esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Path Planning for Robots with Improved A* Algorithm under Bidirectional JPS Strategy
## 4 Simulation Analysis and Experimental Validation
### Experimental Validation  
In order to verify the feasibility of the improved A* algorithm in the actual operation of the mobile robot, the algorithm was applied to the two-wheel-drive differential robot built by our team, as shown in Figure 14a.The differential robot utilizes the RPLIDAR A1 laser rangefinder to obtain the 2D point cloud data of the external environment. It integrates odometer data for localization and constructs a 2D map using the Amcl and Gmapping function packages. Finally, it performs global path planning using the global planner in the Move Base function package [20]. The experimental scene described in this paper was a section of aisles in the laboratory, as shown in Figure 14b.  
The two path plannings performed by the differential robot at the experimental site are shown in Figure 15. The results of the planning have been displayed using the ROS visualization tool Rviz, where the orange square represents the model of the cart and the green solid line depicts the path planned by the improved A* algorithm.  
The search times of the three algorithms for the two paths are shown in Tables 3 and 4, respectively. It can be concluded that the improved algorithm proposed in this paper further enhances search speed while essentially producing optimal paths. Therefore, the improved algorithm outperforms the other two algorithms significantly.  
Figure 14: Mobile robots and experimental environments: (**a**) differential robot; (**b**) laboratory.  
Figure 15: The results of path planning using the improved algorithm: (**a**) planning for Path 1, (**b**) planning for Path 2.
## 2 Results
### hNudt16 Stability in the Presence of Different Ligands and Its Substrate Affinity Studies
#### 2.3.3 Determination of the Structure of hNudt16 in Solution by SAXS  
To understand why we obtained two binding constants in MST studies we decided to examine the structure of the hNudt16. Several crystallographic structures of this protein are available, which can be used to analyze the molecular docking of the ligand of interest. The interaction studies were performed in solution therefore we decided to analyze whether the tertiary and quaternary structures of hNudt16 in solution were significantly different from those in the crystal structures. The SAXS data confirmed that hNudt16 in solution occurs in a stable dimeric form. Analysis of the SAXS data yielded the structural parameters of hNudt16 in solution. The radius of gyration (R\({}_{g}\)) calculated by fitting experimental data to the Guinier equation was R\({}_{g}\) = 24.16 \(\pm\) 2.74 A. The analysis of the pair distance distribution function \(p\)(R) in GNOM resulted in an identical value of R\({}_{g}\) = 24.17 \(\pm\) 2.74 A, and the maximum size of enzyme molecule D\({}_{\rm max}\) = 79.22 A (Figure 5B).  
First, we compared the experimental SAXS data obtained for hNudt16 in solution with all available crystal structures of hNudt16 (3 COU, 6 XV, 3 MGM, 6 XTU, 5W6X, 6B09, and 2 XSQ). Using CRYSOL software, we calculated the theoretical scattering curves for all crystal structures and compared them with the experimental curve (Figure 5A). This analysis showed that hNudt16 structures have a favorable fit to SAXS data with discrepancy in the range \(\chi\) = 0.75 \(\div\) 0.91. The best fit with a discrepancy \(\chi\) = 0.75 was obtained for the 2 XSQ PDB. Structures with the lowest discrepancy from the experimental data in solution were most similar to the wild-type state of a protein. As a result, the 2 XSQ was used to model the protein-ligand binding in the study of molecular docking.  
Based on SAXS scattering curves, we performed ab initio modeling of the hNudt16 enzyme in solution using DAMMIN software [36]. The resulting low-resolution model of the hNudt16 dimer in solution is presented in Figure 5C. We performed calculations using DAMMIN with no constraints on the local symmetry of the dimer. Twenty independently received models were averaged using the DAMVER program. Figure 5C(I,II) show a superposition of the obtained low-resolution 3D model of hNudt16 in solution (semitrans  
Figure 4: Comparison of the free energy associated with the dissociation of the complex (\(\Delta\)G\({}_{\rm diss}\)) calculated for stronger binding site (with K\({}_{\rm d1}\) obtained from MST) with \(\Delta\)T\({}_{\rm m}\) obtained from dye-free and DSF with dye measurements for tested hNudt16E76Q-ligand pairs.  
parent shape) with the crystallographic 2 XSQ in projections along the two different axes of the molecule. The crystal structure of hNud16 fits very well with the low-resolution bead model.
## 3 Applications of WSNs
### Applications Classification
Such systems cannot rely on a fixed infrastructure and have to be very robust, because of the inevitable impairments encountered in open environments. The system should respond to environment changes as quick as possible. The environment to be observed will mostly be inaccessible by the human all the time. Hence, robustness plays an important role. Also security and surveillance applications have some demanding and challenging requirements such as real-time monitoring and high security. An other application that could belong to both the above defined categories is devoted to the realisation of energy efficient buildings. In this application, in fact, sensor nodes could aim at estimating a process (SPE), but also events (ED). In this case the WSN is distributed in buildings (residential or not) to manage efficiently the energy consumption of all the electric appliances. Consequently, nodes have to continuously monitor the energy consumed by all appliances connected to the electrical grid. Therefore,sensors have to estimate a process, that is the energy consumption which varies with time, but in some cases, they could be used to detect some events. As an example, sensors could detect the arrival of a person in a room to switch on some electrical appliances. The European Joint Undertaking ARTEMISIA has funded the project eDIANA (Embedded Systems for Energy Efficient Buildings), focused on the above described application scenario. The project, in fact, aims at achieving energy efficient buildings through innovative solutions based on networked embedded systems. The eDIANA approach is to achieve greater efficiency in the use of resources, prioritizing energy as scarce resource, more flexibility in the provision of resources and better situation awareness for the citizen and for service and infrastructure owners. This will be achieved through the deployment and inter-operation of embedded systems throughout the eDIANA environment of buildings and intra-building units.
## 4 Conclusions  
In this study, the effect of material parameters such as yield strength, Young's modulus, and tangent modulus on the safety factor determined for autofrettaged cylinders under a working pressure of 400 MPa was investigated. A series of FE simulations were conducted based on the Taguchi experimental design, and then, the data were analyzed through ANOVA and regression analysis. For the regression analysis conducted based on the numerical results for all three scenarios, a validation simulation was performed using data for AISI 4340 steel. Additionally, analytical calculations were performed to validate the finite element model and compared with the FE simulation results. The following conclusions are drawn:  
* According to the Taguchi analysis, the most influential parameter on the safety factor determined for autofrettaged cylinders under a working pressure of 400 MPa is the yield strength. For all three scenarios, the effect of Young's modulus and tangent modulus on the safety factor is very low. Although the effect of Young's modulus and tangent modulus on the response is minimal, tangent modulus is more influential on the response than Young's modulus for all three scenarios. While Young's modulus decreases and tangent modulus increases, there is a slight tendency for the safety factor to increase.
* The numerical and analytical results show a very good agreement. The safety factors calculated using the analytical method are lower than those obtained using the numerical method in all three scenarios. It can be said that the analytical method is more conservative in calculating safety factors compared to the numerical method.
* According to the analysis results, it was observed that the internal machining process leads to more residual stress loss compared to the external machining process.
* It was found that selecting materials with higher yield strength and tangent modulus and lower stiffness would have a positive impact on the safety factor of the structure in all three scenarios investigated in the study.
* According to the results of ANOVA, using materials with higher yield strength maximizes the benefit obtained from the autofrettage process.
* When comparing the results obtained from the validation simulation with those from the regression equation, it is evident that the regression equations exhibit a high prediction capability.  
Conceptualization, V.C.; Methodology, M.O., H.Y. and V.C.; Software, B.O.; Validation, H.Y. and M.Y.; Formal analysis, H.Y. and M.Y.; Investigation, M.O.; Resources, H.Y.; Data curation, M.O., M.Y., Y.C. and M.A.E.C.; Writing--original draft, M.O.; Writing--review & editing, M.O. and M.Y.; Visualization, B.O., Y.C. and M.A.E.C.; Supervision, V.C.; Project administration, V.C.; Funding acquisition, M.O. 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 conflict of interest.
## 4 Collision Avoidance Decision-Making Method  
Ships will encounter many unknown factors during navigation which are also extremely difficult to forecast, such as TSs' uncoordinated collision avoidance maneuvers, which makes research on ship maneuvering decision-making approaches extremely difficult. To deal with the TS's unpredictable maneuvering, this research developed a maneuvering decision method based on time series rolling calculation and produced adaptive decisions by rapidly updating input data.
# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
## 2 Forecasting Method and Application
### Combined Prediction  
The combined prediction was first proposed by [PERSON] and [PERSON] The combined forecasting model is constructed by assigning different weights to the prediction values of single forecasting models [34]. The classification of combined prediction can be divided into the following two types:  
* According to the functional relationship between the combined and the single forecasting models, the combined forecasting model can be divided into linear and non-linear combination prediction [35].
* According to the weight coefficients of the single models, the combined forecasting model can be divided into fixed weight and variable weight combination prediction [36].  
The linear combination prediction has relatively large errors and has great limitations, while the fixed weight combination prediction cannot dynamically adjust the combination weight, therefore it is necessary to use a non-linear and variable weight combined forecasting model. For example, neural networks and SVM are non-linear combiners, and these two combination methods can realize non-linear and variable weight combination of the single forecasting models. However, neural networks are not suitable for processing few samples data. There will be overfitting problems and the prediction accuracy is not satisfactory [37]. SVM or LSSVM has obvious advantages in solving few samples, non-linear and high-dimensional problems [38]. Therefore, LSSVM optimized by QGA is adopted in this paper as a non-linear and variable weight combiner. The flow chart of the proposed combined prediction is shown in Figure 3.  
Figure 2: The flow chart of the Least Squares Support Vector Machine (LSSVM) optimized by quantum genetic algorithm (QGA).
# A Hybrid Routing Protocol Based on Naive Bayes and Improved Particle Swarm Optimization Algorithms
## 2 Related Works
### Clustering Routing Algorithms
Node \(P_{j}\) selects the cluster head with the smallest cost(\(j\),\(i\)) to join, so as to ensure that the load is balanced between clusters.
# Vertex Cover Reconfiguration and Beyond +
## 1 Introduction
In Section 4, we start by showing that the VCR problem parameterized by \(\ell\) remains W[1]-hard when restricted to bipartite graphs. To obtain this result, we introduce the \((t,d)\)-bipartite constrained crown problem and show that it plays a central role for determining the complexity of the reconfiguration problem. As the vertex cover is solvable in polynomial time on bipartite graphs, this result provides an example of a search problem in P whose reconfiguration version is W[1]-hard parameterized by \(\ell\), answering a question left open by [PERSON] et al. [10]. In Section 5, we characterize instances of the VCR problem solvable in time polynomial in \(|V(G)|\) and apply this characterization to trees, graphs with no even cycles and (with some additional restrictions) to cactus graphs (we incorrectly claimed to have proved the result for cactus graphs in its full generality in an earlier version of this paper [13]). We note that a polynomial-time algorithm for even-hole-free graphs was also independently obtained by [PERSON] et al. [8] for solving several variants of the closely-related independent set reconfiguration problem. Moreover, VCR is known to be PSPACE-complete on graphs of bounded treewidth [14] (for some constant value of treewidth), but it remains open whether the problem is PSPACE-complete already for graphs of treewidth at most two, or even outerplanar graphs. Our result on cactus graphs is a first step towards settling these questions. In Section 6, we present the first fixed-parameter tractable algorithm for VCR parameterized by \(\ell\) on graphs of bounded degree after establishing the NP-hardness of the problem on four-regular graphs. Finally, we show using completely different techniques, and at the cost of a much worse running time, that VCR, as well as a host of other reconfiguration problems are fixed-parameter tractable on nowhere dense classes of graphs.
## 5 Discussion  
As introduced in the introduction, to the best of our knowledge, there is no relevant research on the 3D point cloud extraction method for CS-GBSAR. However, research on the CA-CFAR method for 2D SAR image target detection has been deeply studied and widely applied, which can be used as the candidate for solving the problem. For the problem of multi-target detection, [PERSON] et al. [31] proposed a CFAR target detection method based on iterative filtering in 2011, which avoids the interference of multiple targets. [PERSON] et al. [32] proposed a ship detection algorithm based on CFAR cascade in SAR images in 2012. It can solve the problem of multi-target and clutter edge at the same time. [PERSON] et al. [33] proposed an automatic region screening SAR target detection algorithm in 2016. It has stable detection performance and false alarm suppression ability. [PERSON] et al. [34] proposed a semantic CFAR target detection algorithm. Compared with the traditional CFAR using only intensity information, it can remove a large number of strong clutter false alarms without shadows. To dispose of the effect of the target pixels near the PUT (Pixel Under Test), [PERSON] et al. [35] proposed a superpixel-level CFAR  
Figure 16: Modified DBSCAN method clustering results with strong sidelobes.  
Figure 17: The clustering results of removing sidelobes are compared with the optical images.  
detection method in 2018. It utilizes superpixels instead of rectangular sliding windows to define CFAR guard areas and clutter. The objective is to enhance target exclusion from outliers while reducing false detections. [PERSON] et al. [36] proposed the BTSR-CFAR (Robust Bilateral-Trimmed-Statistics-CFAR) detector in 2021, it eliminates the capture effect arising in conventional CFAR detectors. In 2023, [PERSON] et al. [37] proposed the Multi-source Fusion CFAR Detection Method Based on the Contrast of Sliding Window, the proposed algorithm fully exploits the distribution characteristics of target energy in multi-station fusion images and provides an effective way for multi-source fusion target enhancement detection.  
The above-mentioned CFAR methods are all based on on two-dimensional SAR images, which cannot be applied to CS-GBSAR. As will be shown in the following section, the conventional layer by layer method cannot accurately extract the 3D point cloud. The strong sidelobe will degrade the extraction performance. The proposed two-step CFAR can avoid such influence, which demonstrates the necessity of proposed method.  
Section 5.1 is the comparison of the proposed method with the conventional method. Section 5.2 gives a summary of the proposed method and the future research directions.
## 4 Results and Discussion
### Performance Comparison of Different Models for Identifying the Surface Defects  
To further investigate the effectiveness of the proposed method for the surface defect detection in this work, we selected current lightweight models that are widely available in a large number of object detection studies, including SSD-VGG16, YOLOv3-tiny, YOLOv4-tiny, original YOLOv5s, and YOLOx-nano, for comparison with our model. The performances of these selected models were all obtained from the same testing set used for our model. The corresponding performance comparison results are exhibited in Figure 9. The results show that our model was consistently minimal in terms of TNNP, FLOPS, and MS. In the case of TNNP, our model was 98.136%, 94.704%, 92.190%, 93.505%, and 48.775% smaller than the other five models, respectively. Similar reductions were observed in the other two indicators (FLOPS and MS): for example, 98.685%, 93.751%, 88.146%, 88.337%, and 22.562% in FLOPS; 97.957%, 94.208%, 91.463%, 92.932%, and 48.457% in MS. For the  
Figure 8: Identification results for the testing set: (**a**) confusion matrix of identification results; (**b**) visualization of identification results.  
identification speed, compared to the original YOLOv5s and YOLOx-nano, our model reduced AITC by 12.867% and 44.017%, separately. Due to the simpler model architectures of SSD-VGG16, YOLOv3-tiny and YOLOv4-tiny, they produced shorter AITCs than our model. However, all three of them were significantly worse than our model as well as the original YOLOv5s and YOLOx-nano in the _Precision_ and _Recall_ indicators. This indicates that their accuracy was measurably weaker than our model, such that the faster speeds of these three models do not have potential for practical application. The significant results mentioned above demonstrate that our model had a lower complexity, a smaller scale, a weaker computing power dependence, and a faster running speed. Our model also offers notable improvements in mAP, _A\({}_{D}\)_, _Precision_, and _Recall_ in identifying the eight types of surface defects. Our model outperformed the others in the indicators related to the accuracy. In particular, it exclusively achieved 100% for _A\({}_{D}\)_ and all Recalls for defective arc magnets where other models did not. Unlike the other models, the false identification rate of our model on the Recall indicator occurred only in the defect-free arc magnets and was merely 1%, which is widely acceptable for actual production. Such results suggest that our model is more conducive to accurately identifying defective arc magnets. The above performance comparison shows that our model had obvious advantages in the deployment (1.921 MB in MS), speed (9.46 ms in AITC), and accuracy (100% in Recall for different defective arc magnets) for identifying surface defects on arc magnets, signifying more reliable application.
## 4 Discussion  
The results of the experiments not only provided valuable insights into the performance of the neural network regression model in predicting the hardenability of steels, but also shed light on the effects of the chosen hyperparameters and cross-validation strategies on the robustness of the model and the prediction accuracy through a comparative analysis.  
The results obtained from the neural network regression (_fitrnet_) models, particularly Model 4208 (8-85-141-1), demonstrate their remarkable effectiveness in predicting the hardness of steels. The rigorous hyperparameter tuning, facilitated by Bayesian optimization, contributes significantly to the performance of the models. The 5-fold and 10-fold cross-validation schemes ensure the reliability and generalizability of the models and provide robust insights into their predictive capabilities. The 10-fold cross-validation proved to be the optimal choice in this study, which is in line with recommendations from the previous literature [34]. When comparing various evaluation metrics, such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R\({}^{2}\)), the 4208 model comes out on top. With an impressive RMSE of 1.0790 and an R\({}^{2}\) of 0.9900, the model demonstrates excellent accuracy and reliability across the assessment measures.  
The illustrative example in the research paper shows the practical application of Model 4208 in determining the hardenability band for a particular steel grade. This application highlights the effectiveness of the model in predicting and optimizing heat treatment results and underscores its practical utility in materials engineering.  
In summary, the experiments highlight the predictive capabilities of _fitrnet_ models in determining the hardenability of steel, with Model 4208 demonstrating exceptional accuracy and robustness. The inclusion of Bayesian optimization improves the efficiency of hyperparameter tuning and highlights its importance in improving model performance.
## 5 MATLAB-Simulink System Model
### Traction Machine  
The selected vehicle's motor is a 350 kW permanent magnet synchronous machine (PMSM). The electric motor model is based on data extracted from the manufacturer [21] and from the literature [33; 34; 35].  
The equations used in the model are as follows [36] (amplitude-invariant Clarke transformation) [37]:  
\[\begin{bmatrix}u_{d}\\ u_{q}\end{bmatrix}=R_{S}\cdot\begin{bmatrix}i_{d}\\ i_{q}\end{bmatrix}+\frac{d}{dt}\begin{bmatrix}\lambda_{d}\\ \lambda_{q}\end{bmatrix}+\omega_{dec}\cdot\begin{bmatrix}0&-1\\ 1&0\end{bmatrix}\cdot\begin{bmatrix}\lambda_{d}\\ \lambda_{q}\end{bmatrix} \tag{7}\]  
\[T_{em}=\frac{3}{2}\cdot p\cdot\left(\lambda_{d}\cdot i_{q}-\lambda_{q}\cdot i_ {d}\right) \tag{8}\]  
\[T_{em}-T_{load}=J\cdot\frac{d\omega_{mcc}}{dt}+\text{B}\cdot\omega_{mcc} \tag{9}\]  
where  
* \(\text{u}_{d},\text{u}_{\text{q}}\): stator voltages in dq reference frame [V].
* p: pole pairs [-].
* \(\text{i}_{d},\text{i}_{\text{q}}\): stator currents in dq reference frame [A].
* Tload: load torque [Nm].
* R\({}_{S}\): stator winding resistance [\(\Omega\)].
* \(\omega_{\text{elec}}\): rotor electrical speed [rad/s].  
Figure 7: Power electronics topology (simplified scheme).  
\(\bullet\)\(\mathrm{T_{em}}\): electromagnetic torque [Nm].
* J: total moment of inertia [kg\(\cdot\)m\({}^{2}\)].
* \(\omega_{\mathrm{mec}}\): rotor mechanical speed [rad/s].
* B: total friction [Nm/(rad/s)].
* \(\lambda_{\mathrm{d}}\), \(\lambda_{\mathrm{q}}\): stator flux linkages in dq reference frame [Wb].