esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Enterprise Architecture Model Slicing
## 2 Background and Related Work
### EAR Management and Data Reuse  
Effective **EAR management** is vital for realizing the more significant potential of EA. This is because it means developing new artifacts that can be based on existing EA data (to save cost and improve consistency). It also means the fusion of multiple EA artifacts to improve the alignment among EA artifacts. In addition, the utilization of various computing power for analysis, prediction, and visualization of the EA artifacts can be employed to enable more empowerment.  
Traditional EAR management has not sparked much discussion [14], because it was assumed that EA data are developed according to a (pre-defined and fixed) metamodel, and the data are complete, structured, rigorous, and consistent. For instance, in [15], BPMN choreography diagrams can be automatically generated based on existing models stored in a web-based repository that uses the BPMN ontology to describe each component's structural properties. Furthermore, in [16], metadata about the content of UML class diagrams can be automated and archived into a pre-existing repository.  
However, modern Agile EAM practices may bring new challenges for EAR management and model reuse. The pursuit of more non-professional participation [17], rapid model development and validation, and high Return on Investment (ROI) for model development around a specific purpose can lead to reduced model quality, which challenges EAR management. Researchers noticed such potential repository pollution issues and proposed to use machine learning techniques to avoid adding duplicates to the repository [18].  
**Reusing existing model** data might be challenging because EARs are usually complex and large, consisting of many components and intricate relationships in multiple dimensions, making them difficult for a human to grasp completely. Moreover, such repositories rely on a scattered accumulation of workforce because it is often created by humans and aims for humans (to understand). As a result, the rigor and consistency of the data may be limited. The complexity, significant volume, and low consistency of EARs make reusing model data difficult for humans or computers. The vague requirements to develop new models make model reuse even more difficult. To make it clear, reusing existing model data in this article does not mean integrating data from other sources, but recommending and reusing data in the existing model repository for new views/purposes.  
Many relations exist implicitly within multiple views. Thus, it is difficult to remember and find all relevant model data by humans. Due to the diversity of underlying metamodelsand the inconsistency of the data, it is not easy to implement an automatic search with a pre-written (one-time) script.
# Automatic Optimal Robotic Base Placement for Collaborative Industrial Robotic Car Painting
## 4 Construction of the First Cost Function: Minimizing the Possibility of Collision
In particular, we consider the point clouds \(\mathcal{P}_{i}(\mathbf{0})\), \(1\leq i\leq M\), obtained from the pre-processing stage (see Section 3). Given a collection of base positions \(\mathbf{p}_{i}\), \(1\leq i\leq M\), we translate each of the given point clouds \(\mathcal{P}_{i}(\mathbf{0})\) to obtain their translated versions \(\mathcal{P}_{i}(\mathbf{p}_{i})\), \(1\leq i\leq M\), by utilizing Equation (7). Subsequently, we obtain a planar point cloud by projecting \(\mathcal{P}_{i}(\mathbf{p}_{i})\), \(1\leq i\leq M\), onto the sides and top of the vehicle. This is equivalent to the projection of \(\bigcup\limits_{i=1}^{M}\mathcal{P}_{i}(\mathbf{p}_{i})\) onto the car sides and top. Focusing on the vehicle top for brevity, we can assess its coverage using a computationally efficient technique like ray-casting (see, e.g., [54]). This method, which can determine if a point lies within a traced boundary from the Moore-Neighbor algorithm, can be used to provide a quantitative measure of the top's (resp., sides') paint coverage. Algorithm 2 provides the procedure for computing the top paint coverage ratio. The same algorithm can be utilized for computing the paint coverage ratio of the vehicle sides, whose details we skip for brevity. Figure 8: Boundary detection in the vehicle’s CAD model and creation of the grip map representations. In summary, the second cost function \(\mathcal{F}_{2}^{\text{top}}(\mathbf{p}_{1},\cdots,\mathbf{p}_{M})\), which is calculated according to Algorithm 2, can be expressed as  
\[\mathcal{F}_{2}^{\text{top}}(\mathbf{p}_{1},\cdots,\mathbf{p}_{M})=\frac{N_{ \text{covered}}^{\text{top}}}{M_{\text{G}}^{\text{top}}\times N_{\text{G}}^{ \text{top}}}, \tag{13}\]  
where the resulting value of \(\mathcal{F}_{2}^{\text{top}}(\mathbf{p}_{1},\cdots,\mathbf{p}_{M})\) lies in the interval \([0,1]\). We can repeat the same process (details omitted for brevity) for obtaining the coverage ratio of the sides of the car to obtain the cost functions \(\mathcal{F}_{2}^{\text{side1}}(\cdot)\) and \(\mathcal{F}_{2}^{\text{side2}}(\cdot)\) according to  
Figure 5.0  
Figure 5.1  
**Computational complexity for computing the paint coverage ratios:** The worst-case computational complexity for testing the inclusion of \(N_{i}\) points from a point cloud in a boundary from a grid map of size \(M_{\text{G}}^{\text{top}}\times N_{\text{G}}^{\text{top}}\) is \(O\left(N_{i}\cdot M_{\text{G}}^{\text{top}}\cdot N_{\text{G}}^{\text{top}}\right)\). It is remarked that the worst case-complexity of ray casting for point inclusion in a general 2D polygon is \(O(n)\), where \(n\) is the number of the polygon edges (see, e.g., [55; 5]). Similar computational complexity expressions can also be stated for computing \(\mathcal{F}_{2}^{\text{side}}(\cdot)\) and \(\mathcal{F}_{2}^{\text{side2}}(\cdot)\).
## 4 Discussion
Synchronization through the generation of a specific event was the method chosen by our research group to synchronize the EMG recordings of masticatory muscles and mandibular movements. In our case, we used a \"switch\" device that comes with EMG equipment and a sound recorder that comes with AG501 3D-EMA equipment. Activation of this switch generated an independent and distinguishable event in both registers, which in the case of EMG was recorded as a square electrical signal, and in the EMA was recorded as a peek in the acoustic signal that was used to synchronize both recordings. Our recording method is a novelty in the scientific literature, and it has the advantage of the high accuracy of the movement-recording method demonstrated in previous articles from our research group [25; 26; 27]. In addition, this synchronization was performed using only the components that already accompany both pieces of equipment selected, a fact that besides facilitating its realization and replication, does not add costs to its realization. In general, our results obtained during synchronized recordings showed no distortion/alteration outside the limit values informed by the manufacturers. Even though both instruments are very sensitive to electromagnetism, the EMA AG501 and the EMGs proposed in this methodology do not generate interferences between them during the simultaneous recording. This is probably because the bandwidths with which each system work do not overlap. This analysis could be improved by incrementing sample size and making a more complex experiment, e.g., determining the natural variability of EMG and EMA recordings for the application considered.
## 2 Governing Equation and Calculation Setting
### Turbulence Model  
In RSM, the Reynolds-averaged Navier-Stokes equation is derived by solving the transfer equation of Reynolds stress. The transfer equation of Reynolds stress can be formulated as follows:  
\[T_{ij}+C_{ij}=D_{T,ij}+D_{L,ij}+P_{ij}+G_{ij}+\varphi_{ij}+\varepsilon_{ij}+F_ {ij}+S_{vm} \tag{4}\]  
where \(T_{ij}\) is the local time derivative, \(\frac{\partial}{\partial t}\Big{(}\rho\overline{u_{i}^{\prime}u_{j}^{\prime}} \Big{)}\); \(C_{ij}\) is the convection, \(\frac{\partial}{\partial x_{k}}\Big{(}\rho u_{k}\overline{u_{i}^{\prime}u_{j}^ {\prime}}\Big{)}\); \(D_{T,ij}\) is the turbulent diffusion, \(-\frac{\partial}{\partial x_{k}}\bigg{(}\rho\overline{u_{i}^{\prime}u_{j}^{ \prime}u_{k}^{\prime}}+p^{\prime}\Big{(}\delta_{ij}u_{i}^{\prime}+\delta_{ik} u_{j}^{\prime}\Big{)}\bigg{)}\); \(D_{L,ij}\) is the molecular diffusion, \(\frac{\partial}{\partial x_{k}}\Big{[}\mu\frac{\partial}{\partial x_{k}}\Big{(} \overline{u_{i}^{\prime}u_{j}^{\prime}}\Big{)}\Big{]}\); \(P_{ij}\) is the stress production, \(-\rho\Big{(}\overline{u_{i}^{\prime}u_{j}^{\prime}}\frac{\partial u_{j}}{ \partial x_{k}}+\overline{u_{i}^{\prime}u_{k}^{\prime}}\frac{\partial u_{i}}{ \partial x_{k}}\Big{)}\); \(G_{ij}\) is the buoyancy production, \(-\rho\beta\Big{(}g_{i}\overline{u_{i}^{\prime}\theta}+g_{j}\overline{u_{i}^{ \prime}\theta}\Big{)}\); \(\varphi_{ij}\) is the pressure strain, \(\overline{p^{\prime}\bigg{(}\frac{\partial u_{j}^{\prime}}{\partial x_{j}}+ \frac{\partial u_{j}^{\prime}}{\partial x_{i}}\bigg{)}}\); \(\varepsilon_{ij}\) is the dissipation, \(-2\mu\overline{\frac{\partial u_{i}^{\prime}}{\partial x_{k}}\frac{\partial u_ {j}^{\prime}}{\partial x_{k}}}\); \(F_{ij}\) is the production by system rotation, \(-2\rho\Omega_{k}\Big{(}\overline{u_{j}^{\prime}u_{m}^{\prime}}\varepsilon_{km} +\overline{u_{i}^{\prime}u_{m}^{\prime}}\varepsilon_{km}\Big{)}\).
# Edge Couplers in Silicon Photonic Integrated Circuits: A Review
## 1 Introduction
Section 4 depicts the derivative structures of edge couplers in the vertical direction, including edge couplers assisted with multiple upper waveguides, couplers based on cascaded multi-stage tapers, couplers with refractive index-matching cladding, couplers made of three-dimensional tapers, and couplers composed of cantilever structures. Finally, in Section 5, a brief comparison between edge couplers and grating couplers is given. We also discuss some key issues related to the preparation and packaging of edge couplers, and prospective technologies that can be utilized to improve the coupling performance in future are proposed in Section 5. Figure 1: Schematic of optical interconnects between fiber and photonic chip.
# Sustainability-Based Analysis of Conventional to High-Speed Machining of Al 6061-T6 Alloy
## 4 Analysis of Experimental Data: Transitional Speed Machining (TSM)
### Surface Finish  
The effect of cutting parameters on Ra in TSM was analyzed using ANOVA and main effect plots. The results of ANOVA are presented in Table 10. The R\({}^{2}\) value of the ANOVA model is 92.78%, which is sufficiently high and shows that all relevant process parameters have been included in the analysis. The results from ANOVA establish the strong statistical contribution of every parameter. From the viewpoint of percentage contribution, feed has the highest influence, followed by cutting speed and depth of cut. The high effect of feed on Ra is an expected outcome and has been discussed in the previous section. Cutting speed has been observed to be slightly more significant than depth of cut. The main effect plots developed to analyze the effect of cutting parameters on Ra are shown in Figure 7. As expected, feed has to be set at the lowest possible level to achieve minimum surface roughness. Ra has been observed to first decrease with an increase in cutting speed and depth of cut and then to increase. The optimal value of Ra is observed at 1000 m/min and a depth of cut of 3 mm.
## 2 Materials and Methods
### Details of the Element Formulation  
The displacement field \(\{\Phi\}\) of a layer defined in Equation (5) can be written using the following general form.  
\[\{\Phi\}=[H]\ \{u\} \tag{8}\]  
where the matrix \([H]\) indicates the hierarchical shape functions for nodal and non-nodal variables \(\{u\}\). The element equations for a layer can be expressed by using the principle of virtual work  
\[\delta U_{\varepsilon}-\delta W=0 \tag{9}\]  
where \(\delta U_{\varepsilon}\) is the internal virtual strain energy and \(\delta W\) is the external virtual work. The internal virtual strain energy can be written with the strain vector \(\{\varepsilon\}\) and the stress vector \(\{\sigma\}\) defined in Equations (6) and (7).  
\[\delta U_{\varepsilon}\ =\ \int\limits_{V}\delta\{\varepsilon\}^{T}\ \{\sigma\}\ \ dV \tag{10}\]  
If the virtual displacements are defined as  
\[\delta\{\Phi\}=[H]\ \delta\{u\} \tag{11}\]then the virtual strain can be written as  
\[\delta\{\epsilon\}=[B]\;\delta\{u\} \tag{12}\]  
The external virtual work due to the loading \(\{F\}\) on surface \(A\) takes the following form:  
\[\delta W\ =\ \int\limits_{A}\delta\{u\}^{T}\{F\}\ dA \tag{13}\]  
Based on these definitions, the virtual work equation shown in Equation (9) can be expressed as  
\[\int\limits_{V}\delta\{u\}^{T}\ \left[B\right]^{T}[D]^{l}\left[B\right]\{u\}\ dV =\ \int\limits_{A}\delta\{u\}^{T}\{F\}dA \tag{14}\]  
Then, the element stiffness matrix of a layer can be obtained from  
\[\left[K\right]^{l}=\iiint\limits_{V}\left[B\right]^{T}\left[D\right]^{l}[B]\ dx\ d\theta\ dr \tag{15}\]  
Figure 1 illustrates the modeling approach for the proposed model for a two-layer laminated system. If there are no gaps and empty spaces at the layer interfaces, compatibility conditions can be applied. The displacement components within the in-plane direction vary linearly across the thickness of the lamina, while the out-of-plane displacement is assumed to remain constant across the thickness of the lamina, similar to two-dimensional modeling.
## 2 Host Directed Therapy
### Promoting Phagosome Maturation and Enhancing Autophagy
#### 2.1.3 Imatinib and Other Tyrosine Kinase Inhibitors  
A key feature of _M. tuberculosis_ is represented by its ability to inhibit phago-lysosome fusion, and thus potentially limit the efficacy of the autophagy process, thanks to the presence of specific virulence factors like lipoarabinomann in the cell wall [30], the ESX-1 secretion system [31; 32] and other key components such as the Eis protein which modulates autophagy and inflammation and suppresses host innate immune responses [33]. This inhibitory effect can be overcome by tyrosine kinase inhibitors, such as imatinib and the second-generation inhibitors nilotinib and dasatinib, which target the BCR-ABL fusion protein and are used for treating chronic myeloid leukemia [34]. Different studies explored the effect of imatinib on _M. tuberculosis_-infected macrophages and revealed that it increases acidification of lysosomes thereby halting bacterial multiplication [35]. Moreover, imatinib was shown to reduce the number of granulomatous lesions in mice and to act synergistically with first-line anti-TB drug rifampicin [36].
## 3 Resolution of Degeneracy  
### System Overview  
Figure 1 shows the block diagram of the proposed visual-inertial odometer method, which fuses vanishing points and structural lines based on point features. The overall framework of the proposed method is divided into front-end and back-end data. The raw data collected by the camera and IMU were mapped onto camera frames to participate in the VIO system.  
After calculating the deviation of the IMU, the pre-integration and bias correction process is performed on the IMU. Meanwhile, the points, vanishing points, and structured line features obtained from the camera are processed in parallel in the system. For the point features on the camera frames, optical flow is used for tracking and matching, and vanishing point features are filtered by RANSAC and the basic matrix model [13]. After detecting vanishing points using the two-line method [40], processing this feature is similar to point features. Following the detection of line features using the ELSED algorithm [28], structured line features meeting the criteria are identified based on Section 4.2. Keyframes are selected based on the average disparity between adjacent camera frames and the tracking quality of features, after which pose estimation of the feature and reconstruction of the environment are performed on these keyframes.  
After the camera and IMU initialization phase, the system enters the triangulation stage. Degeneracy detection is applied to line features involved in epipolar constraints to calculate the position of epipoles. Then, the direction vectors of line features are corrected by incorporating vanishing points based on the relationship between vanishing points and epipoles, while features on the 2D image frames are restored to their correct positions in 3D space, as detailed in Sections 3.4 and 3.5. Next, traditional line features are categorized into structured and unstructured lines based on vanishing point features under the Manhattan world assumption.  
Finally, the line features, vanishing point features, structured line features, and prior information along with a priori information are entered into a sliding window to construct  
Figure 1: Block diagram for the proposed method.  
a visual-inertial bundle adjustment formulation, and the CERES solver [42] is used to compute the pose, velocities, and biases for each frame.
# Self-Organizing and Scalable Routing Protocol (SOSRP) for Underwater Acoustic Sensor Networks
## 3 Proposed Protocol
### Path Selection Criteria  
The path selection criterion for SOSRP is based on hop count and distance between source and destination. The protocol selects the shortest path between source and sink. On sensing the event, the path formulation begins with the selection of next hop by source node to transmit the data to the sink node.  
To diminish the energy consumption during data transmission, the selection criteria of next node is based on smallest hop count and distance from source node to sink node. When source node has data to send, it will look up in its neighbor table to select the next node. The node with the least hop count value will be selected as the next hop. If two neighboring nodes have the same hop count value in its neighbor table, the node with the shortest hop count distance will be selected as the next one. Since, each entry in the table contain an accumulated distance from source to destination as mentioned in Section 3.2, the selected node represents the overall selected path leading to the sink node as an optimal case.
# Research on Authentic Signature Identification Method Integrating Dynamic and Static Features
## 2 Related Works
[21] designed an online signature verification system to extract three different types of features from the online signature, namely, physical features, frequency-based features and statistical features. The first ensemble using the feature classifier strategy combines the results of the seven classifiers using the sum of the normalized distribution, while the second ensemble, using the majority voting strategy, uses the decision of the first ensemble to make the final prediction, which is evaluated on the SVC 2004 and MCYT-100. The dataset includes real signatures and skilled forged signatures with 98.43% accuracy on the SVC 2004 dataset and 97.87% accuracy on MCYT-100. [PERSON] et al. [22] obtained a total of 42 feature vectors containing static and dynamic features, such as average velocity, pen up/down ratio, maximum pressure, pressure range, x-velocity variance, signature width, signature height, etc. They obtained better accuracy and faster training time on the SVC2004 Dataset but used fewer static features and weak interpretability. [PERSON] [23] extracted GLCM and LBP features and fused them. After extracting texture features, he proposed an extraction method for signature stroke depth features, taking depth as the dynamic feature of the signature. The rules of using the GPDS dataset are consistent with the local dataset and are divided into three parts: real signature, skilled forged signature and random pseudo-signature, collecting 10 real signatures and 10 skilled signatures for a total of 20 groups. The highest overall correct rate of 87.75% for texture feature identification and 97.378% for depth feature identification was achieved, but an attempt was not made to combine the two, fusing dynamic and static feature information. [PERSON] et al. [6] proposed a handwritten signature verification method based on improved combined features. Based on the acquisition of offline images and online data, texture features were extracted using GLCM and HOG and nine geometric features were extracted. In addition to the horizontal and vertical coordinates and pressure contained in the online data, four dynamic features, velocity, acceleration, angle and radius of curvature, were also extracted. Support vector machine (SVM) and dynamic time warping (DTW) were used to verify the results. The forged signature is obtained by finding 2-3 experimenters to provide real signatures and forging them after pre-training. A total of 20 authors was collected and 1200 signatures were forged. Thus, 3, 5, 8 and 10 real signatures were selected for training small samples. The remaining signatures were used as test samples. After feature fusion, the highest accuracy rate of 10 samples was 97.83% and the false accept rate (FAR) value was 1.00%, The value of false reject rate (FRR) was 3.33%, but the characteristics of Chinese signatures are not well utilized and the task of forging signature is not detailed enough.
## 3 Results
### The Results of Performance Evaluation Metrics
The performance criteria values obtained for the test phase of this model were calculated as 0.006, 0.078, 0.052, and 0.365, respectively. The performance criteria values obtained for the training phase of the same output parameter of the same algorithm were calculated as 0.005, 0.068, 0.043, and 0.587, respectively. The performance values of ML algorithms are obtained from the estimation data to generate estimation data of time and energy costs, which show the advantages of choosing \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Output Features**} & \multirow{3}{*}{**Algorithms**} & \multicolumn{8}{c}{**Performance Scores**} \\ \cline{3-11}  & & \multicolumn{4}{c}{**Test Data**} & \multicolumn{4}{c}{**Train Data**} \\ \cline{3-11}  & & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** & **StDev** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** & **StDev** \\ \hline \multirow{3}{*}{Delivery time (min)} & RF & 44.13 & 6.64 & 3.08 & 0.95 & 29.05 & 37.85 & 6.15 & 3.08 & 0.96 & 29.15 \\  & GB & 70.69 & 8.41 & 4.74 & 0.93 & 28.96 & 70.69 & 8.41 & 4.74 & 0.93 & 29.34 \\  & \(k\)NN & 132.55 & 11.51 & 6.22 & 0.86 & 28.62 & 132.55 & 11.51 & 6.22 & 0.86 & 28.53 \\  & NN & 104.22 & 10.21 & 5.65 & 0.89 & 29.22 & 104.22 & 10.21 & 5.65 & 0.89 & 28.87 \\ \hline \multirow{3}{*}{Energy cost} & RF & 0.00 & 0.02 & 0.01 & 0.95 & 0.10 & 0.00 & 0.03 & 0.02 & 0.92 & 0.10 \\  & GB & 0.00 & 0.02 & 0.01 & 0.96 & 0.10 & 0.00 & 0.02 & 0.01 & 0.95 & 0.11 \\  & \(k\)NN & 0.01 & 0.08 & 0.05 & 0.36 & 0.08 & 0.00 & 0.07 & 0.04 & 0.59 & 0.08 \\  & NN & 0.01 & 0.08 & 0.05 & 0.40 & 0.11 & 0.00 & 0.04 & 0.03 & 0.86 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The comparison of performance evaluation metrics results based on the train and test data. e-scooter vehicles for mail or package delivery. Performance measurement metric values of ML algorithms based on forecast data are shared in Table 7.  
The GB algorithm has the best performance values for both time and energy costs among the developed ML algorithms. For the time output parameter, the performance measurement metrics of the GB algorithm were calculated as 149.32, 12.22, 6.08, and 0.845, respectively. For the energy cost output variable of the same algorithm, the performance measurement metrics of the GB algorithm were calculated as 0.001, 0.019, 0.009, and 0.963, respectively. The \(k\)NN algorithm has the worst delivery time and energy cost performance among the ML algorithms. For the time output parameter, the performance measurement metrics of the GB algorithm were calculated as 205.59, 14.34, 7.74, and 0.786, respectively. For the energy cost output variable of the same algorithm, the performance measurement metrics of the GB algorithm were calculated as 0.006, 0.078, 0.052, and 0.365, respectively.