esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Datasets and Preprocessing
### Data Preprocessing  
Data preprocessing involves cleaning the data from unnecessary and unhelpful information to obtain better results. Preprocessing functions aim to minimize information loss while maintaining maximum data dimensionality. When one works with Arabic text, preprocessing is an essential step in data classification due to the unique Arabic morphology, such as diacritics and dots, which are commonly misplaced or unmarked, causing word ambiguity. Normalization, tokenization, and stop-word removal were applied to both datasets to address the previous challenges.  
* Tokenization is splitting a sentence into a set of tokens (words), which is important because unstructured text can be converted into independent words that can be easily analyzed. The dataset was tokenized using Keras' utility tokenizer class, automatically removing all punctuation and tokenizing the text.
* Normalization is undertaken to overcome misplaced dots or glitches in a word. This study normalized by replacing letters, as shown in Figure 4.
* Removal of stop-words, non-Arabic characters, and punctuation  
Figure 3: Sample of Jordanian articles about Brexit.  
Figure 2: Sample of KSA articles about the Hajj.  
\begin{table}
\begin{tabular}{l l l l} \hline  & **Saudi** & **Egypt** & **Jordan** \\ \hline
**No. of articles** & 486 & 481 & 485 \\ \hline
**Avg. no. of sentences** & 8 & 10 & 8.8 \\ \hline
**Avg. no. of characters** & 1163 & 1995 & 1404 \\ \hline
**Avg. word length (number of characters in word)** & 6.11 & 5.9 & 6.03 \\ \hline
**Avg. sentence length (number of words)** & 25 & 30 & 32 \\ \hline \end{tabular}
\begin{tabular}{l l l l} \hline \multicolumn{1}{|c|}{_med
## 1 Introduction
In this case, multiple nodes detect the pipeline leak simultaneously when a leak occurs, but affected by themonitoring position, different nodes may generate conflicting diagnosis results, which make it difficult for the monitoring system to make a right decision, so we need to devise a conflicting results solution method for making a reliable decision according to the diagnosis results from different nodes, avoiding the false alarm problem. In view of the issues above, in this paper we analyze the benefits and drawbacks of existing methods and propose a novel hierarchical pipeline leak detection and localization method. At different levels, we adopt different detection methods to process leak information. The effectiveness of the new method is verified by simulation experiments. Our results demonstrate that the hierarchical method can detect pipeline leaks and localize leak points accurately and effectively. The rest of the paper is organized as follows: in Section 2, we review some of existing methods for pipeline leak detection and localization. The preliminaries, including the architecture of pipeline monitoring sensor networks and hierarchical leak detection and localization model, are briefly illustrated in Section 3. Section 4 describes the algorithm in detail. Section 5 evaluates the performance of hierarchical leak detection and localization method through simulation experiments. Finally, Section 6 concludes this paper.
# A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
## 3 Intelligent Application of Deep Learning in Tunnel Engineering
### Mountain Tunnel
#### 3.1.2 Prediction of Collapse and Outburst and Water Inflow
The network structure was combined with the time series prediction method to realize the successful prediction of the unfavorable geological section of the tunnel. In the prediction of tunnel water inrush, the time series data of water inrush are normalized as the input and output variables of the model. It is a common method by which to establish a BP neural network model to predict water inrush, which has been used in the risk analysis of long-buried tunnels and karst tunnels [59; 60]. [PERSON] et al. [61] proposed an automatic detection method to quantify the water inflow on the tunnel face in view of the problems of personal subjective judgment, labor, and time consumption in the process of on-site manual monitoring of tunnel water leakage. This method utilizes the high-efficiency level of the convolution neural network (CNN) model in semantic segmentation. First, it identifies the tunnel face image samples without water inrush; then, it segments the image samples in the water inrush areas. Finally, it uses the ResNet-101 model to realize the probability classification of whether water inrush and inflow occurs in the image of the tunnel face. Currently, there are abundant research results on the prediction of tunnel rockbursts, collapse, and water surges using deep neural networks, and relying on the powerful no-linear computational ability and learning ability of neural networks, we can realize the preliminary analysis, prediction, and forecasting of disasters in mountain tunnels under complex environmental factors. In the context of a \"dual-carbon strategy\", the use of deep neural networks to establish a tunnel carbon emission calculation and analysis system [62], a dynamic design and decision-making system [63], and a construction mechanization system carbon emission calculation model [64] will also become major hot directions in the future construction of mountain tunnels.
## 4 Conclusions  
In conclusion, the proposed non-periodic pinhole array filtering method of the spatial light modulator was verified with a numerical simulation and a practical experiment in this study. First, the high-order intensity distribution was verified by varying the \(C\) value. Thus, the first-order intensity became seven times lower compared to the periodic intensity when the SLM fully became non-periodic pixels. Although non-periodic pixels eliminate high-order diffraction, a hologram based on non-periodic pixels acts as noise in the high-order region. Hence, the noise tends to increase in the high-order region when the reconstruction distance is closer to the hologram plane. Finally, a custom-made pinhole array mask was fabricated and attached to the SLM to reorganize the pixels. As a result, the practically reconstructed hologram showed results equivalent to those from the simulation. The proposed method was not able to solve all the noise problems in the holographic display. However, we believe that a lens-less holographic display will be realized by combining the results here with other ideas.  
Conceptualization Y.K.K. and J.S.L.; methodology Y.K.K. and J.S.L.; software W.J.R. and J.S.L.; writing--original draft Y.K.K. and J.S.L.; writing--review and editing, Y.K.K., W.J.R. and J.S.L.; supervision, J.S.L. All authors have read and agreed to the published version of the manuscript. This research was funded by an Institute for Information & Communication Technology Promotion (IITP) grant funded by the Korean government (MSIP) (No. 2020-0-01842, Development of holographic lithography equipment and printing technology for security and books). The authors declare no conflict of interest.
## 2 Preparation of Magnetic-Fluorescent Nanocomposites
### Fluorescent Polymer Coated Magnetic Nanoparticles  
#### 2.2.1 Silica-Polymer Magnetic-Fluorescent Nanocomposites  
Conjugated polymers are a very promising class of functional materials in the production of fluorescent nanoparticles. These polymers can be conveniently incorporated onto silica coated magnetic nanoparticles by the layer-by-layer self-assembly (LbL) technique. For example, using this technique, the cationic conjugated polymer PFV was chosen as a luminescent polymer to be incorporated onto the CoFe\({}_{2}\)O\({}_{4}\)@SiO\({}_{2}\) core-shell nanoparticles [37]. [PERSON] et al. also synthesised fluorescent and magnetic core-shell nanospheres by grafting a glycol-copolymer consisting of 6-O-methacryloylgalactopyranose (MAGal) and 4-(pyrenyl)butyl methacrylate (PyMA) onto silica encapsulated iron oxide particles (Figure 3) [38].  
Figure 2: Schematic of Fabrication of (CdTe/Fe\({}_{3}\)O\({}_{4}\))@SiO\({}_{2}\) FMNS with Different Magnetic Potentials and TEM images of (CdTe/Fe\({}_{3}\)O\({}_{4}\))@SiO\({}_{2}\) FMNS: (**A**) W-FMNS, (**B**) M-FMNS, and (**C**) S-FMNS. Adapted from ref. [29].  
In another study, Fe\({}_{3}\)O\({}_{4}\)@mSiO\({}_{2}\) nanoparticles were prepared by grafting fluorescent polymethacrylic acid and folic acid onto the silica coated magnetic material [39]. Polyethyleneimine (PEI) modified yolk-shell nanocapsules were also produced from PEI coated fluorescent mesoporous SiO\({}_{2}\) shells and Fe\({}_{3}\)O\({}_{4}\) cores [40].  
Carboxyl-functionalized superparamagnetic Fe\({}_{3}\)O\({}_{4}\) nanoparticles and individual amino-funtionalised silica coated fluorescent NaYF\({}_{4}\):Yb, Er up-conversion nanoparticles were synthesized, resulting in magnetic luminescent multifunctional nanoparticles (Figure 4) [41]. [PERSON] et al. prepared a monodisperse core-shell-structured Fe\({}_{3}\)O\({}_{4}\)@nSiO\({}_{2}\)@mSiO\({}_{2}\)@NaYF: Yb\({}^{3+}\), Er\({}^{3+}\)/Tm\({}^{3+}\) nanocomposites with mesoporous, up-conversion luminescent and magnetic properties [42].  
Figure 3: Synthesis of Fluorescent, Magnetic, Glycopolymer-Grafted Nanoparticles and Microscope images of A549 cells exposed to glycopolymer-grafted nanoparticles. (**A–D**) epifluorescence microscopy, (**E–G**) confocal microscopy. (**A**) Transmission image, (**B**,**E**) merged fluorescence images, (**C**,**F**) fluorescence images that exclusively show the green light emitting structures in the cell (nuclear membrane), and (**D**,**G**) fluorescence images that exclusively show the blue light emitting structures (pyrene in the glycopolymer covered nanospheres). Adapted from ref. [38].
## 2 Optimization without Consideration of the Reliability Criteria
### Objective Functions and Constraints for Market Settlement without Reliability Criteria
#### 2.1.3 Operating Constraints of the BESS with the Renewables without Reliability Constraints  
The BESS can charge and discharge power within its state of charge (SoC) limit at the manufacturers' discretion. Hence, the SoC of the BESS with the PV generator and the BESS with the WPG, represented by \(SoC_{sb,t}\) and \(SoC_{wb,t}\), respectively, is limited by the minimum and maximum SoC levels:  
\[\begin{split} SoC_{wb}^{min}\leq SoC_{wb,t}\leq SoC_{wb}^{max}, \quad\forall\;wb,t\end{split} \tag{12}\]  
\[\begin{split} SoC_{sb}^{min}\leq SoC_{sb,t}\leq SoC_{sb}^{max}, \quad\forall\;sb,t\end{split} \tag{13}\]  
Equations (14) and (15) represent management of the SoC through continuous scheduling periods. The efficiency coefficients represented by \(\beta\) and \(\alpha\) are the efficiency when charging and discharging, respectively.  
\[\begin{split} SoC_{wb,t}=SoC_{wb,t-1}+P_{wb,t}^{W2B}.\beta_{wb}- \frac{P_{wb,t}^{B2G}}{\alpha_{wb}},\quad\forall\;wb,t\end{split} \tag{14}\]  
\[\begin{split} SoC_{sb,t}=SoC_{sb,t-1}+P_{sb,t}^{S2B}.\beta_{sb}- \frac{P_{sb,t}^{B2G}}{\alpha_{sb}},\quad\forall\;sb,t\end{split} \tag{15}\]  
The coordinated BESS cannot be simultaneously charged and discharged. Thus, the binary control variables for charging and discharging, represented by \(\delta_{wb,t}^{C}\), \(\delta_{wb,t}^{D}\), \(\delta_{sb,t}^{C}\) and \(\delta_{sb,t}^{D}\) in each portfolio as the constraints, can be expressed as follows:  
\[\begin{split}\delta_{wb,t}^{C}+\delta_{wb,t}^{D}\leq 1,\quad \forall\;wb,t\end{split} \tag{16}\]  
\[\begin{split}\delta_{sb,t}^{C}+\delta_{sb,t}^{D}\leq 1,\quad \forall\;sb,t\end{split} \tag{17}\]The energy to be discharged and charged is represented in Equations (18)-(21), indicating the output constraints for each resource portfolio, specified at the manufacturers' discretion.  
\[0\leq P_{wb,t}^{B2G}\leq\delta_{wb,t}^{D}\cdot Pmax_{wb}^{PCS},\quad\forall\;wb,t \tag{18}\]  
\[0\leq P_{wb,t}^{W2B}\leq\delta_{wb,t}^{C}\cdot Pmax_{wb}^{PCS},\quad\forall\;wb,t \tag{19}\]  
\[0\leq P_{sb,t}^{B2G}\leq\delta_{sb,t}^{D}\cdot Pmax_{sb}^{PCS},\quad\forall\; sb,t \tag{20}\]  
\[0\leq P_{sb,t}^{S2B}\leq\delta_{sb,t}^{C}\cdot Pmax_{sb}^{PCS},\quad\forall\; sb,t \tag{21}\]
# Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing
## 5 Performance Evaluation
### The Impact of the Reliability of Each VNF on Different Algorithms  
In this part, we vary the reliability of VNF in each SFC. The number of all VNF instances decreases with the reliability of VNF, and the reliability provided by all parallel instances for each VNF in SFC increases with the reliability of VNF, as shown in Figure 10a,b. When the reliability of VNF increases, the reliability enhancement achieved by deploying the same number of parallel instances increases. Thus, fewer BVNFs are deployed to satisfy the reliability requirement.  
Figure 10c shows that the throughput increases with the reliability of VNF. The total number of VNF instances deployed to satisfy the reliability requirement decreases with the reliability of VNF. The fixed resource capacity in the network can accept more user requests.  
Figure 10: (**a**) Throughput when the reliability of VNF in SFC changes; (**b**) the number of VNF instances and VNF reliability of all accepted user requests when the reliability of VNF in SFC changes; (**c**) the average reliability provided by one VNF instance, all active VNF instances and all VNF instances of all accepted user requests when the reliability of VNF in SFC changes.
# Performance Characteristics of an Orthopter-Type Vertical Axis Wind Turbine in Shear Flows
## 1 Introduction
[PERSON] and [PERSON] [26] examined the power performance of an O-VAWT with three blades whose cross-sectional shape was the upstream half of a NACA0010-65 section reflected about the mid-chord by conducting theoretical analysis with a multiple-stream tube model and field measurements. Our group [23] conducted wind tunnel experiments to compare the performance of an O-VAWT with elliptic blades and one with flat-plate blades. By considering the mechanical loss torque, we obtained the maximum power coefficient of 0.246 at a tip-speed ratio of 0.4 for the O-VAWT with elliptic blades and of 0.288 at a tip-speed ratio of 0.4 for the O-VAWT with flat-plate blades. It should be noted that these studies on O-VAWTs were conducted in conditions where the approaching flows had uniform distribution. To properly conduct a micro-sitting of an O-VAWT in the built environment, it is important to understand the effects of the strong shear approach flow with on the performance of the O-VAWT. Figure 1 illustrates the approaching wind flow to a building. As the wind flow approaches the building, the wind speed decreases, and the pressure increases. Then, the wind flow proceeds along the upwind face of the building and separates at the corners on the roof and the side walls. As the separated wind flow is not obstructed by the building, the pressure decreases, and the wind speed increases. Near the upwind corners, the wind speed increases more than that of the approaching wind. In addition, reverse flow regions are formed between the separated shear layer and the building's walls. As a result, strong shear flows are formed vertically over the roof surface and horizontally over the side walls. Due to the mixing of momentum, the shear becomes weaker as the flow proceeds downstream. To utilize the increased wind speed over the roof of a building, the effects of building shapes and wind directions on the wind conditions have been investigated (e.g., [27]). Furthermore, the effects of wind conditions, such as wind speed, turbulent intensity, and skew angles, on the potential energy yield and the power performance of a wind turbine have been studied (e.g., [28; 29]). In this study, we investigated the effects of horizontal shear flow on the performance of the O-VAWT by conducting wind tunnel experiments and three-dimensional CFD simulations. A uniform flow and two types of shear flow were employed as the approaching flow to the O-VAWT. One type had a higher velocity on the advancing side of the rotor. The other type had a higher velocity on the retreating side of the rotor. For each type of shear flow, we set three different shear strengths.
# Event Detection Using a Self-Constructed Dependency and Graph Convolution Network
## 4 Experiments
### Ablation Experiments
#### 4.4.2 Analysis of Influence of GCN Layers L and Attention Heads K on Model Effect  
In this study, we built an L-layer GCN model and used the multi-head attention mechanism to improve the GCN model. To achieve the best performance, we explored the impact of the numbers of GCN layers and attention heads K on the performance of the model. While other parameters remained constant in the experiment, we increased the number of hidden layers and attention heads from one to five for the experiment; the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline MaxEnt & 74.5 & 59.1 & 65.9 \\ CrossEntity & 68.7 & 68.9 & 68.8 \\ \hline DMCNN & 75.6 & 63.6 & 69.1 \\ JRNN & 66.0 & 73.0 & 69.3 \\ DBRNN & 74.1 & 69.8 & 71.9 \\ \hline RGCN & 68.4 & **79.3** & 73.4 \\ JMEE & 76.3 & 71.3 & 73.7 \\ MOGANED & 79.5 & 72.3 & 75.7 \\ EEGCN & 76.7 & 78.6 & 77.6 \\ \hline Ours & **79.7** & 77.2 & **78.4** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Exploring the effect of different compatibility functions on the overall performance of the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline - & 78.3 & 76.8 & 77.5 \\ Pruning & 78.9 & 76.5 & 77.7 \\ Entity Edges & 78.1 & 76.2 & 77.1 \\ Pruning + Entity Edges & **79.7** & **77.2** & **78.4** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Influence of different graph structures on the model.  
event detection results are shown in Table 4. Here, we only focus on the F1 score. From the results in the table, we can see that the efficacy of the model first increased but subsequently declined, as the numerical values of the two parameters increased. The best result was obtained when both values were three, so we finally determined that three GCN layers and attention heads should be used.
# Three-Dimensional Printing and Digital Flow in Human Medicine: A Review and State-of-the-Art
## 5 Digital Flow in 3D Printing
### Image Acquisition  
Usually, for healthcare applications, we start with medical images, especially when discussing diagnosis and treatment. The flow chosen will depend on the clinical needs. When they are internal structures such as those obtained by MRI, CT, and 3D Ultrasound, the information will be stored in the DICOM (Digital Imaging and Communication on Medicine) format. In a complementary way, there are surface scanners such as intraoral scanners of structured light to scan teeth, mucous membranes, and others, as well as for extraoral scanning that can be Photogrammetry (Monoscopic and Stereophotogrammetry), Laser, Structured light in its variants of white, blue, and infrared light, which allow us to create a digital copy of surfaces of faces. Depending on the technology, the latter is saved directly in the STL format, OBJ combined with JPEG texture maps, or others. Of these files, the oldest and most commonly used is STL, which loads only the geometry of the 3D file. This article does not focus on making an in-depth comparison between 3D file types. Still, each can load different information, such as geometry, appearance, scene, and motion [24; 25; 26]. Of these, only geometry can be 3D printed, hence the still widespread use of STL files and software that allows the transformation of DICOM.  
In many cases, the visualization software of recent equipment allows rendering for viewing only (diagnostic/therapeutic evaluation). On the other hand, transforming to STL is not visualizing but directly changing the format to another type of communication file to continue with the next steps. The act of converting the DICOM format to STL using specialized software is called \"Segmentation\". This term should not be confused with surgical segmentation of structures. It is important to remember that a tomography or MRI scan in each slice also captures unwanted structures, the air surrounding the face, and even artifacts. In this sense, segmentation is understood as cleaning each of the tomography slices, based on the desired Housefield units to obtain an accurate and professional model.  
The manipulation of software (such as Inveslaus(r), Slicer(r), or others) in inexperienced hands can produce severe errors in the identification of structures, thicknesses, and distances and, consequently, severe mistakes in diagnosis and treatment. Because of that, it is essential to have a formally trained and experienced technical professional to use medical manipulation.
## 3 Results and Discussion
### Particles Generation and Process Kit Inspection  
A \"mini-marathon run\" was performed to simulate a typical production procedure, where 25-wafer lots were automatically processed through the AltaCVD tool. A total of 500 wafers were processed, depositing 0.8 \(\upmu\)m of SiO\({}_{2}\) on to the Si substrates followed by the F\({}_{2}\)-based cleaning process. The optical inspection showed no unusual wear of the process kit parts, no degradation of O-rings made of perfluoroelastomer, no tool downtime, and no noticeable chamber damage. The measured particles density was compared with the NF\({}_{3}\) clean. These results are a good indication for a possibility of substituting the NF\({}_{3}\) via the F\({}_{2}\)-gas mixture clean. The substitution does not necessitate any modification of the existing hardware used by the semiconductor industrial companies.  
Figure 3: SiO\({}_{2}\) etch rates per minute as a function of the N\({}_{2}\) percentage in an F\({}_{2}\)–gas mixture for different total mass flows of fluorine in the F\({}_{2}\)–gas mixture. The following parameters were kept constant: total flow, spacing between 200 mils to 650 mils, T = 400 \({}^{\circ}\)C, pressure between 3 Torr for inner clean and 4 Torr for outer clean, clean time = 45 s. The error bar is \(\pm\)10% for plasma cleaning using an F\({}_{2}\)–gas mixture.