esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Continuous-Wave Micro-Doppler Radar  
In this study, fall detection is based on a continuous-wave (CW) micro-Doppler millimeter-wave (MMW) radar. A CW is a sine wave of constant amplitude and frequency, and it is considered to be of infinite duration. The Doppler effect refers to the shift in frequency caused by a moving object. An additional shift in frequency can be caused by the object vibrating or spinning. When this happens, the Doppler-shifted signal becomes modulated; this is known as the micro-Doppler effect. This modulation can have a specific pattern or signature for which algorithms can be developed for required applications; this will be expanded on later.  
Figure 1 presents the basic scheme of the CW micro-Doppler radar used in this study. A CW signal, \(\tilde{E}_{T}\), with a constant carrier frequency \(f_{0}\) is transmitted toward an object:  
\[\tilde{E}_{T}(t)=\Re(A_{T}e^{|2\pi f_{0}t}) \tag{1}\]  
where \(t\) indicates the dependence on time, \(\Re(\ \cdot\ )\) denotes the real part of the complex number and \(A_{T}\) is the amplitude of the transmitted signal. The received signal returning from the moving object at distance \(r(t)\) is:  
\[\tilde{E}_{R}(t)=\Re\{A_{R}e^{-|2\pi r(t)-\theta|}\cdot e^{|2\pi f_{0}t}\} \tag{2}\]  
Figure 1: A general scheme of a continuous-wave (CW) micro-Doppler radar.  
where \(A_{R}\) is the amplitude of the received signal, \(k=2\pi f_{0}/c\) is the wavenumber, \(c\) is the speed of light and \(\theta\) is a constant for the phase shift. A factor of 2 is applied to account for the path from the radar to the object and the return. At the radar, the transmitted signal (1) is multiplied with the received signal (2). After low pass filtering (LPF), the intermediate frequency (IF) signal obtained is:  
\[\tilde{V}(t)=\tilde{E}_{T}(t)\cdot\tilde{E}_{R}^{*}(t)=\Re\{A_{T}A_{R}e^{i[2k \cdot r(t)-\theta]}\} \tag{3}\]  
From this equation, it can be seen that the resulting phase changes over time:  
\[\varphi(t)=2k\cdot r(t)-\theta \tag{4}\]  
Through the use of (4) and the familiar connections from mechanics, \(\dfrac{d}{dt}r(t)=v(t)\) and \(\dfrac{1}{2\pi}\dfrac{\partial\varphi(t)}{\partial t}=f_{d}(t)\), it is possible to express the instantaneous Doppler frequency shift as:  
\[f_{d}(t)=\dfrac{1}{2\pi}\dfrac{\partial\varphi(t)}{\partial t}=\dfrac{1}{2 \pi}2k\cdot v(t)\cdot cos\alpha=\dfrac{2f_{0}}{c}\cdot v(t)\cdot cos\alpha \tag{5}\]  
where \(\alpha\) is the angle between the direction of the transmitted/reflected signal and the direction of moving of the person. The resulting instantaneous Doppler frequency \(f_{d}(t)\) of the IF signal obtained at the multiplier output is proportional to the object velocity \(v(t)\).
# A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer
## 4 The System Hardware and Software Implementation Options
### Example of Black-and-White Image Transmission with MATLAB Computational Engine  
The encryption system described in the previous subsection (Figures 12 and 17) can be used to encode any type of information, including graphic information, which is one of the most popular application examples for chaotic data transmission. Therefore, to illustrate the operability of this system, we considered confidential image transmission. As a test image, a black-and-white image \(200\times 200\) pixels in size with 256 shades of gray level (a portrait of the writer [PERSON] (1860-1904)), shown in Figure 22a, was selected.  
When transmitting images, the information signal is a two-dimensional function \(f(x,y)\), where \(x\) and \(y\) are the spatial coordinates, and the amplitude \(f\) is the intensity (brightness) or gray level at any point with a pair of coordinates \((x,y)\).  
Figure 21: Statistical analysis after message delivery. (**a**) Error rate vs. number of transmission repetitions; and (**b**) Transmission speed vs. number of transmission repetitions.  
Figure 20: Testbed main window after message delivery. (**a**) number of iterations: 20,000; (**b**) number of iterations: 8000.  
To submit an image to the system input, we used a block _From file_ to receive data from an external file. The data are presented in the form of a matrix  
\[\left[\begin{array}{cccc}t_{1}&t_{2}&\dots&t_{end}\\ u_{1}&u_{2}&\dots&u_{end}\end{array}\right] \tag{29}\]  
where the first row of the matrix represents time samples in ascending order, and the second one is the image elements (pixels) at these points in time.  
To convert a BMP image into data of the form in Equation (1), it is necessary to execute a script in the workspace of the MATLAB system as a standard MATLAB procedure from the digital image processing libraries. The input of the script is the initial image BMP-file, the output is a MATLAB data MAT-file. After simulation of the image's transmission, the script is executed in reverse order to generate an output BMP-image file.  
Model parameters as per Equation (1) can be changed in the model topology file, or through the graphical user interface in the MATLAB/Simulink system. During the simulation, the following parameters were selected:  
* fixed parameters of the Rossler system: \(a=b=0.2\);
* the parameter of the synergetic observer that determines the rate of estimation of the parameter, \(\alpha=0.01\); and
* the modulating signal is presented in the form of a stepwise time dependence, the height of each step corresponding to the gray level of the black-and-white image.  
The resulting restored image is shown in Figure 22c and the encoded signal in the communication channel shown in Figure 22b.
## 3 Biphasic and Linear Fractional Poroelastic Models
### Correspondence of Parameters between the Biphasic and the Linear Fractional Poroelastic Model  
The biphasic model essentially depends on two parameters: the aggregate modulus \(H_{A}\) and the averaged axial permeability \(\kappa\). The linear fractional poroelasticity model of Biot depends instead on three parameters: the diffusion coefficient \(\lambda_{\beta}\), the fractional power \(\beta\) and the ratio \(k/\mu\). These parameters depend on the material parameters of the constituent phases or have to be determined, for example using numerical experiments curves fitting. The biphasic and linear fractional Biot models are equivalent under the above assumptions when \(\beta=0\) (i.e., in the case of a non-fractional [PERSON]'s model) and when \(H_{A}=\lambda_{0}\).  
The Equations (8) and (24) show that the biphasic and Biot models are equivalent when \(H_{A}=\lambda_{0}\). Similarly, the displacement solution from the [PERSON]'s model in the case of the stress relaxation test can be obtained by replacing \(H_{A}\) by \(\lambda_{0}\) in Equation (9). Hereinafter, Tables 1 and 2 summarize the relationships between the parameters for each models.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Notation** & **Name** & **Expression** & **Unit** \\ \hline K & drained bulk modulus & Pa \\ \hline G & drained shear modulus & Pa \\ \hline \(K_{u}\) & undrained bulk modulus & Pa \\ \hline \(\alpha\) & Biot’s coefficient & Unitless \\ \hline B & [PERSON]’s coefficient & \((K_{u}-K)/(\alpha K_{u})\) & Unitless \\ \hline \(k\) & averaged intrinsic permeability & \(\mathrm{m}^{2}\) \\ \hline \(\mu\) & fluid viscosity & \(\mathrm{N}\cdot\mathrm{s}\cdot\mathrm{m}^{-2}\) \\ \hline \(\kappa\) & averaged axial permeability & \(\kappa=k/\mu\) & \(\mathrm{m}^{4}\cdot\mathrm{N}^{-1}\cdot\mathrm{s}^{-1}\) \\ \hline \multirow{3}{*}{\(\lambda_{0}\)} & non-fractional diffusion coef \\  & - compressible case & \(((4G+3K)(K_{u}-K))/(\alpha^{2}(4G+3K_{u}))\) & \(\mathrm{m}^{3}\cdot\mathrm{s}\cdot\mathrm{kg}^{-1}\) \\ \cline{1-1}  & - incompressible case & \((4G+3K)/3\) \\ \hline \(\lambda_{\beta}\) & fractional diffusion coef. & \(\mathrm{m}^{3}\cdot\mathrm{s}^{1+\beta}\cdot\mathrm{kg}^{-1}\) \\ \hline \(\beta\) & fractional power & Unitless \\ \hline \hline \end{tabular}
\end{table}
Table 1: Linear fractional Biot model.
# Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems +
## 1 Introduction
This paper discusses the need for SRA based on dynamic programming at the base station to support diverse 5G use-cases, as depicted in Figure 1.
* We extend our SRA algorithm for 5G use-cases: eMTC, URLLC and eMBB. The problem formulation shows the scalability of the utility function to adopt all these use-cases with LTE to support the first generation 5G NSA architecture. * An extended discussion of related work is given in Section 2 to review state-of-the-art SRA techniques and discuss the need for our work presented in this paper. * In this paper, we extend the performance evaluation to consider the eMTC and URLLC in addition to LTE. For LTE, our SRA algorithm outperforms the greedy approach [13] by up to 60%, 2.6% and 1.6% in terms of goodput, goodput fairness and delay fairness, conforming to [17]. For eMTC and URLLC associated with more demanding performance requirements, our SRA algorithm continues to outperform the greedy cross-layer approach [13] by up to 17.24%, 18.1%, 2.5% and 1.5% in terms of average goodput, correlation impact, goodput fairness and delay fairness, respectively. The remainder of this paper is organized as follows. Section 2 reviews state-of-the-art SRA algorithms. Section 3 formulates the SRA problem. In Section 4, our SRA algorithm is described and its time complexity analyzed. In Section 5, the performance of the proposed SRA algorithm is evaluated in comparison to [13] for LTE, eMTC and URLLC use-cases. Finally, the paper is concluded and future work is discussed in Section 6.
## 2 Materials and Methods
### Irradiation Conditions  
The volume of the phyto-chamber was divided by opaque panels into four equal compartments. In three of the four zones, in addition to the main lighting, there were installed additional LEDs emitting their radiation in the UV-A, UV-B, and UV-C ranges. (The fourth zone was for control). The spectral characteristics of the emitters in each zone are shown in Table 1. The UV irradiators consisted of an aluminum radiator with printed circuit boards with LEDs attached to them. The technical specifications of the installed LEDs are presented in Table 2. As a percentage of visible light, the intensity of the used UV emitters, comparable to that of sunlight, are for the Moscow summer season for 2019 and 2020 (with the exception of UV-C, which is absent under normal conditions, but it can be present in places with ozone holes in earth/s atmosphere).  
In the photosynthetically active radiation zone, the exposure was provided by the combined LED-based irradiators of various spectral compositions. The light period dura  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**No.**} & \multirow{3}{*}{
\begin{tabular}{c} **Experiment** \\ **Variant** \\ \end{tabular} } & \multicolumn{4}{c}{**Main Lighting**} & \multicolumn{4}{c}{**UV-Radiation,**} \\  & & \multicolumn{2}{c}{**W m\({}^{-2}\)**} & \multicolumn{4}{c}{**W m\({}^{-2}\)**} \\ \cline{3-8}  & & **B** & **G** & **R** & **FR** & **UV-A** & **UV-B** & **UV-C** \\  & & **400–500 nm** & **500–600 nm** & **600–700 nm** & **700–800 nm** & **315–400 nm** & **280–315 nm** & **100–280 nm** \\ \hline
1 & UV-A & & & & & 1.5 \(\pm\) 0.2 & - & - \\
2 & UV-B & \(5\pm 0.5\) & 9.5 \(\pm 0.3\) & 15.8 & 1.5 & - & 0.015 \(\pm\) 0.005 & - \\
3 & UV-C & & & \(\pm\) 0.4 & \(\pm\) 0.1 & - & - & 0.010 \(\pm\) 0.002 \\
4 & Control & & & & & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spectral properties of radiation sources.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multicolumn{4}{c}{**Type of UV-LEDs**} \\ \cline{2-4}  & **UV-A Raytron Solution** & **UV-B Szynju** & **UV-C Epistar** \\ \hline Peak current, mA & 750 & 40 & 40 \\ Output power, W & 2.4 \(\div\) 2.6 & 2 \(\div\) 4 & 4 \(\div\) 6 \\ Direct voltage, V & 3.2 \(\div\) 3.8 & 5 \(\div\) 8 & 5 \(\div\) 8 \\ Viewing angle, \({}^{\circ}\) & 60 & 120 & 120 \\ Peak wavelength, nm & 365 & 310 & 275 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of UV-LEDs.  
tion was 16 h. The average irradiation intensity was 30 \(\pm\) 1,2 Wm\({}^{-2}\), which corresponds to the lower recommended level required for the normal basil cultivation.  
The UV irradiators worked simultaneously with the main light. The UV irradiance was monitored with the portable UV radiometer TKA-PCM (STP \"TKA\", Saint-Petersburg, Russian Federation).
## Comparative Study of DE, PSO and GA for Position Domain PID Controller Tuning  
**[PERSON] 1,2,* and [PERSON] 2**  
1 College of Mechanical and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China  
2 Department of Aerospace Engineering, Ryerson University, Toronto 350 Victoria St, ON, Canada, E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.:+1-4169-795-000 (ext. 4928).  
Academic Editors: [PERSON] and [PERSON]  
_Received: 1 July 2015 / Accepted: 18 August 2015 / Published: 21 August 2015_  
**Abstract:** Gain tuning is very important in order to obtain good performances for a given controller. Contour tracking performance is mainly determined by the selected control gains of a position domain PID controller. In this paper, three popular evolutionary algorithms are utilized to optimize the gains of a position domain PID controller for performance improvement of contour tracking of robotic manipulators. Differential Evolution (DE), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are used to determine the optimal gains of the position domain PID controller, and three distinct fitness functions are also used to quantify the contour tracking performance of each solution set. Simulation results show that DE features the highest performance indexes for both linear and nonlinear contour tracking, while PSO is quite efficient for linear contour tracking. Both algorithms performed consistently better than GA that featured premature convergence in all cases.  
**Keywords:** optimization; position domain control; PID; differential evolution (DE); genetic algorithm (GA); and Particle Swarm Optimization (PSO)
# Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
## 2 Background
### ML Models  
In order to develop a reliable mental health monitoring system through emotion recognition, it is important to figure out which machine learning methods perform most efficiently in this sector. To accurately classify emotions, the machine learning methods are fed with input signals (discussed in Section 2.1). The system determines a pattern from this input data and uses this information to correctly classify emotions. Afterwards, when non-classified data are fed to the classifier, it outputs a single discrete emotional category or several numerical values corresponding to a position on an emotional plane.  
Among diverse machine learning methods, the most commonly found methods are: Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Convolutional Neural Network (CNN), Deep Neural Network (DNN), Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), etc. We also came across a few hybrid or ensemble methodologies, such as voting, adaboost, a combination of Principal component analysis (PCA) and Linear discriminant analysis (LDA), etc. These techniques are comprehensively included in several studies, and thus interested readers are referred to [51; 52; 53]. The majority of machine learning methods evaluate performance in accuracy [54]. However, there are a few other evaluation metrics, e.g., correlation coefficient and RMSE (Root Mean Square Error). The correlation coefficient is the statistical measure of how strongly related two variables are [55]. Moreover, RMSE refers to the numerical error between a prediction and its true value [56]. A lower error corresponds to a closer match between the estimator and the ground truth.
## 3 Materials and Methods
### Atmospheric Correction  
In the model presented in Equation 3, three quantities are related to the atmosphere and its physical condition at the time of the flight: the transmittance \(\tau\) along the path from the surface to the on-board sensor, the upwelling radiance \(l_{u}\) emitted by the atmospheric layer in the observing direction and the downwelling radiance \(l_{d}\) emitted by the whole atmosphere toward the surface and reflected in the observing direction. All of these spectral quantities were computed by the Modtran 5 radiative transfer model [17].  
A rigorous parameterization of the model requires measured vertical profiles of the air temperature, pressure and humidity and the volume mixing ratio of each molecular species composing the atmosphere. Of course, such detailed knowledge is normally unavailable. For the present study, however, three different parameterization of the Modtran model were tested and compared. The first run used the profiles obtained by a daily radiosonde launched at 00:00 UTC from the station of the Regional Agency for Environmental Protection (ARPA) at San Pietro Capofiume, located about 30 km NE of Bologna. It provides vertical profiles of temperature, pressure, relative humidity and wind speed from the ground to an altitude of about 20 km. The second run used instead the profiles obtained by a MODIS Terra second level product [18], based on an image acquired at 20:30 UTC, about one hour later than the thermal flight. MODIS data contain also information about the total precipitable water vapor and the total ozone burden in the air column. Finally, the third run of the model simply used the mid-latitude winter (MLW) pre-defined profile, which would be the most appropriate choice, if no other data were available. A comparison between the vertical profiles of air temperature used in the three Modtran runs is shown in Figure 3.  
For the calculation of the upwelling radiance and the transmittance, the radiative transfer was simulated between the ground and the height of flight. Instead, for the calculation of the downwelling irradiance, the simulation was performed from a height of 100 km to the ground level, and it has been repeated, varying the impinging angle from 0 to 90 degrees by a step of 5. For these simulations, when using the sounding data, the vertical profiles were completed with the MLW model above a height of 20 km; instead, the total precipitable water vapor value was used when using MODIS Terra data.  
Figure 2: Orthophoto-mosaic of 30 thermal images acquired over the test area in Bologna.  
The differences in the resulting broadband parameters, following the three approaches in the parameterization of the Modtran model, are summarized in Table 1, where broadband radiances are formally defined as in Equation 2, and the broadband transmittance T is computed as the integral average of the spectral transmittance, weighted by the instrument filter function (_s_) and by the black-body radiance (_ls_). When analyzing those differences, the spatial and time distances between the sounding data and the MODIS ones must be considered.
## 2 Train Occupancy Time and Graphic Analysis
### Graphic Analysis of Two Trains  
For two trains \(i\) and \(j\), their total occupancy time on the timetable cannot be obtained by simply adding up their own occupancy time because the two trains may occupy the same time period repeatedly. In this article, this phenomenon is called the section repeated-occupancy problem. If the section repeated occupancy of trains on a timetable is not considered, only a simple sum will lead to larger calculation results.  
For ease of explanation, there are assumed to be two trains \(i\) and \(j\), and the departure time of train \(i\) is earlier than the departure of train \(j\) from the departure station. In the train timetable, there may be three situations between two trains: no repeated occupancy, partial repeated occupancy and full repeated occupancy.  
Figure 1: Occupancy time of standard trains on a train timetable.  
Figure 2: Occupancy time of one single train timetable.  
As shown in Figure 3, the two trains have no repeated occupancy. In this situation, the total occupancy time \(OT_{ij}\) can be obtained by adding the occupancy time of train \(i\) and train \(j\).  
\[OT_{ij}=OT_{i}+OT_{j} \tag{2}\]  
Figure 4 shows a situation of the partial repeated occupancy of two trains. In this situation, the travel time of train \(j\) is longer than the travel time of train \(i\) due to the differences in speed or stop plans. Therefore, train \(j\) can start from the departure station before the occupancy of train \(i\) is over, resulting in overlapping part \(RT_{ij}\) between the occupancy time \(OT_{i}\) and \(OT_{j}\). In this case, the overlapping part shall be removed from the total occupancy time of the two trains.  
\[OT_{ij}=OT_{i}+OT_{j}-RT_{ij} \tag{3}\]  
Figure 5 shows the situation of full repeated occupancy of two trains. In this situation, because train \(i\) is overtaken by train \(j\) when it stops at the intermediate station, the occupancy time of train \(j\) is completely covered by the occupancy time of train \(i\). In this case, the overlapping part between the two trains is equal to the occupancy time of train \(j\). Therefore, the total occupancy time of the two trains on the train timetabl  
Figure 4: Occupancy time of two trains with partial repeated occupancy.  
Figure 3: Occupancy time of two trains without repeated occupancy.  
To summarize the above three situations, the repeated occupancy time between the two trains \(RT_{ij}\) can be calculated by Equation (4), and the total occupancy time of the two trains \(OT_{ij}\) can be calculated by Equation (5):  
\[RT_{ij}=\left\{\begin{matrix}0&RT_{ij}<0\\ OT_{i}-(d_{j}-d_{i})&0\leq RT_{ij}\leq OT_{j}\\ OT_{j}&RT_{ij}>OT_{j}\end{matrix}\right. \tag{4}\]  
\[OT_{ij}=OT_{i}+OT_{j}-RT_{ij} \tag{5}\]
## 5 Results
### Movement and Navigation Tests  
#### 5.2.1 Speed Tests  
For speed tests, the authors focused on measuring the maximum linear ground speed, acceleration, and deceleration. For this purpose, tests were carried out on the four different surfaces shown in Figure 1 and Table 2, with a slope of 0\({}^{\circ}\) and different payloads. The results are reported in Table 6.
# Preliminary Validation of Surface Reflections from Fengyun-3C Radio Occultation Data
## 1 Introduction
In the past decade, FY-3C products have been evaluated via comparisons with other ROs' profiles. For example, FY-3C refractivity products from the near surface to the altitude of 30 km are as good as COSMIC refractivity products [33; 34]. Over a long-term perspective, the latitudinal and seasonal variations of refractivity, temperature and specific humidity data produced by FY-3C were fully investigated and showed slight differences from ERA-Interim data [35]. It is noticeable that the quality analysis in such an evaluation mentioned above only refers to the influence of multipath and super refraction in the lower atmosphere, without taking the reflected signals of FY-3C into account. It is well-known that the signal travels to lower height and can be reflected at the surface and received by RO receivers. Therefore, it is possible to detect the surface-reflected signals of FY-3C RO events and provide the opportunity to improve FY-3C products resolution. In this paper, we put the emphasis on the surface-reflected signals of FY-3C, which have not been studied before. Firstly, we systematically compared the mean bias and standard deviation between FY-3C profiles and COSMIC refractivity profiles. We selected COSMIC events with both reflected and non-reflected signals. Subsequently, we used a radio holographic technique to visualize and identify the reflection of the FY-3C signals. At last, we reconstructed the models of reflected signals of FY-3C RO events. Section 2 introduces the available data and data preprocessing. Section 3 shows the methodology of the research and details the results. Section 4 discusses the results and Section 5 is our final conclusion to the present study.