esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning
## 2 Literature Review
### Introduction to Transfer Learning  
All models of NER are for improving accuracy and portability. In NER's field, the application of a model across different domains has always been a considerable challenge [10]. Supervised learning involves building statistical models from labeled training data. However, if the training data are insufficient, the model cannot perform well due to the sparseness of the data [10]. As the official dictionary for formal terms and the existing reviews of school building safety inspection for the current research are inadequate to build a brand-new NER model, we attempt to introduce transfer learning, which is one of the most popular frontiers of machine learning, to solve the issue of data shortage.  
Since 1995, transfer learning has attracted the attention of scholars around the world under different names [15]. For example, multitask learning attempts to learn multiple target tasks simultaneously and find out the function that benefits all tasks [15; 16]. Nowadays, the source task's roles and the target task in transfer learning are no longer symmetrical; transfer learning focuses on target tasks instead of learning all tasks at the same time. In other words, under the current definition, transfer learning aims to learn from one or more original tasks and apply the result to the target tasks. Based on the existing model of NER, transfer learning is applied in three different levels [17]. (1) Cross-domain transfer: Under the premise of the same language, the adequately named entities of each field are mere \"sublanguages.\" They still share the patterns and characteristics of the language. Cross-domain transfer learning can divide into two types: label remapping and disparate label sets. (2) Cross-application transfer: On the premise of the same language, named entities still share the same primary language characteristics even if their applications differ. The model architecture is the same as the previous one with disparate label sets. (3) Cross-lingual transfer: Transfer learning across languages mostly relies on additional packages, and the performance is sensitive to package size and quality [18]. Therefore, cross-language transfer, which involves the sharing of character embedding and character-level neural networks, is proposed for similar languages [17]. In the current research, we implement the NER module via the cross-application as mentioned above transfer, that is, the cross-domain transfer with disparate label sets. Figure 1 shows the model architecture.  
Figure 1: Cross-Domain transfer with disparate label sets and Cross-Application transfer [17].
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Study Group** & \multicolumn{4}{c}{**Weibull Shape (\(\beta\))**} & \multicolumn{4}{c}{**Weibull Scale (\(\eta\))**} \\ \hline  & **Estimate** & **St Error** & **Lower** & **Upper** & **Estimate** & **St Error** & **Lower** & **Upper** \\ \hline
0 s & 8.1790 & 1.0424 & 6.3712 & 10.4999 & 1257.379 & 25.5617 & 1208.264 & 1308.4906 \\
60 s & 6.9078 & 0.8808 & 5.3803 & 8.8690 & 1018.7335 & 24.5392 & 971.7553 & 1067.9829 \\
120 s & 8.0244 & 0.9069 & 6.4301 & 10.0141 & 467.0982 & 9.7300 & 448.4118 & 486.5633 \\
0 cycles & 2.7040 & 0.4074 & 2.0127 & 3.6328 & 195.6014 & 13.9108 & 170.1516 & 224.8576 \\
1 cycle & 2.6640 & 0.4060 & 1.9761 & 3.5913 & 192.7207 & 13.8859 & 167.3392 & 221.9521 \\
5 cycles & 2.9873 & 0.4529 & 2.2195 & 4.0208 & 185.5872 & 11.9333 & 163.6121 & 210.5138 \\
10 cycles & 3.1770 & 0.4761 & 2.3685 & 4.2617 & 195.9868 & 11.8612 & 174.0652 & 220.6693 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Weibull statistics of the number of cycles to failure for the time of use and sterilization cycles study groups. Figure 6: (**A**) Weibull probability plot of the number of cycles to failure for the time of use and (**B**) sterilization cycles study groups. The ANOVA test revealed statistically significant differences between the number of cycles of in and out movement and the time of use of NiTi endodontic reciprocating files (\(p<0.001\)) (Figure 7). However, no statistically significant differences were observed between the number of cycles of in and out movement and the number of sterilization cycles applied to NiTi endodontic reciprocating files (\(p=0.848\)) (Figure 7). The mean length of the fractured file tip did not show statistically significant differences between the number of cycles of in-and-out movement and the time of use (\(p>0.05\)) or the number of sterilization cycles (\(p>0.05\)) applied to the NiTi endodontic reciprocating files (Figure 7). The scale distribution parameter (\(\eta\)) of Weibull statistics showed statistically significant differences between the number of cycles of in-and-out movement and the time of use of NiTi endodontic reciprocating files (\(p<0.001\)) (Table 6, Figure 8A); however, no statistically significant differences were found between the number of cycles of in-and-out movement and the number of sterilization cycles applied to the NiTi endodontic reciprocating files (\(p>0.05\)) (Table 6, Figure 8B). The shape distribution parameter (\(\beta\)) of Weibull statistics did not show statistically significant differences between the number of cycles of in-and-  
of cycles of in-and-out movement and the time of use (\(p>0.05\)) (Table 6, Figure 8A) or the number of sterilization cycles applied to the NiTi endodontic reciprocating files (\(p>0.05\)) (Table 6, Figure 8B).
# Early Fault Detection and Operator-Based MIMO Fault-Tolerant Temperature Control of Microreactor
## 8 Experimental Results
### Comparison of Simulation and Experiment  
The simulation results shown in Section 7.1 are compared with the experiment results. However, since the target temperatures and fault requirements are different, the comparison is made based on the average absolute error from the reference input after the time of the fault. Specifically, the simulation is calculated using temperatures from 500 to 800 s, while the experiment is calculated using results from 601 to 1000 s. As shown in Table 5, the mean absolute error is smaller for the simulation. Therefore, the simulation results are superior in this comparison.
# Solving the Urban Transit Routing Problem Using a Cat Swarm Optimization-Based Algorithm
## 2 Problem Description  
### Urban Transit Routing Problem (UITRP)  
The UTRP aims at developing a set of vehicle routes for an urban transit network while meeting all passengers' and/or operator's constraints. To model a transportation network, bus stops are modeled as adjacent nodes and, as a result, are linked by an edge. Several nodes connected by edges form a route. Several edges that connect nodes constitute a transportation path. In Figure 1, we present [PERSON]'s Swiss public transit network [25]. This network has fifteen nodes and twenty-one edges. Traveling times between nodes are defined by the numbers on the edges.  
A candidate bus route (transportation path) constitutes several nodes connected by edges. We can define a valid bus route among nodes 8, 14 and 7 since these nodes are connected by existing edges of the network. However, nodes 9, 11 and 14 cannot form a bus route since there are not any existing edges that directly connect these nodes of the network. A route set is formed by one or more transportation paths (valid bus routes). Finally, all the routes of a route set that have been superimposed constitute the route network.  
It is obvious that a route network should contain all nodes of the current transit network but may not include all its edges. As a matter of fact, we can say that each route network constitutes a subgraph of the existing road network (graph). An ideal route network, that is, a network satisfying all traveling demands, is a network with paths that connect each node of the network with every other node. To construct an efficient route network, we must acquire precise estimations of all travel demands. To realize these estimations, one should [44]:  
* [noitemsep]
* Undertake public and private vehicle analysis.
* Carry out surveys on the local population.  
Figure 1: The Swiss road network by [PERSON] [25].  
* Examine current ticket sales, etc.  
However, these estimations are difficult to make, since travel demands are continuously modified and are very sensitive to factors like quality of service, pricing policy, etc. In the ideal case, the most travel demanding routes should be fulfilled with short travel paths and few vehicle transfers. In this way, however, the service level of all other routes will probably be affected. There are also several other factors that affect designing an effective route network, such as the local government's transport management policies, the local area's street environment, etc. [45].
# Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks
## 4 Performance Evaluation
### Simulation Results
#### 4.2.2 Data Delivery Delay  
The data delivery delay is an essential metric for time-constrained real-time applications. In mobile sink-based WSNs, the absence of the sink's current location stands as a primary factor that impacts the delay. In this situation, data packets have to be rerouted towards the sink's latest location, which results in increased delay.  
Figure 8 shows the data delivery delay of VGRQ and QDVGDD. The rationale behind the high data delivery delay in QDVGDD is the unavailability of the updated sink position information to the sensor nodes, which causes the data to travel a long route to be delivered at the sink. In contrast, VGRQ has the provision of periodically providing the location information of the sink to CHs, and they transmit the query as well as data over the chain, respectively, to the AoI and sink. Although QRRP also provides the sink's location to ring nodes, it involves a greater number of hops in its routing path than the VGRQ  
Figure 7: Energy consumption.  
protocol. Thus, QRRP results in more delay as compared to VGRQ. However, QRRP outperforms QDVGDD, since QRRP always ensures the shortest routing path for query and data transmission.
## 1 Introduction
Practically speaking, the coupling coordination degree model is not only applied to the coupling research of the two subsystems, but is also commonly used in the coupling coordination research of three subsystems; some studies entailed a quantitative analysis of the temporal and spatial evolution characteristics based on the model across the tourism, urbanization, and ecological environment subsystems in Heilongjiang Province [24], in Shaanxi Province [25], in Coastal city [26], and in China's other major tourist cities [27]. In addition, the coupling coordination degree model has been applied to explore the coupling relationship between urbanization and ecosystem health [28]; among tourism carbon emissions, economic development, and regional innovation [29]; among tourism, transportation, and low-carbon city systems [30]; and so on. It has been widely used in empirical applications. Some scholars pay attention to the negative impact of tourism urbanization, believing that tourism activities will increase the pressure on the ecological environment, thereby reducing the quality of urbanization [31].These research results have a practical guiding role in promoting the coupling and coordinated development of tourism and urbanization. However, in contemporary contexts, the task of coordinated, high-quality progress faces the problem of unbalanced and inadequate development. High-quality development demands attention and research in the current time. High-quality development has the distinct characteristics of the times and is a major research topic in the new era [32]. The research mainly focuses on connotation [33], characteristics [34], index system construction and measurement [35], etc., which is still in the initial stage of weak theory and practice. Throughout the existing studies, experts and scholars mostly focused on provinces and key tourism cities and made single identifications based on time series. Few scholars took urban agglomeration as the research object and combined time and space to conduct a dynamic analysis of space-time evolution. It can be observed that the discussion on the coupling coordination between tourism and urbanization at the medium scale and its high-quality development is relatively unexplored, as well as being an urgent concern. Consequently, this study not only applies the perspective of spatial-temporal heterogeneity to analyze the coupling coordination characteristics of tourism and urbanization, but also investigates the coupling coordination framework of high-quality development and optimizes the layout of the regional economy for an empirical application of its findings. This elucidation of the high-quality development mechanism between tourism and urbanization coupling coordination will provide a point of reference for regional development in other urban agglomerations, helping to establish a regional small-cycle hub, promote the domestic big cycle, and advance both domestic and international dual circulation.
# UAV Communication Recovery under Meteorological Conditions
## 2 System Model
### Specific Attenuation Model for Rain, Fog, Snow, and Gases
#### 2.3.2 Fog  
Fog, as another common meteorological phenomenon, can significantly impact UAV communications and flights. Based on the ITU model, the specific attenuation caused by fog, \(\gamma_{\rm fog}\), can be described as [14]  
\[\gamma_{\rm fog}=K_{\rm i}R_{\rm fog}\quad({\rm dB/km}), \tag{7}\]  
where \(K_{\rm i}\) represents the attenuation per kilometer with unit liquid water density, in units of \(({\rm dB/km})/({\rm g/m^{3}})\), and \(R_{\rm fog}\) denotes the liquid water density (LWD) in \({\rm g/m^{3}}\). In this study, we focus on the LWD range of 0.05-0.5 \({\rm g/m^{3}}\).
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 4 5G Functions and Features to Support the Information Flow within Industry 4.0
These build upon the communication at the tera-Hertz bands, supported by non-orthogonal multiple access (NOMA), fog and edge computing, artificial intelligence-based technologies, blockchains, and quantum communication, just to mention a few. The expectations are that tactile internet becomes a reality (due to extremely low delay, high reliability and high throughput communication), with real AR/VR capabilities, ultra-high endpoint density and efficient fulfillment of mixed requirements (slices). Digital Twins (DT) and handling Cyber-Physical Systems are also key application areas for 6G [65]. For the time being (without discussing the exact technologies leading there) we can plan the new DT and CPS applications, which should count on the requirement fulfillment of 6G systems in relation to latency, throughput, reliability, positioning accuracy [66] and endpoint density. The flow of information in the automation industry can be depicted according to hierarchical ISA-95 [67] or the RAMI 4.0 reference models [68]. The RAMI 4.0 is a 3D model (shown by Figure 5) describing all vital aspects, including all stockholders of Industry 4.0. In this section, we are interested in the communication layer of RAMI 4.0 [69], which describes the integration and coexistence of industrial networks according to their timely requirement and connection types. Figure 6 gives an idea about the information flow in five-layer industrial automation stack [70], covering all crucial aspects of RAMI 4.0, which will be explained in the following sections. Figure 5: Reference Architecture Model Industries 4.0 (RAMI4.0) [68].
# Numerical Study on Hydrodynamic Performance of Bionic Caudal Fin
## 3 Results and Discussion  
The motion parameters of the caudal fin are selected as follows: \(Re=45000\), \(St=0.3\), \(h=0.4\)\(L\), and \(\alpha_{max}=25^{\circ}\). For flapping foil propulsion, high-efficiency thrust production occurs in wakes with a Strouhal number in the range of \(0.2<St<0.4\)[8]. Furthermore, most fishes have been shown to swim near a \"universal\" optimal value \(St=0.3\) because of the high propulsive efficiency [29; 30; 31]. Therefore, \(St\) adopted in the paper is 0.3. Figure 7 shows the performance curves of caudal fin. It is shown that the curve settle to a similar behaviour with the flapping foil. It is further noticed that, the mean \(C_{x}\) which equals to 0.44 is positive and the mean \(C_{y}\) which equals to 0.002 is almost zero. In other words, the fin gets the thrust force and the resultant force of transverse force is near zero. The thrust efficiency is about 30.3%.  
Figure 8 shows the vortex pattern in the wake. The vortex ring consists of a train of inverted hairpin-like vortices braided together such that the legs of each vortex are attached to the head of the preceding vortex [32; 33]. Specifically, Figure 9 shows the two-dimensional vorticity magnitude in the wake. It is shown that the vortices shedding from the fin range two  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Mean \(C_{x}\)** & **Mean \(C_{y}\)** & **Thrust Efficiency** \\ \hline Reference & 0.604 & 0.0082 & 49.1\% \\ Present & 0.58 & 0.0065 & 47.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reference results of flapping foil.  
Figure 6: Vortex pattern behind the foil.  
Figure 7: Performance curves of caudal fin.  
have different directions of rotation in different ranks. The jet stream can be induced between the two vortices ranks and the fin can obtain reaction force correspondingly. Furthermore, two vortices shed from the fin in a cycle time, resulting in two oscillations of thrust force.
# AR in the Architecture Domain: State of the Art
## 5 AR System
### Tracking and Registration
#### 5.2.2 Markerless Tracking  
According to the survey, the markerless approach is based on the camera's orientation and automatic recognition of some geometric features of the real environment. Compared to the marker approach, this method can allow real-time tracking, but the large amount of data to be processed can slow down the recognition process. [37]. In this case, concerning the three main constraints shown in the use of markers, only the ambient lighting conditions affect feature recognition. Markerless tracking is based on the recognition of specific geometric features (feature-based), such as corners and edges of buildings, or shapes (model-based) within the scene [126]. The concept of a feature-based approach is finding a correspondence between 2D image features and their 3D coordinates in the scene. Recent approaches show the possibility of using a structure from motion (SfM) methodology to generate 3 DCG (three-dimensional computer graphics) model objects on a live video feed of the surrounding landscape acquired via a camera [127].  
On the other hand, model-based methods use CAD models or 2D drawings that outline the main objects by identifying the lines, edges, or shapes present in the model. This method is applicable in indoor and outdoor environments, but the observation point intensely conditions it. In this sense, identifying planes in the environment and applying 3D to 2D feature projection allows the estimation of the camera position without any SDK system [128]. Some recent experiments have shown that it is possible to merge depth information given by a sensor with the position of a camera, establishing a relationship between the 2D space of the image and the 3D space of the scene, resulting in a reliable estimate of the camera position [56]. The markerless approach is much more flexible and intuitive, applicable both indoors and outdoors as long as the image database of the scene is connected to the location, but suffers from processing times that often lead to a delay in data registration.
## 3 Results and Discussion
### Evaluation of the Performance on Different Methods  
The machine learning model can predict each test action as a score of 1-3. To analyze more detailed results about the scoring performance of the improved GMM, we further visualized the confusion matrix based on expert scoring and automatic scoring. Figure 5 shows the confusion matrix obtained by the Naive-Bayes-based method, AdaBoost.M1-based method, and improved GMM-based method. In this study, we consider expert scoring as the gold standard, and we combined the scoring results for each test action. From Figure 5, we can observe that the misclassified samples are prone to be predicted as a score close to its true score, 1-point samples are more likely to be wrongly predicted as 2 points than 3 points, and 3 points samples are more likely to be wrongly predicted as 2 points than 1 point; among these, the most errors occur when 2-point samples are predicted as 3-point samples.  
Table 1 shows the scoring of the Naive Bayes, AdaBoost.M1, improved Gaussian mixture model (GMM), and traditional GMM. The accuracy of improved GMM is higher than Naive Bayes, AdaBoost.M1, and traditional GMM, and the improved GMM has the highest agreement. In general, the FMS assessment based on the improved GMM model outperformed Naive Bayes and AdaBoost.M1. The results indicate that the improved GMM yields considerable improvement over the traditional GMM.  
Figure 4: (**a**) Expert score distribution of FMS movements. (**b**) Expert score distribution of FMS movements based on Borderline SMOTE.