esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Soil Moisture Mapping Methodology
### Artificial Neural Networks (ANN)
This approach, which uses a priori information on the soil moisture, is relevant even when the agricultural plots in the study area are irrigated. If the SAR images are acquired during the dry season with irrigation activities occurring at the study site, the use of a priori information on the \(mv\) (dry to slightly wet soils) could lead to an underestimation of the \(mv\) if the real soil moisture is higher than 30 vol %. First, the analysis of the performance of neural networks is carried out using the noisy validation database for each case (no a priori information, a priori information for dry to slightly wet soils, and a priori information for very wet soils). The estimated \(mv\) from the neural networks is compared to the reference \(mv\) and the errors for the \(mv\) estimates were quantified by means of the bias (Estimated \(mv-\) Reference \(mv\)) and the Root Mean Square Error (RMSE). In addition, the error for the \(mv\) estimates (RMSE and Bias) was analyzed according to the NDVI, \(Hrms\), and \(\theta\) values. Finally, the nine developed neural networks are used to invert real SAR data. The inversion was performed using real SAR data as well as the NDVI values (calculated from the optical images) instead of the noisy backscattering coefficients and NDVI values.
## Materials and Methods  
### Printing of Wall Geometry  
Two PA 6/66 random copolymers in the form of filament spools were used for printing as well as thermal analysis. The weight average molecular weights, M\({}_{\mathrm{w}}\), glass transition temperatures, T\({}_{\mathrm{g}}\), and melting temperatures, T\({}_{\mathrm{m}}\), of both copolymers, as provided by the supplier, are summarized in Table 1. The materials are referred to as HMWPA (high molecular weight PA) and LMWPA (low molecular weight PA) in the presented study. Due to the substantial moisture uptake that is characteristic for polyamides, both materials were dried for 24 h at 80 \({}^{\circ}\)C in a Vacutherm VT 6025 vacuum drying oven (Thermo Fisher Scientific, Waltham, MA, USA) before printing or thermal analysis. When printing, the filament was transferred from the vacuum oven to a PrintDry filament drying station (PrintDry, Windsor, ON, Canada), equipped with silica gel. The drying station, set at 70 \({}^{\circ}\)C, which is the maximum temperature of the device, allowed the filament to stay dry during printing, since the filament was being fed directly from the dryer into the feeder of the Ultimaker 2 FFF printer (Ultimaker, Geldermalsen, The Netherlands).  
A custom G-code was written and uploaded in the Ultimaker 2 FFF printer equipped with a 0.4 mm diameter nozzle to print the required samples. Figure 1 shows the sample geometry, which consisted of a 60 mm wide wall with a thickness of one layer in the y-direction. On top of the brim, defined as Layer 1, 50 layers were added with a layer height of 0.2 mm. Kapton tape was applied on the build plate to ensure sufficient adhesion of the printed sample. During printing, the Ultimaker 2, which is open at the front and top, was covered with the Ultimaker 2 PPMA cover (Ultimaker, Geldermalsen, The Netherlands) to limit the influence of distortions in air flow of the surroundings. However, the temperature of the build environment was not regulated, since the Ultimaker 2 does not allow for environmental control.  
Besides using both provided materials, the liquefier temperature, T\({}_{\mathrm{liquefier}}\), build plate temperature, T\({}_{\mathrm{build\, plate}}\), and print speed, v\({}_{\mathrm{print}}\), were varied. Table 2 gives an overview of the different applied print settings.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Material** & **M\({}_{\mathrm{w}}\) [kg/mol]** & **T\({}_{\mathrm{g}}\) [\({}^{\circ}\)C]** & **T\({}_{\mathrm{m}}\) [\({}^{\circ}\)C]** \\ \hline HMWPA & 58 & 49 & 199 \\ LMWPA & 24 & 41 & 198 \\ \hline \hline \end{tabular}
\end{table}
Table 1: M\({}_{\mathrm{w}}\), T\({}_{\mathrm{g}}\) and T\({}_{\mathrm{m}}\) of the PA 6/66 copolymers.
# Multi-Wavelength Digital-Phase-Shifting Moire Based on Moire Wavelength
## 3 Experiments and Results  
### Experimental Setup  
To demonstrate the method of multi-wavelength digital phase-shifting moire based on moire wavelength, experiments were performed using an optical setup including a liquid crystal display (LCD) projector (PT-AE7000U, Panasonic, Mississaug, ON, Canada) and a single monochrome CMOS camera (Grasshopper 3 GS3-U3-41C6M-C, FLIR Systems, Inc., Vancouver, BC, Canada) 2048 \(\times\) 2048 resolution with a 50 mm 6 MP lens (LM50 SC, Kowa, Duesseldorf, Germany).
## 2 Mathematical Model for Centroid
### Mathematical Modeling for Centroid Calculation  
#### 2.2.1 Geometric Relationship of Helical Flute Curve  
The type of helical flute (1, 2 and 3) is same. The curve of each helical flute is a piecewise function composed of \(l_{s},l_{1},l_{2},l_{3}\). Taking the first quadrant helical flute as an example for calculation, the equation of \(l_{s}\) can be known from the geometric relationship in Figure 2.  
\[y=(x-R)\cdot\tan\delta \tag{3}\]  
Assuming the distance between the center of the circle O and the center of the arc O\({}_{i}\) is \(O_{i}O=p_{i}\cdot R\), where \(p_{i}\) is a parameter coefficient, and the general expression of the arc of the chip flute is:  
\[(x-p_{i}\cdot R\cdot\cos\lambda_{i})^{2}+(y-p_{i}\cdot R\cdot\sin\lambda_{i}) ^{2}={r_{i}}^{2},i=1,2,3 \tag{4}\]  
Figure 2: Radial section design parameters.  
Because \(l_{s}\) and \(l_{1}\) are tangents, according to the geometric relationship of Figure 2:  
\[\left\{\begin{array}{c}r_{1}=\left|\frac{\tan\delta(p_{1}\cdot R\cdot\cos \lambda_{1}-R)-p_{1}\cdot R\cdot\sin\lambda_{1}}{\sqrt{1+\tan^{2}\delta}}\right| \\ p_{1}=\frac{r_{1}-R\cdot\sin\delta}{R\cdot\sin(\lambda_{1}-\delta)}\end{array}\right| \tag{5}\]  
where \(l_{1}\) and \(l_{2}\) are intrinsically tangential while \(l_{2}\) and \(l_{3}\) are external tangents, it can be known that:  
\[\left\{\begin{array}{c}r_{1}-r_{2}=2\sqrt{\left(\frac{W_{1}+2r_{2}}{2} \cdot\cos\lambda_{2}-p_{1}\cdot R\cdot\cos\lambda_{1}\right)^{2}+\left(\frac{ W_{1}+2r_{2}}{2}\cdot\sin\lambda_{2}-p_{1}\cdot R\cdot\sin\lambda_{1} \right)^{2}}\\ \left(r_{2}+r_{3}\right)^{2}=\left(\frac{W_{1}+2r_{2}}{2}\cdot\cos\lambda_{2} -p_{3}\cdot R\cdot\cos\lambda_{3}\right)^{2}+\left(\frac{W_{1}+2r_{2}}{2}\cdot \sin\lambda_{2}-p_{3}\cdot R\cdot\sin\lambda_{3}\right)^{2}\end{array}\right. \tag{6}\]  
The undetermined coefficients, \(\lambda_{i},p_{i}\), can be obtained from Equations (5) and (6), and the coordinates of the tangent points between \(l_{1}\) and \(l_{2}\); \(l_{2}\) and \(l_{3}\); \(l_{1}\) and \(l_{s}\), and the coordinates of the bar boundary points A to E can be obtained, and then the expression of the helical flute curve can be determined:  
\[\left\{\begin{array}{c}y=\left(x-R\right)\cdot\tan\delta\\ \left(x-p_{i}\cdot R\cdot\cos\lambda_{i}\right)^{2}+\left(y-p_{i}\cdot R\cdot \sin\lambda_{i}\right)^{2}=r_{i}^{2},i=1,2,3\end{array}\right. \tag{7}\]
###### Abstract  
The fault frequencies are as they are and cannot be improved. One can only improve its estimation quality. This paper proposes a fault diagnosis method by combining local mean decomposition (LMD) and the ratio correction method to process the short-time signals. Firstly, the vibration signal of rolling bearing is decomposed into a series of product functions (PFs) by LMD. The PF, which contains the richest fault information, is selected to perform envelope spectrum analysis by the Hilbert transform (HT). Secondly, the Hilbert envelope spectrum of the selected PF is corrected with the ratio correction method. Finally, higher precision fault frequencies are extracted from the corrected Hilbert envelope spectrum, and then the fault location is accurately determined. The proposed method of this paper can be used in online real-time monitoring technology of rolling bearing failure.  
Keywords:local mean decomposition; spectrum correction; ratio correction method; frequency accuracy +
Footnote †: journal: Applied
# Advanced Metal Matrix Nanocomposites
## 1 Introduction
In the case of mass production techniques, such as casting, the mixing of reinforcement in molten metal is challenging at the industrial scale without giving significant thermal exposure to reinforcement. The dispersion of ceramic reinforcement in molten metal is commonly attempted using:  
1. [label=a.] 2. Stirring to ensure a vortex [1; 4]; or
3. The ultrasonic method [12; 13; 14]. In the stir-casting method, the parameters such as stirrer design, stirrer speed, and its location within the crucible are most important. Recently, ultrasonic cavitation-based dispersion of reinforcement has been attempted [14; 15; 16] which employs the transient (to the order of nano-seconds) micro hot spots that can reach localized temperatures of about 5000 \({}^{\circ}\)C and pressures above 1000 atm and heating/cooling rates above 1010 \({}^{\circ}\)C/s [16]. Such a strong impact coupled with high temperatures becomes catalytic in breaking up the nanoparticle clusters and cleans the particle surface culminating in promising results. In addition to these dispersion methods, researchers have also attempted to integrate ceramic powders with metallic matrices applying powder preforms through squeeze casting and melt infiltration methods. Gas injection of reinforcement particles in the crucible and gas injection of ceramic particles in disintegrated melt streams, such as in spray atomization and the deposition method, have also been reported [1; 17]. Importantly, the existing conventional infrastructure that is used for metal processing and fabrication in the synthesis of metal matrix composites can be used with minor modifications for easy industrial adaptability. MMCs endowed with superior combinations of properties arising due to the material design freedom are opening up new applications in multiple engineering and biomedical sectors [18]. For example, biocompatibility, specific strength, high bio-corrosion resistance, as well as cell growth capability are important for most biomedical applications, such as those composites used as orthopedic biomaterials [18; 19; 20]. Additionally, properties such as light-weight and high-strength are simultaneously demanded in some industrial applications, like those automotive structures where lightweight but high-strength metal matrix nanocomposites are employed [21]. This review attempts to provide a comprehensive strategy based on the multi-disciplinary knowledge base from researchers in this area which will hopefully be beneficial for students, researchers, and industrialists alike. The key topics discussed are:  
1. Current state-of-the-art in the synthesis of nanocomposites. 2. An insight into microstructural characteristics of composites. 3. Dispersion mechanisms of nano-particles. 4. Mechanical responses, including tensile, compressive, torsion, fatigue, and damping properties. 5. Particular emphasis is placed on lightweight composites, such as those based on magnesium and aluminum due to their capability to mitigate greenhouse gas emissions and global warming due to a rapid expansion in the transportation sector [3].
# Learning-Based Online QoE Optimization in Multi-Agent Video Streaming
## 5 Policy Gradient for MA-Stream
### Evaluation Setup
#### 6.1.2 Reward Functions  
Our proposed MAPG-finite algorithm allows for the maximization of any nonlinear reward in a finite time period. We consider two reward functions in the evaluation, namely QoE and fairness, to compare the performances achieved by different policies.  
For the QoE reward, we use a sigmoid-like function to measure the reward with respect to the stall time. In particular, we choose parameters in Equation (16) to match a stall-to-QoE curve reported by [14]. We plot our fitted reward function in Figure 4a, which matches well with the reported stall-to-QoE curve in [14]. In addition, for the fairness objective, we choose a logarithmic utility function shown in Equation (17) and Figure 4b of the users' received stall time. By maximizing the logarithmic function, the proportional-fair QoE assignment between the users [13] is obtained.  
Naturally, our proposed multi-agent learning algorithm is able to learn and optimize any reward functions, linear or nonlinear. We note that, even when the exact function is unknown, the model-free algorithm can still be trained and evaluated using the real-world user traces.
# Medical Augmented-Reality Visualizer for Surgical Training and Education in Medicine
## 2 Materials and Methods
### Model Database  
The MDB in this study stored a 3D surface model of the liver along with a set of possible rotations and corresponding CSDs. Each surface model is built once in the form of a triangle mesh per physical phantom, using real imaging data. For each orientation (\(\theta\),\(\Phi\)) defined by rotations about the (\(X\),\(\gamma\)) axes of the model (Figure 4), the contour is determined and a corresponding CSD is calculated. Input silhouette images used for CSD calculation are created by projecting the 3D triangle mesh onto a 2D plane, followed by contour extraction. The sampling of angles (\(\theta\),\(\Phi\)) affects the final angular resolution of phantom tracking, and a balance should be maintained between the image analysis requirements regarding accuracy and statistical errors.  
Figure 3: Analyzed points to determine the starting point for the contour descriptor. \(P_{\text{MC}}\) is the pole, i.e., geometric center of mass of the contour, \(L_{\text{BFL}}\) is the best-fitting line of all contour points, and \(P_{\text{CP1}}\) and \(P_{\text{CP2}}\) are points at the intersection between the contour and line \(L_{\text{BFL}}\).  
Figure 2: Examples of contours from the MDB. Left column: prerendered images of model position. Right column: Image descriptor \(d(\alpha)\). The plots show the distance from geometric center of mass (\(\gamma\) axis) with respect to the angular position of the point (\(X\) axis). \(L_{\text{s}}\) is the starting position for descriptor calculations, \(L_{\text{v}}\) is the reference line used to determine the angle about the \(Z_{\text{w}}\) axis, and O is the contour center of mass.
## 3 Experiments and Validation
### Limits of Application  
The quality of the results depends, in particular, on the degree of nonlinearity of the underlying function. In Figure 13, this circumstance is qualitatively illustrated for a true function with two points \(\mathbf{p}_{1}\) and \(\mathbf{p}_{2}\) from a given point cloud. In case of a moderate change rate of the true function (Figure 13a), the approximation error to the true intersection remains small, but if the behavior of the function between these support points is strongly nonlinear, the deviations may also increase; see Figure 13b. In such a situation, it is recommended to increase the resolution of the considered grid.  
Due to numerical inaccuracies, it may be further advisable in case of parallel plane sections to check the results again afterwards to see whether a pair of values exist twice in the section plane. For example, for planes parallel to the \(xy\)-plane with offset \(d\), two pairs \([x^{*},y^{*},d]\) and \([x^{*},y^{*},d\pm\eta]\) with \(\eta\ll 1\) can occur. One option is to only print the first two values, i.e., \(x\) and \(y\) components, since the corresponding value for \(d\) is known, and use the unique() command to remove duplicate pairs to avoid ambiguities.  
Based on the evaluation, we can draw the conclusion that the RooTri() function is significantly more robust than the contour() function in terms of the quality of the computed results. Furthermore, the calculation is performed faster, and less memory is  
Figure 13: Influence of nonlinearities; (a) moderate nonlinear course of the true function, (b) strongly nonlinear course of the true function.  
required, since fewer elements have to be processed. At this point, it should be further emphasized that the quality of the roots could only be determined because the test functions were known in advance. If these are not known, there is no possibility to detect outliers, since the control possibility is omitted. Rather, one is then dependent on the manual preparation of the data to identify the outliers.
## 4 Results and Discussion
### The Requirement for the Single-Station Inversion
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Station** & **Distance (km)** & **R-SNR** & **T-SNR** & **Z-SNR** & **Residual** \\ \hline
**MASB** & 19 & 9.2 & 6.2 & 11.3 & 0.05 \\
**TWGB** & 40 & 7.3 & 6.5 & 9.5 & 0.28 \({}^{*}\) \\
**TWKB** & 68 & 7 & 8.2 & 7.2 & 0.34 \({}^{*}\) \\
**TPUB** & 84 & 3.9 \({}^{*}\) & 7.3 & 7.4 & 0.27 \({}^{*}\) \\
**YULB** & 105 & 6.8 & 10.5 & 10.4 & 0.13 \\
**SSLB** & 137 & 4.0 \({}^{*}\) & 9 & 9.2 & 0.15 \\
**TDCB** & 191 & 3.3 \({}^{*}\) & 9.9 & 10 & 0.20 \\
**NACB** & 196 & 7.6 & 11.1 & 10.4 & 0.18 \\
**NNSB** & 215 & 6.2 & 8.9 & 10.6 & 0.18 \\
**YHNB** & 241 & 7.7 & 10.2 & 10.3 & 0.30 \({}^{*}\) \\
**SBCB** & 248 & 3.9 \({}^{*}\) & 5.8 & 8.3 & 0.34 \({}^{*}\) \\
**KMNB** & 325 & 6.7 & 7 & 6.7 & 0.36 \({}^{*}\) \\ \hline \hline \end{tabular} \({}^{*}\) SNR values less than 4. \({}^{*}\) residual values larger than 0.2.  
\end{table} 
Table 4: The residuals between all-station inversion and single-station version. Figure 8: The synthetic waveforms of the TDCB, NACB, and SBCB stations by the MASB single-station inverted force (red curves) and observed records (black curves). inversion. In summary, we suggest that stations having SNR values higher than 8.5 and located within 200 km of a landslide are appropriate for use in single-station waveform inversion and may have residuals of \(\rm{M_{LQ}}\) values below 0.2. The single-station PFH inversion provides a chance to analyze small-scale landslides. Small-scale landslides generate weak seismic signals and may not be detected by many seismic stations, making it difficult to constrain the results of the multistation waveform inversion. The dynamic processes of a landslide can still be extracted by executing single-station PFH inversion with high-SNR seismograms.
## 3 Results and Discussion
### The Results of Rheological Test
Figure 3D shows the dependences of the effective viscosity on shear stress at 220 \({}^{\circ}\)C. All samples are non-fluid, fragile and easily destroyed when a shear stress exceeding the strength of the sample structure is reached. The higher the strength limit, the higher the graphene oxide content. The sample without graphene oxide does not show an obvious yield strength due to sliding and loss of continuity when a shear stress of 5 MPa is reached. For a sample of 0.10% GO/Al\({}_{2}\)O\({}_{3}\), the strength is \(\approx\)1 kPa, for 0.25% GO/Al\({}_{2}\)O\({}_{3}\)--\(\approx\)2 kPa, for 0.50% GO/Al\({}_{2}\)O\({}_{3}\)--\(\approx\)3 kPa. Rheological tests showed a positive result of adding graphene oxide for each of the selected concentrations of 0.10, 0.25 and 0.50 vol.% as an additive that reduces the viscous and viscoelastic properties of the highly filled polymer compositions in comparison with 0% GO/Al\({}_{2}\)O\({}_{3}\). It is interesting to note that the greatest decrease in viscous and viscoelastic properties can be achieved with the introduction of only 0.10% graphene oxide. An increase in the graphene oxide content leads to an increase in the viscoelastic characteristics of the samples and an increase in the strength limit. The results obtained can be explained by the fact that, in the case of the 0% GO/Al\({}_{2}\)O\({}_{3}\) composition, we are dealing with so-called \"dense\" suspensions in which the resistance to relative movement of particles is determined by intermolecular microscopic forces, as well as dry friction mechanics, depending on the surface roughness of the contacting particles [38; 39]. It is well known that graphene oxide has a positive effect on the tribological properties of materials; namely, it significantly reduces their coefficient of friction [11; 40]. Most likely, the addition of a small amount of well-dispersed graphene oxide to the composition reduces friction between ceramic particles and thereby increases the fluidity of the composition; however, an increase in the graphene oxide content of more than 0.10% begins to suppress the mobility of the polymer chain [34], which, in turn, begins to negatively affect the rheological properties of the filled composition, but the properties of such compositions remain better than those of a composition without graphene oxide.
# High Pressure and Pasteurization Effects on Dairy Cream +
###### Abstract
One observed that 450 MPa treatment (10 or 25 \({}^{\circ}\)C during 15 or 30 min) followed by refrigerated storage (4 \({}^{\circ}\)C) for 8 days did not affect the fat globule size distribution and other physico-chemical properties of pasteurized creams [8]. Regarding microbiological changes, another study showed that it is possible to considerably reduce _Listeria innocua_ load in creams (35% fat), obtaining a decimal reduction time (D) of D450 MPa/25 \({}^{\circ}\)C for 7.4 min [9]. Differently, [PERSON] et al. (2000) obtained D400 MPa/25 \({}^{\circ}\)C = 4 min on ewe's milk (6% fat), showing the potential effect of fat to protect microorganisms against hydrostatic pressure [10]. Other methodologies have also been used for nonthermal pasteurization of dairy products, such as ultraviolet radiation, pulsed electric field (PEF), ultrasound, etc., yet these present lower efficacies compared to HPP, as PEF and US need to be combined with moderate temperatures to increase the inactivation rates of the target microorganisms, while ultraviolet radiation has low penetrance in opaque fluids. Other methodologies such as membrane filtration, despite its possible continuous use, require frequent cleaning and replacing the filters, which are rather expensive and do not allow a proper flow of bulky liquids through the filters [11]. To evaluate the impact and safety of this nonthermal technology and compare it with thermal pasteurization, (a) microbial load (endogenous and inoculated _Escherichia coli_ and _L. innocua_), (b) fatty acid composition, (c) color parameters, (d) viscosity, and (e) volatile compounds were studied. Samples included the raw cream with no treatment (control), after the heat treatment (conventional pasteurization at 75 \({}^{\circ}\)C for 15 s), and after the pressure treatment (at 450 and 600 MPa for 5 min), followed by refrigerated storage (4 \({}^{\circ}\)C). The effect of HPP on inoculated microorganisms in dairy cream was also studied in a second set of experiments, at 600 MPa for 5 and 15 min.