esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### GNSS Network RTK Data Solution Model  
Typically, the carrier phase observation equation and the pseudo-range observation equation can be established between the receiver and the satellite:  
\[\rho_{r}^{i}=R_{r}^{i}+c\delta t_{r}-c\delta t^{i}+I_{r}^{i}+T_{r}^{i}+\xi_{ \varphi,r}^{i} \tag{1}\]  
\[\varphi_{r}^{i}=\lambda^{-1}\Big{(}R_{r}^{i}+c\Big{(}\delta t_{r}-\delta t^{i} \Big{)}-I_{r}^{i}+T_{r}^{i}\Big{)}+N_{r}^{i}+\xi_{\varphi,r}^{i} \tag{2}\]  
where \(\rho_{r}^{i}\) and \(\varphi_{r}^{i}\) represent pseudo-range observations and carrier phase observations, respectively; \(I_{r}^{i}\) is the measured ionospheric delay; \(\xi_{\varphi,r}^{i}\) is the amount of pseudo-range observation noise; \(\xi_{\varphi,r}^{i}\) represents the amount of carrier phase observation noise; \(\delta t_{r}\) represents the amount of receiver clock error; \(T_{r}^{i}\) represents the tropospheric delay error; \(R_{r}^{i}\) represents the distance between the receiver and the satellite; \(\delta t^{i}\) is the satellite clock error; \(N_{r}^{i}\) is the ambiguity that needs to be calculated; \(c\) is the speed of light. \(R_{r}^{i}\) can be determined by the receiver coordinate \((x,y,z)\) and the satellite coordinate \((x^{i},y^{i},z^{i})\) as:  
\[R_{r}^{i}=\sqrt{\big{(}x-x^{i}\big{)}^{2}+\big{(}y-y^{i}\big{)}^{2}+\big{(}z- z^{i}\big{)}^{2}} \tag{3}\]
## 4 Methods  
### Classification of Rainfall Events  
Rainfall events are very rare over the study area. Regional rainfall intensity was analyzed by [PERSON] (2016), using data collected by the Egyptian Meteorological Authority over a period of 55 years (1934-1989). He categorized precipitation into light (0.1 to 1 mm), moderate (1 to 10 mm), and heavy (>10 mm) intensity events. The rainfall intensity distribution revealed that 61% of the yearly events are light, 34% are moderate, and 5% are heavy. In addition, mean annual precipitation received at the site was 77 mm, 43 mm, and 6 mm for light, moderate, and heavy events, respectively. From the above, it is indicated that light events are extremely important. This fact stresses the importance of the capability of any alternative or complimentary system to the rain gauge measurements in estimating such light events. This desired feature of the satellite-based estimations will further be investigated in this study.  
[PERSON] (2016) performed an analysis of the underlying rainfall mechanisms in the study area. He concluded that rainfall can be broadly categorized into three groups. The first group is associated with a convectional mechanism producing rainfall that usually results from excessive heating of the near-surface air during the hot seasons. This type is forming large, thundery clouds that release considerable amounts of water in heavy rainy events. This type is well known in the Saint-Catherine mountain area in the Eastern side of the Gulf of Suez. The second group embraces frontal rainfall and mostly affects the coastal areas, causing intense rainy events with a shorter duration. This type is typical for the area from El-Tor to Sharm El-Sheikh. The third mechanism is orographic, generating significant thundery events, especially in the Eastern part of the Gulf of Suez, whereas the effect of rain shadow renders the lee side mostly dry.  
The reader may refer to the Supplementary Material accompanying this paper and which includes a short discussion on the synoptic evolution for each case study, together with animations of sequences of the respective surface synoptic analyses, analyses at 500 hPa, and satellite images.  
In the following information, a statistical analysis is performed between different satellite-based data (IMERG and TMPA) on one hand, and between the in-situ rain gauge measurements and the satellite-based data, on the other hand. The statistical metrics used are given in the Appendix A.
## 3 Experimental Results  
### Optical Spectrum and Light Current Characteristics  
Figure 2a shows the lasing spectrum at a drive current of 400 mA, and Figure 2b single-mode fiber coupled light output of the laser as a function of drive current. During measurements, a heatsink temperature of 18 \({}^{\circ}\)C was used. The 3 dB bandwidth of the laser is 9.3 nm, from 1545.8 nm to 1555.2 nm, at a central wavelength of 1550.5 nm with 42 lasing modes. The 10 dB bandwidth is 12.5 nm, from 1543.3 nm to 1555.8 nm, at a central wavelength of 1549.6 nm with 56 lasing modes as indicated in the Figure 2a. For this device, an optical signal-to-noise ratio (OSNR) greater than 60 dB is achieved. Moreover, from Figure 2b, it can be seen that a threshold current of 60 mA and fiber coupled optical power of 24.4 mW at 450 mA are obtained.  
Figure 1: (a) Schematic cross-sectional diagram of the ridge-waveguide InAs/InP QDot laser structure; (b) SEM images of the cross-sectional facet of a fabricated F-P InAs/InP QDot laser; and (c) the top view of an InAs QDot layer.
## 3 The Noise Blowing-Up Strategy
### Indicators
To assess the visual variations and the noise between the images (see Section 2.1), we shall compute the \(L_{0}^{norm},L_{1}^{norm},L_{2}^{norm}\), \(L_{\infty}\), and FID values for the following pairs of images:  
* \(\mathcal{A}_{a}\) and \(\mathcal{D}_{scenario}^{\mathcal{C}}(\mathcal{A}_{a}^{\text{hr}})\) in the \(\mathcal{R}\) domain. One writes \(L_{p,\mathcal{R}}^{norm,Adv}\) (\(p=0,1,2\)) and \(L_{\infty,\mathcal{R}}^{adv}\) the corresponding values. * \(\mathcal{A}_{a}^{\text{hr}}\) and \(\mathcal{D}_{scenario}^{\text{hr},\mathcal{C}}(\mathcal{A}_{a}^{\text{hr}})\) in the \(\mathcal{H}\) domain. One writes \(L_{p,\mathcal{H}}^{norm,Adv}\) (\(p=0,1,2\)) and \(L_{\infty,\mathcal{H}}^{adv}\) the corresponding values. * \(\mathcal{A}_{a}^{\text{hr}}\) and \(\lambda\circ\rho(\mathcal{A}_{a}^{\text{hr}})\) in the \(\mathcal{H}\) domain. One writes \(L_{p,\mathcal{H}}^{norm,clean}\) (\(p=0,1,2\)) and \(L_{\infty,\mathcal{H}}^{clean}\) the corresponding values. * \(\mathcal{A}_{a}^{\text{hr}}\), \(\lambda\circ\rho(\mathcal{A}_{a}^{\text{hr}})\) in the \(\mathcal{H}\) domain. One writes \(\text{FID}_{\mathcal{H}}^{clean}\) the corresponding values. * \(\mathcal{A}_{a}^{\text{hr}}\) and \(\mathcal{D}_{scenario}^{\text{hr},\mathcal{C}}(\mathcal{A}_{a}^{\text{hr}})\) in the \(\mathcal{H}\) domain. One writes \(\text{FID}_{\mathcal{H}}^{adv}\) the corresponding values. In particular, when adversarial images are involved, the comparison of some of these values between what occurs in the \(\mathcal{R}\) domain, and what occurs in the \(\mathcal{H}\) domain gives an insight into the weight of the noise at each level, and of the noise propagation once blown-up. Additionally, we shall as well assess the ratio:  
\[\frac{L_{1,\mathcal{H}}^{norm,Adv}}{L_{1,\mathcal{H}}^{norm, clean}}=\frac{L_{1}^{norm}(\mathcal{A}_{a}^{\text{hr}},\mathcal{D}_{scenario}^{ \text{hr},\mathcal{C}}(\mathcal{A}_{a}^{\text{hr}}))}{L_{1}^{norm}(\mathcal{A }_{a}^{\text{hr}},\lambda\circ\rho(\mathcal{A}_{a}^{\text{hr}}))}.\]  
This ratio normalizes the weight of the noise with respect to the effect of the anyhow occurring composition \(\lambda\circ\rho\). Said otherwise, it evaluates the impact created by the noise normalized by the impact created anyhow by the resizing functions.
# Hypergraph Analysis of Functional Brain Connectivity During Figurative Attention
## 6 Hypergraph Construction  
Hypergraph analysis provides a simplified and more generalized representation of the brain network. Instead of relying on the twelve individual graphs presented in the previous section, we can construct four hypergraphs using the summarization technique described in Section 2.2.  
To construct the hypergraphs, we follow these steps:  
1. Compute the label of every node \(i\) for each connectivity measure \(\Delta C\) using Equation (2) (Lab \(C_{i}=\sum_{i}^{n}\Delta C\)).
2. From the probability distribution of all \(n\) labels for each connectivity measure found the mean \(M=\frac{1}{n}\sum_{i}^{n}\text{Lab }C_{i}\) and standard deviation \(\sigma\).  
Figure 10: Disconnectivity graphs based on mutual information loss in the (**upper raw**) \(f_{1}\) and (**lower raw**) \(f_{2}\) frequency regions, associated with (**left column**) left-cube attention (\(\Delta Inf_{L}<0\)) and (**right column**) right-cube attention (\(\Delta Inf_{R}<0\)). Bold lines highlight the links with the largest negative mutual information differences (Min \(\Delta Inf\)). Violet dots denote the nodes with the lowest sums of their link weights, indicated next to these nodes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measure** & **Value** & **Lobes** & **Value** & **Lobes** \\ \hline Max \(\Delta Inf_{L}(f_{1})\) & \(1.41\) & O1–P3 & \(1.43\) & O1–O2 \\ Max \(\Delta Inf_{L}(f_{2})\) & \(1.05\) & Cz–F3 & \(0.97\) & O2–P3 \\ Max \(\Delta Inf_{R}(f_{1})\) & \(0.81\) & P4–Pz & \(0.38\) & Pz–Cz \\ Max \(\Delta Inf_{R}(f_{2})\) & \(1.12\) & FS–FP2 & \(1.05\) & FS–FP1 \\ Min \(\Delta Inf_{L}(f_{1})\) & \(-1.26\) & Cz–Fz & \(-1.18\) & Cz–F4 \\ Min \(\Delta Inf_{L}(f_{2})\) & \(-1.06\) & Cz–Fz & \(-0.81\) & Cz–FC2 \\ Min \(\Delta Inf_{R}(f_{1})\) & \(-1.07\) & F7–F4 & \(-0.92\) & F7–FP1 \\ Min \(\Delta Inf_{R}(f_{2})\) & \(-0.57\) & Pz–Cz & \(-0.22\) & Pz–FC1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maxima and minima mutual information differences.  
3. Calculate threshold value \(C_{th}=M\pm\sigma\) for each connectivity measure (\"+\" for \(M>0\), \"-\" for \(M<0\)).
4. Using the threshold value, determine whether a change in connectivity is significant or not. The change is significant if the label \(|\mathrm{Lab}\,C_{i}|>|C_{th}|\).
5. Using the summarization technique described in Section 2.2 remove insignificant nodes whose labels are smaller than the threshold value. Weak changes in connectivity are considered as brain noise and are not related to attention.
6. Construct incident matrices using Equation (5).  
The probability distributions for each connectivity measure are presented in Figures A7-A9 in Appendix C. One can also find there the values of means (\(M\)), standard deviations (\(\sigma\)), and thresholds (\(C_{th}\)), which are summarized in Table A1.
# Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN
## 6 Conclusions  
In WBAN applications, the energy of sensors is limited and difficult to supplement, so it is very necessary to reduce the energy consumption of sensors as much as possible for achieving smooth transmission of WBAN data and the sustainability of service. Compress sensing data by eliminating redundancy can effectively reduce the amount of data transmission and improve the system operation efficiency. Thus, the paper first considers the accuracy of reconstructed signals and uses the proposed STPCSE model to implement it, which not only reduces the amount of data transmission, but also encrypts the data to a  
Figure 10: Comparison of networking throughput between TTCF, UC-MPRP and CRPBA.  
certain extent through compression and decompression rules. This is the most basic processing measure to ensure lightweight and safe data transmission. Secondly, the performance improvement of the data forwarding mechanism is also a key means to ensuring high and reliable data transmission. This paper improves the WBAN data forwarding mechanism based on social relations by integrating multiple attribute factors, high-performance methods or algorithms, such as the influence factors of the sampling frequency, residual energy and nodal importance, European distance, EW method and Dijkstra optimization algorithm. Then, the TTCF strategy based on human limb dynamic behavior is proposed. It fuses the STPCSE model and the improved data forwarding mechanism, and leverages the latter to pass the compressed data based on the former, and realize the lightweight, safe, reliable and effective data transmission for WBAN. Finally, a large number of simulation experiments and performance comparison analysis results show that TTCF has good performance in data reconstruction error, cumulative delivery rate, energy-saving effect and throughput. For example, its cumulative delivery rate is about 12% and 20.8% higher than that of UC-MPRP and CRPBA, and its residual energy and throughput are 1.22 times and 1.41 times, 1.35 times and 1.6 times of the latter two, respectively.  
Conceptualization and methodology, J.L. and J.Y.; writing, J.L.; supervision, J.Y.; data curation and validation, J.L. and J.X.; investigation and visualization, J.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 62002028, J. Y.).  
Not applicable.  
The authors would like to thank the editors and anonymous reviewers for their insightful suggestions to improve the quality of this paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} WBAN & Wireless Body Area Network \\ TTCF & Two-Tier Cooperation based high-reliable and lightweight Forwarding \\ STPCS & Semi-Tensor Product Compress Sensing \\ ECG & Electrocardiograms \\ EEG & Electroencephalograms \\ EMG & Electromyography \\ QoS & Quality of Service \\ \end{tabular}
## 2 Methodology
### Polarization Decomposition and Feature Extraction  
Processing and analyzing the PolSAR data can effectively extract the polarization scattering features, and further achieve classification, detection and identification of quad-Pol SAR data. Therefore, polarization decomposition for PolSAR data is usually adopted to obtain multi-dimensional features. Here, we propose to consider the 36-dimensional polarimetric scattering features, which were derived from a single temporal PolSAR image using various methods. Some of these features can be directly obtained from the measured data, and others were computed with incoherent decomposition (i.e., Freeman decomposition [32], Yamaguchi decomposition [33], Cloude decomposition [34] and Huynen decomposition [35]) and Null angle parameters [52]. The 36-dimensional scattering features obtained from a single temporal PolSAR image are summarized in Table 1. Then, higher dimensional scattering features can be obtained from multiple temporal PolSAR images. The resulting features involve all the potential information of the primitive PolSAR data.
## Materials and Methods
### Mesh Generation and Independence Study  
For resolving the boundary layers at the fins, ten prism layers were added on the fin surfaces. Prism layers are hexahedral cell layers that are aligned with the surface contour of the fin. The overall thickness of the prism layers depends on the inlet velocities and amounts to between 2 and 8 mm, which corresponds to an inlet velocity between 5 and 18 m/s, yielding a \(y^{+}\) between 30 and 60. Furthermore, a wake refinement of the grid downstream of the fins was performed in STAR-CCM+ to accurately resolve the increasing recirculation with increasing AoA; see Figure 9a.  
Based on these settings, and using unstructured polyhedral cells, a grid independence study was carried out. It includes six different meshes (\(\text{M}_{\text{i}}\)) with an increasing density to identify the required grid resolution. Figure 9b shows the drag coefficient as a function of the number of cells. The deviation of  
Figure 8: The geometry and boundary conditions of the numerical simulation region with the commercial 4-fins configuration at an AoA of \(45^{\circ}\). The surfaces of the fins are also defined as walls.  
the drag coefficient for meshes M\({}_{5}\) and M\({}_{6}\) ranges between 0.3% and 0.4% compared to M\({}_{4}\). We chose that M\({}_{4}\) with approximately 1.2 million cells is the proper grid configuration to perform the 4-fin parameter study.
# Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
## 4 Distributed Algorithm for Subchannel Allocation
### Marginal Contribution-Based Best-Response Algorithm
The updates of subchannel selection probabilities are dependent on learning the step size (\(\beta\)), normalized utility increment (\(\tilde{r}_{i}\)), and the selected subchannel (\(k_{t}\)) at time slot \(t\). To understand the update process, let us again consider example 1 with two subchannels \(\mathcal{K}=\{1,2\}\). Suppose for link \(i\) at time slot \(t\) the subchannel selection probability vector is \(\mathbf{p}_{i}=\{p_{i,1}(t-1),p_{i,2}(t-1)\}=\{0.5,0.5\}\) with \(\beta=0.5,\tilde{r}_{i}=0.5\), and subchannel 1 is selected for transmission in the payload phase, i.e., \(k_{t}=1\). From the update rule in step 11, since link \(i\) has selected subchannel 1, the probability of selecting this subchannel in the next time slot increases and is given by \(p_{i,1}(t)=0.5+(0.5).(0.5).(1-0.5)=0.625\). On the other hand, the probability of selecting subchannel 2 in the next time slot decreases and is given by \(p_{i,2}(t)=0.5-(0.5).(0.5).(0.5)=0.375\). Note that the sum of subchannel selection probabilities of link \(i\) at time slot \(t\) always equals to 1, i.e., \(\sum_{k\in\mathcal{K}}p_{i,k}(t)=1\). Such an approach fpr selecting a new sensing subchannel based on the concept of probability distribution allows for the exploration of more subchannel profiles while minimizing the amount of subchannel sensing and information exchange requirements at each time slot.
## 3 Results and Analysis
### The Trip Level
Additionally, during neighboring time periods, the changes in trip numbers differ. For instance, during 17:00-18:00, more trips are shown in Figure 8a than Figure 8b, but during 19:00-20:00, more trips appear in Figure 8b than Figure 8a. The changes during 17:00-20:00  
Figure 5.0 The distribution of hourly trip numbers. in Figure 8a are relatively sharp compared to those in Figure 8b, indicating that the mobilities for origins are more concentrated than destinations during this period. Another interesting pattern is that although the periods with low trip flows for both origin and destination were 00:00-6:00, the periods with no human mobility (indicated by white areas) were longer for origins than destination. Thus, by using CTM diagrams, one can explore detailed temporal patterns of human mobility on specific days. The CTM can achieve the clear visualization of the days that have the largest trip numbers during all time intervals as well. Figure 9 displays the CTM diagrams with the largest trip numbers during all time intervals from the perspectives of both origin and destination. According to Figure 9, the largest trip numbers appeared on 28 August for both origin and destination. Specifically, the largest trip numbers occurred on 25 August during the first half of the day (00:00-12:00) and on 28 August during the second half of the day (12:00-24:00) for both origin and destination. During the morning and evening peaks, 25 August and 26 August had the largest trip volumes, respectively, for both origin and destination. Nevertheless, some obvious differences between Figure 9a,b exist as well. For example, during the period of 5:00-6:00, the largest trip number for origin was on 22 August, while for destination, it was on 25 August. During the period of 6:00-7:00, the largest trip number for origin was on 25 August, but it was on 26 August for destination. During 9:00-10:00, 28 August had the largest trip numbers for origin, but it was 25 August for destination. Similar differences were observed during the periods of 12:00-15:00, 17:00-18:00, and 21:00-23:00. In summary, both similar and different temporal patterns of intra-urban human mobility regarding trip numbers can be easily characterized using  
Figure 5.1 The CTM diagrams of trip numbers on 15 August: (**a**) origin and (**b**) destination. Figure 5.2 The distribution of hourly trip numbers each day: (**a**) origin and (**b**) destination. CTM diagrams according to one's specific demands. Based on these patterns, such as the peak usage time intervals and the corresponding days, decision-making processes can be informed for bike-sharing companies. For example, by understanding these patterns, companies can better anticipate demand and adjust bike distribution dynamically, ensuring that bikes are available where and when they are most needed.
# FSI Application in the Parametric Analysis of Blade-Disk Connection of Turbojet Engine Compressor
## 3 Parametric Analysis with FSI  
FSI is a multiphysical study of the interaction of fluid that flows around a solid structure, considering the effect of the reverse interaction, i.e., the impact of structural deformation on flow parameters. Fluid-structure interaction is a method that requires calculations using computational fluid dynamics (CFD) and the combination of fluid flow results with a structural analysis model. Analyses of this type should be considered wherever the flow of fluid can exert pressure as well as the thermal load on the structure. The coupling allows fluids and structural models to exchange information and shows how fluid flow can cause structure deformation (and vice versa, how structure deformations can affect flow in the ambient zone). Modeling approaches may vary depending on the degree of physical coupling between fluid and solid and the level of accuracy required. When stresses and deformations are considered, the analysis includes data provided by simulations from flow and structural analysis. The application of the appropriate FSI coupling for analysis depends on the degree of physical coupling of the solution field between the liquid and the solid [24]. Strongly physically coupled fields require strong numerical couplings. Full couplings are then used. Solution fields that are relatively independent can be solved by using a weaker numerical coupling, a one-way fluid-structure coupling.  
In the parametric analysis of the blade-disk connection, one-way fluid-structure coupling was used. First, the pressure distribution on the blade was determined, and then, the resulting data were implemented for structural analysis. In addition, the influence of temperature was omitted in the analysis. This is because one of the first compressor stages made of the Inc718 alloy was being analyzed. The temperature value, in this case, would not significantly affect the properties of the material. ANSYS Workbench 2020 R2 software was used for calculations. A diagram of the one-way analysis is shown in Figure 8.  
The classic design of the high-pressure compressor rotor stage was tested considering two characteristic speed ranges of turbine engines: the idle range (65% N2) and the maximum rotor speed (100% N2). The parametric analysis was carried out to evaluate selected geometrical parameters in terms of their impact on the maximum stresses in the connection. Because of the condition of maintaining the dimensions of the engine, parameters that could change the radial dimensions of the rotor stage were not considered. Parameter P1--width angle (i.e., the angle of inclination of the side surfaces of the blade root)--and Parameter P2--angle defining the thickness of the blade root were selected. They are presented in Figure 9.  
Figure 8: One-way FSI analysis scheme in ANSYS Workbench (red line means input and output parameters, blue line means implementing geometry for structural analysis, violet line means implementing CFD results for structural analysis).