esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Comparison and Verification  
In order to further illustrate the applicability of the method in this paper, the research results of [PERSON] et al. [40] were selected for comparison. The relevant parameters were assumed to be: \(b=0,\ 0.5,\ 1;\ \gamma=18\ \mathrm{kN/m^{3}};\ c_{0}=10\ \mathrm{kPa};\ \varphi_{0}=20^{\circ};\ H=10\ \mathrm{m};\ L=3\ \mathrm{m};\ q=15\ \mathrm{kPa};\ k_{h}=0.2,\ \mathrm{and}\ k_{v}=0.1.\) The horizontal earth pressure distributions of several methods are shown in Figure 10. The traditional Mononobe-Okabe method leads to a linear distribution, while the results of [PERSON] et al. [40] and the method in this paper both demonstrate nonlinear distributions. At the same time, based on the results of shaking-table tests of seismic earth pressure on retaining walls under different foundation conditions by [PERSON] and [PERSON] [24], it is found that a \"convex\" nonlinear distribution in the lower part of the retaining structure is realistic. It can also be seen that the results obtained by the traditional Mononobe-Okabe method were much larger than the results of this paper and those of [PERSON] et al. [40]. Compared with the results obtained by [PERSON] et al. [40], the main gap was concentrated in the lower part of the retaining structure. This may be explained by the fact that the intermediate principal stress and soil arching effect were not considered, and because the linear slip surface was used simultaneously. However, both displayed the same trend, and the earth pressure reduction point was near \(0.8H\), which is consistent with the previous analysis. This also shows that the method proposed in this paper is reasonable.
# Can OCT Angiography Be Made a Quantitative Blood Measurement Tool?
## 2 OCTA Fundamentals
### Hemodynamic Parameters  
What are the main hemodynamic parameters that impact observed OCTA signals? In capillaries (Figure 1A), the RBC flow is single-file, with plasma gaps in between [22; 23]. RBC speed (distance/time), flux (\(\#\)/time), and linear density (\(\#\)/distance) are thus primary hemodynamic parameters in capillaries. Due to the plasma gaps between cells, flux can often be determined by imaging individual capillaries and counting RBCs traversing a single location [23]. Assuming single-file capillary flow, microvascular tube hematocrit (H\({}_{\text{tube}}\)), or RBC volume fraction, is related to linear density (\(\varrho\)) by \(\varrho\ =\ \text{H}_{\text{tube}}\text{A}/\text{V}_{\text{RBC}}\), where V\({}_{\text{RBC}}\) is the red blood cell volume and A is the vessel cross-sectional area. Capillary tube hematocrit is generally a factor of \(\sim\)2-3\(\times\) lower than systemic levels [24], but hematocrit can vary considerably between capillaries. In macroversels (Figure 1B), which include supplying arteries and draining veins, the blood velocity varies across the vessel cross-section. In contrast to microvessels, macrovascular hematocrit approaches systemic levels of \(\sim\)40-45% [25]. Flow is typically laminar with some degree of blunting [24], with the largest shear rate, or velocity gradient, at the edge of the vessel. In macroversels, RBC velocity or speed (distance/time), flow rate (volume/time), and hematocrit (volume/volume) are the primary hemodynamic parameters. All hemodynamic parameters vary over time with respiration and the heartbeat of the subject [24].  
Figure 1: (**A**) Flow in capillaries (microvessels with diameters of <10 \(\upmu\)m) is single-file, usually with highly variable hematocrits that fall below systemic levels; (**B**) On the other hand, macrovascular flow often follows a blunted laminar profile at near-systemic hematocrits. Consequently, different approaches are required to quantify microvascular versus macrovascular hemodynamics via OCTA (Optical Coherence Tomography Angiography) imaging.
## 7 Discussions and Future Directions
### Beyond Communication and Information Theory  
Flying base station channel capacity builds on Shannon channel capacity but adds the mobility control assuming cellular-provision-dedicated flying base station, i.e., its mobility control is to maximize the channel capacity performance. Such information-theoretic analyses have historically driven and informed the research and development in wireless communication and its signal processing, as there is currently no standardized or dominant protocol or algorithm to control the operations of the flying base stations. For example, there have been decades of research adapting and controlling the physical-layer signal processing and coding to approach the Shannon channel capacity, including works from the 2000s (e.g., [25; 26; 27]), 2010s (e.g., [28; 29]), and 2020s (e.g., [30; 31]). However, a hands-on implementation using a software-defined radio (for control, adaptation, and implementation of the base station communication functions) and a UAV drone (for mobility control) will provide proof-of-concept validation and facilitate practicality.
## 3 Experiments and Results
### Motion Pattern Recognition Based on Multilayer Perceptrons
#### 3.1.4 Different Sliding Window Lengths  
In order to study the influence of the length of the sliding window on the accuracy of recognition, all the time-domain and the frequency-domain feature parameters were used, and the length of the sliding window was set to 256 ms, 512 ms, 1024 ms, and 2048 ms, and the feature parameters dataset was established at an interval of 50 ms. Again, 70% of the dataset was used for training, and 30% was used for testing. In the 70% of the dataset used for training, 90% of the dataset was used for training, and 10% of the dataset was used for verification. The classifier uses a three-layer multilayer perceptron. The activation function of the input layer selects the sigmoid function, and the activation function of the output layer selects the softmax function. In the cross-validation method, the number of hidden layer nodes was changed from 4 to 60, and the classifier model was trained separately. The number of hidden layers with the highest accuracy was selected as the final classification model. The final recognition results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Set** & **Number of Hidden Nodes** & **Recognition Accuracy Rate (\%)** & **Prediction Time (ms)** \\ \hline RMS & 34 & 74.49 & 6.5 \\ \hline RMS, VAR, ZC & 38 & 89.89 & 6.2 \\ \hline MPF, MF & 38 & 65.28 & 6.4 \\ \hline RMS, VAR, ZC, MPF, MF & 40 & 95.93 & 6.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recognition accuracy of different feature parameter sets in a three-layer multilayer perceptron model. Root mean square (RMS), variance (VAR), zero crossing (ZC), mean power frequency (MPF), and median frequency (MF).  
It can be seen from Table 3 that, when using all the time-domain and the frequency-domain feature parameters dataset, the recognition accuracy rate is the best when the sliding window length is 1024 ms, reaching 95.93%. When the length of the sliding window is less than 1024 ms, the recognition accuracy of the optimal three-layer multilayer perceptron on the verification set reduces to less than 66%. When the window length is increased to 2048 ms, the recognition accuracy rate is reduced to 88.65%.
_Article_  
**TiO\({}_{2}\)/ZnO Nanofibers Prepared by Electrospinning and Their Photocatalytic Degradation of Methylene Blue Compared with TiO\({}_{2}\) Nanofibers**  
[PERSON] 1, [PERSON] 1, [PERSON] 1\({}^{\copyright}\), [PERSON] 1,2, [PERSON] 2 and [PERSON] 1,3,*  
Received: 19 July 2019; Accepted: 16 August 2019; Published: 19 August 2019  
**Abstract:** TiO\({}_{2}\) nanofibers have high chemical stability and high strength and are applied to many fields such as air pollution sensors and air pollutant removal filters. ZnO nanofibers also have very high absorptivity in that air and are used as germicides and ceramic brighteners. TiO\({}_{2}\)/ZnO nanofibers, which have a composite form of TiO\({}_{2}\) and ZnO, were fabricated and show higher photocatalytic properties than existing TiO\({}_{2}\). The precursor, including zinc nitrate hexahydrate, polyvinyl acetate, and titanium isopropoxide, was used as a spinning solution for TiO\({}_{2}\)/ZnO nanofibers. Electrospun TiO\({}_{2}\)/ZnO nanofibers were calcined at 600 \({}^{\circ}\)C and analyzed by field emission scanning electron microscope (FE-SEM) and X-ray diffraction (XRD). The average diameter of TiO\({}_{2}\)/ZnO nanofibers was controlled in the range of 189 nm to 1025 nm. XRD pattern in TiO\({}_{2}\)/ZnO nanofibers have a TiO\({}_{2}\) anatase, ZnO, Ti\({}_{2}\)O\({}_{3}\), and ZnTiO\({}_{3}\) structure. TiO\({}_{2}\)/ZnO nanofibers with a diameter of 400 nm have the best photocatalytic performance in the methylene blue degradation experiments and an absorbance decrease of 96.4% was observed after ultraviolet (UV) irradiation of 12 h.  
**Keywords:** TiO\({}_{2}\) nanofibers; TiO\({}_{2}\)/ZnO nanofibers; electrospinningning; photocatalyst
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 5 Delay Minimization and Resource Allocation Algorithms
### Discussion  
As seen in Section 5.1, several works consider traffic level and UE number as an evaluation criteria for delay minimization; however the complexity of the algorithms are bounded with the number of users and the level of traffic. To achieve the delay minimization objective without encountering complexity issues, AI/ML-based traffic forecasting methods are highly relevant to exploit the data collected from monitoring and to reveal patterns such as peak hours, thus simplifying the solution space. A forecasting example that uses neural networks for delay minimization is already provided for FiWi networks in [48]. By taking the optimal training time and forecast accuracy trade-off into account, different neural networks approaches can also be used to detect patterns and forecast the network traffic characteristics, such as the traffic forecasting with long-short term memory method in [73].  
Regarding the deployment of MEC solutions, there deep reinforcement learning based delay minimization solutions [74] and edge caching [75] can achieve significant QoS improvements for applications, such as video streaming, but the impact of integrating these application-based decision-making mechanisms to optical and mmWave radio networks has not been studied in the existing papers, providing a novel research direction for delay minimization. The limitations of MEC solutions provided in Table 4 also reveal that the solutions depend on test-bed results and simulations; therefore impact of real network dynamics should be thoroughly studied in future works.  
In delay optimization papers, users [67], network services [68], and cloud servers [72] are also involved in decision-making as external stakeholders. MEC servers could also be third party stakeholders who lease their computing resource blocks to process or store for dynamic function placement, providing AI-as-a-Service [1] or Security-as-a-Service to other network stakeholders and making service providers an external decision-maker in network management for service-specific decisions.
## 2 Data  
We use the Wind spacecraft observations obtained at 1 au during 1995. We analyze the high time-resolution (-92 s) proton parameters and their 1-sigma uncertainties derived from non-linear fittings of bi-Maxwellian distributions to the observed distribution functions [13]. The quality of each fitting is quantified by the chi-squared parameter \(\chi^{2}\). Here, we use the proton plasma density \(n\), bulk speed \(v\), thermal speed \(v_{\text{th}}\), the 1-sigma error of the plasma density \(\sigma_{n}\), and thermal speed \(\sigma_{v,\text{th}}\). We exclude the parameters that are derived with \(\chi^{2}>5\) and all data points with relative density and temperature uncertainty \(\frac{\sigma_{n}}{n}>0.3\) and \(\frac{\sigma_{T}}{T}>0.3\), respectively. Finally, we use the magnetic field \  
overlaps the dynamic term. The dynamic and thermal terms have no common range of values.  
According to Equation (3), the second term of the non-isothermal Bernoulli integral is the thermal \(\frac{1}{2}v_{\rm th}^{2}\) multiplied by \(\frac{\gamma}{\gamma-1}\). Thus, the significance of the second term varies with the polytropic index \(\gamma\). Figure 2a plots the time series of the Bernoulli integral in the solar wind protons, assuming five different values for \(\gamma\). We specifically assume isobaric protons (\(\gamma=0\)), protons with \(\gamma=1/2\), protons with \(\gamma=3/2\), adiabatic protons with three kinetic degrees of freedom (\(\gamma=c_{\rm p}/c_{\rm v}=1.67\)), and protons with a nearly isochoric behavior (\(\gamma\to\infty\)). Figure 2b focuses on a subinterval ranging from day of year (DOY) 305 to DOY 310. In Figure 2c, we show the histograms of the Bernoulli integral as calculated for all of 1995 for the different \(\gamma\) values. Our results shows that there are no apparent differences in the calculated integral, regardless of the \(\gamma\) values.  
The second term of the Bernoulli integral may become the most significant term as \(\gamma\to 1\) (nearly isothermal plasma). In Figure 3a, we show \(\frac{\gamma}{\gamma-1}\) as a function of \(\gamma\). The grey shadow indicates the range \(5/6<\gamma<5/4\) for which the absolute value of \(\frac{\gamma}{\gamma-1}\) exceeds 5. Figure 3b shows the histograms of the dynamic term, and the thermal term multiplied by 5. The two histograms overlap within the small range from \(3.3\times 10^{10}\) to \(3.7\times 10^{10}\) m\({}^{2}\) s\({}^{-2}\). This result means that in the data we analyze, the second term of the Bernoulli integral may become comparable to the dynamic term for \(5/6<\gamma<5/4\).
## 4 Problem Formulation and Brief System Architecture
### Operating Layer  
A massive amount of healthcare data needs to be processed and analyzed, and it requires provision of a storage system which, will be done in the operating layer. HPC collects all unstructured and semi-structured healthcare data from different sources. To get a structured distributed file management system (SDFM), HPC applies the Hadoop MapReduce framework over the unstructuredor semi-structured data. Then, the SDFM is sent to a data warehouse which creates an interaction between the Operating layer and Application layer.
# River Discharge and Saltwater Intrusion Level Study of Yangtze River Estuary, China
## 2 Data and Methodology
### Numerical Model
#### 2.2.2 Parameters  
The time step was \(\Delta\)t = 30 s. The horizontal viscosity coefficient was calculated using [PERSON]'s formula, and the vortex viscosity was estimated based on the velocity gradient [24]. It was found that the roughness field had major impacts on the tides. In this study, the roughness was repeatedly calibrated according to the depth of the different regions. The range of roughness (n) was between 0.0105 and 0.02. The salinity horizontal diffusion coefficient was observed to have a certain influence on the salinity field of the Yangtze Estuary. It was found that the greater the diffusion coefficient, the faster the salinity diffusion, and the greater the diffusion range. Therefore, based on this study's repeated calculations and calibrations, the salinity diffusion coefficient was set as a constant value of 100 m\({}^{2}\)/s [25; 26; 27].
## 2 Background
### Clock Synchronization  
Since TWR-derived positioning relies heavily on precise clock synchronization [22, 23, 24, 25, 26], distributed coherence has become a common requisite for these types of networks. Coarse approaches such as the Network Timing Protocol (NTP) are sufficient for communications applications [27, 28, 29] but do not support the precision we need for rapid localization. Several extensions have improved this performance [30, 31, 32, 33, 34, 35, 36], but many have imposed simplifying assumptions, are not computationally efficient, and lack generality to changes in the underlying models. In response, we proposed a family of novel one-shot estimators in [9] that specifically integrate clock synchronization in the localization algorithm. We demonstrated that these estimators were optimal for single measurements and reduced to a system of linear equations. In this work, we formulate an extended Kalman filter (EKF) extension [37, 38, 39] to these estimators to further reduce the computational complexity and improve the robustness to cycle slips and other outages.
## 3 Methods
### Overall Framework
#### 3.2.2 Data Generation Module
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Layer** & **Parameters Setting** & **Output Size** \\ \hline \multirow{8}{*}{Discriminator} & Input & Reshape: 1024–32 \(\times\) 32 & 1 \(\times\) 32 \(\times\) 32 \\  & Conv1 & Kernel size: 32 \(\times\) 5 \(\times\) 5, 8, 2, LeakyReLU, dropout & 32 \(\times\) 16 \(\times\) 16 \\  & Conv2 & Kernel size: 64 \(\times\) 5 \(\times\) 5, 8, 2, LeakyReLU, dropout, BN & 64 \(\times\) 8 \(\times\) 8 \\  & Conv3 & Kernel size: 128 \(\times\) 5 \(\times\) 5, 8, 2, LeakyReLU, dropout, BN & 128 \(\times\) 4 \(\times\) 4 \\  & Conv4 & Kernel size: 256 \(\times\) 5 \(\times\) 5, 8, 2, LeakyReLU, dropout, BN & 256 \(\times\) 2 \(\times\) 2 \\  & Output\_1 & 1024 \(\times\) 1, Sigmoid & 1 \\  & Output\_2 & 1024 \(\times\) 10, Softmax & 10 \\ \hline \multirow{8}{*}{Generator} & Input & Fully connection layer: 100 \(\times\) 1024; & 256 \(\times\) 2 \(\times\) 2 \\  & Desonv1 & Kernel size: 128 \(\times\) 5 \(\times\) 5, 1, 1, ReLU, BN & 128 \(\times\) 4 \(\times\) 4 \\ \cline{1-1}  & Deconv2 & Kernel size: 64 \(\times\) 5 \(\times\) 5, 1, 0, ReLU, BN & 64 \(\times\) 8 \(\times\) 8 \\ \cline{1-1}  & Deconv3 & Kernel size: 32 \(\times\) 5 \(\times\) 5, 3, 5, ReLU, BN & 32 \(\times\) 16 \(\times\) 16 \\ \cline{1-1}  & Deconv4 & Kernel size: 1 \(\times\) 5 \(\times\) 5, 3, 9, Tanh & 1 \(\times\) 32 \(\times\) 32 \\ \cline{1-1}  & Output & Reshape: 32 \(\times\) 32–1024 & 1 \(\times\) 1024 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The structure of the discriminator and generator.
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 5 Discussion
### Mixed Environments: The Role of AV-Human Interactions  
The mixed-traffic scenarios simulated in this study, where different AV behaviors coexist with human-driven vehicles, produced interesting insights into the interactions between human and autonomous drivers. In particular, the balanced driving patterns of normal AVs and the coordinated movements of platooning AVs helped mitigate the traffic inefficiencies introduced by aggressive and cautious driving styles. This is consistent with the findings of [27; 29; 38; 40; 41; 42; 43], who noted that mixed-traffic environments with diverse AV behaviors can stabilize traffic flow by balancing the extremes of cautious and aggressive driving.
## 3 Results and Discussion
### Analysis and Verification of Hydration Dynamics via TGA  
The pozzolanic reaction and strength development depend on the amount of calcium hydroxide generated by the hydration reaction of cement [38]. TGA is typically used to measure the calcium hydroxide content in cement. TGA was conducted using the DTG curves of HSCs shown in Figures 3-5. The amount of calcium hydroxide (\(\Delta\)m) generated in each specimen was quantified, and the values are plotted in Figures 3-5. The analysis of hydration dynamics using TGA was performed by employing two methods (DTG/TGA), and a stepwise method was used here. The weight loss of C-S-H, ettringite, and monocarbonate occurred in the range below 230 \({}^{\circ}\)C [39]. Calcium hydroxide decomposed in the ranges 380 \({}^{\circ}\)C to 480 \({}^{\circ}\)C [40] and 400 \({}^{\circ}\)C to 560 \({}^{\circ}\)C, and the difference in mass was used to determine the amount of calcium hydroxide [41; 42]. The weight loss in TG was caused by calcium-hydroxide dehydration, and this value was used to quantify the amount of calcium hydroxide. A major quantity of calcium hydroxide was generated rapidly in the C-UHSCC specimens during the initial steam-curing process, and this quantity increased or decreased as the curing period increased, which indicated that internal hydration was in progress. When the pigments were added, the calcium hydrate content increased relative to that of specimen P as the age increased. This phenomenon indicated that the moisture absorbed by the pigments was released, or the hydration was delayed owing to the moisture supplied during the steam-curing process.
# Crystal Plasticity Finite Element Modeling on High Temperature Low Cycle Fatigue of Ti2 AlNb Alloy
## 3 Model Parameters Identification and Validation
### Crystal Plasticity Finite Element Model  
#### 3.2.1 RVEs and Boundary Conditions  
Based on the EBSD phase composition figure, the 3D RVEs containing two phases were established as shown in Figure 6. According to EBSD results, O phase, \(\beta\) phase and \(\alpha_{2}\) phase accounted for 52%, 30% and 7%, respectively. Due to the least content of \(\alpha_{2}\) phase, this was ignored, so the Ti\({}_{2}\)AlNb was regarded as a two-phase material. The RVE contained 100 sets, the proportion of O phase and \(\beta\) phase was 2:1. Figure 6a shows that the O phase is the dominant phase, and the \(\beta\) phase is dispersed in the O phase. The RVEs were generated by Neper [32], the mesh scale was 100 \(\times\) 100, and were divided into 100 areas. Then the RVE files were imported into ABAQUS. The boundary conditions for the RVE model are shown in Figure 6, the periodic boundary conditions (PBC) were implemented, and can be expressed as:  
\[\text{u(CD)} - \text{u(AB)}=\text{u(C)}-\text{u(A)}, \tag{13}\] \[\text{v(BD)} - \text{v(AC)}=\text{v(B)}-\text{v(A)}, \tag{14}\]  
where u(AB), u(CD), v(BD) and v(AC) are the x-direction and y-direction displacements for edges AB, CD, BD and AC. u(A), u(C), v(B) and v(A) are the x and y displacements of nodes A, B and C. To remove the rigid body motion, the vertex A was fixed in both x and y directions, and B and C were fixed in the x and y direction, respectively. A displacement was applied to vertex C in the x-direction to simulate the fatigue loading condition.  
Figure 5: Simulation, (**a**) and experimental results of uniaxial tension and LCF steady-state hysteresis loop at 1.4% strain range, (**b**).  
Figure 6: (**a**) RVE with O phase (red) and \(\beta\) phase (blue); and (**b**) periodic boundary conditions.
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 7 Discussion
Until S1C will not be operational, the nominal temporal resolution of S1 mission will remain 12 days, limiting the capability of such a system in providing timely information about processes in progress on the surface of the Earth.