esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The PSO-SVM algorithm is proven to be an effective method of the rainfall forecast decision. The SVM method is a kind of machine learning method with a high degree of nonlinear problems. It is a kind of machine intelligent learning method with a solid theoretical basis. Moreover, there is no limit to the dimension (number of vectors) of the vector due to the establishment of the SVM model, which facilitates the handling of meteorological problems with time, space and multiple factors. We modeled the parameters of the model by using the PSO algorithm to optimize the parameters of the model. The PSO-SVM model was established and compared with the traditional mesh optimization, the Genetic algorithm and the Ant Colony algorithm.  
The experimental results indicate that the PSO algorithm has a higher accuracy and efficiency. The PSO-SVM is ideal for multiple variable analyses and particularly important in current problem-solving tasks like weather forecasts. Furthermore, some research could be done to increase the accuracy rate of this model. The output of the model is proven to be very promising and absolutely helpful for its improvement.  
Thus, this study is considered significant. If the weather warnings are told to ordinary people ahead of the natural calamity, they can act as life-saving signals, valuable added services and disaster prevention tools. These models provide a good example of the capabilities of Support Vector Machine Regression for modeling high precision and efficient weather forecasting.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MSE** & **M-SVM** & **GA-SVM** & **AC-SVM** & **PSO-SVM** \\ \hline
200 & 0.3162 & 0.3162 & 0.3536 & 0.3536 \\
400 & 0.4183 & 0.3354 & 0.3354 & 0.3354 \\
600 & 0.3873 & 0.3416 & 0.3536 & 0.3651 \\
800 & 0.3708 & 0.3708 & 0.3062 & 0.3354 \\
1000 & 0.3162 & 0.324 & 0.324 & 0.3162 \\
1200 & 0.2814 & 0.25 & 0.25 & 0.2415 \\
1400 & 0.2892 & 0.2809 & 0.2739 & 0.2597 \\
1600 & 0.2622 & 0.25 & 0.2622 & 0.2305 \\
1800 & 0.2934 & 0.2635 & 0.2635 & 0.2582 \\
2000 & 0.3082 & 0.2872 & 0.2872 & 0.2782 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The Mean Square Error (MSE) of four methods.  
Figure 5: The Receiver Operating Characteristic (ROC) curve and Area Under Curve (AUC) drawn from the wired 1000 samples.  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their constructive comments, which greatly helped to improve this paper. This research was supported by the National Nature Science Foundation of China (No. 41575155), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Anomaly Detection from Event Log
### Anomaly Detection Beyond the Business Process  
Similar to conformance checking in the field of business processes, many studies have investigated anomaly detection by comparing process patterns and event logs derived from a variety of fields including manufacturing, transportation, logistics, and information communication networks. [PERSON] et al. [42] modeled material flow using a dynamic network model to manage material flow at work sites. They proposed an algorithm to detect the flow of materials that move out of the network and verified its performance by using industrial data. [PERSON] et al. [43] suggested a method to automatically derive the material flow of a manufacturing system by using input and output signals and expressing them in a petri net. They then detected anomalies using a token replay method. To test whether airplanes keep to their specified routes, [PERSON] and [PERSON] [44] proposed a model-based anomaly detection framework and verified it based on airplane operation scenarios of airplanes that follow their intended trajectory, inappropriate airplanes, and vague airplanes. [PERSON] et al. [45] extracted the trajectories of vehicles from transportation CCTVs, derived a normal trajectory using the spectrum clustering method, and suggested an anomaly detection method that can identify vehicles that deviate from the trajectory. [PERSON] et al. [46] proposed an algorithm that can detect when moving objects have an abnormal trajectory after deriving a normal trajectory of objects in a monitoring video using spectrum clustering. To verify this, they conducted an experiment to detect anomalies using indoor and outdoor CCTV images. [PERSON] et al. [47] proposed a method for detecting isolation-based anomalies after grouping traces with the same origin and detection by using data from the GPS device mounted in taxis. They conducted an experiment to verify the proposed anomaly detection algorithm using actual taxi operation records in China. [PERSON] et al. [48] suggested an algorithm for detecting the transportation of suspicious containers in the transportation schedules by learning general container transportation schedules using SVM. Then they conducted an experiment to verify the algorithm by using the container transportation records for a period of 3 years. [PERSON] et al. [49] proposed a framework that detects abnormal behavior of the network process after learning the general network process in the field of network security by using deep learning algorithms such as a recurrent neural network (RNN) and deep neural network (DNN). They verified the performance of this algorithm using the KDDCup 1999 dataset.
## 4 Experiments
### Results  
This section presents the results obtained by the different models for predicting noise levels (LAeq). Table 2 summarizes the evaluation metrics used: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R\({}^{2}\)), Correlation, and Accuracy. These metrics allow us to evaluate and compare the performance of each model in terms of accuracy and its ability to capture temporal and spatial relationships in the data. The metrics are computed as the average values from the predictions on the test data, i.e., the last 15%, which corresponds to predictions for 492 days.  
Our proposed model, CNN1D+LSTM+TransformerConv, demonstrates superior performance compared to the other evaluated models. With an accuracy of 0.9158 and a correlation of 0.9601, it significantly outperforms the other approaches. Specifically, it reduces the MAE by 35% and the RMSE by 37% compared to the next best model. These results highlight the effectiveness of our approach in accurately predicting noise levels.  
Therefore, Figure 7 shows the evaluation metrics per time step for the best model, CNN1D+LSTM+TransformerConv, illustrating how model performance varies across different forecast horizons. This figure reinforces the robustness of the proposed model, emphasizing its accuracy in predicting noise levels over time.  
In Figure 7, we observe how the evaluation metrics change across different prediction horizons. Both RMSE and MAE increase slightly as longer-term intervals are predicted, indicating a slight reduction in accuracy for more distant time steps. Correlation and R\({}^{2}\) remain high for most steps but also exhibit a slight decline in the longer forecast horizons.  
Figure 7: Metrics per time step for CNN1D+LSTM+TransformerConv. Each time step represents 12 h intervals, differentiating between daytime and nighttime measurements. The vertical axis indicates noise levels.
# Rate of Penetration Prediction Method for Ultra-Deep Wells Based on LSTM-FNN
## 3 Field Data Processing  
### Data Introduction  
Compared with deep well drilling operations, ultra-deep well drilling operations face more difficulties and challenges. Under the harsh drilling environment of high temperature and high pressure in deep formation, the properties of drilling fluid will change, so it is difficult to both carry rock cuttings and cool the bit in time, resulting in a reduced ROP and accelerated bit wear. In addition, with the increase of well depth, the formation abrasiveness increases, the drillability is poor, the formation pressure system is complex and the safe density window is narrow, which increases the difficulty of drilling parameter optimization. Therefore, in the process of establishing the prediction model of ROP in ultra-deep wells, it is necessary to focus on the complex correlation between parameters such as bottom hole temperature (T), formation pressure, rock physical properties, bit type and ROP.  
The drilling and logging data used in this paper come from two vertical and ultra-deep wells in a block of the Tarim oil fields, which are marked as well 1 and well 2, respectively. The straight-line distance between the two wells is 5098 m, and the well depth is more than 6000 m. The deep formation is mainly interbedded with mudstone and siltstone, and the difference in rock physical properties is small. The specific input parameters are shown in Table 1.
## 2 Materials and Methods  
In this rapid review, a specific article selection protocol prioritized the most recent and pertinent literature on the treatment of MRONJ, specifically from 2022 to 2023. All entries from the initial database queries were imported into a reference management tool (EndNoteTM, Clarivate, London, UK), where duplicates were eliminated. The preliminary screening process was carried out by one author ([PERSON]), who reviewed titles and abstracts to exclude articles that did not align with our inclusion criteria as systematic reviews or meta-analyses (Table 1). Search engine was PubMed (Medline). Search query based on the PICOS framework were crafted jointly with the sponsoring organization and a librarian from the university[PERSON] This involved a set of essential terms utilized for scanning the titles and summaries of papers within chosen databases. The primary terms included \"treatment for MRONJ\" or \"treatment for Medication-related osteonecrosis of the jaw\". The next stage involved full-text reviews of the filtered articles by two authors ([PERSON] and [PERSON]) to confirm their suitability for inclusion in the study. Two reviewers ([PERSON] and [PERSON]) independently evaluated the methodological soundness of the selected systematic reviews. Any discrepancies in the quality assessments were reconciled through deliberation.  
The search, conducted via PubMed (MEDLINE), targeted systematic reviews and meta-analyses, using tailored keywords to ensure relevance and currency (Figure 1)[PERSON]  
From an initial pool of 262 articles, 22 were meticulously chosen based on relevance and uniqueness. Following the guidelines for Cochrane rapid reviews, one author ([PERSON]) was responsible for extracting pertinent details from each study, including the title, objectives, methodology, types of treatment, review outcomes, and conclusions. This data extraction was then verified for accuracy and completeness by a second author ([PERSON]). The results from the reviews were combined through narrative synthesis, categorized according to Innocenti domains, and divided into groups focusing on \"epidemiology and diagnosis\", \"drug holiday\", and \"treatment\" (Figure 2).  
\begin{table}
\begin{tabular}{c c c} \hline  & **Inclusion Criteria** & **Exclusion Criteria** \\ \hline Problem & MRONJ treatment & Animal and cadaver studies \\ Intervention & Any type of MRONJ treatment & Not applicable \\ Comparison & Any or none & Not applicable \\ Outcomes & Slowing down the development of & Lack of effectiveness of a given \\  & MRONJ or curing MRONJ & treatment method \\  & English-language reports from & Non-clinical studies, technical reports, and \\ Settings & meta-analysis and systematic & case reports; no access to the full text; \\  & reviews published in 2022–2023 & non-peer-reviewed papers, e.g., preprints. \\ \hline \end{tabular}
\end{table}
Table 1: Inclusion and exclusion criteria for the review.
## 1 Introduction
However, the medical significance behind these selected features data is not well known. Previous studies suggested different features were associated with CAD in different correlation degrees [12]. In addition, morphological features were the most direct features for characterizing CAD in clinical [4]. In other words, the coronary arteries with CAD lesion has special morphological features that are different from the normal arteries. Therefore, morphological features of the coronary arterial trees may be of great potential to build a machine learning classifier for the detection of CAD. The aim of this paper is to propose a new methodology (a combination of using machine learning algorithms and imaging-based morphological measurement methods) for the detection of CAD, which may help to detect CAD at the early stage in a non-invasive way and provide a tool to save more lives. To collect morphological features dataset, we reconstructed three-dimensional (3D) coronary arteries from CCTA images of CAD patients and healthy individuals and measured the morphological features from the reconstructed 3D coronary arterial trees. To evaluate the quality of the morphological features, machine learning algorithms such as logistic regression (LR), DT, linear discriminant analysis (LDA), k-NN, ANN, and support vector machines (SVMs) were then applied to build classifiers for the detection of CAD. The accuracy of these machine learning algorithms was used to evaluate the prediction capability of classification algorithms. Moreover, the algorithm with best performance was further used to find the most important features in detecting CAD. In addition, the effects of the kernel function selection, data sampling, training dataset amount, and the dimension of input features on the classification performances of the machine learning model were also studied. The main contributions of this work were: (1) combined utilization of machine learning and morphological measurement method for CAD detection, (2) achieved superior classification performance compared with existing studies, (3) found the most two important morphological features for CAD detection.
## 4 Materials and Methods
### Model Selection Approach
#### 4.3.3 Huber Regressor  
Lasso, Ridge and Elastic-Net all rely on the ordinary least squares formula for their loss function. One problem with this is that outliers often have too much influence on the predictions. This is true for both the models that implement the L1 and the L2 regularization norms, despite the fact that the L1 norm utilized the median as the central value for the sake of minimizing this effect. There are several different regression approaches that offer the complete elimination of outliers, with one example being RANSAC (RANdom SAmple Consensus).  
However, due to the size of the data, as well as the nature of the task, we propose that it is best to minimize the effect of outliers rather than to completely eliminate them. For this reason, we decided to experiment with the Huber regressor, which is available through Pythons Skalearn package ([[https://scikit-learn.org/stable/modules/generated/sklearn.linear_model_HuberRegressor.html](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model_HuberRegressor.html)]([https://scikit-learn.org/stable/modules/generated/sklearn.linear_model_HuberRegressor.html](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model_HuberRegressor.html)) (accessed on 28 March 2023)). The Huber regressor, much like the Ridge regression model, implements L2 regularization.  
However, it does so by using M-estimators [109] rather than mean of the distribution as its central value, thus, making it more resistant to outliers. We suggest that, due to this property, it will result in slightly better predictions compared with the baseline approach. The loss function of the Huber regressor can be mathematically formulated in the following way:  
\[\min_{w,\sigma}\sum_{i=1}^{n}\biggl{(}\sigma+H_{\epsilon}\biggl{(}\frac{x_{ij }w-y_{i}}{\sigma}\biggr{)}\sigma\biggr{)}+\alpha\sum_{j=1}^{m}w_{j}^{2} \tag{5}\]  
where \(H_{\epsilon}(z)\) takes the values of:  
\[H_{\epsilon}(z)=\begin{cases}z^{2},&\text{if }|z|<\epsilon,\\ 2\epsilon|z|-\epsilon^{2},&\text{otherwise}\end{cases} \tag{6}\]
# Application of Simultaneous Symmetric and Cambered Airfoils in Novel Vertical Axis Wind Turbines
###### Abstract  
Two novel four-blade H-darrieu's vertical axis wind turbines (VAWTs) have been proposed for enhancing self-start capability and power production. The two different airfoil types for the turbines are assessed: a cambered S815 airfoil and a symmetric NACA0018 airfoil. For the first novel wind turbine configuration, the Non-Similar Airfoils 1 (NSA-1), two NACA0018 airfoils, and two S815 airfoils are opposite to each other. For the second novel configuration (NSA-2), each of the S815 airfoils is opposite to one NACA0018 airfoil. Using computational fluid dynamics (CFD) simulations, static and dynamic conditions are evaluated to establish self-starting ability and the power coefficient, respectively. Dynamic stall investigation of each blade of the turbines shows that NACA0018 under dynamic stall impacts the turbine's performance and the onset of dynamic stall decreases the power coefficient of the turbine significantly. The results show that NSA-2 followed by NSA-1 has good potential to improve the self-starting ability (13.3%) compared to the turbine with symmetric airfoils called HT-NACA0018. In terms of self-starting ability, NSA-2 not only can perform in about 66.67% of 360 deg similar to the wind turbine with non-symmetric airfoils (named HT-815) but the power coefficient of NSA-2 at the design tip speed ratio of 2.5 is also 4.5 times more than the power coefficient of HT-8615; the power coefficient difference between HT-NACA0018 and HT-8615 (=0.231) is decreased significantly when HT-8815 is replaced by NSA-2 (=0.076). These novel wind turbines are also simple.  
vertical axis wind turbine (VAWT); CFD; power coefficient; self-starting; symmetric; cambered airfoil; H-darrieu's dynamic stall Article
## 3 Experiments
### Ablation Study  
#### 3.4.1 The Impact of Newton-Schulz Iteration Count  
In this experiment, we investigate the impact of the number of Newton-Schulz iterations on the classification model's accuracy. This experiment is evaluated on histopathological images from the BreakHis dataset at 100\(\times\) magnification. As shown in Figure 5, increasing the number of iterations significantly enhances the model's ability to represent high-order features. As the number of iterations increases from 1 to 5, classification accuracy steadily improves, reaching a peak of 98.56% at 5 iterations. This indicates that a moderate number of iterations better captures covariance information and enhances feature representation quality. However, when the number of iterations further increases to seven, although the model's accuracy remains at a high level, the improvement tends to saturate. Beyond seven iterations, the model's performance starts to decline. This phenomenon is likely due to the accumulation of numerical instability caused by excessive iterations, which weakens the feature's discriminative ability. Meanwhile, a higher number of iterations significantly increases computational cost and reduces inference efficiency. Considering both performance and computational complexity, we ultimately choose five iterations as the optimal setting, ensuring high classification accuracy while effectively controlling computational costs, achieving an optimal balance between performance and efficiency.
## 6 Numerical Results Discussion and Optimization Analysis
### Optimization Analysis
#### 6.3.2 Numerical Results  
A numerical example is carried out for obtaining the joint optimal values (\(c^{*}\), \(\alpha^{*}\), \(\eta^{*}\)). The following set of cost elements and system parameters is given: \(c_{0}=20\), \(c_{1}=100\), \(c_{2}=50\), \(c_{3}=45\), \(c_{4}=8\), \(c_{5}=10\), \(c_{6}=6\), \(c_{7}=5\), \(\lambda=4\), \(\
u=10\), \(\mu_{1}=3\), \(\mu_{2}=1.5\), \(\mu_{w}=1\), \(\delta=0.1\), \(\gamma=1\), \(b_{b}=0.8\), \(b_{r}=0.2\), \(b_{v}=0.6\). Figure 3 depicts the Pareto optimal set. These results provide useful guidelines for system architects and executives (call managers). Table 1 summarizes the Pareto optimal solutions. After obtaining the Pareto optimal solutions, the system architect or executive can select the appropriate parameter combination from Table 1, based on his judgment and experience. For example,  
In Figure 3 and Table 1, one can observe that there is an inverse relationship between \(AC\) and \(W\) for a smaller \(1-U\). The adjusted coefficient of determination is \(R^{2}(adj)=99.98\%\), and the \(p\)-value of the \(F\)-test of the regression equation is less than 0.01. Additionally, Figure 4 presents the histogram and the Kolmogorov-Smirnov test for the regression residuals. It shows that the regression residuals have an approximately normal distribution, which means that this regression model is suitable.  
The regression equation is as follows:  
\[AC/1000=-60.95-0.001(1/W)+0.065W-0.819e^{-W}-0.176\text{log}(W)+\] \[0.659/(1-U)+32.88(1-U)+83.55e^{-(1-U)}+10.48\text{log}(1-U).\]  
In order to help understand this regression, we give a simple example below. If the maximum acceptable server's idle rate is \(1-U=0.084\) and the maximum acceptable time spent in the buffer is \(W=7.068\), the minimum operating cost is approximately \(Ac=1000(-60.95-0.001(1/7.068)+0.065\times 7.068-0.819e^{-7.068}-0.176\text{log}(7.068 )+0.659/0.084+32.88\times 0.084+83.55e^{-0.084}+10.48\text{log}(0.084))=632.5548\). The actual value of the minimum operating cost is 623.538, with an error of 1.43%. Therefore, the system architect or executive can use this regression model to find the minimum operating cost that satisfies the service quality and the utilization of servers.
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
## 5 Results and Validation
### Performance Comparison
#### 5.3.5 Trust Value Comparison  
Figure 6 illustrates a comparative analysis of the KL trust values across five algorithms. Specifically, in the context of the NSFNet-14 dataset, as shown in Figure 6a, it is evident that the Dijkstra algorithm fails to take trust values into account. Consequently, it directs all network traffic along a single path, leading to a consistently static KL trust value of approximately 1. This static value serves as a clear indicator of the Dijkstra algorithm's inability to achieve routing convergence. The TBPPO algorithm, on the other hand, displays a noticeable convergence trend and eventually stabilizes at around 0.5, with intermittent fluctuations hovering around 0.2. In contrast, the PPO algorithm, which does not explicitly address security concerns, exhibits a performance similar to Dijkstra. Both the DRSIR and TBDRSIR algorithms exhibit inadequate adaptation to dynamic DDoS attack defense scenarios, failing to achieve trust value convergence. As a result, the TBPPO algorithm  
Figure 5: Performance comparison of the average time delay of five algorithms: (**a**) NSFNet-14, and (**b**) Germany-50.  
consistently maintains lower KL trust values, suggesting a reduced vulnerability to attacks and an enhanced security performance.  
Figure 6b compares the performance of the KL trust values on Germany-50. Much like the scenario in NSFNet-14, the Dijkstra algorithm does not consider trust values when assessing its performance. It channels all traffic through a single path, resulting in a consistently stable KL trust value of around 1.5 without achieving convergence. Examining the graph, it becomes apparent that the TBPPO algorithm exhibits a noticeable trend towards convergence, although it experiences more fluctuations compared to NSFNet-14. Eventually, it stabilizes at approximately 0.8, with fluctuations hovering around 0.4. In contrast, PPO reaches convergence at 1.3. Regrettably, both the DRSIR and TBDRSIR algorithms fail to achieve trust value convergence. This result shows the capability of the TBPPO model to identify relatively secure routes in a large network environment.