esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Direct-Vibration Actuator for
## 3 Design Optimization
### Coil Optimization  
The variation in the force factor caused by the change of layers and the diameter of the coil is analyzed. The coil resistance is fixed at 6 \(\Omega\). The layer is the number of coil windings for each width. The diameter of the coil refers to the diameter of copper wire through which the current flows, excluding the insulation layer thickness.  
While maintaining the same resistance and layer, decreasing the diameter can decrease the force factor since the resistance per unit length increases, which leads to a decrease in the total coil height. The decreased coil height may cause less magnetic flux to pass through the coil. Similarly, while maintaining the same resistance and diameter, increasing the number of layers decreases the coil height and may decrease the force factor.  
With regard to the geometry constraint in the coil optimization, the distance from the yoke to the top of the coil is maintained at 3.1 mm when the top of the coil is in a fixed position. The exterior coil surface must be positioned at 6.25 mm (longitudinally) and 3.25 mm (laterally), respectively, from the center of the top plate.  
The distance from the coil bottom to the yoke should be at least 0.15 mm. The air gap between the coil and the top plate is fixed at 0.08 mm. While maintaining the coil resistance at 6 \(\Omega\), a diameter from 0.05 mm to 0.15 mm is used, and 4, 6, 8, and 10 layers are chosen to determine the maximum force factor. The reason for choosing an even-numbered layer is that from the manufacturing process, the input wire and output wire should start and end in the same position. To maximize the force factor, it is important to find the best combination between the diameter and the total coil length. The constraints and descriptions for coil optimization are briefly shown in Figure 9.  
Table 4 lists the specifications of four cases that have the maximum force factor on the dedicated coil diameter.  
The eight-layer coil with a diameter of 0.11 mm is found to have the maximum force factor. The maximum force factor in 4, 6, 8, 10 layers in a neutral position is shown in Figure 10.  
By combining all results, the final optimized force factor is obtained, which is shown in Figure 11. The optimized model has a 12% higher force factor than the original prototype.  
Figure 9: Description of coil optimization.
## 4 Numerical Simulation for the Highway Subgrade Stability
### Numerical Results and Discussion  
#### 4.3.1 The Stability of the Subgrade without and with ELB  
Figure 7 shows the shear strain increment contour of the model without ELB. It can be seen that the sliding surface runs through the subgrade and the soft soil layer. That is to say, the sliding failure occurs in the model. The stability factor of the subgrade is only 1.03. It can be concluded that the subgrade is in a critical state and prone to be unstable if it is not reinforced.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Height/H**} & **Unit Weight** & **Elastic Modulus** & **Poisson** & **Cohesion** & **Internal Friction** \\  & \(\gamma\)**/kN/m\({}^{3}\)** & **E/MPa** & **Ratio \(v\)** & _c/kPa_ & **Angle \(\varphi^{c}\)** \\ \hline Subgrade & 19 & 50 & 0.3 & 20 & 24 \\ and ELB & 17 & 24 & 0.35 & 8 & 6 \\ Soft soil & 21 & 60 & 0.22 & 390 & 31.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical parameters of the model.  
Figure 6: Numerical calculation model of ELB.  
Figure 8 shows the shear strain increment contour of the model with ELB. The height and width of the ELB are 6 m and 10 m, respectively. Figure 8 demonstrates that the sliding surface does not run through the subgrade and the sliding failure does not occur in the subgrade. The stability factor of the subgrade is 1.26. This tells us that the stability of the subgrade can be improved by the ELB.
## 1 Introduction
[PERSON] (2016) [18] presented a rapid laser scan planning method that overcomes the computational complexity of planning laser scans based on diverse data quality requirements in the field. The planning goal is to minimize the data collection time while ensuring that the data quality requirements of all objects are satisfied. [PERSON] (2016) [19] combined a touch-trigger probe and a laser scanner to fulfil the accuracy and complexity of measurement requirements. [PERSON] (2000) [20] proposed an algorithm to plan how to place the scanner to satisfy the view angle, view depth, and interference requirements. [PERSON] (2016) [21] offered an error-ellipsoid-based uncertainty model for Laser Radar measurement. The abovementioned planning research focused on the measurement accuracy or collection time. Because the parts under inspection typically have flat surfaces, the occlusion, reachability, and interference are not prominent problems in path planning. Coherent Laser Radar provides a robust and highly accurate form of measurement for the BIW. However, the features to be measured of large-scale spatial distribution and complex inspection constraints become a challenge in the BIW inspection implementation of Laser Radar. In BIW inspection using Laser Radar systems, the laser and the feature are constrained within certain angle and distance ranges to ensure the inspection accuracy. Moreover, the laser must not be obstructed before reaching the feature. For safety, the Laser Radar must not collide with the ground, walls, or BIW. Additionally, the Laser Radar must not be positioned in a location that the robot cannot reach. When these complex constraints are incorporated into the station optimization model, the measurability of the planning result can be ensured. This paper focuses on the abovementioned constraint models. This paper is organized as follows: In Section 2, the station planning of BIW inspection is described in detail. In Section 3, the measurement constraints for BIW inspection are established. Section 4 provides a case study to verify the proposed methods. Finally, conclusions are presented in Section 5.
# Online Determination of Boron Isotope Ratio in Boron Trifluoride by Infrared Spectroscopy
## 3 Results and Discussion
### Online Determination of Boron Isotope Ratio  
Figure 8 illustrates the online determination results from the separation part of a chemical exchange rectification device, showing the enriched stage, the stability stage, and an instability point (where the isotope ratio suddenly drops). With the assistance of IR spectroscopy, the instability point can be found and thus encourage correction of the operation. Figure 9 shows the isotope ratio curve of the complex part where the \({}^{11}\)BF\({}_{3}\) was enriched on the same isotope separation device. The errors of determination results were a little larger when the isotope ratio become smaller with the decrease in SNR. The sampling stage experiments lasted for 12 h and the sampling interval was 2 h. The curve shows the change of the isotope ratio in the process of product collection. The overtopping (exceeding limit value) point indicated that the sampling operation should be stopped at that moment, because the limit of the isotope ratio for \({}^{11}\)B product (\({}^{10}\)B/\({}^{11}\)B \(\leq\) 0.0204) has been exceeded.  
Figure 8: Online determination results of the separation part of a chemical exchange rectification device.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Series No.**} & \multicolumn{2}{c}{**Isotope Ratio of \({}^{10}\)B/\({}^{11}\)B**} \\ \cline{2-3}  & **Results of IR Method** & **Results of ICP-MS** \\ \hline
1 & 1.6385 \(\pm\) 0.0043 & 1.6390 \(\pm\) 0.0006 \\
2 & 0.7536 \(\pm\) 0.0045 & 0.7546 \(\pm\) 0.0010 \\
3 & 0.4815 \(\pm\) 0.0038 & 0.4832 \(\pm\) 0.0009 \\
4 & 0.0915 \(\pm\) 0.0059 & 0.0905 \(\pm\) 0.0010 \\
5 & 0.0504 \(\pm\) 0.0063 & 0.0488 \(\pm\) 0.0012 \\
6 & 0.0120 \(\pm\) 0.0079 & 0.0102 \(\pm\) 0.0007 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the results from the IR method and ICP-MS method.  
Figure 9: The curve of isotope ratio about the complex part of a chemical exchange rectification device.
# Uncertainty Analysis of 3D Line Reconstruction in a New Minimal Spatial Line Representation
## 2 The Proposed Method
### Conversion between the Proposed Line Representation and the Plucker Line
#### 2.3.1 From Plucker Line to the Proposed Representation  
In homogeneous representation, \(\mathbf{L_{1}}=(\mathbf{m}_{1};\mathbf{d}_{1})\) and \(\mathbf{L}_{2}=(\lambda\mathbf{m}_{2};\lambda\mathbf{d}_{2})\) (\(\lambda\
eq 0\)) represent the same line. In this paper, we force the norm of direction component to be 1: \(|\mathbf{d}|=1\). This only prevents representing the lines at infinity(a Plucker line with \(\mathbf{d}=\mathbf{0}\) is at infinity).  
The direction \(\mathbf{d}=(d_{1},d_{2},d_{3})^{\mathsf{T}}\) can be mapped to the unit sphere coordinate as \((\theta,\phi)\) directly:  
\[\begin{split}\theta&=\arccos d_{3};\\ \phi&=atan2(d_{2},d_{1}),\end{split} \tag{11}\]  
where \(\theta\in[0,\pi)\) and \(\phi\in[-\pi,\pi)\). In Plucker coordinates, the ratio of \(|\mathbf{m}|\) and \(|\mathbf{d}|\) implies the distance from the origin to the line. In our case, the distance is \(|\mathbf{m}|\). Then  
\[m_{l}=|\mathbf{m}|\,. \tag{12}\]  
The closest point \(\mathbf{P}_{c}\) on a Plucker line(with \(|\mathbf{d}|=1\)) to the origin can be represented by:  
\[\mathbf{P}_{c}=\mathbf{d}\times\mathbf{m}. \tag{13}\]  
Therefore, the rotation angle \(\alpha\) from \(\mathbf{v}_{s}\) to \(\mathbf{OP}_{c}\) about the axis \(\mathbf{d}\) can be derived by:  
\[\alpha=\begin{cases}\arccos\frac{\mathbf{v}_{s}^{\mathsf{T}}(\mathbf{d}\times \mathbf{m})}{|\mathbf{m}|}&\mathbf{d}^{\mathsf{T}}(\mathbf{v}_{s}\times( \mathbf{d}\times\mathbf{m}))\geq 0\\ 2\pi-\arccos\frac{\mathbf{v}_{s}^{\mathsf{T}}(\mathbf{d}\times\mathbf{m})}{| \mathbf{m}|}&\mathbf{d}^{\mathsf{T}}(\mathbf{v}_{s}\times(\mathbf{d}\times \mathbf{m}))<0\end{cases}. \tag{14}\]  
Given \(\mathbf{m}\) and \(\mathbf{d}\), we can transform the redundant six-vector into a compact four-vector with certain physical meanings: \(\theta\) and \(\phi\) imply the direction in sphere coordinate, \(m_{l}=|\mathbf{m}|\) implies the minimum distance from the origin to the spatial line, \(\alpha\) implies the location around the original.
# Diagnosis of the Multiepitope Protein rMLEEISH3 for Canine Visceral Leishmaniasis
## 3 Results and Discussion
### Diagnostic Performance of rMELEISH3 Antigen in Detecting CVL
infantum-infected dogs_ (n = 23) presenting high (n = 6), medium (n = 8), and low (n = 9) parasite loads. Positive (n = 5) and negative (n = 8) control samples were included in the assays. Cutoff values, area under the curve (AUC), sensitivity (SEN), and specificity (SPC) were calculated. AUC is widely used to assess the accuracy of diagnostic methods, with values close to 1 indicating the high discriminatory ability of an assay. According to [PERSON] [38], AUC values can be interpreted in terms of discriminatory power as no discrimination (AUC \(\leq\) 0.5), low (0.5 < AUC \(\leq\) 0.7), moderate (0.7 < AUC \(\leq\) 0.9), high (0.9 < AUC < 1), and perfect (AUC = 1.0). While sensitivity represents the ability of an assay to correctly identify infected individuals (positive samples), specificity refers to the ability to prevent detection among uninfected individuals (negative samples) [39]. Based on these findings, it is important to consider the potential clinical implications of rMELEISH3 for the diagnosis of CVL. The use of rMELEISH3 in ELISA-based assays for CVL may represent an important step toward improving diagnostic accuracy, particularly in the detection of asymptomatic infections, which are crucial for controlling disease transmission. However, several challenges remain, including the risk of cross-reactivity with other pathogens, variability in antibody responses among dogs, and the need for multicenter validation studies using field samples. Future work should also focus on comparing rMELEISH3 with currently used recombinant antigens to assess its added value and feasibility for routine veterinary diagnostics.
## 3 Function-Oriented Networking
### FON Forwarding Table  
One of FON's main purposes is to provide FON _Function_ with an API abstracting system's routing related-resource. Let us take the Linux machine as an example. For communication, _ARP table_ and _Routing table_ have to be controlled in a lump and also _Flow table_ of Virtual Bridge has to be managed as well. In order to do this, FON constructs FON Forwarding Table by using FON ID. It manages various tables uniformly through this. FON manages these tables and provides APIs related with FON Forward Table, and FON Function can change easily network policy by modifying a FON Forwarding Table only; in other words, FON Function does not have to modify ARP, Routing, and Flow tables directly.
## 3 Tolerancing Informatics
### Enablers of Tolerancing Informatics  
The core enabling technologies can be subdivided in technologies that enable an automatic processing of specification-related tolerancing information and of verification-related tolerancing information (see Figure 3). Regarding the first aspect, fostered by the increasing digitalization of manufacturing, recent standardization efforts allowing the unambiguous specification of tolerances in three-dimensional part views of technical drawings and thus also in three-dimensional part models via model-based definition (MBD) are an important enabler of tolerancing informatics workflows, since they form the basis for creating unambiguous and semantic (three-dimensional) tolerance specification information in engineering design. Moreover, standardization efforts (ISO and ANSI) and their implementation by Computer-Aided Design (CAD) system vendors concerning the exchange of product data, such as STEP AP 242, are important for the automatic processing and exchange of tolerancing information by and between different computer-aided design, computer-aided manufacturing, and computer-aided inspection tools.  
Considering the automatic processing of verification-related tolerancing information, the adoption of Quality Information Framework (QIF) for the exchange of tolerancing and metrology information ensures digital continuity between specification and verification data. Moreover, modern sensor technologies but also Industry 4.0 fostered by the advent of the internet of things (IoT) are important enablers in the form of a technology push towards  
Figure 3: Main enablers for automatic tolerancing information processing. GD&T: Geometric Dimensioning and Tolerancing; PMI: Product Manufacturing Information; QIF: Quality Information Framework.  
Figure 2: Tolerancing informatics as a seamless link between all activities in geometrical variations management.  
tolerancing informatics workflows, since these technologies allow the quick sensing of geometrical part and assembly characteristics and their transfer to data storage and processing units in smart manufacturing systems.  
Beside this, sophisticated tolerance and variation simulation tools in the form of digital twins for geometrical variations management, such as those presented in [15] based on fundamental concepts of standards for the geometrical product specification and verification or as proposed in [16], are important enablers for tolerancing informatics workflows. This is because they allow the modelling and processing of instantiated part variation information in real-time and thus enable the realization of smart assembly and advanced matching strategies [17].
## 3 Results  
### Results for One Motion (GMS EUK)  
Results for GMS EUK at different intensity levels are presented in Figure 7 (interstorey drift \(\Delta\)), Figure 8 (residual interstorey drift \(\Delta_{r}\)), Figure 9 (absolute acceleration \(a_{t}\)) and Figure 10 (energy dissipated by dampers \(W_{damp\prime}\)). The plots show the ratio between analysis results of models MB to HB (first and second columns in plots) or LB to HB (third and fourth column in plots), at every intensity level. Every storey is treated as an independent datum; for every intensity level the 50% percentile is highlighted and its value at the maximum intensity labeled. The results are binned by amount of damping at design intensity (low or high), damper type (linear or non-linear) and storey (first of roof). In the case of residual drift ratio \(\Delta_{r}\), the results are restricted to levels for which \(\Delta_{r}\) at HB is not negligible. Concise results for other variables are given in Appendix B, including: storey results in Figure A3 (drift velocity \(\
abla\), damper shear \(V_{d}\), storey shear \(V_{s}\), total shear \(V_{t}\)); element results in Figure A4 (beam Moment \(M_{b}\), beam rotation \(\theta_{b}\), column moment \(M_{c}\), column rotation \(\theta_{c}\), column axial force \(N_{c}\), storey moment \(M_{s}\)); and energy results in Figure A5 (relative energy input \(E_{t}\), plastic strain energy dissipated by frame \(W_{frame}\), column \(W_{column}\) and beam \(W_{beam}\)). For \(W_{frame}\), \(W_{column}\), \(W_{beam}\) only intensity levels with a non-negligible value of the variables have been selected. Even so, very low values of plastic strain are obtained for specific storeys in model type HB and, as a result, the ordinates of the ratio LB/HB and MB/HB present large peaks; the scale of the ordinate axis has been set ignoring these values.
## 4 Result Analysis and Discussion
### Analysis of Results and Comparison with State-of-the-Art Research  
As discussed in the previous subsection, the utilization of the proposed Real-Time Boundary Stationary Wavelet Transform (RTBSWT) and the improved long short-term memory (LSTM) networks introduces a novel approach for sequence classification tasks. This approach is particularly effective when the input data contain both spatial and temporal features. Consequently, Table 5 illustrates the capabilities of the proposed framework compared to recent state-of-the-art research in fault detection across various scenarios, including internal faults, external faults, inrush currents, and internal faults coinciding with CT saturation. The proposed method and other recent methods were tested under fair and equal conditions using identical case studies.  
The results shown in Table 5 demonstrate that the proposed framework exhibits superior performance. For internal faults, the proposed framework correctly detects 498 out of 500 cases (approximately 99%) with an average detection time of 130 milliseconds. In contrast, other state-of-the-art methods correctly detect 489 out of 500 cases (approximately 97%) but require an average detection time of 340 milliseconds. Regarding external faults, the proposed framework accurately identifies 499 out of 500 cases (approximately 99%) with an average detection time of 110 milliseconds. Meanwhile, other leading methods detect 471 out of 500 cases (approximately 94%) with an average detection time of 310 milliseconds. In the case of inrush currents, the proposed framework successfully detects 497 out of 500 cases (approximately 99%) with an average detection time of 120 milliseconds. Comparatively, other top methods detect 486 out of 500 cases (approximately 97%) with an average detection time of 360 milliseconds. Lastly, for internal faults coinciding with CT saturation, the proposed framework accurately identifies 496 out of 500 cases (approximately 99%) with an average detection time of 170 milliseconds. Other state-of-the-art methods detect 462 out of 500 cases (approximately 92%) with an average detection time of 390 milliseconds. These results highlight the effectiveness and efficiency of the proposed framework, which not only achieves higher detection accuracy but also significantly reduces the detection time compared to other state-of-the-art methods. This demonstrates its potential for real-time applications in fault detection and highlights the advancements made in integrating RBSWT and improved LSTM networks for sequence classification tasks.  
Figure 19: RMSE analysis of a noisy signal test on a power transformer during an internal fault under CT saturation condition.
# CommuSpotter: Scene Text Spotting with Multi-Task Communication
## 2 Related Work  
### Scene Text Spotter of Two-Stage Paradigm  
To address the challenges of arbitrary texts, [PERSON] et al. [13] developed Mask TextSpotter v1, which includes long short-term memory (LSTM) [20] in text recognition tasks to boost spotting results. In Mask TextSpotter v2 [19], text and character instance segmentations are adopted to improve recognition performance. [PERSON] et al. [21] predicted additional latent information from text detection tasks to enhance text instance segmentation. [PERSON] et al. [5] developed Regions of Interest (RoIs) masking to improve segmentation accuracy by selecting and fusing features for instances. These studies focused on improving the text recognition task. Some of the following focus on improving the early text detection task. FOTS [9] and TextNet [22] adopt rotating RoIs and perspective RoIs to handle irregular and multi-oriented text detections, respectively. CRAFT [23] groups character region features from the text detector to reinforce character attention for the recognizer. [PERSON] et al. [15] adopted Transformer to improve the representation from the shared backbone, while text detection and recognition tasks were parallel and independent. SwinTextSpotter [14] adopts the Swin Transformer [24] in the backbone for better text representations. The expertise from the text detection task is transferred to the text recognition task, but there is no inverse interaction. In addition, errors accumulate from early tasks to later tasks. Furthermore, some dataset annotations are tailored to specific methods, such as character and polygon annotations [13; 25], text-line annotations [16], etc.