esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Calculation Results  
### Segment Staggering  
It can be seen from Figure 13a that the first ring and the second ring are only affected by the longitudinal Jack thrust force in the \(Z\) direction of the tunnel axis combined with the numerical simulation calculation structure. They are all less than \(10^{-1}\) mm. That is, no comparative analysis is conducted.  
Figure 13: Maximum Dislocation. (**a**) Upward direction of shield machine. (**b**) Downward direction of shield machine. (**c**) Left direction of shield machine.  
Under the action of jack thrust angle, the statistical results of the maximum segment staggering position of the 3 rd-9 th rings are shown in Table 4.  
According to the statistical results, the maximum staggering number of segments in two adjacent rings occurs at the joints of A2-A3 and A1-A2 segments, respectively. The adjacent faces are lower than the arch waist of the K segment, and the vertical section of the tunnel is symmetrically distributed.  
When the shield machine is upward, the amount of segment staggering in the third ring decreases linearly with the increase of the jack thrust angle, while the segment staggering amount of the fourth to ninth rings increases linearly with the increase of the jack thrust angle. Under the condition of the jack thrust angle of 3\({}^{\circ}\), the segment staggering amount of the fifth ring in the third to ninth rings is the largest, which is 0.324. When the shield machine is downward, the staggering amount of segments in rings 3-9 decreases with the increase of jack thrust angle. When the jack thrust angle is 3\({}^{\circ}\), the segment staggering amount of the fifth ring is the largest, which is 0.31. When the shield machine runs on the left, the segment staggering amount of odd number ring (K segment is on the left side of shield tunneling prevention) decreases linearly with the increase of jack thrust deflection angle, while that of eighth number ring (K segment is on the right side of shield machine tunneling prevention) increases linearly with the increase of jack thrust deflection angle. When the jack thrust deflection angle is 0.2\({}^{\circ}\), the segment staggering amount of the fifth ring is the largest, which is 0.31 mm.  
According to the statistical results of the maximum displacement of segments and the variation law with the jack thrust angle, it can be seen that the amount of segment staggering is jointly controlled by the \(RT\) plane transverse load and the \(Z\)-axis longitudinal load. The location of the maximum segment staggering amount in each ring is the maximum position of the tunnel transverse shear force, which is consistent with the actual situation. The adjustment of the shield machine attitude has no effect on the position of the maximum segment stagger but only changes the development law of segment staggering amount with jack's thrust deflection angle. No matter how the shield machine posture is adjusted, the segment staggering amount of the second ring, which is detached from the shield tail, is the largest.
# Pore Water Pressure Prediction Based on Machine Learning Methods--Application to an Earth Dam Case
## 3 Application Case: Montbel Dam, an Existing Earth Dam
### Development of Machine Learning Models  
All the ML models utilized in this investigation were developed using the Keras package of Python, a high-level application programming interface designed for neural networks. The computations were executed on an Intel(R) Xeon(R) CPU E5-2609 v4 @ 1.70 GHz with 32 G RAM (Intel, Santa Clara, CA, USA). Optimal hyperparameters were obtained based on the prediction performance, assessed using the coefficient of determination (R\({}^{2}\)) and the mean square error (MSE):  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\bigl{(}m_{i}-p_{i}\bigr{)}^{2}}{\sum_{i=1}^{n}(m_ {i}-\overline{m})^{2}} \tag{7}\]  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\Bigl{(}m_{i}-p_{i}\bigr{)}^{2} \tag{8}\]  
where \(m_{i}\) and \(p_{i}\) represent the measurement and prediction of the PWP, respectively; \(\overline{m}\) is the mean of the measurement. The loss function is also defined by MSE. It is important to note that the models exhibiting superior performance are those with higher R\({}^{2}\) values and lower MSE values.
## 2 Background and Related Works
### Background
#### 2.2.3 Key-Value Extraction  
A key value in this paper refers to a specific text span--typically a name, date, location, or number--mentioned in a natural language question that corresponds to a property value of a node (or occasionally a relationship) in the graph. These values are essential for filtering query results and are typically used in the WHERE clause of the Cypher query.  
Key values appear explicitly in the user question. They usually represent identifiable facts such as name (\"[PERSON]\"), year (1994), place (\"New York\"), or numerical conditions. Extracted key values usually can be mapped to the correct property in the graph schema during query generation.  
**Example:**: _\"Can you provide a list of actors who appeared in movies directed by [PERSON]?\"_  
Generated Cypher:  
MATCH (a:Actor)-[:ACTED_IN]>(:Movie)<[:DIRECTED]-(d:Director)  
WHERE d.name = \"[PERSON]\"  
RETURN DISTINCT a.nameIn this example, the key value \"[PERSON]\" is mapped to the name property of the Director node. It appears in the WHERE clause to restrict results to movies directed by that specific person.  
Identifying key values accurately is crucial for ensuring that the Cypher query targets the intended entities. Failure to extract them properly may result in incomplete or incorrect query results.
## 3 Results and Discussion
### Characterization of Epoxy Compositions with Polyurethanes
The thermal data are collected in Table 5. For a weight loss of 5%, the thermal degradation temperature (\(T_{5\%}\)) decreased proportionally to the content of polyurethane in the epoxy binder, indicating reduced thermal stability. Based on the values at \(T_{5\%}\), and \(T_{10\%}\), the epoxy with 10 phr of Ref PU had the highest temperature. This is thought to be related to the morphology of PU in the cured epoxy matrix, as shown in Figure 11, which shows  
Figure 9: (a) Storage moduli of epoxy compositions and (b) tan \(\delta\) values. the scanning electron microscopy (SEM) images of each epoxy sample. Figure 11b1 shows a large amount of phase-separated PUs in the epoxy binder with 10 phr of Ref PU. The thermal degradation of the volatile segments of the phase-separated PUs was delayed by the surrounding epoxy matrix and resulted in the highest thermal degradation temperature. Neat epoxy and epoxy compositions with PUs exhibited similar degradation temperatures within a \(\pm\)2% range. This indicates that the content of the flame-retardant phosphor was too low to provide flame retardancy. Surface images of neat resin (Figure 11a1,a2) showed mainly smooth surfaces with partial wavy patterns. However, an epoxy composition with 10 phr of Ref PU (Figure 11b1) or EPPU (Figure 11c1) showed numerous holes resulting from impact-detached PUs. Other images, including those with >10 phr of polyurethane, did not show phase-separated polyurethane but had rough surfaces as the PU content increased. This suggests that the high polyurethane content in the epoxy matrix promoted flexibility, compensating for the embrittlement of the cured epoxy product, but also lowered the tensile and flexural strengths of the epoxy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Neat** & **EPPU** & **EPPU** & **EPPU** & **Ref PU** & **Ref PU** & **Ref PU** \\  & **Epoxy** & **10 phr** & **20 phr** & **30 phr** & **10 phr** & **20 phr** & **30 phr** \\ \hline \(R\) (L-MPa-K–\(1\)-mol\(-1\)) & & & & & 0.008314 & & \\ Storage modulus, \(E^{\prime}\) (MPa at 180 °C) & 1.1 & 1.1 & 0.965 & 0.873 & 0.946 & 0.908 & 0.811 \\ \(T_{g}\) (°C, with DMA) & 160.8 & 153.6 & 142.8 & 138.2 & 146.8 & 136.7 & 129.4 \\ \(V_{c}\) (mol/L) & 0.097 & 0.097 & 0.085 & 0.077 & 0.084 & 0.080 & 0.072 \\ \hline \end{tabular}
\end{table} 
Table 4: Storage moduli and \(V_{c}\) of epoxy compositions containing PUs. phr: parts per hundred resin; DMA: dynamic mechanical analysis. Figure 10: TGA curves for epoxy compositions including PUs. Figure 11: Scanning electron microscopy (SEM) images of epoxy compositions containing Ref PU or EPPU: (**a1**) neat epoxy at 5000\(\times\) magnification, (**a2**) neat epoxy at 10,000\(\times\) magnification; (**b1–b3**) epoxy compositions with 10, 20, and 30 phr Ref PU; (**c1–c3**) epoxy compositions with 10, 20, and 30 phr EPPU.
# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 4 Experimental Results and Discussion
### Experimental Results and Analyses
#### 4.4.5 Comparative Analysis of Simulated Investments
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **FPV** & **TP** & **SR** & **MDD** \\ \hline Buy-and-hold & 142,599.4903 & 42,599.4903 & 1.4195 & 0.15816 \\ Martingale model & 125,125.7755 & 25,125.7755 & 1.1583 & 0.10183 \\ EL-MTSA & 151,876.9812 & 51,876.9812 & 3.7597 & 0.03587 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulated investment experiment. These results collectively underscore the EL-MTSA model's effectiveness in achieving higher returns, managing risk, and delivering consistent performance compared to traditional strategies. Figure 9 illustrates the trajectory of portfolio value changes over time through a line chart, offering a visual representation of the investment outcomes. From top to bottom, the results correspond to the buy-and-hold strategy, the martingale model, and the EL-MTSA model, respectively.
## 2 Materials and Methods
### Verification by Comparison Between Data from Experiments and Simulations  
To verify the reliability of numerical simulation results, this study establishes a small-scale experimental model with dimensions of 40 cm (L) \(\times\) 10 cm (W) \(\times\) 100 cm (H), consisting of a stainless steel building model and XPS insulation material. Experimental data from Condition 2-1 are selected and compared with corresponding numerical simulation data. Both model experiments and numerical simulations use a two-layer wind input method with a downwind direction (westerly wind). Flame deflection and flame spread speed are analyzed to validate the simulation results.  
(1) Flame deflection  
As shown in Figure 15, the flame deflection patterns in numerical simulations and model experiments are generally consistent. Under the influence of oblique backwind, the flame initially deflects to the left during the early stage of development, then gradually spreads upwards. In the model experiment, the flame reaches the top of the building at 351 s, while in the numerical simulation, it reaches the top at 326 s, a difference of only 25 s. Subsequently, at around 410 s in the model experiment, the flame has spread across the entire front face of the wall, similar to the numerical simulation where the flame spreads across the entire building wall around 415 s. Importantly, both the numerical simulation  
Figure 14: Temperature variation map above the middle of the building on the ignition surface at 100 s under different grid sizes.  
and model experiment show that the flame does not spread to the side walls but remains confined to the fire-exposed wall surface.  
(2) Flame Spread Speed  
As shown in Figure 16, the trend of flame spread speed in model experiments and numerical simulations is generally consistent, exhibiting an initial increase followed by a decrease. In the model experiment, the flame spread speed reaches its peak at 70 s, while in the numerical simulation, the peak occurs at 90 s, with a difference of only 20 s between the two.  
When comparing the results of numerical simulations with those of small-scale experiments, it is evident that the nested, coupled, layered simulation results closely align with the small-scale experimental results, validating the effectiveness of nested, layered simulations.  
Figure 16: Comparison of flame spread velocity between model experiment and numerical simulation.  
Figure 15: Comparison of flame spread velocity between model experiment and numerical simulation.
# Production of Alternative Fat from Adipose-Derived Stem Cell from Bovine in 3D Culture
###### Abstract  
Cultivated meat, developed through cell culture technology, is emerging as a promising solution that closely mimics both the flavor and nutrient profiles of conventional meat. One key component that contributes to the flavor of meat is fat content. In this study, bovine adipose-derived stem cells (bADSCs) were cultured for the production of alternative fat in vitro. The expression of mesenchymal stem cell (MSC) markers (CD29, CD73, and CD105) and colony forming efficiency were assessed to characterize bADSCs. bADSCs were differentiated into adipocytes to produce cultivated fat in 2D or 3D culture. The cultivated fat was analyzed by gas chromatography to verify the similarity of the fatty acids of animal-derived fat. Our results show that bADSCs have characteristics of MSC and could differentiate into adipocyte. The ratio of unsaturated fatty acids and saturated fatty acids in cultivated fat and adipose tissue was similar. Adipogenic differentiation of ADSCs using a textured vegetable protein (TVP) scaffold could form the lipid droplets in the TVP. This study demonstrated the establishment of a culture system for the fat production from bADSCs in vitro. The fat produced through bADSCs shows the potential to be used in the composition of hybrid-cultivated meat.  
borine ADSC; adipogenesis; alternative fat; 3D culture Article
# Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction
###### Abstract  
Accurate traffic prediction is crucial to the construction of intelligent transportation systems. This task remains challenging because of the complicated and dynamic spatiotemporal dependency in traffic networks. While various graph-based spatiotemporal networks have been proposed for traffic prediction, most of them rely on predefined graphs from different views or static adaptive matrices. Some implicit dynamics of inter-node dependency may be neglected, which limits the performance of prediction. To address this problem and make more accurate predictions, we propose a traffic prediction model named Time-Evolving Graph Convolution Recurrent Network (TEGCRN), which takes advantage of time-evolving graph convolution to capture the dynamic inter-node dependency adaptively at different time slots. Specifically, we first propose a tensor-composing method to generate adaptive time-evolving adjacency graphs. Based on these time-evolving graphs and a predefined distance-based graph, a graph convolution module with mix-hop operation is applied to extract comprehensive inter-node information. Then the resulting graph convolution module is integrated into the Recurrent Neural Network structure to form an general predicting model. Experiments on two real-world traffic datasets demonstrate the superiority of TEGCRN over multiple competitive baseline models, especially in short-term prediction, which also verifies the effectiveness of time-evolving graph convolution in capturing more comprehensive inter-node dependency.  
traffic prediction; spatiotemporal network; graph convolutional network; time-evolving graphs; graph generation +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Thermal Analyses (DTA/TGA/DTG)  
Thermal analyses were used to confirm the presence of some of the most important mineral components in the JKB, JHC and JOF samples and results are given in Figure S1.  
For all samples, it is evident that in the first temperature interval (25-400 \({}^{\circ}\)C), due to low humidity, significant peaks at DTA curves, as well as weight losses recorded by TGA, were not recorded. The indication that dehydration and releasing of loosely bound surface water occurs is visible across peaks at 117, 132 and 103 \({}^{\circ}\)C at DTG curves for JKB, JOF and JHC, respectively. The exothermic peaks at DTA curves at 478 \({}^{\circ}\)C for JOF and JHC and 493 and 535 \({}^{\circ}\)C for JOF and JKB were obtained in the second temperature interval (400-650 \({}^{\circ}\)C). In the same temperature range, peaks are visible at DTG curves as follows: 477 and 520 \({}^{\circ}\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Chemical Content**} & \multicolumn{4}{c}{**Content, \%**} \\ \cline{2-4}  & **JKB** & **JOF** & **JHC** \\ \hline SiO\({}_{2}\) & 40.09 & 53.62 & 48.61 \\ Al\({}_{2}\)O\({}_{3}\) & 8.68 & 10.52 & 12.94 \\ CaO & 9.08 & 8.73 & 9.33 \\ MgO & 1.86 & 2.15 & 2.74 \\ Fe\({}_{2}\)O\({}_{3}\) & 16.46 & 10.42 & 11.8 \\ Na\({}_{2}\)O & 0.75 & 0.97 & 0.98 \\ K\({}_{2}\)O & 1.46 & 1.94 & 2.10 \\ TiO\({}_{2}\) & 1.01 & 0.84 & 1.01 \\ Mn & 1.05 & 1.17 & 1.14 \\ S & 8.07 & 2.14 & 1.52 \\ Loss on ignition & 10.67 & 6.91 & 7.23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of the tailings of the flotation facility of the Grot Mine [12].  
for JKB, 573 \({}^{\circ}\)C for JOF and 551 \({}^{\circ}\)C for JHC, and adequate weight losses are visible at TGA curves. The shape of the thermal diagrams has been explained in the literature in various ways. According to [24], the peak at 478 \({}^{\circ}\)C for JOF and JHC and 493 \({}^{\circ}\)C for JKB can be explained by the presence of pyrite in the samples and its endothermic decomposition to Fe\({}_{2}\)O\({}_{3}\) and SO\({}_{3}\), which can be estimated as follows:  
\[4\text{FeS}_{2}+15\text{O}_{2}\to 2\text{Fe}_{2}\text{O}_{3}+8\text{SO}_{3} \tag{1}\]  
The presence of a peak at 535 \({}^{\circ}\)C can be explained by additional decomposition of the Fe\({}_{2}\)O\({}_{3}\), which explains why it was not detected by XRD analysis [25]. According to [PERSON] et al. [26], the DTA and DTG peaks in the second temperature range are indications of the presence of the clinochlore and are the result of its thermal degradation. Endothermic peaks with minima at 796, 812 and 775 \({}^{\circ}\)C were visible for all samples in the third temperature range (650-1000 \({}^{\circ}\)C) at DTA curves, i.e., at 787, 806 and 760 \({}^{\circ}\)C at DTG curves for JOF, JKB and JHC, respectively. These peaks are characteristic of the thermal decomposition of carbonates and originated from calcite (CaCO\({}_{3}\)) which was also detected by XRD analyses. Due to the release of CO\({}_{2}\), the decomposition of calcite was followed by weight loss of 5.66, 6.41 and 6.03% for JOF, JKB and JHC, respectively.
# DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index
## 2 Background
### Related Works  
[PERSON] et al. (2018) were the pioneers in conducting research on BDI prediction using the ARIMA model. In the past several years, there has been some research done on BDI prediction. [PERSON] and [PERSON] used a fuzzy neural network model to analyze and forecast BDI (2018), and [PERSON] et al. (2018) forecast BDI as a high-dimensional multivariate regression problem by using deep neural networks. [PERSON] et al. (2018) predicted one-step-ahead BDI values by their proposed three artificial neural networks, specifically a univariate model and two bivariate models, by harnessing historical BDI data and the world price of crude oil. [PERSON] et al. (2018) proposed a decomposition technique for BDI data, and then used a neural network for prediction. [PERSON] et al. (2018) compared econometric models such as ARIMA and GARCH with artificial neural network models such as BPNN, RBFNN, and ELM. The majority of the previous research has treated BDI prediction as regression and short-term prediction tasks. In the present study, by contrast, we conducted research on both short- and long-term prediction of BDI to facilitate ship-owners' short- and long-term decision-making. In addition, in terms of models, the majority of the previous studies have harnessed artificial neural network models and statistical models. Note that in terms of sequential learning for prediction of time-series data, the recurrent neural network is the state-of-the-art method. Moreover, nowadays, deep learning with a deep architecture is a promising approach for accurate prediction.  
Over the course of the past few decades, there have been many outstanding approaches to the prediction of time-series data, such as ARIMA (2018), Support Vector Regressor (SVR) (2018), fuzzy systems (2018), and deep learning (2018; 2019; 2020; 2021). Nonetheless, by those methods alone, accurate prediction of real data is unobtainable, since real, time-series data commonly is volatile and non-stationary. For enhanced prediction, some researchers have proposed data transformation (2018), decomposition (2018; 2019; 2020), and even ensemble methods (2018; 2019). In the present study, we harnessed and combined deep-learning approaches including a deep recurrent neural network (Deep RNN), a long-short-term memory network (LSTM) and a gated rectified unit neural network (GRU) to obtain more accurate prediction results.
# Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals
## 3 Modeling the Arrival Processes
### Marginal Distribution of the Arrivals
#### 3.1.1 Poisson Distribution  
The Poisson random variable has the probability mass function  
\[\mathbf{P}(X=j)=e^{-\lambda}\frac{\lambda^{j}}{j!},\qquad j\geq 0,j\in\mathbb{Z} ^{+}, \tag{4}\]  
with mean value and variance equal to \(\lambda\).
# Detecting Green Mold Pathogens on Lemons Using Hyperspectral Images
## 1 Introduction
When the filter can pass a spectral bandwidth of more than 10 nm or several different bandwidths, as in using color glasses, it is called multispectral imaging. When the passing bandwidths are near 1 nm to 10 nm, and not more than 1 nm, they are called hyper- and ultraspectral imaging, respectively. As the spectral bandwidth of the filter becomes smaller, the physical and chemical elements contained in the object to be tested can be identified with better accuracy because each individual element has its corresponding spectral wavelength. Among the imaging, the hyper- and multispectral imaging have been used very widely for estimating the quality of fruits, vegetables, meat, grains such as surface damage (bruises, chilling, and insect biting), surface quality (firmness, moisture content, hardness, rottenness, and fungi presence), biochemical components, and quality grades in the food and agricultural industries [22; 23; 24; 25]. However, the multispectral imaging can be hardly used for the accurate detection of the green mold pathogen infection in the fruits, as it has a spectral range too wide to trace the presence of the pathogen, though the hyperspectral imaging was used to identify the pathogen presence in tangerine. In this paper, a spectral intensity ratio and the behavior of spectral intensity changes in a hyperspectral imaging are used to estimate the degree of the green mold pathogen infection and to discriminate between fresh and infected lemons. The presence of green mold pathogens on the surfaces of lemons is identified with the use of the dominant wavelengths of the fresh lemon and the green mold pathogen colors, which are found in this paper.
## 2 Materials and Methods
### Binary Hypothesis Testing and Energy Detection  
The received signal samples, denoted as \(\mathbf{y}\), are utilized to construct the test statistic \(T\) according to Equation [32]:  
\[T=\frac{1}{N}\sum_{n=0}^{N-1}|y[n]|^{2} \tag{2}\]  
Subsequently, this test statistic is compared with a threshold \(\gamma\) to facilitate the hypothesis test:  
\[T\lessapprox_{H_{1}}^{H_{0}}\gamma \tag{3}\]  
In Equation (3), \(\gamma\) represents the decision threshold, dictated by the channel's noise variance. It can be predefined or statistically estimated, and it plays a crucial role in achieving accurate and reliable detection results.