esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Crop Yield Correlation Results Obtained by Three Different Methods  
The relationship between crop yield and CDI equations calculated using three different methods was examined. The results of the correlation analysis for all districts are shared in Table 3. In 7 out of the 40 districts where the study was conducted, correlations were not statistically significant for all methods. Among all the significant models, the SHAP model provided the best results with an average correlation of 0.56. Following the SHAP model, the highest average correlation was 0.47, obtained using the empirical method. Another significant finding is that the correlation results between the PCA and empirical methods are quite similar. Figure 3: Loading plots of PCA analysis results represent the four selected provinces within the study area, with the selected districts for Aksaray, Karaman, Konya, and Nigde being Gülagac, Ayranc, Derbent, and Ciftik, respectively. The correlation values between the drought indices used in this study and crop yield are presented in Table S6. When evaluating all 40 districts, the highest correlation was obtained using SHAP in 33 districts, PCA in 1 district, the empirical method in 3 districts, SPEI in 3 districts, and scPDSI in 0 districts. In the comparison among the SPEI indices themselves, SPEI_3 exhibited the highest average correlation at 0.39, while SPEI_9 had the lowest average correlation (0.26). Among all the methods, scPDSI showed the lowest average correlation (0.22). A box plot illustrating the correlation values for the districts in the training and test sets is presented in Figure 4. This figure, which serves as a visual representation of Table S6, allows for the easy identification of the distribution, minimum and maximum values, and median values of the correlation results for each method. The highest median values in the training and test sets belong to CDI\({}_{\text{[SHAP]}}\), while CDI\({}_{\text{[Empirical]}}\) and CDI\({}_{\text{[PCA]}}\) show a very similar distribution. It is observed that SPEI_1, SPEI_3, and SPEI_6 produce similar results in the training set, but the differences become apparent in the districts within the test set. The spatial distribution of the correlation across the 40 districts in the study area is shown in Figure 5. SHAP demonstrates high correlations in a large number of districts, followed closely by PCA. Higher correlations between the drought indices and crop yield are observed in the northern regions of the study area, while a decrease in correlations is observed in the southern regions, which are at a higher elevation. Topographic variations in mountainous regions have the potential to influence precipitation distribution and the conditions under which wheat can be cultivated. Mountainous regions may give rise to orographic effects, which can result in greater (or lower) precipitation accumulation. Conversely, lower elevations may exhibit a more homogeneous distribution of rainfall. This may result in a weakening of the correlation between the drought index and yield in the southern and mountainous regions.
## 4 Materials and Methods
### Functional Characterization of Class II ApTPSs  
The ORF regions of the _ApTPSs_ were subcloned into a pESC-Leu expression vector via PCR amplification with a pEASY-Uni Seamless Cloning and Assembly Kit (TransGen, Beijing, China), and the amplicons were digested with Notl restriction enzymes (New England Biolab, Massachusetts, USA), and all operations were carried out according to the manufacturer's protocol. The verified vectors were subsequently transformed into yeast strain BY-T20 (BY4742, \(\Delta\)_Trp1_, _Trp1_:_HIS3_-_PPGKI_-_BTS1_/_ERG20_-_TADH1_-_PTDH3_-_SaGGPS_-_TTPI_-_PTEF1_-_HHMG1_-_TCYC1_) [40]. The BY-T20 strain was transformed by integrating _BTS1_/_ERG20_, _SAGGPS_ and _tHMG1_ genes into the _Trp1_ site of BY4742-\(\Delta\)_TRP_. The BY-T20 cells harbouring the expression vector were selected on SD-Leu-His media (synthetic dextrose minimal media without leucine and histidine) (FunGenome, Beijing, China) (consisting of 20 g\(\cdot\)L\({}^{-1}\) glucose) and cultivated at 30 \({}^{\circ}\)C for 48 h in an incubator. The monoclonal plaques were identified in SD-Leu-His media at 30 \({}^{\circ}\)C for 48 h and were expanded at a rate of 1:100 into 100 mL of SD-Leu-His media (consisting of 20 g\(\cdot\)L\({}^{-1}\) glucose) under the same conditions for 48 h (OD\({}_{600}\) = 2-3). The culture was centrifuged at 3000 g for 10 min to collect the cells, resuspended in yeast induction SD-Leu-His media (consisting of 20 g\(\cdot\)L\({}^{-1}\) galactose) and grown at 30 \({}^{\circ}\)C for 48 h in a shaking incubator [41]. Then, 1 mL of the yeast culture was pipetted from the cultured yeast suspension and mixed with an equal volume of \(n\)-hexane (Thermo Scientific, California, USA). The mixture was subjected to cell disruption using an ultrasonic disruptor. After disruption, the suspension was centrifuged at 13,000\(\times\) g for 10 min, and the supernatant was collected. This process was repeated three times. The three aliquots of n-hexane were combined in an EP tube, dried under a stream of nitrogen (N\({}_{2}\)), and re-dissolved in 100 \(\upmu\)L of \(n\)-hexane for GC-MS analysis [42]. The GC-MS analysis was performed using a Trace 1310 instrument (Thermo Scientific, California, USA) equipped with a TSQ 8000 mass detector and then separated with a TG-5 MS column (30 m \(\times\) 0.25 mm LD., DF = 0.25 \(\upmu\)m (Thermo Scientific, California, USA). Helium was used as a carrier gas at a constant flow rate of 1.0 mL/min. The oven programme was as follows: 50 \({}^{\circ}\)C for 2 min, an increase of 20 \({}^{\circ}\)C min\({}^{-1}\) to 300 \({}^{\circ}\)C, and then holding for 20 min. The ion trap temperature was 250 \({}^{\circ}\)C, and the scan range was 40 to 450 \(m/z\)[43]. The sample (1 \(\upmu\)L) was injected in splitless mode.
# Research on the Jiamusi Area's Shallow Groundwater Recharge Using Remote Sensing and the SWAT Model
## 3 Materials and Methods
### Research Methods
#### 3.2.2 SWAT Model  
The SWAT model (Soil and Water Assessment Tool) is a basin-scale semi-distributed hydrological model developed by the Agricultural Research Service (ARS) of the United States Department of Agriculture (USDA) with daily time steps. It mainly includes the hydrologic process, soil erosion, and pollution load sub-models. SWAT models can simulate surface water, soil water, and groundwater processes. The data can also be subdivided into multiple natural sub-basins based on the real topography of the basin, effectively reducing the impact of the spatiotemporal variation in natural factors on the simulation results and further dividing corresponding hydrological units within the scope of the sub-basin for collaborative change feature simulation [26].  
Equation (2) displays the water balance formula based on the hydrological cycle process simulation of the SWAT model.  
\[SW_{t}=SW_{0}+\sum_{i=1}^{t}(R_{day,i}-Q_{surf,i}-E_{u,i}-W_{sep,i}-Q_{gw,i}) \tag{2}\]  
where \(SW_{t}\) is the soil water content at the end of the period, mm; \(SW_{0}\) is the soil water content at the beginning of the period, mm; \(t\) is the calculation period; \(R_{day,i}\) is the rainfall of day i, mm; \(S_{surf,i}\) is the surface runoff of day i, mm; \(E_{a,i}\) is the evaporation amount of day i, mm; \(W_{sep,i}\) is the permeability of day i, mm; \(Q_{gw}\) is the underground runoff on day i, mm.
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 4 Chipless RFID Array Design
### Planar Array Tags  
A planar array configuration is the arrangement of unit cells in a two-dimensional (2D) plane, typically in rows and columns. In this study, a unit cell and three planar array configurations (\(2\times 2\), \(3\times 3\), and \(4\times 4\) arrays) are designed using CST Microwave Studio for the comparison of RCS as shown in Figure 10. The simulations provide information on the performance characteristics of each array, such as the radiation pattern, resonance frequency, and RCS. The array configurations are carefully designed and simulated in a delicate mesh size environment to obtain accurate RCS data, and the results are analysed in subsequent sections for further analysis.  
Figure 8: Linear array configurations of Pi-shaped resonator. (**a**) Unit cell. (**b**) \(1\times 2\) array. (**c**) \(1\times 3\) array. (**d**) \(1\times 4\) array.  
Figure 9: Surface current distribution of \(2\times 2\) array configuration. (**a**) Without removing copper strips. (**b**) Removing copper strips.
# Three-Dimensional Drone Cell Placement: Drone Placement for Optimal Coverage
## 5 Experiment and Results
### Parameter Tuning  
DFO has been implemented as the meta-meta-heuristic to optimize all the algorithms including itself. The meta-meta-heuristic algorithm has been compiled for a total of 100 iteration; five sets of parameters have been gathered for each of the combinations in Figure 5. The set of parameters that results in the highest user coverage has been used for the rest of the experimentation. Table 3 contains the parameters for the meta-meta-heuristic parameter tuning.  
Figure 4: Default and phased restart diversity for suburban 120 environment. (**a**) Suburban 120 diversity without phased restart; (**b**) suburban 120 diversity with phased restart.  
Figure 3: Default and phased restart diversity for suburban 90 environment. (**a**) Suburban 90 diversity without phased restart; (**b**) suburban 90 diversity with phased restart.  
The pseudo code for the meta-meta-optimizer is presented in Algorithm 4. Based on previous experiments conducted in the field in regards to parameter tuning of the implemented algorithm, the upper and lower bounds were selected from several pioneers in the field [48; 53; 54; 55; 56]. The default and optimized parameters for DFO, PSO and DE are tabulated in Table 4.  
```
1:Initialize the flies within the Search Space
2:while Termination Criteria do
3:for i = 1 : N flies do
4: Calculate the fitness for each fly where fitness is user coverage of the respective meta-heuristic algorithm DFO, PSO and DE
5:endfor
6:for\(i=0\to N-1\,and\,\,i\
eq s\)do
7: Find the best neighbor
8:for\(d=0\to D-1\)do
9:if\(U(0,1)<\Delta\)then
10: Update particle position \(\triangleright\) see Algorithm 3
11: Check boundary within parameter range and clamp if outside
12:endif
13:endfor
14:endfor
15:endwhile
```  
**Algorithm 4** DFO Meta-meta-optimizer pseudo code  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Environment** & **DFO** & & & & **PSO** & & & & **DE** & \\ \hline Standard parameters & 25 & 0.001 & 25 & 1 & 1.5 & 2.0 & 0.99 & 25 & 0.8 & 0.9 \\ \hline Suburban 90 & 34 & 0.05 & 213 & 0.36 & 0.43 & 0.5 & 0.3 & 72 & 0.39 & 0.87 \\ \hline Suburban 100 & 36 & 0.056 & 109 & 1 & 0.31 & 1.14 & 0.73 & 211 & 0.79 & 0.8 \\ \hline Suburban 110 & 235 & 0.069 & 137 & 0.49 & 2.15 & 0.2 & 0.17 & 50 & 0.33 & 0.1 \\ \hline Suburban 120 & 55 & 0.032 & 106 & 0.14 & 0.8 & 2.05 & 0.9 & 183 & 0.11 & 0.89 \\ \hline Urban 90 & 19 & 0.055 & 245 & 0.24 & 1.5 & 1.05 & 0.48 & 168 & 0.35 & 0.54 \\ \hline Urban 100 & 5 & 0.048 & 195 & 0.86 & 0.78 & 0.8 & 0.22 & 110 & 0.71 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of standard and optimized parameters.  
Figure 5: Parameter tuning tree.
## 5 Experiments and Results
### Ablation Studies
#### 5.2.3 The Number of Supporting Frames  
* Due to GPU memory limitations, we extract the features of each image individually and then feed them into memory before inputting them into the sampling and aggregation modules.
* We experiment with different numbers of supporting frames (5, 7, 9, 11, 13, and 15) during inference and 2 or 4 supporting frames per mini-batch during training.
* The results in Table 9 indicate that using more supporting frames during training does not lead to higher accuracy, and the improvement saturates at 13 frames during the inference stage.
* Consequently, we default to sampling 2 supporting frames during training and aggregating features from 13 supporting frames during inference.  
Overall, the ablation studies provide a comprehensive evaluation of our design on the PoseTrack2018 validation dataset. They highlight the importance of motion information on the feature level, the effectiveness of the proposed feature sampling and aggregation modules, the minimal impact of different embedding network structures, and the optimal number of supporting frames for inference and training.
# Topology Optimization and Testing of Connecting Rod Based on Static and Dynamic Analyses
## 4 Results
### Results from Motion Loads  
The dynamic study was carried out to extract the motion loads to evaluate the safety factor of the connecting rod. As discussed in Section 3, the connecting rod was subjected to simultaneously tensile and compressive loads, and only the maximum reaction forces acted while the connecting rod moving toward the TDC and BDC was evaluated. Goodman fatigue life analysis was used to evaluate the FOS and life of the connecting rod and verify that the optimized topology did not fail under the defined conditions. Based on the given reaction forces, the connecting rod had the lowest safety factors of 2.8476 and 2.6182 at the crank end while moving toward the TDC and BDC, respectively, as shown in Figure 12a,b. Given that the FOS needed to be more than 2.5 for the connecting rod, the results satisfied this condition since the obtained FOS values were above 2.5 even in the weaker nodes.  
Figure 11: (**a**) Maximum von Mises stress acting on the initial design. (**b**) Maximum von Mises stress acting on the 5.66% mass-reduced optimized topology.  
Figure 12: (**a**) Safety factor after applying the motion loads on the 5.66% mass-reduced optimized topology at the TDC. (**b**) Safety factor after applying the motion loads on the 5.66% mass-reduced optimized topology at the BDC.  
Goodman fatigue life analysis was used since the chosen material was alloy-steel and to predict the lifecycle of the connecting rod when it was subjected to varying stress [30]. Contributions of this research  
1. The TO process of the connecting rod showed the possibilities of optimizing similar highly dynamic components.
2. An improvement in the existing design was achieved by reducing the mass by 5.66%.
3. The static structural results were validated by the dynamic analysis.
4. The obtained optimized topology was designed in such a way to make it possible to be produced by both conventional manufacturing methods like forging and additive manufacturing.
5. The combination of static structural analysis for the design and rigid body dynamics for the verification of the optimized design can be repeated and applied to similar studies.  
The results clearly show that the connecting rod did not fail under the given motion load conditions while moving toward the TDC and BDC, even at 1 million cycles, as shown in Figure 13a,b.
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 2 CNN Structure  
CNNs are deep neural networks that are good at mining local features of input images. The \"local weight sharing\" and \"down-sampling\" characteristics of CNNs ensures invariance in the shifting, scaling, and rotation of images up to a certain extent. Local connections serve to reduce parameters that need to be trained within the network, thereby speeding up the training process. CNNs are multilayered learning networks comprising an input layer, convolution layer, pooling layer (i.e., down-sampling layer), fully connected layer, and output layer. The basic structure of a CNN is depicted in Figure 1. In a typical CNN, the first few layers of the network usually comprise alternating convolution and pooling layers, whereas the last few layers near the output layer usually represent fully connected networks [33].  
Once an input image has been processed by the convolution layer, multiple feature maps of the image are obtained, and each feature map represents the extraction of a particular feature. During the extraction process, neurons of the same feature map share a set of weights (i.e., convolution kernel), and \(n\) feature maps can be obtained using \(n\) convolution kernels.  
The pooling layer reduces the dimensionality of the convolution result to facilitate reduction in computation. Average pooling retains more image-background information, whereas max pooling retains more information concerning image texture. In view of this, max pooling was favored in this study to ensure retention of more texture information to facilitate accurate image classification.  
The fully connected layer connects all features extracted from the upper layer, reduces these features to their one-dimensional forms, and sends the output value to the output layer for classification.  
Figure 1: Typical convolutional neural network (CNN) structure.  
The output layer solves the multiclass classification problem. The Softmax function used for solving such problems is good at approximating complex nonlinear relationships whilst offering advantages of high training speed and high classification accuracy. Since the number of ship classes within the two datasets considered in this study equaled 4 and 6, respectively, the Softmax function was considered for label prediction of ship images. The output layer (Softmax layer) was placed after the last fully connected layer within the network architecture.
## 6 Evaluated Parameters
### Parameter 3: Rayleigh Damping in the Explicit Method  
Rayleigh damping assumes that the damping matrix \(\mathbf{C}\) is expressed as a linear combination of the mass \(\mathbf{M}\) and stiffness \(\mathbf{K}\) matrices [65], with the form  
\[\mathbf{C}=\alpha\mathbf{M}+\beta\mathbf{K} \tag{9}\]  
here, \(\alpha\) and \(\beta\) are the mass-damping and the stiffness-damping parameters, respectively. For each angular frequency \(\omega_{a}\), the damping ratio \(\xi\) is related to the Rayleigh damping as  
\[\xi=\frac{\alpha}{2\omega_{a}}+\frac{\beta\omega_{a}}{2} \tag{10}\]  
When considering damping in explicit solvers, the time increment \(\Delta t\) is determined as follows [94]:  
\[\Delta t\leq\frac{2}{\omega_{max}}\bigg{(}\sqrt{1+\xi^{2}}-\xi\bigg{)} \tag{11}\]where \(\omega_{max}\) is the highest frequency of the system. If \(\omega_{max}\) is high, the term associated with \(\alpha\) has a negligible effect on the time increment. Conversely, \(\beta\) can significantly reduce the time step, and it may even make the analysis unfeasible for large models. To address this issue and minimize the computational cost, it is usually to set \(\beta\) to zero and only \(\alpha\) is applied [94; 95]. This decision is based on the assumption that the structural response is mainly dominated by low frequencies, which are associated with the mass [67]. On the other hand, if \(\beta\) is applied, it is recommended that its value is either of the same order of magnitude or less than the time increment without damping [87]. It is noticed that Abaqus automatically calculates the time increment. The damping ratio was assumed to be 3% for all the models. For the analysis of the piers, three damping conditions for \(\beta\) were tested: equal to zero, with the value calculated according to Equation (10), and a reduction of that value to 10%.
# Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation
## 3 Methodology
### Loss Function
#### 3.3.2 Center Loss  
In the conventional DNN methodology, features are extracted through a sequence of convolutional layers. Subsequently, a softmax function is employed to compute the probability distribution over the target classes. While this model optimizes the predictions based on the input samples, it typically overlooks the intrinsic relationships among samples within the same class. This oversight can result in suboptimal separation of class features in the feature space.  
To address this limitation, the center loss Ref. [30] incorporates principles from clustering algorithms to enhance class discriminability. Specifically, this loss function aims to increase intra-class compactness--that is, it encourages samples belonging to the same class to cluster closely together in the feature space. The center loss achieves this by establishing a center, \(c_{i}\), for each class and minimizing the following objective:  
\[L_{center}=\sum_{i=1}^{m}\lVert x_{i}-c_{i}\rVert^{2} \tag{3}\]  
where the sum of the squared distances from the center of the discriminative feature for each sample, \(x_{i}\), in a min-batch (m) is expected to be as small (closer) as possible (closer to each other). The learned feature is both discriminative and separable, which, together, improve the performance for detecting the unknowns that are not included in the training dataset. Due to the large number of training samples, the center of each category in the min-batch is different. Ref. [30] used a gradient descent approach to automatically update the center value in each min-batch and obtain the average centroid of all samples for inference. The adapted center loss for our segmentation task can be rewritten as follows:  
\[L_{center}=\frac{1}{T}{\sum_{t=1}^{T}}\Big{\lVert}f^{e}(x^{t})-C_{yi}\Big{\rVert} ^{2} \tag{4}\]  
where \(f^{e}(x^{t})\) is the \(\hat{N}\)-dimensional feature extracted from \(x^{t}\); and \(C_{yi}\) is the center of class \(y^{t}\) in the feature space at time t, which is calculated by taking the average of \(f^{e}(x^{t})\) for all data in a batch. This loss tries to minimize the intra-class distance by pulling the embedded feature into the cluster center.