esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### The Structural Dynamics Model
As a result, 3 translation and 3 rotation nonlinear constraint equations are defined, expressed through the following nonlinear position/orientation equations:  
\[\mathbf{r}_{G}^{r}(P2) = \mathbf{r}_{G}^{m}(P1)\] \[\boldsymbol{\theta}^{r,m} = const\]  
where \(\boldsymbol{\theta}^{r,m}=\big{(}\theta_{x}^{r,m},\theta_{y}^{r,m},\theta_{z} ^{r,m}\big{)}\) is a column matrix that contains the relative rotation angles between the connected points defined in the local coordinate system of node \(P1\) of component \(m\). The relative angles \(\boldsymbol{\theta}^{r,m}\) are given by [53]:  
\[\cos\theta_{i}^{r,m}\Big{(}\mathbf{e}_{j}^{r}\cdot\mathbf{e}_{k}^{m}\Big{)}- \sin\theta_{i}^{r,m}(\mathbf{e}_{k}^{r}\cdot\mathbf{e}_{k}^{m})=0,\ \ \ \ i=1,2,3\]  
where \(i,j,k\) are such that \(e_{ijk}=1\) and \(\mathbf{e}_{i}^{r}\), \(\mathbf{e}_{i}^{m}\) are the two unit vector bases defining the local frames of the connected nodes (see Figure 2). To conclude, the elasto-dynamic equations are linearized and expressed in perturbed form. Equivalent mass (\(\mathbf{M}\)), damping (\(\mathbf{C}\)), and stiffness (\(\mathbf{K}\)) matrices are computed. The linearized equations are solved iteratively using the 2 nd order implicit Newmark method [54]. Modal analysis can be also performed on the calculated \(\mathbf{M},\mathbf{C},\mathbf{K}\) matrices around the defined reference state. Figure 1: Compatibility conditions. Example between 3 sub-bodies (sb1, sb2, sb3). MaPFlow has been validated in pure aerodynamic analyses against experimental measurements and other computational tools [28]. The same holds for hGAST when coupled with BEM and vortex type aerodynamic models [28; 55]. In the present work, the coupled hGAST/MaPFlow tool is validated. More details about the implementation of the under investigation AL module within MaPFlow and the aeroelastic coupling can be found in [55].
## 3 Research Design and Instruments  
At the beginning of this section, we outlined the research and put forward hypotheses to be tested in the research. In the following subsections, we describe the research instrument and metrics we used to identify the key parameters that influence the accessibility of 3D user interactions.  
To create better XR interfaces, it is important to recognize the influence of certain accessibility parameters on task performance and user experience. This research is a pilot study that aims to collect initial data to identify the key parameters that influence the accessibility of 3D user interactions in educational software solutions based on 3D visualization technologies.  
Based on previous research findings and theoretical foundations [20; 35; 36; 37; 42], we formulate three hypotheses that we evaluate using the results of the analysis of 3D user interactions in educational software:  
**H1:**: _In precision-oriented XR applications, smaller 3D objects relative to the virtual hand will improve task accuracy and user satisfaction._
**H2:**: _Visual distractions reduce task performance and user satisfaction._
**H3:**: _Indirect interaction methods lead to better task efficiency, accuracy, and satisfaction than direct interaction._  
By empirically evaluating these hypotheses, this study contributes to the development of the accessibility of XR interfaces. The defined guidelines will be specifically tailored to the needs of 3D interaction. They will enable the creation of UIs that not only fulfill the basic accessibility requirements but also improve the user experience.  
The study consisted of two phases. In the first phase, a research instrument called GeometryGame was used to collect quantitative data. The second phase collected qualitative data related to participants' attitudes toward certain aspects of accessibility and interaction implemented in the GeometryGame. Figure 1 illustrates the experimental environment used in the first phase of the study.  
Figure 1: Schematic representation of the experimental environment used in the study. The layout includes the position of the participant, the sensor, the display, and the interaction area. The user interface of the application is in Croatian. In the upper left corner of each screen, the blue robot in the bubble displays the user’s instructions for a particular sublevel (e.g., “The goal of this game is to place a cube, a sphere and a pyramid in the appropriate space corresponding to their shape.”). In the upper right corner of the screen, the options “Skip sublevel” (hr. _Preskoci razinu_) and “Exit” (hr. _IZLAZ_) are displayed.  
The quantitative data provide a basis for analyzing the effectiveness and accessibility during the interaction with the research tool, while the qualitative data provide information about the users' satisfaction with the achieved precision, the ease of interaction, and the general subjective impressions during the interaction (e.g., the users' frustration).
## Reweighted Factor Selection for SLMS-RL1 Algorithm under Gaussian Mixture Noise Environments  
**[PERSON] 1,2,* and [PERSON] 3**  
1 School of Information Science and Engineering, Chongqing Jiaotong University,  
Chongqing 400074, China  
2 College of Computer Science, Chongqing University, Chongqing 400044, China  
3 Department Electronics and Information Systems, Akita Prefectural University, Akita 015-0055,  
Japan; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel:+86-23-62652751.  
Academic Editor: [PERSON]  
_Received: 6 July 2015 / Accepted: 11 September 2015 / Published: 25 September 2015_  
**Abstract:** The sign least mean square with reweighted L1-norm constraint (SLMS-RL1) algorithm is an attractive sparse channel estimation method among Gaussian mixture model (GMM) based algorithms for use in impulsive noise environments. The channel sparsity can be exploited by SLMS-RL1 algorithm based on appropriate reweighted factor, which is one of key parameters to adjust the sparse constraint for SLMS-RL1 algorithm. However, to the best of the authors' knowledge, a reweighted factor selection scheme has not been developed. This paper proposes a Monte-Carlo (MC) based reweighted factor selection method to further strengthen the performance of SLMS-RL1 algorithm. To validate the performance of SLMS-RL1 using the proposed reweighted factor, simulations results are provided to demonstrate that convergence speed can be reduced by increasing the channel sparsity, while the steady-state MSE performance only slightly changes with different GMM impulsive-noise strengths.  
**Keywords:** Sign least mean square (SLMS); reweighted L1-norm (RL1); reweighted factor selection; Gaussian mixture model (GMM); sparse channel estimation
# Real-Time Precise Point Positioning during Outages of the PPP-B2b Service
## 2 Methodologies
### The Repair Methods of Matching Error  
The B2b signal uses the Issue of Data (IOD) to ensure the relevance of the information content in different message types. The B2b corrections can be used only when the IOD of Navigation (IODN) of the B2b signal broadcast matches the IOD broadcast by the GNSS for the same satellite. The IOD Corr in message type 2 (orbit correction) and satellite message type 4 (clock correction) are used to match the clock and orbit corrections. However, a mismatch between the IODN and the IOD and between the IOD Corr of the orbit corrections and the IOD Corr of the clock corrections can occur at some epochs.  
Because the IOD broadcast via the broadcast ephemeris and the IODN in the PPP-B2b message do not update simultaneously, there can be a mismatch between the IOD and the IODN. The amount of missing data due to this mismatch is approximately 1% daily. In this case, the user should continue to apply the old broadcast ephemeris until the IODN in the B2b signal is updated [11].  
In the PPP-B2b information, the corrections and corresponding IOD Corr are broadcasted in sequence, and thus there are some occasions when they cannot be matched based on the IOD Corr because the clock correction is updated while the orbit correction is not. In this case, it is recommended to extend the validity of the clock corrections from 12 s to 26 s to use the last matched clock and orbit correction pair until the IOD Corr of the orbit correction is updated. The amount of missing data due to this mismatch is approximately 0.9% daily.
## 5 Conclusions  
The static triaxial test, SEM test, and GRA under F-T cycles were conducted, and the following conclusions can be drawn.  
1. The shear strength and its parameters of CMS after F-T cycles were obtained through the static triaxial test. The results showed that the cohesion and shear strength of S0 and S2 decreased as F-T cycles continued, the internal friction angle not being obvious. The shear strength of both increased with the increasing confining pressure. The increase of the cohesion, the internal friction angle, and the shear strength of S2 doubled compared to that of S0.
2. The microscopic parameters D\({}_{\rm p}\), D\({}_{\rm ps}\), C, R, D\({}_{\rm pr}\), H\({}_{\rm m}\), D\({}_{\rm h}\), and D\({}_{\rm hs}\) of S0 and S2 at optimum moisture content after F-T cycles were studied and analyzed by SEM and IPP. With the increase of F-T cycles, the D\({}_{\rm ps}\), D\({}_{\rm h}\), and D\({}_{\rm hs}\) of S0 increased, the D\({}_{\rm p}\) and R decreased, while the C, D\({}_{\rm pr}\), and H\({}_{\rm m}\) had no obvious variations. As F-T cycles continued, the D\({}_{\rm p}\), R, and D\({}_{\rm h}\) of S2 increased and the D\({}_{\rm pr}\) and D\({}_{\rm hs}\) decreased. The D\({}_{\rm ps}\), C, and H\({}_{\rm m}\) fluctuated in a certain range.
3. The correlations between microscopic parameters and macroscopic parameters were calculated on the basis of GRA. The results manifested that the most important factor affecting the cohesion of S0 was D\({}_{\rm p}\), while the cohesion of S2 was more sensitive to D\({}_{\rm ps}\). Concerning the internal friction angle, C was the most important for that of S0, and D\({}_{\rm h}\) was primarily related to that of S2. The use of modifiers to reduce D\({}_{\rm h}\) could most effectively increase the shear strength of soil after F-T cycles.
4. The samples under different moisture contents should be analyzed by SEM to study the effect of moisture content on the microstructure parameters of CMSS under F-T cycles. Meanwhile, more research should be carried out on the fractal dimension of soil.  
Conceptualization, S.S.; methodology, Y.Z.; software, J.W.; validation, H.L., Y.Z. and L.W.; formal analysis, J.W.; investigation, S.S.; resources, H.L.; data curation, S.S.; writing--original draft preparation, S.S., H.L., G.L and L.H.; writing--review and editing, S.S. and H.L.; visualization, J.W.; supervision, L.W.; project administration, H.L.; funding acquisition, Y.Z. and J.W. All authors have read and agreed to the published version of the manuscript.  
Figure 16: The curve of the internal friction angle of S2 versus the five most relevant microscopic parameters after F-T cycles: (a) D\({}_{\rm h}\), (b) R, (c) D\({}_{\rm ps}\), (d) D\({}_{\rm p}\), and (e) D\({}_{\rm pr}\).  
**Funding:** This research was funded by National Natural Science Foundation of China, grant number 51308256.  
**Acknowledgments:** The authors express their appreciation for the financial support of the National Natural Science Foundation of China, grant number 51308256. Thanks to reviewers for their valuable comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Antioxidant Activity of Beetroot Juices  
The antioxidant activities of the studied beetroot juices during storage at different temperatures were determined based on their scavenging effect on the 1,1-diphenyl- 2-picryl-hydrazyl (DPPH) radical. The obtained results, expressed as the Trolox equivalent, are presented in Figure 3. The purchased organic beetroot juice showed a higher ability to scavenge free radicals when stored at 4 \({}^{\circ}\)C. Homemade juice prepared from raw vegetables remarkably lost its antioxidant properties regardless of the storage conditions. It is worth mentioning that the presence of DDT or \(\beta\)ME did not affect the results in DPPH assay for both beetroot juices just after the preparation of these samples. The specific antioxidant activity values of beetroot juices after the addition of DTT or \(\beta\)ME and storage at different temperatures are shown in Table S3. During the prolonged time of storage, as various reactions may take place with numerous final products, the antioxidant activities of juices were changed, but in ambiguous ways. [PERSON] et al. also observed a slow decrease in antioxidative activity in beetroot juice (obtained by maceration of roots in a centrifugal juicer, and then pastewised for 15 min at 90 \({}^{\circ}\)C), which was stored in dark at 4 \({}^{\circ}\)C [47]. The antioxidative capacity of beet juices depends mainly on the concentration of phenolic compounds, most of which belong to the betalain family (several betacyanins and betaxanthins) that serve as colour pigments and possess high antioxidant potential in-vitro [29; 48; 49]. Beet juice is also rich in vitamin C, a strong antioxidant, that is often used as a natural preservative [50]. It was reported that organic cropping of red beetroot plants increased the content of ascorbic acid by 23.3% and antioxidant activity by 30.3% in comparison with conventional cultivation [51]. Table 2 shows the main compounds determined in the studied beetroot juices. The higher concentrations of betanin, a major red pigment, as well as vulgasant I, the predominant yellow pigment from a group of betaxanthins, were found in purchased organic beetroot juice. However, there is no information on what amount of vegetable that juice was prepared from. The concentrations of these pigments decreased during storage of juices, and their faster loss was observed in homemade juice and at a higher temperature of storage. The highest percentage reduction in betanin content was measured in the squeezed juice sample (27%) after storage at 20 \({}^{\circ}\)C, which could be linked to the low thermal stability of betalains [52]. The action of endogenous enzymes (\(\beta\)-glucosidase, polyphenol oxidases,  
peroxidases), present in red beets, may also account for betalain degradation and colour losses [44; 52]. Apart from betalains, small amounts of polyphenic acids, such as \(p\)-hydroxybenzoic (pHA), gallic, and ferulic acids have been identified, particularly in organic beetroot juice.
## 3 Results and Discussion
### Anti-Inflammatory Activity of the Extracts
These data indicated that heather and marigold anti-inflammatory activity could not be only correlated with the concentration of triterpene acids in the extracts, although the anti-inflammatory activity of unsolic and oleanolic acid has been previously reported in the literature [25,30]. A possible explanation regarding the lack of correlation between the amount of triterpene acids and the anti-inflammatory activity of this work could be attributed to the relatively high concentration of these compounds required to exert such anti-inflammatory activity. Thus, [PERSON] and [PERSON] [31] evaluated the anti-inflammatory activity of oleanolic and unsolic acids and observed a considerable reduction on TNF-\(\alpha\) and IL-6 secretion in the presence of 20 uM of unsolic acid and 40 uM of oleanolic acid. Regarding the present work, the highest concentration of triterpene acids (expressed as the sum of the  
Figure 1: Levels of TNF-\(\alpha\) (**a**), IL-1\(\beta\) (**b**) and IL-6 (**c**) secreted by THP-1/M cells activated with LPS in the presence of supercritical fluid extracts from heather (**m**), marigold (-) and a combined heather + marigold (50:50) extraction (\(\equiv\)). Each bar corresponds to the mean of three determinations \(\pm\) SD. * Denotes statistical differences between control positive and other samples at \(p<0.05\). oleanolic, torsolic and betulinic acid) in the supercritical extracts was 13.3 uM (E3-2 from heather). In addition, torsolic acid has also been associated with a pro-inflammatory activity in a dose-dependent manner in mouse macrophages. In this sense, torsolic acid could promote the production and secretion of the pro-inflammatory cytokines TNF-\(\alpha\), IL-1\(\beta\) and IL-6 [32]. This fact could in part explain the practically ineffective anti-inflammatory effect of the E1-S1 extract from heather, which presented the highest concentration of torsolic acid (98.6 mg/g) (see Table 3) and the lowest oleanolic/ursolic concentration ratio (0.14). Additionally, the reduction in the cytokine secretion produced by the extracts with a low concentration in these triterpene acids, such as E1-S2 from heather, could be related to the presence of other bioactive compounds with anti-inflammatory activity. Thus, faradiol- and taraxasterol type compounds presented in marigold extracts have been reported to possess anti-inflammatory activity [10; 21; 23]. Therefore, additional studies should be carried out with the aim of elucidating the effect on the cytokine secretion of the studied triterpene acids and their possible synergisms and antagonisms with other bioactive compounds present in the extracts.
# A New Windage Loss Model for S-CO\({}_{2}\) Turbomachinery Design
## 3 Comparison of Data to Existing Windage Loss Models
It is noted that the variation of the density ratio over time is due to testing many conditions in a single test, and the test section reaches a steady state quite quickly when the test conditions are changed. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & \multicolumn{2}{c}{**Empirical Equation**} & \multicolumn{1}{c}{**Note**} \\ \hline [PERSON] [12; 13] & \(C_{f}=\frac{1.8}{Re_{c}\sigma^{25}}\frac{(1+c_{s})^{2}}{(1+c_{s})^{2}-1}\) & \(\sqrt{Ta}\leq 41.3\) (\(laminar\)) & \(K_{mack}\) is obtained with assuming continuous change of \(C_{f}\) at \(\sqrt{Ta}\leq 41.3\) \\ \cline{2-4}  & \(C_{f}=\frac{K_{mack}}{16\sigma^{25}}\) & \(\sqrt{Ta}>400\) (\(turbulent\)) & Air test \\ \hline [PERSON] [14] & \(C_{f}=\frac{2}{Re_{c}}\) & \((laminar)\) & Couette velocity profile in laminar and air test for \(B=2.04\) in turbulence regime \\ \cline{2-4}  & \(\frac{1}{\sqrt{C_{f}}}=B+1.768\)\(ln\left(Re_{c}\sqrt{C_{f}}\right)\) & \((turbulent)\) & Air test for \(B=2.04\) \\ \hline [PERSON] [15; 16] & \(C_{f}=\frac{0.46\left(c_{s}+c_{s}^{2}\right)^{\frac{1}{4}}}{Re_{c}^{25}}\) & \(400<Re_{c}<10^{4}\) & Pure water, water-glycerin mixture Gap size: 0.95–4.7 cm \\ \cline{2-4}  & \(C_{f}=\frac{0.073\left(c_{s}+c_{s}^{2}\right)^{1/4}}{Re_{c}^{23}}\) & \(10^{4}<Re_{c}<10^{5}\) & \\ \hline [PERSON] [15; 17] & \(\frac{1}{\sqrt{C_{f}}}=7.54+11.5\,log_{10}\left(Re_{c}\sqrt{2C_{f}}\right)\) & Spindle oil test \\ \hline [PERSON] and [PERSON] [15; 18] & \(C_{f}=\frac{0.515\,c_{s}^{0.3}}{Re_{c}^{25}}\) & \(500<Re_{c}<10^{4}\) & Pure water, water-glycerin mixture Gap size: 0.32–4.45 cm \\ \hline [PERSON] [15; 19] & \(C_{f}=\frac{75}{Re^{2}}\) & \(Ta<1700\) & Freezer oil, pure water, water-glycerin mixture Roughness test \\ \cline{2-4}  & \(C_{f}=\frac{800}{c_{s}Re_{c}^{2}}+\frac{0.335\,c_{s}^{0.38}}{Re_{c}^{44}}\) & \(1700<Ta<10^{44}\) & Freezer oil, pure water, water-glycerin mixture Roughness test \\ \cline{2-4}  & \(C_{f}=\frac{K_{f}}{16\sigma^{25}}\) & \(Ta>10^{4}\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Existing empirical windage loss models. Figure 7: Measured windage loss vs. calculated windage loss from [PERSON]’s model.
## 2 Results and Discussion  
In order to investigate the chemical diversity of the headspace and volatile oil composition from two brown algae of the family Dictyotaceae, order Dictyotales collected from the Adriatic Sea from the same area (island Vis, Croatia), two complementary methods were used: headspace solid-phase microextraction (HS-SPME) and hydrodistillation (HD) followed by gas chromatography and mass spectrometry (GC-FID; GC-MS) analysis. In order to avoid the changes that could occur in VOCs from the native samples after a long time of collection or drying, both algae were investigated as fresh samples. Two fibres of different polarity were used for HS-SPME (Polydimethylsiloxane/Divinylbenzene (PDMS/DVB) and Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS)) to obtain more complete chemical profiles. The results pointed out striking differences between the chemical profiles for the headspace and volatile oil of the same algae (depending on the compounds molecular mass and volatility). It should be taken into consideration that the headspace composition does not reflect the composition percentage of the sample (only of the headspace) and hydrodistillation is not an adequate method for the isolation of water-soluble, high-molecular compounds and less volatile compounds such as diterpenes.
# Extending Wireless Reschargeable Sensor Network Life without Full Knowledge
## 9 Full Knowledge Optimal Algorithm
The lowest possible time increase is sufficient for algorithms' performance comparison when similarly applied to all algorithms. The power computing algorithm (Algorithm 7) computes the power needed to charge and reach the nodes in the charging list. Travel cost is the cost to reach the furthest node in the charging list (lines 14 and 15). A cost is assigned to each node, defined as the amount of power needed to bring the node to the target power level and the cost to reach it (in terms of power). Based on the cost, the algorithm identifies the lowest cost nodes to charge, and accumulates the costs (lines 9-13). ```
1:\(d\)\(\triangleright\) Distance between two consecutive nodes
2:\(\Lambda_{m}\)\(\triangleright\) Localization cost in terms of power
3:procedurecomputeNeedeedPower(\(N,targetPower,u\))
4:for\(node\in N\)do
5:if\(node\)\(\geq\)\(targetPower\)then
6:\(cost_{node}\leftarrow\infty\)\(\triangleright\) Eliminate nodes beyond maximum reach
7:else
8:\(cost_{node}\gets targetPower-nodepower+(d_{node}\times\Lambda_{m})\)\(\triangleright\) Compute cost to reach and charge a node
9:endif
10:endfor
11:for\(i\gets 1\ldots u\)do\(\triangleright\) Charge \(u\) cheap nodes
12:\(node\leftarrow\)find\(i^{th}\) lowest cost node in \(cost\) set
13:\(C\gets C+targetPower-node_{power}\)\(\triangleright\) Accumulate charge cost
14:\(c\gets u-1\)
15:endfor
16:\(pose\leftarrow\) - furthest position in the first \(u\) nodes from \(cost\)
17:\(M\leftarrow\)\(pose\times\Lambda_{m}\)\(\triangleright\) Compute moving cost, the cost to reach furthest node to charge
18:return\(C,M\)
19:endprocedure
```  
**Algorithm 7** Compute Needed Power to Charge and Move Given Nodes.