esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
## 4 Results
### Evaluation of Small-Basin-Scale TWSC
#### 4.6.3 Comparison with Soil Moisture Storage Change (SMSC)  
We adopted the CLDAS-V2.0 soil moisture storage (SMS) product to calculate the SMSC of the GRB. Then, SMSC was calculated as the difference between SMS in two consecutive months. We calculated the CC between TWSC and SMSC in 172 sub-basins over the common overlap period (April 2017-December 2018); the spatial distribution is shown in Figure 12. The CC between TWSC and SMSC for 172 sub-basins ranges from 0.26 to 0.88, and the median CC is 0.56. The CC of most sub-basins is greater than 0.5. The sub-basins with larger CC are mainly concentrated in the downstream area, while the sub-basins with smaller CC are mainly concentrated in the upstream area.  
Two sub-basins with high CC (labeled (a) and (b)) and two sub-basins with minimum CC (labeled (c) and (d)) were selected for further analysis. It can be seen from Figure 12c,d that although the CC between TWSC and SMSC in these two sub-basins is small, their changes show a relatively good agreement. The most extreme values occurred in the same month. Overall, time series are similar in both data, and the main maxima and minima are found in a similar month. These three comparisons suggest that the TWSC calculated in our study is reliable.  
Figure 11: Comparison of measured GWLC and TWSC ((**a**) Ganzhou Hotel station, (**b**) Zhangjiawei station, and (**c**) Yichun station).  
Figure 12: The spatial distribution of the CC between TWSC and SMSC (**a,b**) time series of two sub-basins with high CC, (**c,d**) time series of two sub-basins with low CC.
## 3 Results  
The experimental results of optimization process were achieved through optimizing linear and non-linear contour tracking using three different fitness functions in DE algorithm for position domain gain optimization and modified APF which was presented in the previous section. To obtain enhanced path and quantified tracking of the manipulator robot, the presentation of the proposed controller has been assessed through a 6-DOF universal manipulator robot UR5 shown in Figure 1. Experimentally, the Robotics System Toolbox of MATLAB simulation with the ROS platform was used to connect with the manipulator robot UR5. The experimental results presented here show the improvement in path planning and contour tracking through the application of DE algorithm into both the APF algorithm and the PDC-PID controller for the UR5 manipulator. In Figure 2. The overall communication model to control UR5. In the first part, optimization was achieved for the position domain controller (PID) of manipulator UR5 by meta-heuristic algorithm. The main goal is minimizing the contour error level of the manipulator. Three different types of fitness functions (ISE, IAE, and MSDAE) were adopted to calculate the optimization efficiency in linear and nonlinear contour tracking to complete the optimization process. Figure 1: Communicating the UR5 robot with the computer software. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Population size & 30 individuals \\ Number of generations & 50 \\ Probability of crossing (CR) & 0.9 \\ magnetic moment & \(1\) erg/G = 1 emu \\ Step size to be taken in the difference (F) & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: DE algorithm parameter values. Figure 2: The overall communication model to control UR5. The contour errors of DE optimization for linear and non-linear contours are shown in Figures 6 and 7. In this section of the results, we find the optimal contour performance for linear and nonlinear contours. This aim was completed by calculating the optimal fitness function for linear and nonlinear contours, and after this, we calculated the error contour when applied to three functions in linear and nonlinear contour functions. Therefore, the result of this section is to obtain optimal contour tracking of the UR5 manipulator robot. Figure 4: Fitness functions vs. iteration number for non-linear contour. Figure 5: Contour error produced in linear and non-linear contour tracking using gain optimization through fitness functions. Table 6 shows that the optimization algorithm produced similar mean torques \(\mathrm{T_{m}}\) for Joint 1 of the UR5 manipulator using the three fitness functions while other joints produced different values. The DE algorithm in linear tracking produced the smallest contour error with the smallest mean torque required. The same argument applies for non-linear contour tracking, in which, the smallest contour error produced the smallest torque except the mean torque in Joint 2. Figure 8 shows a comparison between linear and non-linear tracking for mean torque required using the three fitness functions.
# L5 IN: Overview of an Indoor Navigation Pilot Project
## 2 L5 IN Project Overview
### Modeling and Digitalization  
Based on the available data and the selected methods, the following structure was chosen for the navigation application (Figure 1). The data basis of indoor navigation serves, on the one hand, to create a map data set. These data can be existing building plans, building plans derived from escape and rescue plans, or other geodata. Apart from geometry, topological and semantic information also play a role. If no data are available for a building, it will be surveyed. Models were derived from these survey data, and all these data were integrated into a BIM. On the other hand, a routing graph for route calculation can form the basis as well. If necessary, this routing graph can be derived from building plans or directly from the BIM. In order to transfer the information about the locations to the navigation system, the data were stored in a database and an effective transfer format via the 5G mobile phone network to the end devices was developed. These data were used together with smartphone sensors and 5G signals for position estimation, as well as for navigation and visualization in the app.  
In the area of digitization of the L5 IN project, a first prototype for a navigation application is to be developed that can demonstrate the functionality and performance. In the first phase, a first 2D navigation model was developed with the existing data and information, containing the basic functionalities (location-to-location navigation, real-time updates, etc.). The essential basis of this prototype was the available planning fundamentals (construction plans, escape plans, etc.). For a later extension by 3D navigation, 3D models of the environment were developed with the help of a game engine based on the images. Corresponding navigation functionalities were implemented. At the end of the L5 IN project, it is planned that all available software tools and class libraries will be provided. For this purpose, a corresponding application documentation is to be developed, which will also allow third parties to use it.
## 4 Proposed Algorithm
### Experimental Results
#### 5.3.1 Results of the JIT-RMOVRPTW
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Instance** & \(\overline{\zeta}\) & **NSACOWDRL** & **NSACO** & **NSGA-III** & **NSGA-II** & **MOEA/D** \\ \hline \multirow{4}{*}{c202} & Small & 0.067098 & 0.111056 & 0.221364 & 0.155425 & 0.149471 \\  & & 9.94E\(-\)05 & 5.76E\(-\)05 & 0.001808 & 0.001132 & 0.000783 \\ \cline{2-7}  & Middle & 0.070933 & 0.095232 & 0.195238 & 0.156117 & 0.142658 \\  & & 0.000124 & 7.45E\(-\)05 & 0.000646 & 0.000893 & 0.000335 \\ \cline{2-7}  & Large & 0.063498 & 0.096462 & 0.22384 & 0.179565 & 0.166596 \\  & & 5.61E\(-\)05 & 5.11E\(-\)05 & 0.001706 & 0.001494 & 0.001324 \\ \hline \multirow{4}{*}{c206} & Small & 0.07471 & 0.123859 & 0.214364 & 0.099614 & 0.157687 \\  & & 0.000159 & 0.000116 & 0.001652 & 0.000463 & 0.000542 \\ \cline{2-7}  & Middle & 0.089647 & 0.121177 & 0.205124 & 0.119925 & 0.15276 \\  & & 6.96E\(-\)05 & 0.000127 & 0.001728 & 0.000238 & 0.001202 \\ \cline{2-7}  & Large & 0.111621 & 0.14161 & 0.207618 & 0.117877 & 0.173173 \\  & & 0.000449 & 0.000345 & 0.003593 & 0.000272 & 0.002181 \\ \hline \multirow{4}{*}{rc202} & Small & 0.091929 & 0.095577 & 0.136415 & 0.12007 & 0.13582 \\  & & 7.24E\(-\)05 & 4.42E\(-\)05 & 0.000804 & 0.000165 & 0.000713 \\ \cline{2-7}  & Middle & 0.092914 & 0.107702 & 0.170686 & 0.158157 & 0.141599 \\  & & 4.08E\(-\)05 & 5.77E\(-\)05 & 0.000682 & 0.000297 & 0.000587 \\ \cline{2-7}  & Large & 0.143131 & 0.157795 & 0.211815 & 0.163551 & 0.184171 \\  & & 0.000346 & 9.64E\(-\)05 & 0.000707 & 0.00036 & 0.000859 \\ \hline \multirow{4}{*}{rc204} & Small & 0.099484 & 0.10002 & 0.155438 & 0.179327 & 0.124656 \\  & & 0.000159 & 9.27E\(-\)05 & 0.001078 & 0.001097 & 0.000573 \\ \cline{2-7}  & Middle & 0.107897 & 0.119688 & 0.217314 & 0.189579 & 0.22512 \\ \cline{2-7}  & Middle & 9.31E\(-\)05 & 6.03E\(-\)05 & 0.001167 & 0.001371 & 0.001906 \\ \cline{2-7}  & Large & 0.109758 & 0.110154 & 0.162679 & 0.119891 & 0.172319 \\  & & 0.000275 & 0.00014 & 0.000765 & 0.000193 & 0.002228 \\ \hline \multirow{4}{*}{RL} & Small & 0.081081 & 0.089546 & 0.114181 & 0.125006 & 0.085965 \\  & & 1.93E\(-\)05 & 2.56E\(-\)05 & 0.000217 & 0.000377 & 0.000176 \\ \cline{2-7}  & Middle & 0.07656 & 0.081223 & 0.12775 & 0.132086 & 0.107114 \\ \cline{1-1} \cline{2-7}  & Middle & 1.51E\(-\)05 & 2.22E\(-\)05 & 0.000873 & 0.000183 & 0.000203 \\ \cline{1-1} \cline{2-7}  & Large & 0.075806 & 0.075908 & 0.166999 & 0.113566 & 0.103795 \\ \cline{1-1} \cline{2-7}  & & 3.33E\(-\)05 & 1.65E\(-\)05 & 0.002057 & 7.47E\(-\)05 & 0.000376 \\ \hline \end{tabular}
\end{table} 
Table 3: Mean and variance metric values of IGD indicators.
## 1 Introduction
In fact, in our previous study the leaf EO chemical composition from Azorean _C. japonica_ was compared with several _C. japonica_ leaf EO samples from other geographical origins. It was clearly demonstrated by principal component analysis (PCA) and hierarchical cluster analysis (HCA) that, in the Azores, the _C. japonica_ leaf EO chemotype is \(\alpha\)-pinene type, while in most Asian countries the leaf EO chemotype is either _ent_-kaurene type or elemol + _ent_-kaurene type. In addition, monoterpeak hydrocarbons dominated in Azorean _C. japonica_ leaf EO, while oxygen-containing sesquiterpenes are the major terpene fraction in _C. japonica_ leaf EOs from East Asia countries [7]. Concerning the biological activity of Azorean _C. japonica_ leaf EO, previous investigations revealed their potential as a natural biocide, namely as a bactericide, fungicide [13], and molluscicide against _Radix peregra_ ([PERSON]), which is a crucial vector of fascidiasis, a disease with a high impact on public health, as well as on animal production losses and consequent economic costs [14]. Many other bioactivities of _C. japonica_ leaf EO samples from other countries have been reported, including analgesic, anti-inflammatory, antimelanogenesis, antioxidant, an titussive, antiulcer, anxiolytic, cancer chemopreventive, neuropharmacological, whitening, antitermite, mosquito larvicidal, mosquito repellent, and silverfish repellent properties ([11] and the references therein). However, the existing literature on the chemical composition of EOs from various _C. japonica_ parts, such as FCs and MCs, and their biological potential remains limited. Furthermore, there is an ever-increasing demand for effective natural antimicrobial agents with applications in the fight against human and animal infectious diseases or in green plant protection and food preservation. According to the literature [15,16], crude EOs or their fractions/individual components, as well as synergistic combinations between  
Figure 1: (**A**) Fresh leaves and strobili (female and male cones) of Azorean _Cryptomeria japonica_. (**B–E**) Dried Azorean _C. japonica_ aerial parts utilised in this study: (**B**) female cones, (**C**) male cones, (**D**) leaves (needle-shaped), and (**E**) foliage. Bar = 1 cm. EOs/components or with antibiotics, can play an important role in inhibiting microbial growth. In this context, and knowing that bioactive secondary metabolites have different distributions in EOs from different plant parts, the aims of this comparative study were: (i) to determine the yield and chemical profile of the EOs extracted by HD from different Azorean _C. japonica_ parts (foliage, leaves, FCs, and MCs) and (ii) to investigate the antimicrobial and brine shrimp lethality (BSL) effects of the obtained EOs and two pure EO components (\(\alpha\)-pinene and terpinen-4-ol). The results will generate more data on the chemical and biological properties of this species to stimulate the development of CJBR's commercial uses and, consequently, to contribute to the local circular economy.
# Unlocking the Potential: How Flying Taxis Will Shape the Future of Transportation
## 3 Customer Need to Use Flying Taxis
### Flying Taxis Benefit People Who Need to Travel for Business Purposes
#### Flying Taxis Benefit People Who Need to Travel Frequently for Work or Personal Reasons  
Flying taxis have the potential to significantly benefit people who need to travel frequently for work or personal reasons by providing faster and more convenient transportation options. Compared to traditional ground transportation or commercial air travel, flying taxis have the ability to save passengers a significant amount of time, allowing them to travel to their destination quickly and efficiently. This can be especially beneficial for business travelers who need to travel frequently and cannot afford to spend a lot of time commuting. Additionally, flying taxis offer more flexibility in terms of scheduling and route options, allowing passengers to better tailor their travel plans to their specific needs [169].
## 6 Simulation Results
### Performance on Lossy Source-Relay and Relay-Destination Links  
In this subsection, we consider the lossy channels, in which both source-relay and relay-destination links have erasures. We assume that each source-relay link has the same erasure probability \(p_{sr}\), and each relay-destination link has the same erasure probability \(p_{rd}\). Setting \(n=2\), \(k=1000\), \(\varepsilon=1\), and using the optimized degree distributions in Table 1, Figure 8 shows the BER performance of EDFC versus the relay-destination erasure rate \(p_{rd}\) for different source-relay erasure probabilities \(p_{sr}\). It reveals that EDFC suffers performance degradation with the increase in \(p_{sr}\) and \(p_{rd}\). The BER performance is similar to the lossless case for \(p_{sr}=0\) and the small erasure rate \(p_{sr}=0.05\). Due to the involvement of the extra cooperative relay, it is still possible for EDFC to provide good performance for each source at a moderate erasure probability such as \(p_{sr}=0.3\). For example, when \(p_{rd}=0.3\) and \(p_{sr}=0.3\), the BER of \(S_{2}\) can still be as low as \(4.3\times 10^{-5}\). The simulation results reveal that EDFC can reduce the impact of obstacles blocking or burst errors of the channels and provide enhanced protection for lossy channels. It also can provide the UEP property for different sources and improve the overall performance.  
Figure 7: BER performance comparison of EDFC and GDFC for four-source networks.
## 3 Measured Uplift Pressure Application Method and Software Development
The same operation should carry out for the integration point and the drainage pipe curve. 5. According to the measured uplift pressure and the position of the integration point in the cross-section, the uplift pressure at the integration point is calculated by interpolation. 6. The applying method judges whether all the current element's integration points have finished the uplift pressure calculation. If not, steps (2) to (5) are repeated. 7. The applying method judges whether all FEM elements on the foundation surface have completed uplift pressure calculation. If not, steps (2) to (6) are repeated. 8. According to the interface requirements of the FEM, the measured uplift pressure of each integration point is output. The uplift pressure is called when the FEM simulation is carried out. According to the above analysis, it can be seen that the application method of measured uplift pressure includes a lot of data conversion, data fitting, loop computation, judgment computation, and calculation. Thus, the manual calculation is time-consuming and prone to errors. To facilitate the popularization and application of this applying method, researchers and designers can quickly complete the application of the measured uplift pressure. The visual application program of uplift pressure of concrete dam is compiled into the software. The operation interface is shown in Figure 4. The software interface includes the shortcut operation command area, parameter setting area, operation area, calculation process display area, results index area, and data visualization area, enabling users to complete the uplift pressure application quickly. In the numerical simulation of a concrete dam, the uplift pressure is an indispensable load for a gravity dam; the uplift load should be considered in the design of a gravity arch dam and medium-thick arch dam. For thin arch dams, uplift pressure accounts for a small proportion of the total load because of the small area of the dam foundation, which can be  
Figure 4: Software interface of the application method of measured uplift pressure. neglected in the design process. As for the distribution of uplift pressure, it is generally considered that uplift pressure will decrease after impervious curtain and drainage pipe, and the drainage pipe is assumed as the reduction point. According to the experience of many concrete dams in China, it is considered that the reduction coefficient at the drainage pipe in the riverbed should be less than 0.2-0.3.
## 5 Materials and Methods
### Proteomic Analysis
#### 5.2.2 In Gel Digestion and Mass Spectrometry Analyze  
The in-gel digestion was conducted according to [PERSON], [PERSON], and [PERSON] [66] with small modifications. Firstly, the gel bands were selected, excised, and transferred to a 1.5-mL micro tube. Subsequently, a solution of 75 mM ammonium bicarbonate (in 40% ethanol) was added to destain the bands. Thereafter, the supernatant was removed, 5 mM dithiothreitol (in 25 mM ammonium bicarbonate) was added, and all samples were incubated at 60 \({}^{\circ}\)C for 30 min (reduction step); next, we added 55 mM iodoacetamide (in 25 mM ammonium bicarbonate) and incubated all samples at room temperature for 30 min in the absence of light. The supernatant of all individual samples was again removed, and the gel pieces dehydrated by adding acetonitrile (ACN). Subsequently, to each sample, 10 \(\upmu\)L of proteomic grade trypsin solution (10 ng/\(\upmu\)L in 50 mM ammonium bicarbonate) was added, and digestion was allowed for 45 min on ice. Thereafter, supernatants were removed, gel pieces were covered with 50 mM ammonium bicarbonate and incubated overnight at 30 \({}^{\circ}\)C. Finally, each sample was suspended in 20 \(\upmu\)L of ACN/5% trifluoroacetic acid (TFA) (1:1, _v/v_) and sonicated for 10 min. The supernatant was again removed and dispensed in a separate tube. We repeated this step three times and combined the supernatants of the same samples. Lastly, we repeated the process using ACN instead of ACN/5% TFA. The obtained supernatant was combined with the previously obtained supernatants.  
The tryptic peptides were analyzed by liquid chromatography-mass spectrometry (LC-MS) using an electrospray-ion trap-time of flight (ESI-IT-TOF) system coupled to a binary ultra-fast liquid chromatography system (UFLC) (20A Prominence, ShimadzuKyoto, Japan).. Briefly, samples were dried, resuspended in 0.1% acetic acid, and loaded onto a C18 column (Discovery C18, 5 \(\upmu\)m, 50 \(\times\) 2.1 mm) operating with a binary solvent system: (A) water:acetic acid (999:1, _v/v_) and (B) ACN:water:acetic acid (900:99:1, _v/v/v_). The column was eluted at a constant flow rate of 0.2 mL/min with a 0 to 40% linear gradient of solvent B for 40 min. The eluates were monitored by a Shimadzu SPD-M20A PDA detector before introduction into the mass spectrometer. The interface voltage was set to 4.5 KV, the capillary voltage used was 1.8 KV at 200 \({}^{\circ}\)C, and the fragmentation was induced by argon collision at 50% 'energy'. The MS spectra were acquired under the positive mode and collected in the range of 350 to 1400 _m/z_. The MS/MS spectra were collected in the range of 50 to 1950 _m/z_.
## 3 Hybrid Computing Architecture of MSD Algorithm
### Correlation Calculation Based on SC
#### 3.1.2 Multiplier  
When signals are mapped from the data domain to the probability domain, the data undergo scaling and bias operations. To ensure uniformity in calculation, the product of two input data points must also undergo the same mapping rule. Assuming the two input data points are \(X_{1}\) and \(X_{2}\), the mapped result of their product can be expressed as  
\[P_{y}=\frac{X_{1}X_{2}+1}{2} \tag{23}\]  
where \(y\) is the product of \(X_{1}\) and \(X_{2}\). Considering that the mapping results of input data in the probabilistic domain can be represented as  
\[P_{x_{1}}=\frac{X_{1}+1}{2} \tag{24}\]  
and  
\[P_{x_{2}}=\frac{X_{2}+1}{2} \tag{25}\]the product can be calculated as follows:  
\[\begin{split} P_{y}&=\frac{X_{1}X_{2}+1}{2}\\ &=\frac{X_{1}+1}{2}\frac{X_{2}+1}{2}+\frac{1-X_{1}}{2}\frac{1-X_{ 2}}{2}\\ &=P_{x_{1}}P_{x_{2}}+(1-P_{x_{1}})(1-P_{x_{2}})\end{split} \tag{26}\]  
In probability calculations, the computation circuit structure for the multiplier is an XNOR gate.  
Similarly, the complex multiplier can be expressed as  
\[\begin{split} P_{y}&=\frac{\frac{\text{R}(X_{1}) \times\text{R}(X_{2})-\text{I}(X_{1})\times\text{I}(X_{2})}{2}+1}{2}+j\frac{ \frac{\text{R}(X_{1})\times\text{I}(X_{2})+\text{I}(X_{1})\times\text{R}(X_{2} )}{2}+1}{2}\\ &=\frac{1}{2}\Big{\{}P_{\text{R}(X_{1})}P_{\text{R}(X_{2})}+(1- P_{\text{R}(X_{1})})(1-P_{\text{R}(X_{2})})\Big{\}}-\frac{1}{2}\Big{\{}P_{\text{I}(x_{1} )}P_{\text{I}(x_{2})}+(1-P_{\text{I}(x_{1})})(1-P_{\text{I}(x_{2})})\Big{\}}\\ &+j\frac{1}{2}\Big{\{}P_{\text{R}(x_{1})}P_{\text{I}(x_{2})}+(1- P_{\text{R}(x_{1})})(1-P_{\text{I}(x_{2})})\Big{\}}+\frac{1}{2}\Big{\{}P_{ \text{I}(x_{1})}P_{\text{R}(x_{2})}+(1-P_{\text{I}(x_{1})})(1-P_{\text{R}(x_{ 2})})\Big{\}}\\ &\text{where R}(\cdot)\text{ denotes the real part of a complex number and I}(\cdot)\text{ denotes the imaginary part of a complex number.}\\ &\text{In the complex multiplication computation, four multipliers and two adders are required. When the input data are random, adders with a scaling factor of two should be employed to prevent result overflow. The computation circuit of \(\frac{x_{1}+x_{2}}{2}\) is a two-input multiplexer in the SC. The whole circuit of the complex multiplier is shown in Figure 5.