esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Target Network Model for Rail Surface Defect Detection
## 2 Methodology
### Unit Clustering Based on the k-means++ Algorithm  
The default sizes of initial anchor boxes in the YOLO V7 algorithm are based on the CoCo training set, which uses the k-means algorithm for calculation and is influenced by the initial cluster centers. If these default sizes were directly used for training, it not only affects the final model's accuracy but also leads to a prolonged training process without convergence. Therefore, it is essential to recluster the dataset to obtain suitable anchor box sizes, which makes the learning process of deep convolutional neural networks smoother and enables better predictions.  
We have improved the accuracy of the proposed object detection network in predicting object positions by using the k-means++ clustering algorithm [38] instead of the original k-means clustering algorithm. Compared with k-means, the k-means++ clustering algorithm improves the accuracy of classification results by optimizing the selection of initial points. By using k-means++ to cluster the dataset, more accurate and representative anchor boxes are generated by reducing the bias in the clustering results caused by the random selection of initial cluster centers.  
When using the k-means++ clustering algorithm to select candidate boxes for rail surface defect samples, the distance between sample \(b_{i}\) and the center of the cluster \(c_{j}\) are as shown in Equation (11), where \(C=\left\{b_{1}(x_{1},y_{1}),\ldots,b_{n}(x_{n},y_{n})\right.\) is the sum of the widths and heights of all labeled rails and rail defects.  
\[d\left(b_{i},c_{j}\right)=1-\text{IOU}\left(b_{i},c_{j}\right) \tag{11}\]  
where \(\text{IOU}\left(b_{i},c_{j}\right)\) represents the intersection over union between \(b_{i}\) and \(c_{j}\).  
Once the cluster centers are determined, the following Equation (12) is shown as follows:  
\[P\left(x\right)=\frac{d\left(b_{i},c_{j}\right)}{\sum_{m=1}^{N}{Dx_{i}}^{2}} \tag{12}\]  
When the input image size is 640 \(\times\) 640, the algorithm will generate three different sizes of feature map outputs: 80 \(\times\) 80  
In this research, applying k-means clustering to our training dataset resulted in the identification of nine anchor box sizes, which are (117, 62), (123, 99), (162, 79), (618, 35), (164, 147), (614, 41), (43, 621), (626, 59), and (85, 619). These numbers represent the anchor frame dimensions, with the first number in parentheses representing the length of the anchor frame and the second number representing the height of the anchor frame. The calculation results are shown in Figure 5, where the x-axis and y-axis represent the width and height of the ground truth bounding box, respectively. Each color represents one cluster, and \"\(\bigstar\)\" represents the centroid of each cluster after clustering. Because there are 9 sets of anchors in the algorithm, there are 9 centroid points in the figure.
## 4 Baseband Modulation Instability  
Baseband modulation instability, the instability due to low frequency disturbances, has been shown to be intimately related to the existence condition of rogue waves [29]. The connection was established theoretically in several dynamical systems [38; 39; 41; 42]. This section focuses on the role of baseband modulation instability in the formation of rogue wave.
## 2 Materials and Methods
### NASA-USDA Soil Moisture Data  
National Aeronautics and Space Administration Goddard Space Flight Center (NASA GSFC) provided the NASA-USDA global soil moisture data by the 1-D Ensemble Kalman Filter (EnKF) data assimilation method. SMOS level 2 soil moisture observations were generated by integrating them into a modified two-layer Palmer model [41]. Due to the low resolution of SMOS itself, as a result, the NASA-USDA global soil moisture data generated based on SMOS data has a low spatial resolution. The spatial resolution of this dataset is only \(0.25^{\circ}\times 0.25^{\circ}\). This dataset includes both surface and subsurface soil moisture data, but the soil moisture based on GNSS-IR can only reflect the variation of soil moisture within 1-6 cm of the soil surface. Therefore, the surface soil moisture of the NASA-USDA global soil moisture data is selected as the initial comparison data for the point-surface fusion results. This paper obtained NASA-USDA global soil moisture through Google  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Parameters** \\ \hline Receiver Type & TRIMBLE NETRS GPS \\ Antenna Type & TRIM29659.00 \\ Rectifier type & SCIT \\ Station height and sampling rate & 2 m, 15 s \\ Effective depth for measuring soil moisture & 0–5 cm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of PBO measurement stations.  
Earth Engine ([[https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_soil_moisture#description](https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_soil_moisture#description)]([https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_soil_moisture#description](https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_soil_moisture#description)), accessed on 6 March 2021) data.
# A Hybrid Adaptive Particle Swarm Optimization Algorithm for Enhanced Performance
## 5 Engineering Application
### Tension Spring Design  
The tension/compression spring problem is a classic design challenge in structural engineering. Its objective is to minimize the spring weight while ensuring safety and stability by meeting four constraints: deflection, shear stress, surge frequency, and outer diameter. The core design variables for this problem include the wire diameter \(d\), mean coil diameter \(D\), and the number of active coils \(N\). The structural design of the tension/compression spring is illustrated in Figure 9.  
The mathematical model of the design is as follows:  
* Design variable: \(x=[x_{1},x_{2},x_{3}]=[d,D,N]\);
* Objective function: \(f(x)=(x_{3}+2)x_{2}x_{1}^{2}\);
* Constraint condition: \(g_{1}(x)=1-\frac{x_{3}^{2}x_{3}}{71785x_{1}^{4}}\leq 0\), \(g_{2}(x)=\frac{4x_{3}^{2}-x_{1}x_{2}}{12566(x_{2}x_{1}^{3}-x_{1}^{4})}+\frac{ 1}{5108x_{1}^{2}}\leq 0\), \(g_{3}(x)=1-\frac{140.45x_{1}}{x_{2}^{2}x_{3}}\leq 0\), \(g_{4}(x)=\frac{x_{1}+x_{2}}{5}-1\leq 0\).
* Variable range: \(0.05\leq x_{1}\leq 2\), \(0.25\leq x_{2}\leq 1.3\), \(2\leq x_{3}\leq 15\).  
In this design, it is necessary to set the boundary of \(x_{1}\), \(x_{2}\), and \(x_{3}\) under constraint conditions. The upper and lower bounds of the particle individual are \(ub=[2.0,1.3,15.0]\) and \(lb=[0.05,0.25,2]\), respectively. If \(x_{1}\), \(x_{2}\), \(x_{3}\) do not satisfy the constraints, we need to define a fitness function and add a penalty function to it. These two functions are \(fit=(x_{3}+2)x_{2}x_{1}^{2}+gp\) and \(gp=factor\times\sum_{i=1}^{i=4}(\max(x,g_{i}(x)))^{2}\), respectively.  
In order to verify the performance of the algorithm, we compared the improved algorithm with other metaheuristic algorithms such as GWO, WOA, SSA and BOA. To ensure the fairness of verification, each algorithm was run 30 times independently, and the minimum of the running results was taken as the optimal solution. The experimental results are shown in Table 8.
## 5 Results and Robustness Analysis
### Exploring Asymmetry Through the Analysis of \"Low Propensity for Business Failure\"  
To explore the asymmetric nature of causality in business failure, we conducted an additional fsQCA using \"low propensity for business failure\" as the outcome.  
This approach allows us to examine the configurations of conditions that are associated with the absence of business failure, providing a contrast to the configurations that lead to its occurrence.  
The analysis involved the same set of predictor variables identified in the original analysis (SOLL, RTAS, ROA, GEAR, TPR, PPE, and TRUCAM). We applied the fsQCA methodology to the new outcome (low propensity for business failure) as described in Section 4.  
The results of this analysis revealed a distinct causal configuration associated with \"low propensity for business failure\", which differs from those found in the original analysis. The solution is displayed in Table 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline Solution: \(\rightarrow\) & FSOLL*-F.TPR*-F.PPE + F.GEAR*-F.TPR*-F.PPE + \\ \(\
eg\)F.RTAS*-F.TPR*-F.PPE*F.TRUCAM \(\rightarrow\) & F.PPE + \\ \(\
eg\)F.  
This solution suggests that a combination of high solvency (F.SOLL), low indebtedness (-F.GEAR), high returns per employee (-F.TPR, -F.PPE), and low environmental risk (-F.TRUCAM) is associated with a low propensity for business failure (F.LOW_VADISP2 BB).  
In contrast, the original analysis, where the outcome was \"business failure\" (Table 5), identified three different causal configurations:  
The differences between these results and the original analysis highlight the asymmetry of causality in business failure. Conditions that contribute to failure do not simply have the opposite effect on \"no failure\", indicating that different causal pathways imply separate causal dynamics. This underscores the importance of examining both outcomes to gain a better understanding of the factors influencing business success and failure.
## 8 Conclusions and Future Work  
In this research, we proposed a combination of machine-learning algorithms and statistical methods. This method consisted of four parts. In the first part, k-means clustering was used to identify k homogeneous clusters, which homogenizes the data and assigns them to similar clusters. In the second part, the data within the clusters were trained for the prediction of RF trees, and weak trees were created with high accuracy. In the third part, shrinkage methods such as lasso, elastic net, and group lasso were used to decrease the number of RF trees. They increased efficiency, reduced error, and improved the model performance. Finally, the remaining trees were ensembled to reduce variance and error. To evaluate the efficiency of the designed method, the ECAPRAF model was applied to both real and simulation data. The results were analyzed using MSE, RMSE, and MAE criteria for each cluster and WMSE, WRMSE, and WMAE criteria for the weighted sum of clusters. The simulation results showed that in all four states, ECAPRAF-EN, ECAPRAF-Lasso, and ECAPRAF-GL were reduced by about 12%, 11.5%, and 11% compared to RF, respectively. The real datasets showed about 12%, 7%, and 11% reduction, respectively. Among the three methods, including the ECAPRAF-EN, ECAPRAF-Lasso, and ECAPRAF-GL, ECAPRAF-EN provided better results and more trees within clusters. Concerning the number of trees, ECAPRAF-EN with 128 trees performed better than ECAPRAF-Lasso with 100 and ECAPRAF-GL with 105. Among the methods, ECAPRAF-EN has the best performance compared to other shrinkage methods. Therefore, it can be concluded from this study that our proposed model, which sought to increase the accuracy and improve the performance of traditional RF with clustering and shrinkage methods, is efficient.  
As a future work, we will use the CNN algorithm instead of random forest. This will be done by replacing multiple parallel CNNs instead of random-forest trees. In other words, we will enhance the CNN network by using shrinkage methods.  
Conceptualization, Z.F. and H.B.; methodology, Z.F.; software, Z.F.; validation, H.B. and M.-R.F.-D.; formal analysis, Z.F.; writing--original draft preparation, Z.F.; writing--review and editing, H.B., M.-R.F.-D., M.F.I. and W.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
[.]Data Availability Statement: Boston house price: [[http://lib.stat.cmu.edu/datasets/boston](http://lib.stat.cmu.edu/datasets/boston)]([http://lib.stat.cmu.edu/datasets/boston](http://lib.stat.cmu.edu/datasets/boston)) (accessed on 7 May 2021); Real Estate Valuation: [[https://archive.ics.uci.edu/ml/datasets/Real+estate+valuation+data+set](https://archive.ics.uci.edu/ml/datasets/Real+estate+valuation+data+set)]([https://archive.ics.uci.edu/ml/datasets/Real+estate+valuation+data+set](https://archive.ics.uci.edu/ml/datasets/Real+estate+valuation+data+set)) (accessed on 7 May 2021).  
The authors declare no conflict of interest.
## 4 Input/Reactions from Participants
### Physiological Data Collection  
It is well known that physiological characteristics reflect the emotion and attention of a person. Hence, it may be desirable to obtain such information during a session of treatment to gain better insight to the state of an individual with ASD. We have seen more autism research that employs various sensors to collected physiological signals, including Pulse Plethysmogram (PPG), Skin Temperature (ST), Electrodermal Activity (EDA), Electrocardiographic (ECG), and Electroencephalography (EEG) [39; 40; 51; 139; 140]. Typically, the classification of such signals was done offline. Hence, the classification accuracy does not impact the flow of the program and cannot cause harm to the participants. Nevertheless, in the long run, obtaining responses through EEG has the potential to develop brain-computer interfaces to improve social skills of individuals with ASD [39]. EEG data were also collected in [141; 142; 143; 144]. In [145], [PERSON] et al. reported a design for wearable companion toys in which biosensors can be embedded to measure the participant's physiological and emotional state. In [146], physiological data, including PPG, Galvanic Skin Response (GSR), and Respiration (RSP), are collected while a participant with ASD engages in a driving task. Note that in some studies, multimodal data are collected [143]. In addition to physiological data, eye gaze and spoken conversations are also collected [143] to enable adaptive social interaction.
## 4 Results and Analysis
### Results and Analysis of Surveys
#### 4.1.2 Effects of Feedback and Contexts in Surveys  
According to the data from the second survey, the effects of feedback and contexts in HRI with interaction errors, i.e., RQ1 and RQ2, were analyzed by [PERSON]'s exact test, respectively. The corresponding results are shown in Table 3. \(\chi^{2}\), \(p\), and Cramer's V represent the values of chi-square, significance, and Cramer's V, respectively. They show that, for contexts, the difference between the count and the expected count is quite small as \(\chi^{2}\) is small. In addition, they show significant differences for feedback (\(p\) < 0.05) and no significant differences for contexts (\(p\) > 0.05). The Cramer's V coefficients in Table 3 indicate the magnitude of the effect size of contexts is quite small, while that of feedback is medium. There also exists a statistical difference between the count and the expected count in the cases where the user emotions are happiness, surprise, anger, and disgust, respectively, as feedback. In the case of repeated CF, the effects of contexts were considered, and the corresponding results are shown in Table 4, where \(i\) represents the number of times that CF is repeated. It is obvious that contexts do not show an impact on user emotions, but feedback does.  
The log-linear model analysis method was employed to analyze the corresponding interaction effects, i.e., RQ4. The main variables include contexts, feedback, and emotions before and after users encounter interaction errors. The corresponding results show that all \(p\)-values in the step summary of the model are larger than 0.05. This reveals there are no interaction effects between contexts, feedback, and previous emotions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(i\) * & \multicolumn{3}{c}{**Contexts**} & \multicolumn{3}{c}{**Previous Emotions**} \\  & \(\chi^{2}\) & \(p\) & Cramer’s V & \(\chi^{2}\) & \(p\) & Cramer’s V \\ \hline
1 & 1.676 & 0.815 & 0.119 & 39.585 & \textless{}0.001 & 0.323 \\
2 & 2.288 & 0.838 & 0.126 & 51.192 & \textless{}0.001 & 0.357 \\
3 & 2.044 & 0.901 & 0.128 & 87.742 & \textless{}0.001 & 0.424 \\ \hline \end{tabular}
* \(i\) represents the number of times that CF is repeated.  
\end{table}
Table 4: The results in the case of repeated CF for the effects of contexts and previous emotions, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \(\mathbf{Factor}\) & \(\mathbf{x}^{2}\) & \(p\) & Cramer’s V \\ \hline Feedback & 43.414 & \textless{}0.001 & 0.419 \\ Contexts & 1.641 & 0.95 & 0.083 \\ Previous emotions & 44.149 & \textless{}0.001 & 0.248 \\ \hline \end{tabular}
\end{table}
Table 3: The results in the case of encountering interaction error for the first time.
## 3 Results and Discussion
### Optimization of HS-SPME
#### 3.2.8 The Effect of Sample Volume  
HS-SPME technology is based on the dynamic balance of analytes among samples, headspace and fiber coatings ([PERSON] et al., 2017). Therefore, sample volume will affect the ionic strength and vapor pressure of volatile analytes, thereby affecting the extraction efficiency. The experimental results showed that some compounds are low in content, so it should be avoided when the sample amount is too small to be adsorbed by the fiber. However, high sampling content is also unfavorable, owing to the fact that fiber easily becomes saturated and absorbed, and the excessive volatile compound will not be continuously collected.  
Figure 6: Effect of desorption temperature on the extraction efficiency of the VOCs.  
According to the experimental results, 8 mL was selected for subsequent investigation. Furthermore, in authentic water sample analysis, samples with a high content of VOCs are diluted into an appropriate quantitative region.
## 4 Experimental Results
### Benchmark Models  
For benchmark comparison, we deploy several conventional deep learning models, such as RNN, LSTM, and GRU, to examine whether the proposed models outperform the benchmarks. In addition, we utilize WaveNet, which combines causal filters with dilated convolutions, so that the model learns long-range temporal dependencies in time-series data [70]. The benchmark models and corresponding architectures are listed below.  
1. RNN: Two RNN layers with 128 units and a dense layer with a look-ahead period of units;
2. LSTM: An LSTM layer with 128 units and a dense layer with a look-ahead period of units;
3. GRU: A GRU layer with 128 units and a dense layer with a look-ahead period of units;
4. WaveNet: A simpler architecture of an audio generative model based on PixelCNN [71], as described in [70].  
Table 2 lists the training setting for the benchmark models. All benchmark models were trained with 50 epochs, an early stopping patience of 10, a learning rate of 0.0005, a batch size of 32, the MSE loss function, the Adam optimizer, and the ReLU activation function.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Number of epochs & 50 \\ Early stopping patience & 10 \\ Learning rate & 0.0005 \\ Batch size & 32 \\ Loss function & MSE \\ Optimizer & Adam \\ Activation function & ReLU \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter setting for the benchmark models.
# Dropped Object Impact Analysis Considering Frequency and Consequence for LNG-FPSO Topside Module
## 5 Numerical Simulation and Test Evaluation
### Drop Impact Analysis of the Cone-Type Object
Meanwhile, to demonstrate the application possibilities of the proposed drop impact analysis, experimental results of strain measured near the impact point (at a point 150 mm from the crash point) and analytical results of strain calculated at the same position were compared as shown in Figures 21 and 22. Figure 21 shows a comparison of the strain values over time immediately after impact (0 s) in the case of a drop height of 3 m. The average value is plotted based on the data shown in Figure 16 and FEA is the value of strain calculated at a point 150 mm from the crash point. As shown in Figure 21, the tendency of the strain change from the moment of impact up to 0.01 s is somewhat different but the plastic strain value, which is closely related to the impact energy, has a margin of approximately 8.6% error with 0.0163 (experiment) and 0.0149 (analysis) at 0.06 s. The elastic strain generated in a very short time is closely related to the friction between two objects and the initial collision conditions. The difference between the analysis result and the experimental result in the elastic region can be considered as a limitation that such a phenomenon cannot be strictly simulated by analysis. Figure 22 shows a comparison of the strain values over time immediately after impact (0 s) in the case of a drop height of 5 m. As shown in Figure 22, although the pattern of instantaneous change in the strain from the moment of impact to 0.015 s is somewhat different, the plastic strain is 0.0189 (experimental) and 0.0207 (analytical) with a margin of approximately 9.5% error at 0.06 s.  
Based on the aforementioned drop impact analysis results, it is possible that the proposed method can sufficiently reproduce drop impact experiments. Therefore, using the proven method, a drop impact simulation for the suction drum bundle was performed to calculate the impact energy considering drop angles.
# Classification of Tropical Cyclone Tracks in the Northwest Pacific Based on the SD-K-Means Model
## 4 Results Analysis
### TCs' Intensity and Destructive Potential  
To better visualize intensity differences across clusters, Figure 5 presents the distribution of TC intensity levels categorized according to the RSMC-Tokyo standard. The legend provides corresponding color codes.  
Figure 4: Representative central tracks of each cluster. Red lines denote the central (median) track within each cluster; gray lines represent the remaining TC tracks in each cluster.  
As shown, Clusters 1 and 2 contain the strongest TCs, with over 50% of the TCs reaching intensity level 6 or higher. Cluster 2, in particular, exhibits a minimum intensity of level 4, with no weak storms. Nearly half of the TCs in Cluster 0 also reach level 6, while Cluster 3 exhibits a relatively uniform and lower intensity distribution, classifying it as the weakest cluster overall.  
Given the established correlation between TC intensity and destructive power, the PDI is used to further assess and compare the potential destructiveness of the TCs in each cluster. A higher PDI indicates greater accumulated energy, and thus, stronger destructive potential.  
From Figure 6, it is evident that Cluster 2 TCs have the highest mean and median PDI, signifying the greatest destructive potential. Cluster 1 ranks second, with a slightly lower but comparable mean PDI. Cluster 0 shows moderate destructive potential. Cluster 3 exhibits the lowest PDI, aligning with its classification as having the weakest storms. These findings are consistent with the intensity-level distributions shown in Figure 5 and validate the clustering model's ability to distinguish TCs with varying impacts and characteristics.