esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability of a Compacted Sand Slope Model Subject to
## 3 Numerical Study
### Built Numerical Model  
Numerical computations were conducted for the 3D slope model shown in Figure 1 and various loading configurations displayed in Figure 3. The three-dimensional finite-element type was selected with the option of an automatically generated mesh by Plaxis 3D code. This mesh counts 29.146 nodes and 17.581 3D finite elements of 10 nodes of 70.324 stress-points.  
Figure 12 shows the generated mesh of the 3D numerical model for the ground slope of angle equal to 30\({}^{\circ}\), referenced as \"case study No.1\"; the slope angle equal to 35\({}^{\circ}\) refers to \"case study No.2\".  
Two constitutive models describe the behavior of compacted sand: the Mohr-Coulomb (MC) model and the hardening soil model (HSM). Table 3 summarizes the adopted geometrical parameters of these constitutive models. MC constitutive law remains the current reference to describe the soil behavior by using few parameters, easily determined from routine laboratory tests.  
Figure 12: Mesh and boundary conditions of the 3D Plaxis model (\(\alpha\) = 30\({}^{\circ}\)).  
The HSM necessitates further parameters to describe, in a more realistic manner, the nonlinear soil behavior and the hardening effect missed by the MC model. Appendix A, presents the method of determining the parameters of HSM.  
Adoption of the HSM proved to be suitable for describing loose sands susceptible to liquefaction, [8]. Moreover, dilatancy is a specific behavior observed for sands that occurs in pre-failure phase. [PERSON] and [PERSON], 2018, [16] reported on the importance of the dilatancy on the evolution of failure mechanism of horizontal ground loaded by a rigid plate.
# Rain Monitoring with Polarimetric GNSS Signals: Ground-Based Experimental Research
## 2 Experiments and Data
### Data Processing Algorithm
#### 2.3.3 Method to Deal with Multipath Effect  
Multipath effect is a source of error for GNSS applications, which affects carrier phase measurements [29]. If GNSS signal falls on the ground before reaching a receiver, the reflection occurs. Thus, this signal may arrive at an antenna finally. In this situation, the receiver combines records of direct signals with multipath artefacts [30]. Since our polarized antenna is on the ground and its gazing angle is at the horizontal direction, multipath effect is inevitable.  
The multipath effect is related to the environment in which the GPS receiver is located. Thus, the characteristic of multipath effect on the signal is stable in the same condition. This means that the term \(\Delta m\) of a satellite should repeat after a sidereal day. Therefore, the \(\Delta m\) can be regarded as background data. Its feature can be shown by average and standard deviation, which can be calculated as a function of angle \(\beta\) from no-rain days. To get more accurate multipath feature, all the no-rain days' data are used. Here, multipath effect can be removed by  
\[\Delta\phi^{\prime\prime}(\beta)=\Delta\phi^{\prime}(\beta)-\overline{\Delta m (\beta)} \tag{6}\]  
where the mean \(\Delta m\) over a sidereal day is obtained over the period of non-rainy days, which depends on the trajectory of the satellite and the surrounding of the instrument [24].
# UVSQ-SAT, a Pathfinder CubeSat Mission for Observing Essential Climate Variables
## 3 Materials and Methods
### The Space Segment: The UVSQ-SAT CubeSat Payload
#### 3.2.2 The DEVINS Sensors
From the knowledge of the Sun's direction (\(\theta\)), we will determine the DEVINS solar spectral irradiance (SSI(\(\lambda\))) from the following instrumental equations based on our experience and history in solar instrumentation developments and observations [42; 43; 44]. \[\text{SSI}(\lambda)=\frac{I_{d}}{S(\lambda,V,T)\;\Sigma\tau( \lambda)\;\cos(\theta)}\left(\frac{f^{\prime}-d}{f^{\prime}}\right)^{2}\frac{( \text{1u.a.})^{2}}{d^{2}}\left(1-\frac{1}{c}\frac{\partial z}{\partial t} \right)^{2} \tag{9}\] \[\text{and}\;\frac{1}{f^{\prime}}=(n-1)\left(\frac{1}{R_{1}}-\frac {1}{R_{2}}\right) \tag{10}\]  
where \(I_{d}\) is the DEVINS photodiode current measured, \(S(\lambda,V,T)\) is the DEVINS photodiode responsivity, which depends on voltage (\(V\)) and temperature (\(T\)), \(\Sigma\) is the DEVINS photodiode active area, \(\tau(\lambda)\) is the DEVINS photodiode transmission of the sapphire window, \(d\) is the distance between the DEVINS aperture and the active area, \(n\) is the refractive index of the window, and \(R_{1}\) and \(R_{2}\) are the curvature radii of the sapphire window lens. A dark current and a flat-field corrections will be necessary for a full correction of the DEVINS data. The DEVINS absolute calibration can be performed using the primary standard of spectral irradiance (Physikalisch-Technische Bundesanstalt (Germany) and/or the Laboratory for Atmospheric and Space Physics (LASP) facilities (USA)). The UVSQ-SAT UV SSI expected performances (Table 1) depend mainly on the absolute calibration of the photodiode responsivity (\(S(\lambda,V,T)\)) and on the DEVINS noise detection. The targeted technical performances of the DEVINS sensors are provided in Table 6, which are compliant with the expected performances in the absolute determination of UV SSI that are of \(\pm 2.5\%\) at 1 \(\sigma\) in the 200-242 nm spectral region (compatible with uncertainties obtained with SOLAR-ISS spectrum [17]). The stability per year of the DEVINS sensors will be demonstrated in orbit as its low aging in UV. Figure 4: DEVINS manufacturing process. This action (collaborative project between LATMOS, Nanovation, and Observatoire de Versailles Saint-Quentin-en-Yvelines) is co-funded by the French Agence Nationale de La Recherche (ANR).
## 2 Materials and Methods
### Aligner Production  
The Tera Harz TC-85 (Graphy Inc., Seoul, Republic of Korea) was used for aligner production and aligners were directly 3D printed using the instructions provided by the manufacturer. Hereby, the Phrozen Sonic XL 4k 2022 printer (Phrozen Technology, Hsinchu, Taiwan) was employed for the printing. The aligners were designed with the thickness recommended by the producer (0.5 mm), and an offset of 0.05 mm was set between the aligner and the model. Printing was conducted in successive layers of 100 \(\upmu\)m, with a down speed of 200 and a delay of 1000 ms. The first 6 layers were cured for 30 s. The height was 8 mm and the up speed was 100. The following layers were cured for 4 s with a 7 mm height and an up speed of 100. Supports were designed and planned using the Uniz software, Version 2.6.1.11 (Uniz, San Diego, CA, USA) to ensure the correct position with an angulation of 60\({}^{\circ}\) on the printer plate (Figure 1). When the printing process was terminated, aligners were removed from the plate and then the supports were manually removed.  
The resin excesses were accurately removed from the intaglio surface using a centrifuge spinning at 1000\(\times\)\(g\) revolutions per minute, which was used for 5 min two times. Thereafter, aligners were divided into 7 groups (Table 1). Each group consisted of 5 aligners and were post-cured (Figure 2) according to a different curing time in a post-curing unit (Cure M, Graphy, Seoul, Republic of Korea). In total, 6 groups out of 7 were post-cured with a nitrogen generator, the 7 th one was cured without a nitrogen generator. Curing times were the following: 15 min, 20 min, 25 min, 30 min, 40 min, and 50 min. The group without the nitrogen generator was cured for 30 min. After curing, aligners were put in boiling water at 100 \({}^{\circ}\)C for 2 min.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Printer Plate Angulation** & **Curing Time** & **Nitrogen Generator** & **N** \\ \hline
60\({}^{\circ}\) & 15 min & yes & 5 \\ \hline
60\({}^{\circ}\) & 20 min & yes & 5 \\ \hline
60\({}^{\circ}\) & 25 min & yes & 5 \\ \hline
60\({}^{\circ}\) & 30 min & yes & 5 \\ \hline
60\({}^{\circ}\) & 40 min & yes & 5 \\ \hline
60\({}^{\circ}\) & 50 min & yes & 5 \\ \hline
60\({}^{\circ}\) & 30 min & no & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Group production characteristics and composition.  
Figure 1: Support design and printed samples.
# Influence of Mouse Grip Type on Flicking and Tracking Tasks Performance
## 3 Results
\begin{table}
\begin{tabular}{l l c c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & \multicolumn{1}{c}{**Mouse Grip Type**} & \multicolumn{1}{c}{**Mean (SD)**} & \multicolumn{1}{c}{**F**} & \multicolumn{1}{c}{\(p\)**-Value**} \\ \hline \multirow{3}{*}{Score (n)} & Palm Grip & 6711.11 (258.38) & \multirow{3}{*}{1.081} & \multirow{3}{*}{0.358} \\  & Claw Grip & 6618.50 (506.17) & & \\  & Fingertip Grip & 6918.30 (428.93) & & \\ \hline \multirow{3}{*}{Reaction time (s)} & Palm Grip & 0.82 (0.03) & \multirow{3}{*}{0.299} & \multirow{3}{*}{0.745} \\  & Claw Grip & 0.83 (0.06) & & \\  & Fingertip Grip & 0.81 (0.06) & & \\  & Palm Grip & 51.57 (2.88) & & \\ \multirow{3}{*}{Precision (\%)} & Claw Grip & 54.46 (3.35) & 1.565 & 0.235 \\  & Fingertip Grip & 55.40 (3.31) & & \\ \multirow{3}{*}{Aim Time (s)} & Palm Grip & 0.85 (0.03) & \multirow{3}{*}{1.085} & \multirow{3}{*}{0.327} \\  & Claw Grip & 0.86 (0.06) & & \\  & Fingertip Grip & 0.81 (0.09) & & \\ \hline \multirow{3}{*}{Total distance (px)} & Palm Grip & 20,693.67 (1210.84) & \multirow{3}{*}{0.855} & \multirow{3}{*}{0.441} \\  & Claw Grip & 21,833.96 (1588.70) & & \\ \cline{1-1}  & Fingertip Grip & 22,074.37 (1703.33) & & \\ \hline \multirow{3}{*}{Minimum distance (px)} & Palm Grip & 37.70 (7.77) & \multirow{3}{*}{1.341} & \multirow{3}{*}{0.285} \\  & Claw Grip & 42.07 (9.04) & & \\ \cline{1-1}  & Fingertip Grip & 34.70 (10.84) & & \\ \cline{1-1} \cline{2-2} \multirow{3}{*}{Trajectory (\%)} & Palm Grip & 78.98 (1,02) & \multirow{3}{*}{4.585} & \multirow{3}{*}{**0.024**} \\  & Claw Grip & 71.46 (4.62) & & \\ \cline{1-1}  & Fingertip Grip & 77.21 (5.389) & & \\ \cline{1-1}  & Palm Grip & 370.66 (23.06) & & \\ \cline{1-1}  & Claw Grip & 445.82 (38.55)\({}^{\mathrm{palm}}\) & 5.448 & **0.013** \\ \cline{1-1}  & Fingertip Grip & 404.40 (39.56) & & \\ \hline \multirow{3}{*}{Minimum cursor (px)} & Palm Grip & 103.11 (17.58)\({}^{\mathrm{daw}}\) & \multirow{3}{*}{11.784} & \multirow{3}{*}{**<0.001**} \\  & Claw Grip & 140.37 (10.68) & & \\ \cline{1-1}  & Fingertip Grip & 106.57 (20.07)\({}^{\mathrm{daw}}\) & & \\ \cline{1-1} \cline{2-2} \multirow{3}{*}{Average velocity (m/s)} & Palm Grip & 0.026 (7.69\({}^{-5}\)) & \multirow{3}{*}{0.201} & \multirow{3}{*}{0.820} \\  & Claw Grip & 0.028 (0.005) & & \\ \cline{1-1}  & Fingertip Grip & 0.028 (0.004) & & \\ \cline{1-1} \cline{2-2} \multirow{3}{*}{Average acceleration (m/s\({}^{2}\))} & Palm Grip & 5.27\({}^{-4}\) (4.58\({}^{-6}\)) & & \\ \cline{1-1}  & Claw Grip & 5.61\({}^{-4}\) (1.02\({}^{-4}\)) & 0.160 & 0.853 \\ \cline{1-1}  & Fingertip Grip & 5.54\({}^{-4}\) (8.59\({}^{-5}\)) & & \\ \cline{1-1} \cline{2-2} \multirow{3}{*}{Hand movement (mm)} & Palm Grip & 133.00 (0.98) & & \\ \cline{1-1}  & Claw Grip & 143.30 (24.25) & 0.250 & 0.782 \\ \cline{1-1}  & Fingertip Grip & 140.47 (21.67) & & \\ \hline \hline \end{tabular} Note: Pair differences are marked by the name of the condition that is different in the column of the Mean (SD) value.  
\end{table} 
Table 1: Score and kinematic variables in the flicking task according to mouse grip type.
## 2 Macroscopic Unit-Cell
### In-Plane Kinematics  
The variational principle of energy conservation and the principle of virtual displacement were followed to achieve the discrete macro-element elastic stiffnesses. Figure 2 presents the two directions of the in-plane system, i.e., the x- and y-directions, with both the tributary areas of each in-plane FE truss and the associated deformation modes (kinematics).  
Let us consider that \(\overline{E}_{ii}\) is the elastic modulus of the equivalent homogeneous material - is the average operator as it is generally defined to represent a composite or intrinsically homogeneous material), \(i\) is the corresponding axis x or y, \(\overline{\sigma}_{xy}\) the shear modulus of the media, V the volume of the region under study, and \(\Delta\) a displacement increment under the studied direction. For the in-plane case, the total strain energy density is generically given by Equation (1):  
\[U=\frac{1}{2}\int_{V}(\Sigma)^{\mathsf{T}}\{E\}\cdot dV \tag{1}\]  
Figure 2: In–plane unit cell: a tributary area for each truss and kinematics of each allowable (admissible) deformation mode.  
where \(\left\{\Sigma\right\}^{T}=\left\{\begin{array}{cc}\Sigma_{xx}&\Sigma_{yy}&\Sigma_{ xy}\end{array}\right\}\) and \(\left\{E\right\}=\left\{\begin{array}{cc}E_{xx}&E_{yy}&E_{xy}\end{array}\right\}\) are the stress and strain quantities of the media constitutive relationship, respectively, ideally obtained experimentally or via a homogenisation procedure. For the mode-I, the calibrated elastic stiffness of each in-plane truss is derived by imposing that the stored strain energy in volume V of both the discrete \(U_{discrete}^{mode-I}\) and the continuum homogenised media \(U_{continuum}^{mode-I}\) are equal.  
\[U_{continuum}^{mode-I}=\frac{\sigma\varepsilon}{2}V=\frac{\overline{E}_{ii} \varepsilon^{2}}{2}V=\frac{H.t.\overline{E}_{ii}}{2}\frac{\Delta^{2}}{2L+e} \tag{2}\]  
\[UU_{discrete}^{mode-I}=V\frac{\sigma\varepsilon}{2}=\frac{H.t.E_{ii}^{in- plane\text{ \ \it axial\ \it truss}}}{2}\frac{2\Delta^{2}}{e} \tag{3}\]  
If both energies are equated, the [PERSON]'s modulus of the in-plane axial trusses of a macro-unit cell for each plane direction (if orthotropy is considered) are derived and reads as:  
\[E_{xx}^{in-plane\text{ \ \it axial\ \it truss}}=\frac{\overline{E}_{xx}e}{4L+2e} \tag{4}\]  
\[E_{yy}^{in-plane\text{ \ \it axial\ \it truss}}=\frac{\overline{E}_{yy}e}{2(H+e)} \tag{5}\]  
The same methodology is followed for the mode-II trusses, namely the in-plane shear trusses. The stored strain energies of the continuum and discrete model are:  
\[U_{continuum}^{mode-I}=\frac{H.t.\overline{G}_{xy}}{4}\frac{\Delta^{2}}{2L+e} \tag{6}\]  
\[U_{discrete}^{mode-II}=\frac{e.t.E_{xy}^{in-plane\text{ \ \it shear\ \it truss}}\Delta^{2}}{H} \tag{7}\]  
Consequently, the Young's modulus of the in-plane shear trusses are given by:  
\[E_{xy}^{in-plane\text{ \ \it shear\ \it truss}}=\frac{\overline{G}_{xy}H^{2}}{4e (2L+e)} \tag{8}\]  
For more details regarding the IP formulation, the reader is referred to [83, 84].
# Cellular Antioxidant Effects and Bioavailability of Food Supplements Rich in Hydroxyrosol
## 3 Results
### Bioassays
#### 3.2.3 Cellular Peroxidation  
Isoprostanes are a family of eicosanoids produced by the random oxidation of tissue phospholipids by oxygen radicals. Several studies have shown that the content of F2-isoprostanes in the human body (i.e., measured in vivo) is directly related to oxidative damage to lipids. In particular, the 8-epi-prostaglandin F2-alpha isomer (8-isoprostane), an end product of the lipid peroxidation chain reaction, has been proposed as a reliable signaling molecule for antioxidant deficiency and oxidative stress [25; 26; 27; 28]. The degradation of lipids by lipoxidation occurs as a result of oxidative damage, and consequently the levels of isoprostanes increase, contributing in turn to the development of many diseases related to oxidative stress.  
To evaluate the potential of olive-derived supplements in protecting cellular lipids from oxidative damage, we measured the levels of free 8-isoprostanes in cultured cells. Modest amounts of 8-isoprostanes were present in the culture media under normal culture conditions (untreated control), which were increased by oxidative stress after induction by AAPH treatment, but these decreased after incubation with olive-derived extracts or quercetin treatments (Figure 2 and Table 4); compared with untreated cells (baseline level), treatment with P-1 caused a significantly (\(p<0.05\)) greater reduction in the content of free 8-isoprostanes (\(\sim\)29% reduction), while P-2 showed a moderate yet not significant effect on lipid oxidation protection (\(\sim\)6% reduction).  
Figure 2: Box plots represent the free isoprostanes (8-epi PGF2\(\alpha\)) released in culture, showing median, 25–75% quartiles, standard error, and total range of values from 3 experiments. UTC: untreated control cells; +: lipid peroxidation initiator; Q: quercetin control. * \(p<0.05\) versus UTC.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Treatment** & **U SOD/mg Protein** & **U Catalase/mg Protein** \\ \hline UTC & 6.87 \(\pm\) 0.44 * & 5.52 \(\pm\) 0.04 \\ P-1 & 7.93 \(\pm\) 0.13 * & 5.51 \(\pm\) 0.03 \\ P-2 & 9.23 \(\pm\) 0.24 * & 7.39 \(\pm\) 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Antioxidant enzyme activity measured in cell-lysates after 72 h of treatment with the sample material. Average results are expressed in terms of activity units per mg of protein \(\pm\) standard deviation. UTC: untreated control cells. * \(p<0.01\) according to \(t\)-test.  
The products derived from olives appear to cut down the number of oxidative attacks on the cell lipid membrane; in fact, the basal oxidative stress is effectively quenched compared to the untreated control cells, thus promoting cellular health. Similarly, in humans, oils rich in olive polyphenols have been shown to reduce the urinary excretion of F2-isoprostanes [29].
## 3 Results and Discussion
### Figures of Merit  
The linearity of the proposed method was evaluated by constructing calibration curves for each element. Thus, the peak area of each selected emission line was plotted against the concentration of a series of standard solutions ranging between 0.1 mg L\({}^{-1}\) and 10.0 mg L\({}^{-1}\), which were prepared in matrix-matched diluent containing 20% \(v/v\) concentrated HNO\({}_{3}\) and 2% \(v/v\) H\({}_{2}\)O\({}_{2}\) in order to mimic the acidic/oxidative conditions after the digestion procedure. Subsequently, least square linear regression analysis was employed and the slope, intercept and coefficient of determination of the calibration curves for each toxic and nutrient metal was calculated. For the calculation of the limits of detection (LODs) and the limits of quantification (LOQs) for the examined analytes, ten blank solutions containing 20% \(v/v\) concentrated HNO\({}_{3}\) and 2% \(v/v\) H\({}_{2}\)O\({}_{2}\) were prepared and analyzed. The LOD value of each analyte was considered to be the concentration that was equal to three times the standard deviation of the responses of the blank solutions versus the slope of the calibration curve. Similarly, the LOQ value of each analyte was considered to be the concentration that was equal to ten times the standard deviation of the responses of the blank solutions versus the slope of the calibration curve. Table 1 summarizes the results of the linearity, as well as the LOD and the LOQ values for the examined analytes. As it can be observed, good linearity was obtained for all analytes, while the LOD values were 0.08-5.95 mg kg\({}^{-1}\) and the LOQ values were 0.27-19.83 mg kg\({}^{-1}\). For the evaluation of the trueness and the precision of the proposed method, two different spice samples (i.e., clove and chili) were spiked with different amounts of metals (i.e., 100 mg kg\({}^{-1}\), 250 mg kg\({}^{-1}\) and 500 mg kg\({}^{-1}\), since no certified reference material containing the 17 analytes was available. Spiked samples were prepared by adding appropriate amount of multi-element standard solution containing at the digestion vessel containing the sample and the digestion mixture. The spiked samples were subjected to the experimental procedure described in Section 2.4. The trueness was evaluated in terms of relative recovery (RR%) by comparing the experimentally found concentration of each metal with the added concentration, while the precision of the proposed method was evaluated in terms of relative standard deviation (RSD) of three different complete preparations of the spiked samples [14]. Table 2 summarizes the results for method trueness and precision. Moreover, the herein method was employed for the analysis of the standard reference material 1573 (tomato leaves) and the trueness ranged between 96.2-102.4% for the analytes contained in this sample (i.e., Cr, Cu, Fe, Mn and Zn).
## 3 Key Internet of Things Scenarios
### Smart Manufacturing  
Smart manufacturing is an important recipient of mMTC. Application needs are evolving from those supported by the so-called Narrowband Internet of Things (NB-IoT) and LTE machine-type communication (LTE-M), which were essentially used for sensor data transfer. Present trends include massive sensor deployment, cameras, tablets and wearables. The relevant communication needs include data acquisition from much more differentiated sources, based on human-machine interaction, and even augmented reality. The technology evolution is converging towards throughput in the order of 10 Mbps and, in some specific services with real time requirements, a maximum latency of 2-3 ms, compliant with uRLLC requirements.  
Furthermore, in this case, serverless computing, with its responsiveness and limited resource footprint, can be a suitable solution for implementing mMTC in an industrial environment. The ability to deploy services on edge nodes helps in limiting the overall latency in message delivery and processing. However, serverless computing is not a one-size-fits-all solution and may not be suitable for services that require very low latency, such as remote robot control.