esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### HA-MVD Process Facing Walnut Shell Breaking
#### 3.2.4 Mean Square Error of Moisture Content  
MSE is an indicator to quantify differences in the MC of walnuts and is responsible for their engineering properties. As shown in Table 4, the MSE ranges from 1.89% to 8.42%. Figures 12C and 13C present the response surfaces of each variable to the MSE. It can be observed from Figure 12C that there is an interaction between the HA temperature and the microwave power. The variation in MSE is small in the range of high HA temperatures (60-70 \({}^{\circ}\)C) or low microwave power (500-600 W). However, the MSE changes significantly in the range of low HA temperatures (50-60 \({}^{\circ}\)C) or high microwave power (600-700 W). The results suggest that there are both sensitive and non-sensitive zones for the effect of HA temperature and microwave power on MSE. In addition, the combination of low HA temperature (50-60 \({}^{\circ}\)C) and high microwave power (600-700 W) led to a significant increase in the MSE, which reached a peak of approximately 8.48%.  
It can also be seen from Figure 12C that the MSE decreases with increasing HA temperature, whereas it increases with increasing microwave power. This discrepancy is presumably attributable to the variance in heating mechanisms between HA and microwave radiation. The thermal energy is translated from the external shell to the internal kernel through thermal conduction in terms of the HAD, resulting in the fact that the shell is heated rapidly and has a higher temperature compared to the kernel [16]. Hence, the drying rate of shells is obviously faster than that of kernels when the HA temperature increases, which reduces the MC difference between the shell and kernel, thus reducing the MSE. In contrast, microwave heating stands out due to its distinctive aspects of selective and internal heating. Therefore, microwave heating can act directly on the moisture inside the walnut, and through high temperature produce a certain vapor pressure inside the walnut, spreading the moisture from the inner kernel to the surface of the shell [40]. Increasing the power significantly increases the drying rate of the kernels, which increases the MC difference between shell and kernel and thus increases the MSE.  
In addition, as summarized in Figure 13C, the MSE is greater at the higher MC of the transition point and MV thermostatic temperature. This suggests that the combination of a higher MC of the transition point and MV thermostatic temperature can increase the difference in the MC of individual walnuts. Meanwhile, we found that the MC of the transition point had a more significant effect on MSE compared to the MV thermostatic temperature, which is consistent with the ANOVA results (Table 5).
## 3 Results
### Test Setup  
Laboratory autonomy testing is performed at the Jere A. Chase Ocean Engineering Laboratory (Figure 7a) at the University of New Hampshire (UNH). With dimensions of 18 m \(\times\) 12 m \(\times\) 6 m, the UNH Engineering Tank allows for rapid, multi-seasonal testing with both surface and underwater vehicles. The experimental platforms used for this research are small-scale, differential thrust Autonomous Surface Vehicles (ASV), referred to as Testing Unmanned Performance PlatformS (TUPPS). The testbed vehicle has a base width of 0.6 m and a length of 0.9 m and is outfitted with a Velodyne VLP-16 lidar for obstacle detection (Figure 7b). This laboratory test environment is used as the basis for the simulations in this paper to first demonstrate proof-of-concept of PERFORM. It is emphasized that all test data generated for this study are via analytical simulations mimicking test data obtained from the UNH Engineering Tank.  
For the first two test cases (Case Study I and Case Study II), the goal of this validation test for PERFORM (from a test engineer's point-of-view and without loss of generality) is to evaluate a vehicle's ability to detect an unknown stationary obstacle (if any), replan a path to safely avoid any such obstacles, and to reach the goal waypoint(s) with an acceptable path length. To accomplish this, two performance criteria are selected to evaluate performance: the vehicle's total distance traveled and the closest point of approach (CPA) to any existing obstacle. These criteria serve as the two inputs to the IT2-FL system. For the third case study (Case III), the goal of the validation test is to evaluate how efficiently the autonomy engine is able to complete an area survey mission, so path percent error and average speed are the chosen inputs. The overall performance score of the path-planning autonomy is the output for all three cases.
## 3 Results
### Correlation Analyses' Results  
Correlational analyses revealed several significant associations among the performance metrics, demographics, and users' feedback (see Table 2). Age was negatively correlated with Task Time A and Task Time B and with Accuracy A and Accuracy B. Education revealed a negative association with Task Time B, Accuracy A and B, but a positive one with Task Time A. The GSQ negatively correlated with Task Time B and Accuracy A but revealed a positive relationship with Accuracy B. Additionally, User Experience (UX) was negatively correlated with Task Time A and both Accuracy A and B. Lastly, Usability and Task Time A exhibited a positive association, while Acceptability demonstrated a positive relationship with Task Time A and Accuracy A but a negative one with Accuracy B.  
In addition, age showed a positive correlation with User experience (UX) but negative correlations with Usability and Acceptability (see Table 3). Also, Education did not reveal any significance with User experience, yet negative correlations were detected with Usability and Acceptability. Also, GSQ did not reveal a significant correlation with User experience and Usability, but a negative correlation with Acceptability was revealed. Lastly, PC and smart phone use were positively correlated with User Experience, Usability, and Acceptability (see Table 3). Therefore, the second exploratory hypothesis is partially confirmed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Task Time A** & **Task Time B** & **Accuracy A** & **Accuracy B** \\ \hline Age & \(-\)0.102 *** & \(-\)0.038 *** & \(-\)0.071 *** & \(-\)0.221 *** \\ Education & 0.002 ** & \(-\)0.038 *** & \(-\)0.097 *** & \(-\)0.038 *** \\ GSQ & 0.054 & \(-\)0.017 *** & 0.040 * & 0.004 ** \\ UX & \(-\)0.050 *** & 0.085 & \(-\)0.023 *** & 0.005 ** \\ Usability & 0.012 * & 0.118 & 0.112 & 0.102 \\ Acceptability & 0.036 * & 0.040 * & 0.008 ** & \(-\)0.093 *** \\ \hline \hline \end{tabular} \(p<\) 0.05 *, \(p<\) 0.01 **, \(p<\) 0.001 ***; GSQ = Gaming Skills Questionnaire; UX = User Experience Questionnaire; Usability = Usability Questionnaire; Acceptability = Acceptability Questionnaire.  
\end{table}
Table 2: Correlations: performance with demographics and user evaluation.
# Dense Small Object Detection Based on an Improved YOLOv7 Model
## 3 Proposed Method
### SPD Conv Downsampling  
Traditional downsampling methods, such as max pooling and strided convolutions (SConv), have been fundamental components of CNNs. In most research scenarios, images have good resolutions and moderate-sized objects, which allows stride convolutions and pooling layers to skip large amounts of redundant pixel information while still enabling effective feature learning. However, these methods have several shortcomings, especially in tasks like small object detection in YOLO models. Strided convolutions, for instance, apply filters to the input with a step size greater than one, effectively reducing the spatial dimensions. This reduction in spatial resolution can cause critical details of small objects to be missed. Figure 4 shows the downsampling process of SConv with a stride size of 2 as an example. We can clearly see that SConv compresses the input feature map by a \(3\times 3\) convolution kernel with stride size of 2, which is 1/2 of the width and height of the original image, and directly filters out part of the feature information.  
Figure 3: Structure of C3R2N (**left**) and original C3 (**right**). We used Res2 Net to replace the residual module of C3.  
To reduce the feature loss of small targets, we have introduced the SPD Conv model. By converting spatial dimensions into depth channels, SPD Conv allows the network to process high-resolution features without reducing the image's spatial dimensions. This provides a richer representation of the input, which is beneficial for detecting small and densely packed objects. SPD Conv slices the input feature map and obtains four subgraphs with double downsampling, which contains the global spatial information of the original image. The subgraphs were spliced along the channel dimension and the channel dimension was adjusted by the non-stride convolution layer. As shown in Figure 5, SPD Conv maintains a higher resolution in the intermediate feature maps compared to traditional downsampling methods. Replacing downsampling layers with SPD Conv can lead to a more effective and accurate model for detecting small objects in various applications.
## 3 Results and Discussion
### Reduction of OC Circulation
This increase in syngas content also goes in hand with a decrease in the total net heat release from the CLG process (\(\dot{Q}_{net}\)), which can be calculated from the difference in the enthalpies of the streams entering (\(i_{in}\)) and leaving (\(o_{ut}\)) the air and fuel reactor (see Equation (15)), as the enthalpy of the FR products increases. \[\dot{Q}_{net}=\sum_{FR,in}\dot{m}_{i}\cdot h_{i}-\sum_{FR,out}\dot{m}_{i}\cdot h _{i}+\sum_{AR,in}\dot{m}_{i}\cdot h_{i}-\sum_{AR,out}\dot{m}_{i}\cdot h_{i} \tag{15}\]  
The decrease in net process heat release with decreasing \(\phi\), indicating the retaining of chemical energy in the FR products, also becomes visible upon consideration of Figure 5c, depicting the relative net heat release of the process for the different OC-to-fuel ratios. For the given boundary conditions, an autothermal process, for which syngas yields are maximized without relying on external heat addition (\(\dot{Q}_{net}=0\)) is attained for an OC to fuel ratio of approx. 0.5. The resulting cold gas efficiency for this operating point amounts to approx. 60% (see Figure 5d) at a FR temperature of 775 \({}^{\circ}\)C. Although the equilibrium model predicts full char and volatile conversions for these temperatures (see Figures 4a  
Figure 5: Simulation results for CLG operation through reduced OC circulation. OC-to-fuel ratio as a function of the OC circulation rate (**a**). Fuel reactor temperature (**b**), relative net process heat (**c**), and cold gas efficiency (**d**) for different values of \(\phi\) (\(\lambda=1.2\)). and 5d), char, volatile, and tar conversion are known to be kinetically governed processes in chemical looping systems [25; 55; 56; 59], leading to product compositions deviating strongly from equilibrium composition [35; 52]. Due to this reason, temperature differences in the range of 50 to 100 \({}^{\circ}\)C are generally targeted in dual fluidized bed gasification [16], in order to obtain sufficiently high gasifier temperatures, allowing for decent char, volatile, and tar conversions. Accordingly, FR temperatures in the range of 850-950 \({}^{\circ}\)C are desired in CLG, in order to attain high carbon capture efficiencies and cold gas efficiencies as well as low syngas tar loads [20; 23; 37; 60; 61]. These considerations underline that, although the desired reduction in \(\phi\) is possible, attaining an efficient autothermal CLG process through a reduction in the OC circulation rate is not a recommendable strategy as it entails low fuel reactor temperatures, due to the dual-purpose of the OC circulation (i.e., oxygen and heat transport). Consequently, alternative approaches, allowing for a decoupling of oxygen and heat transport between the AR and FR and hence increased FR temperatures are required, in order to attain a CLG process exhibiting the desired characteristics.
# Identification Procedure for Design Optimization of Gravitational Electromagnetic Energy Harvesters
## 2 Materials and Methods  
The devices analyzed in this work are cylindrical 1 DOF GVEHs. The main components of these systems include a suspended moving magnet, a fixed magnet at the bottom and coils wrapped around a tube. Three different configurations are studied, varying the magnets, the tube dimensions, the presence of the internal guide, the number of turns, and the axial location of the coils. For sake of simplicity, the configurations are referred to as EH1, EH2, and EH3 with increasing sizes. A schematic representation of the three energy harvesters can be seen in Figure 1. The values of the highlighted main dimensions are reported in Table 1. These devices have magnets sizes that are chosen to tune the resonance frequency in the range of 3-6 Hz. The configurations have different stiffness tuning and global dimensions, in order to experimentally evaluate different dynamics and power generations, thus having a deeper knowledge of the design optimization procedure. To achieve this purpose, the configurations present moving and fixed magnets with different dimensions, thus changing the system resonance frequency and the magnetic flux linkage with the coils. All the magnets in the three configurations are made of NdFeB42. The dimensions of moving and fixed magnets for all configurations are reported in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Configuration** & \(D_{ext}\) **(mm)** & \(H_{tot}\) **(mm)** & \(D_{g}\) **(mm)** & \(z_{0}\) **(mm)** \\ \hline EH1 & 31 & 90 & 8 & 20 \\ EH2 & 36 & 170 & - & 37 \\ EH3 & 56 & 220 & 5 & 46 \\ \hline \end{tabular}
\end{table}
Table 1: Main dimensions of the energy harvesters.  
Different tube lengths are adopted in order to enable adequate strokes compatible with the specific configuration stiffness. Each device has coils with different dimensions, number of turns, and resistance, but has the same material (enameled copper) and the same cross-section diameter of 0.1 mm. The coils in each configuration are placed along the tube at different heights to find the axial location of the coil that optimizes the generated power. This phenomenon is linked to the variation of the magnetic flux linkage of the coils during the motion of the suspended mass. Further details on this behavior can be found in Section 2.1. Table 3 shows the main characteristics of the coils for EH1, EH2, and EH3.
# Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery
## 3 Results and Discussion
### Experiments on Preprocessing  
Figure 7a demonstrates the mean classification accuracy (MCA) curve of the training process without batch normalization. After that, batch normalization was performed on the input images, which set the mean and variance value of each channel (red, green, and blue channels in total) to 0.5 and 0.5. The training curve after batch normalization is demonstrated in Figure 7b. The comparison shows that using batch normalization can significantly accelerate the network training. In this case, we decided to use batch normalization as a preprocessing operation. However, from Figure 7a,b it can be observed that a risk of over fitting exists, which remains to be solved.  
Figure 6: Field images in different HLB disease categories.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories** & **Training** & **Validation** \\ \hline normal & 84 & 56 \\ light & 44 & 29 \\ medium & 20 & 13 \\ heavy & 0 & 0 \\ Total. & 148 & 98 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training and validation samples.  
Figure 7: Comparison of performance with and without batch normalization. (**a**) Without batch normalization; (**b**) With batch normalization.  
Figure 8a, while a too-large learning rate leads to the neural network divergence. A better classification performance can be obtained by choosing an appropriate learning rate, as shown in Figure 8b.  
The classifier performance of different momentums is shown in Figure 9. It can be observed that appropriately increasing the momentum coefficient can well accelerate the convergence of the cost function. However, using a too-large momentum coefficient (0.97) destabilizes the training process at the initial stages. A better selection of the momentum coefficient is shown in Figure 9b,c.  
The performance of different batch sizes is shown in Figure 10. It can be observed that a too-small batch size can cause oscillation at initial stages, as shown in Figure 10a. However, a too-large batch size slows down the learning process and degrades the classification accuracy, as shown in Figure 10d. A better classifier performance can be obtained by using an appropriate batch size, as shown in Figure 10b,c.  
The performance of different weight decays are shown in Figure 11. From Figure 11, it can be observed that using weight decay has no impact on improving the classifier performance. In this case, we decided not to use weight decay in this study. After hyper-parameter tuning, the selected hyper-parameter values are shown in Table 4.
## 4 Application Examples for Turbomachinery
### Rans
#### 4.1.2 Pratt and Whitney Energy-Efficient Engine High-Pressure Turbine  
The high-pressure turbine designed by Pratt and Whitney for the NASA Energy-Efficient Engine program (PW E3) is a single-stage turbine characterized by high load and high efficiency, and has 24 guide vanes and 54 rotor blades. The test [30; 31] includes an uncooled rig program, a supersonic cascade rig program, and a leakage flow rig program. In this paper, Build 2 with a 43% reaction force in the uncooled rig program is utilized for verification. Specific data are shown in Table 2.  
The wall of the grid is encrypted, and the minimum grid spacing is \(1\times 10^{-6}\) m to ensure that the minimum grid spacing on the wall is \(\Delta y^{+}<1\). The total grid number of a single blade channel is approximately 2.27 million nodes. The grid distribution is shown in Figure 6. In order to ensure that the calculation results are independent of the grid distribution, steady calculations with different coarse and fine grids are used, demonstrating that 2.27 million nodes are sufficient for this study and can meet the required accuracy. The wall and hub casing are set as no-slip boundary conditions, and the inlet and outlet parameters, as well as the rotor speed, are set according to Table 2.  
A comparison of the steady-state efficiency obtained in our experiments with that of the NASA experiments is shown in Figure 7. Compared with the experimental results, it is found that the design-point state prediction is better, the overall prediction efficiency is high, and the error is within 1%. Figure 8 shows a comparison of the static pressure distribution on the blade surface at the root, middle, and tip of the guide vane at the design point with WENO-ZQ3 and the experimental results. It can be seen that the static pressure distribution on the pressure surface in these three sections closely matches the experimental values, while the trailing edge of the blade-root suction surface shows slight differences.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Total inlet temperature & 431.17 K \\ Total inlet pressure & 375.56 Kpa \\ Speed & 9789 rpm \\ Total pressure ratio & 4.12 \\ Efficiency & 90.8\% \\ Correct rotor clearance & 0.3683 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design-point test performance and status of uncooled rig program.  
Figure 6: Computational grid of PW E3.
## 3 Theoretical Background
### Fade Margin  
The fade margin is the difference between the level of the received signal, and the sensitivity of the receiver (in dB), aimed to ensure the required quality of service, is defined as  
\[FM^{\mathrm{[dB]}}=R_{X}^{\mathrm{[dBm]}}-P_{RXth}^{\mathrm{[dBm]}} \tag{3}\]  
where \(R_{X}\) is the received signal level power, \(P_{RXth}\) is the sensitivity threshold of the receiver.
## 2 Basic Calculus of Fractional-Order System  
**Definition 1**: [38]_. For a given function \(f(t)\), the Riemann-Liouville fractional-order integration is defined by_  
\[{}_{0}I_{t}^{\gamma}f(t)=D_{t}^{\gamma}f(t)=\frac{1}{\Gamma(\gamma)}\int_{0}^{ t}\frac{f(\tau)}{\left(t-\tau\right)^{1-\gamma}}d\tau, \tag{1}\]  
_where \(\gamma\in R^{+}\), the initial time is zero, and \(\Gamma(\cdot)\) is the Gamma function which is defined by \(\Gamma(z)=\int_{0}^{\infty}t^{z-1}e^{-t}dt\)._  
**Definition 2**: [38]_. Let \(m-1<\gamma<m\), \(m\in N\), the Riemann-Liouville fractional-order derivative of order \(\gamma\) for a given function \(f(t)\) is defined by_  
\[{}_{0}^{RL}D_{t}^{\gamma}f(t)=\frac{d^{\gamma}f(t)}{dt\gamma}=\frac{1}{\Gamma (m-\gamma)}\frac{d^{m}}{dt^{m}}\left[\int_{0}^{t}\frac{f(\tau)}{\left(t-\tau \right)^{\gamma-m+1}}d\tau\right], \tag{2}\]  
**Remark 1**: [38]_. The following relationship is obtained based on the Definitions of 1 and 2:_  
\[{}_{0}^{RL}D_{t}^{\gamma}f(t)\big{(}{}_{0}I_{t}^{\gamma}f(t)\big{)}=f(t). \tag{3}\]  
**Definition 3**: [38]_. The Caputo fractional-order derivative of order \(\gamma\) is defined by_  
\[{}_{0}^{C}D_{t}^{\gamma}f(t)=\begin{cases}\frac{1}{\Gamma(m-\gamma)}\int_{0}^ {t}\frac{f^{m}(\tau)}{\left(t-\tau\right)^{\gamma-m+1}}d\tau,&m-1<\gamma<m\\ \frac{d^{m}}{dt^{m}}f(t),&\gamma=m\end{cases}. \tag{4}\]  
**Remark 2**: [38]_. One can see that the relationship because the definition of Caputo fractional-order integration is identical to the definition of Riemann-Liouville integration_  
\[{}_{0}^{C}D_{t}^{\gamma}f(t)\big{(}{}_{0}I_{t}^{\gamma}f(t)\big{)}=f(t). \tag{5}\]  
**Property 1**: [38]_. An The following relationship holds for both Riemann-Liouville and Caputo derivatives:_  
\[{}_{0}^{RL,C}D_{t}^{\gamma}\left({}_{0}^{RL,C}D_{t}^{-\alpha}f(t)\right)={}_{0}^{ RL,C}D_{t}^{\gamma-\alpha}f(t), \tag{6}\]  
_where \(\gamma\geq\alpha\geq 0\)._  
**Property 2**: [38]_. The following result is obtained from the [PERSON] derivative:_  
\[{}_{0}^{RL}D_{t}^{-\gamma}\left({}_{0}^{RL}D_{t}^{\alpha}f(t)\right)={}_{0}^{ RL}D_{t}^{\alpha-\gamma}f(t)-\sum_{j=1}^{m}\left[{}_{0}^{RL}D_{t}^{\alpha- \gamma}f(t)\right]_{t=0}\frac{t^{\gamma-j}}{\Gamma(1+\gamma-j)}, \tag{7}\]  
_where \(m-1\leq\alpha\leq m\)._  
**Theorem 1**: [38]_. We set \(x=0\) as an equilibrium point for the non-autonomous fractional-order system_  
\[D^{\gamma}x(t)=f(x,t), \tag{8}\]  
_where \(f(x,t)\) satisfies the Lipschitz condition and \(0<\gamma<1\). Assume that there are a Lyapunov candidate \(V(x,t)\) and class \(\kappa\) function \(s\eta_{1},\ \eta_{2},\ \eta_{3}\) satisfying_  
\[\eta_{1}\|x\|\leq V(x,t)\leq\eta_{2}\|x\|, \tag{9}\]  
\[D^{\gamma}V(x,t)\leq-\eta_{3}\|x\|. \tag{10}\]  
The equilibrium point of system (8) is then asymptotically stable.