esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Mix Proportions on Rheological Properties, Air Content of Wet Shotcrete--A Case Study
## 3 Results and Discussion
### Influence of Various Factors on Rheology and Air Content of Fresh Wet Shotcrete
#### 3.1.6 Effect of Fly Ash  
As shown in Figure 14, with the increase of fly ash content from 5% to 15%, the slump value increases more than twice, and the relative yield stress and viscosity decrease by about 50%. Generally speaking, the fluidity of shotcrete can be significantly improved by adding fly ash. Similar results with positive effect of fly ash on fluidity are also reported in references [66; 67]. According to the pumping test in Table 3, adding fly ash can improve the pumping performance, reduce the pressure drop, but reduce the sprayability, and the build-up thickness is small. This phenomenon may also be attributed to the \"ball effect\" and \"free paste effect\". The content of CaO in the fly ash used in this test is less than 10%, which belongs to Class F fly ash. The fly ash particles have spherical geometry and smooth surface (Figure 15 [68]) to enhance the \"ball bearing effect\" by reducing friction and promoting sliding between angular particles. [PERSON] et al. [69] pointed out that this effect will be amplified with the widening of fly ash particle size distribution. Fly ash, as a fine particle with a particle size of 0.4-100 um, fills the gap between larger particles, and extrudes the original water in the gap to increase the volume of free paste, which is called free paste effect. The use of fly ash instead of cement reduces the cement concentration and further reduces the flocculation of cement particles. In addition, because the specific gravity of fly ash particles is lower than that of cement particles, the volume of paste will increase when fly ash replaces cement with the same quality. Therefore, more free paste volume as lubricant increases the fluidity of wet shotcrete, which is called dilution effect. The influence of fly ash on the fluidity of fresh concrete is shown in Figure 15.  
Figure 14: Effect of fly ash on slump, air content, relative yield stress, and viscosity.  
Figure 15: Effect of fly ash on the flowability of fresh wet shotcrete.  
This does not mean that the more fly ash is, the higher the fluidity of the new concrete. [PERSON] et al. [66] found that when a higher content of fly ash is mixed, the yield stress increases. This is also related to the types of fly ash, for example, the Class C fly ash in Figure 15a is irregular and porous, and the lubrication is weak. CaO in Class C fly ash is more, and its viscosity is relatively higher after hydration.  
According to the fitting results in Figure 14, the fitting effect of fly ash on slump and relative plastic viscosity is good, R\({}^{2}\) = 0.9668, R\({}^{2}\) = 0.9504 respectively, with good prediction effect. With the increase of fly ash content, the air content changes greatly, leading to the bad fitting effect of fly ash and air content, R\({}^{2}\) is less than 0.5.
# Minimization of the Decoupling Circuit (DC-Link) in Induction Motors
## 4 Conclusions  
This paper presents a solution to avoid oversizing the decoupling system belonging to the electric vehicle propulsion system by using an equation that optimally calculates the minimum capacitance required for the decoupling element, analyzing the energy flow in the system's power electronic converters.  
The equation is based on the critical scenario of the highest energy return in the electric vehicle motor mode operation, which occurs when the motor speed varies from 0% to 100% of its rated speed, at maximum load, in the shortest possible time, ensuring the correct operation in less demanding scenarios. Employing this equation reduced the capacitance  
Figure 13: Comparison between proposed voltage ripples and voltage ripples obtained with developed minimum capacitance equation.  
required in the decoupling system by 49.9% to 62.2%, compared to existing equations in the literature. In addition, errors of 3.75% to 6.25% were obtained between what was obtained theoretically and in simulation using the equation proposed in conjunction with a full three-phase bridge inverter with SPWM modulation using a modulation index of 0.9, and a switching frequency of 3300 Hz.  
The implementation of the equation developed allows selecting and using capacitors with more reliable technologies and with a longer useful life in the decoupling element belonging to the propulsion system, and at the same time avoiding its oversizing, which has a positive impact on the performance of the electric vehicle propulsion system.  
Finally, it is important to emphasize that the minimum capacitance equation developed in this paper is easily scalable for its implementation in different working schemes, broadening its application spectrum.  
Conceptualization, M.P.-S., G.C.-L. and L.M.C.-S.; methodology, L.M.C.-S., M.P.-S., G.C.-L., L.H.-G.; software, L.M.C.-S., Y.R.-S., G.C.-L.; validation, L.M.C.-S., M.P.-S., G.C.-L. and L.H.-G.; formal analysis, L.M.C.-S., M.P.-S., G.C.-L.; investigation, M.P.-S., L.H.-G., G.C.-L.; resources, L.H.-G., Y.R.-S., O.U.J.-S., G.C.-L.; data curation, Y.R.-S., O.U.J.-S., G.C.-L; writing--original draft preparation, L.M.C.-S., M.P.-S., Y.R.-S., G.C.-L.; writing--review and editing, L.M.C.-S., M.P.-S., L.H.-G., G.C.-L.; visualization, Y.R.-S., O.U.J.-S., G.C.-L.; supervision, M.P.-S., L.H.-G., Y.R.-S., G.C.-L.; project administration, M.P.-S., L.H.-G., G.C.-L; funding acquisition, M.P.-S., L.H.-G., O.U.J.-S., G.C.-L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data obtained in this study are available on request from the authors.  
The authors declare no conflict of interest.
## 2 Fault Identification Method for Thermal Generator Sets
### Fault Feature Extraction Method  
#### 2.3.1 Discrete Spectrum Interpolation Method  
The rotating frequency and its harmonic components can reflect the fault features concerning misalignment, rub-impact, and dynamic unbalance. These features are usually extracted from the frequency spectrum. However, the FFT and spectrum analysis would cause energy leakage owing to time domain truncation and the interference of noise [26; 27]. This may lead to great errors in the frequency, amplitude and phase in the FFT and spectrum analysis. In order to improve the accuracy, a discrete spectrum interpolation method is adopted.  
Let \(x(t)\) be a harmonic signal sequence with frequency \(f_{0}\), amplitude \(A_{0}\) and phase \(\theta_{0}\). Suppose the amplitude and phase first calculated by a Discrete Fourier Transform (DFT) and then corrected by the interpolation method are:  
\[\hat{A}_{0}=X_{w}(k)/W(\
abla f^{1}) \tag{1}\]  
\[\hat{\theta}_{0}=\arctan(I_{k}/R_{k})+\pi\
abla f^{1} \tag{2}\]  
where \(X_{w}(k)\) means the \(kth\) line of the harmonic signal, _i.e._, the maximum value of the main lobe. \(W(\
abla f^{1})\) expresses the frequency spectrum for a rectangular window with the value \(W(\
abla f^{1})=\sin(\pi\
abla f^{1})/\ (\pi\
abla f^{1})\). \(R_{k}\) and \(I_{k}\) represent the real and imaginary parts of DFT, respectively. When rotating frequency is input, the system would correct the amplitude and phase of the rotating frequency and search for the accurate amplitude and phase of its harmonic components automatically.
## 2 Materials and Methods
### The Revised Universal Soil Loss Equation  
The RUSLE model stands as a widely adopted and recognized model extensively utilized for the prediction of soil erosion rates. The rationale behind selecting the RUSLE model lies in its robust scientific validity, which has been established through rigorous research and empirical validation. This model's proven accuracy and reliability in assessing and forecasting soil erosion processes, contribute to more informed and effective soil conservation and land management strategies [12].  
Its widespread acceptance and extensive use in various geographical regions attest to its reliability and effectiveness in predicting soil erosion rates [12; 13]. The rigorous scrutiny and validation carried out by the scientific community enhance the confidence in using RUSLE as a valuable tool in tackling soil erosion issues and adopting sustainable land management practices.  
Additionally, the RUSLE model provides a practical framework for estimating soil erosion using readily available data and relatively simple mathematical equations. The integration of this model with GIS technology provides a powerful synergy that facilitates comprehensive spatial analysis and visualization of soil erosion patterns across landscapes [13].  
Then it allows for customization and adaptation to diverse geographical regions and a wide spectrum of land use scenarios. It has been widely used for several decades and has demonstrated its effectiveness in various settings worldwide.  
Finally, RUSLE serves as a prominent model extensively employed in the fields of agriculture, soil conservation, and land use planning. It performs an essential role in developing erosion control plans and evaluating the effectiveness of erosion control practices in mitigating soil loss [12; 13]. The RUSLE Equation (2) is expressed as follows:  
\[GSL=R\times K\times LS\times C\times P \tag{2}\]  
where  
_GSL_ represents the estimated annual gross soil loss in tons per hectare.  
In this equation \(R\) is the rainfall erosivity factor (MJ mm ha\({}^{-1}\) h\({}^{-1}\) year\({}^{-1}\)),  
\(K\) denotes the soil erodibility factor (t ha h h\({}^{-1}\) MJ\({}^{-1}\) mm\({}^{-1}\)),  
\(LS\) represents the topographic factor (dimensionless),  
\(C\) signifies the cropping and land cover factor (dimensionless), and  
\(P\) stands for the erosion control practice factor (dimensionless).  
Indeed, each component within the equation is assigned a specific numerical value, tailored to the unique conditions and data of the particular site under consideration.
## 6 Discussion and Future Work  
Our results indicate slightly higher RMSE and MAE compared to the referenced models. However, the metrics remain comparable to those achieved by machine learning methods.  
With a loop time of 4 ms, our algorithm is efficient enough for real-world applications, making our model, or similar ones, suitable for being a replacement of ML models as part of larger systems.  
ML models based on gradient methods, such as gradient boosting or neural networks, can achieve efficient prediction times but often require substantial computational resources during training due to iterative optimization processes. In contrast, fuzzy logic systems typically involve simpler computations, leading to comparable prediction times, though designing and fine-tuning the rules may require manual effort.  
Our fuzzy logic model is characterized by its ease of understanding, ease of fine-tuning, and robustness. An even better performance could be achieved using a more optimal embedded platform and with further code optimization.  
For future work, we plan to investigate the potential of integrating specific components of our fuzzy logic model into a larger hybrid system based on machine learning models. We also plan to experiment with different rules for sub-systems of features and explore various settings for defining the fuzzy logic system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model 1** & **RMSE** & **MSE** & **Corr** \\ \hline _Fuzzy Logic 3–5 (best)_ & 0.126 & 0.101 & 0.690 \\ Medium Tree & 0.124 & 0.098 & 0.412 \\ Linear SVM & 0.122 & 0.097 & 0.436 \\ Linear Regression & 0.121 & 0.097 & 0.447 \\ Quadratic SVM & 0.121 & 0.094 & 0.458 \\ Gaussian SVM & 0.115 & 0.089 & 0.529 \\ Random Forest & 0.111 & 0.088 & 0.566 \\ XGBoost & 0.110 & 0.087 & 0.788 \\ LightGBM (all) & 0.109 & 0.086 & 0.796 \\ LightGBM (best) & 0.108 & 0.085 & 0.801 \\ \hline \hline \end{tabular} 1  
\end{table}
Table 10: Performance of the selected machine learning models and _Fuzzy Logic 3–5_ model used in this paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MSE** & **Corr** \\ \hline _Fuzzy Logic 3–5_ & 0.126 & 0.101 & 0.690 \\ _Fuzzy Logic 3–7_ & 0.137 & 0.113 & 0.700 \\ _Fuzzy Logic 5–7_ & 0.135 & 0.111 & 0.690 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance of three fuzzy logic models (_Fuzzy Logic 3–5_, _Fuzzy Logic 3–7_, and _Fuzzy Logic 5–7_).
# Developments on Perovskite Solar Cells (PSCs): A Critical Review
## 9 Summary  
A comprehensive review of PSCs was conducted in this study. PSCs are the upcoming cost-effective PV technology, promising to supply sustainable, green, and affordable electricity in the next coming decades. The development in the PSC technology in both industry and academic research is constantly improving. New ideas and milestones are constantly being published. Therefore, this review serves as a guide to interested scientists and new researchers in the field by providing detailed information on PSC device background and stepwise scientific efforts applied to improving the device performance with time. This work reviews previous studies in modelling the perovskite crystal structure and electronic structure, including factors that affect and determine both perovskite intrinsic structures. This study further highlights major developments in perovskite device architecture and fabrication methods towards achieving flexible and high-performance PSC devices. In addition, efforts in the perovskite ABX\({}_{3}\) designing and processing condition are elucidated; progress in the perovskite film fabrication is given; good correlation in perovskite film quality or morphology with each fabrication method is clearly explained. Additional efforts in this review have been made to outline recent challenges to PSC commercialization. This  
Figure 16: Simplified schematic images of PSC encapsulation, the PSC device architecture, and low-resolution images of the actual PSC device from the top, front, and back [92].  
includes the use of the toxic lead (Pb) element in most ABX\({}_{3}\) perovskite compounds and, more importantly, the perovskite material intrinsic and extrinsic instability issues. Among other solutions, the replacement of the toxic lead (Pb) element and encapsulation of the PSCs are highlighted.
## 3 Results and Discussion
### X-ray Diffraction Analysis
These results highlight the existence of a strong interaction between PP, G, C and E especially in the PP-25G-C-E composite, partially due to the presence of dispersion and compatibilizing agents. In this composite, it is possible that the ash particles are oriented on the surface in a parallel plane and the elastomer particles are randomly distributed [30]. The analysis of the diffraction peaks showed no noticeable alterations in the crystalline structure of PP-25G composites. However, some changes were observed in the interplanar distances and the width at half-maximum intensity of the crystalline peaks. There seems to be a slight increase in interplanar distances (the concentration of defects in the crystals increases) beyond what can be attributed to experimental errors, particularly for PP-25G-E and PP-25G-C-E (Table 2). This implies a possible penetration of block copolymer particles into the spherulitic structure of PP, as a similar result was obtained in a study by [PERSON] et al. [20]. The expected decrease in crystallinity for PP-25G-E and PP-25G-C-E composites in comparison to PP-25G can be attributed to the impact of the elastomeric phase interfering with the crystallization process of PP. This interference is particularly noted in cases where ethylene-butylene blocks are interpenetrating the PP structure. The XRD crystallinity index was 66% for PP-25G, 70% for PP-25G-C, 63% for PP-25G-E and 58% for PP-25G-C-E. It can be noticed that the CI decreases with the increase in composite complexity. The sample PP-25G-C has the highest values for CI and OI as well as larger crystals, and maybe, according to [PERSON] et al. [31] and [PERSON] [32], increased imperfection. We expect this behavior to be reflected in decreased toughness (impact strength and elongation at break). On the contrary, in the case of the PP-25G-C-E composite, which showed the lowest value for CI, we expect improved toughness (increased impact strength and elongation at break) as well as an improvement in surface hardness. The FWHM data in Table 2 show small variations across all composites with an exception at the (111) crystallographic plane. PP-25G and PP-25G-E have higher values for FWHM, hence smaller crystals [27], compared to PP-25G-C and PP-25G-C-E. Studies by [PERSON] and [PERSON] revealed an increase in the FWHM of the X-ray diffraction peaks due to the presence of residual surface stress [33].
# Investigation of Cavitation Noise in Cavitating Flows around an NACA0015 Hydrofoil
## 2 Mathematical Model
### FW-H Equation  
The acoustic analogy method is the key to solve flow-induced noise. This method is based on the FW-H Equation, which is widely accepted in engineering. The FW-H Equation is derived from the continuity equation and the N-S equation:  
\[\frac{\partial^{2}p^{\prime}}{\partial t^{2}}-c^{2}\frac{\partial^{2}p^{ \prime}}{\partial x_{\mathrm{i}}^{2}}=\frac{\partial T_{\mathrm{ij}}}{ \partial x_{\mathrm{i}}\partial x_{\mathrm{j}}}-\frac{\partial}{\partial x_{ \mathrm{i}}}\bigg{[}P_{\mathrm{ij}}\delta(f)\frac{\partial f}{\partial x_{ \mathrm{j}}}\bigg{]}+\frac{\partial}{\partial t}\bigg{[}\rho_{0}u_{\mathrm{i} }(f)\frac{\partial f}{\partial x_{\mathrm{j}}}\bigg{]} \tag{8}\]  
where \(p^{\prime}\) represents sound pressure, \(\delta(f)\) represents the _Dirac-\(\delta\)_ function, and \(T_{\mathrm{ij}}\) represents the Lighthill stress tensor.  
The three terms on the right side of the FW-H equation represent the quadrupole acoustic source, dipole acoustic source, and monopole acoustic source, respectively. The quadrupole acoustic source expresses high-frequency noise radiated by the hydrofoil boundary layer and vortex shedding. The dipole acoustic source expresses low-frequency noise radiated by the unsteady pulsating force, which is induced by an inhomogeneous flow field and the turbulent flow field around the hydrofoil surface. The monopole acoustic source expresses noise induced by bubble development and collapse. To hydrofoil cavitation, the quadrupole acoustic source is far less than the other two acoustic source. Thus, this article ignored the quadrupole acoustic source.
## 1 Introduction
Therefore, instead of proposing a new metaheuristic algorithm, this paper focuses on presenting a hybrid method for optimization problems. In our previous work [16], we focused on using a genetic algorithm to achieve optimal, or near-optimal, solutions to the target coverage problem. However, the accuracy of the solutions obtained by GA is affected by the selection and mutation operators and the convergence rate is slow. This paper first presents a discrete particle swarm optimization (DPSO) algorithm for the target coverage problem. Particle swarm optimization has gained increasing popularity in solving complex optimization problems due to its simplicity and high convergence speed. This paper also presents a hybrid metaheuristic algorithm based on the DPSO method combined with GA operators, namely DPSO_GA, to solve the target coverage problem. The use of GA operators aims to achieve the goal of intelligent exploration-exploitation. The hybridization of PSO and GA involves using two approaches sequentially or in parallel or using GA operators within the PSO framework [17; 18; 19; 20; 21; 22; 23]. In this study, the hybridization consists of a two-phase mechanism where the evolution process is accelerated by using DPSO and diversity is maintained by using GA. Our presented algorithm can achieve more precise solutions than conventional heuristic algorithms can. The main contributions of this paper are as follows:  
1. We defined and proved the complexity of the target coverage problem in directional sensor networks with rotatable sensors, and we also mathematically formulated the problem;
2. We proposed a greedy algorithm according to the targets' maximally being covered by a number of directional sensors--the proposed greedy solutions are used as base for comparison;
3. We firstly proposed a metaheuristic algorithm, namely DPSO, for the target coverage problem to determine the schedule of the covered sector for each directional sensor with the goal of not only maximizing the total number of covered targets but also minimizing the number of active sensors to conserve energy;
4. We secondly proposed a hybrid metaheuristic algorithm based on the DPSO method and combined with GA operators, namely DPSO_GA, to solve the target coverage problem;
5. We performed experiments to evaluate the performance of the proposed algorithms, described the results, and compared them with the results of a greedy algorithm;
6. We discussed the quality of the solutions generated by proposed DPSO and DPSO_GA algorithms along with the solutions generated by previous proposed GA, highlighting the most suitable algorithm for the target coverage problem in various environments. The remainder of this paper is organized as follows. In Section 2, we present a review of the related work of target coverage problem. In Section 3, we define the target coverage problem in the DSN. Section 4 presents the details of our proposed algorithms. Section 5 presents the experimental results. In Section 6, we present a discussion of the experimental results. Finally, we conclude the paper and propose some future research topics in Section 7.