esa-sceva/satcom-chunk-collection huggingface dataset:
# In Vitro Gene Expression Responses of Bovine Rumen Epithelial Cells to Different pH Stresses
## 3 Results
### Functional Analysis of 94 Shared DEGs in BRECs between \"Control vs. SARA\" and \"Control vs. ARA\"  
A total of 1381 and 283 significantly ([log2 (FC)] \(>1\), \(p<0.05\)) DEGs were documented in the two comparisons of \"control vs. SARA\" and \"control vs. ARA\", respectively. Of these DEGs, 94 genes were commonly shared among the three comparisons. A total of 20 BP and 3 MF terms were significantly enriched (\(p<0.05\)), and most were major molecular functions, e.g., immune effector processes, defense responses, regulation of transport, regulation of cell communication, response to organic cyclic compounds, and response to stress. Seven downregulated DEGs (e.g., PIK3 CG, ATF4, PCK2, FIBIN, NRIP1, SLC7A11, IFFT1, DDIT4, and TMF1) and one upregulated DEG (e.g., CYP1A1) were enriched in BP and are associated with the response to organic cyclic compounds. The three major molecular functions related to DEGs consisted of receptor-ligand activity, signaling receptor activator activity, and receptor regulator activity. A complete set of GO terms and DEGs involved are provided (Supplementary Materials Table S6).  
The results of KEGG pathway analysis showed that 18 pathways (\(p<0.05\)) (Supplementary Materials Table S6), mainly related to PI3K-Akt signaling pathways (e.g., PIK3 CG, ATF4, FGF19, PCK2, VEGFA, LPAR1, DDIT4, FGF21, and IL7R), and MAPK signaling pathways (e.g., ATF4, FGF19, VEGFA, RASGRP1, and FGF21), were significantly enriched; the top ten KEGG pathways are shown in Figure 4.  
Quantitative reverse transcription-polymerase chain reaction (RT-qPCR) analyses of six random genes were carried out on the same samples to corroborate the expression changes of genes found in the RNA-Seq data. The results demonstrated that all of these genes' expression patterns matched those discovered by RNA-Seq. The dependability of our RNA-Seq data was indicated by the consistency between the RT-qPCR and RNA-Seq results (Supplementary Materials Figure S1).  
Figure 4: Significant KEGG pathways of 94 shared DEGs in BRECs between “control vs. SARA” and “control vs. ARA”. The significances of the identified KEGG pathways were determined by \(p<0.05\). Red bars indicate upregulated genes and blue bars indicate downregulated genes.
# Integrating Digital Twins of Engineering Labs into Multi-User Virtual Reality Environments
## 4 Results
### Validation
#### 4.1.3 Student Satisfaction Questions and Answers  
A satisfaction survey was conducted using a Likert scale to better visualize the students' responses in each of the surveys. the distribution of the responses of both groups is shown in Figures 10 and 11.  
The integration of virtual reality into the laboratory experience significantly enhanced all evaluated aspects, including interest, safety, participation, and perceived educational value. This technology not only deepened participant immersion and engagement but also reduced perceived risk and improved process comprehension. Additionally, it made the experience more engaging and widely recommended. These findings underscore the potential of virtual reality as a powerful tool for teaching technical processes in engineering, fostering both interaction and knowledge retention.  
As part of the assessment of student satisfaction with the virtual reality experience, participants were also asked which tool within this multi-user environment they found most beneficial for their engineering education. In both groups, approximately 80% favored real-time data visualization.  
Furthermore, students were asked whether this new methodology fostered a more collaborative learning environment. The results were unanimous, with 100% of participants acknowledging an improvement in collaboration.  
Figure 10: Questions and answers for Group 1.
# Optimization of BP Neural Network Model for Rockburst Prediction under Multiple Influence Factors
## 7 Conclusions  
The present paper selected the ratio of the maximum tangential stress of surrounding rock to the rock uniaxial compressive strength (\(\sigma_{\rm o}/\sigma_{\rm c}\)), the ratio of the rock uniaxial compressive strength to the rock uniaxial tensile strength (\(\sigma_{\rm c}/\sigma_{\rm t}\)) and the elastic energy index (\(W_{\rm et}\)) as prediction indexes, and extensively collected 100 groups of typical rockburst samples. Based on standard algorithms, four optimization algorithms and four index dimensionless methods, this paper established sixty BP models in total for rockburst prediction with sample sizes of 40, 70 and 100. The following conclusions can be drawn:  
(1) Comparative study and engineering application results show that the LM-BP neural network based on the unified extreme processing method has the best prediction effect, which is the best optimal model.  
(2) The selection of dimensionless methods has different influences on the prediction results of the BP neural network model, the SCG-BP model has the greatest influence and the GDM-BP model has the least influence. Compared with the dimensionless method, the selection of the training function of the BP neural network has a more significant influence on the prediction results.  
(3) With the addition of training samples, the average and maximum prediction accuracy rates of the BP model both increase nonlinearly. Therefore, it is necessary to increase the number of typical rockburst samples and gradually improve the prediction accuracy of the BP model in the future.  
**Author Contributions:** C.W.: methodology, formal analysis and writing--original draft. J.X.: writing--review and editing and project administration. Y.L.: conceptualization, methodology and formal analysis. T.W.: data curation and validation. Q.W.: data curation and validation. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the Science and Research Fund from the Educational Department of Yunnan Province (2021J0060), the National Natural Science Foundation of China (51934003, 52264019), the Major Science and Technology Special Project of Yunnan Province (20220 AG050014) and the Yunnan Innovation Team (202105 AE160023).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data in the study are from the literature, which is publicly accessible.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Prediction Indexes**} & \\ \cline{2-5}
**No.** & \(\sigma_{0}\)/\(\sigma_{\mathbf{c}}\) & \(\sigma_{\mathbf{c}}\)/\(\sigma_{\mathbf{t}}\) & \(W_{\mathbf{et}}\) & **Actual Risk Level** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: Cont.
# Efficient and Accurate Hemorrhages Detection in Retinal Fundus Images Using Smart Window Features
## 3 Experiment and Result Analysis
### Segmentation Result  
The proposed segmentation algorithm (SWAT) is compared with five state-of-the-art segmentation techniques: spatial fuzzy clustering with level set (FCLS) [37], active contour mean separation (ACMS), active contour Chan-Vese (ACC-V) [38], k-means clustering (KMC) [39], and region growing (RG) [40]. All the methods are applied on DIARETDB1 and DIARETDB0 datasets. The statistical results of the DIARETDB1 dataset are provided in Table 1 and can be depicted in Figure 6. The statistical results of the DIARETDB0 dataset are provided in Table 2, and visual inspection can be seen in Figure 7.  
The proposed algorithm segments hemorrhage attached to blood vessels or with a black background. Although, ACMS and FCLS perform well with 67.45% and 78.23% F1 scores, respectively. However, the proposed method performs better than other methods in terms of precision rate, recall rate, and F1 score by achieving an 83.85% F1 score.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision Rate (\%)** & **Recall Rate (\%)** & **F1 Score (\%)** \\ \hline KMC & 48.88 & 86.49 & 62.46 \\ RG & 69.38 & 58.58 & 63.53 \\ ACC-V & 65.34 & 82.67 & 72.99 \\ ACMS & 74.68 & 61.51 & 67.45 \\ FCLS & 78.80 & 77.67 & 78.23 \\ SWAT & 83.97 & 83.74 & 83.85 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical analysis of different methods on DIARETDB1 dataset.  
Figure 6: _Cont._  
Figure 6: Results of the different segmentation methods on the DIARETDB1 fundus images dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision Rate (\%)** & **Recall Rate (\%)** & **F1 Score (\%)** \\ \hline KMC & 57.89 & 74.45 & 65.14 \\ RG & 55.92 & 61.50 & 58.58 \\ ACC-V & 59.86 & 71.48 & 65.15 \\ ACMS & 66.35 & 51.66 & 58.09 \\ FCLS & 65.73 & 67.41 & 66.56 \\ SWAT & 70.51 & 74.08 & 72.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical analysis of different methods on DIARETDB0 dataset.  
Figure 7: _Cont._  
Figure 7: Cont.
# Recognition of Vehicle License Plates Based on Image Processing
## 2 License Plate Recognition System
### Distortion Correction
#### 2.4.1 Binarization  
First, the license plate area is binarized for the perspective distortion correction. In the image segmentation Equation (1), \(f\) and \(g\) are the input and output images, respectively, and \(T\) is the threshold value. For the input image \(f\), when the value of the pixel \((x,y)\) is less than the threshold value \(T\), the corresponding pixel value is 0. However, when the value is greater than or equal to the threshold value, the corresponding pixel values become 1. Thus, the divided image can be obtained.  
\[g(x,\ y)=\left\{\begin{array}{l}if\ f(x,y)\ \geq T,\ 1\\ if\ f(x,y)<T,\ 0\end{array}\right. \tag{1}\]  
Figure 5 shows the binarization image. This image is used to detect the parallelogram corresponding to the license plate area. This is an essential technique for selecting a threshold value. It distinguishes the area corresponding to the license plate in the obtained image. In this study, \(T\) is selected using the Otsu [13] binarization technique to automatically determine the threshold value.  
Figure 4: (**a**) Low-resolution image; (**b**) Improved image.
## 2 Results and Discussion  
Diatom microalgae and their fossil form (diatomaceous earth, DE) represent a low-cost and easily available source of nanostructured biosilica produced by single-celled microorganisms (diatoms) that uptake silicic acid from seawater via a complex metabolic biomineralization process to afford intricately patterned and hierarchically organized silica layers. In the last decades, biosilica from diatoms have been extensively studied for the possibility of functionalizing them, starting both from in vivo and in vitro methods [13; 14; 15; 16] with a plethora of multifunctional molecules for applications ranging from biomedicine [17; 18; 19; 20; 21] to photonics and imaging [22; 23; 24].  
Very recently, the in vitro removal of a series of phenolic compounds used as model pollutants was achieved by a surface chemical modification of diatomaceous earth with a polydopamine (PDA) layer easily produced in aqueous solution [25]. In fact, PDA exhibits excellent adhesive and entrapping properties [26; 27] for different application fields.  
In this frame, we aimed to investigate whether a PDA layer could improve the uremic toxin entrapping capacity of two adsorbent materials, such as DE and polyvinylpyrrolidone-co-styrene (PVP-co-S) co-polymer, a commercially available, nontoxic, totally organic bulk filler already proposed in the literature for dialysis processing [9].  
The PDA coating of DE and PVP-co-S was easily obtained by in situ dopamine monomer oxidative polymerization in aqueous solution, in green conditions, without adding any toxic solvents or requiring further processing. In order to find the best entrapping properties, the formation of a single PDA coating layer around the support materials was compared with a double PDA coating by a full characterization with spectroscopic techniques in solution and solid state, and after use in the removal of indoxyl sulfate (IS) and p-cresyl sulfate (PCS).  
Figure 1: Model sulfated kidney toxins tested in experiments: (**a**) p-cresyl sulfate; (**b**) indoxyl sulfate.
# Detection of Thermal Strain in Steel Rails with BOTDA
## Laboratory Test
### Test Setup
To better mimic climate conditions of real applications, the temperature range of the experiment was set to be from \(-40\,^{\circ}\)C to \(+50\,^{\circ}\)C with a variation step of \(5\,^{\circ}\)C. It took 1 h for the chamber to increase from one temperature step to the next, and each temperature step was held for 4 h for repeated measurement.
# RGloVe: An Improved Approach of Global Vectors for Distributional Entity Relation Representation
## 5 Conclusions and Future Work  
In this paper, we have proposed an improved method of global vectors RGloVe for distributional entity relation representation. Unlike traditional relation extraction, distributional relation representation aims to train the entity vectors and measure the degree of closeness of relationship between two entities. The major advantage of distributional relation representation is that it is no longer limited to predefined relation types, which makes it easy to be applied to open domain question answering and information retrieval.  
The statistics of entity co-occurrences in a corpus is the primary source of information available to distributional relation representation between entities. We first obtain a co-occurrence matrix, each of whose elements represents the co-occurrence weight of two entities. Then, in order to train the entity vectors more efficiently, we have developed an improved global vectors model of RGloVe by using the cosine similarity to approximate the entity occurrences instead of dot product. Finally, a graph database of Neo4j is introduced for building, storing and querying the relationships between named entities. The final comparative experiments show the superiority of our methods. In the future work, we will explore better classification criteria than cosine similarity between entity vectors and relationship type vector. In addition, it is significant to extend our model to perform experiments on the English corpus.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Word2 Vec (\%)** & **GloVe (\%)** & **RGloVe (\%)** \\ \hline \(N=1000\) & 0.18 & 0.2 & 4.7 \\ \(N=5000\) & 0.65 & 0.7 & 12.4 \\ \(N=10\),000 & 1.13 & 1.24 & 15.98 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Top \(N\) precision.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **SVM (\%)** & **Word2 Vec (\%)** & **GloVe (\%)** & **RGloVe (\%)** \\ \hline average accuracy & 90.7 & 75.6 & 76.8 & 79.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average accuracy of relationship classification.  
**Acknowledgments:** We are very grateful to our word group of text mining for their help with the data sets and evaluation. The work described in this paper was supported by National Natural Science Foundation of China (NO.61331017).  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] contributed analysis tools; [PERSON], [PERSON] and [PERSON] wrote the paper. Authorship must be limited to those who have contributed substantially to the work reported. All authors have read and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 FDM-DEM Coupling Model  
The discrete element method (DEM) decomposes the soil mass into a series of particles in space, enabling for the calculation of friction between particles and walls. This study focuses on the restraint effect caused by friction between isolation piles and soil, which is suitable for simulation by discrete element method. However, the computational efficiency of the discrete element method is far lower than that of the continuum approaches, such as the finite element method (FEM) and the finite difference method (FDM). Therefore, the modeling size of DEM will be severely constrained. In order to satisfy the requirement of engineering analysis scale and reflect micro mechanical properties, this study introduces the FDM-DEM coupling technique [23; 24; 25; 26] to realize the efficient coupling analysis between discrete medium and continuum medium.
# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
## 4 Experimental Works
### Performance Metrics  
In this study, MSE, RMSE, R-Squared, and MAE performance metrics are used to evaluate the performance of the proposed model. In this study, RMSE, MSE, MAE, and R-Squared performance metrics were preferred to ensure the accuracy of electric vehicle demand forecasting. The selection of these metrics aims to provide high accuracy and reliability in a complex and dynamic market such as EV demand. RMSE and MSE are important in increasing the reliability of the model by emphasizing large deviations. Large errors in demand forecasting in the electric vehicle market can directly affect strategic decisions, so low RMSE and MSE values provide more accurate forecasts. MAE presents the error rate in a wider framework by taking the absolute value of each deviation and does not exaggerate large errors. In this respect, it is suitable for demonstrating the general accuracy of EV demand forecasting. R-Squared reflects the data explanatory power of the model and shows how successful the model is in a market that depends on many factors, such as EV demand. These metrics play a critical role in correctly modeling fluctuations in EV demand and evaluating the overall performance of the model. These metrics are as follows:  
* RMSE is the square root of the average of the squared differences between actual and forecasted values. RMSE quantifies the amount of deviation between predicted and actual values. A smaller RMSE value signifies superior performance of the model. \[RMSE=\sqrt{\frac{\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})}{N}},\] (10)
* MSE is the average of the squared differences between actual and anticipated values. MSE calculates the mean of the squared errors, emphasizing significant errors. A smaller mean squared error (MSE) indicates a superior model. \[MSE=\frac{1}{N}\underset{i=1}{\overset{N}{\sum}}(y_{i}-\hat{y}_{i})^{2},\] (11)
* MAE is calculated as the average of the absolute differences between actual and anticipated values. MAE calculates errors using absolute values, giving less weight to huge errors. A smaller MAE signifies superior performance of the model. \[MAE=\frac{\sum_{i=1}^{n}|y_{i}-x_{i}|}{n}\] (12)
* R-Squared is a statistical metric that quantifies the extent to which independent factors account for the variability in the dependent variable. The R-Squared number represents the proportion of variation in the data that is accounted for by the model. A model is better fitted the closer its value is to \(1\).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Hyperparameters** & **Search Ranges** & **Optimum Values** \\ \hline Optimization method & AdaGrad, RMSProp, Adam, and SGD & Adam \\ \hline Epoch & 1–5000 & 1000 \\ \hline Batch size & 2–128 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._\[R\text{-}Squared=1-\frac{\sum_{i}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i}(y_{i}-\mu)^{2}} \tag{13}\]
## 2 Materials and Methods
### Cylindrical Concrete Samples  
In order to perform splitting tensile strength (Brazilian) tests on concrete elements added with _Arundo donax_ natural fibers, some cylindrical concrete samples (Figure 4) with 10 cm diameter and 15 cm height were prepared with the addition of 1% by weight of _Arundo donax_ vegetal fiber (added as an initial reference following a random distribution), having a different aspect ratio (AR)--defined as the ratio between the length and equivalent diameter--as follows:  
* 4 samples were reinforced with natural fiber having AR = 30: length equal to 90 mm and transversal equivalent diameter equal to 3 mm;
* 4 samples were reinforced with natural fiber having AR = 50: length equal to 150 mm and transversal equivalent diameter equal to 3 mm;
* 4 samples were reinforced with natural fiber having AR = 70: length equal to 210 mm and transversal equivalent diameter equal to 3 mm.  
Figure 3: Tensile tests on _Arundo donax_ fibers.  
Moreover, in order to evaluate the practical results, one cylindrical sample of the same concrete but without any additive fiber was also prepared.  
The concrete samples consisted of:  
* Pozzolanic cement \"CEM IV 325\", according to UNI EN 197-1:2011 [86], in a percentage of 20% of the total volume, that constitutes the active component of concrete. Compared to Portland cement, it has a slower hardening process, thus improving workability and durability [87]. Today, industrial cement manufacturers are increasingly using materials such as pozzolan, fly ash, and volcanic ash, to replace part of Portland cement, in order to reduce CO2 emissions and the relative cost, and to improve the resistance to chemical attack by sulphates and sea water.
* sand particles, consisting of loose sedimentary rock with a grain size measuring less than 1.5 mm and a humidity content below 10%, in a percentage of 30% of the total volume;
* quarry gravel, crushed and rounded rock with granule size less than 30 mm, in a percentage of 40% of the total volume;
* water, which has the fundamental function of hydrating the cement and whose quantity greatly influences the strength characteristics of the hardened concrete, in a percentage of 10% of the total volume.  
In the preparation of the mixture, the grain size of the aggregates was mixed between fine and coarse, in order to, as much as possible, fill the spaces between contiguous elements and reduce the volume of voids, which reduce the mechanical strength of hardened concrete. Finally, after vibrating the concrete mix, aimed at improving compactness and adherence of blocks to formworks, the samples were subjected to maturation for 28 days in a humid environment, with the aim of conferring compactness and mechanical resistance over time to the fluid mixture described above.
## 3 IT, IoT, IIoT, IoUT, and OT
The battery life of the UAVs may limit this method, particularly if the head node is mobile and gathers data from the sensor nodes. There are a number of industrial and scientific applications that require the connectivity of underwater objects, including oil exploration, environmental monitoring, disaster prevention, and disaster recovery [2]. Ships, buoys, and autonomous surface vehicles can serve as data collection stations, or sinks, by gathering data from underwater sensor networks and transmitting them to a control center via radio waves. This approach allows for efficient and cost-effective data collection and monitoring in marine environments.
# Review of Adaptive Shock Control Systems
## 3 Discussion
### Concept 3--SMA Wire Bundle Actuators
#### 3.3.1 Advantages  
SMA actuators can be designed to be highly compact, thus, they can be used in wing regions with limited installation space. The SMA actuation requires only an electrical power supply to introduce heat to the SMA wires which is expected to mitigate the overall system weight increase with respect to other actuation technologies such as hydraulic fluid systems. Furthermore, due to the fact that several actuators are placed in chordwise direction, the bump is not only variable in its height (depending on the amount of supplied heat) but also in its position as well as shape. Also, the formation of both 2D and 3D bumps is conceivable.