esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
The methodological workflow (Figure 3) integrates four synergistic phases:  
(1) Image preprocessing. Satellite imagery from Sentinel-2, Landsat-8/9, and Sentinel-1 SAR (2019-2023) was collected. Cropland areas were masked using the Dynamic World dataset to constrain the classification extent. Cloud and shadow masking was applied to optical imagery, followed by atmospheric correction. Vegetation indices (Table 2) were calculated, and SAR data were extracted.  
(2) Feature selection. Vegetation index time series were composited at 15-day intervals and smoothed using Savitzky-Golay and mean filtering, followed by least squares fitting to preserve phenological trends. Based on these trends, the optimal classification window (June 15-August 20) was determined by analyzing intercrop separability. Statistical features within this window, along with seasonal SAR backscatter coefficients, were used as classification inputs.  
(3) Supervised classification. A Random Forest model was trained using the selected features on the GEE platform. Annual crop-type maps distinguishing maize, oats, potato, sesame, and other crops were generated for 2023, with accuracy assessed through pixel-level confusion matrices and county-level area consistency evaluations.  
(4) Spatiotemporal application and analysis. The trained model was retrospectively applied to classify crop distributions for 2019-2022 using the same procedures. Annual crop maps were produced, and interannual rotation dynamics were quantified using pixel-level crop transition matrices. Dominant rotation types were identified based on high-frequency transitions across consecutive years. Sankey diagrams were used to visualize the evolution of rotation trajectories. In addition, spatial distribution analyses related to topographic gradients (elevation and slope) were conducted.  
Figure 3: Flowchart of the proposed method for crop-type mapping. Satellite data were sourced for 2019-2023. Ground truth data were sourced in 2023.
# A Review of Treatment Techniques for Short-Chain Perfluoroalkyl Substances
## 3 Treatment Techniques for Short-Chain PFASs
### Adsorption Technique
#### 3.1.4 Adsorption with Other Materials  
In recent years, the performance and construction of new materials that could be controlled and modified easily were synthesized by researchers. [PERSON] et al. used a covalent triazine-based framework (CTF) to eliminate PFBS, with an adsorption capacity of 92.03 mg/g [25]. Subsequently, [PERSON] et al. explored the adsorption capacity of AC, CTF, and IRA910 for PFBA and found that the order followed the rules of micropore AC \(<\) CTF \(<\) IRA910, which demonstrated the adsorption mechanism was an electrostatic effect between the triazine group and the PFAS anion head [32]. Ionic strength might be another major factor of other materials that influenced the adsorption efficiency. For example, the adsorption efficiency of poly-styrene carboxylic acid (PS-COOH) for short-chain PFCas in seawater was better than river water due to the large ionic strength in seawater [39].  
Therefore, in various kinds of adsorption materials, the adsorption efficiency of anion exchange resin and electrocoagulation for short-chain PFAs were remarkable. However, the elution ability of the anion exchange resin was inferior when the short-chain PFAs were adsorbed, and the flocs of electrocoagulation were still required for further treatment. Secondly, AC owned the property of low cost and high removal efficiency, while the regeneration ability was unsatisfactory. Finally, new adsorption materials could improve electrostatic attraction effectively, but the application should be further explored.
## 3 General Opportunities, Challenges, and Applications
### Main Challenges
However, consistently encrypting these interactions without affecting the performance poses a challenge [44]. The delivery of consistent and accurate results is challenging. Variabilities in data quality, distribution, and computational capabilities can influence models' performance. The statistical variations in data sources add to this complexity. While techniques like data preprocessing and augmentation offer some solutions, maintaining privacy while achieving large-scale optimization in FL is a complex task [45; 46; 47]. Challenges such as adapting methodologies to specific project demands, managing non-independent and identically distributed (non-IID) data, and optimizing communication are critical tasks in FL. However, as research advances, solutions to these challenges are expected to emerge, paving the way for the broader adoption of FL [48; 49; 50; 51].
# Comparison of Profit-Based Multi-Objective Approaches for Feature Selection in Credit Scoring
## 2 Related Work  
### Profit Scoring  
Credit scoring is defined as \"... a set of decision models and their underlying techniques that aid lenders in granting consumer credit\" [1]. Its core purpose is to assess the risk of lending to a prospective borrower. Historically, credit decisions were based the lender's knowledge of the borrower. In modern times, statistical and machine learning approaches have taken precedence. The goal of these approaches to credit scoring is to distinguish borrowers who are likely to show some negative behavior. Recent research has trended towards evaluation of profit as part of the credit-scoring process because it allows for improved decision-making by lenders. A profit measure comprised of benefits versus losses due to misclassification was proposed, with varying data variable acquisition costs also being considered [5]. The Internal Rate of Return (IRR) was used to measure profitability of peer-to-peer loans [16]. A new measure, expected maximum profit (EMP), which is composed of the benefits of correct classification and costs of misclassification, was suggested [17] and reworked for consumer credit scoring [18].
# ForecastNet Wind Power Prediction Based on Spatio-Temporal Distribution
## 2 Materials and Methods
### Wind Power Prediction Model  
#### 2.2.1 Euclidean Distance and Differential Distance  
In order to obtain the spatio-temporal distribution characteristics of wind turbines, the spatial distance perception function \(G_{d}\) is constructed based on the Euclidean distance, and the differential distance perception function \(G_{s}\) is constructed based on the differential similarity. \(G_{d}\) can reflect the explicit neighboring relationship between wind turbines, and \(G_{s}\) can reflect the invisible neighboring relationship. We calculate the Euclidean distance between two nodes, take the K nodes with the closest distance as neighboring nodes, and form the set \(N_{d}(i)\) to obtain the spatial matrix \(A(i,j)\). The expression of \(A(i,j)\) is shown in Equation (3). We calculate the differential similarity \(Sim(i,j)\) between two nodes, which is calculated as in Equation (4), and the closest K nodes are taken as the differential proximity nodes to form the set \(N_{s}(i)\).  
\[A(i,j)=\begin{cases}1,j\in N_{d}(i)\\ 0,j\
otin N_{d}(i)\end{cases}, \tag{3}\]  
\[Sim(i,j)=\sum_{t=1}^{T}{((x_{i,w}^{t}-x_{i,w}^{t-1})\cdot(x_{j,w}^{t}-x_{j,w}^ {t-1}))}, \tag{4}\]  
where \(x_{i,w}\in R^{T\times 1}\) denotes the wind speed sequence of the \(i\)-th wind turbine.  
For wind turbines, both \(N_{d}(i)\) and \(N_{s}(i)\) are combined to aggregate wind speed information from two neighboring turbines and merged as input features to improve the performance of the wind power prediction.
## 5 Discussion
### Results of the Application and the Influence of Current Regulation  
For the regulation-related influence, there are five key influential factors as shown in Figure 8. Firstly, compliance with existing regulations must be considered. Secondly, regulations depend on the location as they can change depending on the region. Third, the use of the building will also influence the Self-Protection Plan; thus, the intended processes and activities are to be considered. Moreover, the building itself where the activities take place is a fourth factor that influences the plan. Finally, the use rate of the building depends on the user and influences the plan.
# GNSS Performance Modelling and Augmentation for Urban Air Mobility +
## 1 Introduction
Service coverage has also been expanded through the introduction of new constellations and multi-constellation receivers. In spite of the obvious benefits of GNSS-based positioning, several error sources and fault modes exist: signal propagation through the ionosphere and troposphere introduces ranging error that translates to receiver positioning error through the visible satellite geometry. In the urban environment, positioning availability and accuracy are adversely affected by receiver antenna obscuration and severe signal multipath. This paper has two key objectives that are prerequisites for the implementation of a safe UAM framework. First, the model-based prediction of GNSS performance for operations in urban environments; Second, the development of a guidance-based augmentation strategy that allows the assessment of planned UAS trajectories in terms of navigation performance metrics. The scope of this paper is restricted to unintentional fault and error modes and an augmentation strategy that supports their mitigation. Beyond this scope, other important research initiatives that are necessary for fully realizing UAM operations include jamming and spoofing detection, cyber-security and Assisted-GNSS, which are not included in this work. This paper draws on work from [8; 9; 10], and adds to it comprehensive modelling of multipath/NLOS conditions based on ray-tracing which accounts for, in particular, geometrical and material properties of the urban environment, and of the airframe. The multipath modelling includes the effect of receiver tracking loop characteristics on overall positioning error. The NLOS/multipath models are used to characterize the expected GNSS error in urban canyon airspace. A path-planning strategy is developed which takes into account the expected error for the airspace and generates trajectories that avoid zones of degraded navigation performance. The remainder of this paper is structured as follows: Section 2 introduces the proposed system architecture followed by comprehensive modelling of GNSS performance in Section 2; Section 4 presents a simulation case study applying the developed methodology to a representative UAS operation in an urban environment, along with the relevant results.
# Heat Sink Equivalent Thermal Test Method and Its Application in Low-Orbit Satellites
## 3 Application of Heat Sink Equivalent Test Method
This finding shows that the error of the heat sink equivalent method is consistent with the theoretical expectation and proves that the new thermal test method has good accuracy and can fulfill the purpose of the thermal test. The new method of simulating heat flow using heat sinks not only ensures the reliability of satellite development, but also significantly reduces the duration of thermal testing and the cost of operating equipment compared to traditional thermal testing. According to the statistics of the thermal test results of the GF02F satellite, the traditional thermal test takes about 300 h and consumes about 0.7 m\({}^{3}\) of liquid nitrogen per hour, while the heat sink equivalent thermal test takes about 200 h and consumes about 0.3 m\({}^{3}\) of liquid nitrogen per hour. The test's duration is thus shortened by one-third and the consumption \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Serial**} & \multirow{2}{*}{**Unit**} & \multicolumn{2}{c}{**Traditional Thermal Test**} & \multicolumn{2}{c}{**Heat Sink Thermal Test**} & \multirow{2}{*}{**On-Orbit Temperature**} \\ \cline{3-3} \cline{5-6}  & & **Cold** & & **Hot** & & **Cold** & **Hot** \\ \multicolumn{1}{c}{**Number**} & & **Conditions** & **Conditions** & **Conditions** & **Conditions** & **Conditions** & **Conditions** \\ \hline
1 & Three-axis gyroscope & 24.3 & 26.4 & 24.4 & 25.8 & 25.8 \\
2 & X+ star sensor & 20.4 & 21.8 & 23.5 & 24.3 & 24.0 \\
3 & X\(-\) star sensor & 20.8 & 22.4 & 23.5 & 24.2 & 24.0 \\
4 & Nano-star sensor & 18.3 & 19.2 & 21.7 & 22.4 & 21.5 \\
5 & Single-axis gyroscope & 18.3 & 20.5 & 18.5 & 20.9 & 19.5 \\
6 & Power controller & 17.4 & 30.1 & 21.4 & 30.6 & 25.8 \\
7 & X wheel & 4.8 & 18.1 & 6.8 & 16.0 & 11.5 \\
8 & Y wheel & 5.7 & 20.4 & 10.5 & 20.5 & 13.5 \\
9 & Z wheel & 7.6 & 21.9 & 10.5 & 20.5 & 15.3 \\
10 & S wheel & 5.1 & 18.0 & 8.9 & 17.8 & 12.2 \\
11 & Data transmission electronics A & 8.9 & 27.7 & 8.1 & 24.1 & 14.0 \\
12 & GPS receiver & 2.9 & 19.4 & 7.6 & 14.8 & 11.5 \\
13 & X-band transponder & \(-\)2.2 & 19.1 & 3.9 & 18.1 & 11.5 \\
14 & Imaging processing unit & \(-\)7.4 & 17.6 & 3.0 & 28.0 & 15.0 \\
15 & Central computer & \(-\)2.9 & 15.4 & 7.1 & 22.6 & 17.0 \\
16 & Data transmission electronics B & 6.1 & 21.8 & 9.2 & 22.8 & 12.0 \\
17 & S-band transponder & 10.4 & 23.5 & 13.0 & 22.6 & 17.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Temperatures of GF02F in thermal test and on orbit (\({}^{\circ}\)C). Figure 6: Comparison of equivalent thermal balance test results and on-orbit temperature. of liquid nitrogen per hour is reduced by half. The specific data from the test are shown in the Figure 7.
## 3 Results  
The results for the LC resonant circuit test, aimed at characterizing the behavior of each type of alternator core, are based on calculating the resonance frequency to determine the inductance using the given Equation (2) and analyzing the damping. This approach allows for a detailed comparison of how different core materials impact the alternator's performance, which are critical for optimizing the efficiency and output of alternators in practical applications.  
Figure 5 represents the comparative response signals for the LC circuit of the alternators with different core materials (air, SMC, and iron). It highlights the variations in resonance frequency, inductance, and damping characteristics for each core type.  
As observed, the air and SMC cores exhibit a resonance frequency of 501.5 Hz and similar inductance values of 10.07 pH. However, they differ in damping characteristics, with the air core at 67% and the SMC core at 60%. In contrast, the iron core displays a notably lower resonance frequency of 309.12 Hz, a higher inductance of 27.15 pH, and similar damping value of 59%. Table 1 summarizes the results for the three types of alternators tested, according to the core material.  
Figure 4: Vortepsa wind tunnel scheme. (**Top**) Plant view. (**Bottom**) Side view.  
Regarding the performance test results, the signal generated in an open circuit for the different alternator prototypes were analyzed (Vpeak and Vrms). The tests were conducted by setting a constant wind speed of 7 m/s in the wind tunnel test chamber, a speed within the operational range of the VIV blackeless wind turbine selected for the testing.  
Furthermore, Figure 6 represents the comparative signals generated by the alternator prototypes with different core materials (air, SMC, and iron), allowing for a comparative analysis of their performance test under a constant wind speed of 7 m/s.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Core** & **Frequency [Hz]** & **Capacitance [\(\mu\)F]** & **Inductance [pH]** & **Damping** \\ \hline Air & 501.50 & 10 & 10.07 & 67\% \\ SMC & 501.50 & 10 & 10.07 & 60\% \\ Iron & 309.12 & 10 & 27.15 & 59\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results for the three types of alternators for the LC test.  
Figure 5: Damping response curves for all alternator prototypes.  
Figure 6: Signals generated for all alternator prototypes.  
As can be seen, all prototype cores exhibit the same frequency of 30.92 Hz but differ in their voltage outputs. The air core has a Vpeak and Vrms values of 763 mV and 216 mV, respectively, while the SMC core shows higher values of 1150 mV and 316 mV. In contrast, the iron core displays a significantly higher Vpeak of 3830 mV and Vrms of 1019 mV, while maintaining the same frequency of 30.92 Hz. This demonstrates the superior performance of the iron core, followed by the SMC core and, lastly, the air core for the alternators of VIV bladeless wind turbines.  
Finally, Table 2 summarizes the results obtained from the performance tests for the 3 types of alternators tested, according to their core material.
## 5 Discussion  
### The Contribution of Each Load to Strength and Reliability  
Previous studies have not discussed the contribution of each load to the strength and reliability. To explore the contribution of each load to strength and reliability, the 750 kN hook load, 250 stands, and 20 m/s back wind speed are taken as an example. Then, the strength analysis and reliability analysis under seven different conditions, including the single hook load, single stand load, single wind load, composite load I (gravity, hook load and stand load), composite load II (gravity, hook load and wind load), composite load III (gravity, stand load and wind load) and complex load, are investigated and discussed. Four main front columns are selected to monitor the performance, which are section III-1, section II-1, section I-1 and the bottom, respectively. The stress proportion is defined as the ratio of the stress of the single load condition or the composite load condition to the stress of the complex load condition. Then, we discuss the stress proportion and minimum reliability index (\(\beta_{min}\)) of the four selected columns under the above seven conditions.  
As shown in Figure 11, with the decrease in the column's height, the stress proportion of the hook load gradually decreases from 0.68 to 0.18, the stress proportion of the stand load increases from 0.31 to 0.38, and the stress proportion of the wind load is almost unchanged. Under the composite load, with the column's height decreasing, the stress proportion of composite load I decreases from 0.80 to 0.49, the stress proportion of composite load II decreases from 0.73 to 0.33, and the stress proportion of composite load III increases from 0.51 to 0.89. The results show that composite load I has the maximum stress proportion at the position of section III-1, and for the bottom column, composite load III has the maximum stress proportion. Therefore, as to the column at section III-1, a reasonable value of the hook load can ensure its safety and strength. For the bottom column, a windshield can be set to weaken the influence of wind, and stacking too many stands should be avoided.  
The effect of each load on the minimum reliability index \(\beta_{min}\) is studied as shown in Figure 12. In the composite load III and the complex load conditions, when the height of the column decreases, the \(\beta_{min}\) of the column decreases sharply, which are 26.8% and 24.3%, respectively. Under other working loads, the maximum varying degree of the reliability index is only 8.8%. It shows that when the back wind load and the stand load exist at the same time, the reliability of the derrick experiences a sharp drop with decreasing height. Therefore, it is particularly important to arrange the drilling load reasonably according to the environmental load.  
Figure 10: Variation curve of column reliability index along with height under different wind speeds.
# Dynamic Heat Transfer Calculation for Ground-Coupled Floor in Emergency Temporary Housing
## 2 Solving the Heat Transfer Equation  
### Heat Transfer Equation  
#### 2.1.1 Temperature Boundaries  
The heat balance of the ground surface includes the conduction with soil, the convection with air, and the radiation with the sun. In order to facilitate the study, the ground surface is treated as the Dirichlet boundary condition [30].  
\[T_{\text{os}}=T_{\text{oa}}+\gamma I/h_{\text{o}}, \tag{1}\]  
where \(T_{\text{os}}\) is outdoor ground surface temperature, \({}^{\circ}\)C; \(T_{\text{oa}}\) is the outdoor air temperature, \({}^{\circ}\)C; \(\gamma\) is the absorptivity of ground surface; \(I\) is the solar radiation intensity, W/m\({}^{2}\); \(h_{\text{o}}\) is outdoor convective heat transfer coefficient, W/(m\({}^{2}\cdot\)K). Then it can be expanded into the Fourier series,  
\[T_{\text{os}}(t)=T_{\text{om}}+\sum_{n=1}^{\infty}(B_{n}\sin\omega_{n}t+C_{n} \cos\omega_{n}t), \tag{2}\]  
where \(T_{\text{om}}\) is annual mean temperature of outdoor air, \({}^{\circ}\)C; \(\omega\) is angular frequency, rad/s; \(t\) is time, s.  
The heat balance of the floor surface includes the conduction with the ground, the convection with indoor air, and the radiation with the interior surfaces of the walls. Moreover, sometimes, there might be insulation on the floor. Assuming the heat storage capacity of insulation is so small as to be negligible, the indoor surface convective coefficient could be equivalent to its heat resistance,  
\[h_{\text{e}}=1/(R+1/h_{\text{i}}). \tag{3}\]  
Here, \(h_{\text{e}}\) is the equivalent convective coefficient, W/(m\({}^{2}\cdot\)K); \(R\) is the heat resistance of insulation, m\({}^{2}\cdot\)K/W; \(h_{\text{i}}\) is the floor surface convective coefficient, W/(m\({}^{2}\cdot\)K). Thus, the floor surface is considered as the Robin boundary condition. Similarly, indoor air temperature can also be expanded into the Fourier series.  
\[T_{\text{ia}}(t)=T_{\text{im}}+\sum_{n=1}^{\infty}(D_{n}\sin\omega_{n}t+E_{n} \cos\omega_{n}t), \tag{4}\]  
where \(T_{\text{im}}\) is the annual mean temperature of indoor air, \({}^{\circ}\)C.
# Noise-Free Security Assessment of Eviction Set Construction Algorithms with Randomized Caches
## 5 Analysis of Eviction Set Construction Algorithms
### Behavior Analysis of Eviction Set Construction
#### 5.2.3 Group Testing Algorithm  
Figure 6 depicts the probability of finding an eviction set as a function of the candidate set size using the group testing algorithm. As shown by the theoretical curve (see Equation (1)), the probability of finding a candidate set with at least \(W\) congruent addresses increased with its size. The experiment results show that when the candidate set had at least \(W\) addresses, the group testing algorithm could reduce its size to construct a valid eviction set. Furthermore, it can be observed that group testing reduction in a noise-free environment closely matched the theoretical prediction. [PERSON] et al. [48] observed similar trends in a real system.  
Figure 5: (a) The success rate of the single address elimination algorithm with different candidate set sizes. (b) The time required to find an eviction set using the single address elimination algorithm.  
Figure 6: The success rate of the group testing algorithm with different candidate set sizes.
## 3 Results  
### Evaluation of Precision of Precipitation Data  
The CLM3.5 model daily precipitation data were aggregated to obtain monthly precipitation data for the Northeast region from 2008 to 2014. In this study, both daily and monthly precipitation data were used for accuracy validation within the study area. As shown in Figure S2, the results indicate that the Satellite daily precipitation data correlation coefficient with site data R = 0.5313 (\(p<0.05\)). The overall coefficient of determination R\({}^{2}\) = 0.8532, slope K = 1.0798, and correlation coefficient R = 0.9237 between the monthly satellite precipitation data and the measured monthly precipitation at the sites passed the significance test at \(p=0.05\). Satellite monthly precipitation tends to overestimate the actual precipitation in areas with low values and underestimate the actual precipitation in areas with high values. The correlation coefficient between ERA5 and the daily precipitation data at the meteorological sites R = 0.3605 (\(p<0.05\)). The overall coefficient of determination R\({}^{2}\) = 0.6294, slope K = 4071.4, and correlation coefficient R = 0.7933 for the monthly precipitation data of ERA5 and the measured rainfall data at the sites indicate that the accuracy of the ERA5 precipitation data is relatively low compared to the measured rainfall data. Overall, the monthly precipitation data from the CLM3.5 model exhibit a more pronounced correlation and consistency with site-measured precipitation in the study area, making them suitable for use as a data source for precipitation-related studies.