esa-sceva/satcom-chunk-collection huggingface dataset:
# A Voltage Multiplier Circuit Based Quadratic Boost Converter for Energy Storage Application
## 5 Experimental Verification of the Proposed Converter
### Experimental Results at \(V_{in}=\) 12 \(V\)  
As shown in Figure 11a, the measured output voltage is 90 volts, which is slightly lower than the ideal calculated voltage at 0.4 duty ratio and 12 volts input. It can further be observed that the inductor currents are continuous, and the converter is operating in CCM. The capacitor voltages are shown in Figure 11c. The measured voltage across capacitor C\({}_{1}\) is 19.9 volts; for C\({}_{2}\), it is 37.5 volts, and for C\({}_{3}\), it is 49 volts. The voltage stress across the switches is lower than half of the output voltage. In the case of the first switch, the stress is around 20 volts and in the case of the second switch is around 35 volts, as shown in Figure 11d It can be validated from hardware results that the converter is working satisfactorily and the high voltage gain along with reduced voltage stresses.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Elements** & **Specification** \\ \hline Input Voltage (V\({}_{\text{in}}\)) & 12 V/20 V \\ \hline Maximum Output Power & 200 W \\ \hline Switching Frequency & 50 kHz \\ \hline Load Resistance & R = 200/250 \(\Omega\), Electronic load simulator \\ \hline Inductors & L\({}_{1}\) = 550 uH and L\({}_{2}\) = 330 uH \\ \hline Capacitors & C\({}_{1}\) = 220 uF/63V, C\({}_{2}\) = C\({}_{3}\) = 47 uF/100 V and C\({}_{4}\) = 47 uF/200 V \\ \hline Power MOSFET (S\({}_{1}\) and S\({}_{2}\)) & SPW52N50C3 \\ \hline Diodes (D\({}_{1}\), D\({}_{2}\), D\({}_{3}\) and D\({}_{4}\)) & PFCD86 \\ \hline Gate Drivers IC & TLP250H \\ \hline Gate Driver Voltage Regulator IC & MCWI03-48S15 \\ \hline Microcontroller & STM32 Nucleo H743 ZI2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specifications of the proposed converter.  
Figure 10: Experimental Setup.
# Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems
## 1 Introduction
The main contributions are outlined as follows:  
1. A deep learning-based method for locating FDIAs in power systems is proposed, utilizing a CNN-LSTM classifier to locate the compromised bus or line. 2. The method employs a GA to optimize various hyperparameters of the neural network, thereby achieving a model with superior positioning performance. 3. The performance and practicality of the model are demonstrated through extensive experiments, and this method is compared with other advanced FDIA localization methods. The rest of this paper is organized as follows. Section 2 provides a brief introduction to power system state estimation and the false data injection attack model. Section 3 elaborates on the architecture and implementation issues of the proposed FDIA localization method. Section 4 demonstrates the performance of the proposed FDIA localization method through experimental simulations. Section 5 discusses the paper and provides an outlook for future research. Finally, Section 6 summarizes this paper.
# State-of-the-Art Review of Structural Vibration Control: Overview and Research Gaps
## 1 Introduction
### General
Therefore, the initial section of this paper provides an overview of various structural control systems, outlining their general limitations and challenges. Upon reviewing the literature, it becomes evident that semi-active control systems have emerged as promising techniques, offering the versatility and adaptability of active control systems without the need for a substantial energy supply while retaining the reliability characteristic of passive control systems. The following section provides a comprehensive examination of various semi-active control devices and their associated \begin{table}
\begin{tabular}{c c} \hline \hline
**List (40)** & **Publications** \\ \hline General (13) & [4; 8; 10; 12; 14; 18; 19; 20; 21; 22; 23] \\ Passive (4) & [15; 16; 24; 25] \\ Active (3) & [11; 12] \\ Active and Semi-active (3) & [2; 26; 27; 28] \\ Semi-active (3) & [13; 28; 29; 30] \\ MR Damper (7) & [6; 31; 32; 33; 34; 35; 36] \\ Hybrid (2) & [3; 5] \\ Control Strategies (5) & [27; 29; 32; 37; 38] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of publications on the state of the art of structural control. advantages. The practical implementation of a semi-active control system crucially depends on the chosen control strategy, presenting a foundational challenge. The following section provides a systematic review of the control theories and algorithms for semi-active control systems. This section summarizes the advantages and disadvantages of each control algorithm, highlighting efforts to address the distinct challenges associated with each. In discussions related to control theory, emphasis is placed on issues directly tied to the physical behavior of civil structures, rather than delving into intricate developments in control system theory. The subsequent section explores the ongoing efforts of various researchers to refine control strategies tailored to specific issues and requirements, outlining different approaches to modify algorithms for enhanced system performance. The final section consolidates the research findings, offering a conclusion and outlining potential avenues for future research. This structured approach thoroughly explores the diverse aspects of structural control, ranging from devices to theories and strategies, and provides valuable insights for researchers and practitioners.
## 3 Results and Discussion
### Computer Color Matching
At a high concentration, the possibility of the formation of dye aggregates and having insufficient dye molecules for an even distribution in the fabrics would be generally higher than that at low concentration. Furthermore, from Tables 7 and 8, the results show that at a high concentration (3%), the difference in recipe prediction for Sample 3 dyed in water and Sample 6 dyed in D5 is large. This means that the recipe prediction accuracy of sample dyed in D5 at a high concentration is lower than that in water. Nevertheless, this difference is considered to be tolerable and, thus, acceptable for computer color matching. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Formulae**} & \multirow{2}{*}{**Color**} & \multicolumn{3}{c}{**D5 Solvent-Assisted Dyeing**} \\ \cline{3-5}  & & **Sample 4 (0.3\%)** & **Sample 5 (1.5\%)** & **Sample 6 (3\%)** \\ \hline \multirow{3}{*}{Theoretical} & Yellow & 0.10 & 0.50 & 1.00 \\  & Blue & 0.10 & 0.50 & 1.00 \\  & Red & 0.10 & 0.50 & 1.00 \\ \hline \multirow{3}{*}{CIEL*a*b*} & Yellow & 0.15 & 0.60 & 1.33 \\  & Blue & 0.13 & 0.46 & 1.11 \\  & Red & 0.11 & 0.49 & 1.24 \\ \hline \multirow{3}{*}{CIEL*u*v*} & Yellow & 0.15 & 0.60 & 1.15 \\  & Blue & 0.13 & 0.46 & 0.86 \\  & Red & 0.11 & 0.49 & 1.07 \\ \hline \multirow{3}{*}{ANLAB} & Yellow & 0.15 & 0.60 & 1.33 \\  & Blue & 0.13 & 0.46 & 1.11 \\  & Red & 0.11 & 0.49 & 1.24 \\ \hline \multirow{3}{*}{Hunter lab} & Yellow & 0.15 & 0.66 & 1.33 \\  & Blue & 0.13 & 0.63 & 1.11 \\  & Red & 0.11 & 0.58 & 1.24 \\ \hline \multirow{3}{*}{FMC2} & Yellow & 0.15 & 0.60 & 1.15 \\  & Blue & 0.13 & 0.46 & 0.86 \\  & Red & 0.11 & 0.49 & 1.07 \\ \hline \multirow{3}{*}{JPC79} & Yellow & 0.15 & 0.66 & 1.33 \\  & Blue & 0.13 & 0.63 & 1.11 \\  & Red & 0.11 & 0.58 & 1.24 \\ \hline \multirow{3}{*}{CMC 1.0} & Yellow & 0.15 & 0.60 & 1.33 \\  & Blue & 0.13 & 0.46 & 1.11 \\  & Red & 0.11 & 0.49 & 1.24 \\ \hline \multirow{3}{*}{BFD 1.0} & Yellow & 0.15 & 0.60 & 1.33 \\  & Blue & 0.13 & 0.46 & 1.11 \\  & Red & 0.11 & 0.49 & 1.24 \\ \hline \multirow{3}{*}{CIE94 1.0} & Yellow & 0.15 & 0.66 & 1.33 \\  & Blue & 0.13 & 0.63 & 1.11 \\ \cline{1-1}  & Red & 0.11 & 0.58 & 1.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Color matching recipes of solvent dyed standard samples with different color difference formulae. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Water-Based Dyeing**} & \multicolumn{4}{c}{**DS Solvent-Assisted Dyeing**} \\ \cline{3-8}
**Formulae** & **Color** & \begin{tabular}{c} **Sample 1** \\ **(0.3\%)** \\ \end{tabular}  & \begin{tabular}{c} **Sample 2** \\ **(1.5\%)** \\ \end{tabular}  & \begin{tabular}{c} **Sample 3** \\ **(3\%)** \\ \end{tabular}  & \begin{tabular}{c} **Sample 4** \\ **(0.3\%)** \\ \end{tabular}  & \ \begin{tabular}{c} **Sample 5** \\ **(1.5\%)** \\ \end{tabular}  & \begin{tabular}{c} **Sample 6** \\ **(3\%)** \\ \end{tabular}
## 4 Conclusions  
This study employs FLUENT numerical simulation to investigate the impact of the sphericity factor on the deposition of aerosol particles on the surfaces of photovoltaic components. It elucidates the mechanisms of particle deposition and accumulation while exploring the influence of the sphericity factor on particle distribution across the surfaces of the components through flow field analysis. The key findings are as follows:  
1. Under the set boundary conditions, the airflow velocity near the surface of the photovoltaic component shows an increasing trend from bottom to top, with the lowest velocity on the near-ground side. The minimum value is 2.2 m/s, and the maximum value is 3.89 m/s. The air pressure near the surface of the photovoltaic component shows a decreasing trend from bottom to top, with the highest pressure on the near-ground side. The maximum value is 10 pa, and the minimum value ranges between 3.33-5.56 pa;
2. Irrespective of particle size, the accumulation of particles during settling is shown to increase as the sphericity factor increases;
3. After reaching the surface of the photovoltaic component, particles predominantly accumulate on the near-ground side. The maximum deposition amount of various spherical coefficients on the surface of the photovoltaic panel near the ground side is 1.5 kg/m\({}^{2}\), with the minimum value ranging from 1.4-1.434 kg/m\({}^{2}\). Nevertheless, with a gradual increase in the sphericity factor, the particle distribution becomes more dispersed and covers the entire surface.  
The study was conducted in Hohhot City, which served as the research location. However, the small wind tunnel used in the experiments was not fully enclosed, which prevented us from controlling the influence of other factors unrelated to the study, such as  
Figure 12: Error distribution chart.  
local temperature and air humidity; this led to an error. Furthermore, the limited number of sampling sites may restrict the generalizability of the experimental results.  
Conceptualization, Y.Q. and C.W.; methodology, Y.Q.; software, C.W.; validation, Y.Q. and C.W.; formal analysis, Y.Q.; investigation, C.W.; resources, Y.W.; data curation, Y.Q.; writing--original draft preparation, Y.Q.; writing--review and editing, X.G.; visualization, C.W.; supervision, X.G.; project administration, X.G.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Inner Mongolia Autonomous Region (2021 MS05030), Inner Mongolia University of Technology Student Innovation Experiment Project (202203010), Special Fund for Central Guiding Local Science and Technology Development (2022 ZY0048), Optical-electro-thermal coupling mechanism and performance optimization of concentrated photovoltaic modules: 2023 LHMS05047.  
Not applicable.  
**Data Availability Statement**: Laboratory data will not be uploaded to the database for the time being. If necessary, please email the corresponding author for consultation.  
The authors declare no conflict of interest.
# Upgrading _In Vitro_ Digestion Protocols with Absorption Models
## 2 Technological Approaches of Intestinal Epithelium Models  
Intestinal epithelium models can be useful tools at the beginning of nutritional studies and during the development of functional foods, because they enable cost-effective screening of samples [22]. They also facilitate the investigation of the cause-effect relationship between different factors, because they provide a well-controlled and characterized experimental setup.  
During the investigation of the absorption of food components, several different approaches can be applied (Figure 3). In cases of some high-throughput experiments, non-cell-based transport models, brush border membrane vesicles, or dialysis can be used for fast estimation of the passive absorption of food components [1; 22; 32]. Still, the complex processes that determine the absorption rate of food components through the intestinal barrier [22] can only be precisely simulated with models that include mammalian cells. Due to the complexity of the intestinal tissue, numerous approaches (the application of different cell lines or stem cells, various cell culture techniques) were developed to imitate the epithelium. The simpler methods mainly focus on the establishment of proper epithelial barrier integrity, and the development of mucus layer on the surface of cells.  
Intestine-on-a-chip models with immortalized cell lines have been used to study nutrient absorption [17]. Organoids, microfluidic systems, and _ex vivo_ models can also be used for absorption studies. In numerous cases, these more complex models are chosen because of their increased precision, even if they are more expensive and more laborious than the simple Transwell systems.
# Energy-Optimized 3D Path Planning for Unmanned Aerial Vehicles
## 4 Creating the Polygonal Graph over the Work Space
### Trajectory Optimization Methods
#### 5.2.2 Constraint Optimization  
This increases the safety constraints on flight safety. This method is split into two main categories: hard constraints that increase the boundary value and soft constraints that increase the binding force. The hard constraint method considers all safe areas to be equivalent. Its disadvantage is that some parts of the trajectory come too close to the obstacle, and when the controller cannot follow the generated path, it may cause a collision and further, the flight speed is also low. The soft constraint method applies a \"push force\" (using the gradient method to calculate the proximity to the obstacle) to push the trajectory away from the obstacle. Summary: hard constraint--ensuring global optimality with the convex formulation. Soft constraint--pushing the trajectory far from obstacle (safety), if there is a local minimum, the success rate and the kinematic feasibility are low.  
It must be emphasized that this list is far from complete, but this study will lead to further examination and discussions regarding which of these methods, or what combination of them, proves most suitable for creating the energy-optimal path.
###### Abstract  
Paraxial beam modes, which propagate in space and focus without changing their transverse intensity pattern, are of great value for multiplexing transmitted data in optical communications, both in waveguides and in free space. The best-known paraxial modes are the Hermite-Gaussian and Laguerre-Gaussian beams. Here, we derive explicit analytical expressions for Ince-Gaussian (IG) beams for several first values of the indices \(p=3\), \(4\), \(5\), and \(6\). In total, we obtain expressions for the amplitudes of 24 IG beams. These formulae are written as superpositions of the Laguerre-Gaussian (LG) or Hermite-Gaussian (HG) beams, with the superposition coefficients explicitly depending on the ellipticity parameter. Due to simultaneous representation of the IG modes via the LG and HG modes, it is easy to obtain the IG modes in the limiting cases wherein the ellipticity parameter is zero or approaches infinity. The explicit dependence of the obtained expressions for the IG modes on the ellipticity parameter makes it possible to change the intensity pattern at the beam cross-section by continuously varying the parameter values. For the first time, the intensity distributions of the IG beams are obtained for negative values of the ellipticity parameter. The obtained expressions could facilitate a theoretical analysis of properties of the IG modes and could find practical applications in the numerical simulation or generation of such beams with a liquid-crystal spatial light modulator.  
Ince-Gaussian beams; Laguerre-Gaussian beams; Hermite-Gaussian beams; elliptic beams; characteristic equation +
Footnote †: journal: Applied sciences
## 3 Results
Figure 11 presents the SHAP summary plot, showing the relative importance and direction of influence of each input variable. The analysis reveals that unemployment rate, number of tourists, net income, and trade balance are consistently among the most influential features. SHAP values also confirm that the model's predictions align with established criminological assumptions--for example, higher unemployment contributes positively to the prediction of higher crime risk categories. This post-hoc explanation technique demonstrates that while the ANN component is nonlinear, its decision process can be transparently understood, making the overall hybrid model suitable for informed, real-world decision-making in public safety. The plot illustrates the importance and impact of various features on the hybrid ARIMA-ANN model's'very high' crime risk predictions. Each point on the plot represents a Shapley value for a specific feature and an instance in the dataset. The interpretability of the ANN's hidden layer was further examined, with particular attention given to its capacity to model higher-order interactions between features, which are often critical in complex social contexts. SHAP interaction values were applied to the trained ANN to highlight and quantify the key feature combinations influencing predictions. Notably, a joint increase in unemployment rate and average temperature--representing combined economic and seasonal stressors during summer months--was found to have a pronounced nonlinear effect on predicted crime risk. Other significant interactions included trade balance and net salary, as well as tourists and temperature, supporting empirical observations that tourism-driven fluctuations are intensified in hotter periods. SHAP dependence and interaction plots revealed the logic embedded within the ANN's hidden layer and enhanced model transparency for policy audiences. This interpretability step, now standard in machine learning practice, ensures that the hybrid model delivers both  
Figure 11: SHAP beeswarm for ‘very high’ class. strong predictive performance and actionable, comprehensible mechanisms for public safety decision-making.
## 2 Materials and Methods
### Data and Data Processing
#### 2.2.2 Data Padding  
Our dataset potentially includes missing values and outliers due to factors such as equipment failures, station maintenance, and technical complexity. The original dataset contains some null values, and the presence of incomplete data can significantly impact the accuracy of the model's predictions. If eliminated directly, this action results in a reduction in the available data and can also lead to inadequate model training. Considering the significant correlation observed among the parameter data, the k-means algorithm has been selected as the preferred method for analysis in this study.  
The k-means algorithm serves as the fundamental clustering algorithm for predefined clustering categories [28; 29]. The algorithm utilized in this study is a traditional distance-based clustering approach that uses distance as a metric to assess similarity. Specifically, the algorithm considers objects with shorter distances to be more similar to each other. The algorithm takes into account the proximity of objects to define clusters, with the ultimate objective of acquiring compact and autonomous clusters. The measurement is conducted using the Euclidean distance metric. The software has the capability to process and manage extensive datasets with high efficiency [30]. The inputs of this algorithm include a dataset and the number of categories. The Euclidean distance formula is expressed in Equation (3):  
\[dist(X,Y)=\sqrt{\sum_{i}^{n}{(X_{i}-Y_{i})^{2}}} \tag{3}\]  
In Equation (3), \(X\) and \(Y\) denote two samples, \(i\) represents the number of eigenvalues, and \(n\) represents the number of data points.
## 3 Results
### Transmit Subsampling
#### 3.2.2 Verification of the Transmit Element Selection Scheme
For future investigations, the effect could be decreased by separating the front wall and back wall from the remainder of the data. The measures indicate that, except for FISTA on the \(z~{}=~{}13\) mm defect, choosing a narrower distribution yields more energy for the defect in the reconstruction, compared to the overall energy. When choosing a wider aperture, the performance slowly starts to decrease, as the ultrasonic waves from the outer transmitters that are reflected by the defect have less amplitude due to the transducer directivity and boundary. On the other hand, choosing a wider distribution is beneficial if the defect reflects stronger to the side than back to the center, as might be the case for a crack. This mismatch is due to the model being derived for perfect point scatterers, which have, unlike the side-drilled holes used in the simulations, an omnidirectional reflection pattern. Consequently, as the measures in Figure 7 are derived for side-drilled holes as reflectors, the results indicate the aperture has been chosen slightly too large. Still, the choice seems suitable, as the algorithm performance is good, and will be more robust to directional reflectors or off-center than a more narrow aperture. The given measures reflect the performance of the algorithms for a side-drilled hole that is located perfectly in the center of the aperture. It is also indicated that the aperture choice should not be too narrow by the FISTA reconstruction in Figure 7a and the slight increase of most curves from scaling factor 0.1 to 0.25. We therefore deem the transmit pattern selection scheme suitable for the application at hand.
## 8 System Design
### Link Budget  
The communications system for the OpenOrbiter spacecraft consists of the TT&C board which includes two deployable antennas (released from packing configuration by neoprene wire). Note that the deployment is actually from the rear (exterior-facing) side of the board, so as to be exterior to the rails (as wrapping around the rails is not allowed by the CubeSat specifications [24]). This is indicated via shading over this area in Figure 2. A transmission speed of 1200 baud is presumed. This is based on the 16.8 cm\({}^{2}\) of antenna surface from this configuration and presuming a worst-case orbit of 400 km. The ground station configuration for the OpenOrbiter mission is currently unknown; thus this estimate is based on the designs of other previous CubeSats (e.g., Cute-1, UWE-1, GeneSat-1 [25]).