esa-sceva/satcom-chunk-collection huggingface dataset:
# Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images
## 2 Fundamentals of the Classification Algorithms Applied
### Random Forest  
It is an ensemble model of several decision trees randomly organized and individually trained. Each tree makes a classification decision where the class with the maximum number of votes is determined for the input data [8]. Each tree is independently organized so successive trees are independent from the previous ones [18]. Random forest (RF) has shown good performance in linear and nonlinear models known for balancing bias and variance [7]. This algorithm is widely used on satellite images data due to the high accuracy of its classifications [22]. One of its outstanding features is that it can successfully handle high data dimensionality and multicolinearity since both are insensitive to overfitting [23] and training algorithm is fast [24]. Moreover, RF has high accuracy for datasets external to those considered during training, which shows that spatial autocorrelation has a low impact on the prediction performance [25].
# Physicochemical Properties and Volatile Profile of Chito: A Traditional Dry-Cured Goat Meat Product
## 3 Results and Discussion
### Texture Evaluation
[5] found that maturation significantly affects meat texture through proteolysis and other enzymatic processes. However, chito has a short processing time and, therefore, it does not allow for an intense action of muscle proteases on myofibrillar protein. Furthermore, their salt content could decrease their proteolytic activity due to chloride sodium concentrations above 8%, which could have completely inhibited muscle protease activity [20]. It should be noted that the hardness of the pressed samples is not a problem for the consumer, since chito is typically consumed in broth, where heat and hydration soft \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Processing Year**} & \multicolumn{2}{c}{**Treatments**} & \multicolumn{1}{c}{**SEM**} & \multicolumn{1}{c}{**P-Level**} \\ \hline  & & **NP** & **P** & \\ \hline Texture profile analysis & & & & \\ Hardness (N) & 2021 & 55.39 \({}^{\text{bB}}\) & 223.67 \({}^{\text{aB}}\) & 55.19 & * \\  & 2022 & 154.4 \({}^{\text{bA}}\) & 574.01 \({}^{\text{aA}}\) & 84.31 & *** \\  & 2023 & 75.75 \({}^{\text{bB}}\) & 328.01 \({}^{\text{aB}}\) & 77.86 & * \\ SEM & & 29.55 & 53.55 & & \\ \(P\)-level & & ** & *** & & \\ Springiness & 2021 & 0.54 \({}^{\text{aA}}\) & 0.66 \({}^{\text{aA}}\) & 2.91 & NS \\  & 2022 & 0.59 \({}^{\text{aA}}\) & 0.69 \({}^{\text{aA}}\) & 2.10 & NS \\  & 2023 & 0.69 \({}^{\text{aA}}\) & 0.56 \({}^{\text{aA}}\) & 3.28 & NS \\ SEM & & 3.21 & 3.09 & & \\ \(P\)-level & & NS & NS & & \\ Cohesiveness & 2021 & 0.56 \({}^{\text{bA}}\) & 0.63 \({}^{\text{aA}}\) & 3.52 & NS \\  & 2022 & 0.59 \({}^{\text{aA}}\) & 0.69 \({}^{\text{aA}}\) & 4.27 & NS \\  & 2023 & 0.72 \({}^{\text{aA}}\) & 0.57 \({}^{\text{aA}}\) & 5.08 & NS \\ SEM & & 3.35 & 3.38 & & \\ \(P\)-level & & NS & NS & & \\ Resilience & 2021 & 0.19 \({}^{\text{aA}}\) & 0.20 \({}^{\text{aA}}\) & 1.24 & NS \\  & 2022 & 0.09 \({}^{\text{aB}}\) & 0.06 \({}^{\text{aB}}\) & 2.19 & NS \\  & 2023 & 0.09 \({}^{\text{aB}}\) & 0.05 \({}^{\text{aB}}\) & 2.64 & NS \\ SEM & & 1.74 & 2.07 & & \\ \(P\)-level & & * & ** & & \\ \hline \hline \end{tabular}  
* NP: non-pressed (immediate consumption); P: pressed (for sale). SEM: standard error of means. \({}^{\text{aB}}\): Means values within the same row with different letters are significantly different (\(P<0.05\)). \({}^{\text{AB}}\): Means values in the same column with different letters are significantly different (\(P<0.05\)). \(P\)-level, Significance: NS: not significant; \({}^{\star}\) (\(P\leq 0.05\)); \({}^{\star\star}\) (\(P\leq 0.01\)); \({}^{\star\star}\) (\(P\leq 0.001\)).  
\end{table} 
Table 3: Mean values of instrumental measurement texture of chito. Finally, between the different years, NP showed the highest values (\(P<0.05\)) in hardness for 2022, whereas P showed a similar behavior for 2022 and 2023. For resilience, 2021 was the year that showed the highest resilience in NP and P compared with the other years, indicating that both samples had the ability to recover from deformation.
## 2 Materials and Methods
### Physicochemical Characterization
respectively, to the stretching vibrations of water molecules, while those at 1190-1082 cm\({}^{-1}\) and then at 1010 and 789 cm\({}^{-1}\) expressed, respectively, the vibrations of the Si-O-Si and Si-O-Al groups of the network. The bands at 921 and 789 cm\({}^{-1}\) indicated the presence of the stretching vibration of Al-OH where Al is in coordination with VI. The band at 542 cm\({}^{-1}\) indicated the vibrations of the Si-O-Si and Si-O-Al groups of the network. Thermal treatment of kaolin generally causes its transformation to metakaolin. The FT-IR spectra of metakaolin confirmed the occurrence of kaolinite dihydroxylation during the heat treatment of kaolin, whereby no fingerprint bands at 3695-3621 cm\({}^{-1}\) were observed on the spectra; contrarily, the metakaolin spectra were mainly characterized by the 1065 cm\({}^{-1}\) band which corresponded to the stretching vibration of the Si-O-Si and Si-O-Al groups of the metakaolin network [40]. The thermogravimetric analysis (TGA) was conducted to observe the mass loss during heating. The heating rate was maintained at 2 \({}^{\circ}\)C per minute using TGA/DSC 2 HT Mettler Toledo (Columbus, OH, USA), and the testing was done up to the temperature of 1200 \({}^{\circ}\)C.  
The morphology of the specimens was obtained from various locations on the assintered specimens using a Vega Tescan scanning electron microscope (SEM) (Tescan, Brno, Czech Republic) equipped with energy-dispersive X-ray spectroscopy (EDS). The analysis of the tensile strength was performed on the sintered specimens in the form of cylindrical pallets (15 mm in diameter and 5 mm in height) in accordance with ASTM C 496. The diametral tensile strength (DTS) values were measured on three specimens from each mix design input using a Shimadzu, AG-X Plus universal testing machine (Shimadzu, Kyoto, Japan). The crosshead speed was 0.600 mm/min. The DTS values were calculated using Equation (1):  
\[\sigma=\frac{2F}{\pi DT} \tag{1}\]  
where:  
\(\sigma\) = diametral tensile strength (DTS) [MPa],  
\(F\) = force causing the destruction of the specimen [N],  
\(D\) = diameter of the specimen [mm],  
\(T\) = thickness of the specimen [mm]. The mean value \(\pm\) standard deviation values of at least three independent specimens were used for obtaining all the results [44]. Figure 3: FT-IR spectra of kaolin and metakaolin.
## 2 Study Area and Data
### Data
#### 2.2.2 Site Validation Data  
To assess the accuracy of the generated all-weather surface temperature, field measurement data from the Heihe River Basin Integrated Remote Sensing Experiment (HiWATER) project [22; 23] in the Heihe River Basin were collected. These data can be downloaded from the National Tibetan Plateau Data Center ([[http://www.tpdc.ac.cn/zh-hans/data](http://www.tpdc.ac.cn/zh-hans/data)]([http://www.tpdc.ac.cn/zh-hans/data](http://www.tpdc.ac.cn/zh-hans/data)) (accessed on 23 March 2024)). The datasets include four-component radiation, surface radiative temperature, soil heat flux, and other observational data, which were collected from ground-based observation stations. Four-component radiation is measured by the CNR4 net radiometer, with a maximum nonlinear error of 1%. The in situ surface temperature, calculated from upward and downward longwave radiation based on four-component radiation, has been widely used to validate the accuracy of all-weather surface temperature.  
This study selected nine sites from different land cover types in the years 2017-2019, namely Aruo Station, Daman Station, Dashalong Station, Huangmo Station, Huazhaizi Station, Hunhelin Station, Sidaoqiao Station, Yakou Station, and Zhangye Station. Detailed information for these monitoring stations is provided in Table 2.  
The calculation of in situ surface temperature is based on the validation site datasets. The specific calculation process is based on the Stefan-Boltzmann law, and the formula is as follows:  
\[LST=\left[\frac{R_{u}-(1-\varepsilon_{b})R_{d}}{\sigma e_{b}}\right]^{1/4} \tag{1}\]  
In the above formula, \(R_{u}\) represents the upward longwave radiation; \(R_{d}\) represents the downward longwave radiation; and \(\varepsilon_{b}\) represents the surface broadband emissivity and its value ranges from 0.97 to 0.98. In this study, it is fixed at 0.97; \(\sigma\) is the Stefan-Boltzmann constant, with a value of 5.67 \(\times\) 10\({}^{-8}\) Wm\({}^{-2}\)K\({}^{-4}\).  
Combining the information from the validation site data, the final surface temperature calculation formula is as follows:  
\[LST=\left(\frac{ULR_{Cor}-0.03\cdot DRR_{Cor}}{0.97\cdot 5.67\cdot 10^{-8}} \right)^{\frac{1}{4}} \tag{2}\]  
In the above formula, \(ULR_{Cor}\) represents the upward longwave radiation, and \(DLR_{Cor}\) represents the downward longwave radiation.
# A Multi-Input Fusion Model for Privacy and Semantic Preservation in Facial Image Datasets
## 3 Preliminary  
### Affine Transformation  
In order to obtain a different face that still preserves original features, our proposed method utilizes affine transformation [61; 62], while maintaining both affine subspace dimensions and the ratios of the lengths of parallel line segments. As a result, units of parallel affine subspaces stay parallel after affine transformation. Affine transformation consists of translation, scaling, homothety, similarity, reflection, rotation, shear mapping, and combinations of them in any combination and sequence. Generally written in homogeneous coordinates, the affine transformation is as shown in Equation (1)  
\[\begin{pmatrix}a_{2}\\ a_{2}\end{pmatrix}=A\cdot\begin{pmatrix}a_{1}\\ a_{1}\end{pmatrix}+B \tag{1}\]  
where \((a_{1},b_{1})\) represents the value of pixel intensity in an original image and \((a_{2},b_{2})\) is the new coordinate after the transformation.  
\[A=\begin{pmatrix}cos\theta&-sin\theta\\ sin\theta&cos\theta\end{pmatrix},B=\begin{pmatrix}0\\ 0\end{pmatrix} \tag{2}\]  
Matrix \(A\) depicted in Equation (2) represents the pure rotation  
Now, given the old coordinates \((x,y)\), the new coordinates \((x^{\prime},y^{\prime})\) for the image are obtained considering the initial angle \(\theta\) and the angle of rotation \(\theta^{\prime}\). \(x\) and \(y\) are depicted in Equation (3)  
\[x=rcos\theta,y=rsin\theta \tag{3}\]  
As \(x^{\prime}=rcos(\theta+\theta^{\prime})\) and \(y^{\prime}=rsin(\theta+\theta^{\prime})\), \(x^{\prime}\) and \(y^{\prime}\) are presented in Equation (4)  
\[x^{\prime}=rcos\theta cos\theta^{\prime}-rsin\theta sin\theta^{\prime},y^{ \prime}=rsin\theta cos\theta^{\prime}-rcos\theta sin\theta^{\prime}. \tag{4}\]  
So, the conversion between \((x^{\prime},y^{\prime})\) and \((x,y)\) can be described by Equation (5).  
\[x^{\prime}=xcos\theta^{\prime}-ysin\theta^{\prime},y^{\prime}=ycos\theta^{ \prime}+xsin\theta^{\prime} \tag{5}\]
# Efficient Zero-Sequence Impedance Measurement in Autotransformers Using Low-Voltage Excitation
###### Abstract  
In accordance with the IEEE standard, the zero-sequence impedance test of transformers necessitates an open or short circuit test on the high-voltage side winding. Power source excitation is applied to the high-voltage test winding using a high-capacity test power supply. To circumvent the need for a large-capacity, high-voltage test power supply in field tests, this paper proposes a method for measuring zero-sequence impedance in autotransformers based on low-voltage excitation and disconnection testing. By applying a three-phase power supply to the lowest voltage side of the autotransformer and creating a disconnection condition, an unbalanced test scenario is established. Subsequently, the composite sequence network equivalent circuit for one-phase and two-phase disconnections between the high-voltage side and the low-voltage side of the transformer is formulated. Calculation formulas for the zero-sequence impedance of the autotransformer under various conditions are derived. The accuracy of the zero-sequence impedance is verified using MATLAB/Simulink simulation software 2018b, evaluating double-winding and three-winding auto-transformers in breaking tests under different connection modes. The error is found to be less than 3%. The results of this study affirm the high accuracy of the proposed method.  
autotransformer; zero-sequence impedance; on-site testing; low-voltage excitation; disconnection test +
Footnote †: journal: Applied Sciences
## 6 Conclusions  
The lifting lug, as one key structure of the geotechnical centrifuge, is the focus of this research. This paper presents an effective and systematic structure design and shape optimization for the lifting lug with multi topological configurations based on the weighted B-Spline FCM and GCMMA. It provides a complete shape optimization process for the lifting lug of the geotechnical centrifuge, which addresses the problem of the light-weight design of the lifting lug while satisfying the requirement of the geotechnical centrifuge strength. Four topological configurations, i.e., original single web, porous dual web, open deep groove dual web, and connected closed dual web, are investigated and optimized. Two main conclusions can be drawn.  
First, through shape optimization design based on the weighted B-Spline FCM and GCMMA, the final mass of the four configurations is greatly reduced compared with the initial configurations, where the greatest weight loss was observed in case 4 is 10.6%. This indicates that the weighted B-Spline FCM and GCMMA can be applied for shape optimization of the complex structure in the engineering design.  
Second, in contrast to the final single web adopted in the traditional basket design in case 1, the final configuration in case 4, i.e., connected closed dual web, has the least mass. The final mass was reduced by 133.38 kg with the centrifuge strength requirement being satisfied. Therefore, the final configuration in case 4 is superior to other three configurations.  
In this work, the design procedure extending the weighted B-Spline FCM and GCMMA into shape optimization is developed for the rotational structure concerning the important role it plays in engineering applications. However, our mechanical analysis was restricted to the static loads; vibrating loads, also as a common load condition during the operation of centrifuges, were not tested. Further investigation on this problem will make the design of the centrifuge within the FCM and GCMMA framework more comprehensive.  
Conceptualization, X.L.; methodology, X.L.; software, L.H.; validation, X.L. and L.H.; analysis, L.H.; investigation, X.L. and L.H.; writing--original draft preparation, X.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Positioning Errors Measurement of CNC Machine Tools Based on J-DBB Method
## 1 Introduction
This type of error is caused by machining errors of the guide rail, improper assembly of the guide rail, the thermal deformation of the guide rail, the incorrect initial installation position of the guide rail is incorrect, etc. The positioning errors such as the uniformity contraction error and opposite clearance error of the ball screw will significantly reduce the motion accuracy of the machine tools [24; 25; 26]. The positioning errors are related to the positioning mechanism and the servo control system. The errors of the positioning mechanism mainly include the uniform contraction error of the ball screw, the periodic error caused by the pitch error, vibration, and installation error of the ball screw. Opposite clearance error caused by servo system out-of-step and inaccurate numerical control compensation. To continue to improve the accuracy of the machine tool, the measurement of positioning errors is indispensable [27; 28; 29; 30]. However, the above-mentioned positioning errors have not been thoroughly studied. In addition, the J-DBB is a new device for the geometric errors of machine tools. Therefore, it's necessary to further study the positioning errors of machine tools based on the J-DBB method. This paper measured the positioning errors of the CNC machine tool at full scale using the J-DBB method. This paper is organized as follows: Section 2 introduces the basic principle of the J-DBB method. Section 3 analyzes four typical positioning errors of the CNC machine tool. Section 4 simulates these positioning errors and analyzes the results. The rest summarizes the advantages and measurement methods.
# A Review on Distribution System State Estimation Algorithms
## 3 DSSE Fundamentals and Main Algorithms
### DSSE Algorithms  
The DSSE algorithms can be categorized as model-based, forecasting-aided, and data-driven. The main mathematical formulas, advantages, disadvantages, and related research are presented below.
# Thermal Performance Analysis of a Double-Pass Solar Air Collector: A CFD Approach
## 3 Results and Discussion
### Efficiency  
Table 8 shows the temperature increments in the first and second passes and the outlet temperature for each configuration. The temperature increase resulting from the first pass (\(\Delta T_{ps1}\)) and the second pass (\(\Delta T_{ps2}\)) are more significant with flow 1. Moreover, for each flow, the U-shape double-pass CPC collector outlet temperature (\(T_{ps2,out}\)) of all the configurations is very similar, with differences between 0.7 K and less.  
Table 9 shows the contribution of the first pass (cavity) and second pass (receiver duct) to the useful energy gain inside the U-shape double-pass CPC collector and the efficiency for each configuration applying Equations (16) and (17). For instance, in configurations (a) and (b), between 40% and 45% of the total heat is extracted in the first pass with both flows. While in configurations (c) and (d), the heat recovery depends on the operating flow and is between 36% and 42% in the first pass.  
Moreover, the efficiencies are higher with flow 2 due to the better heat transfer and lower heat losses to the ambient. In addition, it is observed that configuration (a) Down-Down is the most efficient, while configuration (c) Up-Up provides the lower efficiency of the cases analyzed at flow 2.  
Configuration (a) has the highest efficiency because the air flows predominantly through the bottom section of the cavity, which is closest to the receiver plate. This surface has the highest temperature, thus is where heat recovery is desired. In addition, as the air descends, the final part of the cavity recovers heat since the fluid is forced to exit from the bottom.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Pressure Drop (Pa)**} \\ \hline  & **Configuration** & **Flow 1: 0.01 kg/s** & **Flow 2: 0.02 kg/s** \\ \hline (a) & Down–Down & 10.97 & 36.85 \\ (b) & Down–Up & 9.01 & 31.31 \\ (c) & Up–Up & 8.84 & 31.33 \\ (d) & Up–Down & 10.70 & 37.58 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Pressure drop in the system.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Flow 1: 0.01 kg/s**} & \multicolumn{3}{c}{**Flow 2: 0.02 kg/s**} \\ \hline  & **Configuration** & \(\Delta T_{ps1}\) **(K)** & \(\Delta T_{ps2}\) **(K)** & \(T_{ps2,out}\) **(K)** & \(\Delta T_{ps1}\) **(K)** & \(\Delta T_{ps2}\) **(K)** & \(T_{ps2,out}\) **(K)** \\ \hline (a) & Down–Down & 12.84 & 16.10 & 327.09 & 6.74 & 8.33 & 313.22 \\ (b) & Down–Up & 12.31 & 15.90 & 326.36 & 5.98 & 8.99 & 313.12 \\ (c) & Up–Up & 10.30 & 17.94 & 326.39 & 5.87 & 8.54 & 312.57 \\ (d) & Up–Down & 11.62 & 16.60 & 326.37 & 6.29 & 8.66 & 313.10 \\ \hline \hline \end{tabular}
\end{table}
Table 8: U-shape double-pass CPC temperature increments and outlet temperature (\(T_{f1,in}\) = 298.15 K).
## 3 Results
### Scaffold's Geometrical Parameters and Main Findings
#### 3.5.6 Interconnectivity  
Scaffold interconnectivity was addressed variably across the studies included in this review. This parameter is critical for successful bone regeneration, as it directly influences nutrient diffusion, vascularization, and cell migration [10; 11; 16]. Quantitative data on interconnectivity were reported only by [PERSON] et al. 2022 [36], who described 100% interconnectivity in PCL/\(\beta\)TCP scaffolds (50% porosity) and \(\beta\)TCP scaffolds (70.6% porosity). These findings suggest an open and interconnected pore network, facilitating the efficient transport of fluids and cells.  
Although not all studies provided quantitative measurements, some employed 3D printing techniques that favor the construction of interconnected porous architectures, as seen in [PERSON] et al. (2021) [26] and [PERSON] et al. (2021) [27]. Additionally, the presence of macropores ranging from 0.1 to 1 mm, reported by [PERSON] et al. 2006 [28], suggests a high interconnectivity potential, despite the lack of precise numerical data. The variability in methodology and the lack of standardization in reporting interconnectivity highlight the need for consistent evaluation criteria in future studies.
## 4 Semantic Management Component
### Get MappingSuggestion Service
The difference between the approach used in this work and previous solutions is that the weighted approach is used, which distinguishes the weight, and thus \"importance\", of relationships between concepts. Moreover, the weight is not static; it changes with each repetition of the relationship that is based on the co-occurrence principle. This principle assumes that if two related concepts are repeated, the \"closeness\" of them to each other grows, so the result of the mapping can be different in different time stamps. Another novel point is that getMappingSuggestion compares the \"closeness\" of a set of concepts to one chosen concept and orders them accordingly. This has good potential for further reasoning, allowing patterns in the data to be found and related concepts to be discovered.