esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep-Learning Approach for Identifying a Drunk Person Using Gait Recognition
## 2 Materials and Methods
### Gait Analysis
#### 2.2.2 Gait Energy Image (GEI)  
A gait energy image (GEI) is a normal cumulative image acquired over time for the entire gait cycle. This is a representative gray-level image with gait information as normalized pixels of the silhouette. To avoid noise from individual images and to ensure smooth synchronization, GEIs are generated based on a continuous sequence in the gait cycle. In particular, GEI shows several major characteristics of gait, such as operating frequency, temporal and spatial changes of the human body, and general shape of the overall body. To generate an accurate GEI, one or more cycles of continuous walking must be included as shown in Figure 2. In addition, compared to other expression methods, GEIs have the advantage of being less sensitive to noise and more likely to obtain excellent results.  
Pixels shown in white have the highest intensity and correspond to body parts (head, torso, etc.) that show little movement during the gait cycle. This mainly corresponds to the static domain of the GEI, which contains information related to body type and posture.  
Figure 2: Manufacturing and augmentation process of a gait energy image (GEI). (**a**) Images were captured from background-subtracted video and combined to add up to a consequential GEI. (**b**) Gait Energy Image (GEI).  
On the other hand, pixels with medium intensity appear gray, and it can be seen that the lower part of the leg and the arm are constantly moving. This region shows how movement occurs during walking, and it is the dynamic region of the GEL.
# Rock Crack Types Identification by Machine Learning on the Sound Signal+
## 2 Automatic Recognition Method-Based Spectrograms for Hard Rock Crack Types
### Identification Model for Tensile and Shear Crack Using GPC
#### 2.4.2 Implementation Steps  
There are four main steps, including data collection, training samples construction, recognition model establishment, and prediction samples recognition, involved in the application of the proposed method, as shown in Figure 14 and described in the following sections.  
Step 1: Collect tensile and shear sound signals. The sound signals of tensile cracks are collected from Brazilian splitting tests, and the sound signals of shear cracks are collected from direct shear tests. A sound sample set including both sound signals is established.  
Step 2: Pre-process the sound signals by denoising, framing, and adding windows.  
Step 3: Generate spectrograms. The short-time Fourier transform (STFT) is used to draw the sound spectrograms.  
Step 4: Extract characteristics of the spectrograms using pre-trained ResNet-18. The pool5 layer of ResNet-18 is used to extract the deep characteristics of spectrograms. All spectrograms in the sample set are extracted deep characteristics to construct training samples to train the GPC model.  
Step 5: Establish the GPC model. Before iterative training, some initial parameters of the GPC model that need to be determined include the initial hyper-parameter range, kernel function type, and adaptive parameter optimization times. These necessary parameters are finally determined after iterative training.  
Step 6: Verify the GPC model. During the training process, the trained GPC model is self-validated. If the accuracy of the verification results meets the requirements, the determination method is accurate, and the next step is continued. Otherwise, return to step 5; the distribution of training samples or the initial parameters of the GPC model must be adjusted again.  
Step 7: Input the prediction sample. All the predicted sound signals are processed according to steps 2-4, and the corresponding deep characteristics of the spectrograms are extracted. The extracted characteristics are used as inputs to the trained GPC model in order to obtain the final classification probability. According to the output results, the crack type can be predicted. The meaning of the output probability \(p^{\star}\) of the GPC model is as follows: When \(p^{*}>0.5\), the sound signal is determined to be a tensile signal. The closer the \(p^{*}\) value is to \(+1\), the greater the probability that the sound signal is a tensile crack. When the probability value is \(p^{*}<0.5\), the sound signal is predicted to be a shear signal. The closer the \(p^{*}\) value is to \(-1\), the greater the probability that the sound signal is a shear crack.  
A MATLAB-based program was developed according to these steps.
## 5 Discussion
Furthermore, variations in the CubeSat inertia also increase the pointing error as seen in Figure 16. Even though changing the inertia of a CubeSat on orbit could be possible only by degeneration of the nanosatellite, this approach is useful for considering new control configurations if the physical design is changed during the development process. Indeed, it is necessary to measure the center of mass, center of inertia, and mass of the satellite to adjust the initial algorithm to meet the requirements of the launcher. Once the launcher has been chosen, adjustment of the structure design can be made from the original design, affecting the mass properties of the CubeSat and subsequently the controller. In this regard, model-based design is necessary for analyzing the possible impact of making minor changes in the design without necessarily implementing those changes. Even though inertia is critical for the system performance, it is not a variable that could be easily set by the developers. Alternatively, the initial orbit parameters are variables that could be set by the deployers. Thus, the selection of the deployer could be guided to approximate an optimal selection based on Monte Carlo analysis, since the simulation demonstrates that the most optimal points for the controller to begin acting occur at approximately 34.72 and 109.83 degrees, resulting in a mean pointing error of less than 20 degrees, as seen in Figure 18. Finally, from the results shown in Figure 17, it could be inferred that the pointing error in nadir control tends to remain constant for the simulated initial pointing error during two orbits, highlighting the importance of optimally completing the detumbling state to less than 0.1\({}^{\circ}\)/s as seen in Figure 1 to decrease the pointing error once the state is changed. As could be seen, each approach is limited by their own configurations, and also contributes significantly to the understanding and analysis of the whole system performance. Hence, all simulation environments are complementary to perform an enhanced model-based design and also to carry out a comprehensive V&V process.
## 2 Materials and Methods
### Light Conditions  
Artificial light conditions were created in the growth chamber by exploiting ad hoc light recipes made using a PHYTOFY(r) RL LED module from OSRAM (Munich, Germany), which is specifically designed for indoor horticulture applications allowing for fully tunable light spectra in terms of intensity per wavelength. The intensity associated with every wavelength is expressed as photosynthetic photon flux density (PPFD), thus, the portion of emitted photons that contributes to the photosynthesis in the photosynthetic active region (PAR) is constituted by wavelengths in the range 400-700 nm. The LED light recipe chosen for these preliminary growth tests is a standard composition summarized in Figure 1.  
The PPFD emitted by the panels was approximately 212 umol m\({}^{-2}\) s\({}^{-1}\) with a photoperiod of 16 h/day, equal to 12.15 mol m\({}^{-2}\) d\({}^{-1}\). These technical data have also been confirmed by several studies carried out on lettuce growth where the use of white and green light has shown an improvement in the fresh mass of the plants [22], the combination of red and blue lights can favor lettuce growth, and the application of red/far red stimulates the germination process, also increasing light capture [23]. The distance of the LED panels from the plants was 75 cm, kept constant for the entire duration of the test. According to the layout and the chosen light recipe, the PPFD map of the area under the LED panel is exposed in Figure 2. The pots used for growth experiments were placed within the two active areas highlighted by the black rectangles. This allowed ensuring as uniform lighting conditions as possible for each lettuce plant.  
Figure 1: Graph of the light recipe used during growth tests for lettuce vegetative growth. The vertical axis displays the photosynthetic photon flux density (PPFD).
# Bayesian Estimation of Simultaneous Regression Quantiles Using Hamiltonian Monte Carlo
## Appendix B Univariate Linear Quantile  
Suppose the error terms \(\epsilon_{i=1,\ldots,T}\stackrel{{ i.i.d}}{{\sim}}\mathcal{N}(0,1)\). As the Bayesian approach requires a likelihood, we use the ALD to make Bayesian inference, and our quantile of interest is \(\tau=0.25\), thus we use \(ALD(0,\sigma,\tau=0.25)\). We suggest detecting the quantile of order \(\tau=0.25\) by placing a prior on \(\sigma\). For all algorithms, we generate 300 independent observations issued from the model defined in Equation (5). Since we are using simulated data, the theoretical quantiles are known, which are \(\beta_{0}=4.33\) and \(\beta_{1}=2\).  
We run all the algorithms for 3000 iterations, with half of them designated as burn-in.  
It is noteworthy that for both the Metropolis-Hastings and Gibbs algorithms using R, we received the following message: \"Cannot compute Gelman & Rubin's diagnostic for any chain segments for variables \(\beta_{0}\). This indicates convergence failure\". We received the same message for \(\beta_{1}\). By computing \(\hat{R}\) by hand, we obtained \(\hat{R}\) values less than 1, indicating that the standard deviation between chains is less than the standard deviation within chains. Hence, we did not achieve convergence of the chains. This is not the case when using HMC. Therefore, if we are interested in estimating a univariate linear quantile (not simultaneously), it is preferable to use HMC based on the results obtained in Table A1. Additionally, the estimated quantiles using HMC are better than the ones estimated using Gibbs and Metropolis-Hastings. Hence, for a more complex model like simultaneous regression quantile, it is definitely better to use HMC. This is the main motivation behind using HMC.
## 2 Methods  
### Preprocessing  
#### 2.1.1 Common Spatial Patterns  
Common Spatial Patterns (CSPs) are derived from the common spatial subspace decomposition (CSSD) algorithm, which is a spatial filtering method for the analysis of multichannel EEG data. The main goal of CSP is to find spatial filters that maximize the variance of EEG signals in one category and minimize the variance of EEG signals in another category. This is achieved by calculating the covariance matrices of EEG signals of different categories and then applying eigenvalue decomposition to find the spatial filter. The resulting filter is called the CSP and is used to transform the original EEG signal into a new eigenspace where the classes are more easily separable [14]. The CSP is shown as follows:  
\[W=\frac{\omega^{T}X_{1}^{T}X_{1}\omega}{\omega^{T}X_{2}^{T}X_{2}\omega}=\frac{ \omega^{T}M_{1}\omega}{\omega^{T}M_{2}\omega} \tag{1}\]  
where \(W\) is the spatial filter matrix, \(T\) is the transpose of the matrix, \(X\) represents the data matrix of the class, and \(M_{1}\) and \(M_{2}\) are the covariance matrices of the two classes of EEG signals, respectively.  
CSP does not require the selection of a specific frequency band. Therefore, it is suitable for all types of EEG signals and provides a feature vector that captures signal components relevant to the task, while reducing noise and irrelevant components. CSP is sensitive to noise, but EEG signals typically have low signal-to-noise ratios. CSP typically requires EEG recordings from multiple electrodes for an effective spatial filter, which may cause it to have a low performance on signals with only a few channels.
# Design Analysis of a Passive Buffer System for Space-Assembled Segmented Mirrors
## 4 Simulation Analysis  
In order to test and verify the buffer ability of the proposed theory, there should be evaluation to assess whether the capture contact meets the requirements of optical assembly. Due to a lack of experiment environment and equipment, it is an effective method for the dynamic simulation on computer to validate the parameters of the buffer system designed.  
A stack of parameters with zero gravity environment were chosen to compare the magnitude of the collision, such as axial docking velocity, lateral deviation, and roll angular velocity. One PMA is designed to remain stationary and stable at the center of the simulated environment. Meanwhile, another PMA approaches and connects with it via the parameterized docking maneuver. Referring to docking data from Automated Rendezvous and Docking of Spacecraft [22] and SPHERES docking experiment [23], typical initial docking conditions of SMALL are listed in Table 2.  
The thrust value is set to 100 N. Taking the androgyny characteristic of SMALL into account, there will be two contact positions for the probe-cone docking process. Due to the existence of the docking deviation, there is always a case where a set of pin and hole is contacted, and then another set is contacted. Under the above conditions, the docking simulation between the two PMAs is performed by Adams software. The contact force diagram of a typical rigid collision system is shown as Figure 8. Likewise, that of the double-elastic collision system is shown as Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Axial Docking** & **Maximum Lateral** & **Roll Angular** & **Pitch/Yaw Angular** & **Roll Bias Error** \\  & **Velocity (mm/s)** & **Bias Error (mm)** & **Velocity (\({}^{\circ}\)/s)** & **Velocity (\({}^{\circ}\)/s)** & **(\({}^{\circ}\))** \\ \hline Value & 5 & 10 & \(-\)1\(\cdot\)1 & \(-\)1\(\cdot\)1 & \(-\)10\(\cdot\)10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial docking conditions of SMALL.  
Figure 7: The relationship between the driving force and the maximum compressive stress under different depths of propulsion.  
Under the same docking conditions, the contact forces between the first set are almost the same, as the red lines shows in Figures 8 and 9. However, by comparison, it should be noted that the contact force of the second set of pins and holes produced a significant change. The connection mechanism, using the passive buffer system, generates a collision force much smaller than that of the conventional un-buffered system. The contact force decay rate under the double-elastic contact model is 58%, which is much larger than 10% under the traditional rigid collision. Therefore, it can be concluded that the double-elastic contact model is feasible.
## 4 The Proposed Heuristic Scheme
### Decomposition Selection Algorithm  
Whenever a service request \(S\) arrives, the decomposition selection algorithm selects a decomposition from the decomposition set of services \(dc=G_{S}\in s_{dc}\). A cost function is used to determine which decomposition is going to be selected, as expressed in Equation (20) below:  
\[cost(dc)=w_{e}*n_{e}(dc)+w_{p}*n_{p}(dc)+w_{n}*n_{f}(dc) \tag{20}\]  
The cost function has three selection factors, which are number of edges \(n_{e}(dc)\), number of nodes \(n_{f}(dc)\), and number of paths \(n_{p}(dc)\). Each factor has a weighting parameter that is used to control the impact of that factor. These weighting parameters, \(w_{e}\), \(w_{p}\), and \(w_{n}\), are used to represent the number of edges, number of paths, and number of nodes, respectively. The pseudo code of the decomposition selection algorithm is illustrated in Algorithm 1.  
```
Data:\(s_{dc}\): Service request decomposition set, \(w_{e}\): edges weighting parameter, \(w_{p}\): paths weighting parameter, \(w_{n}\): nodes weighting parameter. Result: Service graph of selected decomposition with minimum cost
1 begin
2\(minCost\gets infinity\)for\(G_{s}\in s_{dc}\)do
3\(cost=w_{e}*n_{e}(G_{s})+w_{p}*n_{p}(G_{s})+w_{n}*n_{f}(G_{s})\)if\(minCost>cost\)then
4\(minCost=cost\)\(SelectedDc=G_{s}\)
5 end if
6 end for
7 Return: SelectedDc
8 end for
```  
**Algorithm 1**Decomposition Selection Algorithm
# Optimization of Ship Permanent Magnet Synchronous Motor ADRC Based on Improved QPSO
## 3 Active Disturbance Rejection Controller
### TD Controller  
The Tracking Differentiator (TD) smooths the approximation of the generalized derivative of the input signal using the nonlinear function in the Active Disturbance Rejection Controller (ADRC). Taking a second-order system with an integral-series configuration as an example, the state-space equation is as follows:  
\[\left\{\begin{matrix}fh=fhan(v_{1}-v,v_{2},r,h),\\ v_{1}=v_{1}+hv_{2},\\ v_{2}=v_{2}+hfh,\end{matrix}\right. \tag{5}\]  
where \(v\) represents the input signal, while \(v_{1}\) and \(v_{2}\) are the two output signals, with \(v_{1}\) being the tracking value of \(v\), and \(v_{2}\) representing the generalized derivative of \(v\), which is calculated from \(v_{1}\). \(r\) and \(h\) are the transition coefficient and the sampling step size, respectively. As \(r\) increases, the speed at which \(v_{1}\) tracks \(v\) also increases, depending on the transition rate and the system's capacity. The control synthesis function \(fhan(v_{1},v_{2},r,h)\) is used in the equation, and its form is as follows:  
\[fhan=\left\{\begin{matrix}d=rh^{2},\\ a_{0}=hv_{2},\\ y=v_{1}+a_{0},\\ fsg(y,d)=\frac{sign(y+d)-sign(y-d)}{2},\\ a_{1}=\sqrt{d(d+8|y|)},\\ a_{2}=a_{0}+sign(y)\frac{(a_{1}-d)}{2},\\ a=(a_{0}+y)fsg(y,d)+a_{2}(1-fsg(y,d)),\\ fh=-r_{0}^{d}fsg(a,d)-rsign(a)(1-fsg(a,d)),\end{matrix}\right. \tag{6}\]  
Figure 2: ADRC Structure Diagram.
# Reliability Analysis of Technical Means of Transport
## 2 Methodology  
This section describes the theoretical grounds for developing a mathematical model. The functional characteristics of reliability, considered in terms of probability and described in Section 2.1, are related to continuous random variables and may be also applied during mathematical transformations in order to determine specific functions analytically. If the considered objects are unrepairable, it is necessary to know relevant probability distributions in order to determine a value of a specified reliability factor. Then, a value of this factor should be determined based on the analytical methods of a probability theory. From a practical perspective, only the values of reliability factors in a certain population and statistical methods for the estimation of these values may be referred to. Functions characterizing a random time of object fitness, described in Section 2.1, are simply interpreted statistically in the case when the probability of events is replaced with their occurrence frequency. There can be distinguished two groups of methods for the statistical presumption of reliability:  
* Non-parametric methods, in which the probabilistic characteristic of a random variable is deduced directly;
* Parametric methods, in which the probabilistic characteristics of random variables are defined indirectly through determining the probabilistic characteristics of specific parameters from an experiment.  
A correlation of parametric and non-parametric methods, for a statistical test of object reliability, is presented in the form of a chart (Figure 2).  
Figure 2: Flowchart of the correlation of parametric and non-parametric methods.  
If it is required to estimate the functional characteristics of reliability based on the empirical data, obtained during either observations or experimental tests, the object reliability may be characterized through a set of numerical data randomly selected from a given population (set) of objects. Therefore, it is crucial to appropriately estimate the parameters of the distribution of the considered variable as well as to determine the empirical characteristics, both numerical and functional, described in Section 3.
## 4 Conclusions  
An OFDM-QPSK transmission link accompanied by the spatial diversity reception was proposed to improve the performance of FSO communications. The OFDM signal exhibited better performance than the single-carrier signal, and the BER was reduced from 2.87 \(\times\) 10\({}^{-3}\) to 2.98 \(\times\) 10\({}^{-4}\) at an SNR of 6 dB by using the OFDM-QPSK signal instead of the SC-QPSK signal. The receiving sensitivity was further improved by the spatial diversity reception. Under moderate turbulence, the BER decreased from 2.45 \(\times\) 10\({}^{-3}\) to 6.10 \(\times\) 10\({}^{-4}\) at an SNR of 6 dB by using two-aperture receivers. Under strong turbulence, the BER was reduced to 6.87 \(\times\) 10\({}^{-4}\) by using six-aperture receivers from 2.14 \(\times\) 10\({}^{-2}\). More aperture numbers are required with turbulence strengthening, and it should be selected according to the used channel conditions.  
[PERSON] contributed to the paper in terms of methodology, program, visualization, and data analysis and paper draft. [PERSON] and [PERSON] contributed to the paper in terms of investigation, resources, and project administration. [PERSON] contributed to the paper in terms of conceptualization, supervision, review, and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (61875172, U2141231), the Zhejiang Provincial Natural Science Foundation of China (LD19F050001), and the National Key Research and Development Program of China (2019 YFB2205202).  
Not applicable.  
Figure 8: Required aperture number of receivers for the BER below 1 \(\times\) 10\({}^{-3}\) at the SNR of 6 dB.