esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The fluidized catalytic cracking (FCC) process converts heavy cuts into light ones. Generally, the feed is vacuum gas oil, providing boiling points around 450 \({}^{\circ}\)C due to the high molecular weight of this type of hydrocarbon. The products obtained include dry gases, liquefied petroleum gas, light oil, heavy oil and mainly gasoline, diesel, and kerosene.  
One of the characteristics of the FCC process is that the catalyst used for this unit is selective and must be designed according to the specific process and conditions (operation, feeding characteristics) in the Esmeraldas State Refinery in which it will be applied. Therefore, the catalysts can have a great effect on the production and on the economic aspect due to the volume of feed processed daily [1].  
Alophane is a nanoporous micro clay with an undefined crystalline structure. It consists of organic material and has a high moisture content due to its hygroscopic property and extensive surface area. It adsorbs large amounts of water due to its hygroscopic property; its adsorption capacity can be in a range of [20, 50]% by weight of water [2].  
The geographical location of allophane sample collection was established by the Institute of Geological and Energy Research (IIGE), located in the province of Santo Domingo de los Tsachilas, covering an area greater than 4000 km\({}^{2}\).  
This process began with the drying and grinding of different samples of natural allophane; consequently the samples were physically characterized by BET (Brunauer, Emmett, Teller) surface area tests, particle size, and Fourier transform infrared spectroscopy (FT-IR), and then alkaline fusion was performed followed by a hydrothermal treatment. Finally, the samples were washed, filtered, and dried. The activated samples were evaluated and characterized by the following tests: BET surface area (Brunauer, Emmett, Teller), particle size, Fourier transform infrared spectroscopy (FT-IR), thermogravimetry (TGA), chemisorption, X-ray diffraction (XRD), atomic force microscopy (AFM), X-ray fluorescence (XRF), and chromatography.  
The test method for the determination of the activity of catalytic fluidized cracking (FCC) catalysts in equilibrium or deactivated in the laboratory is evaluated on the basis of the mass conversion percentage of the diesel feed in a microactivity unit, with equipment constructed according to ASTM D-3907/D3907M-2019.  
The Navier-Stokes 3D equations govern the behavior of any fluid, allowing molecular dynamics to be studied, contributing substantively to chemical kinetics.  
The dynamics of catalysis attempts to quantitatively explain the forces and vector fields of moving fluids, such as crude oil in the fluidized catalytic cracking process.
# An Energy Efficient Sink Location Service for Continuous Objects in Wireless Sensor Networks
## 3 Proposed Scheme
### Origin-Based Continuous Object Tracking(Ocot)
#### 3.2.1 Construction of Origin-Based Grid Structure  
When a continuous object occurs at the origin point, the first source node becomes the origin node that starts the construction of grid structure. Figure 10 shows the grid construction process where \(\alpha\) is the grid size. The origin node sends _grid construction messages_ to the four neighboring grid points. When the location of the origin node is \(L_{s}=(x,y)\), the grid points are \(L_{p}=(x_{i},y_{i})\) such that:  
Figure 9: The efficiency of using a location agent.  
\[x_{i}=x+i\cdot\alpha,y_{i}=y+j\cdot\alpha;i,j=\pm 0,\pm 1,\pm 2,\ldots \tag{2}\]  
The messages spread the whole network and the node closest to each grid point becomes the grid node. A grid node keeps the information about the grid point \(L_{p}\) and upstream grid point. Duplicated messages from different neighboring grid nodes are dropped so a grid node has one upstream grid point. Then, a grid node becomes a bluster head and organizes a cluster by broadcasting a cluster construction message. The cluster size \(\frac{\sqrt{2}}{2}\cdot\alpha\) is selected for thorough cluster construction. A sensor becomes a cluster member by sending its ID to the cluster head and ignores the cluster construction messages from other grid nodes.  
Figure 10 shows a grid for the origin node \(O\) indicated with a black square, and its virtual grid structure. The black dots indicate grid points and the white triangles around each grid point are the grid nodes. The gray area indicates a cluster. After the grid structure and clusters are completely built in the sensor field, all sensor nodes become to know their cluster heads, to whom they should deliver their detection information. The cluster head receiving data from ordinary nodes becomes boundary cluster head and has responsibility for data reporting to sinks.
## 2 Materials and Methods
### Spectral Data Pre-Treatment  
There are many uncontrollable physical variations, such as the non-homogeneous distribution of the particles, changes in the refractive index, and particle size distribution, that make samples exhibit differences in spectral information during measurement. These also cause light scattering effects and then result in additive or multiplicative effects that are not related to the chemical response and lead to inaccurate results. Thus, to address these effects, the spectra for each sample set were subjected to pre-treatment to minimise background noise (Figure 2). The pre-treatment used standard variate normalisation (SNV), the first derivation was calculated using a Savitzky-Golay filter with a first-order polynomial and a 15-point window smoothing (order 1, windows 15 points), followed by linear detrending as reported by [PERSON] et al. (2018).
## 3 System Methodology
### Integration of DEA Efficiency Scores and HMM Predictions into ILP  
The ILP formulation incorporates the efficiency scores obtained from DEA and the predictions from the HMM. This integration allows for a comprehensive optimization approach that considers both historical efficiency and dynamic predictions.  
\[Maximize\ \rho_{ij} \tag{23}\]subject to \(\frac{\sum_{k=1}^{n}\lambda_{k}\cdot x_{kj}}{\sum_{k=1}^{n}u_{k}\cdot c_{kj}}\leq \rho_{ij}\).  
This constraint ensures that the inclusion of an arc in the multicast tree is influenced by its historical efficiency (\(\rho_{ij}\)) obtained from DEA.  
\[Maximize\ \delta_{ij} \tag{24}\]  
subject to \(\delta_{ij}\leq q_{ij}\).  
Here, \(\delta_{ij}\) represents the QoS parameter associated with the arc \((i,j)\), and the constraint ensures that the QoS parameter is satisfied.
## 5 Discussion  
So far, we have compared and measured the difference in fps and the maximum number of nodes that can be expressed in the same time step environment. Following this, the time step was increased to check when the cloth object became abnormal, to check the maximum calculation section limit of PBD and VBD.  
In the hanging simulation, the fps of each algorithm was compared using the standard 0.01 s time step. However, when the time step was increased to 0.02 s, the cloth simulated with the PBD algorithm exhibited sagging, as highlighted by the red circle in Figure 7. A similar sagging phenomenon was also observed in the object-to-object collision scenario, further increasing the inaccuracy of the algorithm's physical calculations. This inaccuracy  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Node \(\times\) Node (Total Nodes)** & **fps** \\ \hline
**Hanging** & & \\ VBD & 120 \(\times\) 120 (14,400) & 30.1 \\ PBD & 93 \(\times\) 93 (8649) & 30.6 \\ \hline
**Object-to-object collision** & & \\ VBD & 92 \(\times\) 92 (8464) & 30.2 \\ PBD & 80 \(\times\) 80 (6400) & 30.1 \\ \hline
**Self-collision** & & \\ VBD & 23 \(\times\) 23(529) & 30.1 \\ PBD & 21 \(\times\) 21(441) & 31.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Maximum number of nodes for each simulation.  
Figure 6: Graph of average fps for each node in the self-collision simulation.  
resulted in abnormal energy accumulation and dissipation, which negatively affected the stability of the simulation. Table 7 presents the variation in the cloth's area during the hanging simulation for each algorithm. A larger increase in the cloth's area indicates reduced computational accuracy.  
In the self-collision simulation, while significant sagging did not occur, errors were observed in the collision process itself. When the time step was increased to 0.1, it was noted that PBD produced artifacts, with many objects penetrating the overlapping parts of the cloth, as Figure 8 shows. In contrast, VBD demonstrated relatively stable collision results, even with the same 0.1 time step. The red circle in Figure 8 shows where the mesh is broken. This indicates that the calculation accuracy of PBD was lower than that of VBD, as the inaccuracy in the physical simulation increased with the larger time step. Therefore, this test confirms that VBD can simulate larger time steps more stably compared to PBD.
# Criteria for Cutting Head Clogging Occurrence during Slurry Shield Tunneling
## 6 Conclusions  
The traditional methodology of evaluating clogging potential relying only on soil types is sometimes unrealistic and impractical due to the soils and properties of the soils being unknown. In this study, criteria for cutting head clogging during soft ground shield tunneling were established based on the machine's main driving parameters. Three slurry shield tunneling cases concerning cutting head clogging are introduced for validation. Changes in driving parameters of the machines, including total thrust, cutting head torque, advance rate, and cutting head rotation speed--before, during, and after the cutting head clogging--are presented and analyzed. Based on the analysis, the criteria for cutting head clogging occurrence during slurry shield tunneling are suggested. The conclusions that can be drawn are as follows:  
1. When evaluating the total thrust and the cutting head torque of a shield tunneling machine, the parameter of penetration depth per revolution should be considered.
2. The total thrust and the cutting head torque of tunneling machines will increase, or will not, once cutting head clogging occurs. Either of the composite parameters of total thrust/penetration depth and cutting head torque/penetration depth is suggested to do the cutting head clogging evaluation.
3. According to the three construction cases presented, maximum increases of the total thrust/penetration depth and cutting head torque/penetration depth (suggested criteria) by 2-6 times can be found when cutting head clogging occurs. But regarding the minimum, a 30-50% increase of the composite parameters requires attention.  
**Author Contributions:** [PERSON]. ([PERSON]) and Y.Y.: conceptualization, methodology, project administration, and writing the original draft. X.L. ([PERSON]) and H.L.: data collection, plotting curves and analysis, and review. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Support from the Chinese National Natural Science Foundation (No. 51978040) is acknowledged.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Swing Analysis and Control Research of the Space Tethered Combination in the Maneuver Process
###### Abstract  
Swing usually occurs in the maneuver process of a tethered combination, which is constituted of a platform, a tether and a target (i.e., space debris) for capture. Therefore, a dynamical model of the space tethered combination was established, based on the maneuver of the mission platform in a short time. The conditions for the three swing formations of the tethered combination were obtained according to the analysis of the dynamical model. In order to solve the swing problem, anti-swing control strategies, based on linear feedback control, approximate linearization control and variable structure control, were proposed, respectively. Furthermore, simulation results verified the correctness and effectiveness of the above strategies. To test the validity of the control strategies, a ground experiment setup was built according to the similarity of dynamics. The experimental results show that linear feedback control and approximate linearization control can suppress the in-plane and out-of-plane swing of the combination rapidly.  
space tethered combination; anti-swing control; linear feedback control; approximate linearization control; variable structure control +
Footnote †: journal: Applied Sciences
# Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis
## 5 The ALPR System
### Detection
#### 5.1.4 Candidate-Regions Classifications  
Depending on the corresponding aspect ratio (\(AR\)), each candidate region is classified as either a plate or a non-plate. The \(AR\) is obtained from:  
\[AR=\frac{W}{H} \tag{1}\]  
where \(W\) is the plate width and \(H\) is the plate height.
# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 4 Multi-Type Anomaly Detection in Civil Aviation
### Anomaly Detection  
The XGBoost model iteratively builds multiple weak classifiers, each of which classifies the data and then combines them into a strong classifier. The final output is the result of the joint action of multiple classifiers. For a sample, the model gives K values, where k is the number of categories. The sum of these k values is one. In this paper, we choose the category with the highest probability as the prediction result. After the detection of the MAD-XFP method, the output results, \(\hat{Y}\), represent different abnormal events, and the relationship between each abnormal event and the abnormal label is shown in the following Table 3.  
During the training phase, we record the probability of abnormal Event B occurring under the conditions of abnormal Event \(A\), denoted as \(P(B|A)\). In the validation phase, we consider the case where the probability of the sample being predicted as a certain category is the highest as the final result, and record the result as Event \(A\), with a probability of occurrence of \(P(A)\). Then, according to the conditional probability formula \(P(AB)=P(B|A)P(A)\), we can calculate the probability of both Event \(A\) and Event  
Figure 5: Distribution of feature importance of MAD-XFP (top 20).  
occurring simultaneously, denoted as \(P(AB)\). If \(P(AB)>\theta\) (\(0<\theta<1\)), we believe that event A and event B occurred simultaneously in this sample. At this point, we have realized the identification of abnormal events of multi-event coupling in QAR data.
## 4 Conclusions  
The effects of the beam diameter and hatch spacing on the bendability and microstructural behavior of the AISI 316 stainless-steel sheet were investigated. The conclusions of this study could be summarized as follows.  
1. At a constant beam diameter, with an increase in hatch spacing, the strain on the spacing between the scan tracks increased, while the strain on the heating line decreased. The increase in strain of the spacing between the scan tracks was responsible for the lower bendability of the material, which demonstrated the counter bending between the scan paths.
2. The total plastic strain increased with a decrease in beam diameter and increase in hatch spacing. This was attributed to the increase in temperature and implied a correlation between the melting and an increase in the total strain.
3. When the beam diameter was increased and the hatch spacing was decreased, the formation of the small equiaxed dendrite grains instead of coarse and inhomogeneous austenite grains increased the bendability of the material. This could be attributed to the decrease in anisotropy in the microstructure, which increased the work hardening capacity.
4. The grain sizes were increased in the reheated overlap regions of the samples. The reduction in the yield stress of the material in the overlap region owing to the grain growth led to a larger deformation.
5. The grain boundary strengthening effect with the larger strain hardening of the material were responsible for the increase in microhardness with an increase in beam diameter and decrease in hatch spacing. However, at the small beam diameter and large hatch spacing, the increase in the cooling rate was responsible for the microhardness improvement.  
Formal analysis, D.A.; Investigation, D.A.; Methodology, M.H.G.; Project administration, S.M.H.S.; and Supervision, Y.H.M.  
This research was funded by the National Research Foundation of Korea, grant-funded by the Korean government (no. 2012R1A5A1048294).  
Figure 12: Obtained cooling rates from FEM analysis.
## 2 Materials and Methods
### The Median Filter  
We will use the Median Filter as a complementary tool. Since the encrypted images are damaged, there is a loss of sharpness in the decrypted images. For this reason, in the present research, the median filter 5 \(\times\) 5 is proposed to improve its sharpness. The application of this instrument proceeds as follows: given a point \((x_{1},x_{2})\) of the pixel map, an adjustment is performed on the neighboring pixels of size \((w,h)\). In summary, the median filter 5 \(\times\) 5 builds a mask of 25 pixels from a pixel \((x_{1},x_{2})\). In such a way that the point \((x_{1},x_{2})\) is in the center, and the others are around it. Figure 1 illustrates it. In this sense, the point \((0,0)\) represents \((x_{1},x_{2})\).  
Afterward, values are ordered according to their intensities. Let us denote the medians of each color as \(M_{r_{s}(x_{1},x_{2})}\), \(M_{g(x_{1},x_{2})}\) and \(M_{b,(x_{1},x_{2})}\). The median per color is greater than or equal to \(\lceil\frac{25}{2}\rceil-1\) and less than the other values. Subsequently, these nine values are ordered according to their intensity. Then, the median is chosen as the value that meets the following: it is greater than or equal to the first; in other words, 50%, and less than the remaining points.
## 2 Experimental Setup and Procedures
It can be seen that the intrinsic interference spectrum generated by the BS is no longer visible because its lower power has been overshadowed by the main interference with higher power. Nevertheless, the intrinsic interference generated by the BS still has an effect on the laser wavelength output, as will be shown in the subsequent experimental results. For the two complementary interference spectra depicted in Figure 2b, it is observed that the yellow line exhibits a higher interference power compared to the blue line. Additionally, the difference in power between the interference peak of the yellow line and the interference trough of the blue line is greater than that between the interference peak of the blue line and the interference trough of the yellow line. Consequently, the fiber ring laser structure in question displays a multi-wavelength laser spectrum, as indicated by the dark line in Figure 2b. Each lobe of interference is characterized by a pair of head horns. In addition, the lasing of the laser wavelength will be at the half-upper-waist of the lobe of the two main interferences, where the two main interferences are superimposed and have the highest power. The concept of \"spatial hole-burning\" as an effect of lasers is derived from [67; 68]. The operating principle of hole-burning in a laser involves selectively depleting certain frequency components within the gain profile of the amplifying medium, creating a \"hole\" in the spectral line. This process is achieved by preferentially exciting atoms or molecules in the upper energy level of the laser transition, causing a reduction in the population of those specific energy states. As a result, the gain at those frequencies decreases, leading to a spectral \"hole\" where the gain is lower than the surrounding frequencies. Hole burning can be induced by various mechanisms, such as optical pumping, where external light sources excite the atoms to specific energy levels or have inhomogeneous broadening effects in the medium. The creation of these holes in the gain profile can have significant implications for the operation and performance of the laser, affecting parameters such as linewidth, power dependence, and mode pulling. Therefore, in this scheme, the hole will be formed in the gain spectrum corresponding to the node of the interference pattern according to inhomogeneous broadening, resulting in a decrease in the lasing threshold at the half-upper-waist of the lobe of the interference spectrum.
## 5 Discussion
### Limitations and Future Work  
Despite the encouraging results, we note several certain limitations. Expert judgments, despite being thorough, retained elements of subjectivity, and the review process relied on the standards and norms of a single institution. Employing larger reviewer pools, multi-annotator consensus, or multi-institutional replication would help mitigate potential human-bias effects and increase generalizability. Partial real-world validation focused on Task1 with 100 newly created questions, leaving Tasks 2 and 3 to be tested more extensively and in a wider range of academic disciplines or question designs. This study also concentrated largely on text-centric, STEM-based courses; further exploration of humanities or social sciences, where interpretive or highly subjective items are common, could reveal different patterns of performance or bias.  
Another limitation concerns the nature of the course materials themselves. The approach hinged on carefully curated lecture notes, slides, and condensed texts rather than raw, unstructured sources. If only dense textbooks or research articles were available, additional pre-processing or fine-tuning may be necessary. The meta-evaluator was similarly tied to a particular large language model and prompt design, so reproducing these results within different LLM ecosystems might introduce new biases or necessitate different calibration strategies. Scalability likewise presents challenges, as fine-tuning large models, maintaining knowledge graphs, and implementing real-time retrieval can require significant computational resources and specialized expertise--constraints that smaller institutions may find difficult to meet.  
Finally, the absence of direct student feedback on AI-generated items leaves open questions about the effects on learner motivation, fairness perceptions, and actual educational outcomes. Future work could examine these dimensions by incorporating student perspectives, validating tasks beyond alignment (Tasks 2 and 3) at scale, exploring further refinements to the meta-evaluator prompts, and assessing the viability of more lightweight or distributed infrastructures. Furthermore, future work could also incorporate more sophisticated retrieval (e.g., multi-hop chaining, graph-based expansions, multi-modal models) or advanced multi-agent collaboration, provided that basic transparency and reliability standards are upheld. Such research would not only enrich the understanding of how these AI-driven tools function across diverse contexts but would also advance their integration into real-world academic environments.