esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Design of the Proposed System
#### 5.4.1 The High-Level Architecture of the Proposed System  
The section below illustrates the high-level architecture and implementation details of Hyperledger Fabric as a solution.  
**The Logical Structure of Hyperledger Fabric**  
1. Presentation layer: the patient can access their PHR from a mobile app or web browser and interact with a Fabric blockchain network through a system development kit (SDK).
2. Data access layer: the SDK provides a simple application programming interface (API) that encapsulates all access to the ledger by allowing an application to interact with Fabric. The system provides a security layer so that the system's members can access their related documents and information based on their role.
3. Logic layer: this layer represents the functions of the chaincode for different members, which are healthcare centers, patients, physicians, etc. In addition, CouchDB and the ledger store the healthcare center's information and the transaction history on the Hyperledger Fabric blockchain to maintain immutability and security. Each member can request access to the PHR, but they cannot view the record unless the patient grants them access.
4. Data storage layer: the system solution contains data that are off-chain and data that are on-chain to maintain security and efficiency simultaneously; these are: * Off-chain: this stores a user's basic information through the health provider, including their mobile numbers, email addresses, and hashed passwords, in a regular secure database to provide them with the ability to delete and remove their accounts from the system at any time; * Interplanetary file system (IPFS): a peer-to-peer decentralized storage service that stores vast PDF files such as X-rays with the proper security access, which will reduce the load on the blockchain and enhance performance [51].  
Figure 4 illustrates the logical layers.  
Figure 4: Logical layers of the solution.
## 3 Results
### Running Initiation Key Events of Interest/Running Initiation Performance
#### 3.3.1 Group Scale  
As shown in Figure 4, the three key initiation events of interest exhibit a similar pattern in the two conditions. Wave initiations occur first in both the RWE and VE conditions with respective average timings of \(-\)2507 (\(\pm\)721) ms and \(-\)2704 (\(\pm\)1191) ms before the partner crosses the mark. Gaze releases and foot lifts occur later and closer together. The average timings of gaze release are \(-\)349 (\(\pm\)216) ms in RWE and \(-\)331 (\(\pm\)186) ms in VE. Foot lifts occur on average at 5 (\(\pm\)90) ms in RWE and 72 (\(\pm\)129) ms in VE. The repeated measures ANOVA revealed that the three events occur at different times (F (2,66) = 57.930; \(p\) < 0.001; \(\eta_{p}^{2}\) = 0.840). Post hoc comparisons with Bonferroni corrections revealed that wave initiations occur significantly earlier than gaze releases (\(p\) < 0.001; \(d\) = 3.371) and foot lifts (\(p\) < 0.001; \(d\) = 3.935) in both conditions. Although there was no significant difference between the average times of gaze releases and foot lifts, gaze releases seem to temporally precede foot lifts (\(p\) = 0.132; \(d\) = 0.564). The comparison between conditions shows no significant difference between VE and RWE behavior (\(p\) = 0.707; \(d\) = 0.055), and there was no interaction between conditions and key events (\(p\) = 0.826; \(\eta_{p}^{2}\) = 0.002).
# Deep Anomaly Detection Based on Variational Deviation Network
## 4 Methods
### Variational Auto-Encoder
#### 4.3.2 Principle of Variational Auto-Encoder  
The variational auto-encoder generates a latent space representation through the encoder, and then generates a \(\hat{x}\) similar to the normal data \(x\) through the decoder, where we only know the original normal data \(x\), and we want to infer the characteristics of \(z\). The distribution, that is, calculating \(p(z|x)\), is expressed as follows:  
\[p(z|x)=\frac{p(x|z)p(z)}{p(x)} \tag{4}\]  
However, calculating \(p(x)\) is quite difficult, so let us approximate \(p(z|x)\) with another distribution \(q(z|x)\), we defined it to have a scalable distribution. If we can define the parameters of \(q(z|x)\) so that it is very similar to \(p(z|x)\), we can use it to approximate the complex distribution, so here we used the KL loss function. The KL dispersion degree is the difference between two probability distributions, so if we want to ensure that \(q(z|x)\) is similar to \(p(z|x)\), we can minimize the KL divergence between the two distributions.  
\[minKL(q(z|x)||p(z|x)) \tag{5}\]  
In the image generation model, we can use \(q\) to infer potentially hidden variables (latent states) that can be used to generate observations. We can further construct this model into a neural network structure, where the encoder model learns the mapping from \(x\) to \(z\), and two neural networks are constructed in the encoder \(\mu_{k}=f_{1}(x_{k}),log\sigma^{2}=f_{2}(x_{k})\) to calculate the mean and variance in the normal distribution, and the decoder model learns the mapping from \(z\) to \(x\), as shown in Figure 5.  
Figure 5: Schematic diagram of the basic principle of a variational auto-encoder. \(X\) is trained by two encoders separately to obtain the mean and variance, which are kept close to the normal distribution by an additional loss function to obtain a probability distribution \(Z\) conforming to \(X\). \(Z\) is reduced to \(\hat{X}\), similar to \(X\) by a decoder and continuously optimized by reconstructing the error loss function.  
In order to prevent the noise from being zero, we stipulated that \(p(z|x)\) were all aligned with the standard normal distribution while ensuring that the model had the ability to generate, that is, it could sample from \(N(0,1)\) to generate an image.  
The loss function includes two items. The first penalizes the reconstruction error, and the second encourages us to learn that the distribution \(q(z|x)\) is similar to the real prior distribution \(p(z)\). We assumed the unit Gaussian distribution, where each is the latent space of dimension \(j\).  
\[\mathcal{L}(x,\hat{x})+\sum_{j}KL(q_{j}(z|x)||p(z)) \tag{6}\]  
In order for \(p(z|x)\) to be in line with the standard normal distribution, the loss function here is specifically expressed as:  
\[\mathcal{L}(x,\hat{x})+\sum_{j}KL(q_{j}(z|x)||N(0,1)) \tag{7}\]
# Prediction of Remaining Fatigue Life of In-Service Bridge Cranes
## 3 Rainflow Counting Method and Stress Spectrum Acquisition  
The two-parameter rainflow [22] counting method is a method used to obtain stress spectra for metallic structural components. It divides the load history by taking the amplitude and mean value as two parameters and calculates the stress amplitude and stress mean value corresponding to the obtained mini-cycles, which can be used for fatigue life estimation and compilation of fatigue load spectra. Because the two-parameter rainflow counting method takes into account both dynamic strength (magnitude) and static strength (mean value), it is in line with the inherent characteristics of fatigue loading itself. Therefore, it is widely used in fatigue life for statistics and analysis of data.  
The load history to which most engineering components are subjected is complex and consists of a combination of variations in multiple loads. This combination effect can lead to fatigue damage of metal components. Therefore, the study of fatigue life and the reliability of metal components requires the analysis of their stress spectra.  
The stress spectrum of an overhead crane refers to the plot of the relationship [3] between the magnitude of stress and the frequency number of stress occurrences of the main box girder under different operating conditions, which are obtained by statistically processing a certain number of stress-time histories; regarding the acquisition of the stress spectrum, the relevant literature has many mature methods to acquire the stress-time histories of the fatigue failure points through field experiments or simulation model dynamics simulations and to obtain the stress-time histories at the point of fatigue failure by using the two-parameter rainflow counting method cycle counting to obtain the desired stress spectrum.
# Statistical Characterization of the Magnetic Field in Space during Magnetic Storms
## 3 Autocorrelation Function for Each Component of the Magnetic Field during a Magnetic Storm  
The autocorrelation function is a measure of the similarity between a signal and a delayed signal and is based on an inverse Fourier transform of the power spectrum. The estimation of the autocorrelation function directly affects the accuracy of the estimation of the power spectrum. It thus has an impact on the performance of radar, navigation, and communication systems. For example, when detecting the position and velocity of a target using Doppler radar, the autocorrelation function can be used to directly determine the fuzziness of the signal and, thus, the resolution of the estimates of distance and velocity [28]. When performing array processing, the autocorrelation function determines the properties of the autocorrelation matrix, which has an impact on the performance of adaptive beamforming and direction estimation algorithms [29; 30; 31; 32]. In wireless communications, autocorrelation estimation is an effective method for estimating fundamental tones in noise and is also the key to blind signal separation and channel equalization techniques for distributed MIMO communication systems [33; 34].  
For stationary stochastic processes, the autocorrelation function is only related to the time interval between two samples and is independent of the initial observation moment, i.e.,  
\[R(t,t+\tau)=E(x(t)x(t+\tau))=R(\tau) \tag{4}\]  
However, we cannot guarantee that the magnetic fields during a magnetic storm are always stationary processes; therefore, when estimating the autocorrelation function, Equation (4) needs to be applied based on the time delay unit at different initial observation moments. In reality, the mathematical expectation operator in Equation (4) cannot be computed directly through averaging. The specific procedure is implemented by replacing  
Figure 2: Probability densities of the magnitude of each component of the magnetic field. **(a)** 0:00 on 6 March 2016, to 23:00 on 7 March 2016; **(b)** 0:00 on 13 October 2016, to 23:00 on 14 October 2016; **(c)** 0:00 on 27 May 2017, to 23:00 on 28 May 2017; **(d)** 00:00 on 8 September 2017, to 23:00 on 9 September 2017; **(e)** 0:00 on 27 September 2020, to 23:00 on 28 September 2020; **(f)** 00:00 on 4 November 2021, to 23:00 on 5 November 2021.
# A Capacity Enhancement Method for Frequency-Hopping Anti-Jamming Communication Systems
## 3 Results
### Comparison with OFDM Signals
#### 3.5.2 Comparison of FH Capacity Enhancement Signals with OFDM Transmission Signals  
OFDM transmission signals, generally used to transmit broadband information, occupy one or more sub-bandwidths, up to all sub-bandwidths within the bandwidth, when the transmission rate is at its highest. OFDM transmission signals give priority to the utilization of frequency resources, so higher-order modulation is generally used.  
OFDM transmits the signal using a coding efficiency of \(\eta\) and a modulation order of \(\lambda\), when the demodulation threshold of OFDM is ThoFDM, with an occupied bandwidth of \(W_{\rm s}\) and a transmission information rate of \(r=W_{\rm s}\lambda/\eta\). For ease of calculation, the bandwidth of the 1/2 sub-bandwidth is ignored, as shown in Figure 18.  
The information transmission rate is \(2r_{\rm FH-A}\) and the demodulation threshold is Th\({}_{\rm A}\) for the case of doubling the signal rate with FH capacity enhancement.  
According to Equation (22), the ratio of the immunity of the two types of signals depends on the following equation:  
\[\gamma=\frac{r_{\rm FH-A}(Th_{\rm A})}{r_{\rm OFDM}(Th_{\rm OFDM})} \tag{24}\]  
According to Equation (24), for a certain occupied bandwidth, when the BPSK regime is used for FH capacity enhancement signals and the 16 QAM regime is used for OFDM signals. The demodulation SNR requirement for OFDM high-order modulated signals is four times that of FH capacity-boosted signals (6 dB). At this point, when \(r_{\rm OFDM}\) is 1000 (30 dB) times as large as \(r_{\rm FH-A}\), the FH capacity-boosting signal is 36 dB more resistant to interference than the OFDM transmission signal. When \(r_{\rm OFDM}\) and \(r_{\rm FH}\) (the total rate of messages A and B) are equal, FH capacity-boosting signal A is more resistant to interference than the OFDM transmission signal by 9 dB.
## 2 Modern Physical Layer
### Underwater Acoustic Channel Characterization
#### 2.1.4 Channel Propagation Models  
In conclusion, both frequency-dependent absorption and noise and low and variable propagation speed have a significant impact on the design of underwater communication networks. It has been demonstrated that different scenario characteristics and even current different time spots lead to different channel responses.  
Simulations are a very convenient way of testing algorithms and protocols before their actual deployment. However, one has to be certain that the simulation results are as accurate as possible. The use of, for example, a too simple of a physical model can alter the behavior and, sometimes, lead to wrong or incomplete conclusions [14,15].  
The most simple propagation model is the empirical Thorp's formula presented in Equation (2). Although the simulation can be very fast, attenuation formula accuracy is very poor, as long as it is only based on the frequency and the distance from the nodes, ignoring other important physical criteria, such as ocean waves, depth, holography, _etc_.  
The next model in complexity is the Monterrey Miami Parabolic Equation (MMPE) [16], implemented in OPNETin a simulator called Xie Gibson [17]. Its main equation is shown in Equation (8) This model offers a better description of the attenuation calculation by including the effects of ocean waves, the depth of the nodes and the seafloor multipath. However, several parameters need to be computed before running any simulation [18].  
\[PL(t)=m(f,s,d_{S},d_{R})+w(t)+e() \tag{8}\]  
where:  
* \(PL(t)\) : propagation loss from node S to node R.
* \(m()\) : propagation loss without random and periodic components.
* \(f\) : signal frequency (kHz).  
* \(d_{S}\) : sender's depth (meters).
* \(d_{R}\) : receiver's depth (meters).
* \(s\) : distance between S and R (meters).
* \(w(t)\) : function to approximate periodic signal loss due to wave movement.
* \(e()\) : signal loss due to random noise or error.  
Last, some models are based on the solution of ray equations, described in detail in [19], to determine the ray coordinates of the acoustic signal propagation. Recent simulators, such as the World Ocean Simulator System [20] in Network Simulator 2, and some interesting OPNET simulator extensions [18] incorporate this model. Several woks have been performed to validate this model with real measurements [20, 21, 22, 23, 24, 25, 26]. Integrating empirical data from world databases that measure the sound speed profile, bathymetry, floor sediment and ocean wave motion, this model shows a behavior that is very close to reality.  
Recently, some extensions of the previous models have been developed to simulate sea surface dynamics [27], variable attenuation and the Doppler effect produced, as described in Section 2.1.2. These models use successive runs of the propagation model as the surface evolves, so simulation performance decreases significantly.
# Advanced Assistive Maintenance Based on Augmented Reality and 5G Networking
## 6 Prototyping and Testing
### Remote Assistance  
In view of a fully-capable on-field adoption, the system was also equipped with an augmented reality remote assistance service that allows the on-field operator to interact with the remote expert. The two share a real-time audio and video streaming based on _Skype for HoloLens(r)_ with virtual objects and 3D markers in overlay, as depicted in Figure 8.
# Real-Time Interactive Parallel Visualization of Large-Scale Flow-Field Data
## 3 Data-Partitioning Strategy Based on Grid Segmentation
### Preprocessing of File Data
#### 3.1.1 The EnSight Gold Data Format  
The EnSight Gold format is used to store scientific and engineering visualization data. It can be used directly for 3D visualization and supports various effects such as stereoscopic display and projection. Additionally, the EnSight Gold format can directly describe vector data and is scalable, supporting multiple software platforms. Due to its significant advantages in visualization and scalability, the EnSight Gold format has been widely used in industrial and scientific fields [5]. The format consists of multiple files, with different types of data files stored in files with different suffixes. For example, geometric model files use the.geo suffix, whereas model-related attribute data are stored in files with user-defined suffixes. The EnSight Gold format is commonly used to store unsteady flow-field data and uses.case files to maintain the file-association relationships between various geometry models and attributes, as well as the data organization relationships of different time steps. Figure 2 shows the structure of an EnSight Gold file.  
Figure 1: After initializing the MPI (Message Passing Interface) environment, in order to reduce communication overhead, each process scans and calculates the data area for which it is responsible for processing and constructs metadata. On this basis, the grid is partitioned and mapped.
## 2 Materials and Methods  
### Numerical Methodology  
The finite volume method in ANSYS Fluent 2022 R1 software performed numerical simulations of substrates mixing in jet reactors. Mixing conditions were analyzed for inlet flow ranges 20, 40, and 60 mL/min, T- or S-type reactors, and citric acid (CA) or formic acid (FA). The geometries of the investigated reactors are presented in Figure 1. The simulations were conducted in a 3D model representing the reactors divided into two inlets (one for ammonium sulfide, AS, and the other for CA/FA), an outlet, and a center where bulk mixing occurs. The mesh on the inlets and outlet was generated by a sweep method using quad\(\backslash\)tri mesh type. Tetrahedrons created the center mesh (Figure 2). The mesh consists of approx. 450,000 elements, of which the center itself approx. 120,000. It has been proved that an increase in the number of calculation cells does not affect the quality of the calculations.  
Despite the laminar flow with a parabolic velocity profile in the inlet channels, the wall boundary layer y was determined (Equations (1)-(3)) due to the turbulent flow in the mixing and outlet area. The maximum velocity used was assumed for further calculations.  
\[\frac{u}{u^{*}}=u^{+}=5=y^{+}=y\frac{u^{*}}{\
u} \tag{1}\]\[u=\frac{4Q}{\pi d^{2}} \tag{2}\]  
\[y=\frac{\
u u}{u^{*2}}=\frac{25\mu}{\rho u}=\frac{25\mu\pi d^{2}}{\rho 4Q} \tag{3}\]  
where \(u\) is the mean velocity; \(u^{*}\) is dynamic velocity; \(u^{+}\) is dimensionless velocity; \(y^{+}\) is dimensionless length; \(\
u\) is kinematic viscosity; \(Q\) is inlet flow rate; \(d\) is the diameter; \(\rho\) is density; \(\mu\) is dynamic viscosity.  
For flow simulation, the Standard k-epsilon model was chosen. The reagent mixing was simulated by the Species Transport model, where the mixture's density and viscosity (Table 1) were determined with volume and mass-weighted mixing laws. Mass diffusivity was assumed to be \(10^{-9}\) m\({}^{2}\)/s. Inlet boundary conditions were exported from separate simulations to obtain velocity profiles of reagents. Simulations were computed with First Order Upwind discretization and then with Second Order Upwind discretization to increase the accuracy of the solution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Solution** & **Density [kg/m\({}^{3}\)]** & **Dynamic Viscosity [Pa\(\cdot\)s]** \\ \hline CA & 1028.63 & \(1.216\times 10^{-3}\) \\ FA & 1072.17 & \(1.194\times 10^{-3}\) \\ AS (same as water) & 998.20 & \(1.003\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physicochemical parameters of reagents used [27].  
Figure 1: (**a**) S-type and (**b**) T-type reactors with their dimensions.  
Figure 2: Mesh of (**a**) T-type and (**b**) S-type reactors.  
Based on CFD results, turbulence intensity \(I\) was determined (Equation (4)). In addition, the zones above 70% of the maximum value of the mixing intensity were designated for both types of reactors.  
\[I\equiv\frac{u^{\prime}}{U} \tag{4}\]  
where \(u^{\prime}\) is the root-mean-square of the turbulent velocity fluctuations; \(U\) is the mean velocity (Reynolds averaged).
# Slope Stability Analysis of Open-Pit Mine Considering Weathering Effects
## 1 Introduction
This approach can lead to inaccurate predictions of slope stability and potentially underestimate the risks of slope failure. Therefore, it is imperative to incorporate weathering effects into slope stability assessments to ensure the safe and efficient operation of mines. Additionally, a thorough understanding of the mechanical properties of rocks at a given time and location is crucial for studying related geotechnical engineering. The process of ore mining itself involves studying the mechanical properties of rocks. Gaining a comprehensive understanding of the physical and mechanical properties of surrounding rocks, particularly under the influence of weathering, holds significant theoretical and practical value. It is essential for ensuring the safe mining of open-pit mines and optimizing mining boundaries. Although weathering effects play a crucial role in determining slope stability, few researchers have investigated the effects of weathering and the traditional analysis methods either overlook these factors or oversimplify them. In this study, a comprehensive analysis of slope stability in open-pit mines, taking into account the influence of weathering effects, was conducted. Laboratory tests were performed to investigate the impact of weathering on the mechanical properties of black-shale specimens. Numerical simulations were employed to evaluate slope stability and determine safety factors under various weathering conditions. By incorporating weathering effects into slope stability analysis, this study aimed to provide a more accurate and reliable assessment of slope stability in open-pit mines. The findings of this research could significantly contribute to enhancing mine safety, mitigating slope failure risks, and optimizing mine planning and operations.
# Space Charge Accumulation and Decay in Dielectric Materials with Dual Discrete Traps
## 3 Bipolar Charge Transport Model
### Self-Consitent Equations  
The charges in dielectric materials are governed by a set of self-consistent equations [22; 30; 31].  
(a) Charge advection-reaction equations,  
\[\frac{\partial q_{free}(x,t)}{\partial t}+\frac{\partial j_{c}(x,t)}{\partial x }\ =\ S_{free}(x,t) \tag{27}\]  
\[\frac{\partial q_{trapi}(x,t)}{\partial t}\ =\ S_{trapi}(x,t),(i\ =\ 1,2) \tag{28}\]  
(b) Charge transport equation,  
\[j_{c}(x,t)\ =\ q_{free}(x,t)\mu_{0}F(x,t) \tag{29}\]  
(c) [PERSON]'s equation,  
\[\frac{\partial^{2}\phi(x,t)}{\partial x^{2}}\ =\ -\frac{q_{free}(x,t)+\sum\limits_{i \ =\ 1}^{2}q_{trapi}(x,t)}{\varepsilon_{0}\varepsilon_{r}} \tag{30}\]  
(d) The spatial integration of electric field,  
\[V_{appl}\ =\ \int_{0}^{L}F(x,t)dx \tag{31}\]  
where \(q_{free}\) and \(q_{trap}\) are the free and trapped charge densities in the dielectric in cm\({}^{-3}\), respectively; \(j_{c}\) is the conducting current density in the material in Am\({}^{-2}\); \(D\) it the diffusion coefficient of free charges which is equal to \(\mu_{0}k_{B}T/q_{e}\); \(\phi\) is the potential in the material in V; and \(V_{appl}\) is the applied voltage on the sample in V. The summation of free and trapped charge densities is named as net charge density, \(q_{net}\)