esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Methodology  
### Optical Engine Facility  
The tests were carried out in a [PERSON] optical engine design, as can be seen in Figure 1, based on a medium-duty CI platform. This engine has the typical overhead valve design, where the camshaft is located at the engine block and connected to the crankshaft via a gear. Push rods are used for opening and closing the valves. In this facility, a specific box was installed just on the cylinder side to allocate the camshaft, as the distance between the head and block is increased due to the optical access design. Due to this adaptation, there are no lateral optical accesses to the combustion chamber, allowing just the visualization through the piston bottom. The optical engine uses the same original cylinder head as the conventional version. It has four valves and a centered solenoid injector per cylinder. In Table 1, the main geometric parameters of the optical engine are summarized.  
The fuel was delivered to the injector using a high-pressure pump (Bosch CP3, Gerlingen, Germany) and a conventional common rail. A Controlled Diffusive Spray (CDS) injector with eight holes was employed for the current work. This injector allows an improving the distribution of the spray inside the chamber. The result is a shorter penetration of the spray and a wider cone angle. The result is a closer combustion reaction to the center of the piston bowl, as opposed to the traditional nozzles, that the combustion reactions appear close to the walls [30]. Several numerical [31] and experimental [23; 32; 33] studies have shown a significant reduction in pollutant emissions, particularly in particulate matter and hydrocarbon emissions. Thermodynamic analysis was performed thanks to a fully instrumented engine. The instantaneous intake and exhaust pressures were measured using a piezoresistive transducer (Kistler 4049A5 Switzerland). A piezoelectric transducer (Kistler 6124A, Switzerland) was used for registering the in-cylinder pressure. The pressure signals were synchronized with a crankshaft encoder with a 0.5 crank angle degree (CAD) resolution. In addition, intake and exhaust temperatures and oil and coolant temperatures  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Number of cylinders [-] & 1 \\ Number of valves [-] & 4 \\ Compression ratio & 13.3 \\ Stroke/Bore & \(\sim\)1 \\ Displacement [L] & \(\sim\)0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optical engine characteristics.  
Figure 1: Optical engine parts. Reprinted with permission [24].  
were measured with K-type thermocouples. The first thermodynamic law calculates the apparent heat release rate (aRoHR). A screw compressor provides the intake air at the required pressure, and a valve at the exhaust system simulates the back pressure of 0.2 bar. An air heater was located just before the intake port to set the intake air temperature required for each test. The engine was run in skip-fire mode to reduce thermal stress, window fouling, and thermal transients [34]. As a result, just one combustion cycle occurred each 20 motored cycles.
# Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model
## 7 Conclusions  
Fly-rock is an undesirable phenomenon that is generated by mine blasting. It is evaluated as the most dangerous phenomenon among the side effects of blasting. This study introduced a state-of-the-art technology of fly-rock prediction with high accuracy based on ANN models and their robust combination (i.e., EANNs). The findings indicated that the proposed EANNs' performance was improved significantly when compared to the sub-models (i.e., ANN models). In other words, individual ANN models have served as positive input variables to improve the accuracy of the proposed EANNs model. This finding is impressive, and it should be applied in experimental conditions to control the distance of fly-rock, reducing the risks to humans and equipment.  
Data collection and experimental works: H.N., X.-N.B.; Writing, discussion, analysis: H.N., X.-N.B., H.M., P.R.; Revise and preparing the revision: H.N., X.-N.B., T.N.-T.  
This research received no external funding.  
The authors would like to thank Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam; Duy Tan University, Da Nang, Vietnam; and the Center for Mining, Electro-Mechanical research of HUMG.  
Figure 10: Sensitivity analysis results for input parameters.  
Figure 9: Comparison of the proposed EANNs and ANN models with the same structure.
## 1 Introduction
Calibration equations need to be applied to use portable Chl meter readings to convert these readings to actual chlorophyll concentration. Remote sensing offers an alternative set of techniques to estimate chlorophyll concentration. These can be grouped into two main categories: (i) Radiative transfer models and (ii) vegetation indices (VIs). The physically based canopy reflectance model relies on the principle that canopy reflectance is controlled by a combination of canopy and soil background biophysical variables such as vegetation structure, leaf composition, and illumination angle [33, 34, 35]. To estimate Chl from observed reflectance data, the physical model must be inverted. The inversion consists of adjusting the input biophysical variables to reduce the error between the simulated and measured reflectance [36, 37]. While these techniques have been applied with success [38, 39], they can be computationally demanding. In addition, they suffer from the so-called ill-posed problem [40, 41] due to model and measurements uncertainties; that is, different model parameters might result in very similar spectra [42]. The VI approach is based on the statistical or empirical relationship between arithmetic combinations of two or more spectral bands and a particular leaf or canopy characteristic (_i.e._, chlorophyll concentration) [43]. It has been argued that this approach is sensor-specific, site-dependent, and does not account for variability in LAI. However, the VI approach offers computational simplicity and accuracy, and its potential for predicting vegetation variables is well supported by numerous published studies [40, 44]. VIs can be derived from hyperspectral and multispectral data. Several studies have examined the relationship of VIs and CC leaf hyperspectral response at the leaf level [19, 41, 45]. Sensors on board different satellites have estimated vegetation CC using VIs at varying spatial resolutions, from a few to hundreds of meters [46, 47, 48]. While field spectroscopy and satellite-derived VIs have been used to estimate the chlorophyll content in leaves and canopies in different vegetation types, only few studies have focused on mangrove forests [21, 22, 30, 32]. The spatial distribution and seasonal dynamics of mangrove forest CC is not well understood as previous studies have been spatially localized. Given the importance of foliar pigments as surrogates of mangrove physiological status, phenology, health condition, and potentially GPP, it is fundamental to assess the accuracy of VIs to predict CC at the leaf and landscape level. Our main goal is to show for the first time that the multispectral sensor Landsat 8 can be potentially used to produce maps of spatial distribution and temporal variation of chlorophyll concentration in mangrove forests. The objectives of this research were to (a) assess the performance of selected hyperspectral and broad band VIs for predicting CC at the leaf level, and (b) relate the estimated CC on the ground with Landsat 8 data to map the spatial distribution and temporal variability of mangrove CC at the landscape level.
# Stress Estimation Using the Acoustoelastic Effect of Surface Waves in Weak Anisotropic Materials
## Appendix A  
Considering the uniaxial stress (\(\sigma_{1}=\sigma,\sigma_{2}=0\)) and angle condition of \(\Phi=\theta\), Equation (17) can be rearranged as follows,  
\[\frac{V_{\theta}-V_{R}}{V_{R}}=\frac{1}{2\mu}A_{2222}\cos^{4}\theta C_{11}^{ \prime}+(a_{\phi 0}+\sum_{k=1}^{3}\frac{a_{\Delta k}\
u_{k}}{\mu})\frac{ \sigma}{\mu}-(a_{\Delta 0}+\sum_{k=1}^{3}\frac{a_{\Delta k}\
u_{k}}{\mu})\cos 2\theta \frac{\sigma}{\mu} \tag{18}\]  
By substituting \(\sigma=0\), the velocity in the unstressed state, in the \(\theta\) direction can be obtained as follows:  
\[V_{\theta,R}=(\frac{1}{2\mu}A_{2222}\cos^{4}\theta C_{11}^{\prime}+1)V_{R} \tag{19}\]  
On the other hand, the velocity in the stressed state in the \(\theta\) direction is represented as follows,  
\[V_{\theta}=\Bigg{[}\frac{1}{2\mu}A_{2222}\cos^{4}\theta C_{11}^{\prime}+(a_{ \phi 0}+\sum_{k=1}^{3}\frac{a_{\Delta k}\
u_{k}}{\mu})\frac{\sigma}{\mu}+(a_{ \Delta 0}+\sum_{k=1}^{3}\frac{a_{\Delta k}\
u_{k}}{\mu})\frac{\sigma}{\mu}\cos 2 \theta+1\Bigg{]}V_{R} \tag{20}\]  
Considering the condition of Case 1A shown in Tables 1 and 2, for \(\theta=0^{\circ}\), \(45^{\circ}\) and \(90^{\circ}\), three-directional surface wave velocities in the unstressed state can be expressed by Equations (A4)-(A6)  
\[V_{0,R}=(\frac{1}{2\mu}A_{2222}\cos^{4}0C_{11}^{\prime}+1)V_{R}=(\frac{1}{2\mu} A_{2222}C_{11}^{\prime}+1)V_{R} \tag{21}\]  
\[V_{45,R}=(\frac{1}{2\mu}A_{2222}\cos^{4}45C_{11}^{\prime}+1)V_{R} \tag{22}\]\[V_{90,R}=(\frac{1}{2\mu}A_{2222}\cos^{4}90C_{11}^{\prime}+1)V_{R}=V_{R} \tag{10}\]  
The three-directional velocities in the stressed state are represented by Equations (10)-(11).  
\[V_{0}=\Bigg{[}\frac{1}{2\mu}A_{2222}C_{11}^{\prime}+(a_{00}+\sum_{k=1}^{3}\frac {a_{0k}v_{k}}{\mu})\frac{\sigma}{\mu}+(a_{\Delta 0}+\sum_{k=1}^{3}\frac{a_{\Delta k }v_{k}}{\mu})\frac{\sigma}{\mu}+1\Bigg{]}V_{R} \tag{11}\]  
\[V_{45}=\Bigg{[}\frac{1}{2\mu}\frac{1}{4}A_{2222}C_{11}^{\prime}+(a_{00}+\sum_{ k=1}^{3}\frac{a_{0k}v_{k}}{\mu})\frac{\sigma}{\mu}+1\Bigg{]}V_{R} \tag{12}\]  
\[V_{90}=\Bigg{[}(a_{90}+\sum_{k=1}^{3}\frac{a_{0k}v_{k}}{\mu})\frac{\sigma}{\mu }+1\Bigg{]}V_{R} \tag{13}\]  
The acoustoelastic coefficients defined in Equations (11) and (12) then become the following:  
\[K_{1}=\frac{(a_{00}+\sum\limits_{k=1}^{3}\frac{a_{0k}v_{k}}{\mu})\frac{1}{\mu }+(a_{\Delta 0}+\sum\limits_{k=1}^{3}\frac{a_{0k}v_{k}}{\mu})\frac{1}{\mu}}{( \frac{1}{2\mu}A_{2222}C_{11}^{\prime}+1)} \tag{14}\]  
\[K_{2}=(a_{90}+\sum_{k=1}^{3}\frac{a_{0k}v_{k}}{\mu})\frac{1}{\mu} \tag{15}\]  
Finally, Equations (10)-(15) are substituted into the stress estimation equations shown in Equations (13)-(15) to obtain \(\sigma_{1}=\sigma\), \(\sigma_{2}=0\) and \(\theta=0\), which are identical to the given conditions.
## 1 Introduction
Another algorithm, CBLARHM (Cluster Based Location-Aware Routing Protocol for Large Scale Heterogeneous Mobile _Ad hoc_ Networks) [14], is based on GPS (Global Position System). This mechanism is utilized for clustering large scale networks, but involves high cost due to the use of GPS. These algorithms have difficulty in clustering and managing when the network size is variable. To solve this problem, this paper proposes a novel Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) that efficiently clusters and manages sensors using the fuzzy information of node status in the network. The proposed FRCA uses the Fuzzy Relevance Degree (FRD) with fuzzy value \(\mu\)[15] to perform and manage clustering. We regard the Fuzzy Relevance Degree (FRD) with fuzzy value \(\mu\) as FRD. Therefore, in this algorithm, FRD performs clustering by choosing some nodes that act as coordinators of the clustering. The fuzzy state viewing structure, which is performs clustering, consists of 5 parameters: ID, \(\mu\), Level, M-hop, and Balance. The cluster head ClusterHead (CH) and cluster members ClusterMember (CM) are selected using fuzzy value \(\mu\) in the fuzzy state viewing structure. In the proposed algorithm, FRD is used to solve expandability and to control the generation of multi-hop cluster. FRD controls the number of clusters to improve efficiency. The clustering based on FRD helps in maintaining the structure of the cluster as stable as possible, and thus minimizing the topology changes and associated overheads during ClusterHead changes. We compared the proposed algorithm with existing methods, such as CBRP (Cluster-Based Routing Protocol) [8], WACA (Weighted-based Adaptive Clustering Algorithm) [3], and SCAM [1] (Scenario-based Clustering Algorithm for Mobile _ad hoc_ networks), in terms of performance. According to the simulation result, the proposed algorithm achieves better performance than the existing ones. The rest of the paper is organized as follows. Related works are reviewed in Section 2. In Section 3, details of the proposed FRCA algorithm are presented. In Section 4, the simulation results of the proposed FRCA algorithm are given and the algorithm's performances are discussed. Finally, in Section 5, some conclusions are drawn.
## 5 Reliability Evaluation of the Nonlinear Frame Structure  
### EEV-Based Reliability Evaluation Strategy  
The maximum story drift ratio of each story as the dynamic response reference indicators is recommended by the Chinese Code for Seismic Design of Buildings (GB 50011-2010, 2016) [46], and the dynamic reliability of the frame structure of a single-story can be written as follows, based on the first-passage failure criterion with a symmetrical double boundary [47]:  
\[R_{Tj}(\varphi_{b})=\Pr\bigl{\{}\Phi_{\max,j}<\varphi_{b}\bigr{\}}=\int_{0}^{ \varphi_{b}}p_{\Phi_{\max,j}}(\varphi)\mathrm{d}\varphi \tag{28}\]  
where \(j\) denotes the number of stories of the frame structure. \(R_{Tj}(\varphi_{b})\) denotes the reliability of the \(j\)-th story of the frame structure over the interval \([0,T]\). \(\varphi_{b}\) is the threshold. \(\Phi_{\max,j}=\max\limits_{t\in[0,T]}\left\{\left|\Phi_{j}(t)\right|\right\}\) is the EEV of \(\Phi_{j}(t)\), and \(p_{\Phi_{\max,j}}\) denotes the PDF of the extreme value \(\Phi_{\max,j}\), respectively. It is clear that the single-story dynamic reliability \(R_{Tj}(\varphi_{b})\) is the cumulative distribution function (CDF) of the extreme value \(\Phi_{\max,j}\).  
To this end, the global dynamic reliability of the frame structure can be expressed as follows:  
\[R_{T}(\varphi_{b})=\Pr\{\Phi_{\max}<\varphi_{b}\}=\int_{0}^{\varphi_{b}}p_{ \Phi_{\max}}(\varphi)\mathrm{d}\varphi \tag{29}\]  
Figure 9: Probability information of the story drift ratio of the eighth story: (**a**) the PDFs of the story drift ratio of the eighth story at three typical time instances; (**b**) probability information of the story drift ratio of the eighth story from the time instance \(t=2\) s to 4 s.  
where \(\Phi_{\max}=\max_{j\in[1,10]}\left\{\Phi_{\max,j}\right\}=\max_{j\in[1,10]}\left\{ \max_{t\in[0,T]}\left\{\left|\Phi_{\left(t\right)}\right|\right\}\right\}\) is the EEV of the story drift ratio of the frame structure and \(p_{\Phi_{\max}}(\varphi)\) is the PDF of \(\Phi_{\max}\), respectively. Similarly, the global dynamic reliability of the frame structure \(R_{T}(\varphi_{b})\) is the CDF of the extreme value \(\Phi_{\max}\).  
It is important to note that the out-crossing-process theory on the first-passage reliability problem frequently fails to provide a precise solution. However, by employing the EEV event, the complete correlational information is already present, making it simple to determine the precise solution.
## 3 Results and Discussion
### Forecasting Model Based on Single NTL Index
To further explore the nonlinear relationship between the NTL data and the TOVAFAF, the PSO algorithm was employed to optimize the input weights and hidden layer biases of the ELM model. The results show that the PSO-ELM model achieves the best forecasting performance with the R\({}^{2}\) value for 0.8974 and the MRE value for 23.42%.
# A Hierarchical Generative Embedding Model for Influence Maximization in Attributed Social Networks
## 5 Experiment Setup
### Baselines  
We evaluated the performance of the proposed HGE model compared with the following baselines:  
* GraphSAGE [40]: an attributed network embedding model which leverages node attributes and generates embeddings by sampling and aggregating features from a node's local neighborhood;
* AANE [41]: a model which learns attributed network embedding efficiently by decomposing complex modeling and optimization into sub-problems;
* M-NMF [27]: a single-layer community structure preserving baseline, which integrates the community information through a matrix factorization;
* GNE [33]: a multi-layer community structure preserving baseline, which embeds communities onto surfaces of spheres;
* SpaceNE [36]: a method which applies subspace to the hierarchical network embedding model and preserves the proximity between pairwise nodes and between communities;  
Moreover, we evaluate the performance of the proposed HGE-GA algorithm compared with the following baselines:  
* PMIA [12]: This is a heuristic algorithm that defines the maximum influence in-arborescence (MIIA) and leverages sequence submodularity to compute the influence spread;
* IMM [9]: This utilizes a classical statistical tool, martingale, as well as RR sets (reverse reachable sets), and can provide higher efficiency in practice;
* TSH-GA : [PERSON] et al. [20] propose a method to compute the influence probability of node pairs, which considers social ties, general network structure, and node attributes  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **\#Nodes** & **\#Edges** & **\#Attributes** & **\#Labels** \\ \hline
**Cora** & 2708 & 5429 & 1433 & 7 \\
**DBLP** & 17,716 & 105,734 & 1639 & 4 \\
**BlogCatalog** & 5196 & 171,743 & 8189 & 6 \\
**Flickr** & 7575 & 239,738 & 12,047 & 9 \\
**Pubmed** & 19,717 & 44,338 & 500 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the datasets used in the experiments.  
as factors underlying influence but executes feature extraction of these three factors by typically hand-crafted rules. TSH-GA is an influence maximization algorithm jointing the method to compute the influence probability of node pairs proposed in [20] and the general greedy algorithm;
* HGE-1N-GA: This is a variant of our HGE-GA which uses the one-normal form to compute the similarity of two nodes.
## 4 Conclusions  
_N. takakii_ has been previously isolated from a soil sample; however, this is the first report of the secondary metabolites from a marine-derived strain of this species. Besides the indole alkaloids aszonalenin; acetylaszonalenin; and tryptoquivalines L, H, and F, and the meroditerpene aszonapyrone A, which are common among the members of this genus, a new tryptoquivaline analog (trytoquivaline U), a new meroditerpene with an uncommon side chain containing an enol function (sartorend), a prenylated indole alkaloid (takakiamide) and the previously reported isocomarnin derivative (6-hydroxymellein) were also produced by the culture of the marine-derived _N. takakii_ KUFC 7898. Although sartorend, takakiamide and tryptoquivaline U did not exhibit any antibacterial activity against the Gram-positive (_Staphylococcus aureus_ ATCC 25923 and _Bacillus subtilis_ ATCC 6633) and Gram-negative (_Escherichia coli_ ATCC 25922 and _Pseudomonas aeruginosa_ ATCC 27853) bacteria as well as methicillin-resistant _S. aureus_ (MRSA) and vancomycin-resistant Enterococci (VRE) from the environment in our assay protocol, it does not mean that these new metabolites do not have other interesting biological activities. Thus, these new metabolites should be explored in other bioassay protocols so that their potential can be further discovered.
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 6 Conclusions  
We have improved the traditional D* Lite algorithm and realized multi-goal path planning and obstacle avoidance for USVs in unknown, dynamic and complex environments. By expanding the algorithm to search more neighbor nodes and increasing the safe distance, we have solved the issue of the limited steering maneuverability of USVs during autonomous navigation. A path optimization algorithm has been proposed to eliminate sharp turns and reduce the path length. The minimum binary heap has been used to optimize the priority queue of the D* Lite to reduce the path planning time and improve the search efficiency. Experimental results show that the improved D* Lite has much better path planning performance and is more realistic in practice.  
In the future, we will further test and improve the stability and efficiency of the improved D* Lite with other obstacle detection sensors such as a stereo camera and millimeter-wave radar to further enhance the USV.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11177863/s1](https://www.mdpi.com/article/10.3390/app11177863/s1)]([https://www.mdpi.com/article/10.3390/app11177863/s1](https://www.mdpi.com/article/10.3390/app11177863/s1)), Videos S1: Path planning and dynamic collision avoidance. Videos S2: Keeping a safe distance from riverbank during autonomous navigation.  
Conceptualization, X.Z. and Y.Y.; methodology, X.Z. and B.Y.; writing--original draft preparation, B.Y. and X.Z.; writing--review and editing: X.Z.; supervision, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 24: Procedure of dynamic obstacle avoidance.  
**Funding:** This was partly funded by the AI University Research Centre (AI-URC) through XJTLU Key Programme Special Fund (KSF-P-02 and KSF-A-19) and Research Development Fund of XJTLU (RDF-19-02-23).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.