esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of TiO2 on the Electrochemical Performance of Sb2O3 Anodes for Li-Ion Batteries
## 3 Results and Discussion
### Electrochemical Performance
With the addition of TiO\({}_{2}\) to Sb\({}_{2}\)O\({}_{3}\), the rate performance was improved--by maintaining a high capacity over 300 mAh g\({}^{-1}\)--and there were excellent signs of cycling stability of the composite electrode. Particularly, when cycled back at a current density of 50 mA g\({}^{-1}\), the (3:1) TiO\({}_{2}\)/Sb\({}_{2}\)O\({}_{3}\) electrode recovered its capacity, demonstrating great reversibility. The nanocomposite electrode delivered a capacity of 354.8 mAh g\({}^{-1}\) at the 10 th cycle and at 50 mA g\({}^{-1}\), while at the 60 th cycle the capacity was 336.08 mAh g\({}^{-1}\). The electrochemical performance results of the proposed (3:1) TiO\({}_{2}\)/Sb\({}_{2}\)O\({}_{3}\) composite electrode are similar to those reported by [PERSON] et al. [37,50] and [PERSON] et al. [49] on similar composite electrodes in albeit different systems. However, the synthesis method and the systems reported in this work are different from those reported by [PERSON] et al. and [PERSON] et al. Moreover, the morphology and structure of the TiO\({}_{2}\)/Sb\({}_{2}\)O\({}_{3}\) composite materials discussed in the present work are different from those reported by [PERSON] et al. and [PERSON] et al. [37,49,50]. Figure 11: Rate performance of (3:1) TiO\({}_{2}\)/Sb\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\) and Sb\({}_{2}\)O\({}_{3}\) electrodes at different current densities.
## 2 Study Area and Data  
### Study Area and Ground Data  
The field campaign was conducted over an alpine grassland located in the eastern part of the Qinghai-Tibet Plateau of China, called Ruoergai Prairie (approximately centered at 33 deg37'N; 102 deg54'E) (Figure 1). Ruoergai Prairie has a cold, humid plateau monsoonal climate with an average annual temperature of 0.7 degC and a total precipitation of 656.8 mm [27]. It is characterized by rolling hills that range from 3422 m to 3704 m in elevation. The frozen period of the Ruoergai Prairie is eight months. The snow begins to fall in October.  
Coincident with the Radarsat-2 satellite overpasses, field campaign measurements of soil moisture and vegetation parameters were conducted over the study area. There are 97 sampling sites along a road distributed over the study area. Topography of the sampling sites is relatively flat. Each sampling site was 30 m \(\times\) 30 m. Within each sampling site, soil moisture was measured using a Time-Domain Reflectometer (TDR) probe (Model CS659, manufactured by Campbell Scientific) at a soil layer of approximately 10 cm by inserting the rod into the ground vertically and characterized by a relative accuracy of 3% (Campbell Scientific, [[http://www.campbellsci.com/cs659](http://www.campbellsci.com/cs659)]([http://www.campbellsci.com/cs659](http://www.campbellsci.com/cs659))). To reduce the measurement error, soil moisture was measured ten times at each of the sampling sites, and the average was then computed. Soil samples for moisture measurements in 50 sampling sites were also taken to calibrate the TDR. However, due to the small number of soil samples, it is possible that the calibration of TDR was imperfect. Meanwhile, the following parameters were measured in 50 sampling sites: vegetation biomass, leaf area index (LAI), vegetation height, vegetation water content, vegetation coverage, single leaf area, leaf density, physiological and biochemical parameters, leaf structure parameters and vegetation spectral properties. The vegetation biomass samples were clipped from three randomly selected plots (0.5 m \(\times\) 0.5 m) within each sampling site weighed _in situ_, and dried in an oven at 120 degC for 30 min and then at 80 degC for 24 h. An LAI-2200 instrument was used to measure the LAI. The LAI was evenly measured for ten times. The average value of ten LAI values was considered to represent the LAI value of the sampling site. The average value of the measured biomass in the three plots was assumed to represent the value of the full sample site. The water content of samples was determined by weighing each sample before and after oven drying to estimate the vegetation water content (wet weight minus dry weight). A Full Range Portable Spectroradiometer (PSR-3500) was used for vegetation spectral measurements. In addition, to avoid mixed-pixel problem, the sampling sites were restricted to a distance of at least 300 m away from the roads.
## 3 Methodology
### Model Construction
#### 3.3.1 Fuzzy Logic Background and Modeling
Each if-then rule in the Mamdani technique results in a fuzzy set for the output variable; hence, the defuzzification step is essential when obtaining the output variable's values with precision. Instead of a fuzzy set for the output variable, the TS technique produces a scalar as the result of each if-then inference rule. In the TS technique, the defuzzification process is easily finished by calculating the weighted average of the rule result. The selection of the parameters in TS fuzzy logic modeling is the primary source of difficulty. Because of this, [PERSON] (1993) proposed the first proposed ANFIS (Adaptive Network-based Fuzzy Inference System) methodology is used to estimate the membership's parameters and the subsequent functions [108].
# Contribution of Climate Change and Grazing on Carbon Dynamics in Central Asian Pasturelands
## 2 Materials and Methods  
### Study Area  
The regions of Central Asia, including Kazakhstan, Turkmenistan, Uzbekistan, Tajikistan, Kyrygzstan, and Xinjiang province in China, extend from 34.3\({}^{\circ}\) to 55.4\({}^{\circ}\)N latitude and from 46.5\({}^{\circ}\) to 96.4\({}^{\circ}\)E longitude, encompassing a land area of approximately 5.7 million km\({}^{2}\). The overall population of Central Asia was approximately 101 million by 2020, which equaled to about 18 people per km\({}^{2}\), according to the statistics provide by the World Bank ([[https://data.worldbank.org/indicator/SP.POP.TOTL](https://data.worldbank.org/indicator/SP.POP.TOTL)]([https://data.worldbank.org/indicator/SP.POP.TOTL](https://data.worldbank.org/indicator/SP.POP.TOTL)); accessed 12 February 2022) and China Statistical Yearbook 2021 ([[http://www.stats.gov.cn/tjsi/ndsj/2021/indexch.htm](http://www.stats.gov.cn/tjsi/ndsj/2021/indexch.htm)]([http://www.stats.gov.cn/tjsi/ndsj/2021/indexch.htm](http://www.stats.gov.cn/tjsi/ndsj/2021/indexch.htm)); accessed 12 February 2022). The climate is arid and semiarid with the annual precipitation less than 300 mm in more than 70% of the region. Coexisting with native and domestic grazers for thousands of years, Central Asian rangelands are the largest contiguous grazed land on this planet. However, they have been heavily degraded [10; 11] and subjected to climate changes over the past decades [12; 13]. According to the distribution elevation and climate conditions, we divided the pasturelands of Central Asia into three typical types: forest meadow (FM, >1650 m a.s.l., arid index > 0.7), temperate grasslands (TG, 650-1650 m a.s.l., arid index \(\approx\) 0.3), and dry grasslands (DG, <650 m a.s.l., arid index \(\approx\) 0.1) [14] (Figure 1).  
Figure 1: Study area and the distribution of different grassland types of Central Asia. Forest meadow is mainly distributed in Kyrygzstan, Tajikistan, and the central and western Xinjiang. Desert grassland mainly covers southern Kazakhstan, Uzbekistan, and Turkmenistan. Temperate grassland mainly occupies northern Kazakhstan.
## 4 Simulation Analysis of 3D Network Deployment Experiments
We can find that all coverage rates may reach a maximum value around 0.8, which is consistent with the above analysis. For different \(\lambda_{D}\), when \(\lambda_{D}\) is larger, \(C\) is generally larger and \(U\) is generally smaller. When \(\lambda_{D}\) is smaller, \(U\) is generally larger and \(C\) will be smaller. Especially, for a fixed \(\lambda_{D}\), when the network requires better system utilization, the coverage rate has to decrease. It can be generally seen that the ideal results show \(U\sim\) 0.8 and \(C\sim\) 0.7. Furthermore, If we choose the maximum value of \(C\times U\) marked by black dots in Figure 9a as the best system performance, the corresponding optimal \(R_{S}\) can be determined by Equations (21) and (22) and is shown in Figure 9b. It gives the changes of the optimal \(R_{S}\) depending on the Debye length \(\lambda_{D}\). We can find that the optimal \(R_{S}\) for a sensor node in a 3D WSN application decreases when the \(\lambda_{D}\) increases. It shows that, when a small \(\lambda_{D}\) is used as the parameter to calculate the network deployment, a relatively ideal system utilization \(U\) and coverage rate \(C\) can be obtained by using a larger \(R_{S}\). Figure 8: The changes of the final system coverage rate at 3000 time steps developed by our 3D algorithm as a function of node sensing radius \(R_{S}\), corresponding to different values of \(\lambda_{D}\).
###### Abstract  
Background: Little is known regarding the association between physical fitness and anthropometric parameters in HIV-diagnosed children and adolescents. Therefore, this study aimed to investigate the association between anthropometric parameters and physical fitness in this population. Methods: A cross-sectional study was conducted with HIV-diagnosed children and adolescents (aged 5-15 years). Body composition was assessed by anthropometric measurements and dual-energy X-ray absorptiometry, cardiorespiratory fitness by peak oxygen consumption (VO2 peak), muscle strength/endurance by handgrip strength, standing broad jump, abdominal and modified push-up tests, and flexibility using the sit-to reach test. Linear regression analyses (simple and multiple) were applied to investigate the association between anthropometric parameters and physical fitness. Results: In total, 86 children and adolescents (mean age: 11.44 \(\pm\) 2.20 years) participated in the study. A significant association was observed between anthropometric parameters, whereby the sum of four skinfolds could explain 69% of the fat mass percentage and 30% of VO2 peak; the sum of two bone diameters could explain 70% of fat-free mass, 55% of bone mineral content, and 43% of bone mineral density; calf skinfold and subscapular skinfold tests could explain the distance of standing broad jump, and the number of modified push-ups explained 16% of the standing broad jump and 19% of the modified push-up test results. Conclusions: Adding the measurements of four skinfolds and two bone diameters to a follow-up routine can provide relevant information regarding fat accumulation, bone development, cardiorespiratory fitness, and muscle strength/endurance status in HIV-diagnosed children and adolescents, supporting decision-making and measures for the adequate development of this population.  
bone density; child; pediatric; youth Article
# A Dynamic Route-Planning System Based on Industry 4.0 Technology
###### Abstract  
Due to the availability of Industry 4.0 technology, the application of big data analytics to automated systems is possible. The distribution of products between warehouses or within a warehouse is an area that can benefit from automation based on Industry 4.0 technology. In this paper, the focus was on developing a dynamic route-planning system for automated guided vehicles within a warehouse. A dynamic routing problem with real-time obstacles was considered in this research. A key problem in this research area is the lack of a real-time route-planning algorithm that is suitable for the implementation on automated guided vehicles with limited computing resources. An optimization model, as well as machine learning methodologies for determining an operational route for the problem, is proposed. An internal layout of the warehouse of a large consumer product distributor was used to test the performance of the methodologies. A simulation environment based on Gazebo was developed and used for testing the implementation of the route-planning system. Computational results show that the proposed machine learning methodologies were able to generate routes with testing accuracy of up to 98% for a practical internal layout of a warehouse with 18 storage racks and 67 path segments. Managerial insights into how the machine learning configuration affects the prediction accuracy are also provided.  
artificial neural network; route-planning system; machine learning; A-star; shortest-path model +
Footnote †: journal:  
_Article_  
A Dynamic Route-Planning System Based on Industry 4.0 Technology  
D. Nguyen Duc  
School of Management Technology, Sirindhorn International Institute of Technology, Thammast University, Pathum Thani 12120, Thailand; [EMAIL_ADDRESS] (D.N.D.); [EMAIL_ADDRESS] (T.T.H.)
# Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms
## 2 Materials and Methods
### GAs
#### 2.2.3 Parent Selection  
The purpose of selecting parent candidates is to obtain better individuals or solution candidates than the previous generation. This study uses the tournament selection method to perform this selection. The selected parent candidate is the fittest individual among \(k\) randomly chosen individuals in the population, with \(k\) representing a predetermined hyperparameter in the tournament selection method. Individuals with the smallest error values will have the highest probability to be selected.
# Sharp Switching Characteristics of Single Electron Transistor with Discretized Charge Input
## 1 Introduction  
Single-electron transistors (SETs) have unique characteristics based on a Coulomb blockade (CB) effect. In the past few decades, various applications were proposed such as an elemental charge sensor [1], low-power consumption analog and digital circuits [2; 3; 4; 5; 6], dc current standards by combination of superconductive island or electrodes [7; 8] and a composite device utilizing ferromagnetic materials [9; 10; 11]. Since the shape of CB region of SETs is a rhombus with respect to axes of bias and gate voltages, threshold voltage is unwillingly changed by the bias voltage. This property is unfavourable from the viewpoint of the current switch application or applications for digital circuits. We proposed a single electron device, which we called input discretizer (ID), generates a discretized charge output from a continuous input voltage signal, and demonstrated that the ID improves the performance of capacitively-connected single electron devices [12]. In terms of providing discontinuous thresholds to the CB, the ID enhances their gain characteristics [13; 14]. Nevertheless, those studies were based on analytically or numerically calculated results. Here we report an experimental result of the SET with the ID comprising aluminium by shadow evaporation technique. The device exhibited steep responses to the input signal compared to an SET without the ID.
## 2 Procedures in Deriving Rainfall Maps from Attenuation
### Rainfall Mapping  
Although line-integrated rainfall observations are certainly valuable for many hydrometeorological studies and applications, the need to provide the spatial distribution of rainfall, i.e., generate rainfall maps from the estimations of CMLs, is more necessary. The simplest approach is to treat the CMLs as virtual rain gauges located at their midpoint location [45]. However, such an assumption is not reasonable given the heterogeneity of rainfall over the path of long-distance CMLs. Therefore, [PERSON] et al. [85] proposed an iterative algorithm to represent CML measurements using multiple data points along the path. Several studies [86; 87] compared the two strategies and found that the dominance of the latter is positively correlated with the spatial variability of rainfall. Then, spatial interpolation techniques such as inverse distance weighting (IDW) and ordinary kriging (OK) can be employed to reconstruct sparse rainfall estimates to continuous spatial distribution. [PERSON] et al. [88] quantitatively analyzed the performance of different interpolation  
Figure 4: Scatter plots of specific rain-induced attenuation and rain rates at different frequencies. It is worth noting that the rain rates are obtained from an OTT Parsivel dishormeter deployed in Nanjing, whereas the specific rain-induced attenuations are simulated using the T-matrix algorithm based on the DSD data recorded by the dishormeter.  
algorithms. The results showed the OK algorithm, which uses more priori information, performs slightly better than IDW. More sophisticated algorithms that consider rainfall inhomogeneity over the paths of CMLs include tomographic analysis techniques [32; 89; 90], the block kriging type approach [91], stochastic reconstruction algorithms [92; 93] and the compressed sensing theory [94; 95; 96; 97]. Furthermore, the literature [98] reported that CMLs can provide information on the spatial structure of rainfall fields and that the error in the estimation of the spatial autocorrelation function is only 5%.
# State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks
## 3 Deep Learning Theoretical Background
### MLP Network  
MLP is a class of FFNN. The term MLP or \"multilayer perceptron\", refers to a number of perceptrons that are organized into several layers [24]. A single perceptron can calculate one output from multiple inputs by mathematically making a linear combination [25, 27]. A perceptron is illustrated in Figure 1. In the figure, \(x\) is denoted as the input signal vector, \(w\) as the weight vector, \(b\) as the bias, and \(y\) as the output. The output \(y\) can be obtained by putting the weighted sum of \(x\), \(w\), and \(b\) into the activation function \(\varphi\)[23, 25]. The equation is written as  
\[y=\varphi\left(\sum_{i=1}^{n}w_{i}x_{i}+b\right). \tag{6}\]  
Figure 1: A perceptron.  
Several perceptron can be used as the building blocks for a more structurally complex MLP since one perceptron has a limited ability to map the output. Conventionally, an MLP is composed of at least three layers, which are the input layer, the hidden layer, and the output layer. Each layer can have several neurons or nodes. Consider an MLP with one hidden layer illustrated in Figure 2. The calculation for each layer is done in a similar manner to Equation (6), and it can be written as  
\[z=\varphi\left(\sum_{j=1}^{n}w_{\
eg j}\left(\sum_{i=1}^{m}w_{j}x_{i}+b_{j} \right)+b_{\
eg}\right), \tag{7}\]  
where \(z\) is the output vector; \(x\) is the input vector; \(w_{j_{i}}\) and \(b_{j}\) are the weight matrix and the bias vector of the input layer, respectively; \(w_{\
eg j}\) and \(b_{\
eg}\) are the weight matrix and the bias vector of the hidden second layer, respectively; and \(\varphi\) is the activation function. The nonlinear activation function can be either sigmoid, tanh, rectified linear unit (ReLU), etc [28].  
The MLP network can be trained using a supervised learning technique called the backpropagation algorithm [25; 27; 29]. The backpropagation algorithm is composed of two steps--the forward pass and the backward pass. The forward pass would calculate the predicted output with the corresponding input using Equation (7). At the end of the forward step, an error would be obtained by calculating the predicted output and the real output difference. The error would be evaluated with a cost function. Next, in the backward pass, the partial derivative of the cost function with respect to different parameters would be calculated and propagated back through the network with chain rule. Based on the result, the model would adjust the parameters for the highest accuracy. The weight of the network would be adjusted using gradient-based optimization. This whole process would be done iteratively until the weight values converge or the weight value that would minimize the error is found [25].
###### Abstract  
Background: The aim of this study was to evaluate the macro- and micro-structure and the chemical composition of the surface of 5 different commercially available dental implants. Roughness values were also calculated. Materials and Methods: 1 zirconia implant (NobelPearl of Nobel Biocare) and 4 titanium implants, Syra (Sweden&Martina), Prama (Sweden&Martina), T3 (Biomet 3i), and Shard (Mech&Human), were analyzed through SEM-EDX analysis and quantitative evaluation of surface roughness (1 sample), and XPS chemical analysis (1 sample). Surface roughness was quantitatively assessed using the stereo-SEM method (SSEM). The following area roughness parameters were calculated, according to ISO25178: Sa, Sz, and Sdr. Results: From the SEM observations, all the implants analyzed presented modern well-developed micro-structures as the result of the specific process of double acid etching alone or combined with other additional treatments. Roughness values were generally greater at the level of the implant body and lower at the collar. The chemical characterization of the implant surfaces exhibited excellent results for all of the implants and indicated good care in the production processes. Conclusions: All the samples were well-conceived in terms of topography and surface roughness, and clean in terms of chemical residues.  
Keywords: dental implants; titanium; zirconia; XPS; roughness +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
employing FFT can serve as a valuable approach to examining these characteristics through the use of time-domain simulations, provided these operations are computed over a single operating period in a steady state. **The proposed methodology** entails the utilization of automatic differentiation (AD), alongside mathematical properties to compute the derivatives, an exact method of calculation, applicable to both time-domain simulations and FFT computation, necessitating access to the implementation code of the computing program. Indeed, the black-box finite differences solution has been largely dismissed within the realm of derivative-free optimization literature, primarily due to its prohibitive costs in terms of function evaluations and its impracticality when the objective function is subject to noise; nevertheless, it remains a topic of ongoing research [15]. The initial iteration of this methodology has been elaborated on in the publication COMPEL [16]. It is noteworthy that in [16], the intricacies of the frequency-domain features were not thoroughly explored, and the application showcased, involving a three-phase diode rectifier, was of lesser complexity compared to the aircraft power channel that will be discussed in this paper. In light of this introduction, the structure of the paper is delineated as follows: Section 2 presents the methodology, Section 3 discusses the implementation within an open-source software framework, and Section 4 explores its application in regard to the optimal design of an aircraft's electrical power channel. Given that power electronics offers promising applications involving hybrid ODEs, wherein the ODEs vary according to specific events, such an application is introduced and employed to exemplify the more general approach proposed herein.