esa-sceva/satcom-chunk-collection huggingface dataset:
# The Ethanol Oxidation Reaction Performance of Carbon-Supported PtRuRh Nanorods
## 1 Introduction
Although theoretical studies suggest that Rh is not as active as Ru for OCS formation, Rh can enhance the EOR performance of Pt-based catalysts by synergistic effects and electron modification [38]. Moreover, the PtRuRh/C catalysts with different Pt/Ru/Rh compositions have been prepared for methanol oxidation reaction (MOR) [39; 40]. It can be observed that the ternary PtRuRh can provide higher MOR activity than binary PtRu at low potential. Furthermore, the catalysts prepared at the ratio of Pt\({}_{1}\)Ru\({}_{1}\)Rh\({}_{2}\) possess the highest current density at 0.5 V (vs. RHE) in 0.5 M H\({}_{2}\)SO\({}_{4}\) and 1 M CH\({}_{3}\)OH at 60 \({}^{\circ}\)C, suggesting the synergistic effect of Ru and Rh addition on their MOR performance. Therefore, ternary PtRuRh/C can be a promising EOR catalyst. Moreover, controlling the morphologies of Pt nanostructure is a promising method to promote their catalytic activity, especially the structure-sensitive reactions, such as EOR [41; 42]. The Pt 1-D structures, such as nanorods (NRs) and nanowires (NWs), exhibit higher catalytic activities toward MOR and EOR when compared with 0-dimensional NPs [43]. This can be attributed to its anisotropic nature, high aspect ratios, few lattice boundaries and low surface defect sites [44; 45; 46]. Pt NWs can display lower onset potential and higher current density than Pt NPs, attributed to a decrease in the activation energy for EOR of 1-D structures [47]. Similar enhanced EOR performances can be observed on PtRh NWs [48]. PtRh NWs have obviously lower onset potential and higher activity than PtRh NPs and Pt/C. After 2000 cycles, the PtRh NWs maintains 86% current density, which is the most durable catalyst among the prepared ones. It seems that 1-D structures such as NWs or NRs with second metals can possess higher activity and stability than NPs, which may be associated with unique structure and surface properties. In order to prepare highly effective catalysts, in this study, carbon-supported PtRu, PtRh, and PtRuRh catalysts have been prepared by taking the advantages of the bifunctional mechanism and 1-Dstructure. The presence of OCS can help the desorption of adsorbed intermediates on Pt active sites, which enhances the activity and stability of Pt catalyst, and 1-D structures with high aspect ratios can have better EOR performance than NPs, owing to its anisotropy and unique structure. The compositions, structures, morphologies, surface compositions and electrochemical performances of the prepared catalysts are analyzed by a field emission scanning electron microscope and an X-ray energy dispersive spectrometer (SEM-EDS), X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS)/H\({}_{2}\)-temperature-programmed reduction (TPR)/CO-stripping/cyclic voltammograms (CV), and linear sweep voltammograms (LSV)/ chronoamperometry (CA), respectively.
# Green Approach in Water-Based Drilling Mud Design to Increase Wellbore Stability
## 4 Discussion

Table 5: Decrease in API filtration, PPT filtration, and spurt loss for all tested mud samples in regard to base mud (BM). Regardless of the concentration of MPP and particle size, a decrease in pellet swelling between 17.4% (B1) and 45% (B4) was observed at room temperature, and a decrease in swelling between 14.2% (A1) and 48.6% (B4) was determined at 90 \({}^{\circ}\)C. When the concentration of MPP increased, the reduction in pellet swelling increased for mud samples containing particles from 0.1 to 0.16 mm (mud samples B1-B4), from 17.4% (B1) at a concentration of 0.5% by volume of water up to 45% measured with mud B4 containing 2% by volume of water at room temperature. At 90 \({}^{\circ}\)C, results were similar to those measured at room temperature, but the reduction in pellet swelling was slightly greater, from 25.2% (B1) to 48.6% (B4). At room temperature, a similar trend was observed in measurements with mud containing MPP particles less than 0.10 mm (mud samples A1-A4), with one exception. The greatest reduction in swelling was observed for mud A3, containing MPP at a concentration of 1.5% by volume of water (40.5%). However, at a concentration of 2% by volume of water (A4), the reduction in pellet swelling was less than that (37.5%), indicating that it is not necessary to increase the concentration of MPP beyond 1.5% by volume of water. At 90 \({}^{\circ}\)C, the reduction in pellet swelling at a concentration of MPP of 1% by volume of water or more was similar (from 39.9% to 43.9%); at a low concentration of 0.5% by volume of water (A1; 14.2%), a slightly lower value of reduction in pellet swelling was observed compared to the reduction measured at room temperature (26.8%). Although this research shows that the best results were obtained at higher concentrations of MPP (1.5% and 2% by volume of water), the effects on other properties of the drilling mud, especially rheology, need to be further studied to determine the potential of these muds for adequate wellbore cleaning. Considering that satisfactory results were also obtained at lower concentrations (up to 1% by volume of water), it is necessary to select those concentrations that had a satisfactory effect on API filtration and shale swelling, but that also present other adequate properties to allow for the wellbore to be safely drilled while reducing the negative environmental impact of drilling mud.
# Inter-Session Repeatability of Marker-Less Motion Capture of Treadmill Running Gait
## 2 Materials and Methods
### Instrumentation  
A stadiometer (Tanita Corporation; Arlington Heights, IL, USA) was used to record each participant's mass and standing height prior to the first data collection session. A Phys-Tread Pressure Treadmill (Noraxon USA, Scottsdale, AZ, USA) containing 3120 sensors collected force and pressure data (100 Hz) during the running trials. Eight Sony RX0 II cameras (Sony Corporation; Minato, Japan; 120 Hz) were synchronized via Sony camera control boxes and positioned to accommodate a capture volume of approximately 2.4 \(\times\) 2.4 \(\times\) 3.1 m dimensions with the treadmill centralized (Figure 1). The cameras were moved minimally throughout the entire investigation, but they were recalibrated prior to each day of data collection. The treadmill was moved between sessions; however, tape placed on the floor allowed the treadmill position to be nearly identical. Participants were permitted to use any running footwear that they desired (Table 1); however, footwear was maintained across the three testing sessions. Participants were instructed to wear their \"normal\" running attire such that it consisted of shorts (or short tights) and a shirt, which was to be tucked into the shorts. Previous work has reported that clothing type only induced negligible influences during overground walking with the ML motion capture software used in this study [17].
## 4 Evaluation and Results
### Ablation Study
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Binary Classification**} & \multicolumn{4}{c}{**Multi-Class Classification**} \\ \cline{2-11}
**Feature Set** & **Acc.** & **Prec.** & **Rec.** & **F1- Score (\%)** & **Train.** & **Acc.** & **Prec.** & **Rec.** & **F1- Score (\%)** & **Train.** \\  & **(\%)** & **(\%)** & **(\%)** & **Score (\%)** & **Time (h)** & **(\%)** & **(\%)** & **(\%)** & **Score (\%)** & **Time (h)** \\ \hline DEX & 98.64 & 98.54 & 99.66 & 99.10 & 1.24 & 96.77 & 96.79 & 96.77 & 96.78 & 1.15 \\ Manifest & 96.29 & 96.75 & 98.35 & 97.54 & 1.22 & 91.64 & 91.73 & 91.64 & 91.66 & 1.15 \\ Resources & 98.99 & 99.32 & 99.32 & 99.32 & 1.19 & 91.83 & 92.09 & 91.83 & 91.77 & 1.15 \\ DEX + Manifest & 99.08 & 99.20 & 99.58 & 99.39 & 1.51 & 98.07 & 98.08 & 98.07 & 98.07 & 1.11 \\ DEX + Resources & 99.33 & 99.49 & 99.62 & 99.56 & 1.20 & 98.57 & 98.57 & 98.57 & 1.12 \\ Manifest + Resources & 99.37 & 99.62 & 99.53 & 99.58 & 1.13 & 95.50 & 95.57 & 95.50 & 95.50 & 1.17 \\
**DEX + Manifest + Resources** & **99.68** & **99.87** & **99.70** & **99.79** & **0.81** & **99.56** & **99.56** & **99.56** & **1.07** \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Ablation study comparing individual and combined APK features (DEX, Manifest, and Resources) for binary and multi-class malware classification. Figure 9: Top-1 accuracy training curves for the ablation study. The full RGB combination consistently outperforms individual and partial feature sets in both binary and multi-class settings.
# Finite-Time Height Control of Quadrotor UAVs
## 1 Introduction
The dynamic model of quadrotor UAVs in the height direction is divided into an inner-loop velocity control system and an outer-loop height control system. A finite-time ESO is developed for the inner-loop velocity system to estimate disturbances in real time, and a finite-time super-twisting controller is constructed to improve the convergence speed and robustness of the system. An integral sliding mode controller is designed for the outer-loop height system to improve control accuracy. The main contributions of this paper are summarized as follows:  
**I. **: A dual closed-loop control frame is proposed with a finite-time super-twisting controller in the inner loop and an integral sliding mode controller in the outer loop for a quadrotor UAV. **II. **: An upper bound of convergence time is introduced for a finite-time ESO, which is applied in the inner loop to assist the finite-time super-twisting controller in estimating disturbances. **III. **: With excellent robustness, an integral sliding mode controller is designed to both generate a desired height velocity signal for the inner loop and reject disturbances of the outer loop. In this paper, \(|\cdot|\) and \(\|\cdot\|\), respectively, represent absolute values and Euclid norms. The superscript \(T\) represents the transposition of the matrix and \(I\) represents the identity matrix with the appropriate dimension. \(\text{sign}(\cdot)\) is a common symbolic function. \(\lambda_{\max}\{E\}\) and \(\lambda_{\min}\{E\}\), respectively, represent the largest eigenvalue and the smallest eigenvalue of the matrix \(E\).
## 1 Introduction
### Initial Damage Distribution Finite Element Model of Strand Steel  
According to the theory of continuum damage mechanics [40; 51], these continuously distributed initial micro-defects can be introduced into the damage analysis in the form of initial damage so that the damage evolution caused by the initial defects (such as microcracks, structural defects, material inhomogeneity, and segregation) of steel wire and its influence on the cable deterioration process can be considered in the simulation of the cable deterioration process. In addition, due to the random and disordered distribution of the initial defects of the wire, the initial damage degree of each wire in the wire body will often show a certain difference, and this difference in the initial damage degree will also cause the uneven distribution of the wire stress. Therefore, when analyzing the initial damage of wires in the cable, it is assumed that the initial damage of each wire is independent of each other and obeys the same probability distribution model. According to [PERSON]'s suggestion [52], when analyzing structural safety and reliability, if the control ability of material parameters is weak, it is more appropriate to use the Weibull distribution function to obtain material parameters. Moreover, according to the study of [PERSON] et al. [53], the Weibull distribution is stable, and the fatigue parameters obtained using the Weibull function to analyze the fatigue behavior of the cable will not fail due to the change in cable length. [PERSON] et al. [54] also used this distribution function to study the corrosion fatigue damage evolution behavior of cables. Based on the above analysis, the initial damage of steel wire here will also be randomly generated by the Weibull distribution function; that is, the probability density of the initial damage of the steel wire \(D_{i}\) obedience is Formula (13) [55]:  
\[f(D_{i};\lambda,k)=\left\{\begin{array}{cc}\frac{k}{\lambda}\left(\frac{D_{ i}}{\lambda}\right)^{k-1}e^{-(n/\lambda)^{k}}&D_{i}\geq 0\\ 0&D_{i}<0\end{array}\right. \tag{13}\]  
where \(\lambda>0\) is the scale parameter, \(k>0\) is the Shape parameter.  
Based on the above modeling ideas, the cable finite element model shown in Figure 4 is established here to analyze the evolution process of corrosion fatigue damage of the cable.
## 2 Literature Review
### Effectual Routing Strategies
The proposed research compares the dataset to the \"BHT dataset\" mentioned in the reference. Assess the relevance and representativeness of the datasets used in both works. Similarly, ref. [34] introduced an efficacious routing algorithm known as the Dyte algorithm, designed for optimizing the delivery of packets while concurrently minimizing packet delays. Within the framework of the Dyte algorithm, a trilateral zone is thoughtfully delineated by the source node, precisely ascertaining the final location of the destination node. This delineation culminates in compiling a roster of nodes encompassing the last known location of the destination node. The source node appends the destination node's identity and other pertinent information to the packet header before dispatching it to the node located within the trilateral zone. Every constituent node within the trilateral zone scrutinizes the packet's information and responds as dictated by its operational state. Should a node possess knowledge of the destination node's whereabouts, an additional trilateral zone is meticulously charted. Subsequently, a novel packet is engendered and directed towards the destination. Conversely, in cases without the knowledge of the destination, intelligence regarding the trilateral zone is shared with neighboring nodes until the message reaches the intended destination.
# The Impact of Eclipsing GNSS Satellites on the Precise Point Positioning
## 3 Results and Discussion
### The Impact of Eclipsing Satellites on GNSS PPP
#### 3.3.1 The Impact on the Positioning Accuracy of the Single-System Static PPP
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Constellation**} & \multirow{2}{*}{
\begin{tabular}{c} **Average** \\ **Percentage** \\ \end{tabular} } & \multirow{2}{*}{**Direction**} & \multicolumn{4}{c}{**Static**} & \multicolumn{4}{c}{**Kinematic**} \\ \cline{4-9}  & & & **S1** & **S2** & **S3** & **S1** & **S2** & **S3** \\ \hline \multirow{4}{*}{G} & \multirow{4}{*}{9.2\% (31)1  & N & 4.25 & 4.24 & 16.80 & 16.89 & 16.76 \\  & & E & 8.49 & 8.56 & 8.49 & 24.13 & 24.15 & 24.01 \\  & & U & 10.35 & 10.42 & 10.32 & 41.56 & 41.68 & 41.40 \\ \hline \multirow{4}{*}{R} & \multirow{4}{*}{27.3\% (22)} & N & 5.63 & 6.16 & 5.47 & 43.38 & 43.33 & 38.33 \\  & & E & 11.26 & 12.50 & 10.96 & 71.72 & 73.49 & 65.97 \\  & & U & 12.57 & 13.20 & 12.35 & 106.40 & 112.07 & 99.12 \\ \hline \multirow{4}{*}{E 2  & 22.2\% (18) & E & 15.78 & 15.50 & 15.56 & - & - & - \\  & & U & 26.33 & 25.76 & 25.86 & - & - & - \\ \hline \multirow{4}{*}{C 3  & 14.3\% (14) & E & 15.41 & 19.90 & 12.27 & 40.73 & 64.52 & 30.84 \\  & & U & 24.56 & 27.96 & 21.70 & 95.09 & 165.78 & 68.02 \\ \hline \multirow{4}{*}{GR} & \multirow{4}{*}{16.6\% (53)} & N & 3.77 & 3.75 & 11.44 & 11.64 & 11.27 \\  & & E & 6.57 & 6.56 & 6.57 & 15.63 & 15.79 & 15.53 \\ \cline{1-1}  & & U & 8.58 & 8.61 & 8.56 & 29.47 & 30.21 & 29.33 \\ \hline \multirow{4}{*}{GREC} & \multirow{4}{*}{13.0\% (85)} & N & 3.62 & 3.61 & 3.61 & 10.18 & 10.23 & 10.11 \\  & & E & 5.94 & 5.96 & 5.95 & 13.41 & 13.46 & 13.23 \\ \cline{1-1}  & & U & 8.53 & 8.55 & 8.53 & 26.28 & 26.40 & 26.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The average RMS values (mm) under different scenarios.
# Influence of Impeller Speed Patterns on Hemodynamic Characteristics and Hemolysis of the Blood Pump
## 2 Materials and Methods
### Modeling and Meshing  
The simulation model of the flow field of the blood pump consisted of five parts to simulate the flow characteristics of blood pumps accurately, as shown in Figure 2a. The structural grid had better calculation accuracy and convergence, and could capture the flow details of the internal flow field of the blood pump. Therefore, structural meshes were divided for each part of the simulation model of a blood pump in the ANSYS ICEM software (ANSYS, Inc., PA, USA), as shown in Figure 2b.  
An impeller was a significant driving component of the blood pump. The boundary layer grids were divided for the impeller channel to obtain accurate simulation results. In addition, hemolysis might occur in the small clearance between the impeller and the pump shell and the small size of the spiral groove of the hydrodynamic bearing. Therefore, refined structural grids, as shown in Figure 3, were divided into these areas to obtain accurate simulation results. The number of grid elements in each region is shown in Table 1.  
Figure 1: Schematic diagram of the blood pump. The arrow indicates the blood flow path and the secondary flow path.  
Figure 2: (**a**) Blood pump simulation model; (**b**) structured mesh of the blood pump, in which the area mesh of hydraulic bearing was refined.
## 1 Introduction
[14] suggested a model to describe reactive absorption processes, using the Maxwell-Stefan equations. The model is comprised of several terms of film transport, reversible chemical reactions, the mass transfer between multicomponents, thermodynamics and hydrodynamics [14]. [PERSON] et al. [15] used a PED model for the reactive absorption of carbon dioxide into ammonia solution based on unsteady-state and non-equilibrium conditions. Various equations, including mass, charge, and ionic balance equations are used in such non-equilibrium models. Non-equilibrium models describe rigorously by solving complex equations [15]. However, users unfamiliar with numerical analyses, such as industrial engineers, have difficulties in using these models [16]. To easily predict and design reactive absorption, equilibrium models, which are comprised of several ordinary differential equations (ODE), are commonly applied. [PERSON] [16] suggested a method to describe the performance of packed columns with chemical reactions using ODEs. He converted PEDs to ODEs using various equations, such as those related to mass transfer and the chemical reaction rate [16]. In addition, many models for unit processes of carbon capture have been reported recently, such as a model based on mass and energy balance [17], and a model using dimensionless variables and parameters [18]. In this study, a numerical model for EWS, which is composed of a mass transfer coefficient and a zero-order kinetic constant, was developed for ease of use for engineers. In the model, mass transfer represents the rate of transfer from the gas to liquid phase. Various factors affect the rate, such as the superficial gas velocity and pressure. The mass transfer rate can be expressed using the mass transfer coefficient. For electrochemical oxidation, pollutants are oxidized by both direct and indirect oxidation mechanism. The removal rate or the type of oxidant generated are influenced by the current density, electrolyte type, and electrode types [19]. However, EWS performance cannot be evaluated by separating the direct and indirect oxidation effects. The performance can be expressed by the kinetic constant, which represents the oxidation rate of the pollutants. The aim of this study was to develop a model for predicting EWS performance, using simple ODE consisting of mass transfer coefficients and kinetic constants. A model was developed to predict the performance and derive an appropriate operation plan of EWS. The experiments were performed using a lab-scale experimental setup, and a model was established and validated by estimating the parameters to accurately simulate the experimental results. Additionally, the performance of the EWS was predicted, using this model.
# Research Trends and Future Direction for Utilization of Woody Biomass in Japan
## 3 Supply Potential and Availability of Woody Biomass
### Methods and Results of Availability Estimation
#### 3.2.2 Results and Influencing Factors of Availability Estimation
(3) Mechanization  
Mechanization refers to the transition from a conventional low-efficiency, primarily manpower-dependent forestry operation system to a high-efficiency, primarily machine-dependent forestry operation system. This transition involves the selection of suitable machinery based on the topographical characteristics of the operational area. The essence of mechanization is to enhance operational efficiency and reduce production costs by appropriately selecting efficient machinery. Earlier studies have estimated the availability of woody biomass by comparing traditional and mechanized operation systems, revealing that in large-scale harvesting activities, mechanized operation systems significantly improve availability [18; 19]. However, recent studies have tended to treat the operational system as a fixed condition for estimation, neglecting mechanization as a factor influencing availability. Cases 4, 5, 25, 26, and 27 in Table 1 establish five forestry operation systems based on information from harvesting sites in the study area. Additionally, Cases 6 through 24 in Table 1 set up more complex forestry operation systems based on the slope and relief volume in the study area. The selection of an appropriate forestry operation system has a substantial impact on reducing the cost of woody biomass production. The choice of a forestry operation system is closely tied to topography, making it challenging to quantify the effects of mechanization. Cases 4, 5, and 8 in Table 1 investigated the availability of woody biomass in different forestry operation systems within the same location (Tochigi Prefecture, Japan). By comparing the results of these three cases, the impact of mechanization on increasing the availability can be elucidated. In contrast to Cases 4 and 5, Case 8 employed highly efficient machinery such as harvesters, with a more precise set of nine operation systems based on the local topography. As a result, when subsidies are applied, Cases 4, 5, and 8 estimate availability rates of 10%, 9%, and 87%, respectively, with Case 8 showing a significantly higher availability rate than Cases 4 and 5 [20; 21; 22].