esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Path Planning of Unmanned Sweepers Based on a Fusion Algorithm
## 2 Traditional A* Algorithm with Improvements  
Principles of the Traditional A* Algorithm  
The A* algorithm is one of the traditional global-path-planning algorithms applied in various addressing scenarios. A* algorithm is an improvement of [PERSON]'s algorithm, which optimizes for a single objective point using a heuristic function, giving priority to the path closer to the objective. Its evaluation function consists of two components, the actual generation value and the estimated generation value, and the definition can be expressed as in Equation (1).  
\[F(n)=G(n)+H(n) \tag{1}\]  
where \(n\) denotes the current node, \(G(n)\) denotes the actual generation value from the starting point to node \(n\), and \(H(n)\) is a heuristic function which denotes the estimated generation value from node \(n\) to the target point. If we want to find the optimal solution for the two-point path, the key is the selection of the estimation function \(H(n)\). If \(H(n)\) is less than or equal to the actual value of the distance from the current node to the target node, then the search nodes are many, the search range is large, and the efficiency is low, but the optimal solution can be obtained. If \(H(n)\) is equal to the actual value, then the search will be carried out strictly according to the shortest path; if \(H(n)\) is greater than the actual value, the search nodes are fewer, the search range is smaller, and the efficiency is high, but the optimal solution cannot be guaranteed. Therefore, an exact heuristic function is constructed to calculate the length of the shortest path between any two nodes. In two-dimensional maps, calculating the actual distance value between nodes and target points as a surrogate value can achieve a better result. The traditional A* algorithm heuristic functions are usually Euclidean distance, Chebyshev distance and Manhattan. Their distance equations are shown as follows, Equation (2), Equation (3), Equation (4), respectively.  
\[\text{Euclidean distance}: d=\sqrt{\left(x_{2}-x_{1}\right)^{2}+\left(y_{2}-y_{1} \right)^{2}} \tag{2}\] \[\text{Chebyshev distance}: d=max(|x_{1}-x_{2}|,|y_{1}-y_{2}|)\] (3) \[\text{Manhattan}: d=|x_{1}-x_{2}|+|y_{1}-y_{2}| \tag{4}\]  
Figure 1 shows a graphical representation of the three distance formulas. The traditional A* algorithm generally uses Euclidean distance or Manhattan distance, but Chebyshev distance only needs to calculate addition and subtraction, which greatly improves the efficiency of operation, and there will be no error no matter how many times the superposition is calculated. Therefore, the Chebyshev distance formula was chosen in this paper.  
Figure 1: Diagram of distance measurement methods.
## 3 Macroscopic Transmission Control: Virtual Hub-Based Transmission
### Transmission Link Decision and Route Reply
#### 3.2.1 Virtual FIFO Flooding-Based Search  
This search in Algorithm 1, performed virtually at the destination node using the constructed reduced-hub topology map, is based on a first-come first-served (FIFO) flooding-based search. However, in FIFO cases, the RREQ with lower link costs could arrive later. Therefore, this search has a subpath update process after the first selection of the candidate hub-path.  
```
1:At any intermediate hub node \(H_{j}\) in the reduced hub map,
2:while (1) do
3:if the stochastic delay constraint is not violated then
4:if the hub node has never flooded the received RREQ before then
5: the hub node floods the RREQ packet;
6:else
7: the node does not flood it, but records the RREQ information;
8:endif
9:else
10: discards it;
11:endif
12:endwhile
13: At destination, collect hub paths into \(\Psi\) from the received RREQs;
14: At destination, select best hub-path, \(P^{*}\leftarrow\arg\min_{P\in\Psi}\sum_{(i,j)\in P}c_{ij}\);
15: At each intermediate hub node, if there are any better sub-hub-paths that can provide lower cost than \(P^{*}\) while not violating stochastic delay constraint, the destination node updates the hub paths and saves them into \(\Pi\);
16: At destination, reselect the best hub-path, \(P^{*}\leftarrow\arg\min_{P\in\Pi}\sum_{(i,j)\in P}c_{ij}\);
```  
**Algorithm 1** Virtual FIFO flooding-based heuristic search.
# Multicriteria Route Planning for In-Operation Mass Transit under Urban Data
## 4 Multicriteria Route Planning for Mass Transit
### The Algorithm
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**City** & **LSD** & **Indicator** & **BiasSpan** & **Dijkstra**’s** & **BFS** & **IDDFS** & **Best-First** & **Distance-A*** & **PF-A*** \\ \hline \multirow{3}{*}{Chicago} & \multirow{3}{*}{0.03461} & Mean & **0.25280** & 0.15798 & 0.09373 & 0.09114 & 0.08128 & 0.15277 & 0.09747 \\  & & Std. & **0.31434** & 0.23687 & 0.16829 & 0.17891 & 0.14793 & 0.22618 & 0.17577 \\  & & Grouping & **A** & B & C & C & B & C \\ \hline \multirow{3}{*}{Tainan} & \multirow{3}{*}{0.00475} & Mean & **0.08129** & 0.04597 & 0.03702 & 0.03378 & 0.03255 & 0.04262 & 0.03597 \\  & & Std. & **0.14666** & 0.09651 & 0.09355 & 0.07890 & 0.08870 & 0.09504 & 0.09162 \\ \cline{1-1}  & & Grouping & **A** & B & C & D & B & C \\ \hline \hline \end{tabular}
\end{table} 
Table 10: [PERSON]’s LSD test based on one-way ANOVA. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**City** & **Source** & **DF** & **SS** & **MS** & **F-Value** & \(p\)**-Value** \\ \hline \multirow{3}{*}{Chicago} & Method & 6 & 1.203675884 & 0.200612647 & 21.56892294 & 4.55 \(\times\) 10\({}^{-22}\) \\  & Error & 413 & 3.841314821 & 0.009301004 & & \\  & Total & 419 & 5.044990705 & & & \\ \hline \multirow{3}{*}{Tainan} & Method & 6 & 0.034645522 & 0.005774254 & 32.96143117 & 1.88 \(\times\) 10\({}^{-32}\) \\  & Error & 413 & 0.072350218 & 0.000175182 & & \\ \cline{1-1}  & Total & 419 & 0.106995740 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: One-way ANOVA test for different methods and constraints towards unit PF performance. Meanwhile, despite the discussion of BiasSpan against other comparative methods, the \(p\)-values in Wilcoxon signed-rank test for all pairs of methods towards unit PF performance are shown in Table 12, in which the disadvantage where the targeted method has more negative ranks than positive ranks against comparative methods is noted as \"D.A.\". This demonstrates a similar result to the [PERSON]'s LSD test recorded in Table 10; nonetheless, there are some differences between these two somewhat clustering results. Precisely, BiasSpan still holds a performance significantly outperforming other competitors; while _[PERSON]'s_ and _Distance-A_* are still grouped in the second tier, though _[PERSON]'s_ secures better results. The discrepancy seems to lie in the \(PF\)-\(A\)*. It seems that _[PERSON]'s_ significantly surpasses \(PF\)-\(A\)* especially in Tainan dataset, for which _[PERSON]'s_ and \(PF\)-\(A\)* are clustered in the middle tiers as listed in Table 10. Indeed, there is no significant difference among the unit PF performance achieved by \(BFS\), \(PF\)-\(A\)*, _IDDFS_, and _Best-First_, though \(PF\)-\(A\)* slightly outperforms the other three methods.
## 3 Results and Discussion
### Chemotaxonomic Characteristics  
The major fatty acids (>1%) of strain OS4T were C18:1 ou6c/ ou7c (73.3%), C18:0 (9.9%), C100 3 OH (5.4%), C20:1 ou7c (2.9%), and C16:0 (1.6%) which were different in composition to those of other species in genus _Jannaschia_ (Table 2). The main differences in fatty acids were listed in Table 2, where strains _J. helgolandensis_ 14858T and _J. aquimarina_ GSW-M26T contained branched chain fatty acids (i.e., iso-C15:0, antieq-C15:0, iso-C16:0, iso-C17:0, and antieq-C17:0), which were not detected in strain OS4T. Analysis of the respiratory quinone of strains OS4T, 14858T, and GSW-M26T indicated that Q-10 predominated in those strains [7,18]. The polar lipid composition of OS4T was virtually identified (Figure S2), with the phospholipids mainly comprising phosphatidylcholine (PC), phosphatidylethanolamine (PE), and phosphatidylglycerol (PG). Two unknown amino-lipids (i.e., aminolipids (AL) and aminophospholipid (APL)) and four unknown glycerolipids (i.e., glycerolipids 1-4) were also being detected in OS4T (Figure S2). Taken together, both respiratory quinone and polar lipid of OS4T were in line with the previous reports on the genus _Jannaschia_, and the major fatty acids clearly distinguished OS4T from other _Jannaschia_ strains (e.g., 14858T and GSW-M26T).  
Figure 3: Phylogenetic relationship of strain OS4T and other related species. Neighbor-joining phylogenetic tree based on 16S rRNA gene sequences, showing the relationships of strain OS4T and related species. Bootstrap values \(\geq\) 70% (based on 1000 replications) are shown at branching nodes. _Stappia stellulata_ IAM12621T (GenBank accession number, D88525) was used as an outgroup. Bar, 0.01 substitutions per nucleotide position.
# Knitted Microwave Transmission Line for Wearable Electronics
## 5 Conclusions  
The presented research demonstrates the feasibility of knitting technology for fabricating textile-based transmission lines suitable for high-frequency applications in the 2.4 GHz band. The fabricated line exhibits good impedance matching and acceptable attenuation levels, despite the inherent dielectric losses associated with textile materials. The simulation results align well with the measured performance, validating the proposed modeling approach. Importantly, bending the line with a 100 mm radius of curvature has a minimal impact on its electrical characteristics.  
The proposed method offers a unique advantage by eliminating the need for post-processing techniques like sewing or gluing, thereby simplifying the manufacturing process and enhancing the overall reliability of fabricated structures. The precise control over line geometry and the close agreement between theoretical predictions and experimental results underscore the potential of this approach.  
However, challenges remain in terms of scalability, cost-effectiveness, and long-term durability. Future research will focus on addressing these limitations by exploring advanced knitting techniques, optimizing material selection, and implementing robust packaging strategies. Additionally, investigating the integration of these lines with various antenna types and developing techniques for efficient signal transmission and reception will be crucial for practical applications.  
Conceptualization, I.N. and L.J.; methodology, I.N. and L.J.; software, L.J.; validation, I.N. and L.J.; formal analysis, I.N. and L.J.; investigation, I.N. and L.J.; resources, I.N.; data curation, I.N. and L.J.; writing--original draft preparation, I.N. and L.J.; writing--review and editing, I.N. and L.J.; visualization, I.N. and L.J.; supervision, L.J.; project administration, I.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 5 Conclusions  
This paper proposes a new medical image hiding and authentication algorithm based on double POB system. Firstly, the ROS is extracted, and then the ROI and RONI are separated by an image segmentation algorithm. After bit plane separation and cross-reorganization, two encrypted shares are obtained through the lossless compression and image encryption algorithm. In the double-hiding process, the secret message and authentication bits are embedded using the POB number system. In the recovery process, authentication is carried out first; if it is not tampered with, the receiver will extract the secret message and complete the recovery of the original image. If the authentication fails, the repeatedly filled data are directly used for lossless recovery.  
The experimental results showed that the scheme in this paper significantly enhances the embedding capacity without damaging the perceptual quality of the image. Compared with other schemes, it has good performance in data embedding and lossless recovery. Additionally, the bit plane separation and cross-reorganization method effectively safeguard highly sensitive pixels within the ROI of medical images, which is crucial for their utilization. However, when using double POB system, compression and re-encryption operations are performed at the same time, which leads to a slightly higher time overhead. In future work, we will mainly focus on how to reduce the time cost of using the double POB function.  
Figure 12: The application scenario of the proposed scheme.  
**Author Contributions:** Conceptualization, X.S.; Methodology, X.S.; Software, X.S. and E.T.; Writing--review and editing, F.R.; Formal analysis, E.T.; Investigation, M.Z.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper was supported by the National Nature Science Foundation of China (Program No. 62202377), the Natural Science Basic Research Plan of Shaanxi Province of China (Program No. 2024)JC-YBMS-524, 2022 JM-353), the Key Research and Development Program of Shaanxi (Program No. 2023-YBGY-015), and the Scientific Research Program Funded by Shaanxi Provincial Education Department (No. 22 JK0560).  
**Institutional Review Board Statement:** Not applicable for studies not involving humans or animals.  
**Informed Consent Statement:** Not applicable for studies not involving humans.  
**Data Availability Statement:** All data generated or analyzed during this study are included in this published article. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Coupling and Optical Analysis of a Round-Cornered Square-Shaped Microresonator
## 4 Results and Discussion
### Effects of Propagation (\(\alpha\)) and Transmission (\(\beta\))
Using the general formulation developed here and discussed in the preceding paragraphs, the specific case of RCSS microresonators [27] is now considered. The system in Figure 1, with geometrical dimensions and operational details (shown in Figure 7 (left)) is examined by numerical analysis. To enable a comparison with previous publications [27; 28; 36], the width of the input waveguide and the microresonator are kept at 0.35 \(\upmu\)m, the gap between the microresonator and the input waveguide is 0.2 \(\upmu\)m, the refractive index of the microresonator and input waveguide is 3.5, and the total optical path length of the microresonator is 400 \(\upmu\)m (see Figure 7 (left)). The simulation utilizes the electromagnetic frequency domain (EMFD) analysis, which uses the Finite Element Analysis (FEA) to solve the frequency domain form of [PERSON]'s equations [37]. A hybrid 2D fine meshing technique (a combination of structured and unstructured) is applied, which generates 733,920 domain elements and 30,429 boundary elements in the entire (150 \(\upmu\)m \(\times\) 150 \(\upmu\)m) computational domain (see Figure 7 (right)). Figure 6: Effect of transmission (\(\beta\)) and propagation (\(\alpha\)) on the quality factor \(Q\), represented by contours of log \(\mathcal{F}\) (Equations (19) and (25)). Figure 7: RCSS \(R_{c}=15\)\(\upmu\)m; (**left**) Schematic with dimensions; (**right**) Hybrid meshing for FEA analysis. The maximum element size in each domain is equal to \(\lambda/5\sqrt{n}\), where \(\lambda\) is the wavelength and \(n\) is the refractive index of the particular domain. The boundary condition at the inlet has a 1 V/m electric field in the \(z\)-direction (\(E_{z}=1\), TE Mode). The scattering boundary condition that makes the boundaries transparent to a scattered wave and an incoming plane wave is used everywhere else [37]. Multiple cases are simulated using 64-bit windows-based Intel Xeon dual CPUs with 3.20 GHz speed and 8 cores each that share 144 GB RAM. On average, each case was simulated for approximately 18 h and 25 min, and the spectral response was numerically generated.
# Point-Line-Aware Heterogeneous Graph Attention Network for Visual SLAM System
## 3 Methodology
### Point-Line Feature Extraction Network
#### 3.1.5 Network Output Distillation  
In order to reduce the increasing computation cost caused by the introduction of the attention mechanism and the point-line perception module, we further compressed the point-line detection model PL-Net. A transfer-aware method is presented to transfer the information from the teacher model to the student network. The knowledge distillation strategy (KD) [30; 31; 32] was used to fine-tune the accuracy of the recovery model. As shown in Figure 2, the multiples tasks were combined with the teacher network and the student network to guide the training of the student feature extraction. In the training process, the adaptive weighted multitask distillation was realized, and \(X_{tea}\), \(Y_{tea}\), and \(Z_{tea}\) represented the key point of the teacher model, the center point of the line segment, and the output of the line-segment regression feature layer, respectively. The student model corresponded to  
Figure 6: The parallel attention block (PAB). The PAB is designed to transfer important information to the line branch output (\(X_{F}^{\text{SC}}\)). The pink box in the lower-left corner shows the structure of the CAEU.  
the feature layers X\({}_{stu}\), Y\({}_{stu}\) and Z\({}_{stu}\). The mean squared error (MSE) function was applied to the multitask distillation. The loss function of the training distillation was:  
\[\left\{\begin{array}{l}L_{p}^{S}=\|\mathbf{X}_{tea}-\mathbf{X}_{stu}\|^{2}\\ L_{root}^{S}=\|\mathbf{Y}_{tea}-\mathbf{Y}_{stu}\|^{2}\\ L_{dis}^{S}=\|\mathbf{Y}_{tea}-\mathbf{Y}_{stu}\|^{2}\end{array}\right. \tag{16}\]  
where \(L_{p}^{S}\), \(L_{root}^{S}\), and \(L_{dis}^{S}\) denote the key point, the line segment's center point, and the distillation loss of the line-segment regression task, respectively. Then, the weighted distillation loss was defined as:  
\[L_{MSE}^{s}=\sum_{l}\omega_{l}L_{l}^{s} \tag{17}\]  
where \(L_{MSE}^{s}\) is the multitask distillation loss, and \(\omega_{l}\) represents the weight value of the verification loss.
# Framework for Fast Experimental Testing of Autonomous Navigation Algorithms
## 5 New Localization Module: GNSS/SLAM Fusion  
One of the research lines that our group wants to develop in the future is the fusion of SLAM algorithms with GNSS-based systems. The motivation for this fusion is the complementarity of both localization systems since SLAM algorithms need relatively close landmarks to extract positional information--which can cause occlusions and multi-path problems to GNSS positioning--while GNSS systems work better in clear areas that are challenging for SLAM algorithms because of landmark scarcity. As a starting point for our future research and in order to demonstrate the versatility of our framework, we developed a new localization system based on GNSS/SLAM fusion. Figure 7 shows the ROS nodes scheme of our new localization module, and Figure 8 details its connection with the rest of the system. In our solution, we fuse the positions obtained by the two systems using a Kalman filter in which these positions will be considered asynchronous observations and the odometry is used as a control signal in the prediction phase of the filter. This module allows off-map localization, so it would be difficult to integrate it with the navigation stack but its integration is straightforward using our framework. We next describe each subsystem shown in Figure 7.  
Figure 6: Block diagram that shows the proportional controller described in (1). The set-point is the position and orientation of the point closest to the line shown in Figure 5, left. The error signals are obtained depending on the position and orientation of the vehicle (Figure 5, right). Both, the set-point and the vehicle pose are expressed in map coordinates. The control signal \(u\) is the steering value expressed in degrees, that we send to the low-level control module of our vehicle.
# Sustainable Urban Water Management in China: A Case Study from Guangzhou and Kunming
## 6 Water Sensitive Solutions for Guangzhou and Kunming
### Solutions for Guangzhou
#### 6.1.2 Steady and Good-Quality Urban Water Services  
Real-time automated control and monitoring systems, such as Supervisory Control and Data Acquisition (SCADA), should be implemented in the water service system, at the source, water and wastewater treatment plants, and other networks to reduce the need for human labor during a pandemic [49]. Automatic monitoring of key water quality parameters, particularly virus concentrations, in lakes and rivers to ensure good water quality should also be initiated at the regional levels [50]. This will serve as a criterion for determining the appropriate level of disinfection achieved at water treatment plants. Real-time automated central control of the processing flow and dose at treatment plants can help in reducing the workers workload, while providing a consistent supply of high-quality water [50].  
In order to maintain acceptable water quality, the microbial communities present in the water, particularly the virus, should be monitored on a daily basis. To ensure adequate water quantity, the water supply network should be continuously monitored using sensors to determine the residential water demand as a control signal [51]. Additionally, to prevent pipe corrosion, an automatic monitoring system for water quality and pipe condition should be implemented. When wetland treatment is used as a final step in the sewage treatment process, real-time automated control and monitoring can assist engineers in determining the current state of the wetland and managing it more effectively. Computer-based management will become increasingly prevalent in urban service systems in the future. Additionally, citizen science has the potential to boost participants' knowledge and understanding of different water issues, thereby, increasing participants' confidence in both the water's quality and the importance of the water business [52]. Additionally, citizen participation in monitoring can boost individuals' willingness to pay for improved water quality.