esa-sceva/satcom-chunk-collection huggingface dataset:
# New Four Points Initialization for Digital Image Correlation in Metal-Sheet Strain Measurements
## Appendix A  
In this section, an image process is made to binarize the ellipses engraved on the metal sheets. With this, the initial condition for 2D-DIC is found. The first step is to situate the threshold in the image by using the Otsu method; that way, absolute-white circular marks will be obtained. Immediately after this, a number must be assigned to the ellipses (blobs) so they can be indexed onto the process of labelling the connected components. By the end, these connected components are discharged from areas bigger than 80 pixels. Considering that the pair of images is represented in monochrome, with a size of \(N\times M\) and a pixel resolution of b bits, the pair of images can be represented as:  
\[I^{s}(x,y,z)\in\left\{0\leq\mathbb{Z}\leq 2^{b}\right\}\forall\{1\leq x\leq M \},\{1\leq y\leq N\} \tag{1}\]  
where \(s\) represents the right or the left camera.  
We receive an image in grayscale. Following this, we make a complement of this image to enhance the importance of the circles, as indicated in Equation (1), and to obtain fair marks:  
\[C^{s}(x,y)=2^{b}-I^{s}_{g}(x,y) \tag{2}\]  
where \(s\) refers to left or right image.  
To obtain a wide image-operation range, these images are normalized, as indicated in Equation (1):  
\[NI^{s}(x,y)=2^{b}\frac{C^{s}-min(C^{s})}{max(C^{s})-min(C^{s})} \tag{3}\]  
A difficulty of the non-uniform-illumination is presented on the metal-sheet surface by the nature of the problem. To decrease it, an adaptive filter of average window (1) is applied to the normalization process on the image, where the size of the window is three times the average of the circular mark's ratios, in pixels. The filter is defined by Equations (2) and (3):  
\[SM^{s}(x,y)=\sum_{k=1}^{M}\sum_{l=1}^{N}NI^{s}(k,l)\frac{1}{(3r)^{2}}I^{s}_{3r, 3r}(x-k,y-l) \tag{4}\]  
\[SI^{s}(x,y)=NI^{s}(x,y)-\alpha*SM^{s}(x,y) \tag{5}\]  
Afterwards, another normalization is applied, through Equation (1) on Equation (5), and a thresholding with the Otsu method is applied to obtain a binarized version \(B^{s}(x,y)\) of \(SI^{s}(x,y)\). In such a process, we receive as a result the marks in white and the other regions in black. The next step is to label the binarized image elements \(O^{s}_{i}(x,y)=labeling(B^{s}(x,y))\). Here, every binarized segment of the image (blob) is independent: \(O^{s}_{i}\cap O^{s}_{j}\in\varnothing\ \forall\ j\
eq i\) for \(i,j=1,2,\ldots,LN\). Nonetheless, it is common to have small non-representative stains in that set of blobs \(O^{s}_{i}(x,y)\), and this is why an interconnected pixels selection is applied with areas less than 80 pixels (1):  
\[L^{s}_{i}(x,y)=\left\{O^{s}_{i}(x,y)\ \forall\ area\Big{[}O^{s}_{i}(x,y)\Big{]}< \delta^{s}\right\} \tag{6}\]  
The overall previous process is shown in Figure 1 and dot matrix position indexes Figure 2.
## 4 Discussion
### Implications of Assumptions and Boundaries  
The current work assumes that the utilized technologies are able to fabricate and detect the targeted deviations with sufficient accuracy to yield valid results. This is especially relevant for the choice of probe size for the CMM, which acts as a filter for the surface roughness [40]. A probe diameter of 3 mm was selected not only for practical reasons but also to facilitate the analysis of geometric deviation without the noise imposed by surface roughness. A smaller probe could enable the analysis of narrow grooves, thus potentially exposing additional surface variations and defects, while the filtering could still be conducted numerically to enable an analysis of larger variations. Such inspections and analyses could be compared to the collected data but are--for now--left for future work.  
The present study was limited to external geometric and dimensional accuracy where the CMM was the measuring instrument of choice. Investigations of surface roughness, mechanical properties, and internal structures are outside the scope of this study and are left for future work. While these areas are the subject of many research efforts, the preliminary results of the current study, as well as results of related research, suggest that the variations between positions in the build space are substantial and cannot be neglected in the design of experiments for LB-PBF/P. Closer examination of these effects in other AM technologies constitutes an avenue of future research--first of all for LB-PBF/M, but perhaps also other powder bed systems.
# A Unified Theory for 3D Gear and Thread Metrology
## 2 State of the Art in Gear and Thread Metrology
### Classical Screw Thread Metrology  
One of the most important pioneers in screw thread metrology was Prof. [PERSON] (1880-1972) of the Technical University of Dresden in Germany. He was the first to describe the mathematics needed to determine the pitch diameter from tactile measurements in double-flank contact referred to as the three-wire method for plugs and the two-ball method for rings [27; 28]. His algorithms for rake correction and compensation of probing element deformation were summarized and compared to other methods by [PERSON] and [PERSON] in 1974 [29], and this work formed the foundation for international calibration guide cg-10 published by EURAMET [30]. Though this guideline is still the most relevant international standard on the calibration of screw threads, it is limited to pitch diameter measurements of parallel threads. In other words, it does not cover other screw thread determinants like lead and flank angles nor can it be used for tapered threads.  
State-of-the-art screw thread metrology relies on inspection in two axial sections that are perpendicular to each other. Reference [30] defines five categories of calibration that differ in the amount of cost and effort required and, as a consequence, in the quality  
Figure 1: Trinity in gear metrology. The classical line- and point-based gear measurements are divided into the three main categories profile, helix, and pitch.  
and accuracy of the pitch diameter measurements. The idea is to measure only some of the quantities and assume the others to be nominal or at least within tolerance. When a thread gauge is calibrated for the first time, it is recommended to apply the most elaborate category with all determinants to be measured. However, even measurements from this category may prove to be prone to error, as seen in the results of a national intercomparison conducted in 2016/2017 with 20 participants [31]. This report lists 711 individual results, 110 of which indicated \"unsatisfactory performance and generated an action signal\" as described in ISO/IEC 17043:2010 [32].  
Two weaknesses of the cg-10 measurement strategy are primarily responsible for the poor performance in comparison measurements. One relates to the sophisticated method of self-centering probing in double-flank contact. Although special artifacts have been designed for this type of probe characterization, the reproducibility is still much lower when compared to single-flank probing [33]. The second drawback obviously arises from the fact that the screw threads are only inspected in two axial sections. This means that errors in the roundness of the part may not be identified properly if the sections under assessment do not correspond to the angular sectors with maximum/minimum deviations. An example of this effect is given in Section 5 (Figures 6-8).  
The two most relevant measurement strategies in established screw thread metrology are depicted in Figure 2.
# Sixty-Nine-Element Voice Coil Deformable Mirror for Visible Light Communication
## 2 Theory
### Electromagnetic Force of Micro VCA  
The amplitude of electromagnetic force outputted by micro VCA is one of the key factors that determine the phase stroke of VCDM. In addition, the parameters of the structure and the external conditions including the current and the cooling element should also be considered [16]. Micro VCA as shown in Figure 1 eliminates the mechanical connection between the stationary part and the moving part of an actuator through electromagnetic forces. It includes a thin mirror, springs, struts, a permanent magnet, and a coil from top to bottom. The geometry of the micro VCA is cylindrical. In addition, \(r_{m}\) is the magnet's radius, \(h_{m}\) is the magnet's height, \(d_{c-in}\) is the coil's inner diameter, \(d_{c-out}\) is the coil's outer diameter, \(h_{c}\) is the coil's height, and \(h_{g}\) is the height of the air gap.  
The mathematical relation between output force and structural parameters of micro VCA could be derived according to the Biot-Savart law. The field vector \(H\) of the current-carrying coil at the position vector \(r\) is  
\[\mathbf{H}=\frac{\mathbf{B}}{\mu_{0}}=\frac{\int_{\mathrm{L}}\frac{\mu_{0}}{4\pi} \frac{\mathrm{d}\mathbf{l}\times\mathbf{e_{r}}}{\mathbf{r}^{2}}}{\mu_{0}}=\int_{\mathrm{L} }\frac{1}{4\pi}\frac{\mathrm{d}\mathbf{l}\times\mathbf{e_{r}}}{\mathbf{r}^{2}}, \tag{1}\]  
where \(\mu_{0}\) is vacuum magnetic permeability, \(\mathrm{I}\mathrm{d}\mathbf{l}\) is the current element, and \(\mathbf{e_{r}}\) is the unit vector along a position vector \(r\). The permanent magnet in the VCA is in the magnetic field generated by the current-carrying coil, so the electromagnetic force can be expressed as  
\[\mathbf{F}=\int_{V}B_{r}\
abla\mathbf{H}\mathbf{\mathrm{d}}\mathbf{v}=\int_{V}B_{r}\
abla\int_ {\mathrm{L}}\frac{1}{4\pi}\frac{\mathrm{d}\mathbf{l}\times\mathbf{e_{r}}}{\mathbf{r}^{2}} \mathrm{d}\mathbf{v}, \tag{2}\]  
where \(V\) is the volume of a permanent magnet, and \(B_{r}\) is the remanence of the permanent magnet.  
From Equation (2), it is obvious that the factors that affect the electromagnetic force of a micro VCA include the following factors: (1) remanence that depends on the material of a magnet; (2) surface magnetism of the permanent magnet, and it is related to the magnetization direction and size of the magnet; (3) the current through the coil; (4) the geometrical size of the coil; (5) the distance between the magnet and the coil, that is, the air gap of the micro VCA. The accurate optimization of micro VCA is based on the finite element analysis of the electromagnetic field.
# RANDnet: Vehicle Re-Identification with Relation Attention and Nuance-Disparity Masks
## 4 Experiments
### Parameter Analysis
We tested five distinct values of \(\beta\), as tabulated in Table 1: 0.01, 0.05, 0.1, 0.2, and 1.0. With \(\beta=0\), the branch with the relation attention module ceases gradient contribution, reducing the overall framework to the baseline approach. Optimal performance is observed at \(\beta=0.1\), with the mAP and CMC@1 reaching 81.6% and 96.3%, respectively. In addition, we conducted experiments on the Veri776 dataset to investigate the impact of the weighting of triplet loss relative to the overall loss. The experimental results are depicted in Figure 9. Based on the obtained curve results, we determined the weighting of triplet loss to be one, indicating an equal weight allocation between triplet loss and cross-entropy classification loss, with a ratio of 1:1. Figure 8: Numerical analysis of RANDnet components. Figure 9: The weighting of triplet loss represents the ratio between triplet loss and cross-entropy loss. When the weight equals 1.0, the CMC results are relatively optimized. Results from the parameter analysis concerning the number of self-attention modules \(L\) and feature length \(d_{RA}\) are presented in Figure 10a,b. The chosen values for \(L\) in our experiments are 1, 2, 3, and 4, while for \(d_{RA}\), they are 128, 256, and 512. These experiments reveal that the highest accuracy is achieved with \(L=1\), and the impact of varying \(d_{RA}\) values appears to be inconclusive. Consequently, the parameter configuration for subsequent experiments is set as follows: \(\beta=0.1\), \(L=1\), and \(d_{RA}=256\).
# Measuring Method of Node Importance of Urban Rail Network Based on H Index
## 3 Method for Calculating the Importance of Urban Rail Network Nodes Based on \(h\)-Index  
The characteristics of urban rail network topology and passenger flow distribution represent the characteristics of the network to a certain extent. In an urban rail network, the passenger flow at the station changes with time. There are similarities and correlations between the passenger flow of stations in a certain spatial range. Considering the spatial correlation characteristics, a method based on \(h\)-index is proposed to study node importance. The main contents include: (1) correlation analysis of urban rail network node passenger flow; (2) the measurement index of node importance based on \(h\)-index is proposed, and on this basis, the measurement method of node importance based on \(h\)-index is constructed, and the corresponding flow chart is given.
# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 4 System Implementation
### Planning of Data Collection  
To ensure that the collected pedestrian-crossing data accurately reflect real-world driving conditions, we focused on acquiring images captured from actual vehicle operation. The initial dataset was collected along Kaixuan Road, located near Feng Chia University in Xitun District, Taichung City, Taiwan. This roadway, as shown in Figure 3, is a relatively new urban development and features consistently marked pedestrian crossings, all equipped with standardized signage and a regular layout. The digital map background used in Figure 3 is adapted from Google Maps and is included for illustrative and academic purposes. The data collection route began at the Dapeng intersection, proceeded along Kaixuan Road, and turned at the Liming intersection before returning along the same route to the original starting point. Along this segment, a total of ten intersections with designated pedestrian crossings were recorded. This well-structured and visually consistent environment was selected as the training dataset for the first version of the pedestrian-crossing detection model and the automatic labeling module. The clarity and regularity of the markings in this area facilitated initial model development and annotation efficiency, laying the groundwork for subsequent system iterations and performance optimization.  
After acquiring initial data collection experience, training insights, and conducting observations of real-world road conditions, we extended our data collection to include pedestrian crossings at intersections without signage, particularly in areas near school  
Figure 2: The functions of automatic labeling module.  
zones. These locations often feature irregular or non-standard crossing markings, making them valuable for enhancing the model's generalization capabilities. To this end, we selected seven schools, including Feng Chia University and other institutions in its vicinity, as target areas for data collection. The pedestrian crossings around these schools were documented to capture a diverse range of crossing styles and environmental conditions. The geographic locations of the selected schools are illustrated in Figure 4, with the map background also based on Google Maps.  
Figure 4: Map of the school locations for collecting pedestrian-crossing data. Map background: Google Maps.  
Figure 3: The driving route of pedestrian crossing on Kaixuan Road, Taichung City, Taiwan. Map background: Google Maps.
# An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs
## 4 Proposed Algorithm
### Cluster Size Optimization in Clustered Corona Based WSNs
#### 4.2.1 Minimum Angle of Sectors  
The lower bound of the cluster size interval is obtained according to the density of nodes in the network. If the cluster size is too small, some interior clusters will be empty; thus, networks encounter connectivity and coverage problems. In a corona based WSN, according to Equation (3), a corona will be empty if, and only if, the innermost corona is empty:  
\[N_{i}=0\ IIFN_{1}=0,\ 2\leq i\leq k \tag{14}\]  
To calculate the lower bound of the interval, it is considered that interior zones of the clusters belonging to innermost corona have the lowest number of nodes. With respect to the uniform distribution of nodes, the allocated space to nodes will be as follows:  
\[space=\frac{S}{M} \tag{15}\]  
where \(S\) and \(M\) denote the area of the network and the total number of nodes in the network, respectively. The innermost corona is divided into sub-coronas with equal thickness as shown in Figure 2.  
There is a relationship between the number of nodes, sectors and sub-coronas in innermost corona, which can be written as follows:  
\[a\cdot\sum_{j=1}^{b}(2j-1)=N_{1}\ or\ a\times b^{2}=N_{1} \tag{16}\]  
where a denotes the number of sectors with the minimum angle which should be obtained in this section, \(b\) shows the number of sub-coronas of the innermost corona. \(N_{1}\) is the number of nodes located  
Figure 2: (**a**) Dividing the innermost corona into sub-coronas; (**b**) enlarged view of the innermost corona.  
in the innermost corona. In order to calculate the number of sub-coronas in the innermost corona, the width of sub-coronas should be obtained. It is considered that the occupied space by nodes is circular, then, the diameter of the occupied space demonstrates the thickness of each sub-corona which can be given by:  
\[x=\sqrt{\frac{space}{\pi}}\times 2 \tag{17}\]  
By dividing the width of the coronas by the width of sub-coronas the number of sub-coronas can be obtained as follows:  
\[b=\left\lfloor\frac{r}{x}\right\rfloor \tag{18}\]  
By substituting Equation (18) in Equation (16) the number of sectors is derived as:  
\[a=\frac{N_{1}}{b^{2}} \tag{19}\]  
It is considered that each sector has only one sensor node in the innermost sub-corona, so the minimum angle of sectors is as follows:  
\[{}_{Min}=\frac{360}{a} \tag{20}\]
# Feature Mining and Sensitivity Analysis with Adaptive Sparse Attention for Bearing Fault Diagnosis
## 2 Introduction of Relevant Basic Models  
This section presents the theoretical principles of the basic network layers used in the proposed model. The proposed model first extracts the primary features of the signal sequence by the convolutional layer. Next, it extracts the long sequence signal features by the BiLSTM layer to capture the dependency between the before and after time features. Then, it focuses on the important features via the sparse attention mechanism. Finally, it completes the fault diagnosis via a softmax layer. Therefore, the basic network layers of the proposed method include the convolutional layer, the BiLSTM layer, and the attention mechanism layers. They are described as follows.
## 4 Conclusions  
1. Comparison of the macrostructure of the matrix alloy with the structure of the composite, in which there was no zone of columnar crystals, suggests that the reason for the different solid phase growth pattern is the higher thermal conductivity of the composite due to the addition of graphene.
2. Metallographic techniques were employed to establish that graphene used as reinforcement did not have modifying properties and did not affect the size and morphology of the structural constituents in the as-cast composites. Nonequilibrium crystallization of the AlFe alloy and the AlFeGn composites led to the formation of dendrites of Al solid solution 15-20 \(\upmu\)m in size with degenerate eutectic along the grain boundaries and primary needle-like Al\({}_{13}\)Fe\({}_{4}\) crystals of 10-40 \(\upmu\)m long and 1-3 \(\upmu\)m wide.
3. HPT produces an UFG structure with an average grain size of 200 nm and induces the breakage and fragmentation of the eutectic and primary crystals of iron aluminides and their partial deformation-induced dissolution with the formation of a supersaturated solid solution of Fe in Al.
4. The hardness of the deformed composite is four times higher than the hardness of the composite in the as-cast state, and its electrical resistance is two times less. This strengthening was achieved via the sum of contributions from various structural mechanisms, i.e., grain-boundary (the Hall-Petch relationship), dispersion (the Orowan mechanism), dislocation (proportional to density of dislocftion), and solid-solution mechanisms (proportional to the content of the alloying element in the Al solid solution).
5. The micro-addition of graphene increases the specific electrical conductivity of the composite both in the as-cast and in the deformed states compared to the matrix alloy.  
Conceptualization, I.G.B. and L.A.Y.; Methodology, I.G.B., A.N.P., D.Y.R. and L.A.Y.; Investigation, R.V.M., D.Y.R., A.N.P., V.V.M., B.M.F. and A.A.M.; results analysis, I.G.B., A.N.P., D.Y.R., V.V.M. and L.A.Y.; Writing--Review and editing, I.G.B. and A.N.P. All authors have read and agreed to the published version of the manuscript.  
This research was carried out within the state assignments of the Ministry of Science and Higher Education of the Russian Federation (Theme \"Struktura\" No 12202100003-2 (IMP UB RAS) and the Research Program N 122020100210-9 (HITE UB RAS)).  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Data can be transmitted on demand.  
**Acknowledgments:** The electron microscopic studies were performed at the Center of Collaborative Access Test Center of Nanotechnologies and Advanced Materials, Institute of Metal Physics of the Ural Branch of the Russian Academy of Sciences (Ekaterinburg, Russia). The Raman spectra for graphene and thermal conductivity were collected using the equipment of the Shared Access Center Composition of Compounds of Institute of High-Temperature Electrochemistry of the Ural Branch of the Russian Academy of Sciences (Ekaterinburg, Russia).  
**Conflicts of Interest:** The authors declare no conflict of interest.