esa-sceva/satcom-chunk-collection huggingface dataset:
# Unveiling the Role of Minerals and Trace Elements of Thermal Waters in Skin Health
## 2 Minerals, Trace Elements, and Other Compounds of TSW in Skin Health
### Cations
#### 2.2.2 Sodium  
Sodium is an essential nutrient that the body needs in relatively small amounts (as long as there is no substantial sweating) to keep body fluids in balance and muscles and nerves healthy. In addition to the classic two compartments (intracellular and extracellular), new investigations have shown that sodium can be stored without commensurate water retention in the interstitium and endothelial surface layer, forming a dynamic third compartment for sodium. Sodium storage in the skin and endothelial surface layer may function as a buffer during periods of dietary depletion and excess, representing an extra-renal mechanism for regulating sodium and water in the body [79].  
Most sodium-rich thermal waters are chloride waters, but sodium sulfate waters can be also found; chloride and sulfate TSWs were already discussed in Sections 2.1.1 and 2.1.2.  
[PERSON] and [PERSON] [42] investigated the wound-healing effects of Yuda hot spring water in female albino guinea pigs. Yuda hot spring water is classified as a thermal alkaline spring water; the main cations are sodium ions, the main anions are chloride ions, and sulfur is also present. Animals were bathed in Yuda TSW, and the control group was bathed in tap water. Differences in the concentrations of several amino acids associated with wound healing were found between the two groups, and image analysis showed that wounds made on the abdominal skin of guinea pigs were significantly contracted by hot spring bathing compared to bathing with tap water, and their histopathological findings demonstrated that wound healing was accelerated.  
A single-center, evaluator-blinded, split-face, randomized study investigating the effects of sodium-rich thermal spring water in improving the efficacy and tolerability of standard acne therapy showed an improvement in skin hydration and a feeling of skin supplements; QoL also improved [43].
# Experimental Characterization of the Engineering Properties of Landfill Compost-Biocover
## 3 Results and Discussions
### Moisture and Density Relationships
From Figure 8, it can be observed that as the water content increases up to the optimal water content, the FAS curve sharply decreases, which shows the reduction of voids in the compost materials. Then, the FAS continues to decrease slowly  
Figure 8: Relationship between free air space vs. compaction curve of compost material. Figure 9: Free air space, relative diffusion coefficient, and degree of saturation relationships of compost material. with increased water content until it attains a value close to zero. This means that most voids are filled with water. The compost does not allow sufficient gas flux to pass through the sample at a certain water content level, affecting the microbial oxidation phenomenon due to an insufficient supply of O\({}_{2}\). Now in Figure 9, the relative diffusion coefficient is strongly correlated to the free air space and degree of saturation, which shows a gradual decrease with a decrease in free air space and with an increase in the degree of saturation, similar to the findings of [PERSON] et al. [24], until it attains a value of 0.062 at 85% degree of saturation. Furthermore, the test data of the studied material show a high relative diffusion coefficient of 0.20 to that of 0.17 for soil, 0.10 for soil:compost = 30:70, and 0.10 for soil:compost = 70:30 [24], at a particular free air space of 0.31. Generally, the relative diffusion coefficient varies from 0.204 to 0.12 for changes in free air space from 31 to 20%, corresponding to water content from 38 to 65% and degree of saturation between 46% and 71%. The 20% to 35% FAS values in compost established the maximum O\({}_{2}\) consumption rate, i.e., 95% according to previous field and experimental studies [9; 22]. The test data presented in Figures 6-9 highlight that the studied compost material with water content of 38% to 71% shows the highest consumption rate and thereby highest CH\({}_{4}\) oxidation potential; thus, it can effectively be used as landfill cover materials in compacted conditions for methane oxidation.
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 4 Challenges of PV-DT
### Standardization  
One of the current challenges of an evolving DT development is the issue of standardization. Standardization aids the interoperability and connectivity of distinct items. PV-DT is a relatively new research area, as such it still has a long way to go in terms of standardization. The authors of [10] provided some insight into how the models applied in digitalizing solar modules could provide different results under low-light conditions. The work compared models in commercial platforms and open-source simulation models. Furthermore, due to the varying levels of approximation applied in modeling the physical counterpart, it is currently difficult to establish the accuracy of items from different digital twins, hence the need for standardization [27; 134].
# Overview and Perspectives for Vehicle-Integrated Photovoltaics
## 2 Results and Discussions
### Examination and Outlook of PV Technologies for PV-Powered Vehicles
#### 2.2.3. Curved Photovoltaic Technologies  
Glass-backsheet or glass-glass arched photovoltaic modules have a similar structure to standard PV panels with a thick front glass cover of 2.0 to 3.2 mm. This architecture utilizes 3D curved glass and, as listed in Table 2, commercial offers are available [74; 77; 78; 79; 80; 81; 82].
## 6 Conclusions  
A predictive model of concrete strength using measurements of the P-, S-, and R-wave velocities was proposed as a non-destructive method using support vector machine (SVM) and artificial neural network (ANN) models. The strength prediction models were developed using the correlation between the ultrasonic velocities and core strengths using six slabs in a strength range of 24-60 MPa. The SVM and ANN models predicted the strengths with much higher accuracies than the traditional linear regression, and the following conclusions were drawn.  
* In the prediction of concrete strength, the predictive models that used three types of ultrasonic velocities were more accurate than the models that used only one or two velocities, and they converged more quickly with smaller errors.
* The SVM model was able to obtain more accurate predictions than the ANN because of the less generalizable performances and over-fitting issues of ANNs.
* A slight change in the ultrasonic velocity was observed at around 50 MPa, and individual models are required for normal- and high-strength concretes to obtain higher accuracy.
* For strengths less than 30 MPa, the differences and dispersion of the ultrasonic velocities and core strengths were large, and various factors, such as the type, size, and distribution of the aggregate and cement as well as the water to cement ratio, should be considered to obtain more accurate predictions.  
[PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON] contributed device/analysis tools; [PERSON] and [PERSON] wrote the paper.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No.2018R1D1A1A02085377). This work was supported by Research Assistance Program (2019) in the Incheon National University.  
The authors declare no conflict of interest.
# Hierarchical Concept Learning by Fuzzy Semantic Cells
## 3 Method
### Optimization of Fuzzy Semantic Cells
Because there are too many fuzzy semantic cells at this time, the computational complexity is high. Therefore, according to the premise of high purity, the FSCs in the same category, the better. As a result, we should include the constraint that \(M\) be as small as possible while \(L_{i}\) be as pure as possible. Finally, the learning problem of the hierarchical structure becomes an optimization problem of the objective function. Due to the difficulty in calculating the closed-form solution of this optimization problem, we use the iterative method instead. To obtain the optimal fuzzy semantic cell, we apply to Adam method [17], which is an efficient method to solve the unconstrained nonlinear optimization problems to minimize the objective function Equation (4). Existing works also use gradient descent [24], evolutionary optimization [26] or particle swarm optimization [27] to optimize the models. We believe numerical methods are more stable in optimization and Adam can avoid saddle points compared with gradient descent. The procedure of the hierarchical concept learning with fuzzy semantic cells is summarized in Algorithm 1. ```
0: Training set \(DB=\{(x_{1},y_{1}),\ldots,(x_{n},y_{n})\}\) of \(K\) categories with concept information where \(y\in\{C_{1},\ldots,C_{K}\}\)
0: Fuzzy semantic cell \(L=\langle P,d,\delta\rangle\)
1:function\(ConceptLearning(DB,M)\)
2:\(DB\): The dataset with concept information \(M\): The numbers of fuzzy semantic cells
3: Begin
4: Partition:\(D_{j}=\{(x,y)\mid y=C_{j}\}\quad j=1,2,...,K\)
5: Initialize:\(P_{i},\sigma_{i}\quad i=1,2,...,M\)
6:for each data \(\mathrm{x}\in DB\)do
7: Compute:\(d(x,P_{i})=\|x-P_{i}\|_{2}^{2}\)
8: Compute:\(\mu_{L_{i}}(\mathrm{x})=\exp(-\frac{d(x,p_{i})}{2\sigma_{i}^{2}})\)
9:endfor
10: Compute: minimize the following by [PERSON]: \[\min O_{(P,\sigma)}=-\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{K}\frac{\sum \limits_{x\in D_{j}}\mu_{L_{i}}(x)}{\sum\limits_{k=1}^{M}\sum\limits_{x\in DB }\mu_{L_{k}}(x)}\ln\frac{\sum\limits_{x\in D_{j}}\mu_{L_{i}}(x)}{\sum\limits_{ x\in DB}\mu_{L_{i}}(x)}\]
11:return\(O(P,\sigma)\)
12:endfunction
```  
**Algorithm 1** Hierarchical Concept Learning by Fuzzy Semantic Cells  
The main runtime in our training is the computation of Equation (4), namely the conditional entropy. The computation complexity of Equation (4) is \(\mathcal{O}(nM)\). This scales linearly with \(n\), the number of samples. Empirically the computation only takes less than a second on a CPU of Intel Xeon 4116 in Ubuntu 16.04. Prototypes and \(\sigma\) are the parameters of our fuzzy semantic cells to optimize, and the only hyper-parameter having an influence is the number of prototypes. We will discuss it in Section 4.3. In summary, we describe the learning method to build the hierarchical structure from the data to the concepts. Based on this structure, given a new sample \(x\), we also introduce the decision-making approach to infer its abstract concept.
## 3 The Proposed Chaotic Stream Cipher Based on Compound Coupled Map Lattices
### The Improved Coupled Chaotic Map
#### 3.2.1 The Improved CML Based on Logistic Map  
In this subsection, we present the scheme of the improved four stage CML based on the logistic map. First, we add a counter to each stage. Based on the modified CML function, Figure 7 shows the structure of the improved four stage CML. _Key_ is the input, and the final throughput is _Output_. The input key, initial values and the parameters of chaotic map are defined as the same as Section 3.1. In this figure, the modified function \(L(x_{t+1}^{n})\) is defined as Equation (7).  
\[L(x_{t+1}^{n})=\left\{(1-\varepsilon)f(x_{t}^{n})+\frac{\varepsilon}{2}[f(x_{ t}^{n+1})+f(x_{t}^{n-1})]\right\}*i\bmod 1,1\leq i\leq Q \tag{7}\]  
where \(n=1,2,\ldots,N,N=4\), and \(L(x)\) are the improved chaotic CML based on the logistic map. The \"mod 1\" means the fractional part of \(L(x_{t+1}^{n})\). The \(f(x)\) is the chaotic logistic map, \(i\) is a counter and \(Q=512\) is a fixed integer. Our program environment is Matlab2017, installed on the Windows 10 operation system, with Intel Core i5 CPU 2.80 GHz. Figure 8 is the simulation algorithm for the improvement, where the \(f(\ )\) is the logistic map. The initial value of the counter \(i=Key\bmod Q\). In this simulation, the number of rounds \(r\) is 2000. Each iteration has a real output \(x_{t}^{4}\).  
Figure 6: The simulation result of the original four stage CML based on the tent map.  
Figure 7: The structures of the improved four stage CML based on the logistic map.  
Figure 9 is the simulation results of the improved CML based on the logistic map, where \(p(x_{t})\) is the probability density, \(t\) is the time index and \(x_{t}\) is the output. In these figures, we can find that the probability density of the original CML is improved.  
To improve the security, we discard both the first 64 outputs. Because the keystream includes the binary sequences, the CML output is the real number. It needs the output transformation. The output transformation is defined by Equation (8).  
\[\text{keystream}=Output*2^{32} \tag{8}\]  
Here, the keystream is a 32-bit binary sequence per transformation.
## 3 Results and Discussion
Figure 5: E\({}_{\rm g}\) vs. theoretical efficiencies of the MEGSC with fixed \({}_{\rm NR}\) under full-light concentration (C = 46,200) For this simulation, we used IQY and NQY (E\({}_{\rm th}\) = 2\(\cdot\)E\({}_{\rm g}\), 3\(\cdot\)E\({}_{\rm g}\), 4\(\cdot\)E\({}_{\rm g}\)). f\({}_{\rm NR}\) is 1, 10\({}^{-1}\), 10\({}^{-3}\), and 10\({}^{-5}\)). Table 1 presents a comparison of the experimental results (PbSe [27; 28] and CdTe [29]) and MEGSC DB approaches considering the effect of nonradiative recombination. For this simulation, we used the ratio of \(\mathrm{f_{NR}}\) up to \(10^{-12}\) to find an appropriate range of \(\mathrm{f_{NR}}\) to compare with the low experimental results of an MEGSC. These results indicate low efficiencies of the colloidal QDs of an MEGSC due to the high nonradiative recombination. As shown in Figure 3 and Table S1, and in agreement with a previous study that used ERE approaches in GaAs QD solar cells [38], we detected similarities between the MEGSC DB and the experimental results. If the GaAs QD bandgap is 1.4-1.5 eV, the efficiency range at \(\mathrm{f_{NR}}=10^{-4}\) and \(10^{-5}\) is 23%-25%. These values show similar efficiency ranges at \(10^{-4}\)-\(10^{-5}\) of ERE, even if the GaAs QD solar cell does not consider the MEG effects [38]. A small ratio of ERE has a significant impact on \(\mathrm{V_{oc}}\) owing to the increased nonradiative recombination rate. For instance, if the ERE is in the order of \(10^{-8}\), the overall voltage drop in \(\mathrm{V_{OC}}\) is 0.5-0.6 V. Thus, its corresponding efficiency also decreases [38]. Other studies have also explained the voltage drop of QDSCs due to nonradiative recombination [38; 39; 40].
## 6 Influence Analysis of Construction Parameters and Field Verification
### Engineering Application Analysis  
To verify the rationality of the refined numerical simulation method, the measured data of three monitoring sections were adopted for comparative analysis with the numerical simulation results. The monitoring segments were located on the surface above the segments, and the calculation parameters considered the average value of the parameters measured in the 10-segment distance range near the monitoring segments. Chamber pressure, synchronous grouting quantity, and jack pressure could be directly monitored in the process. Based on other scholars' achievement [29], the friction between shield skin and soil was adopted as 40% of the jack pressure. The measured and simulated values of TY-56 are shown in Figure 28. The simulated value of the chamber pressure was detected as 176 kPa (\(P_{s}^{*}=0.91\)); the simulated value of the friction between the shield skin was measured as 4858 kN (\(F_{g}^{*}=0.33\)); and the simulated value of the synchronous grouting quantity was 6.07 m\({}^{3}\) (\(V_{g}^{*}=0.33\)). The measured and simulated surface settlements of TY-56 are presented in Figure 29. The measured values were found to be distributed near the simulated curves, indicating that the simulation results were essentially consistent with the measured results. Figure 30 depicts the measured and simulated values of segment ovality in monitoring section TY-56. According to the findings, good agreement between the simulated and measured values was revealed. Table 4 shows the measured and simulated maximum surface settlement and segment ovality for various monitoring sections. Table 4 shows that the minimum error between the measured values and the simulated values was 4% and the maximum error was 20%. The simulated values were in good agreement with the measured values, which indicated the suitability of the established numerical simulation method for the deformation character analysis caused by shield tunneling in water-rich soft stratum. In addition, the model was capable of calculation support for similar projects.  
Figure 28: Excavation parameters at TY-56.
# Reflective Inverse Diffusion
## 1 Introduction  
All materials scatter light, either in a transmissive manner or reflectively, to some degree. The ground work for imaging with scattered light was put forth by [PERSON] in which the random scatter was treated as a complex field that interfered with incident light to produce speckle patterns. It was theorized that with properly-modified incident light, the scattering object could be used to simulate different optical devices, such as a lens or mirror [1]. There was no distinction between scattering by transmission or reflection.  
Motivated by the biomedical community's search for methods of imaging through biological tissues, phase modulation techniques have demonstrated the ability to shape a wavefront causing light to refocus to a single point after transmission through a scattering medium [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. Just as [PERSON] predicted a decade earlier, properly-modified incident light allowed the scattering medium to behave as a lens [1]. This process, called \"inverse diffusion\" by the authors, is also capable of simulating a phase grating that allows control of the focus spot location in the observation plane by adjusting the wavefront shape.  
Reconstructing images from reflected light would provide the ability to see around corners and is a growing interest in the remote sensing and intelligence communities. Other methods for imaging with reflectively-scattered light require an illumination source in the occluded scene [14] or require an illumination source with a line of sight to the occluded scene [15]. This presents an application problem as access to the occluded scene may not be available. Reflective inverse diffusion provides a method for illuminating occluded scenes without direct access or line of sight. By refocusing light after reflection a simulated light source can be achieved from almost any scattering surface, satisfying the line of sight requirement of existing imaging techniques, such as dual photography [15].  
We demonstrate reflective inverse diffusion using feedback-based wavefront shaping methods adapted from the original transmissive inverse diffusion research. Our method was successful at refocusing light after being scattered by diffusely-reflecting surfaces. It was tested on six different surface types and produced various levels of enhancement (as defined previously [9]). Phase modulation also provided a method of non-mechanical beam steering; the ability to control the location of the refocused spot in the observation plane using reflective inverse diffusion was also demonstrated.
## 4 Analysis, Results and Discussion
### Descriptive Analysis
Based on the trend of the red line in Figure 10, it can be assumed that this cluster corresponds to a grid containing nightclub-type establishments, as evidenced by the peak that can be observed in the early hours of Saturday. It is notable that from 1 a.m. on Saturday the trend is toward an increase of mobile phones in Cais da Viscondessa, prolonging until around Saturday at 3 a.m. From then, the tendency is to decrease until 9 a.m. on Saturday, during the period that tourists are leaving. Figure 8: Time series for Cluster 79. Figure 7: Grids corresponding to Cluster 79, the Cais do Sodre area. It is worth mentioning that the grids identified as nightlife areas have some flaws. One clear example of an area incorrectly recognized as a nightlife area is the Alfama area, with its corresponding grids in Figure 11.  
As can be seen by the red trend line in Figure 12, there is a notable decrease in mobile phones around dinner time, which lasts the whole night and returns around Saturday at 9 a.m. This shows that this area does not offer much nightlife activity, despite containing many of the establishments present in the list provided by the City Council, which means that some of the entries in the list prevent the results from being even better. These may be restaurants that are open until late (thus being identified as nightlife establishments), as Alfama is a very touristic area. It is impossible to determine which establishments from the list limit the process, as each entry is composed solely by its latitude and longitude. A  
Figure 10: Time series for Cluster 70. Figure 9: Grids corresponding to Cluster 70, the Cais da Viscondessa area. viable solution to this problem would be for the Lisbon City Council to provide a dataset with more information on each establishment.
# A LoRaWAN Architecture for Communications in Areas without Coverage: Design and Pilot Trials
## 5 Results
### Data Rate Analysis  
In the data rate analysis, all possible combinations depending on the \(SF\), \(CR\) and bandwidth were taken. In Figure 10, the graphs that we obtained are shown. In the first duplex, \(SF\) was fixed, and the behavior was evaluated depending on the rest of the parameters. In the second duplex, the bandwidth value was fixed at 125 KHz, and finally, the \(CR\) value was fixed at 4/5, since it was the one that was providing the best data bit rates.  
This first simulated analysis of the data rate gave us an insight into the capacities of the system depending on the LoRa modulation configuration and the opportunity to know all the alternatives, since other works [24; 45] only showed this theoretical analysis of the data rate in a partial way. The analysis of Figure 10 shows that when the \(SF\) was higher, then the bit rate was lower. If the bandwidth increased and the coding rate was high (4/5), the bit rate transmitted also increased. On the other hand, the coverage range theoretically behaved in the opposite way to the data rate (i.e., the lower the \(SF\) and the \(CR\), the more robust the transmitted signal would be to noise and interference, so the coverage range should have been higher). It is important to reach a trade-off between the data rate and the desired coverage range.  
Table 3 shows a summary of the data results. The results show that the above-mentioned deductions were verified.  
Figure 9: (a) Test areas used in the TeamUp system data captures. (b) Coverage range in urban area (red), rural area (blue) and natural area (green).  
Figure 10: Simulated data transmission rate measurements. (**a**) Results when setting \(SF\) to 7 and changing \(CR\) and \(BW\). (**b**) Results when setting \(BW\) to 125 KHz and changing \(CR\) and \(SF\). (**c**) Results when setting \(CR\) to 4/5 and changing \(BW\) and \(SF\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spreading Factor** & **Bandwidth (KHz)** & **Coding Rate** & **Data Rate (Kbps)** \\ \hline
7 & 125 KHz & 4/5 & 5.4688 \\
7 & 125 KHz & 4/8 & 3.4180 \\
7 & 250 KHz & 4/5 & 10.9375 \\
7 & 250 KHz & 4/8 & 6.8359 \\
12 & 125 KHz & 4/5 & 0.2930 \\
12 & 125 KHz & 4/8 & 0.1831 \\
12 & 250 KHz & 4/5 & 0.5859 \\
12 & 250 KHz & 4/8 & 0.3662 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A summary comparison of data rate as a function of \(SF\), bandwidth and \(CR\).