esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Polyamine Levels in Peripheral Blood Cells and Plasma for Patients and the Control Group
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Predictor Variables of LVEF** & **B** & **SE B** & **\(\bolds{\rho}\)** & \(\bolds{p}\)** \\ \hline Acute Myocardial Infarction Patients (R\({}^{2}\): 0.666; ANOVA \(p<0.001\)) & & & & \\ \hline Constant & 43.26 & 5.04 & & \(<\)0.001 \\ Putrescine in Erythrocytes & \(-\)701.03 & 140.71 & \(-\)0.70 & \(<\)0.001 \\ Putrescine in Mononuclear & 27.54 & 8.03 & 0.48 & 0.003 \\ Cells (mmol/mg protein) & & & & \\ \hline Chronic Heart Failure Patients (R\({}^{2}\): 0.86; ANOVA \(p=0.013\)) & & & & \\ \hline Constant & \(-\)66.13 & 18.32 & & 0.007 \\ Isoamylamine in Mononuclear & \(-\)0.15 & 0.03 & \(-\)1.55 & 0.002 \\ Cells (mmol/mg protein) & & \(-\)55.75 & 16.85 & \(-\)1.63 & 0.011 \\ Spentine in Mononuclear & 25.36 & 6.01 & 2.88 & 0.003 \\ Putrescine in Erythrocytes & 11,841.35 & 4311.38 & 1.63 & 0.025 \\ Spermine in Erythrocytes & & \(-\)1952.00 & 508.61 & \(-\)3.57 & 0.005 \\ Isoamylamine in Plasma & & 12.21 & 3.55 & 1.29 & 0.009 \\ Spentine in Plasma & & 4650.32 & 932.87 & 1.27 & 0.001 \\ Spentine-to-Spermidine Ratio & & 71.69 & 22.20 & 0.97 & 0.012 \\ Spentine-to-Spermidine Ratio & & 19.98 & 5.88 & 0.67 & 0.009 \\ \hline Predictor variables of Heart Rate & & & & \\ \hline Chronic Heart Failure Patients (R\({}^{2}\): 0.512; ANOVA \(p=0.006\)) & & & & \\ \hline Constant & 79.33 & 2.56 & & \(<\)0.001 \\ \hline Spermidine in Plasma & 1511.67 & 444.93 & 0.72 & 0.006 \\ \hline Predictor variables of Septum Size & & & & \\ \hline Chronic Heart Failure Patients (Adjusted R\({}^{2}\): 0.55; ANOVA \(p=0.003\)) & & & \\ \hline Constant & 11.41 & 0.6 & & \(<\)0.001 \\ Isoamylamine in Erythrocytes & 1.64 & 0.38 & 0.88 & \(<\)0.001 \\ Putrescine in Plasma & \(-\)4.69 & 2.05 & \(-\)0.47 & 0.037 \\ \hline \hline \end{tabular} B: unstandardised coefficients; SE B: standard error of B.  
\end{table} 
Table 3: Multiple regression analysis with coefficients of constants and model of independent variables to predict left ventricular ejection fraction ( LVEF) in patients with acute myocardial infarction and chronic heart failure and septum size in patients with chronic heart failure.
# A C-Based Framework for Low-Cost Real-Time Embedded Systems
## 7 Internet Access
### Design and Implementation
#### 7.2.1 Shared Variable Enumeration  
Regarding the first translation step, the underlying idea is that the controller card and the IMS have access to the shared variables configuration file described in Section 6 at compile time. From it, they can both generate an identical enumerated data type definition, together with the enumerated values associated with it, by means of the code listed in Figure 10. The code relies on techniques backed by the C language standard [20] and widely supported by C compilers like _gcc_. Moreover, this technique is commonly used in the implementation of various other open-source tools, such as the lwIP protocol stack [38].  
With this approach, the enumerated values corresponding to the shared variable names are mentioned in the type definition in the same order as they appear in the configuration file when the C pre-processor scans the file. This is the same order followed when generating the input shared memory table for the controller card firmware. Considering that enumerated values are represented as integers by the compiler, and both enumerated values and array indexes start at zero, they can be used as an unambiguous index in the input shared memory table by both the controller card and the IMS.  
For conciseness, the example code only shows how to generate the aforementioned data type definition from the declaration of input shared variables, but the same method is also appropriate for outputs. A finer-granularity, variable-by-variable approach is possible as well, such as the one shown for user interfaces and proxies in Section 4.5, at the expense of a higher complexity of the generation macros. However, this is not a concern because, being expanded at compile time, they do not introduce any runtime overhead.  
For the same reason, the example generates a data type with a fixed name, shmem_input_var_name_enum, assuming that the IMS will communicate with a single controller card. When dealing with multiple cards, name clashes can easily be avoided by  
Figure 10: Generation of the enumerated data type for Internet access to shared (input) variables.  
adding an appropriate, card-specific prefix or suffix to the data type name, by means of the C pre-processor token concatenation operator ## (double hash). This technique is already used in Figure 10 to add the prefix E_ to enumerated values, in order to avoid clashes with the corresponding input shared variable names, which are globally accessible.
# IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
## 7 Simulations and Discussions
### Discussion about IPCBs with Different Initial Flight Altitudes  
IPBs' initial flight altitudes have a significant impact on IPCB geometry performance. In this section, the initial flight altitudes of IPB5 and IPB6 in Table 2 are modified to 20 km, 21 km, 22 km, 23 km, and 24 km, respectively, while other conditions remain untouched. The planning results of different IPCBs are listed in Figures 9-11.  
From the comparison of Table 3, it can be seen that there are no significant performance differences in 20 km, 21 km, 22 km, and 23 km, but there is a declining tendency in 24 km. The result of 24 km can probably be attributed to the short service time of IPB5 caused by the big wind velocity. All of these are reflected in Figures 9-11.  
Figure 9: Flight altitude comparison for IPCBs with different initial flight altitudes: (**a**) IPB5 and IPB6 at 20 km initially; (**b**) IPB5 and IPB6 at 21 km initially; (**c**) IPB5 and IPB6 at 23 km initially; (**d**) IPB5 and IPB6 at 24 km initially. (Flight altitude for IPCB with IPB5 and IPB6 at 22 km initially, please refer to Figure 6).  
In addition, from Figure 11, it can be seen that for the listed initial flight altitudes, the higher the initial flight altitude is, the better the initial GDOP is. However, the IPCB with the highest initial flight altitude has the fastest performance deterioration due to the big wind velocity at the highest altitude. Therefore, if short-term performance is pursued, higher initial flight altitudes may be preferred over lower altitudes. If long-term performance is pursued, initial flight altitudes near the quasi-zero wind layer may be preferred.  
Figure 11: GDOP comparison for IPCBs with different initial flight altitudes.  
Figure 10: Horizontal trajectory comparison for IPCBs with different initial flight altitudes: **(a)** IPB5 and IPB6 at 20 km initially; **(b)** IPB5 and IPB6 at 21 km initially; **(c)** IPB5 and IPB6 at 23 km initially; **(d)** IPB5 and IPB6 at 24 km initially. (Horizontal trajectory for IPCB with IPB5 and IPB6 at 22 km initially, please refer to Figure 7).
## 6 Thematic Taxonomy of UWSNs
### Deployment
#### 6.1.3 Enabling Technologies  
There have been many recent advances in UWSNs, which have gone through rapid growth in various fields. Technological innovations in communication facilitate underwater sensor nodes and applications to interact with each other to provide services for users and adapt to varying requirements and preferences [45]. This innovation has become a driving force in Industry 4.0 that supports the deployment of Industry Internet of Things (IIoT). IIoT allows data to be forwarded securely to the cloud network from various sensor nodes and updated from time to time. According to [46; 47], IIoT is the integration of industrial wireless networks and IoT technologies as a single system that consists of cloud networks, machines, equipment, and terminals. Consequently, recent developments in the field of IoT and UWSNs have led to a renewed interest in the Internet of Underwater Things (IoUT). IoUT allows a variety of underwater nodes to communicate with each other by collecting and transferring data to the surface station with high internet speeds. A study in [10] proposed an architecture of IoUT that consists of three layers: perception layer, network layer, and application layer. Remarkably, a Green IoUT was introduced as a new advancement to decrease the negative consequences of greenhouse gases on the environment [48]. Apart from this greenhouse effect, ref. [49] stated that underwater nodes and vehicles require high power consumption, which can stop the operation of critical missions or applications prematurely. This issue led the researchers in [50] to initiate emerging energy-efficient design for UWSNs, particularly for exploring offshore gas and oil environments. Within the next few years, underwater communication systems will confront some challenges in the integration of heterogeneous nodes or underwater vehicles, complex architectural design, and various underwater applications. For those reasons, ref. [51] introduced the next-generation network paradigm, known as Software Defined Networking (SDN), to support innovation practices, improve network flexibility, conduct efficient resource allocation, and control the operational cost. The architecture of SDN IIoT integrates three layers [47]. The sensor data from the physical layer moves to the control layer through a southbound interface and subsequently is transmitted to an application layer via a northbound interface and API. Figure 6 provides apparent relationship of emerging technologies among SDN, IIoT, IoUT, and Industry 4.0.
###### Abstract  
The evaluation of vegetation cover after post-fire treatments of burned lands is important for forest managers to restore soil quality and plant biodiversity in burned ecosystems. Unfortunately, this evaluation may be time consuming and expensive, requiring much fieldwork for surveys. The use of remote sensing, which makes these evaluation activities quicker and easier, have rarely been carried out in the Mediterranean forests, subjected to wildfire and post-fire stabilization techniques. To fill this gap, this study evaluates the feasibility of satellite (using LANDSAT8 images) and drone surveys to evaluate changes in vegetation cover and composition after wildfire and two hillslope stabilization treatments (log erosion barriers, LEBs, and contour-felled log debris, CFDs) in a forest of Central Eastern Spain. Surveys by drone were able to detect the variability of vegetation cover among burned and unburned areas through the Visible Atmospherically Resistant Index (VARI), but gave unrealistic results when the effectiveness of a post-fire treatment must be evaluated. LANDSATS images may be instead misleading to evaluate the changes in land cover after wildfire and post-fire treatments, due to the lack of correlation between VARI and vegetation cover. The spatial analysis has shown that: (i) the post-fire restoration strategy of landscape managers that have prioritized steeper slopes for treatments was successful; (ii) vegetation growth, at least in the experimental conditions, played a limited influence on soil surface conditions, since no significant increases in terrain roughness were detected in treated areas.  
V 2021 115423. [[https://doi.org/10.3390/app11125423](https://doi.org/10.3390/app11125423)]([https://doi.org/10.3390/app11125423](https://doi.org/10.3390/app11125423))  
1
## 3 WRF-CFD Wind Field Downscaling Technology
### Simulation of Typhoon Field by WRF Model
#### 3.1.2 Physical Scheme Selection and Parameter Settings  
In the WRF model, the Arakawa C mesh is used in the horizontal direction and the terrain follows the mass coordinate system in the vertical direction. Through the use of triple-nested mesh technology, the WRF simulation accuracy is 1.5 km. The mesh areas of each layer are referred to as D01, D02, and D03, respectively. Area D03 is the center of the simulation area and is set as the Guangdong region (110.3\({}^{\circ}\) N, 20.3\({}^{\circ}\) E). The outermost coarse mesh spacing is 13.5 km and the number of nodes is 211 \(\times\) 211. The second mesh spacing is 4.5 km with 217 \(\times\) 217 nodes. The innermost mesh spacing is 1.5 km with 241 \(\times\) 241 nodes. The map projection uses the Lambert isometric projection scheme. Based on the Hybrid Vertical Coordinate, the computational domain is divided into 30 layers along the height direction.  
During the numerical simulation of the typhoon, after several screening tests, the selection was the Mellor-Yamada-Janjic\({}^{\prime}\) (MYJ) boundary layer scheme and the Kain-Fritsch cumulus convection parameterization scheme. To analyze the daily changes in the boundary layer, Typhoon Rammasun was simulated for 48 h. For precise numerical simulation, the first 7 h was selected as the mode's spin-up time. The output frequency of the simulation result was once per hour. The detailed parameters of the WRF model are shown in Table 1. For details on each scheme, please refer to the WRF user manual [25].
# Spatial Performance Indicators for Traffic Flow Prediction
## 3 Materials and Methods
### Case Study
#### 3.2.1 PeMSD8 Dataset  
In this case study, we evaluated the traffic flow prediction results of the PeMSD8 dataset, a subset of the well-known PeMS dataset that contains highway traffic data from California [19]. The PeMSD8 dataset includes both traffic flow data and graph adjacency matrix of the California freeway networks. This dataset was chosen specifically because it is widely used as a reference benchmark to evaluate the traffic flow prediction performance.  
The PeMSD8 dataset used in this study was derived from real-time traffic measurements obtained by the Caltrans Performance Measurement System (PeMS). These measurements were collected every 30 s and aggregated into 5-min intervals. In this study, we chose the PeMSD8 dataset, a subset of the PeMS dataset that specifically focuses on traffic data from San Bernardino between July and August 2016. To remove redundancy, each detector in this dataset was chosen to be located at least 3.5 miles from the neighboring detectors, resulting in a total of 170 detectors [6].  
The dataset comprised three types of traffic states: traffic flow, traffic speed, and occupancy. However, in this study, we focused only on the traffic flow predictions. The dataset also includes an adjacency matrix that represents the network graph, explaining the spatial relationship between traffic sensors as shown in Figure 2. This adjacency matrix enables the use of graph-based traffic flow prediction methods that rely on predefined graph structures. Further details of the PeMSD8 dataset are presented in Table 1.
## 4 Conclusions  
In this paper, we proposed and demonstrated a carrier phase recovery and polarization tracking scheme. This scheme is based on adaptive Kalman filter aided by pilot symbols. Compared with the algorithm that we mentioned in the introduction, such as EKF and AKF without the pilot symbols, this new scheme has advantages in high convergence speed, tolerance to initial Q value, and improvements of tracking performance. Simulations and experiments have investigated all this.  
In the simulation part, the optimal FR is suggested regarding OSNR sensitivity, dynamic polarization rotation, and laser linewidth. The tradeoff between tracking ability improvements and spectrum efficiency determines the optimal FR. Then the convergence speed is evaluated by MSE.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Our Scheme** & **CMA+BPS** \\ \hline Real multiplication & 585 & 16M+16+6 NB+4N \\ Real addition & 459 & 16M+4+6 NB-B+2N+2 \\ Comparison & 0 & B \\ LUT & 32 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The calculation comparisons between our joint scheme and CMA+BPS.  
Figure 13: Polarization rotation tracking performance of AKF and EKF with pilot symbols.  
AKF with pilot symbols can converge within 50 symbols, and AKF without pilot symbols achieves convergence within 2500 symbols. The investigations about the tolerance to initial Q value, including Qpol and Qphase are carried out. The results show that AKF has a high tolerance to initial parameter Q. It achieves almost the same tracking performance for different initial Q while EKF is exclusively relying on Q.  
In the experiment part, we choose FR=1:7 to verify the improvements in convergence speed and tracking ability. AKF with pilot symbols can converge within 20 symbols, and AKF without pilot symbols achieves convergence within 800 symbols. In the following, the tracking performance improvement of dynamic polarization rotation is proved by the result of the experiment.  
Finally, we also compare our scheme with CMA+BPS. The result shows that the calculation complexity is lower than that of CMA+BPS. In conclusion, the proposed scheme can be considered a good candidate for parameter estimation and channel equalization in the next generation of flexible and dynamic optical communication networks.  
Investigation, J.C., Y.Y., Q.X. and Q.Z.; Methodology, Q.X.; Writing--original draft, J.C.; Writing--review & editing, Y.Y., Q.Z., L.F., and Y.Y.  
This work was supported by the National Natural Science Foundation of China (Contract No. 61205046 and 61575051) and Shenzhen Municipal Science and Technology Plan Project (JCYJ20150327155705357, KQCX2015032409501296, JSGG20150529153336124 and JCYJ20150529114045265).  
The authors declare no conflict of interest.
## 3 Setup and Diagnostic Analysis of the Transformer Using the FDS Method
The permittivity of oil, spacers, and barriers comprises complex functions dependent on both angular frequency (\(\omega\)) and temperature (\(T\)), influencing the overall measured frequency response  
\[\varepsilon^{*}(\omega,T)=\varepsilon t(\omega,T)-j\varepsilon^{\prime\prime }(\omega,T) \tag{5}\]  
Or  
\[\text{tg}\delta(\omega,T)=\frac{\varepsilon^{\prime\prime}(\omega,T)}{ \varepsilon t(\omega,T)} \tag{6}\]  
Considering the number of barriers and spacers, the overall response of the insulation channel (\(ch\)) can be represented by  
\[\varepsilon^{*}(\omega,T)_{ch}=\frac{Y}{\frac{1-X}{\varepsilon^{*}\text{ spacers}}+\frac{X}{\varepsilon^{*}\text{ barriers}}+\frac{1-Y}{\frac{1-X}{\varepsilon^{*}\text{oil}}+\frac{X}{ \varepsilon^{*}\text{buries}}}} \tag{7}\]  
Based on these defined relationships and calculated parameters, the MEGGER IDAX-350 diagnostic system assesses the insulation condition of high-voltage transformers by comparing measured and modeled frequency curves. The system's software utilizes an evaluation table, presented beneath the displayed curves in the device's interface, as shown in Figure 6.  
The MEGGER IDAX-350 system summarizes the quality assessment criteria for transformer oil and paper-insulation systems. The evaluation is based on parameters such as the  
Figure 5: Graphical representation of the components contributing to the total permittivity of the transformer, used for frequency curve modeling and analysis with the MEGGER IDAX system. loss factor at a standard frequency of 50 Hz normalized to 20 \({}^{\circ}\)C, paper moisture content (in %), and oil conductivity (in pS/m) normalized to 25 \({}^{\circ}\)C.  
The basic limit between good and deteriorated condition of the insulation system of a high-voltage transformer is a value of 0.5% for the loss factor, 2% for the moisture in the paper, and 3.7 pS/m for the oil conductivity.
## 2 Mathematical Basis of Montgomery Modular Multiplication over Binary Extension Field
 \tag{6}\]  
Let us denote the results of the \(i\)th iteration of Equations (5) and (6) by \(A_{i}\) and \(B_{i}\), respectively. These values can be computed recursively based on the outcomes of the previous \((i-1)\)th iteration. Specifically, for the \(i\)th step, where \(1\leq i\leq\frac{k+1}{2}\), the recursive version of Equation (5) can be stated in the following manner:  
\[A_{i}=A_{i-1}F\bmod F+Cd_{(k-i)} \tag{7}\]  
with \(A_{0}=0\). Equation (6) can be recursively formulated in a similar manner to Equation (5). The recursive form for Equation (6) at step \(i\) can be expressed as  
\[B_{i}=B_{i-1}F^{\,-1}\bmod F+Cd_{(i-1)} \tag{8}\]  
with \(B_{0}=d_{(k-1)/2}=0\). It is crucial to keep in mind that \(d_{(k-1)/2}=0\) is necessary in order to calculate the final product, \(B_{(k+1)/2}\). It is also possible to determine \(A_{i}\) and \(B_{i}\) concurrently because they do not share any data. Equations (7) and (8) provide an illustration of this concurrent calculation. The expansion of \(F^{\,k}\) in Equation (7) can be changed to obtain the reduced form of \(A_{i}\) for \(1\leq i\leq\frac{k+1}{2}\) at the bit level. Therefore, \(A_{i}\) can be written in the following manner in terms of its bit-level representation [21]:  
\[ \begin{split} A_{i}=& a_{k-2}^{i-1}F^{k-1}+\cdots+a_{1 }^{i-1}F^{2}+a_{0}^{i-1}F+\\ & a_{k-1}^{i-1}(f_{k-1}F^{k-1}+\cdots+f_{1}F+f_{0})+\\ & d_{k-i}(c_{k-1}F^{k-1}+\cdots+c_{1}F+c_{0})\end{split}  \tag{9}\]  
The expression for the recursive representation of \(A\) at step \(i\) should be  
\[ \begin{split} a_{k-1-j}^{i}=& a_{k-2-j}^{i-1}+a_{k-1 }^{i-1}f_{k-1-j}+d_{k-i}c_{k-1-j}\end{split}  \tag{10}\]  
with \(a_{j}^{0}=a_{-1}^{i-1}=0\) and \(0\leq j\leq k-1\). By multiplying each side of equation \(F\) by \(F^{-1}\), we can obtain \(F^{-1}=\sum_{j=1}^{k}f_{j}F^{j-1}\). This manipulation is valid because \(F\) is a root of \(F\), and for any irreducible polynomial, the coefficients \(f_{0}\) and \(f_{k}\) are both equal to \(1\). \(B_{i}\) can be expressed in a similar manner to Equation (9) by substituting the expansion of \(F^{-1}\) from Equation (8). The revised representation of \(B_{i}\) should be as follows [21]:  
\[ \begin{split} B_{i}=& b_{k-1}^{i-1}F^{k-2}+\cdots+b_ {1}^{i-1}+b_{0}^{i-1}(f_{k}F^{k-1}+\cdots+\\ & f_{2}F+f_{1})+d_{i-1}(c_{k-1}F^{k-1}+\cdots+c_{1}F+c_{0})\end{split}  \tag{11}\]  
Once again, the recursive representation of \(B\) at step \(i\) can be written as follows:  
\[b_{j}^{i}=b_{j+1}^{i-1}+b_{0}^{i-1}f_{j+1}+d_{i-1}c_{j} \tag{12}\]  
with \(b_{j}^{0}=b_{k}^{i-1}=d_{(k-1)/2}=0\) for \(0\leq j\leq k-1\). To combine \(A^{(k+1)/2}\) and \(B^{(k+1)/2}\) to obtain the intended outcome \(P\), a certain number of \(m\) two-input XOR gates are required. These XOR gates are utilized to carry out bitwise exclusive-OR operations between \(A^{(k+1)/2}\) and \(B^{(k+1)/2}\) corresponding bits. The bits of the final outcome \(P\) will be produced by the output of these XOR gates.