esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Comparison of Clustering Analysis Methods  
Fixation clustering analysis is a common technique used to explore visual attention patterns and their distribution characteristics [58]. From a cognitive perspective, the spatial clustering of fixations reflects the viewer's preferential allocation of attention and task-driven visual strategies. According to selective attention theory, gaze behavior is not random but guided by cognitive relevance, making clustered fixations indicative of perceptual salience and goal-oriented processing [17]. Clustering methods thus offer an analytical window into how viewers segment scenes into meaningful units based on internal cognitive priorities. In current research, three main methods are employed to process fixation data: Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Hierarchical Clustering, and K-means algorithms. DBSCAN is a density-based clustering method that does not require predefining the number of clusters and adapts well to irregularly distributed data [59]. However, studies indicate that when datasets contain regions of varying density, their stability may be compromised, making it difficult to ensure consistent results in all scenarios [60]. Hierarchical Clustering constructs a hierarchical tree structure to represent relationships between data points, allowing for more detailed cluster-level information [61]. This method enables researchers to observe clustering patterns at different levels, making it particularly suitable for interpretive analysis and visualization. However, due to its iterative computation requirements, it has a relatively high computational cost when processing large-scale data, limiting its suitability for real-time applications. K-means algorithms are widely used in fixation clustering analysis due to their computational efficiency and interpretability [58]. The K-means++ algorithm improves the selection of initial centroids, enhancing stability [62]. Compared to other methods, K-means generates clustering results more quickly, and its straightforward interpretability makes it highly advantageous for real-time analysis [63]. Empirical studies show that K-means performs consistently in fixation data analysis and provides easily interpretable visual results.
## 3 Results
### Analysis of Prediction Results of MSFNet  
To comprehensively assess the model's performance, we computed the accuracy, precision, recall, and \(F1\)-_score_ of each category, as well as the overall classification results. Additionally, PR curves were plotted for each class, and a confusion matrix was generated to provide deeper insights into the model's classification behavior. The PR curves illustrate the balance between precision and recall across varying decision thresholds, while the confusion matrix offers a detailed analysis of misclassifications, highlighting both the model's strengths and areas for improvement. These evaluations serve as a crucial basis for the further refinement and optimization of the model.  
As shown in Table 4, all classification metrics exceed 90% across categories. However, the accuracy for others and stripe rust is relatively lower, at 92.4% and 91.5%, respectively. The confusion matrix (Figure 7) indicates a pronounced misclassification between these two categories, with 35 others samples misclassified as stripe rust, and 25 stripe rust samples misclassified as others. The others category encompasses diverse regions, including soil, buildings, and low-density weed areas, some of which--such as shadowed soil or sparsely vegetated backgrounds--may exhibit texture and spectral characteristics similar to the canopy of wheat stripe rust regions, thereby increasing the difficulty of accurate discrimination. This feature overlap is likely a key factor contributing to misclassification and highlights a potential direction for improving the model's discriminative capability in future research. Furthermore, Figure 7 presents the P-R curves for each category, where all AP values exceed 0.95, indicating that the model maintains a good balance between precision and recall, ensuring the robustness of the detection results. Notably, the AP values for healthy and yellow dwarf reach 1.0, demonstrating the model's exceptional discriminative ability for these categories and further validating its superior classification performance.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Metrics** & **Healthy** & **Others** & **Stripe Rust** & **Yellow Dwarf** & **Total** \\ \hline Accuracy & 99.5\% & 92.4\% & 91.5\% & 99.1\% & 95.4\% \\ Precision & 99.0\% & 90.6\% & 93.8\% & 99.1\% & 95.6\% \\ Recall & 99.5\% & 92.4\% & 91.5\% & 99.1\% & 95.6\% \\ \(F1\)-_score_ & 99.2\% & 91.5\% & 92.7\% & 99.1\% & 96.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance evaluation of MSFNet.  
Figure 7: The confusion matrix and PR curve of MSFNet.
## 2 Review Methodology  
The protocol for this review was developed in six stages; the first three were searching stages and the next three were screening phases. Figure 1 shows the stages of the protocol we followed to obtain the articles to be reviewed. In the first stage, the key terms were identified that encompassed the basic concepts we wanted to investigate, such as 'affective disorder','serious games', 'autism' and 'neurotypical', as well as their synonyms from the Emtree hierarchy [86], such as'mood disorder', 'affective illness', 'videogame' and 'VR simulator'. Then, in the second stage, the queries implementing our search strategies were developed using these key terms and their synonyms, as shown in Table 1. Afterward, in the third stage, the search strategies were applied to four scientific databases (IEEE [87], Embase [88], Web Of Science [89] and Scopus [90]); according to the guidelines provided by AMSTAR [91], searching in at least two databases should be sufficient for reviewing the literature in a field. At this stage, we obtained 507 articles from the database searches. The fourth through sixth stages correspond to the three phases in which these articles were screened. At the fourth stage, phase one of the screening commenced where the duplicate articles, or articles that appeared in more than one of the four databases we searched in, were eliminated, which left us with 494 articles. Then, in stage five, phase two of screening was undertaken and here, articles were screened based on their titles according to our eligibility criteria, which are further discussed in Section 2.1. A total of 98 articles were obtained, which were then further screened in the same manner based on their abstracts in stage six, which implemented screening phase three. After this stage, the final 37 articles that were reviewed for this study were obtained. Stages four through six are discussed in further detail in Section 2.2. Section 2.3 discusses  
Figure 1: Six stage protocol implemented to obtain the articles for this literature review. the key points of analysis that we focused on to perform the comparative analysis of the extracted data about the reviewed studies.
# Seismic Coherent Noise Removal of Source Array in the NSST Domain
## 2 Theory and Method  
### Shearlet Transform  
Shearlet transform is an asymptotically optimal sparse representation of multidimensional functions constructed by a special form of affine systems with synthetic expansion, which is composed of the Laplace pyramid transform and a set of directional filters.  
When the dimension is two-dimensional, the scale matrix and the shear matrix are:  
\[A=\begin{bmatrix}j&0\\ 0&\sqrt{j}\end{bmatrix},B=\begin{bmatrix}1&-l\\ 0&1\end{bmatrix} \tag{1}\]  
In Equation (1), \(A\) is the scale matrix, which can be divided into scales; \(j>0,j\) is the scale parameter; \(B\) is the shear matrix, which can be dissected into directions; and \(l\) is the shear parameter.  
The affine system with synthetic expansion is defined as:  
\[\begin{split}\psi_{AB}(\varphi)=\Big{\{}\varphi_{j,l,k}(x)=& \Big{|}det\ A\Big{|}_{j/2}.\\ \varphi\Big{(}B^{l}A^{j}x-k\Big{)}:j,l\in R,k\in R^{2}\Big{\}}\end{split} \tag{2}\]  
In Equation (2), \(\varphi\in L^{2}\big{(}R^{2}\big{)}\), \(\varphi\) is the shear wave function, \(L\) represents the productable space, \(x\) is the data to be processed, \(det\left(\cdot\right)\) is the determinant, and \(k\) is the translation parameter.  
If \(\psi_{AB}(\varphi)\) satisfies the Parseval framework (the tight framework), that is, for any function, it satisfies the following equation:  
\[C_{j,l,k}=S\{f\}=\Big{\langle}f,\varphi_{j,l,k}\Big{\rangle} \tag{3}\]  
In Equation (3), \(C_{j,l,k}\) is the Shearlet domain coefficient, \(S\) is the Shearlet forward transform, and \(\langle\cdot\rangle\) is the inner product. \(\psi_{AB}(\varphi)\) is called the composite wavelet. Parameters \(j,\ l,\ k\) can control the division of the direction and the scale. By taking the inner product of the function and the basis function \(f\) and the basis function \(\varphi_{j,l,k}\) of different scales, directions, and positions, the Shearlet domain coefficients in the corresponding scale, direction, and position can be obtained. This process essentially uses the basis function after translation, expansion, and rotation to approximate the signal.  
The frequency domain subdivision of the shear wave and its support interval are shown in Figure 1. As can be seen from Figure 1, the support interval of any shear wave element \(\varphi_{j,l,k}\) in the frequency domain is a trapezoidal region symmetric about the origin; the direction is distributed along a straight line with the slope of \(l2^{-j}\). The size of the trapezoidal region is about \(2^{2j}\times 2^{j}\).  
Figure 1: Frequency domain subdivision and its support interval.
# Spatial Data-Based Automatic and Quantitative Approach in Analyzing Maintenance Reachability
## 3 Materials and Methods
### Intersection Detection-Based Spatial Relationship Judgment between Arm and Product
#### 3.3.1 Application of Typical Intersection Detection Methods Illustration
If the end point is in the cylinder, then the line segment intersects with the cylinder. If all conditions are not met, then the line segment does not intersect with the cylinder. As shown in Figure 8a-d, line segment AB intersects the bottom surface of the cylinder, line segment AB intersects with the side surface of the cylinder, line segment AB is inside the cylinder, and line segment AB does not intersect with the cylinder, respectively. Figure 7: Application of the Intersection Detection Between a Line Segment and a Cylinder. (4) Intersection of a line segment and a capsule  
The intersection of a line segment and a capsule is detected by comparing distance \(d\) from the capsule axis to the line segment and the radius \(R\) of the capsule. As shown in Figure 9, if \(d>R\), then line segment AB does not intersect with the capsule, but if \(d<R\), then line segment AB intersects with the capsule. (5) Intersection of a line segment and a triangular patch  
The intersection of a line segment and a triangular patch is detected by the method of finding the intersection point. Whether the line where the line segment is located would intersect with the plane where the triangular patch is located should be determined. If it intersects, then the intersection point would be located. Then, if the intersection point is in the line segment and the triangular patch, then the line segment intersects with the triangular patch, otherwise it does not intersect.
## 3 Generic Routing Solutions
### Distance Vector Routing Protocols (DVRPs)
Both operations, path discovery and path maintenance, operate entirely on demand [73]. Therefore, unlike other protocols, DSR does not use any periodic routing advertisements, thus reducing traffic and control overheads. Path selection is based on hop counts, similar to other DVRPs, which repeat the possibility of routing over unreliable paths in terms of link quality. The main advantage of DVRPs is routing based on local information received from direct neighbours rather than global knowledge of the entire network topology, such as LSRP. Hence, it significantly reduces overhead packets and bandwidth consumption compared to LSRP. In contrast, the main disadvantage of DVRPs is the disregard for link quality. Routing in DVRPs is mainly based on the minimum cost of distance, i.e., hop counts. It does not consider the link quality, an essential factor for Lossy channels as in LLNs, but not wired networks or hop-by-hop networks as MANETs for which these protocols are mainly designed. Therefore, using a DVRP in LLNs with a hop count metric only without taking into consideration the link quality may result in routing over unreliable paths with low PDRs.
# Analysis of Bidirectional ADR-Enabled Class B LoRaWAN Networks in Industrial Scenarios
## 2 LoRaWAN  
LoRaWAN technology is a network protocol that makes use of Long Range (LoRa) technology, which acts as a physical layer. It is used for communication and device administration. Each of the two technologies, LoRa and LoRaWAN, will be explained in more detail below.
## 11 Responses for the Incremental-N
### Second-Order Type Reference and Output Disturbance  
In order to verify the performance of incremental-3, the system is subjected to second-order output references and disturbances. The results are presented for a simulation that spans 5000 s.  
The second-order reference for the flow rate starts at 710 s and ends at 885 s, ranging from 4 (m\({}^{3}\)/s) to 6 (m\({}^{3}\)/s). For temperature, it ranges from 50 (\({}^{\circ}\)C) to 60 (\({}^{\circ}\)C), starting at 1410 s and ending at 1585 s. In both cases, the final value is maintained until the end of the simulation.  
Figure 14: Response of \(Q_{s}\) and \(T_{s}\) to step-type references and output disturbances for incremental-5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Incremental** & **IAE (\(Q_{s}\))** & **IAE (\(Q_{s}\))** & **ISE (\(Q_{s}\))** & **ISE (\(T_{s}\))** \\ \hline
0 & 457.44 & 1532 & 115.73 & 1628 \\
1 & 39.34 & 186 & 42.1 & 1075 \\
2 & 49.24 & 366 & 58.92 & 2075 \\
3 & 132.37 & 754 & 163.44 & 4530 \\
4 & 154.48 & 1328 & 197.14 & 7446 \\
1 & 344.14 & 3118 & 401.73 & 16,094 \\ \hline \hline \end{tabular}
\end{table}
Table 5: IAE and ISE values of \(Q_{s}\) and \(T_{s}\) for all incrementals under step-type references and output disturbances.  
The second-order output disturbances for the flow rate begin at 2400 s and end at 3000 s, ranging from 0 (m3%) to 0.5 (m3%). For temperature, the disturbance ranges from 0 (\({}^{\circ}\)C) to 5 (\({}^{\circ}\)C), starting at 3400 s and ending at 4200 s. In both cases, the final value is maintained until the end of the simulation.  
Figure 18 presents the results obtained using incremental-2. This incremental does not achieve zero steady-state error in response to second-order output references and disturbances. The response attempts to follow the parabolic input but consistently exhibits an error.  
Figure 16: Response of \(Q_{s}\) and \(T_{s}\) to ramp-type references and output disturbances for incremental-2: (a) ramp-type reference, (b) output disturbances.  
Figure 17: Response of \(Q_{s}\) and \(T_{s}\) to ramp-type references and output disturbances for incremental-3: (a) ramp-type reference, (b) output disturbances.  
Figure 15: Response of \(Q_{s}\) and \(T_{s}\) to ramp-type references and output disturbances for incremental-1: (a) ramp-type reference, (b) output disturbances.  
In contrast, when using incremental-3, zero steady-state error is achieved for second-order output references and disturbances (see Figure 19). It can be observed that the response successfully follows the parabolic trajectory in both the flow rate and the output temperature.
## 4 Results of Optical-System Design and Simulation  
In order to detect the spectrum of an exoplanetary atmosphere, an optimized optical system is essential. According to the above astronomical parameters and optical-system indices, a feasible instrumentation proposal was designed, and performance is evaluated in this section.  
The researchers conducted simulation experiments [38] to determine the appropriate operating temperature. The major effect in the temperature variation of the detector is that of a changing dark current. Peak-to-peak variation in the temperature of optical elements results in the modulation of its thermal emission. When the operating temperature of the optical elements is set to \(\leq\)70 K, and that of detectors to \(\leq\)40 K, the thermal-radiation noise caused by elements and the detector noise could be limited. Devices performance and refractive lens indices should be considered in optical-system design at such temperatures.  
The overall optical system consists of four modules: telescope, dichroic system, pointing system, and infrared spectrometer. The telescope is used to collect spectral radiation from stars. The dichroic system splits light and delivers beams to modules. The pointing system is utilized for photometry and orientation. The infrared spectrometer is the main scientific payload to provide dispersive spectra with a specific spectral resolution and SNR in the detection waveband.
# Exploring CeO\({}_{2}\)-Doped Co/SBA-15 Catalysts for Acetic Acid Oxidative Steam Reforming
###### Abstract  
This work explores the effect of the incorporation of CeO\({}_{2}\) into Co/SBA-15 catalysts in hydrogen production through acetic acid oxidative steam reforming as a bio-oil aqueous phase model compound. CeO\({}_{2}\) was incorporated (5-30 wt.%) to improve the physicochemical properties of the catalyst. XRD analysis confirmed that the addition of CeO\({}_{2}\) resulted in smaller CeO\({}^{0}\) mean crystallite sizes, while H\({}_{2}\)-TPR showed enhanced reducibility properties. The catalytic performance was evaluated in the 400-700 \({}^{\circ}\)C range, S/C molar ratio = 2, O\({}_{2}\)/C molar ratio = 0.0375, WHSV = 30.2 h\({}^{-1}\), and P = 1 atm. Catalysts containing 10 and 20 wt.% of CeO\({}_{2}\) exhibited the best catalytic performance, achieving nearly complete conversions and H\({}_{2}\) yield values, approaching thermodynamic equilibrium at 550 \({}^{\circ}\)C. Both samples maintained an acetic acid conversion above 90% after 30 h of time-on-stream, with H\({}_{2}\) yields above 55% along the steam reforming tests. This agrees with their lower coke formation rates (7.2 and 12.0 mg\({}_{\text{coke}}\)g\({}_{\text{cat}}\)\({}^{-1}\).h\({}^{-1}\) for Co/10 CeO\({}_{2}\)-SBA15 and Co/20 CeO\({}_{2}\)-SBA15, respectively).  
Keywords:hydrogen; biomass pyrolysis; oxidative steam reforming; cobalt; mesoporous catalysts +
Footnote †: journal: Applied sciences
## 2 Project Overview  
Relying on the underground tunnel between Hengli Station and Panyu Square Station of Guangzhou Rail Transit Line 22 (as shown in Figure 1), a field test of the mechanized construction of the prefabricated SRS arch support is carried out at the mileage of ZDK34+370-ZDK34+400, and the tunnel in the test area is mainly located in moderately weathered granite (as shown in Figure 2). The overburden thickness of the vault is 26.5 m, mainly composed of miscellaneous fill, muddy soil, silty medium-coarse sand, and sandy clay. The maximum excavation span of the tunnel is 14.9 m, the height is 11.95 m, and the cross-sectional area is 143 m\({}^{2}\). The tunnel passes under Luojiayong Bridge, and the minimum horizontal clear distance between the bridge pile and the tunnel is only 2.06 m. In addition, the buildings on the surface are densely built, and the deformation of the surrounding rock of the tunnel should be strictly controlled to minimize the impact on the surrounding environment.  
The design parameters of the primary support of the tunnel (as shown in Figure 3) are as follows: the grid arch used in the non-test area adopts a four-leg grid arch with a main reinforcement diameter of 32 mm; the SRS arch used in the test area adopts a nine-leg type with a steel tubes diameter of 50 mm and thickness of 6 mm; \(\pi\)-shaped and U-shaped reinforcement diameter is 14 mm, with a spacing of 1.0 m; and steel mesh diameter is 8 mm, with a spacing of 15 cm \(\times\) 15 cm. The shotcrete grade is C25, and the thickness is 35 cm; early-strength mortar bolts diameter is 22 mm, the length is 3.0 m, and the row spacing is 1.0 m \(\times\) 1.0 m. The arch foot is equipped with 42 mm diameter locking anchor tubes, and the single length is 2.0 m.  
Figure 1: The geographical location of underground tunnel project in urban area in Guangzhou, China.  
Figure 2: Longitudinal profile of geological layer of site project.
## 2 Background
### K-Means Clustering  
K-Means clustering, a popular unsupervised machine learning algorithm, divides data into K clusters. It aims to minimize the within-cluster variance by iteratively updating cluster centroids, as explained in Equation (1).  
\[\underset{C}{\text{argmin}}\sum_{i=1}^{K}\sum_{x\in C_{i}}||x-\mu_{i}||^{2} \tag{1}\]  
where \(C\) is the set of clusters, \(C_{i}\) is the \(i\)th cluster, \(\mu_{i}\) is the centroid of cluster \(C_{i}\), and \(||\cdot||\) denotes the Euclidean distance. K-Means clustering is explored as a precursor to Operational Modal Analysis (OMA) in monitoring rotating machines used in energy generation [22]. By distinguishing suitable excitation conditions through statistical features, this approach enhances OMA accuracy, ensuring effective Structural Health Monitoring (SHM) and potentially preventing shutdowns or catastrophic failures, thereby safeguarding economic and social interests [22].
## 2 Theory Model
### Numerical Model  
Numerical computational models of the WU and the LC were established through the software COMSOL Multiphysics 6.2. The pressure acoustic interface and thermo-viscous acoustic interface were used. The numerical models of the LC and the WU have nearly identical boundary conditions, being set up, as shown in Figure 2a,b, respectively.  
The goal of the elaboration is the performance of the metamaterial labyrinth itself. Therefore, the ventilated area was extended to both sides, and a perfectly matched layer (PML) was defined at both ends to absorb acoustic waves. A background pressure field was defined at the incident end with a pressure amplitude of 1 Pa. The waves were assumed to be completely reflected at the wall, so rigid wall-boundary conditions were applied. The labyrinth structure was considered the thermo-viscous losses, and the remaining part was defined as a pressure acoustic region; it contained 369,075 elements in the WU numerical model and 607,267 elements in the LC numerical model.