esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Empirical Validation and Practical Application
### Future Likelihood of Adoption  
Figure 9 on the future likelihood of adoption reflects the students' perspectives regarding the continued use of immersive technologies, such as the Metaverse, in their academic and professional pursuits. Preintervention responses demonstrated a notable level of uncertainty, with many students expressing neutrality or moderate likelihood (\"neither likely nor unlikely\"). However, post-intervention data showed a significant change, with a higher proportion of students indicating that they were \"somewhat likely\" or \"extremely likely\" to integrate such technologies into their future practices. This change reflects a growing confidence in the potential of the Metaverse to provide meaningful applications in professional and educational contexts. Although positive responses increased, a subset of students remained hesitant, reflecting concerns about the scalability and real-world practicality of the Metaverse in long-term use. This hesitation underscores the importance of addressing perceived and actual barriers to adoption, such as technical limitations, lack of industry standardization, or lack of familiarity. Institutions aiming to promote broader adoption of immersive technologies could benefit from more targeted exposure to use cases where such platforms are actively deployed in the industry. By aligning Metaverse applications with evolving professional demands and fostering technological adaptability through continuous practice, the likelihood of adoption can be further enhanced. The intervention led to significant changes in students' likelihood of future use and recommendation of the Metaverse (\(Z=-1.23\), \(p<0.05\)). The effect sizes were small (\(r=-0.07\) for negative ranks and \(r=-0.30\) for positive ranks), suggesting that while the statistical changes were significant, the practical influence of the intervention on students' attitudes toward adopting and recommending the Metaverse was modest.
## 4 Discussion  
The main objective of this study was to construct an algorithm to retrieve turbidity in the Ramganga River using LSSR. Statistical techniques [54; 55] have been applied to determine the relationship between surface reflectance and measured turbidity. Bands from b1-b5 showed weak correlations for March and November. Different band ratios were utilized--for example, b2/b5, b3/b4, b3/b5, and b4/b5. The b2/b4 ratio was observed to be the most proficient for the estimation of turbidity for March, whereas b2/b3 was the most effective ratio for the estimation of turbidity in November for the Ramganga River. That was because vegetation indices (VIS) (b1, b2, and b3) and near infrared (NIR) (b5) are the most sensitive bands to SSC changes in water surface [56].  
Such a monitoring system by remote sensing could be used as an early forewarning system for turbidity exceedance, which could help to make timely decisions about allowed emissions into the river water. Thus, simple and less expensive regular monitoring can be applied at a considerably larger spatial scale than continuous conventional sampling methods. However, errors related to satellite data, which reduce the accuracy of the resulting maps, are as follows:  
* The samples collected may not be representative in relation to the total area of the water body;
* Water contains many soluble substances that hinder the process ofobtaining the precise signature of the studied parameters;  
Figure 9: Turbidity map of the Ramganga River on 11 November 2014.  
* The difference in date between the acquisition of the satellite data and the insitu data;
* The relatively low spatial resolution of satellite images may affect their accuracy;
* The uncertainty of the locations of the pixels and insitu samples;
* The small number of samples affects the regression model, as well as the validation process.  
A major problem with medium-resolution satellite data like Landsat 8 is that the Ramganga River is irregular in shape, generally narrow (about 100 m wide), and includes small islands. The reflected radiation from the shore and the vegetation near the shore is generally stronger than the radiation from the water. Therefore, the retrieval water quality parameters might not be possible if even a small portion of a pixel is covered with land. Also, the distinguished turbidity models are probably not relevant for different streams, and are along these site-specific lines. In all cases, testson freshwater bodies with comparable attributes should be undertaken to access the suitability of the models.
# Cumulative Failure Rate Prediction of EDCU in Subway Vehicles Based on RF-CNN-LSTM Model
## 2 Basic Theory
### Introduction to LSTM Principles
#### 2.3.2 Optimization Algorithms and Inverse Normalization  
Adam Optimizer is an optimization algorithm with an adaptive learning rate that combines the advantages of the RMSprop and Momentum methods. Adam Optimizer maintains a learning rate for each parameter, which is adaptively adjusted according to the historical gradient of the parameter. The main formulas of the algorithm are:  
1. Gradient first-order matrix estimation  
\[m_{t}=\beta_{1}\cdot m_{t-1}+(1-\beta_{1})\cdot g_{t} \tag{12}\]  
where \(m_{t}\) is the first-order moment estimate of the gradient at the moment t; \(\beta_{1}\) is the decay rate of the first-order moment, which is usually set to \(0.9\); \(g_{t}\) is the gradient at the moment t; and \(m_{t-1}\) is the first-order moment estimate of the previous moment.  
2. Gradient second-order matrix estimation \[v_{t}=\beta_{2}\cdot v_{t-1}+(1-\beta_{2})\cdot{g_{t}}^{2} \tag{13}\]  
where \(v_{t}\) is the second-order moment estimate of the gradient at the moment t; \(\beta_{2}\) is the decay rate of the second-order moments, which is usually set to 0.999; \(g^{2}t\) is the square of the gradient at the moment t; and \(v_{t-1}\) is the second-order moment estimate at the previous moment.  
3 Deviation correction  
\[\hat{m}_{t}=\frac{m_{t}}{1-\beta_{1}^{t}} \tag{14}\]  
\[\hat{v}_{t}=\frac{v_{t}}{1-\beta_{2}^{t}} \tag{15}\]  
where t is the current iteration step.  
4 Parameter update  
\[\theta_{t}=\theta_{t-1}-\eta\cdot\frac{m_{t}}{\sqrt{\hat{v}_{t}}+\varepsilon} \tag{16}\]  
where \(\theta_{t}\) is the value of the parameter at the moment t, \(\theta_{t-1}\) is the value of the parameter at the previous moment, \(\eta\) is the learning rate, and \(\varepsilon\) is a very small constant to prevent division by zero.  
By optimizing the algorithm, convergence can be accelerated, and model performance can be improved.  
Denormalization  
Denormalization involves converting the normalized predicted values back to the original scale. The formula is as follows:  
\[X_{original}=\frac{X_{norm}-a}{b-a}\cdot(X_{max}-X_{min})+X_{min} \tag{17}\]  
where \(X_{norm}\) is the normalized value, usually within a specific range; a and b are the lower and upper limits of the range used for normalization; and \(X_{max}\) and \(X_{min}\) are the maximum and minimum values in the original data, which are used to determine the normalized range.
## 4 Conclusions  
In summary, the effects of bottom layer sputtering pressures and annealing temperatures on the microstructures and properties of Mo bilayer films prepared by RF/DC magnetron sputtering were investigated. The results show that both the bottom layer sputtering pressures and annealing temperatures have great influences on the structures and properties of Mo bilayer films. The sputtering kinetic energy and mobility of Mo particles in Mo films increased with decreasing sputtering pressures and increasing annealing temperatures, leading to the increase of grain sizes, uniformity and compactness of Mo films and the decrease of RMS roughness. Correspondingly, the electrical conductivity and reflectance of Mo bilayer films were also increased. Resistivity ranging from 2.40 to 4.62 \(\times\) 10\({}^{-5}\)\(\Omega\).cm was obtained at 0.4 to 1.2 Pa bottom layer sputtering pressure and 2.31 to 0.92 \(\times\) 10\({}^{-5}\)\(\Omega\).cm at 100 to 400 \({}^{\circ}\)C annealing temperature. The strain decreased with increasing annealing temperatures and bottom layer sputtering pressures. Moreover, the particles' morphology of Mo bilayer films changed from spindles to triangles with increasing annealing temperatures. Prepared Mo bilayer films exhibited better adhesion, higher reflectance and lower resistivity which could be used suitably for the back electrode of CIGS solar cells. The Mo bilayer film prepared at 0.4 Pa of bottom layer sputtering pressure, 0.3 Pa of top layer sputtering pressure and annealed at 400 \({}^{\circ}\)C demonstrated excellent comprehensive performance: its resistivity reached 0.92 \(\times\) 10\({}^{-5}\)\(\Omega\).cm, the reflectance was above 55%, and the maximum photo-conversion efficiency of 13.5% was achieved for the CIGS solar cell. From the above results, we can see that under the same sputtering conditions, the adhesion and reflectance of Mo bilayer films prepared by RF/DC mode were higher than that of DC/DC mode, and the conductivity and efficiency of CIGS solar cells were close to that of DC/DC mode.  
Conceptualization, data curation, formal analysis, validation and writing--original draft (H.Z.); Conceptualization, funding acquisition, methodology, project administration (J.X.); Conceptualization, formal analysis, investigation and writing--review and editing (A.M.). This research was funded by the Key Project of Education Department of Henan Province (grant number 17A430018), the Key Research Project of Henan Colleges and Universities (grant number 18B430008).  
The authors declare no conflict of interest.  
Figure 6: I–V parameters of copper indium gallium selenium (CIGS) solar cells as a function of annealing temperature of Mo electrodes: (**a**) change of the open circuit voltage (Voc) with different annealing temperature; (**b**) change of the short circuit current density (Jsc) with different annealing temperature; (**c**) change of the fill factor (FF) with different annealing temperature; and (**d**) change of the photo-conversion efficiency(n) with different annealing temperature.
# NMR and LC-MS-Based Metabolomics to Study the Effect of Surfactin on the Metabolome of Flax
## 3 Results and Discussion
### Distribution of Surfactin in Flax Tissues
#### 3.2.2 No Detection of Surfactin in Aerial Part Extracts  
The same analyses carried out on the root extracts were carried out on the leaf and stem extracts and revealed no peak corresponding to the three forms of surfactin. In LC-MS chromatograms, there was no additional peak in the area at the retention time range of 6.2-6.5 min when comparing the surfactin-treated samples with the control samples. These results were also confirmed by the absence of a doublet signal at 0.85 ppm in \({}^{1}\)H NMR spectra. This reveals that surfactin stays at the root level and does not transfer to stems and leaves.  
This finding is consistent with previous research showing a general response to exogenous substances, attacks by pathogens and pests, and interaction with microbiota, reporting that systemic effects induced by local effects can alter the way plants defend themselves and respond to stress, with all of these elements being considered as part of the complex interactions between plants and their environment that can activate molecular signaling [32; 33].
## 2 Materials and Methods
### Design of Key Components  
#### 2.3.1 Wireless Serial Communication Measurement System  
The Arduino controller is used for wireless serial port transmission of the measurement data. In addition, the serial port debugging software is used on the computer to receive the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & **Total Mass** & **Theoretical Ground** & **Actual Ground** & **Error** \\
**Counterweights** & **(kg)** & **Pressure (Pa)** & **Pressure (Pa)** & **(\%)** \\ \hline
0 & 3.854 & 306.69 & 310.32 & 1.18\% \\
1 & 6.258 & 498.00 & 505.46 & 1.50\% \\
2 & 8.662 & 689.30 & 694.15 & 0.70\% \\
3 & 11.066 & 880.60 & 887.13 & 0.74\% \\
4 & 13.470 & 1071.91 & 1079.45 & 0.70\% \\
5 & 15.874 & 1263.21 & 1270.33 & 0.56\% \\
6 & 18.278 & 1454.52 & 1462.81 & 0.57\% \\
7 & 20.682 & 1645.82 & 1648.17 & 0.14\% \\
8 & 23.086 & 1837.13 & 1831.71 & \(-\)0.30\% \\
9 & 25.490 & 2028.43 & 2019.19 & \(-\)0.46\% \\
10 & 27.894 & 2219.73 & 2203.88 & \(-\)0.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ground pressure calibration data.  
Figure 7: Ground pressure calibration device. 1. 12 V power supply. 2. Arduino controller. 3. Wireless serial port transceiver module. 4. Signal amplifier. 5. Pressure sensor. 6. Mounting base. 7. Counterweight plate. 8. Bottom plate.  
measurement data and display the measurement data in real time. In the meanwhile, the values of 12 pressure sensors are recorded, as well as forward tilt angle, roll angle, forward speed, time, and so on. Figure 8 shows the measurement software. The measurement data are saved by the measurement software and it can be processed and analyzed immediately.
# Research on Vibration Comfort of Non-Motorized Lane Riding Based on Three-Axis Acceleration
## 3 Experimental Data Collection and Processing
### Calculation of ISO 2631 [34] Comfort Indicators  
Mechanical vibration significantly impacts human health and comfort, and vehicle smoothness reflects the extent of bumps experienced, as per the international standard (ISO 2631-1:1997). During riding, it is assumed that the rider is seated, and lateral swaying of the vehicle body is disregarded. Additionally, considering the rider's strong self-regulatory ability, hand-transferred vibration is also overlooked [35; 36; 37; 38]. Consequently, rider vibration is influenced by acceleration in three dimensions: \(x\) (forward direction), \(y\) (lateral direction perpendicular to forward), and \(z\) (vertical direction). Due to varying human sensitivity to acceleration in different directions, the contribution of acceleration in each direction to comfort differs. Thus, calculating total weighted acceleration requires considering the influence of three-axis acceleration on comfort [39; 40; 41]. Accordingly, when computing total weighted acceleration, different weights are assigned based on the impact of three-axis acceleration on comfort [42]:  
\[a_{y}=({k_{x}}^{2}{a_{x}}^{2}+{k_{y}}^{2}{a_{y}}^{2}+{k_{z}}^{2}{a_{z}}^{2})^ {\frac{1}{2}} \tag{1}\]  
where \(a_{x}\), \(a_{y}\), \(a_{z}\) correspond to the acceleration on the coordinate axes \(x\), \(y\), \(z\), respectively. \(k_{x}\), \(k_{y}\), \(k_{z}\) are the direction factors, according to the standard sitting state, where \(k_{x}=1.4\), \(k_{y}=1.4\), \(k_{z}=1\). The correlation between the calculated total vibration and comfort is shown in Table 1.  
The data collected during the experiment were processed to determine the weighted acceleration using Equation (1). Subsequently, the average of these weighted accelerations over the course of the running interval was employed as the characteristic parameter for assessing riding vibration comfort [43; 44; 45].
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 8 Evaluation
### Experimental Setup  
The experimental analysis of CrossDeFi is performed between two different DeFi-based test networks: Cosmos and Polkadot. Cosmos employs the inter-blockchain communication (IBC) protocol, while Polkadot uses the cross-chain message passing (XCMP) protocol for enabled cross-chain communication. Both test networks feature interoperability layers configured to closely simulate real-world scenarios, enabling cross-chain asset transfers between DeFi X and DeFi Y.  
Cosmos utilizes CosmWasm for smart contract execution across different blockchains, facilitating a wide range of DeFi asset transfer functionalities. In Polkadot, the relay chain does not natively support smart contracts, although its parachains can. The relay chain is the heart of Polkadot, providing security, consensus, and interoperability. The parachains are independent blockchains connected to the relay chain, leveraging interoperability and security features while maintaining Polkadot's unique functionalities. Notably, Moonbeam and Astar are platforms within Polkadot that support smart contracts. This evaluation utilizes various test networks to provide sufficient transaction execution time computation and gas usage estimation on both Cosmos and Polkadot.  
CrossDeFi conducts tests in intervals of 10 \(\times\) 5 times, totaling 50 asset transfers of one ATOM from DeFi X on the Cosmos network to DeFi Y on the Polkadot network. The evaluation is performed on a computer powered by the Ubuntu 18.02 LTS operating system, equipped with a multi-core Intel i7 CPU, 64 GB of RAM, and 512 GB SSD storage, ensuring a reliable internet connection.
## 1 Introduction  
As the vulcanization process is the most known chemical reaction associated with rubber, it is not the definitive method to determine the final properties of rubber goods. Elastomer reinforcement with fillers defines, together with the basic properties of the elastomeric matrix, the target application of a rubber formulation, or rubber compound. Carbon black is the most classical reinforcing filler in the tire industry, and its colloidal characteristics such as Specific Surface Area (SSA) and agglomerate particle structure represented by Oil Adsorption Number (OAN) are strictly correlated with the synthetic methodology employed [1]. Nowadays, the most industrially produced carbon black grades are standardized by ASTM and the resulting properties in the model compound are included in the standard as well; in other words, it is relatively easy to predict the mechanical effect of a certain grade of CB inside a rubber compound. The advent of silica in rubber formulations in the mid-1980s stimulated further research on the understanding of the relation between reinforcing filler characteristics and compound properties [2]. To mitigate the high level of hydrophilicity in silica, the use of a coupling agent became necessary to obtain a good level of dispersion. This also allowed the insertion of a specific surface chemistry thanks to the industrial availability of different alkoxysilanes bearing diverse functional groups [3]. The currently mostly employed silane in rubber compounding is able not only to disperse but also to covalently bind the polymer chains, allowing a further tuning of mechanical properties.  
Nanoclays are also well-known raw materials in materials science [4; 5; 6] and more specifically in rubber compounding. The basic structural unit is the T-O-T sandwich of two T (tetrahedral) silicate sheets with O (octahedral) coordinated cation in between. The TOT silicate sandwich is found in several combinations in nature since the geological conditions together with the availability of specific elements yield diverse particulate materials [4]. The most common structures are lamellar nanoclays, such as bentonite and montmorillonite, consisting of 2D foils of TOT sandwic stacks into pillared structures, with exchangeable cations balancing the TOT layer negative charge, as in Figure 1.  
L lamellar nanoclays possess high specific surface area, and thanks to the presence on the surface of exchangeable cations (Na, Ca, Mg, Fe... ) it is relatively easy to apply different chemical modifications simply by choosing adequate quaternary ammonium or phosphonium salts [4]. The industrial use of lamellar clays exploits the filler ability to reduce gas diffusion through rubber goods, thanks to the generation of tortuous paths, which hinder the permeation of small molecules. This characteristic found application in tire inner liner, but also gas masks, high pressure, and high vacuum rubber sealings, and others [8]. Another possibility for nanoclay particle shape is rod-like, among which at-tapulgite, paligorskite, imogolite, wollastonite, and sepiolite clays were chosen for literature studies [4].
## 3 Materials and Methods
### Data Preprocessing  
We first aligned the collection cycle of CCTV images, weather observation, and public weather data with one minute for data preprocessing. The preprocessing for image analysis consists of image data processing, an image data task scheduler, frame-by-frame extraction and ROI setting, and meteorological and image data matching. Specifically, the image data were first divided and processed into small units of image data on the DB server, and the image data were converted to frame-by-frame image data using the scheduler. The image data are reprocessed into the set ROI area within the converted image data. The location and visual data of public weather, sensing, and image data are combined via linear stretching and then calibrated according to each data generation/collection cycle to construct one dataset according to a certain cycle.  
The preprocessing process in the program is as follows: starting with the library call, the preprocessing process is completed in the following steps: accessing the database, browsing the image area, retrieving atmospheric sensor data, retrieving weather sensor data, retrieving public information data, separating the data per sensor, combining data frames, converting timestamp attributes to string attributes, generating columns of data frame file names, checking and interpolating missing values, rounding PM values, matching data between file lists and data frames, and exporting CSV.  
Finally, we developed an ROI derivation algorithm based on correlation analysis between PM observations and CCTV images and selected an ROI optimized for PM estimation. In detail, after constructing converted images for CCTV images, we derived the correlation between each pixel value of the converted images and the corresponding PM observation values. This allowed us to select the pixels with relatively high correlations as optimal ROIs for constructing a deep learning model.
## 2 Nanofluid Synthesis and Specific Heat Measurements  
### Materials  
Binary carbonate salt mixtures were employed as a base fluid of the nanofluids in this study. The base fluids were composed of lithium carbonate (Li\({}_{2}\)CO\({}_{3}\), Sigma-Aldrich, St. Louis, MO, USA) and potassium carbonate (K\({}_{2}\)CO\({}_{3}\), Sigma-Aldrich, St. Louis, MO, USA) in various mole fractions. According to a previous study [38], lithium carbonate and potassium carbonate have a eutectic composition (62 mol % of Li\({}_{2}\)CO\({}_{3}\) and 38 mol % of K\({}_{2}\)CO\({}_{3}\)) in which the carbonate salt eutectic has a high melting temperature of 488 \({}^{\circ}\)C. Recently, [PERSON] and [PERSON] reported that the specific heat of carbonate salt mixtures in the liquid phase changes drastically with the molar fraction of the two salts [31]. As mentioned previously, the base fluid (chemical composition of two salts in a binary salt mixture) affects the enhancement of the specific heat of the salt mixtures as well [28]. For this reason, five different salt compositions were chosen for the base fluid of the nanofluid tested in this study. Graphite nanoparticles (Sigma-Aldrich, St. Louis, MO, USA) less than 50 nm in diameter were dispersed into the base carbonate salt mixtures. Figure 1 shows a transmission electron microscope (TEM, JEOL JSM-2010) image of the graphite particle. Gum arabic (GA, Sigma-Aldrich, St. Louis, MO, USA)--a dispersing agent for carbon nanoparticles, particularly carbon nanotubes in water [39; 40]--was added for the homogeneous dispersion of the graphite nanoparticles. GA was observed to be appropriate for molten salt-based carbon nanoparticle nanofluids, compared with conventional surfactants such as sodium dodecyl sulfate and sodium dodecyl benzene sulfonate [21]. All the chemical agents were used as received.