esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Data Processing  
Through the ArcMap [21] \"Extract Values to Points\" tool [22], we collected each pixel value from the multiple layers image (bands and days) at the specified sampling points (sensor). Data of different bands of the Sentinel-2 were processed together with the moisture values sensed by in-situ sensors. Soil moisture from the sensors was estimated as the average values gathered from 9:00 a.m. to 1:00 p.m. since the Sentinel-2 image was always taken at 10:56 a.m. The calculated average soil moisture value is used for the statistical analyses.  
The data matrix was processed with Statgraphics Centurion XVIII [23] to analyze if there was any correlation between the moisture values and the values from the satellite  
Figure 2: Details of the used sensors (**a**) the sensor, (**b**) installation of sensor in one of the selected greens.  
images. First, general multivariate analyses, with all sorts of soil, and specific multivariate analyses, for individual sort of soil, were performed to find which satellite image bands correlate with the soil moisture. The multivariate analyses were performed according to the predefined options of Statgraphics Centurion XVIII [23]. No modifications were applied to the obtained results. The data of dispersion matrix and correlation matrix were used. All cases were included in the analyses. No outlier results were deleted for this initial test.  
Then, simple and multiple regression models are compared according to the results of multivariate analyses. Regarding the simple regression models, a total of 27 possible regression models are evaluated. All of them include the use of a constant number, and the adjustment was performed according to the value of least squares. Concerning the multiple regression models, only linear regression is evaluated. The regression models will be calculated for all soil classes, a group of soils, or individual soils classes (sandy, sandy-clay, and clay) according to multivariate analyses. We assume R\({}^{2}\) as the best indicator of the accuracy of the model. Attending to previous experiences, models characterized by R\({}^{2}\) higher than 50% should be considered as good models for this case due to the heterogeneity of the soil. This issue will be further analyzed in the discussion.  
Once the mathematical models are obtained, the validation includes qualitative and quantitative analyses. The first one applies both mathematical models in ArcMap using the tool \"Raster Calculator\" [24], and results are checked and compared with other soil moisture indexes. The \"Raster Calculator\" is a simple tool that allows applying a mathematical model to one or more bands. This tool applies the mathematical model based on the pixel values of the bands included in the mathematical model generating a new raster. Then, the predicted value of soil moisture obtained in ArcMap was extracted and compared with the sensed one.
# Deep Learning Methods for Space Situational Awareness
## 4 Results and Discussions
Finally, in Table 4, we show the results in terms of precision, recall, and F-measure for each considered network. The SqueezeNet was found to be the best-performing network in terms of overall accuracy. In addition to being a \"squashed\" network, therefore with much fewer parameters, it was the fastest to train. The AlexNet network follows in terms of performance. Using CNN with fewer layers has the advantage of lower hardware needs and shorter training times than VGG-16 and GoogLeNet. Indeed, shorter training times allow for testing more hyperparameters. This makes the entire training process easier. One of the key design  
Figure 8: Learning curves of the SqueezeNet DL method. choices of the VGG-16 network was to use smaller (\(3\times 3\)) convolutional filters than those of previous networks. This allows an accurate recognition of the images (with an overall accuracy of 87.50%, as we can see in Figure 9), at the cost of dramatically increasing the depth of the network. the VGG-16 model is an extremely heavy net (thanks also to its depth and the number of fully connected layers), characterized by the slowest training times. GoogLeNet network, although it has reached a good accuracy (85%), has turned out to be the least performing, but still faster than the VGG-16 (which exceeds by only 2% accuracy).
# Model Updating of a Freight Wagon Based on Dynamic Tests under Different Loading Scenarios
## 5 Calibration
### Correlation Analysis  
Figure 19a shows the error values between numerical and experimental frequencies, before and after calibration, for the unloaded and loaded vehicle, taking as reference the values of the experimental frequencies. The relative errors on the natural frequencies were significantly reduced for all five modes, leading to a reduction in the average error from 11.8% before calibration to 3.2% after calibration, for the unloaded vehicle, and from 20.4% before calibration to 4.8% after calibration, for the loaded vehicle.  
Figure 19b shows a comparison of the MAC values between the numerical mode shapes, before and after calibration, and experimental mode shapes. The average MAC value improved from 0.85 to 0.87, and from 0.82 to 0.85, in the unloaded and loaded configurations, respectively.  
Figure 20 compares the experimental and numerical modal configurations after calibration, for the unloaded (Figure 20a) and loaded (Figure 20b) configurations. As can be observed, an evident similarity between numerical and experimental modal configurations was achieved after calibration.  
Figure 19: Errors between experimental and numerical modal responses, before and after calibration: (**a**) natural frequencies, (**b**) MAC.
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 4 Experiments
### Ablation Studies
#### 4.7.1 Loss Function  
To evaluate the effectiveness of each component in the proposed loss function, we conduct a series of tests with different loss settings, as shown in Table 4.  
Supervision loss (\(L_{sup}^{A}\) and \(L_{sup}^{B}\)) underpins the independent optimization of each model; removing this component forces the affected model to depend predominantly on transfer loss and pseudo-labels for training. As an example, omitting \(L_{sup}^{A}\) strips Model A of its capacity for standalone optimization, resulting in a notable performance drop. Concurrently, the pseudo-labels transferred to Model B deteriorate in quality, undermining the efficacy of knowledge transfer. However, since Model B still retains \(L_{sup}^{B}\), its performance decline is relatively limited. Similarly, removing \(L_{sup}^{B}\) leads to a significant reduction in Model B's performance, especially when capturing strict boundaries (e.g., \(IoU=0.7\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Experimental Setting**} & \multicolumn{4}{c}{**Rank@1**} & \multirow{2}{*}{**mIoU**} \\ \cline{3-4}  & & **IoU = 0.3** & **IoU = 0.3** & \\ \hline \multirow{5}{*}{MMCL (VSLNet)} & Ours & 72.31 & 58.86 & 38.52 & 51.96 \\  & w/o \(L_{sup}^{A}\) & 68.56 & 52.82 & 33.89 & 48.11 \\  & w/o \(L_{sup}^{B}\) & 70.12 & 56.71 & 36.25 & 49.92 \\  & w/o \(L_{trans}^{A}\) & 70.85 & 56.71 & 36.79 & 50.25 \\  & w/o \(L_{trans}^{B}\) & 71.92 & 57.91 & 37.65 & 51.35 \\ \hline \multirow{5}{*}{MMCL (2D-TAN)} & Ours & 60.58 & 46.95 & 28.26 & 42.17 \\  & w/o \(L_{sup}^{A}\) & 59.41 & 45.63 & 27.11 & 41.03 \\ \cline{1-1}  & w/o \(L_{sup}^{B}\) & 55.81 & 38.89 & 22.18 & 37.84 \\ \cline{1-1}  & w/o \(L_{trans}^{A}\) & 59.83 & 46.42 & 27.94 & 41.72 \\ \cline{1-1}  & w/o \(L_{trans}^{B}\) & 58.78 & 45.56 & 27.21 & 40.85 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of MMCL with different losses on Charades-STA dataset.  
Figure 4: Performance comparison of isomorphic models on the Charades-STA dataset under the MMCL framework.  
Model A is also affected by the degraded quality of pseudo-labels, but to a lesser extent, as it still depends on \(L_{sup}^{A}\). Excluding the transfer losses (\(L_{trans}^{A}\) and \(L_{trans}^{B}\)) disrupts the inter-model knowledge-sharing mechanism. Removing \(L_{trans}^{A}\) prevents Model A from assimilating supplementary insights from Model B, leading to a performance dip. Model B, retaining \(L_{trans}^{B}\) and its corresponding supervisory signals, suffers only marginal setbacks. Conversely, removing \(L_{sup}^{B}\) robs Model B of its ability to acquire knowledge from Model A, causing a significant performance drop, whereas Model A's performance sees only a minor decline since it can still rely on its own supervision signals for independent optimization.
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
## 2 Theory and Methodology
### Relocation of the Edge in Subpixel-Level
#### 2.2.2 ZOM-Based Edge Location Calculation  
After improving the edge model, the ZOM-based method is used to calculate the subpixel-level coordinates of the edges, as detailed in [PERSON] and Mehrotra [PERSON] and [PERSON] (2018). The Zernike moment of order \(n\) and repetition \(m\) for the 2D intensity function \(f(u,v)\) is defined as  
\[Z_{nm}=\frac{n+1}{\pi}\iint\limits_{u^{2}+v^{2}\leq 1}f(u,v)T_{nm}(r,\theta) dudv=\frac{n+1}{\pi}A_{nm}, \tag{3}\]  
where \(f(u,v)\) is the gray value of pixel \((u,v)\) in the unit circle, \(T_{nm}(r,\theta)\) is the Zernike moment kernel function expressed in polar coordinates.  
The edge coordinates are determined based on the rotation invariance of ZOM; that is, the modulus of ZOM remains the same before and after the rotation of the image, only the phase angle changes. When the image rotates with an angle \(\psi\) as shown in Figure 3, the relationship between \(A_{nm}\) and \(A^{\prime}{}_{nm}\) can be obtained through the polar coordinate transformation as,  
\[A^{\prime}{}_{nm}=A_{nm}e^{-jm\psi}, \tag{4}\]  
where \(j=\sqrt{-1}\), \(A_{nm}\) is the Zernike moment of the pixels in the original \(u-v\) coordinate system, and \(A^{\prime}{}_{nm}\) is the Zernike moment of the pixels in the rotated \(u^{\prime}-v^{\prime}\) coordinate system aligned with the edge.  
According to the condition that the imaginary part of \(A^{\prime}{}_{11}\) is equal to zero, i.e., \(\mathrm{Im}[A^{\prime}{}_{11}]=0\)[PERSON] and [PERSON] (2018), the edge orientation can be calculated in combination with Equation (4) as  
\[\psi=\tan^{-1}(\frac{\mathrm{Im}[A_{11}]}{\mathrm{Re}[A_{11}]}), \tag{5}\]  
Figure 3: Image rotation.  
where \(\text{Im}[A_{11}]\) is the imaginary part of \(A_{11}\), and \(\text{Re}[A_{11}]\) is the real part of \(A_{11}\). It is seen that only one Zernike moment \(A_{11}\) is required to calculate the edge orientation. Meanwhile, the distance of the edge to the center can be obtained as  
\[l=\left[1-\omega^{2}-\sqrt{\left(\omega^{2}-1\right)^{2}-2\omega^{2}A_{20}/A^{ \prime}{}_{11}}\right]/\omega^{2}, \tag{6}\]  
Subsequently, the coordinate of the edge point in subpixel-level is determined as  
\[\begin{bmatrix}u\\ v\end{bmatrix}=l\begin{bmatrix}\cos(\psi)\\ \sin(\psi)\end{bmatrix}, \tag{7}\]  
Two edge images generated by software and processed with Gaussian blurring, i.e., straight edge and curved edge, as illustrated in Figure 4, were used to compare the performance of the pixel-level and subpixel-level edge-detection algorithms. The pixel-level edge points extracted by the Canny operator and the subpixel-level edge points extracted by the improved ZOM-based method are shown in Figure 4, which shows that the subpixel-level edges are closer to the true edges than the pixel-level edges.
## 2 Background
### Bi-Level Optimization  
Bi-level optimization is a mathematical programming framework where a constraint in an optimization problem is another optimization problem. The main optimization problem is generally called upper (leader) model while the constraint which is another optimization problem is called lower (follower) model. This type of optimization framework arises in situation where hierarchical decision-making is involved. In other words, a decision from one task affects the decisions of the other task and vice versa. This framework has two types of variables, the upper-level variables and the lower-level variables [29].
## 1 Introduction
In the present study, two hybrid intelligent models, namely, ICA-ANN and GA-ANN, were utilized and implemented to forecast SF of RWs under dynamic conditions. These two models used the most effective factors of ICA and GA to create a proper relationship for prediction of the SF values. The hybrid models are less applicable in engineering and still have not been used in the SF forecast of RWs under dynamic conditions. The present study provides explanations in terms of databases after introducing applicable intelligent methods. Afterwards, modeling procedures were explained in detail and finally, the best predictive model was selected for forecasting the SF values.
# Robust Engineering for the Design of Resilient Manufacturing Systems
## 3 Methods and Tools
### Data Analysis
#### 3.3.1 Arrival Time Analysis  
For the analysis of the arrival data, the software tool MATLAB [38] has been utilized. The first dataset consists of the arrivals. Each arrival refers to a certain product and includes the date and the number of products of the respective order. The strategy of the company is after receiving a specific order, determining the amount of the raw material that it is going to be used. The raw material is translated into number of 500 kg billets. Since every order was kept tracked in kilograms by the company accordingly, the kilos are converted into parts of 500 kg, as it happens in the real production line and they rounded up. For example, an order of 1300 kg will be transformed into 3 parts in the data analysis process. Similarly, a 6200 kg order will be transformed into 13 parts and so forth. The next step is the pre-process, which is focused on grouping the arrivals into weeks as indicated from the data format by the company. In fact, a new series is formed by displaying the sums of arrivals that arrived into the system during the same weeks. Afterwards, the attention is focused on finding a distribution that efficiently fits the final series. However, due to the fact that the number of observations is significantly low, namely only 56, no statistical analysis tool is valid for recruitment. In this context, normality is assumed without further investigation. From the process described above, it is defined that the distribution that best follows the amount of parts arriving at the system every 168 h, which in turn denotes that the interarrival time is assumed 168 has suggested from the company data. The results of the normal distribution approach regarding the mean value and the standard deviation are listed in the Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Mean Value** & **Standard Deviation** \\ \hline A & 74 & 45 \\ \hline B & 107 & 68 \\ \hline C & 48 & 31 \\ \hline D & 40 & 27 \\ \hline E & 133 & 75 \\ \hline F & 149 & 82 \\ \hline G & 131 & 73 \\ \hline H & 30 & 21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the Normal distributions for the arrival time of the parts.
# Initial Structure Design and Optimization of an Automotive Remote Head-Up Display
## 3 Proposed Methods
### Anti-Occlusion Optimization Method Based on the Off-Axis Triple Inverse Structure  
Due to the advantages of compact structure, no shading, a large field of view, and so on [12], the off-axis triple inversion is usually used as the initial structure for optimal design in the HUD design process. However, because its structure is very flexible, the eccentricity of a small distance or the adjustment of a small angle of rotation may bring about problems in the optimization process, such as structural occlusion. Inspired by the research on the off-axis three-reverse control occlusion method [13], as shown in Figure 6 below, by controlling the height of the light at the edge of the adjacent surface in the optimization process, the light occlusion problem in the optimization process can be prevented. The constraint conditions can be expressed as follows:  
\[\begin{split} y_{1_{down}}-y_{3_{up}}&>a\\ y_{2_{down}}-y_{4_{up}}&>b\\ y_{3_{down}}-y_{5_{up}}&>c\end{split}\, \tag{11}\]  
where \(y_{i_{down}}\) denotes the \(i\)th ordinate below the first surface, \(y_{i_{up}}\) denotes the \(i\)th ordinate above the first surface, and \(a,b,c\) are constants.  
Figure 6: Off-axis triple inverse structure.  
In the structure of this paper, the source image plane in the HUD can be regarded as surface 5 in Figure 6, surface M\({}_{1}\) as surface 3, surface M\({}_{2}\) as surface 4, and the windshield can be regarded as surface 2. According to the relationship between the relevant structure and surface position, as shown in Figure 7, the following expression can be used to control the surface position:  
\[\begin{array}{c}\text{minMF}^{2}=\frac{\sum\limits_{i=1}^{\pi}W_{i}(V_{i}-T_{ i})^{2}}{\sum\limits_{i=1}^{\pi}W_{i}}\\ x_{Q_{4}}-x_{P_{2}}\geq X\\ s.t.\begin{array}{c}y_{P_{3}}-y_{Q_{2}}\geq Y\\ z_{Q_{3}}-z_{P_{1}}\geq Z\end{array}\end{array}, \tag{12}\]  
where MF represents the value of the system optimization function, \(W_{i}\) represents the weight of the \(i\)th optimization, \(V_{i}\) represents the actual value of the \(i\)th optimization variable, \(T_{i}\) represents the standard value of the \(i\)th optimization variable, and \(x_{Q_{i}}\) represents the \(x\) direction coordinate value of the \(Q_{i}\). \(X,Y,Z\) are constants and need to be determined according to the relative position of the HUD structure and the size of the reflection mirrors. In this experiment, they are set to 10 mm, 20 mm, and 200 mm, respectively.  
In Zemax, an optimization function is used to express the constraint condition (12), and the weight of the constraint term is set to limit the structure so as to prevent the mirror position from serious deviation, occlusion, and structure chaos during the optimization.
## 4 Simulation Study
### Nonlinear Load  
A benchmark nonlinear load is formulated in IEC standard, and this nonlinear load circuit is shown in Figure 10. We use this benchmark nonlinear load to test the capability of matrix converter with nonlinear load. Figure 11 shows the simulation results under nonlinear load conditions. The load conditions are: Phase A uses this benchmark nonlinear load, capacitance is 1000 \(\mu\)F, resistance is 50 \(\Omega\), both B-phase and C-phase resistance are 15 \(\Omega\). Figure 11a shows the three-phase output voltage waveform, and Figure 11b,c show the current and neutral current waveform. The cusp current waveform in the three-phase output current waveform diagram is the A-phase output current. It can be seen from the figure that the A-phase output current waveform tends to be stable in a short time after the simulation starts, the waveform is no longer changed. It can be seen from Figure 11d that the phase A output voltage has only a small number of low-order harmonics, the total harmonic distortion rate is 2.59%, fundamental amplitude is 136.4. It can be seen from these waveforms that the distortion and imbalance of the output voltage are very small even when a high-power non-linear load is attached. The waveform of current flowing through \(U_{\text{dc}}\) under nonlinear load is shown in Figure 11e.  
Figure 11: _Cont._  
Figure 10: Nonlinear load circuit.  
It can be seen from the simulation results of the converter with the above three unbalanced loads that the proposed control strategy enhances the ability of HFL three-phase four-leg MC to adjust the output balance voltage. A stable three-phase symmetrical sine wave can still be output when the load fluctuates. It can effectively reduce the harmonic distortion rate, and the system has a strong load carrying capacity.
# Boundary-Aware Hashing for Hamming Space Retrieval
## 4 Experiments
### Hyper-Parameter Sensitivity  
We conduct experiments to test the sensitivity of the \(\alpha\) parameter towards search performance. As shown in Figure 8, the R@\(H\leq 2\) performance of BAH increases rapidly at first with the increase of \(\alpha\) and then decreases. As the hyper-parameter \(\alpha\) increases, the real-value continuously approaches the hash codes to reduce the quantization error and improve the pruning. To achieve both satisfactory precision and recall, we provide the hyper-parameter \(\alpha\) of the four datasets set to 0.01, 0.01, 0.05, and 0.01, respectively.
_Applied_  
_sciences_  
_Article_  
**Drying Performance and Aflatoxin Content of Paddy Rice Applying an Inflatable Solar Dryer in**  
**Burkina Faso**  
**[PERSON] 1,***, **[PERSON] 1**, **[PERSON] 2** and [PERSON] 1**  
Footnote 1: [[https://github.com/journals/sab/s](https://github.com/journals/sab/s)]([https://github.com/journals/sab/s](https://github.com/journals/sab/s))  
is found in food mostly before and after being harvested [6; 10]. Therefore, EU regulation applies a maximum limit of 5 \(\upmu\)g/kg for AFB1 and 10 \(\upmu\)g/kg for total aflat [11]. Aflatoxin concentration could be reduced by employing a drying practice [12].  
Various alternative low-cost drying practices, like solar, infrared, convective, desiccant, fluidized and spouted bed dryers, have been designed for developing countries [13]. Among all those practices, the use of solar dryers is one of the most suitable methods, due to their low external energy requirement and the considerable reduction of contamination compared to conventional sun drying [13; 14; 15; 16]. At a village or farmer's cooperative scale, high demand for solar dryers that are cheap, efficient, easy-to-operate and flexible has been acknowledged [13; 14; 16; 17].  
Drying performance of a solar dryer is affected by different factors, such as air movement, operating conditions, geometrical properties and thermal energy storage system [18; 19]. Thermal energy for a solar dryer can be transferred by natural convection (passive) or forced convection (active) [19; 20; 21].  
Over the last three decades, inflatable solar dryer (ISD), also known as GrainPro(r) Solar Bubble Dryer(tm), which is an active solar dryer, has been constantly developed by the University of Hohenheim, the International Research Institute (IRRI) and GrainPro Inc. In the interest of improving drying operation and preserving product quality, the dryer has been tested to dry various crops such as paddy rice, maize, cassava, red beans and amaranth leaves, in Africa and Asia [22; 23; 24].  
The objective of the present study is to evaluate an inflatable solar dryer (GrainPro SBD 50) to dry paddy rice in the Southwest region of Burkina Faso, with regard to drying performance and product quality, such as impurities and aflat contamination.
## 5 Experimental Results and Discussions
### Evaluation Measures
#### 5.2.2 Packet Delivery Ratio (\(PD_{ratio}\))  
The fraction of delivered packet to the transmitted packet is termed as the packet delivery ratio.  
\[PD_{ratio}=\frac{Total\;\;number\;\;of\;delivered\;packets}{Total\;number\;of\; transmitted\;packets} \tag{29}\]