esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### The Tectonic Control  
Regional tectonism may play a crucial role in the formation and evolution of the channels. This is manifested in that the channels in the study area were built on a pre-existing syn-rift depression zone (Figures 3 and 4). In the Late Miocene, the direction of regional plate movement along the Alpine Fault plate boundary underwent a change from mostly strike-slip to strongly transpressive (~10-8 Ma) [9; 15; 58; 59; 60; 61; 62]. This tectonic reorganization is evidenced by a regional phase of tectonism and volcanism that occurred in the Late Miocene (Kaikoura Orogeny; about 15-10 Ma), marked by the eruption of alkalic volcanoes, regional faulting, and folding of sediments up to the early Late Miocene age throughout the Bounty Trough [9; 14; 15; 62]. This phenomenon could be related to an increase in the convergence rate between the Pacific and Australian plates, leading to the uplift of the Southern Alps and increasing rates of sediment supply to the Bounty Trough [9; 58; 59; 63]. These tectonic changes likely favored the initiation of channels in the study area, which is consistent with the previous assumption that the modern physiography of the Bounty Trough, including the present Bounty Channel and abyssal Bounty Fan, all date from the Kaikoura event [9; 14]. Meanwhile, the pre-existing NE-SW-oriented uplift zone may be strengthened by this tectonic reorganization, which in turn regulates the direction of the early-stage NE-SW-oriented channels (Figures 4 and 7A). Along with the inherited depression zone gradually filling over time, its control on the distribution of the channels diminished. This resulted in the early NE-SW-oriented channels decreasing activity and finally being abandoned (Figures 4D and 7B-F).  
Uplift of the Southern Alps in central South Island started to speed up at ~5 Ma, related to the acceleration of the convergence rate between the Pacific and Australian plates [9; 59; 64]. This progress may result in the formation of an obvious erosion uncomformity, i.e., the base of sequence S4 (corresponding to N60) (Figures 3 and 4), which indicates a new tectonic tilting and potentially causes an overall change in the orientation of the Pliocene to Quaternary tributary channels. Moreover, this can greatly increase the supply of terrigenous sediments to the study area, promoting the formation of modern tributary channels C1 and C2. These tributary channels mainly aligned with the slope gradient of the continental slope (Figures 4 and 7D-F), suggesting the control of modern slope topography on the distribution of the channels. Well-developed levees and sediment waves on both sides of tributary channels C1 and C2 and their V-shaped channel thalwegs (Figures 4 and 7D-F) indicate that turbidity currents processes have been highly active in the channels since the Pliocene. Previous studies have indicated that the Bounty Channel and Bounty Fan probably originated as early as the Late Miocene but have mostly been built during the last 3 million years (Plio-Pleistocene), which is consistent with our observations [7; 8; 9; 11; 62].
## 2 Experimental Section
### Measurement of PEP Activity  
#### 2.7.1 zGly-Pro-pNA Assay for PEP Activity  
Each of the protein lysates, from the soluble fraction and from the refolded insoluble fraction, as well as the purified enzyme, were assayed for PEP activity using zGly-Pro-pNA (benzyloxycarbonyl-Gly-Pro-p-nitroanilide) (Bachem Americas, Inc., Torrance, CA, USA). The reaction buffer consisted of 0.025 M ethanolamine, pH 8.5, with 0.3 mM zGly-Pro-pNA substrate. In a 96-well microtiter plate, 150 \(\mu\)L of reaction buffer were added to all wells. Enough enzyme in 0.1 M sodium phosphate, pH 7.5, was added to separate wells, so that 50 \(\mu\)L of enzyme could be added to each well containing substrate to start the reaction. Immediately after the addition of the enzyme, the plate was placed in a Benchmark Plus microtiter plate reader (Bio-Rad Laboratories, Hercules, CA, USA). The absorbance at 410 nm was measured in kinetic mode. The \(\varepsilon_{410}\) for cleaved zGPpNA is 8,800 L\(\cdot\cdot\cdot\)mol\({}^{-1}\cdot\cdot\)cm\({}^{-1}\)[2]. One unit of activity is defined as the amount of enzyme that produces a change in absorbance of 0.1 at 410 nm per minute [2]. The specific activity of the PEP in each fraction is reported as \(\mu\)M of product\(\cdot\cdot\)min\({}^{-1}\).
## 3 Results
### Calibration of Biome-BGCMuSo Mode  
After the EFAST method was used to analyze the sensitivity of the physiological and ecological parameters of the Biome-BGCMuSo model, the results of calibrating the sensitive parameters of the model for different forest types were shown in Figure 5. The GPP values simulated by the optimized MOD_17 model are in agreement with the GPP values simulated by the calibrated Biome-BGCMuSo model. The best effect is DNF, R\({}^{2}\) = 0.84, RMSE = 24.16 g/C/m\({}^{2}\)/16d; followed by MF, with R\({}^{2}\) = 0.81 and RMSE = 17.25 g/C/m\({}^{2}\)/16d; the last one is DBF, with R\({}^{2}\) = 0.76 and RMSE = 15.39 g/C/m\({}^{2}\)/16d.  
Through the dendrochronological measurements of 46 sample plots collected in the Greater Khingan Mountains area of Inner Mongolia in 2013 and 2016, some samples with serious interference and large measurement errors were screened. After conversion to forest NPP, they were verified with the calibrated Biome-BGCMuSo model. The results are as follows (Figure 6). The verification results showed that the calibrated Biome-BGCMuSo model simulates the forest NPP better, with R\({}^{2}\) = 0.64 and RMSE = 26.55 g/C/m\({}^{2}\)/year.  
Figure 5: Comparison of 16-day GPP values fitted by the optimized MOD_17 model and the calibrated Biome-BGCMuSo model.
## 4 Calculation of Structure Stress Fields
### Computing Results and Analysis  
To analyze the relationship between the dynamic stress characteristics and spatial positions of impeller blades under different working conditions, a static coordinate (\(x,y\)) and a rotating coordinate (\(\psi\), \(\xi\)) are defined in Figure 7. In a rotating period, the position of an impeller blade is defined by the anti-clockwise rotation angle \(\varphi\) between the positive \(\psi\) axis of the rotating coordinate and positive \(y\) axis of the static coordinate. When the analyzed blade is located at the highest point of rotation and the blade's axial direction points in the direction of gravity, that means \(\varphi\) = 0\({}^{\circ}\).  
Figure 8a shows the equivalent stress of the suction side of the impeller blade at \(\varphi\) = 0\({}^{\circ}\), and a stress concentration region is found near the root and leading edge. This is consistent with the crack areas on the prototype blade, as shown in Figure 8b. This means the cracks may be influenced and caused by the high equivalent stress and stress concentration.  
Figure 9 provides the changing rule of the maximum dynamic stress of blades within a spatial rotation cycle under different working conditions. Considering the influence of gravity, the maximum dynamic stress of the blade first increases and then decreases in an anticlockwise rotation cycle. The maximum equivalent stress appears at \(\varphi\) = 66\({}^{\circ}\) and is 150.25 MPa and 173.14 MPa under \(H\) = 2.5 m and \(H\) = 3 m working conditions, respectively. Under \(H\) = 3.5 m and \(H\) = 4 m working conditions, the maximum equivalent stress appears at \(\varphi\) = 140\({}^{\circ}\) and is 197.21 and 221.37 MPa, respectively; thus, the higher a head is, the greater its maximum dynamic stress would be. Meanwhile, the maximum dynamic stress of blades all appear on the suction surface near the leading edge of the blades and the root.  
Figure 8: Comparison of the simulated stress distribution of the impeller blade suction side at \(\varphi\) = 0\({}^{\circ}\) and cracks on the prototype impeller blade, (**a**) simulated region of stress concentration, (**b**) clustered cracks on the prototype impeller blade.  
Figure 7: Definition of coordinate systems.
## 3 Materials and Methods
### Analysis of the Forecasting Methods
#### 3.1.1 Naive Forecasting Method  
The Naive Forecasting Method, or naive models, assume that the occurrence level for the forecast period is equal to the occurrence level from the last available period, i.e., forecast = last month's data. Based on the last data set (training data), a forecast was made (Figure 2). All values are the same because the prediction was made relative to the values of the last data set. The evaluation criterion for this model could not be applied, as this model repeats the last month's data.  
Figure 1: Examples of (**a**) graphical display of data; (**b**) graphical display of data trends and seasonality.  
Figure 2: Example of forecasting using the Naive Forecasting Method.
## 4 Materials and Methods
### Methodology
#### 4.2.2 The Workflow of the Proposed Approach  
This study systematically addresses the challenges of multi-scale feature extraction and small-sample class recognition in sedimentary facies image classification through the development of a lightweight convolutional neural network (CNN) integrating multi-scale dynamic inputs and cross-modal fusion with sequence stratigraphic analysis. The experimental workflow comprises four core components: data preprocessing, augmentation strategy design, model architecture optimization, and performance evaluation.  
The model architecture adopts a lightweight dual-branch design:  
(1) The image input branch contains three convolutional modules (32 \(\rightarrow\) 64 \(\rightarrow\) 128 channels), extracting local texture features via dynamic convolution kernels, combined with global average pooling to compress spatial dimensions;  
(2) The system tract input branch embeds prior geological knowledge (system tract classifications) through fully connected layers. These features are concatenated with image features along the channel dimension before final classification via fully connected layers into six sedimentary facies probabilities. To suppress overfitting, a dropout layer (rate 0.5) is introduced before fully connected layers, supplemented by L2 regularization constraints post convolution. The optimizer employs the [PERSON] algorithm with an initial learning rate set to \(1\times 10^{-3}\), dynamically adjusted via an exponential decay strategy (decay rate 0.9, step size 500). The loss function utilizes weighted cross-entropy, addressing class imbalance through computed class weights.  
Experimental evaluation employs five-fold cross-validation, with primary metrics including overall accuracy, the Jaccard similarity coefficient, and the Matthews correlation coefficient. Feature space distributions are analyzed via confusion matrices and t-SNE-based dimensionality reduction visualization to verify the model's discrimination capability between thin-bed channels (<1 m) and thick-bed shoals (>3 m).  
The overall procedure of the proposed method is shown below in Figure 9.  
Figure 9: Flowchart of the method.
# Function Optimization and Parameter Performance Analysis Based on Gravitation Search Algorithm
## 2 Gravitational Search Algorithm
### Basic Principles of Gravitation Search Algorithm  
Because there is no need to consider the environmental impact, the position of a particle is initialized as \(X_{i}\). Then in the case of the gravitational interaction between the particles, the gravitational and inertial forces are calculated. This involves continuously updating the location of the objects and obtaining the optimal value based on the above mentioned algorithm. The basic principal of gravitational search algorithm is described as follows in detail.
# Coherence Enhancement Based on Recursive Anisotropic Scale-Space with Adaptive Kernels
## 5 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Isotropic**} & \multicolumn{2}{c}{**Anisotropic**} & \multicolumn{2}{c}{**Coherence-Enhancing**} & \multicolumn{2}{c}{**Our Method**} \\ \hline
**Threshold** & **Precision** & **Recall** & **Precision** & **Recall** & **Precision** & **Recall** & **Precision** & **Recall** \\ \hline \(\theta=3\) & 0.6405 & 0.7365 & 0.7750 & 0.0620 & 0.7272 & 0.8670 & 0.7754 & 0.8581 \\ \hline \(\theta=2\) & 0.5556 & 0.8470 & 0.7073 & 0.6099 & 0.6139 & 0.9335 & 0.7706 & 0.9324 \\ \hline \(\theta=1\) & 0.3384 & 0.9288 & 0.6473 & 0.8856 & 0.4893 & 0.9335 & 0.7639 & 0.9393 \\ \hline \(\theta=0\) & 0.2849 & 0.9288 & 0.5868 & 0.8856 & 0.3480 & 0.9592 & 0.5606 & 0.9429 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative evaluation of the detection of curvilinear structures corresponding to roads in aerial images and fissures in coronal, axial, and sagittal plane CT images in terms of precision and recall. Figure 7: Quantitative comparison of the ROC curves for curvilinear structure detection in (**a**) an aerial image, and in (**b**) coronal, (**c**) axial, and (**d**) sagittal plane lung CT images. Our proposed algorithm (in blue color) is compared with the others, namely isotropic diffusion (in green color), anisotropic diffusion by proposed Perona–Malik (in red color), and coherence-enhancing diffusion proposed by [PERSON] (in pink color).
# Evaluating Packaging Design Relative Feature Importance Using an Artificial Neural Network (ANN)
## 3 Results
### Case Study 1: Relative Feature Importance of the Synthetic Dataset  
The synthetic dataset was created by inputting the box perimeters, depths, ECTs, and thicknesses of 3009 commonly used commercial boxes [43] into the simplified McKee formula, as detailed in Equation (7) [44] to compute the BCS values.  
\[\text{BCS}=5.87\times\text{ECT}\times\sqrt{\text{Thickness}\times\text{P}} \tag{7}\]  
where  
ECT--Edge crush strength (lb/in).  
Thickness--Thickness of the corrugated board (in).  
P--Perimeter of the box (in).  
Using the simplified McKee formula and the concept of derivative, the theoretical relative importances of the four BCS features were calculated, as shown in Figure 5. ECT has a weight of 0.500, both perimeter and thickness have a weight of 0.250, and depth (abbreviated as d below) has no importance (0).
# Development of a Novel Concrete Curing Method Using Induction Heating System
## 3 Properties of Concrete after Curing
### Results and Discussion
#### 3.2.2 Compressive Strength Results
Figure 11: Comparisons of compressive strength ratios: (**a**) effects of maximum curing temperature; (**b**) effects of duration of maximum curing time; (**c**) effects of maturity for IHC specimens; (**d**) effects of cross-sectional size. Figure 10: Appearance of cylinders after compression tests at 15 h: (**a**) LTC specimens; (**b**) ATC specimens; (**c**) IHC specimens; (**d**) HTC specimens. The effects of the cross-sectional dimensions of the specimens on the early age strength development of heat-cured concrete can be examined using the results of Figure 11d. Both IHC and HTC specimens having the smallest cross-sectional dimension, \(\phi\)100, exhibited early age strength values that were significantly higher than those of larger specimens. This result can be attributed to the effective transfer of heat from the steel form to the entire cross-section of concrete (see Figure 9a), providing sufficient curing. The compressive strength ratio of IHC specimens continually decreased as the size of the specimens increased and this result can be possibly explained by the results of temperature distributions in Figure 9. As previously indicated, the non-uniformity of the temperature distributions inside concrete increased as the specimen size increased because the heat generated in the steel form for curing concrete is not sufficiently transferred to the center of concrete if the specimen size is large. Thereby, induction heat-cured concrete of larger specimens exhibited lower strength development at an early age. Meanwhile, HTC specimens with a cross-section of \(\phi\)300 mm exhibited slightly higher strength than that of specimens with a cross-section of \(\phi\)150 mm. This is inconsistent with the results of IHC specimens and may be attributed to the difference in heat transfer mechanisms between HTC and IHC. As mentioned above, IHC initially heats the steel form located outside the concrete, and then the generated heat is transferred to the interior of the concrete. In the case of HTC, concrete curing was carried out in the test chamber at elevated temperature and therefore, the entire cross-section of the cylindrical specimens in this chamber was heated uniformly. This resulted in quite similar early-age strength development for the various cross-sectional dimensions. In summary, the effectiveness of IHC decreases as the specimen size increases because the heat generated in the steel form could not be sufficiently transferred to the center of the concrete. This resulted in lower strength development at an early age for induction heat-cured concrete with lager cross-sectional dimensions that should be properly addressed for the practical utilization of the IHC method in construction fields.
## 4 Results and Discussion
### Results of SIFT
Following this, the blue axis (horizontal) is closer to a normal distribution with zero, which should mean that the displacement of the matching in this direction is near to zero as well. However, the red axis (vertical) has a different distribution; in this case, the majority of the matched pixels are within the limits of the search window. For this reason, there is supposed to be a great displacement between the matched DEMs on this axis. Finally, we composed both displacements at a distance expressed in the pixels of the image. The rotation between rings was not considered due to their values being zero in almost all cases, which is explained by the fact that all the DEMs have the same orientation. The composed distance is featured in Figure 6, which presents two different results. The comparison between DSM05 and DTM05 has similar distance values, between two and three pixels. However, between DTM25 and the other DEMs, the distance is around ten pixels, which is two multiplied by the difference in scale of the DEMs. For this reason, both comparisons show a similar result. With regard to the specific cells that were matched between the DEMs, the images in Figure 7 can be used as an example. In this figure, the red pixels represent cells that have a correlation coefficient better than 0.99. The first idea that we want to highlight is the difference in the matched cells; zone 946 has a smaller number of highlighted cells because there are great differences between MDT and MDS, even with the same spatial resolution. These differences are the expected results because height changes are focused on zones with vegetation and buildings. On the other hand, zone 1027 has a high number of matched cells, which we consider a good result because the DEMs are in areas with low vegetation or even bare ground. The results in Figure 7 also highlight riverside vegetation, for example, in the zoomed areas of zones 1027 and 1034. Moreover, vegetation areas, like the zoomed part of zone 1008, have been detected and discarded as highlighted cells for matching both DEMs. To end this section, the results of the matching process are shown in Table 4. As we can see, the matched keypoints show almost no rotation and a scale that is almost equal to the scale of definition of the DEMs, which are one between DTM05 and DSM05 and five between DTM25 and the other two. The number of keypoints used for this calculation is more than 500,000 points in the lower cases, and the precision is near the distance of Figure 6 for transformations between DTM05 and DSM05 and five times higher between DTM25 and DSM05 or DTM05.
## 3 Results and Discussion
### ANN Validation and Comparison with ANOVA  
After training the ANN, the network should be verified with the data intended for testing. The test data in this study included 15% of the total data that had not been used in the ANN training phase. Table 7 also compares the prediction accuracy of different types of ANN with that presented by ANOVA.  
According to Table 7, the best networks of each type have been chosen to compare the prediction accuracy. For basic ANN, the trained network in row 3 is considered as the selected basic ANN with the best performance. The type of activator function is \"TanSig\" for the first layer and \"Linear\" for the second layer. For ANN + PSO, the trained network in row 7 is regarded as the most suitable algorithm of this type. The trained network in row 11 has also been chosen as the most suitable ANN + GA combination. The mutation rate in this algorithm is 15%, and the cross-over rate is 65%. Additionally, Figure 10 exhibits a comparison between the predicted values via the regression formula provided by ANOVA, selected neural networks of all three types (basic ANN, ANN + GA, and ANN + PSO) with the value obtained from the FEA. Furthermore, Table 8 presents the values predicted by each algorithm for all the data sets compared with ANOVA predicted values and FEA results.  
As it is clear from Figure 10 and Table 8, ANOVA with 86.5% accuracy and basic ANN with 99.99% accuracy provide the farthest and the closest predictions to the FEA response, respectively. Additionally, according to these data, the basic ANN presented in row 3 of Table 7 (with eight neurons in the hidden layer) has been chosen as the most appropriate prediction model. The graph presented in Figure 11 indicates the training procedure of the basic ANN. During 91 epochs, the basic ANN converged. The MSE trend of training data can be seen alongside this trend for validation and test data. In addition, this figure represents the regression analysis of the basic ANN model for training, validation, and test data. Additionally, the regression model for the whole data set can be seen in the bottom right corner of Figure 11.