esa-sceva/satcom-chunk-collection huggingface dataset:
# Micro-Gear Point Cloud Segmentation Based on Multi-Scale Point Transformer
## 2 A Dataset for Micro Metal Gears Based on MIM
### Collection and Processing of MIM Micro Metal Gear Dataset  
Due to the need for comprehensive acquisition of gear surface data, an efficient, precise, safe batch inspection method is required. Traditional contact measurement techniques generally have a limit of a 0.2 module for measuring gear moduli, whereas non-contact measurement techniques based on diverse optical principles do not have this limitation.  
Among 3D measurement technologies, the structured light projection method stands out for its high speed and precision. The core advantage of this measurement method lies in its non-contact nature and high degree of automation. It enables rapid and accurate measurements without touching the object's surface, significantly enhancing the efficiency and reliability of the measurements.  
The German GOM ATOS Core three-dimensional optical scanning system, depicted in Figure 4, is employed for these measurements. The optical 3D scanning measurement system primarily consists of a high-precision stereo measurement head; an automated turntable; and the ATOS Professional 2018 measurement software. The equipment setup is illustrated in Figure 4. The point cloud data acquisition process is outlined in Figure 5.  
It is important to note that before scanning, it is necessary to determine if the surface of the object being measured has any characteristics, such as reflectiveness, that could affect imaging. Surfaces that are too smooth require the application of a developer spray. In this experiment, the developer used is a mixture of 5% TiO\({}_{2}\) and 95% anhydrous ethanol, which is thoroughly mixed and then uniformly sprayed onto the surface of the component. The results are displayed in Figure 6.  
Following the acquisition of raw point cloud data, a data refinement process is employed, involving trimming and removal of unnecessary planes and point cloud data to optimize the dataset. Subsequently, the ATOS software is utilized for polygonalization operations, enabling the generation of refined, non-overlapping triangular mesh data from the original point cloud data. The data before and after this process are illustrated in Figure 7. Such operations serve to further enrich the diversity of the dataset. Through experimental design and software processing, a total of 1101 high-quality point cloud data points were successfully collected.  
Figure 4: ATOS Core 3D optical scanning system.  
Figure 5: Point cloud data acquisition process.  
Figure 6: Comparison of gears before and after treatment with modifier.  
Figure 7: Comparison of ATOS software optimization and processing of point cloud data. (**a**) Pre-processed point cloud data; (**b**) processed point cloud data.
## 3 Numerical Results & Discussion
### Effective Apoptosis Ratio  
As described earlier, photothermal therapy needs to maximize apoptosis in tumors and minimize thermal damage to surrounding normal tissues. Therefore, the effective apoptosis ratio was evaluated by simultaneously considering the apoptosis ratio, which represents the rate at which apoptosis occurs in the tumor, and the thermal hazard value of the normal tissue, which represents the degree of thermal damage to the surrounding normal tissue.  
Figure 12 shows contour graphs of the effective apoptosis ratio according to the volume fraction of the injected GNRs and the intensity of the irradiated laser per unit area for various tumor lengths and depths.  
Depending on the geometry of the tumor, the length and depth of the tumor increase, and the intensity of the laser resulting in the maximum effective apoptosis ratio increases. As described earlier, as the length of the tumor increases, a higher laser intensity is required to cause a wider range of temperature rise, and as the depth of the tumor increases, the distance from the surface to the tumor tissue becomes longer. This is because normal tissue absorbs the energy of the laser; hence, higher laser intensity is required to maintain the temperature range where apoptosis occurs in deep tumors.  
In the case of the intensity of the irradiated laser and the volume fraction of the injected GNRs, the apoptosis ratio and the thermal hazard value of the normal tissue show a tendency to constantly increase or decrease; however, in the case of the effective apoptosis ratio, there was a condition in which the maximum value was derived. This indicates that, although the thermal damage to the surrounding normal tissue can increase if the focus is only on maintaining the temperature range in which apoptosis occurs in the tumor, there is a certain condition that minimizes thermal damage to surrounding normal tissue while inducing apoptosis in as many regions as possible within the tumor. Table 6 summarizes the laser intensity and the volume fraction of the injected GNRs for the length and depth of all tumors.
## 2 Basic Concepts
### Granular Computing  
Granulation aims at grouping elements based on their similarity, functionality, proximity, or indistinguishability. Granular Computing (GrC) formally represents a granule as a subset of a universal set [26]. In machine learning, the classification problem can be seen as a model of inductive learning, which exploits the training sets to build a model that provides an efficient representation for all testing sets. Granular computing employs the partitioning process by learning a classifier. It employs a rule induction algorithm that is applied using supervised machine learning techniques [27].  
Computing the granules can be regarded as knowledge processing. Granules can be defined as the basic unit of knowledge. Each granule can be perceived by a human's knowledge processing. Therefore, unknown concepts (a subset of the universe dataset) can be approximated by known grains according to the axioms of approximation [27].  
The rule induction technique is used in granular computing to emulate the supervised machine learning methods used to classify the dataset. In rule induction, a set of rules (r1, r2,..., rm) is expressed to map the input space to the output space. Each rule consists of a logical implication, and the results become valid if the prediction is satisfied [28; 29]. AQ-family [30] and CN2 [31] are examples of traditional rule induction methods. In GrC, every rule, ri, is associated with a certain subset of instances; it might be considered a granule from the GrC point of view [27].  
**Definition 1**.: _In the two-dimensional plane space of the information system \(S=(U,\,R)\), the elementary granulation in each granulation is designated by a point in the plane, known as the granular node. For elementary granulations within the same granulation, there is no line segment connecting; however, for elementary granulations within different granulations, a line segment connecting in the plane space denotes that the two granulations have the intersection set. This line segment is referred to as the granulation side, and the intersection set on the granulation side denotes the granulation side's weight value. As a result, a granular network in two dimensions is created [32]._  
**Definition 2**.: _A directed weighted graph can be used to represent the entire granular network of a decision table since each granulation side is represented by a directed line segment from left to right, as shown in Figure 2[32]._  
The following stages are used to build granular computing network (GrC) for the dataset \(S=(x,\,yi)\)\(\mid\) I = 1, 2,..., n in N-dimensional space. First, the point in S is symbolized by the indivisible atomic grains. Second, based on the set's view, a distance between two granules is suggested. Thirdly, the union process is jointly determined by distance and granularity. Finally, the class of test data is predicted using the granule set, which is obtained at this point.
## 6 An Example of a Standard Diagnostic granule System
### Standard Diagnostic Granule System
Hence \(\mu_{p}(a)\leq\inf\{\mu_{y}(a),\mu_{z}(a)\}\wedge\max\{v_{y}(a),v_{z}(a)\}\leq v _{p}(a)\) iff \(\langle\mu_{p}(a),v_{p}(a)\rangle\leq_{n}\langle\inf\{\mu_{y}(a),\mu_{z}(a)\}, \max\{v_{y}(a),v_{z}(a)\}\rangle=\langle\mu_{y}(a),v_{y}(a)\rangle\otimes \langle\mu_{z}(a),v_{z}(a)\rangle\) iff \(x=\langle a,\langle\mu_{p}(a),v_{p1}(a)\rangle\rangle\in_{G}y\cap_{G}z\). Therefore \(\cup_{G}\{t\in C_{0}:\exists x\in G_{inst}(x\in_{G}t\wedge x\in_{G}y\wedge x \in_{G}z)\}\subseteq_{G}y\cap_{G}z\). From Theorem 14: \(y\cap_{G}z\subseteq_{G}\cup_{G}\{t\in C_{0}:\exists x\in G_{inst}(x\in_{G}t \wedge x\in_{G}y\wedge x\in_{G}z)\}\). Hence and from Theorem 11: \(y\cap_{G}z=\cup_{G}\{t\in C_{0}:\exists x\in G_{inst}(x\in_{G}t\wedge x\in_{G} y\wedge x\in_{G}z)\}\). The proof of the point (2) is analogous. The First Element of the Sequence \(Sc_{n}\) that satisfies the \(n\)-SclFS  
Table 1 shows two **diagnostic generators**\(p_{1}\), \(p_{2}\) which define all considered diagnostic granules \(p=\langle s,f\rangle\) belonging to \(n\)-SclFS using the operations described in Formulas (**S1**)-(**S5**). The number \(n\) satisfying the Definition of \(n\)-SclFS is searched for as follows. For the diagnostic generator \(p\), each pair \((\mu,v)\) has function values \(\mu_{p},v_{p}\). Then, the _smallest_ natural number \(n\) is searched, such that for each \(x\in X\):  
\[0\leq Sc_{n}^{-1}(\mu_{p}(x))+Sc_{n}^{-1}(v_{p}(x))\leq 1, \tag{10}\] \[0\leq Sc_{n}(\mu_{p}(x))+Sc_{n}(v_{p}(x))\leq 1. \tag{11}\]  
Then, \((\mu,v)\in n\)-SclFS. From among the numbers determined in this way, \(n\) is selected as the largest, for all diagnosed objects \(o\in X\). The largest number \(n\) for the map scale is 2, for the logarithmic scale it is 3, and for the power scale it is 4. According to Theorem 5, for the map scale \(p\in n\)-SclFS, when \(n>1\), for the logarithmic scale \(p\in n\)-SclFS when \(n>2\), and for the power scale \(p\in n\)-SclFS, when \(n>3\). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Diagnosis** & **Object** & **Symptom** & **Fault** & **Number \(n\) for** & **Number \(n\) for** & **Number \(n\) for** \\ \(p\) & \(o\) & \(s\) & \(f\) & \(Sc_{n}(x)=x/n\) & \(Sc_{n}(x)=1-\log_{(1-y_{n})}x\) & \(Sc_{n}(x)=x^{n}\) \\ \hline \multirow{4}{*}{1} & 1 & 0.5 & 0.4 & 1 & 1 & 1 \\  & 2 & 0.8 & 0.8 & 2 & 3 & 4 \\  & 3 & 0.8 & 0.4 & 2 & 2 & 2 \\  & 4 & 0.5 & 0.9 & 2 & 2 & 3 \\  & 5 & 0.4 & 0.9 & 2 & 2 & 2 \\ \hline \multirow{4}{*}{2} & 1 & 0.7 & 0.4 & 2 & 2 & 2 \\  & 2 & 0.9 & 0.4 & 2 & 2 & 2 \\ \cline{1-1}  & 3 & 0.9 & 0.4 & 2 & 2 & 2 \\ \cline{1-1}  & 4 & 0.8 & 0.6 & 2 & 2 & 3 \\ \cline{1-1}  & 5 & 0.8 & 0.8 & 2 & 3 & 4 \\ \hline \end{tabular}
\end{table} 
Table 1: Symptoms, faults, fuzzy degree, and determination of the number \(n\) for \(n\)-SclFS.
## 5 Analysis of Countermeasures for Yellow Plume
### Discussion on Control Measures from the HRSG Side  
As shown in Figure 15, the exhaust temperature of gas turbine and the inlet temperature of the HRSG can reach about 600 \({}^{\circ}\)C. This temperature range makes NO transform rapidly to NO\({}_{2}\), which makes the NO\({}_{2}\) content at Test Point 2 obviously higher than at Test Point 1. At the same time, due to the existence of CO, O\({}_{2}\), H\({}_{2}\)O and other appropriate factors, the conversion conditions of NO to NO\({}_{2}\) are better in the HRSG.  
Based on the above research and analysis, several measures to suppress yellow plume from the HRSG side are proposed as follows:  
1. CO catalytic oxidation device [20] should be installed in the diffusion section, which connects the gas turbine with the HRSG, which can reduce the content of CO and NO\({}_{2}\). Simultaneously, the flue gas temperature should rise to improve the efficiency of the HRSG.
2. Catalytic reduction method [21] should be adopted in the flue. The NO\({}_{2}\) would be reduced to NO, thus yellow plume would be eliminated.
3. The flue gas should be sprayed and NO\({}_{2}\) absorbed in the flue or chimney of the HRSG [22], thus the sewage would be neutralized by alkaline substances, or the alkaline solution could be directly sprayed inside the chimney.
## 3 Results and Discussion
### Effects of Thermal Treatment on Tensile Strength  
According to Figures 1-7 the geotextiles have a higher fiber orientation along the CD, and samples taken along this direction are thus used for this section. Recycled Kevlar(r)/PET/LPET nonwoven geotextiles are composed of LPET fibers that have a sheath-core structure. The sheath has a melting point of 110 \({}^{\circ}\)C, based on which the thermal treatment is set to be at 120 \({}^{\circ}\)C. The tensile strength of nonwoven fabrics in relation to thermal treatment conditions is presented in Table 4. Regardless of the environment being neutral, acid, or alkaline, the tensile strength of the nonwoven geotextile is greater when they are thermally treated, rather than when they are not thermally treated, as indicated in Figure 8. A \(t\)-test is conducted in order to examine the influence of thermal treatment on the tensile response of the nonwoven geotextiles tested the tensile strength. The \(t\)-test results report \(p<0.05\) that there is a statistical significance as thermal treatment results in a higher tensile strength. However, the statistical analyses for NaOH solution is absent in Table 4 due to the fact that the immersion in a strong alkaline solution for 4 months results in the disappearance of tensile strength of nonwoven geotextiles. The available data is insufficient to satisfy the hypothesis for a normal distribution, and a \(t\)-test cannot be performed. One-way ANOVA and Scheffe's test are used to compare the tensile strength of the thermally treated and non-thermally treated geotextiles in relation to immersion periods (1, 2, 3, and 4 months) and solution temperatures (23 \({}^{\circ}\)C and 50 \({}^{\circ}\)C). For geotextiles immersed in H\({}_{2}\)O, there is no statistical significance in the tensile strength between different immersion periods and solution temperatures. This statistical analysis is in conformity with the results in section 3.2. When applying CH\({}_{3}\)COOH at 23 \({}^{\circ}\)C, the non-thermally treated geotextiles show the statistical significant difference as 1 \(<\) 0 (months), while the thermally treated geotextiles show the statistical significant difference as 2, 3, 4 \(<\) 0. In addition, the thermally treated geotextiles also have a higher alkali resistance than non-thermally treated geotextiles, as indicated in Figure 8e. Such a result is due to the constituent LPET fibers that undergo softening as a result of the thermal treatment for the nonwoven geotextiles. The fibers that are crossed are bonded by the bonding points, and thereby increase the contact areas between fibers and eventually improve the strength of nonwoven fabrics. Therefore, thermally treated nonwoven geotextiles have a higher tensile strength.  
Figure 7: FT-IR spectra of nonwoven geotextiles that are immersed in NaOH at (**a**) 23 \({}^{\circ}\)C and (**b**) 50 \({}^{\circ}\)C.
## 3 Methods  
### Log-Polar Transformation  
In the image processing algorithm, the log-polar transformation has the characteristics of scaling invariance and rotation invariance and is usually used for target extraction and recognition [25; 26]. However, the log-polar transformation in our method is inspired by analyzing the characteristics of the imaging system, as derived above. Here, we present the steps and flow charts that describe the application of the log-polar transformation method to achieve resolution recovery.  
* Step 1 Obtain the image data \(f(x,y)\) of M*N by photoelectric sensors and the point spread function \(PSF(x,y)\) of M*N by simulation of the ideal point source in FEKO.
* Step 2 Transform the image data \(f(x,y)\) and \(PSF(x,y)\) coordinate into the polar-coordinate to get the new image \(g(r,\theta)\) and the new \(PSF(r,\theta)\) Equation (8), and interpolate the new image using the bicubic interpolation method. \[r=log(\sqrt{x^{2}+y^{2}})\qquad\theta=arctan(y/x)\] (8)
* Step 3 Use the Lucy-Richardson algorithm with \(g(r,\theta)\) and \(PSF(r,\theta)\) to reconstruct the high-resolution image \(g^{\prime}(r,\theta)\).
* Step 4 Inverse transform \(g^{\prime}(r,\theta)\) into the Cartesian coordinate system and interpolate it by the bicubic method to obtain the final result with resolution recovery.  
Figure 4 shows the transformation process. As we can see, the space-variant blur in the Cartesian-coordinate turns to be space-invariant in the log-polar-coordinate. However, this method has a blind spot at \(r=0\), and it will not work well close to the blind spot since there is too few pixels to be sampled. The mathematical method can hardly improve it, and now, the feasible way is to avoid putting the detected object around the blind point.  
Figure 3: (**a**): \(y\)-\(z\) plane of the blur image with three points located at \((x,y)\) = (0, 0.5 m), (0, 1 m), and (0, 2 m); (**b**): the result of log-polar transformation of the left one.The unit of \(x\)-axis is pixel.  
In the process of transformation, bicubic interpolation will result in an uneven rise of values in the different locations of the image, which can be depressed by choosing appropriate filter methods, such as median filter, or by improving the interpolation procedure.
## 6 Conclusions  
This work has focused on the seismic geomorphology aspect of four different types of continental slope channels complexes in the Canterbury Basin, Offshore New Zealand. The conclusions from this research are that:  
1. The channels complexes in the study area can be categorized into four main types, based on the shape of the erosional surfaces and the reflection configuration of channels fills. These are vertical migrating channels (Type 1), lateral migrating channels (Type 2), V-shaped channels (Type 3), and U-shaped channels (Type 4).
2. The morphometric analysis of the channels complexes in the study area shows an important aspect (geometry, facies, and scaling relationship) in the quantitative interpretation of its sedimentary processes and paleoenvironment. Thus, it is applicable to a similar setting worldwide.
3. The morphometry of the submarine channels is also influenced by local factors in these submarine environments (e.g., channel wall collapse, different maturity of turbidity flows, etc.)
4. The evolution of the channel's morphometry and trajectory in the study area indicates a less significant regional plate movement.
5. Seismic facies and morphometry analysis of channels complexes in the study area revealed that channels complexes are contributing to differential compaction, horizon interpretation pitfalls, and burial effects that might influence petroleum prospectivity in the study area.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12094386/s1](https://www.mdpi.com/article/10.3390/app12094386/s1)]([https://www.mdpi.com/article/10.3390/app12094386/s1](https://www.mdpi.com/article/10.3390/app12094386/s1)), Figure S1: Seismic attribute of four types of the channels complexes in the study area; (a) curvature, (b) instantaneous frequency, (c) sweetness, and (d) variance with channel delineation (dashed line).  
Conceptualization, D.H.; methodology, D.H.; investigation, D.H. and W.R.R.; data curation, D.H. and W.R.R.; writing--original draft preparation, D.H. and W.R.R.; writing--review and editing, D.H. and W.R.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Department of Geosciences, College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals.  
Not applicable.  
Data Availability StatementData are available in a publicly accessible repository that does not issue DOIs. Publicly available datasets were analyzed in this study. This data can be accessible here: www.nzpam.gov.nz/maps-geoscience/exploration-database. Last accessed on 24 April 2022.  
The authors thank New Zealand Petroleum and Minerals (NZPM) for making this dataset into a public domain. We also thank King Fahd University of Petroleum and Minerals (KFUPM), as well as [PERSON], [PERSON] and [PERSON], for supporting this research. [PERSON] is thanked for PETREL software donated to KFUPM.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### New Pictogram Events
#### 4.2.1 What Is the Indexing Level of Newly Created Pictograms Depending on Their Category?  
Punctual mode offers a system of indexed pictograms. This way, \"folder\" pictograms can be established, serving as a container for related concepts that will be selected in the next level, adding extra precision to the sentence. We consider the level of indexation as the number of folders and subfolders in which a new pictogram is created. To illustrate this methodology, the default configuration of PicoDroid Lite contains the pictogram \"apple\" inside the pictogram subfolder \"fruit\", which is included in the pictogram folder \"eat\". This means that the pictogram \"apple\" presents a second level of indexation.  
This analysis studies which categories show the highest degree of indexing. We know the level of indexation by observing the length of the list present in the **category** field (see Table 5). Figure 7 shows the distribution of new pictograms according to their level of indexing and the category they belong to.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Samples** \\ \hline Social interaction & 56.90\% \\ Nutrition & 59.91\% \\ Care & 27.94\% \\ Leisure & 48.55\% \\ Emotions & 54.17\% \\ Health & 29.41\% \\ Names & 53.66\% \\ \hline \hline \end{tabular}
\end{table}
Table 16: Percentage of equal-category pictograms created together.  
Figure 7: Distribution of pictograms regarding their indexation and the category they belong to.  
Results clearly show that the nutrition category is the one that benefits the most from this indexing system, reaching up to four levels and with a broad representation of alternatives in each of them. Another relevant aspect is that the social interaction category tends to host all pictograms in an unindexed format, presumably for quick, straightforward access, in comparison with categories such as emotions, names, or health, with almost anecdotal representation of unindexed pictograms. In the case of names, they are created mostly in indexed form to be used as objects in the generated sentences (\"_Quiero estar con profe [PERSON]_\", I want to be with teacher [PERSON]).