esa-sceva/satcom-chunk-collection huggingface dataset:
## Methods and Materials
### Extraction and Analysis of Sterols by GC-MS  
For sterol extraction, dry cell matter was heated in 1 mL of 10% KOH ethanolic solution at 90 \({}^{\circ}\)C for one hour. Sterols were extracted from cooled material in three volumes of 400 \(\upmu\)L of hexane. An internal standard, 5a-cholestane, was added to each sample. Hexane fractions were dried under a gentle N\({}_{2}\) stream, and derivatized with 50 \(\upmu\)L of 99% BSTFA + 1% TMCS (N,O-Bis(trimethylsilyl))trifluoroacetamide, Trimethylsilyl chloride at 70 \({}^{\circ}\)C for one hour. The resulting extractions were resuspended in 50 \(\upmu\)L of fresh hexane prior to GC-MS injection.  
Gas chromatography/mass spectrometry (GC-MS) analysis was performed using an Agilent 7890 instrument equipped with a HP-5 capillary column (30 m; 0.25 mm inner diameter, film thickness 0.25 \(\upmu\)m) coupled to an Agilent quadrupole MS (5975 N) instrument. The following settings were used: oven temperature initially set to 50 \({}^{\circ}\)C, with a gradient from 50 \({}^{\circ}\)C to 250 \({}^{\circ}\)C (15.0 \({}^{\circ}\)C min\({}^{-1}\)), and then from 250 \({}^{\circ}\)C to 310 \({}^{\circ}\)C (8 \({}^{\circ}\)C min\({}^{-1}\), hold 10 min); injector temperature = 250 \({}^{\circ}\)C; carrier gas helium flow = 0.9 mL min\({}^{-1}\). A split-less mode of injection was used, with a purge time of 1 min and an injection volume of 5 \(\upmu\)L. Mass spectrometer operating conditions were as follows: ion source temperature 230 \({}^{\circ}\)C; quadrupole temperature 150 \({}^{\circ}\)C; accelerating voltage 200 eV higher than the manual tune; and ionization voltage 70 eV. Full scanning mode with a range from 50 to 650 Dalton was used.  
Sterol peaks were identified based on retention time, mass spectrum, and representative fragment ions compared to the retention times and mass spectrum of authentic standards. The NIST (National Institute of Standards and Technology) library was also used as reference. The area of the peaks and deconvolution analysis was carried out using the default settings of the Automated Mass Spectral Deconvolution and Identification System AMDIS software (v2.6, NIST). Peak area measurements were normalized by both the weight of dry matter prior to extraction and the within-sample peak area of the internal standard 5a-cholestane. Sterol standards used to calibrate and identify GC-MS results in this study included: cholest-5-en-3-\(\beta\)-ol (cholesterol); (22E)-stigmasta-5,22-dien-3\(\beta\)-ol (stigmasterrol); stigmast-5-en-3-\(\beta\)-ol (sitosterol); campest-5-en-3-\(\beta\)-ol (campesterol); (22E)-ergosta-5,22-dien-3-\(\beta\)-ol (brassicosterol); (24E)-stigmasta-5,24-dien-3\(\beta\)-ol (fucosterol); 9,19-Cyclo-24-lanosten-3\(\beta\)-ol (cycloartenol);; 5-\(\alpha\)-cholestane; and the derivatization reagent bis(trimethyl-silyl) trifluoroacetamide and trimethylchlorosilane (99% BSTFA + 1% TMCS) and were obtained from Sigma-Aldrich, Sydney, Australia.
## 3 Control Strategy of the Proposed MMC  
The main part is the SM, which is may be a DC-DC or DC-AC power converter. Selection of better SM can provide fair control complexity, voltage blocking capability, and bipolar operation at a minimal cost. A new circuit performance of MMC with HB-SM is discussed in [2] by interchanging inductors with a transformer and also it offers twice the DC-AC voltage gain. The conventional MMC is having the advantage of power devices voltage ratings are halved, and the capacitor size of the SM is also lessened.  
This paper uses neutral point clamped (NPC) SMs for the converter arrangement. The schematic (Figure 6) of the proposed MMC has slightly changed the SM with NPC SM with every single PV-panel, and the DC-DC converter and the DC-link to the MMC has been maintained by the PV array followed with the DC-DC converter. Figure 7 shows the controlling structure. For this controller implementation, initially, we are sensing the line-to-line three-phase voltages, and then these are transformed to two-phase voltage quantities by _alpha-beta_ park's transformation. Using these _alpha-beta_ voltages, PLL is implemented, _alpha-beta_ voltages are then converted into _dq_ voltages using [PERSON]'s transformation. Now from here, we are sensing the inverter side currents for the controller implementation. These currents are then transformed to the _alpha-beta_ domain using park's transformation and then transformed to the _dq_ domain using [PERSON]'s transformation. Here, _ld_ corresponds to active current, and _lq_ corresponds to reactive current. Later _Id&lq_ are then subtracted from reference currents to find the error, and the error is fed to the _PI_ controller to produce _Eq and Ed_. It is then transformed to _abc_ voltages to get the references for PWM generation and, finally, we get the PWM generation block.  
Figure 6: Schematic of the proposed modular multilevel converter.  
Generally, arm components in the traditional MMC are designed using the following equations.  
\[L_{arm}\cdot C_{arm}=\frac{1}{\omega^{2}}\Bigg{\{}\frac{2(h^{2}-1)+m^{2}h^{2}}{8h^{2 }(h^{2}-1)}\Bigg{\}} \tag{1}\]  
\[C_{arm}=\frac{1}{L_{arm}\cdot\omega^{2}}\Bigg{\{}\frac{2(h^{2}-1)+m^{2}h^{2}}{8h^{ 2}(h^{2}-1)}\Bigg{\}} \tag{2}\]  
where \(\omega\) is the operating frequency, \(h\) is the harmonic order, and \(m\) is the modulation index.  
However, in the proposed strategy, we use the transformers instead of arm inductors in the traditional MMC.  
\[(L_{arm}+mutual\ inductance)\cdot C_{arm}=\frac{1}{\omega^{2}}\Bigg{\{}\frac{2(h^ {2}-1)+m^{2}h^{2}}{8h^{2}(h^{2}-1)}\Bigg{\}} \tag{3}\]  
From Equations (1) and (3), the inductance component is more in the proposed strategy, because of the mutual inductance of the transformer when compared to the traditional MMC for the same harmonic order and modulation index. Hence, from Equations (2) and (4), the proposed system requires a lesser value of capacitance when compared to the traditional MMC.
# Theoretical Approach to Predicting the Diffusion Radius of Fracture Grouting in Soil-Rock Mixtures
## 4 Results and Discussion
### Mechanism of Slurry Diffusion of Field Grouting Tests  
Figure 16 shows the morphological image of the test concretion of groups #11-13. Figure 16 shows clear fracture paths in the morphological image of the concretion of groups #11-13, and the slurry veins are clearly visible, showing the typical characteristics of fracture grouting. However, during the process of removing the concretion, it was found that there was a small amount of grout and soil, and stone was interbedded and mixed evenly on the surface of the concretion.  
Figure 15: Detailed drawing of the concretion of Group #7.  
Figure 17 shows the concretion fragment of groups #11-13, as shown in Figure 17. The concretions taken out only contain the cementation body composed of slurry and rock, without soil. This is because the slurry filled the void between the rock and wrapped the rock. The loose soil in the void and the soil in contact with the rock were squeezed out by the slurry, and the slurry in contact with the rock formed strong cementation of the slurry-rock cementation interface. According to measurement, the diffusion radius of the fracture grouting of the concretions in groups #11-13 was 30-40 cm. Although there was a small range of permeation grouting, permeation grouting mainly occurred on both sides of the fracture surface. The permeation grouting was attached to the fracture grouting, and the permeation width was approximately 4-6 cm, which had little influence on the overall diffusion radius. Therefore, the slurry diffusion form of groups #11-13 was fracture diffusion.
## 3 Results
### WSS-Based Hemodynamic Indices
The RRT, by definition, is an indicator of disturbed blood flow, marked by a low magnitude and high oscillatory WSS. In other words, it represents the time of residence the blood spent at the endothelium for the further formation of atherosclerosis localization. As shown in Figure 18, maximum RRT values are found at the roots of the leaflets, while minimum values occur at the leaflet edges due to the short blood residence time there. Increased calcification correlates with elevated RRT values, particularly at the roots of calcified leaflets. This suggests that blood stays longer behind calcification deposits. Notably, in Grade 5, where calcification is aligned along the coaptation line, elevated RRT values were observed in the belly region on both the aortic and ventricular sides. This can be attributed to the limited motion of leaflet tips during acceleration and deceleration, resulting in a concave leaflet shape that tends to keep blood for extended periods. Generally, RRT values are lower on the ventricular side due to continuously streaming blood flow as expected. Comparisons between uniform and helical flow reveal that RRT values decrease in helical flow scenarios, likely due to enhanced blood washout in the Valsalva sinus region. Moreover, the OSI and the RRT, which are both derived from the wall shear stresses, were used as further indicators of the increased risk of thrombus formation. Specifically, the regions with low OSI and high RRT values are good candidates for this. The last WSS-based hemodynamic index analyzed in this study is transWSS, which measures the average of the WSS components perpendicular to the temporal mean WSS vector over a cardiac cycle. A low transWSS value indicates that the flow remains predominantly parallel to an axis along the main flow direction throughout the cardiac cycle. In contrast, a high transWSS suggests significant fluctuations in the radial flow direction. As displayed in Figure 19, calcification generally leads to increased transWSS values near the leaflet tips and belly region. Enhanced transWSS patterns were notably seen along the coaptation line due to the high flutter in this valve region. Higher transWSS values were observed in both Grade 3, with two leaflets without calcification, and Grade 4, featuring one with slight calcification. A broader transWSS distribution extending to the belly region was observed in Grade 6, likely due to reduced flutter at the leaflets' tips and decoupled leaflet interactions with the ventricular blood flow. The results show that helical flow also contributes to elevated transWSS values on both ventricle and aortic sides. This is expected as transWSS primarily evaluates stress variations in directions perpendicular to the main flow. Thus, increases in transWSS arise from greater lateral load components, particularly in cases with generally asymmetric calcification patterns that induce temporally and spatially unstable fluctuations.
# A Simulation Study of Global Evapotranspiration Components Using the Community Land Model
## 1 Introduction
Although these improvements achieved good results and became part of the CLM4.5 parameterization, due to the interaction between the ET components (i.e., the water required for the Et comes from soil, therefore, Et and Es interact with each other), the resulting improvement in CLM4.5 in comparison with CLM4.0 with regard to ET components and latent heat flux was not analyzed. Therefore, some scholars still choose CLM4.0, rather than CLM4.5, to study ET [10; 19]. In this paper, we will use CLM4.5 and CLM4.0 for offline simulation tests to evaluate the performance of CLM4.5 on simulating Ec, Et, Es and latent heat flux compared with FLUXNET data [20; 21] and reanalysis data provided by National Oceanic and Atmospheric Administration (NOAA) [22] and Climate Forecast System Reanalysis (CFSR) [23] in the form of energy. In addition, to compare different versions of CLM and to provide a reference for further model improvement, another objective of this study is to test and analyze CLM4.5 relative to CLM4.0 in terms of improvements to the analysis of Et, Es, and latent heat flux. In the following, Section 2 includes a brief description of CLM4.5, experiment design, and data. Section 3 presents the results analysis. The conclusion and discussion are given in Section 4.
# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
###### Abstract  
In a disaster site, terrestrial communication infrastructures are often destroyed or malfunctioning, and hence it is very difficult to detect the existence of survivors in the site. At such sites, UAVs are rapidly emerging as an alternative to mobile base stations to establish temporary infrastructure. In this paper, a novel deep-learning-based multi-source detection scheme is proposed for the scenario in which an UAV wants to estimate the number of survivors sending rescue signals within its coverage in a disaster site. For practicality, survivors are assumed to use off-the-shelf smartphones to send rescue signals, and hence the transmitted signals are orthogonal frequency division multiplexing (OFDM)-modulated. Since the line of sight between the UAV and survivors cannot be generally secured, the sensing performance of existing radar techniques significantly deteriorates. Furthermore, we discover that transmitted signals of survivors are unavoidably asynchronized to each other, and thus existing frequency-domain multi-source classification approaches cannot work. To overcome the limitations of these existing technologies, we propose a lightweight deep-learning-based multi-source detection scheme by carefully designing neural network architecture, input and output signals, and a training method. Extensive numerical simulations show that the proposed scheme outperforms existing methods for various SNRs under the scenario where synchronous and asynchronous transmission is mixed in a received signal. For almost all cases, the precision and recall of the proposed scheme is nearly one, even when users' signal-to-noise ratios (SNRs) are randomly changing within a certain range. The precision and recall are improved up to 100% compared to existing methods, confirming that the proposal overcomes the limitation of the existing works due to the asynchronicity. Moreover, for Intel(R) Core(TM) i7-6900K CPU, the processing time of our proposal for a case is 31.8 milliseconds. As a result, the proposed scheme provides a robust and reliable detection performance with fast processing time. This proposal can also be applied to any field that needs to detect the number of wireless signals in a scenario where synchronization between wireless signals is not guaranteed.  
U
# A Mathematical-Based Model for Estimating the Path Duration of the DSDV Routing Protocol in MANETs
## 3 Mathematical Model
### Estimating the Number of Hops from Source to Destination  
In this mathematical-based model, it is assumed that the nodes move within a square dimensional area. This means that (Figure 6) the length of each side is one unit. Then, the average distance (\(D\)) between two nodes located inside this square area can be approximated by Equation (14) [36,37].  
\[D\approx 0.5217 \tag{14}\]  
Therefore, if it is assumed that the length of square base is \(L\), then the average distance (\(D\)) between the two nodes located in this area can be calculated using Equation (15).  
\[D\approx 0.5214\times L \tag{15}\]  
From Equation (9), the expected distance between the two adjacent nodes is represented as follows (Equation (16)).  
\[E_{R}(r)=\int_{0}^{R}\frac{2r^{2}}{R^{2}}\text{d}r \tag{16}\]  
Figure 7 demonstrates an example of the aforementioned concepts, where \(r\) represents the distance between the two adjacent nodes and \(D\) represents the distance between the source and the final destination.  
Figure 6: Average distance between any two nodes.  
From the above findings, the estimated number of hops can be computed using the following equation (Equation (17)):  
\[E_{H}(h)\approx\frac{D}{E_{R}(r)} \tag{17}\]
# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
## 2 Materials and Methods
### Machine Learning and Fuzzy Logic Models for Soil Temperature Estimation
#### 2.2.5 K-Nearest Neighbors (KNN)  
KNN is a non-parametric, instance-based learning algorithm introduced by [PERSON] and [PERSON] [52], widely used for both classification and regression tasks. The core principle of KNN is that the class or predicted value of a given data point is determined by the properties of its k nearest neighbors, which are usually calculated using a distance metric, such as Euclidean distance. The performance of the model depends heavily on the hyperparameters, including the number of neighbors (\(k\)), the weighting scheme applied to the neighbors, and the choice of distance metric. In this study, these hyperparameters--particularly the optimal \(k\) value and the most appropriate distance metric--were systematically selected to improve model accuracy. While KNN is appreciated for its simplicity and ability to effectively capture local data structures, it also faces limitations, such as increased computational costs for large datasets and sensitivity to feature scaling, which can affect performance if not properly addressed.
# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 2 Railway Infrastructure Monitoring Use Cases and Satellite Data Potential
### Monitoring of Water Level Around the Rail Track (WL)  
Railway infrastructure is under high risk of damage from flooding, considering recent climate changes, such as increasing temperatures, changes in precipitation intensity and amount, and increases in heavy storms [16]. This has resulted in more water-induced damage to railway infrastructure in recent years. The water level near the rail track affects the safety of passengers and cargo on board. It is currently determined by visual inspection carried out by trained personnel. These inspections are subjective, limited in space and time, affected by traffic frequency [36; 37], and do not include water areas out of the inspectors' sight but close enough to cause damage. Dynamic water surface areas near the rail track can be mapped using satellite optical images. However, to determine the changes in vertical water level, it is necessary to use other, complementary techniques.  
Recent advances in satellite-based Earth surface water monitoring offer possibilities for water monitoring near railway tracks [17]. Satellites like Sentinel-3 and ICESat-2 use radar and laser altimetry to measure water levels in rivers, lakes, and reservoirs. These data can be used to monitor changes in water levels near rail tracks, helping to predict and prevent flooding. SAR satellites, such as those in the Sentinel-1 and Radarsat Constellation, can provide high-resolution images that detect changes in surface water extent. This is useful for identifying areas where water levels are rising and could potentially impact rail infrastructure. Optical satellites like Landsat and Sentinel-2 provide multispectral imagery that can be used to monitor water bodies. Changes in water extent can indicate rising water levels or potential flooding risks.
# Power Control Techniques for Interference Management-
## 7 Conclusions  
In this paper, the authors conducted a systematic literature review to answer four research questions. The methodology involved a bibliometric analysis of the literature published on power control in wireless networks. The main goal was to inform us of the current and future research status of power control taxonomy and its applications to addressing interference in 5G networks. A bibliometric analysis software from an R package was used to analyze (1) the current publication trends, (2) the most influential authors, countries, and journal articles, (3) the current state of collaboration, and (4) the areas involving power control that need additional study. The authors obtained both numerical and graphical visualizations. The visualization results make several contributions to the current literature. First, it informs researchers in this area to quantify and visualize the thematic evolution of power control in 5G networks. Secondly, it provides insight into the current state of the topic under study and predicts the direction of future research. Thirdly, it tracks the words of authors, the impact of papers, and the trajectory of research ideas by measuring citation counts in critical research databases and online sources. Finally, it investigates the recurring themes in a research field and identifies possible future research directions. Overall, the visualization results confirmed continuous growth in the study, meaning that there is a need for further exploration of power control techniques in wireless networks. Based on the results, there is a definite need to establish the impact of power control in 5G and beyond wireless networks.  
Conceptualization, N.R.N. and M.S.; Methodology, N.R.N. and M.S.; Investigation, N.R.N. and M.S.; Resources, N.R.N. and M.S.; Software, N.R.N.; Validation, N.R.N.; Supervision, M.S.; Visualization, N.R.N.; Writing--original draft, N.R.N.; Writing--review and editing, N.R.N. and M.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data is contained within the article. This work was supported by the University of South Africa by making services available through Information and Communication Technology (ICT) department and Library support services.
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 5 Experimental Simulation Scenarios Topology
### Medium-Voltage Power Line  
This scenario is based on the real topology of a smart grid application. The topology consists of underground power cables for the energy distribution and distribution substations. Figure 6 show this topology with all substation nodes (S1-S6) which are connected by the cable junctions (J1-J9). These junctions create crossroads for other power line connections of the distribution network. From the network structure point of view, two types of cables are used in this case. Cable type 1 is AXEKCY, and cable type 2 is AXEKVCEY. Both types of power line are designed for middle-voltage power transmission.  
The NS-3 simulator does not define similar types of MV cables, so they were created for this simulation. Table 7 shows selected values of the cable primary parameters. The values were approximated from available sources and from the catalogue of the cable manufacturer.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cable Type** & \(L\) **[H/m]** & \(C\) **[F/m]** & \(A_{\text{R}}\) **[\(\Omega\)/(m \(\cdot\) Hz\({}^{2}\))]** & \(B_{\text{R}}\) **[\(\Omega\)/(m \(\cdot\) Hz\({}^{2}\))]** & \(C_{\text{R}}\) **[\(\Omega\)/m]** \\ \hline AXEKCY & \(5.7\times 10^{-7}\) & \(4.5\times 10^{-10}\) & \(-4.58\times 10^{-15}\) & \(1.1\times 10^{-8}\) & \(8.7\times 10^{-3}\) \\ AXEKVCEY & \(5.2\times 10^{-7}\) & \(3.2\times 10^{-10}\) & \(-4.58\times 10^{-15}\) & \(1.1\times 10^{-8}\) & \(8.7\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameters and coefficients for implementation of MV management.  
Figure 5: Simulation setup—swept parameters in point-to-point topology.  
Figure 6: Diagram of real MV power lines with BPLC communication.  
As stated in papers [80; 81], the frequency-dependent primary parameter is the resistance \(R\prime\).  
\[R\prime(f)=A_{R}f^{2}+B_{R}f+C_{R}\left[\frac{\Omega}{\mathrm{m}}\right], \tag{5}\]  
where \(A_{R}\), \(B_{R}\) and \(C_{R}\) are the coefficients of the polynomial function and \(f\) is the frequency. Inductance \(L^{\prime}\) and capacitance \(C^{\prime}\) are frequency independent:  
\[L\prime(f)=L\left[\frac{\Omega}{\mathrm{m}}\right], \tag{6}\]  
\[C\prime(f)=C\left[\frac{\mathrm{F}}{\mathrm{m}}\right]. \tag{7}\]  
The parameter conductance \(G^{\prime}\) is neglected in the simulations and only resistance is considered. The only difference between the cables in Table 7 is the electrical isolation which has an effect on the noise interference.
## 2 Antenna Design and Optimization
### Parameter Optimization  
This section describes the impact that some key parameters have on antenna impedance matching and CP characteristics. The simulated S11 results with different thicknesses of the substrate (H\({}_{1}\)) and superstrate (H\({}_{2}\)) are shown in Figure 6a. The H