esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Work Functions  
Table 2 shows that the work function decreases when Pd atoms are adsorbed on the ZGO surface. The reduction in work function ranges from \(-\)0.44 eV to \(-\)0.15 eV. The larger magnitude of \(\Delta\phi\) is better. Therefore, the Pd-ZGO1 structure, i.e., Pd on the top of the Ga\({}_{3\mathcal{C}}\) site, is the most sensitive.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & \(\Phi_{S}\) (**eV**) & \(\Phi_{S,\text{gas}}\)(**eV**) & \(\Delta\Phi\) (**eV**) & \(\Delta E\) (**eV**) \\ \hline ZGO & 4.09 & - & - & - \\ NO\({}_{2}\)-ZGO & - & 5.01 & +0.92 & +0.64 \\ H\({}_{2}\)S-ZGO & - & 2.40 & \(-\)1.69 & +2.32 \\ Pd-ZGO1 & 3.65 & - & \(-\)0.44 & - \\ Pd-ZGO2 & 3.84 & - & \(-\)0.25 & - \\ Pd-ZGO3 & 3.94 & - & \(-\)0.15 & - \\ Pd-ZGO4 & 3.75 & - & \(-\)0.34 & - \\
1 NO\({}_{2}\)-Pd-ZGO1 & - & 5.02 & +1.37 & \(-\)1.30 \\
1 NO\({}_{2}\)-Pd-ZGO2 & - & 5.06 & +1.22 & \(-\)2.94 \\
1 NO\({}_{2}\)-Pd-ZGO3 & - & 5.23 & +1.29 & \(-\)2.25 \\
1 NO\({}_{2}\)-Pd-ZGO4 & - & 5.08 & +1.33 & \(-\)2.14 \\
2 NO\({}_{2}\)-Pd-ZGO1 & - & 6.02 & +2.37 & \(-\)2.64 \\
2 NO\({}_{2}\)-Pd-ZGO2 & - & 5.61 & +1.77 & \(-\)3.72 \\
2 NO\({}_{2}\)-Pd-ZGO3 & - & 5.38 & +1.44 & \(-\)3.06 \\
2 NO\({}_{2}\)-Pd-ZGO4 & - & 5.62 & +1.87 & \(-\)2.53 \\
1H\({}_{2}\)S-Pd-ZGO1 & - & 2.93 & \(-\)0.72 & \(-\)0.73 \\ \hline \end{tabular}
\end{table}
Table 2: Calculated work functions \(\Phi_{S}\) and \(\Phi_{S,\text{gas}}\), work function differences \(\Delta\Phi\), and adsorption energies \(\Delta E\) for the ZGO surfaces, NO\({}_{2}\) and H\({}_{2}\)S molecules on ZGO surfaces, Pd-adsorbed ZGO surfaces, and NO\({}_{2}\)/H\({}_{2}\)S on Pd-adsorbed ZGO surfaces.  
Figure 4: Side view of the \(n\)H\({}_{2}\)S molecules on Pd-ZnGaO\(i\) surface. The equilibrium bond lengths of \(n\)H\({}_{2}\)S-Pd-ZGO\(i\) are listed in Table 1.  
When a NO\({}_{2}\) molecule was adsorbed on the surface of Pd-ZGO\(i\), we found that 1 NO\({}_{2}\)-Pd-ZGO1 had the largest \(\Delta\Phi\) (+1.37 eV). In the 1 NO\({}_{2}\)-Pd-ZGO1 structure, N-Pd and Pd-Ga have the largest bond lengths. The magnitude of \(\Delta\Phi\) ranges from +1.22 eV to +1.37 eV in 1 NO\({}_{2}\)-Pd-ZGO1. This increase in \(\Delta\Phi\) is further increased as the number of NO\({}_{2}\) molecules becomes two. Note that when H\({}_{2}\)S is adsorbed on the ZGO surface, the work function difference \(\Delta\Phi\) changes significantly to \(-\)1.69 eV. When H\({}_{2}\)S molecules are adsorbed on the surface of Pd-ZGO\(i\), the magnitude of \(\Delta\Phi\) is about half that of H\({}_{2}\)S-ZGO, ranging from \(-\)0.49 eV to \(-\)0.90 eV in 1H\({}_{2}\)S-Pd-ZGO\(i\). The 1H\({}_{2}\)S-Pd-ZGO3 structure has the largest \(\Delta\Phi\) (\(-\)0.90 eV). When two H\({}_{2}\)S molecules are adsorbed on the surface of Pd-ZGO\(i\), the magnitudes of \(\Delta\Phi\) of 2H\({}_{2}\)S-Pd-ZGO1 (\(-\)1.77 eV) and 2H\({}_{2}\)S-Pd-ZGO4 (\(-\)1.82 eV) are larger than that of H\({}_{2}\)S-ZGO without the Pd atom (\(-\)1.69 eV), showing that the sensitivity of the ZGO surface doped with palladium becomes higher.
## 4 Results and Discussions
### Ultimate Compressive Strength of Cracked Stiffened Panels
Figure 12 and Tables A4-A6 present the results of biaxial loading on cracked stiffened panel, emphasizing the effect of crack size and type on the ultimate compressive strength. Figure 12: Effect of crack on ultimate strength of stiffened panel under biaxial compression. As shown in Figure 12d, a small crack (10% of the structural dimensions) did not significantly affect the ultimate strength of the panel under biaxial compression, similar to the behavior observed in the plate (Figure 8d). This is because the stress distribution remained largely unaffected by the crack, as illustrated in Figures A16-A30. In contrast, the combined cracks, each with a size of 50% of the plate's length and breadth (or web height), led to a longitudinal disconnection between the plate and stiffener (Figure A27d). As a result, both the strength and stiffness of the panel decreased significantly, as the structural components were no longer able to transmit loads effectively. The impact of the cracks on the panel's performance was found to diminish as the loading ratio (\(\sigma_{y}/\sigma_{x}\)) increased (Figure 12). In other words, when the longitudinal load was much smaller than the transverse force, the cracks had a negligible effect on the ultimate strength of the stiffened panel.
## 4 Conclusions  
The study showed that both the method of drying raspberry pomace and its percentage had a significant impact on some physicochemical properties of the tested bread. After freeze-drying, raspberry pomace was characterized by a higher content of IDF, SDF, TDE, ash, energy value, ABTS, and TAC than that dried by convection drying. Convection-dried pomace had a higher carbohydrate content.  
The acidity of the bread increased statistically significantly from 2.13 mL \(\text{N}_{\text{NOH}}/10\) g d. b (control bread) to 7.78 mL \(\text{N}_{\text{NOH}}/10\) g d. b. (10 FD). Fortifying the bread with pomace also resulted in a statistically significant reduction in the total baking loss, from 15.1% (control bread) to 10.62% (10 FD). There was no statistically significant effect of the addition of raspberry pomace on the protein and water contents in the tested breads, nor on their energy values.  
The addition of raspberry residues increased the content of total fiber, fat, and ash, as well as the content of antioxidant properties (TPC and DPPH). Fortifying the bread  
Figure 2: Principal component analysis: (**a**) projection of the samples (cases) and (**b**) projection of the variables.  
with raspberry pomace resulted in a change in the fiber content from 18.13% d. b. (control sample) up to 19.97% d. b. (10 FD). Fat and ash contents in the bread increased accordingly, from 5.74% and 2.83% d. b. (control sample) to 7.18% and 3.12% d. b. (10 FD). However, the sugar content decreased from 65.71% d. b. (control sample) to 63.68% d. b. (5 FD).  
In the case of TPC, an increase in the value of this parameter was noted from 1.37 mg GAE/g d. m. (control bread) up to 2.49 mg GAE/g d. m. (10 FD), and in the case of antioxidant activity determined by the ABTS method, from 5.61 ugTE/g d. m. up to 14.56 ugTE/g d. m., and by the DPPH method from 1.96 ugTE/g d. m. up to 3.85 ugTE/g d. m., respectively.  
The principal component analysis (PCA) showed that the most beneficial effect on the nutritional, bioactive, and antioxidant properties of gluten-free breads was with the 10% addition of freeze-dried raspberry pomace. In addition to nutritional quality, an important element of introducing a new product to the market is its acceptance by consumers. Thus, our future research will concern the functional properties, texture, color assessment, and sensory analysis of baked goods with the addition of raspberry pomace. The obtained results would allow the development of an appropriate recipe for gluten-free bread enriched with fruit by-products. The designed bread could be both sensory acceptable and of high nutritional quality.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 2 Dark Current Measurement and Noise Correction Method Based on Focal-Plane Temperature  
The main structure schematic of this paper is shown in Figure 1. Firstly, the response model and dark current model of the LWIR QWIP detection system are introduced. Then, the dark current measurement method is highlighted, and a dark current noise correction method is proposed. Then, the components of each module of the satellite-borne LWIR QWIP detection system used in the study are introduced. Finally, the experimental part is presented.
## 3 Proposed Method for Automatic Classification of Heart Sounds
### Classification  
In this stage, four machine learning models were used to compare their precision, specificity and sensitivity. The models are the support vector machine (SVM), K-nearest neighbors (KNN), random forests (RF) and multilayer perceptron (MLP). These models are selected because they have been widely used in the state-of-the-art approaches (see Table 2) and have given good classification results. The different models used in this work are described below.  
* SVM: This classification method, developed by [PERSON] and his team ([PERSON] et al. 2002), is one of the most used in pattern recognition and binary classification problems, such as for discriminating between normal and abnormal heart sounds. This model is based on constructing a hyperplane that allows the separation of the data into two classes [52].
* KNN: The K-nearest neighbor classifier computes the distance between the data that we want to classify and labeled training data, selecting the nearest neighbors. Based on the neighbor's category, it is determined to which class the test data belongs [53]. The Euclidean distance is commonly used to determine nearby neighbors for the test data. This model is also suitable for binary classification.
* RF: Random forest is a combination of tree-structured classifiers, where each tree depends on the values of a random vector. To classify an input vector, each tree that belongs to the forest gives a \"vote\" for classification. Subsequently, the model determines the classification according to the highest number of votes. Random forest is very efficient when used for binary classification on a large number of samples, guaranteeing good specificity and sensitivity in the results. However, it is confusing in terms of its interpretation compared to decision trees [54].
* MLP: The multilayer perceptron (MLP) is a direct-feed artificial neural network and is the most widely used neural network classifier. The MLP has an input layer that takes the characteristics/patterns of the training data, a hidden layer and an output layer with one node per class. The inverse propagation algorithm is used to calculate the weights transported by the network connections. The number of nodes in the hidden layer is determined experimentally [55].  
The well-known tool for data mining and machine learning Weka (Knowledge Environment of Waikato University) was used to construct classification models [56]. The power characteristics were used as inputs for the three classifiers (SVM, KNN, RF and MLP). A cross validation of 10 folds was used to evaluate the performance of each classifier.
# Effect of Zr Modification on NH\({}_{3}\)-SCR Reaction Performance of Cu-Ce/SAPO-34 Catalysts
## 3 Results and Discussion
### In Situ DRIFTS Measurements
#### 3.5.2 Transient Reaction of NO+O\({}_{2}\) after NH\({}_{3}\) Adsorption Saturation  
After NH\({}_{3}\) was saturated for 40 min, NO+O\({}_{2}\) was introduced for transient reaction, and the samples were scanned at 3, 6, 10, 20, and 30 min to analyze the change in the curve. As shown in Figure 6, with the introduction of NO+O\({}_{2}\), the characteristic peaks of Lewis acid at 3117 and 3034 cm\({}^{-1}\) disappeared, and the N-N extension vibration at 2360 cm\({}^{-1}\) was enhanced, which may be due to the reaction between adsorbed NH\({}_{3}\) and gaseous NO+O\({}_{2}\), resulting in the formation of N\({}_{2}\), which enhanced the characteristic peak at 2360 cm\({}^{-1}\) attributed to N\({}_{2}\), the rest of the characteristic peaks did not change significantly, but new  
Figure 5: In situ DRIFTS of NH\({}_{3}\) adsorbed on the 4 Cu-4 Ce-4 Zr/SAPO-34 catalyst.  
characteristic peaks were formed at 1383 cm\({}^{-1}\) and 1265 cm\({}^{-1}\), among which the adsorption species at 1265 cm\({}^{-1}\) belonged to the bridged nitrate species [26; 28], and the adsorption peak at 1383 cm\({}^{-1}\) was the strongest and belonged to the CIS-N\({}_{2}\)O\({}_{2}\)\({}^{2-}\) species [29; 30], indicating that new nitrate species were generated on the catalyst surface. In general, the characteristic peaks of Lewis acid disappear, which proved that the adsorbed NH\({}_{3}\) reacted with gaseous NO+O\({}_{2}\). This process followed the E-R mechanism, while the characteristic peaks of Bronsted acid did not change significantly. Therefore, the weak Lewis acid site played a leading role in the reaction process.
## 4 System Components
### Networking System
#### 4.1.2 Networking Challenges
On one hand, small-scale fading results from reflection, scattering and diffraction effects emanating from constructive and destructive interference between wireless transmitter and a receiver, resulting in multi-path signals. The size, shape and texture of these objects defines the wireless channel characteristics [46]. Reflection could occur in air-to-ground channels of drones due to objects at the earth's surface such as trees dispersing the propagation signals in a LOS channel into multi-paths [52]. Reflection can also occur from the airframe of the drone itself as well as from the flat segment of mountain slopes [53]. Diffraction occurs due to forest vegetation and foliage from various parts of trees such as trunk and top levels to impede the LOS air-to-ground propagation wave into multi-paths [54]. However, scattering of LOS paths of drones could be accredited to the composition of structures and objects located within the confines of the ground station as the drones operates at altitudes above ground levels [55]. Hence, drastic reflections, scattering and diffraction degrades propagation signal and may even result in complete loss of signals. Finally, mostly air-to-ground communications may potentially interfere with signals from other cells such as macro-cells and pico-cells thereby contributing to reduced channel capacities and throughput, which is referred to as interference [56].
## 3 Analysis and Discussion of Results
### Discussion  
Table 7 presents the results of both the regression analyses and the best ANN architecture. Regression models and their fit are presented, relating UCS (dependent variable) first with \(v_{p}\) or \(n\) (linear, exponential, potential and quadratic models) and then including all rock parameters for the traverting stone studied (multiple regression and neural network models). Simple regression analyses found that UCS is related to \(v_{p}\) and \(n\), and the best fit was obtained with a quadratic model (R\({}^{2}\) = 0. 7573), dependent only on the porosity of the rock, while the neural network model is the one that presents the best fit for multivariate modeling, considering the totality of adjusted models.  
Neural network analyses are competitive with single and multiple linear regression analyses, but not with the quadratic multiple model (model that presents the best fit). However, due to the lack of sample data, the adjustment potential of the tool and its ability to abstract the modeled system is not evident.
# Brazilian Propolis: Nature's Liquid Gold with Anti-Inflammatory and Anticancer Potential
## 7 Use of Nanotechnology in Research on Brazilian Propolis  
Nanotechnology is an interdisciplinary field of science concerned with the design, synthesis and application of structures of nanometric size (1-100 nm). Due to their unique properties, nanoparticles have found widespread applications in medicine, pharmaceutical industry and biomedical engineering. One of the most promising directions is the use of nanoparticles aimed at improving the bioavailability and efficacy of bioactive substances [17; 105]. The most commonly used nanocarriers include lipid nanoparticles, liposomes, polymeric and metallic nanoparticles. Numerous studies support the application of different nanoparticles in combination with compounds of natural origin [106; 107; 108].  
Brazilian propolis is a natural product rich in various bioactive compounds, such as artepilin C, flavonoids and phenolic acids, with proved anti-inflammatory, anticancer and antioxidant effects. However, their therapeutic efficacy is reduced by their low water solubility, chemical instability and limited bioavailability [109; 110; 111]. In this context, nanotechnology opens new prospects of improving the pharmacokinetic and biological properties of propolis, as well as increasing the stability, bioavailability and precision of drug delivery to target tissues and cells. Despite the increasing interest in nanotechnology applications regarding Brazilian propolis, the number of studies remain limited, as the complex chemical composition and properties suggest great potential still to be studied and explored for this purpose. Therefore, all the articles obtained are presented in Table 5 below.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type of Propolis**} & \multirow{2}{*}{**Nanoparticles**} & \multirow{2}{*}{**Dose**} & \multicolumn{2}{c}{**Disease**} & \multirow{2}{*}{**Activity/Mechanism/Effect**} & \multirow{2}{*}{**Ref.**} \\  & & & & **of Cells** & \\ \hline \multirow{4}{*}{**Red Brazilian**} & Polymeric & & & NC-BRPE enhanced the & \\  & nanoparticles & & OVCAR-3 & antitumor activity of & \\  & (nanocapsuled & Brazilian red & (ovarian cancer) & propolisils in ovarian cancer & \\  & propolis extract & & & cells, both in 2D and & \\  & NC-BRPE) & & & 3D models. & \\ \hline \multirow{4}{*}{**Red Brazilian**} & Gold & & Bladder cancer & The cytotoxicity of & \\  & nanoparticles & & cells (T24) and & nanoparticles with the & \\  & & & prostate cancer & hydroethanonic extract of & \\  & & & & prostate cancer & Brazilian red propolisils was & \\  & & & & cells (PC-3) & induced by mechanisms & \\  & & & & associated with apoptosis. & \\ \hline \multirow{4}{*}{**Red Brazilian**} & Polymeric & & & NCBRPE effectively & \\  & nanoparticles & & & & \\  & (nanocapsuled & 2–1024 mg/L & OVCAR-3 cells & \\  & Brazilian red & (2–1024 mg/mL) & & & to reduce ROS generation \\  & propolis extract & & & in neutrophils, thereby & \\  & NCBRPE) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Biological effect of nanoparticles with Brazilian propolis extracts.
## 4 The Third-Order Beam Theory
### Beam Constitutive Relations  
In the RBT, as in in the case of CBT and TBT, we invoked the inextensibility of the transverse normal lines, which amounts to setting \(\varepsilon_{zz}=0\). Therefore, we can use one-dimensional constitutive relations. In particular, the one-dimensional constitutive relations are  
\[\sigma_{xx} =E\,\varepsilon_{xx}, \sigma_{xz} =G\,\gamma_{xz}, \tag{82}\] \[\mathcal{M}_{xy} =2\ell^{2}G\,\chi_{xy}, \mathcal{M}_{yz} =2\ell^{2}G\,\chi_{yz}. \tag{83}\]Substituting the constitutive relations from Equations (82) and (83) into the definition of the stress resultants in Equations (75a)-(75c), we obtain  
\[\left\{\begin{array}{l}N_{xx}\\ M_{xx}\\ P_{xx}\end{array}\right\}=\left[\begin{array}{cccc}A_{xx}&B_{xx}&E_{xx}\\ B_{xx}&D_{xx}&F_{xx}\\ E_{xx}&F_{xx}&H_{xx}\end{array}\right]\left\{\begin{array}{l}\varepsilon_{xx} ^{(0)}\\ \varepsilon_{xx}^{(1)}\\ \varepsilon_{xx}^{(3)}\end{array}\right\}, \tag{84a}\] \[\left\{\begin{array}{l}N_{xz}\\ P_{xz}\\ P_{xy}\\ R_{xy}\\ Q_{yz}\end{array}\right\}=\left[\begin{array}{cccc}A_{xz}&D_{xz}&0&0&0\\ D_{xz}&F_{xz}&0&0&0\\ 0&0&A_{yy}&D_{xy}&0\\ 0&0&D_{xy}&F_{xy}&0\\ 0&0&0&0&D_{xy}\end{array}\right]\left\{\begin{array}{l}\gamma_{xz}^{(0)}\\ \gamma_{xz}^{(2)}\\ 2\chi_{xy}^{(0)}\\ 2\chi_{yz}^{(1)}\end{array}\right\}. \tag{84b}\]  
where (see Equation (21))  
\[(A_{xx},B_{xx},D_{xx},E_{xx},F_{xx},H_{xx})=\int_{A}(1,z,z^{2},z^{3},z^{4},z^{6 })E(z)\,dA, \tag{85a}\] \[(A_{xz},D_{xz},F_{xz})=\frac{1}{2(1+\
u)}\int_{A}(1,z^{2},z^{4})E(z)\,dA,\] (85b) \[(A_{xy},D_{xy},F_{xy})=\frac{\ell^{2}}{2(1+\
u)}\int_{A}(1,z^{2},z^{4})E(z)\,dA, \tag{85c}\]
# LVRT and Stability Enhancement of Grid-Tied Wind Farm Using DFIG-Based Wind Turbine
## 4 Design of the DFIG System  
Figure 7 illustrates the arrangement of VSWT-DFIG together with its control strategy. Here, an aerodynamic WT scheme consisting of a drive train, pitch angle controller, wound rotor induction generator (WRIG), and an AC/DC/AC converter formed from two levels of insulated gate bipolar transistors (IGBTs) operated by the rotor side and grid side controllers comprised the overall system. Nevertheless, WRIG was connected to the WT, which transfigured the WP into electrical power. The rotor position \((\theta_{r})\) and \(\omega_{r}\). were computed using the WRIG rotor shaft. As shown in Figure 7, the stator terminal was firmly tied to the power grid, while the rotor was attached to the grid using RSC and GSC. The rating of the power converter was 30% of the WRIG. The pulse width modulation (PWM) approach was imposed in this research, and the carrier frequency was taken 3.0 kHz for AC/DC/AC converter. The DC-Link circuit contained a DC chopper. The comparator block controlled it, enabling the DC chopper switch whenever the DC-link voltage exceeded or equaled the predetermined limit (Vdc \(\geq\) 1.15 pu). Moreover, the rated DC-Link voltage was 1.2 kV.  
Figure 5: Drive train model.  
Figure 6: Characteristics of a wind turbine with maximum power point tracking (MPPT) for a doubly fed induction generator (DFIG).
# Estimation of SMPS' Conducted Emission According to CISPR Standards
## 2 Materials and Methods
### Studied System
#### 2.2.2 Test Setup with Ground Reference Plane and Harness  
According to Figure 2, the complete test setup should consist of LISN, ground reference plane, harness, and DUT. For this purpose, the model of this test setup was prepared using the ANSYS HFSS tool and is shown in Figure 7.  
It is worth noting that DUT on this level is only the place where the electrical signals will come in and out. The physical layout of the SMPS will be extracted on a separate level. It was decided to take this approach for an important reason. In order to make the simulation run with the minimum required time and computing resources required, it is beneficial to follow the submodeling approach. This approach induces that if the system consists of small and large components (when compared with each other), the system should be divided into submodels where each submodel is meshed with mesh of a similar size. In cases where the test setup and layout system were to be extracted immediately, there would be regions with very dense mesh layout with respect to the rest of the system. This situation could lead to a very large number of mesh elements and increase computing time significantly.  
To properly extract S-parameters for cables used in the analyzed model, an additional third conductor is added. This conductor is a continuation of the LISN reference plane and, on the second side, it is terminated with a plane simulating the presence of DUT. This plane is also on the same potential as the LISN reference plane. Ports connected to the DUT end of cables are terminated to that plane. This approach was used because if the DUT side ports were terminated to a floating object, then the calculated S-parameters would not be correct due to the lack of a reference and current return path. This approach is also limited to the orientation of the additional reference conductor in space relative to the actual DUT conductors. The orientation of this conductor should be as similar as possible to the actual mutual spacing between the DUT conductors, which is the case in this work. S-parameters were not extracted for additional reference conductors. The impedance diagram of the modeled system for harness parameters extraction is shown in Figure 8.  
A detailed model of this approach is shown in Figure 9. The terminal ports were marked in yellow. With the approach  
However, The LISN on this level of simulation is represented by only the metal housing that is part of the main current loop and electrical model, shown in Figure 11, of the real device that will be included in the electrical level of simulation. LISN_P and LISN_N are the positive and negative supply outputs of the LISN, while V_EMI_P is the node which the conducted emission will be calculated from.