esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Vehicle Routing Problem with Time Windows
### Mathematical Formulation of VRPTW  
Based on the formal definition introduced by [PERSON] and [PERSON] [30], the VRPTW is presented in Equations (1)-(11). In this formulation, the problem is represented through a directed graph composed of a set of nodes containing the customers and the central depot. These nodes are joined by arcs symbolizing the possible route of a vehicle between two nodes. The graph is formally defined as \(G(V,E)\), where \(V=\{0,\,\ldots,n\}\) is the set of \(n\) nodes, and \(E\) is the set of edges joining pairs of nodes in \(V\). Furthermore, \(K\) is the set of delivery vehicles, and \(N=V\backslash\{0\}\) is the set of customers, where the node \(0\) in \(V\) is the vehicle depot.  
On the other hand, \(c_{ij}\) is the travel cost from the \(i\)-th node to the \(j\)-th node, and \(x_{ijk}\) is equal to \(1\) if the arc joining the \(i\)-th node and the \(j\)-th is used by \(k\)-th vehicle, otherwise it is \(0\). \(d_{i}\) is the demand of \(i\)-th customer, and \(C\) is the vehicle capacity. Furthermore, \(w_{ik}\) specifies the start service time of \(i\)-th customer by the \(k\)-th vehicle, \(s_{i}\) is the service time of \(i\)-th customer, \(t_{ij}\) is the time travel from the \(i\)-th node to the \(j\)-th node, and \(a_{i}\) and \(b_{i}\) are the time window limits of \(i\)-th node. \(E\) and \(L\) represent the earliest possible departure time and the latest possible arrival time at the depot, respectively. \(\Delta^{+}(i)\) denotes the set of nodes directly reachable from the \(i\)-th node, and \(\Delta(i)\) denotes the set of nodes from which the \(i\)-th node is reachable.  
\[min\!\!\!\sum_{k\in K}\sum_{(i,j)\in A}c_{ij}x_{ijk} \tag{1}\]  
subject to:  
\[\sum_{k\in K}\sum_{j\in\Delta^{+}(i)}x_{ijk}=1\quad\forall i\in N \tag{2}\]  
\[\sum_{j\in\Delta^{+}(0)}x_{0 jk}=1\quad\forall k\in K \tag{3}\]  
\[\sum_{i\in\Delta^{-}(j)}x_{ijk}-\sum_{i\in\Delta^{+}(j)}x_{jk}=0\quad\forall k \in K,j\in N \tag{4}\]  
\[\sum_{i\in\Delta^{-}(n+1)}x_{i,n+1,k}=1\quad\forall k\in K \tag{5}\]\[x_{ijk}\Big{(}w_{ik}+s_{i}+t_{ij}-w_{jk}\Big{)}\leq 0\quad\forall k\in K,(i,j)\in A \tag{6}\] \[a_{i}\sum_{j\in\Delta^{+}(i)}x_{ijk}\leq w_{ik}\leq b_{i}\sum_{j \in\Delta^{+}(i)}x_{ijk}\quad\forall k\in K,i\in N\] (7) \[E\leq w_{ik}\leq L\quad\forall k\in K,i\in\{0,n+1\}\] (8) \[\sum_{i\in N}d_{i}\sum_{j\in\Delta^{+}(i)}x_{ijk}\leq C\quad \forall k\in K\] (9) \[x_{ijk}\geq 0\quad\forall k\in K,(i,j)\in A\] (10) \[x_{ijk}\in\{0,1\}\quad\forall k\in K,(i,j)\in A. \tag{11}\]  
The objective function in Equation (1) is the minimization of the total travel cost. Furthermore, Equation (2) indicates that a single vehicle must serve only one customer. The path of each vehicle is represented by Equations (3)-(5) while Equations (6), (8), and (9) guarantee both the time feasibility and the capacity of the vehicles. Equation (7) indicates that one service should start in the time window [a, b]. Finally, Equation (10) are non-negativity constraints and Equation (11) indicate that variables must have binary values.
# User Identity Linkage across Social Networks by Heterogeneous Graph Attention Network Modeling
###### Abstract  
Today, social networks are becoming increasingly popular and indispensable, where users usually have multiple accounts. It is of considerable significance to conduct user identity linkage across social networks. We can comprehensively depict diversified characteristics of user behaviors, accurately model user profiles, conduct recommendations across social networks, and track cross social network user behaviors by user identity linkage. Existing works mainly focus on a specific type of user profile, user-generated content, and structural information. They have problems of weak data expression ability and ignored potential relationships, resulting in unsatisfactory performances of user identity linkage. Recently, graph neural networks have achieved excellent results in graph embedding, graph representation, and graph classification. As a graph has strong relationship expression ability, we propose a user identity linkage method based on a heterogeneous graph attention network mechanism (UIL-HGAN). Firstly, we represent user profiles, user-generated content, structural information, and their features in a heterogeneous graph. Secondly, we use multiple attention layers to aggregate user information. Finally, we use a multi-layer perceptron to predict user identity linkage. We conduct experiments on two real-world datasets: OSCHINA-Gitee and Facebook-Twitter. The results validate the effectiveness and advancement of UIL-HGAN by comparing different feature combinations and methods.  
user identity linkage; social network; heterogeneous graph; neural network +
Footnote †: journal: Applied Sciences
## 2 Research Background
Therefore, the approach is inappropriate for identifying the artworks in 360-degree images. A fully affine invariant image matching method called affine-SIFT (ASIFT) was proposed in [72]. The ASIFT extends the SIFT to a fully affine invariant device. It simulates scale, longitude and latitude angles of camera. It also normalizes translation and rotation. It transforms images by simulating all possible affine distortions caused by a change in the camera optical axis direction. These distortions depend on the longitude and latitude. The images undergo directional subsamplings. The tilts and rotations are performed for a small and finite number of longitude and latitude angles. The simulated images are then compared by the SIFT for final matching. However, there are a lot of false matched keypoints between original artwork images and 360-degree images using the ASIFT. In [73] authors investigated the possibility of synthesizing affine views of successive panoramas of street facades with affine point operators. Final keypoint matches were obtained by applying the ASIFT features. In [74], the authors evaluated some feature extraction methods to automatically extract the tie points for calibration and orientation procedures. The considered feature extraction methods are mainly the SIFT and its variants. Several computer vision algorithms have been proposed to detect features and recognize objects for many applications. However, research has yet to develop a technology that specifically recognizes objects in 360-degree images.
# Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM
## 4 Experimental Study
### Experimental Comparison Analysis  
#### 4.4.1 Experiments and Analysis of Different Models  
For the purpose of verifying the effectiveness of the model suggested in this paper, B1_1, B1_2 and B1_4 of working condition 1 are selected as the training set and B1_3 as the test set. Meanwhile, three models are compared and analyzed with CNN, BiLSTM and CNN-BiLSTM. Figure 11 illustrates the change of the loss value of model introduced in this paper during training, which gradually decreases and eventually stabilizes as the number of training rounds increases. Figure 12 illustrates the RUL results for each model on the test bearings.  
Figure 11: Train Loss.  
Figure 10: Functional relationship between Score and Error.  
From Figure 12, the prediction curve of the CNN is clearly upward compared to the true curve, and there is a risk of overestimating the RUL of the bearing. The BiLSTM has the worst prediction effect. The model of CNN-BiLSTM has some improvement over CNN and BiLSTM. It is clear that the RUL prediction by the model of MBCNN-BiLSTM is closest to the real value.  
To reflect on the model's strengths and weaknesses more clearly, three evaluation indicators, MAE, RMSE and Score, are utilized in this paper to assess model's performance. So as to prevent the effect of randomness of the experiments on the results, the average results of 20 experiments are used as the final results in this paper. The evaluation metrics of the prediction of each model are illustrated in Table 3.  
As evidenced by Table 3, the MAE, RMSE and Score of MBCN-BiLSTM outperformed the other three comparison models. Compared with CNN, BiLSTM and CNN-BiLSTM, the MAE values decreased by 33.3%, 50.0% and 14.3%, respectively, and the RMSE values decreased by 30.0%, 56.3% and 12.5%, respectively, and the scores improved by 46.5%, 26.0% and 23.5%, respectively, demonstrating the accuracy of the suggested model for bearing RUL prediction.  
Figure 12: The prediction results obtained from different models on the test bearings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MAE** & **RMSE** & **Score** \\ \hline CNN & 0.09 & 0.10 & 0.43 \\ BiLSTM & 0.12 & 0.16 & 0.50 \\ CNN-BiLSTM & 0.07 & 0.08 & 0.51 \\ MBCNN-BiLSTM & 0.06 & 0.07 & 0.63 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation metrics values of the four models.
# Research Progress of Polysaccharide-Gold Nanocomplexes in Drug Delivery
## 1 Introduction
The combination of polysaccharides and nanocarriers improves the biological activity of polysaccharides [32] and enhances the biocompatibility of nanocarriers. This increased biocompatibility allows cells to accept the nanocarriers readily and facilitates the efficient delivery of drugs to the desired target location. Meanwhile, polysaccharide nanocarriers enhance drug-loading activity by gradually releasing the loaded medicines [35]. Additionally, gold nanoparticles have unique properties that enable drug release in specific locations, including pH response, temperature response, or photothermal response.
## 2 Hybrid Graphene-Based Materials (Hybrid GBMs)
### Silica Derivatives
Figure 2 shows the process of obtaining GO particles anchored to silica. Nonetheless, the support of graphene sheets on the surface of silica derivatives still allows for their functionalization with other compounds to further enhance these materials' extraction capacity. The modification of Si@GO particles with \(\beta\)-cyclodextrinrin was addressed by [PERSON] et al. (2018) to extract isoflavones from human urine. In their work, the authors reported using the sorbent as a coating in a \"needle-sleeve\" device in a fully automated solid-phase microextraction coupled with liquid chromatography (SPME-LC) system. The hybrid sorbent showed excellent performance for the intended purpose, attributed to the high stability and selectivity of GO and silica particles and both hydrophobic and hydrophilic characteristics of cyclodext. The authors highlighted the versatility of the sorbent by providing enhanced affinity with enrichment factors better than 3.8 times. The reported device could also be used more than 200 times while demonstrating adequate precision ([PERSON] et al., 2018). The studies mentioned in this topic demonstrate that incorporating graphene derivatives onto the surface of silica expands the application possibilities of these sorbents in various microextraction devices, especially in packing techniques (offline or online). Such a remarkable achievement is attributed to the improved mechanical resistance and morphology of the GBMs. Furthermore, the structural changes in these materials caused by these modifications result in the high stability and adequate reusability rates of these devices. Furthermore, the reusability of these materials significantly impacts green analytical practices since it reduces waste generation and contributes to the method's sustainability, promoting a more environmentally friendly approach to sample preparation.
# Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
## 3 Basic System Design
### Data Processing and Mapping  
A sensed event \(E\) can be defined by an \(l\)- dimensional tuple, \(\left(A_{1},A_{2},A_{3},\dots\ A_{l}\right)\) where \(A_{g},\forall_{g}\in[1,l]\) denotes the \(g\)th attribute and \(D_{Ag}\) is the domain of attribute \(A_{g}\). Each member node of a sector transmits the sensed event as an \(l\)-tuple \(\left\langle v_{i1},v_{i2},.....,v_{il}\right\rangle_{k}\), where \(1\leq i\leq M_{k}\), \(M_{k}\) is the total number of member nodes in \(kth\) sector and \(v_{ij}\) denotes the value of the \(jth\) attribute received from \(ith\) node of \(kth\) sector. A \(SH\) node after collecting tuples from all the member nodes aggregates them at the end of each epoch before finding the mapping for the target \(SH\).  
Hence, after aggregation at epoch \(t\)  
\[\mathrm{E_{k}}\left(\mathrm{Agg}\left(t\right)\right)=\int\limits_{\mathrm{ Agg}_{i=1}}^{M_{k}}\left\langle v_{i1},v_{i2},......,v_{il}\right\rangle\left(t\right) \tag{5.1}\]  
\[=\left\langle\psi_{1},\psi_{2},......,\psi_{l}\right\rangle\left(t\right) \tag{5.2}\]  
Here,  
\[\psi_{j}=\left\{\max_{i=1}^{M_{k}}v_{ij},\min_{i=1}^{M_{k}}v_{ij},\mathit{av} \mathit{Sa}_{i=1}^{M_{k}}\right\},j\in[1,I],k\in[1,S] \tag{6}\]  
Figure 4: (**a**) Data mapping; and (**b**) range query example.  
In Equation (6), \(M_{k}\) denotes the number of member nodes in \(kth\) sector. It is assumed that all attribute's aggregated values of \(\psi_{i}\) have been normalized to be between 0 and 1. As shown in Table 1, weights have been assigned to different attributes based on their importance in the event description. Hence, an attribute with higher weight has higher influence in deciding the similarity among events.
## 2 Mathematical Formulation of Pseudointensity Vector
### Overview of Signal Intensity and Pseudointensity  
The intensity of active vector can be described as a magnitude of average direction and time of total energy flow, and can be expressed as [35]  
\[\mathbf{I}(k)=\frac{1}{2}\Re\{p(k)^{*}\mathbf{u}(k)\}, \tag{1}\]  
where \(p(k)\) represents omnidirectional pressure; \(\mathbf{u}(k)=\left[u_{x}(k)\ u_{y}(k)\ u_{z}(k)\right]^{T}\) is the particle velocity vector in the Cartesian direction; \(\Re\{\cdot\}\) denotes the actual operator. \(\mathbf{u}(k)\) can be used to estimate the DoA because energy flows in the wave propagation direction. The particle velocity vector of plane wave associated with DoA \((\theta,\phi)\) is [33]  
\[\mathbf{u}(k)=-\frac{p(k)}{\rho_{0}c}\begin{bmatrix}sin\theta cos\phi\\ sin\theta sin\phi\\ cos\theta\end{bmatrix} \tag{2}\]  
where \(c\) and \(\rho_{0}\) are speed of light and ambient density, respectively. The components of \(\mathbf{u}(k)\) exhibit dipole's directivity patterns, which are in alignment with the Cartesian axes. The resultant vector faces the opposite direction from the estimation direction.  
The beamformer that has a dipole directivity pattern is extracted from a first-order coefficient of spherical harmonic as  
\[D(k,\varphi,\mathbf{a}_{lm}(k))=\sum_{m=-1}^{1}Y_{1}^{m}(\varphi)a_{1(m)}(k) \tag{3}\]  
With \(\varphi\) representing the direction of steering. Hence, by approximating Equation (1) using the coefficients of the plane wave density function in the spherical domain, the PV is expressed as [35]  
\[\mathbf{I}(k)=\frac{1}{2}\Re\left\{\mathbf{a}_{00}(k)^{*}\begin{bmatrix}D(k,\varphi_{- x},\mathbf{a}_{lm}(k))\\ D\left(k,\varphi_{-y},\mathbf{a}_{lm}(k)\right)\\ D(k,\varphi_{-z},a_{lm}(k))\end{bmatrix}\right\} \tag{4}\]  
with \(\varphi_{-x}=(0.5\pi,\pi)\), \(\varphi_{-y}=(0.5\pi,-0.5\pi)\) and \(\varphi_{-z}=(\pi,0)\).
## 4 Case Study  
The proposed framework has been tested using the SCADA system's database of a hydropower plant belonging to the Romanian HydroPower Company. The plant, identified through the red circle in Figure 7, is the first from a hydro arrangement located on an important river in eastern Romania. The plant has six Francis-type units. The first water pipe supplies the last two units, and the second piper the first four units. The first four units have a total installed power of 27.5 MW, while units five and six have 50 MW. Figure 7: The hydro arrangement of which the analyzed plant is a part (adapted after [36; 37]). The SCADA database includes fields completed at every hour with the following technical parameters: the individual water flows of each pipe, the total water flow of the two pipes, the total produced power of the first four-generation units (GU1-GU4), the produced power by the GU5, the produced power by the GU6, the total produced active and reactive power by the plant, the frequency, the stator voltage, the stator current, the produced active and reactive powers, the excitation voltage, the excitation current of each generation unit, and water levels of the reservoir (upstream and downstream). The SCADA file associated with a day from the database containing the fields highlighted above is shown in Figure 8. The signification of the blank cells corresponds with the case of the non-loading of the generation unit. The obtained results at the year level for each task integrated into the proposed framework are presented in the following. The DEMA can see after data processing the summary information on the operation of the plant regarding the number of hours and the total energy produced by each generation unit (GU1-GU2), see Figure 9.  
Table 1 presents the extracted results from the advanced statistical analysis containing the mean (m), standard deviation (\(\sigma\)), and quintiles (Q0, Q1, Q2, Q3, and Q4) for each technical parameter from the database. The values have been calculated only for the hours when at least one unit was in operation. Also, the DEMA has available boxplots from which outliers are identified quickly. Figure 8: SCADA file associated with a day from the database. Figure 9: The summary information regarding the operation of the plant regarding the number of hours and the total energy produced by each generation unit. Two situations are presented in the following. The first refers to the loading of the generation units when the outliers identified associated with the different operating regimes compared to most regimes did not lead to the violation of the allowable limits, see Figure 10. In these cases, an attention message is launched to the DEMA, who must verify the regimes and establish if any disturbance appeared.
## 3 Results and Discussions  
### Characterization of Fine Lateritic Materials  
#### 3.1.1 Mineralogical Composition and MBT  
The XRD patterns and mineralogical semi-quantitative calculations of samples from profiles BG1 and BG2 are given in Figure 5 and Table 1. The XRD results show that the two lateritic soil profiles have almost the same mineral compositions with a slight variation in terms of proportions. These mineral compositions are dominated by newly formed secondary minerals which include kaolinite (28.6-47.0%), hematite (6.3-28.4%), goethite (10.5-24.0%), gibbsite (4.6-17.4%), anatase (0.9-11.7%), ilmenite (0.2-6.7%), and magnetite (0.0-5.9%) (Table 1). Quartz and feldspars are the residual primary minerals in the studied materials, with low proportions that range from 1.4% to 6.8% and from 1.3% to 4.4%, respectively (Table 1). The mineral compositions of the studied lateritic materials are found in the weathered products of basaltic rocks in the West Region of Cameroon [46; 69].  
The MBT of samples (Table 1) is very low and ranges between 0.2 and 0.6 g/100 g. These values are very close to those obtained by [41] (0.4-0.67 g/100 g) and allow the studied lateritic materials to be identified as silts [70; 71]. Thus, these clay-rich lateritic soils are sensitive to water and are, therefore, not suitable for engineering purposes [72; 73].
## 4 Adjustment of the TMC Simulations
### The TMC Sensitivity Calculations  
The accuracy of the TMC method inherently depends on the number of Monte Carlo repetitions that naturally assumes a logical rule for its application: with more repetitions, the more accurate the final results will be. This reasonable approach can be applied until the gain in the accuracy of the TMC calculations exceeds the accuracy of the MCNP statistical calculations adopted in this work, i.e., 0.1%. This limit was used to assess the required number of the TMC cycles to get the TBR statistical distributions. To assess this number, several huge TMC runs were performed for 5000, 10,000 and 30,000 repetitions using the HCPB geometry model discussed above and 300 random files for each nuclide. Presented in Figure 5 are the statistical distributions of the TBR results obtained for these three cases. The presented results for each case were fitted by normal (Gaussian) distributions to obtain the most probable TBR response and a standard deviation \(\sigma\). For 10,000 and 30,000 iterations, the mean TBR = 1.181 and the standard deviation in both cases is \(\sigma\) = 0.030. For less repetitions, the mean TBR is slightly less: TBR = 1.174 (\(\sigma\approx\) 0.031). In all these cases, the standard deviation is almost the same but the mean TBR tends towards bigger numbers with an increase in the TMC repetitions. Therefore, the number of the TMC cycles affects the position of the maximum of the normal distribution, the most probable or mean TBR number, however, the effect for the standard deviation is not big. Nevertheless, the standard spread of the results is significant, assuming its final effect for the DEMO design. Hence, the final assessments of this effect should be carried out with the big enough TMC repetitions number, that is, at least 10,000, to ensure its justified quantification.  
Figure 4: Results of the sensitivity calculations of the TBR as a function of the random files number.