esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Mix Design Study of Asphalt Mixtures with SSA Resulting from the Preliminary Study
#### 3.4.2 Results of the Marshall Mix Design Method for the Selected Asphalt Mixtures  
The optimum binder contents (OBC) of AC 14 and AC 20 mixtures with 75% SSA were determined according to the Marshall mix design method. Table 8 presents the average densities (MD and bulk density) and the corresponding values of Va and VMA obtained for each binder content used in the study. After the specimens' volumetric characterization, they were subjected to the Marshall stability test, through which the results of maximum force (stability) and deformation at failure are obtained, as shown in Table 9.  
\begin{table}
\begin{tabular}{c c c} \hline
**Fraction** & **AC 14 Surf 35/50 (BB)** & **AC 20 Bin/Base 35/50 (MB)** \\ \hline SSA 0/20 & – & 62\% \\ SSA 10/14 & 29\% & 13\% \\ SSA 4/10 & 31\% & – \\ SSA 0/4 & 15\% & – \\ NA 14/20 & – & 6\% \\ NA 0/4 & 20\% & 17\% \\ Commercial filler & 5\% & 2\% \\ \hline \end{tabular}
\end{table}
Table 7: Composition of the selected asphalt mixtures with 75% SSA.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Equivalent Binder Content** & \multicolumn{2}{c}{**MD (kg/m3)**} & \multicolumn{2}{c}{**Bulk Density (kg/m3)**} & \multicolumn{2}{c}{**Va (\%)**} & \multicolumn{2}{c}{**VMA (\%)**} \\ \cline{2-9}  & **AC 14** & **AC 20** & **AC 14** & **AC 20** & **AC 14** & **AC 20** & **AC 14** & **AC 20** \\ \hline
4.0\% & 3013 & 3049 & 2746 & 2617 & 8.9 & 14.2 & 17.9 & 22.8 \\
4.5\% & 2937 & 3011 & 2747 & 2686 & 6.5 & 10.8 & 16.6 & 20.7 \\
5.0\% & 2914 & 2962 & 2793 & 2714 & 4.1 & 4.9 & 15.6 & 16.2 \\
5.5\% & 2873 & 2907 & 2794 & 2699 & 2.7 & 7.5 & 15.3 & 19.6 \\
6.0\% & 2842 & 2854 & 2833 & 2736 & 0.8 & 4.3 & 14.6 & 17.7 \\ \hline \end{tabular}
\end{table}
Table 8: Volumetric characteristics of mixtures with 75% SSA for different binder contents.  
Figure 7: Grading curve adjustments for the conventional asphalt mixtures with natural aggregates (NA) and the selected asphalt mixture with 75% SSA: (**a**) AC 14; (**b**) AC 20.  
The results obtained in the Marshall method were then analyzed to determine the OBC, considering the binder contents resulting in the maximum bulk density and Marshall stability and the mean value of Va specified for each type of mixture.  
Based on the analysis of the results of the surface course mixture (AC 14), the OBC is situated at 5.0%. This binder content also results in values of VMA and deformation at failure that meet the specified criteria. A similar result was observed for the AC 20 mixture regarding the optimum binder content parameters. Likewise, the use of 5.0% bitumen would result in VMA values and deformation at failure that meets the specification limits. Thus, the remainder of this work used a binder content of 5.0% for both asphalt mixtures (AC 14 and AC 20).
## 4 Results and Discussion
### Test Systems
#### 4.2.2 Test System 2  
Four multimodal functions are included in the test functions. They are compared to four metaheuristic algorithms that are currently in use. PSO, DE, GWO, and SOGWO are the algorithms utilized for comparison. Table 4 displays the specifics of the multimodal test function. The two-dimensional parameter space of these four unimodal functions is depicted in Figure 7.  
Table 5 contains the average and standard deviation for ten independent runs. The convergence rates of the four test functions for several algorithms are displayed in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function Name** & **Expression** & **Search Space** & **Dim** \\ \hline F8 & F8 & [-500,500] & 30 \\ F9 & F9 & [-5.12,5.12] & 30 \\ F10 & F10 & [-32,32] & 30 \\ F11 & F11 & [-600,600] & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Details of the multimodal test function.  
Figure 7: The results of parameter space. (**a**) The parameter space of F8 and (**b**) the parameter space of F9 and (**c**) the parameter space of F10 and (**d**) the parameter space of F11.  
Table 5 demonstrates that, despite the ALGWO algorithm's poor performance in the multimodal test function in the F8 function, the average search accuracy achieves the minimum value of 4.44 \(\times\) 10\({}^{-15}\) in F10 and the optimal solution of 0 in F9 and F11. As a result, the ALWO algorithm's indication is much superior to that of other algorithms. Figure 8 shows that, for the test functions of F10 and F11, the ALGWO algorithm yields superior solution outcomes and accelerates convergence speed. Therefore, when solving multimodal test functions, the ALGWO method generally outperforms other algorithms.
# Interference Suppression in EEG Dipole Source Localization through Reduced-Rank Beamforming
## 4 Discussion  
It is known that LCMV-based solutions may perform sufficiently well when solving the neuroelectric inverse problem of estimating the location of brain sources only if certain conditions are satisfied: zero or low correlations between the sources, high enough signal-to-noise ratios (SNRs), and sufficiently large spatial separation of sources [8]. However, the LCMV beamformer tends to suppress source-power estimates from sources that have highly correlated time-courses, given that it assumes that source time-courses from different generators are uncorrelated, which cannot always be attained [3; 29]. Figure 9: Results of the source localization process through our reduced-rank beamforming approach in EEG SSVEP data of Subject 10 in [27] and for a specific frequency.
## 4 Performance  
The proposed method is implemented in MATLAB 2021a with 16 GB RAM on an i7 processor. The main role is to encrypt the confidential data. If any government/private department or any group wants to share their confidential data with the service of a CSP, the security level will go down or become worthless. With our implemented technique, we can share our data securely over the cloud and no hacker can crack or destroy/update the data.  
Hence, by following our implemented technique, anyone can share their confidential data. Therefore, sharing images should be encrypted by our implemented technique.  
To obtain the best results to share the data over the cloud securely, first, we downloaded different collections of  
Here, the mean-squared error (MSE) can be computed as in Equation (15):  
\[\text{MSE}=\frac{1}{\text{mn}}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}[\text{I}(\text{i}, \text{j})-\text{K}(\text{i},\text{j})]^{2} \tag{15}\]  
Here, I shows the input image. K shows the encrypted image. (i, j) denotes the pixel coordinates, and m, n shows size of the input image. Entropy is a well-known measure which indicates indegree of randomness in the image. The entropy (E) of an image can be computed as in Equation (16):  
\[\text{E}=\sum_{i=0}^{m-1}\text{p}(\text{m}_{i})\log_{2}\ \frac{1}{\text{p}( \text{m}_{i})} \tag{16}\]  
where, m\({}_{i}\), \(\epsilon\), E, and m\({}_{i}\) together denote the probability of image occasion m\({}_{i}\).  
The attackers sometimes explore the relation among the adjacent pixels of an image for statistical attacks. Actually, the adjacent pixels of the plain image are highly correlated to each other in all three directions, such as horizontally (HC), vertically (VC), and diagonally (VC). This relation should be minimum so that no statistical information should be disclosed to the attackers. The relation among the adjacent pixels can be calculated as:  
\[\text{r}=\frac{\sum\left(\text{x}_{i}-\text{\mu}_{\text{x}}\right)(\text{y}_{ i}-\text{\mu}_{\text{y}})}{\sqrt{\sum\left(\text{x}_{i}-\text{\mu}_{\text{x}} \right)^{2}(\text{y}_{i}-\text{\mu}_{\text{y}})^{2}}} \tag{17}\]  
Here, r is the correlation coefficient. x and y represent the adjacent pixels. \(\text{\mu}_{\text{x}}\) and \(\text{\mu}_{\text{y}}\) are the means of x and y, respectively. For this experiment, we randomly selected 3000 pairs of adjacent pixels (x; y) from plain and encrypted images. A palm X-ray image is taken for this test, as shown in Figure 6.
## 7 Applications of HAR in Healthcare
### Mental Health Issues  
HAR has the potential to detect mental health issues (anxiety, depression, and dystymia) and even monitor symptoms of mental health issues by analysing daily activity and sleeping patterns. Mental health issues is usually characterised by a change in the pattern of daily activities, with the patient becoming less interested in certain activities. Less physical activity has generally been associated with people dealing with mental health issues. People with anxiety are likely to experience or carry out certain anxiety behaviours such as biting their nails, pulling their hair, pacing, and so on. They might also experience diarrhoea or constipation. Insomnia is also one of the symptoms of mental health issues. These activities can be detected by HAR systems, and this can be the foundation of a HAR-based mental health support system.  
People with major depressive disorder and who are at risk of self-harm can also be monitored using HAR-based systems. While current mental health detection and monitoring depends on self reporting, it is flawed because it relies on the patient being able to accurately, and without bias, report their activities. However, with HAR systems, these activities can be automatically and accurately detected, and it gives the mental health professional accurate information to work with.  
The detection and monitoring of mental health issues using wearable or ambient sensors has been a long-standing research goal [116]. One of the limitations faced in this research area is the availability of large datasets and also the lack of uniformity in the data collected. This is a problem that needs to be tackled for the adoption of HAR systems in the detection and monitoring of mental health issues.
# Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning
## 4 Two-Level Path Planning Method  
The K-L algorithm is similar to other local path planning algorithms, which achieve obstacle avoidance and pathfinding of dynamic obstacles by sacrificing time; however, if the whole process is accomplished only using only the K-L algorithm, it will considerably reduce the transportation efficiency of AGVs. Therefore, the area where the K-L algorithm is used needs to be as small as possible, in addition to a reasonable timing requirement to maximize the advantages of the algorithm. In this paper, we first use the improved genetic algorithm to obtain the global path; then, when the AGV's radar detects an obstacle that cannot be avoided, we use the K-L algorithm to complete obstacle avoidance in a limited area. In addition, because the dynamic working environment is generally narrow, if there is no reasonable scheduling method, considerable congestion of multiple AGVs will occur, reducing the transportation efficiency of AGVs. In this paper, the scheduling of multiple AGVs can be accomplished through the node busy factor. In other words, AGVs can avoid the more congested nodes by detouring, which maximizes the transportation efficiency of multiple AGVs.  
The GA-KL method flow is shown in Figure 5. The main steps of a two-level path planning method combining the K-L algorithm and an improved genetic algorithm are as follows:  
1. The working environment is scanned by the AGV's own SLAM system, which obtains map information to create a raster map.
2. First, the optimal set of global path nodes is obtained using the T-GA algorithm; then, the shortest path between nodes is generated using the A* algorithm, and the algorithm aims at the shortest path length.
3. Due to the global set of path nodes, we divide the whole path into multiple subpaths to obtain multiple local target points.
4. For each subpath, we first detect dynamic obstacles using the detection radar of the AGV; then, when dynamic obstacles appear, the K-L algorithm is enabled to complete obstacle avoidance and select the shortest path length as the goal to obtain the local optimal path; if there are no obstacles, the path planned by the A* algorithm continues to be used.
## 3 Phenomenological Model of Flexible Membrane Damper with Slits
### Vibrations of the Circular Membrane  
Consider the vibrations of the circular membrane with radius \(a\) in the axisymmetric case. The deflection of the membrane from the state of equilibrium \(u\) does not depend on the angle, and the wave equation reads as follows:  
\[\frac{\partial^{2}u}{\partial t^{2}}=c^{2}\bigg{(}\frac{\partial^{2}u}{ \partial r^{2}}+\frac{1}{r}\frac{\partial u}{\partial r}\bigg{)} \tag{1}\]  
where \(t\) is time; \(r\) is the radial coordinate; and \(c\) is the wave speed. The solutions in the separable variables \(u(r,t)=R(r)T(t)\) read as follows:  
\[T^{\prime\prime}\left(t\right)=Kc^{2}T(t) \tag{2}\]  
\[rR^{\prime\prime}\left(r\right)+R^{\prime\left(r\right)}-KR(r)=0, \tag{3}\]  
where \(K\) is a constant. The equation for \(T(t)\) has periodic solutions for \(K<0\). Assuming \(K=-\lambda^{2}\) yields the following:  
\[T(t)=A\cos c\lambda t+B\sin c\lambda t \tag{4}\]  
The equation for \(R(r)\) is a special case of [PERSON]'s differential equation. After dropping the non-physical part of the solution, \(R(r)\) reads as follows:  
\[R(r)=J_{0}(\lambda r) \tag{5}\]  
where \(J_{0}\) is a zero-order Bessel function of the first kind. The fixed boundary conditions (\(u\) must be zero on the boundary) yield the following:  
\[R(a)=J_{0}(\lambda a)=0 \tag{6}\]  
For the fundamental natural frequency, we assume that \(\lambda a=\alpha_{01}\), where \(\alpha_{01}\approx 2.40483\) is the first root of \(J_{0}\). Thus, finally, the fundamental axisymmetric vibrations of a circular membrane read as follows:  
\[u_{01}(r,t)=\Big{(}A\cos\frac{c\alpha_{01}}{a}t+B\sin\frac{c\alpha_{01}}{a}t \Big{)}J_{0}\left(\frac{\alpha_{01}}{a}r\right) \tag{7}\]  
Note that \(0\leq r\leq a\).
# _remote sensing_
## 2 Methodology
### Antenna Offset Correction  
The precise ephemeris product of IGS provides the center-of-mass (CoM) coordinate of the respective space vehicle, but the position reference point of BDS-3 broadcast ephemeris is the antenna phase center (APC) of a single-frequency B3I signal [3; 4; 5; 32; 33]. With respect to the orbit error, this inconsistency causes meter-level differences in the direction of the antenna in the spacecraft frame [14]. In order to assess the position accuracy of the BDS-3 broadcast ephemeris, antenna offset vectors ([[http://www.csno-tarc.cn/datacenter/satelliteparameters](http://www.csno-tarc.cn/datacenter/satelliteparameters)]([http://www.csno-tarc.cn/datacenter/satelliteparameters](http://www.csno-tarc.cn/datacenter/satelliteparameters)), accessed on 1 January 2023) provided by the satellite manufacturer correct the position reference point of the broadcast ephemeris from APC to CoM. The correction in detail from APC to COM can be found in reference [14].
## 5 Conclusions  
In this study, we propose a joint optimization of a multi-cell massive MIMO network towards three objectives: the power consumption, the DL EMF exposure and the UL EMF exposure. Both the DL and UL EMF exposure have been addressed differently. In a first step, the DL and UL EMF exposure are studied separately (E-field for the DL exposure and SAR for the UL exposure), while in a second step, the two objectives are jointly expressed in terms of the dose metric. Assuming the optimization objectives equally important, the results of the simulations show that the DL EMF exposure is more impacted (+174% and +507% for \(E\) and DL dose, respectively) by the variation of the number of users than the UL EMF exposure (+5% and +1.3% for the \(SAR\) and UL dose, respectively). Also, for the first time, it has been shown that the increase of the number of BS antennas elements contributes to a 12% reduction of the DL EMF exposure, at the expense of a higher UL EMF exposure (70% increase of the UL dose). Furthermore, using Pareto front analysis, we show that the optimal network required to serve 224 users, with a better trade-off of the power consumption and the total dose, is obtained with 37 64-antenna BSs for a power consumption of 2.2 kW and a total dose of \(1.54\times 10^{-4}\) (\(\frac{\text{W}\cdot\text{s}}{\text{kg}}\)). Finally, the massive MIMO networks achieve the same performance in terms of user coverage with a DL exposure 5 times lower, compared to the 4G reference network. Experiments remain the future focus of this research to validate the results in a realistic environment.  
Conceptualization, M.D., E.T., D.P., S.G. and W.J.; methodology, W.J.; software, M.D., M.M.; formal analysis, M.D., D.P., S.G. and W.J.; writing-original draft preparation, M.M.; writing-review and editing, S.S., M.D., E.T., D.P., S.G., K.E.P., L.M. and W.J.; supervision, M.D. and W.J.; project administration, L.M. and W.J.; funding acquisition, W.J.  
This research was funded by the Research Foundation at UGent (BOF-UGent) under the grant agreement number 01W0014.  
[PERSON] is post-doctoral fellow of the FWO-V (Research foundation-Flanders).  
The authors declare no conflict of interest.
## 5 Stability of Carotenoids and Betalains  
All natural pigments, especially carotenoids, are deeply unstable and prone to alteration due to heat, light and oxygen exposure [12]. To address the stability problem, microencapsulation and nanoencapsulation have been extensively investigated. [PERSON] [75] and [PERSON] et al. [76] comprehensively reviewed the encapsulation methods and the materials applied to produce carotenoid-rich microparticles (e.g., modified starch, maltodextrin, \(\beta\)-glucan, carboxy-methylcellulose, inulin, pectin, chickpea protein, gelatin, whey protein isolate and gum Arabic). Other prominent studies are reported in Table 2.  
[PERSON] et al. [77] assessed the stability of carotenoid-containing chromoplasts (Chr) (recovered by a tailored enzymatic mix) under different temperatures (4, 25 and 40 \({}^{\circ}\)C) and light exposure (dark and UV-light irradiation), in comparison to the free carotenoids (Free car) obtained by solvent extraction. Irrespective of the different storage conditions (temperature and UV-light exposure), the lowest pigment degradation rates were found for Chr [0.014 days\({}^{-1}\) (25 \({}^{\circ}\)C in the dark)-0.057 days\({}^{-1}\) (40 \({}^{\circ}\)C under UV-light)] rather than Free car [0.018 days\({}^{-1}\) (25 \({}^{\circ}\)C in the dark)-0.071 days\({}^{-1}\) (40 \({}^{\circ}\)C under UV-light)]. Furthermore, Chr better retained the colorimetric parameters, especially at 4 \({}^{\circ}\)C (\(\Delta\)E-4.0) and 25 \({}^{\circ}\)C (\(\Delta\)E-4.5) in the dark. These results proved that the chromoplast envelope better preserves the red-orange nuance of natural pigments recovered by EAE than carotenoids from solvent extraction [77]. Concerning betalains, one of the main obstacles that limit their application in the food sector is their poor stability under operational conditions. Numerous studies have been focused on exploring approaches to stabilize them, extending their applications [78; 79]. Some of the stabilization methods that have been studied include the addition of antioxidants, chelating agents and encapsulation techniques [80; 81]. [PERSON] et al. [82] demonstrated that encapsulation by freeze drying, using polysaccharide-protein matrices, is a suitable method to enhance betalains stability (Table 2). [PERSON] et al. [83] studied the synergistic action of UV light and heat on the visual colorimetric perception of betalains enzymatically extracted from unsold beets. The study demonstrated that if the pigment was kept at temperatures close to room temperature, the effect of UV light was negligible. This is evident considering the colorimetric parameters at 4 and 25 \({}^{\circ}\)C. By storing the pigment at medium to high temperatures (40 \({}^{\circ}\)C), stability is strongly affected by temperature rather than exposure to UV light.
## 2 Materials and Methods
### Irradiance Reflectance Model for Translucent Substances  
There is one more common case we have to concern ourselves with: translucent organic materials growing on a mineral substrate. In order to model this situation we use the irradiance reflection model for finite bottom depth of Albert and Mobley [1]. The irradiance reflectance of the underlying material is used as a bottom irradiance reflectance \(R_{b}\) in this case. The irradiance reflectance of the combination of translucent overlay of reflectance \(R_{\infty}\) and thickness \(z_{b}\) with a substrate of reflectance \(R_{b}\) is modeled by the following equations.  
\[R_{t}=R_{\infty}\Big{[}1-A_{1}e^{-\gamma(\lambda)z_{b}}\Big{]}+R_{b}A_{2}e^{- \delta(\lambda)z_{b}}\;, \tag{20}\]  
with:  
\[\delta(\lambda)=\big{[}\kappa_{0}+(1+x(\lambda))^{\kappa_{1w}}(1+\kappa_{2w}) \big{]}\bigg{(}\frac{b_{b}(\lambda)}{x(\lambda)}\bigg{)}\;, \tag{21}\]  
\[\gamma(\lambda)=\big{[}\kappa_{0}+(1+x(\lambda))^{\kappa_{1b}}(1+\kappa_{2b}) \big{]}\bigg{(}\frac{b_{b}(\lambda)}{x(\lambda)}\bigg{)}\;, \tag{22}\]  
Table 3 gives the coefficients of Equations (20)-(22).  
The new parameter that controls the behavior of the combined solution is \(u_{b}=b_{b}(\lambda)z_{b}\). Assuming the overlaying vegetation completely covers the substrate then, as the thickness of the overlay \(z_{b}\) increases, the combined reflectance \(R_{t}\) goes to the reflectance of the overlay \(R_{\infty}\) while when \(z_{b}\) becomes small the combined reflectance approaches \(R_{b}\) as expected. What the model above shows is that the usual approach of linearly combining the separate reflectance signatures of the mineral substrate and the organic cover according to the weights of their relative areas only works in the limit where the cover is thick enough. For thin organic covers there is an exponential transfer of signature from substrate to cover which is a strong function of wavelength through the backscattering albedo of the translucent overlay \(x(\lambda)\).
# Atenea Exams, an IMS LTI Application to Solve Scalability Problems: A Study Case
## 3 Solution and Results
### A solution to Cope with Scalability Issues in Atenea
#### 3.2.3 Virtualization and Private Cloud  
From 2005, the Data Processing Center (CPD) of the UPC began to incorporate virtualization systems. By 2009, the entire Atenea service was moved to virtualized servers in the UPC Private Cloud, in virtualization clusters. A virtualization Cluster is a group of hosts. When a host is added to a cluster, the host's resources become part of the cluster's resources. The cluster manages the resources of all hosts within it. Clusters enable high availability and distributed resource scheduling [27; 28].  
This move to a virtualized system does not mean a change of architecture. The scheme of 2 load balancers, 6 front-ends, fiber optic connected disk cabinet and a server for the database engine is still maintained. However, virtualization allows certain advantages over using physical servers:  
* Better server recovery capability in case of crash. The virtualization cluster manages the recovery of virtual servers in the event of a crash on the physical machine where the virtual machine is running.
* Possibility of implementing high availability strategies, forcing the presence of redundant services in different clusters. For example, not putting all front-ends or load balancers in the same cluster.
* Possibility to dynamically distribute physical resources (processors, memory, etc.) to one service or another depending on the workload. In this way, load peaks can be addressed, and resources redirected to other services when the system has less load. It turns out that the workload at Atenea varies greatly depending on the calendar (enrollment periods, course, homework assignments, exams, holidays), day of the week, time, or even time of day: when there is an exam all students start the quiz at the same time.