esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials Used  
Topographical map of the study area (sheet no. 78B/5) in 1: 50,000 scale was collected from the Survey of India (SOI), Kolkata which was visually compiled with Google Earth (GE) images from 2017 to 2019 for updating the river map, settlement, and road map. Land use/land covers categories were classified using Sentinel-2 satellite imagery of 10 x 10 m resolution downloaded from the US Geological Survey Earthexplorer [34] which was crosschecked using Cohen's Kappa coefficient through field truth, GE imagery. The Geological Survey of India provided a detailed lithological map in 1:250,000 scale, which was resampled to match to scale with other thematic layers. To map the seismically disturbed zones, an earthquake zoning map was downloaded and extracted from National Center for Seismology, under the Ministry of Earth Sciences, India in 30 m x 30 m resolution. The lineament was extracted in 1:50,000 scale from the web platform of ISRO (Indian Space Research Organization) that is Bhuvan [35]. Slope, aspect, topographical wetness index (TWI), and stream power index (SPI), were extracted from advanced land observation satellite (ALOS) phased array type L-band synthetic aperture radar (PALSAR) derived digital elevation model (DEM) of 12.5 x 12.5 m resolution downloaded from the Alaska Satellite Facility. Spatial variation of soil depth and texture at 1:50,000 scale was gathered from NBSSLUP Regional Centre, Kolkata and classified based on the texture classification method of U.S. Department of Agriculture (USDA). The rainfall data was collected from the Indian Meteorological Department (Table 1). However, the spatial resolution of these factors are not same. For producing the landslide susceptibility map, the resolution of the ALOS PALSAR DEM (12.5 m x 12.5m) was considered as the base resolution and all the causative factors were resembled into \(12.5\times 12.5\) m resolution.
# Research on a Noise Reduction Method Based on Multi-Resolution Singular Value Decomposition
## 2 Method Overview
### Mrsvd
#### 2.1.2 Multi-Division Structure MRSVD  
MRSVD is constructed by constructing a Hankel matrix with a row number of 2 (described later as the two-division method). Drawing on the idea of it, this paper studies the method of constructing singular value decomposition layer by layer by constructing a Hankel matrix with different structures. The singular value decomposition with different rows of Hankel matrices will form different decomposition methods L, and each decomposition method has different decomposition types N. In this paper, L = 3 and L = 4 (described later as the three-division method and the four-division method) are the main research objects. The component corresponding to the larger singular value in the two-division method is the approximation signal, and the component corresponding to the smaller singular value is the detail signal. The three-division method and the four-division method have three and four singular values respectively, so there are different methods for constructing approximate signals and detailed signals. The decomposition of the three-division method is shown in Figure 2.  
There are two types of decomposition in the three-division method (L = 3):  
(1) Figure 2a is a Hankel matrix with a row number of three. Each decomposition will result in three signal components. The component corresponding to the first singular value will be regarded as the approximate signal A, and the components corresponding to the last two singular values will be regarded as the detail signal D and d. In this way, D and d is separated as a detail signal during each decomposition, leaving only the approximate signal A for the next layer of decomposition. This type of decomposition is denoted as N = 1.  
(2) Figure 2b is another decomposition type of the three-division method. Each decomposition will result in three signal components. The component corresponding to the first two singular values  
Figure 2: Schematic diagram of the decomposition of the three-division method. (**a**) N = 1, (**b**) N = 2.  
will be regarded as the approximate signal A and a. And the components corresponding to the third singular values will be regarded as the detail signal D. Then the two approximate components A, a are superimposed to construct a Hankel matrix for decomposition of the next layer, and D is separated as a detail component. This type of decomposition is denoted as N = 2.  
Similarly, the four-division method (L = 4) has three types of decomposition, N = 1, 2, 3. The specific decomposition and reconstruction process of the three-division and the four-division method is similar to the two-division method. By analogy, the multi-division method is also decomposable, and the p-division method has p-1 types of decomposition.
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## 3 Algorithms for Computing the 3D Zernike Moments for a Surface Triangular Mesh
### Computing the Integrals \(Q_{nl}\)  
We consider the different integrals \(S_{nl}^{k}(r)\) of the radial functions \(R_{nl}(r)\):  
\[S_{nl}^{k}(r_{0})=\int_{0}^{r_{0}}r^{k}R_{nl}(r)\mathrm{d}r\]  
[PERSON] and [PERSON] have derived relationships for similar integrals for the 2D Zernike radial polynomials [58]. We expand their results here. In Appendix A, we show the following proposition that allows us to compute \(S_{nl}^{0}(r_{0})\):  
**Proposition 1**.: _For non negative integers \(n\) and \(l\) with \(l\leq n-2\) and \(n-l\equiv 0\) (mod 2), the following relationship hold:_  
\[S_{nl}^{0}(r_{0})=\frac{2l+3}{(2n+3)(l+1)}\Big{(}R_{n+1,l+1}(r_{0})-R_{n-1,l +1}(r_{0})\Big{)}-\frac{l+2}{l+1}S_{n,l+2}^{0}(r_{0}), \tag{25}\]  
_and for \(l=n\):_  
\[S_{nn}^{0}(r_{0})=\frac{r_{0}^{n+1}}{n+1}.\]  
In Appendix B, we then establish a recurrence on the \(S_{nl}^{k}(r_{0})\), in steps of 2 in \(k\):  
**Proposition 2**.: _For non negative integers \(n\) and \(l\) with \(l\leq n-2\) and \(n-l\equiv 0\) (mod 2), and for non negative integer \(k\), the following relationship hold:_  
\[K_{1}(n+2,l)S_{nl}^{k+2}(r_{0})=-S_{n+2,l}^{k}(r_{0})+K_{2}(n+2,l)S_{nl}^{k}( r_{0})+K_{3}(n+2,l)S_{n-2,l}^{k}(r_{0}), \tag{26}\]  
_where \(K_{1}\), \(K_{2}\), and \(K_{3}\) are defined in Equation (21)._  
The special case \(k=0\) leads to the following recurrence for the integral \(Q_{nl}(r_{0})=S_{nl}^{2}(r_{0})\):  
\[Q_{nl}(r_{0})=\frac{(n+2-l)(n+l+3)}{(2n+3)(2n+5)}S_{n+2,l}^{0}(r _{0})\\ +\frac{1}{2}\bigg{(}\frac{(2l+1)^{2}}{(2n+5)(2n+1)}+1\bigg{)}S_{ nl}^{0}(r_{0})+\frac{(n-l)(n+l+1)}{(2n+3)(2n+1)}S_{n-2,l}^{0}(r_{0}), \tag{27}\]  
for \(l=n\), we additionally need:  
\[Q_{nn}(r_{0})=\frac{r_{0}^{n+3}}{n+3}.\]  
Finally, we show that for all non negative integer \(n\), and all integer \(l\) with \(0\leq l\leq n\) and \(n\) and \(l\) of the same parity, there exists a polynomial function \(U_{nl}\) of degree \(n-l\) such that  
\[Q_{nl}(r_{0})=r_{0}^{l+3}U_{nl}(r_{0}). \tag{28}\]  
The proof is simple. First, we notice that \(R_{nl}\) is written as:  
\[R_{nl}(r_{0})=r^{l}V_{nl}(r_{0}), \tag{29}\]where \(V\) is a Jacobi polynomial (see Equation (19)) of degree \(n-l\). Then  
\[Q_{nl}(r_{0})=\int_{0}^{r_{0}}r^{l+2}V_{nl}(r)\mathrm{d}r. \tag{30}\]  
Using the polynomial expansion of \(V_{nl}\), it is easy to show that \(Q_{nl}(r_{0})\) is a polynomial function of degree \(n+3\), with \(r_{0}^{l+3}\) as a factor.
## 4 Results and Discussion
### Speed Performance  
In order to obtain an equalizer with the best comprehensive properties, the equalization speed should be considered in addition to the equalization performance. As mentioned earlier, only a high-speed equalizer can be used with actual hardware. This is why a 1D-CNN, which can parallelize, was chosen as the student model. Therefore, we also conducted experiments to record the time for the teacher and student networks to complete signal equalization. Since KD does not change the reasoning time of the same network structure, we only needed to compare the teacher and student networks, not the two networks that undergo KD or not. Using the same CPU, the time for the teacher network and the student network to complete signal equalization is shown in Table 1 below. The average equalization time for the student model 1D-CNN was 0.49 s, while the average equalization time for the teacher model biGRU was 14.36 s. Compared with the biGRU, the proposed 1D-CNN equalizer reduced the computational time consumption by 97%, with only a 0.5 dB Q-factor penalty. This also verifies the theory that the parallel structure of a 1D-CNN can greatly reduce inference delay compared with the recursive structure of a biGRU. In  
Figure 8: Q-factor comparison and spectrum diagram of the LMS, biGRU, 1D-CNN and 1D-CNN with KD with a changing baud rate.  
Figure 9: Q-factor comparison of the LMS, biGRU, 1D-CNN and 1D-CNN with KD with a changing RoP, and the eye diagrams of the in-phase signal before and after equalization.  
addition, the 1D-CNN only used 0.7% of the number of trainable parameters as biGRU, which means a far lower computation complexity and shorter training time.  
It can be seen that the equalization time of the 1D-CNN structure was only 3% that of biGRU. Although the biGRU had a better equalization performance than the 1D-CNN, the equalization performance of the 1D-CNN greatly improved after KD, reaching the same level as that of the biGRU. Thus, such a large rate improvement brought about by parallelization makes the 1D-CNN after KD the best choice of equalizer for minimalist deployment.
# Development of Automatic Berthing Support Program for Autonomous Ships
## 3 Results  
### Automatic Berthing Support Program  
#### 3.1.1 Thruster Output and Output Angle Calculation Procedure  
The process by which the automatic berthing support program calculates the thruster output and output angles based on the environmental input conditions is shown in Figure 4. First, under the specified wind direction/speed and current direction/speed conditions, the program calculates the external forces acting on the hull as the ship berths at the desired speed and then determines the resulting turning moments. Next, the total forces are divided into longitudinal and lateral components. Finally, the program calculates the thruster output and output angles, considering the forces and turning moments acting on the hull.
## 3 Results and Discussion
### Master Curves Result Analysis
#### 3.2.2 G* and \(\delta\) after Aging  
G* evolution and \(\delta\) evolution of the HCRMB with different amounts of sulfur along with aging are shown in Figure 6. As described in Figure 6, the G* of the HCRMB with different amounts of sulfur decreases gradually by temperature sweep tests. Compared to 70# and 20 TB_0.25 ul, 40 TB_0.25 ul shows an insignificant increase in G* after RTFOT aging, indicating that the short-term aging resistance of 70# and conventional RMB is worse than that of HCRMB. Moreover, compared to 40 TB_0.25 ul, 40 TB_0.45 ul shows an insignificant decrease in \(\delta\) after PAV aging, indicating that the long-term aging resistance of 40 TB_0.25 ul is worse than that of 40 TB_0.45 ul. In other words, sulfur can improve the long-term aging resistance of HCRMB.
# Dynamic Compensation of a Fluxgate Magnetometer Based on a Hybrid Optimizing Algorithm
## 3 Simulation and Signal Test
### Simulation Results and Discussion
#### 3.1.2 Comparison of Compensation Effects of the Optimization Algorithm  
In order to illustrate the improvement of the dynamic performance of the magnetometer by the hybrid optimization algorithm of GA-SA, GA, and SA, these three algorithms are, respectively, utilized to calculate parameters of the second-order dynamic compensator. Taking the step response of the magnetometer model as the input of the dynamic compensator and the step input as the ideal output of the dynamic compensator, different equations of the dynamic compensator can be obtained as follows:  
\[GA:\] \[u(k)-1.2195u(k-1)+0.2473u(k-2)=-0.0736y(k)+1.7834y(k-1)-1.6818y(k-2) \tag{10}\] \[SA:\] \[u(k)-0.7282u(k-1)-0.2229u(k-2)=1.8223y(k)-0.4910y(k-1)-1.2828y(k-2)\] (11) \[GA-SA:\] \[u(k)-1.8995u(k-1)+0.9020u(k-2)=2.7937y(k)-5.3875y(k-1)+2.5963y(k-2) \tag{12}\]  
The simulation and comparison results are shown in Figure 12. As shown in Figure 12a, the fitness of the GA algorithm converges after 300 iterations, and the final fitness is higher than that of the SA and GA-SA algorithms. It falls into the local optimum too early and fails to obtain the global optimal solution, which is the shortcoming of the GA algorithm. The fitness of the SA algorithm converges slowly in the late stage and does not change in some iterations of 1000-4000, resulting in low efficiency and a long algorithm execution time. The GA-SA algorithm overcomes the problems of the above two algorithms. In the iterative process, it converges fast, has the lowest fitness and obtains the global optimal solution. As shown in Figure 12b, the GA-SA algorithm has the lowest fitness, so the output compensated by the GA-SA algorithm follows the step input the fastest, minimizing the dynamic error. The specific evaluation indicators are shown in Table 3.  
Figure 11: Dynamic compensator compensation results for different orders: (**a**) order 1 to 6; (**b**) order 7.
## 2 Materials and Methods
### Colorectal Carcinoma  
Colorectal carcinoma (CRC) in AYA is rare. However, in contrary to the older patients benefiting from screening programs, the CRC incidence in this age group is constantly increasing. Moreover, due to experienced diagnostic delays, AYA are more likely to be diagnosed in the advanced stage of disease. The diagnosis of CRC in the young may be a sign of an underlying genetic disorder, such as familial adenomatous polyposis (FAP) or Lynch syndrome; however, most cases occur sporadically [205].  
Many studies reported altered expression of various Hsps, including Hsp 20 [206], Hsp 40 [207], Hsp60 [208; 209; 210], Hsp70 [207; 211], and Hsp90 [212] in the CRC specimens. The tissue Hsp status was found to be associated with tumor grade, lymph node involvement, distant metastases, response to CHT, recurrence and overall survival [206; 209; 212; 213]. The serum concentration of Hsp60, Hsp70 and Hsp90\(\alpha\) were assessed as possible CRC biomarkers [214; 215; 216; 217]. Consequently, several in vitro trials with Hsps inhibitors have been conducted, yielding promising results [219; 211; 218; 44; 119]. However, the data regarding the presence and function of Hsps in the circulating exosomes released from CRC cells is scant.  
[PERSON] et al. for the first time reported that Hsp60 is actively released from cancer cells via the exosomal pathway [107; 108]. These studies were based on previous research reporting increased expression of Hsp60 in preneoplastic colon lesions and CRC, suggesting its role in tumorigenesis [208; 210; 210]. Subsequently, the same research group reported the presence of Hsp60 in the exosomes obtained from the peripheral blood of patients with CRC. Hsp60 was also detected in the inflammatory natural killer (NK) cells and macrophages within the tumor microenvironment. The levels of Hsp60 in the exosomes were significantly increased at diagnosis but comparable to healthy controls after the tumor removal. Moreover, Hsp60 was detected in the membrane of exosomes before the tumor excision, but neither in the same patients after the surgery nor in healthy controls [100].  
The authors therefore suggest that the exosomal Hsp60 blood levels could serve as a promising biomarker of CRC recurrence.  
Another chaperone that was found in exosomes derived from human CRC cell lines is Hsp70 [91; 95; 139]. Being presented at the exosomal plasma membrane, it was found to induce natural killer (NK) lymphocyte anti-tumor activity [91]. Later, [PERSON] et al. reported that Hsp70-rich exosomes from a mice CRC cell line also elicit an immune anti-tumor response via inducing Hsp70-dependent interleukin (IL)-6 secretion from dendritic cells. The exosomes secreted after heat stress had higher concentration of Hsp70 and were more effective immune stimulators than exosomes released spontaneously [92].
# Categorization of the Condition of Railway Embarments Using a Multi-Attribute Utility Theory
## Methodology
### Implementation of Multi-Attribute Utility Theory  
To effectively implement MAUT into a methodology for railway embankment condition assessment, several steps need to be considered. A detailed description of the mathematical basis of the MAUT approach is presented in [36; 37; 38]. After the selection and determination of the quantification values of the attributes (QV\({}_{\text{C}}\)) using Equations (1)-(5), the following step includes calculation of the utility function values for the selected '\(n\)' number of attributes (five attributes are proposed in the paper) and '\(m\)' number of alternatives. An alternative (S) is defined as 'a sub-section of pre-defined  
Figure 4: Determination of deeper irregularities: (**a**) Ground Penetrating Radar trace in case of deep irregularity presence and (**b**) radagram showing the detected hyperbolic phenomena. length'. In this case, 100 m long sub-sections were considered, which determine the resolution for categorization. Any other resolution length, adapted to the character of specific problem and to the needs of infrastructure managers, can also be considered. The overall MAUT problem can be expressed by an \(m\times n\) decisional matrix, having the form as shown in Table 3.  
The normalized utility function values can be determined using the following equation:  
\[\overline{\mathrm{U}_{\mathrm{i}}}\big{(}\mathrm{S}_{\mathrm{j}}\big{)}=\frac{ \mathrm{QV_{C,i}}\big{(}\mathrm{S}_{\mathrm{j}}\big{)}}{\sum_{\mathrm{j=1}}^{m} \mathrm{QV_{C,i}}\big{(}\mathrm{S}_{\mathrm{j}}\big{)}}\,, \tag{6}\]  
where  
\(\overline{\mathrm{U}_{\mathrm{i}}}\big{(}\mathrm{S}_{\mathrm{j}}\big{)}\)--normalized utility function value for Attribute \(i\) and Alternative \(j\)  
\(\mathrm{S}_{\mathrm{j}}\)--Alternative \(j\), subsection of 100 m in length; j = 1, 2,..., m  
\(m\)--number of alternatives  
\(\mathrm{QV_{C,i}}\)--quantification value of Attribute \(i\); i = 1, 2,..., n  
\(n\)--number of attributes  
The sum of all utility function values for a specific alternative is equal to 1:  
\[\sum_{\mathrm{i=1}}^{n}\overline{\mathrm{U}_{\mathrm{i}}}\big{(}\mathrm{S}_{ \mathrm{j}}\big{)}=1. \tag{7}\]  
The next step in the implementation of MAUT includes the evaluation of the importance of each selected attribute. To do this, a grading scheme is established, where the grading of importance of each attribute for the embankment condition assessment is conducted by attributing a grade ranging from 1 (negligible importance) to 10 (extremely important). This information was collected using a questionnaire completed by experienced engineers working in the field of infrastructure management. The determination of each attribute's importance is thus by nature subjective, reflecting the experience and risk acceptance of the engineers in question.
# The Complex Relationship between Virulence and Antibiotic Resistance
## 3 Virulence Mechanisms
### Virulence Factors  
Virulence encompasses not only the ability of bacteria to cause disease in the host (i.e., degree of pathogenicity) but also the ability of bacteria to infiltrate and colonize a host. In the late 1980s and early 1990s, virulence was thought of as a pathogen centered attribute and the concept of pathogenicity islands of genetic virulence factors was introduced in the late 1990s [109; 110]. Genetic virulence factors can regulate physical attributes of the bacteria such as flagella, curli, fimbiae, adhesions, biofilm, [111; 112; 113; 114] or biochemical factors, including host cell surface modifying enzymes, toxins, and antibiotics to provide a competitive advantage [115; 116; 117]. Genetically encoded antibiotic resistance in many aspects can be considered a subtype of virulence factors as they promote host pathogenesis, allowing persistent or chronic diseases [118]. Identifying pathogenic islands of virulence and antibiotic resistance genes along with their associated transcriptional and translational regulators is critical to recognize and assess druggable targets in the search for new tools in the antibacterial arsenal. Equally important in antibiotic drug discovery efforts is a consideration of virulence transmission, and the potential combinatorial approach with anti-virulence therapeutics.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 4 Experimental Results and Discussion
### Experimental Results and Discussion  
#### 4.4.1 Results on the RSOD Dataset  
Benchmark evaluations against ten state-of-the-art detectors in Table 1 demonstrate the superior performance of ProSAF-Net, which establishes a new benchmark with a peak mAP of 97.12%. This performance breakthrough validates the technical efficacy of our design, particularly excelling in small object detection with class-leading AP scores of 96.56% for aircraft (predominantly sub-pixel targets) and 98.37% for oil tanks. The technical superiority stems from the synergistic integration of SDAM and PSFM modules: SDAM preserves high-resolution spatial details through shallow feature retention, while PSFM hierarchically aggregates contextual semantics via cross-layer attention mechanisms. Their co-optimization generates discriminative representations that balance localization precision and semantic richness. Equally important, ProSAF-Net achieves second-ranked AP on large-scale overpass detection, confirming its multi-scale adaptability. Visualizations in Figure 6 empirically validate this capability, demonstrating simultaneous high-precision detection of macro-scale structures (e.g., playgrounds) and micro-scale targets (e.g., aircraft) under complex backgrounds. These results collectively evidence the framework's robustness across extreme scale variations in remote sensing scenarios.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **mAP** & **Aircraft** & **Oil Tank** & **Overpass** & **Playground** \\ \hline soft-NMS [48] & 86.60 & 76.10 & 90.30 & 81.30 & 98.80 \\ Faster R-CNN [10] & 88.10 & 71.30 & 90.7 & 90.90 & 99.70 \\ YOLOv3 [12] & 89.40 & 88.60 & 94.50 & 75.90 & 99.90 \\ FPN [13] & 90.91 & 90.58 & 94.47 & 80.18 & 98.49 \\ Cascade R-CNN [49] & 91.30 & 94.20 & 96.10 & 83.20 & 99.00 \\ KLDet [50] & 93.22 & 94.46 & 94.88 & 86.58 & 96.95 \\ YOLOv5 [33] & 93.64 & 95.58 & 97.79 & 81.68 & 99.50 \\ ABNet [16] & 94.17 & 91.49 & 96.14 & 89.61 & 99.44 \\ DA\({}^{2}\)FNet [51] & 94.78 & 95.72 & 97.73 & 90.56 & 95.12 \\ SESA-Net [52] & 95.15 & 95.69 & 97.89 & 90.23 & 96.82 \\ TBNet [53] & 96.98 & 95.24 & 96.76 & **95.92** & **100** \\ ProSAF-Net (Ours) & **97.12** & **96.56** & **98.37** & 94.71 & 98.86 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with the current state-of-the-art on the RSOD dataset is presented. The top-performing results are shown in bold, and the runner-up results are underlined.