esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Comparison and Theoretical Analysis of the Proposed Design  
Table 1 presents the theoretical comparison results of a monopole crescent-shaped antenna operating in the triple-notched frequency range. The study demonstrates that the monopole UWB antenna employs various methods to achieve notches at different frequencies. The notching in the C-band is achieved using an L-shaped stub method described in [21], while rectangular slots are used in the slot antenna for notching in WiMax and WLAN [22]. In [23], slots and PIN diodes are used for notching, and [24] employs SRR for the same purpose. Different shaped methodologies, such as W-shaped conductor and T-shaped slot, three open quarter-wavelength slots, switches, inverted V-shaped slots, embedding stubs, parasitic strips, slits, and C-shaped and parasitic stubs, are utilized to create notches at WiMax, C-band, WLAN, and the satellite network band, as observed in [25; 26; 27; 28; 29; 30; 31], respectively. In the proposed design, two CSRRs are employed at the feed point, ground plane, and a C-shaped curve in the center, enabling triple notching at the C-band, WLAN, and satellite networks. Table 2 presents a comparison of different techniques for improving isolation in the MIMO antenna system. Mutual coupling, which affects the antenna characteristics and degrades the system's performance, is a challenge in MIMO systems and can lead to spectral regrowth. To overcome this coupling effect, various techniques, such as the plus-shaped slot technique [35], decoupling structure [36], and the use of slot antenna asymmetrical placement [37], have been employed. Other techniques, including m-IFSD, common radiator, partial ground plane and slots, T slots and stub, Varactor diode, Periodic EBG, SICBS, MPS radiator, and reactive load, have also been used to mitigate the coupling effect [38; 39; 40; 41; 42; 43]. In the proposed work, high isolation in a four-port MIMO antenna system is achieved by incorporating CSRR at the ground plane and patch, along with an orthogonal  
Figure 10: Diversity parameters ECC, DG, MEG, and CCL of MIMO system. symmetrical distance within the system. This proposed concept minimizes the size of the MIMO antenna array while maintaining excellent isolation between the elements. The high isolation among the MIMO system can be achieved through the coupling of CSRR at the ground and patch, and then the CSRR performs multitasking in the proposed design.
###### Abstract  
In optical camera communications (OCC), the provision of both flicker-free illumination and high data rates are challenging issues, which can be addressed by utilizing the rolling-shutter (RS) property of the image sensors as the receiver (Rx). In this paper, we propose an RS-based multiple-input multiple-output OCC scheme for the Internet of things (IoT) application. A simplified design of multi-channel transmitter (Tx) using a 7.2 \(\times\) 7.2 cm\({}^{2}\) small 8 \(\times\) 8 distributed light emitting diode (LED) array, based on grouping of LEDs, is proposed for flicker-free transmission. We carry out an experimental investigation of the indoor OCC system by employing a Raspberry Pi camera as the Rx, with RS capturing mode. Despite the small area of the display, flicker-free communication links within the range of 20-100 cm are established with data throughput of 960 to 120 bps sufficient for IoT. A method to extend link spans up to 1.8 m and the data throughput to 13.44 kbps using different configurations of multi-channel Tx is provided. The peak signal-to-noise ratio of \(\sim\)14 and 16 dB and the rate of successfully received bits of 99.4 and 81% are measured for the shutter speeds of 200 and 800 \(\upmu\)s for a link span of 1 m, respectively.  
optical camera communications; rolling-shutter camera; Internet of things; multiple-input multiple-output; light-emitting diodes +
Footnote †: journal: Sensors
# Numerical Study of Viscoplastic Flows Using a Multigrid Initialization Algorithm
## 2 Numerical Methodology
### Pressure-Velocity Coupling  
Since momentum equations are integrated over co-located control volumes, checkerboard oscillations may appear if central difference is used to approximate face velocities in discretized continuity equation and pressure gradient in momentum equations [34]. Indeed, in this case, the interpolated expression for face velocity will not include a pressure gradient across the faces of the control volume and the resulting solution can be oscillatory. [PERSON] and [PERSON] [35] interpolation is used to prevent spurious oscillations in the solution by ensuring strong pressure-velocity coupling. Their procedure has been extremely successful in co-located grids because of its inherent advantages, such as convenience in implementation on an unstructured grids and economical storage of velocity and pressure data. The classical expression of Rhie-Chow momentum interpolation can be written as follows:  
\[u_{e}=\overline{u_{e}}-\overline{D_{e}}(\overline{(\
abla P)_{e}}-(\
abla P)_ {e}) \tag{19}\]  
where the over-bar refers to linear interpolation. In this way, Rhie and Chow's special pressure-velocity coupling involves the addition of a higher order pressure gradient term. The restored linkage between the pressure differences across the control volume faces and the face velocities provides damping of the spurious oscillations in the solution due to the co-located arrangement.
## 2 Materials and Methods
### Phenological Metrics  
Owing to the fact that [PERSON] et al. (2013) demonstrated the capability of singular spectrum analysis (SSA) in the analysis of nonlinear dynamics in NATT [4], we also employed the same method to smooth and reduce the noise in satellite-based SIF, EVI time series. Correspondingly, following [PERSON] et al. (2013) [4], 37 composite periods of the window length and four leading components were selected to configure the parameters in the SSA implementation (Figure 2). After interpolating to daily time series from SSA-reconstructed SIF, EVI series, we used the PhenoDeriv function from the \"GreenBrown\" package to derive the key phenological metrics. Five metrics were extracted:  
1. The start of the growing season (SOS), defined as the date halfway between the minimum value and the fastest greening rate;
2. The peak of the growing season (POS), the date of the maximum value;
3. The end of the growing season (EOS), the date halfway between the fastest brown-down rate and minimum value;The rate of spring green-up (RSP), the amplitude of EVI or SIF between POS and SOS divided by the periods (days) between POS and SOS;
5. The rate of autumn senescence (RAU), the amplitude of EVI or SIF between POS and EOS divided by the periods (days) between POS and EOS  
To examine the interaction of environmental drivers and vegetation variables, the coefficient of determination (\(r^{2}\)) was calculated across four major biome types. A \(t\)-test was utilized to examine the statistical significance level of the relationships (\(p\)-value). To further explore the difference between SIF and EVI, we interpreted SIF with fPAR and SIF\({}_{\text{yield}}\), expressed as:  
\[SIF_{PAR}=\frac{SIF}{PAR} \tag{3}\]  
\[SIF_{yield}=\frac{SIF}{EVI\times PAR} \tag{4}\]  
Data processing, analysis, and visualization were conducted in the open-source R scientific computation environment (Version 3.6.2) and the associated packages contributed by the R user community ([[http://cran.r-project.org](http://cran.r-project.org)]([http://cran.r-project.org](http://cran.r-project.org)), accessed on 1 August 2020).
# Vertical Seismic-Profile Data Local Full-Waveform Inversion Based on Marchenko Redatuming
## 2 Principles and Methods
### VSP-Driven Local FWI Method
In the general case, the quantities of \(\mathsf{x}_{s}\) and \(\mathsf{x}_{vsp}\) differ  
Figure 6: Schematics of the location of the virtual receivers in the local FWI based on the standard Marchenko receiver redatuming (**a**) and the VSP-driven local FWI (**b**). The internal part of the area surrounded by the rectangle of red dots is the target zone. The red dots represent the virtual source points \(\mathsf{x}_{vgs}\), the green dots are the virtual receiver points \(\mathsf{x}_{vr}\) (**a**) and \(\mathsf{x}_{vpr}^{vsp}\) (**b**), the blue dots are the physical surface sources \(\mathsf{x}_{s}\), and the pink dots are the subsurface VSP receivers \(\mathsf{x}_{vsp}\). The black dots are other grid points within the target volume. significantly and the redatumed wavefields \(P_{all}^{mar}(\mathrm{x_{cr}},\mathrm{x_{s}})\) and \(P_{all}^{mar}(\mathrm{x_{cr}^{vsp}},\mathrm{x_{vsp}})\) also differ in energy, so that \(\alpha\) is necessary. The gradient of Equation (19) can be easily calculated using the adjoint state method [27]. The gradient of Equation (19) can be expressed as:  
\[\delta E=\sum_{\mathrm{x_{s}}}\omega^{2}diag(\mathrm{F}(m_{in},\omega))^{*}v_{ in}+\alpha\sum_{\mathrm{x_{vsp}}}\omega^{2}diag(\mathrm{F}^{vsp}(m_{in},\omega))^{*}v_{ in}^{vsp}. \tag{20}\]  
The \(v_{in}\) and \(v_{in}^{vsp}\) are the local adjoint wavefields in the target volume corresponding to the surface source \(\mathrm{x_{s}}\) and the virtual source \(\mathrm{x_{vsp}}\), respectively. Local adjoint wavefields can be obtained as follows:  
\[A(m_{in},\omega)v_{in}=\mathbb{P}_{vr}^{T}(\mathbb{P}_{vr}\mathrm{F}(m_{in}, \omega)-P_{all}^{mar}(\mathrm{x_{cr}},\mathrm{x_{s}},\omega)), \tag{21}\]  
and  
\[A(m_{in},\omega)v_{in}^{vsp}=(\mathbb{P}_{vr}^{vsp})^{T}(\mathbb{P}_{vr}^{vsp }\mathrm{F}^{vsp}(m_{in},\omega)-P_{all}^{mar}(\mathrm{x_{cr}^{vsp}},\mathrm{x _{vsp}},\omega)). \tag{22}\]  
Finally, the commonly used L-BFGS quasi-Newton algorithm is used to iteratively calculate the updates of target local model parameters.
###### Abstract  
Warping and cross formation are the main defects of an overhang structure formed by laser powder bed fusion. In order to study these defects, a seven\(-\)shaped overhang structure with different lengths and heights of the overhang was printed. The influence of the temperature and stress field on the overhang structure was investigated using a 3D finite element (FE) model. The results of the simulation showed that the molten pool in the powder support zone was much larger than the molten pool in the solid support zone. The molten pool sank due to the actions of gravity and the capillary force. This led to the powder melting, which then formed a droplet\(-\)like cross formation on the lower surface. The temperature difference between the regions led to a large residual stress. When the residual stress exceeded the material strength, warping deformation occurred in the top area, affecting the subsequent powder\(-\)laying process. The warping zone was remelted when the next layer was processed. As the number of forming layers increased, the thermal conductivity and stiffness increased continuously, and the deformation of the top area gradually decreased. The experiment results showed that the longer the overhanging length was, the more serious the warpage was. When the overhanging length was below 3 mm, the warping of the top area continued to decrease to zero as the building process proceeded. Meanwhile, the cross formation appeared at the bottom of the overhanging area in all experimental groups. Studying the process of warping and cross formation was helpful to understand the defect change process in the manufacturing process of an overhang structure.  
laser powder bed fusion; overhang structure; numerical simulation; warping deformation; cross formation +
Footnote †: journal: Medical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China  
V.A.; [PERSON]; [PERSON] Research on the Warping and Dross Formation of an Overhang Structure Manufactured by Laser Powder Bed Fusion.
## 3 Results  
### Purification and Prebiotic Activity Score of Red Sawed Oligosaccharide Fractions  
Red seaweed oligosaccharidesacharide fractions undergoing anion-exchange column yielded three distinct fractions assigned ED-F1, ED-F2 and ED-F3 (Figure 1). The first fraction eluted out with water resulted in the major peak, followed by a peak eluted using 0.2 M and 0.4 M NaCl, while no peak was detected through the elution using 0.8 M-1.6 M (results not shown). Monitoring was conducted using the phenol-sulfuric assay and similar fractions were pooled together into one single fraction. Anion-exchange chromatography was extensively used to fractionate oligosaccharidesacharides including fucooligosaccharides derived from the enzymatic hydrolysis of brown seaweed Sargassum honeri [31] as well as ulvan oligosaccharidesacharides from green seaweed _Ulva_ sp. [32]. Gradient increase in the ionic strength of the mobile phase (CI\({}^{-}\)), competes with the binding site of the positively charged DEAE sepharose resin that will gradually enable the release of negatively charged molecules that binded earlier [33]. Ionized sulphate ester groups are the prime contributors to the natural anionic properties of the seaweed oligosaccharidesacharides [34].  
The three oligosaccharidesacharide fractions subjected to prebiotic assay showed positive prebiotic activity score values against five different commercial probiotics (Figure 2). ED-F1 displayed the highest prebiotic activity score against all probiotics with the highest value against \(L\). _paracasei_ LC01 at 1.64 \(\pm\) 0.17. The prebiotic activity score values of ED-F1 were significantly higher compared to commercial prebiotic F97 against _B. animalis_ BB12, _B. longum_ BB46, _L. paracasei_ LC01 and _L. acidophilus_ LA05. These values are higher than the reported prebiotic activity score of pectin oligosaccharidesacharide fractions from citrus peel against _L. paracasei_ (0.17-0.38) and _Bifidobacterium bifidum_ (0.09-0.93) [35], but comparable to the score of seaweed polysaccharides extracted from _Sargassum withii_ (1.42) and _Enteromorpha compressa_ (1.44) against _Lactobacillus plantarum_ [36]. The current study displayed a slightly lower prebiotic activity score values of ED-F1 against \(L\). _plantarum_ LP8014. The variation observed in the prebiotic activity score of different fractions for both bifidobacteria and lactobacilli strain relates to its metabolic diversity [27] and their different preferential utilization of various oligosaccharides in the fraction [27; 37; 38]. Depending on the species, lactobacilli strains have been known to possess a great variety of genes involved in the metabolism of both complex oligosaccharides and simple sugars which can be switched on depending on the availability of the type of carbohydrates available [39].  
Figure 1: _E. denticulatum_ low molecular weight oligosaccharidesacharide fractions obtained through anion exchange chromatography (DEAE-Sepharose\({}^{\text{\textregistered}}\) Fast Flow).
## 4 Regional Scale Analysis  
### Hazard Indicators  
The actions that perturb the natural equilibrium on slopes can cause geological-hydraulic instability phenomena; they originate from many interrelated factors, whose different combinations have an extremely variable impact on the balance of the natural environment. The indices proposed in this section try to summarize the main factors that may contribute to the occurrence of a landslide event. It must be specified, however, that this is an assessment made at a large scale. The objective of this first phase of the work was to provide a useful tool for large-scale planning that can later coordinate more specific actions on areas particularly at risk. Each indicator quantifies, on a scale of values from 1 to 5, the most favourable situation for a landslide event to occur (5) and the least favourable (1). However, it must be specified that at this stage of the work, knowledge of the mechanisms and contexts of landslide activation is not detailed enough to identify a dominant factor; therefore, the influence of each factor is equal. To give weights to the indices, it would also be necessary to choose a type of event for which to make an assessment, a choice that would exclude, or give less weight to, events that would occur with the same probability. The factors analyzed were the following:  
1. Acclivity;
2. Slope aspect;
3. Paraglacial areas;
4. Glacial geomorphological features;
5. Permafrost degradation;
6. Vegetation and protection forests;
7. Existing gravitational processes;
8. Distribution of precipitation;
9. Lithology and geological setting. A summary of the indices is given in Table 1 and Figure 3, which specifies the subdivision of the index into the five scores from 1 to 5, the sources taken as reference for its determination, and the database used for the calculation applied to the case study of the Valle d'Aosta region. Finally, an ideal update frequency of the data is also suggested: the data can be subdivided into those points that are more or less static and those that are dynamic and need to be updated regularly. Examples of static datasets are related to geology, soil types, geomorphology, and morphography. The time frame for the updating of dynamic data may range from hours and days, for example, for meteorological data and their effect on slope movements, to months and years for land use and population data (used in the next sections for exposure and vulnerability assessment).
## 2 Materials and Methods
### Characterization Techniques  
#### 2.4.1 Compositional Analysis  
A comprehensive series of analytical techniques was employed to characterize the chemical composition of the samples. These analyses included determining the alpha-cellulose content (TAPPI T203 cm-09) [45], ash content (TAPPI T211 om-02) [46], moisture (TAPPI T412 om-02) [47], dry matter content (TAPPI T264 cm-07) [48], and Kappa number, which indicates the lignin content (TAPPI T236 cm-85) [49]. The samples analyzed included initial grass, alkali-treated, and bleached samples.  
The composition of grass clippings was studied using the Van Soest method, which is applied for the quantitative gravimetric analysis of all cell wall components, such as cellulose, hemicellulose, lignin, soluble compounds, and ash. This study was conducted using a semi-automatic FIWE Advance device. After each treatment, the samples were dried at 105 \({}^{\circ}\)C for 16 h and then weighed. The Van Soest method, a quantitative cell wall analysis, was used to determine changes in the proportions of different families of molecules with various grass treatment methods.
# Velocity Control of a Multi-Motion Mode Spherical Probe Robot Based on Reinforcement Learning
## 5 Conclusions  
This paper presents an adaptive PID controller based on reinforcement learning to address the motion stability of MMSPR during rolling and its rapid braking after jumping to avoid obstacles. This controller adopts the SAC algorithm for adaptively tuning the parameters of the PID controller and introduces a switching control strategy to reduce the system's static errors.  
The adaptive PID control method based on reinforcement learning proposed in this paper outperforms baseline algorithms in optimality and robustness. It ensures the motion stability of the MMSPR during its rolling process and facilitates rapid braking after crossing obstacles to avoid it reentering obstacles. Regarding motion stability, ARLPID facilitates the robot's convergence within 0.02 s. Furthermore, this controller significantly reduces the overshoot observed in PID and eliminates the static errors in RLPID. In cases with the robot having random landing postures, ARLPID ensures the robot's braking within 0.0013 m, which is about 38% better than PID. Furthermore, it has strong robustness and eliminates oscillation in RLPID. Under conditions with random landing speed, the simulation results show that ARLPID enables the MMSPR to brake rapidly when its initial velocity ranges from 0 to 0.24 m/s after landing. It attains a success rate of 100% and a braking time of less than 0.045 s, which is 20% faster than PID. In addition, ARLPID maintains better stability than RLPID considering external interference and collisions that might occur in the practical application.  
In future work, we will further study the performance of this control method on the prototype of MMSPR. Furthermore, we will modify and optimize our control algorithm according to the experimental results.  
Conceptualization, W.M. and B.L.; data curation, C.C. and S.P.; methodology, W.M. and Y.C.; software, W.M. and Y.C.; supervision, B.L. and P.W.; writing--original draft, W.M., B.L. and M.L.; writing--review and editing, W.M., B.L., Y.C. and P.W. All authors have read and agreed to the published version of the manuscript.  
This work was sponsored by Technology 173 Program Technical Field Fund (2019-JCJCJ-J459).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Initial Velocity** & **Error** & **PID** & **RLPID** & **ARLPID** \\ \hline \multirow{2}{*}{0.08 m/s} & \(e_{x}\) & 0.065 s & 0.065 s & 0.045 s \\  & \(e_{y}\) & 0.065 s & 0.025 s & 0.045 s \\ \hline \multirow{2}{*}{0.16 m/s} & \(e_{x}\) & 0.06 s & 0.02 s & 0.04 s \\  & \(e_{y}\) & 0.06 s & 0.03 s & 0.04 s \\ \hline \multirow{2}{*}{0.24 m/s} & \(e_{x}\) & 0.055 s & 0.03 s & 0.045 s \\  & \(e_{y}\) & 0.055 s & 0.035 s & 0.045 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: The convergence time of each algorithm.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality regulations of the laboratory.  
The authors declare no conflict of interest.
## 4 Results
### Results for Different Training Windows  
Figure 7 and Table 5 present the AUROC scores for the test set for a decreasing training window at 6 h increments. Overall, it was shown that RandF performed best for the training time \(t_{0}=-48\) to \(t_{0}=-36\). As a result of overlapping confidence intervals, it was observed that after this period, both GB and RandF performed equally well. Interestingly, due to overlapping confidence intervals, it was observed that for the training window \(t_{0}=-6\), besides RandF and GB  
Moreover, it was shown that the linear models (i.e., NB and LogR) performed best for the shortest training window (i.e., \(t_{i}=-6\)). In particular, it was shown that LogR performed similarly well when compared with the non-linear methods when trained using \(t_{i}=-6\) (AUROC: 0.915). In contrast, except for DT, the non-linear models were relatively stable for each training window increment (i.e., \(t_{i}=-48\) to \(ti=-6\)), with generally slightly better performances for the longest training window (i.e., \(t_{i}=-48\)). Lastly, for all of the training windows, we found that all models performed better on the regular dataset (i.e., \(\Delta\) Significant_Features) compared with the PCA variant of the dataset (i.e., \(\Delta\) PCA_Significant_Features), see Figure 11 in the Appendix A.