esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of NOx Reduction Effect and Impact on Asphalt
## 3 Experimental Results and Analysis
### Asphalt Aging Evaluation by UV
#### 3.3.2 SARA Analysis Result  
In general, changes in the SARA components occur in asphalt owing to aging. At the beginning of the aging reaction, the aromatic hydrocarbon component (aromatics, Ar) increased as the aliphatic saturated hydrocarbon component (saturates, Sa) decreased, and the asphaltene component (As) increased as the petroleum resin component (Resin, Re) decreased. Thereafter, as a two-step change, the Sa component maintained a constant content without further reduction, the Re component increased as the Ar component decreased, and As continuously increased. As asphalt aging by UV continued, the contents of Sa and Ar decreased and the contents of Re and As increased, leading to an increase in the brittleness of asphalt. This resulted in relatively reduced ductility, which ultimately exceeded the fracture toughness and caused cracks [46; 47].  
The results of SARA analysis through the UV aging experiment are shown in Tables 9-11. As a result of the experiment, we found that the asphalt containing TiO\({}_{2}\) exhibited reduced Sa and Ar content and increased Re and As content compared to general asphalt, indicating that UV-induced aging was accelerated. However, in the case of asphalt mixed with TiO\({}_{2}\) and the rejuvenator, the difference in the SARA components before and after the UV aging test was not significant. Therefore, the rejuvenator is effective in preventing UV aging caused by TiO\({}_{2}\).
## 5 Result Analysis  
The results of the dynamic characteristic experiment are mainly used as the initial input of the calculation of the frequency response function matrix of the multi-axis milling process. Using the extended Sherman-Morrison-Woodbury formula, one will eventually get the original frequency response function. Then, the modal parameters are extracted from the original frequency response function, and the variation curves of the modal parameters with the change of the machining path are drawn. To verify the accuracy of the obtained results, a finite element model is built according to the division of the cutting steps, the modal analysis of the finite element model is conducted, and the modal parameters are obtained. Then the results of finite element analysis are compared with the results obtained by the extended Sherman-Morrison-Woodbury formula. The finite element analysis results are in good agreement with the prediction using the extended Sherman-Morrison-Woodbury formula. The results show that the proposed method is of high accuracy. Besides, the machining of blades as a typical case of multi-axis milling of a thin-walled part with a complex curved surface is studied. The research of the multi-axis milling process is one of the tops of the blade during the semi-finishing machining process. Using the proposed method, we obtained the variation of natural frequency, modal mass, modal stiffness, and modal damping of the blade with the cutting path in the multi-axis milling process. The effectiveness of this method is validated.
## 3 Results
### ASE Dual-Selectively Targets the NRF2/STAT3 Axis and Suppresses Survivin and HSP70 Expressions  
To investigate the mechanisms through which the ASE induces cell death, we evaluated expressions of prosurvival proteins in NSCLC cells after ASE exposure. According to the results of [PERSON]'s study [32], a major artemisinin derivative, dihydroartemisinin, significantly suppressed the phosphorylation of the STAT3 signaling molecule in colon cancer cell death. Furthermore, the previous study provided evidence that STAT3/NRF2 signaling might be involved in regulating feroptosis by indicating that STAT3/NRF2 activation could enhance the antioxidant capacity of cells and protect against oxidative damage [33]. Thus, we analyzed NRF2 and STAT3 levels after ASE treatment in both cell lines. As shown in Figure 4, the ASE suppressed NRF2 expression mainly prevailed in A549 cells in a dose-dependent manner but slightly upregulated its expression in the H23 cell line. However, despite triggering multiple cell death modes, ASE strongly inhibited the STAT3 phosphorylation level in both A549 and H23 cell lines. At a 200 \(\upmu\)g/mL concentration, respective inhibition rates of p-STAT3 were 61.5% \(\pm\) 0.5% and 50.6% \(\pm\) 0.1% in A549 and H23 cells with statistical significance (Figure 4B,C). Among known prosurvival proteins downstream of the STAT3 signaling molecule, HSP70 and survivin were shown to impact against apoptosis in tumor cells, and thus we further examined HSP70 and survivin protein levels after ASE treatment. As expected, HSP70 expression was downregulated by approximately 53.5% \(\pm\) 0.05% at the indicated concentration in A549 cells, whereas it had decreased by 53.8% \(\pm\) 0.07% in H23 cells at 48 h (Figure 4A,B). Statistically significant dose-dependent effects were also observed in survivin expression in both cell lines exposed to ASE. Collectively, dual-selective alteration of NRF2, which could be defined as regulatory crosstalk between two distinct forms of cell death, together with downregulation of p-STAT3 and the downstream prosurvival proteins in both cell lines suggests that ASE showed antitumor activity through attenuating the defense mechanisms of cells.  
Figure 4: \(Cont.\)
## 6 Modeling Language Implementation
### Language Translation  
Generators and transformations allow the conversion of the DSML model into different output models or source codes [40]. In the case of RT-Sequencer, the translation of the model into the AST is responsible for generating the final code in the C++ programming language and, finally, target the machine code.  
Besides the metamodel, we use additional rules and _validations_ (as shown in Figure 7 on the left side) that we perform before translation. In our case, we divided the validation into two parts: warnings and errors. Warnings represent potential errors that can be checked before compiling, and can be critical while running the program. Examples of such warnings are, e.g., the incoherence of some visual blocks, which may represent unused visual blocks, or incorrect naming of variables. The second type of validation is errors that represent a semantic error in which further translation of the language is impossible. This type of error is, for example, the use of a non-existent variable or an undefined model or visual block.  
After validating the model successfully, the next step is the _translation into the AST_ of individual models with data and symbols. The sequence tree is built based on the model's execution flow of individual tasks. Note that each task has its execution tree. Model variables are also evaluated in addition to tasks, models (a set of individual visual blocks) and visual blocks. Building an AST tree helps us later to connect and convert to other forms of notation (in our case, the C++ program code).  
Once we have an individual AST tree for each task, we combine them in a _linking_ process. In this process, we link different AST task trees and their dependencies. We  
Figure 7: Model to code in RT-Sequencer.  
determine the dependencies and relationships between local and global variables, visual blocks, and models. Following this procedure, we can do _optimization_ easily and select which blocks are used. Only these used blocks will be translated into the generated C++ code.  
The next step is _generating_ the C++ code from AST trees. On the right side of Figure 7 we can observe an example of a visual block's class declaration that is created automatically from the metablock (see the metamodel class _SeqGPLLogic_ description in the previous subsection, again). In addition to the basic inherited methods, the input and output variable properties are also created (attribute _properties_).  
When the entire C++ code is generated, it needs to be translated into the target machine code. We used the MinGW compiler to create an RTC program for a Windows host computer. In the case where it is intended to compile the RTC program on a remote system with the Linux platform, it is necessary to transfer the generated code to the RT controller, where the gcc compiler is installed. Through remote access, the RTC program is translated according to the required operating system and architectural platform. After successful translation, the RTC program can be run within the system environment.
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 4 Principle of NOMA System
### Principle of Transceiver  
Figure 6 illustrates a typical scheme for the power allocation, the uplink and downlink of the NOMA system management process per sub-channel. Multiple users send their signals to the BS in the same Resource Block (RB) in uplink transmission, as illustrated in Figure 6b. The BS identifies all the users' messages with the assistance of SIC. The main differences between uplink NOMA and downlink NOMA can be summarized as follows:  
* **Transmit Power**: in regards to downlink NOMA, the users' transmit power in uplink NOMA does not have to be different. It is based on the channel conditions of each user. If the users' channel conditions are considerably different, their received SINR can be somewhat different at the BS, irrespective of their transmit Power.
* **SIC Operations:** the uplink NOMA and downlink NOMA users' SIC operations and interference are also somewhat different. In particular, as displayed in Figure 6c for downlink NOMA, the signal of user n is disinfected from the interference caused by user m. In precise, that is performed by first detecting the more powerful signal of user m, modulating it and then deducting it from the composite signal. That implies SIC operation is carried out on a strong user in the downlink for cancelling the weak user's interference. By contrast, SIC is performed at the BS in uplink NOMA to distinguish the strong user n first by treating User m as interference, as presented in Figure 6b. Then it demodulates the recovered signal and subtracts the interference imposed by user n to identify user m.
# Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
## 4 Methodology
### RB Allocation: XueBlockSolver Algorithm
As an example, and taking into account a system with four CUEs and four VUEs, one possible solution could be represented in the following way:  
\[X:\{CUE\_02,VUE\_03;CUE\_01,VUE\_04;\]  
\[CUE\_03,VUE\_01;CUE\_04;VUE\_02\}\]  
In this case, this specific solution is composed by three pairs (\(\{CUE\_02,VUE\_03\}\), \(\{CUE\_01,VUE\_04\}\), \(\{CUE\_03,VUE\_01\}\)) and two lone elements (\(CUE\_04\) and \(VUE\_02\)). The working way of the developed MP-BSA has been summarized in the Algorithm 2. As can be seen in this algorithm, the first steps of the MP-BSA is to randomly generate the initial population and to define the above described objective function. After that, an iterative process starts, in which every individual of the population performs a small modification on its structure. If this newly generated solution improves the previous one, it is accepted. In any other case, the new solutions is discarded. For the modification of individuals, the well-known exchange operator has been used, which has been widely used in other fields [39]. For this movement, first, two elements of the solution are randomly selected, in this case the elements in boldface. Then, the position of these two elements are exchanged. After this interchange, the blocks are built again. This step is used with the aim of no generating infeasible solutions. For this reason, it may be that new solutions have a different number of blocks than the previous ones. Taking the previous individual \(X\), and assuming, for example, \(CUE\_01\) and \(CUE\_04\) have been randomly selected:  
\[X:\{CUE\_02,VUE\_03;\textbf{CUE\_01},VUE\_04;\]  
\[CUE\_03,VUE\_01;\textbf{CUE\_04};VUE\_02\}\]  
One possible new individual could be the following one:  
\[X^{\prime}:\{CUE\_02,VUE\_03;\textbf{CUE\_04},VUE\_04;\]  
\[Y:\{T\_VUE_{1},T\_VUE_{2},T\_VUE_{3},\ \ldots,T\_VUE_{m}\}\]0  
Finally, this iterative process is repeated until the termination criteria is reached. In this case, this termination criteria is composed by two conditions: a fixed number of total generations and a number of generations with no improvements in the best found solution. If any of these conditions are fulfilled, the algorithm finishes its execution and returns it best individual. ```
Data:\(VUE\_Throughput\_Matrix\), \(CUE\_Throughput\_Matrix\), \(Paired\_Throughput\_Matrix\), Result: Best individual found  Define the objective function \(f(x)\);  Initialize the initial population \(X=x_{1},x_{2},...,x_{n}\); repeat foreach individual \(x_{i}\) in the populationdo  Generate new solution \(x^{\prime}_{i}\) from \(x_{i}\); if\(f(x^{\prime}_{i})\)\(>\)\(f(x^{\prime}_{i})\)then  Accept the new solution;  end if  end foreach until termination criterion not reached;  Rank the individuals and return the current best individual of the population;
```  
**Algorithm 2**Pseudocode of the proposed MP-BSA.
## 3 Results  
### Lifestyle-Dietary Patterns and Patient's Characteristics  
The patient's characteristics can be seen in Table 1. The study group's mean age was 26 \(\pm\) 5 years old, and BMI was 25.4 \(\pm\) 5.2 kg/m\({}^{2}\). Three lifestyle-dietary patterns have been distinguished among women with PCOS: (1) Western (WDLP), (2) Prudent (PDLP) (3) Active (ADLP). The factor loadings of each dietary pattern are depicted in Figure 2 and Supplementary materials Table S1. The WDLP was characterised by high visceral fat, high-frequency intake of animal foods, sweets and sweetened beverages, white grains, junk and fried foods, and low-frequency intake of plant foods. PDLP was characterised by high-frequency intake of plant foods and dairy, high meal frequency per day, and intense exercise. ADLP was characterised by high visceral fat, high-frequency intake of plant products, intense exercise and low intake of junk and fried food.  
The mean frequency intakes of different food groups for each adherence can be found in Supplementary Tables S2-S5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Mean** & \(\pm\)**SD** & **Median** & \multicolumn{2}{c}{**CI (95\%)**} \\ \hline Age (years) & 26 & 5 & 25 & 25 & 27 \\ Height (cm) & 165 & 15 & 167 & 163 & 168 \\ Body mass (kg) & 70.8 & 14.9 & 68.0 & 68.3 & 73.3 \\ BMI (kg/m\({}^{2}\)) & 25.4 & 5.2 & 24.1 & 24.5 & 26.3 \\ Waist circumference (cm) & 81.5 & 13.0 & 79.5 & 79.3 & 83.7 \\ Hips circumference (cm) & 100.5 & 11.4 & 100.0 & 98.6 & 102.4 \\ WHR (\(-\)) & 0.81 & 0.08 & 0.80 & 0.80 & 0.82 \\ FM (\%) & 36 & 8 & 35 & 35 & 37 \\ VAT (g) & 467 & 530 & 283 & 378 & 555 \\ TC (mg/dL) & 178 & 31 & 176 & 173 & 184 \\ HDL (mg/dL) & 66 & 16 & 63 & 63 & 68 \\ LDL (mg/dL) & 95 & 30 & 93 & 90 & 100 \\ TG (mg/dL) & 87 & 58 & 69 & 77 & 97 \\ Fasting glucose (mg/dL) & 89 & 7 & 88 & 88 & 9 \\ Fasting insulin (uU/mL) & 11.09 & 7.15 & 8.89 & 9.84 & 12.34 \\ HOMA-IR (\(-\)) & 2.52 & 1.81 & 1.94 & 2.20 & 2.84 \\ FSH (mlU/mL) & 6.6 & 8.00 & 5.9 & 5.1 & 8.00 \\ LH (mIU/mL) & 10.4 & 6.1 & 8.4 & 9.3 & 11.5 \\ LH/FSH (\(-\)) & 1.8 & 1.1 & 1.5 & 1.6 & 2.0 \\ T nmol/L & 1.9 & 0.9 & 1.6 & 1.7 & 2.0 \\ \hline \hline \end{tabular} Abbreviations: BMI—Body Mass Index; WHR—Waist-to-Hip Ratio; WHtR—Waist-to-Height Ratio; FM—fat mass; VAT—Visceral Fat Tissue; TC—Total cholesterol; HDL—High-density lipoprotein; LDL—Low-density lipoprotein, TG—Triglycerides; HOMA-IR—Homeostatic Model Assessment—Insulin Resistance; FSH—Follicle Stimulating Hormone; LH—luteinising hormone, T—Total testosterone.  
\end{table}
Table 1: Patient’s characteristics (\(n=140\)).  
Figure 2: Factor loadings diagram characterising dietary-lifestyle patterns distinguished among women with PCOS.
## 2 Materials and Methods
### Turbulence Clearance
#### 2.2.1 Background
This conversion is needed, since the classification of mountain waves (light, moderate, severe) is developed according to the wind speed that is perpendicular to the mountain at that pressure altitude (V\({}_{\mathrm{norm}}\)) [39], as shown in \[\Delta\alpha= \tan^{-1}\left(\frac{\mathrm{V_{gust}}}{\mathrm{V_{\infty}}}\right) \tag{2}\]2 That is why it is necessary to have an altitude associated with a wind speed in order to calculate the vertical displacement of an aircraft flying over a mountainous area. Below, the steps developed to obtain the wind speed that is perpendicular to the mountain range at a 700 hPa pressure are defined. First, the altitude corresponding to a 700 hPa pressure according to the ISA model was calculated from the following equations:  
\[\Delta\alpha= \tan^{-1}\left(\frac{\mathrm{V_{gust}}}{\mathrm{V_{\infty}}}\right) \tag{2}\]3  
Following the expression, first, it is necessary to calculate the temperature, T, at that pressure, using the formula below [45]:  
\[\Delta\alpha= \tan^{-1}\left(\frac{\mathrm{V_{gust}}}{\mathrm{V_{\infty}}}\right) \tag{2}\]4  
where the following applies:  
* p is the atmospheric pressure at altitude h, expressed in the same units as p\({}_{0}\). In this case, it is 700 hPa. * p\({}_{0}\) corresponds to sea level pressure (1013.25 hPa). * T\({}_{0}\) is the sea level temperature, expressed in \({}^{\circ}\)K (288.15 K). * T\({}_{\mathrm{h}}\) is the temperature gradient of the troposphere (\(-\)0.0065 \({}^{\circ}\)K/m). * R is the ideal gas constant (287.053 J/Kg/K). * g is the gravity at sea level (\(-\)9.80665 m/s\({}^{2}\)). Secondly, once the altitude at a 700 hPa pressure has been calculated by replacing the indicated values (3010 m), [PERSON]'s exponential law or power law is used to calculate the perpendicular wind speed. [PERSON]'s exponential law was proposed in 1915 and is useful for estimating the wind speed at a given altitude based on data measured at another given altitude and is obtained as follows [47; 48; 49]:  
\[\Delta\alpha= \tan^{-1}\left(\frac{\mathrm{V_{gust}}}{\mathrm{V_{\infty}}}\right) \tag{2}\]5  
where the following applies:  
* h\({}_{0}\) is the reference altitude. In this study, this would be equivalent to the flight altitude. * v\({}_{0}\) is the wind speed at the reference altitude. In this case, it corresponds to the wind speed at the flight segment (V\({}_{\mathrm{wind}}\)). * h is equivalent to the altitude found above, relative to a 700 hPa pressure according to the ISA model, which is 3010 m.
* v is the wind speed to be estimated, that is, the wind speed that is perpendicular to the ridge (V\({}_{\mathrm{norm}}\)) at 700 hPa. * \(\alpha\) is the power law index or Hellman exponent, which is obtained using the expression shown below [47]:  
\[\Delta\alpha= \tan^{-1}\left(\frac{\mathrm{V_{gust}}}{\mathrm{V_{\infty}}}\right) \tag{2}\]6  
Thus, following the expressions, the relative wind speed at a 700 hPa pressure is obtained for each case, which could cause mountain waves if its intensity exceeds 15 kt.
# A Heuristic Methods-Based Power Distribution System Optimization Toolbox
###### Abstract
Many of them use heuristic methods because they are simple and applicable to different problems. For example, in [16], the authors use the marine predators algorithm to solve the power source allocation problem in distribution networks. In a recent work, the Moth Flame Optimization algorithm (MFO) was modified to efficiently accommodate fuel cells consisting of wind turbines and photovoltaics [17]. PSO was used in [18] to solve the planning problem in distribution networks by considering economic constraints. Various studies have used an ALO [19; 20], GWO [21; 22; 23], and WOA [24; 25] for solving power systems-related problems. In 2016, [PERSON] et al. applied the ALO [26], and in 2017, [PERSON] et al. applied the WOA [24] for solving the optimal power flow problem. In 2019, an article was published on optimal renewable power placement and sizing using GWO [22]. A combination of GWO with Crow Search Optimization (CSO) was utilized for home appliance scheduling in [27]. In [28], the authors analyzed microgrids consisting of multiple renewable units and a diesel generator and battery considering the energy cost and the probability of insufficient power supply. They formed an optimization problem that was solved by hybridizing PSO and GWO. Several studies have also been conducted to evaluate the effects of installing tap changers in the distribution system. In these studies, it was found that the use of tap changers to adjust the voltage profile can positively affect the voltage profile [23; 29; 30; 31; 32]. Recently, BESS systems have been used in power distribution systems due to several advantages such as the injection/absorption of power. Therefore, researchers focus on the optimal implementation of BESS systems, focusing on optimizing their locations in power systems [33; 34] while others focus on optimizing the charging and discharging times of BESSs [35]. In addition, there are also studies that focus on BESS capacity optimization and operating costs [36]. The main contributions of this work can be described as follows. * We develop an open source toolbox for power distribution systems by using the classical PSO and several recently developed heuristic algorithms: GWO, WOA, and ALO optimization methods, combined with the free power systems software Matpower. * The optimization functions of the toolbox are designed to determine the near optimal set points of tap changer voltage regulators, the charge/discharge behavior of BESS and power output of inverters connected close to PVs on 33- [37], 69- [38], and 141-bus [39] systems. The rest of the paper is organized as follows: The optimization models are described in Section 2. Section 3 describes the implementation of the optimization methods. Section 4 provides details about the toolbox graphical user interface (GUI). Finally, Section 5 shows the simulation and test results. Lastly, Section 6 concludes the paper.
## 3 Solvent-Based Microextraction Techniques
### Dispersive Liquid-Liquid Microextraction (DLLME)  
Dispersive liquid-liquid microextraction (DLLME) and its modifications--such as ultrasound-assisted DLLME (UA-DLLME), ionic liquid-based dispersive liquid-liquid microextraction (IL-DLLME), deep eutectic solvent-based dispersive liquid-liquid microextraction (DES-DLLME), and sugaring-out assisted liquid-liquid extraction (SULLE)--offer unique benefits, such as a high pre-concentration factor for the target analytes, low cost, simplicity, and the possibility of combined use with almost every analytical measurement technique [90; 91; 4]. A large number of solvent microextraction techniques,including single drop microextraction, DLLME, liquid phase microextraction (LPME), have been reported. This extraction procedure allows a better analytical performance in comparison to HF-LPME, as reported by [PERSON] and [PERSON] [92]. The organic phase, which contains the target analyte(s), can be used for quantification by means of different types of instrument configurations [5]. LPME is usually performed to analyze water samples or aqueous solutions. Analysis of solid samples is commonly done in two steps; the solid sample is converted to aqueous solution using a suitable pretreatment procedure, and then LPME is applied.