esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Wind Shear Characteristics of Different Terrains  
Figure 3 shows the wind shear characteristics as the diurnal and monthly changes. As expected, the monthly variations of wind shear exponent at SJB and HNH are relatively high in Winter and low in Summer. The main reason is that, in Summer, the temperature is high, the mixing of near-surface air is sufficient, and the wind shear exponent is small. In Winter, the temperature is low and the air mixing is weak, usually resulting in a large wind shear exponent. However, the wind shear exponent at GFC is low in Winter and high in Summer, which deviates from the trend at SJB and HNH. This is because the area has more rains in Spring and Summer, making the air more humid and the frequency of low-level jets higher. The terrain has a dynamic lifting effect on the mountainous airflow and is prone to a strong wind shear. Furthermore, the daily change of wind shear exponent is closely related to the ambient temperature as seen in Figure 3b. The main reason for the diurnal variation of wind shear exponent is the cyclic variation of temperature. The wind shear exponent in the daytime is lower than that of the nighttime. This result is coincident with the results in the previous studies [31; 32; 33].  
Figure 4 shows the monthly and daily variations of atmospheric stability at SJB. Comparing the atmospheric stability variation with the variation of wind shear exponent, it is found that the wind shear exponent is lower when the atmosphere is more unstable.  
Figure 3: Monthly and diurnal variations of _h1_–_h2_ wind shear exponents in the three cases: (**a**) monthly variation; (**b**) diurnal variation.
# Characterization of the Evolution of Energy Loss Rate in Cyclic Load Testing of Marine Soft Soil
## 4 Stabilized State of Marine Soft Soil Based on Envelope Surfaces of Energy Dissipation Rates  
### Project Cases  
A subway shield tunnel section in Shenzhen passes through an area with operating freight trains, with a tunnel burial depth of 10.0 m and a tunnel diameter of 7.6 m. The tunnel traverses an area beneath the operating freight train, and the surrounding stratum primarily consists of miscellaneous fill, marine soft soil, and strongly weathered granite, with no other buildings or structures in the vicinity. A three-dimensional computational model of the shield tunnel passing through the operating freight train area was established using a finite element calculation program, as shown in Figure 8. In the numerical model, a cyclic load of 100 kPa was applied to the area affected by the train, simulating the influence of the train on the marine soft soil and the tunnel. This model is typically used to simulate soil and rock behavior under shear stress, suitable for describing material damage caused by both shear and normal stresses. The mechanical parameters for the model are provided in Table 3, with the assumption that the water content of the marine soft soil is 55%. By analyzing the stress state of the marine soft soil at different depths, the effect of the cyclic loading from the train on the ground and the tunnel can be assessed. This analysis can assist in designing appropriate reinforcement measures or determining the need for additional pre-strengthening prior to tunnel construction, particularly considering the soil layer composition and tunnel depth.  
Figure 8: Three-dimensional finite element numerical calculation model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stratigraphic Name** & \begin{tabular}{c} **Bulk Density** \\ **(kN/m3)** \\ \end{tabular} & **Cohesive Force (kPa)** & \
\begin{tabular}{c} **Internal Friction** \\ **Angle (\({}^{\circ}\))** \\ \end{tabular} &
\begin{tabular}{c} **Elastic Modulus** \\ **(MPa)** \\ \end{tabular} \\ \hline Soil filling & 15.7 & 7 & 8 & 4.3 \\ Marine soft soil & 17.5 & 13 & 16 & 8.6 \\ Strongly weathered granite & 21.5 & 18 & 23 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Basic mechanical parameters of strata.
## 3 Results
### Agreement between GM and CM  
The Hr, WBr, and H/WBr obtained using the GM method showed a high correlation with ones derived using the CM. When evaluated in Xeleris, [PERSON]'s CCC was 0.93 [0.87-0.96], 0.94 [0.88-0.97], and 0.99 [0.98-0.99] for H/WBr, Hr, and WBr, respectively, while Pearson's linear correlation coefficient was always higher than 0.87, with R\({}^{2}\) > 0.94. For H/WBr, the Bland-Altman analysis resulted in a bias of 0.46%, and the CI ranged between \(-\)0.35% and 1.27%. Similarly, Hr showed a bias of 0.33% and a CI between \(-\)0.31% and 0.97%, and the WBr showed a bias of \(-\)0.91% and a CI between \(-\)2.20% and 0.38%. When the comparison between GM and CM was performed within MIM (that is, using exactly the same ROIs set on anterior and posterior views and on the geometric mean image), the H/WBr bias decreased to 0.12% [CI: 0.04%; 0.19%], the Hr bias decreased to 0.07% [CI: 0.01%; 0.13%], and the WBr bias decreased to \(-\)0.50% [CI: -1.22%; 0.22%]. Therefore, the assisted workflow developed in MIM guarantees a better agreement between the GM and CM and justifies the use of the GM in the following evaluation on the larger dataset of patients. The Bland-Altman plot of H/WBr with the GM versus the CM calculated in the MIM-assisted workflow is reported in Figure 3, while all data are summarized in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variable** & **Occurrences (Percentage)** \\  & **or Median [Range]** \\ \hline
**NM scintigraphy parameters** & \\ WBr (\%) & 82.4 [31.5–99.5] \\ Hr (\%) & 4.3 [1.6–9.7] \\ H/WBr (\%) & 5.4 [2.0–10.7] \\  & 0: 2 (1\%) \\ Visual score & 1: 3 (2\%) \\  & 2: 120 (61\%) \\  & 3: 73 (37\%) \\ \hline Time between early and & 2.8 [1.8–4.7] \\ late acquisitions (h) & 2.8 [1.8–4.7] \\
**Etiology** & \\ \hline ATTRwt & 100 (65\%) \\ ATTRm & 54 (35\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Referring Workstation**} & \multicolumn{2}{c}{**Assisted Workflow**} \\ \hline  & _Bias_ & _95\% CI_ & _Bias_ & _95\% CI_ \\ \hline
**Hr** & 0.33\% & \(-\)0.31\%; 0.97\% & 0.07\% & 0.01\%; 0.13\% \\
**WBr** & \(-\)0.91\% & \(-\)2.20\%; 0.38\% & \(-\)0.50\% & \(-\)1.22\%; 0.22\% \\
**H/WBr** & 0.46\% & \(-\)0.35\%; 1.27\% & 0.12\% & 0.04\%; 0.19\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bland–Altman analysis of Hr, WBr, and H/WBr calculated with the GM vs. the CM. The agreement is expressed in terms of bias and 95%CI.
# A Novel Ensemble Belief Rule-Based Model for Online Payment Fraud Detection
## 5 Experimental Study  
### Dataset Information  
In this section, two datasets from the Kaggle competition of credit card fraud detection is used to validate the effectiveness of our method. Dataset #1 \"[[https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud)]([https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud)) (accessed on 11 July 2023)\" is widely used in fraud detection research. To further validate the effectiveness of our method on multiple datasets, we also conducted experiments on Dataset #2 \"[[https://www.kaggle.com/datasets/nelgiriyewithana/credit-card-fraud-detection-dataset-2023](https://www.kaggle.com/datasets/nelgiriyewithana/credit-card-fraud-detection-dataset-2023)]([https://www.kaggle.com/datasets/nelgiriyewithana/credit-card-fraud-detection-dataset-2023](https://www.kaggle.com/datasets/nelgiriyewithana/credit-card-fraud-detection-dataset-2023)) (accessed on 10 January 2025)\". Dataset #2 is very recent, collected in 2023 from European cardholders. Both  
Figure 7: Model selection process.  
datasets have been anonymized to protect users' personal information. The basic information of these two datasets is shown in Table 1.  
From Table 1 we can find out that both Dataset #1 and Dataset #2 are extremely large. The number of samples in Dataset #1 exceeds 280,000, while the number of samples in Dataset 2 surpasses 560,000. Also, there are only 492 instances of fraud samples in Dataset #1. Therefore, there is an extreme class imbalance between these two classes. The imbalance ratio can reach more than 500. Also, the numbers of attributes in both datasets are 30 and 29, respectively, which is also very large for constructing a BRB model.
# A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph
## 4 Experimental Results and Discussion
### Results without Using Data Augmentation  
The results of the four metrics (Dice, accuracy, precision and recall) for models trained with the dataset without data augmentation can be seen in Table 4.  
The DoubleU-Net model obtained 92.695% of Dice followed by Nano-Net, U-Net and, finally, the DCU-Net with 91.451%. This same order was seen for the recall metric, with 91.283% for DoubleU-Net and 87.846% for DCU-Net. Regarding accuracy, the order was changed to DoubleU-net, Nano-Net, DCU-Net and U-Net. With regard to precision, DCU-Net presented the best result with 95.390%, followed by U-NET, DoubleU-Net and Nano-Net with 93.783%.  
DoubleU-Net continues to show great prominence, as it also obtained the best results in three of the four metrics analyzed in this experiment. However, another model that stood out was Nano-Net, which for training without data augmentation presented results close to DoubleU-Net with fewer trainable parameters.  
Figure 6 shows the same three examples of images tested in the previous experiment (with data augmentation) with the outputs of the models that were trained without data augmentation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dice (\%)** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **Parameter** \\ \hline U-Net & 91.681 & 96.191 & 94.898 & 88.700 & 31,031,745 \\ DCU-Net & 91.451 & 96.123 & **95.390** & 87.846 & 10,069,928 \\ DoubleU-NET & **92.695** & **96.552** & 94.184 & **91.283** & 29,264,930 \\ Nano-Net & 91.739 & 96.173 & 93.783 & 89.815 & **235,425** \\ \hline \hline \end{tabular}  
* **Bold cells** show the best result for that metric.  
\end{table}
Table 4: Segmentation models without augmentation data.  
Figure 6: Example of three panoramic radiograph images tested by segmentation models without data augmentation.  
The conclusions are the same as the experiment with data augmentation, but with some negative points for the DCU-Net Model. The U-Net showed some flaws in the details of the roots, the Nano-Net also showed some flaws and the DoubleU-Net was the model with more satisfactory performance.  
It was observed that the models had more difficulties in detailing the roots, with the exception of the Double-UNet, which better managed this task. In Figure 6B (example one), the roots present a little square and do not follow exactly the design of the root as we can see in the real image of the radiograph, Figure 6A (example one). The Double-UNet model was able to better draw the image and be closer to the real one, as shown in Figure 6E (example one).
## 2 Methods
### Attention Mechanism
#### 2.4.2 Iterative Attentional Feature Fusion (iAFF) module  
Existing attention-based methods only focus on the features in the same layer, iAFF module [52] can integrate cross layer features, making the fusion scheme more heuristic and fuse the receive features in a contextual scale-aware way. The structure of iAFF is shown as Figure 4:  
Figure 4a is the core component of iAFF called Multi-scale Attention Mechanism(MS-CAM), which aggregates local feature and global features via channel-dimension attention and point-wise convolution. The input of MS-CAM is an intermediate feature \(\mathbf{X}\in\mathbb{R}^{\mathbb{C}\times H}\) with C channels and \(H\)-dimension features in one channel, the local channel context \(\mathbf{L}(\mathbf{X})\) is designed as  
\[\mathbf{L}(\mathbf{X})=\mathcal{BN}(PWConv_{2}(\delta(\mathcal{BN}(PWConv_{1}(\mathbf{X})) ))) \tag{11}\]  
where \(PWConv\) denotes point-wise convolution, \(\mathcal{BN}\) denotes batch normalization. \(\delta\) denotes ReLU function. We define \(g(\mathbf{X})\) as global average pooling(Global Avg Pooling) that can computed as  
\[g(\mathbf{X})=\frac{1}{H}\sum_{i}^{H}\mathbf{X}_{i} \tag{12}\]  
\(PWConv_{1}\) has \(\frac{C}{F}\) channels and \(PWConv_{2}\) has \(C\) channels, \(PWConv_{1}\) and \(PWConv_{2}\) construct a convolution bottleneck. The left part of MS-CAM can be seen as global channel feature extraction and the right part is local channel feature extraction. The output feature \(\mathbf{X}^{{}^{\prime}}\) is defined as  
\[\mathbf{X}^{{}^{\prime}}=\mathbf{X}\otimes\mathbf{M}(\mathbf{X})=\mathbf{X}\otimes\delta(\mathbf{L}( \mathbf{X})\oplus g(\mathbf{X})) \tag{13}\]  
where \(\otimes\) is the element-wise multiplication and \(\oplus\) means broadcasting addition. \(\mathbf{M}(\mathbf{X})\) is the sum of global channel extracted features and local channel extracted features. iAFF module in Figure 4b can be expressed as  
\[\mathbf{X}\uplus\mathbf{Y}=\mathbf{M}(\mathbf{X}+\mathbf{Y})\otimes\mathbf{X}+(1-\mathbf{M}(\mathbf{X}+\mathbf{Y}) )\otimes\mathbf{Y}, \tag{14}\] \[\mathbf{Z}=\mathbf{M}(\mathbf{X}\uplus\mathbf{Y})\odot\mathbf{Y}+(1-\mathbf{M}(\mathbf{X}+\bm {Y}))\odot\mathbf{Y},\]  
\(Z\) denotes the output feature, \(Y\) can be output features of the interlayer. In this paper, \(Y\) is the input of residual input. The dotted arrow from MS-CAM means \(1-\mathbf{M}(\cdot)\). The iAFF module can be separated into two parts. The first part is \(\mathbf{X}\uplus\mathbf{Y}\), the second part takes \(\mathbf{X}\uplus\mathbf{Y}\) as the input of the next MS-CAM, and assigns its output as weight to \(\mathbf{X}\) and \(\mathbf{Y}\).  
Figure 4: The structure of the iterative Attentional Feature Fusion (iAFF) module. (**a**) The core component of iAFF module called Multi-scale Attention Mechanism (MS-CAM). (**b**) The structure of iAFF module.
# Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem
## 5 Conclusions  
This paper proposes a graph-based DRL method called GDNN-PPO to solve the AEOSSP with time-dependent attitude transition time. We model the AEOSSP with the time-attitude adjacency graph and reformulate the problem as the MDP. Then, we extract the features of the AEOSSP, including node and edge features, and design a GDNN to guide task choice. Finally, we train the GDNN by PPO and design experiments to verify the validity of the proposed method. Experimental results show that GDNN-PPO outperforms all construction heuristics in ASP by at least 45% and surpasses high-quality competitors except for the state-of-the-art algorithm GRILS with regard to ASP and AST. Regarding GRILS, the difference between the GDNN-PPO performance in all instances is less than 7%; However, the AST of GRILS is 345 times longer than that of GDNN-PPO on average. Thus, GDNN-PPO performs well at large scales and with rapid responses when solving the AEOSSP and has strong potential for future applications in large constellations and new management models.  
Although GDNN-PPO demonstrates significant advantages in solution time, there is potential to enhance its scheduling priority. In future work, we plan to further improve the GDNN solving efficiency by optimizing the feature selection, structure design, etc. Additionally, we plan to combine the proposed method with other algorithms to solve more complex satellite scheduling problems, such as multi-satellite scheduling problems.  
Conceptualization, W.Y.; methodology, W.Y. and J.C.; software, W.Y. and J.C.; validation, J.C., G.W and L.H.; resources, L.H. and X.L.; writing--original draft preparation, J.C. and L.H.; writing--review and editing, X.L. and L.X.; supervision, G.W.; funding acquisition, L.X. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (72001212), Young Elite Scientists Sponsorship Program by CAST (2022 QNRC001) and Hunan Postgraduate Research Innovation Project (CX20210034).  
Not acceptable.  
The authors declare no conflict of interest.  
Figure 10: Gap between the solutions of GDNN-PPOs and the optimal solution.
# Stroke Dataset Modeling: Comparative Study of Machine Learning Classification Methods
## 2 Materials and Methods
### Application of the Methodology
#### 2.4.6 Model Estimation  
Model evaluation is crucial, especially when working on unbalanced datasets of this kind. More common measures from the confusion matrix are applied in the analyzed studies, such as precision, recall, F1-score, and precision. The entire analysis was performed in Python. To eliminate any possibility of high accuracy masking the performance of the models on the minority class, we provided results for each class. The findings justified the efficiency of the models in predicting stroke and underscored the significance of the right metric in the case of imbalanced datasets. Such outcomes raise the question of proper evaluation and indicate the strategies that can be followed in other fields with such issues.  
* Accuracy: the proportion of all predictions that are correctly identified.  
\[\text{Accuracy = (True positive + True negative)/(Total samples)} \tag{1}\]* Sensitivity: the proportion of all true positives, otherwise known as recall or the true positive rate (TPR). \[\text{Recall}=\text{True positive}/(\text{True positive}+\text{False negative})\] (2)
* Specificity: the capacity of a system to produce accurate negative predictions, otherwise known as the true negative rate (TNR). \[\text{Specificity}=\text{True negative}/(\text{True negative}+\text{False positive})\] (3)
* Precision: the ratio of correctly diagnosed positive samples among all positive samples. \[\text{Precision}=\text{True positive}/(\text{True positive}+\text{False positive})\] (4)
* F-Measure: the harmonic mean of both precision and recall. F-Measure = 2 \(\times\) ((Recall \(\times\) Precision)/(Recall \(\times\) Precision)) (5)
* ROC: The Receiver Operating Characteristic (ROC) curve evaluates a classifier's ability to distinguish between classes by plotting the true positive rate (TPR) against the false positive rate (FPR) across different thresholds. The Area Under the ROC Curve (AUROC) quantifies this ability, with 1 indicating perfect separation and 0.5 suggesting no discriminative power. AUROC is calculated as the integral of the ROC curve, often approximated using the trapezoidal rule, which involves summing the areas of trapezoids formed between consecutive points on the curve.
# How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?
## 2 Radiomics
### Prognostic Prediction  
The use of radiomics for the prognostic prediction of AIS patients is the most commonly published. Avery et al. in their study of 829 patients used radiomic features (RFs) obtained from CTA images of patients with anterior circulation Large Vessel Occlusion (LVO). They used masks of the middle cerebral artery (MCA) territory to extract the RF. They used four different input sets: radiomics (only RFs of MCA territory), radiomics + treatment (RF, mTCI score and intravenous thrombolysis), clinical + treatment (clinical variables and treatment) and combined (RF, treatment and clinical variables). The clinical variables included were sex, age and admission NIHSS. The best results were very similar between the clinical + treatment model and the combined model. The area under the curve (AUC) for long-term outcome prediction was 0.81 and 0.82 for the clinical + treatment and combined models, respectively. The AUC for predicting discharge outcome was 0.82 and 0.81 for the clinical + treatment and combined models, respectively. In the internal independent and external test performances, the best results of the different inputs were very similar. There was no significant difference between the best results of the radiomics only model, the radiomics + treatment model, the clinical + treatment model and the combined model. The authors concluded that RFs could help with patients where the collection of clinical information is limited [21].  
Other studies of some prognostic predictions have found similar results [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. In these studies, we can see that the performance of radiomics based on different types of  
Figure 3: Distribution of articles by topic.  
Figure 2: (a) Radiomics articles about AIS patients since 2018. (b) Radiomics articles about AIS published per year.  
images (MRI, CT and CTA) gives good results in predicting prognostic factors such as the mRS scale after AIS or the presence of disability after AIS. Many of these studies compare prediction models based on clinical features, radiomic features and finally evaluate the performance of a combined model by combining clinical and radiomic features. In general, the combined model is the one that performs best.
# A Phase Correction Model for Fourier Transform Spectroscopy
## 5 Simulation Experiment
### Experiments for PCM-APF Model  
Experiments were conducted using the HITEMP database when comparing the proposed PCM-APF with the Mertz method. HAPI is the Python interface for this database, which allows for various operations such as downloading spectral lines and simulating spectra using encapsulated functions [30]. A simulated emission spectrum was created using air with 10% acetylene gas under standard atmospheric conditions (296 K environmental temperature and one standard atmosphere pressure). The spectral line shape function used was Voigt, and the wavenumber range was 1000-1200 cm\({}^{-1}\). Figure 7a displays the normalized spectra after intensity normalization.  
Figure 7a also shows the error phase curve (error phase function: \(\varphi=466.905-765.471\omega+461.631\omega^{2}-122.509\omega^{3}+12.132\omega ^{4}\)). The proposed PCM-APF model and the traditional Mertz method were employed to correct the nonlinear phase error (with a small number of sample points in the small double-sided interferogram, set to 128). In the PCM-APF model, we set the order of the delay line \(N_{shift}=9\) and the order of the all-pass filter \(N=8\); for the optimization algorithm, the population size \(M=20\) and the number of iterations \(n_{max}=200\), using the proposed GS-SABO algorithm.  
Figure 7b,c show the spectral curves recovered using the PCM-APF model and the conventional Mertz method in different wavenumber ranges. It can be found that the spectra recovered using the Mertz method have obvious aberrations, especially at the low frequencies of the spectra, which seriously affects the quality of the recovered spectra, which also proves that the Mertz method is indeed deficient in dealing with the problem of  
Figure 6: (**a**) Optimization results (residual phase error) of GS-SABO. (**b**) Optimization results (residual phase error) of PSO. (**c**) Phase error convergence curves for GS-SABO and PSO (within 400 iterations).  
nonlinear phase errors. However, the spectra can be well recovered using the proposed PCM-APF model. Figure 7d shows the absolute values of the normalized spectral errors recovered using these two methods. The maximum normalized spectral error of the spectra recovered using the Mertz method is about 0.047, while the value using the PCM-APF model is about 0.008, which is a huge difference, showing the great improvement of the calibration accuracy of the PCM-APF model.  
Figure 7: **(a)** Normalized spectra and error phase curve. **(b)** Recovered results of Mertz method and PCM-APF model (wavenumber: 1008–1020 cm\({}^{-1}\)). **(c)** Recovered results of Mertz method and PCM-APF model (wavenumber: 1055–1065 cm\({}^{-1}\)). **(d)** Spectral error after Mertz method and PCM-APF model.  
Finally, the parameter settings for the filter calculated by the PCM-APF model are \(\{a_{0}=1,a_{1}=-1.756691,a_{2}=1.301406,a_{3}=-0.871274,a_{4}=0.626228,a_{5}=-0.5 25851,\)\(a_{6}=-0.133867,a_{7}=0.097102\) and \(a_{8}=-0.001376\}\).