esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Wear Resistance of Ti-6 Al-4V Alloy Composite Coating Prepared by Laser Alloying
## 3 Results and Discussion  
### Phase Composition and Microhardness Analysis of Alloying Coating  
The phase structure of the alloying coating was analyzed by D/Max-2500 X-ray diffractometer from Japan. Figure 3 shows the X-ray diffraction pattern of the alloying coating.  
It can be seen from Figure 3 that the coating contains Ti, SiC, TiN, TiB, Al\({}_{3}\)Ti, Ti\({}_{5}\)Si\({}_{3}\), and other phase structure. TiN, TiB, Ti\({}_{5}\)Si\({}_{3}\) have high hardness, strength, and wear resistance, which can improve the hardness and wear resistance of the coating. Under high temperature conditions, Al3 Ti is easily oxidized to form uniform and dense Al\({}_{2}\)O\({}_{3}\), which can improve the oxidation resistance of the titanium alloy surface layer. Hardness test is to characterize the force energy of wear resistance by friction with loading abrasive tools, so hardness can be used as the main parameter of wear resistance of a material. Figure 4 shows the microhardness distribution of the alloy coating. It shows that the microhardness of the alloyed coating is significantly higher than that of the titanium alloy. The average microhardness of the alloyed coating is 685 HV\({}_{0.5}\), and the microhardness of Ti-6 Al-4V alloy is 343 HV\({}_{0.5}\), which is about twice of that. Because the alloy coating contains high hardness Ti\({}_{5}\)Si\({}_{3}\) and other materials, the specific strength and specific stiffness of Ti Al compound are higher. The microhardness in the dilution zone is significantly lower than that in the alloying zone. Due to the dilution effect of the substrate on the coating, the microhardness of the coating near the substrate increases slightly. In the alloyed coating,  
Figure 2: Multi-functional friction and wear testing machine. (UMT-TL).  
the hardness far away from the surface is slightly higher, which is caused by the uneven distribution of hard particles in the coating.  
From the energy spectrum in Figure 5, it can be seen that the middle part of the coating mainly contains Ti, N, Si, and a small amount of B. Moreover, the X-ray diffraction pattern in Figure 3 shows that the coating mainly contains TiN and a small amount of TiB, Ti\({}_{5}\)Si\({}_{3}\). In the composite of Ni60A, B elements account for 3.0% to 4.5%, so Ni60A is almost completely decomposed under the action of higher laser energy [24; 25]. Although the chemical bond between Ti and N is strong, TiN is partially decomposed. Free Ti element and B, Si elements can be in situ synthesis of new phase TiB and Ti\({}_{5}\)Si\({}_{3}\). The new phase TiB, Ti\({}_{5}\)Si\({}_{3}\) and TiN are both hard particles, which can effectively increase the hardness of the coating.  
Figure 4: Microhardness distribution of alloying coating.  
Figure 3: X-ray diffraction pattern of alloying coating.
## 2 Classes of Secondary Metabolites
### Polyhydroxaysterol and Steroids
Zahramycin A **404** was characterized by the existence of oxirane ring at carbon-5 and carbon-6, while zahramycin B **405** possessed a keto-hydroxy sterol structure [117]. Figure 6: Sesquiiterpenes reported from _Saropython_ sp. Ten polyhydroxylated steroids were isolated from _Saroclipton_ sp., (23\(R\),24\(R\),17Z_)-11\(\alpha\)-acetoxy-16\(\beta\)-methoxy-23,24-dimethylcholest-17(20)-en-3\(\beta\),5\(\alpha\),6\(\beta\)-triol **406**, (24_R_)-gorgost-25-en-3\(\beta\),5\(\alpha\),6\(\beta\),11\(\alpha\)-tetraol **407** and 11\(\alpha\)-acetoxycholest-24-en-1\(\alpha\),3\(\beta\),5\(\alpha\),6\(\beta\)-tetraol **408**, (24_R_)-methylcholest-7-en-3\(\beta\),5\(\alpha\),6\(\beta\)-triol **409**, 11\(\alpha\)-acetoxy-cholest-24-en-3\(\beta\),5\(\alpha\),6\(\beta\)-triol **410**, (22\(E\),24S_)-11\(\alpha\)-acetoxy-ergost-22,25-dien-3\(\beta\),5\(\alpha\),6\(\beta\)-triol **411**, (24_S_)-11\(\alpha\)-acetoxy-ergost-3\(\beta\),5\(\alpha\),6\(\beta\)-triol **412**, (24_R_)-11\(\alpha\)-acetoxy-gorgost-3\(\beta\),5\(\alpha\),6\(\beta\)-triol **413**, (24_S_)-ergost-3\(\beta\),5\(\alpha\),6\(\beta\),11\(\alpha\)-tetraol **414**, and (24_S_)-23,24-dimethylcholest-22-en-3\(\beta\),5\(\alpha\),6\(\beta\),11\(\alpha\)-tetraol **415**. Their structural elucidation was based on spectral data, and it was found that all isolated compounds have a distinguishable 39,5\(\alpha\),6\(\beta\)-trihydroxy group; however, they differ in side chains and substitutions. These steroids could be alienated structurally into four categories including, cholesterol, ergosterol, gorgosterol and 23,24-dimethyl cholesterol. (23\(R\),24\(R\),17Z_)-11\(\alpha\)-acetoxy-16-methoxy-23,24-dimethylcholest-17(20)-en-3\(\beta\),5\(\alpha\),6\(\beta\)-triol **406** has a distinctive 17(20)-en-23,24-dimethyl side chain, while (24_R_)-gorgost-25-en-3\(\beta\),5\(\alpha\),6\(\beta\),11\(\alpha\)-tetraol **407** was a gorgosterol having a 25-ene side chain [118]. Ethanol-soluble fraction of the acetone extract of _S. trocheliorbourum_ yielded 9,11-secosteroid named, 25(26)-dehydroascomilasterol **416** and three polyhydroxylated steroids, 7\(\alpha\)- hydrocrassorosterol A **417**, 11\(\alpha\)-acetoxy-7\(\alpha\)-Hydrocrassorosterol A **418**, sarcomilasterol **419**, 3\(\beta\),6\(\alpha\),11-trihydroxy-9,11-seco-5\(\alpha\)-cholest-7-ene-9-one **420** and 3\(\beta\),6\(\alpha\),11-trihydroxy-24-methylene-9,11-seco-5\(\alpha\)-cholest-7-ene-9-one **421**. The 9,11-secostroids nucleus can be described as the chemotaxonomic indicators for genus _Saroclipton_[119]. Beside the abovementioned isoprenoids obtained from _S. glaucum_, 16-deacetylhalicrasterol B **422**, together with sarcoaldesterol B **396**, sarglaucsterol **423** were isolated too and their structures were elucidated by spectral data [70]. Furthermore, from _S. elrenbergi_ the isolation of two formerly isolated hyppurine **424** and **425**[120] alongside pregnenolone **426** were reported [121]. Figure 7 summarizes polyhydroxylated sterols isolated from _Sacropython sp_.
# Investigation of High-Q Lithium Niobate-Based Double Ring Resonator Used in RF Signal Modulation
## 3 Results and Discussion
### Double Ring Electro-Optic Modulator  
After the analysis of the waveguide loss, characteristic parameters of the coupling region and critical coupling conditions, the modulator with high electro-optic modulation efficiency can be designed according to the factors that affect the performance of the double ring resonator. To analyze differences between double ring resonator and single ring resonator, the parameters are determined as \(\rho=3\) dB/m, \(t_{2}=0.9\), \(t_{3}=0.995\), \(t_{1}=0.779\) and the resonator satisfies the critical coupling condition. The simulation result is shown in Figure 10. The single ring resonator has a Q-factor of \(6.2\times 10^{4}\), and \(23\%\) of its light is confined to resonating in the microring. The double ring resonator has a Q-factor of \(6.5\times 10^{5}\), FWHM = 2.4 pm (300 MHz), FSR = 35 GHz and almost all of the light is confined to resonating in the inner ring. Figure 11 shows the relationship between coupling coefficient and coupling gap in the coupling region between straight-ring waveguide and ring-ring waveguide. The coefficients of the coupling region are determined: \(t_{1}=0.779\), \(t_{2}=0.9\) and \(t_{3}=0.995\), the corresponding gap parameters are \(Gap_{1}=565\) nm, \(Gap_{2}=520\) nm and \(Gap_{3}=1205\) nm.  
With 35 GHz RF signal applied to the electrodes, the electric field generated between the electrodes will change the refractive index of LN, which will shift the transmission spectrum of the microring. This eventually results in output light intensity change. The  
Figure 11: (**a**) The coupling coefficient of the coupling region of the straight-ring waveguide at different coupling gap and (**b**) the coupling coefficient of the coupling region of the ring–ring waveguide at different coupling gap.  
Figure 10: Transmission characteristic curves of the single ring (green line) and the double ring (blue line) resonators at \(\rho=3\)dB/m, \(t_{2}=0.9\), \(t_{3}=0.995\), \(t_{1}=0.779\).  
applied RF signal and the effective refractive index \(n_{eff}\) of the waveguide can be represented by Equation (12):  
\[n^{\prime}_{eff}=n_{eff}-\frac{1}{2d}n^{3}_{eff}\gamma_{33}\Gamma\overline{V} \tag{12}\]  
\[\overline{V}=\frac{2}{T}V\int_{0}^{\frac{T}{2}}\sin\biggl{(}\frac{2\pi}{T}t \biggr{)}dt=\frac{2V}{\pi} \tag{13}\]  
where \(d\) is the electrode spacing, \(\Gamma\) is an electric-optical overlap factor; its value is 0.7, and \(\overline{V}\) is the average voltage applied on the electrode as shown in Equation (13), where \(V\) is the peak voltage of the input RF signal.  
Figure 12a shows the relationship between the transmission characteristic curves of the double ring resonator and the applied voltage \(V\). Figure 12b shows the drift of resonant peak under different applied voltages, and the electro-optic tunability of the double ring resonator is 6 pm/V by linear fitting.
## 2 Shaft and Skip Construction  
The research was conducted in one of the shafts of a coal mine in the Upper Silesian Coal Basin. A simplified mine shaft plan and side views are shown in Figure 1. This is a typical two-compartment arrangement that is characteristic of shafts whose main function is to transport excavated material.  
Figure 1: Shaft plan (**a**) and side (**b**) views.  
The structure of the shaft steelwork was bisymmetric. The guides were made of C200 channels connected by a butt weld into a box section. The length of a single guide was 9 m. The buttons were made of C180 channels and, like the guides, were welded into a hollow profile. The vertical spacing of the buttons was 4.5 m, so the guides were connected to three buttons. A similar steelwork structure is employed in many shafts in Upper Silesia. The connections between the guides and buttons were made as bolted joints, the construction details of which could vary [7; 25]. The connections between the buttons and the guides in the analyzed shaft are shown in Figure 2. They were made of L150 \(\times\) 90 \(\times\) 10 angles welded to the buttons and 20 mm thick plates welded to the guides. The whole was connected with four M24 bolts. The conditions for maintaining the steelwork required reviewing the connections and eliminating any gaps. Tests conducted on these connections [7] showed that, despite the corrosion of the guides and buttons, they retained rotational stiffness and should be classified as semi-rigid rather than pinned connections.  
There were two pairs of skips moving in the shaft, with a capacity of 20,000 kg each. These were typical skips manufactured in Poland during a certain period. The main elements of the skip were the top and bottom transoms, between which was a bucket made of sheet steel. The skip structure was stiffened laterally with beams made of channel sections. The three elements were connected by a bridle that was also made of steel channels [33]. The bottom of the bucket was equipped with a loading lip that allowed its emptying. According to the technical documentation, the mass of the conveyance, including the excavated material, was 34,716 kg, and the height from the top to the bottom transom was 14.55 m. The top transom was constructed from a 10 mm thick steel plate. The stiffener beams were made in C180 channels, while the bottom transom was made from C300 channels and 10 mm plates. The bucket brace was made from C200 channel sections. The skip conveyance was made from 10 mm thick plates. Connections between the elements of the skip structure were made as welded. A structural diagram of the skip is shown in Figure 3. The drawing also includes non-structural elements, such as a canopy or cable fasteners. These elements do not affect stiffness.  
Figure 2: Guide for button connections. Drawing (**a**) and photo (**b**) of the construction.
# Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture
## 2 Materials and Methods
### Description of the Base Station Coordinator Node
#### 2.2.4 Embedded Microcontroller SAM3X8E  
The embedded microcontroller SAM3X8E is integrated into the Arduino DUE platform so that connectivity to use the I2C and SPI (Serial Peripheral Interface) communication resources is provided for the connection of the RTC and microSD modules, in addition to having several UART ports for serial communication, as in the case of the connectivity of the XBee 868 LP device. The tasks performed by the microcontroller in the coordinator node are:  
* Selecting the RICS node that has to take an image using live, timed or on-demand mode. This selection is done by the configuration of the XBee module of the local coordinator node with the adequate address of the remote XBee module.
* Sending the adequate set of AT instructions to the A20/A6C module to get a fully readable image format without need of data edition. With this feature, an improvement in the data transmission speed is achieved.
* Checking the integrity of the received image and, in case of errors in the image data, launching a new capture to obtain an image without data losses.
* Storing the image in a database, formatted with timestamp data to get a set of temporal readable images. In this way, the web control and monitoring system can make requests for any image in the database. The database is stored in a first-level SRAM (Static RAM) memory for quick access and in removable media storage (microSD).
* Setting the image capture to the time interval mode, configurable by the control node, to get the information required for irrigation, fertilization or harvesting decisions.
* Processing and transmission of the image received by HTML-POST on the remote server that links to the web application that contains the SCADA system [45].  
The selection of the RICS node by the local coordinator node is done by modifying a record in its XBee hardware module. The development of the image transmission system makes use of the AT communication mode of the XBee hardware instead of the API mode. The purpose is to increase the transmission speed and to simplify the transmission process of the image. In this way, in the reception of the data stream, the complete image is obtained with an adequate header and end-of-file format that would allow reading it without the need of editing in hexadecimal format to restore its correct format (as usually happens in these transmission systems). The final purpose is to directly transmit the image point-to-point between the coordinator and remote nodes, without the need of splitting the  
Figure 8: Steps of the protocol for image request and transmission.  
images into small packages. In addition, through the appropriate order, the microcontroller can also transfer on demand the image received via USB to a PC, in case the coordinator system is connected to it. In case the API mode is used, the images should be split into blocks of 255 bytes; an image between 35-50 Kbytes would be split into 140-200 packages, thus requiring 120-150 s.
## 2 Path Planning
### Pathfinding Using Grids
#### 2.1.1 Regular Grids  
Regular grids are most extensively used in the field of pathfinding by developers since they provide uniformity to the environment. These are usually represented in 2-dimenional or 3-dimensional Euclidean space in the form of a tessellation. Every smallest unit of these grids have a definite uniform shape of equal sides such as a triangle, square, hexagon, or a cube. On the basis of these shapes, regular grids are further classified into triangular, octile, hexagonal, and cubic as shown in Figure 3.  
Figure 2: Hierarchical classification for pathfinding using grids.  
1. 2D triangular grid: These grids are the least popular when it comes to pathfinding in games. As compared to hexagonal and octile grids, their usage is less, but these grids have some desirable properties. Here, the smallest unit is an equilateral triangle connected using one side and vertices. Figure 3a shows a 2D triangular grid with three obstacles. The gray triangles represent the border area of the grid whereas the blue triangles are the ones with no obstacles present. The area covered with black triangles shows the region covered by the yellow obstacles.
2. 2D square (octile) grid: These are the most widely used grids to represent graphs in the gaming industry particularly for pathfinding as well as in the field of robotics. This grid system is the most researched system for pathfinding as several pathfinding Algorithms have been proposed and tested using this grid system. Figure 3b shows a 2D octile grid with three obstacles. The gray squares represent the border area of the grid whereas the blue squares are the ones with no obstacles present. The area covered with black squares shows the region covered by the red obstacles.
3. 2D hexagonal grid: Some of the properties of these grids are similar to the 2D square grids. The graphs formed using this grid system requires less search time and memory complexities as compared to those formed from the octile system. Figure 3c shows a 2D hexagonal grid with three obstacles. The gray hexagons represent the border area of the grid whereas the blue hexagons are the ones with no obstacles present. The area covered with black hexagons shows the region covered by the green obstacles [17].
4. 3D cubic grid: Apart from the other grid systems which used the 2D environment, the 3D Cubic grid shown in Figure 3d is based on a continuous 3-dimensional environ  
Figure 3: (**a**) Top left: 2D triangular grid with obstacles, (**b**) top right: 2D square (octile) grid with obstacles, (**c**) bottom left: 2D hexagonal grid with obstacles, (**d**) bottom right: 3D cubic grid without obstacles.  
ment. Not much study has been done on this grid system for pathfinding, but it can be applied in other fields such as image processing and computer graphics [18].
## 3 Performance of IWP with Steel Anchor Box
### Parametric Study
#### 3.2.2 Effect of End Plate Thickness  
Figure 26 shows the influence of end plate thickness on the shear force of the connectors and the utilization rate of the steel anchor box. When the thickness of the end plate increases from 15 mm to 30 mm, the shear force of the connectors changes slightly: the average shear force on the connectors of the end plate increases by 5.4 kN, while the average shear force on the connectors of the perforated side plates decreases by 1.7 kN. The utilization rate of the steel anchor box decreases by 0.7%. This indicates that the thickening of the end plate increases the local stiffness of the pylon wall, resulting in a slight reduction in the utilization rate of the anchor box. On the other hand, the thickening of the end plate enhances its ability to bear vertical forces, leading to a slight increase in the shear force of its connectors. In addition, the principal tensile stress of the concrete on the outer side of the pylon wall remains at 5.55 MPa, and the vertical force borne by the end plate increases by approximately 1.6%.
## 2 Methodology
### Feature Extraction  
After image segmentation, we reconstructed the binary image into a grayscale image using morphological reconstruction, and then we differentiated the retinal health and DR images based on the pixel density in binary image (see in Figure 4e) and the information of texture, color, and intensity of the detected regions (see in Figure 4f). As illustrated in Table 1, 208 features from eight feature extractors namely morphological features, intensity features, color features, first order statistical features, Gray Level Co-occurrence Matrix (GLCM) features, Gray Level Run Length Matrix (GLRLM) features, local binary pattern features and [PERSON]'s texture features were extracted.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & & **Description** \\ \hline \multirow{2}{*}{Morphological features} & F1 & Total area of detected regions \\ \cline{2-3}  & F2 & Total length of boundary of detected regions \\ \hline \multirow{2}{*}{Intensity features} & F3–F5 & The mean values of maximum intensity, mean intensity and minimum intensity of the segmented image. \\ \hline \multirow{2}{*}{Color features} & F6–F8 & The mean intensity in hue, saturation and value in HSV color space of the segmented image. \\ \hline \multirow{2}{*}{First order statistical features} & F9–F15 & The mean, standard deviation, smoothness, variance, skewness, kurtosis and energy of intensity of the segmented image. \\ \hline Gray Level Co-occurrence Matrix & F16–F103 & Four orientations of 22 features in GLCM matrix of the segmented image [19] \\ \hline Gray Level Run Length Matrix (GLRLM) features & F104–F147 & Four orientations of 11 features in GLRLM matrix of the segmented image [20] \\ \hline Local Binary Pattern Features & F148–F206 & 59 features from local binary pattern features [21] \\ \hline Tamura’s Texture Features & F207–F208 & Coarseness value and Contrast value from Tamura’s texture features of the segmented image [22] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Features extraction and its description.  
Figure 5: The processing steps of image segmentation: (**a**) Retinal image; (**b**) DR lesions detection; (**c**) blood vessels elimination; (**d**) rectangular mask (**e**) resulted image after post processing step and (**f**) segmented result in grayscale image.
## 3 Transfer Learning Applied to Fault Diagnosis Methods
### The Foundational Theory
#### 3.1.2 Domain Adaptation  
Due to the significant differences in data feature distributions between the source and target domains, the fault diagnosis knowledge acquired from the source domain cannot accurately classify the unlabeled samples in the target domain. As a result, directly applying the source domain features to diagnose target domain data may lead to inaccurate diagnostic results. To address this, a domain adaptation module is incorporated during model development to align the data distributions between the source and target domains, ensuring that the diagnostic knowledge from the source domain can accurately identify target domain samples [17]. The domain adaptation diagram is illustrated in Figure 9.  
Figure 8: The fundamental framework of the RECJ model.  
Figure 9: Domain adaptation graph.  
Domain adaptation is a subfield of transfer learning focused on addressing the issue of feature migration when the source and target domains share the same feature and category spaces but differ in feature distribution. The core concept of domain adaptation involves adding extra loss terms to the model's loss function, enabling the feature extractor to focus on the distributional differences between the source and target domains. This approach helps identify features that are applicable to both domains, thereby enhancing the model's generalization ability [18].  
In domain adaptation, the source domain data is denoted by \(D_{s}\), and the target domain is denoted by \(D_{t}\).  
\[D_{s}=\{x_{i}^{s}\},i=1,2,\ldots\ldots,n_{s},x_{i}^{s}\in R^{d} \tag{3}\]  
\[D_{t}=\left\{x_{j}^{t}\right\},j=1,2,\ldots\ldots,n_{t},x_{j}^{t}\in R^{d} \tag{4}\]  
where \(n_{s}\) represents the number of samples in the source domain, \(x_{i}^{s}\) denotes the i-th sample in the source domain, \(R^{d}\) refers to the real domain, and \(d\) is the feature dimension. Additionally, \(n_{t}\) is the number of samples in the target domain, and \(x_{j}^{t}\) represents the j-th sample in the target domain.  
The label of the source domain is \(y^{s}\), the label of the target domain is \(y^{t}\), and the label space is \(Y=\{1,2,\ldots\ldots,m\}\), m health states. The condition in this paper is \(y^{t}\in y^{s}\in Y\), meaning that the label of the target domain matches the label of the source domain and is included in the source domain's label space.  
Domain adaptation reduces the distributional differences between the source and target domain data and mitigates the sample distribution mismatch in the feature space. This allows for better extraction of transferable features from both domains, enabling the trained model to achieve good classification performance in the label-free target domain [6; 19].