esa-sceva/satcom-chunk-collection huggingface dataset:
# The BCL Method for DSSC: Basis and Applications +
## 5 The \(\mathbf{I_{T}}\) Parameter: A Measure of the Electronic Density on the Anchor Group
### Theoretical Background
#### 6.1.4 Effectiveness of Regeneration (F3)  
The third parameter is the F3 index, which is designed to measure the effectiveness of the regeneration of the oxidized dye by the redox mediator couple (\(R^{-}/\text{R}^{0}\)) added to the solvent. The regeneration process is represented as an equilibrium between two competing species:  
\[\left[\text{D}^{+}\right]-\text{Ti}(\text{OH})_{3}+R^{-}\rightleftharpoons\ \left[\text{D} \right]-\text{Ti}(\text{OH})_{3}+\text{R}^{0} \tag{34}\]  
For an efficient regeneration process, it is necessary that Equation (34) is displaced to the right, \(\text{K}_{\text{eq}}>1\). Considering the Nernst equation, an exponential relationship existing between the equilibrium constant and the free energy change is observed. This can be related to the difference between standard redox potentials, \(\Delta E=\varepsilon_{ox}^{0}(R^{-})-\varepsilon_{red}^{0}\left(\text{D}^{+}\right)\). In this way, F3 is a measure of the amount of displacement to the right of Equation (34), and is expressed as  
\[\text{F3}=\ \alpha\text{e}^{-\Delta\varepsilon} \tag{35}\]  
where \(\alpha\) is a proportionality constant and \(\Delta\varepsilon\) as the difference between the ionization potential of \(R^{-}\) and the electron affinity of \(\text{Ru}^{-3}\) oxidized dye, which is roughly the negative of the ionization potential of \(\text{Ru}^{+2}\).  
\[\Delta\epsilon=\varepsilon_{HOMO}\big{(}R^{-}\big{)}-\varepsilon_{HOMO}\Big{[} \left(Ru^{+2}\right)-\text{Ti}(\text{OH})_{3}\Big{]} \tag{36}\]As \(\Delta\varepsilon\) increases, the equilibrium constant also increases, and Equation (34) is displaced to the right favoring the regeneration of the dye and the beginning of a new photo catalytic cycle. Figure 26 compares the ionization potential of Iodide, di-Iodide and tri-Iodide species, commonly employed as regenerators for DSSC, with the calculated electron affinity for the set of ruthenium dyes.  
The results show that iodide and di iodide present a low value of the oxidation potential, which allows us to reduce, and consequently regenerate, the entire series of complexes from C1 to C6. On the other hand, C3, the cyclometalated complex, has the higher electro affinity and, therefore, is the most difficult to reduce, while C1 would be the easiest. This last complex has the particularity that it can also be reduced by the triiodide when it is present in the electrolyte.
# A Routing Optimization Method for LEO Satellite Networks with Stochastic Link Failure
## 4 Simulation Experiment and Analysis
### Optimization Results  
This section compares the experimental results of GA and GA-AS. The experiment randomly generates several tasks and environments, including satellites, user terminals and ground stations. The parameter settings are as follows, where mutation probability \(\eta_{m}=0.05\), genetic probability \(\eta_{g}\)=0.5, crossover probability \(\eta_{c}=0.5\), weight \(\mathbf{w}=[0.8,0.1,0.1]^{\top}\), transmission power \(P_{T}=10\) mW. Other parameter settings are consistent with Section 4.1.  
Table 2 lists the optimization results of GA-AS with different task sizes under the condition of population size \(NP=700\). The final value refers to the final result obtained by the GA-AS. It can be seen from Table 2 that when the number of tasks \(NS_{c}\) is set to 41, the average fitness value of the initial population is roughly 81% of the final value. As the task size grows, the distribution range and mean value decrease. When \(NS_{c}=98\), the fitness value of the initial population is distributed in 61%\(\sim\)88% of the final value, and the mean value is around 78% of it, indicating that the new initial-solution generation strategy can produce a good initial population.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Transmit Power (mW)** & **Transmission Distance (km)** & **Probability of Failure** \\ \hline \multirow{4}{*}{10} & 500 & \(2.85\times 10^{-6}\) \\  & 1000 & \(4.56\times 10^{-5}\) \\  & 5000 & 0.03 \\  & 10,000 & 0.45 \\ \hline \multirow{4}{*}{30} & 500 & \(7.13\times 10^{-7}\) \\  & 1000 & \(1.14\times 10^{-5}\) \\ \cline{1-1}  & 5000 & 0.01 \\ \cline{1-1}  & 10,000 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Failure probability with different transmission distances and power.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Mean Value** & **Max Value** & **Min Value** & **Final Value** & **Variance** \\ \hline \(NS_{c}=41\) & \multirow{2}{*}{\(1.93\times 10^{3}\)} & \multirow{2}{*}{\(2.12\times 10^{3}\)} & \multirow{2}{*}{\(1.72\times 10^{3}\)} & \multirow{2}{*}{\(2.38\times 10^{3}\)} & \multirow{2}{*}{\(3.58\times 10^{3}\)} \\ \(NA=50\) & & & & & \\ \hline \(NS_{c}=98\) & \multirow{2}{*}{\(4.24\times 10^{3}\)} & \multirow{2}{*}{\(4.74\times 10^{3}\)} & \multirow{2}{*}{\(3.94\times 10^{3}\)} & \multirow{2}{*}{\(5.78\times 10^{3}\)} & \multirow{2}{*}{\(1.24\times 10^{4}\)} \\ \(NA=100\) & & & & & \\ \hline \(NS_{c}=300\) & \multirow{2}{*}{\(1.30\times 10^{4}\)} & \multirow{2}{*}{\(1.48\times 10^{4}\)} & \multirow{2}{*}{\(1.04\times 10^{4}\)} & \multirow{2}{*}{\(1.68\times 10^{4}\)} & \multirow{2}{*}{\(2.28\times 10^{6}\)} \\ \(NA=150\) & & & & & \\ \hline \end{tabular}
\end{table}
Table 2: Initial population distribution with different scales.  
Figure 4: Trends of average fitness values of GA-AS and GA with different population sizes.  
Figure 5: Task execution probability of GA-AS and GA with different population sizes.
# A Cloud Microservices Architecture for Data Integrity Verifiability Based on Blockchain
###### Abstract
On the other hand, following the new technological demands, in terms of software development, we identified that the microservice approach offers an evolutionary step concerning monolithic ones. Thus, we provided an architecture following the microservice approach. However, a microservice system design remains challenging since it can be independently deployed, has lower coupling, and is self-contained. For cloud applications development, microservice allows more freedom to evolve and requires a mechanism to inform others about what has changed or to track past events in the system. With the highly decentralized development and design of microservices, it becomes difficult to maintain a centralized architectural design reference [18]. As a result, the system might be vulnerable to data integrity attacks. Hence, this paper aims to provide a decentralized architecture with two general services. Firstly, a data integrity mechanism in which all events (records) triggered by a cloud system are stored within a blockchain in a hashed form. Secondly, a verifiability process that checks the data integrity. The mechanism was built through a microservice architecture to be integrated with any system as a service. We implemented our architecture, and our experiments show that it can be successfully suited within other cloud systems. Our main contributions are listed as follows:  
* A mechanism for auditing issues storing logs from the initialization phase of a system until the execution of user transactions guaranteed by blockchain strengths concerning integrity. * A consulting mechanism that provides certainty about the integrity of the logs stored in the blockchain. * Traceability of each log transaction carried out by the corresponding users of a cloud system in distributed environments. * An auditing mechanism where cloud systems (following the microservice architecture) can plug it. Our proposal falls in a design science research in the information systems area [19]; specifically, in the software engineering topic. The methodology of our work follows that proposed in [20], commonly applied in engineering. Such a methodology comprehends the following steps: (a) Motivation and problem identification; (b) Objectives; (c) Design; (d) Development; and (e) Demonstration and validation. Following the methodology described previously, we structured the paper as follows: (Step a) is deepened in Section 2, which also grounds some technologies used throughout the document and some related works; Section 3 raises the objectives (Step b) and describes our general architecture (Step c) and main components; Sections 4 and 5 detail the design of our architecture (continuing Step c); Section 6 explains our prototype (Step d), together with a case study shown in Section 7; some demonstrations and validations (Step e) are provided in Section 8. Finally, we outline our discussion and conclusions in Section 9.
# Orientation of Liquid Crystalline Molecules on PDMS Surfaces and within PDMS Microfluidic Systems
## 2 Materials and Methods
### Optical Measurements and Observations  
A digital microscope (KEYENCE VHX 5000) with a universal zoom lens VH-Z20 (20-200\(\times\)) and a long-working-distance, high-performance zoom lens VH-Z50 (50-500\(\times\)) was used for the observations, dimensional measurements, and the capturing of high-resolution images (1600 \(\times\) 1200 pixels in the standard and 4800 \(\times\) 3600 pixels in the stitching mode). The LED transmitted lighting could be used in conjunction with reflected illumination from the lens. The light from each source can be adjusted independently, making it possible to perform observations with an optimum balance of light intensity. The polarization observation setup included in the system allowed for standard polarizing microscope (POM) measurements with white light transmitted through the sample placed between crossed polarizers (see Figure 2m). In addition, a polarization optical microscope (Zeiss Jenalab POL) with a Bertrand lens (introduced between the analyzer and the ocular) was used to obtain the conoscopic images in the PDMS:PDMS cells. The conoscopic technique helps to identify the exact nature of the surface anchoring in the LC samples.  
Figure 1: (**a–d**) Schematic illustration of the main steps of the PDMS microchannel fabrication based on the cast-molding technique. (**a**) Master mold with the desired surface pattern. (**b**) PDMS precursor poured into the mold. (**c**) Thermally cured PDMS peeled from the mold, with the vertical insets and outlets which were drilled. (**d**) Pattern PDMS attached to the substrate. Microchannels ready to be infiltrated with LC. (**e1–e3**) Exemplary samples analyzed in this paper.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 4 Heuristic 5G Core Network Slice Provisioning Algorithm Design
### Node Ranking Based on Network Resource Attributes and Topology Attributes
#### 4.1.1 Local Resource Attributes  
The local resource metric of a node is obtained by multiplying the CPU capacity of the node by the sum of bandwidths of all its adjacent links.  
\[LR(v_{i})=c(v_{i})\sum_{e\in E(v_{i})}b(e), \tag{12}\]  
where \(E(v_{i})\) is the set of all the adjacent links of \(v_{i}\). The reason we define this metric is that the larger \(LR(v_{i})\) is, the more slice nodes can be hosted by the physical node.
# Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data +
## Appendix B Peak-Sidelobe Ratio  
The image quality of a reconstructed SAR scene \(h\) can be evaluated in terms peak-sidelobe ratio [32]. The peak-sidelobe ratio (PSLR) is a dimension that characterizes the ability to measure weak reflective targets in the presence of strong reflective targets in their neighborhood. PSLR can mathematically be expressed in \(\,\mathrm{dB}\) as  
\[\mathrm{PSLR}|_{\mathrm{dB}}=10\log_{10}|I_{\mathrm{SL}}|-10\log_{10}|I_{ \mathrm{ML}}|, \tag{15}\]  
where \(I_{\mathrm{SL}}\) and \(I_{\mathrm{ML}}\) denote the peak-intensity value in the sidelobe and in the main-lobe area, respectively.
## 2 Preliminaries and Problem Formulation  
### RBFNN Approximation  
Consider an unknown smooth nonlinear function \(f(\mathbf{x}):\mathbb{R}^{m}\rightarrow\mathbb{R}\) can be approximated on a compact set \(\mathbf{\Omega}\in\mathbb{R}^{m}\) by the following RBFNN:  
\[f(\mathbf{x})={\mathbf{W}^{*}}^{T}\boldsymbol{\varphi}(\mathbf{x})+\varepsilon \tag{1}\]  
where \(\mathbf{x}\in\mathbf{\Omega}\) is the input vector, \(\varepsilon\) is the approximation error and satisfies \(|\varepsilon|\leqslant\bar{\varepsilon}\) and \(\bar{\varepsilon}\) is a constant, and the node number of the NN is \(l>1\). \({\mathbf{W}^{*}}\in\mathbb{R}^{l}\) represents the optimal weight vector, which is defined by  
\[{\mathbf{W}^{*}}=\arg\min_{\hat{\mathbf{W}}\in R^{l}}\left\{\sup_{\mathbf{x} \in\mathbf{\Omega}}\left|f(\mathbf{x})-\hat{\mathbf{W}}^{T}\boldsymbol{ \varphi}(\mathbf{x})\right|\right\} \tag{2}\]  
where \(\hat{\mathbf{W}}\) is the estimation of \({\mathbf{W}^{*}}\). \(\boldsymbol{\varphi}(\mathbf{x})=[\varphi_{1},\varphi_{2},\ldots,\varphi_{l}] ^{T}:\mathbf{\Omega}\rightarrow\mathbb{R}^{l}\) represents the radial function vector, the element of which is chosen as the Gaussian function:  
\[\varphi_{i}(\mathbf{x})=\exp\left(-\frac{(x-b_{i})^{T}\left(x-b_{i}\right)}{c _{i}{}^{2}}\right)(i=1,2,\ldots,l) \tag{3}\]  
where \(\mathbf{b}=[b_{1},b_{2},\ldots,b_{l}]^{T}\) and \(\mathbf{c}=[c_{1},c_{2},\ldots,c_{l}]^{T}\) are the centers of receptive field and spread of the Gaussian function, respectively.
## 5 The Elevation Difference Method
In Poland, the National Geodetic and Cartog  
Figure 5: A fragment of a master map representing the northern part of the area under study for the state of 1978. There are topographical pickets, boundary of old drill mining (dashed red line) and localizations of old sinkholes (red continuous line). data for a Digital Terrain Model (DTM) based on aerial images, airborne laser scanning and topographic maps. All elevation data are created in the PUWG1992 coordinate system, and the heights of points relate to the Normal Height \"Kronszdatd 86\". Data for the area of Poland are available as part of the geoportal service: [[https://mapy.geoportal.gov.pl/imap/Imgp_2.html](https://mapy.geoportal.gov.pl/imap/Imgp_2.html)]([https://mapy.geoportal.gov.pl/imap/Imgp_2.html](https://mapy.geoportal.gov.pl/imap/Imgp_2.html)) (accessed on 28 March 2023). There, you can view DTM using various services. Due to its high accuracy, this measurement technique has been widely used in displacement measurements for various purposes [34; 35; 36]. Generally, there are two basic types of DTM, which are based on different geometric structures, i.e., GRID--a model in the form of a regular grid of squares, and TIN--a model in the form of an irregular grid of triangles. In Poland, the basic numerical terrain model is a model in a 1 m \(\times\) 1 m grid, which is systematically updated on the basis of Airborne Laser Scanning (ALS), but also--for urban areas--on the basis of stereoscopic measurements as part of the production of an orthophotomap with pixels of 10 cm or smaller. DTM is also developed in a 5 m \(\times\) 5 m grid based on stereoscopic measurements in the production of an orthophotomap with a pixel of 25 cm. This gives 1,000,000 points for 1 km\({}^{2}\) of DTM in a 1 \(\times\) 1 m grid. The area of the study was almost this size. Additionally, the high-resolution DTM was collected from the National Geodetic and Cartographic Resources obtained via airborne laser scanning (ALS) in 2018. The basic assessment tool used in the analysis was a vertical displacement, the accuracy of which was determined by the errors in height measurement in individual years of a given time interval. Denoting the vertical displacement of the \(i\)-th point for the exemplary time interval 1978-2018 as:  
\[\Delta H_{i}=H_{i}^{2018}-H_{i}^{1978} \tag{1}\]  
the mean errors of the vertical displacement of the \(i\)-th point in this time interval were determined from the relationship:  
\[m_{\Delta Hi}=\pm\sqrt{m_{Hi1978}^{2}+m_{Hi2018}^{2}} \tag{2}\]  
The accuracy of determining the heights of the topographical pickets in territorial mapping from the 1970s and 1990s was determined by allowing for an average error of 0.10 m for \(m_{Hi1978}\) and \(m_{Hi1998}\) (using surveying measurements), for LIDAR data using: \(m_{H2018}\) = \(\pm\)0.15 m, the error of displacement amounted to: \(m_{\Delta H(1978-1998)}\) = \(\pm\)0.141 m and \(m_{\Delta H(1998-2018)}\) = \(\pm\)0.180 m, and it is the same for \(m_{\Delta H(1978-2018)}\).
## 5 Evaluation of Keypoint Matching
### Environment of the Evaluation  
#### 5.2.1 The GAN Structure and Loss Function of the Experiment  
Figure 6 shows the network structure used for learning. U-Net [37] was used for the generator, and PatchGAN [11; 38] was used for the discriminator. U-Net and PatchGAN, which were used in the original Pix2 pix, showed good and stable image translation results in its paper. Therefore, our proposed method was based on the Pix2 pix network structure, and we mainly evaluated the effectiveness of the edge enhancement filter.  
U-Net has skip structures in the layer, which makes it possible to pass information to the previous layer without loss before the convolution layer compresses the information. The number of down/up convolution layers was set to 8 (\(=\log_{2}256\)), depending on the size of the training data, which was 256 \(\times\) 256 pixels. To introduce randomness to the network, dropout (probability = 0.5) layers were added to three layers, as shown in Figure 6. The U-Net is considered appropriate for optical and SAR image translation because the proposed method preserves the edges and corners of the features, which are common in both optical and SAR images.  
For the discriminator, the PatchGAN discriminator was used to discriminate the separated input images. The internal structure uses a general 7-layer convolutional encoder.  
For the loss function, we adopted a function that uses L\({}_{1}\) norm, which is the same as that used in Pix2 pix. The Pix2 pix loss function is given by the following:  
\[\begin{array}{rl}\mathcal{L}_{\text{Pix2 pix}}(G,D)&=\mathcal{L}_{\text{cGAN }}(G,D)+\lambda\mathcal{L}_{11}(G)\\ &=\mathcal{L}_{\text{cGAN}}(G,\ D)+\lambda\mathbb{E}_{\textbf{x},\ \textbf{y}, \textbf{z}}[\ \|\ \textbf{y}-G(\textbf{x},\ \textbf{z})\ \|_{1}].\end{array} \tag{5}\]  
Referring to Equations (3) and (5), our proposed loss function is given by the following:  
Figure 6: Structure of the generator and discriminator networks.  
\[\mathcal{L}_{\text{Proposed}}(G,D)=\mathbb{E}_{\mathbf{y}}\Big{[}logD\Big{(}F_{\text{ edge}}(\mathbf{y})\Big{)}\Big{]}+\mathbb{E}_{\mathbf{x},\mathbf{z}}[log(1-D(\mathbf{x},\ G(\mathbf{x},\mathbf{z})))]+\lambda\mathbb{E}_{\mathbf{x},\ \mathbf{y},\mathbf{z}}\Big{[}\parallel F_{\text{edge}}(\mathbf{y})-G(\mathbf{x},\ \mathbf{z}) \parallel_{1}\Big{]}, \tag{6}\]  
where \(\lambda\) indicates a constant value, which was set to 10 [11] in this experiment, and \(F_{\text{edge}}()\) indicates edge enhancement. Similar to Equation (2), the objective generator \(G^{**}\) is given by the following:  
\[G^{**}=\underset{G}{arg\ \underset{G}{min}\ \underset{D}{max}\ \mathcal{L}_{\text{ Proposed}}(G,D)}. \tag{7}\]
# Dual-Band MIMO Antenna for 5G/WLAN Mobile Terminals
## 5 Conclusions  
A four-element dual-frequency MIMO antenna system which can cover N78 (3.3-3.8 GHz), 4.9 GHz (4.8-5 GHz) and IEEE 802.11 ac (5.15-5.35 GHz, 5.725-5.85 GHz) is proposed in this paper. The total efficiency is more than 70%, and the isolation performance is better than 15 dB. The measured results are in good agreement with the simulated results. The proposed MIMO antenna could be a good candidate for both sub-6 GHz 5G and WLAN applications in mobile terminals.  
Conceptualization, J.H.; methodology, Z.C.; optimization, G.D.; investigation, Q.C.; measurement, J.H.; writing--original draft preparation, J.H.; writing--review and editing, L.L., G.L.; supervision and funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded in part by the Science and Technology Department of Zhejiang Province under Grant No. LCG19F010009, the National Natural Science Foundation of China under Grant No. 61671330, and Wenzhou Municipal Science and Technology Program under Grant No. C20170005 and No. 2018 ZG019.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
Conflicts of Interest: The authors declare no conflict of interest.
## 3 Conceptual Models and Numerical Considerations
### Conceptual Model and Parameters for Surface Water Flow  
In addition to the full model for the entire PPGB, the study further considered the PPGB as two different sub-regions (i.e., watersheds for upstream and downstream fans) based on the different flow patterns of the river network (see Figures 1 and 4). These two sub-regions also reflect the long-term conditions of the landforms, especially the terrestrial slopes and land-use types. The following briefly describes the conceptual model and the associated modeling parameters for the PRMS model. In the GSFLOW model, the data consists of the daily maximal and minimal temperatures, precipitation,and streamflow for the period from 1 January 2015 to 31 December 2017. In the study, the number of MODFLOW-2005 cells for the aquifer layer is 3600, including active cells with 1327 and inactive cells declared in the dimension item. Note that the size of each cell is \(1\times 1\) km in the study. The stream segments and stream reaches were 13 and 166, respectively. The corresponding PRMS subsurface reservoirs include 17 hydrologic response units (HRUs) for the full PPG model (see Figure 5). Note that the artificial recharge lake was assigned as the 18 th HRU for the full basin model (Figure 5). The determination of the HRUs mainly depends on the properties such as the drainage boundary, land-surface altitude, land-surface slope, land use, and flow direction [11]. A watershed can include many HRUs. The study determined the HRUs by using the flow accumulation technique to define the boundaries of small watersheds. The small watersheds were then slightly modified to become the HRUs for the GSFLOW model. In the GSFLOW model, the parameters in each HRU can significantly influence the response of SGI for the HRU. Based on the available data for the study area, the simulations of the watershed responses considered the daily stress period for the three-years model. Other input parameters were integrated based on the observations from weather stations, land-use maps, and geology maps in the PPG. With the limited observations, parts of dimensions and parameters might be assigned based on the default values used in previous investigations (e.g., [11]). The typical values associated with streamflow observations are the basis to conduct the trial and error calibration of the GSFLOW model. Table 1 lists the ranges of the calibrated parameters for the GSFLOW model. In the GSFLOW model, the calibration is based on the observations from the stream gauges and the regional groundwater network. Similar to the recharge calibration in the MODFLOW model, the transient pumping rates in the GSFLOW model were calibrated again with the data obtained from the local government because the available data was the year-averaged pumping volume.  
Figure 5: Hydrologic response units (HRUs) defined for the proposed simulation domains (note that HRU 18 is the artificial recharge lake).