esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 4 Experiments
### Evaluation Metrics  
The performance evaluation metrics included accuracy, recall, and precision. In addition, because the security alert data are highly imbalanced, we used the FPR, true negative rate (TNR), F-score, and balanced classification rate (BCR) for evaluating the performance of the proposed method. In this study, true positives (TPs) represent security alerts correctly classified as positive, FPs represent negative alerts incorrectly classified as positive, FNs represent positive alerts incorrectly classified as negative, and true negatives (TNs) represent correctly classified negative alerts. Precision can be more insightful than FPR because TNs are not used in its calculation; thus, they are not affected by class imbalances. We used precision-recall (PR) curves [72] and the PR area under the curve (PR-AUC) to evaluate the performance of the model. The PR-AUC is a model performance metric for binary responses appropriate for highly imbalanced datasets and is not dependent on model specificity. The PR curve plots the precision and recall values over the possible decision thresholds. The recall is plotted on the horizontal axis against the precision on the vertical axis. The performance of a classifier can then be assessed by computing the PR-AUC.  
Accuracy is the percentage of security alert data samples correctly classified by a classifier, as shown in Equation (6).  
\[Accuracy=\frac{TP+TN}{n} \tag{6}\]  
The recall provides the proportion of correctly classified positive samples, as shown in Equation (7).  
\[Recall=\frac{TP}{TP+FN} \tag{7}\]  
Figure 5: Visualizing positive, negative, and synthetic samples using \(t\)-SNE.  
Precision provides the proportion of correct positive class predictions, as shown in Equation (8).  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
The FPR is the number of FPs divided by the total number of actual negatives, as expressed using Equation (9).  
\[FPR=\frac{FP}{FP+TN} \tag{9}\]  
The TNR is the number of correctly classified negative alerts divided by the total number of negative alerts, as expressed using Equation (10).  
\[TNR=\frac{TN}{FP+TN} \tag{10}\]  
The F-score can be interpreted as the weighted harmonic mean of the precision and recall, as shown in Equation (11).  
\[F\text{-}score=2\frac{Precision\times Recall}{Precision+Recall} \tag{11}\]  
BCR is the average of the recall and TNR, as shown in Equation (12).  
\[BCR=\frac{Recall+TNR}{2} \tag{12}\]
## 1 Introduction  
Over the past 40 years, the finite-element method (FEM) has been widely used to study biomechanics in the fields of orthopedics and dentistry. Finite-element models enable researchers to understand data, such as the distribution of stress and strain, that are difficult to record usingmeasurement devices. The FEM is widely used in dentistry for the research and development of artificial implants and surgical instruments, prediction of jawbone growth and developmental behaviors, orthodontic treatment or oral surgery, and preoperative and postoperative evaluations [1, 2]. According to pressure-tension theory [3, 4, 5, 6] in orthodontics, tooth movement is mainly caused by the biomechanical response of the periodontal ligament (PDL) surrounding the tooth. An orthodontic bracket exerts force on a tooth to compress or stretch the PDL. Previous studies have indicated that bone resorption and apposition are required in the compression and tension zones, respectively [3, 4, 5, 6]. In other words, tooth movement is achieved through bone remodeling [3, 4, 5, 6]. Finite-element modeling of the jawbone and orthodontic force can be used to evaluate changes in the stress, strain, and hydrostatic pressure of the PDL surrounding the tooth. In coordination with pressure-tension theory, FEM can be used to predict tooth movement and movement direction.  
In the literature, FEM has been used for models with a single tooth [7, 8, 9], partial jawbone models with several teeth [10, 11, 12], and complete jawbone models with all teeth [13, 14, 15, 16]. Additionally, these finite-element models have been used to explore the effects of the design and material properties of different orthodontic forces, brackets, and archives on the biomechanical responses of the PDL during tooth movement. In studies that have employed the FEM to analyze such biomechanical responses, the assumed material properties of the PDL have affected the simulation results. Some studies have assumed that the PDL is a linear elastic material [10, 11, 17, 18, 19, 20], but animal and cadaver studies [21, 22, 23, 24] have indicated that the actual PDL is more similar to a bilinear elastic material. Other researchers have assumed that the PDL is a bilinear elastic material for conducting FEM simulations of biomechanical responses in tooth movement [25, 26, 27, 28].  
Numerous researchers have used FEM to analyze the biomechanical responses of the PDL during tooth movement due to orthodontic treatment. However, most of these studies have developed partial jawbone models with several teeth and used orthodontic force only as a loading condition. Therefore, in the present study, we evaluated the effect of partial and complete mandible models on the biomechanical responses of the PDL with and without occlusal force. Additionally, we discuss the determination of the material properties of the PDL by using a finite-element model.
# A Comparative Analysis of Active Learning for Biomedical Text Mining
## 4 Results and Discussion  
The results (Tables 2-13) show that the DDI dataset, which applies BERT for feature extraction, has the best performance in accuracy when we apply an SVM algorithm with an AL framework which builds based on MRD query strategies. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**TF-IDF**} & \multirow{2}{*}{**DDI**} & \multicolumn{2}{c}{**Supervised**} & \multirow{2}{*}{**RS**} & \multirow{2}{*}{**LC**} & \multirow{2}{*}{**IDD**} & \multirow{2}{*}{**Margin**} & \multirow{2}{*}{**MRD**} \\  & & & **Learning** & & & & & \\ \hline \multirow{4}{*}{SVM} & Accuracy & 82.17 & 82.92 & 81.88 & 81.44 & 81.79 & 82.49 \\  & F1 & 87.08 & 89.75 & 86.9 & 87 & 86.71 & 88.73 \\  & Accuracy & 81.08 & 82.99 & 83.04 & 82.61 & 83.01 & 81.84 \\ \multirow{2}{*}{NB} & F1 & 86.34 & 90.69 & 90.7 & 89.25 & 90.71 & 88.78 \\  & Accuracy & 64.92 & 74.99 & 68.84 & 67.45 & 69.87 & 70.6 \\ KNN & F1 & 61.42 & 75.71 & 66.78 & 64.91 & 68.72 & 69.63 \\  & Accuracy & 82.87 & 82.62 & 82.85 & 79.93 & 83.06 & 82.07 \\ XGBoost & F1 & 89.82 & 88.56 & 88.63 & 84.58 & 89.03 & 88.05 \\ Random & Accuracy & 81.46 & 81.03 & 82.87 & 81.6 & 81.27 & 81.44 \\ forest & F1 & 84.94 & 85.65 & 87.45 & 86.77 & 85.86 & 86.73 \\  & Accuracy & 78.11 & 83.01 & 82.83 & 80.56 & 82.9 & 82.38 \\ AdaBoost & F1 & 81.31 & 82.1 & 90.31 & 86.32 & 90.53 & 89.78 \\  & Accuracy & 81.18 & 90.71 & 91.43 & 89.93 & 90.5 & 89 \\ CatBoost & F1 & 86.01 & 87.4 & 90.8 & 89 & 90.1 & 90.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of results with and without AL methods (TF-IDF) for DDI dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**TF-IDF** & **HoC** & **without** & **RS** & **LC** & **IDD** & **Margin** & **MRD** \\ \hline \multirow{3}{*}{SVM} & Accuracy & 93.64 & 91.39 & 93.20 & 93.12 & 93.16 & 92.93 \\ \cline{2-7}  & F1 & 91.26 & 90.39 & 91.08 & 90.83 & 91.08 & 90.74 \\ \hline \multirow{3}{*}{KNN} & Accuracy & 86.50 & 88.12 & 86.05 & 86.35 & 86.05 & 89.08 \\ \cline{2-7}  & F1 & 93.51 & 91.88 & 93.36 & 93.42 & 93.36 & 93.28 \\ \hline \multirow{3}{*}{Random Forest} & Accuracy & 93.51 & 90.35 & 92.99 & 92.79 & 92.99 & 93.12 \\ \cline{2-7}  & F1 & 81.82 & 86.81 & 81.63 & 81.39 & 81.63 & 81.07 \\ \hline \multirow{3}{*}{CatBoost} & Accuracy & 94.32 & 92.09 & 94.90 & 94.10 & 93.40 & 92.10 \\ \cline{2-7}  & F1 & 84.36 & 83.38 & 83.82 & 84.26 & 83.86 & 84.64 \\ \hline \multirow{3}{*}{Random Forest} & Accuracy & 78.85 & 78.32 & 78.55 & 78.50 & 78.60 & 78.58 \\ \cline{2-7}  & F1 & 83.81 & 84.28 & 83.04 & 84.42 & 83.49 & 84.39 \\ \hline \multirow{3}{*}{AdaBoost} & Accuracy & 76.46 & 79.38 & 77.69 & 77.83 & 75.51 & 78.46 \\ \cline{2-7}  & F1 & 82.77 & 86.63 & 82.79 & 85.41 & 81.22 & 86.62 \\ \hline \multirow{3}{*}{CatBoost} & Accuracy & 78.92 & 78.81 & 79.80 & 78.80 & 79.10 & 79.20 \\ \cline{2-7}  & F1 & 84.36 & 83.63 & 85.80 & 84.90 & 84.20 & 84.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of results with and without AL methods (TF-IDF) for HoC dataset.
## 5 Experimental Results
### Training Setup and Results
The dotted line indicates that the average cumulative rewards obtained by the agent after this step tend to stabilize. Figure 12: Comparison of positioning performance with different weighting strategies during training. In the initial training phase, the agent predominantly output random weights with occasional selections of actions corresponding to the maximum Q-value. However, since DDQN-EW critically relies on stable value targets provided by the target network--whose parameters at this stage are derived from extremely limited and thus highly inaccurate data--the agent's autonomous decisions were statistically suboptimal. Consequently, its positioning performance initially underperformed both the \"default\" and \"random\" strategies. As training progresses, the agent continuously learns from batched experiences sampled from the replay buffer, progressively improving the accuracy of the target network's Q-value estimations. This enables the agent to determine contextually appropriate weights based on varying epoch states, ultimately surpassing the \"random\" strategy in positioning accuracy. Nevertheless, likely due to convergence to a local optimum during network training, the agent's final positioning performance eventually plateaued at a level comparable to that of the \"default\" strategy. Incorporating more epochs could enhance the agent's positioning performance but risks overfitting to specific epoch characteristics. To ensure generalization, we deliberately limited training iterations. This constraint maintains robust performance across diverse scenarios while preventing over-specialization.
## Appendix A Examinations of Smaller-Sized Cylinders and Multiple Cylinders  
### Experiment Configuration  
In the main text, we investigated the measurement performance with only a single cylinder with a specific diameter. In this appendix, we investigate the consistency of the results of PA velocity measurement with respect to the light-absorbing cylinder. The measurements were performed in two cases, one with a smaller cylinder and another with multiple cylinders. In the first case, we employed a single GC with a diameter of 0.5 mm, which was smaller than the one employed in the main text. In the second case, an object consisting of three GCs with a diameter of 0.5 mm aligned laterally with center-to-center distances 0.6 mm each other. Figure 11 illustrates the experimental configurations. The conditions and procedures were identical to the one performed for the single GC with a diameter of 2.0 mm in the main text. In each experiment, measurements were repeated five times and the ensemble averages were analyzed. Figure 12 compares the results with a single GC at a diameter of 2.0 mm (black) and at a diameter of 0.5 mm (blue), and with the three GCs at a diameter of 0.5 mm (orange).
# Damage and Failure in a Statistical Crack Model
## 5 The Fracture Process in Graphs
### Qualitative Summary of Graphs  
Beyond the quantitative results exhibited in the graphs, one may formulate the following qualitative conclusions:  
1. Despite the bimodal or multimodal nature of hotspots, the ensemble-averaged curves of damage versus time and versus strain-rate are continuous and smooth. They can replace detailed knowledge of the evolving crack distribution greatly simplifying the SCM.
2. Further, the scaling laws are relatively simple and invariant (in form) over more than 3 decades of strain rates.
3. Values of damage at failure are much less than would be predicted by the percolation thresholds. The long range effect of the entire spectrum of cracks is small and dominated by the local effect of nearest neighbors. Typical percolation thresholds occur at damage levels of \(\alpha=9/16\), see [27], whereas Figure 4 shows failure occurring in the array model with crack interactions at damage levels \(\alpha<0.23\).
4. There is relatively small uncertainty due to the assumed size of the process zone. There is also small uncertainty to the size of the ensemble beyond the minimum of 100 used as a baseline in the simulations reported. Further, even the ensemble of 500 simulations of our two-dimensional problems took only a few minutes on an Apple workstation.
5. I have not systematically explored the uncertainty of our results to the choice of the maximum crack size. The direct relation of stress at failure to the maximum crack size suggests that this parameter may be effectively deduced from experimental data. I have chosen the maximum crack size to be four times the average crack size in the exponential distribution, Equation (8). A smaller maximum would mean more hotspots and perhaps imply qualitative changes in the fracture process.
# Performance Analysis of an Improved Gravity Anchor Bold Expanded Foundation
## 1 Introduction
However, with the popularization and application of high hub and large capacity wind turbines, the anchor cage ring has gradually become the configuration used for the connection of large capacity wind turbines, with it replacing the foundation ring. As shown in Figure 4, the circular foundation [30; 31; 32] and annular foundation are both extended foundations, which are often used in wind turbines. However, both types of foundations are inconvenient for the maintenance and replacement of anchors. Therefore, this paper proposes an improved foundation form suitable for steel towers that expands the space in the lower area of the foundation, which is not only conducive to the maintenance and replacement of bolts but also reduces the cost of materials. To facilitate the study, we selected a foundation in practical engineering and took its specific size, materials, and load parameters. Figure 5 shows the size parameters of the foundation and the details of the anchor bolts. A total of 192 anchor bolts were used on the foundation, which were evenly distributed in the overhanging part of the foundation. Figure 4: Schematic diagram of (**a**) a circular foundation, (**b**) a ring foundation under a towering structure, and (**c**) a ring wind turbine foundation under a concrete tower. Figure 3: Wind turbine tower foundation configurations [27]. Table 1 explains the meaning of the size parameters and the corresponding values. The size of the lower maintenance space can be changed by changing the parameters h1 and b3. The purpose of this paper is to study the influence of parameters h1 and b3 on the stress of the foundation via numerical simulation. According to the simple mechanical principle, the author puts forward a preliminary conjecture: when the load is constant, the maximum tensile stress value of the most dangerous place in the upper part of the foundation increases with the increase in h1 and decreases with the increase in b3.
# FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs
## 4 Results and Discussion
### Environment Description  
NSL-KDD is a newer version of the existing KDD'99 dataset. It is an efficient reference dataset that helps researchers in comparing various methods of intrusion detection. Every single set of records has different characteristics of the flux with a label indicating whether it is an attack or normal behavior. Because of the limited open data sets for the use of IDS based on networks, NSL-KDD is actually the most effective dataset that can be used to evaluate various intrusion detection approaches [44]. The CICIDS2017 dataset includes benign attacks and the latest current attacks, resembling real-world data (PCAP). In addition, it contains analysis results from network flow based on CICFlowMeter, havingflows that are labeled by timestamp, the source and destination IPs, the protocols, and the attacks (CSV files) [45]. ToN-IoT holds a collection of different information sources taken from the whole IIoT system, as well as telemetry data, linux records, and also IoT system network traffic. The ToN-IoT network is accessible on ToN-IoT repository [46]. In addition, the datasets were displayed in CSV format with a column labeled that represents the attack or the normal behavior, indicating the types of attacks.  
We run our tests on an Intel(R) Core(TM) i7-8650U CPU @ 1.90 GHz 2.11 GHz, and 16 GB in RAM, with Windows 10 \(\times\) 64-bit. We implemented our model and feature engineering using Python v3.10.6.  
* TP: True positives define the amount of well identified intrusion.
* FP: False positives represent the amount of badly classified intrusion.
* TN: True negatives define the amount of correctly well normal occurrence.
* FN: False negatives represent the amount of misclassified normal occurrence.  
To evaluate our model we used the following metrics: accuracy, precision, recall, and f1-score, and the equations to calculate these metrics are respectively (8), (9), and (10). Those metrics are calculated based on the elements of the confusion matrix represented in the Table 2.  
\[\text{Accuracy}:\ \frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{8}\]  
\[\text{Precision}:\ \frac{\text{TP}}{\text{TP}+\text{FP}} \tag{9}\]  
\[\text{Recall}\ (\text{True Positive Rate TPR}):\ \frac{\text{TP}}{\text{TP}+\text{FN}} \tag{10}\]  
\[\text{False Positive Rate}\ (\text{FPR}):\ \frac{\text{FP}}{\text{FP}+\text{TN}} \tag{11}\]  
\[\text{F1-score}:\ 2\ \times\ \frac{\text{Precision}\ \times\ \text{Recall}}{\text{ Precision}\ +\ \text{Recall}} \tag{12}\]
# Evaluation of the Effectiveness of Surface Defect Removal by Slide Burnishing
###### Abstract  
This study determines the influence of technological parameters of slide burnishing on the size of surface defects (scratches). The experiment was performed on ring-shaped samples of C45 steel. The samples had scratches made on their surface with a nominal depth from 10 \(\upmu\)m to 70 \(\upmu\)m. Slide burnishing was carried out using a variable force and feed. It was observed that regardless of the applied force and feed, scratches with a nominal depth of 10 \(\upmu\)m and 20 \(\upmu\)m were completely removed, and a \"crushing\" effect occurred. As for other surface defects, they were 2 to 27 times smaller compared to their values before burnishing. The surface roughness parameters Ra, Rt, Rpk, Rk, and Rvk decreased. Their values were 42% to 91% lower than those observed after grinding. The thickness of the strengthened layer ranged from 10 \(\upmu\)m to 15 \(\upmu\)m, and the degree of strengthening was from 20% to 38% at a depth of 1 \(\upmu\)m. Compressive residual stresses occurred in the surface layer. Taking into account the surface layer properties and the effectiveness of surface defect removal, it should be noted that the most beneficial effects were obtained at F = 150 N and f = 0.03 mm/rev.  
surface defects; slide burnishing; surface defect removal; surface layer properties Article
## 4 Experiment  
In order to verify the correction effect after correction by the grating ruling system, we conducted two sets of grating ruling comparison experiments. Firstly, two 79 g/mm medium-step gratings were ruled, both with a ruling area of 800 mm \(\times\) 90 mm. One of them was not compensated for the error, and the intensity of the grating ghost lines and stray light was measured to be \(10^{-3}\) after the ruling, and the diffraction efficiency of the \(-36\) diffraction order of the medium-step grating was less than 30% with a diffraction wavefront of 0.456 \(\lambda\) (\(\lambda\) = 632.8 nm), as shown in Figure 15a. The other grating was ruled with error compensation, the measured grating ghost line intensity was less than \(10^{-5}\), and stray light intensity was less than \(10^{-4}\). The diffraction efficiency of its \(-36\) diffraction order was better than 50% with a diffraction wavefront of 0.108 \(\lambda\), as shown in Figure 15b.  
Figure 15: Wavefront comparison of 79 g/mm mid-step grating before and after error compensation: (**a**) no error compensation; (**b**) with error compensation.  
Next, two 600 g/mm conventional gratings were ruled, both with a ruling area of 40 mm \(\times\) 25 mm. The stray light from the grating without error compensation forms bright lines between the two diffraction stages, as shown in Figure 16a, with a maximum stray light intensity above \(10^{-3}\) and a diffraction efficiency of less than 70% for the \(-1\) diffraction order grating. The stray light intensity of the error-compensated grating reaches the order of \(10^{-5}\), and the diffraction efficiency of the \(-1\) diffraction order grating is better than 80%. The diffraction wavefronts of the two gratings are shown in Figure 17. From Figure 17, the diffraction wavefront mass of the grating before and after error compensation is reduced from 0.207 \(\lambda\) to 0.101 \(\lambda\) (\(\lambda\) = 632.8 nm).  
Through two groups of comparative experiments, it can be observed that the error sources affecting the spectral performance of the grating are corrected, and then the grating ruling experiment can be verified. The modified grating ruling machine can effectively improve the spectral performance of the grating after the ruling.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 3 Data Acquisition Methods
### Sensor Calibration
#### 3.2.4 Turbidity Sensor Calibration  
The calibration process for turbidity sensors requires the use of a standard turbidity calibration solution (e.g., 0 NTU, 20 NTU, 100 NTU, or 400 NTU) [24]. The sensor is immersed in the standard solution, and the calibration settings are adjusted to match the known value. After each measurement, the sensor is thoroughly rinsed with deionized water to prevent sediment contamination from affecting the optical surface and measurement accuracy. In addition, to preserve measurement accuracy, we avoid prolonged exposure to highly turbid or abrasive solutions, which could scratch the optical surface. If contamination or deposits occur on the optical surfaces, they are gently wiped with a non-abrasive cleaning solution to restore sensor performance.
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 1 Introduction
Indeed, ML tools are capable of automatically extracting from the huge amount of available data valuable hidden information, and can take into account plenty of parameters, which are typically not considered even in an oversimplified manner by model-based approaches. Additionally, ML models can easily incorporate different metrics, such as energy consumption of UAVs for hovering and transmitting/receiving information, into a single feature called information bit per joule of flying time [30]. Motivated by the above considerations, the main focus of this paper is to highlight the possible U-RAN scenarios in which the use of ML can allow system designers to take into account specific issues of UAVs, by evidencing the fundamental limits of model-based approaches. The paper is articulated as follows. Related work is discussed in Section 1.1, where, moreover, the novel issues covered by our survey are underlined. In Section 2, the main application scenarios are introduced, whereas technical challenges and requirements are discussed in Section 3, including the main disadvantages of conventional model-based solutions [31; 32; 33; 34; 35] when applied to this problem. The principles of ML techniques are discussed in Section 4, with reference to a number of typical problems in U-RAN design. Finally, promising future research topics and their potential impacts are outlined in Section 5.