esa-sceva/satcom-chunk-collection huggingface dataset:
# On Training Data Selection in Condition Monitoring Applications--Case Azimuth Thrusters
## 3 Results
### Identified Operating Areas for Monitoring  
Figure 9 illustrates the shaft speed ranges that became selected for monitoring from the candidate subsets when all models (time-domain, bearing and gear) were identified together. Both plots show that the selected ranges were relatively wide when datasets were small, such as N = 200. When the datasets were larger, the selected ranges were typically narrower.  
With the 'basic training subsets' approach (on the left), the highest number of selected subsets was five, in the case of N = 200. Then, the minimum number of samples in the subset could be as low as twenty (10% of N). Therefore, some of the subsets could be only faintly representative of the operation. When N was increased, areas around 410 and 500 rpm became selected, which could be explained by the frequent operation within such ranges (see Figure 4).  
Figure 9: Selected rotational speed ranges when all models were identified together. ‘Basic training subsets’ approach was used on the left, whereas ‘adaptive training subsets’ was used on the right. Values on horizontal axis represent the sample size (N) and the vertical lines with different colors represent selected non-overlapping ranges.  
The plot on the right illustrates that generally a large proportion of the operating area was identified for monitoring irrespective of the sample size (N) when the 'adaptive training subsets' approach was used. The monitoring of a broad area is useful when the thruster is probably operated within a broad rotational speed range during typical use. The highest number of selected operating ranges for monitoring was nine in the case of N = 2000. When N = 200 or 400, only two ranges became selected. If many ranges are selected for use, multiple models and parameters must be identified, which may increase complexity. However, the use of simple models, as shown in Equation (1), helps to keep the complexity of the monitoring approach tolerable. Appendix C illustrates the ranges selected for the time-domain, bearing and gear models, when the models were identified separately from each other.
## 2 Establishment of the Active-Passive Single Pile Calculation Model
### Phase Description of the Additional Load Caused by an Adjacent Surcharge
#### 2.3.2 Elastoplastic State
 \tag{13}\]  
Calculation of the Additional Load on the Pile Side  
We assumed that the earth pressure at rest \(q_{0}(z)=K_{0}\gamma z\) is the equilibrium stress of the soil mass between piles. When there is a horizontal stress at the \(AA^{\prime}\) surface \(\sigma_{AA^{\prime}}(z)>q_{0}(z)\), the plastic flow around the soil mass behind the pile occurs (which will be discussed later), and we now present the calculation method for \(\sigma_{AA^{\prime}}(z)\leq q_{0}(z)\). Step 1: calculate the total lateral pressure of surface \(BB^{\prime}\) under the influence of the surcharge. \[\sigma_{BB^{\prime}}(z)=\sigma_{x}(z)+K_{0}\gamma z \tag{14}\]  
where \(\sigma_{x}(z)\) is the additional stress calculated by Equation (6), \(K_{0}\) is the coefficient of earth pressure at rest, and \(K_{0}=1-\sin\varphi_{0}\), \(z\) is the depth of the calculated point. Step 2: calculate the lateral pressure of surface \(AA^{\prime}\) under the influence of the surcharge. \[ \begin{array}{rl}\sigma_{AA^{\prime}}(z)=&\frac{1}{N_{\varphi} \tan\varphi}\cdot\Bigg{\{}\frac{c\cdot\left(1+2N_{\varphi}^{1/2}\tan\varphi \right)}{\exp\left[(D_{1}-D_{2})G_{3}/D_{2}\right]}+\left[(D_{2}-D_{1})^{G_{1}} (\sigma_{BB^{\prime}}(z)\cdot G_{1}+c\cdot G_{2})-\right.\\ &\left.c\cdot G_{2}\right]\cdot\frac{N_{\varphi}\tan\varphi}{G_{1} \cdot\exp\left[(D_{1}-D_{2})G_{3}/D_{2}\right]}\Bigg{\}}-\frac{c\cdot\left(1+2 N_{\varphi}^{1/2}\tan\varphi\right)}{N_{\varphi}\tan\varphi}\\ \end{array}  \tag{15}\]  
Step 3: when \(\sigma_{AA^{\prime}}(z)\leq K_{0}\gamma z\), find the new earth pressure equilibrium point. Substituting \(\sigma_{1x}=K_{0}\gamma z\) into Equation (13) gives \(x=x_{l}\), and the next step is to determine the region where \(x_{l}\) is located. If \(x_{l}\) is in the region \(AA^{\prime}E^{\prime}E\), then the pile side passive line load concentration can be written as  
\[q(z)=\sigma_{BB^{\prime}}(z)D_{1}-\sigma_{1x|x=x_{l}}\cdot D_{2} \tag{16}\]  
If \(x_{l}\) is not in the region \(AA^{\prime}E^{\prime}E\), \(\sigma_{2D}=K_{0}\gamma z\) should be substituted into Equation (12), giving \(D=D_{l}\), so that the pile side passive line load concentration can be written as  
\[q(z)=\sigma_{BB^{\prime}}(z)D_{1}-\sigma_{2D|D=D_{l}}\cdot D_{2} \tag{17}\]
###### Abstract  
The effect of strength mismatch (ratio between the yield stress of weld metal and base metal, \(M_{y}\)) on the ductile crack growth resistance of welding pipe was numerically analyzed. The ductile fracture behavior of welding pipe was determined while using the single edge notched bending (SENB) and single edge notched tension (SENT) specimens, as well as axisymmetric models of circumferentially cracked pipes for comparison. Crack growth resistance curves (as denoted by crack tip opening displacement-resistance (CTOD-R curve) have been computed using the complete Gurson model. A so-called CTOD-\(Q\)-\(M\) formulation was proposed to calculate the weld mismatch constraint \(M\). It has been shown that the fracture resistance curves significantly increase with the increase of the mismatch ratio. As for SENT and pipe, the larger \(M_{y}\) causes the lower mismatch constraint \(M\), which leads to the higher fracture toughness and crack growth resistance curves. When compared with the standard SENB, the SENT specimen and the cracked pipe have a more similar fracture resistance behavior. The results present grounds for justification of usage of SENT specimens in fracture assessment of welding cracked pipes as an alternative to the traditional conservative SENB specimens.  
Keywords: weld strength mismatch; ductile tearing; pipelines; mismatch constraint +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion
### Effect of Cold Plasma Treatment on Physical Change of Seed's Surface
#### 3.1.2 Effect of Cold Plasma Treatment on Seeds' Surface Morphology  
During early germination steps, the seed coating acts both as physical protection against the environment and as a constraint for root protrusion. Therefore, any structural modification of this coating could affect seed germination. To investigate the effect of cold plasma treatment on the seed's coating surface, scanning electron microscopy (SEM) was employed to characterize the seed's surface morphology.  
SEM images suggest that the plasma directly induced changes in the seed coating (Figure 3). There are only slight deformations after 1-min plasma treatment and some wrinkles emerging on the seed surface after 2-min plasma treatment. However, the epidermis is deformed and shrunken in the seeds treated for 4 min. The surface topography of the seeds is comprised of crack features with sizes varying from 0.2 to 1.5 \(\upmu\)m after 4 min of plasma treatment. The degree of seed coat deformation and the size of the cracks are proportional to the plasma treatment time. We rule out the effect of UV radiation produced by the plasma jet in forming these cracks and deformation features, since exposing seeds to similar UV radiation by a UV lamp (peaked at 254 nm) at the same time as plasma treatment results in no change in the seed coating. Therefore, we relate these crack features to the interaction of the electrons and ions of the plasma with the seed coating. Our findings also agree with other reports, in which the cold plasma was observed to induce change in the outer coating of different seeds [7; 21].
## 2 Materials and Methods
### Fabrication and Digital Impression of Working Models  
Fifteen working models per group were fabricated on the basis of the master model. A customized ray for taking impressions of the master model was designed with the appropriate shape. A regular space of 2-mm was assigned to the impression materialsusing CAD software. The 15 customized trays per group were fabricated using a 3D printer (ZENITH; Dentis, Daegu, Korea, 2017) that used stereolithography (STL). A resin (ZMD-1000B; Dentis, Daegu, Korea, 2017) was selected as the 3D printing material. To make an impression of the master model, the heavy-body impression material (Aquasil Ultra Rigid; Dentsply, USA, 2017) was poured into the customized tray, whereas the light-body impression material (Aquasil XLV; Dentsply Sirona, York, PA, USA, 2017) was poured around the abutment tooth. After removing the master model, all impressions were carefully observed under a video microscope (IMS; Sometech, Seoul, Korea, 2017) to check for any damage or errors. Type IV dental modeling material (Fujirock; GC, Leuven, Belgium, 2017) was poured into the impression, and the model was removed after the modeling material had set. The working models were fabricated by repeating the fabrication process for the plaster models, and all models were inspected using the same video microscope that was used to observe the impressions.  
Digital impressions of the working models were taken with the following intraoral scanners: Omnicam (Dentsply Sirona, York, PA, USA, 2017), EZIS PO (DDS, Seoul, Korea, 2017), and CS3500 (Carestream Dental, Atlanta, GA, USA, 2017). The models were scanned according to the manufacturer's recommendations without any special adjustment of the scanner. All intraoral scanners used in this study were used by one skilled operator (K. S.). The scanning strategy was followed by the occlusal, buccal, and lingual of the die. The models incorporated details such as margins, buccal side, lingual side, mesial surface, distal surface, and the triangular structure between the two abutment teeth that were used to align the scanned data.
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 6 Analysis of Experimental Results  
The results obtained in Section 5 demonstrate that the features selected for each feature selection method and classification algorithm are different, and the evaluation index performance differs according to the number of features used. In this section, we compare the base model of Section 5.1 and the best performance with the classification algorithm of each feature selection method. We also compared the results with related works that are similar to this experiment. In addition, by increasing the amount of data, the time complexity change according to the feature selection application is checked and analyzed.
## 4 Analysis of the Results
### Analysis of the Experimental Results  
In the experiment, 50 CT images were selected for training and 47 images were used to test the accuracy and time complexity of the algorithm. In order to verify the accuracy of the segmentation algorithm, we used the proposed method, region-growing, watershed, and rolling-ball methods to segment the CT images of juxtapleural nodules. Considering the interference of multiple factors, the accuracy of segmentation was not judged in the experimental process. Therefore, the results of various segmentation algorithms were compared with the comprehensive lung parenchyma area manually segmented by four experienced radiologists. In the course of the experiment, the manually segmented images in the medical records were the ultimate gold standard.
# Experimental Study of the Performance of a Novel Vertical-Axis Wind Turbine
## 4 Results and Discussion
Based on the results of the experiment, the average power coefficient due to the actual power generated by the AVAWT is \(0.188\pm 0.0168\); meanwhile, the average aerodynamic power coefficient of the AVAWT rotor is about \(0.350\pm 0.0332\), the power coefficient of a Savonius rotor is 0.30 [4], the power coefficient of a DVAWT is between 0.4 and 0.42 [5] and the power coefficient of a modified Savonius rotor with a \(45^{\circ}\) angle of twist is 0.3385 [9]. It is worth noting that, unlike a DVAWT that is not self-starting, the AVAWT is self-starting.
# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 4 Results and Discussion
### Comparison with Previous Studies  
This study expands on earlier research by [PERSON] et al. [1], who predicted students' degrees of adaptation throughout the pandemic using the same dataset. By utilizing an extensive range of Explainable AI (XAI) methodologies, our research expands on previous findings and offers a more profound understanding of factors affecting adaptability.  
[PERSON] et al. [1] used a variety of machine learning algorithms in their study, including Random Forest, Decision Tree, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network. The Random Forest classifier yielded the best results, with an accuracy of 89.63% and precision values of 0.89, 0.90, 0.88 for low, moderate and high adaptability, respectively. Using the Random Forest model, our study, in contrast, obtained a higher accuracy of 91% and precision values of 0.93, 0.94, and 0.94 for high, low, and moderate adaptation levels, respectively. To provide a more thorough interpretation of the model predictions, we also used XAI techniques including SHAP, LIME, Anchors, ALE, and Counterfactual explanations to highlight certain feature contributions. We demonstrate the complementary nature of our approach and verify the robustness of our technique by comparing our results with those of [1]. Our work presents a more thorough knowledge of  
Figure 10: Comparative analysis of original and Counterfactual feature values for Instance 3.  
student adaptation in the context of education, not only validating their findings but also providing improved interpretability using XAI approaches.  
This comparison underscores the value of integrating XAI methods with traditional machine learning models to achieve both high predictive performance and interpretability, thereby contributing to more informed and effective educational interventions.
# Implementation and Evaluation of HTTP/3 Connectivity Check Using Happy Eyeballs Algorithm
## 4 Implementation and Experimental Results
### Results of Packet Loss Setting  
The HTTP/3 adoption rate and connection establishment time when the packet loss was intentionally reproduced are displayed in Table 3. The maximum values are also presented because the standard deviations were large.  
Table 4 presents the measured connectivity establishment time for Alt-Svc, which was performed as a control experiment. Note that when the loss ratio was set to 50% or higher, Alt-Svc failed owing to a timeout (30,000 ms), and it was difficult to establish 1000 consecutive connections, which is why the results are not shown.  
Table 4 shows the measured connectivity establishment time for Alt-Svc, which was performed as a control experiment. Note that when the loss ratio was set to 50% or higher, Alt-Svc failed owing to a timeout (30,000 ms), and it was difficult to establish 1000 consecutive connections, which is why the results are not shown.
###### Abstract  
In the detection of gravitational waves in space, during the science phase of the mission, the point-ahead angle mechanism (PAAM) serves to steer a laser beam to compensate for the angle generated by the relative motion of the two spacecrafts (SCs) during the approximately 10 s of flight time a laser beam will take from one SC to reach a distant SC of three million kilometers away. The Tilt-to-length (TTL) noise budget for the PAAM is constrained to less than 8 \(\mathrm{pm}/\sqrt{\mathrm{Hz}}\) within the frequency range of 1 mHz to 1 Hz. This constraint requires that the measurement noise of the interferometer remains below this threshold to guarantee the precision needed for gravitational wave detection in space. In the present work, an equal-arm heterodyne interferometer, which is fixed in a vacuum system with multilayer thermal shields, is proposed for the OPD (Optical Path Difference) measurement. The background measurement noise of the system is smaller than 60 \(\mathrm{pm}/\sqrt{\mathrm{Hz}}\) within the frequency range of 1 mHz to 1 Hz. This corresponds to an 84.6% noise reduction at 1 mHz compared to similar unshielded interferometers utilizing conventional bonding methods, demonstrating that the proposed system effectively suppresses measurement noises, particularly thermal noise, in the low-frequency range.  
heterodyne interferometer; thermal shield; optical path difference; hydroxide-catalysis bonding; thermal noise +
Footnote †: journal: Applied sciences
## 2 The Proposed Method
### The RUL Probability Prediction Framework  
This section will introduce the proposed probabilistic RUL prediction framework in this paper. The overall framework is depicted in Figure 4.  
First of all, the sensor data is subjected to a filtering process, where only pertinent information is retained and subsequently normalized using the minimum-maximum normalization technique, as in Equation (12). This normalization aims to mitigate the adverse effects arising from singular sample data.  
\[x^{*}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{12}\]  
where \(x\) is the sensor data, \(x^{*}\) is normalized data, \(x_{min}\) is the minimum value in the data, and \(x_{max}\) is the maximum value in the data.  
Then, the widely employed data compression technique, PCA, is applied. Numerous studies have demonstrated its significant impact on data dimensionality reduction and denoising. PCA is utilized to extract the principal features of the data, thereby reducing its dimensionality and enhancing the computational efficiency.  
Subsequently, the core prediction model BiGRU-IVBI is composed by leveraging the Bayesian network into BiGRU. The deterministic weight value in BiGRU is replaced by our prior probability distribution to analyze the uncertainty factors in the neural network and train the model with gradient descent to minimize the loss function to obtain the optimal distribution parameters and, finally, obtain the RUL probability distribution prediction based on which the point prediction and CI of the aero-engine RUL prediction also can be obtained.
# Transparent CoAP Services to IoT Endpoints through ICN Operator Networks +
## 3 Scenario and Problem Statement
### CoAP Observe  
Request aggregation is not common in the conventional IP network. There are some cases where the constraint devices (e.g., temperature sensors) may receive a large number of requests that require significantly higher processing capabilities. In the case of CoAP observe, a CoAP server needs to maintain state for each client and respond separately to each of them.  
The details of supporting CoAP observe extension in the NAP of the POINT architecture is presented in [11]. In brief, CoAP requests for the same resource are aggregated in the NAP, hence only a single CoAP client is visible to the CoAP server. In addition, when the status of a resource changes, the updates are transmitted using ICN multicast, thus safeguarding network resources.
# Investigation into the Construction Response of Tunnels through Fault: Model Test
## 5 Numerical Simulation Verification
### Model Verification  
The comparison analysis of the model test and numerical simulation results on the surrounding rock pressure and displacement at 60\({}^{\circ}\) fault dip is presented in Figure 15, where \"NS\" represents numerical simulation and \"MT\" represents model test. It is observed from the figure that both the numerical simulation and model test exhibit a similar increasing trend in the pressure and displacement of the surrounding rock with the excavation steps. According to the model test results, the pressure and displacement of the surrounding rock in the numerical simulation initially increased at a slow rate, but then grew rapidly with the excavation steps until reaching a stable state. As the material parameters and dimensions used in the numerical simulation are in a 1:30 proportion to those used in the model test, there exists a certain gap in their values. However, this difference does not affect the verification of the reliability of the model test results.  
Figures 16 and 17 show the vertical displacement of the strata and the stress distribution of the lining after the tunnel is excavated. The vertical displacement of the fault zone is clearly visible, indicating the disturbance caused by tunnel excavation. This disturbance can lead to the instability of the fault zone, reconsolidation of the strata, and significant vertical displacement. The unstable surrounding rock in the fault zone can also increase the pressure on the lining, resulting in the instability of the support structure. The stress  
Figure 14: The numerical fault tunnel model of 60\({}^{\circ}\) fault dip.  
Figure 15: Comparative analysis of the surrounding rock construction response: (**a**) surrounding rock pressure and (**b**) surrounding rock displacement.  
distribution of the lining shows that the stress is concentrated at the vault, side walls, and haunches, indicating that these areas are under high stress and are more prone to instability. Therefore, it is important to carefully consider the design and construction of support structures in fault zones to ensure the stability and safety of the tunnel.
## 2 Multi-Objective Message Routing in EnFVs
### Objectives and Constraints in Routing Optimization  
Routing optimization in the context of EnFVs involves the determination of efficient and optimal routes for message transmission, considering various objectives and constraints [17; 18]. The objectives in multi-objective message routing optimization typically include minimizing message delivery time, maximizing network throughput, reducing energy consumption, and ensuring reliable communication. These objectives are crucial for enhancing the overall performance and efficiency of the transportation system described below.  
Message delivery time is a primary objective in routing optimization. For EVs, minimizing the message delivery time is essential for timely coordination, ensuring efficient charging, and enabling effective vehicle-to-grid communication. In the case of flying vehicles, timely message delivery is crucial for maintaining situational awareness, enabling efficient air traffic control, and supporting safe operations [1]. Maximizing network throughput is another key objective in routing optimization. High network throughput ensures the efficient utilization of communication resources and facilitates the exchange of messages among vehicles. This objective is particularly relevant in scenarios where a large number of vehicles are present, such as in urban environments or during peak travel periods [19]. Reducing energy consumption is a critical objective for EnFVs. In the case of EVs, minimizing energy consumption during message routing helps extend the vehicle's driving range and optimize battery usage. For flying vehicles, energy efficiency is vital for increasing flight endurance and reducing the need for frequent refueling or recharging [20]. Furthermore, the routing optimization process must consider various constraints that arise in the context of EnFVs. These constraints may include the availability and location of charging stations for EVs, flight restrictions and airspace regulations for flying vehicles, road or airspace congestion, and real-time traffic conditions. Incorporating these constraints ensures that the generated routes adhere to the specific operational requirements and limitations of the vehicles and the transportation system.