esa-sceva/satcom-chunk-collection huggingface dataset:
# RM-Line: A Ray-Model-Based Straight-Line Extraction Method for the Grid Map of Mobile Robot
## 3 Results
### Comparison Results in Two-Room Environments  
Experiment 2 was performed in two rooms. Figure 10 shows the experimental results.  
Figure 10a shows the experimental environment. It consisted of two rooms connected by a door. The environmental area was 5 m \(\times\) 11 m. Compared with Experiment 1, the environment area of Experiment 2 was larger and had more obstacles.  
Figure 10b shows the grid map of the robot, which had a larger extent than Figure 9b and contained more contours. The size of each grid was 20 cm \(\times\) 20 cm.  
Figure 10c shows the line segments obtained using RM-Line. It can be seen from the figure that, in a more complex grid map, RM-Line could also obtain an ideal straight line extraction effect.  
Figure 10d shows the line segments obtained using CannyLine.  
Table 2 shows the comparison of two algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Average Dis (cm)** & **Number of Lines** & **Time (ms)** \\ \hline CannyLine & 20.5 & 101 & 121 \\ RM-Line & 2.6 & 53 & 36 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of two algorithms in experiment 2.  
Figure 10: The experiment in two rooms: (**a**) the environment; (**b**) grid map; (**c**) line segments obtained using RM-Line; (**d**) line segments obtained using Canny-Line.  
Table 2 shows that RM-Line saved 70% of the time compared to CannyLine, and the average point-to-line distance was reduced by 87%. RM-Line extracted 53 lines from the grid map, and CannyLine extracted 101 lines. When these lines were used for map compression, the proposed RM-Line saved 47.5% more storage space than CannyLine. Similar to the results in Section 3.2, the straight line obtained using CannyLine was quite different from the real obstacle grid, while the straight line obtained using the proposed RM-Line accurately described the obstacle information in the map.
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 2 Problem Statement
### Impacts of Subset Size on Accuracy and Integrity Performances
#### 2.2.1 Accuracy Performance Improved by Subset Size Growth
This verifies the importance of a good satellite selection method during the positioning process.
###### Abstract
Instead of feeding the received signal directly to the matched filter (MF), we first feed it to a sequence of three subsystems (Doppler estimator, phase corrector, and waveform recovery) that form our proposed add-on module. The Doppler estimator applies a nonlinear transformation to the received signal to remove the bi-phase waveform and calculate an estimate for the Doppler value. The phase corrector uses this estimate to reduce the Doppler effect from the received signal and then check if any remainder of the Doppler effect needs to be suppressed. If that is the case, the adjusted signal from the phase corrector is re-fed to the Doppler estimator for further processing. This treatment is repeated until an accurate estimate of the Doppler is obtained. The waveform recovery subsystem uses the boosted estimates of the Doppler value along with the received signal to detect the target speed and recover the original waveform with a minimal Doppler effect. The recovered waveform is then fed to the conventional range radar detector. It is worth mentioning that the proposed module does not affect the transmitter and is powerful enough to enable the designer to optionally disregard MTD, which requires many pulses to obtain an accurate Doppler estimate without resolving the Doppler ambiguity. This paper is structured as follows: in Section 2, the algorithm is described analytically, providing an overview of its key components. The simulation results of the proposed algorithm are presented in Section 3, highlighting its performance in a simulated environment. Section 4 investigates the performance of the system and analyzes its effectiveness in practical scenarios. The validation of the theoretical model and the implementation of the algorithm using software-defined radio (SDR) are discussed in Section 5. Furthermore, Section 6 identifies the limitations of the current algorithm and offers potential solutions to overcome them. Finally, in Section 7, the paper concludes by summarizing the key findings and discussing future work that can be pursued in the radar field.
## 6 Valorization Technologies for Avocado-Derived Biomass through Obtaining Bioactive Compounds
### Phenolic Compounds
Although the integration of these technologies in a biorefinery could address more sustainable processes, food and pharma grade solvents should replace highly toxic ones, such as methanol. Thus, water, aqueous-ethanolic solutions, and CO\({}_{2}\) as solvents could be some options. For example, [PERSON] et al. [83] have shown that microwave-assisted extraction (400 W) with 58% ethanol can be applied to obtain phenolic compounds from avocado stone in a short time (4.8 min). This enables the recovery of an even higher TPC than using ultrasound-assisted extraction. Alternatively, [PERSON] et al. [20] opted for the use of direct drying methods when wastewater was studied as a source of antioxidants. The best option was spray-drying at 160 \({}^{\circ}\)C when compared with lower temperatures and lyophilization (Table 5). Among the phenolic compounds identified in avocado waste, [PERSON] et al. [81] found procyanidin B2 and epicatechin in the peel and trans-5-O-caffeoyl-b-quinic acid, procyanidin B1, catechin, and epicatechin in the stone. [PERSON] and coworkers [80] characterized 29 phenolic compounds, fourteen flavan-3-ols ((epi)catechin derivatives), nine flavonols (quercetin, kaempfer and isorhamnetin glycoside derivatives), and six phenolic acids (chlorogenic and coumaric acid derivatives). Peel and stone presented very different phenolic profiles, with only two common compounds.
# Failure Prediction of the Rotating Machinery Based on CEEMDAN-ApEn Feature and AR-UKF Model
###### Abstract  
A novel failure prediction method of the rotating machinery is presented in this paper. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to decompose the vibration signals of the rotating machinery into a number of intrinsic mode functions (IMFs) and a residual (Res), and the metric of maximal information coefficient (MIC) is used to select eligible IMFs to reconstruct signals. Then, the approximate entropy (ApEn)-weighted energy value of the reconstructed signals are calculated to track the degradation process of the rotating machinery. Furthermore, the Chebyshev inequality is introduced to determine the prediction starting time (PST). Finally, the auto regress (AR) model and unscented Kalman filter (UKF) algorithm are used to predict the remaining useful life (RUL) of the rotating machinery. The method is fully evaluated in a test-to-failure experiment. The obtained results show that the proposed method outperforms its counterparts on failure prediction of the rotating machinery.  
failure prediction; rotating machinery; complete ensemble empirical mode decomposition with adaptive noise; maximal information coefficient; approximate entropy; unscented Kalman filter 2020
# UAV Dynamic Non-Terrestrial Transmission Channel Analysis Based on SSCM-RT Model
## 3 SSCM-RT Channel Model
### Modeling Scenario Characterization
 \tag{6}\]  
where \(\omega=\frac{G_{far}}{G_{max}+G_{far}}\), \(G_{far}\) and \(G_{near}\) are the total gain in the far\(-\)field and the total gain in the near\(-\)field, respectively. It follows that the result is related to \(\frac{d_{1}}{d_{2}}\). This provides the theoretical basis for the cascade channel system and also provides the theoretical basis and feasibility support for the SSCM-RT channel modeling reference in this paper. Figure 4: Long-distance UAVs communication scenario of SSCM-RT. The red point represents a high-altitude UAV that carries TX; the blue point represents a low-altitude UAV that carries RX. The SSCM-RT hybrid channel model proposed in this paper combines the advantages of the statistical channel modeling method and ray tracing method. Taking path loss as an example, when assuming that both path \(d_{1}\) and \(d_{2}\) of the channel are known, the path loss in segment \(d_{1}\) can be written as  
\[P_{L-SSCM}(d_{1})[\mathrm{dB}]=P_{L-SSCM}(d_{1}+d_{2})[\mathrm{dB}]-P_{L-SSCM} (d_{2})[\mathrm{dB}]. \tag{7}\]  
The total path loss can be written as  
\[ \begin{array}{ll}PL[\mathrm{dB}]&=P_{L-SSCM}(d_{1})[\mathrm{dB}]+P_{L-RT}(d_ {2})[\mathrm{dB}]\\ &=P_{L-SSCM}(d_{1}+d_{2})[\mathrm{dB}]-P_{L-SSCM}(d_{2})[\mathrm{dB}]+P_{L-RT} (d_{2})[\mathrm{dB}]\\ &=20 lg\frac{1}{\frac{d_{1}}{d_{2}}+1}+10n\cdot lg\Big{(}\frac{d_{1}}{d_{2}}+1 \Big{)}+X_{\sigma}(d_{1}+d_{2})-X_{\sigma}(d_{2})+P_{L-RT}(d_{2})[\mathrm{dB}] \\ &=10(n-2)lg\Big{(}\frac{d_{1}}{d_{2}}+1\Big{)}+X_{\sigma}(d_{1}+d_{2})-X_{ \sigma}(d_{2})+P_{L-RT}(d_{2})[\mathrm{dB}].\end{array}  \tag{8}\]  
It is consistent with the definition and range of PLE. According to the simulation results in Section 2.1, the first part of Equation (8) can be calculated by bringing in the corresponding value of \(n\). \(X_{\sigma}\) depends on the variance \(\sigma\) and the relationship of \(d_{1}\) and \(d_{2}\). \(P_{L-RT}(d_{2})\) is calculated by an equivalent RT simulation. This only applies to the case of two segments. \(X_{\sigma}(d_{1}+d_{2})\) and \(X_{\sigma}(d_{2})\) obey the same zero\(-\)mean Gaussian random variable. This implies that the estimated PL's variance has not doubled. According to the results in the reference [17], electromagnetic waves transmitted over long distances can be approximated as parallel line propagation if the free space propagation model is used as a reference. This also demonstrates the validity of the equivalent RT part of the simulation in this SSCM-RT model. The GSCM-RT hybrid model proposed in reference [14] also provides a theoretical basis for this paper. While the long-distance path loss in the scenario of this paper is non-negligible, the results of the statistical channel model have greater practicality compared with reference [14].
# Status, Features, and Future Development of the LIFUS5/Mod4 Experimental Facility Design
## 3 Materials and Methods
### Connection to W-Hydra  
During an in-box LOCA, the PbLi-water interaction occurring inside the TBM box is strongly influenced by the dynamical behavior of the respective connected loops. The inertia, inventory, and thermodynamical parameter in the water loop regulate the final amount of water injected into the system; the inertia of the PbLi loop counteracts the water injection and mitigates the LOCA effects. For this reason, it is extremely important that not only the PbLi circuit mimics exactly the behavior of the real system but also that the coupled water circuit can do the same.  
To achieve this goal, the LIFUS5/Mod4 facility utilizes as a water source the Water Loop (WL) facility, which is being simultaneously built at Brasimone Research Centre and which mimics in a 1:1 configuration the ITER Water Cooling System (WCS). This allows performing the tests with the injected water with the same thermodynamical parameters and dynamical effects that are expected to occur during a real in-box LOCA event [14].  
The connection between the two facilities is obtained using pipelines that have the same length as the WCS pipe forest, including the presence of delay tanks. These pipelines will be connected to the WCLL TBM mock-ups through an injection device, which will allow controlling the test execution and water injection. The exact layout and working principle of the injection device are still to be determined and it will be described in detail in future works, however, the following high-level requirements have been defined:  
* It must allow for precise control of the timing of the water injection to allow the synchronization of the acquisition system with the event time;
* It must cause a net and fast surge of water into the test section, to simulate a complete pipe breakage and to allow for the water flashing;
* It must maintain a similar geometrical shape and the same hydraulic characteristics as the real water circuit.  
The design of the injection system will be carried out to obtain the highest quality in the data generated by the facility and to maintain as much as possible the incidental scenario representativeness.
## 4 Seepage Control System in the Xigou Tailings Dam with an Increased Height  
### The Finite Element Model  
Before the calculation is performed, the construction of a 3D finite element model can be performed as follows: specify the range of the numerical simulation, determine the type of elements and the type of analysis, locate the position of geometry, input the material parameters, input the boundary conditions, mesh generation, and set the initial conditions and calculation phases.  
As shown in Figure 8a, a three-dimensional finite element mesh was generated for evaluating the performance of the seepage-proof system design and the impact of increasing the height at the Xigou tailings dam project. This model consisted of 356,221 nodes and 985,641 brick elements (with a small number degenerated into tetrahedral elements). The size of the numerical model was 700 m in the direction of the dam axis and 1000 m in the direction parallel to the river. The bottom of the model was at an elevation of 800 m, and the highest elevation of the model was 1202 m. The topographic and tailings material zone, as well as the seepage-proof system including the horizontal pipes and the bending slotted pipes, are shown in Figure 8b-d.  
Figure 7: The Xigou tailings dam in 2017. (**a**) Downstream area. (**b**) Drainage pipes at an elevation of 955 m.  
The boundary conditions were defined as follows: corresponding to the water level of a tailings pond, the hydraulic head on the upstream dam surface was variable at different raising stages, and the water head was constantly 910 m on the downstream dam surface. The lateral and bottom boundaries in the model were taken to be impermeable. The potential seepage boundaries were applied at the remaining boundaries, including the tailings dam surface and the ground above the upstream and downstream water levels, the embankment, and the boundaries of the drainage pipe. The potential seepage boundary satisfied [PERSON]'s complementary condition.
# A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision
## 4 Experiments
### Discussion  
The evaluation of this method is described in the experiment section. The experimental results are similar to the design expectation, but there are still shortcomings in the accuracy of depth perception extraction of the object being watched. This may be due to the inadequacy of the estimation strategy spatial coordinate of the PoR. We use the middle point of the nearest points of the binocular visual axes as the result of 3D PoR estimation. Although this strategy is easy to use, it is also relatively brute. If we only consider the errors on \(X\) or \(Y\) direction, it can be found that the accuracy of the visual axes' directions has reached a high level. But in our method, the 3D PoR estimation is formed by the spatial intersection of the binocular visual axes, so the slight angle deviation of the visual axis can increase the error of the depth estimation which is much more than the errors on \(X\) and \(Y\) directions.  
Since the depth perception in human visual attention is often inaccurate, the system is difficult to learn human vision accurately. Therefore, it is the focus of future work to propose a more effective gaze estimation strategy.
## 5 System Overview
### System Service Architecture  
There is a service running on each data node to accomplish all the functionalities that the system has. This program listens to the client applications by using the server socket structure through the port allocated to it (443 which is reserved for HTTPS, but we used this port). When the service routine is enabled:  
1. It identifies all data storage devices in the computer and prepares the device list.
2. It creates a large empty file that covers 80% (optimum file size according to our experiment/test) of each element in the device list (this file has been created for each drive only once). This description is defined in Figure 3. The file corresponding to a disc drive in the computer has a disc header and array-based bunch list in which each bunch has 10 MB of size. Disc header contains a bitmap of bunch list and each bunch is represented in a header by one bit whether it belongs to a file or not.
3. The structure of this file is described below as a virtual file system.
4. SystemDataFile located on the first node of the system is read by the data node when activated. This file contains comprehensive information for all data nodes, replica nodes and client applications in the system. This size is a rather small size. As shown in Table 1, all data nodes get this list from the first node that has constant IP value when the computer restart. Then, when a new node is added to the system or there is an update in the list, the system administrator sends the CheckServerList function of the service routine to all the data nodes. Each client application is connected to the data node that is assigned to it. (At least one data node). The functions used are GetAvailableDatanodelp, SaveFile, ReadFile, DeleteFile, CheckServerList, and GetFileFromReplica.
# Assessment of Cranial Deformation Indices by Automatic Smartphone-Based Photogrammetric Modelling
## 3 Results
### Three-Dimensional Transformation Assessment
#### 3.2.1 Without Scale Correction  
A similarity transformation was determined between the coordinates of each PhotoMeDAS model and the CNC CMM coordinates. The mean and standard deviations obtained for the three shifts, three rotations, and scale factor are presented in Table 2. A  
Figure 6: Histograms displaying the differences in coordinates between all PhotoMeDAS coordinates and the ground truth. Units mm.  
scale transformation was also computed, and the resulting values are shown in Table 2, as well.  
As can be seen in Table 2, the scale factor, with a value of around 0.99, is significant; that is, the PhotoMeDAS 3D model needs to be scaled down a bit to make it compatible with the ground truth.  
As mentioned before, the Bland-Altman plot is the best tool for comparing PhotoMeDAS and the ground truth. In our case, we have point coordinates from 54 PhotoMeDAS models, and the mean value of each of them was computed. The coordinate standard deviations were taken from the initial accuracy assessment, that is, 0.86, 0.97, and 0.74 mm, respectively. Figure 7 shows no null slopes in the Bland-Altman plots for the \(x\)-, \(y\)-, and \(z\)-coordinates, which confirms that a scale difference from unity between the PhotoMeDAS solution and the CNC CMM exists.  
Figure 7: Bland–Altman plots of the coordinate differences between PhotoMeDAS and CNC CMM (ground truth).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Similarity Transformation**} & \multicolumn{2}{c}{**Scale Transformation**} \\ \cline{2-5}  & **Mean** & **Std. Deviation** & **Mean** & **Std. Deviation** \\ \hline X translation (mm) & 0.50 & 0.30 & & \\ Y translation (mm) & 0.00 & 0.31 & & \\ Z translation (mm) & \(-\)0.27 & 0.17 & & \\  & \(-\)0.0065 & 0.0039 & \(-\)0.0096 & \\ Differential scale & (0.9935) & 0.0039 & (0.9904) & 0.0043 \\ Rotation in X (deg) & \(-\)0.07 & 0.32 & & \\ Rotation in Y (deg) & 0.03 & 0.66 & & \\ Rotation in Z (deg) & \(-\)0.36 & 0.66 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the similarity and scale transformations between PhotoMeDAS coordinates and the CNC CMM coordinates (ground truth): mean and standard deviation. The scale factor in brackets equals 1 + differential scale.  
The PhotoMeDAS model is slightly larger than the ground truth, although this is of little importance since the results do not deteriorate by more than 1 mm. This problem of scale, which was already noted in [11] (\"a general overestimation is appreciated\"), impacts the infant's head size only in the order of 1 mm (or less), since the coordinates are below 100 mm (see Figures 3 and 4).  
The next section presents the results after applying a scale factor correction to the PhotoMeDAS coordinates.
## 6 Conclusions
**Nomenclature**  
\(m\): Rotor weight in kg  
\(g\): Gravitational force  
\(Fx,Fy\): Suspension forces, N  
\(F_{dx},F_{dy}\): Uncertain disturbances, N  
\(K_{X},K_{Y}\): Equivalent constant matrices of suspension forces  
\(K_{xxp}\), \(K_{yyp}\), \(K_{xyp}\), \(K_{yxp}\), \(K_{xxn}\): Equivalent suspension force constants in both positive and negative directions,  
\(K_{ygn}\), \(K_{xym}\), \(K_{yxn}\): respectively  
\(I_{x}J_{Y}\): Suspension current vectors  
\(ixp\), \(ipy\), \(ixn\), \(iyn\): X and Y-directional individual suspension currents  
\(\psi\): Per phase flux linkages  
\(r\): Phase resistance, ohms  
\(N\): Phase-inductance reversal vector  
\(N_{0}\), M\({}_{0}\): Initial reversal flux and position vector constants, respectively  
\(\theta\): Position of the rotor per phase in degree  
\(V\): Phase voltage vector, volts  
\(w\): Speed of the rotor, rpm  
\(T_{e},T_{l}\): Net actual torque and applied load torque  
\(B,J\): Damping coefficient, actual moment of inertia of rotor  
\(i\): Main phase current vector, amps  
\(X_{ref}\), \(Y_{ref}\), \(X_{d}\), \(Y_{d}\): Rotor reference and desired displacements  
\(X_{2},Y_{2}\), \(\hat{X}_{2}\hat{Y}_{2}\): Actual and estimated rotor displacements  
\(K_{\psi}\),\(K_{w}\),\(K_{\theta}\): Flux linkage, speed and position observer coefficients  
\(e_{x},e_{y},e_{w},e_{\psi},e_{\theta}\): Displacement errors, error speed, flux linkage and positions, respectively  
\(C_{x},C_{y},C_{w}\): Rotor X and Y displacements and speed-switching function positive constants,  
\(S_{x},S_{y},S_{w}\): Rotor X and Y displacements and speed-sliding mode switching equations,  
\(U_{x},U_{y},U_{w}\): Rotor X and Y displacements and speed control equations, respectively  
\(\sigma_{x},\sigma_{y},\sigma_{w}\): New dynamic sliding mode switching constants of rotor displacements and  
speed, respectively  
\(\lambda_{x},\lambda_{y},\lambda_{w}\): Rotor displacements and speed control design constants  
\(\eta_{x},\eta_{y},\eta_{w}\): Signum function constants