esa-sceva/satcom-chunk-collection huggingface dataset:
# Genetic Algorithm-Based Intelligent Selection Method of Universal Shield Segment Assembly Points
## 2 Establishment of Assembly Point Selection Objective Function
### Related Concepts
#### 2.1.2 Universal Shield Segment and Its Assembly Point  
A formed tunnel consists of a large number of lining rings assembled in segments. Figure 3 shows the structure of a 16-points universal shield segment ring. The segment is generally composed of three categories, A, B and K [17]. The segment ring is an isosceles trapezoid from the side view, where \(S\) is the unilateral wedge shape of the ring, \(\theta\) is the wedge angle, \(b\) is the central width, and \(D_{g}\) is the diameter of outer wall. Due to the wedge shape of ring, the turning of the tunnel and the auxiliary control of the shield machine are realized. Longitudinal bolt holes numbered 1 to 16 are evenly distributed on the end face of the segment ring, and the rings are connected to each other by longitudinal bolts. In this connection, the relative rotation angle between the two adjacent rings cannot be arbitrary but must be an integer multiple of the unit rotation angle (22.5\({}^{\circ}\)). The K-block, also called a capping block, is usually assembled last in a ring as the most critical step. The assembly point of the segment is the installation position of the K-block, which is used to describe the rotation angle of the segment ring. As shown in Figure 4a, the K-block is located at the top of the ring; in this case, the assembly point is 16. While in Figure 4b, the K-block rotates 3 \(\times\) 22.5\({}^{\circ}\) clockwise, and the assembly point is 3.  
Figure 2: Schematic diagram of shield machine and segment ring.  
Considering the quality of the tunnel and the difficulty of assembling segments, the lowermost point 8 and the uppermost point 16 are usually not allowed to be selected. In addition, assembling segment rings need to meet the principle of staggered-joint assembly [16], i.e., the stitches between the connecting blocks of two adjacent segment rings need to be staggered. To achieve staggered assembly, the next possible segment assembly point \(i_{n+1}\) and the previous segment assembly point \(i_{n}\) need to satisfy the correspondence in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Previous Segment Assembly Point** & \multicolumn{4}{c}{**Next Segment Possible Assembly Point**} \\ \(i_{n}\) & & & \(i_{n+1}\) & & \\ \hline
1 & 3 & 6 & 9 & 12 & 15 \\
2 & 4 & 7 & 10 & 13 & \\
3 & 5 & 11 & 14 & 1 & \\
4 & 6 & 9 & 12 & 15 & 2 \\
5 & 7 & 10 & 13 & 3 & \\
6 & 11 & 14 & 1 & 4 & \\
7 & 9 & 12 & 15 & 2 & 5 \\
9 & 11 & 14 & 1 & 4 & 7 \\
10 & 12 & 15 & 2 & 5 & \\
11 & 13 & 3 & 6 & 9 & \\
12 & 14 & 1 & 4 & 7 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The correspondence between \(i_{n+1}\) and \(i_{n}\).  
Figure 4: Assembly points diagram: (**a**) assembly point 16; (**b**) assembly point 3.  
Figure 3: Universal segment ring structure: (**a**) front view; (**b**) side view.
# Weighting of Multi-GNSS Observations in Real-Time Precise Point Positioning
###### Abstract  
The combination of Global Navigation Satellite Systems (GNSS) may improve the accuracy and precision of estimated coordinates, as well as the convergence time of Precise Point Positioning (PPP) solutions. The key conditions are the correct functional model and the proper weighting of observations, for which different characteristics of multi-GNSS signals should be taken into account. In post-processing applications, the optimum stochastic model can be obtained through the analysis of post-fit residuals, but for real-time applications the stochastic model has to be defined in advanced. We propose five different weighting schemes for the GPS + GLONASS + Galileo + BeiDou combination, including two schemes with no intra-system differences, and three schemes that are based on signal noise and/or quality of satellite orbits. We perform GPS-only and five multi-GNSS solutions representing each weighting scheme. We analyze formal errors of coordinates, coordinate repeatability, and solution convergence time. We found that improper or equal weighting may improve formal errors but decreases coordinate repeatability when compared to the GPS-only solution. Intra-system weighting based on satellite orbit quality allows for a reduction of formal errors by 40%, for shortening convergence time by 40% and 47% for horizontal and vertical components, respectively, as well as for improving coordinate repeatability by 6%.  
multi-GNSS; real-time; PPP; stochastic modeling; weighting +
Footnote †: journal: Remote Sensing, 10, 84
# A Registration Method for Historical Maps Based on Self-Supervised Feature Matching
## 3 Materials and Methods
### Map Preprocessing  
During the model training phase, we addressed the heterogeneity of historical map images, which exhibit significant variations in resolution, size, and cartographic style. Given the prohibitive cost of complete manual annotation (particularly for high-resolution maps), we adopted a transfer learning strategy with self-supervised fine-tuning. First, maps were tiled into \(256\times 256\) pixel patches for coarse annotation of dominant features (primary roads, building footprints, and major waterways). Drawing inspiration from vascular structure segmentation [14]--a task that shares topological similarities with road network extraction--we initialized our model using a pretrained U-Net architecture. This baseline model was then fine-tuned on coarsely labeled map patches. By prioritizing the alignment of macro-level structures, minor discrepancies in local features (e.g., branch roads) were rendered less impactful, thereby simplifying downstream feature matching. To enhance cross-style generalization, we integrated Instance-Batch Normalization (IBN) [25] into the network, which enabled robust performance across diverse map aesthetics.  
To handle the high-resolution historical maps (typically > 1500 \(\times\) 1500 pixels), we implemented a tiling-and-stitching pipeline. The original maps were divided into \(512\times 512\)-pixel overlapping patches (with a stride of 256) to preserve contextual continuity. Each patch was processed by the segmentation network, and the local predictions were aggregated through weighted averaging in overlapping regions, resulting in a full-resolution segmentation mask identical to the input dimensions.  
To mitigate boundary artifacts in stitching results, we designed an overlapping tiling strategy with adaptive fusion weighting. Adjacent patches were generated with 20% overlapping regions, ensuring sufficient coverage of edge regions. During reconstruction, pixel-wise confidence scores (derived from the network's softmax outputs) were used to blend predictions in overlapping zones, with higher weights assigned to high-confidence regions near patch centers. This approach effectively suppressed edge discontinuities while maintaining global consistency. The result of our map image segmentation method is shown in Figure 3.
## 3 Results and Discussion
### Characterizations of PENMs  
Figure 2 and Table 2 show that when the electric field decreases from 2.5 to 1.5 kV/cm, and when the flow rate increases from 0.5 to 1.0 mL/h, PENMs have good morphology and are composed of bead-free nanofibers. Specifically, with the combination of an electric field of 1.5 kV/cm and a flow rate of 1.0 mL/h, PENM-9 has the smallest diameter distribution of nanofibers of 240-390 nm, as well as good formation of nanofibers. Moreover, PENM-9 also has the finest average diameter of 316 nm (\(p<0.01\)) among all PENMs studied. The properties of polymer represent a key factor influencing the electrospun nanofibers. In this section, a constant concentration of PVA was used, making manufacture parameters the only variables that would affect the morphology of nanofibers. [PERSON] (1964) found that a voltage that exceeded 6 kV created Taylor cones, facilitating the electrospun nanofibers. In addition, different electric fields required different corresponding suitable flow rates. An electric field that exceeded the range employed to create stabilized Taylor cones had a greater pull force than the flow rate, resulting in a concave Taylor cone and unstable jets. As a result, there was a greater proportion of the presence of bead-shaped, flatten, or flaw nanofibers, as seen in Figure 2a,b,d,e. Conversely, under an electric field that is less powerful than that required for Taylor cone formation, the jets became drops and are unable to reach the collect board. As the electric fields used in this study are higher than those required for Taylor cone formation, all the aforementioned flaws are absent. The optimal PENM-9, which was made with the combination of an electric field of 1.5 kV/cm and a flow rate of 1.0 mL/h, was further used for the following disucssions.  
Figure 1: Cell viability of shikonin component. \({}^{**}p<0.01\) compared with the control group.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 3 Proposed Hardware Architecture
### Routing Networks  
To enable seamless data transfer between various locations within the architecture, three routing networks (RN_1, RN_2, and RN_3) are employed as depicted in Figure 1. The RN_1 and RN_2 include the following three inputs: base coordinates x and y, the output of ROM_DATA, and the outputs of RF_1 and RF_2. The selection of data for processing is controlled by the signals RN_1_ADDR and RN_2_ADDR. The dimension for these control signals is \(5\times 1\). On the other hand, the input of RN_3 is the output of ALU. Moreover, RN_3 has a size of \(3\times 1\). It performs a critical role in selecting the desired result produced by the ALU.
# Advances and Future Perspective on Detection Technology of Human Norovirus
## 2 Detection Techniques
### Immunological Methods
ICG-based kits mainly include NOROTOP(ALL.DIAG SA, Strasbourg, France), ImmunoCardSTAT(r) Norovirus (Meridian Bioscience Europe, Nice, France), Ridaquick Norovirus (R-Biopharm, Darmstadt, Germany), and SD Bioline Norovirus (Standard Diagnostics, Inc., Kyonggi-do, Korea). Both commercial EIA- and ICG-based kits were evaluated for clinical sample testing [41; 42; 43; 44; 45; 46; 47]. The ICG-based kits have a shorter detection time (about 15-30 min) compared to EIA-based kits. Given the large antigenic differences and infectious ability amongst different genotypes of HuNoVs, the viral loads in foods and environmental samples were low. The viral loads in clinical samples were relatively high, but large differences were observed in different genotypes. Therefore, immunological methods such as ELISA and immune colloidal gold cannot satisfy the strict testing requirements because of the low sensitivity [48; 49]. However, immunological methods have easy and fast operation and simple equipment requirements. As important techniques, immunological methods should be further improved. Immunological methods can be used for early screening of HuNoVs infection and for efficient diagnosis of disease in combination with molecular detection methods.
## 3 Results
### Cox-CSDPC Disease Prediction Model Application and Results Analysis  
We used Kaplan-Meier method to analyze the characteristics and screened 40 features. By establishing the Cox-CSDPC disease prediction model, we obtained prognostic index, ACC value, OR value, PEV value, ROC curve, AUC value and other indicators. The ROC curve of the Cox-CSDPC disease prediction model is shown in Figure 4. Among them, AUC = 0.807, ACC = 80.0%, training time is 29.5 h, with certain accuracy. The Yoden index is calculated from the ROC curve. The maximum value is 0.531, which is the cut-off point. The sensitivity at this time is 0.742 and the specificity is 0.789. According to the Formula (1), the prognostic index PI can be established. When PI > 0.531, the risk of circulatory diseases can be judged to be large. When PI < 0.531, the risk of circulatory diseases can be judged to be small. In this way, the individual's prediction results are obtained. In addition, the interpretation of the variation \(PEVp_{\omega}^{2}\) = 0.911, approaching 1, shows a strong correlation between the predicted value and the exposure factor.  
The indicator data perform relatively good, and it can be seen that the overall model has a good prediction effect, but the model prediction ability has a large space to develop. The Cox proportional hazard prediction model is essentially a survival analysis, using survival time as a covariate, and has  
Figure 4: Cox-CSDPC disease prediction model ROC curve.  
a high utilization rate for large cohort data. This model can be used for predictive studies of other chronic diseases.
###### Abstract
In this work, we introduce a pioneering framework for retinal vessel segmentation, specifically tailored for applications in DR diagnosis. We advance the field of retinal vessel segmentation for DR diagnosis by introducing a pioneering model that seamlessly integrates transformer blocks, a Joint Pyramid Upsampling (JPU) module, and a Convolutional Block Attention Module (CBAM) for enhanced feature extraction and representation. Our model goes beyond conventional approaches, leveraging the power of attention mechanisms and joint pyramid upsampling to capture intricate vessel structures across multiple scales. Furthermore, we contribute a comprehensive evaluation of the model's performance on benchmark datasets, demonstrating its effectiveness in addressing key challenges such as intensity inhomogeneity and variations in vessel characteristics. This work not only introduces a novel segmentation model but also provides a significant stride toward accurate and efficient automated diabetic retinopathy screening. The key contributions of this work are as follows,  
* We introduced transformer blocks into the model architecture to facilitate contextualized feature extraction, allowing the model to capture long-range dependencies and improve feature representations. * The addition of the Joint Pyramid Upsampling (JPU) module enhances multi-scale feature fusion, enabling the model to effectively capture information across different scales and improve segmentation accuracy. * Channel-Attention and Spatial-Attention Mechanisms (CBAM) are integrated into the model to enhance channel and spatial attention, enabling the model to adaptively recalibrate feature maps and focus on relevant spatial regions, therefore improving segmentation performance. * Our proposed framework achieves superior accuracy in retinal vessel segmentation, particularly for diabetic retinopathy diagnosis, surpassing existing methods in terms of performance metrics such as Mean IoU, Recall, and F1 Score.
# Contribution of Gray and White Matter Abnormalities to Cognitive Impairment in Multiple Sclerosis
## 2 Results
### Multiple Sclerosis (MS) Patients vs. Healthy Controls (HCs)
#### 2.1.3 Voxel-Based Morphometric (VBM) Comparison--MS Patients vs. HCs  
VBM analysis revealed GM volume decreases in several regions in MS patients compared with HCs, including the right insula, middle frontal gyrus, inferior frontal gyrus and left cingulate gyrus (\(p<0.05\), family-wise error (FWE) corrected for multiple comparisons, see Table 2 and Figure 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cognitive Test** & **HC (_n_ = 29)** & **MS Patients (_n_ = 39)** & \(p\) \\ \hline AVLT-Total recall & 0 \(\pm\) 1.00 & \(-\)0.60 \(\pm\) 1.51 & 0.294 \\ AVLT-Delayed recall & 0 \(\pm\) 1.00 & \(-\)0.50 \(\pm\) 1.44 & 0.588 \\ VFT & 0 \(\pm\) 1.00 & \(-\)0.31 \(\pm\) 0.87 & 0.892 \\ SDMT & 0 \(\pm\) 1.00 & \(-\)0.83 \(\pm\) 1.27 & 0.014 \\ TMT-A & 0 \(\pm\) 1.00 & 0.78 \(\pm\) 1.97 & 0.290 \\ TMT-B & 0 \(\pm\) 1.00 & 0.79 \(\pm\) 1.90 & 0.254 \\ BVMT-R-Total recall & 0 \(\pm\) 1.00 & \(-\)0.79 \(\pm\) 1.80 & 0.194 \\ BVMT-R-Delayed recall & 0 \(\pm\) 1.00 & \(-\)0.58 \(\pm\) 1.79 & 0.661 \\ PASAT-3\({}^{\prime\prime}\) & 0 \(\pm\) 1.00 & \(-\)0.83 \(\pm\) 1.50 & 0.039 \\ JLOT & 0 \(\pm\) 1.00 & 0.06 \(\pm\) 0.81 & 1.000 \\ \hline \hline \end{tabular} Values are mean \(\pm\) standard deviation (SD); Abbreviations: AVLT = Auditory Verbal Learning Test; VFT = Verbal Fluency Test; SDMT = Symbol digit modalities test; TMT = Trail making test; BVMT-R = Brief Visuo-patial Memory Test-Revised; PASAT-3\({}^{\prime\prime}\) = Paced Auditory Serial Addition Test (3 seconds); JLOT = Judgment of Line Orientation Test; HC = healthy control.  
\end{table}
Table 1: Comparison of cognition between MS patients and HC.  
Figure 1: Extent of white matter (WM) integrity damage in diffusion tensor images (DTI) analyses. The bars indicate the extent of WM integrity damage. The blue and red bars indicate differences of WM diffusion between the multiple sclerosis (MS) patients and healthy control (HC) subjects, and between the cognitive impairment (CI) and cognitive preservation (CP) groups, respectively. FA, MD, RD, and AD refer to fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity, respectively. FA: fractional anisotropy; MD: mean diffusivity; RD: radial diffusivity; AD: axial diffusivity.
# IoT Group Membership Management Using Decentralized Identifiers and Verifiable Credentials
## 2 Background and Related Work
### Related Work  
The use of DIDs and VCs has been explored in the context of the IoT by some research efforts. [PERSON] et al. [5] are using DIDs and VCs to provide secure firmware/software update on IoT devices. In that system, DIDs and VCs are used for authenticating software providers to IoT devices. Our system, on the other hand, uses DIDs and VCs for authenticating IoT devices to clients, as well as to prove CoAP group membership. [PERSON] et al. [6] leverage DIDs to provide IoT device authentication for the Modbus protocol. Their approach leverages a DID method which is based on Hyperledger Fabric blockchain. [PERSON] et al. [7] use DIDs and VCs to express access rights related to vehicles as well as to implement the delegation of these rights. Similar to [6], they rely on a blockchain for implementing a DID registry. [PERSON] et al. [8] also utilize blockchain (and smart contracts) to implement a DID registry for DIDs used by IoT devices and manufacturers. Our solution does not require any external entity (such as a blockchain) for retrieving DID documents; instead, all the required information is stored in a DNS server, and it is resolved as part of the CoAP group URI DNS resolution. [PERSON] et al. [9] study the business aspects of DIDs in the context of \"IoT as a service\" platforms. Their work is orthogonal to our approach, which proposes a security solution for the IoT using DIDs and VCs.  
In our previous work, we use DIDs to protect the routing layer of a Next-Generation Internet architecture from poisoning attacks [10], as well as for providing authenticity for content items stored in the Inter-Planetary File System (IPFS) [11]. In those systems, all DID documents are integrated in the protected resources, and these resources are identified by a DID (i.e., a public key). In our system, we leverage DNSSEC to store some DID documents. This has the following advantages: (i) protected resources' identifiers (i.e., CoAP group URIs) can be of arbitrary form (including human readable and memorable names), (ii) every time a DID is revoked, the corresponding protected resource does not have to be renamed.
## 2 Principles of Rainfall Field Retrieval by ESLs and HMLs  
Microwave signals will have significant attenuation due to scattering and absorption of rainfall. This physical phenomenon can lead to serious rain attenuation of the radio wave signals from communication satellites to the earth and the communication links between ground-based communication base stations due to rainfall. The ingenuity of the new technique of retrieving rainfall by using rain attenuation from ESL and HML is to reverse the interference of rainfall in the atmosphere to microwave signals to obtain rainfall information. In addition, ESLs and HMLs can be combined to retrieve rainfall field when multiple ESLs and HMLs are present in the area. Figure 1 depicts the process of retrieving rainfall field by combining ESLs and HMLs. Firstly, a link network for rainfall observation can be built by a combination of ESLs and HMLs in the area. Then, the sparse rain attenuation at the location of the link network can be obtained by using the forward model of rain attenuation for ESL and HML. Furthermore, the inversion model of microwave rain attenuation can be used to convert the rain attenuation into sparsely distributed rain rates in the area. Finally, the spatial reconstruction method can be used to obtain the continuously distributed rainfall field.
## 3 Results
### Performance of VIs
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**VI** & **Intercept** & **Slope** & \(\mathbf{R^{2}}\) & **RMSE** & **Signif.** \\ \hline VOG\({}_{2}\) & 26.277 & \(-\)274.101 & 0.881 & 0.6 & *** \\ VOG\({}_{3}\) & 27.114 & \(-\)238.934 & 0.880 & 0.6 & *** \\ MTCI & 26.178 & 10.039 & 0.866 & 0.6 & *** \\ VOG\({}_{1}\) & \(-\)26.703 & 50.467 & 0.865 & 0.6 & *** \\ mSR\({}_{705}\) & 19.105 & 7.395 & 0.852 & 0.7 & *** \\ CI green & 27.373 & 6.728 & 0.841 & 0.7 & *** \\ SR\({}_{750}\) & 15.436 & 9.852 & 0.838 & 0.7 & *** \\ TCARI & 60.562 & \(-\)57.535 & 0.835 & 0.7 & *** \\ mND\({}_{705}\) & 12.596 & 58.631 & 0.830 & 0.7 & *** \\ WDRVI green & 21.085 & 41.491 & 0.830 & 0.7 & *** \\ NDVI green & 14.663 & 53.726 & 0.809 & 0.8 & *** \\ mCARI\({}_{705}\) & 26.561 & 21.353 & 0.804 & 0.8 & *** \\ WDRVI & 24.994 & 41.655 & 0.545 & 1.2 & *** \\ SR & 23.427 & 2.278 & 0.541 & 1.2 & *** \\ NDVI & \(-\)22.222 & 82.947 & 0.536 & 1.2 & *** \\ EVI\({}_{1}\) & \(-\)13.213 & 135.008 & 0.422 & 1.4 & *** \\ EVI\({}_{2}\) & \(-\)10.006 & 138.282 & 0.420 & 1.4 & *** \\ NDVI\({}_{705}\) & 16.729 & 65.825 & 0.384 & 1.4 & *** \\ SR\({}_{680}\) & 29.778 & 0.964 & 0.139 & 1.7 & *** \\ NDVI\({}_{680}\) & 19.155 & 43.457 & 0.059 & 1.7 & *** \\ \hline \hline \end{tabular} Notes: Statistical significance, 0.01 “**”; 0.001 “***”; 0.001 “***”.  
\end{table} 
Table 4: Relationship between CC and VIs for \(L\). _racemosa_ (\(n=151\)). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**VI** & **Intercept** & **Slope** & \(\mathbf{R^{2}}\) & **RMSE** & **Signif.** \\ \hline VOG\({}_{2}\) & 18.188 & \(-\)451.792 & 0.693 & 5.7 & *** \\ VOG\({}_{3}\) & 21.345 & \(-\)373.644 & 0.688 & 5.7 & *** \\ VOG\({}_{1}\) & \(-\)87.574 & 96.967 & 0.672 & 5.8 & *** \\ mND\({}_{705}\) & \(-\)42.674 & 163.958 & 0.672 & 5.8 & *** \\ MTCI & 22.359 & 15.054 & 0.670 & 5.9 & *** \\ mSR\({}_{705}\) & 14.913 & 9.963 & 0.650 & 6.0 & *** \\ SR\({}_{750}\) & 4.131 & 15.024 & 0.621 & 6.3 & *** \\ NDVIgreen & \(-\)54.493 & 174.230 & 0.609 & 6.4 & *** \\ WDRVIgreen & \(-\)13.384 & 100.923 & 0.607 & 6.4 & *** \\ Clgreen & 24.163 & 8.929 & 0.584 & 6.6 & *** \\ mCARI\({}_{705}\) & 23.182 & 33.436 & 0.582 & 6.6 & *** \\ TCARI & 93.665 & \(-\)174.992 & 0.577 & 6.6 & *** \\ NDVI\({}_{705}\) & 29.339 & 81.311 & 0.114 & 9.6 & *** \\ WDRVI & 31.829 & 51.978 & 0.056 & 10.0 & *** \\ SR & 44.576 & 1.352 & 0.055 & 10.0 & *** \\ NDVI & \(-\)43.281 & 123.583 & 0.055 & 10.0 & *** \\ EVI\({}_{1}\) & 15.393 & 110.420 & 0.043 & 10.0 & *** \\ EVI\({}_{2}\) & 20.601 & 104.767 & 0.037 & 10.1 & *** \\ NDVI\({}_{680}\) & 44.377 & 33.138 & 0.011 & 10.2 & ** \\ SR\({}_{680}\) & 60.735 & 0.049 & 0.000 & 10.2 & ns \\ \hline \hline \end{tabular} Notes: Statistical significance, not significant “ns”; 0.05 “**”; 0.01 “**”; 0.001 “***”.  
\end{table} 
Table 3: Relationship between CC and VIs for \(R\). _mangle_ (\(n=579\)). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**VI** & **Intercept** & **Slope** & \(\mathbf{R^{2}}\) & **RMSE** & **Signif. ** \\ \hline VOG\({}_{2}\) & 4.968 & \(-\)1013.500 & 0.834 & 5.0 & *** \\ VOG\({}_{3}\) & 6.924 &