esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Artificial Neural Networks  
The ANN was performed based on the previous three principal components as an input dataset. The optimum numbers of epochs and neurons in the hidden layer were defined by using an iteratively tuned configuration for predicting B1, AR, and TZ dye  
Figure 5: Principal component analysis (PCA) score plot using the first and second principal components, PCA points are labeled based on (**a**) TZ, (**b**) AR and (**c**) B1 dyes concentrations.  
Figure 6: Principal component analysis (PCA) score plot using the first and third principal components; PCA points are labeled based on (**a**) TZ, (**b**) AR and (**c**) B1 dyes concentrations.  
concentrations in the mixtures. As shown in Figure 7, the model could train the neurons well after about 175 epochs since, from this value, the minimum MSE and MAPE were found for the training and testing data.  
Once the number of epochs for training had been decided, the next step was to find the number of neurons in the hidden layer. To do this, we used a cross-validation method, which consisted of starting to evaluate the model with a small number of neurons and gradually increasing the number until we found the minimum MSE of the training and testing sets.  
As shown in Figure 8, the optimal number of neurons in the hidden layer was found to be 500.  
The optimum number of epochs and neurons in the hidden layer were used to fit the database with the same architecture. Regression coefficients (\(\mathbf{r}^{2}\)) for predicting TZ, AR, and B1 dye concentrations in the mixtures achieved by the model are shown in Figure 9a-c.  
The closer the \(\mathbf{r}^{2}\) values of the PCA-ANN model were to one, the higher the fitting effect of the model was. It can be observed from Figure 9 that the \(\mathbf{r}^{2}\) values of the three dyes are greater than 0.99. This means the PCA-ANN model is highly accurate in the prediction of dye concentrations in ternary mixtures.  
Figure 8: MSE vs. number of neurons in the hidden layer.  
Figure 7: Loss vs. Epoch Graphs. (**a**) MAPE vs. Epoch, (**b**) MSE vs. Epoch.
## 4 Point Cloud Segmentation
### Graph-Based Segmentation
#### 4.3.2 Graph Construction  
Once the setting of weights between points was completed, segmentation could commence. We employed the graph-based segmentation method using adaptive thresholds to determine the degree of similarity between two neighboring points. We first regarded points \(V_{i}\) of a supervoxel as a single group \(S_{i}\). The image segmentation algorithm is outlined in the following:  
Step 1. Calculate the degree of similarity between each supervoxel centroid and its neighboring centroids, and obtain a graph with \(n\) points and \(m\) edges;  
Step 2. Arrange similarity \(e\) of the edges in ascending order to obtain \(\mathbf{E}=(e_{1},\ldots,e_{m})\), where \(\mathbf{E}\) is a set of similarity values \(e_{1}\), \(\ldots\), \(e_{m}\);  
Step 3. Apply Step 4 to edge \(q\), \(q=1,\ldots\), \(m\);  
Step 4. Perform comprehensive judgment of \(e_{q}\). Suppose that there are two connected vertices. If they do not belong to the same component, then the maximum degree of internal similarity between the two, \(\tau\), is calculated as follows:  
\[\tau=\max(I_{i}+\frac{\delta}{|S_{i}|},I_{j}+\frac{\delta}{|S_{j}|}) \tag{10}\]  
where \(I_{i}\) and \(I_{j}\) denoted the internal difference of a component, \(\delta\) indicated the threshold value, and \(|S|\) represented the number of points within point group \(S\).  
If \(\omega_{ij}\), (i.e., the degree of similarity between the two) exceeds \(\tau\), then the two groups were combined and updated as follows:  
\[\begin{array}{ll}S_{k}=S_{i}\cup S_{j}\\ I_{k}=\omega_{ij}+\frac{\delta}{|S_{k}|}\end{array} \tag{11}\]
###### Abstract  
Bimatoprost is a prostaglandin analog used in cosmetic products designed to stimulate hair growth, including eyebrows. However, limited study-based evidence confirming its efficacy, safety, and patient satisfaction is available. The aim of this study was to investigate whether, and to what extent, bimatoprost affects the density and length of eyebrow hair in young women in comparison to sweet almond oil. The study group included 27 healthy women who used bimatoprost cosmetic preparation once daily for 5 weeks and then crossed over to use sweet almond oil once daily for 5 weeks. Bimatoprost preparation, in comparison to sweet almond oil, improved eyebrow density (70% vs. 30%; \(p=0.003\)) and eyebrow hair elongation (59% vs. 26%; \(p=0.014\)), but not eyebrow hair hydration (59% vs. 89%; \(p=0.001\)) and hair darkness (22% vs. 11%; \(p=0.278\)). The comfort of use was comparable for both preparations (63% vs. 67%; \(p=0.996\)), but treatment satisfaction was significantly higher with bimatoprost (66% vs. 22%; \(p<0.001\)). All reported adverse events were minor, transient, and resolved spontaneously. We conclude that the bimatoprost preparation was significantly more effective than sweet almond oil in improving the density and length of eyebrow hair, with a similar level of safety. Therefore, bimatoprost can be considered an ingredient in cosme designed to enhance eyebrow growth; however, further larger studies with extended follow-ups are needed.  
hair growth; skin care; eyebrow hypotrichosis; bimatoprost; prostaglandin analogs +
Footnote †: journal: Applied sciences
## 4 Conclusions  
In summary, this study was designed to test the functional properties of _C. vulgaris_ and assess the potential of its combination with probiotics and bovine colostrum for developing new dietary supplements or functional foods and thus create a new segment of products. The results demonstrated the prebiotic and immunomodulatory effects of the _Chlorella_ powder and its cholesterol-lowering effects in the tested combinations. In addition, the nontoxicity of _Chlorella_ toward mammalian cells was confirmed. The limitations of this study relate to missing information regarding the influence of the cultivation conditions of _C. vulgaris_ or a more detailed analysis of the chemical composition of the tested algae as the content of saccharides or other functional compounds with immunomodulatory, prebiotic, or hypocholesterolemomic effects can be modified by cultivation. The acquired results appear to depend on the functional abilities of the selected probiotic strains or lactic acid bacteria and the defined content of bioactive compounds in the colostrum. Although the results are promising, additional studies are needed to characterize further the synergic effects of algae, probiotics, and/or colostrum, e.g., identification/characterization of bioactive compounds of _Chlorella vulgaris_ and their methods of extraction, as well as growth promotion effect on other probiotics (_Lactobacillus_), effective dose, etc. Nevertheless, the application of natural resources such as algae, probiotics, and colostrum that offer health benefits for humans and animals in supplements or foods holds considerable potential owing to their ready availability, low cost, and high nutrient qualities.  
Conceptualization, I.H., G.K., and J.S.; methodology, I.H., I.D., B.S., and M.H.; formal analysis, I.H., J.S., B.S., H.M., M.H., and I.D.; investigation, A.K., G.K., and I.H.; resources, I.H.; data curation, I.H., A.K., and B.S.; writing--original draft preparation, I.H., A.K., and J.S.; writing--review and editing, T.B., L.C., and M.H.; supervision, I.H., G.K., and A.K.; project administration, I.H.; funding acquisition, I.H. and G.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Agriculture of the Czech Republic, Institutional support, No. MZE-RO1421 and No. QK1910300. The METROFOD-CZ research infrastructure project (MEYS Grant No.: LM2018100) includes access to its facilities.  
This study was conducted in compliance with the laws of the Czech Republic (311/1997 column.) and the European Community Council recommendations (86/609/EEC) regarding the protection of animals used for experimental and other scientific purposes. This experimental study was approved by the committee of the Ministry of Education, Youth, and Sports of the Czech Republic, approval No. MSMT-46654/2015-8.  
Not applicable.  
All data are presented in the paper.  
The authors declare no conflict of interest.
## 3 Trust-Region Reflective (TRR) Search
### Subspace Approach Solving Trust-Region Sub-Problem  
Although accurate algorithms exist to solve Equation (21), they usually involve the computation of all eigenvalues of \(H\) and a Newton process for the secular equation (27). To circumvent costly solving Equation (27) in the full-space of _dim_ variables and be beneficial for large-scale problems, an approximate subspace trust-region interior reflective algorithm [32] was proposed to restrict the full-space sub-problem to a 2-D subspace \(S\). Once \(S\) is determined, it is trivial to solve the subspace sub-problem Equation (28) since it is just two-dimensional.  
\[\frac{1}{\Delta}-\frac{1}{||s||}=0 \tag{27}\]  
\[\min\{\hat{q}(\hat{s}):\ \|\hat{s}\|\leq\Delta,\ D\hat{s}\in S\} \tag{28}\]  
The 2-D subspace \(S\) is formed as the linear space spanned by \(\hat{s}_{1}\) and \(\hat{s}_{2}\), where \(\hat{s}_{1}\) is the gradient direction \(\hat{g}\), and \(\hat{s}_{2}\) is the output of a preconditioned conjugate gradient (PCG) process, either an inexact Newton direction defined by Equation (29) or a direction of negative curvature satisfying Equation (30).  
\[\hat{M}\ \hat{s}_{2}=-\hat{g} \tag{29}\]  
\[\hat{s}_{2}^{T}\hat{M}\ \hat{s}_{2}<0. \tag{30}\]  
After Cholesky factorization and the manipulation of the conjugate gradient (CG) method, the trial step \(s\) can be determined with the best of three directions: (1) the 2-D trust-region solution, (2) the reflection of the 2-D trust-region solution [33] and (3) the affine-scaled gradient direction \(-D^{2}g\). The philosophy behind this choice is to force global convergence via the steepest descent direction or the negative curvature direction and achieve fast local convergence via the Newton step when it exists.
## 2 IoT and Opportunistic Networks--State of the Art  
IoT systems involve interconnected smart devices equipped with sensors and communication modules. Recent research emphasizes the importance of efficient data routing, scalability, and interoperability in IoT architectures [1]. Opportunistic networks operate in intermittently connected environments, relying on node mobility and opportunistic contacts for communication. Recent studies focus on routing protocols, mobility models, and resource optimization techniques to enhance communication reliability and efficiency [2]. The integration of IoT with opportunistic networks enables applications in disaster management, wildlife monitoring, and rural connectivity. Advanced routing protocols play a crucial role in facilitating data dissemination, energy-efficient routing, and adaptive forwarding strategies [3].
## 5 Conclusions
### Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
3D-CMCC CNR-BGC & 3-D Forest Ecosystem Model of the Euro-Mediterranean Centre for Climate Change \\  & with Carbon-Nitrogen-Respiration Biogeochemistry \\
3D-CMCC FEM & 3-D Forest Ecosystem Model of the Euro-Mediterranean Centre for Climate Change \\
3-PG & Physiological Processes Predicting Growth model \\
4C/FORESEE & Forest Ecosystems in a Changing Environment model \\ ANAFORE & ANAlysis of FORest Ecosystems model \\ ANPP & Aboveground net primary production \\ API & Application programming interface \\ BASFOR & BASic FORest simulator model \\ BGC & Biogeochemistry \\ Biome-BGC & Biome BioGeochemical Cycles model \\ CARAIB & CARbon Assimilation In the Biosphere model \\ CoFoLaMo & Comparison of Forest Landscape Models \\ COST & Cooperation in Science and Technology \\ CPU & Central processing unit \\ CSV & Comma-separated values \\ DLEM & Dynamic Land Ecosystem Model \\ DoD & Department of Defense \\ EC & Eddy covariance \\ ED/ED2 & Ecosystem Demography model \\ EMS & Environmental Measurement Station \\ ForClim & Forests in a changing Climate model \\ ForHyCS & Forest and Hydrology Change in Switzerland model \\ FORMIND & Forest Model Individual-based \\ FVS & Forest Vegetation Simulator \\ GO+ & GRAECO and ORCHIDEE plus CASTANEA model \\ GOTILWA+ & Growth of Trees is Limited by Water model \\ GPGPU & General-purpose graphics processing unit \\ HF & Harvard Forest, Massachusetts, USA \\ IBIS2 & Integrated Biosphere Simulator version 2 \\ iLand & Individual-based forest landscape and disturbance model \\ ISMIP/ISMIP2 & Inter-Sectoral Impact Model Intercomparison Project \\ JERC & Jones Ecological Research Center, Georgia, USA \\ JULES & Joint UK Land Environment Simulator \\ L-systems & Lindenmayer systems \\ LandClim & LANDIS-ForClim model \\ LANDIS & Landscape Disturbance and Succession model \\ LANDIS-II & Landscape Disturbance and Succession model, C\# version \\ LM3 & Land Model version 3 \\ Landscape-DNDC & Landscape DeNitrification DeComposition model \\ LINKAGES & Linked forest productivity-soil process model \\ \end{tabular}  
Figure A1: HF-EMS tower daily averages.
###### Abstract  
Due to the space and time constraints of turbulence measurement equipment and the experiment scene, it is difficult to obtain the atmosphere refractive index structure constant over the ocean. In this paper, the characteristics of atmospheric optical turbulence in offshore and open ocean conditions are summarized by analyzing the meteorological data obtained from two ocean atmospheric optical parameter field experiments. Because of the influence of land undersurface, the turbulence strength in offshore conditions is roughly the same as that on land and presents different characteristics in open ocean. Compared with the offshore area, the turbulence strength over the open ocean near-surface decreases during the day and increases at night, and the diurnal variation characteristics weaken. The turbulence strength profiles over the offshore area show different characteristics at different times, where the turbulence strength in the morning is higher than that in the evening. By retrieving the meteorological factors affecting the turbulence, it is found that the temperature gradient and wind shear are in good agreement with turbulence strength in both offshore and open ocean areas. Furthermore, the integrated parameters for astronomy and optical telecommunication are derived from profiles over the offshore and open ocean areas. It is of great significance to research the turbulent characteristics of ocean atmosphere for optical transmission and astronomical observations.  
optical turbulence; astroclimatic parameter; atmospheric physics; ocean and atmospheric optics +
Footnote †: journal: Appl. Sci.  
**Authors**
# Statistical Multipath Model Based on Experimental GNSS Data in Static Urban Canyon Environment
## 2 Multipath Parameters Extraction Method
The signal amplitude of each path ray is  
\[A_{k}=\sqrt{\left(\hat{a}_{k,I}\right)^{2}+\left(\hat{a}_{k,Q}\right)^{2}}, \tag{3}\]  
where \(\hat{a}_{k,I}\) and \(\hat{a}_{k,Q}\) are the estimated amplitudes of the I and Q branches of the \(k\)-th multipath, respectively. Then, the power attenuation proportionality coefficient is defined as  
\[Att_{dB}=20\log\alpha_{k}=20\log(A_{k}/A_{0}). \tag{4}\]  
The carrier phase of each path ray is computed as  
\[\hat{\theta}_{k}=\text{arctan2}\big{(}\hat{a}_{k,Q},\hat{a}_{k,I}\big{)}. \tag{5}\]  
Therefore, the multipath frequency fading of the \(k\)-th path ray is computed as  
\[f_{fading}^{k}=\frac{d\Phi_{k}(t)}{2\pi\cdot dt}=\frac{d\big{(}\hat{\theta}_{ k}(t)-\hat{\theta}_{0}(t)\big{)}}{2\pi\cdot dt}. \tag{6}\]  
One hundred and twenty-seven correlators are used in CADLL to make the multipath-detection scope extend to 4 chips, which cover most of the possible multipath delays encountered in the urban environment. A Spinrent SSS8000 simulator is used to test the performance of the CADLL algorithm. The configuration of the case is shown in Table 2.  
Figure 2: Block structure of Coupled Amplitude and Delay Lock Loop (CADLL). Figure 3 shows the estimation results of the simulated case processed by the CADLL algorithm. The subgraphs at the top-left, the top-right, the bottom-left and the bottom-right show the estimated multipath time delay \(\tau_{k}\), the multipath power attenuation \(Att_{dB}\), the multipath carrier phase difference \(\Phi_{k}(t)\), and the correlation waveform, respectively. The carrier phase difference will be integrated if the multipath signal is continuous. The multipath fading frequency \(d\Phi_{k}(t)/dt\) is the gradient of the carrier phase difference. From Figure 3, it can be seen that the two multipath signals are correctly estimated. The green line represents multipath 1, and the blue line represents multipath 2. In the subgraph of the correlation waveform, the black curve, which represents the compound signal, is severely distorted. After the CADLL algorithm separated each path ray from the compound one, the correlation waveform of each path signal recovered to a regular triangular-wave. The average estimation errors for each multipath are listed in Figure 1:0 \begin{table}
\begin{tabular}{c c c} \hline \hline
**Constellation** & **GPS LICA/PRN2** \\ \hline LOS CNR & \multicolumn{2}{c}{38 dB} \\ Multipath number & MP 1 & MP 2 \\ Multipath delay & 0.8 chip/234 m & 0.3 chip/87 m \\ Power attenuation & \(-\)10 dB & \(-\)5 dB \\ Fading frequency & 0.01 Hz & 0.1 Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Configuration of Spient CSS8000 simulator. Figure 3: Time delay, power attenuation, carrier phase change and correlation shape of two multipath signals extracted by CADLL.
## 4 Discussion  
### Influence of the Black Si Surface on the Growth of the Multilayers  
The morphology of the 3D-RMS differs drastically from the standard RMS (Figures 2 and 3). While continuous and compact layers grew on the flat silicon substrate, the topography of the Si needles originated the growth of conical (mace) shaped Ni/Al multilayer domains on top of the black Si. The effect of the shadow instability causes that longer needles collect more material during film growth compared to shorter needles [24]; this promotes a preferential growth of the domains that originate the needles with the highest peaks, while the domains that originate in the shorter needles stop growing when neighboring domains increase in diameter. The heterogeneity in the speed of growth generated is due to the shadow effect, which also causes formation of gaps between domains. An important effect that must also be considered is the incidence angle (\(\theta\)) of the arriving atoms, which will vary according the needle shape. For the atoms that reach the tip of the silicon needles, \(\theta\) will be close to 0\({}^{\circ}\) while for the atoms that reach the lateral sides of the needles, \(\theta\) will be close to 90\({}^{\circ}\). Previous studies demonstrated the influence of the incidence angle on the microstructure of the films deposited by magnetron sputtering [33; 34]. The increase in incidence angle results not only in a higher porosity in surface morphology but also in a rougher surface [33]. Additionally, in the domains the curvature of the multilayers is observed, which is caused by the shape of the tip of the silicon needles. Figure 7d reveals the convex shape of the needle tips. During sputter deposition, a curved surface affects the coating's thickness, generating a lateral gradient with the maximum thickness at the center of the tip and the minimum thickness at the corners [34]. Due to the curvature, atoms will arrive at more grazing incidence for points farther from the center of the tip. This results in an increase of interfacial roughness of the multilayer in comparison with the multilayers deposited on flat surfaces [35]. Figure 5b depicts the multilayer's curvature changes with the growth of the domain. Therefore, it can be expected that the curvature will have a greater impact on the morphology of the first layers than in the last layers where the curvature decreases.
# A Model-Based Investigation of the Performance Robustness of the Deflector Jet Servo Valve
## 5 Results of Model Validation
### Pressures in Receivers  
With the actual parameters substituted, the pressures of the two receivers were calculated, with the results recorded in Appendix A. Additionally, the corresponding experimental data are listed in Appendix B.  
A graphical comparison, as illustrated in Figure 18, shows that the theoretical calculation conforms to the experiment fairly well, except for an abnormal pressure deviation in the right receiver when a large deflector displacement is exerted, which is presumed to be caused by asymmetry of machining. Additionally, Figure 19 reflects the calculation performance of the theoretical model in terms of the pressure differential driving the spool valve. In addition, we compare our pressure differential results with the simulations of [PERSON] (2016) [31] and the experimental data of [PERSON] et al. (2021) [32]. However, since they do not take account of the outlet pressure, the pressure differential simulation results are lower. The agreement between our theoretical calculation and experimental results is satisfactory. Consider the calculation accuracy of the model, as shown in Figure 20. Except for the abnormal point, the calculation error fluctuates in a small range for both the pressures and the pressure differential. The errors are derived from the phenomenon whereby the pressure gradient of each receiver fluctuates slightly and irregularly as the deflector moves; that is, some tiny unknown factors are functioning in the actual hydraulic amplifier.  
Figure 19: Comparison of pressure differential [31; 32].  
Figure 18: Comparison of receiver pressures.  
Still, the validity of the theoretical model has been materially proven; hence research on the DJV can be furthered based on it.
## 2 Materials and Methods
### Plate and 'Plate-Sole' Models  
Carbon plate surface models with three curvature radii (200 mm, 250 mm, and 300 mm) were created in ANSYS DesignModeler (Figure 2b). Epoxy carbon woven (230 GPa, wet) was selected from the ANSYS materials database [32], and its properties are listed in Table 3. As shown in Figure 2a, the thickness of each carbon fiber fabric layer was set to 0.125 mm in Ansys Composite Prep (ACP) [33]. A stacking configuration of two fabric layers with fiber orientations of \(\pm\)45\({}^{\circ}\) relative to the reference axis formed a single layer with a total thickness of 0.25 mm. To achieve the required carbon plate thicknesses of 1 mm, 1.5 mm, and 2 mm, a total of four, six, and eight stacked layers of carbon fiber fabric were used, respectively, resulting in fully laminated composite carbon fiber plates with the desired thicknesses [34; 35]. The coupling position of the carbon plates within the sole is shown in the lower part of Figure 2b, with the embedded position at the heel remaining fixed. The bending areas of the carbon plates are located in the forefoot, while the heel section remains flat. Bonded contact was defined as being between the carbon plates and the sole, and the contact conditions between the loading head, support beams, shoe sole were set as described in the model validation section. The support beams were fixed at the bottom, and the loading head was set to move vertically downward at a rate of 15 mm/s, with a displacement of 15 mm. The analysis time was determined based on the displacement and rate. Force-displacement data for the loading head were obtained, and the linear region was selected to calculate the FLBS of the plates and the combined 'plates-sole' structures using Equation (1).