esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning
### Model Revision for Disaster Management  
The module is initially developed for related work [1]. Our related work (The Human-Computer Interaction Lab, 417 Department of Civil and Construction Engineering, NTUST) is mainly a chatbot to collect the post-disaster data and feedback. With those data in hand, we can analyze the data and provide disaster managers with compact information. However, when there are more and more data and feedback provided by the assessors, the analyzing work must be tough and inefficient without the assistance of NLP techniques. Hence, with the module of this paper, the back-end can input the raw text into the module and get the parsing results. In that way, the displaying module in the related work can get better and efficient information for the manager. Take another example in the chatbot, according to the question in the examination. The conversation module of the related work (chatbot script) has expected to get the \"accurate\" answer. Without the assistance of the module, the chatbot relies on merely a rule-based method to check if the assessor provides \"enough\" information. Hence, the result may be flawed and not accurate at all. However, with the module, the conversation module can enhance the overall quality of data collected.  
The current research aims to recognize named entities in damage information gathering and analysis tasks in disaster management. However, without sufficient reviews of safety inspections, a reliable NER model is challenging to build. Hence, this research utilizes transfer learning, which is one of the most popular techniques in machine learning. With the assistance of transfer learning, a NER model can build based on existing models with minimal training data. As this NER model designed to target disaster management, the model should recognize specific terms; that is, the model needs further modification through data augmentation. After the appropriate enhancement, the NER model can recognize not only familiar Mandarin entities but also jargons in the domain.
# Control Synthesis as Machine Learning Control by Symbolic Regression Methods
## 4 Symbolic Regression Methods  
All methods of symbolic regression encode the mathematical expression and search for optimal solution on the space of codes by the genetic algorithm. To encode a mathematical expression it is needed to create the base sets of elementary functions. These sets will be alphabets for coding, and their elements are letters for creating words or codes.  
The base sets of functions can be combined by the number of arguments. The following base sets are possible:  
* a set of functions with two arguments \[\text{F}_{2}=\{f_{2,1}(z_{1},z_{2}),\ldots,f_{2,v}(z_{1},z_{2})\}.\] (26) All functions with two arguments have to possess the following properties:
- be commutative \[f_{2,i}(z_{1},z_{2})=f_{2,i}(z_{2},z_{1}),\;i=1,\ldots,v\] (27)
- be associative \[f_{2,i}(z_{1},f(z_{2},z_{3}))=f_{2,i}(f_{2,i}(z_{1},z_{2}),z_{3}),\] (28)
- have a unit element \[f_{2,i}(z,e_{i})=f_{2,i}(e_{i},z)=z,\] (29) where \(e_{i}\) is a unit element for the function, \(i=1,\ldots,v\). These base sets (24)-(26) are generally enough to describe any mathematical expression of the control function based on the Kolmogorov-Arnold representation (or superposition) theorem, which states that any complex function can be represented as a combination of one-dimensional functions [25; 26]. Let us consider an example of codding of a mathematical expression by different symbolic regression methods. Let the mathematical expression be given \[y=(q_{1}x_{1}^{2}+q_{2}x_{2}^{2})\exp(-q_{2}x_{1})\sin(q_{1}x_{2}+x_{3}).\] (30) To present this mathematical expression the following base sets are enough:
- the set of arguments of the mathematical expression (30) \[\text{F}_{0}=\{f_{0,1}=x_{1},f_{0,2}=x_{2},f_{0,3}=x_{3},f_{0,4}=q_{1},f_{0,5} =q_{2},f_{0,6}=0,f_{0,7}=1\};\] (31)
- the set of functions with one argument \[\text{F}_{1}=\{f_{1,1}(z)=z,f_{1,2}(z)=z^{2},f_{1,3}(z)=-z,f_{1,4}(z)=\exp(z),f_{1,5}(z)=\sin(z)\};\] (32)
- the set of functions with two arguments \[\text{F}_{2}=\{f_{2,1}(z_{1},z_{2})=z_{1}+z_{2},f_{2,2}(z_{1},z_{2})=z_{1}z_{2 }\}.\] (33)
# Optimization Methods of Compressively Sensed Image Reconstruction Based on Single-Pixel Imaging
## 1 Introduction
However, the sparse representation performance of the traditional wavelet transform is not good enough, and reconstruction of higher precision images with a lower sampling compression rate (SCR) requires high enough sparsity of the original signals. On the other hand, in CS-based single-pixel imaging system, a digital mirror device (DMD) and a spatial light modulator (SLM) are usually used to encode and modulate spatial optical signals. However, due to the hardware limitations of DMD or SLM, the sensing matrix of CS must only be deterministic binary matrix in single-pixel imaging system, so deterministic binary matrices need to be designed and constructed [32, 33, 34, 35]. In order to facilitate the hardware implementation, the traditional binary sensing matrices such as 0-1 binary sparse random matrix (with the elements of \(+1\) and 0) and part Hadamard random binary matrix (with the elements of \(+1\) and \(-1\)) are usually fixed as the deterministic binary matrices in single-pixel imaging system. However, the structure and performance of traditional binary sensing matrices are usually not optimal, which extremely limits the improvement space of expanding compressed sensing measurement matrix and further enhancing imaging quality in single-pixel camera. Therefore, on the basis of traditional methods, it is very necessary to further optimize the signals sparse decomposition and the binary sensing matrix in compressed sensing, and to that end related work is reported in this paper. First, based on the traditional DWT, the original wavelet decomposition basis was optimized by our proposed constraint condition, which enhancing its sparse representation performance. Then, we propose a new algorithm model and a new initialization method to construct more optimized binary sensing matrices than traditional binary sensing matrices by gradient projection (GP) strategy. Finally, the above proposed key technologies were applied to our single-pixel imaging system and effectively improved the image reconstruction accuracy and imaging quality. The remaining contents of this paper are organized as follows: Section 2 introduces the basic theoretical background and analyzes some related works as the foundation of our contributions; in Section 3, our single-pixel imaging system is built, we design a new optimization method based on the traditional wavelet decomposition basis and propose a new compressed sensing measurement matrix optimization algorithm; in Section 4, we chose appropriate parameters that perform relatively well by the simulation experiments, and test the performance and demonstrate the effectiveness of our methods by practical imaging experiments using our single-pixel camera; then our proposed methods are analyzed and the experimental results discussed in Section 5. Finally, Section 6 summarizes our work and this paper is concluded.
## 2 Materials and Methods  
The section adds the particulars about the data acquisition setup that is shown in Figure 1, feature extraction process, employed ML techniques and DL architectures, the conceived validation workflows, and the statistics approaches for the performance comparisons. The overall flow diagram of the experimental study is illustrated in Figure 2.  
Figure 1: **(A) Welding setup prior to start of welding process, showing the laser source (in green), mounted on an industrial 6-axes robot; two synchronized IR cameras that record heating and cooling transients; battery modules fixed to a linear guide rail that cyclically moves towards cameras, stopping once reaching the target position where tab and busbar are under the cameras field of view. (B) View of the setup from behind one of the two cameras before welding starts. The IR camera (in yellow) is mounted on a support framed with the clamping unit (in blue) with an orientation that enables the recording of the welding process, avoiding collision with the robotic arm. (C) View of a new configuration in which the linear guide rail has reached the target position under the camera. Once in this condition, the clamping unit (in blue) presses on the aluminum tab (electrode, in gray) in order to ensure a repeatable coupling with the copper busbar (in brown). Then, the robot can approach, and finally it welds the joint with a constant speed, following the laser path (dashed red line) whose length is 40 mm. The clamping unit is designed to allow both the welding process and the recording through the camera, as well as to ensure a repeatable gap between the layers. (D) The industrial 6-axes robot with the mounted laser head. (E) The result after the weld: the laser path (dashed red line), the electrode (blue), part of the busbar (green). (F) Graphic representation of the measuring instrument probes position during the three executions of the electrical resistance measurement, using the four-wire method.**  
Figure 2: Video processing starts with cropping IR camera resolution of \(256\times 320\) pixels to \(20\times 300\) pixels which cover the welded joint area. Three pipelines correspond to the three workflows proposed in this study. In the first, each frame is subdivided into 20 adjacent regions in order to evaluate time transients distribution (_cooling analysis_); _image analysis_ starts with pixel binarization to encode high/low-temperature patches, allowing welding nuggets shape description, and _video analysis_ performs global process statistical representation at a pixel level. The pooled data features of workflow I feed both ML and DL classifiers, while reduced and selected groups of features are provided to the ML classifiers only for workflows II and III, respectively.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 2 Energy-Efficient IoT
### Emerging Trends and Future Directions
#### 2.4.2 Artificial Intelligence  
Machine learning and artificial intelligence show promise for optimizing the resource allocation and data collection in IoT systems. Edge computing and edge AI can make local real-time decision making feasible for IoT devices and thus reduce their reliance on cloud services, thereby lowering the power consumption for communications and cloud computing. AI-powered data analysis can also facilitate optimizing energy management such that IoT devices can operate intelligently and efficiently.
## 2 Optimization Methodology
### Contour Tracking Control
#### 2.1.1 Position Domain Mapping with Relative Derivative  
To convert the slave dynamic motions from time domain into the position domain, the relation must be developed in terms of master and slave motions in the position domain. This relation is represented by the relative derivative of the master motion (\(q_{m}\)) and slave motion (\(qs_{i}\)).  
To explain master/slave is a model of communication for hardware devices where one device has a unidirectional control over one or more devices. This is often used in the electronic hardware space where one device acts as the controller, whereas the other devices are the ones being controlled. In short, one is the master, and the others are slaves to be controlled by the master.  
\[q^{\prime}_{si}=\frac{dq_{si}}{dq_{m}}=\frac{q^{\prime}_{si}}{q_{m}} \tag{1}\]  
where \(q_{i}\) is the relative position velocity, which represents \"the ratio of the slave motion velocity to the master motion velocity\", and it also explains the procedure of motion relation between slave motions and master motions. The relative acceleration of the slave motion which is the second relative derivative of Equation (1) is given by:  
\[q^{\prime\prime}_{si}=\frac{dq^{\prime}_{si}}{dq_{m}} \tag{2}\]  
According to Equations (1) and (2), in order to convert the slave velocity and acceleration from the time to the position domain, the following equations are considered:  
\[\left\{\begin{array}{c}q^{\prime}_{si}=q_{m}q^{\prime}_{si}\\ q^{-}_{si}=q^{\prime\prime}_{si}(q_{m})^{2}+q_{m}q^{\prime}_{si}\end{array}\right. \tag{3}\]  
Equation (3) describes the relation between the relative and the absolute motions, i.e., to transform the absolute velocities and accelerations in the time domain to relative velocities and accelerations in the position domain. The above equation transforms a dynamic system from time-domain to position-domain [20,21].
# Establishing a Generic Geographic Information Collection Platform for Heterogeneous Data
## 3 Result and Analysis
### Illustrative Examples
#### 3.2.2 Challenge 1: Support the Entire Process of Field Collection  
The collection area for this task is vast, and many personnel are involved. Gathering all personnel to discuss and assign tasks is difficult and inefficient. Our platform effectively resolves these issues.  
Managers configure the necessary collection tools and contents interactively on the platform, as shown in Figure 4(a). Once configured, they upload the task area to the server and add all personnel. The server divides the task area evenly among the personnel based on the area, number of personnel, and selected division level. As shown in Figure 4(b), managers can manually adjust these divisions on the platform. Task information is then transmitted in real-time to the collection software of the respective task personnel. During the collection process, managers can track the location of the personnel and monitor progress in real-time. They can also view the field data uploaded by the personnel and check if it has been verified by inspectors, as shown in Figure 4(c).
# Design of an Axial Turbine for Highly Downsized Internal Combustion Engines
## 3 Methodology
### Preoptimization of the Applied Solutions with Meanline and Streamline Analysis  
The preliminary solution, consisting of the principal meridional channel dimensions, thermodynamic parameters and initial velocity triangles and blade chord data (Figure 7), is then exported to the preoptimization module of AxSTREAM, allowing for aerodynamic optimization of the blades' relative pitch and chord of the selected design solution, also considering the structural requirements with an automatic parametrized stress calculator (not the Finite Element (FE) method). This module recalculates the thermo-fluid-dynamic parameters via meanline analysis and allows the designer to manually edit the meridional channel section after checking the physical properties' contour in order to further improve the efficiency of the machine.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Data** & **Unit** & **Min** & **Max** \\ \hline Boundary Conditions & & & \\ \hline Inlet Total Pressure & kPa & 262 & 262 \\ Inlet Total Enthalpy & kJ/kg & 1597 & 1597 \\ Stat. Pressure at Outlet & kPa & 124 & 124 \\ Mass Flow Rate & kg/h & 254 & 254 \\ Inlet Flow Rate & tan. deg & 90 & 90 \\ Shaft Rotational Speed & rpm & 178,422 & 178,422 \\ \hline Parameters & & & \\ \hline Hub Diameter & mm & 9 & 18 \\ Rotor Diameter Ratio (D2/D1) & - & 1 & 1 \\
1 st Stage Blade Height l1 & mm & 5 & 10 \\ Hub Reaction & - & 0 & 0.800 \\ Isentropic Velocities Ratio (u/Co) & - & 0.2400 & 0.4600 \\ Heat Drop Gradient (H\_z/H\_1) & - & 0.800 & 1.000 \\ Meridional Velocities Gradient (Cm\_z/Cm\_1) & - & 0.800 & 1.200 \\ Module Admission Ratio & - & 1 & 1 \\ \hline Constraints & & & \\ \hline Number of Stages & - & 1 & 1 \\ Hub Diameter Limit & mm & 1.000 & 10,000.000 \\ Tip Diameter Limit & mm & 1.000 & 50.000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of predesigned boundary conditions (AxSTREAM Inverse Task solver).  
Then, an axisymmetric 2D thermodynamics solver was used to calculate the streamlines distribution in the meridional channel, as well as increasing the precision of the meanline calculator. In the case of 3D blades, this module was also used to perform a minimization of the incidence angle and change the blade twist parameters in such a way as to allow for better off-design performance, reducing the chance of stalling at different flow conditions.  
A map of the turbine was then calculated with the same solver to check the suitability for the application in off-design points. However, a weakness of this solver is that it does not consider the actual profile shape, as it is based on 1D-2D calculations and loss models. For this reason, the final turbine suitability was to be determined via increasingly accurate CFD simulations.
## 2 Important Terms  
### Singular Value Decomposition (SVD)  
It is the most often used technique for dividing matrices into component matrices and showing the main attributes of the source matrices. The source may be rebuilt using the matrices that make up the input. The compression method relies on altering the decomposition to produce low-rank approximation matrices. The approach achieves lossy compression as the recovered matrices vary slightly from the source matrix. SVD has a wide range of applications, including rank, numerical error, and low-rank approximation [22; 23]. One such use that will be covered in this study is data compression.  
One of the benefits of SVD is its capability to work with both kinds of matrices (viz. square and rectangular). An image is just a 2D matrix of numbers that represent the image's pixels. Figure 1 and Equation (1) depict the three matrices \(U\), \(d\), and \(V\) that result from the matrix \(M\) being split in half. \(U\) and \(V\) are perpendicular to each other, and \(d\) is diagonal; their single values are arranged in ascending order along \(U\), \(V\), and \(d\).  
\[M_{m\times n}=U_{m\times r}d_{r\times r}(V_{n\times r})^{T} \tag{1}\]
### Fourth-Generation (4G) Technology  
\"Connect anytime, anywhere, anyhow\" is the guiding principle of 4G communication. Its primary characteristics are seamless access, personalization, Quality of Service (QoS) management, and an IP-based system (end-to-end IP transmission). The sole packet-switching technology, 4G, sometimes referred to as IMT-Advanced, was initially introduced in 2010. It integrates with one Core Network and many Radio Access Networks (RANs) to offer a comprehensive IP-based network with speeds ranging from 100 Mbps to 1 Gbps, with a frequency of 2-8 GHz [5]. Due to the high speed, the coverage area was reduced,necessitating the use of numerous smart antennas to provide long-range communication [6]. Good Quality of Service (QoS) and data speeds in 4G support services with improved clarity; such services include, but are not limited to, Mobile Television, HDTV content, Digital Video Broadcasting (DVB), Video Chat, Multimedia Newspapers, and High-Quality Live Streaming. Compared to 3G, 4G delivers incredibly reduced latency. However, there are some drawbacks to 4G cellular networks. The increased number of antennas and infrastructure required for extensive coverage leads to higher deployment and maintenance costs. Moreover, 4G networks are more susceptible to interference, especially in densely populated urban areas where signal congestion can occur. The battery consumption in 4G devices is higher, due to the continuous need for high-speed data transmission, which can reduce the overall battery life of mobile devices. Furthermore, despite the high speeds, the actual performance may vary significantly, depending on the user's location, network traffic, and the number of connected devices. Lastly, the transition from older networks to 4G requires compatible devices, which may not be affordable for all users, creating a digital divide.
# Automated Real-Time Assessment of Stay-Cable Serviceability Using Smart Sensors
###### Abstract  
The number of cable-stayed bridges being built worldwide has been increasing owing to the increasing demand for long-span bridges. As the stay-cable is one of critical load-carrying members of cable-stayed bridges, its maintenance has become a significant issue. The stay-cable has an inherently low damping ratio with high flexibility, which makes it vulnerable to vibrations owing to wind, rain, and traffic. Excessive vibration of the stay-cable can cause long-term fatigue problems in the stay-cable as well as the cable-stayed bridge. Therefore, civil engineers are required to carry out maintenance measures on stay-cables as a high priority. For the maintenance of the stay-cables, an automated real-time serviceability assessment system using wireless smart sensors was developed in this study. When the displacement of the cable in the mid-span exceeds either the upper or the lower bound provided in most bridge design codes, it is considered as a serviceability failure. The system developed in this study features embedded on-board processing, including the measurement of acceleration, estimation of displacement from measured acceleration, serviceability assessment, and monitoring through wireless communication. A series of laboratory tests were carried out to verify the performance of the developed system.  
stay-cable; serviceability; monitoring; automation; real-time; Raspberry Pi; smart sensor +
Footnote †: journal: Applied Sciences