esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Shear Strength Model with Structural Damage for Red Clay in the Qinghai-Tibetan Plateau
###### Abstract
The accuracy of its results has been verified in many tests [30; 31; 32], providing a new idea for revealing the change mechanisms of the soil strength after FTCs from the micro level. [PERSON] et al. [33] performed SEM and NMR tests on expansive soil in seasonally frozen areas. The results revealed that, during the FTCs, the porosity of expansive soil decreased, with micropores remaining largely unchanged. Mesopore content increased while macropore content decreased. [PERSON] et al. [34] performed NMR tests on loess-paleosol after FTCs. The test results revealed that an increase in FTCs led to a rise in the signal amplitude of the \(T_{2}\) spectrum curve of paleosol. Mesopore content decreased and that of macropores and maximum pores increased. Currently, scholars have achieved significant advances in the research of red clay's strength and permeability alterations subjected to FTCs [35; 36]. However, there has been little exploration of the micro mechanism of red clay strength degradation. The shear strength criterion is an important theory to describe the shear strength of soils. Shear strength criteria were proposed to describe the soil strength under different FTC conditions. [PERSON] et al. [37] proposed a strength model of loess based on the variation in cohesion with the number of FTCs and water content. The strength degradation of loess after FTCs was well predicted. [PERSON] et al. [38] proposed a new shear strength criterion based on the Mohr-Coulomb strength model, considering the influence of the number of FTCs on the cohesion and the internal friction angle of clay. Actually, FTC leads to strength degradation by changing the structure of the soil. However, the change in structure was not considered in the above shear strength criteria. Therefore, it is urgent to propose a shear strength criterion to consider the FTC effect on soil microstructure. In this research, to clarify the impact of FTCs on the shear strength and microstructure of red clay, triaxial shear and NMR tests were performed. According to the multiscale analysis of shear strength and NMR tests, an extended Mohr-Coulomb strength model was proposed. The model can reflect the macroscopic strength deterioration through the soil structural damage by the FTC effect. Valuable references about engineering de sign involving red clay and ecological conservation in the QTP can be provided through research findings.
## 3 Results and Discussions
### Adsorption and Diffusion of H\({}_{2}\) in a Microporous Model  
To reveal the mechanisms of lignite structure on H\({}_{2}\) adsorption and diffusion and to investigate the interactions between H\({}_{2}\) molecules and the microporous structures of LR@1 and LR@2, Figure 6 summarizes the specific surface area, porosity, micropore structure, H\({}_{2}\) adsorption capacity, H\({}_{2}\) diffusion coefficient, and gas diffusion models for both microporous structures of LR@1 and LR@2. From Figure 6a, it can be seen that the microporous structure of LR@2 has a higher specific surface area and porosity compared with LR@1, with values of 2402.60 m\({}^{2}\)/g and 26.905%, respectively. This indicates that LR@2 contains more effective adsorption sites, which facilitate H\({}_{2}\) storage. The adsorption capacity of H\({}_{2}\) for both coal samples is shown in Figure 6b. The simulated adsorption capacities of the two coal samples are in good agreement with experimental results from related studies ([PERSON] et al. [10] and [PERSON] et al. [35]), which confirms the reliability of the simulation. The H\({}_{2}\) adsorption capacity for LR@2 is higher than that for LR@1, with maximum adsorption of 0.474 mmol/g. This is attributed to LR@2 having a higher specific surface area and porosity compared with LR@1. Additionally, element analysis and coal molecular structure analysis confirm that LR@2 has a higher proportion of oxygen-containing functional groups than LR@1. Combined with electrostatic potential analysis, it is found that H\({}_{2}\) molecules are more likely to interact with these oxygen-containing functional groups in the coal structure. The \(MSD\) and diffusion coefficients of H\({}_{2}\) in the microporous structures of the two coal samples were calculated using Equations (2) and (3), as shown in Figure 6c. From Figure 6c, it can be observed that H\({}_{2}\) has a larger \(MSD\) and diffusion coefficient in the micropores of LR@1 compared with LR@2. This suggests that the presence of a higher proportion of oxygen-containing functional groups in LR@2 leads to stronger interactions between H\({}_{2}\) and the microporous structure, making the diffusion of H\({}_{2}\) more difficult.  
Figure 5: Coal structure and hydrogen electrostatic distribution and front molecular orbital alignment (**a**) Electrostatic distribution (**b**) Front molecular orbitals.
## Appendix A
### Pseudocode of K-Nearest Neighbors Algorithm  
```
1:Input:\(D_{\text{train}}\): Training dataset; \(D_{\text{test}}\): Test set; \(\alpha\): Learn rate; \(n\_epochs\): Training epochs
2:Output:\(D_{\text{predicted}}\): Predicted probabilities for the test dataset
3:Program Body
4:Initialize \(\theta\) (Parameter vector) to zero or random values
5:for epoch = 1 to \(n\_epochs\)do
6: let \(gradients\) be a vector of zeros
7:for each \((x,y)\) in \(D_{\text{train}}\)do
8: let \(z\) be the dot product of \(\theta\) and \(x\); let \(\hat{y}\) be the sigmoid function of \(z\), \(\hat{y}=\frac{1}{1+e^{-z}}\)
9: let \(error\) be \(\hat{y}-y\); update \(gradients\) by adding \((error\cdot x)\)
10:endfor
11: update \(\theta\) by subtracting \(\alpha\cdot gradients\)
12:endfor
13:Prediction for \(D_{\text{test}}\)
14:for each \(x_{\text{test}}\) in \(D_{\text{test}}\)do
15: compute \(\hat{y}\) as the sigmoid of the dot product of \(\theta\) and \(x_{\text{test}}\); assign \(\hat{y}\) to \(D_{\text{predicted}}\)
16:endfor
17:Return \(D_{\text{predicted}}\)
```  
**Algorithm A3** Logistic Regression Algorithm [50]
## 4 Influence of Dataset Features
### Influence of Sequencing Depths and K  
Overall, KMC3 and CHTKC perform stably on datasets with various sequencing depths regardless of k-mer length, but KMC3 is more efficient for calculating long k-mers in large datasets with high sequencing coverage (Figure 4(a), Supplementary Table S5). CHTKC has better competitive when dealing with low sequencing depth or short k-mers because the short k-mer or low sequencing depth counting tasks have a relatively small number of distinct k-mers (Table 3). CHTKC only needs 1-2 passes to complete these tasks when it has enough memory in this case. The case of counting long k-mer for high coverage sequencing data (k \(\geq\) 32 and coverage \(\geq\) 30x) means CHTKC must visit a large number of distinct k-mers and needs more batches to complete the task. More passes lead to more memory accesses (Figure 4(b)) and disk operations. More disk accesses imply the algorithm tends to be increasingly affected by the IO bottleneck, making it difficult to improve the performance. On the contrary, the partition-based KMC3 is related to the total k-mers, and the running time depends linearly on sequencing depth/number of total k-mers for each task with the same k-mer length.
###### Abstract  
Landslides are one of the primary geological disasters posing significant threats to life and property. Strengthening the monitoring of rainfall-induced landslides is, therefore, crucial. The Direct Resistivity (DC) method can accurately map the subsurface electrical resistivity distribution, making it an essential tool for predicting the position of the slide face. However, when conducting landslide surface DC surveys, various undulating terrains such as ridges and steep slopes often pose accessibility challenges. In such topographies, conventional regular grid measurements become very difficult. Additionally, when the terrain is highly undulating and complex, interpreting apparent resistivity data can lead to erroneous results. In this study, we propose using the DC method to monitor rainfall-induced landslides. By moving away from traditional device setups and utilizing an arbitrary dipole-dipole observation system, we aim to improve efficiency, enhance data resolution, and reduce costs. The resistivity of the slope was found to change significantly during the incubation, formation, and development of a landslide in physical model experiments. Furthermore, the feasibility of our proposed method for assessment rainfall-induced landslides was illustrated by a real case study in South China.  
landslide assessment; electrical resistivity tomography; arbitrary dipole-dipole configuration Article
# Laboratory Test of a Vehicle Active Noise-Control System Based on an Adaptive Step Size Algorithm
## 5 Discussion and Conclusions  
In this paper, an adaptive step size FxLMS algorithm (AASFxLMS) and an adaptive step size FxLMS algorithm (AASFxLMSPO) with particle swarm optimization are proposed. Through mathematical modeling, simulations, and experiments, the following conclusions are drawn:  
1. The AASFxLMS algorithm proposed in this paper adjusts the step size according to the size of the reference signal, which can effectively cope with the impact noise signal and further improve its performance via particle swarm optimization.
2. The MATLAB simulation results show that, compared with the FxLMS algorithm, MNFxLMS algorithm, NASFSxLMS algorithm, and NASFSxLMSPSO algorithm, the proposed AASFxLMS algorithm and AASFxLMSPSO algorithm have better noise reduction performance. In the case of faster convergence, the steady-state error is lower.
3. As shown by the collection of the interior noise signal and the hardware-in-the-loop test in the noise suppression room, the noise reduction amplitude can reach 12 dB, which verifies the reliability and effectiveness of the proposed algorithm in terms of interior noise active control.
4. In this paper, the noise reduction effect of the proposed algorithm is verified only in the laboratory. The most necessary future development is a test conducted in a car running over speed bumps.  
Figure 17: Experimental noise reduction effect of a 60 km/h constant driving speed. (**a**) The time domain, (**b**) The frequency domain.  
* 5 In addition to the classical particle swarm optimization algorithm to optimize parameters \(a\) and \(c\), the process can also be realized by using a neural network algorithm, genetic algorithm, ant colony algorithm, or bee colony algorithm.
* 6 As another future development direction, the potential applications of the in-vehicle active noise control system proposed in this paper can also include aircraft, ships, offices, and other indoor application scenarios.  
Conceptualization, T.J. and S.W.; Validation, T.J.; Investigation, C.P.; Resources, S.W.; Data curation, T.J.; Writing--original draft, T.J.; Writing--review & editing, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Shandong Province under grant no. ZR20211180079, and the State Key Laboratory of Automotive Simulation and Control Open Foundation under grant no. 20210226.  
Not applicable.  
Some or all data and materials generated or used during this research are available from the corresponding author by reasonable request.  
The authors also express their great gratitude to the research team and the editors for their help.  
The authors declare no conflict of interest.
## 5 Conclusions  
In the present study, a preliminary analysis of _DcTPSb1_ in _Dendobium chrysotoxum_ was conducted, encompassing examinations of the exonic intron structure distribution, conserved motifs, subcellular localization, spatiotemporal expression pattern, cis-acting elements, phylogenetic analysis, in vitro functional validation, and gene overexpression. _DcTPSb1_ was found to exhibit differential expression patterns throughout three floral phases and various tissues, underscoring its role in regulating monocotyled biosynthesis in flowers. DcTPSb1 localized in chloroplasts, was a member of the TPS-b subfamily, closely related to homologous plants. Functionally, DcTPSb1 could convert geranyl diphosphate (GPP) to linalool, ocimene, and (-)-\(\alpha\)-pinitol in vitro. This study offers insights into the regulatory mechanisms of TPS genes in _Dendrobium_ spp. and Orchidaceae, laying a foundation for future investigations and potential modifications of floral scents in these plants.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/cimb47010025/sl](https://www.mdpi.com/article/10.3390/cimb47010025/sl)]([https://www.mdpi.com/article/10.3390/cimb47010025/sl](https://www.mdpi.com/article/10.3390/cimb47010025/sl)), Figure S1: Agarose gel electrophoresis of cDNA amplification of the _DcTPSb1_ gene; Figure S2: Sequence analysis of _DcTPSb1_ genome in _Dendrobium chrysotoxum_, including 6 introns, 7 exons and UTR sequences at both ends; Figure S3: Second class structure of DcTPSb1; Figure S4: Specific distribution of three important motifs commonly distributed in TPS proteins in Figure 1; Figure S5: Validation of genetically modified tobacco; Table S1: DNA sequence of _DcTPSb1_ and protein sequence of DCTPSb1 from the _D. chrysotoxum_; Table S2: Information on primers used for sequence amplification and validation in this paper; Table S3: Information about proteins with relatively similar homology to the target protein and other proteins known to belong to the TPS subfamily used in Figures 1 and 2; Table S4: Cis-acting elements in the promoter region of _DcTPSb1_ gene; Table S5: The upstream promoter 2 kb of _DcTPSb1_ gene.  
Conceptualization, Z.C.; methodology, Z.C. and Z.D.; software, S.Z.; validation, W.W.; data curation and writing--original draft preparation, Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Guizhou Provincial Basic Research Programs (Natural Science) (grant number ZK [2024]general542); the Guizhou Provincial Basic Research Programs (Natural Science) (grant number ZK [2023]general189); the Youth Foundation of the Guizhou Academy of Agricultural Sciences (grant number 202230); the Project of Guizhou Provincial Department of Science and Technology (grant number Qiankehe Fuqi [2022]005)and the Germplasm Resources Project of the Guizhou Academy of Agricultural Sciences (grant number 2024).  
Not applicable.  
Data Availability Statement: Data are contained within the article and Supplementary Materials.  
The authors declare no conflicts of interest.
## 3 The Joint Relay Selection and Power Control Optimization Method via Stackelberg Game
### Stackelberg Game Model  
In the anti-jamming relay communication, the user transmits messages firstly and selects a relay to help broadcast messages to base station, after having sensed the user's signals, the jammer releases jamming signals immediately. Take a perspective of Stackelberg game, the user acts as leader and jammer acts as follower in the proposed scenario. The proposed game can be denoted as \(\mathcal{G}=\{\mathcal{P},\mathcal{Q},\mathcal{J},U,V\}\), where \(\mathcal{P}\) and \(\mathcal{Q}\) denote the user's power strategy space and relay selection strategy space respectively, \(\mathcal{J}\) denotes jammer's power strategy space, \(U\) and \(V\) denote the user's and jammer's utility, respectively.  
Based on the utilities given in Section 2.2, both the user and jammer aim to maximize their utilities to get a better communication effect or jamming effect. For the jammer, given a user's joint strategy \(P\in\mathcal{P},Q_{n}\in\mathcal{Q}\), it makes the optimal power strategy to destroy normal communication, and the optimization problem is expressed as:  
\[\max_{J}V(P,Q_{n},J). \tag{7}\]  
For the user, it makes a joint relay selection and power control strategy to guarantee anti-jamming communication quality, and the user's optimization problem can be expressed as follows:  
\[\max_{P,Q_{n}}U(P,Q_{n},J). \tag{8}\]  
Based on the analysis above, the joint optimization problem can be solved by hierarchical decision-making method via Stackelberg game frame, which is shown as follows:  
\[\left\{\begin{array}{l}\max_{P,Q_{n}}U(P,Q_{n},J)\\ \text{subject }to:P\in\mathcal{P},Q_{n}\in\mathcal{Q}\\ \quad\quad\text{The optimal solution}:(J^{*})\\ \quad\quad\left\{\begin{array}{l}\max V(P,Q_{n},J)\\ \text{subject }to:J\in\mathcal{J}\end{array}\right.\end{array}\right. \tag{9}\]
## 2 Materials and Methods
### Model Validation
#### 2.2.2 The Validation Method  
To evaluate the accuracies of the model, residual sum of squares error (SSE), mean square error (MSE), and root mean square (RMS) error were considered as the criteria for the accuracy evaluation and calculated by the following (Equations (9)-(11)).  
\[\text{SSE }=\ \sum_{i\ =\ 1}^{n}\left(\vec{y_{i}}-y_{i}\right)^{2} \tag{9}\]  
\[\text{MSE }=\ \frac{\text{SSE}}{n}\ =\ \frac{1}{n}\sum_{i\ =\ 1}^{n}\left(\vec{y_{i}}-y_{i}\right)^{2} \tag{10}\]  
\[\text{RMS }=\ \sqrt{\text{MSE}}\ =\ \sqrt{\frac{\text{SSE}}{n}}\ =\ \sqrt{\frac{1}{n}\sum_{i\ =\ 1}^{n}\left(\vec{y_{i}}-y_{i}\right)^{2}} \tag{11}\]  
where \(\vec{y_{i}}\) is the value calculated by the model, \(y_{i}\) is the value of the experimental data, and n is the number of samples. SSE, MSE, and RMS error reflect the reliability and stability of  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Compounds** & **Concentration (mg/L)** \\ \hline Biotin & 2.00 \\ Folic acid & 2.00 \\ Riboflavin & 5.00 \\ Nicotinic acid & 5.00 \\ p-Aminobenzic acid & 5.00 \\ Thiotic acid & 5.00 \\ Calcium D-(+)-pantothenate & 5.00 \\ Pyridoxine hydrochloride & 10.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The components of the vitamin solution.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Compounds** & **Concentration (g/L)** \\ \hline FeSO\({}_{4}\) 4H\({}_{2}\)O & 1.80 \\ CoCl\({}_{2}\) 6H\({}_{2}\)O & 0.25 \\ CuCl\({}_{2}\) 2H\({}_{2}\)O & 0.01 \\ NiCl 6H\({}_{2}\)O & 0.01 \\ ZnCl\({}_{2}\) & 0.10 \\ H\({}_{3}\)BO\({}_{3}\) & 0.50 \\ EDTA & 2.50 \\ MnCl\({}_{2}\) 4H\({}_{2}\)O & 0.70 \\ (NH\({}_{4}\))6 Mo\({}_{7}\)O\({}_{24}\) 4H\({}_{2}\)O & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The components of the mineral solution.  
the model. It is commonly accepted that the closer the sum of squares error (SSE) is to 0, the better the model fits the data.
## 3 Results  
In this section, the accuracies of BDS-3 POD and time synchronization are analyzed using ground station data and ISL data. Joint orbit determination experiments with different LEO satellite constellations and ground stations were also carried out. The factors affecting the orbital accuracy and time synchronization of the BDS-3 satellites are discussed in detail.
# Heat Transfer Enhancement of Nanofluids with Non-Spherical Nanoparticles: A Review
## 3 Convective Heat Transfer of NSN Nanofluids
### Numerical Predictions and Mechanism Discussion
#### 3.2.1 Particle Concentration Distribution and Interaction  
Sonication promotes the uniform dispersion of nanoparticles in the base fluid during preparation. Nonetheless, there is no uniform standard for optimal ultrasonication duration for different types of nanofluids [13]. In addition, the uniform distribution of nanoparticles is overlooked by researchers. [PERSON] et al. [107] simulated the particle number concentration in a straight pipe using the method of moments. The results revealed the non-uniformity of the cylindrical nanoparticle volume concentration across the section. [PERSON] et al. [108] modeled the Poiseuille flow and CHT of nanofluids in a circular minichannel and found that the nanorod volume fraction distribution is non-uniform near the boundary.  
In contrast, the particles are more uniform when their aspect ratio is larger. The thermal resistance and temperature gradient are largest near the wall and are sensitive to particle concentration. [PERSON] et al. [109, 110, 111] investigated the distribution and deposition of cylindrical nanoparticles in turbulent straight and curved pipes. The coupled model considered the effect of turbulent diffusivity on particle concentration distribution and interaction. The results revealed that the distribution of the particle concentration in the cross-section becomes non-uniform along the flow direction. In the curved duct, the extent of non-uniformity of the distribution of the particle number concentration increases with increasing Stokes number, Dean number, Reynolds number, and particle AR. They showed that particle transport and deposition in curved tubes are important; however, limited studies have considered the effect. Their numerical results demonstrated that the penetration efficiency increases as the Dean number, Reynolds number, and particle aspect ratio decrease. The penetration efficiency is highest when the Stokes number is approximately 0.02.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 4 PADP Algorithm Description
### Initial Deployment  
We calculate the initial deployment is referring to the scheme in the paper [39]. In Figure 4, the tangent plane of the communication ball of an underwater node and surface of the water can be regarded as some surface disks. In addition, these intersecting disks can divide the water surface into several logical regions which got the order depending on the level of coverage it is covered. We select the high order logical regions centroid will be the initial position of the surface gateway node.
## 4 Methodology
### User Spacecraft Orbit Determination
#### 4.4.1 Gravitational Forces  
The gravitational force model is composed of various intricate components, among which the spherical harmonic representation of Earth's gravity field is crucial. It characterizes the gravitational impact of Earth's mass distribution on objects within its gravitational field, and to incorporate this force, the JGM-2 50x50 gravity model [15] is utilized. This gravity model was utilized to maintain consistency with the FDF's operational solutions. The perturbations caused by ocean and solid Earth tides, characterized by a mean Love number k\({}_{2}\) = 0.29, were also incorporated into the force budget.  
Furthermore, the gravitational force model also accounts for point mass and relativistic perturbations induced by the Sun, Earth, Moon, Mercury, Venus, Mars, Jupiter, and Saturn. These perturbations are determined utilizing DE421 planetary ephemerides [16]. This approach enables accurate predictions of gravitational effects on user spacecraft.
## 4 Fault Location Principle
### Fault Location Process  
Combined with the above analysis, The positioning flow chart is shown in Figure 4. The fault location process of this paper is as follows:  
Figure 4: Positioning flow chart.  
The time-domain voltage traveling wave waveform from 1 ms before the fault to 3 ms after the fault is intercepted, and the line-mode traveling wave and zero-mode traveling wave are extracted by the phase-mode transformation shown in Formula (2).
2. The optimal decomposition mode number \(\hat{K}\) and optimal penalty factor \(\hat{\alpha}\) of MVMD are obtained by MPA optimization algorithm considering the minimum comprehensive entropy kurtosis ratio.
3. The MVMD is used to simultaneously decompose the line-mode traveling wave and the zero-mode traveling wave, and the IMF with the smallest entropy/kurtosis ratio is extracted as the line-mode traveling wave characteristic signal and the zero-mode traveling wave characteristic signal, respectively.
4. The wave head calibration of the traveling wave characteristic signal is carried out by the morphological gradient, and the arrival time \(t_{1}\) of the linear mode traveling wave head and the arrival time \(t_{0}\) of the zero-mode traveling wave head are obtained.
5. The line-mode traveling wave velocity and zero-mode traveling wave velocity are calculated by Formula (5). If it is a single cable line, the fault distance \(d_{\text{m}}\) is calculated by Formula (22). If it is a hybrid line, the fault distance \(d_{\text{m}}\) is calculated by Formula (25).