esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Hybrid A* Algorithm Improvement
#### 2.5.4 The Modified Hybrid A* Algorithm in Detail  
The modified Hybrid A* algorithm considering terrain roughness is improved on the regular Hybrid A* algorithm, which has the same essential parts. Before planning starts, it is necessary to set vehicle parameters, including length, width, tire information, and minimum turning radius, etc. Generally, tire information includes information on wheelbase and tire size. After that, the C-MAP is input, which includes both obstacle and roughness information to the path planner, and then the essential planning parameters are set, e.g., FC, RC, EI, etc. The pseudo code for Modified Hybrid A* algorithm (Algorithm 1) is as follows.  
```
1: Set Vehicle properties: \(VP\leftarrow(Length,Width,Tire\) in formation, Minimum turning radius)
2: Set planner: planner \(\leftarrow(CostMap,\) Planning parameters\((ML,\)\(NMP,FC,RC,SC,EI))\)
3: Input: \(startNode(x_{s},y_{s},\phi_{s}),goalNode\((x_{g},y_{g},\phi_{g})\)
4: Procedure Hybrid A* planner \((startNode,goalNode,VP,\) planner)
5: Node extension
6: Validate the node
7: RS curve check
8:return Path Points
9: Processing Path Points by ConjugateGradientSmoother \((CostMap,\)\(PathPoints)\)
10:return the Path Points after CG optimization
11:end procedure
12: Procedure gScore \((n_{current},n_{next},planner,VP)\)
13:\(gScore_{parent}\gets gScore\) of \(n_{current}\)
14:\(Length_{current\gets next}\leftarrow ML\)
15: Generate the tire path besides the extension line according to \(VP\)
16: Calculate the occupied grid coordinates of the tire path
17: Delete duplicate grid coordinates
18: \(TireCost\) is the total cost of the occupied gird
19:if the direction of \(n_{next}\
eq\) the direction of \(n_{current}\)then
20:\(gScore=gScore_{parent}+Length_{current\gets next}+TireCost+SC\)
21:else
22:\(gScore=gScore_{parent}+Length_{current\gets next}+TireCost\)
23:endif
24:end procedure
```  
**Algorithm 1** Modified Hybrid A*  
The procedure gScore is an improved \(g\)-value estimation function. In each node extension step, it accumulates the total cost of the occupied grid of the corresponding tire path which is calculated from vehicle parameters. This allows Hybrid A* algorithm can incorporate surface roughness into motion cost estimation. Steps 19 to 23 mean that only when the motion status changes, in other words, when the motion direction changes from forward to backward, the SwitchCost will penalize the motion cost.
## 5 Experimental Results
### Training Results  
The confusion matrix, precision-recall curve, and F1 curve are shown in figures below. As shown in the confusion matrix in Figure 14, the accuracy for the seven categories of PCB defects demonstrates ideal results.  
Figure 15 shows an ideal trend for the accuracy of the seven classes of PCB defect detection. The precision curve indicates the proportion of true positives among all predictions. All classes show an accuracy of 1.0.  
Figure 16 shows the proportion of true positive predictions among all actual positive objects. Each curve demonstrates an ideal trend for all categories, indicating that most defects present in the PCB images are detected.  
Figure 14: Confusion matrix for each class.  
Figure 15: Graph of precision.  
Figure 19 shows the model's performance during the training and validation stages. High performance was observed across all metrics, including the bounding box loss, object loss, classification loss, accuracy, recall, and mean Average Precision (mAP). As shown in the Figure 19, both mAP@50 and mAP@50-95 demonstrate high performance, with values of 0.995.  
As shown in Figure 20, defects were successfully detected by the RT-DETR model. All seven types of defects present in the validation images were detected.  
Figure 19: Model performance at each learning and validation stage.  
Figure 18: F1-score curve.  
Figure 20: _Cont._  
For comparative analysis with other object detection techniques, we applied YOLOv5 compared the results with RT-DETR. It was confirmed that RT-DETR had a faster processing time and better bounding box detection accuracy, as shown in Table 3.
## 2 Methodology  
### Extreme Gradient Boosting  
Extreme gradient boosting (XGBoost) is an efficient, flexible, and scalable machine learning method based on the gradient boosting framework [12]. Its core principle involves iteratively improving the model by constructing a sequence of weak learners, each attempting to correct the errors of its predecessor.  
Unlike other gradient boosting algorithms, XGBoost offers significant advantages in performance and flexibility. It simplifies the calculation of the loss function through a second-order Taylor expansion and includes a regularization term in the objective function to control model complexity. The objective function of XGBoost is shown in Equation (1). Additionally, XGBoost supports parallel computing and cache optimization, which enhances training speed and efficiency. It also introduces a sparsity-aware algorithm to handle missing data, ensuring high accuracy and robustness in the presence of incomplete data. Given these advantages, XGBoost is an optimal choice for the rock mass classification database used in this study.  
\[Ob_{j}=\sum_{i=1}^{n}L(y_{i},\hat{y}_{i})+\sum_{k=1}^{K}\Omega(f_{k}) \tag{1}\]  
where \(Ob_{j}\) is the objective function; \(L\) is the loss function; \(y_{i}\) and \(\hat{y}_{i}\) represent the actual and predicted values of sample \(i\), respectively; \(\Omega\) is the regularization term; \(K\) is the number of weak learners; and \(f_{k}\) denotes the complexity of the \(k\)-th weak learner.  
Despite its many strengths, XGBoost has some limitations. For example, the hyperparameter tuning process requires substantial computational resources and time. Therefore, suitable optimization methods are crucial for enhancing its efficiency and effectiveness. To address these challenges, metaheuristic optimization algorithms have become an area of significant interest.
# Compton Scattering Imaging of Liquid Water in Porous Carbon-Based Materials
## 3 Results and Discussion
### Relative Volume Fraction of C Fiber/C Composites and Liquid Water  
To better understand the liquid water distribution along the \(z\) direction, the variation in the relative volume fraction of the liquid water was evaluated. From Equation (1), the intensity of the Compton scattered X-rays, integrated over a cross section, is given by  
\[I\propto F_{\text{C}}\cdot\rho_{e,C}+F_{H_{2}O}\cdot\rho_{e,H_{2}O} \tag{2}\]  
where \(F_{\text{C}}\) and \(F_{H_{2}O}\) are the volume fraction of carbon fiber and carbon composite and that of liquid water, averaged over the cross section, and \(\rho_{\text{C}}\) and \(\rho_{H_{2}O}\) are the effective electron density of the carbon fiber and carbon composite and the liquid water, respectively.  
Figure 6: Imaging liquid water in cross-section 5: (**a**) wet GDL material sample; (**b**) dry GDL material sample; (**c**) difference image between the wet and dry samples. In (**c**), liquid water is observed in the pore spaces in the GDL material sample, which is indicated by the yellow lines. Negative contributions are observed on the region of the carbon fibers, indicating interactions between the GDL materials and liquid water.  
Figure 7: Imaging liquid water in cross-sections 5–7, together with the image of the dry sample.  
Assuming that the porosity of the GDL material is 80% and the density of the carbon composite is 1.8 g/cm\({}^{3}\), Equation (2) is approximated by  
\[I\propto 18+0.6x \tag{3}\]  
where \(x\) is the volume fraction (%) of liquid water.  
Figure 8 shows the depth dependency of the relative volume fraction of liquid water, together with that of the carbon fiber and carbon composite. Here, the X-ray beam position, Z, at 0 \(\upmu\)m corresponds to the cross section nearby the Cu cover plate, and that at 140 \(\upmu\)m corresponds to the cross section close to the Al base. This result show that liquid water penetrates into the GDL materials at 50 \(\upmu\)m from the surface, which is consistent with the observation of the difference images in Figure 7. Some water penetration from the Al base side is also observed. This water comes from the liquid water that has moved outside the GDL material sample.
# Evolutionary Game Analysis of Power Generation Groups Considering Energy Price Fluctuation
## 3 Analysis of Power Generation Cost under Primary Energy Price Fluctuation
### Coal-Fired Power Generation Cost Analysis  
The power generation cost of coal-fired power generation should first consider the spot price of thermal coal, transportation costs, and tax rates, and calculate the arrival price of thermal coal; then calculate the variable cost of coal-fired power generation; finally, calculate the marginal cost of coal-fired power generation. It includes the following steps:  
First, the power coal arrival price is derived from the power coal spot price, as described in Equation (13).  
\[\lambda_{facocal}=[\lambda_{coal}\eta_{coal}(1-T_{coal})+TR_{coal}(1-T_{ trecal})] \tag{13}\]  
In the formula: \(\lambda_{facocal}\) is the arrival price of thermal coal; \(\lambda_{coal}\) is the spot price of thermal coal; \(\eta_{coal}\) is the standard coal conversion rate; \(T_{coal}\) is the thermal coal tax rate; \(TR_{coal}\) is the coal transportation cost; \(T_{trecal}\) is the coal transportation tax rate.  
The variable cost of coal-fired electricity generation is then calculated based on the arrival price of power coal, as shown in Equation (14).  
\[C^{VAR}_{coal}=(\lambda_{facocal}\sigma_{coal}+C_{nut}+C_{env})(1+T_{coallec}) \tag{14}\]  
In the formula: \(C^{VAR}_{coal}\) is the variable cost of coal-fired power generation; \(\sigma_{coal}\) is the comprehensive coal consumption; \(C_{quat}\) is the water production cost; \(C_{env}\) is the environmental cost; \(T_{coallec}\) is the coal-fired power generation tax rate.  
Finally, the marginal cost of coal-fired power generation can be obtained from the variable cost of coal-fired power generation, as shown in the following Equation (15):  
\[C_{coal}=(C^{VAR}_{coal}+C^{Const}_{coal})(1+\eta_{faclec}) \tag{15}\]  
In the formula: \(C_{coal}\) is the marginal cost of coal-fired power generation; \(C^{Const}_{coal}\) is the fixed cost of coal-fired power generation; \(\eta_{cofaclec}\) is the power consumption rate of coal-fired power plants.
## 5 Discussion
Secondly, from a computational perspective, combining multiple models can reduce the likelihood of encountering bad local minima. Finally, from a symbolic standpoint, some learning tasks may have yet to be considered in the algorithm's current hypothesis space. The proposed model is highly adaptable as different sub-models can be combined to generate hybrid models tailored to specific tasks. Moreover, ensemble learning or combination strategy can employ other models or tasks, such as Vision GNN (ViG) [57], graph neural networks [58], and RNN-CNN hybrid models [59], using alternative voting strategies or stacking methods. Consequently, combining hybrid models with ensemble learning provides ample room for further exploration. A single learner is typically inadequate to approximate such hypotheses effectively, whereas multiple learners can improve the approximation. There is still room for further improvement in our work. For instance, the utilization of Grad-CAM can be employed to examine the information extracted by individual submodels and the complete model, providing supporting evidence via transparent design methods for the relevant theories. In the future work, the system can be expanded to include other diseases or incorporate additional features. The model itself can be replaced or modified, and the model parameters can be retrained based on updated data to facilitate version updates and enhancements. Because the WSNs-aided technology involves a large amount of personal and private information (e.g., EMR data), it is important to ensure the data security and confidentiality. To achieve this, effective encryption and security measures are needed to protect the data from unauthorized access. In addition, the fundus image may become blurry due to the limitation of device quality, influencing the accuracy and precision of the data transmitted via wireless sensor networks. Professional technicians are needed to maintain and manage the network to ensure its proper operation. For the deep learning-based DR grading model, although the HybridLG framework has effectively addressed the DR grading, some concerns still need to be considered. Firstly, when extending the framework to integrate other models, the time required for training multiple sub-models without pre-trained parameters on different GPUs is still higher than using a single model. Although the performance is improved, it may need to be more cost-effective. Secondly, despite the HybridLG framework demonstrating good generalization performance on the dataset, when applied in practice, the quality of extracted fundus images during diagnosis should be of high quality to prevent misdiagnosis and ensure accurate grading and treatment of the patients.
## 2 Basic Characteristics of Novel SCPC Connection
### Mechanical Behavior Analysis of Hysteretic Dampers
The analysis demonstrates hysteretic damper has ideally a much higher initial stiffness, it can be modelled as elastic plastic model. As shown in Figure 6b, the theoretical hysteresis curve of hysteretic dampers is an elastic-plastic model and the stiffness after yielding is zero. Hence, combined with the PT tendons and hysteretic dampers, the novel connection can be modelled as the bilinear elastic spring taken parallel with elastic plastic model. The theoretical hysteresis curve of novel hybrid joint is modelled as a typical double flag-shape hysteretic model. Figure 4: Cyclic loading test set up. Figure 5: Comparisons of hysteretic behavior of numerical simulation and test results. The mechanical principle of the novel hybrid joint with hysteretic dampers is defined as the gap opening and closing alternately under the near-fault earthquake wave actions. Figure 7 shows the mechanical principle of novel connection composites when it bears the positive moment under seismic actions--where \(T\) is the resultant tension of all the unbonded tendons; \(N_{t}\) and \(N_{b}\) are the horizontal resultant forces of the upper and lower hysteretic dampers, when the beam-column joint subjected to the positive moment under the loadings, the upper hysteretic damper bear compressive force and the lower hysteretic damper bear tensile force; \(V_{c}\) is the shear force taken by the fiction, reflecting the fictions caused by prestressed tendons and high strength bolts, respectively; \(d_{1}\) is the distance between the top edge of the beam and the resultant point of PT tendons; \(d_{t}\) and \(d_{b}\) are the distances between the center point of the top and bottom hysteretic dampers and the top edge, respectively; The moment \(M\) at the beam end includes the moment provided by unbonded tendons and the hysteretic dampers. \(N_{PT}\) is the pre-pressure of PT tendons at the end beam, \(\mu_{1}\) is the fiction coefficient of the beam-column interface, \(N_{v}^{b}\) is the shear bearing capacity of single high strength bolt on the steel connection, the formula is:  
\[M=T\cdot d_{1}-N_{t}\cdot d_{t}+N_{b}\cdot d_{b} \tag{1}\]  
\[V_{c}=F_{f}=N_{PT}\times\mu_{1}+4\times 2\times N_{b}^{v}. \tag{2}\]  
The value of initial resultant force of unbonded tendons is \(T_{0}\), when the bending moment at beam-column full contact interface exceed the critical opening moment \(M_{IO}\) under the external load, the joint gap has opened, the formula of \(M_{IO}\) is as follows:  
\[M_{IO}=T_{0}\cdot d_{1}-N_{t}\cdot d_{t}+N_{b}\cdot d_{b} \tag{3}\]  
Figure 4.0 Theoretical hysteretic model of the novel connection. Figure 4.1 Theoretical hysteretic model of the novel SCPC connection.
## 2 Data Description and Preparation
### Data Processing  
Before training the deep learning autoencoder, the dataset was preprocessed to be consistent, noise-free, and prepare the data for time series analysis. the preprocessing steps included imputing missing values, normalizing numeric features, and structuring data into time sequences. For maintaining consistent feature scaling, all numeric parameters were normalized by z-score normalization, provided as follows:  
\[x^{\prime}=\frac{x-\mu}{\sigma}, \tag{1}\]  
where \(x\) represents the original value, \(\mu\) is the mean, \(\sigma\) is the standard deviation, and \(x^{\prime}\) is the normalized value (Equation (1)). This transformation ensures that all features have a mean of zero and a standard deviation of one, improving model convergence and stability. Since time dependency is crucial for detecting stuck pipe events, the dataset was restructured into sequences using a lookback window of five time steps. This process,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Unit** \\ \hline Date & Date/Time \\ Average Hookload & Kkgf \\ Hole depth (MD) & M \\ Weight on Bit & Kkgf \\ Block Position & M \\ Rate of Penetration & m/h \\ Average Rotary Speed & Rpm \\ Average Surface Torque & KN-m \\ Rate of Penetration (5 ft avg) & m/h \\ Bit Depth (MD) & M \\ Average Standpipe Pressure & kPa \\ Mud Flow In & L/min \\ Stuck cases & Categorical \\ \hline \hline \end{tabular}
\end{table}
Table 2: Drilling parameters and units [45].  
Figure 1: Experimental setup: (**a**) Volve 3D model. (**b**) The Volve development was based on production from the Mersk Inspirer jack-up rig [44].  
known as temporalization, allows the model to learn patterns across consecutive drilling states. The transformation is mathematically expressed as  
\[X_{i,j,k}=\Big{\{}X_{i,j},X_{i,j-1},\ldots,X_{i,j-(k-1)}\Big{\}}, \tag{2}\]  
where \(X_{i,j,k}\) represents the time series data for sample \(i\) at time \(j\) within a window of \(k\), and \(xj\) is the original value at time \(j\), as presented in Equation (2). After processing, the dataset was split into training (80%), validation (10%), and testing (10%) subsets, ensuring that only normal drilling operations (non-stuck cases) were used for training. This setup enables the model to effectively learn normal operational patterns while detecting anomalies indicative of stuck pipe incidents.
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 5 Prototype Development and Validation
### WebAuthn Authentication Web Application (WAWA)
#### 5.1.1 WebAuthn Standard and Credentials  
As explained in Section 1.6, the WebAuthn standard considers the registration and authentication of credentials. There are two types of WebAuthn credentials related to authenticators (like security keys): discoverable and non-discoverable. For this purpose, we have to consider two different authentication flows.  
With discoverable credentials, the user does not need to identify themselves with a username during the process. The userHandle included in the authenticator response is used as the userId to find the registered device for verifying the authenticator response.  
Hence, using discoverable WebAuthn credentials implies that the user is identified _after_ the authentication of the credential.  
On the contrary, with non-discoverable credentials, the authentication response from the authenticator will not contain a userHandle that identifies the user. Therefore, the username should be specified at the authentication form. When the server generates the WebAuthn authentication options, a list of the registered credentials for the corresponding user must be provided.
## 3 Machinery Monitoring
### Feature Extraction  
The calculation of feature values is achieved using a window with data length determined by the user, such as an enterprise employee. Generally, the length should not exceed half the wavelength of the signal. In accordance with the recommendations in a previous study [25], 50% of each window is overlapped by the following window to enhance the precision of recognition. Figure 3 presents an example in which the signal was collected from a machine in a spring manufacturing facility. Based on a signal wavelength of approximately 200 Hz, the length of the window is set to 80 points with a 40-point overlap of the preceding and succeeding windows.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Hotel** & **Cost** & **Distance to the Beach** & **Internet Ratings** \\ \hline A & $100 & 5 km & 3 \\ B & $750 & 3.5 km & 4 \\ C & $230 & 1 km & 3 \\ D & $450 & 2 km & 1 \\ E & $320 & 8 km & 3 \\ \hline \end{tabular}
\end{table}
Table 2: Information pertaining to hotels in the vicinity of the beach.  
Figure 2: The example of skyline hotels.  
Window values are represented as \([X_{1},\,X_{2},\,\ldots,\,X_{|w|}]\), where \(|W|\) refers to the length of a window, and extracted features are presented as follows:  
1. Mean: Mean value of signal data in each window
2. Energy: Sum of squares of data in each window divided by the window length: \[\text{Energy}=\frac{\sum_{i=1}^{|W|}{|X_{i}|}^{2}}{|W|},\] (1)
3. Root mean square: \[\text{RMS}=\sqrt{\frac{1}{|W|}{\sum_{i=1}^{|W|}{x_{i}^{2}}}}\] (2)
4. Variance: \[\text{variance}=\frac{1}{|W|-1}{\sum_{i=1}^{|W|}{(x_{i}-m)}^{2}},\] (3) where \(m\) is the mean value of _Xi_.
5. Average absolute deviation: \[\text{MAD}=\frac{1}{|W|}{\sum_{i=1}^{|W|}{|x_{i}-m|}}.\] (4)
6. Standard deviation: Root mean square of variance
7. Maximum value: Maximum value of each window.
## 2 Materials and Methods
### Numerical Simulation for Mixers  
As a key component of the gas system, the mixer plays an important role in enhancing gas mixing efficiency and the concentration uniformity of the output gas [19; 20; 21; 22]. Here, a static mixer is utilized instead of a dynamic one. The static mixer has no moving parts and can realize movements such as diversion, confluence, and rotation to achieve effective dispersion and thorough mixing of different fluids by its mixing unit assembled in the pipe. Static mixers can be categorized into various types based on the structure of the mixing units [23]. Among them, the SV mixer is particularly suitable for gas mixing. Hence, the SV mixer is chosen for use in this study.  
The static mixer consists of a closed thin-walled cylindrical tube filled with SV mixing units. The length-diameter ratio (L/D) is a crucial parameter for the design and selection of the mixing chamber in addition to internal volume. To investigate the optimal L/D of the mixer for the gas system, three mixer structures with L/D ratios of 0.75, 2.5, and 6 are developed, all with an internal volume of 800 mL, as shown in Figure 2. In each mixer, the thickness of the spoiler elements is 1 mm, and three groups of SV units are arranged along the length at a dislocation angle of 60\({}^{\circ}\). To compare the mixing efficiency of mixers with different L/D ratios, a binary gas mixing simulation is employed as a research model to simplify numerical analysis and reduce computational load. Each mixer includes two gas inlets and one gas outlet.  
The mixer structure model is created using 3D software (UG NX 10.0) and then imported into the COMSOL multi-physics software (v.6.1) for simulating gas mixing. The simulation is conducted in coupled physical fields involving single-phase fluid--turbulent flow and chemical species transport--concentrated species transport. The mesh is automatically generated with tetrahedral elements and a general element size. The Realizable k-\(\varepsilon\) model is selected in the turbulence flow physics field with default parameters [24]. The inlet flow rate is set at 2 L/min and the outlet pressure at 0 kPa. The Maxwell-Stefan model is used for the diffusion model in the concentrated species transport physics field [25], with a mass diffusion coefficient of 1 \(\times\) 10\({}^{-5}\) (m\({}^{2}\)/s). The initial gas mass fraction at the two inlets is set to 100% and 0, respectively. The properties of the gas under study are based on air properties, selected directly from the system material library.