esa-sceva/satcom-chunk-collection huggingface dataset:
# Star Identification Algorithm Based on Dynamic Distance Ratio Matching
## 2 Star Pattern and Catalog
### Theory  
The imaging principle of a star tracker can be approximated as a projection transformation [16]. As shown in Figure 1, star 1 and star 2 are imaged onto the image plane through the lens \(O\), with points \(F\) and \(F^{\prime}\) representing the center points of the plane. The lengths \(OF\) and \(OF^{\prime}\) correspond to the focal lengths. Due to the variation in focal length, the star points are imaged from \(S_{1}\), \(S_{2}\) to \(S^{\prime}_{1}\), \(S^{\prime}_{2}\) on the image plane.  
According to the principle of similar triangles, \(\triangle OFS_{1}\) and \(\triangle OF^{\prime}S^{\prime}_{1}\), \(\triangle OFS_{2}\) and \(\triangle OF^{\prime}S^{\prime}_{2}\) are corresponding similar triangles. Therefore, we can derive the following Equation (1):\[\left\{\begin{array}{cc}\frac{FS_{1}}{FS_{1}}&=\frac{FS_{2}}{FS_{2}^{\prime}}\\ \angle S_{1}FS_{2}&=\angle S_{1}^{\prime}F^{\prime}S_{2}^{\prime}\end{array} \right.\Rightarrow\left\{\begin{array}{cc}d_{r}&=\frac{FS_{1}}{FS_{1}^{ \prime}}&=\frac{FS_{2}}{FS_{2}^{\prime}}\\ \theta&=\angle S_{1}FS_{2}&=\angle S_{1}^{\prime}F^{\prime}S_{2}^{\prime}\end{array}\right. \tag{1}\]  
In this context, \(d_{r}\) represents the ratio of the distances between the star points on the image plane, and \(\theta\) is the angle between the two lines formed by these distances. Both \(d_{r}\) and \(\theta\) remain invariant with changes in focal length. Therefore, using \(\{d_{r},\theta\}\) as features in the star identification algorithm can effectively enhance the algorithm's robustness to variations in focal length and image distortion [17].
# Prediction of Intraday Electricity Supply Curves
## 3 Methodology
### Preprocessing
#### 3.2.4 Approximation Procedures for Curves  
Since our goal was to predict a curve, we needed a method to synthesize its information and work with this synthesis as input and output for our models. Two straightforward approaches are to approximate it with curves from a parametric family (and work with these parameters) or to approximate it using a grid of points (and work with them), interpolating the intermediate values. We chose the second option since the use of a parametric family usually requires assumptions about the smoothness of the functions, and we know that the supply functions are step functions.  
There are three issues to consider:  
* Interpolation technique. Since we were working with nondecreasing step functions, it was reasonable to interpolate consistently with this property, using right-side constant interpolation (i.e., the interpolated values are equal to the largest grid point value smaller than them).
* Uniformity of the grid. Despite the broad price ranges presented by the markets, in reality, most offers occur within a more limited range. Given our interest in accurately representing this subinterval, it made more sense to use a non-uniform grid, allocating more density to these empirically observed regions. In Figures 2 and 3, we observe that, although both interpolations generally fit the original curve well, the non-uniform interpolation captures the section of the curve with a higher density of transactions much better.
* Grid size. There is a trade-off between precise representation and the informational weight of the grid (which we wanted to reduce compared to the raw data). We opted for a grid of 150 points.  
Figure 2: Comparison between the uniform (**left**) and non-uniform (**right**) grids.
# Improving Graphite Ore Grade Identification with a Novel FRCNN-PGR Method Based on Deep Learning
## 3 Proposed Novel FRCNN-PGR Model
### Models That Fuse Multi-Scale Features  
In this paper, the graphite ore images are cut multiple times, and there are large differences in the size of the positive samples in the images. Some ore images have a large quantity (Area), while others have a very small proportion of ore. The FR-CNN algorithm model continuously extracts image features under the deep convolutional neural network. After layering by layer convolution, the image will gradually lose some detailed information, which will cause the model to predict small objects and details inaccurately. Single feature extraction is shown in Figure 8.  
Figure 8 shows that the size of the feature map decreases as the network depth increases, and only the top-level features are used for prediction. Therefore, this paper adds Feature Pyramid Network (FPN) [27] to the backbone feature extraction network. The structure of the combination of feature maps at different levels is shown in Figure 9.  
Figure 9 shows the performance of a \(1\times 1\) convolution operation on the four feature extraction convolution blocks of the ResNet50 network. Then, in this structure, the number of channels in each convolution layer can be the same to facilitate subsequent operations, upsample the high-level feature map after changing the channel and adding a shallow layer feature, and then go through a \(3\times 3\) convolution to complete the entire FPN process. The final feature map fuses high-level and low-level features, bringing ideal results to graphite ore images' detailed texture and small target recognition.
# An Investigation on Base Metal Block Shear Strength of Ferritic Stainless Steel Welded Connection
## 3 Finite Element Analysis and Parametric Study
### Analysis Model
#### 3.1.1 Welded Connection Modeling  
The finite element analysis model for the ferritic stainless steel (STS430) fillet-welded connection is displayed in Figure 3. Four parts (test part, coupling part, rigid part, and weld metal) of welded connections were modeled with continuum solid element type (C3D8R). However, base metal and heat affected zone (HAZ) in the test part were not divided into different regions and were assumed to have same material properties [12; 13]. Coupling part (6.0 mm thick plate) in the center and weld metal were modeled to be deformed. In the fixed parts (rigid part and clamping part) on the right side of Figure 3, the weld length of both sides in the loading direction was set to 80 mm to induce a block shear fracture of the base metal in the test part. The mesh area of the plate was divided into two different parts based on the line of 30 mm away from the toe of the weld metal in the direction perpendicular to applied force in the test part, and the mesh size around the weld metal where the fracture was predicted was divided into by 0.1 mm for the surface and three equal parts for the thickness direction referring to previous studies for weld connection conducted by [PERSON] et al. [12; 13]. Another part far from the fillet-welded connection was less refined and the maximum element aspect ratio for test part and rigid part was limited to 5:1 in order to minimize analysis time.
## 4 Simulation Experiment and Results
### Definition of Simulation Cases  
In this paper, the A* algorithm, RRT* algorithm, and PRM algorithm, which are commonly used in path planning problems, are selected and compared with the PH-RPM algorithm proposed in this paper. Among them, the A* algorithm has a more direct path search, while the RRT* and PRM algorithms are efficient and fast, and all of them have good path planning performance. In addition, the traditional RPM algorithm is also applied in the examples, and the results of the algorithm are compared with those of other algorithms. Some of the intelligent optimization algorithms, such as the genetic algorithm (GA) popular in path planning, are not selected as comparison algorithms because the parameter configurations of such algorithms have some uncertainty. Generally, such methods require optimization of parameter configurations and algorithmic strategies according to the problem environment to achieve the application and performance improvement of the algorithms; directly adopting the parameter configurations and algorithmic strategies from other studies in this paper's problem does not guarantee that a reliable solution can be obtained.  
The experiments were conducted in the construction environment of the first to third main vertical zone (MVZ) on deck 8 of a large cruise ship. This environment is mainly occupied by passenger cabins, and most of the cabin types are prefabricated cabins that need to be pushed and transferred from the reversed side opening to the target positions for installation. The experiments are designed with two scenarios. Case 1 is a simple scenario that corresponds to the cabin-pushing process of the first predioricated cabin in the region. The path planning problem in this scenario mainly needs to consider the fixed structural obstacles on the deck environment and part of the reserved installation space. Case 2 is a complex scenario; in addition to the obstacles in Case 1, we also need to pay attention to the impact of the prefabricated cabin that has been installed on the path. Both cases need to ensure that the speed of the prefabricated cabin is 0 m/s when it reaches the target position and the angle meets the process requirements for subsequent connection and installation, which means that the prefabricated cabin needs to stay at the target position according to the specified position. In both cases, the length and width of the PMCU are 6.2 m and 2.8 m.  
Figure 6: The overall architecture of the PH-RPM algorithm.  
All experiments were run on a computer with an AMD Ryzen5 4600H 3.00 GHz processor and 16 GB RAM and were solved using the well-established solver SNOPT. The relevant parameters used are shown in Table 2.
## 2 Materials and Methods  
### Security Requirements  
The integration of WSN in low power agriculture for the internet and society 5.0 requires adequate security mechanisms, which can offer essential safety safeguards for WSN applications, equipment, and communications in agriculture. The conventional Internet connections require sufficient security to use end-to-end communications between low-power farm WSN sensing devices and other external or internet companies. According to recent studies in [2; 3; 5; 16; 21; 40; 47; 48] about the security of smart agriculture, the agriculture WSN authentication scheme must satisfy the security and functionality of agriculture WSN in Society 5.0. These security and functionality requirements are as following:  
* Mutual authentication: the agriculture professional Ui and the sensor node Sn should authenticate each other with the help of the gateway node (GWN).
* Anonymity: an adversary should not get the real identity of the agriculture professional \(U_{i}\).
* Multi-gateway: agriculture WSN has many sensor nodes and IoT devices that are distributed over large agriculture fields. Hence, a single-gateway node can hardly manage this number of nodes, causing a single point of failure. Therefore, the agriculture environment should support multi-gateway communication.
* Physical Attack: The attacker disturbs the protocol by causing a collision packet, inserting and interrogating packets to obtain information about the communication template, or delaying communication. Thus, the WSN should withstand physical attacks, such as sensor capture attacks and gateway attacks.
* Network Attacks: The WSN authentication scheme for agriculture must resist several attacks, such as an offline password guess attack, the user impersonation attack, the node impersonation attack, the modification attack, the man-in-the-middle attack, and the replay attack.
## 2 Materials and Methods
### Characterization of MIL-88A Carriers  
#### 2.2.1 Powder X-ray Diffraction (XRD)  
XRD analysis was carried out using a D8 Advance diffractometer (Bruker AXS, Karlsruhe, Germany) with Johansson monochromator (copper radiation K\(\alpha\)1 = 1.5406 A) and silicon strip detector LynxEye. The measurements were performed in the 2\(\theta\) range of 6-40\({}^{\circ}\) at room temperature with a step size of 0.01\({}^{\circ}\) and a scan rate of 0.05\({}^{\circ}\)/s in continuous mode.
# Study of a Small Robot for Mine Hole Detection
## 2 Functional Analysis and Mechanical Modeling  
The design of mine hole-inspection small robots faces two challenges to ensure the robots work properly in coal mine holes. The first challenge is designing the robot to navigate swiftly and smoothly within a mine hole; the second challenge is ensuring that a robot can maintain its mobility even when encountering steep slopes or variations in hole diameter.  
The functional analysis of a robot is shown in Figure 1. For the first problem, the crawler wheel is used as the walking mechanism of the small robot during mine hole inspection. The motor's rotation is conveyed to the crawler wheel via the drive arm, providing a stable driving force for the robot. For the second problem, a new type of adjustment mechanism is designed, in which the drive arm of the robot can be extended and retracted with the cooperation of the stepper motor, the screw nut, and other components. This design enables the robot to adjust the positive pressure, stabilize its body, and navigate through mine holes with significant inclines.
# Slug Regime Transitions in a Two-Phase Flow in Horizontal Round Pipe. CFD Simulations
## 4 Conclusions  
The main objective of the study was to reproduce the slug flow in a circular pipeline using numerical simulation in the ANSYS software package. The process of initiation of the slug flow regime was simulated.  
The slug begins to nucleate at a distance of 25% of the pipeline length from the inlet section. At this distance, the slug has poor stability, since its volume is small. In moving downstream, the slug draws in an ever-larger volume of the heavy phase, which makes the regime stable. At a distance of 45-50% from the inlet section, the slug acquires its maximum volume. However, it does not have time to influence the upstream flow characteristics, so there is no significant pressure increase behind the slug. Then, the slug moves downstream to the end of the computational domain. This movement is accompanied by an increase in pressure in the pipeline area before the slug.  
The technical solution was proposed to change the flow regime. The solution was the mesh partition that facilitates a change in the flow regime from slug to layered. The location of such promoter was chosen based on the results of calculations performed for the pipeline without it. The mesh partition was installed in the middle of the pipeline. Its resistance was chosen in [39] that pressure drops on such promoter did not exceed 10% of the pressure drop along the length of the pipeline. The proposed technical solution provides a change in the flow regime from slug to layered, which will help to increase the service life and operational safety of real pipelines, at insignificant energy costs, to overcome the additional resistance integrated into the pipeline.  
Therefore, further research is needed to address the following challenges:  
* Determine the minimum resistance and the shape of the promoter, providing a change in the flow regime.
* Create a method for determining the location of the promoter in the streams.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Volume Fraction of the Liquid Phase** \\
**A Moment** & **Phase 1.Volume Fraction** \\
**in Time, s** & \\ \hline
0.2 s & \\
0.4 s & \\
0.6 s & \\
0.8 s & \\
1.0 s & \\
1.2 s & \\ \hline \hline \end{tabular}
\end{table}
Table 8: The volume fraction of the liquid phase in consecutive moments of time in a system with mesh partitions.  
**Author Contributions:** Conceptualization: VS., N.V., E.K., D.N., and S.K.; methodology: E.K. and S.K.; software: E.K.; validation: E.K. and D.N.; formal analysis: E.K.; investigation: E.K. and D.N.; resources: E.K.; data curation: S.K.; writing--original draft preparation: N.V.; writing--review and editing: N.V. and V.S.; visualization: E.K.; supervision: V.S.; project administration: D.N.; funding acquisition: V.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research work and APC was funded by the Academic Excellence Project 5-100 proposed by Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Object Detection with Low Capacity GPU Systems Using Improved Faster R-CNN
## 3 Proposed Improved Faster R-CNN Method for Remote Sensing Object Detection  
The model we recommend is based on the latest faster R-CNN, a state-of-the-art object detection system. We attempted to solve the weakness of the regular convolution structure used in the faster R-CNN model for detecting small and mixed objects in remote sensing using the deformable convolution technique. With the FPN technique, the high-resolution features in the shallow layers of the remote sensing images are transferred to the network. WS technique, which reduces batch size in order to provide deep learning training without performance problems in low power/single GPU systems such as single GPU, was added to our model. In our opinion, this is the first study in which these techniques are used in conjunction with the faster R-CNN algorithm and provides an effective remote sensing object detection model for a system with low/single GPU power.  
In the proposed method, ResNet50 [14] with deformable convolution is used to extract high-resolution features. The object is detected using the multi-scale features via the FPN module. The output_stride, which is the ratio of the input resolution to the output resolution, is set to 32 to produce a more intense attribute map. The network structure of our proposed method is shown in Figure 6.  
In the proposed model, features are extracted by the ResNet50 backbone, which is a deformable convolution network. While the standard ResNet50 backbone consists of a convolution neural network, the proposed model uses a deformable convolution network. This allows the backbone to extract features from more receptive areas.  
In the model, using FPN structure, features obtained from P2, P3, P4, P5 layers are given to the faster R-CNN model and object detection is performed. In order to provide more effective training on low GPU systems, the WS structure completes the training with one image per GPU. When this value is considered 32 BN, sometimes 64 images/GPU, the effectiveness of WS appears.
## 3 The Proposed Model
### Image Pre-Processing  
At the initial stage, the XAITO-DRGC model applies MF and contrast enhancement as a pre-processing step. The MF technique adopts a non-linear approach for noise exclusion from scaled input images [17]. It functions by sliding pixel by pixel, replacing every pixel value with a median value of adjacent pixels. The window pattern having a size of \(3\times 3\) can be utilized to slide pixel by pixel over the neighbors in a scaled input image. The calculation of the median is conducted by sorting every pixel value primarily in arithmetical order in the window paradigm and interchanging the pixel values with the central pixel value. The histogram equalization technique can be implied for enhancing the contrast of the scaled input image by making use of its histogram. The procedure is done through the distribution of pixel intensity values that were appearing frequently and thus, low contrast regions of an image obtain high contrast.
## 3 Results
### Effect of satP Deletion on Drug Resistance Formation  
To verify the effect of the _satP_ gene on ENR resistance of \(Pm\), an in vitro method of inducing drug resistance was used to detect the development time of drug resistance of the gene deletion strain, wild-type strain and replenishment strain. The results showed that in generation 1-5, the drug resistance of \(PmS\) was the same as that of the \(\Delta PmS\) and the \(CPmS\), with an MIC of 0.5 \(\upmu\)g/mL. The drug resistance formation time of sensitive strains and gene deletion strains was different in generation 5-10. The resistance of \(PmS\) and the \(CPmS\) developed rapidly, with an MIC of 64 \(\upmu\)g/mL in the 10 th generation. While the drug resistance of \(\Delta PmS\) developed slowly, and the MIC was 16 \(\upmu\)g/mL in the 10 th generation (Figure 3A).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Strains**} & \multicolumn{6}{c}{**MIC Range (\(\upmu\)g/mL)**} & \multicolumn{6}{c}{**Substitution in**} \\ \cline{2-10}  & **CIP** & **ENR** & **ENR + CCCP** & **ENR + Verapamil** & **GyrA** & **GyrB** & **ParC** & **ParE** \\ \hline \(\Delta PmS\) & 0.25 & 0.25 & 0.125 & 0.125 & - & - & - & - \\ \(\Delta PmR\) & 2 & 2 & 2 & 2 & Arg88 lle & Ser467 Phe & Glu84 Lys & - \\ \(\Delta PmHR\) & 64 & 64 & 64 & 64 & Arg88 lle & Ser467 Phe & Glu84 Lys & - \\ \hline \end{tabular}
\end{table}
Table 2: QDRR mutation genotypes and fluoroquinoline MICs for \(\Delta PmS\), \(\Delta PmR\) and \(\Delta PmHR\).  
Figure 2: (**A**) Confirmation that the deletion of the _satP_ strains were genetically stable beyond 40 generations by PCR. M: DL 2000 Marker; 1–4, deletions strain _satP_; 5, wild-type strain. (**B**) The genetic stability of _satP_-deficient strains over 40 generations was verified by pRE112 primers. M: 2000 plus DNA Marker; 1–4, deletions strain _satP_; 5, wild-type strain. (**C**) Confirmation that the \(CPmS\) was genetically stable beyond 40 generations by PCR. M, 2K plus DNA Marker; 1, wild-type strain; 2, \(CPm\). (All data are expressed as the mean \(\pm\) SD. \(n=3\)). (**D**) The expression level of the _satP_ gene in the wild-type strain, deletion strains and \(CPmS\).
## 3 Results and Discussion
### Feature Extraction and Selection
#### 3.2.6 Variance Value  
Variance value (VARV): Variance reflected the degree of data dispersion, and the response signal variance was used to represent the characteristic value of its signal strength.  
\[VARV=\frac{\sum_{i=1}^{N}{(x_{i}-\overline{x})^{2}}}{n} \tag{8}\]  
where \(\overline{x}\) is the mean value of the sensor response signal to a sample, \(x_{i}\) is the response value of the sensor to the ith second of a sample, and \(n\) is the time points of the sensor
## 3 Results and Discussion
### Relationships between Radar Signals and Vegetation Parameters
Overall, the use of X-band radar signal with medium incidence angle (\(\sim\)30\({}^{\circ}\)) for the retrieval of LAI, HVE, BIO, and VWC of our grassland is very limited. Only the canopy height could be retrieved for heights lower than 50 cm and in using HV polarization. Results show that the opportunity to estimate the soil moisture even with dense vegetation covers (vegetation height up to 1 m). Indeed, the X-band radar signal penetrates vegetation cover and always follows the evolution of soil moisture. In the future, the opportunity to estimate soil water content using semi-empirical backscattering models (such as water cloud model) will be investigated. Figure 9: Sensitivity of X-band ((**left panel**) \(\rm HH\), and (**right panel**) \(\rm HV\)) to LAI in three soil moisture (Mv) classes. Figure 10: Sensitivity of X-band ((**left panel**) HH, and (**right panel**) HV) to vegetation height in three soil moisture (Mv) classes. Figure 11: Sensitivity of X-band ((**left panel**) \(\rm HH\), and (**right panel**) HV) to BIO in three soil moisture (Mv) classes.
## 3 Hazard Analysis Foundations and Key Terms of STPA and ISO26262
### Key Terms of ISO26262 Concept Phase and STPA
Therefore, if STPA is to be used for hazard analysis in ISO26262, it is necessary for STPA to adapt to the current requirements of the concept phase of ISO26262. In this paper, certain key terms in STPA will be limited to the scope of ISO26262 requirements, such as \"loss\" in STPA and \"harm\" in ISO26262, and some could be equivalent in this paper, such as \"accident\" in STPA and \"consequence of hazardous event\" in ISO26262. So that the work products required by the concept phase of ISO26262 can be obtained through the analysis of STPA. Moreover, we will explain how to map corresponding key terms of STPA and ISO2626 in Section 5. In a word, all the distinctions are understandable, since STPA is a general safety analysis method, and ISO26262 only serves road vehicles. So, if we make some appropriate adjustments to STPA, it can fully meet the requirements of ISO26262 for hazard analysis.