esa-sceva/satcom-chunk-collection huggingface dataset:
# Internal Force Analysis of Buried-boring Piles in the Yuanzishan Landslide
## 4 Influence of Parameter Changes on the Internal Force of the Buried-Boring Pile  
### Influence of Depth of Buried Length above Slip Surface on the Internal Force of the Pile  
The internal force of the bored pile is an important parameter for anti-slide pile structure design [23]. In order to study how the depth of the buried-boring pile head from the surface influenced the internal force of the buried-boring pile, the pile head depths were varied at 0, 2.5, 5, and 7.5 m, with corresponding pile length above the slip surface values of 10, 7.5, 5, and 2.5 m, respectively. The diameter of the bored pile was 0.5 m, the pile length below the slip surface was 5 m, and all the other parameters were the same as for the buried-boring pile analysis of the Yanzishan landslide. The internal force of the buried-boring pile was calculated for the different cases (Figure 6).  
Figure 5: Relationship between the driving force of the pile-shared ratio and the buried depth ratios.  
The maximum internal force that can be withstood by the bored pile and the soil reaction force are the critical parameters for the failure of an anti-slide pile [23]. To study the variation of the soil reaction  
Figure 6: Calculated values of the internal forces for buried-boring piles of various buried depth with D = 0.5 m: (**a**) shear force; (**b**) bending moment; and (**c**) soil reaction force.  
force and the maximum bending moment with buried depth, a sliding depth ratio, \(\mathrm{R_{L}}\)[38], was defined as the pile length above the slip surface, \(h\), divided by the pile length, \(L\), where \(L=h_{4}+h\). When \(h=5\) m, \(L=10\) m, and \(\mathrm{R_{L}}=0.5\), then the maximum soil reaction force \(\mathrm{S_{max}}=413\) kPa (Figure 6c) and the maximum bending moment \(\mathrm{M_{max}}=469\) kN.m (Figure 6b). The relationship between the maximum internal force for varying buried depth and the sliding depth ratio was also investigated (Figure 7).
## 6 Physical Simulation for Weakening Mine Pressure by Key Strata Fracturing  
### Physical Simulation Model and Key Strata Fracturing Scheme  
Similar simulation studies are carried out according to the similarity simulation theory, and the weakening effect of overlying key strata fracturing on the mine pressure behavior is further explored. Two sets of similar models are produced: the geometric similarity ratio, the density similarity ratio, the stress similarity ratio, and the Poisson's ratio, which are 1:200, 1:1.60, 1:320, and 1:1, respectively.  
Similar simulation experiments mainly use the primary materials of sand, gypsum, calcium carbonate, and water. Different proportions of these materials are mixed and laid in the model frame to simulate the fracture and movement characteristics of the overlying strata above the coal mining face. Based on the above-mentioned rock mechanics test results, a proportioning experiment was conducted on similar simulated materials, and the material proportioning was ultimately obtained, as shown in Table 2.  
Model 1 is a control group in which the key strata are not fractured, as shown in Figure 11a. Model 2 is the experimental group in which the key strata will be fractured before mining, and an embedded hose with holes is placed in KS2 and KS3 in the production process. Red water is injected into KS2 and KS3 through the grouting pump through the small hole on the hose, which can simulate the weakening effect of the key strata, as shown in Figure 11b,c. During this process, the flow rate of the injected water is constant to realize the weakening effect on the material parameters of the key stratum. On this basis, the thin film stress distributed sensor monitors the goaf stress after the overlying strata collapse. The stress test film size is 6 cm \(\times\) 6 cm, the size of a single measurement point microelement is about 1.5 mm \(\times\) 1.5 mm, and the distribution density of stress test points is 36/cm\({}^{2}\). The range of a single stress measurement point microelement is 1000 Pa, and 1600 stress measurement points are integrated with a test film, as shown in Figure 11d. The weakening effect of key strata fracturing on mine pressure behavior is analyzed by comparing the fracture characteristics of key strata and the goaf stress in models 1 and 2.  
Figure 11: Test plan and related monitoring instruments. (**a**) Key strata unfractured; (**b**) key strata fractured; (**c**) prefabricated hoses and water injection fracturing; (**d**) vertical stress test with thin film stress tester in goaf.
## 4 Numerical Examples
### TPS Model

Table 3: Interval parameter bounds of TPS model. Figure 5: TPS model: 1, 2, and 3 respectively represent node 1, node 2, and node 3. Figure 6: TPS temperature response at uncertainty level 5%: (**a**) Node 1; (**b**) Node 2; (**c**) Node 3. Table 4 shows the bounds and relative errors of the temperature response of Node 2 at different times in the TPS model. As the uncertainty and time increase, the AIFEM's relative error increases slightly. When the uncertainty reaches 10%, the maximum error of the AIFEM is 3.08%, and the AIFEM is still valid for uncertainty analysis. The accuracy of the AIFEM in uncertain TPS transient heat transfer analysis was verified by comparing the relative errors.
# Design of a Compact Planar Magnetic Levitation System with Wrench-Current Decoupling Enhancement
## 3 Working Principle and Modeling
### Magnetic Flux Density Computation  
It is difficult to find the analytical solutions to Equations (2) and (3) for the magley system in real-time control. Alternative solutions, numerical methods, such as the Gaussian quadrature method [28], can be applied to solve for forces and torques in real time. However, it could not be reimplemented on the proposed system due to limitations on the computation power of the controller in this work. In addition to the numerical method, the magnetic nodes approach [25] can be used to find the magnetic flux density \(\mathcal{B}_{c(p)}\) generated by the magnet mover at any location in the mover frame expressed as  
\[\mathcal{B}_{k(p)}=\frac{(-1)^{k-1}B_{\mathrm{r}}}{4\pi}\begin{bmatrix}\ln(-y_ {r}+\|\vec{r}_{p}-\vec{r}_{k}\|_{2})\\ \ln(-x_{r}+\|\vec{r}_{p}-\vec{r}_{k}\|_{2})\\ \arctan(\dfrac{x_{r}y_{r}}{2\|\vec{r}_{p}-\vec{r}_{k}\|_{2}})\end{bmatrix}, \tag{4}\]  
where \(x_{r}=x_{p}-x_{k},y_{r}=y_{p}-y_{k},z_{r}=z_{p}-z_{k}\), and \(\|\vec{r}_{p}-\vec{r}_{k}\|_{2}=\sqrt{(x_{r})^{2}+(y_{r})^{2}+(z_{r})^{2}}\).  
In this work, the disc magnet was modeled with 116 nodes for both top and bottom levels. Therefore, there were 232 nodes in total. The total magnetic flux density at arbitrary point \(p\) could be computed by  
\[\mathcal{B}_{m(p)}=\sum_{k=1}^{232}\mathcal{B}_{k(p)}. \tag{5}\]  
The equations shown above were all expressed in the mover coordinate system. Later, the obtained magnetic flux density was transformed into the world coordinate system. Numerical results of the magnetic flux density of the 4-inch disc magnet were verified with simulation results in ANSYS Maxwell as shown in Figure 9. The magnetic flux density components were computed at 20 mm above the center of the magnet along the \(x\) axis from \(-200\) mm to 200 mm.  
With the obtained magnetic flux density components, the Lorentz forces and torques could be computed for each square coil with the following coil segmentation strategies.
# Unlocking Wellness: Probiotics as Key Drivers in Functional Food Innovation and Health Promotion
## 5 Technological Innovation of Probiotics in Functional Foods
### Multidimensional Environmental Adaptation Techniques  
Technological innovations in probiotic applications focus on enhancing strain environmental adaptability and optimizing stress tolerance through multidimensional strategies [151]. Studies demonstrate that probiotics can induce cross-protective effects through pretreatment. For example, _L. plantarum_ KLDS 1.0628, heat-acclimatized at 45 \({}^{\circ}\)C for one hour, showed a 31.38-fold increased tolerance to subsequent high-temperature challenges. The proportion of unsaturated fatty acids in its cell membranes significantly increased from  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \
\begin{tabular}{c} **Encapsulation** \\ **Materials** \\ \end{tabular} & **Advantages** & **Disadvantages** & **Reference** \\ \hline \multirow{4}{*}{Proteins} & \multirow{2}{*}{Whey protein} & High nutritional value, good & High temperature variability, limiting & [143] \\  & & emulsification, strong solubility & hot working applications & [143] \\ \cline{2-5}  & \multirow{2}{*}{Gelatin} & Biodegradability, excellent film-forming & High purification costs, low & \\  & & properties, strong oxygen resistance & concentration gelatin chemical & [144] \\  & & properties unstable & & \\ \hline Lipids & Fatty acids &
\begin{tabular}{c} Biocompatibility, low-temperature \\ processing adaptability \\ \end{tabular} & Weak mechanical strength, poor & [145] \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._32.7% (control group) to 47.5%, suggesting enhanced thermal stability through membrane fluidity regulation [152]. At the genetic level, membrane reorganization constitutes a key acid tolerance mechanism. [PERSON] et al. [153] subjected _B. coagulans_ to atmospheric pressure room-temperature plasma (ARTP) mutagenesis combined with 40 days of adaptive laboratory evolution, obtaining the artp-aleBC15 mutant strain. This strain exhibited a 22.4% survival rate after three hours of simulated gastric treatment at pH 2.5, 4.8-fold higher than the wild-type strain, with significantly improved cell membrane permeability and hydrophobicity. Research indicates that strain-specific pretreatment strategies enhance environmental adaptation. [PERSON] et al. [154] demonstrated that _L. plantarum_ pre-acclimated to 2% NaCl achieved a maximum growth rate of 0.52 OD/h, a 40% increase over untreated controls, with the cell length extending from 2.8 \(\upmu\)m to 4.2 \(\upmu\)m to cope with osmotic stress. These techniques significantly increase probiotic functional stability in food processing, gastrointestinal, and medical applications, providing theoretical foundations and technical pathways for the development of precise probiotic formulations.
# Application of Riemannian Seismic Ray Path Tracing in Salt Dome Prospecting
## 5 Discussion
### Strengths and Weaknesses of the Proposed Methodology
#### 5.2.1 Strengths  
1. **Accurate modelling of Complex Geometries.** * Precise Representation: Differential geometry allows for the accurate modelling of the intricate shapes and structures of salt domes, which are often highlyirregular and complex. This precision is crucial for understanding the subsurface environment.
* Curved Space Analysis: The use of differential geometry facilitates the analysis of curved spaces, providing a more realistic representation of geological formations compared to traditional Euclidean methods.
2. **Enhanced Data Interpretation.** * Detailed Structural Insights: By employing differential geometry, geoscientists can gain deeper insights into the structural complexities of salt domes, improving the interpretation of seismic data.
* Geophysical Data Integration: Differential geometry provides a robust framework for integrating various types of geophysical data, such as seismic, gravity, and magnetic data, leading to a more comprehensive understanding of the subsurface.
3. **Improved Seismic Imaging.** * Advanced Algorithms: Differential geometry enables the development of advanced algorithms for seismic imaging, such as those used in ray tracing and wave propagation modelling. These algorithms can more accurately predict the paths of seismic waves in complex media.
* Enhanced Resolution: The application of differential geometric methods can lead to enhanced resolution in seismic imaging, allowing for better identification and delineation of salt domes and associated structures.
4. **Theoretical Foundations.** * Rigorous Mathematical Framework: Differential geometry provides a rigorous mathematical framework for modelling and analysing geological structures. This foundation ensures that the methods used are theoretically sound and can be systematically improved and validated.
## 2 Mathematical Model
### The Motion Governing Equation of Gas Turbine Rotor with Blade off
\(\mathbf{M}\) and \(\mathbf{G}\) are the constant components of the global mass matrix and gyroscopic matrix, respectively, while \(M_{\text{b}}(t)\) and \(\mathbf{G}_{\text{b}}(t)\) are the corresponding time-varying components due to blade-loss, respectively. \(\mathbf{K}\) denotes the stiffness matrix. \(\mathbf{F}(t)\) is the sudden unbalance excitation when blade off occurs in a blade-disk. The structural damping matrix, \(\mathbf{C}\), is obtained by using the Rayleigh damping formulation [38], which can be written as  
\[\mathbf{C}=\alpha\mathbf{M}+\beta\mathbf{K} \tag{15a}\] \[\alpha=\frac{4\pi\omega_{\text{n1}}\omega_{\text{n2}}(\zeta_{1}\omega_{\text{ n2}}-\zeta_{2}\omega_{\text{n1}})}{(\omega_{\text{n2}}^{2}-\omega_{\text{n1}}^{2})}, \ \beta=\frac{\zeta_{2}\omega_{\text{n2}}-\zeta_{1}\omega_{\text{n1}}}{\pi( \omega_{\text{n2}}^{2}-\omega_{\text{n1}}^{2})} \tag{15b}\]  
where \(\omega_{\text{n1}}\) and \(\omega_{\text{n2}}\) are the first- and second-order undamped natural frequencies calculated; \(\xi_{1}\) and \(\xi_{2}\) represent the first two-order modal damping ratios, which are set to 0.02. The obtained sparse matrices \((\mathbf{M},\mathbf{K},\mathbf{G})\) can improve the calculation speed for vibration responses. However, the obtained mode shapes are not smooth enough because the continuous rotor is discrete in a finite number of elements. Similar to other modeling methods, such as the lumped mass method, finite element method with beam theory, etc., [3; 17; 18], this is an inevitable defect, but it will not affect the calculation accuracy of natural frequency, mode, and response.
# Precise Positioning of BDS, BDS/GPS: Implications for Tsunami Early Warning in South China Sea
## 2 Real-Time Kinematic Precise Positioning Performance of BDS in South-East Asia
This demonstrates the known fact that, for reliable near-field tsunami early warnings, a trustworthy slip distribution is favorable. From this synthetic inversion test, we conclude that a BDS (or a BDS/GPS) network with real-time processing, if deployed at the Luzon Island, will be able to contribute to a fast and reliable tsunami source inversion at the Manila trench.
# Performance Evaluation of UAV-Enabled LoRa Networks for Disaster Management Applications
## 5 Conclusions  
In this paper, a new network system called the UAV-enabled LoRa network has been designed. The main objective of the system is to extend the network coverage offered by a BS to a set of mobile GNs using relaying gateways installed on UAVs. This objective is achieved thanks to a double-layer network that consists of a LoRaWAN segment for the communication between the GNs and the gateways and a WiFi ad hoc network for all the communications between the gateways and the BS. The core of the system is the UAV-enabled LoRa network mobility algorithm, specifically designed to adapt as much as possible to the ever-changing position of the GNs. It is shown that the activation of all the UAV-enabled LoRa network algorithms (VSF + CRM + MP) allows us to obtain an AE-PRR above 0.9 in a representative scenario with three teams of GNs and 12 UAVs. An acceptable AE-PRR, between 0.8 and 0.9, is obtained with 8 or 10 UAVs. We anticipate that our work would be helpful in the practical realization of such disaster management networks.  
Although the presented architecture shows promise, there are some additional features that can be explored to continue and possibly upgrade what was accomplished in the present work. For instance, UAVs are subject to strict energy constraints. The impact of such constraints is of great practical interest. The developed algorithm may need to be modified to limit as much as possible unnecessary movements and transmissions. A future iteration of the system might also consider the usage of fixed-wings drones to increase the maximum air-time. Moreover, the impact of outdated channel estimation can also be investigated for practical systems. These challenging tasks are left for future studies.  
**Author Contributions:** Conceptualization: O.A.S.; Data Curation: I.K.; Writing Original Draft: P.U.; Writing--review and editing: O.A.S., I.K., A.A.; Funding acquisition: P.U.; Project Administration: O.A.S.; Validation and Visualization: A.A.; Supervision and Resources: P.U. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research work is supported by the SUT reseach and development fund.  
Figure 8: Average percentage of buffered packets in a scenario with 3 teams of 20 GNs.  
**Acknowledgments:** The authors would like to say thanks for the reviewers and editors for reviewing this manuscript.  
**Conflicts of Interest:** The authors declared no conflict of interest.
## 4 Experiments and Results
### Results
#### 4.4.3 Results of DFU-Helper Framework  
To demonstrate the effectiveness of the experiments, we obtained images from several published articles [4; 57; 58] and we tried to compare if the findings of the model correlated with the descriptions given in the articles we are using as baseline. We present the results  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Metrics** & **Our SNN** & **[PERSON] et al. [53]** & **[PERSON] et al. [54]** & **[PERSON] et al. [55]** & **[PERSON] et al. [56]** \\ \hline
**Rank** & **BEST** & 1 st & 2 nd & 3 rd & 4 th \\ Macro F1-Score & **0.6455** & 0.6216 & 0.6077 & 0.5959 & 0.5691 \\ \hline None F1-Score & **0.7607** & 0.7574 & 0.7453 & 0.7157 & 0.7466 \\ \hline Infection F1-Score & 0.6348 & 0.6388 & 0.5917 & **0.6714** & 0.6281 \\ \hline Ischemia F1-Score & **0.6189** & 0.5282 & 0.558 & 0.4574 & 0.467 \\ \hline Both F1-Score & **0.6009** & 0.5619 & 0.5359 & 0.539 & 0.4347 \\ \hline Macro Precision & **0.6507** & 0.614 & 0.6207 & 0.5984 & 0.5814 \\ \hline Macro Recall & **0.6697** & 0.6522 & 0.6246 & 0.5979 & 0.6104 \\ \hline W.Avg. Precision & **0.7136** & 0.7009 & 0.6853 & 0.6730 & 0.6800 \\ \hline W.Avg. Recall & **0.6931** & 0.6856 & 0.6657 & 0.6711 & 0.6636 \\ \hline W.Avg. F1-Score & **0.6839** & 0.6801 & 0.6532 & 0.6714 & 0.6577 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SNN used in the DFU Framework compared to published work in the literature on classification tasks.  
of multiple use cases using both cosine similarity and Euclidean distance calculations. By utilizing these different measures of similarity, we aim to provide a comprehensive analysis and ensure the reliability of our conclusions.
## 3 Results and Discussion
### Results for the Engine Tests  
The value of the fuel auto-ignition delay angle in a diesel engine is one of the most important parameters of the combustion process. In modern engines with a common  
Figure 10: ID parameter values for the tested fuel samples.  
Figure 9: Pressure changes in a constant volume combustion chamber for the tested fuel samples.  
rail injection system, control over the auto-ignition delay is achieved through an appropriate injection strategy implemented by the ECU algorithm. The auto-ignition delay in a traction compression ignition engine depended mainly on the physicochemical properties of the fuels, i.e., the cetane number of the fuel (self-ignition capability), viscosity, density and surface tension (affecting the quality of atomization and dispersion of the fuel), the turbulence of the air in the cylinder, the mixing of fuel particles with the air, and subjecting the fuel to physical (evaporation) and chemical (partial oxidation of the fuel) transformations. The above parameters had a significant effect on the angle of onset of combustion in the engine during static engine operation and during acceleration, which directly affected the auto-ignition delay period. The effect of the addition of n-hexane to canola oil on the auto-ignition delay was found, as shown in Figure 14. The differences in auto-ignition delay obtained are mainly due to the different physicochemical properties of the canola oil, particularly viscosity. The issues discussed are described in detail in the authors' publication [18].  
Figure 11: CD parameter values for the tested fuel samples.  
Figure 12: DCN parameter values for the tested fuel samples.  
Figure 15 shows a comparison of maximum engine combustion chamber pressures. It can be seen that the maximum combustion pressures were higher for the diesel-fueled engine compared to canola oil-based fuels. The effect of the addition of n-hexane on the increase in maximum cylinder pressure is also apparent.  
Indeed, the course of the combustion process in an internal combustion engine depends not only on the type of fuel, but on many parameters, such as the temperature and pressure in the combustion chamber at the beginning of fuel injection, the fuel injection timing, the fuel amount and its distribution. This is also confirmed by the results of tests on other fuel blends carried out in a constant volume chamber, however, not for the diesel fuel normative method but for different values of combustion chamber temperature at the beginning of fuel injection [33], fuel injection pressure and fuel-air ratio [34; 35]. Considering n-hexane, a study in the article [36] showed that the initial fuel temperature is also an important factor affecting the ID parameter.  
Figure 14: Values of the auto-ignition delay angle (\(\alpha_{\text{id}}\)) for a diesel engine operating at a crankshaft speed of 3000 rpm.  
Figure 13: MPRR parameter values for the tested fuel samples.