esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Cooperative Optimization Algorithm  
A crucial aspect for solving the GSPDP is that determining the suitability values \(w_{r,\rho}\) is no trivial task. While a user may be able to list a small number of best suited service station locations for each of his/her SPRs, we have to consider it practically infeasible to obtain reasonably precise suitability values for all potential service station locations \(V\) of each SPR by directly asking the users. A complete direct questioning would not only be extremely time consuming, but users would easily be overwhelmed by the large number of possibilities, resulting in incorrect information. For example, users easily tend to only rate their preferred options as suitable and might not consider certain alternatives as also feasible, although they actually might be, on second thought, when no other options are available. The problem of user fatigue substantially impacting the quality of obtained feedback when too much information is asked from a user is, for example, discussed in Reference [35].  
Hence, interaction with users needs to be kept to a minimum and should be done wisely to extract as much meaningful information as possible. Moreover, users must be confronted with easy questions whose answers at the same time provide strong guidance for the target system. Based on this philosophy, we present a _Cooperative Optimization Algorithm (COA)_ for solving the GSPDP if no a priori information about the use cases of the users, their respective demands, or the suitability values is known. The general concept of COA was originally introduced in Reference [13]. In this paper, this framework is substantially adapted and extended towards better scalability and for solving instances of the more general GSPDP, as specified in the previous section.  
In Section 4.3, we formally define how users can interact with COA. Note, however, that COA does not put a strict limit on the number of allowed interactions with each user. Instead, we will measure the effectivity of our COA framework by the number of user interactions the framework requires for generating a (close to) optimal solution.
# A Comprehensive Survey for Deep-Learning-Based
## 4 Abnormality Detection with Images over Long Range
### The Application of Satellites in Smart Grid Abnormality Detection  
In this section, we introduce the abnormality detection applications in the power grid from the three aspects of satellite positioning, optical imagery, and SAR. We will summarize the abnormality detection applications suitable for each method and summarize them in Table 3.
## 3 Numerical Verification
### Case 1: A Cylindrical Shell  
#### 3.1.1 Model Details  
The radius of the semi-circular shell is 10 m and the unbounded fluid was simulated as a far field boundary whose radius was 500 m (shown Figure 1(a)). The material properties of the fluid were set up as follows: Density = 1.225 kg/m\({}^{3}\); sound velocity, c = 340 m/s. The viscosity is so small that it can be neglected in both the theoretical analysis and CFD calculation. The purpose of this paper is to investigate the principle of pressure under vibrations of a structure. The movement of a semi-circular shell was set as a uniform vibration in the radius direction. As for the calculation parameters, the far field boundary was set to be the far field pressure, the reference pressure value was zero, boundary movement (it is the same as the shell vibration) was set as U(t) = sin(10t), which is shown in Figure 1(b), the calculating time was set to 1.0 s, and the delta-time to 0.001 s. A simple scheme second order method was accepted in the CFD simulation and the initial condition in fluid was set to a stationary condition. For comparison, two cases of a compressible and an incompressible fluid were carried out, respectively.  
In order to present the effect of the discretization, numerical solutions with different meshing sizes are displayed. Table 1 shows the comparison of the total energy in the fluid, including kinetic energy and pressure potential energy, at t = 0.2 s. As the results in the case of a size of 0.2 m and 0.1 m are the same, the size of 0.2 m was thus adopted in this paper. Afterward, there were a total of 24,885 structural hexahedron elements in the numerical model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Density of Shell Mesh (cm)** & 2.0 & 1.0 & 0.8 & 0.5 & 0.3 & 0.2 & 0.1 \\
**Total energy (10\({}^{4}\) J)** & 4.52 & 4.21 & 3.93 & 3.90 & 3.89 & 3.88 & 3.88 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The total energy with different densities of mesh.  
Figure 2: _Cont._
# Neutrosophic Linear Equations and Application in Traffic Flow Problems
## 4 Solving a System of Neutrosophic Linear Equations
To illustrate the solving process of the neutrosophic matrix equation by the MATLAB software, we use the inverse matrix method to solve the neutrosophic matrix equation by the following example. **Example 3**. : _Assume that there is the following neutrosophic matrix equation:_  
\[\left[ \begin{array}{ccc}2&-4&1\\ -3&6&-5\\ 4&-3&7\end{array} \right]\left[ \begin{array}{c}x_{1}\\ x_{2}\\ x_{3}\end{array} \right]=\left[ \begin{array}{c}4I\\ -2I\\ 3I\end{array} \right],\]  
_where \(A=\left[ \begin{array}{ccc}2&-4&1\\ -3&6&-5\\ 4&-3&7\end{array} \right]\), \(X=\left[ \begin{array}{c}x_{1}\\ x_{2}\\ x_{3}\end{array} \right]\), and \(B=\left[ \begin{array}{c}4I\\ -2I\\ 3I\end{array} \right]\). Then, we need to find the solution of \(X=[x_{1},x_{2},x_{3}]^{T}\)._  
Thus, we apply the inverse matrix method for solving this neutrosophic matrix equation by the MATLAB software, which is shown in the following program:  
% The MATLAB solution program of the neutrosophic matrix equation  
clc  
_syms I;_  
\(A\) _= [2, \(-4\), \(1\); \(-3\), \(6\), \(-5\); \(4\), \(-3\), \(7\)];_  
\(B\) _= [4!, \(-2\)!, 3! ];_  
\(C\) _= inv(\(A\))_  
\(X=C\times B^{\prime}\)  
Running results of the program are as follows:  
C =  
0.7714 0.7143 0.4000  
0.0286 0.2857 0.2000  
\(-0.4286\) \(-0.2857\) 0  
\(X=\)  
\((20^{\bullet}I)/7\)  
\(I/7\)  
\(-(8^{\bullet}I)/7\)  
If \(I\in[1,\,2]\) is considered as a possible interval of the indeterminacy \(I\), then the solution of the system is \(x_{1}=[2.8571,5.7143]\), \(x_{2}=[0.1429,0.2857]\), and \(x_{3}=[-1.1429,-2.2857]\). Especially, when \(I=1\)the system is degenerated to the system of classical linear equations and its solution is given by \(x_{1}=2.8571\), \(x_{2}=0.1429\), and \(x_{3}=-1.1429\). Obviously, the classical linear equations and solution are special cases of the neutrosophic linear equations and solution. However, the classical linear equations cannot deal with real problems with indeterminate information, while the neutrosophic linear equations can do them with determinate and/or indeterminate information.
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 2 Comparative Study of BPv7 and IPv6 Functionalities
### Feature Analysis  
This section provides a detailed row-by-row analysis of the comparison outlined in Table 1 of the two standards, highlighting key features such as addressing, reliability support, routing, and fragmentation handling.
# GPS Time Series Analysis from Aboa the Finnish Antarctic Research Station
## 3 Results
### Time Series Analysis  
It is generally accepted that the GPS coordinate time series contain some form of temporally correlated noise along the expected white noise [33]. The effect of this noise has direct effect on the uncertainty of the estimated parameters. We used Hector [67] software that enables modelling the temporal correlation of the data and thus providing more realistic uncertainty estimates. First, daily Cartesian coordinate time series were converted to local NEU coordinates with respect to the mean position, and the time series of each component was analysed separately. We found only one discontinuity in the time series analysis. It was linked to the switch from IGb08 to IGS14 in CODE's operational solutions, which were used for the RTKLIB solution. Therefore, we solved an offset in the case of RTKLIB time series at 29 January 2017. The frame of the JPL products (used in the GIPSY solution) was the same for the entire period of the time series. Second, we removed the outliers using an IQR factor of 2.2. In addition, the offsets as well as annual and semiannual effects were taken into account in the outlier rejection. About 50 epochs (i.e., 1%) were rejected from the GIPSY solution and 100 epochs (i.e., 2%) from the RTKLIB solution, respectively. Third, we estimated the trends using three different noise models: Generalised Gauss-Markov (GGM), power-law (PL) and flicker noise implementation of the Hector (FlickerGGM). Typically, the white noise is added to the model, but in our time series the fraction for white noise was zero and, therefore, it was omitted from the model. The choice of the final model was based on the analysis of the power spectral analysis (PSD) and the Akaike and Bayesian information criteria (AIC/BIC). The model with lowest AIC or BIC values should fit best the time series. Figure 11 depicts the PSD plots, whereas Tables 5 and 6 summarise the AIC/BIC values. The results suggest that the GGM and PL models fit mostly equally best to the estimated spectrum. For the GIPSY Up component, the AIC/BIC analysis supports more GGM model, but the PSD plot shows that none of the model fits very well to the data. There are a few power peaks at roughly 27 cpy and 60-70 cpy corresponding roughly 14 and 5 day cycle, respectively. Those peaks probably cause the misfit of the models to the estimated spectrum. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Conventional** & **North** & **East** & **Up** & **Robust Statistics** & **North** & **East** & **Up** \\ \hline Count & 4886 & 4858 & 4886 & & & & & \\ Min & \(-7.9\) & \(-7.8\) & \(-17.9\) & Q1 (25\%) & \(-1.8\) & \(-1.8\) & \(-4.1\) \\ Max & 7.8 & 7.8 & 17.8 & Q3 (75\%) & 1.8 & 1.8 & 4.1 \\ Spread & 15.8 & 15.7 & 35.6 & IQR & 3.6 & 3.6 & 8.2 \\ Avg & 0.0 & 0.0 & 0.0 & MED & 0.0 & 0.0 & 0.0 \\ Std & 2.6 & 2.6 & 6.0 & MAD & 1.8 & 1.8 & 4.1 \\ Rms & 2.6 & 2.6 & 6.0 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Conventional and robust statistics to assess the accuracy of RTKLIB solution with respect to GIPSY solution [unit: mm].
# Research on Key Technologies of Elastic Satellite Optical Network Based on Optical Service Unit
## 3 Analysis and Discussion
### Performance Evaluation  
Following the analysis of delay performance, we have conducted a quantitative evaluation of the delay optimization mechanism of OISL-OSU compared with OISL-MPLS from three perspectives: network architecture, protocol processing, and switching mode.  
1. Network architecture Table 8 presents a comparative overview of the network architectures of OISL-OSU and OISL-MPLS.  
2. Protocol processing The OISL-MPLS protocol stack processing delay comprises IP/Label header processing (\(t_{HdrProc}\)), forwarding table lookup (\(t_{FIB}\)), and QoS shaping (\(t_{QoS}^{shaper}\)), which is \[\mathcal{T}_{OISL-MPLS}=t_{HdrProc}+t_{FIB}+t_{QoS}^{shaper}\] (14) Meanwhile the OISL-OSU protocol processing delay is burst scheduling (\(t_{BurstSch}\)): \[\mathcal{T}_{OISL-OSU}=t_{BurstSch}\] (15)
3. Switching mode Store-and-forward processing (\(t_{store}\) and \(t_{forward}\)) contributes to the delay in OISL-MPLS packet switching, which is \[\mathcal{T}_{OISL-MPLS}^{\prime}=\sum_{i=1}^{n}\left(t_{store}^{(i)}+t_{forward }^{(i)}\right)\] (16)  
Figure 13: Back-to-back test results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Delay Source** & **OISL-OSU** & **OISL-MPLS** \\ \hline Encapsulation/decapsulation & Fixed-length & IP packet parsing \\ Label lookup & Optical layer wavelength selection & Electrical layer forwarding \\ Optical (O)/electrical (E) conversion & Optical switching & O/E/O conversion \\ Queue Buffering & Reserved bandwidth in hard channel & Bandwidth contention delay \\  & eliminates contention delay & Bandwidth contention delay \\ \hline \hline \end{tabular}
\end{table}
Table 8: Network architecture delay comparison.  
In contrast, OISL-OSU ensures deterministic delay through pre-configured optical paths (\(t_{config}\)) and implements cacheless forwarding based on burst header packets (\(t_{BHP}\)) as follows:  
\[\mathcal{T}^{\prime}_{OISL-OSU}=t_{config}+t_{BHP} \tag{17}\]
# A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction
## 4 Results and Discussion
### Experiment Analysis and Discussion
#### 4.2.2 Effects of Spatial Dimension on the Predictive Models  
The urban functional area is divided into 19 categories according as presented in Section 3.2. The model used in this paper predicts the waiting time of passengers according to different functional area categories, the MAE value of predicted passenger waiting times for different ribbon categories is shown in Figure 9 below.  
Figure 8: Prediction effect of different time segments.  
As shown in Figure 8, the predicted MAE values of different functional areas are within a certain range, the highest MAE value is 73.28 s while the best one is only 17.68 s.  
Among all the functional areas, locations close to taxi companies have the smallest error. This is because the mobility characteristics of taxis near companies are the most obvious whether on weekdays or holidays, so their taxi operation patterns are also the easiest to capture. As areas such as Government agencies and social groups, and bus stations are often located in remote locations with fewer taxis, the waiting time for taxis is thus longer and has strong randomness there. The prediction error is relatively big in such areas. Areas without function category identification are difficult to predict its taxi waiting time due to its fuzzy characteristics, so its prediction effect is worse than other areas.  
To solve the relatively high prediction error of taxi duration near no function area category identification, the surrounding trajectory data can be used to model its urban functional attributes. Furthermore, trajectory data can fit such non-characteristic geographic areas by time periods to better simulate the real situation.
## 4 Discussion
### Estimating the FPAR in Canola Growth Periods with Hybrid Models  
This study demonstrated that the hybrid inversion models combining ANN and VIs were reliable for estimating the FPAR across the canola growth stages. However, comparing the FPAR estimated by the ANN-VIs hybrid models with the in situ measured FPAR, there was an underestimation trend at the flowering stage. Yellow flowers emitted red light radiation (yellow = green + red) for canopy horizontal signals, and this increased radiation caused an increase in the reflectance of the red band canopy [95; 96]. The proportion of green and blue bands was positively correlated with the number of flowers per unit area [97]. This led to the estimated FPAR value being less than the measurement, inducing the lowest estimation for the canola flowering period. Furthermore, the stem gradually changed from green to withered yellow, while the pod gradually changed from green to yellow and gray, leading to errors in the estimation of the FPAR by the VIs at the maturity stage.  
Traditional FPAR inversion studies mainly established correlations between the VIs and FPAR, such as SR and NDVI [98; 99]. However, the relationships between the VIs and FPAR could be sensitive to change and unstable. [PERSON] et al. [100] mentioned that the simple proportional assumption between the NDVI and FPAR was valid only when the canopy background was ideally black. There are also machine learning algorithms that can estimate the FPAR. [PERSON] et al. [101] demonstrated the feasibility of estimating the maize FPAR using an ANN and stepwise multiple linear regression (SMLR) method (R\({}^{2}\) > 0.6). Although there were some errors in the FPAR estimation at the flowering and maturity stages, the hybrid model still exhibited acceptable performance during the seedling stage. This could provide a possibility for the accurate estimation of canola FPAR in the growth stages.
# Logistics Space: A Literature Review from the Sustainability Perspective
## 4 Impact of Logistics Space: On Sustainability
### Social Dimension  
The development of logistics space has brought about great social effects, such as employment, urban traffic safety and urban geography. On the one hand, some municipalities favour logistics activities, considering that they provide industrial jobs requiring little qualifications when more traditional manufacturing jobs have disappeared from metropolitan areas. On the other hand, many municipalities reject logistics activities, because they generate noise, truck traffic and safety problems [49]. They are also accused of consuming space, with a low ratio of jobs per hectare. Frequent shipments put pressure on local traffic safety and affect the normal operation of other social activities. Freight vehicles should avoid direct conflicts with other social vehicles, and its route and time is determined by the location of logistics facilities. In addition, heavy-duty trucks will cause wear on the road surface, destroy road infrastructure, and bring serious security risks. Generally speaking, the increase in freight capacity itself may very well increase the employment level. Logistics facilities need a reasonable position to ensure the sustainable development of society.
## 6 Conclusions  
In this paper, a hybrid immersed boundary-lattice Boltzmann and finite difference method for fluid-structure interaction and heat transfer in non-Newtonian flow is presented, with the geometry-adaptive Cartesian mesh technique to enhance the computational efficiency. The present numerical method includes four parts: the fluid solver, heat transfer solver, structural solver, and immersed boundary method for fluid-structure interaction and heat transfer. Specifically, the multi-relaxation-time-based LBM is adopted for the computation of non-Newtonian flow, with the geometry-adaptive Cartesian mesh technique to enhance the computational efficiency. The heat transfer equation is spatially discretized by a second-order up-wind scheme for the convection term, a central difference scheme for the diffusion term, and a second-order difference scheme for the temporal term. The structural dynamics is numerically solved using a finite difference method. The no-slip boundary is achieved using an immersed boundary method.  
The proposed method and developed solver is validated with standard benchmark problems involving the non-Newtonian fluids flow, i.e., the developing flow of non-Newtonian fluid in a channel, heat transfer around a stationary cylinder and flow around a stationary cylinder with a detached  
Figure 11: Instantaneous vorticity (left column, ranging from \(-2U_{0}/D\) to \(2U_{0}/D\)) and temperature (right column, ranging from 0 to 1.0) contours at Re = 100, Pr = 1.0, \(H_{0}=1.0\), \(n=0.6\), and \(f^{*}=0.8\) (**top** row), 1.0 (**middle** row), and 1.2 (**bottom** row).  
filament. The good agreements achieved by the present method with the published data show that the present extension is an efficient way for fluid-structure interaction and heat transfer involving non-Newtonian fluid.  
The heat transfer around an oscillating cylinder in non-Newtonian fluid flow is also numerically studied using the present method, considering the effects of the oscillating frequency and amplitude. The results show that the increasing frequency and amplitude tend to enhance the heat convection, resulting in larger time-averaged Nusselt numbers. The enhancement that occurs in the shear-thinning fluid is much more significant than that in the shear-thickening fluid. The present results may be used to expand the currently limited database of FSI and heat transfer benchmark studies.  
[PERSON] is the recipient of an Australian Research Council Discovery Early Career Researcher Award (project number DE160101098).  
[PERSON] and [PERSON] conceived the ideal. [PERSON] developed the code and conducted the simulation. [PERSON] and [PERSON] analysed the results, drafted and revised the manuscript.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
CFD computational fluid dynamics  
LBM lattice Boltzmann method  
IBM immersed boundary method  
BGK Bhatnagar-Gross-Krook  
TRT two-relaxation time  
MRT multi-relaxation time  
FSI fluid-structure interaction
## 2 Methodology
### Ionization Rate at Different Heights
#### 2.2.1 Geometric Models and Particle Source  
As shown in Figure 4, a geometric model was built that involved 38 identical detectors, recording energy deposition at different altitudes. Each detector is a thin slice with a radius of 5 m and a height of 1 mm. We configured one detector per 1 cm interval for 0-9 cm, one detector per 10 cm interval for 10-90 cm, one detector per 1 m interval for 1-9 m, and one detector per 10 m interval for 10-100 m. Since the atmospheric density varies very little  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Particle Type** & **Number** & **Average Energy** & **Range in Air** \\ \hline \({}^{222}\)Rn & 100,000 & 0 & 0 \\ alpha & 400,000 & 6.123 MeV & 4.94 cm \\ e & 520,866 & 263.4 keV & 63.16 cm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stabilization products of radon decay.  
Figure 3: Decay paths of radon and its daughters.  
within 0-100 m, we assumed sea level air for the world and the detector material. Sea level air is comprised of 0.0000124 C, 0.755267 N, 0.231781 O and 0.012827 Ar with a density of \(1.20479\times 10^{-3}\) g/cm\({}^{3}\).  
Whereas alpha particles have a range of 4.94 cm in air, all isotropic sources were arranged \(\pm\)5 cm around the detector; if this distance was exceeded, alpha particles could never access the detector. Moreover, surface sources were established at 1 cm intervals to measure the alpha emitted within a volume of 5 m radius and 1 cm height at that location. For instance, the detector located at 1 m was surrounded by an alpha source of 5 m radius at 0.95, 0.96, 0.97, 0.98, 0.99, 1, 1.01, 1.02, 1.03, 1.04, and 1.05 m. The range for beta particles is 63.16 cm. Due to the longer range of the beta particles, we did not utilize all 38 detectors as we did for alpha particles. Only 10-100 m detectors were used, and a beta isotropic surface source was designed with a radius of 5 m and spacing of 10 cm. The beta surface sources around the ten detectors were positioned at \(\pm\)60 cm. As with alpha particles, surface sources were placed at 10 cm intervals to measure the beta emitted within a volume of 5 m radius by 10 cm height at that location. The number of particles emitted per second from the source at different heights was fixed from the distribution of alpha and beta particles with height.
## 3 The Variety of Effects the Broken Cloud Arrays Could Have on a Light Field
### Sky Luminance Distribution  
The sky luminance distributions were calculated for solar coordinates (\(A_{S}=180^{\circ}\), \(z_{S}=60^{\circ}\)) and three nadir-view cloud fractions: (a) 0.15, (b) 0.30 and (c) 0.45. Assuming the cloud-base altitude is \(H_{C}=1\) km and the cloud radius is \(R_{C}=0.5\) km, the respective hemispherical sky cover fractions (\(C_{F,humsf}\)) are as follows: (a) 0.22, (b) 0.45, and (c) 0.67. The clouds situated opposite to the position of the sun can contribute essentially to the ground-reaching radiation if the sun is low and cloud albedo \(\rho_{C}>0.5\) (see Figure 2). Therefore, the beams of light entering the upper interface of a straight pipe from the anti-solar quadrant can have an important contribution to the light field below the pipe. It is also shown in Figure 2 that the luminance typically drops as the cloud fraction increases.  
Figure 2: Sky luminance distribution under broken cloud arrays (kcd/m\({}^{2}\)) computed using the UniSky simulator for a solar altitude at \(30^{\circ}\) and cloud fractions as follows: (**a**) 0.15, (**b**) 0.30, and (**c**) 0.45.