esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Discussion
Based on the results from Section 3.1 to Section 3.4, the optimal mean error ratios under different configurations are provided in Table 25. It is summarized that in terms of the blunt trailing edge based on the Airfoil tools, SST + 12 m combined with ROE is the recommended simulation configuration and the optimal results are (1.97%, 4.52%, 1.6%). In terms of the sharp trailing edge based on NACA4, the preferred configuration is SST + 12 m combined with ROE and the results are (2.28%, 3.96%, 1.04%). In terms of the definition formula, applying 200 points sourced from NACA4 combined with SA + 16 m + ROE could achieve the optimal results of (2.54%, 1.86%, 1.74%). Moreover, attention should be paid that improper parameter selection may lead to a large decline in the simulation accuracy. As shown in Table 22, the maximum decline degrees of the four parameter types, which are highlighted in bold, are 31.73%, 19.88%, 18.45%, and 53.2% in order. In particular, an inappropriately adopted sharp trailing edge modeling method could lead to an accuracy decline of 34.76%. The above conclusions suggest that the inappropriate sharp trailing edge modeling method is not able to predict the performance of the proposed model. Figure 37: The optimal performance comparisons of trailing edge shapes and the modeling methods. edge modeling method may introduce more error ratio than the inappropriate trailing edge shape and the unsuitable flux type pick would bring the largest drop degree in prediction accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Parameters**} & \multirow{3}{*}{**Values**} & **Optimal** & **Optimal** & **Optimal** & **Optimal** & **Optimal** & **Maximum Accuracy** \\  & & **Mean** & **Mean** & **Mean** & **Mean** & **Mean** & **Decline Degree** \\  & & **Error** & **Ratio of** & **Ratio of** & **Error** & **Between Optimal** \\  & & **P/F\({}_{\mathbf{t}}\)** & **T/T\({}_{\mathbf{t}}\)** & **U/U\({}_{\mathbf{t}}\)** & **Ratios** & **Results** \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Blunt \\ trailing edge \\ \end{tabular} } & \multirow{3}{*}{Airfoil tools} & \multirow{3}{*}{132 points} & \multirow{3}{*}{264 points} & \multirow{3}{*}{2.62\%} & \multirow{3}{*}{4.00\%} & \multirow{3}{*}{1.65\%} & \multirow{3}{*}{2.76\%} \\  & & & & SST + 16 m + ROE & & \\ \cline{3-5}  & & \multirow{3}{*}{200 points} & & SST + 16 m + ROE & & \\ \cline{3-5}  & & \multirow{3}{*}{200 points} & & SST + 16 m + ROE & & \\ \cline{3-5}  & & & & SA + 16 m + ROE & & \\ \cline{3-5}  & & \multirow{3}{*}{400 points} & & & SST + 20 m + ROE & & \\ \hline \hline \end{tabular}
\end{table} 
Table 25: The mean error ratios and the maximum accuracy decline under different parameters. Figure 38: The optimal performance comparisons of the far-field distances, turbulence models, and flux types.
_Article_  
**Study on Driving Decision-Making Mechanism of Autonomous Vehicle Based on an Optimized Support Vector Machine Regression**  
**[PERSON] \({}^{\star}\), [PERSON], [PERSON] \({}^{\star}\) and [PERSON]**  
College of Transportation, Shandong University of Science and Technology, Huangdao District,  
Qingdao 266590, China; [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (J.H.)  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS] (J.Z.); [EMAIL_ADDRESS] (S.W.); Tel.: +86-139-0532-3314 (J.Z.); +86-186-0532-6013 (S.W.)  
Received: 16 November 2017; Accepted: 20 December 2017; Published: 22 December 2017  
**Featured Application: This work is specifically applied to the driving decision-making system of autonomous vehicles, allowing autonomous vehicles to run safely under complex urban road environment.**  
**Abstract: Driving Decision-making Mechanism (DDM) is identified as the key technology to ensure the driving safety of autonomous vehicle, which is mainly influenced by vehicle states and road conditions. However, previous studies have seldom considered road conditions and their coupled effects on driving decisions. Therefore, road conditions are introduced into DDM in this paper, and are based on a Support Vector Machine Regression (SVR) model, which is optimized by a weighted hybrid kernel function and a Particle Swarm Optimization (PSO) algorithm, this study designs a DDM for autonomous vehicle. Then, the SVR model with RBF (Radial Basis Function) kernel function and BP (Back Propagation) neural network model are tested to validate the accuracy of the optimized SVR model. The results show that the optimized SVR model has the best performance than other two models. Finally, the effects of road conditions on driving decisions are analyzed quantitatively by comparing the reasoning results of DDM with different reference index combinations, and by the sensitivity analysis of DDM with added road conditions. The results demonstrate the significant improvement in the performance of DDM with added road conditions. It also shows that road conditions have the greatest influence on driving decisions at low traffic density, among those, the most influential is road visibility, then followed by adhesion coefficient, road curvature and road slope, while at high traffic density, they have almost no influence on driving decisions.**  
**Keywords: autonomous vehicle; driving decision-making mechanism; road conditions; support vector machine regression; PSO algorithm**
# Tsunami Modeling and Satellite-Based Emergency Mapping: Workflow Integration Opportunities
## 2 Materials and Methods: Tsunami Modelling and Satellite-Based Emergency Mapping
### The Global Disaster Alert and Coordination System (GDACS) Integrated TSUNAMI Model  
The fully automatic tsunami model [2], integrated in the GDACS workflow, consists of different steps, namely: (a) preparing the input deck providing epicenter, magnitude and depth through a dedicated software together with a database of seismic faults worldwide; (b) resampling the GEBCO (Gridded Bathymetry Data, [[https://www.gebco.net](https://www.gebco.net)]([https://www.gebco.net](https://www.gebco.net))) 30 arc second resolution bathymetry to obtain the required ancillary dataset for the calculation; (c) preparing the expected soil deformation, obtained applying the Okada deformation model and a global database of possible sources; (d) running the tsunami software to get the propagation of the prepared initial deformation; (e) preparing the outputs to be published online in the GDACS web site. The whole tsunami calculation chain is performed automatically whenever a new event or a new estimation of the same event is available.  
The tsunami propagation code adopted at the time of the event (2015) was the SWAN code [3]; currently the HySea code, from Malaga University, is adopted to improve the efficiency of the calculations (overall duration time of few minutes). Full references for this code can be found here: [[https://edanya.uma.es/hysea/index.php/references](https://edanya.uma.es/hysea/index.php/references)]([https://edanya.uma.es/hysea/index.php/references](https://edanya.uma.es/hysea/index.php/references)).  
Among the output of the calculations, a particularly important file is the list of locations that are reached by the tsunami wave, including details on the height and arrival time. These calculations are obtained using the initial coarse model, refined with further additional calculations performed by restricting the domain to the largest impact zone and increasing the spatial resolution. The minimum spatial resolution is 500 m by 500 m, which is considered suitable for a first estimation of the impact. For a more detailed calculation of the inundation zone, much higher spatial resolution datasets are necessary (cell size in the order of few meters): unfortunately, those datasets are not available worldwide and their calculations are time consuming.  
The alerts of GDACS (Green, Orange, Red) are elaborated based on the severity of the event (the maximum height in the case of tsunamis), the population involved and the vulnerability of the countries. Apart from the alerts published on the web, GDACS also sends e-mail and SMS alerts to subscribed recipients (about 29,000). GDACS also posts alerts in the main social media platforms, reaching more than 8600 followers on Facebook and over 4100 on Twitter.
## 3 Proposed Adaptive KF-Based Tracking
### The System Stability Detection  
If the system remains stable, there is no need to employ the adaptive factor in the KF process. Excessive use of adaptive factors may reduce the filtering accuracy and increase the computational load. The adaptive factor is used only when the system is not steady. Hence, the hypothesis testing method is employed to test the system. Then, two hypotheses can be introduced:  
\(\gamma_{0}\): the system operates in a stable state;  
\(\gamma_{1}\): the system operates abnormally.  
When the tracking system is stable, the innovation sequence \(\mathbf{d}_{k}\) follows Gaussian distribution with a zero mean [26]. Otherwise, when the tracking system is not stable, \(\mathbf{d}_{k}\) will not obey the Gaussian distribution. Therefore, the detailed expressions are as follows:  
If the system is stable, then \(\mathbf{d}_{k}\sim\text{N}(0,\mathbf{\hat{C}}_{k})\);  
If the system is not stable, then \(\mathbf{d}_{k}\) does not obey the Gaussian distribution.  
Therefore, the system stability detection is realized by these characteristics.  
According to the chi-square test, based on \(\mathbf{d}_{k}\), the statistics function is defined as follows:  
\[\beta_{k}=\mathbf{d}_{k}^{\text{T}}\mathbf{\hat{C}}_{k}^{-1}\mathbf{d}_{k} \tag{29}\]  
If \(\mathbf{d}_{k}\) obeys a Gaussian white noise sequence, \(\beta_{k}\) has \(\chi^{2}\) distribution with \(s\)-degree-of-freedom (\(s\)-DOF) and \(s\) denotes the innovation sequence's dimension.  
If \(\alpha\) is chosen as the significance level, then  
\[\text{P}\Big{\{}x>\chi_{\alpha}^{2}(s)\Big{\}}=\int_{\chi_{\alpha}^{2}(n)}^{ \infty}f(x)dx=\alpha,\ 0<\!\alpha\leq 1 \tag{30}\]  
where \(x\) is the statistic of the \(\chi^{2}\) distribution. \(\chi_{\alpha}^{2}(s)\) is the threshold value which is decided by the given \(\alpha\) and \(s\)[31]. Figure 2 shows the PDF (probability density function) of the chi-square distribution.  
Accordingly, if the hypothesis \(\gamma_{1}\) is established, \(\beta_{k}\) will be greater than \(\chi_{\alpha}^{2}(s)\), which indicates that the system is not stable. Otherwise, the system is in a stable state.
# Hierarchical Self-Supervised Learning for Knowledge-Aware Recommendation
## 4 Methodology
### Triple-Graph Masked Autoencoder
#### 4.1.2 Node Feature Reconstruction  
Distinct from the edge reconstruction and the degree restoration that focuses on the structural-wise learning of the KG, the node feature masking reconstruction we propose focuses on enhancing entity representations from a node-wise perspective. Specifically, we randomly mask a certain proportion of the first-order neighborhood structural features of nodes, followed by reasoning and reconstruction. Node feature extraction. Considering the prevalent absence of attribute information in real-world KG, we extract the first-order neighborhood structural features of the nodes as the initial features to enrich the structural information of node attributes. Inspired by the entity-entity adjacency matrix in the KG, we construct the entity-relation association matrix \(\mathbf{X}\) as input features:  
\[x_{ik}= \begin{cases}\text{count}(r_{k})&\text{if }\exists(i,r_{k},\cdot)\text{ in the KG}\\ 0&\text{otherwise}\end{cases}  \tag{8}\]  
where \(x_{ik}\) is the element in the \(i\)th row and the \(k\)th column of \(\mathbf{X}\), \(k\) denotes the number of types of \(r\), \(\text{count}(r_{k})\) represents the count of triplets involving node \(i\) and relation \(r_{k}\) within the KG. If there is no triplet between relation \(r_{k}\) and node \(i\), the value of corresponding \(x_{ik}\) is zero. This strategy addresses the challenge associated with the dimension explosion of the traditional adjacency matrix. Masked feature reconstruction. At a predefined masking rate, we selectively disturb a subset of entities \(\mathcal{E}_{mask}\). For each entity \(i\) within this masked set, its initial feature \(\mathbf{x}_{i}\) is replaced with a masking token [MASK] represented as \(\mathbf{x}_{[M]}\). We feed the perturbed matrix \(\mathbf{X}^{\prime}\) into the GNN encoder for information propagation and aggregation, where the \(\mathbf{x}^{\prime}_{i}\) for \(i\in\mathcal{E}\) can be defined as  
\[\mathbf{x}^{\prime}_{i}= \begin{cases}\mathbf{x}_{[M]}&\text{if }i\in\mathcal{E}_{mask}\\ \mathbf{x}_{i}&\text{if }i\
otin\mathcal{E}_{mask}\end{cases}  \tag{9}\]  
Then, we choose MLP as the decoder. Motivated by the challenge of MSE to handle values close to zero, we opt the scaled cosine error (SCE) as the loss function. The training objective is to compute the loss between the predicted feature \(\hat{\pi}_{i}\) and the original feature \(\mathbf{x}_{i}\) using SCE:  
\[\mathcal{L}_{f}=\frac{1}{|\mathcal{E}_{mask}|}\sum_{i\in\mathcal{E}_{mask}} \left(1-\frac{\mathbf{x}_{i}^{\top}\hat{\pi}_{i}}{\|\mathbf{x}_{i}\|}\right)^{\gamma} \tag{10}\]  
where the scaling factor \(\gamma>=1\). Finally, the overall objective to be minimized about T-GMAE is  
\[\mathcal{L}_{m}=\mathcal{L}_{con}+\lambda_{3}\mathcal{L}_{f} \tag{11}\]  
The designed T-GMAE, integrating three reconstruction strategies, achieves enhanced node embeddings, which effectively capture the global structural characteristics of the KG. Algorithm 1 is the pseudo-code of the T-GMAE algorithm.
## 2 Study Area  
### Greater Metro Manila Area  
The study examined datasets from two regions in the Philippines: the Greater Metro Manila Area (GMMA) and Leyte Province.  
GMMA encompasses the cities and municipalities of Metropolitan Manila, the national capital, as well as the adjacent provinces of Bulacan, Cavite, Laguna, and Rizal. For this study, the data utilized were from Metro Manila, Bulacan, and Cavite. This densely populated area is located at the southernmost extension of the Central Luzon Basin, bordered by Manila Bay to the west and the Sierra Madre Mountain Range to the east.  
Metro Manila, the primary survey area, is morphologically divided into three morphological units: the Coastal Lowland, the Central Plateau, and the Marikina Valley [25]. The Coastal Lowland and Marikina Valley are primarily composed of Quaternary Alluvium, which is characterized by soft and unconsolidated sediments with interlayers of sand, clay, and silt. The Coastal Lowland is a flat low-lying plain facing Manila Bay, formed by deposits from sandbars, back marshes, tidal flats, the Pasig River Delta, and reclaimed land. The Marikina Valley, in contrast, consists of flood plains along the Marikina River and a delta along Laguna de Bay. This valley formed as a pull-apart basin linked to the Marikina Valley Fault System, consisting of the West Valley Fault (WVF) and East Valley Fault (EVF), which together form a dominantly dextral strike-slip fault system [26].  
Situated between these two landforms is the Central Plateau, which comprises the Guadalupe Formation. This formation is characterized by a sequence of conglomerates, sandstones, and mudstones, along with flat-lying medium to thin-bedded fine-grained vitric tuff and welded pyroclastic breccias.  
In GMMA, microtremor HVSR data from 25 public schools on quaternary alluvium deposits were utilized, representing the coastal lowlands. These sites were selected from the dataset utilized by [PERSON] et al. (2023) [27] in their study on site response measurements and implications to soil liquefaction potential in Metro Manila. Figure 1 shows the distribution of ReMi and three-component microtremor (3 CMT) sites in GMMA. These sites were selected for their natural ground conditions (i.e., without fill materials), which are expected to minimize errors in Vs30 estimation. The data consist of a 1:1 ratio of ReMi and three-component microtremor (3 CMT) datasets. While this selective approach provides detailed insights into the Coastal Lowland region, it may limit the generalizability of findings to other geomorphological units or areas within GMMA. The Leyte dataset, which includes a diverse set of geomorphological units, offers an opportunity to assess the GA's performance in a different geological context and compare it with findings from GMMA.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 8 Future Research Directions
### Offloading Algorithms  
In various operations, multiple tasks should be conducted simultaneously. There is an increasing demand of multi-UAVs for such tasks. These tasks are divided among deployed UAVs such that each task gets proper focus in a timely manner. Owning to high mobility scenarios, commutation-sensitive and time-sensitive UAV applications, cost-efficient task offloading is a critical concern [121]. Researchers should introduce novel task offloading algorithms in order to reduce the total energy consumption for performing predefined tasks.
## 4 Experiments and Results
### Comparison of Classification Results of the WaveNet Network Using Different Training Sets

Table 6: Accuracy results of WaveNet under the same experimental parameters using training sets based on traditional data augmentation and DCGAN-generated data augmentation. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Traditional Data** & **Data Augmentation Based** \\  & **Augmentation/\%** & **on the Modified DCGAN/\%** \\ \hline Precision of cloud recognition & 92.38 & 98.28 \\ Precision of land recognition & 91.44 & 98.73 \\ Precision of ocean recognition & 91.62 & 98.25 \\ Precision of oceanic internal waves recognition & 97.43 & 99.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of precision of WaveNet under the same experimental parameters using a training set based on traditional data augmentation and DCGAN-generated data augmentation. enhancement method proposed in this article, the WaveNet model has also been greatly improved on the test set in terms of precision and recall indicators of each category. For example, the recognition accuracy of land increased by 7.29%, while the internal accuracy of identifying waves increased by 1.82%; the recall rate of identifying oceans increased by 8.25%; and the recall rate of identifying internal waves also increased by 4.25%.
## 1 Introduction
### Case Study Background
After observing and discussing the 2019/2020 wildfire events, in particular the events unfolding in Mallacoota, the E Voices of the Valley brought the concept to the Latrobe Valley Authority, who then supported them in collaborating with other key regional actors, including state government representatives, academia, and industry. An Emergency DES was subsequently defined as a decentralised energy system powered by solar PV, combined with battery storage, which is installed on public buildings and provides low-cost and clean electricity supply daily, and especially during climate emergencies when the connection to the main grid becomes unreliable. The detailed conceptualisation of the Emergency DES was guided by community concerns around energy supply and ensuring the well-being of vulnerable community members during times of crisis.
# Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks
## 5 Experiments
### Baselines  
To evaluate the effectiveness of our proposed EPEM, we compared it against several baselines. These baselines include traditional link prediction methods, network embedding methods and graph neural network-based methods, as follows:  
* \(SVM\)[25]. SVM, a machine learning model, is capable of assigning labels to objects based on a training dataset. The model is trained by using normalized structural feature representations and existence label sets.
* \(SLiCE\)[18]. SLiCE is a framework that aims to bridge static representation learning methods with global information from the entire graph and local attention-driven mechanisms. This framework aims to learn contextual node representations that incorporate both local and global information.
* \(PME\)[12]. PME is an embedding model that is specifically designed for heterogeneous information social networks. PME builds object and relational embeddings in independent object and relational spaces, and learns embeddings by projecting nodes from the object space to the corresponding relational space and calculating similarities between projected nodes.
* \(HHNE\)[24]. HHNE is a representation learning method that aims to obtain embedding vectors of each node in heterogeneous networks. To achieve this, the method utilizes the naive active learning approach.
* \(HAN\)[26]. HAN is a heterogeneous network embedding representation method that utilizes generative adversarial networks. The model involves training a discriminator and a generator in a minimization game, and the generator is designed to generate a better negative sample by learning the node distribution and incorporating it into the learning process of the sample features.
* \(SEAL\)[27]. SEAL is a link prediction method that utilizes graph neural networks to learn heuristics from local sub-graphs in heterogeneous information social networks. SEAL extracts the local enclosing sub-graphs around it and uses graph neural networks to learn general graph structure features for link prediction.
* \(TALP\)[21]. TALP is a unified framework designed to predict anchored links between nodes. TALP aligns the anchored user nodes, uses the graph's attentional learning to assist in learning the type perception vector and type fusion vector associated with each user node, and obtains the \(n\)-tuple representation of each user node.
## 3 Methodology  
Ground station deployment optimization (i.e., with respect to number and placement) is conducted based on an evolutionary algorithm. In this section, two cases of GS deployment are shown: global deployment optimization and regional deployment optimization. In each case, the objective and methodology are defined.