esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Quantitative Models  
#### 3.2.1 Multiple Assets Portfolio Construction  
Suppose that a portfolio consists of \(N\) stocks, and \(S_{0}\) is the set of initial value for each stock in the portfolio, denoted as \(S_{0}=\left(s_{1}^{0},\ldots,s_{N}^{0}\right)\). The number of each stock in the portfolio is denoted as \(X=\left(x_{1},\ldots,\ x_{N}\right)\). The initial value of the portfolio \(V_{0}\) is calculated as follows:  
\[V_{0}=x_{1}s_{1}^{0}+\cdots+x_{N}s_{N}^{0}=\sum\
olimits_{i=1}^{N}x_{i}s_{i}^ {0} \tag{7}\]  
The decision on the number of shares in each asset will follow the decision on the division of our capital, which is our primary concern, and is expressed as the weights \(W=\left(w_{1},\ \ldots,\ w_{N}\right)\) with the constraint \(\sum\limits_{i=1}^{N}w_{i}=1\), defined by \(w_{i}=\frac{x_{N}s_{i}^{0}}{V_{0}}\) with \(i=1,..,N\).  
At the end of the period \(t\), the values of the stocks change \(S_{t}=\left(s_{1}^{t},\ldots,s_{N}^{t}\right)\), which gives the final value of the portfolio \(V_{t}\) as a random variable,  
\[V_{t}=x_{1}s_{1}^{t}+\cdots+x_{N}s_{N}^{t}=\sum\
olimits_{i=1}^{N}x_{i}s_{i}^ {t} \tag{8}\]  
The actual return of a portfolio \(R_{P}=\left(r_{1},\ \ldots,\ r_{N}\right)\) is the set of random returns on each stock of the portfolio, and the vector of expected return by \(\mu=\left(\mu_{1},\mu_{2},\ \ldots,\ \mu_{N}\right)\) with \(\mu_{i}=E(r_{i})\) for \(i=1,2,\ldots,\ N\). The actual return on the portfolio of multiple assets over some specific time period is straightforwardly calculated as follows:  
\[R_{P}=w_{1}r_{1}+w_{2}r_{2}+\cdots+w_{N}r_{N} \tag{9}\]  
The expected portfolio return is the weighted average of the expected return of each asset in the portfolio. The weight assigned to the expected return of each asset is the percentage of the market value of the asset to the total market value of the portfolio. Therefore, the expected return \(E(R_{P})=\mu_{P}\) of the portfolio at the end of the period \(t\) is calculated as follows:  
\[E(R_{P})=w_{1}E(r_{1})+w_{2}E(r_{2})+\cdots+w_{N}E(r_{N})=\sum\
olimits_{i=1} ^{N}w_{i}\mu_{i} \tag{10}\]  
Variance of return for the portfolio used above part as follows:  
\[Var(R_{P})=E(R_{P}-\mu_{P})^{2}=E\Big{(}{R_{P}}^{2}\Big{)}-\mu_{P}^{2} \tag{11}\]  
The variance of the return can be computed from the variance of \(S_{t}\),  
\[Var(R_{P})=Var\Bigg{(}\frac{S_{t}-S_{0}}{S_{0}}\Bigg{)}=\frac{1}{{S_{0}}^{2}} Var(S_{t}-S_{0})=\frac{1}{{S_{0}}^{2}}Var(S_{t}) \tag{12}\]  
The standard deviations of various random returns is \(\sigma_{P}=\sqrt{Var(R_{P})}\). The covariance between asset returns will be denoted by \(\sigma_{ij}=Cov\Big{(}r_{i},r_{j}\Big{)}\), in particular \(\sigma_{ii}=\sigma_{i}^{2}=Var(r_{i})\). These are the entries of the \(N\times N\) covariance matrix \(Cov\).  
\[Cov\Big{(}r_{i},r_{j}\Big{)}=E\Big{[}(r_{i}-\mu_{i})\Big{(}r_{j}-\mu_{j})\Big{]} \tag{13}\]  
\[Cov=\left[\begin{array}{cccc}\sigma_{11}&\sigma_{12}&\cdots&\sigma_{1N}\\ \sigma_{21}&\sigma_{22}&\cdots&\sigma_{2N}\\ \vdots&\vdots&\ddots&\vdots\\ \sigma_{N1}&\sigma_{N2}&\cdots&\sigma_{NN}\end{array}\right] \tag{14}\]
## 3 Results
### Determination of Mineral Content  
In the treatments with 0-40 mg dm\({}^{-3}\) ZnONPs, there were significant differences in the content of minerals in the Navaho blackberry plantlets (Table 2).  
The content of zinc in the blackberry plantlets increased with the concentration of ZnONPs in the medium, whereas increasing the concentration of zinc oxide nanoparticles in the medium decreased the content of iron in the plantlets. The plants growing on medium with 10 mg dm\({}^{-3}\) ZnONPs contained the most potassium (2150 mg 100 g\({}^{-1}\) DW), calcium (322 mg 100 g\({}^{-1}\) DW), magnesium (152 mg 100 g\({}^{-1}\) DW), manganese (15.3 mg 100 g\({}^{-1}\) DW), and copper (0.61 mg 100 g\({}^{-1}\) DW) (Table 2). At concentrations of 20 and 30 mg dm\({}^{-3}\) ZnONPs, the content of potassium, calcium, magnesium, manganese, and copper was also higher than in the control. At the highest concentration (40 mg dm\({}^{-3}\) ZnONPs), the content of these elements was similar to the control values, except for calcium and manganese, whose content was statistically significantly lower (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**ZnONPs [mg dm\({}^{-3}\)]**} & \multicolumn{6}{c}{**Mineral Content [mg 100 g\({}^{-1}\) DW]**} \\ \cline{2-9}  & **K** & **Ca** & **Mg** & **Fe** & **Zn** & **Mn** & **Cu** \\ \hline
0 & 1600 \({}^{\text{a}}\) & 285 \({}^{\text{a}}\) & 141 \({}^{\text{a}}\) & 18.5 \({}^{\text{a}}\) & 53.9 \({}^{\text{a}}\) & 12.8 \({}^{\text{a}}\) & 0.44 \({}^{\text{a}}\) \\
10 & 2150 \({}^{\text{b}}\) & 322 \({}^{\text{b}}\) & 152 \({}^{\text{b}}\) & 17.9 \({}^{\text{a}}\) & 75.0 \({}^{\text{b}}\) & 15.3 \({}^{\text{b}}\) & 0.61 \({}^{\text{b}}\) \\
20 & 2010 \({}^{\text{c}}\) & 310 \({}^{\text{b}}\) & 149 \({}^{\text{b}}\) & 16.1 \({}^{\text{b}}\) & 87.4 \({}^{\text{c}}\) & 14.9 \({}^{\text{b}}\) & 0.58 \({}^{\text{b}}\) \\
30 & 1860 \({}^{\text{d}}\) & 304 \({}^{\text{b}}\) & 148 \({}^{\text{b}}\) & 15.8 \({}^{\text{b}}\) & 90.8 \({}^{\text{c}}\) & 14.6 \({}^{\text{a}}\) & 0.46 \({}^{\text{a}}\) \\
40 & 1640 \({}^{\text{a}}\) & 254 \({}^{\text{c}}\) & 139 \({}^{\text{a}}\) & 14.5 \({}^{\text{c}}\) & 99.6 \({}^{\text{d}}\) & 10.9 \({}^{\text{c}}\) & 0.43 \({}^{\text{a}}\) \\ LSD\({}_{P}\)\({}_{=\) 0.05} & 74.0 & 16.15 & 4.28 & 0.95 & 5.35 & 2.25 & 0.07 \\ \hline \hline \end{tabular} Different letters indicate significant differences at \(p\) = 0.05.  
\end{table}
Table 2: Influence of ZnO-NPs on mineral contents in plantlets of blackberry (_Rubus fruticos_ L.) of the Navaho variety after 3 months of in vitro culture.  
Figure 1: Influence of different treatments of ZnONPs in MS medium on the growth and development of blackberry (_Rubus fruticos_ L.) of the Navaho variety. (**A**) Developing explants on micropropagation medium; (**B**) regenerated plantlets (from left): 0 (control), 10, 20, 30, and 40 mg dm\({}^{-3}\) ZnONPs.
## 3 Results and Discussion
### Biogenic Components
#### 3.3.2 Equilibrium Tests
In the case of phosphates, the material with the highest removal capacity was activated carbon (\(Q_{max}\) in the range from 2.114 to 2.634 mg/g), followed by halloysite (from 1.224 to 1.617 mg/g), diatomite (from 1.329 to 1.477 mg/g), zeolite (from 0.668 to 0.797 mg/g) and limestone sand (from 0.204 to 0.300 mg/g). It should be emphasized that a slight decrease of \(Q_{max}\) with regard to NH\({}_{4}\)-N was noted for diatomic, limestone sand and halloysite, but these changes were below 0.146 mg/g. Langmuir parameters for the removal of ammonium ions were determined at \(R^{2}\) from 0.982 to 0.999 (Table 4). The only exception are results obtained for zeolite (\(R^{2}\) from 0.771 to 0.833). Lower values of \(R^{2}\) could be caused by the non-exhaustion of the maximum sorption capacity of the materials in the established range of contaminants. The results of phosphate removal (\(R^{2}\) from 0.548 to 0.986) were described least precisely, despite the fact that numerous authors have shown the usefulness of the  
Figure 10: Maximum adsorption capacities of reactive materials with regard to NH\({}_{4}\)-N and PO\({}_{4}\)-P at 3 \({}^{\circ}\)C, 10 \({}^{\circ}\)C and 22 \({}^{\circ}\)C (\(R_{L}\) value ranges are presented in parentheses). Langmuir isotherm for the description of phosphate sorption on minerals and organic materials [78; 79]. This could be caused by the low abilities of the applied materials for the removal of such contaminants. According to [PERSON] and [PERSON] [75], the increase of removal capacity with increasing temperature may be related to the increase of the bonding energy of phosphorus sorption and precipitation rates. Evaluations of parameter \(R_{L}\) calculated on the basis of the Langmuir adsorption constant indicated that the sorption of ammonium and phosphate ions in the analyzed range of concentrations and temperatures is favorable (0 < \(R_{L}\) < 1), and removal of phosphates at low concentrations of these contaminants in the solution is similar to linear and less favorable. This is confirmed by values of \(R_{L}\) close to one. Moreover, the most favorable adsorption process was determined for the highest temperatures for all materials and contaminants, as evidenced by lower \(R_{L}\) values. However, these differences were insignificant in the case of NH\({}_{4}\)-N removal on zeolite and phosphates on all materials. [PERSON] et al. [64] also observed an increase of the maximum sorption capacities of halloysite with regard to ammonium ions with temperature increase (from 0.514 mgNH\({}_{4}\)-N/g at 15 \({}^{\circ}\)C to 0.737 mgNH\({}_{4}\)-N/g at 40 \({}^{\circ}\)C), but in these investigations the values of Langmuir equilibrium constant \(K_{L}\) decreased (therefore \(R_{L}\) increased) with temperature change from 15 \({}^{\circ}\)C to 40 \({}^{\circ}\)C as opposed to the analysis in this paper.
## 3 Results  
### PM\({}_{10}\), PM\({}_{0.49}\) and Endotoxins  
From the descriptive analysis shown in Figure 2, a significant variability in PM (Figure 2A) and endotoxin (Figure 2B) concentrations across the different samples is observed. The mean PM concentrations were found to be 574.7 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) for PM\({}_{0.49-10}\) and 564 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) for PM\({}_{0.49}\). For endotoxins, the concentrations range from low levels (few EU/m\({}^{3}\)) to around 90 EU/m\({}^{3}\), with an average of 13.6 EU/m\({}^{3}\) for PM\({}_{0.49-10}\) and 11.7 EU/m\({}^{3}\) for PM\({}_{0.49}\).  
When calculating the total endotoxin mass, it is observed that despite high PM concentrations, the total endotoxin amount is less than one millionth of the total PM mass in each filter. However, the levels of PM and endotoxins are significantly positively correlated (Spearman's rho = 0.654; \(p<\) 0.01) (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **XRCC1** & **ERCC1** & **XPF** & **XPA** & **IL-8** & **EU/m\({}^{3}\)** & **PM\({}_{10}\)\(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)** \\ \hline \multirow{3}{*}{XRCC1} & 1.000 & 0.505 \({}^{*}\) & \(-\)0.352 & \(-\)0.623 \({}^{**}\) & 0.222 & 0.618 \({}^{**}\) & 0.036 \\ \cline{2-7}  & & 0.019 & 0.118 & 0.003 & 0.333 & 0.003 & 0.876 \\ \hline \multirow{3}{*}{ERCC1} & & & 0.016 & \(-\)0.251 & 0.531 \({}^{*}\) & 0.519 \({}^{*}\) & 0.043 \\ \cline{2-7}  & & & 0.947 & 0.273 & 0.013 & 0.016 & 0.854 \\ \hline \multirow{3}{*}{XPF} & & & & 0.552 \({}^{**}\) & \(-\)0.318 & \(-\)0.370 & \(-\)0.229 \\ \cline{2-7}  & & & & 0.009 & 0.160 & 0.099 & 0.319 \\ \hline \multirow{3}{*}{XPA} & & & & & \(-\)0.096 & \(-\)0.221 & 0.204 \\ \cline{2-7}  & & & & & 0.679 & 0.336 & 0.375 \\ \hline \multirow{3}{*}{IL-8} & & & & & 0.662 \({}^{**}\) & 0.325 \\ \cline{2-7}  & & & & & 0.001 & 0.151 \\ \cline{1-1} \cline{2-7}  & & & & & 0.654 \({}^{**}\) \\ \cline{1-1} \cline{2-7}  & & & & & & \(<\)0.001 \\ \hline \end{tabular}
\end{table}
Table 2: [PERSON]’s correlations among the changing of expression level of the different gene target, with respect to the control, and the PM\({}_{10}\) and endotoxin levels. Asterisks show significant and highly significant correlations.  
The organic waste treatment plant showed the highest level of both PM and endotoxins, and when the concentrations of the two PM sub-fractions (PM\({}_{0.49\text{-}10}\) and PM\({}_{0.49}\)) are summed, the maximum values are quite high, with approximately 18,000 ug/m\({}^{3}\) of PM and 138 EU/m\({}^{3}\) of endotoxins, exceeding the recommended exposure limits for particulates not otherwise regulated, total and respirable dust (PNOR) (15 mg/m\({}^{3}\) and 5 mg/m\({}^{3}\)), and for endotoxins (90 EU/m\({}^{3}\)) [29].  
Finally, when comparing the two PM sub-fractions (PM\({}_{0.49\text{-}10}\) and PM\({}_{0.49}\)) using the paired \(t\)-test, no significant differences in PM and endotoxin concentrations were found, indicating that these contaminants are evenly distributed across the investigated environments (\(p>0.05\)).
## 4 Conclusions  
In this study, the effect of insert-angle factors on cutting temperature in the hard-turning process is explored using a combined approach. Several conclusions have been determined, as follows:  
* The cutting temperature from the experiment and the simulation results have consistent values, reaching about 900 \({}^{\circ}\)C. The difference in the experiment and simulation results is small--approximately 2%.
* Only the tool-nose radius cuts during the hard-turning operation, and the chip makes contact with the cutting edge's chamfered face. At each cutting point, the local tool-geometry parameters and the thickness of the undeformed chip change, and the local rake angle becomes noticeably negative.
* Increasing the negative rake angle and negative inclination angle improves the local negative rake angle of the cutting-edge elements at the tool-nose radius during the hard-turning process, accounting for 65.84% and 31.43%, respectively.
* The most significant factor influencing cutting temperature is the inclination angle, which accounts for 40.75% of the total variability. The rake angle has a smaller effect, accounting for 32.39% of the total variability, and the cutting-edge angle accounts for 7.03%.  
The interactions between the components are not significant. Increasing the negative rake angle and negative inclination angle raises the cutting temperature. This study's findings could bring about additional benefits for applications of the hard-turning technique.  
Conceptualization, P.M.D. and L.H.G.; methodology, P.M.D.; software, V.T.N.; validation, P.M.D., L.H.G. and V.T.N.; formal analysis, P.M.D.; investigation, P.M.D.; resources, P.M.D.; data curation, P.M.D.; writing--original draft preparation, P.M.D.; writing--review and editing, P.M.D., L.H.G. and V.T.N.; visualization, V.T.N.; supervision, L.H.G.; project administration, P.M.D.; funding acquisition, P.M.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
We are grateful to Ho Chi Minh University of Technology and Education for providing the facilities for us to carry out this study.  
Figure 11: Cutting temperature vs. rake and inclination angles’ response surface in hard turning.
# Output Power Smoothing Control for a Wind Farm Based on the Allocation of Wind Turbines
## 4 Simulation Results
Thus, the proposed power smoothing control strategy through the reasonable allocation of the WTs in the wind farm and the rotor inertia was verified by the simulation results. The rotor inertia based power smoothing method for PWTs can reduce the torque ripple and mechanical stress significantly. The CWTs can output the appropriate power according to the reference value. The configured BESS were not working all the time to extend the life. The control for the grid-side converter was also verified through the constant DC link voltage. The proposed control strategy can respond to different needs of the grid, which was verified by the simulation results under different working modes.
## 4 Wind-Induced Fragility Analysis of Corroded Transmission Tower
### Definition of Structural Limit Damage Modes  
Pushover analyses were performed on the finite element models of the transmission tower with different corrosion ages, and the inverse triangular distributed lateral loading mode was used to obtain pushover curves between the base shear and the top maximum displacement, as shown in Figure 20. The maximum displacements at the top in the elastic stage for the transmission tower with corrosion ages of 0, 30, 60 and 90 years are all at 0.4 m, which is much larger than the displacement limit given by the code [34] (the code limit is 3H/1000, i.e., 0.1 m). From Figure 20, it can also be seen that the ultimate displacements of the transmission tower with different corrosion ages are all at 1.86 m. Based on the above analysis, and according to the research results related to the transmission tower damage modes and failure laws [9; 10; 11], three types of damage modes were defined in this paper, i.e., minor damage, moderate damage, severe damage and collapse, and the quantitative index limits for each damage mode of the corroded transmission tower were determined, as shown in Table 4.  
Figure 19: Wind load effect functions of the transmission tower under different corrosion ages: (a) 0 year; (b) 30 years; (c) 60 years; (d) 90 years.
# Noise Modeling to Build Training Sets for Robust Speech Enhancement
## 3 Noise Modeling and Building the Training Sets
### Building the Training Sets
#### 3.2.1 Noise Dataset  
Before building the paired training dataset, a set of approximate noise blocks (or patches) must be extracted from the given noisy speech recordings. In this way, the noise distribution becomes the principal learning objective for the NM-GAN, enhancing its accuracy. Thus, a Speech Endpoint Detection Method [24] can extract the noise segments from the noisy speech.
###### Abstract  
Surface \(\alpha\)-particle emissivity testing and spectral characterization of two leaded tin spheres (Sn\({}_{10\%}\)Pb\({}_{90\%}\), Sn\({}_{63\%}\),Pb\({}_{37\%}\)) and one lead-free tin sphere (Sn\({}_{96.5\%}\),Ag\({}_{3.0\%}\),Cu\({}_{6.5\%}\), SAC305) were carried out. The results show that Sn\({}_{10\%}\)Pb\({}_{90\%}\) Sn spheres have the highest \(\alpha\)-particle emissivity; Sn\({}_{63\%}\)Pb\({}_{37\%}\) Sn spheres are the next highest, which is an order of magnitude lower than the \(\alpha\)-particle emissivity of Sn\({}_{10\%}\)Pb\({}_{90\%}\) Sn spheres; and SAC305 Sn spheres have the lowest emissivity, which is reduced by about 55.6% compared to the emissivity of Sn\({}_{63\%}\)Pb\({}_{37\%}\) Sn spheres. All three types of tin spheres, after purification treatment, achieved the grade of ultra-low alpha particle emissivity (\(<\)0.002 \(\alpha\)/(cm\({}^{2}\)-h)). The internal radionuclide traceability of the tin sphere, combined with the energy spectrum, reveals that the emission spectrum of the tin sphere exhibits an obvious \"single peak\" characteristic, with the peak energy in the interval of 5 MeV-5.5 MeV. Comparative analyses revealed that \({}^{210}\)Po is the main nuclide that produces alpha particles, and \({}^{210}\)Po originates from the decay of \({}^{210}\)Pb. Further Monte Carlo simulations show that \(\alpha\)-particles with energies greater than 4.1 MeV in the measured energy spectrum all come from the contribution of radionuclides within 5 \(\upmu\)m of the surface layer of the tin sphere, which accounts for 60% of the total radioactivity. Combining the experimental and simulation results, it is found that the internal radionuclides of the tin sphere are characterized by more surface layer and less internal layer. The above results are of great significance for the establishment of \(\alpha\)-particle mitigation methods for tin spheres.  
Keywords:tin spheres; alpha particles; emissivity; simulation +
Footnote †: journal: Applied Sciences
# A Hybrid Routing Protocol Based on Naive Bayes and Improved Particle Swarm Optimization Algorithms
## 1 Introduction  
A wireless sensor network (WSN) is a multi-hop self-organizing network composed of a large number of microsensors with a dynamic topology. Data gathering and transmission are the main goals of WSNs [1]. By integrating different types of sensors, such as temperature, humidity, and pressure sensors, for data acquisition and monitoring, WSNs can be used in medical treatments, industry, environmental protection, agriculture, etc. [2]. Because WSN nodes are powered by a battery, energy-efficient utilization is the key component of WSN design and optimization [3, 4]. On the other hand, due to the complex occlusion and other characteristics of the network's application environment, the data transmission process between WSN nodes is vulnerable to interference and the influence of the surrounding environment, and the link characteristic is a probabilistic connectivity state of time connection and time disconnection. In recent years, many researchers have carried out energy consumption optimization research on routing path selection in wireless sensor networks. Cluster head multi-hop hybrid routing based on a cluster structure is a current research hotspot [5]. In this hybrid routing structure, the nodes are first clustered locally, then the data collected by the sensing nodes are collected by the cluster head, and finally the data are returned to the base station (BS) through multiple hops between cluster heads [6]. The advantage of the hybrid routing structure is that it can simplify the network's structureand effectively avoid energy consumption hotspots and energy consumption funnels. The cluster head is the key node in the hybrid routing structure [7]. Cluster head selection and multi-hop path selection between cluster heads are key to determining the overall energy consumption performance of the network [8].  
The routing protocol proposed in this paper considers cluster head selection and multiple hops between cluster heads in unreliable link networks. A machine learning method is adopted in order to select the cluster head and the multi-hop path according to the node residual energy, link quality, and hops for the purpose of saving energy. In unreliable link networks, a relay node and path selection problem can be transformed into a characteristic probability problem of nonlinear classification. In this paper, a classification method based on a Naive Bayes algorithm is used to establish a relay selection method for energy consumption optimization. The classification method based on the Naive Bayes algorithm is a very efficient machine learning method that is widely used in various application scenarios.
### Radio Detection AND Ranging (RADAR) Theory
#### 1.2.2 Continuous-Wave RADAR  
Continuous-Wave (CW) RADAR is a type of RADAR where an EM wave with known frequency is emitted within a given medium (example: air) and reflected by any object (example: car). This method allows the removal of unrelated signals that, in turn, allows the reflected weak signals to be detected [33; 34].  
Without modulating the frequency, the sensor can not measure the distance of the objects due to the Doppler effect. Modulating the frequency allows having a better assessment of the object's velocity and allows estimating the object's position [35].  
Frequency-modulated continuous wave radar FMCW technology uses UWB RADARs' components, i.e., the antennas, the voltage-controlled oscillators, and the low noise amplifiers, to make cost-effective RADARs with frequency bandwidths up to 4 GHz [36].