esa-sceva/satcom-chunk-collection huggingface dataset:
# Adapting Mechanisms for In-Pipe Inspection Robots: A Review
## 3 Analysis of Adapting Mechanisms
### Adapting Mechanisms Based on Pantograph Linkages
A mathematical model for the relationship between wall pressing force and mechanism geometry is available in [46]. The total length of the robot is 390 mm and the radial size can vary between 400 and 700 mm. Although the robot is adaptable to changes in diameter and pipe inclination, it may experience problems with loss of contact when changes of direction are required in a branched pipe. An interesting constructive solution is proposed by [PERSON] et al. in [48]. The inspection robot consists of three modules which are a front and a rear driving vehicle and one control module. Each driving vehicle has two independent modules connected by an active double universal joint that offers omnidirectional steering capability. As the adaptive mechanism should be as simple as possible, it uses a pantograph linkage with one slider that allows natural folding and unfolding. The proposed adapting mechanism is shown in Figure 14.  
Figure 12: Inspection robot with steering wheels. Figure 13: Active adapting mechanism based on a pantograph linkage with two sliders. The wall pressing forces are obtained by means of a spring that acts on slider \(1\) of the pantograph linkage. When the proposed mechanism contracts or expands the wheels are pressed only along the radial direction. The relationship between the pressing force, the spring constant and its initial length is available in [48]. This relation is determined considering the length of the element \(5\) and the measured value of the angle \(\theta\). Thus, forces can be easily preset according to the payload, by adjusting the spring constant and the initial deformation.
## 1 Introduction
[21] proposed an incipient fault detection method using Kullback-Leibler (KL) divergence and PCA. On the basis of [PERSON] et al. 's research, [PERSON] et al. [22] proposed an improved method. The proposed method monitors the derivations in principal and residual subspace. [PERSON] et al. [23] investigated a method of setting the threshold for fault detection using KL divergence for unknown distribution. However, existing methods for detecting incipient faults based on PCA and KL divergence depend entirely on the off-line normal data to obtain the principal components. Each principal component is essentially a projection vector (PV). Once the normal data have been determined, the PVs used to detect faults also remain fixed. Unfortunately, the PVs obtained by PCA are not necessarily the optimum PV to detect the incipient fault from the view of fault detection. Fixed PVs may result in lower fault detection rates for some types of incipient faults [24; 25]. To address the problem of detecting incipient faults in satellites, this paper proposes a novel incipient fault detection method based on local optimum PV (LOPV) and KL divergence. The main contributions of the present research are summarized in the following three points:  
1. This paper puts forward the argument that the PVs obtained by PCA are not necessarily the optimum PV for using KL divergence to detect an incipient fault. 2. The problem of finding the optimum PV to detect the incipient fault is modeled as an optimization problem, and the KL divergence is used to detect the incipient fault on the LOPVs. 3. The application of the incipient fault detection method based on PCA and KL divergence is extended to the satellites. The effectiveness of the proposed method is proven in a real satellite fault. This paragraph outlines the structure of this paper. Section 2 provides a brief introduction about PCA and KL divergence. In Section 3, the incipient fault detection method based on LOPV and KL divergence is presented in detail. In Section 4, the proposed method is validated and analyzed by using a numerical example and a real satellite fault. Finally, the conclusions are presented in Section 5.
## 4 Data Pre-Processing
### Data Analysis and Processing  
Based on the collected data, we identified the connections between maritime routes for coastal passenger ships, marine accidents, and past MTSA-assessed project sites. By performing this process, three types of data were derived: MTSA-related routes associated with past MTSA-assessed project sites, MTSA-unrelated routes, and marine accidents that occurred along each route.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Column** & **Description** \\ \hline \hline \multicolumn{1}{c}{} & \\ \hline \multicolumn{1}{c}{} & \\ \hline \multicolumn{1}{c}{} & \\ \hline \multicolumn{1}{c}{} & \\ \hline \multicolumn{1}{c}{} & \\ \hline \multicolumn{1}{c}{} & \\ \hline \multicolumn{1}{c}{} & \\ \hline \hline \multicolumn{1}{c}{} & \\ \hline \end{tabular}
\end{table}
Table 4: Major columns from data for coastal passenger ship operation and accidents.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Column** & **Description** \\ \hline \multicolumn{1}{c}{} & \\ \hline \multicolumn{1}{c}{} & \\ \hline \multicolumn{1}{c}{} & \\ \hline \multicolumn{1}{c}{} & \\ \hline \multicolumn{1}{c}{} & \\ \hline \hline \multicolumn{1}{c}{} & \\ \hline \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Columns from data for MTSA scheme.  
Here, we denote the combination of MTSA-unrelated route information and MTSA-related accident data as the original dataset set U. The configuration of the original dataset U is shown in Figure 3. In addition, as mentioned in Section 4.4, the dataset obtained after applying SMOTE was named SMOTE dataset U.  
The modeling process involved training the algorithm on the SMOTE Data U set and then testing it on MTSA-related route information. This process was conducted twice. The analysis and processing steps for each dataset are illustrated in Figure 3.  
The process of deriving MTSA-unrelated route information and MTSA-related route information and the extraction of MTSA-related accidents were performed using QGIS software. QGIS is the most popular free geospatial software available worldwide. We used QGIS version 3.28 and executed it on a machine with 8 GB of RAM and an Intel Core i3 3.60 GHz CPU.
# A Distributed Hybrid Community Detection Methodology for Social Networks
## 2 Background
### Classic Methods
#### 2.1.2 User Content Oriented  
Due to the homophily [9; 11], which is the immanent human tendency where individuals tend to get associated with similar peers, the application of community detection in social graphs can alternatively be translated as the identification and the classification of the underlying social contexts [1].  
Without any doubt, the community detection concept is inherently interwoven with the complex user profile information that cannot be explicitly presented with any other kind of network objects association. Thereafter, there are many approaches proposed [4; 11; 13] which are specifically designed for social network analysis that strive to reveal the subjacent communities by solely depending on the user content information.  
The majority of the existing user content oriented methods are, to the best of our knowledge, based on the concept of the Homophilic FCA (Formal Concept Analysis) [11; 13], which is a special case of the original FCA (Formal Concept Analysis) [11]. Homophilic FCA endeavor to form groups of vertices by searching for potential semantic relationships between network objects that are either explicitly or implicitly connected and share similar social interests. Bear in mind that the original FCA is the knowledge extraction method that follows the notion of the Galois lattice hierarchy [11] detection concept where the existing network connections are believed to imply intention to linking rather than actual linking between network objects.
## 4 Creep Model of Surrounding Rock and Parameter Determination
### Determination of Creep Model of Surrounding Rocks
If \(\sigma\geq\sigma_{\rm s}\), the total strain rate of the Burgers-Mohr model consists of the Kelvin strain, Maxwell strain, and M-C plastic strain; therefore, the strain-time curve of the Burgers-Mohr model is given as follows: \[\varepsilon=\frac{\sigma_{0}}{E_{\rm M}}+\frac{\sigma_{0}}{\eta_{\rm M}}t+ \frac{\sigma_{0}}{E_{\rm K}}(1-e^{-\frac{E_{\rm K}}{\eta_{\rm K}}t})+\dot{ \epsilon}_{\rm ij}^{\rm P}\] (3) Here \[\dot{\epsilon}_{\rm ij}^{\rm P}=\lambda\frac{\partial g}{\partial\sigma_{\rm ij }}-\frac{1}{3}\dot{\epsilon}_{\rm vol}^{\rm P}\delta_{\rm ij}\] (4) \[\dot{\epsilon}_{\rm vol}^{\rm P}=\lambda[\frac{\partial g}{\partial\sigma_{11} }+\frac{\partial g}{\partial\sigma_{22}}+\frac{\partial g}{\partial\sigma_{3 3}}]\] (5) \[\dot{\sigma_{0}}=K(\dot{\epsilon}_{\rm vol}-\dot{\epsilon}_{\rm vol}^{\rm P})\] (6)  
where \(\sigma_{0}\) refers to the constant stress, \(\varepsilon\) refers to the strain, \(E_{\rm M}\) refers to the Maxwell shear stiffness, \(\eta_{\rm M}\) refers to the Maxwell coefficient of viscosity, \(E_{\rm K}\) refers to the Kelvin shear stiffness, \(\eta_{\rm K}\) refers to the Kelvin coefficient of viscosity, \(P\) refers to the plastic body, \(\dot{\epsilon}_{\rm ij}^{\rm P}\) refers to the Mohr-Coulomb deviator strain, g refers to the potential function, \(\sigma_{\rm ij}\) refers to the principal stress, \(\dot{\epsilon}_{\rm vol}^{\rm P}\) refers to the plastic volumetric strain, \(\delta_{\rm ij}\) refers to the stress deviator, \(\sigma_{11}\) refers to the maximum principal stress, \(\sigma_{22}\) refers to the intermediate principal stress, \(\sigma_{33}\) refers to the minimum principal stress, \(\lambda\) refers to a coefficient, \(K\) refers to the volumetric modulus, and \(\dot{\epsilon}_{\rm vol}\) refers to the volumetric strain ratio. Figure 7: Elements of the Burgers–Mohr model.
## 3 Solution of Optimal Cruise Point
### Steady-State Cruise Parameter Solution  
In steady-state cruise, the elevation angle of trajectory is constant at 0, and the altitude and Mach number remain unchanged; thus, Equation (4) is simplified into:  
\[\left\{\begin{array}{l}\frac{dMa}{dt}=\frac{T\cos a-D-mg\sin\gamma}{m\cdot c}= 0\\ \frac{d\gamma}{dt}=\frac{T\sin a+L}{m\cdot Ma+c}+\cos\gamma\ (\frac{Ma \cdot c}{R_{e}+h}-\frac{g}{Ma\cdot c})=0\end{array}\right. \tag{20}\]  
Figure 3: Comparison between the parametric model and original data.  
Lift and drag are related to altitude, Mach number, and angle of attack, while thrust is related to altitude, Mach number, angle of attack, and equivalence ratio. In short, there are two equations with four unknowns. From Equation (20), it can be obtained that  
\[\left\{\begin{array}{c}T\cos\alpha-D-mg\sin\gamma=0\\ T\sin\alpha+L+\cos\gamma\cdot\frac{m\cdot\left(Ma\cdot c\right)^{2}}{R_{e}+h}- mg\cos\gamma=0\end{array}\right. \tag{21}\]  
If the upper equation in Equation (21) is multiplied by tan\(\gamma\), \(T\) can be eliminated:  
\[D\tan\alpha+L-mg+m\frac{\left(Ma\cdot c\right)^{2}}{\left(R_{e}+h\right)}=0 \tag{22}\]  
Then, substitute Equation (5) into Equation (22) and it can be obtained that  
\[C_{D}\cdot\frac{1}{2}\rho(Ma\cdot c)^{2}\cdot S\cdot\tan\alpha+C_{L}\cdot\frac {1}{2}\rho(Ma\cdot c)^{2}\cdot S-mg+m\frac{\left(Ma\cdot c\right)^{2}}{\left( R_{e}+h\right)}=0 \tag{23}\]  
If the value of \(h\) and \(Ma\) are given, \(\rho\) and \(c\) can be obtained. Due to the fact that \(C_{L}\) and \(C_{D}\) are related to \(Ma\) and \(\alpha\), Equation (23) is an equation only related to \(\alpha\), which can be solved by the method of bisection. After the value of \(\alpha\) is obtained, there is only one unknown left. Then, the equivalent ratio can be solved by Equation (21) so as to solve the fuel consumption.  
The fuel consumption averaged by the range is solved in the range of Mach 4 to 7 and 20 km to 29 km by the traversing method at a Mach number interval of 0.1 and an altitude interval of 200 m. The contour of fuel consumption at different altitudes and Mach numbers is displayed in Figure 4. Since the equivalence ratio ranges from 0.45 to 1, there are boundaries in the contour caused by the constraints of the equivalence ratio, which means there is no solution of Equation (21) and steady-state cruise cannot be achieved in the region outside the boundaries. The distribution of fuel consumption is similar to a basin. If the Mach number or altitude is too high or too low, the fuel consumption will increase up to 1.2 kg/km, while the minimum is only about 0.4 kg/km. The group of the Mach number and altitude with the minimum fuel consumption is called the optimal steady-state cruise point and is denoted by \((Ma_{opt},l_{opt})\), which is in the range of Mach 4.5 to 5.5 and 24 km to 26 km.
## 2 Materials and Methods
### TRS Measurements of a Polyurethane-Based Phantom  
In this section, we consider time-resolved measurements for a phantom. The solid phantom (biominic optical phantom) is made of polyurethane to simulate biological tissues (INO, Quebec, QC, Canada). The absorption coefficient and reduced scattering coefficient are \(\mu_{a}=0.0209\,\text{mm}^{-1}\) and \(\mu_{s}^{\prime}=0.853\,\text{mm}^{-1}\) at wavelength 800 nm. The refractive index of the phantom is \(n=1.51\). The measurements were performed by the TRS (time-resolved spectroscopy) instrument (TRS-80, Hamamatsu Photonics K.K., Hamamatsu, Japan). Two optical fibers from TRS-80 are attached on the top of the phantom with the separation \(13\,\text{mm}\) (\(M_{s}=M_{d}=1\)). The phantom is illuminated by near-infrared light of the wavelength \(760\,\text{nm}\) through one optical fiber and the reflected light is detected by the other optical fiber. The time interval of the measured data is \(10\,\text{ps}\) and we used the data from \(t_{1}=2.00\,\text{ns}\) to \(t_{M_{t}}=8.00\,\text{ns}\) (\(M_{t}=601\)). Measured counts, i.e., the number of photons, are shown in Figure 1. The upper panel of Figure 1 shows the instrument response function (IRF), which is given as a property of the experimental device. The measured reflected light is shown in the lower panel of Figure 1. The function \(q(t^{k})\) is the instrument response function divided by the maximum value of the measured reflected light. The peak of \(q\) is at \(2.56\,\text{ns}\). \(\{U\}_{11k}=y_{11k}\) is the measured reflected light normalized by its maximum value. In the lower panel of Figure 1, \(t_{1}\) and \(t_{M_{t}}\) are marked by vertical dashed lines. The size of the phantom is large enough that we can assume the three-dimensional half space. Then the energy density of the detected light is computed by Equation (6). The parameter \(\ell\) is obtained from Equation (3).
# Experimental & FEM Analysis of Orthodontic Mini-Inplant Design on Primary Stability
## 2 Materials and Methods
### Bone Characteristics  
The measurement of the cortex thickness on mini-implant insertion zone is performed with Cone Bean computed tomography. The examination of samples was performed in the Faculty of Dental Medicine of University of Sarajevo, using the GALILEOS Comfort plus instrument (Sirona Dental System GmbH Fabrikstrasse 31 D-64625 Behshein, Germany, September 2013) shown on Figure 3a. Cone Beam images are simple to use; they obtain marrow and soft tissue structures and give more data than regular 2D images [21; 22; 23; 24].  
The middle of a flat rib surface is marked for implant insertion. Location positioning is enabled with a cotton wool roll and ligature wire. The cotton wool roll provides wire separation from the measuring region, where wire shining and cortex overlap is avoided. This way, measurement accuracy is preserved. With the help of the laser, the bone is positioned so that the implant insertion location is in the middle of the measuring region Figure 3b. The cortex measurement is performed with Galile  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Implant Type/Geometric Parameter** & **Perfect Anchor** & **Tomas®-pin** \\ \hline Core diameter, d [mm] & 1.6 & 1.6 \\ \hline Thread length, h [mm] & 7.7 & 6 \\ \hline Minimum core diameter, d\({}_{\text{1 min}}\) [mm] & 0.8 & 1.04 \\ \hline Maximum core diameter, d\({}_{\text{1 max}}\) [mm] & 1 & 1.2 \\ \hline Pitch, p [mm] & 0.8 & 0.9 \\ \hline Bearing depth, D [mm] & 0.3 & 0.26 \\ \hline Thread coil climb angle, \(\beta\) [\({}^{\circ}\)] & 16 & 16 \\ \hline Core cone half-angle, \(\alpha\) [\({}^{\circ}\)] & 1.5 & 2 \\ \hline Conicity of thread core, 1:k & 1:24 & 1:17 \\ \hline TSF, [\%] & 37.5 & 29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of geometric parameters established with measuring.  
Figure 3: CBCT scanner GALILEOS Comfort plus (**a**) and specimen positioned for scanning (**b**).  
(V 1.9, SICAT GmbH & Co. KG, Bonn, Germany, 2014). This software enables multiple measuring features for anatomic structures (bone density, length and width, etc.). The swine rib cortex is measured in sagittal cross-section, with a 90\({}^{\circ}\) angle, regarding the ligature wire. The measurement is carried out on all 40 specimens, where cortex thickness in an interval of 0.6-0.75 mm is determined.
## 4 Methodology
### Domain Clustering Based on Multi-Path Transmission
#### 4.2.1 Domain Clustering and Network Management
By introducing a weighted mechanism for the distance between nodes, we ensure that each node is assigned to the same domain as its neighboring nodes, thus reducing the control overhead across the entire network. Furthermore, through numerical simulations, we validate that this domain clustering method improves network reliability. Assume that there are \(N\) nodes in the network, and the reliability of each node is \(R_{ij}\). After the domain division, the final reliability is given by the following formula:  
\[R_{\text{total}}=\prod_{i=1}^{N}\prod_{j\in D_{k}}R_{ij} \tag{31}\]The optimal network reliability is achieved by adjusting the weighting factor \(\lambda\) and the path assignment strategy.
## 4 Results and Discussion
### Visualization through Discriminative Localization  
The customized VGG16 model has a CAM-compatible architecture owing to the presence of the GAP layer. This helps in visualizing the model predictions using both CAM and grad-CAM visualization tools. Figures 11 and 12 demonstrate the results of applying these visualizations to localize the discriminative ROI in pneumonia-infected CXRs.  
Figure 11: Visual explanations through gradient-based localization using CAM: (**a**) Input CXRs; (**b**) Bounding boxes localizing regions of activations; (**c**) CAM showing heat maps superimposed on the original CXRs; (**d**) Automatically segmented lung masks; (**e**) CAM showing heat maps superimposed on the cropped lungs.  
Figure 10: Performance of customized VGG16 model in multiclass classification: (**a**) Confusion matrix; (**b**) ROC curves.  
CXRs are fed to the trained model and the predictions are decoded. The heat maps are generated as a two-dimensional score grid, computed for each input pixel location. Pixels carrying high importance with respect to the expected class appeared bright red with distinct color transitions for varying ranges. The generated heat maps are superimposed on the original input to localize image-specific ROI. The lung masks that are generated with the boundary detection algorithm are applied to extract the localized ROI relevant to the lung regions. We observed that CAM and grad-CAM visualizations generated heat maps for the pneumonia class to highlight the visual differences in the \"pneumonia-like\" regions of the image.  
We applied our novel method of average-CAM and average-grad-CAM to visualize the class-specific ROI, as shown in Figures 13 and 14. Lung masks are applied to the generated heat maps to localize only the ROI specific to the lung regions. We observed that the class-specific ROI localized by the average-CAM and average-grad-CAM for the viral pneumonia class follows a diffuse pattern. This is obvious for the reason that viral pneumonia manifests with diffuse interstitial patterns in both lungs [30]. For the bacterial pneumonia class, we observed that the model layers are activated on both sides of the lungs, predominantly on the upper and middle right lung lobes. This is for the reason that bacterial pneumonia manifests as lobar considerations [30]. The pneumonia dataset under study has more pediatric patients with right lobar consolidations.  
Figure 12: Visual explanations through gradient-based localization using grad-CAM: **(a)** Input CXRs; **(b)** Bounding boxes localizing regions of activations; **(c)** Grad-CAM showing heat maps superimposed on the original CXRs; **(d)** Automatically segmented lung masks; **(e)** Grad-CAM showing heat maps superimposed on the cropped lungs.
## 3 Automated BIM-Based Alarm System  
### Research Method  
The research outlined in this work follows the Design Science Research Methodology (DSRM), which aims to develop an artifact that can solve a concrete problem within a particular context [93]. Figure 2 presents the DRSM adapted for this research addressing the aim of integrating web-based data into a BIM model with RPA composed of four stages, objectives, activities, and tools.  
Figure 2: Adapted DSRM for integrating web-based data into a BIM model with RPA.  
According to Figure 2, the first stage seeks to identify the current state-of-the-art and challenges associated with applying RPA within BIM and weather data processing. The primary activities in this stage involve exploring the applications of RPA in the AEC industry, identifying its specific uses in BIM processes, and investigating how weather data are integrated into BIM models. A literature review is employed as the main tool to gather insights on these topics, enabling the identification of gaps and challenges in current practices. This stage has been addressed in Section 2.  
The second stage focuses on defining a solution to integrate web-based weather data into BIM models using RPA. This involves defining the specific requirements for the solution, such as the types of weather data to be integrated and the extent of automation needed within the BIM environment. Additionally, this stage includes formulating goals to achieve a proper RPA bot performance. A literature review is crucial to address this stage.  
In the third stage, the objective is to design and develop an automated BIM-based alarm system capable of processing and incorporating weather data into BIM models through RPA. Key activities include selecting tools and software for developing the system, such as RPA platforms, BIM software, and weather data. The system is then developed, including the configuration and integration of the chosen RPA tool with the BIM model to automate weather data input. This stage also relies on using the literature to validate the selection of tools and methods, ensuring the system's development is aligned with current industry standards and research. This stage is addressed further in Section 3.  
Finally, the fourth stage addresses achieving the project's established goals. This stage evaluates the functionality of the developed model through practical applications, typically by conducting a case study. In this case, the system is applied to a simulated project where weather data are integrated into a BIM model, and the results are analyzed. The case study method serves as a crucial tool for demonstrating the system's effectiveness and assessing whether the integration of weather data enhances decision-making. This stage is covered in Section 4.