esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling Methodology for Blade Profile with SV
### Numerical Method  
A quasi-three dimensional code MISES by [PERSON] et al. [8] was utilized in order to evaluate the blade profile performances. The code is functional in both direct and inverse design of compressor blade sections. Considering its advantages in terms of simplification and time saving, MISES has been widely validated and used for both scientific studies and industry applications [1; 9; 12; 13; 14].  
The calculated flow domain in MISES is divided into an inviscid outer flow region and a boundary layer region, which are iteratively solved using different equations. The inviscid flow is computed by the Euler equations, and boundary layer computations are based on the classic Karman integral momentum equation and the integral kinetic energy equation. The grid points are established by using an elliptic grid generator in the domain of inviscid flow, determining the streamlines and quasi-normal lines. The flow field is discretized by a finite volume method so that the Newton-Raphson solver can be used to simultaneously solve the non-linear equations of the two zones. The edge velocity and density are first derived from the equations of inviscid outer flow to promote the integral boundary solutions for the displacement thickness, which corrects the edge of inviscid outer flow in turn. The iteration is performed until the convergence criterion is reached. The work of [PERSON] [15] can be referred to for more details about the numerical formulation.  
In order to validate the code, Figure 5 gives a comparison between MISES calculation results and experimental data for a hub section of the first stage in an industrial compressor inversely designed by [PERSON] et al. [16]. It can be seen in Figure 5a that MISES results are in good agreement with the experimental data as the difference of about 0.2\({}^{\circ}\) in reference minimum-loss definition is acceptable in this study. Figure 5b shows the comparison of blade surface pressure distribution at the inflow angle of 47.0\({}^{\circ}\). It is indicated that the MISES-predicted surface pressure distribution is basically in agreement with the experimental results despite a slight difference of suction peak location. Table 2 shows the performance results from experiments and MISES computations at inflow angle of 47.0\({}^{\circ}\)  
Figure 4: Systematic variation of CR with constant ES.  
It is indicated that the outflow angle, the pressure ratio, and the total pressure loss coefficients used for performance analyses are in excellent agreement. The validation indicates that the code can accurately predict not only reference minimum-loss definition but also surface pressure distribution and profile performances required for analyses and modeling in the present study.
## 3 Results
### Correlations between SOS/EOS and Temperature/Precipitation  
In order to identify which factor determines the timing of SOS/EOS, we analyzed the correlations of SOS/EOS with pre-SOS/pre-EOS temperature and precipitation. Figure 5a,b showed that significant negative and positive correlations between SOS and pre-SOS temperature both occurred in 1.2% of pixels. Meanwhile, SOS was significantly negatively correlated with pre-SOS precipitation in 12.9% of pixels while significant positive relationships between SOS and pre-SOS precipitation were only found in 0.1% of pixels. Similarly, a significant negative correlation between SOS and pre-SOS precipitation was found in 13.2% of meadow steppe pixels, 13.2% of typical steppe pixels, and 11.3% of desert steppe pixels. Meanwhile, significant negative correlations between SOS and pre-SOS temperature were only detected in 3.5% of meadow steppe pixels, 0.2% of typical steppe pixels, and 0.2% of desert steppe pixels.  
In contrast to SOS, significant positive correlations were detected between EOS and pre-EOS temperature in 8.8% of total pixels, and between EOS and pre-EOS precipitation in 3.8% of total pixels (Figure 5c,d). Significant negative correlations between EOS and pre-EOS temperature/precipitation were found only in few pixels (0.1%/0.3%). Moreover, significant positive relationships were discovered between EOS and pre-EOS temperature/precipitation in 9.8%/3.5% of meadow steppe pixels and 11.1%/4.8% of typical steppe pixels. In desert steppe, significant positive correlations were found between EOS and pre-EOS precipitation in 1.0% of pixels, while no significant positive relationships were found between EOS and pre-EOS temperature.  
Figure 4: Spatial pattern of multi-year average SOS (**a**) and multi-year average EOS (**b**). SOS (**c**) and EOS (**d**) comparison among meadow steppe, typical steppe, and desert steppe. Different letters (a–c, marked from the largest average to the smallest average) denote that there is a significant difference of SOS/EOS between the two grassland types at the level of 0.05.
# Cluster Analysis with K-Mean versus K-Medoid in Financial Performance Evaluation
## 3 Materials and Methods
### Statistical Methods  
The starting point for the statistical analysis was the calculation of some basic statistical indicators, such as mean, standard deviation, minimum, maximum, skewness and kurtosis. These were chosen to examine the homogeneity of the database values. The results justified the need for group breakdown; the elements of the database were heterogeneous, showing large differences from each other. Following the formation of clusters, homogeneous groups were obtained. For homogeneous grouping, a cluster analysis with two widely used non-hierarchical grouping methods was used, namely the K-Mean and K-Medoid methods.  
The clustering procedures can be hierarchical (tree-like structure) or non-hierarchical. The hierarchical method forms clusters gradually, and consequently this method has very high process cost because all objects are compared before every clustering step [59]. In the case of a big database, it can lead to a significantly high execution time. The algorithms of the K-Mean or K-Medoid methods (which are non-hierarchical methods) \"generally change centers until all points are related to centers\" [59] (p. 7). When compared with hierarchical classification, non-hierarchical classification is characterized by low cost in terms of calculation time [59]. Besides the execution time, in the case of larger samples the interpretation and use of the results of hierarchical cluster analysis are significantly more complicated. Therefore, it is advisable to use the K-Means method [60] or the K-Medoid method.  
Taking into account the above aspects, and bearing the main purpose of this study in mind, we focused only on the two non-hierarchical methods to compare the results of the K-Mean and K-Medoid methods in evaluating financial performance in the case of two large databases containing data related to three main financial indicators (ROS, ROA, ROE) for five years of active retail food companies from one Hungarian and one Romanian county. The grouping of companies was based on the ROS (Return on Sales), ROA (Return on Assets) and ROE (Return on Equity) ratios, in both cases. We worked with predefined group numbers which were determined using the Elbow method (described in the previous section). Additionally, in order to find out the number of clusters, for both databases we applied hierarchical cluster analysis. Ward's method and the squared Euclidian distance were employed. Finally, in the case of the Hungarian database, we chose five clusters, while in the case of the Romanian database we decided on fifteen clusters.  
In the final step, we compared the specific data obtained with the K-Mean and K-Medoid clustering methods separately for each database, based on the five-year average values of the groups.  
The statistical analysis of the databases and the editing of the graphs and diagrams were performed using Microsoft Excel spreadsheets, IBM SPSS Statistics 26.0 (IBM, Armonk, NY, USA) and R statistical software (version R, 3.5.0, R Core Team/R Foundation for Statistical Computing, Vienna, Austria).
# Simulation of Electronic Quantum Devices: Failure of Semiclassical Models
## 4 Fully Quantum-Mechanical Device Modeling Based on the Density-Matrix Formalism
### Non-Local Boltzmann-Type Models  
The very same generalization procedure employed to derive the nonlocal RTA model in Equation (32) can also be adopted to overcome the limitations of the Boltzmann-like treatment pointed out in Section 3.1 (see Figures 6 and 7). Instead of defining the scattering superoperator directly within the Weyl-Wigner phase-space \((\mathbf{r},\mathbf{k})\), one can start from a reliable dissipation model for the density matrix, and then translate it into the Wigner-function picture. To this aim, we refer to the nonlinear density-matrix approach proposed in [73], which: (i) applies to any generic nanostructure; (ii) accounts for high-density effects; (iii) provides the correct thermal-equilibrium state; and (iv) preserves the positive-definite character of the single-particle density matrix. More specifically, for both carrier-phonon and carrier-carrier interaction mechanisms, energy dissipation and decoherence is described by the following nonlinear scattering superoperator  
\[\frac{\partial\rho_{\alpha_{1}\alpha_{2}}}{\partial t}\bigg{|}_{\text{diss}}= \frac{1}{2}\sum_{\alpha^{\prime}\alpha_{1}^{\prime}\alpha_{2}^{\prime}}\left( \left(\delta_{\alpha_{1}\alpha^{\prime}}-\rho_{\alpha_{1}\alpha^{\prime}} \right)P_{\alpha^{\prime}\alpha_{2}\alpha_{1}^{\prime}\alpha_{2}^{\prime}} \rho_{\alpha_{1}^{\prime}\alpha_{2}^{\prime}}-\left(\delta_{\alpha^{\prime} \alpha_{1}^{\prime}}-\rho_{\alpha^{\prime}\alpha_{1}^{\prime}}\right)P_{ \alpha^{\prime}\alpha_{1}^{\prime}\alpha_{2}^{\prime}}^{*}\rho_{\alpha_{2}^{ \prime}\alpha_{2}}\right)\;+\;\text{H.c.}\;, \tag{36}\]  
where \(P_{\alpha_{1}\alpha_{2},\alpha_{1}^{\prime}\alpha_{2}^{\prime}}\) are generalized scattering rates, whose explicit form is given in [73]. The nonlinearity factors \((\delta_{\alpha_{1}\alpha_{2}}-\rho_{\alpha_{1}\alpha_{2}})\) can be regarded as the quantum-mechanical generalization of the Pauli factors of the conventional Boltzmann theory [see Equation (10)]. The key step is then to apply to the density-matrix scattering superoperator in Equation (36) the Weyl-Wigner transform in Equation (31). The resulting Wigner-function scattering superoperator still has the in-minus-out structure of Equation (16), with the local terms of the semiclassical theory in Equations (17) and (18) replaced with the following nonlocal generalizations.
## 3 Weighted Least Square Support Vector Regression Model
### Data Selection and Pretreatment  
Data are chosen from the data monitoring center of the key laboratory of anti-ice disaster. A 500 kV overhead transmission line called \"Zhe-Ming line\", located in Hunan Province, China, is chosen as the real-world case study. The icing data points are collected every fifteen minutes from 0:00 on 4 January 2015 to 11:45 on 11 January 2015, which total up to 720 data points, as shown in Figure 3. The former 576 data points are used as a training sample, and the last 144 as testing sample. Furthermore, the main micro-meteorology data, including temperature, humidity, and wind speed, are shown in Figure 3.  
Hunan Province is located in the middle and lower reaches of the Yangtze River and in the south of the Dongting Lake, surrounded by mountains on three sides; the Xiangzhong basin mainly includes hills, mounds, and valley alluvial plains. The terrain is very conducive for winter strong cold air to sweep from the North, and the Nanling stationary front is formed at the confluence of the cold air and the South China Sea subtropical warm air at the north slope of the Nanling mountains. In the areas covered with the stationary front, the super-cooled water in the condensation layer is very unstable, and extremely easy to adhere to relatively cold hard objects such as wires to form time and glaze. In 2008, Hunan power grid suffered severe freezing damage, which caused many incidents such as tower collapse, line breakage, ice flash, tripping, etc., leading to a large scale blackout disaster. The freezing time, covering area and icing conditions of this damage had been the most serious since 1954. Therefore, the high voltage transmission lines in Hunan Province have been selected as a case study, which has a certain universality.  
Before the program running, the historical micro-meteorology data and icing thickness data must be pre-processed to ensure the requirements of the W-LSSVM are met. First, each input should be weighted by the formula (12), which can reflect the forecasting differences between actual icing values and predictive values. Second, to avoid the scaling problem, the sample data should be scaled to the range [0, 1] by using their maximum and minimum values. Finally, it should be noted that the predicted values must be re-scaled back by using the reverse formula in order to guarantee the convenience and maneuverability for the results analysis. The scaled formula of sample data is shown as follows:  
\[Z=\{z_{i}\}=\frac{y_{i}-y_{\min}}{y_{\max}-y_{\min}}\qquad i=1,2,3,...,n \tag{27}\]  
where \(y_{\max}\) and \(y_{\min}\) are the maximum and minimum value of sample data, respectively.  
Figure 3: Original data chart of icing thickness, temperature, wind speed, and humidity.
## 9 Mobility Environment
### Conclusions  
The cuckoo search algorithm was optimized utilizing the levy flight distribution to address the routing challenges in CRAHNs. The performance of the proposed scheme was evaluated in NS2 simulator. From the experimental results, we observed that the proposed scheme OCS-AODV outperformed the CS-DSDV and ACO-AODV routing protocols, as it obtained a highest PDR and the least packet losses. When the number of nodes increased from 60 to 100, the proposed scheme outperformed the other two routing protocols. The proposed scheme can also sustain the network for a long time with residual energy because when mobile nodes are idle, they switch to the sleep mode to save battery life. Based on the observation of the experimental results for this study, we can further deduce that, according to simulation parameters and their respective values, better performing, QoS-aware routing protocols in CRAHNs are required for improved performance. The proposed scheme obtained 3.87% higher PDR and 2.56% lower packet loss. The scheme enabled the mobile nodes to adjust accordingly to minimize energy consumption. The node switched to an idle state to save battery power when inactive.  
[PERSON] was responsible for research design, investigation, and also wrote the manuscript. [PERSON] was the project leader who conceptualized the idea and edited the manuscript. Both authors have read and agreed to the published version of the manuscript.  
This work is based on the research support in part by the National Research Foundation of South Africa (Grant Numbers: 1141155), the Council of Scientific and Industrial Research, and the University of Limpopo.  
Not Applicable.  
Data Availability StatementData available in publicly accessible repositories including this article and other published outputs.  
This work is based on the research support in part by the National Research Foundation of South Africa (Grant Numbers: 1141155) and Council of Scientific and Industrial Research and University of Limpopo.  
We declare that this work is original and has not been submitted elsewhere for review or publication and that all sources utilized has been referenced.
## 3 Results and Discussion  
### Evaluation of the Total Phenolic and Flavonoid Contents  
_C. sativa_ leaf extract was calorimetrically assessed for total phenolic (TPC) and flavonoid (TFC) contents. The results obtained are presented in Table 1.  
The TPC in _C. sativa_ leaf methanolic extract was 458.22 mg GAE/g, and TFC was 24.55 mg QE/g. The obtained TPC and TFC were much higher compared to previously published studies. In the reports of [PERSON] et al. (2021), [PERSON] et al. (2017), [PERSON] (2007), [PERSON] et al. (2011), [PERSON] et al. (2010) and [PERSON] et al. (2010), the TPC in _C. sativa_ leaves extracts ranged from 14.0 to 298.96 mg GAE/g, while the TFC, expressed so far in units other than those in this work, ranged from 3.2 to 23.4 mg CE/g [11; 26; 27; 28; 29; 30]. This significant difference in the TPC between our own results and the cited reports may result from different methods of performing the assay, as well as from the fact that the preparation obtained as a result of fractionation on the RP-18 resin was cleaned of ballast compounds, thus enriched in phenolic compounds and flavonoids. This fact is also confirmed by the article published by [PERSON] et al. (2022), whose analyzes assessed the macerate obtained from _C. sativa_ leaves, the fraction from liquid-liquid extraction and the preparation obtained from the Amberlite XAD-4 absorption resin [31]. The highest TPC and TFC was also found for the preparation obtained from the deposit (872.4 mg GAE/g and 83.3 mg QE/g, respectively). The obtained value was much higher than that of the other extracts assessed, as well as the result obtained in this study, which may indicate that polymer resins with larger particle sizes than silica resins are more advantageous for the recovery of polyphenic compounds from _C. sativa_ leaves.  
With respect to the remaining morphological parts of the _C. sativa_ tree, the TPC in the tested leaf extract was 1.5 times higher compared to that of flowers [28], 122.8 times higher compared to that of fruits [27], 16.2 times higher than that of bur [32], 2.2 times higher for the shell [33], including 1.04 and 1.11 times lower than those estimated for the inner and outer shells [27], respectively. In turn, the TFC in other morphological parts of the _C. sativa_ tree was expressed in different units in previous reports, hence these values cannot be compared.
## 5 Numerical Results
### Validation of FEM and SEA Models  
In this section, a vibroacoustic coupling analytical model is established for the validation of the numerical vibroacoustic models described in Section 4. The simplified plate-cavity geometry shown in Figure 20 is considered, featuring a homogeneous rectangular plate with all four sides simply supported. A cuboid cavity shape was used to represent the window-cabin system of the HSM. The modal approach was strongly penalized by the high modal density of the cavity. About 290,000 cavity normal modes were found below 10 kHz, leading to a massive size of the coupled matrix system to be solved. [PERSON] et al. (2019) reported that there was no significant difference between the solution obtained using the full coupled system and the two-step approach above 100 Hz. The modal equation governing the plate and the acoustic pressure inside the cavity could be written as follows:  
\[m_{s}\Big{(}\omega_{M}^{2}-\omega^{2}-i\eta_{M}\omega_{M}\omega\Big{)}a_{M}( \omega)=-P_{M}(\omega), \tag{67}\]  
Figure 19: Workflow of the aero-vibroacoustic model.  
\[\Lambda_{N}\Big{(}\omega_{N}^{2}-\omega^{2}-i\eta_{N}\omega_{N}\omega\Big{)}b_{M}( \omega)=\omega^{2}\rho_{0}c_{0}^{2}\sum_{M=1}^{\infty}a_{M}(\omega)\mathrm{C}_{ MN}. \tag{68}\]  
Figure 21 compares the numerical results obtained using the current vibroacoustic FE and SEA models with those obtained from the analytic model for the simplified rectangular cavity model shown in Figure 20. The excitation surface pressure obtained from the LES result of HSM was used for the current validation computation. It can be found that there was good agreement between the two results. Note that the comparison for the SEA results is available only in the larger frequency bands, while that for the FEM results is available only in the narrower frequency bands.
# Noncoherent Channel Combining for GNSS Signal Tracking with an Adaptive Antenna Array
## 2 Mathematical Model
### Signal Model  
Without loss of generality, assuming that one GNSS signal is received by an \(N\)-element antenna array as well as \(Q\) interfering signals, the received signal vector can be expressed, using the complex intermediate frequency (IF) representation, as  
\[\mathbf{x}(t)=\mathbf{a}_{0}s(t)+\sum_{q=1}^{Q}\mathbf{a}_{q}j_{q}(t)+\mathbf{n }(t) \tag{1}\]  
where \(s(t)\) is the GNSS signal, with \(\mathbf{a}_{0}\in\mathbb{C}^{N\times 1}\) being the corresponding steering vector, \(j_{q}(t)\) is the \(q\)th interfering signal, and \(\mathbf{a}_{q}\in\mathbb{C}^{N\times 1}\) is the corresponding steering vector. \(\mathbf{n}(t)\in\mathbb{C}^{N\times 1}\) is the additive noise vector, the elements of which are independent and have a complex Gaussian distribution with zero mean and variance \(\sigma_{n}^{2}\). Moreover, the GNSS signal, interfering signals, and noise are assumed independent of each other.  
The IF received GNSS signal in Equation (1) can be formulated as  
\[s(t)=\sqrt{P}D(t-\tau)c(t-\tau)\mathrm{e}^{\mathrm{i}(2\pi(f_{\mathrm{IF}}+f_{ \mathrm{d}})t+\varphi)} \tag{2}\]  
where \(P\) is the signal power, \(D(t)\) is the navigation data bit, \(c(t)\) is the pseudo-random noise code, \(\tau\) is the time delay, \(f_{\mathrm{IF}}\) is the IF, \(f_{\mathrm{d}}\) is the Doppler frequency, and \(\varphi\) is the initial carrier phase. The interfering signals are assumed to be much stronger than the GNSS signal, such that the spreading spectrum signal gain is not sufficient to maintain the reception of the GNSS signal.
## 2 Materials and Methods
### Evaluation Indexes of Complex Ecospatial Network
In this paper, the robustness of the ecospatial network was evaluated by simulating a random attack and a malicious attack on it, where the random attack represented the random removal of several nodes from the network and the malicious attack represented the removal of the node with the largest degree from the network and its corresponding corridor. The robustness of the ecospatial network can be measured from two aspects: node recovery robustness and corridor recovery robustness, which reflect the recovery capabilities of ecological nodes and ecological corridors, respectively, and they are calculated as follows [44]:  
\[D=1-(N_{r}-N_{d})/N \tag{7}\]  
\[E=1-(M_{r}-M_{e})/M \tag{8}\]  
where \(D\) is the node recovery robustness index, \(N_{r}\) is the number of nodes removed, \(N_{d}\) is the number of nodes recovered, and \(N\) is the number of nodes. \(e\) is the corridor recovery robustness index, \(M_{r}\) is the number of corridors removed, \(M_{e}\) is the number of corridors recovered, and \(M\) is the number of corridors;  
(5)  
Correlation analysis  
In this paper, the Pearson correlation coefficient was used to calculate the correlation coefficient and significance level between NPP and the ecospatial network topological indices so as to analyze the correlation between NPP and the ecospatial network. The formula for calculating the Pearson correlation coefficient is [45]:  
\[r_{xy}=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sqrt{ \sum_{i=1}^{n}(x_{i}-\overline{x})^{2}\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}}} \tag{9}\]  
where \(r_{xy}\) is the correlation coefficient between \(x\) and \(y\); \(n\) is the number of nodes; \(x_{i}\) is the NPP of the \(i\) node; \(y\) is the degree or clustering coefficient of the \(i\) node; and \(\overline{x}\) and \(\overline{y}\) are the mean of \(x\) and \(y\), respectively.