esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Characteristics Study of the Bump Foil Thrust Gas Bearing
## 2 Numerical Simulation Research
### Calculation Process and Parameters  
In this paper, the energy equation of the gas film in cylindrical coordinate system is established as follow:  
\[\begin{array}{l}\rho(T)c_{p}(T)\Big{(}v_{r}\frac{\partial T}{\partial T}+\frac{ v_{r}}{r}\frac{\partial T}{\partial T}+v_{z}\frac{\partial T}{\partial z}\Big{)}=k_{ a}(T)\Big{(}\frac{1}{r}\frac{\partial}{\partial r}\Big{(}r\frac{ \partial T}{\partial r}\Big{)}+\frac{1}{r}\frac{\partial^{2}T}{\partial \partial^{2}}+\frac{\partial^{2}T}{\partial z^{2}}\Big{)}+\\ \Big{(}v_{r}\frac{\partial p}{\partial r}+\frac{v_{z}}{r}\frac{\partial p}{ \partial\theta}\Big{)}+\mu(T)\Big{(}\Big{(}\frac{\partial v_{z}}{\partial z} \Big{)}^{2}+\Big{(}\frac{\partial v_{p}}{\partial z}\Big{)}^{2}\Big{)}\end{array} \tag{1}\]  
where \(c_{p}\) is the specific heat capacity of gas at constant pressure (J/(kg\(\cdot\)K)); \(k_{a}\) is the thermal conductivity of the gas (W/(m\(\cdot\)K)); \(\mu\) is the gas viscosity which changes with temperature, according to the reference [5], the relationship between \(\mu\) and temperature is expressed as:  
\[\mu=a\Big{(}T-T_{ref}\Big{)} \tag{2}\]  
where the slope \(a=4\times 10^{-8}\), when the unit of temperature \(T\) is degrees Celsius, \(T_{ref}=-458.75\).  
The energy equation is nondimensionalized as follow:  
\[\begin{array}{l}\overline{p}(T)\Big{(}\overline{v}_{r}\frac{\partial T}{ \partial T}+\frac{\overline{v}_{z}}{r}\frac{\partial T}{\partial\theta}+ \frac{1}{h}\overline{v}_{z}\frac{\partial T}{\partial z}\Big{)}=k_{1}\Big{(} \frac{  
\[g(\theta)=\left\{\begin{array}{cc}\delta_{h}[1-\theta/(b\beta)]&0\leq\theta<b \beta\\ 0&b\beta\leq\theta\leq\beta\end{array}\right. \tag{8}\]  
\[w(r,\theta)=\frac{F(r,\theta)}{K_{\rm b}\times\Delta l},\qquad K_{\rm b}=\frac{ Et_{b}{}^{3}}{2(1-v^{2})l^{3}} \tag{9}\]  
where \(h_{2}\) is the gas film thickness in the plane area (m); \(g(\theta)\) is the initial gas film thickness distribution which minuses the value of \(h_{2}\) (m); \(\beta\) is the angle of top foil (\({}^{\circ}\)); \(b\) is the pitch ratio; \(w(r,\theta)\) is the deformation of top foil under aerodynamic force, and \(K_{\rm b}\) is the stiffness of the bump foil per unit of transverse length (N/m\({}^{2}\)) [18]; \(\Delta_{\rm thermal}\) is the total thermal expansion of all bearing structures (m).
# Numerical Simulation and Parameter Optimization of Air Slide Based on CFD-DEM
###### Abstract  
The aim of this study was to investigate the influence of operational and design parameters on the conveying efficiency and material layer stability of air slides and to optimize the parameters of the XZ200 air slide. A gas-solid coupled simulation of the conveying process was conducted using ANSYS v2023 and Rocky v23R1 software. Three key variables--inclination angle, input air velocity, and permeable layer porosity--were analyzed to evaluate their effects on wheat flour conveying efficiency and layer stability. Orthogonal experiments and matrix analysis were applied to comprehensively assess the numerical simulation results. The findings reveal that the conveying ratio is positively correlated with input air velocity and inclination angle but negatively correlated with permeable layer porosity. Meanwhile, material layer fluctuation and stability increase with inclination angle but decrease with higher porosity. Through orthogonal testing and matrix analysis, the optimal parameter combination was determined as follows: input air velocity of 1.8 m/s, porosity of 37.84%, inclination angle of 6\({}^{\circ}\), conveying ratio of 96.52%, and material layer fluctuation of 4.39 mm. This study provides a reference methodology for gas-solid coupled simulation in air slide design and offers practical guidance for parameter optimization in air slide systems.  
gas-solid simulation; conveying ratio; stability; matrix evaluation +
Footnote †: journal: Applied sciences  
0000-0001-5000-0002-3899][PERSON]  
0000-0002-3300-3330][PERSON]. [PERSON]  
0000-0002-3330-3300][PERSON]
## 2 Linearization of the Power Deficit Calculation
### Comparison of Traditional Aggregation Methods and the Proposed Method
In summary, by comparing the map for power deficit, as in Figure 2, an equivalence is detected with regard to the distributions of deficits and gradients. The reduced time required for the evaluation of AEP using LAPD is due to the fact that by introducing linearity into the calculation of power deficits, this magnitude behaves like the potential energy of a conservative field. Accordingly, when a wind turbine is included or suppressed, or its location modified, it is not necessary to recalculate all the multiple wakes in which the wind turbines are involved (as shadowed or shadower turbines). Instead, taking advantage of linear properties, only the individual interactions between pairs of turbines are considered, which can be reduced to a function of two parameters: the distance between turbines \(s_{i,j}\) and the orientation between them, respectively. Consequently, an offline LUT can be created, and bilinear interpolation can be used to derive the power deficit between pairs of turbines, significantly reducing the number of operations. This is not possible if an aggregation (quadratic or linear) of the speed deficits is used.
## 3 Construction of the Control Strategy  
### PID Control Algorithms  
The block diagram of a conventional PID control system is shown in Figure 3. PID control is the differential between the actual output signal of the control object and the given signal for making differences and tracking. The control rate u of the system is derived from the proportional P, the integral I and the differential D as a means of regulating the performance indicators of the fertilizer. However, due to the effects of time lag and nonlinearity in the fertilizer mixing process, the effect of using a PID control strategy to regulate pH is often unsatisfactory and it is difficult to track the changes in the pH of the fertilizer solution [24].
# Characterization and Crystallinity of Two Bioactive Sealers: Qualitative and Quantitative Analysis
## 3 Results
### XRD Results  
#### 3.4.1 Qualitative Analysis of XRD  
The XRD spectra revealed the phase composition and the percentage (%) of each sealer before and after immersion (Figure 5A,B and Figure 6A). The spectra of raw Cerafil (Figure 5A(a)) showed calcium silicate; wollastonite-2M [CaO\({}_{3}\)Si; card # 00-901-1913], alite [Ca\({}_{3}\)O\({}_{5}\)Si; card # 00-901-6125], cosite [O\({}_{2}\)Si, card # 00-900-0805], calcite [CCAO\({}_{3}\); card # 00-900-0966], lime [CaO; card # 00-900-6743], triclcium phosphate [Ca\({}_{3}\)(PO\({}_{4}\))\({}_{2}\); card # 00-151-7238], calcium sulfate anhydrite [CaSO\({}_{4}\); card # 00-500-0040], zirconia oxide [ZrO\({}_{2}\); card # 00-152-2143], and bismite [Bi\({}_{2}\)O\({}_{3}\); card #00-901-2546]. After the immersion period, further phases were detected, including portlandite [CaH\({}_{2}\)O\({}_{2}\); card # 00-900-6837] in water and hydroxyapatite [Ca\({}_{3}\)HO\({}_{13}\)P\({}_{3}\); card # 00-901-3627] in PBS (Figure 5A(b,c)).  
The spectra of raw Endosequence (Figure 5B(a)) detected wollastonite-2M [CaO\({}_{3}\)Si; card # 00-901-1913], alite [Ca\({}_{3}\)O\({}_{5}\)Si; card # 00-901-6125], Dicalcium silicate [Ca\({}_{2}\)(SiO\({}_{4}\)); calcite [CCAO\({}_{3}\); card # 00-900-0966], triclcium phosphate [Ca\({}_{3}\)(PO\({}_{4}\))\({}_{2}\); card # 00-151-7238], calcium di-aluminum oxide [CaAl\({}_{2}\)O\({}_{4}\); card # 00-200-2888], and zirconia oxide [ZrO\({}_{2}\); card # 00-152-2143]. Further phases were detected, including portlandite [CaH\({}_{2}\)O\({}_{2}\); card # 00-900-6837] and/or hydroxyapatite [Ca\({}_{3}\)HO\({}_{13}\)P\({}_{3}\); card # 00-901-3627], after immersion in water and PBS, respectively (Figure 5B(b,c)).  
All spectra of AH-26 before and after immersion showed similar phase composition with no big difference in their percentages (Figure 6A(a-c)), including bismite [Bi\({}_{2}\)O\({}_{3}\); card # 00-901-2546], quartz [O\({}_{2}\)Si; card # 00-412-4074], silver [Ag; card # 00-150-9145], calcium tungstate [Ca(WO\({}_{4}\)), card # 00-154-16035], and titanium oxide [Ti\({}_{2}\)O\({}_{3}\); card # 00-101-0582].
# Zero-Inflated Patent Data Analysis Using Generating Synthetic Samples
## 1 Introduction  
In social network services and internet environments, a large part of data are provided in the form of text documents. Since they are text big data, various analysis methods need to be studied. A lot of research has already been done for the analysis of text data, and a significant number of their results have been used in practical domains. However, there are still some problems to be solved in the text big data analysis. One of them is a zero-inflated problem when the preprocessed data contain too many zero values [1; 2; 3]. If there are too many zero values in the data, the explanatory power of the predictive model would be decreased. It is one of the reasons we should take care of the problem for text document analysis. In this paper, we apply patent data to text big data. The patent data also have the zero-inflated problem as other text data do. Since a patent is a document that contains the results of a developed technology in detail, we could forecast future technologies and make research and development (R&D) planning for management of technology (MOT) from the patent data analysis.  
Various studies have been conducted to solve the zero-inflated problem [3; 4], and zero-inflated models suggested popular approaches to modeling the data with excess zeros [1]. They used probability distributions such as Poisson and negative binomial distributions for the zero-inflated data modeling and separated given data into two components with zeros and non-zeros [5; 6]. However, if there are too many zero values in the data, the performance of the suggested zero-inflated models deteriorates. Since most of the patent data in these studies have too many zero values, they are difficult to analyze [2]. Therefore, other approaches are needed to overcome the zero-inflated problem in the patent data analysis. We try to generate synthetic data from given original patent data using statisticalmachine learning methods. To verify the improved performance of our method, real patent documents and simulation data are used and experiments are conducted. The experimental results are compared between comparative models to confirm the validity of the proposed method. In the next section, research backgrounds will be illustrated such as patent data analysis and the zero-inflated problems in the text data. The proposed method and experimental results are shown in Sections 3 and 4, respectively. The contribution and conclusions of our study are provided and possible future works of our research are represented in the last section.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 1 Introduction
It is built on residual CNN (RDCNN), edge attention, and multi-scale channel attention. Three channels are used to perform picture fusion, and a distributed fusion framework is used to fully utilize the output of the previous step's fusion. In order to extract the features of infrared and visible pictures, two channels use residual modules. This minimizes the loss of visible image texture information and infrared image target information. Figure 1 shows the approximate chronology of the infrared and visible image fusion domain approaches. Figure 2 displays the five-year publication history of works on the fusion of visible and infrared images. All scientific journal papers pertaining to infrared visible image fusion are represented in the bar chart's blue portion, and review articles are represented in the orange portion. With the naked eye, we can observe that from 2019 to 2022, there was an increase in the number of papers published annually on infrared and visible-light-related themes, which amply illustrates the rapid growth of infrared and visible light image fusion. Since there are not many review articles, this study will briefly discuss the infrared and visible image fusion field in an effort to inspire researchers just starting out in their careers. Figure 1: Approximate timeline of methods in the field of infrared and visible image fusion. This paper is organized as follows: In the second section, the infrared and visible image fusion techniques are explained in depth, and the benefits and drawbacks of each technique are outlined. The third section lists the fusion image evaluation indexes and explains how these indexes assess the fusion image's quality. The infrared and visible image fusion datasets are sorted in the fourth section, and the distinctions between the various data types are outlined. The fifth section provides a brief overview of infrared and visible image fusion applications. Section 6 describes the problems and potential future prospects for infrared and visible image fusion research. Section 7 concludes by summarizing the thesis. Figure 3 presents a schematic illustration of the article's structure. Figure 3: Structure of the survey. Figure 2: The number of articles with the topic of infrared and visible light image fusion (Statistics with Web of Science).
## 7 Results and Discussion
### Impact of Device Heterogeneity on FL Performance
#### 7.1.4 Effect of Heterogeneous Client Distribution on FL Trade-Offs  
To gain a deeper understanding of how the distribution of client types affects FL performance, we analyze different configurations by adjusting the proportions of IoT, edge, and smartphone devices within the client pool. Each configuration represents a unique allocation strategy, from highly heterogeneous setups (e.g., 60% IoT, 20% edge, and 20% smartphone) to more balanced combinations (e.g., 33% of each device type). We then evaluate the performance of each setup using three key metrics: accuracy, battery consumption, and privacy loss. The results of this comprehensive analysis are summarized in Figure 14.  
Figure 11: Impact of device heterogeneity on privacy over 300 rounds (EMNIST).  
Figure 12: Impact of device heterogeneity on privacy over 300 rounds (Fashion-MNIST).  
Figure 13: Impact of device heterogeneity on privacy over 300 rounds (CIFAR-10).  
Figure 14 clearly shows that client composition has a significant effect on the overall trade-off between accuracy, battery usage, and privacy preservation. Configurations dominated by smartphones (e.g., 60% smartphone) achieve the highest accuracy and the lowest privacy loss but also incur higher battery consumption due to their higher computation demand. In contrast, IoT-heavy configurations consume less battery but perform poorly in accuracy and privacy metrics. The most balanced results are observed in heterogeneous distributions, particularly the setup with approximately 33% of each client type, which yields stable accuracy while moderately preserving battery and privacy. These results reinforce the value of maintaining device diversity in federated training, and they highlight the effectiveness of RBPS in navigating trade-offs in heterogeneous environments.
# Development and Application of a Novel Tsunami Monitoring System Based on Submerged Mooring
## 4 Sea Trial Verification and Result Analysis
### Laboratory Copying Situation  
A laboratory copying machine is mainly used to verify the working mechanism of the entire system and the stability and reliability of the system operation. The main verification content includes the success rate of data transmission and reception, the switching of the working mode of the tsunami monitoring system, and the release of the small water-surface-communication buoy device.  
The laboratory copying process goes from deployment mode to standard mode, from standard mode to alarm mode, and from alarm mode to standard mode. The success rate of data transmission and reception during the mode switching process is 100%, and several working mode switches are successfully simulated without any abnormalities in the working mechanism. The data reception is detailed in Table 2, and the pressure data curve is shown in Figure 8.  
The release test on the sea surface buoy ejection device was conducted at the dock. After testing, it was found that the underwater release performance of the small water-surface-communication buoy release device was good, and the communication cable had slight positive buoyancy and no entanglement after being ejected. The shore test process of the release device is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Working Mode**} & **Number of** & **Actual Number of** & **Data Reception** \\  & **Actually Sent Data** & **Received Data** & **Success Rate** \\  & **Packets** & **Packets** & \\ \hline Layout mode (lasting for & & & \\ actual 3 h) & 47 packets & 47 packets & 100\% \\ Alarm mode (actual & & & \\ duration of 6 h) & 53 packets & 53 packets & 100\% \\ Standard mode (actual & & & \\ duration of 9 h) & 9 packets & 9 packets & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prototype system copying and data transmission situation.  
Figure 8: Normal pressure and alarm pressure curves simulated by laboratory copying machine. (**a**) Normal pressure curve, (**b**) Alarm pressure curve.
## 2 Materials and Methods  
### Study Site  
The study site on the east coast of Peninsula Malaysia, comprised three states, namely the Pahang, Terengganu, and Kelantan states, which were selected for the case study. This study site is located on the eastern side of Peninsula Malaysia and is bordered by the South China Sea to the east. It has a unique topography that influences the rainfall pattern in the region. The land usage patterns for this study site vary across different categories, including residential areas, agricultural land, natural reserves, industrial zones, and commercial development. This region was mainly selected based on its great history of flood and high rainfall variability due to the monsoon cycle, and is thus in need of flood mitigation and water resource management. This study site experiences two monsoon seasons, the northeast monsoon from November to March and the southwest monsoon from May to September. During the northeast monsoon, the region might be experiencing heavy rainfall, while the southwest monsoon brings drier conditions to the region. This study showed that there were rainfall-related extreme events during the monsoon periods for the east coast of Peninsula Malaysia [57; 58; 59]. Among all the rain gauge data downloaded from DID, there are 184 rain gauge stations for the study site, encompassing the region within 2\({}^{\circ}\)30\({}^{\prime}\)0\({}^{\prime\prime}\)-6\({}^{\circ}\)30\({}^{\prime}\)0\({}^{\prime\prime}\) N and 101\({}^{\circ}\)-104\({}^{\circ}\) E being selected for this study. These rain gauge stations are selected due to the data completeness and the uniformity of the location within the study area. The rain gauge stations are shown in terrain view of Google Maps [60] as in Figure 1. In their research, [PERSON] et al. [61] sought to define and measure terrain information from digital elevation model (DEM) to provide clear information for the various patterns of the land surface. It is noticed that the rain gauge station located at the boundary of Negeri Sembilan is included here due to the record as a rain gauge station in the state of Pahang. The mountainous regions located in the central part of the study area, particularly in the state of Pahang, have a lower density of rain gauge stations compared to the more residential areas in the region. For the deep learning training process, a total of 131 IMERG grids encompassing 184 rain gauge stations were utilized during the period 2011-2020.  
The 184 rain gauge stations are selected based on their location in the state of Pahang, Terengganu, and Kelantan. With the longitude and latitude of the rain gauge stations, the 184 rain gauge stations are mapped into the 131 IMERG grids. If there is more than 1 rain gauge station in the same grid, the rain gauge data will be averaged. The IMERG data are then trained with the rain gauge data using the LSTM model developed in Python.
# Evaluation of Tip Loss Corrections to AD/NS Simulations of Wind Turbine Aerodynamic Performance
## 3 Tip Loss Corrections
### A Newly Developed Correction  
A new tip loss correction was recently proposed for BEMT by [PERSON] et al. [33], based on a novel insight into tip loss. In contrast with the Prandtl/Glauert series corrections that begin with an actuator disc and estimate the effect of the finite number of blades, the new correction begins with a non-rotating blade and estimates the effect of rotation on tip loss. It has been validated in BEMT computations and showed superior performances in the cases involving flow separation or high axial interference factor.  
The correction was realized by using two factors of \(F_{R}\) and \(F_{S}\) that treat the rotational effect and the 3D effect, respectively.  
\[F_{R}=2-\frac{2}{\pi}\arccos\left(\exp\left[-2B(1-r/R)\sqrt{1+\lambda^{2}} \right]\right), \tag{34}\]  
\[F_{S}=\frac{2}{\pi}\arccos\left\{\exp\left[-\left(\frac{1-r/R}{\overline{c}/R }\right)^{3/4}\right]\right\},\ \overline{c}=\frac{S_{\rm t}}{R-r}, \tag{35}\]  
where \(\overline{c}\) denotes a geometric mean chord length, and \(S_{\rm t}\) is the projected area of the blade between the present cross-section and the tip. The purpose of introducing \(\overline{c}\) is to deal with tapered blades and those with sharp tips.  
The factor \(F_{R}\) was applied to the BEMT approach by using:  
\[\frac{aF_{R}(1-aF_{R})}{(1-a)}=\frac{\sigma\left(\tilde{C}_{l}\cos\phi+\tilde{ C}_{d}\sin\phi\right)}{4\sin^{2}\phi}, \tag{36}\]  
\[\frac{a^{\prime}F_{R}(1-aF_{R})}{(1+a^{\prime})(1-a)}=\frac{\sigma\left(\tilde {C}_{l}\sin\phi-\tilde{C}_{d}\cos\phi\right)}{4\sin\phi\cos\phi}, \tag{37}\]  
in which the employed lift and drag coefficients were corrected by factor \(F_{S}\), rather than the direct use of the airfoil data.  
\[\tilde{C}_{l}=\frac{1}{2}\left[C_{l}(\alpha)F_{S}+C_{l}^{*}\right], \tag{38}\]  
\[\tilde{C}_{d}=\frac{1}{\cos^{2}\alpha_{i}}\left[C_{d}(\alpha_{e})\cos\alpha_{ i}+\left(\tilde{C}_{l}\cos\alpha_{i}+C_{d}(\alpha_{e})\tan\alpha_{i} \right)\sin\alpha_{i}\right]; \tag{39}\]  
where  
\[C_{l}^{*}=\frac{1}{\cos^{2}\alpha_{i}}[C_{l}(\alpha_{e})\cos\alpha_{i}-C_{d}( \alpha_{e})\sin\alpha_{i}], \tag{40}\]  
\[\alpha_{i}=\frac{C_{l}(\alpha)}{m}(1-F_{S}), \tag{41}\]  
where \(m\) is the slope of the lift-curve of the airfoil before flow separation, \(\alpha_{i}\) is called the downwash angle, and \(\alpha_{e}\) is called the effective angle of attack.  
By comparing with the Glauert tip loss correction, the new tip loss correction appears more complicated in application. It is simplified in the present study: First, Equations (36) and (37) will not be employed naturally in the AD/NS method; Second, Equations (38)-(41) are further simplified to  
\[\tilde{C}_{l}=\frac{1}{2}[C_{l}(\alpha)F_{S}+C_{l}(\alpha_{e})],\ \alpha_{e}=\alpha- \alpha_{i}, \tag{42}\]\[\tilde{C}_{d}=C_{d}(\alpha_{e})+\tilde{C}_{l}\tan\alpha_{i},\ \alpha_{i}=\frac{C_{l}( \alpha)}{m}(1-F_{S}). \tag{43}\]  
The simplification is derived by using the fact that \(\alpha_{i}\) and \(C_{d}\) are relatively small. More detailed applications are shown in the next section.