esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Review Methodology
### Article Filtration and Selection  
There were 663 articles in total that were identified after the initial database search by using the Boolean strings as outlined above. Then, some filtering strategies were applied to narrow down the search results. Firstly, duplicated results from different searches were removed to lower the number to 375. Secondly, the title and abstract of the articles were screened to filter out further irrelevant articles, which reduced the total number to 193. In this stage, articles that were not related to the educational and research environment were excluded. Finally, the full content of the articles that remained from the previous stage was investigated to finalise the list of articles for review. In full-text screening, articles that discussed both Industry 4 integration and educational facilities development were considered. As a result, 145 articles that studied the development of Industry 4 facilities in the academic teaching field were selected and reviewed in this research. Another set of 22 articles that discussed Industry 4 systems generally and the related technologies was included in the review. A PRISMA-style flow diagram for the screening process is demonstrated in Figure 1.  
A grouped summary of the 145 reviewed articles that were organised by technology, design principles, and key challenges is provided in Appendix A.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & **Boolean Strings** & **No. of Articles** \\ \hline \multirow{7}{*}{SpringerLink} & (“Learning Factory” OR “Testlab” OR “Learning Laboratory”) & \\  & AND & \\  & (“Industry 4” OR “Digital Twin” OR “Smart Factory” OR “CPS” OR “CPPS” OR “IoT” OR “Intelligent Manufacturing” OR “AI” & \\  & OR “Machine Vision” OR AR OR VR OR 5G) & \\  & TITLE-ABS-KEY ((“Learning Factory” OR “Testlab” OR “Learning Laboratory”) & \\  & AND & \\  & & AND & \\ \multirow{2}{*}{Scopus} & (“Industry 4” OR “Digital Transformation” OR “Digitisation” OR “Smart Factory” OR “Intelligent Manufacturing” OR “Digital Twin” & 218 \\  & OR “CPS” OR “CPPS” OR “IoT” OR “IIoT” OR “Intelligent Robotics” & \\  & OR “Machine-to-Machine” OR “AI” OR “Cloud Computing”) & \\  & _TS_ = ((“Learning Factory” OR “Testlab” OR “Learning Laboratory”) & \\  & AND & \\ \multirow{2}{*}{Web of Science} & (“Industry 4” OR “Digital Transformation” OR “Digitisation” OR “Smart Factory” OR “Intelligent Manufacturing” OR “Digital Twin” & 120 \\  & OR “CPS” OR “CPPS” OR “IoT” OR “IIot” OR “Intelligent Robotics” & \\  & OR “Machine-to-Machine” OR “AI” OR “Cloud Computing”) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Search Boolean strings/keywords used and the initial numbers of identified articles.
# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 3 Preliminaries
### Fuzzy String Matching
#### 3.1.3 Levenshtein Distance  
In contrast to the Jaccard similarity coefficient, the Levenshtein distance algorithm computes the edit distance between two sequences. This distance corresponds to the minimum number of basic editing operations required to transform the matched string into a dictionary word, which is the first string being matched to. Specifically, given two strings \(S\) and \(T\) with lengths \(m\) and \(n\), respectively, the algorithm constructs a ma trix LD of \((n+1)\times(m+1)\) dimension and computes the value of each cell _LD(i,j)_ using Equation (4) [45].  
\[LD(i,j)=\left\{\begin{array}{cc}0,&i=0,j=0\\ j,&i=0,j>1\\ i,&i>0,j=0\\ Min,&i>0,j>0\end{array}\right. \tag{4}\]  
The matrix is populated based on a recursive formula, where each element in the matrix indicates the edit distance between a prefix of string \(S\) and a prefix of string \(T\). Specifically, the edit distance between two prefixes is calculated using Equation (5).  
\[\text{Min}=\min\{LD(\text{i}-1,\text{j})+1,LD(\text{i},\text{j}-1)+1,LD(\text {i}-1,\text{j}-1)+\text{f}(\text{i},\text{j})\} \tag{5}\]  
The algorithm fills the Levenshtein distance matrix row by row, where each cell represents the edit distance between a prefix of \(S\) and a prefix of \(T\). Specifically, the function f(\(i\),\(j\)) takes the value of 1 if the \(i^{\text{th}}\) word of \(S\) is different from the \(j^{\text{th}}\) word of \(T\), and 0 otherwise. The final edit distance between \(S\) and \(T\) is determined by the value in the bottom-right corner \(LD(\text{m},\text{n})\)of the matrix.
## 3 Results and Analysis
### Compressive Strength  
Figure 8 shows that when the W/C was fixed at 0.5 and at the age of 91 days, the compressive strength of the control group was 53.2 MPa, the compressive strength of the 5% TPSW substitution group was 48.8 MPa (\(-\)8.3%), the compressive strength of the 10% TPSW substitution group was 45.3 MPa (\(-\)14.8%), and the compressive strength of the 15% waste PS substitution group was 40.1 MPa (\(-\)24.6%). The findings show that as the waste PS had a lower water absorption than the natural sand, there were more water molecules in the specimen, which formed many pores and reduced the compressive strength; when the W/C was 0.5 and at the age of 91 days, the strength decreased by 8.3% when the substitution amount was 5%. When the substitution amount exceeded 10%, the strength decreased by more than 10%. It could be inferred that a W/C of 0.5 and a TPSW substitution amount of 5% were the most effective at removing waste to attain the goal of waste recycling.  
Since the water absorption rate of the TPSW material was lower than that of natural sand and gravel, there were more water molecules in the mix, which led to the formation of additional pores, which, in turn, reduced the compressive strength. As shown in Figure 9, under different water-cement ratios (W/Cs), the compressive strength decreased linearly as the proportion of TPSW increased. The slopes of these three curves were nearly identical, indicating that the reduction in the compressive strength was independent of the W/C ratio, which is consistent with previous research findings [38].  
Figure 7: Setting times of cement mortars with different W/C ratios and TPSW amounts.
## 1 Introduction
In [3], a model predictive control approach with an adaptive cost function along with a terminal sliding mode disturbance observer has been utilized to regulate the speed of PMSG coupled to a PKGS. However, only cases with a constant speed reference have been examined. In [15], a UDE control approach based on a first-order reference model system has been proposed to handle periodic variation in the speed reference for an open-orbit KGS. Nevertheless, that analysis only accounted for the generation mode of operation. Additionally, although the reliance on a first-order dynamic model enabled robust stability, it led to certain limitations on transient response time. This paper introduces a model-free controller (MFC) designed to effectively address the above challenges. MFC, also called intelligent PID controllers, was proposed in [24; 25] to address the challenges posed by uncertainties and modeling errors in controller design. The MFC does not rely on the control model of the system [26]; instead, it uses an on-line perturbation estimation where only the knowledge of the system's input-output behavior is needed for automatic updating of the controller. The MFC strategy has demonstrated its effectiveness in various fields, including vehicle yaw-rate control [27], pneumatic muscles [28], electro-hydraulic system [29], direct injection fuel system [30; 31], and automated vehicle path-tracking [32]. This paper introduces a novel MFC strategy specifically designed for a PKGS. While MFC offers several advantages, such as independence from a precise system model and adaptability to uncertainties and disturbances through online estimation based on input-output behavior, it faces limitations when dealing with step references due to its reliance on derivative calculations. Previous solutions, like setting the derivative to zero as adopted in [26], are unsuitable for a PKGS, particularly during the traction phase where the speed reference exhibits a periodic component. To address this challenge, a nonlinear hyperbolic tangent tracking differentiator (TD) is integrated into the MFC framework. This innovative approach resolves the compromise between achieving stable transition phases and maintaining good tracking performance during generation phases, thereby ensuring optimal power generation. The remainder of this paper is organized as follows: Section 2 presents a comprehensive model of the power kite generator system. In Section 3, we introduce the proposed model-free control approach for the machine speed regulation, detailing its structure and the integration of the nonlinear hyperbolic tangent tracking differentiator. Section 4 showcases the simulation results, demonstrating the feasibility and effectiveness of the proposed control strategy highlighting its superior performance in handling the unique challenges of PKGS. Finally, conclusions are drawn in Section 5.
# RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification
## 1 Introduction
In reference [56], the same authors used the recurrent neural network (RNN) instead of the CNN to further improve classification accuracy. When the training samples of radar plots exceed 10000, the recognition accuracy can reach 0.991, which is impressive. Therefore, in order to effectively characterize the uncertain data and also improve the recognition accuracy, a radar plot recognition algorithm based on adaptive evidence classification (RPREC) is proposed in this paper. In the RPREC, a confidence recognition framework is first created that includes target, clutter, and uncertainty, and an updatable classifier based on deep neural networks is also designed. Then, based on the network classification model obtained in each round, the category of all radar plots can be confirmed. If the network classification model does not have samples for training, the class of each radar plot will be randomly initially given. Finally, the class of each radar plot is updated through the fusion of belief functions, and the plots after updating the category label can be used for classifier training and parameter optimization. The optimized classifier can also be reused to obtain the class of each plot. This cycle continues until the category labels of each plot are no longer updated or iterated to a certain number. The performance of the RPREC is verified through some experiments based on the real radar plot dataset. The results show that the RPREC can effectively handle clutter and uncertain data compared to other typical algorithms. So, it can improve the recognition accuracy of radar plots. In addition, the RPREC has less dependence on training samples, making it easy to apply to other scenarios. The rest of this paper is organized as follows. In Section 2, we will recall the belief functions and the evidence \(K\)-nearest neighbor classification, respectively. In Section 3, we will introduce the proposed radar plot recognition algorithm based on adaptive evidence classification. Finally, experiments will be presented in Section 4, and the paper will be concluded in Section 5.
## 5 Results and Analysis
### Tight Coupling Solutions
#### 5.2.3 Segment 3  
The trajectories obtained with the different TC approaches for this scenario are presented in Figure 15. As expected, the results are very close to the reference due to the good GNSS coverage. The GPS/INS solution shows some disagreements during the GPS outages at the beginning of the segment. Again, including the GLONASS measurements yields a reduction of the above mentioned disagreements. Adding the GNSS-derived yaw aiding and the velocity and height motion constraints, the obtained trajectory is close to the reference during the whole segment.
# Energy Efficiency Analysis of MIMO Wideband RF Front-End Receivers
## 3 Transmission Schemes
### Outage Probability and Transmitted Power  
An outage event in a communication link occurs whenever the received SNR falls bellow a threshold \(\xi_{0}\) that allows correct decoding. In other words, the outage probability is defined as \(\Pr\left\{\overline{\xi}<\xi_{0}\right\}\), where assuming the use of capacity achieving error correcting codes, \(\xi_{0}=2^{R}-1\) and \(R\) is the spectral efficiency in bit/s/Hz [22].
## 2 Problem Formulation and Preliminary
### Preliminary  
Consider the autonomous dynamical system  
\[\dot{\mathbf{x}}=\mathbf{f}(\mathbf{x};\mathbf{\varphi}),\mathbf{x}(0)=0, \tag{7}\]  
where \(\mathbf{x}\in\mathbb{R}^{n}\) denotes the state, and \(\mathbf{f}\colon\mathbb{R}^{n}\to\mathbb{R}^{n}\) is a nonlinear locally Lebesgue-integrable function. \(\mathbf{\varphi}\in\mathbb{R}^{l}\) represents the tunable parameters.  
**Definition 1** ([26]).: _The origin of Equation (7) is said to be globally stable in predefined time if, for any \(T_{c}\in\mathbb{R}_{+}\), there exists some \(\mathbf{\varphi}\in\mathbb{R}^{l}\) such that \(\|\mathbf{x}(t)\|=0\) holds for \(\forall t\geq T_{c}\) regardless of \(\mathbf{x}(t_{0})\), where \(\|\cdot\|\) stands for the Euclidean norm._  
**Definition 2** ([27]).: _A smooth function \(\rho(t):\ \mathbb{R}_{+}\to\mathbb{R}_{+}\) is called a performance function if both of the following conditions are satisfied:_  
(1) \(\rho(t)\) _is decreasing and positive;_  
(2) \(\lim_{t\to\infty}\rho(t)=\rho_{\infty}>0\)_._  
**Lemma 1** ([28]).: _For the system in Equation (7), assume that the Lyapunov function \(V(\mathbf{x})\colon\mathbb{R}^{n}\to\mathbb{R}_{\geq 0}\) satisfies_  
\[\dot{V}(\mathbf{x})\leq-\frac{\pi}{aT_{p}}\Big{(}V^{1-\frac{\pi}{2}}(\mathbf{x})+V^{1+ \frac{\pi}{2}}(\mathbf{x})\Big{)}, \tag{8}\]  
_where \(T_{p}>0\) and \(0<a<1\) are positive constants. Then, this system is globally stable in predefined time, and \(T_{p}\) is the convergence time._  
**Theorem 1**.: _For the system in Equation (7), if the radially unbounded Lyapunov function \(V(\mathbf{x}):\mathbb{R}^{n}\to\mathbb{R}_{\geq 0}\) satisfies_  
\[\dot{V}(\mathbf{x})\leq-\frac{\sqrt{3}\pi+2\sqrt{3}\mathrm{arctan}\frac{1}{\sqrt{3} }}{(3+2\alpha)T_{c}}\Big{(}V^{1/2-a/3}(\mathbf{x})+V^{2+2a/3}(\mathbf{x})\Big{)}, \tag{9}\]  
_where \(-1.5<a<1.5\), \(T_{c}>0\), then the system is globally stable in predefined time. Meanwhile, the convergence time is \(T_{c}\)._  
**Proof of Theorem 1.** Please refer to Appendix A. \(\square\)  
**Lemma 2**.: _For any \(y_{i}>0\)\((i=1,\,2,\,\ldots,\,n)\), there is_  
\[\sum_{i=1}^{n}y_{i}^{\overline{\gamma}}\geq\left(\sum_{i=1}^{n}y_{i}\right)^{ \overline{\gamma}},\,\,\,\mathrm{if}\,\,0<\overline{\gamma}\leq 1\quad\sum_{i=1}^{n}y_{i}^ {\overline{\gamma}}\geq n^{1-\overline{\gamma}}\left(\sum_{i=1}^{n}y_{i} \right)^{\overline{\gamma}},\,\,\,\mathrm{if}\,\,\overline{\gamma}>1, \tag{10}\]  
_where \(\overline{\gamma}>0\)[29]._  
**3. PTPPF and Error Transformation**  
In this section, a PTPPF is designed, and then the inequality constraint is converted to an unconstrained one, laying a foundation for the nonsingular predefined-time attitude controller design.
# Biochemistry of Terpenes and Recent Advances in Plant Protection
## 1 Introduction
### General Overview of Terpenes  
The term terpene, proposed by [PERSON] in 1866, originates from the Latin word 'turpentine' (_Balsamun tetebinthinae_), a liquid extract from pine trees. Terpenes are the largest natural products, with significant structural variation, including linear hydrocarbons or carboxylic skeletons. Approximately 55,000 members are known [18; 19]. Terpenes undergo oxygenation, hydrogenation, or dehydrogenation to form terpenoids. Terpenes classification is based on [PERSON]'s, 1887 proposed isoprene units (CSH8), a 5-carbon compound that forms terpenes backbones [20; 21]. The isopentenyl diphosphate (IPP) unit and its isomer, dimethylallyl diphosphate (DMAPP), are the biosynthesis precursors of terpenes. Terpenes are abundant in higher plants, citrus, conifers, and eucalyptus and are widely distributed in the leaves, flowers, stems, and roots of these plants. The development of chromatographic and spectroscopic methodologies in 1945 propelled the explosive discovery of terpenids and terpene-derived products. Terpenoidal molecules are antifungal, antimicrobial, antiviral, and antiparasitic. They deter feeding herbivores and are used as insecticides to store agricultural products [18]. However, their full potential has not yet been attained.
# Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks +
## 3 Problem Statement
### Link Performance  
NB-IoT is a low-power system with a small EIRP [38]. Therefore, link performance is essential for communicating with LEO satellites farther than the base station. LEO satellites have a Doppler shift, which further degrades the link performance of NB-IoT. Terrestrial NB-IoT improves link performance through retransmission using HARQ [39]. However, NB-IoT NTN has difficulty tolerating many retransmissions due to the long _RTT_ of LEO satellites. In particular, for LEO satellites, the communication time varies depending on the orbit and the communication time is short and variable. Therefore, service providers are trying to establish links through multiple LEO satellites to solve the LEO satellite communication problem. Although an analysis of the minimum number of satellites is required to establish a link, there are not many studies for NB-IoT NTN [40; 41]. Therefore, a new method to improve the link performance in NB-IoT NTN is required, and an analysis of the number of satellites, applying satellite orbit, is required. In addition, since NB-IoT periodically transmits data such as notification messages, it is also necessary to analyze the communication time for varying numbers of LEO satellites.
## 3 Problem Statement and Formulations
### An Illustrative Example  
To facilitate a better understanding of the problem and acquire insight into the influence of the depot capacity, we provide a simple example. It considers one terminal, one depot, two trucks, two IF requests, one OF request, and one OE request. All locations are generated on an imaginary time plane spanning 3 h in width and length. The time it takes to travel between two locations is calcuated by their Euclidean distance. In particular, the coordinates of the terminal and depot are (1.500, 1.500) and (1.666, 1.913), respectively. A truck incurs a fixed cost of 10, and the travel cost is computed using the fixed rate of 1 per unit of travel time. We assume that 1 empty container is initially stored at the depot and the depot capacity is \(1\). The planning horizon is 8. Table 4 provides parameters related to inland container transportation requests.  
An optimal set of routes with two trucks is indicated by the black and red arrow lines in Figure 2(a). The numbers on the arrows denote the visiting order. These routes incur a total cost of 25.968. We can observe from Figure 3 that one truck begins its journey from the depot and travels to the terminal. Afterwards, the truck picks up the container associated with IF request 2 from the terminal and subsequently transports it to the designated receiver. After unpacking, the emptied container is transported to the shipper's location associated with OF request 1 for packing cargoes. Once the container has been packed, the fully laden container is hauled to the terminal by the truck. Finally, the truck goes back to the depot. Clearly, the other truck starts from the depot and picks up a full container from the terminal. Later on, the full container is transferred directly to the receiver's site associated with IF request 1. After unpacking, the truck hauls the emptied container to the terminal for export and heads back to the depot.  
When we relax the limited depot capacity constraint and assume that the depot has sufficient capacity to accommodate empty containers, a totally different solution is obtained and shown in Figure 3. It is noted that in this situation, the truck takes an intermediate stop at the depot along its route to store an emptied container, and only one truck is required to complete these requests. Accordingly, the total cost reduces to 16.0259. Therefore, this example attests that the depot capacity has a significant impact on the optimal truck routes, and neglecting this constraint might result in an impractical routing plan.
## 1 Introduction  
Pu-erh tea (PET) is a traditional Chinese tea produced from sun-dried leaves of _Camellia sinensis_ (Linn.) var. _assamica_ (Masters Kitamura) in Yunnan, China. Based on its processing technology and quality characteristics, PET can be classified into Pu-erh shengcha (non-fermented Pu-erh tea, NFPT) or Pu-erh shucha (post-fermented Pu-erh tea, PFFT). NFPT is prepared by pressing sun-dried green tea leaves into a disk or bowl shape, and PFFT is produced from the solid state fermentation (SSF) of sun-dried green tea leaves at 40-60 \({}^{\circ}\)C and high humidity conditions [1,2].  
Currently, PFFT has become increasingly popular in Southeast Asia and is beginning to be recognized in Western culture. PFFT has unique sensory characteristics, including a reddish-brownish color, mellow taste, stale flavor, and long shelf life. Additionally, PFFT has anti-obesity [3,4], neuroprotective [5], antioxidant [6], anticancer [7], constipation-relieving [8], anti-allergic, anti-inflammatory [9],antibacterial [10], and anti-viral (hepatitis B and HIV) properties [11], with retrogressive effects on immunosenescence [12]. Furthermore, clinical studies have shown that the daily consumption of PFPT reduces body weight and prevents obesity [13; 14; 15].  
The health benefits of PFPT have been attributed to its components. Flavonoids [16], theabrownin (TB) [17; 18], gallic acid (GA) [19], and statin [20] have anti-obesity/hypolipidic effects and regulate lipid metabolism. Caffeine (CAF) [21] and polysaccharides [22; 23] have blood glucose-lowering effects, and 8-C N-ethyl-2-pyrrolidone substituted flavan-3-ols [24], ethanol-soluble pigment [25], polysaccharides [23; 26], and phenolic compounds [27] (including GA, (+)-catechin, (-)-epicatechin, (-)-epicatechin-3-O-gallate, (-)-epigallocatechin-3-O-gallate (-)-epiafzelechin-3-O-gallate, kaempfer, and quercetin) have antioxidant properties. However, these components are generally present in other teas (e.g., CAF and catechins), and levels of polyphenols (catechins, theaflavin, thearubigin, myricetin, quercetin, kaempfer, and flavonoid glycosides), amino acids, and soluble sugars decrease during SSF of PFPT [2].  
Recently, compound **1** was detected during SSF of PFPT, but not in the raw material (Figure 1). To our knowledge, only theabrownin (TB), polysaccharides, \(\alpha\)-tocopherol, GA, and CAF increase during SSF of PFPT [2]. In this study, we isolated and identified this novel compound and assessed its antioxidant activity _in vitro_. Furthermore, fungi responsible for the biosynthesis of this compound were identified.