiding the generator during adversarial training by directly linking the generated speech to perceptual quality metrics.  
During the training of the discriminator, the PESQ/STOI metric scores are maximized using the amplitude spectra of clear speech signals as the model's inputs. Then, the metric scores for enhanced speech are estimated, using the amplitude spectra of the denoised speech signals as the inputs, to approach the score values of clean signals of speech.
## 3 Meso-to-Macro Upscaling
### Mesoscale damage model  
The initial elastic moduli of the continuum are assumed to be spatially homogeneous. The isotropic damage model with an equal degradation of the bulk and shear moduli postulates the stress-strain law in the following form [8]:  
\[\sigma=(1-\eta)D^{\epsilon}\varepsilon \tag{14}\]  
where \(\sigma\) is the stress tensor, \(\varepsilon\) is the strain tensor, \(D^{\epsilon}\) is the elastic material stiffness, and \(\eta\) is a scalar damage variable. For quasi-static problems, the damage index \(\eta(x,t)\) is a RF varying in the fictitious time \(t\). For an undamaged material, \(\eta\) is set to be zero, and the response is linear elastic. When the continuum is progressively deformed, the initiation and propagation of micro-cracking decreases the stiffness of the meso-scale SRVE or individual finite elements, and \(\eta\) increases. When \(\eta=1\) for a particular element, the stiffness of this element vanishes, corresponding to complete damage.  
By focusing on probabilistic strength RF, the following exponential softening law is used in this study  
\[\eta=\begin{cases}0&\text{if \ }k<\varepsilon_{0}\\ 1-\dfrac{\varepsilon_{0}}{\varepsilon_{e}}\exp\!\left(-\dfrac{\varepsilon_{e}- \varepsilon_{0}}{\varepsilon_{f}-\varepsilon_{0}}\right)&\text{if \ }k\geq \varepsilon_{0}\end{cases} \tag{15}\]  
where \(\varepsilon_{e}\) is the largest equivalent strain reached in the previous history up to the current state, \(\varepsilon_{0}=s/E\) the limit elastic strain, \(E\) Young's modulus, and \(\varepsilon_{f}\) a parameter determining the fracture energy.  
By employing the above softening condition and the Kuhn-Tucker complementary conditions, nonlinear finite element equations  
\[K(u)u=f \tag{16}\]  
can be solved by using the Newton-Raphson algorithm  
\[u_{i+1}=u_{i}-T^{-1}(u_{i})\!\left[K(u_{i})u_{i}-f\right] \tag{17}\]  
where \(T\) is the tangent stiffness given by
# Three-Dimensional Guidance Laws for Spacecraft Propelled by a SWIFT Propulsion System
###### Abstract  
This paper discusses the optimal control law, in a three-dimensional (3D) heliocentric orbit transfer, of a spacecraft whose primary propulsion system is a Solar Wind Ion Focusing Thruster (SWIFT). A SWIFT is an interesting concept of a propellantless thruster, proposed ten years ago by [PERSON] and [PERSON], which deflects, collects, and accelerates the charged particles of solar wind to generate thrust in the interplanetary space. To this end, the SWIFT uses a large conical structure made of thin metallic wires, which is positively charged with the aid of an electron gun. In this sense, a SWIFT can be considered as a sort of evolution of the [PERSON]'s E-Sail, which also uses a (nominally flat) mesh of electrically charged tethers to deflect the solar wind stream. In the recent literature, the optimal performance of a SWIFT-based vehicle has been studied by assuming a coplanar orbit transfer and a two-dimensional scenario. The mathematical model proposed in this paper extends that result by discussing the optimal guidance laws in the general context of a 3D heliocentric transfer. In this regard, a number of different forms of the spacecraft state vectors are considered. The validity of the obtained optimal control law is tested in a simplified Earth-Venus and Earth-Mars transfer by comparing the simulation results with the literature data in terms of minimum flight time.  
Solar Wind Ion Focusing Thruster; three-dimensional heliocentric transfer; optimal guidance; interplanetary trajectory optimization Article
# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 7 Conclusions  
In this paper, we mainly studied the problem of flight anomaly detection and causal analysis during the approach phase of civil aviation aircraft, and proposed an optimization method based on the XGBoost algorithm. A large amount of experimental data show that the method proposed in this paper can effectively detect abnormal events during the aircraft approach phase, making up for the shortcomings of traditional over-limit detection, which cannot simultaneously detect abnormal events coupled with multiple events. In addition, experts have only conducted scientific research on accident data, but our method can detect relatively small anomalies in daily flight processes. Through factor analysis, we  
Figure 11: Multi-type anomaly detection.  
Figure 12: Features ranking for detected anomaly events. **(a)** ILS Heading Deviation; **(b)** Large Decline Rate; **(c)** ILS Glide Slope Deviation.  
can clarify the source of anomalies and provide warnings for human factors involved in abnormal events. Frequent occurrences of the same abnormal event on the same model should be noted.  
Considering that there are still a large number of unused features in QAR data, there is still room for improvement in constructing new composite features. In addition, there is a risk of overfitting when using the Borderline SMOTE algorithm for data balancing. Other balancing techniques should be considered.  
Therefore, in future research, we will further explore abnormal events during other aircraft flight phases, such as the takeoff phase, landing phase, etc. We will further expand the feature usage of QAR data and explore the spatiotemporal information behind a large number of parameters in different flight stages, making the model more universal. In terms of data preprocessing, we will discuss the applicability of balanced weighted extreme learning machine for imbalance learning and a novel parallel-series data-driven model for IATA-coded flight delay prediction and features analysis, as well as QAR data balancing.  
Conceptualization, C.R.; Software, C.H.; Validation, X.D.; Formal analysis, X.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Research Fund of the Tianjin Natural Science Foundation (No. 20 JCYBJC00500).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Maximal Ratio Combining Detection in OFDM Systems with Virtual Carriers Over V2V Channels
## 6 Simulation and Results
In order to observe the degree of impact on the system performance when increasing the diversity in the data during MRC detection, the parameter \(\
u\) was modified to \(\
u=4\) and \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter (Units)** & **Value** \\ \hline \(\{N,N_{D},CP,N_{p}\}\) (Samples) & \(\{64,48,16,8\}\) \\ Bandwidth (MHz) & 10 \\ Modulation & OFDM \\ Frequency sampling (MHz) & 10 \\ Diversity order: \(\
u\) & \(\{2,4,8\}\) \\ Data modulation & 4 QAM \\ PDF \(E\{h^{2}[n,l]\}\) & \(\lambda e^{-0.4l}\) \\ Number of Multipaths & 6 \\ Doppler frequency: \(f_{D}\) (kHz) & 1 \\ Delay spread: \(\tau_{rms}\) (us) & 0.4 \\ Vehicle speed (km/h) & 100 \\ Channel estimation & Perfect channel knowledge \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. Figure 4: Comparison of BER for linear/nonlinear and MRC detectors in a V2V channel with an RMS delay of 0.4 μs and a Doppler frequency spread of 1 kHz, modeling a non-line-of-sight scenario with Rayleigh fading. The channel model uses a Jakes Doppler profile for each channel tap. 8. Figure 5 illustrates a BER vs SNR comparison of the proposed system for two different values of \(\
u\). For the specific case of SNR = 15 dB, the proposed system configured with \(\
u=4\) and \(\
u=8\) outperformed the system configured with \(\
u=2\) by 5 dB and 10 dB, respectively. Increasing factor \(\
u\) leads to a significant improvement in the system's performance. This is due to the increased diversity at the receiver. With a factor of \(\
u=4\), the proposed system behaves like an equivalent single-input multiple-output (SIMO) system of \(1\times 4\), receiving four replicas of the transmitted data vector. Similarly, with \(\
u=8\), it behaves like an SIMO \(1\times 8\) system, receiving eight replicas of the transmitted data vector.
## Methods  
Twenty-one children with VFNs (16 males, Mean (\(M\)) = 9.8 years, standard deviation (stdev) = 1.8 years) and thirty-five control speakers (19 males, \(M\) = 8.4 years, stdev = 1.6 years) completed a two-alternative forced choice (TAFC, [2,3]) listening task. During each trial, participants heard two sustained tokens of \(\alpha\)/ produced by the same speaker and were asked to judge if the tokens were the'same' or 'different.' One token (base) always had a \(f_{o}\) of 216 Hz, whereas the other token (test) was experimentally manipulated to have an \(f_{o}\) that was either the same, higher, or lower. The \(f_{o}\) of the test token was adaptively modified via the TAFC procedure based on the participant's judgements: correct judgements moved the \(f_{o}\) of the test token closer to the base token in a subsequent trial, whereas incorrect judgments moved the \(f_{o}\) further away. Using this stairstep procedure, a just-noticeable-difference (JND) was acquired for each participant, as a measure of their auditory acuity to \(f_{o}\). A linear regression was used to examine whether JND was significantly predicted by age, group (VFNs, controls), or an interaction between age and group.
## 3 Research Method
### Build Geological Models
#### 3.2.1 Single Horizontal Layer Stratum Model  
The horizontal layer is the most familiar stratum. Figure 2 is the schematic diagram of the single horizontal layer stratum model. This study discusses the effect of different resistivities, layer depths, and noise intensities. The upper resistivity is R1, and the lower resistivity is R2. Different resistivities were set up for comparison. There were eight groups of resistivity variation, which were (R1, R2) = (R1 = 50, R2 = 500, 200, 150, 75) and (R1 = 500, 200, 150, 75, R2 = 50). The layer depth is h; the depths were set as 10 m and 20 m. The noise interference generated by the ambient environment was set up by Gaussian noise. The Gaussian noises were set as 0%, 2%, 4%, 6%, and 8%. Table 1 shows the parameters of the single horizontal layer stratum model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Single Horizontal Layer Stratum Model**} \\ \hline \hline
**Impact Factors** & **n** & **R1** & **R2** & **h** & **N** \\ \hline \multirow{6}{*}{\begin{tabular}{c} Resistivity ratio \\ (n = R1/R2) \\ \end{tabular} } & 0.1 & & 500 & & \\ \cline{2-6}  & 0.25 & & 200 & & \\ \cline{2-6}  & 0.33 & & 150 & 150 & \\ \cline{2-6}  & 0.66 & & 75 & & \\ \cline{2-6}  & & 10 & 500 & & \\ \cline{2-6}  & n \(>\) 1 & 4 & 200 & 50 & 10 & 0 \\ \cline{2-6}  & 3 & 150 & & & \\ \cline{2-6}  & 1.5 & 75 & & & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Layer depth \\ (h) \\ \end{tabular} } & n \(<\) 1 & 0.25 & 50 & 200 & 10 & 0 \\ \cline{2-6}  & n \(<\) 1 & 0.25 & 50 & 200 & 10 & 0-8\% \\ \cline{2-6}  & n \(<\) 1 & 0.25 & 50 & 200 & 10 & 0-8\% \\ \cline{2-6}  & n \(>\) 1 & 4 & 200 & 50 & 10 & 0-8\% \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Noise intensity \\ (N) \\ \end{tabular} } & n \(<\) 1 & 0.25 & 50 & 200 & 10 & 0-8\% \\ \cline{2-6}  & n \(>\) 1 & 4 & 200 & 50 & 10 & 0-8\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter table of the single horizontal layer stratum model.  
Figure 2: Schematic diagram of the single horizontal layer stratum model.
## 1 Introduction
Generally, the objects attached to the beam and plate belong to the subsystem of the whole system, and the subsystem is usually simplified as a lumped (or concentrated) mass in the calculation. In engineering practice, researchers often encounter vibration problems occurring in beam-like structures with lumped masses attached to them, such as automobiles, spacecraft, etc., [59; 60; 61]. From previous studies, it is found that the rotational inertia of the lumped mass has a significant effect on the dynamic characteristics of mass loaded beams, especially for higher order vibration modes [62; 63; 64]. In addition, ignoring the effect of concentrated mass may overestimate the natural frequency and may lead to incorrect system response. Therefore, it is important to consider the effect of the lumped mass. Considering the lumped mass not only improves the calculation accuracy of the system frequency, but also increases the efficiency of the captured energy of the piezoelectric energy harvester [65]. For example, [PERSON] [59] performed a free vibration analysis on a lumped mass uniform beam containing translational and rotational inertia. [PERSON] et al. [66] studied the mechanism of the effect of lumped mass on the flutter characteristics of structures, and the optimal ranges for the weight and location of the lumped mass are provided. Therefore, in this work, we propose a piezoelectric and flexoelectric energy harvester based on the NSGT for FG porous material sandwich nanobeams under axial compressive loading. According to the amplitude of axial load, the energy harvester can work in prebuckling state or postbuckling state. The work is organized as follows. In Section 2, the basic equations are derived. An energy harvester model sandwich piezoelectric beam composed of FG porous material was developed, in which the FG layer is considered with three porosities. The von Karman type nonlinear vibration behavior was considered. Then the nonlinear electromechanical coupling control equation was obtained using the Hamiltonian principle. In Section 3, the static buckling, natural frequency, and output voltage are solved using the Galerkin method. In Section 4, the model is validated and the effects of different types of distribution, porosity coefficients, length scale parameters, nonlocal parameters, excitation frequency, and axial loads on the vibration frequency and output voltage of the nanobeam are discussed. In Section 5, the main results of this paper are summarized, and some conclusions are drawn.
## 5 Summary and Conclusions  
Currently, most attempts to derive cloud microphysical properties from satellite observations involve the disjointed use of MW and IR measurements, which are only subsequently integrated. In this review, we have addressed the problem of developing algorithms that simultaneously/hierarchically combine all the complementary information obtained from different spectral ranges. As described, the importance of strategies for integrating data acquired at MW, IR and VIS wavelengths has emerged as a powerful resource for improving microphysical cloud-parameter retrieval products. The development of methodologies and algorithms to fully exploit the synergistic content of the information carried via the different bands creates exciting opportunities to improve our understanding of cloud-scale microphysical variables, the hydrogeological cycle, and moisture redistribution. This review paper has covered the fundamental aspects of cloud interaction with MW and IR radiation and provided a comprehensive overview of the current state of retrieval algorithms, considering methodologies that derive cloud properties from IR and MW satellite observations separately and in synergy. The review raised the following considerations.