esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Flexural Strength and Flexural-Compression Ratio of AN-LAC  
The flexural strength of AN-LAC is shown in Figure 7a. It was evident that the change trend of flexural strength was the same as that of compressive strength and splitting tensile  
Figure 6: The test results and analysis of the splitting tensile test: (**a**) the splitting tensile strength of AN-LAC; (**b**) the relationship between splitting tensile strength and compressive strength; ACI 363R-92 [40]; [PERSON] et al. [41].  
strength. When the average particle size of NSLA decreased, the flexural strength of the concrete showed an increasing trend. The flexural strength of AN-LAC-1 and AN-LAC-3 at 28 days reached the maximum value of 8.65 MPa and minimum value of 6.60 MPa among all groups, respectively. This phenomenon was attributed to the strength of NSLA, which was similar to its influence on the splitting tensile strength.  
In addition, the ratio of flexural strength to compressive strength is abbreviated as the flexural-compression ratio, which can be used to evaluate the toughness and crack resistance of concrete. A higher value of this ratio indicates the better toughness and crack resistance of concrete. The regression equation for the flexural-compression ratio of AN-LAC was established according to CEB-FIP Model Code 1990 [36]. As shown in Figure 7b, Equations (7) and (8) were used to fit the experimental data and the correlation coefficients \(R^{2}\) were more than 0.99:  
\[f_{\text{f}}=0.05f_{\text{cu}}^{1.29} \tag{7}\]  
\[f_{\text{f}}=0.40f_{\text{cu}}^{0.75} \tag{8}\]
## 1 Introduction
Reference [13] proposed a deep neural network method aiming at the problem that traditional numerical simulation methods are computationally intensive and difficult to achieve a balance between accuracy and efficiency, which realized the rapid prediction of aerodynamic noise under the premise of maintaining accuracy. Reference [14] established a rapid calculation method of force based on Deep Neural Networks (DNN) that aimed at the difficult problem of modeling ironless permanent magnet synchronous linear motors and provided a high-precision and high-efficiency model for global optimization of motor parameters. The combination of numerical simulation and deep learning technology has attracted the attention of scholars in recent years and has led to some achievements in electromagnetics. Reference [15] used numerical simulation data and Convolutional Neural Networks (CNN) to predict two-dimensional potential distributions, and the prediction error was less than 1%. Reference [16] trained CNN in a supervised manner and learned the mapping of coils to motor magnetic field distributions, and the result showed good accuracy in magnetic field prediction. Reference [17] proposed a sequence-based modular network and an end-to-end network to predict the motor drive efficiency maps, which showed good accuracy in prediction. However, in the field of electromagnetic rail launch, there is a lack of application research combining numerical simulation and deep learning technology. This paper combines the numerical simulation data of the electromagnetic railgun with depth learning and proposes an extrapolation prediction method of the armature electromagnetic force using the DBN-DNN model. This paper is organized as follows: Section 2 presents the governing equation, which considering the influence of armature movement, then establishes the finite element simulation model to analyze the dynamic characteristics and numerical solution stability of the armature electromagnetic force under different conductivity. Section 3 aims at the \"pseudo-oscillation\" problem of the numerical solution at high speed affected by \(P_{\mathrm{e}}\), proposes an extrapolation prediction method of armature electromagnetic force with standard armature conductivity, and then describes the network architecture and working principle of DBN-DNN. Section 4 uses two electro magnetic railgun cases to test the extrapolation prediction performance of the model and further proposes the acceleration effect of DBN-DNN model parameters when solving low conductivity transfer to standard conductivity, which improves the training efficiency of the model.
# Computational Aspects of \(L_{0}\) Linking in the Rasch Model
## 6 Discussion  
This article examined the computational aspects of \(L_{0}\) linking in the Rasch model. \(L_{0}\) linking serves as a robust alternative to MM linking when fixed DIF effects are present. Since the \(L_{0}\) loss function is not differentiable at \(x=0\), the ratio loss function and the Gaussian  
Figure 5: Empirical example: DIF parameter estimates (displayed with a black triangle) along with their 95% confidence intervals.  
loss function were used as differentiable approximations. Both approximations rely on a tuning parameter \(\varepsilon\), which controls the approximation error. A numerical illustration and a simulation study demonstrated that the optimal \(\varepsilon\) value minimizing the RMSE of the linking parameter estimate \(\hat{\mu}\) depends on the magnitude of DIF effects, the number of items, and the sample size. A data-driven selection of \(\varepsilon\) provided better performance than a fixed \(\varepsilon\) across all conditions. Additionally, the Gaussian loss function showed a slight advantage over the ratio loss function, though the differences are likely negligible in practice.  
This study aimed to determine group differences in a robust manner, ensuring insensitivity to DIF effects. Detecting DIF items was treated as a nuisance, as the goal was not to identify deviating items but rather to obtain a consistent estimate of the mean difference in ability between the two groups. In contrast, much of the psychometric literature focuses on detecting DIF items [22; 27; 55; 56; 57], with group difference estimation being, at best, a by-product of the procedure.  
Using the \(L_{0}\) loss function effectively removes items with large DIF effects from the linking process. As a result, the estimated group difference is based solely on items with little to no DIF. This approach may pose a threat to validity, as it alters the construct being measured by treating items with DIF effects as construct-irrelevant [58; 59]. Restricting the item set in this way can change the interpretation of the ability variable [60; 61; 62; 63; 64].
## 5 Results
### RGB Band Features  
In the first set of models, RGB band features are selected for the three methods including SVM, RF with default parameters, and the RF with Bayesian hyperparameter optimization. The Bayesian hyperparameter optimization results are shown in Figure 5, where subplot A shows the estimated minimum objective over evaluation time, and subplot B shows the estimated objective over different hyperparameter combinations. It can be observed that the estimated objective function achieves equilibrium after a few evaluations and is close to the observed objective, and the minimum objective function is achieved by the optimized hyperparameters vector \(mls=1\) and \(npts=2\) (default parameters: \(mls=1\), \(npts=1\)).  
The confusion matrices for the three machine learning based classifiers are displayed in Figure 6. In these matrices, target classes denote the truth labels, whereas the output classes mean the classifier predicted labels. The diagonal cell in green shows the number  
Figure 5: (**A**) Minimum objectives over evaluation time by using RGB features; (**B**) estimated fitness function values over the combinations of \(npts\) and \(mls\) using RGB features.  
and the corresponding percentage for correctly classified pixels and the off-diagonal cell indicates the misclassified pixels. Taking the proposed algorithm as an example, for the \"building\" class, 12,338 pixels in green is TP, another 1794 (162 + 1123 + 17 + 492) pixels in red in the first row is FP, and 1102 (295 + 498 + 7 + 302) pixels in red in the first column is FN. Thus, Precision for \"buildings\" class is 12,338/(12,338 + 1794) = 87.3% and similarly Recall for \"buildings\" is 12,338/(12,338 + 1102) = 91.8%. The overall accuracy is 87.9%. In comparison to SVM and the RF with default parameters, the proposed method obtains the best classification performance, which marginally improved against the conventional RF by 0.5%. However, the overall accuracy of SVM algorithm is only 46.1%, which is much less than random forest classifiers. The main reason is the inappropriateness of the SVM algorithm for the land cover classification problem with only RGB band information. A comparison for different methods is shown in Table 3 showing that the optimized random forest method achieves the highest OA and kappa value.  
Figure 6: Confusion matrices of three machine learning based classifiers (**a**) SVM, (**b**) Random forest, (**c**) Random forest with Bayesian optimization) by using RGB band features.
## 2 Numerical Simulation
### Data Processing  
Figure 4 illustrates a schematic diagram representing the aerodynamic load experienced by a train under crosswind conditions. This study primarily focuses on the lateral force, lift, and rolling moment of the HST. In order to simplify the analysis of the train's aerodynamic performance, it is common to normalize the aerodynamic load. The mathematical expressions for the aerodynamic load coefficients of the HST are presented below:  
\[C_{D}=\frac{F_{x}}{0.5\rho V_{a}^{2}A} \tag{6}\]  
\[C_{L}=\frac{F_{y}}{0.5\rho V_{a}^{2}A} \tag{7}\]  
\[C_{M}=\frac{M_{Z}}{0.5\rho V_{a}^{2}Ah} \tag{8}\]  
where \(C_{D}\), \(C_{L}\), and \(C_{M}\) represent the coefficients for the lateral force, lift, and rolling moment, respectively. \(F_{x}\), \(F_{y}\), \(M_{Z}\) represent the lateral force, lift force, and rolling moment, respectively. \(\rho\) is air density of 1.225 kg/m\({}^{3}\). The combined speed \(V_{a}=\sqrt{V_{t}^{2}+U_{w}^{2}}\), \(V_{t}\) is the running speed of the HST, while \(U_{w}\) represents the wind speed. The head car and the  
Figure 3: Schematic diagram of the mesh model: (**a**) cross-section view, (**b**) surface mesh of the head car, and (**c**) surface mesh of the curved wind barrier.  
tail car each have a side area of 89.97 m\({}^{2}\), referred to as A, while the middle car has a side area of 95.42 m\({}^{2}\); h denotes the characteristic height of the carriage, which is 3.82 m.  
During the instantaneous moments as a HST enters or exits the wind barrier area in a crosswind environment, there is a drastic change in the aerodynamic load coefficients. Therefore, \(\Delta C\) is introduced to represent the amplitude of changes in the aerodynamic load coefficients [22]  
\[\Delta C=C_{\text{max}}-C_{\text{min}} \tag{9}\]  
where \(C_{\text{max}}\) and \(C_{\text{min}}\) represent the maximum and minimum aerodynamic load coefficients, respectively.  
Furthermore, \(\Delta C^{\prime}\) is introduced to represent the rate of changes in the aerodynamic load coefficients for a HST within a time interval of 0.035 s. A time interval of 0.035 s is essential for assessing the safety of train operation [23].  
\[\Delta C^{\prime}=\frac{|C_{t+0.035}-C_{t}|}{0.035} \tag{10}\]  
The degree of the sudden change in the aerodynamic load during the HST entering or exiting the bridge wind barrier area is evaluated in this study by using \(\Delta C\) and \(\Delta C^{\prime}_{\text{max}}\).
# Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
## 4 Discussion  
### Accidental Flaring  
Examining the discrepancy between Nightfire estimates and the reported data for the Khanty-Mansi Autonomous Okrug region in 2016 (see Figure S4 in the Supplementary Materials text section), we found a significant (c. 50%) increase in Lukoil regional subsidiary gas flaring volumes, with no increase of such scale observed for any other company in the region. According to the company's annual report, no significant change in the production rates occurred this year [50]. However, according to the same report and major Russian news outlet [51], an emergency event (fire) took place at the major gas processing plant (GPP) of the company in the region in June 2016, resulting in complete facility shutdown for six months. According to the report, annual gas processing levels at the plant dropped from 1.94 to 0.95 BCM. This in turn should have resulted in an increase in on-site flaring at the dependent oil fields, as it is unlikely that the company could have brokered a temporary deal with the other refineries in the region.  
While the incident is admitted to be the reason for decreased APG processing in the annual report, reported flaring volumes appear to be identical in 2015 and 2016 on the company scale [52] (p. 35), at approximately 0.95 BCM (i.e., as high as temporary shutdown processing capacities for that year). It is also plausible that flared volumes were not considered as over-limit, as according to the state regulations, the flaring could be accounted for as occurring during maintenance procedures (see Section 1.1).  
Compared with the reported data (loss in APG processing volumes), the Nightfire-estimated spike in flaring volumes is slightly higher, at 1.2 BCM. On top of that, it is possible that some portion of excess APG could have been utilized locally on the on-site utilizing infrastructure if the latter was not used at its maximum or sent to the major company refineries in the European part of Russia, as, while examining the dashboards of the related oil fields, it has been observed that some had their flaring normalized before the re-commissioning of the GPP. Considering the observation described in Section 4.2, it is possible that Nightfire may tend to overestimate moderate-sized but persistent flares in Russia, which are common for Lukoil; this can also be one of the reasons for the significant discrepancy with Lukoil's reported flaring data observed in Figure 8.  
Figure 9c presents a dashboard for one of the company's oil fields in the region (62.35\({}^{\circ}\) N 75.84\({}^{\circ}\)E), which is reported to be connected with the GPP [51]. On the dashboard, one can observe a significant increase in flaring activity in the second half of 2016, gaps in summer-time observations (shaded area in 3), and the sporadic nature of annual flaring, characterized by surges in 1 and 3 occurring in July (which probably occur during maintenance at the GPP); the lack of Nightfire observations in summer-time for this particular flare could have led to underestimated flaring volumes.
## 5 Experimental Results and Analysis
### Ablation Experiments
#### 5.4.1 Different Channel Data Channels  
The influence of data about joints and bones on final experimental results. On the NTU RGB+D 60 dataset, we compared using only the non-physically connected joint hypergraph as model input, using only the non-physically connected bone hypergraph as model input using both the non-physically connected joint hypergraph and the non-physically connected bone hypergraph as model input, and the experimental results are shown in Table 2. The results show that the action recognition accuracy of using both the non-physically connected joint hypergraph and the non-physically connected bone hypergraph as model input was 90.3% and 95.2% for the CS (cross-subject) protocols and CV (cross-view) protocols, respectively, which were better than that of only using the joint or bone. This shows that the use of multi-channel data containing both joints and bones can provide richer feature information than single-channel data only containing joints or bones, thus enhancing the perceptual capability of the model and achieving a complete representation of the data, thus improving the accuracy of action recognition.  
The influence of different hypergraph construction methods on experimental results. In the NTU RGB+D 60 dataset, where we compared only using physically connected joint hypergraphs as model input, only using non-physically connected joint hypergraphs and physically connected joint hypergraphs as model input. The experimental results for this are shown in  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**NTU RGB+D 60**} & \multicolumn{2}{c}{**Kinetics-Skeleton**} \\ \cline{3-6}  & & **CS(\%)** & **CV(\%)** & **TOP-1(\%)** & **TOP-5(\%)** \\ \hline \multirow{2}{*}{Handcraft feature based} & Lie Group [32] & 50.1 & 82.5 & - & - \\  & Feature Enc [46] & - & - & 14.9 & 25.8 \\ \hline \multirow{4}{*}{RNN based} & ST-LSTM [33] & 69.2 & 77.7 & - & - \\  & VA-LSTM [34] & 79.4 & 87.6 & - & - \\  & AGC-LSTM [35] & 89.2 & 95.0 & - & - \\  & Deep LSTM [28] & - & - & 16.4 & 35.3 \\ \hline \multirow{4}{*}{CNN based} & TCN [36] & 74.3 & 83.1 & 20.3 & 40.0 \\  & HCN [37] & 86.5 & 91.1 & - & - \\  & VA-CNN [38] & 88.7 & 94.3 & - & - \\  & Ta-CNN+ [10] & 90.7 & 95.1 & - & - \\ \hline \multirow{4}{*}{GCN based} & ST-GCN [39] & 81.5 & 88.3 & 30.7 & 52.8 \\  & 2S-AGCN [30] & 88.5 & 95.1 & 36.1 & 58.7 \\ \cline{1-1}  & MS-AAGCN [40] & 90.0 & 96.2 & 37.8 & 61.0 \\ \cline{1-1}  & Shift-GCN [41] & 90.7 & 96.5 & - & - \\ \cline{1-1}  & Sym-GNN [47] & - & - & 37.2 & 58.1 \\ \hline \multirow{4}{*}{HGNN based} & Hyper-GCN(3S) [42] & 89.5 & 95.7 & 37.1 & 60.0 \\ \cline{1-1}  & DHGCN [43] & 90.7 & 96.0 & 37.7 & 60.6 \\ \cline{1-1}  & Selective-HCN [44] & 90.8 & 96.6 & 38.0 & 61.1 \\ \cline{1-1}  & SD-HGCN [45] & 90.9 & 96.7 & 37.4 & 60.5 \\ \cline{1-1}  & Ours & 91.2 & 96.7 & 39.1 & 61.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of classification accuracy with state-of-the-art methods on the NTU RGB+D 60 dataset and the Kinetics-Skeleton dataset.
## 2 System Description
### Transmitting Subsystem  
The function of the transmitting subsystem is to produce the modulated RF waveforms according to the set program. The transmitting subsystem is mainly composed of the synchronization module, a sequential logic controller built in a Field-Programmable Gate Array (FPGA) device, and a signal synthesis and amplification channel. Among them, the synchronization module calibrates the Owen Controlled Crystal Oscillator (OCXO) with the standard Pulses Per Second (PPS) signal provided by Global Positioning System (GPS). The logic part of the synchronization module is implemented in FPGA, nested with the sequential logic controller. The output frequency of OCXO can be adjusted according to the voltage on the specific pin. A chip of Direct Digital Synthesizer (DDS) generate the sounding waveform based on the parameters given by the sequential logic controller. After amplified by the power amplifier, the sounding signal with an appropriate power will be fed into the transmitting antenna.  
The transmitting channel, power amplifier and transmitting antenna are shown in Figure 2. The transmitting channel is packaged in an aluminum alloy mold to shield interference and ensure the purity of the transmitting waveform. An amplifier with a maximum transmitting power of 500 W is adopted. The transmitting antenna is a Log-Periodic Dipole Antenna (LPDA) consists of 18 dipole elements with a boom length of approximately 14 m, and the longest dipole element is almost 15 m long. The main lobe of the antenna points to 113 north by east. In elevation angle, the width of the  
Figure 1: The specific architecture of the system.  
lobe is relatively large. In the following analysis and experiment, we choose \((\varphi=113^{\circ},\theta=35^{\circ})\) cover the incident area of the desired signals with low elevations.
# Path Planning of Underground Robots via Improved A* and Dynamic Window Approach
## 3 Fusion of Improved A* and DWA Algorithm
### Moving Trajectory Model of Handling Robot  
DWA is a local path planning method based on the current state and dynamic window of the robot. Its core goal is to quickly generate an optimal trajectory in a short period of time based on the current position, speed, and environmental information of the robot. These trajectories are achieved by selecting the linear velocity and angular velocity suitable for the current state and environment.  
\[\left\{\begin{array}{l}x_{n+1}=x_{n}+\upsilon\Delta t\cos\theta_{n-1}\\ y_{n+1}=x_{n}+\upsilon\Delta t\sin\theta_{n-1}\\ \theta_{n+1}=x_{n}+\omega\Delta t\end{array}\right. \tag{12}\]\(x_{n+1}\), \(y_{n+1}\), and \(\theta_{n+1}\) are the position information of the handling robot at the next moment; \(x_{n}\), \(y_{n}\), and \(\theta_{n}\) are the position information of the handling robot at the current moment; \(v\) and \(\omega\) are the linear velocity and angular velocity of the handling robot, and \(\Delta t\) is the sampling time.
# Modeling and Analysis of Contactless Solar Evaporation for Scalable Application
## 3 Result and Discussion
### Parametric Analysis of the Scalable SCE
#### 3.3.2 Hole Diameter  
To further explore the effect of the hole diameter on the scalable SCE performance, the absorber-emitter temperature \(T_{\text{ae}}\), water surface temperature \(T_{\text{wat+t}}\) and evaporation rate under different hole diameters, with the same hole area ratio (\(\varphi\) = 20%) and air layer thickness (\(\delta\) = 2.0 mm) were obtained as shown in Figure 9. When the hole diameter increases, the dark evaporation rate increases, due to the enhanced vapor diffusion with larger vertical diffusion. At the same time, the net evaporation rate decreases firstly and then increase slightly after the low point of 0.412 kg\(\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\) is achieved under hole diameter of 10 mm, due to the conflict between the vertical and horizontal diffusion. On the contrary, both \(T_{\text{ae}}\) and \(T_{\text{wat+t}}\) increase firstly and then decrease slightly, due to the same energy input and different latent heat of evaporation. From the above analysis, it can be deduced that the effect of vertical diffusion on the scalable SCE performance is more important than that of horizontal diffusion. Hence, the selection of the hole diameter should be fully considered in real large-scale applications.
## 1 Introduction
[13] investigated the drying time and the quality of pumpkin slices dried by HD, MWD, and MW-HD hybrid dryers at different temperatures, inlet airspeeds, and powers. Their results indicated that drying the pumpkin slices by MWD was faster than the other methods. In addition, it was also described that the highest moisture diffusion coefficient and the lowest rehydration ratio were observed in the drying with MWD, while the lowest shrinkage and the highest color variations were detected in the samples dried by HD. [PERSON] et al. [14] dried brocoli by hot air, freezing, vacuum, MW-vacuum, and MW-hot air methods under different conditions and addressed some of the thermal and physical features of the products. They showed that the lowest SEC was obtained in the MWD; whereas the highest value was earned in the drying with the freezing dryer. The highest and lowest variations in the physical properties (color and shrinkage) were obtained in the HD and freezing dryer, respectively. Moreover, the maximum rehydration coefficient was detected in the freezing dryer. The literature review indicated that a combination of US treatment with different drying schemes can improve the operational indices of the dryers. Regarding the significance of carrot and the need for its proper processing, it seems that the use of this technology can enhance the final product quality in addition to the reduction of processing time and energy consumption. Therefore, the aim of this study was to compare the drying kinetics, SEC, quality (shrinkage, color, and rehydration) of carrots dried through different methods (HD, MWD, INFD, INF-HD, and MW-HD) and US pretreatment. Moreover, a proper model was considered to describe the moisture ratio of the carrot slices dried by any of the mentioned techniques.