esa-sceva/satcom-chunk-collection huggingface dataset:
# Biosorption Potential of Sargassum for Removal of Aqueous Dye Solutions
## 2 Materials and Methods
### Methylene Blue, Brilliant Blue and Congo Red Dye Solutions  
Methylene blue (C\({}_{16}\)H\({}_{18}\)ClN\({}_{3}\)S\(\cdot\)3H\({}_{2}\)O), brilliant blue (C\({}_{45}\)H\({}_{44}\)N\({}_{3}\)NaO\({}_{7}\)S\({}_{2}\)) and congo red (C\({}_{32}\)H\({}_{22}\)N\({}_{6}\)Na\({}_{2}\)O\({}_{6}\)S\({}_{2}\)) were purchased from Sigma Aldrich (Gillingham, UK). The chemical structures are shown in Figure 1. Different stock solutions were prepared from a 1000 mg L\({}^{-1}\) stock solution. Solutions for calibration curves were created by diluting the stock solution (0-312.5 mg L\({}^{-1}\)).  
Figure 1: Chemical structures of methylene blue, congo red and brilliant blue R.
## 2 Materials and Methods
### Real-Time PCR  
Total RNA was isolated from HaCaT cells using RNAiso Plus (TaKaRa Bio, Shiga, Japan). Total RNA served as a template in a reverse transcription reaction using PrimeScriptTM RT Master Mix (TaKaRa Bio). A real-time polymerase chain reaction (PCR) was performed using CFX ConnectTM Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA) and SYBR(r) Premix Ex TaqMan(tm) II (TaKaRa Bio). The following sequences of forward and reverse primer pairs were used: 5'-TCCATATATCTTCTGTTTGCCTTTGC-3' and 5'-TCTGTAAATGTGTTATGATTCTCCAT-3' for hBD3 [19], 5'-AGCAATTGGTGGTG-3' and 5'-ACTCTTGCAAAAAGTTGT-3' for hEGF, 5'-GAGAGGGAAACTGCGCAGA-3' and 5'-GTAGCATTATTATGGGAAGTG-3' for hEGFR, and 5'-AAGGTCGAGAGTCAAACGGATT-3' and 5'-GCAGTGAGGGTCT
# Collaborative Filtering Model of Graph Neural Network Based on Random Walk
## 2 Related Work
### Graph-Based Recommendation System  
Another related research direction is using user-item graph structures for the recommendation. The motivation for applying graph neural network methods to recommend systems lies in two aspects: first, most of the data in RS have a graph structure, and second, GNN techniques are potent in capturing connections between nodes and learning representations of graph data; for example, they have made significant progress in learning node representations [15], improving social network performance [16], and optimizing node embeddings [17]. Due to GNN's capacity to learn representations of potential factors from graphs, researchers have proposed graph-based RS, such as semi-supervised learning models for document recommendation, which can measure item similarity by combining multiple graphs. Inspired by graph/node embedding technology, [PERSON] et al. [18] proposed an algorithm based on a graph convolution network, an automatic graph encoder; it uses the structural information of graphs to identify potential users and items to alleviate the matrix completion problem in deep learning. A graph-based regularization method is added by [PERSON] to the matrix decomposition model to learn the graph structure, resulting in traditional graph-based regularization methods. Since then, many graph neural network-based recommendation systems have been proposed, and these methods have also made considerable progress in alleviating CF-related problems.  
Despite the advantages of traditional CF methods, there are still many shortcomings in dealing with the cold-start problem: firstly, the results are not accurate enough when finding the nearest neighbors for the target users in the cold-start environment, or there are cases when the nearest neighbors cannot be found; secondly, the complexity of the algorithm increases due to the large number of calculations involved, and this problem also increases the time required to train the model. In contrast, the algorithm proposed in this paper can further improve the performance of the recommendation system by taking into account the potential association information between users and items. This method can be directly learned from the bipartite graph constructed by users and items, with no need to add edge-related information. It can quickly discover implicit information about users and items.  
The paper's main contribution can be summarized as follows: (1) Helps users discover more related items. (2) Using finite order polynomials to optimize the convolution process to help users discover items quickly and reduce the computational complexity. (3) The user-item spatial domain information is transformed into a new spectral domain space for the recommendation algorithm.
# Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant
## 5 Application to Predictive Maintenance of LNG Tank Refilling by Means of Trucks
### Prognosis of Remaining Time to Refill Tank Using FIR Methodology
The tank level was computed as  
\[\hat{y}(k)=y(0)-1/K\hat{Q}_{a}(k) \tag{11}\]  
where \(\hat{Q}_{a}\) is the estimation of the accumulated consumption computed by the FIR model. Figure 9 presents the decreasing tank level (black line), and the prediction level on the \(k_{0}=1\) day (24 h; blue line) and on \(k_{0}=2\) days (48 h; red line). In this study, we were interested in computing the RUL until the tank level reached its safety threshold, which meant an NVT of 20%. To this aim, we used Algorithm 1 with the following data: \(k_{h}=15\) days, \(\mathbf{U}(k_{0},n)=T_{m}(k_{0}-144)\), \(\mathbf{x}(k_{0},n)\), which is a set given by \(\{Q_{a}(k_{0}-6),Q_{a}(k_{0}-78),Q_{a}(k_{0}-114)\}\), and \(FT=20\). The first prediction gave an RUL of 342 h, while the second provided an RUL value of 311 h. Comparing with the real data, the prediction errors in the first and second prediction were 31 and 24 h, respectively. The RUL was calculated dynamically using the new observations obtained from the online monitoring system. It is possible to consider other safety indicators, for example, remaining time until the tank level reaches its minimal threshold.
## 3 Numerical Simulation
### The Governing Equations  
Gravity effects are considered based on two-dimensional, steady-state, laminar, and incompressible flow. The general forms of mass, momentum, energy, and species conservation equations are as follows:  
The mass conservation equation  
\[\
abla\cdot\left(\overset{\rightarrow}{\rho V}\right)=0 \tag{6}\]  
where \(\rho\) is the density, and \(\overset{\rightarrow}{V}\) is the velocity vector, which is composed of the axial velocity component (\(V_{z}\)) and the radial velocity component (\(V_{r}\)) of the two-dimensional axisymmetric flow.  
The momentum conservation equation  
\[\
abla\cdot\left(\overset{\rightarrow}{\rho VV}\right)=-\
abla P+\rho \overset{\rightarrow}{g}+\
abla\cdot\left(\overset{\rightarrow}{\tau}\right) \tag{7}\]  
where \(P\) is the static pressure, \(\overset{\rightarrow}{g}\) is the gravity acceleration, and \(\overset{\rightarrow}{\tau}\) is the stress tensor.  
The energy conservation equation  
\[\
abla\cdot\left(\overset{\rightarrow}{V}(\rho E+P)\right)=\
abla\cdot\left(k \
abla T+\overset{\rightarrow}{\tau}\cdot\overset{\rightarrow}{V}-\sum_{i=1 }^{N}e_{i}\overset{\rightarrow}{\overset{\rightarrow}{\underset{j}{\tau}}} \right)+S_{h} \tag{8}\]  
Figure 1: Experimental setup.  
where \(E\) is the energy, \(k\) is the thermal conductivity, \(\
abla T\) represents the temperature gradient, \(e_{i}\) is the enthalpy of species \(i\), \(S_{h}\) includes the heat of chemical reaction and any other defined volume heat source, and \(\overset{\rightarrow}{\int}_{i}\) is the diffusion flux of species \(i\). For laminar flow, \(\overset{\rightarrow}{\int}_{i}\) is expressed as:  
\[\overset{\rightarrow}{\int}_{i}=-\rho D_{m,i}\bigtriangledown Y_{i}-D_{t,i} \frac{\
abla T}{T} \tag{9}\]  
where \(D_{m,i}\) and \(D_{t,i}\) are the mass and thermal diffusion coefficient of species \(i\) in the mixture, respectively.  
The species conservation equation  
\[\
abla\cdot\left(\overset{\rightarrow}{\rho V}Y_{i}\right)=-\
abla\cdot \overset{\rightarrow}{\int}_{i}+R_{i} \tag{10}\]  
where \(R_{i}\) represents the net productivity of species \(i\) in the chemical reaction.
## 3 Results and Discussion
### The Effect of Operation Parameters on AC-CoPcS Catalytic Performance
#### 3.2.2 The Effect of Reaction Time (\(t\))  
The reaction time is one of the most important process parameters that should be optimized firstly, so the effect of reaction time on \(CPP_{SMM}\), _Yield_DMDS, and _Purity_DMDS was investigated. The purity  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **BET Surface Area** & **Total Pore Volume** & \multirow{2}{*}{**Average Pore Size (nm)**} \\  & **(m\({}^{2}\)/g)** & **(cm\({}^{3}\)/g)** & \\ \hline
**AC** & **704.8** & **1.30** & **2.50** \\
**AC-CoPcS (commercial)** & **265.4** & **0.46** & **4.16** \\
**AC-CoPcS (new)** & **301.3** & **0.51** & **3.95** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The surface area test results of the AC, commercial, and newly prepared AC-CoPcS catalysts.  
Figure 5: The unseparated DMDS product (upper layer) after the catalytic oxidation reaction using (**a**) free CoPcS and (**b**) AC-CoPcS catalysts. Experimental conditions: SMM (18.0 wt\(\%\)) 450 g, \(RR\) = 600 r/min, free CoPcS dosage 160 ppm, AC-CoPcS dosage 888.9, \(t\) = 60 min, \(T_{\text{re}}\) = 65 \({}^{\circ}\)C, \(P\)(O\({}_{2}\)) = 0.9 MPa.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Catalysts** & \(C_{\text{ca}}\) & _Yield_DMDS & _Purity_DMDS & **Post-Treatment of DMDS** \\ \hline
**CoPcS** & **160 ppm** & **81.4\%** & **98.7\%** & **AC adsorption** \\
**AC-CoPcS** & **888.9 ppm** & **86.8\%** & **99.8\%** & **None** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance comparison between free CoPcS and AC-CoPcS catalysts 1.  
of the DMDS product was detected by GC-MS, and the spectra are shown in Figure 6. As can be seen, the retention time of DMDS is around 4 min, and there is no other peak of the byproduct in the GS-MS spectra, indicating the high quality of the DMDS is prepared by the new catalytic reaction system. As is shown in Figure 7, when the reaction time increases from 30 min to 60 min, the \(CPP_{\text{SMM}}\) and \(Yield_{\text{DMDS}}\) increase gradually from 62.4% and 44.1% to 98.7% and 86.8%, respectively. However, when the reaction time continues to increase from 60 min to 90 min, the \(CPP_{\text{SMM}}\) decreases slightly to 84.1% at first and then remains nearly constant, while the \(Yield_{\text{DMDS}}\) remains constant at the same time. Nevertheless, the reaction time seems have a little effect on the \(Purity_{\text{DMDS}}\), when the reaction time increases from 30 min to 90 min, the \(Purity_{\text{DMDS}}\) decreases slightly from 99.8% to 99.1%, which verifies the high quality of the DMDS product that was prepared in the new system. As a result, the reaction time of 60 min is optimal, when the reaction time extends to 75 min or 90 min, the DMDS product may react with each other to form the byproduct, causing the decrease of \(Yield_{\text{DMDS}}\) and \(Purity_{\text{DMDS}}\)[30].
## 2 Network Scenarios and Problems
### Observations  
First, we perform simulations for both EDMG with different channel widths to observe the throughputs perceived by the fixed and mobile devices over IEEE 802.11 ay using TCP and MPTCP protocol (see details of the network protocol and parameters later in Table 2). Our test scenario comprises two IEEE 802.11 ay devices connected by a one-meter Line-Of-Sight (LOS) AP. First, we set the two devices to utilize the best beam pattern possible, resulting in a high SNR value and eliminating packet loss. The results of maximum throughput are shown in the first row of Table 1. Thereafter, we repeat the same experiment under the same setup except that the devices are moving across multiple APs at the speed of 10 m/s (see the second row of Table 1). The throughput deteriorates substantially due to mobility (complicated and time-consuming handover process). Furthermore, we repeat the same experiment under the same setup except that the devices are using MPTCP protocol and are associated with two adjacent APs while moving across multiple APs at the speed of 10 m/s (see the third row of Table 1). We can clearly see that MPTCP outperforms TCP when devices are moving from one AP to another. However, the maximum throughput with MPTCP (two paths) when devices are stationary is much lower than expected. We find that this is due to a lack of synchronization and interactions between multipath scheduling at the transport layer with that of the underlying advances in MAC/PHY of 802.11 ay.
# Relational Positioning Method for 2D and 3D Ad Hoc Sensor Networks in Industry 4.0
###### Abstract  
Industry 4.0, or smart factory, refers to the fourth industrial revolution, as a result of which it is expected that massive sensor deployment, high device connectivity, automation, and real-time data acquisition support improve industrial processes. Sensor fault detection and operator tracking allow adequate performance of the sensor network supporting Industry 4.0, and sensor localization has become crucial to enable sensor fault detection. Hence, the development of new localization methods is necessary for environments where GPS localization technology is unfeasible. Furthermore, position location information (PLI) is a crucial requirement for the deployment of multiple services and applications in wireless ad hoc and sensor networks supporting Industry 4.0. Three-dimensional (3D) scenarios are considered to extend the applicability of PLI-based services. However, PLI acquisition presents several challenges within any 3D ad hoc sensor network paradigm. For instance, conventional triangulation algorithms are often no longer applicable due to the lack of direct land-fixed references and the inclusion of a third-dimension coordinate. In this paper, a PL relational searching algorithm suitable for 2D and 3D ad hoc environments was formulated in terms of a discretized searching space and a vertex weight assignment process according to distance relationships from anchor nodes to the node to be located on a feasibility space. The applicability of the proposed algorithm was examined through analytic formulation and simulation processes. Results show small distance errors (less than 1 m) are achievable for some scenarios.  
operator's localization; sensor fault localization; position location information; 3D; sensor networks; discretized space; relational +
Footnote †: journal: Applied Sciences  
1
# The Method of Multi-Camera Layout in Motion Capture System for Diverse Small Animals
## 3 Experimental Verification
### Experimental Results
#### 3.2.3 The Accuracy of Small Animals in Half-Annular Layout  
The ratio of body width to body length (_w/l_) of gecko was about 0.37 \(\pm\) 0.03 and its body height to body length ratio (_h/l_) was about 0.13 \(\pm\) 0.01, so its body shape was close to a flat rectangular. The body width to body length ratio of the spider was about 0.9 \(\pm\) 0.03 and it is the body height to body length ratio was about 0.5 \(\pm\) 0.01, so its shape was close to a slightly flat square. According to the matching relationship between the body shape and the intersection volume of the multi-camera system, the gecko was suitable for half-annular layout, while the spider was suitable for arch layout. However, they are required to perform motion capture experiments on the FMA with a rectangular plane, which required that the viewing intersecting volume must have an inner envelope that was significantly longer than the width. Therefore, the half-annular layout was most apposite. In addition, due to the influence of the depth of field on the standard deviation rate, the working distance of the camera was selected to be 560 mm and the pitch angle was 45\({}^{\circ}\) and the angle between the cameras \(\theta\) was 30\({}^{\circ}\) (Supporting Document S5 for details).  
The \(ACD\) of the geckos was approximately 1.36 \(\pm\) 0.40 mm (\(ACDR\): 3.43 \(\pm\) 1.46%) and the \(ASDR\) was 3.67 \(\pm\) 1.33%. There was no significant difference between the head length and the leg length in each group (ANOVA, \(F\) = 0.537; \(d.f.\) = 3,4; \(p\) = 0.682); the \(ACDR\) of the head length was 2.1 \(\pm\) 0.28% and the \(ACDR\) of the leg length was 4.77 \(\pm\) 0.40%, there was also no significant difference in the head length and leg length between groups (ANOVA, \(F\) = 2.205; \(d.f.\) = 3,304; \(p\) = 0.11); but the \(ACDR\) of the head and the leg was different in each group (ANOVA, GT1:\(F\) = 8.042; \(d.f.\) = 1,208; \(p\) = 0.005; GT2:\(F\) = 370.971; \(d.f.\) = 1,487; \(p\) = 0.000; GT3:\(F\) = 280.534; \(d.f.\) = 1,162; \(p\) = 0.000; GT4:\(F\) = 12.065; \(d.f.\) = 1,191; \(p\) = 0.001) (Table S5).  
The \(ACD\) of the spider was about 0.66 \(\pm\) 0.33 mm (\(ACDR\): 1.74 \(\pm\) 0.13%). Meanwhile the \(ASDR\) was 3.76 \(\pm\) 0.98%. There was no significant difference in the length of posterior segment and tarsal length between experimental groups (ANOVA, \(F\) = 0789; \(d.f.\) = 3,540; \(p\) = 0.501), which was statistically significant; and there was no significant difference between the \(ACDR\) of the tibia segment and theposterior tarsus segment in each group (ANOVA, ST1:\(F\) = 0.512; \(d.f.\) = 1,191; \(p\) = 0.475 ST2:\(F\) = 0.642; \(d.f.\) = 1,179; \(p\) = 0.424; ST3:\(F\) = 0.114; \(d.f.\) = 1,208; \(p\) = 0.736; ST4:\(F\) = 0.904; \(d.f.\) = 2,236; \(p\) = 0.406) (Table S6).
## 4 Results and Discussion
### Results of NACA0012 Models
#### 4.1.2 Sharp Trailing Edge Based on NACA4-Digital Generator  
Figure 7 illustrates the error ratios of \(C_{d}\) and \(C_{l}\) between simulation data and experimental data, and Table 9 provides the corresponding mean error ratios under different AOA ranges. The optimal simulations were performed by SA+12 m, SST+20 m, SST+20 m, SST+20 m, SST+20 m, and SA+20 m and the relevant results are (0.05%, 0.00%), (0.78%, 1.38%), (2.80%, 1.22%), (4.98%, 1.11%), (5.89%, 1.55%), and (7.50%, 1.53%), respectively. At the range of (0\({}^{\circ}\), 2\({}^{\circ}\)), although the \(C_{l}\) of SST+12 is the smallest, the \(C_{d}\) is relatively large. Considering the \(C_{d}\) and \(C_{l}\) comprehensively, the result of SST+20 m is optimum. At the range of (0\({}^{\circ}\), 4\({}^{\circ}\)), despite SST+12 m obtaining the best result for \(C_{l}\), the \(C_{d}\) of SST+20 m is preferred. Due to the \(C_{d}\) being a magnitude greater than the \(C_{l}\), SST+20 m performs the best. At the range of (0\({}^{\circ}\), 6\({}^{\circ}\)), similarly, the ideal result is obtained by SST+20 m. At the range of (0\({}^{\circ}\), 10\({}^{\circ}\)), the result of SST+20 m is the same as that of SA+20 m. On the account of the variance of SA+20 m being lower than that of SST+20 m, SA+20 m behaves the best.
## 3 Analysis of Predictive Modeling Methods
### Gated Recurrent Unit  
A traditional GRU replaces the forgetting and input gates of LSTMs uniformly with an update gate and retains the reset gate, which maintains a balance between prediction accuracy and complexity. The update gate controls the extent to which previous feature information affects feature information at the current moment, and the reset gate controls the extent to which current information is combined with information from the previous moment. The GRU structure is shown in Figure 5, and its specific calculation steps are as follows.  
\[\left\{\begin{array}{l}r_{t}=\sigma(W_{t}\cdot[h_{t-1},x_{t}]+b_{r})\\ z_{t}=\sigma(W_{z}\cdot[h_{t-1},x_{t}]+b_{z})\\ \widetilde{h}_{t}=\tanh(W_{h}\cdot[r_{t}\cdot h_{t-1},x_{t}]+b_{h})\\ h_{t}=(1-z_{t})\cdot h_{t-1}+z_{t}\cdot\widetilde{h}_{t}\end{array}\right. \tag{8}\]  
where \(x_{t}\) is the input data; \(h_{t}\) is the output result; \(h_{t-1}\) is the output of the previous period; \(\sigma\) is the s-type activation function; \(r_{t}\) and \(z_{t}\) are the outputs of the reset gate and the update gate at the time t, respectively; \(\widetilde{h}_{t}\) denotes the hidden state at the current moment; and \(W_{r}\), \(W_{h}\), and \(W_{z}\) denote the weight matrices of the reset gate, the update gate, and the hidden state, respectively. \(b_{r}\), \(b_{z}\), and \(b_{h}\) denote the reset gate, update gate, and hidden state bias.  
Since the inputs are time-series data on bridge deflections, attention must be paid to the links between the different periods. The GRU can mine the temporal information in the data and consider the effect of the period before and after on the current prediction, which is conducive to improving prediction accuracy.
## 2 Existing Leak Detection and Localization Methods
### Methods Based on Model Estimation
#### 2.2.2 System Identification  
This method can effectively distinguish the pipeline model and judge whether a leak happens by comparing it with pipeline actual value. In the case of pipeline integrity, both a fault sensitive model and a fault free model are built. For the fault sensitive model, correlation analysis is used for leak detection. For the fault free model, proper algorithms are introduced for leak point localization. Like the state observer method, this method supposes that both pressure extremes are not affected by the magnitude of the leak. Apart from that, it also needs to exert an M-sequence stimulus signal on the pipeline.
# Solving Partial Differential Equations Using Deep Learning and Physical Constraints
## 2 Methodology
### Artificial Neural Networks  
Artificial Neural Network (ANN) is a research hotspot in the field of artificial intelligence since the 1980s [38; 39]. It abstracts the human brain neurons from the perspective of information processing and models various networks according to different connections. Specifically, the artificial neural network is used to simulate the process of transmitting information from neuron cells in the brain. It consists of multiple connected artificial neurons and can be used to mine and fit complex relationships hidden within the data. Besides, the connections between different neurons are given different weights, each representing the amount of influence of one neuron on another neuron. Figure 1 illustrates the structure of a feedforward neural network (FNN). Feedforward neural network [40] is a simple artificial neural network in the field of artificial intelligence. As can be seen in Figure 1, a feedforward neural network consists of an input layer, one or more hidden layers, and an output layer. Within it, parameters are propagated from the input layer through the hidden layer to the output layer. When designing a neural network, the number of hidden layers, the number of neurons per layer, and the selection of activation functions are all important factors to consider.  
As the number of hidden layers increases, an artificial neuron network can be viewed as an adaptive nonlinear dynamic system that consists of a large number of neurons through various connections, which can be used to approximate a variety of complex functions. Although the structure of artificial neural networks is relatively simple, it is not easy to make artificial neural networks capable of learning. It was not until around 1980 that the backpropagation algorithm effectively solved the learning problem of multilayer neural networks, and became the most popular neural network learning algorithm [39; 41]. Because an artificial neural network can be used as a function approximator, it can be considered as a learnable function and applied to solve partial differential equations. Theoretically, with enough training data and neurons, artificial neural networks can learn solutions to partial differential equations.  
Figure 1: A structural diagram of a feedforward neural network (FNN), which consists of an input layer, one or more hidden layers, and an output layer, each containing one or more artificial neurons.