esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Temperature Field Prediction Model Application
### Acquiring Temperature Data  
The prediction of the temperature field of the motorized spindle depends on the measured temperature at critical positions. The experimental system used is shown in Figure 5, and included an oil-gas lubrication system, a cooling system, a temperature test system, a loading system, and a speed control system. Air pressure, air temperature, and intervals of fuel supply could be monitored in the oil-gas lubrication system. The flow of cooling water and inlet temperature of the cooling system could also be controlled and monitored, but only the outlet temperature of the cooling system could. Consequently, the load and speed of the motorized spindle were also controllable and testable. The  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Rotor external diameter (mm) & 79.4 \\ Rotor iron core length (mm) & 112 \\ Stator internal diameter (mm) & 80 \\ Stator iron core length (mm) & 110 \\ Shaft maximum diameter (mm) & 56 \\ Shaft length (mm) & 170 \\ Motorized spindle external diameter (mm) & 266 \\ Bearing model & 7008C \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main parameters of the motorized spindle.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Material** & **Density (g/cm\({}^{3}\))** & \
\begin{tabular}{c} **Thermal Conductivity** \\ **(W/(m\({}^{\circ}\)C))** \\ \end{tabular} &
\begin{tabular}{c} **Specific Heat Capacity** \\ **(J/(kg.\({}^{\circ}\)C))** \\ \end{tabular} \\ \hline Stator windings & Copper & 8.9 & 400.0 & 386.0 \\ Stator core & Silicon steel & 7.9 & 35.0 & 535.0 \\ Water jacket & 40 Cr & 7.9 & 60.5 & 434.0 \\ Rotor bar & Cast aluminum & 2.8 & – & 875.0 \\ Shaft & 40 Cr & 7.9 & 60.5 & 434.0 \\ Coolant & Water & 1.0 & 0.6 & 4200.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of materials of the model.  
Figure 4: Finite element model of the 170 MD30 motorized spindle. (**a**) Geometrical model, (**b**) finite element analysis meshing model.  
temperature test system consisted of temperature sensors placed on the surface of the motorized spindle. The surface temperature could be measured under different conditions involving the load, temperature of the cooling water, its flow rate, and the rate of flow of the lubricating oil using this test system.
# A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications
## 3 Background and Preliminaries
### Game Theory  
Game theory considers the predictive and actual behavior of individuals in a game and studies their optimization strategies [19]. Different interactions on the surface may exhibit similar incentive structures, so they are a special case of the same game [20]. As in the choice of base station, we need to consider the actual and predictive behavior of the vehicles and show similar incentive structures through different interactions between macrocell and femtocell base stations [21; 22].
## 3 Results
### Reconstruction with Seasonal Segment Time-Series Fitting
#### 3.2.2 Reconstruction with Smoothing Process by Seasonal Segment Fitting  
In this section, the time series after gap-filled and SG filter smoothing processes was defined to be the original time series, and the processed time series was the fitted time series by the four reconstruction models. The \(R^{2}\) and RMSE values between the original and processed time series are calculated in Table 3.  
For the NDVI data, the CCTM model has a lower RMSE compared to other models (Figure 8). More than 95% of the time series points of all the land cover types except EBF  
have fitting \(R^{2}\)-values >0.995, and <5% of all the time series points except shrubland type have a fitting RMSE >0.02 (Figure 9). However, for Exp and Harmonic models, more than 50% of time series points of all the land cover types have a fitting RMSE higher than 0.02. The reconstruction effect of the Ln model is not as good as the CCTM model (especially for the EBF land cover type), but it is significantly better than other models. The CCTM model generally shows considerably better performance than other models for the NDVI time series reconstruction.  
For the GPP dataset (Table 3), more than 95% of the time series points of land cover types except EBF have fitting \(R^{2}\)-values >0.98, and more than 95% of time series points of all the land cover types have fitting RMSEs <0.02 g C m\({}^{-2}\) using the CCTM model. The CCTM model is significantly better than other models, especially for the GPP time series of shrubland, savanna, grassland, cropland, and EBF. For other forest types, except for EBE, there are smaller differences among the four models. Overall, the CCTM model performs well for the GPP time series reconstruction for all land cover types.  
Figure 8: Reconstruction RMSE distribution of NDVI time series data with smoothing process. The frequency is the percentage of time series points in the certain RMSE range, and different landcover types may have different RMSE categories.  
Figure 9: \(R^{2}\) probability distribution of reconstructed NDVI time-series data after smoothing for different landcover types. The frequency is the number of time series points in a certain RMSE range, and different landcover types may have different RMSE categories.  
For the LAI dataset (Table 3), the harmonic model shows the best reconstruction performance, with the CCTM model being slightly inferior. Except for the EBF land cover type, nearly 90% of time series points have fitting \(R^{2}\)-values >0.9, which indicates that the CCTM model also has a great trend-preserving ability. For different land cover types, the reconstruction RMSEs are relatively inconsistent, and the forest types have larger RMSEs than non-forest types such as savanna, shrubland, and cropland. Due to the relatively low RMSE and high \(R^{2}\)-values, the CCTM model is suitable for LAI time series reconstruction.  
For the reflectance dataset, the reconstruction result after smoothing achieved high \(R^{2}\)-values (nearly 100% of time series points have \(R^{2}\)-values >0.9 by any of the models)(Figure 1
# Bias Effects on g- and s-Factors in Westcott Convention
###### Abstract  
For accuracy improvement of neutron activation analysis and neutron capture cross sections, bias effects are investigated on g- and s-factors in the Westcott convention. As origins of biases, a joining function shape, neutron temperature, and sample temperature have been investigated. Biases are quantitatively deduced for two 1/v isotopes (\({}^{197}\)Au, \({}^{59}\)Co) and six non-1/v isotopes (\({}^{241}\)Am, \({}^{151}\)Eu, \({}^{103}\)Rh, \({}^{115}\)In, \({}^{127}\)Hf, \({}^{226}\)Ra). The s-factor calculated with a joining function deduced recently by a detailed Monte Carlo simulation is compared to s-factors calculated with traditional joining functions by Westcott. The results show the bias induced by the sample temperature is small, in the order of 0.1% for the g-factor and in the order of 1% for the s-factor. On the other hand, the bias size induced by a joining function shape for the s-factor depends significantly on both isotopes and neutron temperature. As a result, the reaction rates are also affected significantly. The bias size for the reaction rate is given in the case of an epithermal neutron index r = 0.1, for the eight isotopes.  
neutron activation analysis; neutron capture cross section; bias effect; neutron resonance; joining function; Westcott convention; Doppler broadening; sample temperature; neutron temperature +
Footnote †: journal: Physics Letters B  
[PERSON]  
Nuclear Human Resource Development Center, Japan Atomic Energy Agency, Tokai-mura, Ibaraki 319-1195, Japan; [EMAIL_ADDRESS]
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 5 Guide for Choosing a Correct Accelerometer
### Standards Applicable in the Field of Rail Vehicle Monitoring  
The standards reviewed in this subchapter apply to devices that monitor the rail vehicle's safety and the passenger's comfort, including accelerometers.  
UIC--International Union of Railways is using the online portal to publish standards, regulations, and papers regarding all types of tests for safety and comfort using accelerometers. A dedicated study can be found at [75].  
IEC 60077-2, Railway applications--Electric equipment for rolling stock--Part 2: Electrotechnical components--General rules [76] specifies the general conditions and requirements for all electric equipment installed in power circuits, auxiliary circuits, control and indicating circuits, etc., on railway rolling stocks.  
EN 50155:2021 Railway applications--Rolling stock--Electronic equipment [77] is a standard that impacts the design, manufacturing, and maintenance of electronic equipment for railway vehicles. It outlines the requirements for electronic systems to ensure they can withstand the harsh conditions of railway environments, including temperature variations, humidity, shock, and vibration.  
EN 12299:2024 is a standard that defines the ride comfort for passengers of railway vehicles [78]. This document provides methods for quantifying passenger ride comfort in a rail vehicle. The methods aim to quantify the effects of vehicle body motions on ride comfort and to make the assessment of passenger comfort predictable, repeatable, objective, and meaningful.  
IEC 62974-1:2024 Monitoring and measuring systems used for data collection, aggregation and analysis--Part 1: Device requirements [79] provides the requirements for the design and construction of data used in electronic devices, including the \"management of digital and/or analogue input(s) or output(s)\" and \"Communication connectivity features\".  
IEC 60571:2012 Railway applications--Electronic equipment used on rolling stock [80], covers the conditions of the operation, design, construction, and testing of electronic equipment, as well as basic hardware and software requirements for all electronic equipment installed on rail vehicles related to control, regulation, protection, and supply.  
ISO 16063-32:2016 Methods for the calibration of vibration and shock transducers Part 32: Resonance testing--Testing the frequency and the phase response of accelerometers by means of shock excitation [81] lays down detailed specifications for instruments and procedures for testing the frequency and phase response of accelerometers using shock excitation. It applies to accelerometers of the piezoelectric, piezoresistive, and variable capacitance types, with a damping ratio less than critical and in the frequency range up to 150 kHz.  
More rail vehicle equipment standards are available for online purchase.
# A Single Image Enhancement Technique Using Dark Channel Prior
## 2 Dark Channel Prior Algorithm
### Dark Channel Prior Theory  
The phenomenon of fog usually appears in the scenes of outdoor and urban, so He [11] selected foggy outdoor scenes and urban scenes from the database. They randomly selected 5000 images and manually removed the sky area. In addition, it should be noted that He only paid attention to the fogging images in daytime scenes. These images were resized to 500 \(\times\) 500 pixels, then they used a 15 \(\times\) 15 window to calculate the dark channel values.  
For the images excluding sky areas, the principle of dark channel prior is that at least one of the intensity values of the three channels of R, G, and B in the local area is very small, or even close to zero. For an input image \(J\), the mathematical description of the dark channel prior \(J^{dark}\) is as follows:  
\[J^{dark}(x)=\min_{c\in\{r,g\}}(\min_{y\in\omega(x)}(J^{C}(y))) \tag{6}\]  
where \(J^{C}\) represents the intensity values of the three color channels R, G, and B at location \(y\), and \(x\) is the center of the window \(\omega(x)\).  
In Equation (6), after two minimization operations, the dark channel value is obtained. The first step is to store the minimum value of the three color channels R, G, and B into a gray-scale image, which has the same size as the original image. In the second step, the window \(\omega(x)\) is used to perform the minimum filtering operation on the gray-scale image obtained in the first step. The radius of the filter is determined by the window size.  
There are three factors that result in a lower dark channel value: (1) The existence of shadows, such as the car shadows, buildings, the window of urban images, the shadow of large trees and leaves, and so on. (2) The color of objects, such as green grasses, forests, leaves, red flowers, blue water, etc. Due to the lack of other colors, i.e., the color is concentrated in a certain channel, which causes the values of other channels to be relatively low, a dark channel is generated. (3) Black objects, such as black stones, tree trunks, etc. In the real world, most of the objects have color information or have shadow areas, thereby ensuring the universality of the dark channel prior.
## 2 Literature Overview
### Maintenance Maturity Models
However, little of the published literature has reported on the development and application of maturity models in asset maintenance [11; 12; 13]. Many Capability Maturity Models (CMM) for asset maintenance are reported in unpublished literature sources, developed largely by consultants or individual companies as in-house maturity assessment tools. These models are mainly proprietary and contain limited information, especially regarding their development and use. Whilst several CMMs have been developed in the past few decades, the applicability of such models in asset maintenance is limited [10]. Moreover, all these models reviewed in the study of [PERSON] and [PERSON] [11] allow for assessing the maturity level, but do not indicate or assist in the identification and definition of the actions/activities that must be pursued to reach the highest level. Applying these models to different organisations may not be straightforward due to differences in several aspects, including the organisational structure and business context [12]. Moreover, in the maintenance maturity models available in the literature [11; 12; 13], the information maintenance management domain, if considered, often represents one of the measurement classes and is, therefore, not well detailed. Methods for Maturity Assessment Development  
Whilst maturity models are common and broad in application, few guidelines, procedures and methods are presented on how to develop a maturity model that is theoretically sound, rigorously tested and widely accepted for small and medium enterprises. The main models available in the literature [24; 28; 29; 30; 31; 32] are reported in Table 1. These studies mainly proposed a method to develop a generic maturity model without considering the specific key elements related to the SME context. The models' features were analysed, for the purpose of this study, to apply the best practices in this field for the development of a model for the evaluation of information practices on maintenance management in the SME context, and to provide a more robust and rigorous model from a methodological point of view. The general activities that these methods consider, at the first stage, are the problem identification, the identification of participants, and the scoping and planning of the goals. At the second stage, the activities establish the design strategy and architecture of the model. That is, they define the levels of capability and dimensions (some authors name these processes or focus areas) and establish the best practices expected for each dimension according to its capability level. At the third stage, an instrument to measure the maturity of the object of interest is built, and the procedures for its deployment and management are defined. In the last stage of development, the maturity model and assessment tool are validated. If the model is accepted, it enters a maintenance stage, where changes are managed and, if necessary, the model or instrument is updated.
# Performance of BDS-3: Measurement Quality Analysis, Precise Orbit and Clock Determination
## 3 Results  
In this section, we present the results of measurement quality analysis, including carrier-to-noise density ratio (C/N0), multipath combination, orbit accuracy validation, and clock performance assessment for four BDS-3 satellites. For comparison, some results of BDS-2 satellites are also shown.
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 4 Model Decomposition and Solution
### The Voltage Stability Sub-Model  
This sub-model is established to deal with voltage stability constraints for heavy load center. To improve solving efficiency, this paper adopts the following method: Firstly, according to \(\eta_{\min}\) in Formula (15), we get ultimate load \(P_{1}\)' by Equation (41); then real load of buses in heavy load center and all generators' power output except heavy load center could be increased separately; finally, power flow calculating is processed to verify whether it is convergent. If convergent, it means the UC optimization result satisfies the voltage stability constraint; if not, the new Benders cut should be generated and added to the UC master model, which would be re-solved.  
\[P_{l}^{*}=\left(1+\eta_{\min}\right)\times P_{l0} \tag{41}\]  
\[Min\,w^{t}=\sum\left(MP_{1}+MP_{2}\right) \tag{42}\]  
\[\left[\begin{array}{c}\Delta P_{G}\\ \Delta Q_{G}\end{array}\right]+\left[\begin{array}{cc}H&N\\ J&L\end{array}\right]\left[\begin{array}{c}\Delta\theta\\ \Delta V\end{array}\right]+\left[\begin{array}{c}\Delta P_{dc}\\ \Delta Q_{dc}\end{array}\right]+\left[\begin{array}{c}MP_{1}\\ MQ_{1}\end{array}\right]-\left[\begin{array}{c}MP_{2}\\ MQ_{2}\end{array}\right]=\left[\begin{array}{c}dP\\ dQ\end{array}\right] \tag{43}\]  
\[\left[\begin{array}{c}\Delta P_{dc}\\ \Delta Q_{dc}\end{array}\right]+\left[\begin{array}{cc}O&D\\ E&F\end{array}\right]\left[\begin{array}{c}\Delta X_{ac}\\ \Delta X_{dc}\end{array}\right]+\left[\begin{array}{c}MP_{1}D\\ MQ_{1}\end{array}\right]-\left[\begin{array}{c}MPD_{2}\\ MQ_{2}\end{array}\right]=\left[\begin{array}{c}dP_{dc}\\ dQ_{dc}\end{array}\right] \tag{44}\]  
\[\Delta P_{G}=0\quad\lambda \tag{45}\]  
\[\Delta Q_{\min}\leq\Delta Q\leq\Delta Q_{\max}\quad\underline{\psi}, \overline{\psi} \tag{46}\]  
\[\Delta X_{ac,\min}\leq\Delta X_{ac}\leq\Delta X_{ac,\max} \tag{47}\]  
\[\Delta X_{dc,\min}\leq\Delta X_{dc}\leq\Delta X_{dc,\max} \tag{48}\]  
Benders cut constraints will be generated as shown in Formula (37) when \(w^{t}\) is not equal to zero after several calculations.
# RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking
## 3 Methodology  
Figure 3 shows the overall framework of the proposed method, including the rotation and translation branches. The rotation branch calculates the rotation angle \(\alpha(t)\) of the object to provide an accurate orientation representation. Moreover, the angle is adopted for the translation branch to yield a stable response map. The translation branch synergizes the appearance and motion information contained in adjacent frames for positioning to prevent tracking drift. These two complementary branches are unified for the SOT in SVs.
# The Mechanical Properties of Reinforced Concrete Columns with Longitudinal Pre-Embedded Holes
## 2 Finite Element Model Building  
### Model Establishment  
The reinforced concrete column studied in this paper was extracted from the ground-floor column of a four-story frame structure. Its design parameters strictly comply with current Chinese codes and specifications, and such configurations are widely adopted in multi-story frame structures across China, effectively representing the actual mechanical behavior of frame columns in practical engineering applications, thus possessing typical engineering representativeness. The column has a height of 4200 mm and a cross-sectional dimension of 500 mm \(\times\) 500 mm. The concrete used is of grade C30, with a concrete cover thickness of 25 mm. The mechanical properties of the concrete are presented in Table 1.  
The longitudinal reinforcement consists of HRB400-grade steel bars with diameters of 25 mm and 22 mm, while the transverse reinforcement is made of HPB300-grade steel bars with a diameter of 8 mm, spaced at 150 mm. The stirrup spacing is reduced to 100 mm in the confined regions at both ends of the column. The mechanical properties of the steel are provided in Table 2. The specimen's dimensions and reinforcement details are shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Concrete Strength** & \begin{tabular}{c} **Axial Compressive** \\ **Strength** \\ **\(f_{\text{c}}\) (MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Axial Tensile** \\ **Strength** \\ **\(f_{\text{t}}\) (MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Elastic Modulus** \\ **(MPa)** \\ \end{tabular} \\ \hline C25 & 16.7 & 1.78 & 2.80 \(\times\) 10\({}^{4}\) \\ C30 & 20.1 & 2.01 & 3.00 \(\times\) 10\({}^{4}\) \\ C35 & 23.4 & 2.20 & 3.15 \(\times\) 10\({}^{4}\) \\ C40 & 26.8 & 2.39 & 3.25 \(\times\) 10\({}^{4}\) \\ C50 & 32.4 & 2.64 & 3.45 \(\times\) 10\({}^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic mechanical properties of concrete.  
Figure 1: Specimen dimensions and reinforcement details.
## 4 Results and Discussion
### Load Case  
In order to compare the motion responses of DeepCwind semi-submersible FOWT achieved by our model with the results of UOU in-house codes, seven load cases were conducted including different model settings, i.e., potential flow theory, quadratic damping, mooring model, and wave heading angles. Table 3 summarizes all load cases that would be discussed in the following sections. These simulations were executed in the absence of wind force, and with the rotor locked.  
The numerical data compared in this study were obtained by employing the code FAST (Fatigue, Aerodynamics, Structures, and Turbulence) [6; 19; 20] developed by NREL and the UOU in-house codes [16; 21]. The hydrodynamic coefficients, including added mass, radiation damping and wave exciting forces, of the DeepCwind semi-submersible FOWT are obtained by using a diffraction/radiation solver in the frequency domain. Subsequently, the obtained hydrodynamic and wave loading data from the UOU in-house codes are fed into FAST as a platform loading output. Both of the hydrostatic and quadratic damping forces can be calculated in the UOU in-house codes. In addition, UOU's mooring quasi-static code uses the 4 th order Runge-Kutta method to solve quasi-static equations of two dimensions of a mooring line.  
In our simulation, the linearized potential-flow hydrodynamic radiation and diffraction problems in the frequency domain were first solved by using the three-dimensional panel method. The hydrodynamic coefficients and wave loading data were then transformed to time domain forms by the Cummins time domain equation as a system loading input. In addition, the quadratic transfer function (QTF) matrices with different frequencies and directions are deduced based on the near field integration over the mean wetted hull surface. In order to discuss the influences of mooring forces on the FOWT system, both of the quasi-static mooring model and the dynamic mooring model were considered in this study.