esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology And Implementation
### Metrics
#### 4.4.2 Jaro-Winkler Similarity  
The Jaro-Winkler similarity metric, an extension of the Jaro similarity, is a string comparison algorithm that quantifies the similarity between two strings. Originally conceived for record linkage in the context of the U.S. Census, it has found widespread application in duplicate detection and string matching tasks. In the context of UDC codes, this metric offers a potentially valuable tool for comparing and analyzing classification strings. Formally, the Jaro-Winkler similarity for two strings \(a\) and \(b\) is defined as  
\[sim_{jw}(a,b)=sim_{j}(a,b)+\ell\cdot p\cdot(1-sim_{j}(a,b)) \tag{3}\]  
where \(sim_{j}\) is the Jaro similarity, \(\ell\) is the length of the common prefix (up to four characters), and \(p\) is a scaling factor, typically set to 0.1 [26]. The Jaro similarity itself is computed as  
\[sim_{j}(a,b)=\begin{cases}0&\text{if }m=0,\\ \frac{1}{3}\cdot\left(\frac{m}{|a|}+\frac{m}{|b|}+\frac{m-t}{m}\right)&\text{ otherwise.}\end{cases} \tag{4}\]  
Here, \(m\) is the number of matching characters, and \(t\) represents the number of matching characters that appear in different sequential order in the two strings, divided by two. Characters are considered matching if they are identical and occur within a matching window of \(\lfloor\frac{\max(|a||,|b|)}{2}\rfloor-1\) positions of each other. In the context of UDC codes, the Jaro-Winkler metric's emphasis on prefix matching aligns well with the hierarchical structure of UDC notations. For instance, when comparing UDC codes \"621.39:681.3\" and \"621.39:004\", the metric would assign a high similarity score due to the shared prefix \"621.39\", reflecting their common root in the classification hierarchy. This property makes the Jaro-Winkler metric particularly suitable for tasks such as identifying related UDC codes, detecting potential classification errors, or suggesting alternative classifications in automated systems. However, the application of Jaro-Winkler to UDC codes requires careful consideration. The metric's character-by-character comparison may not fully capture the semantic relationships encoded in UDC's special auxiliaries and faceted structure. For example, the codes \"94(44)\"18\"\({}^{\prime\prime}\)\" (History of France in the 19 th century) and \"94(45)\"18\"\({}^{\prime\prime}\)\" (History of Italy in the 19 th century) would receive a high similarity score due to their structural similarity, despite representing different geographical entities.
## 3 Results
### Failure Characteristics
As mentioned earlier, when a uniaxial compressed specimen undergoes damage and instability, its failure modes mainly include tension failure and shear failure. Different failure modes exhibit different acoustic emission signal characteristics, thus allowing for the inversion of the failure mode of the loaded specimen through acoustic emission signals. Commonly used methods for acoustic emission inversion require parameters such as rise time, amplitude, ringing count, and duration [4]. RA represents the ratio of rise time to amplitude, while AF represents the ratio of ringing count to duration. RA and AF values constitute important parameters for crack model recognition. Tension failure generally exhibits high AF values and low RA values, while shear failure exhibits the opposite characteristics [24]. Therefore, through the characteristics of the acoustic emission signals, it is relatively simple to discriminate the failure mechanism of the specimen under stress loading, as shown in Figure 7.  
Among them, K is the ratio of the maximum AF value to the maximum RA value, and the value of K depends on the properties of the material. The K value for the analysis of the failure mode of the specimen is determined based on the reference of the maximum measured values of RA and AF in the acoustic emission signals during the experiment, which ranges from 1 to 4. Figure 7: Schematic diagram of rock fracture mechanism based on RA and AF values. As shown in Figure 8, RA values mainly range from 0 to approximately 50 ms/v, while AF values mainly range from 0 to approximately 150 kHz, with the most data points near the origin. For a single-rock specimen, the proportion of RA values reaches 90.78%, showing low AF and high RA characteristics, indicating that during uniaxial compression, the main failure mechanism is the formation, development, and extension of shear cracks, corresponding to the macroscopic crack failure characteristics of the rock specimen described earlier, with two cracks appearing on the surface of the rock specimen exhibiting a large inclination angle, and the specimen undergoing shear failure overall. The acoustic emission signals of the single-coal specimen and the composite specimen mainly concentrate near the AF axis, showing clear high AF and low RA characteristics, indicating that tensile failure is the main factor leading to instability and failure. Moreover, as the proportion of coal increases, the proportion of RA values gradually increases, while the proportion of AF values decreases, indicating that shear cracks begin to form, develop, and extend inside the specimen, interconnecting with tensile cracks, eventually transitioning from tensile failure to tensile-shear composite failure. Figure 8: RA–AF of single-coal, single-rock, and combinations.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 3 Antenna Overview
### Array Antenna Model  
Imagine an arbitrary array antenna consisting of several radiating elements \(M\) separated by a distance \(d\). Arrays are primarily used as directors to align the incident and reflected signals to a desired beamwidth. Thus, the array factor (AF) of the incoming signal is modelled as [82; 83]:  
\[\mathrm{AF}(\theta)=1+e^{i\Phi}+e^{2j\Phi}+\cdots+e^{(M-1)j\Phi} \tag{1}\]  
where array phase function \((\Phi)=kdsin(\theta)+\beta\) and the propagation constant \((k)=2\pi/\lambda\), \(\lambda\) denotes the wavelength, \(\beta\) is the phase angle between the elements, \(\theta\) is the desired angle of arrival (AoA) of the incoming signal. The propagation constant is a measure of changes in a sinusoidal electromagnetic wave in terms of amplitude and phase, while propagating through a medium. \(\beta\) denotes a control parameter which steers the antenna between the broad side and the end-fire. In other words, it is responsible for cancelling out the interference, resulting from the various antenna elements. When \(\beta=0^{\circ}\), then \(\theta=90^{\circ}\). This configuration is known as the end-fire condition and the received signal is maximum. On the contrary, when \(\beta=180^{\circ}\), then \(\theta=180^{\circ}\), then broadside occurs, signifying the worst received signal strength. Increasing the array elements results in high directivity, high gain, and low beam width, as shown in Table 1. Thus, having a higher number of array elements, as observed in Yagi, log-periodic, helical antenna results in high gain. The close format of AF is stated as [84]:  
\[\mathrm{AF}(\theta)=\frac{sin\left(\frac{M\Phi}{2}\right)}{sin\left(\frac{\Phi }{2}\right)} \tag{2}\]  
Hence, the directivity of such an array is equal to \(M\). It is plausible to derive the beam width of an arbitrary \(M\) isotropic elements spaced by a wavelength using this formula [83]:  
\[BW=\frac{51\lambda}{Md} \tag{3}\]  
Figure 10: Vivaldi antenna [81].
# Intelligent Multi-Fault Diagnosis for a Simplified Aircraft Fuel System
## 3 Methodology
### The Structural Complexity and Balanced Results  
Following the method outlined in Section 3.2, the structural complexity of the four trained ML algorithms was quantified to facilitate fair comparisons. This process balances complexity across algorithms. For the multinomial logistic regression, the model predicts the class with the highest probability among 32 options. Each probability calculation requires 11 weights (one for each feature) and a bias term, resulting in 384 parameters, defining logistic regression's structural complexity. Adjustments were made to the other algorithms to ensure comparable complexity, such as reducing the number of decision nodes in the decision tree (DT) from 1100 to 400 and shrinking the number of hidden neurons in the artificial neural network (ANN) to nine (from 16).  
It is important to note the structural complexity of the SVM, which employs a one-vs-one strategy and has a complexity of 496, corresponding to the number of sub-learners. However, to align with the simpler structure of the logistic regression, which only involves first-order calculations, the nonlinear kernel function order for the SVM was reduced from fourth to second order. Table 6 summarises the structural complexities of all four algorithms before and after the balancing adjustments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ML Algorithm** & **Original Complexity** & **Balanced Complexity** \\ \hline Logistic regression & 384 & 384 \\ Decision tree & 1100 & 400 \\ SVM & 496 & 496 \\ ANN & 688 & 387 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The structural complexity of each algorithm.  
Figure 12: Confusion matrix of the SVM.  
The four ML models were retrained using the adjusted configurations described above, and the validation results are presented in Table 7.  
The results indicate that the ANN demonstrated the highest predictive performance, while the DT showed the lowest. The SVM and logistic regression ranked second and third, respectively. Reducing model complexity negatively impacted performance, a fact which is unsurprising. However, achieving higher accuracy typically requires increasing model complexity, making the model more challenging to interpret.
## 1 Introduction  
Due to the COVID-19 pandemic, most countries implemented lockdown and social distancing measures, resulting in the closure of schools, training institutions, and further educational activities [1, 2, 3]. The paradigm shift from traditional face-to-face to online classes poses various difficulties for educators and learners, leading to various pedagogical innovations supporting continuing online classes [4]. Yet, the reliability and efficiency of pedagogy for online classes heavily depend on educators' and learners' utilization and exposure to information and communications technology (ICT) [5, 6]. Microsoft Teams, Google Classroom, Zoom, Canvas, and Blackboard are widely-used communication and collaboration platforms to facilitate video meetings, file sharing, storage, quizzes, and rubric-based assessments [7, 8]. Additionally, the flipped classroom is widely adopted for learners to review learning resources such as articles, pre-recorded videos, and YouTube tutorials before classes and deep their understanding through discussion with peers and educators during online classes [9, 10].  
However, there are concerns about the effectiveness and adaptability of online classes [6]. For example, educators struggle with conducting online classes due to a lack of proper training and infrastructure [11]. While open-minded learners quickly adapt to a new learning environment, those with fixed mindsets encounter difficulties adjusting and adapting. Without a widely adopted pedagogy for online classes, the readiness of various stakeholders needs to be flexible and supported accordingly [5]. Research investigating factors impacting online classes indicates that motivation is the most significant factor, contributing to an almost 50% impact on learner performance in online classes. Thus, the biggest challenge for educators is motivating learners to focus in classes [12]. Many learners prefer conventional to online classes because they face challenges in getting a stable Internet connection, understanding the teaching content, interacting with classmates and educators [13], and building up their social networks for social support [14]. Many even believe online classes cannot completely replace traditional schooling [6; 11; 15].  
With the diverse views and stakeholders, it is necessary to investigate broad opinions beyond empirical studies on social media, and similar studies are lacking in this area. Thus, this study employs a social media analytical approach [16; 17; 18] to investigate the sentiments, concerns, and their variance with time regarding online classes by learners and educators.
## 2 FE Model Validation
### Numerical Model
#### 2.2.3 Application of the Axial Compression  
In this study, the shrinkage stress of the prestressing bars is obtained by the cooling method, which drags the steel backing plate downward to press the specimen in order to simulate the device that applies axial compression in the impact test (see Figure 3b in the paper). To implement this method, the linear expansion coefficient \(\alpha\) in the material properties of the prestressing bars is defined first, and then the initial and target temperature fields (0 and \(-1\)\({}^{\circ}\)C, respectively) are defined in the initial step and in analysis Step 1, respectively. Taking the DZ150 series specimens with an axial compression ratio of 0.1 as an example, the linear expansion coefficient \(\alpha\) is specifically calculated as follows:  
\[\alpha=\frac{N_{0}}{2 EA\cdot\Delta t}=\frac{150\text{kN}}{2\times 210\text{GP }\times(\pi\times 7.5\times 7.5\text{mm}^{2})\times 1}=0.002 \tag{5}\]  
where \(N_{0}/2\) is the tensile force of a single prestressing bar, \(E\) is the modulus of elasticity, \(\Delta t\) is the temperature difference, and \(A\) is the cross-sectional area of the prestressing bar.  
As shown in Figure 6, in Step 1, after the prestressing bars shrink according to the preset cooling curve, the axial compression on the specimen gradually increases and precisely reaches the target axial compressive load at the end of Step 1. In analysis Step 2, because the amplitude curve as the input is not smooth but has an inflection point, the axial force loading oscillates with a small amplitude. After that, the prestressing bars bend and elongate with the deformation of the specimen, so the axial compression increases slightly compared to the initial value.
## 4 V-DPAP: Vertex-Constrained Directed Path Allocation Problems
### Theoretical Complexity  
**Proposition 1**.: _For a V-DPAP, determining whether there exists a valid allocation \(\pi\) such that utilitarian evaluation \(u(\pi)\) is greater than or equal to a given value is NP-complete._  
Proof.: First, the problem is NP since \(u(\pi)\) is computable in polynomial time. Then, there exists a polynomial reduction of 3-SAT (which is NP-complete) to our problem. In a 3-SAT formula that contains \(m\) clauses, each clause over the propositional variables \(x,y,z\) can be represented as a weighted DAG \(g\), where:  
1. the set of nodes is \(V_{g}=\{x,-x,y,-y,z,-z,s_{g},t_{g}\}\),  
Figure 3: V-DPAP equivalent to the DPAP example of Figure 1; the set of vertex conflicts, represented as red hypernodes, gives a compact representation of the set of allowed combinations of paths.  
2. the set of paths from \(s_{g}\) to \(t_{g}\) in \(g\) corresponds to the set of truth values for \(x,y,z\) that satisfy the clause (decision diagram representation),
3. the weight of every edge is set to 0, except for edges \(s_{g}\to n\) where \(n\
eq t_{g}\), that have weight 1.  
Last, for every propositional variable \(x\), we can add one conflict \((n,n^{\prime})\) for each pair of nodes labeled by the literals \(x\) and \(\
eg x\) in two distinct graphs.  
For instance, the 3-SAT problem \((x\lor y\lor z)\land(\
eg x\lor y\lor\
eg w)\) can be represented by the V-DPAP illustrated in Figure 4. Clause \((x\lor y\lor z)\) is translated into graph \(g_{1}\) and clause \((\
eg x\lor y\lor\
eg w)\) into graph \(g_{2}\). Vertices linked by dashed edges correspond to conflicts.  
Then, as one path is selected in each graph and as there are \(m\) graphs, determining whether there exists a valid allocation \(\pi\) such that \(u(\pi)\geq m\), with \(m\) the number of clauses in the 3-SAT formula, is equivalent to finding a solution that satisfies all the clauses, hence the NP-completeness result given that all operations used in the transformation are polynomial.  
Proposition 2: _For a V-DPAP, it is NP-complete to decide whether there exists a valid allocation whose leximin evaluation is greater than or equal to a given utility vector. The proposition holds even if there is a unique graph per agent._  
Proof: In the general case, it suffices to consider a problem involving a unique agent owning all the graphs, and to use the result of the previous proposition. If there is a unique graph per agent, it suffices to use the exact same 3-SAT encoding as before. Then, it is possible to show that there exists a valid allocation whose leximin evaluation is greater than or equal to \((1,1,\ldots,1)\) if and only if there exists a solution for the 3-SAT problem. Furthermore, the leximin evaluation of an allocation \(\pi\) can be computed in polynomial time, hence the NP-completeness result.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 5 Challenges and Future Directions
### Security Threats  
#### 5.4.1 Challenges  
Quadcopters can face several security threats, such as unauthorized control, mission disruption, the alteration of flight paths, the exposure of important agricultural data, the disruption of communication and data transmission between quadcopters and ground sensors, and misuse for unauthorized purposes [167, 168, 169, 170].
## 4 Results and Evaluation
### Ablation Experiments
In the second scenario, we mapped these results onto the Gaussian–Voronoi map before converting them into depth maps. Utilizing the Gaussian–Voronoi map significantly boosts terrain diversity and accurately mimics the randomness found in natural landscapes. This approach not only enriches the terrain’s visual appeal but also modifies elevation change rates, offering a more dynamic and realistic terrain modeling. Additionally, the Gaussian–Voronoi map also aids in altering the rate of elevation change across the terrain. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Configuration** & **Regular Score** & **Adjusted Score** \\ \hline Without Gaussian–Voronoi map & 2.15 & 2.46 \\ Without chain-of-thought behavior tree & 2.58 & 2.60 \\ Complete method & 3.33 & 3.27 \\ \hline \hline \end{tabular}  
* Due to the quality assessment experiments already reflecting the effectiveness and quality of the multiagent update strategy adequately, a separate ablation study for this component is not conducted.  
\end{table} 
Table 5: Ablation experiments result. The CoT behavior tree significantly enhances the terrain generation process by accurately interpreting complex textual descriptions and converting them into detailed terrain features. It ensures logical consistency and contextual understanding throughout the text analysis, crucial for generating terrains that closely match the descriptions. It enables the large language model (LLM) to navigate through various layers of text input, extracting and interpreting spatial information and geographical concepts with precision. The chain-of-thought behavior tree guides the LLM in a structured manner, allowing it to maintain context and make logical inferences over multiple iterations of text analysis. Without this component, the terrain generation process becomes significantly less accurate in matching the textual descriptions to the generated 3D models. This loss of fidelity in interpretation underlines the chain-of-thought behavior tree's importance in ensuring that the generated terrains faithfully reflect the described landscapes, thereby enhancing the quality and relevance of the output. Figure 9 illustrates the CoT behavior tree's impact, demonstrating its ability to precisely identify and interpret comparative and positional information from text. This leads to a more accurate and contextually relevant terrain generation, as opposed to models that lack this structured analysis approach.
# Research on Optimization of Multi-Objective Regional Public Transportation Scheduling
## 4 Solution Algorithms
### Obtain Satisfactory Scheme from Pareto Solution Set  
The second step is to obtain a satisfactory scheme from the Pareto solution set. The entropy weight-TOPSIS method is an improvement over the entropy weight method and the TOPSIS method [32]. Moreover, it is a common evaluation method used in multi-objective decision-making problems. Consequently, the entropy weight-TOPSIS method is used to obtain satisfactory schedule from the Pareto set.  
Suppose that the number of Pareto solutions is \(M\). Then, a decision matrix \(D_{M\times 2}\) is obtained with \(M\) Pareto solutions and 2 objectives. The process is stated as follows:  
Step 1: Obtaining the standardization matrix \(B_{ij}=\left[b_{ij}\right]_{M\times 2}\) and normalized matrix \(C_{ij}=\left[c_{ij}\right]_{M\times 2}\).  
Step 2: Calculating the information entropy of each objective \(j\):  
\[K_{j}=-(\frac{1}{\ln M})\sum\limits_{i=1}^{M}c_{ij}\ln c_{ij},j=1,2\ \big{(}\text{if }c_{ij}=0\,\ \text{then}\ c_{ij}\ln c_{ij}=0\big{)}. \tag{15}\]  
Step 3: Computing the weight of objective \(j\):  
\[w_{j}=\frac{1-K_{j}}{\sum\limits_{q=1}^{j}\ (1-K_{q})},j=1,2. \tag{16}\]  
Step 4: Computing the weighted judgment matrix:  
\[Q_{M\times 2}=\left[q_{ij}\right]_{M\times 2},\ \text{where}\ q_{ij}=w_{j}b_{ij},i=1,2, \cdots M,j=1,2. \tag{17}\]  
Figure 4: Illustration of the mutation operator for case 1.  
Figure 5: Illustration of the mutation operator for case 3.  
Step 5: Calculating the Euclidean distance and closeness for each solution:  
\[D_{i}^{+}=\sqrt{\sum_{j=1}^{2}{\left(q_{ij}-q_{j}^{+}\right)^{2}}},i=1,2,\cdots M, j=1,2, \tag{18}\]  
\[D_{i}^{-}=\sqrt{\sum_{j=1}^{2}{\left(q_{ij}-q_{j}^{-}\right)^{2}}},i=1,2,\cdots M,j=1,2, \tag{19}\]  
\[F_{i}=\frac{D_{i}^{-}}{D_{i}^{-}+D_{i}^{+}},i=1,2,\cdots M. \tag{20}\]  
Moreover, the satisfactory schedule can be obtained according to the closeness \(F_{i}\).