esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Mobile virtual reality (VR) technology is expected to become one of the killer applications for future mobile broadband networks. It involves to the transmission of mobile VR video and audio files from cloud servers to user terminal devices, such as head-mounted displays (HMDs), through multi-hop networks, enabling the storage and rendering of VR content by cloud or edge servers [1]. As an example, because of the COVID-19 pandemic, VR-based educational technologies have been drawing increased attention [2]. As a unique application within the broader field of VR, 360\({}^{\circ}\) VR allows users to freely explore and interact with their surroundings from every angle. It has received increasing attention because of its capability of providing users with an immersive experience. However, for current network technologies, delivering 360\({}^{\circ}\) VR videos, which require high bandwidth, low transmission latency, and substantial computational resources, is still challenging [3].  
To alleviate network burden and provide a better viewing experience for VR users, researchers have conducted extensive studies on various aspects. Given the large data transmission requirements of VR, proactive caching of VR videos based on user field-of-view (FoV) prediction is regarded as a key technique to meet the service delay demands [4; 5]. In addition, rendering and processing high-definition VR video frames requires substantial computing resources, while lightweight HMDs often have limited computing capacity, making mobile edge computing (MEC) a key enabling technology for mobile VR applications [6; 7].
# Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides
## 5 Discussion
### Impact of Historical Landslide Events on Susceptibility Assessment  
Most of the factors influencing susceptibility assessments have been explored in several studies [48; 49; 50]. However, much of the historical research overlooks the impact of historical landslide events on the assessment [51]. In contrast, because the slopes where landslides occur become stable in the short term following these events, the susceptibility of the area will consequently decrease. Therefore, historical landslide events have a significant effect on susceptibility assessments.  
The numbers and percentages of historical landslide events at various assessment levels from 2000 to 2009 and the RNA values of the various assessment levels are investigated, allowing the change in the assessment levels of the historical landslide events to be obtained (Table 8 and Figure 11).  
In Figure 11, the circles represent the percentages of historical landslide events or the RNA values at various assessment levels. It can be seen from Figure 11 that over one-third of the historical landslide events (38.71%) occur at a decreasing assessment level. A total of 54.24% of the historical landslide events are within the same assessment level. If the assessment level area is larger, then there may be more historical landslide events within that assessment level. To rule out the possible influence of area size, the RNA is considered to be the most important factor. The RNA at decreased levels (81.31 \(\times\) 10\({}^{-3}\)/km\({}^{2}\)) is more than fifteen times greater than the RNA at increased levels (\(5.25\times 10^{-3}\)/km\({}^{2}\)). Previous studies [52; 53] do not take into account the impact of historical landslide events; therefore, the above results can establish the importance of historical landslide events for susceptibility assessments of landslides.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Area (km\({}^{2}\))**} & \multicolumn{8}{c}{**Level Difference**} \\ \cline{3-10}  & & \(-\)3 & \(-\)2 & \(-\)1 & 0 & 1 & 2 & 3 \\ \hline \multirow{8}{*}{Elevation differences} & \(-\)140 to & & & & & & & \\  & \(-\)100 & & & & & & & \\  & \(-\)100 to \(-\)60 & & & & & & & \\  & \(-\)100 to \(-\)60 & & & & & & & \\  & From 1992 & \(-\)60 to \(-\)20 & & & & & & \\  & to 2000 & \(-\)20 to 20 & & & & & & \\  & 20-60 & & & & & & & \\  & 20-60 & & & & & & & \\  & 60-100 & & & & & & & \\  & 100-1400 & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Numbers of historical landslide events at various assessment levels from 1992 to 2000 and from 2000 to 2009.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 7 UOC Near-Future Network Scenarios and Research Challenges
### UOC--Today's Research Challenges
#### 7.2.9 Multi-Hop-Relay-Based UOC Link to Deliver Medical Services in Remote Areas  
As a possible potential solution to delivering health-related services to remote areas, researchers have recently demonstrated a new technique [503] to improve communication link performance and system reliability. The authors proposed a multi-hop serial UOC link with coherent reception for remote and outlying areas based on 120 Gbit/s DP-16 QAM modulation. For gain optimization and core elements, the authors adopted an all-optical amplify-and-forward technique with an Erbium-doped fiber amplifier (EDFA) in their research article for each relay terminal. Their results revealed that at a target BER of 10-5, the proposed multi-hop link increments of 1.8 km improved the performance in comparison with a direct link under the same atmospheric conditions. The proposed scheme showed an enhancement in link range by 0.7 km per link. The effects of increasing the number of relay nodes to approach a state of convergence was also pointed out. Further research could include creating a mesh link network in which nodes can use AI to choose the best propagation path and set their priorities for the best channel conditions. In this current COVID-19 situation affecting millions of people globally, this proposed high-speed communication network architecture technique can be especially valuable to provide the \"contact-less\" supervision of quarantine patients via video conferencing. UOC technology can therefore be extremely important and timely in helping health-related industries.
## 7 Discussion  
Our study has proven that automatic tire defect detection is difficult but possible. Like other industries, the tire industry is gearing up for a future in which artificial intelligence will play a key role in delivering quality tires to consumers around the world. This is based on both past research and expectations of new technology development in the tire industry.  
The AI methods described in the paper can help in the development of AI-based semi-automated tire quality control and reduce the labor costs associated with technical inspection and defect removal at the earliest possible stages of production, in line with the Industry 4.0 paradigm. Research using DL to detect errors, product defects, and diseases in in-depth images is used in various fields such as manufacturing, industry, and medicine [33; 34; 35; 36; 37].  
The integration of AI into tire quality control processes has brought significant advances to the tire industry. In this article, we have analyzed the current state of quality control in the tire industry, the use of AI in research and development processes, and real-world applications of ML in tire assessment. We also discussed the benefits, challenges, and future prospects of AI in tire quality control.  
In conclusion, the integration of AI in tire quality control processes has revolutionized the tire industry. Using AI technologies, tire manufacturers have improved inspection accuracy and efficiency, improved tire design and performance, and optimized maintenance strategies. The benefits of using AI in tire quality control include improved reliability, early fault detection, predictive maintenance, and data-driven decision-making.  
However, challenges remain, including data quality, the interpretability of AI models, and ethical issues. Addressing these challenges will be critical to the successful implementation and widespread adoption of AI in the tire industry. Furthermore, the future of AI in tire quality control is extremely promising, thanks to advances in sustainable manufacturing, advanced materials, and sensor technologies.  
As tire manufacturers embrace AI and continue to innovate, the tire industry will have safer, more reliable, and more sustainable tires. By prioritizing data integrity, interpretability, and ethical considerations, tire manufacturers can build trust with consumers and regulators by ensuring that AI-based tire quality control processes meet the highest safety and quality standards.
# Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
## 3 Preliminaries
### Classification Algorithms
#### 3.2.2 Logistic Regression (LR)  
LR, a supervised machine learning algorithm, serves as a statistical technique for modeling the probability of events based on one or more independent variables [39]. While it accepts input data of any numeric type, the output variable is binary (0 or 1, false or true, positive or negative).
## 2 Materials and Methods
### Flash Drought Identification  
Flash drought events were determined on the basis of an abrupt change in the soil moisture, mainly using the methodology of [30]. For this, the daily gridded soil moisture was converted into pentad (five-day average). Therefore, for each year, a total of 73 pentads were formed [46]. Then, the soil moisture percentile was determined for each pentad for each year. Finally, the following conditions were applied to identify flash drought. (1) The flash drought begins when the pentad (5d) mean soil moisture falls from above the 40 th percentile to below the 20 th percentile, with an average decline rate of no less than 5% in the percentile per pentad. (2) The flash drought ends when the reduction in the pentad soil moisture rises above the 20 th percentile. (3) Similarly, the drought must last for a minimum of three pentads (15 days). This definition takes into account both drought (soil moisture percentile less than 20%) and flash (decline rate of soil moisture percentile greater than 5%). The drought event calendar time is the period between the beginning and ending of a drought event. A flash drought event's start and recovery phases are described by the first two criteria, and the minimal amount of time that soil moisture stays below the 40 th percentile is described by the third criterion.  
The frequency was defined as the number of flash drought events at each grid point per year, and it represents the number of times. The duration was defined as the number of days during which the flash drought events last. It includes days from the onset to the end of the flash drought. Similarly, we determined the areas affected by flash drought by calculating the ratio of the grid points that experienced flash droughts to the total grid points in the study area [47].
# Harmonized Autonomous-Human Vehicles via Simulation for Emissions Reduction in Riyadh City
## 5 Experiment and Results
### Simulation Results and Analysis  
Table 6 and Figure 9 demonstrate that increasing AV penetration reduces travel time, emissions, and stop frequency while increasing average speed. The 50% AV scenario achieves 25.5% faster trips and 14.6% lower CO\({}_{2}\) emissions than the baseline.
## 3 Photocatalytic Materials for Water Treatment
### Visible-Light-Responsive Photocatalysts: Composition and Properties  
To overcome the limitations of UV activation, considerable efforts have been made to develop photocatalysts able to operate under visible light (\(\sim\)400-700 nm). Examples include the following (Table 2) [65; 68; 75; 88; 89; 90]:  
* Graphitic carbon nitride (g-C\({}_{3}\)N\({}_{4}\)): As a metal-free semiconductor with a moderate band gap (\(\sim\)2.7 eV), g-C\({}_{3}\)N\({}_{4}\) can harness visible light and has shown promising results  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Property/Performance Metric** & **TiO\({}_{2}\)** & **ZnO** \\ \hline Band gap energy & \(\sim\)3.2 eV (anatase phase) & \(\sim\)3.3 eV \\ Electron mobility & 0.1–0.4 cm\({}^{2}\)/V\(\cdot\)s & 200–300 cm\({}^{2}\)/V\(\cdot\)s \\ Surface area & Generally higher & Generally lower \\ Photocatalytic activity & Effective; limited by recombination & Comparable or superior; prone to \\ Photocatalytic disinfection & Effective; may have lag time & Faster with no lag time \\ Stability and durability & High chemical stability & Prone to photocorrosion \\ Light absorption range & Primarily UV & UV and potentially visible with \\ Applications & Environmental remediation, & modifications \\  & self-cleaning, water purification & Antibacterial coatings, sensors, water \\ Composite systems & Effective in heterostructures & Improves performance when combined \\  & with other semiconductors \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of TiO\({}_{2}\) and ZnO photocatalysts.  
for degrading organic pollutants. Its layered structure allows for easy modification and heterojunction formation.
* Bismuth-based compounds: Materials like BiVO\({}_{4}\), Bi\({}_{2}\)O\({}_{3}\), and Bi\({}_{2}\)WO\({}_{6}\) have received attention due to their visible-light responsiveness and photocatalytic activity for oxidation reactions.
* Silver halides (AgX, where X = Cl, Br, I): These materials exhibit strong visible light absorption and can be used alone or as part of composite systems, though stability and toxicity are concerns.  
Table 2 provides a concise overview of various visible light responsive photocatalysts, emphasizing their band gap energies, key features, and typical applications. These materials have been studied for their effectiveness in environmental remediation, particularly in the degradation of organic pollutants under visible-light irradiation.  
For example, titanium dioxide (TiO\({}_{2}\)), a wide bandgap semiconductor, is frequently studied in its rutile and anatase crystalline forms, which exhibit bandgap energies of approximately 3.0 eV and 3.2 eV, respectively. Its sensitivity to ultraviolet (UV) light has spurred not only extensive research in photocatalysis, but also in areas such as super-hydrophilic surface behavior, environmental decontamination, and solar fuel generation. When TiO\({}_{2}\) is photoexcited across its bandgap, it generates electron-hole pairs that are subsequently involved in redox reactions, with surface-adsorbed species acting as electron or hole scavengers, as depicted in Figure 5[91].
## 4 Analysis of Main Cable Curve of Suspension Bridge
### Calculation Accuracy
 } & \multirow{2}{*}{290.0} & \multirow{2}{*}{132.506} & \multirow{2}{*}{132.507} & \multirow{2}{*}{\(-0.001\)} & \multirow{2}{*}{114.904} & \multirow{2}{*}{114.906} & \multirow{2}{*}{\(-0.002\)} & \multirow{2}{*}{114.895} & \multirow{2}{*}{114.896} & \multirow{2}{*}{\(-0.001\)} \\  & & & & & & & & & & \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{ \begin{tabular}{c} 1/16 point of main \\ span (3) \\ \end{tabular}  } & \multirow{2}{*}{362.5} & \multirow{2}{*}{120.987} & \multirow{2}{*}{120.990} & \multirow{2}{*}{\(-0.002\)} & \multirow{2}{*}{100.834} & \multirow{2}{*}{100.837} & \multirow{2}{*}{\(-0.003\)} & \multirow{2}{*}{100.826} & \multirow{2}{*}{100.830} & \multirow{2}{*}{\(-0.004\)} \\  & & & & & & & & & & \\ \hline \multirow{2}{*}{7} & \multirow{2}{*}{ \begin{tabular}{c} 1/8 point of main \\ span (2) \\ \end{tabular}  } & \multirow{2}{*}{435.0} & \multirow{2}{*}{112.759} & \multirow{2}{*}{112.764} & \multirow{2}{*}{\(-0.004\)} & \multirow{2}{*}{90.789} & \multirow{2}{*}{\(-0.006\)} & \multirow{2}{*}{90.778} & \multirow{2}{*}{90.785} & \multirow{2}{*}{\(-0.008\)} \\  & & & & & & & & & & \\ \hline \multirow{2}{*}{8} & \multirow{2}{*}{\ \begin{tabular}{c} 1/16 point of main \\ span (4) \\ \end{tabular}  } & \multirow{2}{*}{507.5} & \multirow{2}{*}{107.820} & \multirow{2}{*}{107.824} & \multirow{2}{*}{\(-0.004\)} & \multirow{2}{*}{84.752} & \multirow{2}{*}{84.756} & \multirow{2}{*}{\(-0.005\)} & \multirow{2}{*}{84.749} & \multirow{2}{*}{84.755} & \multirow{2}{*}{\(-0.007\)} \\  & & & & & & & & & & \\ \hline \multirow{2}{*}{9} & \multirow{2}{*}{ \begin{tabular}{c} Mid-span of main \\ span span (5) \\ \end{tabular}  } & \multirow{2}{*}{580.0} & \multirow{2}{*}{106.164} & \multirow{2}{*}{106.170} & \multirow{2}{*}{\(-0.006\)} & \multirow{2}{*}{82.728} & \multirow{2}{*}{82.736} & \multirow{2}{*}{\(-0.010\)} & \multirow{2}{*}{82.729} & \multirow{2}{*}{82.736} & \multirow{2}{*}{\(-0.00 The maximum difference is 0.009% at Point 9, and the difference ranges from \(-\)0.009% to 0.002%. It is supposed that the results produced by IPSO are basically consistent with those by FEM. The solution to the nonlinear equations in this paper are correct; the solution accuracy can also meet the practical requirements; and thus, the solution can be properly applied in future engineering examples. IPSO is an analytical method in which the results need to be verified by FEM. The unstressed length of each cable and the coordinate results of each node can be obtained via the analytical method. From these results, the finite element model can be established and the displacement of each point can be obtained through the nonlinear finite element solution. If the error is small and meets the accuracy requirements, the calculation result of the analytical method can be considered correct. In this paper, the accuracy of the results produced by the IPSO method is compared with that of N-R and FEM, and the error is proven to be small, which shows that the accuracy of the IPSO calculation can meet the demands.
# Study of Mixed-Mode Cracking of Dovetail Root of an Aero-Engine Blade Like Structure
## 3 Finite Element (FE) Model of the Blade and Disc Slot
### Material Properties and Crack Propagation Law  
The material properties were taken from the work of [PERSON] et al. [11]. The elastic material properties are given in Table 3. All the analyses were performed assuming a metal temperature = 20 \({}^{\circ}\)C. This is quite far from reality, as a gas turbine blade generally works at much hotter conditions. The high temperature will influence material parameters such as Young's modulus and toughness and will also change the friction coefficient at the blade-disc interface. However, for the sake of simplicity, the ambient temperature was assumed as this assumption does not affect the generality of the design procedure.  
A Paris law was used to calculate the number of LCF cycles. The Paris law reported by [PERSON] and [PERSON] [13] in his book is written in the form:  
\[\frac{da}{dN}=C\Delta K^{n} \tag{1}\]  
where:  
* \(a\) is the crack length
* \(N\) is the number of LCF cycles
* \(C\), \(n\) are constants
* \(\Delta K\) is the stress intensity factor range between the maximum and minimum stress field (equals to zero in case of LCF).  
\begin{table}
\begin{tabular}{c c c} \hline
**E (MPa)** & **v** & \(\rho\) **(Mg/mm\({}^{3}\))** \\ \hline
115,000 & 0.33 & \(4.43\times 10^{-9}\) \\ \hline \end{tabular}
\end{table}
Table 3: Elastic properties of Ti-6 Al-4V used in this paper.  
Figure 7: Surface to surface contact definition at the root flanks.  
The crack propagation data used in the analysis are given in Table 4, and they are all taken from Reference [1], in which KIC, the reported value of fracture toughness, is 1739 MPa-mm\({}^{0.5}\). For this paper, however, a design value of 869.5 MPa mm\({}^{0.5}\) was conservatively assumed. In other words, a safety factor of 2 was used for design purposes.  
It is quite interesting to compare the relevant crack propagation data from different sources of the open literature. Three examples, for values of R not greater than 0.1, are given in Table 5. In these three examples, the Paris law coefficients have been calculated for a law of the mathematical form as per Equation (1).  
The reader can appreciate how different the parameters can be. A set of ad-hoc experiments are therefore needed for the specific engineering application.  
In order to predict the number of cycles from the Kth to the unstable propagation of the crack (KIC), the Paris law (Equation (1)) has been integrated (clearly, the Paris law is not applicable to the whole range from Kth to Kc so the crack length range was such that the near threshold and the near fracture parts of the da/dN-\(\Delta\)K curve was not involved).
# A Formula-Structure-Embedding-Based GNN for Formula Equivalence Determination
## 3 Method
### Encoder
#### 3.1.1 Formula Standardization Module  
There are basic algebraic laws for formula transformation, e.g., associative law and commutative law, which make algebraic computation more flexible but increase the diversity of formula expressions, e.g., formulas in Figure 2 have the same mathematical meaning but different abstract syntax trees (ASTs). However, the diversity in form do not reflect any further information about domain knowledge. In order to eliminate the structure differences brought by using the basic algebraic laws, the formula standardization module is introduced to uniform the different abstract syntax trees caused by the use of these algebraic laws. In this module, the following typical operations are performed on each AST:  
1. When processing operators with associative property (such as addition or multiplication), this method examines whether the operators of the current node and its child nodes are the same: if yes, the associative law can be used on these two nodes, and other sub-trees of the current node and the two sub-trees of the child node will be set as the next children of the current node. This operation will be performed recursively operations until all operator nodes have been examined. After this step, the AST will become an N-ary tree (see in Figure 3). 2. To process an operator with commutative property (such as addition or multiplication), we first provide a unique value for each type of operators or operands, then recursively calculate the dictionary order of each sub-tree, and rearrange the order of all sub-trees of the operator in ascending order of the sub-tree dictionary orders. The dictionary order is defined as a unique sequence obtained by recursively performing a breadth-first traversal, where the value of corresponding operator, the number of its sub-trees and the orders of all its sub-trees are sequentially recorded during the traversal process (see Figure 4).  
Figure 3: N-ary tree generation based on the associative law.  
Figure 2: (a) Abstract syntax trees of ‘x + 2y + 2 xy’; (b) Abstract syntax trees of ‘2y + x + 2 xy’.  
By using the above operations, different forms of formulas caused by the application of algebraic laws can be standardized, which improves the accuracy of formula equivalence determination.
# Application of Canola Oil Biodiesel/Diesel Blends in a Common Rail Diesel Engine
## 1 Introduction
[40] studied that with a method of injection in a common rail diesel engine fueled with ultra low sulfur diesel (ULSD) and biodiesel. They reported that higher biodiesel blending rates required higher injection pressure because of the high surface tension of biodiesel. [PERSON] et al. [41] reported that BD20 (20% canola oil blended with 80% ULSD by volume) was the best blend fuel on combustion and exhaust emission characteristics at an engine speed of 2000 rpm with a constant engine load compared with other blend fuels (BD0, BD10, and BD30). Subsequently, [PERSON] et al. [16] presented the effects of pilot injection timing and EGR combustion and emission characteristics by using BD20 in a diesel engine. However, all these findings were revealed at a constant engine load. Therefore, to further investigate the application effect of BD20 and pure canola oil (PCO) in a common rail diesel engine, we extended our study to include various engine loads (0.13 MPa, 0.26 MPa, 0.39 MPa, and 0.52 MPa of BMEP) in the experimental setup. By these research results, we can provide a reference value of research on the development and application of canola biodiesel in a common rail diesel engine.