esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Band Structure Analysis
For the second bandgap, its lower boundary mode is similar to the upper boundary mode of the first bandgap. As shown in Figure 4d, the vibration of the upper boundary mainly occurs at convex structures and the adjacent convex structures are in anti-phase along the \(z\)-axis. The modes of the third and fourth bandgap edges are presented in Figure 5. As indicated in Figure 5a, the connecting parts between the cube and the convex structures are the area where the vibration is most concentrated, resulting in obvious vibration deformation. The vibration of point Figure 5b is also concentrated at the four convex structures and they are all in phase along the \(z\)-axis. The lower boundary vibration of the fourth bandgap appears at the square hollow and the four corners. As can be seen from Figure 5d, two convex structures in the \(x\)-axis direction vibrate up and down. Figure 6 shows the iso-frequency curves that can illustrate the directionality of wave propagation in the piezoelectric phononic crystal. In the center of the figure, squares with lower frequencies have lower degrees of anisotropy. However, as the frequency of the iso-frequency curves gradually increases, their anisotropy becomes more obvious. Moreover, these squares show significant inconsistencies at the four corners and four edges, which means that the propagation of elastic waves in the piezoelectric phononic crystal is anisotropic. In addition, it is worth noting that the flow direction of the wave energy is the vector direction perpendicular to the contour line at each point [37].
# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
## 4 Discussion
### Analyses of the GOCI, MODIS and OLCI Ocean Color Product Differences
#### 4.2.2 Spatial Resolution Difference Analysis  
Different spatial resolutions affect the accuracy of classification results [73]. However, with respect to obtaining classification accuracy, a higher resolution scale (300-1000 m) implies a certain classification advantage. The spectral resolution and spectral range of MODIS are both greater than that of GOCI, but the classification accuracy of GOCI is greater than that of MODIS. Most of the MODIS bands are distributed in the range of 0.4-1 um, and only three bands are in the range of 1-2.5 um. Therefore, spatial resolution is more important than spectral resolution in obtaining high-classification accuracy at this scale.  
On the one hand, the loss of fine patches caused by image pixel scale reduction is the main reason for accuracy reduction [74]. On the other hand, the spectral variability among the ocean objects of interest is large, and the spectral curves between them are discriminatory, as depicted by the target type that is clearly distinguishable from a small number of bands. As the spatial resolution of the image pixels becomes higher, the spectral consistency of the target image pixels becomes stronger, and improving the target classification accuracy becomes more beneficial. Therefore, increasing the spatial resolution is better than increasing the spectral resolution when identifying and classifying marine objects on this spatial scale. The overall accuracy of MODIS is the lowest at 89.3%, which is lower than the 91.9% of GOCI and 95.7% of OLCI. This finding indicates that 10.7% of the pixels in the data-free area of MODIS are misidentified, resulting in a systematic error in the statistics. This order also reflects their increasing spatial resolution from MODIS, GOCI and to OLCI. This undoubtedly reflects the importance of spatial resolution for obtaining excellent classification results at this spatial scale.  
The accompanying phenomenon of the data-free area has been explained in this section with respect to the spatial resolution of data. The spatial resolution of ocean color satellites is generally low because of the vast area of the ocean and the large scale of the obtained data. Low spatial resolution data inevitably have mixed pixels. For example, in the case of the fish-scale clouds mentioned above, the low-resolution data have mixed pixels of clouds and clean water when gaps between the clouds are small. This scenario further affects the ability of the cloud detection algorithm and results in missing inter-cloud pixels. As shown in Figure 14c,f, the number of cloud pixel identification results of MODIS are more than those of GOCI because of the presence of mixed pixels. This is also the case in the turbid water of Figure 17. This difference can further explain the presence of concomitant phenomena in ocean-covered objects in data-free areas.
# Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer
## 3 Golden Sine Grey Wolf Optimizer
### Golden Sine Strategy  
The golden sine algorithm [35] is a mathematical model-based meta-heuristic algorithm proposed by [PERSON] in 2017. It traverses all points on the circle through the sine function and reduces the search space through the golden section coefficient, so that the algorithm can have higher search efficiency without falling into the local optimum.  
The position update equation of the golden sine algorithm is as follows:  
\[X^{\prime}(t+1)=X(t+1)\cdot|\sin r_{1}|-r_{2}\cdot\sin r_{1}\cdot|s_{1}\cdot X _{1}-s_{2}\cdot X(t+1)|, \tag{12}\]  
\[\begin{cases}s_{1}=a\cdot\tau+b\cdot(1-\tau)\\ s_{2}=a\cdot(1-\tau)+b\cdot\tau\end{cases}, \tag{13}\]  
where \(X^{\prime}(t+1)\) is the convergence direction of the golden sine algorithm. \(r_{1}\) and \(r_{2}\) are random numbers between \([0,2\pi]\) and \([0,\pi]\), respectively. \(s_{1}\) and \(s_{2}\) are the golden section coefficients. \(\tau\) is the golden section number, take \(\left(\sqrt{5}-1\right)/2\). The values of \(a\) and \(b\) are set to \(-\pi\) and \(\pi\) and then change with the target value.  
The update rule after introducing the golden sine search strategy into the grey wolf algorithm is as follows: when \(a_{1}<1\), the iteration enters the local search stage; after obtaining \(X(t+1)\), it will converge twice. The golden sine convergence position is calculated by Equation (12), and then the cost between \(X(t+1)\) and \(X^{\prime}(t+1)\) is compared, and the better one is taken. Because of the ergodicity of the golden sine algorithm and the high efficiency of the search range, the grey wolf algorithm is quadratically convergent after the position update is completed. It can also jump out of the local optimum while ensuring the convergence speed in the later stage.
# A Set-Theoretic Approach to Modeling Network Structure
## 3 Network Properties
### Important Nodes  
A fundamental quest in the analysis of many networks is the identification of its \"important\" nodes. They may be a node of high degree in a community, but need not be. In social networks, \"importance\" may also be defined with respect to the path structure [19; 20]. Those nodes, \(C_{d}=\{y\in\mathcal{N}\}\) for which the eccentricity, \(e(y)\), or \(\sum_{x\
eq y}d(x,y)\), is _minimal_, have traditionally been called the **center** of \(\mathcal{N}\)[1; 2]; they are \"closest\" to all other nodes. It is well known that this subset of nodes must be edge connected. One may assume that these nodes in the \"center\" of a network are \"important\" nodes.  
Alternatively, one may consider those nodes which \"connect\" many other nodes, or clusters of nodes, to be the \"important\" ones. Let \(nsp_{xz}(y)\) denote the _number_ of shortest paths \(\sigma(x,z)\) containing \(y\); then, those nodes \(y\) for which \(nsp_{xz}(y)\) is _maximal_ are involved in the most connections. Let \(C_{b}=\{y\in\mathcal{N}\}\), for which \(nsp_{xz}(y)\) is _locally_ maximal. This is sometime called \"betweenness centrality\" [19; 20; 21]. (In [20], [PERSON] proposed the notion of \"random walk betweenness\" as an alternative to shortest path betweenness).
## 2 Materials and Methods
### Pulse-Echo Measurements  
A set of X- and Y-cut wide-band transducers (Olympus Corporation, Tokyo, Japan) was used to send and receive longitudinal and transversal ultrasonic waves in the glass samples under isothermal and isobaric conditions in a pulse-echo configuration. The same piezoelectric element was working both as signal transmitter and receiver of the echoes. A burst generator (TTi Inc., Munich, Germany) was used to excite the transducers at a frequency of 10 MHz, which is near the fundamental frequency of the piezoelectric elements. A second channel of the function generator was utilized to trigger and synchronize a Tektronix digital oscilloscope for accurate measurements of the output signal. The reference and back wall echo trail monitored in the oscilloscope were averaged over time and then sent to a computer for further analysis of the signals. A small amount of couplant, appropriate for common medical applications, was placed in the transducer-glass interface to ensure the maximum ultrasonic radio frequency (RF) signal transmittance in the glass sample.  
Considering the thickness of the glass sample as \(d\), the longitudinal \(u_{l}\) and transversal \(u_{s}\) sound velocity can be estimated as:  
\[u_{l}=\frac{2d}{t_{l}}\text{ and }u_{s}=\frac{2d}{t_{s}} \tag{1}\]  
where \(t_{l}\) and \(t_{s}\) are the time between two consecutive echoes of the longitudinal and transversal waves, respectively, that are monitored in the oscilloscope. For a given sample thickness, the \(t_{l}\) and \(t_{s}\) time intervals are the main sources of error in the determination of the ultrasound speed. A mathematical methodology that permits precise estimation of the time intervals is the cross-correlation technique, which provides an accuracy in sound velocity better than 0.2% [31].  
Considering as \(r_{1}(t)\) and \(r_{2}(t)\) are the two amplitude-normalized signals and \(\tau_{s}\) the time-lag between these signals, then the following relation holds:  
\[r_{2}(t)=r_{1}(t-\tau_{s}) \tag{2}\]  
Subsequently, the cross-correlation function can be estimated as:  
\[R_{12}(\tau)=\frac{1}{2T}\int\limits_{0}^{T}r_{1}(t)r_{2}(t+\tau)dt \tag{3}\]  
This function reveals a global maximum at \(\tau_{s}\), that is the time interval between consecutive echoes. The two amplitude-normalized signals \(r_{1}(t)\) and \(r_{2}(t)\), as well as the cross-correlation function, are presented in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample Notation** & **La\({}_{2}\)O\({}_{3}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **SiO\({}_{2}\)** \\ \hline LAS1 & 0.15 & 0.15 & 0.70 \\ LAS2 & 0.15 & 0.20 & 0.65 \\ LAS3 & 0.15 & 0.25 & 0.60 \\ LAS4 & 0.15 & 0.30 & 0.55 \\ LAS5 & 0.15 & 0.35 & 0.50 \\ LAS6 & 0.25 & 0.15 & 0.60 \\ LAS7 & 0.25 & 0.20 & 0.55 \\ LAS8 & 0.25 & 0.25 & 0.50 \\ LAS9 & 0.25 & 0.30 & 0.45 \\ LAS10 & 0.25 & 0.35 & 0.40 \\ SiO\({}_{2}\) & 0 & 0 & 1 \\ \hline \end{tabular}
\end{table}
Table 2: Mole fraction of the lanthanum aluminosilicate glasses used in this study.
## 5 Conclusions  
In this work, the block version of ISAI (B-ISAI)is studied on multiple GPUs as a sub-domain constructor for the inverses of ILU factors in the Block-Jacobi preconditioning technique. The algorithm is integrated in the open source PETSc framework. The two experiments from the real problems with the block matrix show that BJPB-ISAI algorithm offers a better preconditioner than the parallel level-scheduling based preconditioning in terms of total computing time of GMRES(30), but at the sacrifice of the number of GMRES iterations. It is found that in the extreme cases where strong data dependency exists (for example, block matrices from the five-point finite difference method), the performance of a linear solver using BJPB-ISAI can be much improved on GPUs than that using block triangular solves from the cuSPARSE library. The comparisons on two generations of Tesla GPUs are conducted, compared to Tesla K20 GPU, the Tesla V100 GPU shows great performance improvement on the essential steps of the proposed PBJB-ISAI preconditioner.  
Conceptualization, W.M.; methodology, W.M.; software, X.L.; validation, W.Y. and X.L.; formal analysis, W.M.; investigation, W.M.; resources, W.M., W.Y. and X.L.; data W.M.; writing--original draft preparation, W.M., W.Y. and X.L.; writing--review and editing, W.M., W.Y. and X.L.; visualization, X.L.; supervision, W.M.; project administration, W.M. and W.Y.; funding acquisition, W.M. and W.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant from National Key R&D Program of China (No. 2019 YFB1704202), the National Natural Science Foundation of China (Nos. 61702438), the Nanhui Scholar Program of XYNU and the Innovation Team Support Plan of University Science and Technology of Henan Province (19 IRTSTHN014).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 11: Driven cavity case: the comparisons of overall times with increasing mesh sizes of the problem.
## 3 Results and Discussion
### In Silico Analysis of Physicochemical Properties, Pharmacokinetics, and Drug-likeness
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Compound** & **WLOGP a** & **\% ABS b** & **XLOC93 c** & **Log S d** & **Csp 3 e** & **GI abs f** & **P-gp sub \$** & **BBB h** & **PAINS 1** \\ \hline
**4a** & 3.13 & 85.1 & 3.13 & \(-\)3.74 & 0.00 & High & No & Yes & 0 \\
**4b** & 3.39 & 88.9 & 3.09 & \(-\)3.76 & 0.08 & High & No & Yes & 0 \\
**4c** & 3.87 & 88.9 & 3.39 & \(-\)3.94 & 0.14 & High & No & Yes & 0 \\
**6a** & 2.88 & 81.0 & 3.45 & \(-\)3.93 & 0.00 & High & No & No & 0 \\
**6b** & 2.41 & 86.2 & 2.84 & \(-\)3.44 & 0.00 & High & No & Yes & 0 \\
**6c** & 2.21 & 86.3 & 2.14 & \(-\)3.08 & 0.00 & High & No & Yes & 0 \\
**Albenzazole** & 3.05 & 77.2 & 2.81 & \(-\)3.23 & 0.33 & High & No & No & 0 \\ \hline \hline \end{tabular}  
* Water Partition Coefficient; b % of Absorption = 109\(-\)0.345 \(\times\) TPSA [25]. c Predicts the logP value of a query compound by using the known logP value of a reference compound as a starting point [36]: d Indicator of the aqueous solubility of a compound; e The Fraction of Carbon atoms in sp3 hybridization; f Gastrointestinal Absorption; g Transmembrane Glycoprotein; h Blood-brain Barrier Permeability; i Pan-Assay Interference Compounds.  
\end{table} 
Table 4: In silico estimated physicochemical parameters of the heterocyclic benzimidazolzoles **4a–c**, **6a–c**, and albendazole. Figure 4: Boiled-egg graphs calculated by SwissADME web tool for the benzimidazole-2-yl-methanamines **4a–c**, 1H-benzimidazol-2-yl hydrazones **6a–c** and the conventional anthelminitelbandazole. White region: high probability of passive absorption by the gastrointestinal tract. Yellow region: high probability of brain penetration. \(\bullet\) Non-substrates of P-gp.
# Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey
## 8 Challenges and Future Directions
### On the Effect of the Blockchain Continuous Evolution  
While unsupervised learning methodologies offer remarkable adaptability in detecting anomalies, a significant challenge arises from ensuring the ongoing fit and adaptability of the resulting models to the evolving blockchain architectures and components. As blockchain technology continues to evolve with advancements in consensus mechanisms, network protocols, and the introduction of new features, existing anomaly detection models may become outdated or less effective over time. This problem is exacerbated by the decentralized and distributed nature of blockchain networks, which can lead to heterogeneous data distributions and dynamic transaction patterns [194].  
To address the challenge of model fit and adaptability, researchers must focus on developing techniques that enable continuous learning and adaptation in real time. This requires the integration of mechanisms for model retraining and updating based on the latest blockchain data and network dynamics. Additionally, the incorporation of techniques such as transfer learning and domain adaptation can facilitate the transfer of knowledge between different blockchain environments, improving the generalization and robustness of anomaly detection models [195].  
Moreover, the utilization of blockchain-based governance mechanisms, such as decentralized autonomous organizations (DAOs), can enable collective decision-making processes for model updates and parameter tuning, ensuring community-driven governance. By addressing the challenge of model fit and adaptability, researchers can enhance the long-term effectiveness and reliability of anomaly detection mechanisms in blockchain networks, supporting the integrity and security of decentralized ecosystems.
# A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model
## 2 Structure of the Combined LSCF-MLE-PMM
### The MLE-PMM (2 rd Step)
#### 2.2.3 Convergence of the ML Algorithm  
The MLE-PMM algorithm optimizes iteratively the starting estimates provided by the LSCF. This is accomplished by setting a maximum number of iterations and a value for the relative error between two consecutive iterations of the ML-PMM algorithm (i.e., \(\left|l(\Theta_{i+1})-l(\Theta_{i})\right|/l(\Theta_{i})\), with \(l\) denoting the cost function to be minimized and \(i\) the iteration number). In order to avoid local minima and enlarge the convergence region of the Gauss-Newton algorithm, the following Levenberg-Marquardt form of Equation (11) should be used [23] to ensure that the cost function decreases during the performed iterations  
\[\left(J_{i}^{H}J_{i}+\lambda_{LM}(J_{i}^{H}J_{i})_{D}\right)=-\Delta\Theta_{i} J_{i}^{H}E_{i} \tag{24}\]  
Increasing the Levenberg-Marquardt parameter \(\lambda_{LM}\) in Equation (24) forces the cost function to decrease but reduces the convergence speed. Normally \(\lambda_{LM}=0\) is used as an initial value and then it is adapted in every iteration according to the variation of the cost function. If the cost function increases, the value of parameter \(\lambda_{LM}\) is increased, otherwise, it is decreased [22].
## 4 Results and Discussion
### Web Application Development  
A prototype web-based application is designed and developed to provide real-time RUL predictions. The application is built using the following tools:  
1. Framework: The web application is developed using Streamlit, a Python-based framework known for its simplicity and interactive capabilities. Streamlit allows for rapid prototyping and the creation of visually appealing dashboards and applications. It provides built-in support for integrating machine learning models and simplifies the process of creating forms, charts, and dynamic outputs.
2. Backend: The backend of the application is powered by Python, implementing trained machine learning models. These models are algorithms like LSTM, ANN, Random Forest etc., which have been pre-trained on a dataset of lithium-ion battery degradation patterns. Google Colab is utilized for model training and validation due to its cloud-based GPU acceleration, which speeds up computation-intensive tasks. The trained models are exported and integrated into the application for real-time prediction functionality.
3. Frontend: The front end is designed with a user-friendly interface to ensure intuitive user experience. Users can input key battery parameters such as charge cycles, temperature ranges, and voltage levels through the application as shown in Figure 19 below. The frontend then displays the predicted RUL alongside visualizations like charts and graphs to provide insights into battery health and degradation trends. Streamlit's interactive widgets and charts facilitate this process, enhancing usability.
4. Deployment: The machine learning models are available on Github Repository, furthermore, to ensure accessibility and scalability, the application is hosted on a cloud platform. Google Colab is used in conjunction with Streamlit and Django  
Figure 18: Permutation-based feature importance for the ANN model.  
for model deployment. By linking our Algorithm on Google Colab notebooks directly to the Streamlit application, real-time predictions can be achieved without requiring extensive local computational resources. The cloud-hosted nature of the application ensures that users can access it from any device with internet connectivity, enabling widespread adoption and utility.
## 10 Discussion and Future Developments
### Digital Twin and BIM Model Generation  
The Milan project required a great effort of both preliminary and organizational study because of its highly innovative approach, which required the definition of survey specifications and data processing and management methodologies from zero. The value of the acquired data is currently showing its full significance, and a lot has to be done to give value to the huge surveying project. In fact, having carried out a survey not only photographically but also with the three-dimensional component, in addition to enabling local measurements to be carried out in the mapped areas, as originally planned, is making it possible to investigate the introduction of innovative technologies. Thus, the Milan project achieved the required result of creating a point cloud-based digital twin of all the building complexes managed by the engineering company. The availability of a three-dimensional model of buildings has enabled the implementation of advanced tools to support building management and maintenance operations. In particular, a 3D model allows the generation of simplified BIM models, which, combined with photographic information, facilitate the development of rapid self-localization tools for operators. This can be achieved using simple cameras mounted, for example, on an operator's helmet (Figure 37). Software platforms such as Cupix(r), version 2.2.1. [109] or self-localization functions integrated into Facility management platforms, such as SiteWalk(r) by Dalux(r), version 1.3.1, provide solutions that allow an operator, equipped with a handheld or helmet-mounted camera, to self-locate within a building simply by capturing images of the surrounding environment (Figure 38). These solutions are gaining popularity.  
Figure 37: An operator moves with a helmet-mounted camera. The platform can determine the operator’s position and, consequently, its own trajectory within a building for which the 3D model isknown. Example of the Sitewalk(r) Dalux(r) platform in use at the test site of the \"Modulo\" building at the University of Brescia--Faculty of Engineering.  
The operator knows its own position and can therefore tag geolocated information on objects it observes in the image displayed on the handheld device.  
By moving through the building and/or construction site, it is possible to associate information, tasks, activities, or non-compliance issues with elements simply by observing them in the image and associating the appropriate information. Of course, it is important to note that for the camera to self-localize, a three-dimensional model, even if approximate, of the site must be known.
# Near-Optimal Weather Routing by Using Improved A* Algorithm
## 2 Routing Algorithms
### Bellman-Ford Algorithm
```
1:functionBELLMAN-FORD(\(\mathbf{G}(\mathbf{V},\mathbf{E})\), \(\mathbf{W},s,t\))
2:for all\(u\in V-\{s\}\)do
3:\(d[v]\infty\), \(pred[v]nil\)
4:\(d[s]0\)
5:for all\(i\gets 1\) to \(|\mathbf{V}|-1\)do
6:for all\(e\in[e_{u,v}\in\mathbf{E}\bigm{|}u\in\mathbf{V},\ v\in\mathbf{adjacent}[u]]\bigm{|}\)do : Edge relaxation
7:if\(d[v]>d[u]+\mathbf{W}(e)\)then
8:\(d[v]\stackrel{{ update}}{{\leftarrow}}d[u]+\mathbf{W}(e)\)
9:\(pred[v]\stackrel{{ update}}{{\leftarrow}}u\)
10:for all\(e\in[e_{u,v}\in\mathbf{E}\bigm{|}u\in\mathbf{V},\ v\in\mathbf{adjacent}[u]]\bigm{|}\)do
11:if\(d[v]>d[u]+\mathbf{W}(e)\)then : Negative cycles in \(\mathbf{G}\) detected
12:return\(\varnothing\)
13:return\(d\), \(MAKEPATH(t,\ pred)\)
```  
**Algorithm 1** Bellman-Ford Algorithm  
In Algorithm 1, \(\mathbf{V},\mathbf{E}\), and \(\mathbf{W}\) are the sets of nodes, edges, and weights in the graph \(\mathbf{G}\), respectively; \(pred(n)\) is the parent node of an arbitrary node \(n\); and \(e_{u,v}\) is the weight of edge linking two adjacent nodes, \(u\) and \(v\).