esa-sceva/satcom-chunk-collection huggingface dataset:
# Unlocking Wellness: Probiotics as Key Drivers in Functional Food Innovation and Health Promotion
## 5 Technological Innovation of Probiotics in Functional Foods
### Genetic Engineering  
To address the demands of precision nutrition, personalized medicine, and disease treatment, probiotic engineering has garnered increasing attention. Utilizing gene editing technologies, artificial intelligence, and other advanced methods, researchers perform precise genetic modifications and optimizations of probiotics to confer specific functional characteristics [155]. In gene editing applications, recombinant DNA technology has enabled the development of engineered probiotics that are capable of secreting targeted bioactive molecules. For instance, [PERSON] et al. [156] constructed _Lactobacillus paracasei_ LP-A through genetic modification, enabling angiotensin (1-7) secretion. In an aged rat model, this strain increased circulating Ang (1-7) levels by 25% during the acute phase and 32% during the chronic phase while significantly reducing AngII levels (\(p<0.01\)), thereby effectively modulating renin-angiotensin system function. [PERSON] et al. [157] demonstrated that _E. coli_ Nissle 1917 modified with the manganese superoxide dismutase gene via clustered regularly interspaced short palindromic repeats/Cas9 technology exhibited a 50% increase in survival rate in a DSS-induced colitis model, accompanied by a 62% reduction in inflammatory factor IL-6 levels. In terms of process optimization, [PERSON] et al. [158] developed an artificial neural network model that successfully increased total solids' recovery of water-based sesame milk to 95.18%, achieved a live LAB count of 10\({}^{8}\) CFU/mL, and improved sensory scores by 27% in plant-based dairy products through whey permeate integration. These technological innovations expand probiotic application scenarios while advancing their role in precision medicine and functional foods.
# Depth Image Super Resolution Based on Edge-Guided Method
## 3 Experiments and Analysis
### Experimental Results and Comparative Analysis  
To compare the proposed method quantitatively, we chose RMSE, the Peak Signal Noise Ratio (PSNR), Structural Similarity (SSIM) and Percent of Error (PE) [8] to evaluate experimental results. Tables 1-4 show experimental results of 10 test images from Middlebury Stereo database using different SR methods. These methods included: Neighbor Embedding with Locally Linear Embedding (NE + LLE) [33], Neighbor Embedding with Least Squares (NE + LS) [34], Neighbor  
Figure 4: The sensitivity of patch size \(n\) and stride \(d\). Embedding with Non-Negative Least Squares (NE + NNLS) [35], Global Regression (GR) and Anchored Neighborhood Regression (ANR) [36], Adjusted Anchored Neighborhood Regression for Fast Super-Resolution (AANR) [7], Accurate Image Super-Resolution Using Very Deep Convolutional Networks (CNN) [25], the sparse coding method of [PERSON] [21], the modified sparse coding method of [PERSON] [6], and edge-guided method based Markov random field of [PERSON] [8]. To make the tables readable, we marked the top three reconstruction methods in the four tables. The value in bold is the best. The value with single underline is the second best, and this is the value that is closest to the best value in the optimal direction. Likewise, the value with double underlines denote the third best, which is closest to the second best value in the optimal direction. From the tables, we can conclude that the RMSE and PSNR values of our method both rank the first in the test results. There were seven SSIM values ranked first and three SSIM values ranked third using our method in the \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**RMSE \(\times\)4** & **Bowling** & **Artl** & **Aloe** & **Cones** & **Indian** & **Venus** & **Warrior** & **Tsukuba** & **Hand** & **Dove** \\ \hline NE + LLE & 1.841 & 2.567 & 2.370 & 1.380 & 0.820 & 0.654 & 3.645 & 2.861 & 1.828 & 1.004 \\ NE + LS & 1.811 & 2.512 & 2.332 & 1.357 & 0.802 & 0.634 & 3.630 & 2.860 & 1.839 & 1.000 \\ NE + NNLS & 1.812 & 2.523 & 2.326 & 1.356 & 0.803 & 0.637 & 3.627 & 2.867 & 1.820 & 1.012 \\ GR & 1.986 & 2.838 & 2.585 & 1.492 & 0.906 & 0.726 & 3.864 & 3.142 & 2.004 & 1.088 \\ ANR & 1.807 & 2.634 & 2.429 & 1.393 & 0.836 & 0.662 & 3.723 & 2.946 & 1.885 & 1.022 \\ AANR & 1.855 & 2.713 & 2.478 & 1.456 & 0.855 & 0.674 & 3.707 & 2.972 & 1.925 & 1.043 \\ CNN & 2.238 & 3.798 & 3.245 & 1.778 & 0.987 & 0.845 & 4.424 & 3.505 & 2.174 & 1.214 \\ Yang & 2.112 & 3.361 & 2.865 & 1.514 & 0.908 & 0.733 & 4.360 & 3.186 & 2.026 & 1.098 \\ Zeyde & 1.803 & 2.494 & 2.329 & 1.338 & 0.798 & 0.635 & 3.620 & 2.844 & 1.832 & 0.989 \\ Xie & 1.766 & 2.935 & 2.583 & 1.240 & 0.771 & 0.617 & 4.081 & 3.009 & 1.926 & 1.010 \\ Ours & **1.662** & **2.368** & **2.242** & **1.230** & **0.705** & **0.541** & **3.325** & **2.768** & **1.796** & **0.935** \\ \hline \end{tabular}
\end{table} 
Table 1: RMSE values on the Middlebury Stereo database with scaling factor of 4.
# Analysis of the Efficiency of Slope Evacuation for Dense Crowds in Urban Street-Type Public Spaces
## 8 Limitations and Future Work  
The model constructed in this paper applies to tourist and leisure neighborhoods, which often consist of street-type spaces and a dense flow of people, with the risk of crowd congregation. Despite its contributions, the study has several limitations: this research focuses on theoretical insights derived from numerical simulations. Due to the limitations of the simulation software's functionality, the numerical models used in this study simplify certain conditions in the real world. The simulation does not take into account road surface conditions and terrain features such as steps or sudden elevation changes, which may have an impact on the movement process of pedestrians. While the study includes population groups with different physical abilities, it does not account for cultural, social, or behavioral diversity, which may affect evacuation patterns in heterogeneous populations. This simplification may limit the applicability of the results to diverse populations.  
Future research should include a broader range of pedestrian characteristics, such as cultural backgrounds and social groups. Including more diverse populations will help to validate the generalizability of the study results. Additionally, psychological factors such as panic reactions and risk perceptions should be explored to enhance evacuation strategies and emergency management effectiveness.  
Conceptualization, K.L. and T.Y.; methodology, K.L. and T.Y.; validation, K.L., T.Y. and X.L. ([PERSON]); formal analysis, K.L., T.Y. and X.L. ([PERSON]); investigation, T.Y. and X.L. ([PERSON]); data curation, X.L. ([PERSON]) and X.Y.; writing--original draft preparation, T.Y.; writing--review and editing, all authors; project administration, K.L. and T.Y.; funding acquisition, K.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results
### Comparative Analysis of Modeling Results
#### 3.4.1 PCA-PLSR Modelling Results Analysis  
As shown in Table 4 and in Figures 5 and 6, the PCA-PLSR model using ASD spectral data and GF5 AHSI hyperspectral images have a certain predictive ability with R\({}^{2}\) in the range 0.50-50.682, RMSE in the range 61.65-124.85 g/kg, and RPD in the range 1.13-1.65. Among them, the predicted values of R\({}^{2}\), RMSE, and RPD using ASD spectral data are slightly better than those of GF5 AHSI hyperspectral images. Compared to laboratory data, hyperspectral data have a relatively low signal-to-noise ratio and are susceptible to limitations such as atmospheric attenuation, sensor radiation, and soil background environment.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Particle Size**} & \multicolumn{2}{c}{**ASD**} & \multicolumn{3}{c}{**GF5 AHSI**} \\ \cline{3-8}  & & **R\({}^{2}\)** & **RMSE (g/kg)** & **RPD** & **R\({}^{2}\)** & **RMSE (g/kg)** & **RPD** \\ \hline \multirow{3}{*}{PCA-PLSR} & Sand & 0.644 & 112.23 & 1.23 & 0.573 & 124.85 & 1.28 \\  & Silt & 0.656 & 86.51 & 1.65 & 0.557 & 99.67 & 1.24 \\ \cline{1-1}  & Clay & 0.682 & 61.65 & 1.64 & 0.505 & 72.75 & 1.13 \\ \cline{1-1}  & Sand & 0.853 & 81.55 & 1.65 & 0.857 & 72.46 & 2.23 \\ \cline{1-1} PCA-CNN & Silt & 0.816 & 69.77 & 1.51 & 0.837 & 64.43 & 1.82 \\ \cline{1-1}  & Clay & 0.948 & 26.51 & 3.33 & 0.908 & 31.24 & 3.13 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analysis of model validation accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Source of Data**} & \multicolumn{2}{c}{**ASD**} & \multicolumn{2}{c}{**GF5 HSI**} \\ \multirow{2}{*}{**Indicators**} & \multirow{2}{*}{**Contribution Rate/\%**} & \multicolumn{2}{c}{**Cumulative**} & \multirow{2}{*}{**Contribution Rate/\%**} & \multicolumn{2}{c}{**Cumulative**} \\  & & & **Contribution Rate/\%** & & **Contribution Rate/\%** \\ \hline
1 & 90.87 & 90.87 & 69.97 & 69.97 \\
2 & 4.51 & 95.38 & 17.79 & 87.76 \\
3 & 3.52 & 98.9 & 7.43 & 95.19 \\
4 & 0.42 & 99.32 & 2.54 & 97.73 \\
5 & 0.34 & 99.66 & 1.09 & 98.83 \\
6 & 0.17 & 99.83 & 0.35 & 99.18 \\
7 & 0.06 & 99.89 & 0.24 & 99.42 \\
8 & 0.04 & 99.92 & 0.18 & 99.6 \\
9 & 0.02 & 99.94 & 0.09 & 99.69 \\
10 & 0.01 & 99.95 & 0.07 & 99.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Principal component contribution table.  
In terms of sand, silt, and clay content, the prediction values of clay using ASD and GF5 AHSI data are higher than those of sand, or silt, with an R\({}^{2}\) of 0.682, RMSE of 61.65 g/kg, and RPD of 1.64 for ASD, and an R\({}^{2}\) of 0.505, RMSE of 72.75 g/kg, and RPD of 1.13 for GF5 AHSI, reflecting that low-reflection clay is better predicted under controlled laboratory. Clay often has a strong water absorption ability, and its moisture content has a significant impact on its spectral characteristics, which can provide additional spectral information that helps to improve prediction accuracy. For sand and silt, their moisture content has a relatively small impact on spectral characteristics [45].
# IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
## 4 Adjustment Approaches of IPCB Geometry Configuration
### Vertical Adjustment of an IPB  
An IPB usually keeps its flight altitude by maintaining the balance between gravity and buoyancy, as Equation (10) shows:  
\[\mathbf{B}=\mathbf{G} \tag{10}\]  
In Equation (10), \(\mathbf{B}\) represents its buoyancy, and \(\mathbf{G}\) represents its gravity. Buoyancy \(\mathbf{B}\) is related to the atmospheric density, volume of the balloon, and gravitation acceleration, as shown in Equation (11).  
\[\mathbf{B}=\rho_{air}(h)V\mathbf{g} \tag{11}\]  
In Equation (11), \(V\) represents the volume of the balloon, \(\mathbf{g}\) represents gravitation acceleration, \(h\) represents the flight altitude of the IPB, and \(\rho_{air}(h)\) represents the air density at altitude \(h\). The air density is not a constant, and it varies in a wide range. According to the standard atmosphere model (U.S. Standard Atmosphere, 1976), its change with altitude is illustrated in Figure 3.  
Gravity \(\mathbf{G}\) is related to helium mass, air mass, and the masses of others, as shown in Equation (12).  
\[\mathbf{G}=(m_{He}+m_{air}(h)+m_{other})\mathbf{g} \tag{12}\]  
In Equation (12), \(m_{He}\) represents the helium mass in the main helium bag, \(m_{air}(h)\) represents the air mass in the air ballonet at altitude \(h\), \(m_{other}\) represents the gross mass of balloon envelop, cable, parachute, gondola, balloon controller, payloads and other attachments.  
So if a flight altitude decline is needed, an IPB can pump air into its air ballonet, which will increase its air mass \(m_{air}(h)\) and gravity \(\mathbf{G}\), making \(\mathbf{G}\) greater than \(\mathbf{B}\). The change in air mass can be estimated by Equation (13).  
\[\Delta m=(\rho_{air}(h_{s})-\rho_{air}(h_{d}))\,V \tag{13}\]  
In Equation (13), \(h_{s}\) and \(h_{d}\) represent the flight altitude before adjustment and after adjustment, respectively. Due to the assumption (1) in this section, the time cost of the flight altitude adjustment \(\Delta t\) can be estimated by Equation (14).  
\[h_{s}-h_{d}=v_{\text{sin}\,\text{k}}\Delta t \tag{14}\]  
Figure 3: Atmosphere density from 0 km to 30 km.  
In contrast, if a flight altitude ascent is needed, the IPB should release air from its air ballonet, and the time cost can be estimated by Equation (15).  
\[h_{s}-h_{d}=v_{\text{rise}}\Delta t \tag{15}\]
# Dealing with Low Quality Images in Railway Obstacle Detection System
## 4 Improved Laplacian Edge-Based Blur Detection Method  
It is demonstrated that our application in railway systems requires an algorithm that is both fast and simple while also being sufficiently accurate. As we have already described, the images collected from different cameras are analyzed by SMART2 vision software, which performs object classification and object distance estimation. For these actions, the training of a neural network, which is directly responsible for the decision/notification of class and distance, as well as other important parameters, within the DSS, must be performed on the high-quality image dataset.  
Based on the previously conducted analysis, the use of the Laplace operator is imposed as the ideal candidate for the selection. However, it is discovered that in the presence of noise, this operator can produce incorrect results, hence the focus of this paper is first on noise reduction. Another disadvantage is the manual threshold adjustment, which is addressed below in more detail. Finally, it should be mentioned that during the process of generating a database of clear (non-blur) images that are used to train the neural networks for OD, it is less problematic if an image is wrongly classified as blurred and rejected even if it actually is not than vice versa. The reason lies in the fact that we generated more than enough experimental images for the training set, so we can allow ourselves to emphasize quality training of machine learning models. This fact should be also taken into account while determining the threshold, by pushing the boundaries slightly toward non-blurred images.
# Detection of Mineralization Zones Using Aeromagnetic Data
## 3 Methodology
### First Vertical Derivative (FVD) Method  
The FVD map is critical for detecting near-surface magnetic characteristics associated with geological formations; it resembles the upward continuation map and is used toenhance local anomalies, while attenuating regional anomalies because it is sensitive to shallow magnetic sources. Long wave forms caused by magnetic causative bodies were removed using the first vertical derivative [22]. The zero-contour line on the FVD map helps to clarify the structural and lithological components.
## 2 Heat Balance for an Electric Bus
### Heat Balance Method for Vehicles  
The heat balance method is a common method for calculating heating and cooling loads in a given space or zone [17]. Based on this theory, all heating loads can be written in a comprehensive equation. Four distinct processes can be assumed for the heat balance into an electric bus, as follows:  
1. Outdoor surface heat balance.
2. Indoor surface heat balance.
3. Wall conduction.
4. Inside zone air heat balance.  
For an optimal design of the heating system inside the bus, it is crucial to accurately determine the heating loads, because underestimating them may lead to thermal comfort problems, while an overdesign may increase the manufacturing and operating costs for the vehicle [18].  
Various heat loads must be considered, but there are eight main heat components to estimate in the modeling and calculation of a vehicle cabin's thermal balance [8; 18; 19; 20], as follows (Figure 1):  
1. Metabolic heat load generated by the passengers--\(\dot{Q}_{met}\).
2. Solar heat loads (caused by the Sun's radiation) with three components--\(\dot{Q}_{sun}\), as follows:  
\(\circ\)  Direct radiation from the Sun;  
\(\circ\)  Diffused radiation due to the dispersion in the atmosphere;  
\(\circ\)  Reflected radiation from the ground and other surrounding surfaces.
3. Heat load due to the electric motors--\(\dot{Q}_{met}\).
4. Heat load generated by the traction battery--\(\dot{Q}_{hot}\).
5. Heat load due to the fresh air supplied by ventilation, which is necessary for passengers' needs--\(\dot{Q}_{ven}\).
6. Heat flow changed with the ambient load, due to the temperature gradient between the bus structure (windows, walls, floor, roof, and doors) and the exterior environment--\(\dot{Q}_{amb}\).
7. Heat flow changed with the ambient load, due to the doors opening during stops in stations--\(\dot{Q}_{door}\).
8. Heat load generated by the heating system from the vehicle in order to assure the thermal comfort for passengers--\(\dot{Q}_{bsyst}\).  
Figure 1: Schematic representation of heat loads: a model for a typical electric bus.  
Thus, considering the above heat components, the heat load model for an electric bus is given by Equation (4):  
\[\dot{Q}_{total}=\dot{Q}_{met}+\dot{Q}_{sum}+\dot{Q}_{mot}+\dot{Q}_{bat}+\dot{Q}_{ ven}+\dot{Q}_{amb}+\dot{Q}_{doors}+\dot{Q}_{lbsyst}. \tag{4}\]
## 2 Damage Identification Approach
### Damage Identification Method  
ANNs have an excellent ability to learn and describe highly nonlinear and strongly coupled relationships between multiple-input and multiple-output parameters [49; 50]. The basic structure of a neuron is shown in Figure 1.  
In Figure 1,\(x_{n}\) is the \(n\)th input signal, \(w_{nj}\) is the connection weight, \(b_{j}\) is the threshold value of the \(i\)th neuron, and \(y_{j}\) is the output signal of the \(i\)th neuron. The neuron output signal \(y_{j}\) is calculated by the activation function, as follows:  
\[y_{j}=f(\sum_{i=1}^{n}(w_{ij}x_{i})-b_{j}) \tag{8}\]  
For a damage identification process, the input signals can be damage indices, and the output signals can be damages in structural elements. In this study, we set the damages as the difference of  
Figure 1: Schematic of the basic structure of a neuron. Figure 1 is reproduced from reference [50].  
the elastic modulus between the damaged and intact structural elements. The output signal can be described as follows:  
\[\Delta\mathrm{E}_{i}=\mathrm{E}_{di}/\mathrm{E}_{ui} \tag{9}\]  
where \(\mathrm{E}_{di}\) and \(\mathrm{E}_{ui}\) are the elastic modulus of the \(i\)th damaged element and intact element, respectively. We consider a popular activation function \(f\) for BP networks, the sigmoidal function, in this work:  
\[f(x)=\frac{1}{1+e^{-cx}} \tag{10}\]  
Here, \(c\) is selected as 1.  
The BP neural network is a multilayer feed-forward network, whose basic structural network scheme is shown in Figure 2.  
The number of input and output neurons (\(n\), \(m\)) and the hidden layer (\(k\)) are first determined based on the engineering problem of interest. In our case, the total number of damaged elements and the total number of damage indices are known. The total number of units in the \(i\)th hidden layer (\(h_{i}\)) is determined by the following function:  
\[h_{i}=n-1 \tag{11}\]  
Then, the connection weights (\(\mathbf{w_{ij}}\)) and thresholds (\(\mathbf{b_{j}}\)) are initialized to values between \(-1\) and \(+1\) randomly.  
Training networks is a process of repeatedly updating the \(\mathbf{w_{ij}}\) and \(\mathbf{b_{j}}\) based on the difference between the network outputs and the desired outputs until the output error is within a certain range.  
The most interesting characteristic of ANNs is the capability to familiarize themselves with problems by training and, after sufficient training, to be able to solve unknown problems of the same class. Thus, we can utilize the trained network to identify the structural damage according to the measurement results.
## 2 Upper Bound Solution
### Block Failure Mode  
Figure 1 schematically illustrates the block failure mechanism when a vehicle ascends a slope with angle \(\beta\). In Figure 1, surface \(B\) represents the bottom failure plane, while surfaces \(S_{1}\) and \(S_{2}\) are the lateral failure planes. The dimensions \(H\), \(L\), and \(D\) denote the height, length, and width of a single-track system, respectively. The force \(W_{g}\), aligned with the gravitational force, represents the weight transmitted from the upper vehicle. The force \(F_{b}\), parallel to the slope, indicates the soil thrust. In the block failure mode, the brick-shaped soil block moves at velocity \(V\).  
In these conditions, the total external power \(P\) is given by  
\[P=\left(F_{b}+W_{g}\sin\beta\right)V, \tag{2}\]  
which corresponds to the sum of inner products between external forces (\(W_{g}\) and \(F_{b}\)) and velocity \(V\). As there exist one bottom and two side failure planes, the total dissipation rate \(D\) is expressed as  
\[D=D_{s}+D_{b}, \tag{3}\]  
where \(D_{b}\) and \(D_{s}\) represent plastic dissipation rates along the bottom failure surface \(B\) and side failure surfaces (\(S_{1}\) and \(S_{2}\)), respectively. Along surface \(B\), plastic dissipation rate \(D_{b}\) is written as  
\[D_{b}=c_{u}LDV. \tag{4}\]  
Along two side failure surfaces, plastic dissipation rate \(D_{s}\) is  
\[D_{s}=2c_{u}LHV. \tag{5}\]  
Thus, the total dissipation rate \(D\) is expressed as  
\[D=D_{b}+D_{s}=c_{u}LDV+2c_{u}LHV=c_{u}LV(D+2H). \tag{6}\]  
By assuming that all energy is dissipated to generate plastic deformation, the total external power \(P\) is identical to the total (internal) plastic dissipation rate \(D\); thus, equating \(P=D\) gives the soil thrust \(F_{b}\) as  
\[F_{b}=c_{u}L(D+2H)-W_{g}\sin\beta, \tag{7}\]  
which shows that the soil thrust from the block failure mode is deterministic.
# _photonics_
## 3 FSO Channel Modeling
### Atmospheric Attenuation  
The atmospheric transmittance of the laser beam under clear sky conditions (absence of clouds, rain etc.) is also dependent on the elevation angle and can be given by Equation (7) [46]:  
\[L_{a}=L_{zen}(\frac{1}{\cos(\xi)}), \tag{7}\]  
where \(L_{zen}\) is the vertical link transmittance and \(\zeta\) (rad) is the zenith angle of the link. It is evident that, for low elevation angles, atmospheric attenuation is higher, since the light has to travel a longer path through the atmosphere to reach the receiver.