esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods: Experiment and Numerical Simulation
### Meshing Methodology
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mesh Type**} & **Background** & **Overset** & **Total** & \multirow{2}{*}{\(\mathbf{\theta_{\text{max}}}\) (\({}^{\circ}\))} & **Error (\%)** & \multirow{2}{*}{**\(\mathbf{\theta_{\text{min}}}\) (\({}^{\circ}\))**} & **Error (\%)** \\  & **Cells (M)** & **Cells (M)** & **Cells (M)** & & & & \\ \hline Model test & — & — & — & 12.78 & 0.00 & –3.10 & 0.00 \\ Polyhedral mesh & 1.156 & 3.241 & 4.397 & 11.92 & 6.73 & –1.04 & 66.5 \\ Trimmed mesh & 0.864 & 3.795 & 4.659 & 11.35 & 11.2 & –2.52 & 18.7 \\ Hybrid mesh & 0.864 & 3.241 & 4.105 & 11.83 & 7.43 & –3.54 & 14.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mesh independence analysis summary. The interface between each two overlapping regions is set to overlap boundary for flow information interpolation. Considering the calculation accuracy in \"cutting hole\" and \"interpolation\" processes, the grid dimensions of different grid blocks in overlapping areas should be similar to offer enough donor cells for interpolation. Therefore, the grid around submarine transitions from a refined prism layer to a coarse polyhedral mesh away from submarine to form an overset region as shown in Figure 4a,c. At the same time, several refinement regions are applied to the background region by volume control method. So that the grid dimensions of the background region increase gradually from the overlapping boundary to the far-field boundary. In addition, in order to capture the free surface of the two-phase flow, the grids at the free surface are refined through anisotropy methodology, with densifying along normal direction of the free surface. Eventually, the resulting meshed regions are obtained as shown in Figure 4.  
In order to achieve self-propulsion simulation, an overset mesh approach with multiple regions overlapping are used to model the superposition motion including rudder and propeller rotation. Since overset grids require a minimum of three active cell layers for resolving a gap between two wall boundaries, there are some small gaps between rudders and stabilizing fins that require special treatment. Therefore, when coupling the overset mesh and the background mesh, we activate prism layer shrinkage to morph the cells of the prism layers in these small gaps, so that the above requirement is met. As shown in local enlarged view in Figure 4b, there are at least 8 layers prism cell in small gap, which improves the accuracy of the overlapped grids interpolation.
# A Survey on Mission-Profile-Based Stress Testing of Electric Motor Drives
## 3 Mission-Profile-Based Stress Testing Methods
### Stress Testing Methods
#### 3.2.2 Testing Power Converters
In [83], the Artemis cycle mission profile is used to define the vehicle speed and torque demands. These cycles are employed to determine the current-induced stress in the three-phase inverter. In [84], a lifetime estimation method for a VSI is applied under a wind speed profile. The authors focus solely on one failure mode: die-attach solder fatigue. To improve the testing method, it is necessary to observe additional parameters affected by the mission profile. In the paper [85], an AST procedure using high and low frequency profiles for assessing MOSFET parameters is provided. The testing results imply that no significant degradation of MOSFETs occurs under high and low-frequency testing. A similar approach is used in [86], based solely on low frequency. The authors propose a machine-learning-based method for the online fault prognosis of SiC MOSFETs. The influence of PWM methods for three-phase inverters on semiconductor losses and thermal cycling is investigated in [87]. According to the authors, the inverter's losses include conduction and switching losses, which depend on the PWM method used. However, while the study provides valuable insights, it does not specify which PWM method is most suitable for different practical applications or operating conditions. The mission-profile-based stress test methods are categorized and summarized in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Mission Profile** & **Target Component** & **Indicator** & **MTTF** \\ \hline
[60] & Load cycles & Induction motor & Windings temperature & - \\ \hline
[61] & Thermal and voltage & Electric motor &
\begin{tabular}{c} Rotor bars and short \\ circuit rings \\ \end{tabular} & - \\ \hline
[62] & Speed and load profiles & PMSM emulator & Currents and voltages & - \\ \hline
[63] & IEC 60034-2-1 & Windings, bearings, \\  & standard tests & insulation & Efficiency & - \\ \hline
[64] & Thermal cycles & Insulation & Insulation wearout & 550 h \\ \hline
[36] & \
\begin{tabular}{c} PWM switching \\ frequency \\ \end{tabular} & Insulation &
\begin{tabular}{c} Voltage/current spikes \\ and stress factor \(K_{S}\), \\ Power loss \\ \end{tabular} & - \\ \hline
[65] & Thermal cycles & Insulation & Mechanical stress & - \\ \hline
[66] & Temperature profiles & Insulation & Temperature profile & 88, 143, 201 h \\ \hline
[67] & \
\begin{tabular}{c} Off-line and on-line \\ PDs test \\ \end{tabular} & Insulation &
\begin{tabular}{c} PDs, electric stress, \\ insulation degradation \\ \end{tabular} & - \\ \hline
[68] & Voltage surge test & Insulation &
\begin{tabular}{c} PDs, electric stress, \\ insulation degradation \\ \end{tabular} & - \\ \hline
[69] & Speed profiles & PMSM &
\begin{tabular}{c} Deformation of the \\ rotor core \\ \end{tabular} & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of mission-profile-based stress testing methods.
# Study on the Coupling Relationship of Low Temperature Fluidity and Oxidation Stability of Biodiesel
## 3 Experiment Results and Discussion  
### Low Temperature Fluidity Experiment Results  
#### 3.1.1 Effects of CFI on Low Temperature Fluidity of Biodiesel  
Figures 1-4 were obtained by adding cold fluidity improver (CFI) to the biodiesel blended fuels of B10, B20, B50 and B100 [19]. In Bx, the number x represents the added percentage of biodiesel.  
\begin{table}
\begin{tabular}{c c} \hline
**Reagent Name** & **Manufacturer** \\ \hline BHT (2,6-di-tert-butyl-4-methylphenol) & Shanghai Pengxiang Chemical Co., Ltd. \\  & (Shanghai, China) \\ BHA (2-tert-butyl-4-methoxyphenol) & Shanghai Pengxiang Chemical Co., Ltd. \\  & (Shanghai, China) \\ TBHQ (Tert-butyl hydroquinone) & Zhongshan Jiahui Food Additive Co., Ltd. \\  & (Zhongshan, China) \\ PG (3,4,5-trihydroxybenzoic acid) & Shanghai Yanxintang Biological Technology Co., Ltd. \\  & (Shanghai, China) \\ \hline \end{tabular}
\end{table}
Table 1: Main reagents used in the experiment.  
According to Figure 1, we can see that the biodiesel blending fuel B10 is greatly affected by the addition ratio of CFI. The CFPP of B10 is \(-5\)\({}^{\circ}\)C without any CFI However, with the increasing addition of CFI, the CFPP of B10 decrease. When the added amount of CFI reaches 0.6%, the CFPP of B10 is minimized with \(-17\)\({}^{\circ}\)C. Then, with the addition of increasing CFI is, the CFPP of B10 rises.  
Figure 2 shows that the influence of CFI on B20 is smaller than B10. The CFPP of B20 is \(-3\)\({}^{\circ}\)C when the addition ratio of CFI is 0. As the addition of CFI increases, the CFPP of B20 shows a downward trend. When CFI was added at 1%, the CFPP dropped to a minimum of 16 \({}^{\circ}\)C. Afterwards, as the ratio of CFI increases, CFPP begin to rise rapidly.  
Figure 3 shows that compared with B10 and B20, the effects of CFI on the B50 is not great, and the proportion of the addition amount of CFI is significantly increased. When the addition ratio of CFI is 0, the CFPP of B50 is 1 \({}^{\circ}\)C. The value of CFFP presents a downward trend and then an upward trend overall. When the addition ratio is 2%, the CFPP reaches a minimum of \(-8\)\({}^{\circ}\)C.  
Figure 1: Effects of cold fluidity improver (CFI) on B10.  
Figure 3: Effects of CFI on B50.  
Figure 2: Effects of CFI on B20.  
Figure 4 shows that the CFIP exerts less influence on B100, and the proportion of CFI added amount is remarkably increased. The CFPP of B100 is 7 \({}^{\circ}\)C without any CFI. Different from other biodiesel blended fuel, as the addition ratio of CFI increases, the CFPP of B100 shows a downward trend, without increasing. When the CFI is added at 3.6%, the CFPP of B100 decreases the most, down to 4 \({}^{\circ}\)C, and then added the CFI, the CFPP of B100 tends to remain unchanged.
## 3 Results and Discussion
### Comparative Analysis
#### 3.3.2 Analysis of the Results of the Feature Selection Algorithm Based on CARS-SCA  
The CARS-SCA algorithm combines the global search capability of CARS with the inversion capability of PLS, it reuses the feature enhancement methods to improve the correlation, and finally uses [PERSON]'s correlation analysis to identify the optimal subsets. It can be seen from Table 3 that good inversion results are obtained with the GBRT regression method. For the Honghu area, the LR prediction effect is poor, and the \(R^{2}\), RMSE, and MAE of the validation set are 0.8914, 1.1638, and 0.9212, respectively. The \(R^{2}\), RMSE, and MAE of the GBRT validation set are 0.9711, 0.6007, and 0.4990, respectively, which are the best prediction results for the study area. For the Daye area, the SVMR prediction effect is poor, and the \(R^{2}\), RMSE, and MAE of the validation set are 0.8691, 0.3491, and 0.1935, respectively. Meanwhile, the \(R^{2}\), RMSE, and MAE of the GBRT validation set are 0.9796, 0.1379, and 0.1078, respectively, which represent the best prediction results for this region. In summary, the \(R^{2}\) values of the validation sets of all the models are all above 0.85, which shows that the overall accuracy is high enough to meet the actual needs, while GBRT is superior to the other three models in stability and predictive ability, and the inversion effect is better.
## 4 Control Strategy of the Balancing Actuator
### Phase Detection of Counterweight Discs  
To control the balancing actuator, the phases of the two counterweight discs should be detected and the distribution of the corresponding sensors is shown in Figure 12. Considering several factors, such as response speed and installation size, the unipolar Hall-effect sensor is chosen for phase detection. The detection principle is based on the Hall Effect, that is, each time a magnetic field passes through the sensor, the sensor outputs a pulse voltage. Therefore, three Hall sensors are needed in the stationary part of the actuator, while three permanent magnets are installed in the rotary part to generate magnetic field and a permanent magnet corresponds to the Hall sensor. Two of the permanent magnets are fixed on the counterweight blocks of the counterweight discs and the other magnet is fixed on the cover plate of the rotary part.  
When the rotary part of the actuator synchronously rotates with the rotor system, for each circle of the rotary part, the magnetic field generated by a magnet passes through its Hall sensor one time  
Figure 11: Control principle of the active balancing system.  
Figure 12: Installation position of Hall sensor and permanent magnets.  
and three Hall sensors produce three pulse voltage signals. Because the magnet fixed on the cover plate is still relative to the rotor, its corresponding pulse signal is considered as the reference signal and is also the speed signal of the rotor. The other two voltage signals are considered as the position signals of the two counterweight discs. The three pulse signals are shown in Figure 13. Through the phase difference calculation between the position signals and the reference signal, the phase of the two counterweight discs can be detected by Equation (13):  
\[\left\{\begin{array}{l}\varphi_{\mathrm{A}}^{*}=\mathit{Round}\left(\frac{ \tau_{1}}{\tau}\frac{360}{\Delta\varphi}\right)\times\Delta\varphi\\ \varphi_{B}^{*}=\mathit{Round}\left(\frac{\tau_{1}}{\tau}\frac{360}{\Delta \varphi}\right)\times\Delta\varphi\end{array}\right. \tag{13}\]  
where \(\tau\) is the rotational period of the rotor, \(\tau_{1}\) and \(\tau_{2}\) are the time difference between the position signals and the reference signal and \(\Delta\varphi\) is the phase of each step rotation. Due to the interference signals and installation errors of magnets and Hall sensors, the integral function \(\mathit{Round}()\) is used to modify the detection results.
## 3 Establishment of Integrated Navigation Model
### State Equation and Measurement Equation
#### 3.1.2 LEO's Equation of State  
The state equation of the giant LEO system can be described as [20]:  
\[\dot{U}_{LEO}(t)=B_{LEO}(t)V_{LEO}(t), \tag{43}\]  
where \(V_{LEO}(t)=\left[\begin{array}{c}\sigma_{tu}\\ \sigma_{tru}\end{array}\right]\), \(B_{LEO}(t)=\left[\begin{array}{cc}1&0\\ 0&1\end{array}\right]\), and \(\sigma_{tu}\) and \(\sigma_{tru}\) are Gaussian white noise with mean value of zero.  
Combining Formulas (11) and (23), finally, the state equation of the whole combined system is as follows:  
\[\left[\begin{array}{c}\dot{U}_{INS}(t)\\ \dot{U}_{LEO}(t)\end{array}\right]=A_{INS}(t)U_{INS}(t)+\left[\begin{array}{ cc}B_{INS}(t)&0\\ 0&B_{LEO}(t)\end{array}\right]\left[\begin{array}{c}V_{INS}(t)\\ V_{LEO}(t)\end{array}\right]. \tag{44}\]
# A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data
## 5 Conclusions and Future Works  
As a research hotspot of machine learning, imbalanced classification has attracted the attention of many scholars. In this paper, we propose a novel ensemble framework based on K-means and resampling called EKR to alleviate the adverse effect of imbalanced datasets. On 16 datasets with different imbalance ratios from 1.87 to 41.4, we tested EKR in all aspects and proved the validity and superiority of EKR.  
In order to retain the effective information of positive samples, EKR only uses K-means to cluster negative samples into several sub-clusters, the optimal \(k\)-value is determined by the Average Silhouette Coefficient of each dataset. Then each sub-cluster is combined with the positive samples to form a subset, and the subset is balanced by resampling negative samples. Particularly, when the number of negative samples is more than that of the positive samples, EKR selects negative samples that are closest to the clustering center, because these samples can represent the whole sub-cluster. We evaluated EKR with six different metrics, including Accuracy, Precision, Recall, F1-score, G-mean, and AUC, and the compared AUC of EKR with four baseline models, EKR shows better performance, which indicates that EKR is effective for imbalanced classification.  
Some datasets have fewer positive samples, so the number of samples in the subset may also be less, which has a certain impact on the classification effect of the classifier. However, oversampling the positive samples may lead to class overlapping. Therefore, in future research work, we need to further improve the algorithm in detail to avoid class overlapping and increase the number of samples of a subset. In addition, we will pay more attention to the multi-classification task and design the algorithm suitable for multi-classification.  
Conceptualization, H.D.; methodology, H.D.; software, H.D.; validation, H.D.; formal analysis, H.D.; investigation, H.D.; resources, H.D.; writing--original draft preparation, H.D.; writing--review and editing, H.D., H.Y.; funding acquisition, Y.W., P.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Social Science Fund (19 BYY076), the Science Foundation of the Ministry of Education of China (No. 14 YJC860042), and the Shandong Provincial Social Science Planning Project (No. 9 BJCJC51/18 CXWJ01/18 BJY04). Thanks to all commenters for their valuable and constructive comments.  
The authors declare no conflict of interest.
## 3 Numerical Results and Discussions
### Investigation of the Air Layer Thickness Influence  
The aim of this part is to analyze the influence of the thickness of the DSTSC to determine the optimal thickness for the analyzed cases. The main objective is to make the DSTSC as thin as possible to be easier to install on the facades of buildings, to increase their acceptability towards the beneficiary, and to reduce the footprint of the building integrating such systems. From a realistic point of view, we limited our studies to a 30 cm solar air collector, considering the integration of such system in a building facade.  
First, we investigated the temperature distribution on the perforated absorber plate. We can observe in Figure 14 that the thinnest DSTSC with a thickness of 10 cm overheats in the upper part of the absorber plate. This fact points out a reduction in the heat extracted from the absorber plate, and therefore, a drop in the efficiency of the collector. Additionally, we can notice a homogeneous temperature distribution in the perforated absorber plate of the 30 cm DSTSC.  
These facts led us to investigate the airflow distribution in both solar air collectors in more detail to determine the causes of the overheating of the absorber plate. Figure 15 presents the distribution of the air velocity magnitude in a median plane for both collectors and reveals the main cause leading to overheating. Indeed, part of the air sucked by the collector by-passed the holes in the upper part of the perforated absorber plate, leading to reduced airflow, and thus, reduced heat transfer. On the upper-left part of the Figure, a close-up view of the five upper holes of the absorber is presented, and this allows us to better appreciate and compare the velocity magnitudes for the two models of DSTSC.  
Finally, we extracted data from numerical simulations to analyze the solar collector efficiency for the three analyzed cases, and the results are presented in Figure 16. The efficiency gradually increases when increasing the solar collector thickness, and we can  
Figure 14: Distribution of the temperature on the perforated absorber plate of the 30 cm (**left**) and 10 cm (**right**) thickness DTSC with an air flow of 500 m\({}^{3}\)/h.  
observe that the 20 cm and 30 cm DSTSC reached close values. When varying the thickness from 30 cm to 10 cm, we can notice a variation of about 4% regarding the solar collector efficiency for an air flow of 500 m\({}^{3}\)/h.  
Figure 16: Solar collector efficiency for the three investigated collectors.  
Figure 15: Distribution of the velocity magnitude in a median plane of both DSTSC, 10 cm (**left**) and 30 cm (**right**), for an air flow of 500 m\({}^{3}\)/h.
# Differential Characteristics Based Iterative Multiuser Detection for Wireless Sensor Networks
## 1 Introduction
Reference ([PERSON] and [PERSON], 2018) proposed an iterative multiuser detector for overloaded LDPC coded CDMA systems. This receiver consists of a combination of matched filters in the first stage and a linear MMSE detector and an interference cancellation scheme in the successive stages. In this way, the receiver achieved better BER performance than Turbo coded systems. In this paper, an iterative multiuser detection algorithm based on differential characteristics is proposed for DS-UWB systems. On the basis of optimal multiuser detection, differential characteristics are derived to map the received bit sequence in to a one-dimensional feature space, So that the wrongly received bits are easy to distinguish. Then threshold detection is utilized to get a preliminary detection of the potential wrongly received bit because the optimal threshold is difficult to compute. After that an error bit correct procedure is employed to correct the mistakes in the previous preliminary threshold detection. The above described differential characteristics calculation, preliminary threshold detection and error bit correction procedures are iterated to achieve better BER performance. Simulation results demonstrate performance improvement compared to traditional suboptimal multiuser detection methods. The reminder of this paper is organized as follows: Section 2 introduces the system model DS-UWB system and several typical multiuser detection algorithms. In Section 3, the proposed multiuser detection method is described in detail. Simulations and discussions are given in Section 4. Conclusions are in Section 5.
## 6 Determining the Likely Range of Vertical Impact Angles
### Consequences for the Considered Cage Armour Systems
#### 6.3.2 Mass Efficiency Comparison  
The results as shown in Figures 24 and 29 can also be combined to calculate another performance metric indicative of the mass efficiency of the different cage armour systems:  
\[\text{mass efficiency}=\frac{\text{average efficiency}}{\text{ areal density}} \tag{4}\]  
The results of this analysis are shown in Figure 30 again for horizontal impact angles ranging, respectively, from 0\({}^{\circ}\) to 90\({}^{\circ}\) and from 0\({}^{\circ}\) to 60\({}^{\circ}\). Contrary to the previous analysis, the net systems here clearly outperform all other systems. Especially the polymer fibre-based cage armour shows a very high mass efficiency. As a result, if a cage armour application must be found within a minimal weight envelope, the net systems are likely to give the best solution. It should, however, be reminded here that the analysis does not take into account the additional weight of all mountings, supports, attachments... necessary to mount the cage armour onto the vehicle and that a specific threat, namely the PG-7 projectile, was considered. Nevertheless, a polymer fibre-based net solution will give the highest mass efficiency in all practical cases.
###### Abstract  
This study aimed to develop an approach using Sentinel-1 synthetic aperture radar (SAR) data and the Optitrig crop growth and irrigation model to detect irrigation dates and amounts for maize crops in the Occitanie region, Southern France. The surface soil moisture (SSM) derived from SAR data was analyzed for changes indicating irrigation events at the plot scale in four reference plots located in Montpellier (P1) and Tarbes (P2, P3, and P4). As rain most likely covers several square kilometers, while irrigation is decided at the plot scale, a difference between SSM signals at the grid scale (10 km \(\times\) 10 km) and plot scale is a clear indication of a recent irrigation event. Its date and amount are then sought by forcing irrigation dates and amounts in Optitrig, selecting the most relevant (date, amount) combination from an appropriate criterion. As the observed SSM values hold for a depth of a few centimeters, while the modeled SSM values hold for exactly 10 cm, the best irrigation combination is the one that gives similar relative changes in SSM values rather than similar SSM values. The irrigation dates were detected with an overall accuracy (recall) of 86.2% and a precision of 85.7%, and thus, with relatively low numbers of missed or false irrigation detections, respectively. The performance of the method in detecting seasonal irrigation amounts varied with climatic conditions. For the P1 plot in the semi-arid climate of Montpellier, the mean absolute error percentage (MAE%) was 16.4%, showing a higher efficiency when compared with the humid climate of Tarbes (P2, P3, and P4 plots), where a higher MAE% of 50% was recorded, indicating a larger discrepancy between the detected and actual irrigation amounts. The limitations of the proposed method can be attributed to the characteristics of the Sentinel-1 constellation, including its 6-day revisit time and signal penetration challenges in dense maize cover, as well as the mismatch between the parameterization of Optitrig for SSM simulations and the actual irrigation practices followed by farmers. Despite these weaknesses, the results demonstrated the relevance of combining Optitrig and S1 SAR-derived SSM data for field-scale detection of irrigation dates and, potentially, irrigation amounts.  
Sentinel-1; surface soil moisture; Optitrig; crop growth model; irrigation; maize 1