esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Stress Anisotropy on Petrophysical Parameters of Deep and Ultradeep Tight Sandstone
## 1 Introduction
The results show that with increasing effective stress, both the porosity and permeability of the fracture decreased, showing strong stress sensitivity, and the stress sensitivity of fracture permeability was stronger than that of fracture porosity. The higher the initial values of the reservoir fracture porosity and permeability parameters are, the stronger the stress sensitivity. For a low-permeability fractured reservoir, the stronger the rock plasticity is, the stronger the stress sensitivity. The effective stress is generally the difference between the confining pressure and pore pressure. Scholars at home and abroad have carried out much research on the effect of effective stress on rock sonic velocity [20; 21; 22; 23; 24; 25; 26; 27]. [PERSON] et al. (2021) collected core samples from the Xiling mining area of the Sanshandao gold mine and conducted experimental analysis [28]. They believed that the vertical stress, maximum horizontal stress and minimum horizontal stress in the coastal mining area all increased in an approximate logarithmic function trend with increasing burial depth. The relationship between rock mechanical parameters and in situ stress is roughly logarithmic, and the influence of maximum horizontal stress on rock mechanical parameters is greater than that of minimum principal stress. Rock mechanical parameters have different effects on the value and direction of in situ stress. Rock water saturation affects the sonic velocity [29]. [PERSON] et al. (1995) studied sandstone in the Yulin area of Daqing Ollield and found that shear wave velocity was constant with the change in water saturation [30]. When the water saturation was low, the velocity of the P-wave did not change with the change in saturation; when the water saturation was high, the velocity of the P-wave increased with increasing water saturation. [PERSON] et al. (2011) found that as the gas content of carbonate rocks increased, P-wave velocity decreased, while S-wave velocity changed little [31]. At present, experimental and numerical simulation methods are widely used to study the relationship between in situ stress and rock physical parameters [32; 33; 34]. Rock sonic velocity and resistivity are the key parameters of rock mechanics and reservoir physical properties. The study area of Tarim is in a strong stress environment. Strong stress will have a certain impact on porosity and acoustic velocity, causing effects that hamper a clear understanding of reservoir quality under the strong stress environment. This study combined the distribution of underground stress and designed an experimental scheme of in situ stress on rock physical parameters to explore the influence of stress anisotropy on rock physical parameters in deep and ultradeep tight sandstone.
# Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton
## 5 Physical Experiment
### Implementation Details  
The algorithm in this paper is verified on a digital simulation platform. The visual simulation software used for display can simulate the effect of UAV navigation in various geographical environments, altitudes, and weather environments. The image sequence outputted by the simulation software can simulate the images taken by the dual cameras and output the true values of the real position and attitude. These images can be used as datasets to verify and compare measurement methods for the position and attitude of the UAV. Because of the limited parameters in the simulation software, the roll angle of the aircraft attitude is set to a fixed value of 0.  
The dataset used is shown in Figure 10:  
The proposed method is written in vs2013+OpenCV 2.4.13 using C++ and ran on a computer with an Intel i7-6820 HQ (2.7 GHz) CPU, 16 GB RAM, and an NVIDIA Quadro M2000M GPU. The resolution of the image sequence collected by the simulated binocular stereo vision system is \(888\times 500\) pixels. The internal parameters of the virtual camera are fixed, the focal length is \(a_{x}=a_{y}=1080\) pixels, the principal point coordinates are \((u_{0},v_{0})=(444,250)\) pixels. The rotation matrix in the stereo vision system is the identity matrix, the translation vector is \(T=(0,0,1000)\) mm.  
The proposed method in this study is formed by the combination of motion estimation, an offline model, and online tracking. The proposed method uses ADaboost [23] with the haar feature as the offline model and KCF [24] with the hog feature as the online tracking algorithm. Some parameters must be set in each stage of the proposed method. During classifier pre-training, the number of stages is set to 20 based on experience. The minimum detection rate of each class of strong classifier is set to 0.999. In the tracking component, the linear interpolation factor for adaptation is set to 0.012 and the Gaussian kernel bandwidth is set to 0.6. In the solving stage, the method of feature extraction is ORB feature [29]. Other parameters are set to default values.
## 3 Results
### Covariate Sets
#### 3.2.2 Minimal-Optimal Variable Set  
In both study areas, the GB (greedy backward) sets were composed of similar covariates as AR sets to predict ALR- and ILR-transformed fractions. For the clay\({}_{\text{alr}}\) in the BQ region, HC excluded annual and winter LST on the basis of the AR set, SA (simulated annealing) further excluded vegetation covariates, and GF (greedy forward) merely retained three summer LST covariates. In terms of silt\({}_{\text{alr}}\), HC (hill climbing) excluded terrain covariates, SA further excluded spring LST, and GF merely retained a vegetation covariate, two spring LSTs, and a precipitation covariate. For the prediction of clay\({}_{\text{iIr}}\), HC excluded annual LST, SA excluded summer LST, winter LST, and precipitation, and GF merely retained a topographical covariate and a winter LST. Meanwhile, for silt\({}_{\text{iIr}}\) modeling, compared with the AR set, the SA technique excluded vegetation covariates, HC further excluded annual LST, and GF merely retained a vegetation covariate and a summer covariate (Table S1).  
In the WQ region, based on the AR set of clay\({}_{\text{alr}}\), HC excluded annual LST, SA excluded vegetation covariates, and autumn LST, while GF retained a winter LST covariate and an annual LST covariate. For the silt\({}_{\text{alr}}\) prediction, SA excluded autumn LST, HC further excluded vegetation covariates and annual LST, while GF retained an annual LST and two winter covariates. In terms of ILR-transformed fractions, HC excluded spring and annual LST for clay\({}_{\text{iIr}}\) prediction, SA further excluded summer LST, and GF retained two biotic covariates, and one spring LST covariate. For the silt\({}_{\text{iIr}}\), HC excluded spring, autumn, and annual LST covariates, SA excluded spring, summer, annual LST covariates, and GF excluded vegetation and spring LST covariates (Table S1).
## 3 Results and Discussions
### Intra-LCZ Correlation of LST and Indices of Land Surface Characteristics  
Strength and direction of correlations of LST with Aspect, Elevation, MNDWI, NDBal, NDBI, NDVI, and UI varied between LCZs (Figure 7). This indicated that each LCZ was  
Figure 6: City scale correlation of LST and land surface characteristics in Harare.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Local Climate** & **Average** & **Anomaly D\({}_{\mathbf{t}}\)** & **Coverage** & **Proportion-S** & **Contribution** \\
**Zone (LCZ)** & **Temperature** & **(K)** & **(k)** & **(ha)** & **(\%)** & **Index** \\ \hline LCZ2 & 317.3 & 6.488 & 313.56 & 0.35 & 0.023 \\ LCZ3 & 315.9 & 5.087 & 3337.11 & 3.71 & 0.189 \\ LCZ5 & 311.1 & 0.288 & 1592.37 & 1.77 & 0.005 \\ LCZ6 & 309.0 & \(-1.813\) & 32483.61 & 36.08 & \(-0.654\) \\ LCZ7 & 312.5 & 1.688 & 20,379.51 & 22.54 & 0.382 \\ LCZA & 305.2 & \(-5.613\) & 2473.11 & 2.75 & \(-0.154\) \\ LCZD & 313.5 & 2.688 & 29,130.39 & 32.36 & 0.870 \\ LCZG & 302.0 & \(-8.813\) & 321.12 & 0.36 & \(-0.031\) \\ \hline \end{tabular}
\end{table}
Table 3: Contribution of LCZs to the warming and cooling of the city.  
affected by factors in its own unique way. In LCZ2 and LCZ3, LST negatively and weakly correlated with Aspect, Elevation, MNDWI, NDBaI, NDBI, NDVI, and UI, as evidenced by low correlation coefficients (below 0.3). In LCZ5, there was some negative correlation between LST and Elevation, and negative correlation between LST and MNDWI (r close to \(-0.5\)) and NDVI (r close to \(-0.6\)). A similar correlation between LST and these factors was also recorded in LCZ7.  
Stronger negative correlation between LST and Elevation, and MNDWI and NDVI, was recorded in LCZ6. The correlation between Elevation and LST was very low in LCZA. In LCZD, the extent of urbanization had the highest positive correlation with LST, while surface wetness and vegetation abundance had the strongest negative correlation with LST. Similar to LCZ3 and LCZ7, LST decreased with bareness in LCZD. The thermal behavior of LCZG (water) was adversely affected by the increase in NDVI, NDBI, and NDBaI, resulting in warming.  
Figure 8 shows the influence (significant if \(p\)-value \(<0.05\)) of Aspect, Elevation, surface wetness (MNDWI), bareness (NDBaI), built-up extent (NDBI), vegetation abundance and health (NDVI), and urbanization extent (UI) on LST within a LCZ. Aspect, built-up extent (NDBI), and urbanization extent weakly influenced LST in LCZ2. Similarly, the extent of urbanization was also not a significant factor governing variations of LST in LCZ3, which is also heavily built-up. In LCZ5, other factors significantly influenced LST, with the exception Aspect and bareness (NDBaI). In LCZ6, LCZ7, LCZA, and LCZD, all other factors significantly affected the LSTs spatial distribution (\(p<0.05\)), with the exception of Aspect (\(p=0.18\)). On water bodies (LCZG), all other factors significantly affected LST distribution, except elevation (\(p=0.274\)). The significance of Aspect in affecting the spatial structure of LST was only high in LCZG.  
Figure 7: Intra-LCZ correlation of LST and land surface properties.
## 4 Discussion
### Advantages and Opportunities for Improvement of the Fusing Approach  
The S1 & S2 time series fusing approach presented here offers attractive and powerful opportunities to be further exploited. Firstly, there is the actual feasibility of successfully implementing cloud-free VWC map reconstruction techniques by taking advantage of the all-weather availability of S1 radar data. Fusing S1 SAR and S2 optical imagery proved particularly useful for monitoring crops' VWC in southern Argentina, and can be easily extrapolated to high-latitude cloudy regions worldwide. Secondly, MOGP can also be used for gap-filling S2 time series by combining optical data from other EO systems like Landsat-8 or Sentinel-3. Likewise, also other optically-derived variables can be targeted that align well with radar data, such as LAI [29]. Furthermore, thirdly, the ever-increasing supply of emerging radar satellite constellations in different frequency bands poses a revolutionary paradigm [77]. Altogether, the Earth observation scenario for the next few years is extremely promising and requires further exploration of MOGP techniques.  
On the downside, it is worth emphasizing that the training time was rather long (see Tables 2 and 3) when the data from S1 and S2 were combined to reconstruct the VWC maps (see Section 3.3). The long runtime constitutes the main bottleneck of the MOGP technique. Training MOGP algorithms is a challenging task as it involves a large number of hyperparameters to model all the inter-channel cross-correlations in the spectral domain. Some attempts were explored to further improve the results. Seeking to substitute the per-pixel optimization step [PERSON] et al. [37] suggested optimizing a GP for an averaged ROI and then using the hyperparameters of the optimized model. This approach could reduce the long processing time of S1 & S2 datasets but is at a risk to account for the cropland's spatiotemporal variability. We had tested that approach; however, it appeared that the results in our context were unsatisfactory, and this path was therefore discarded. Follow-up research may attempt to address this limitation. Several alternative mechanisms can be explored to accelerate the training phase of MOGP. They can be divided into two groups: technical and theoretical solutions. Technical solutions include using a more powerful computer (RAM \(\geq\) 64 GB), considering fewer observations for model training, reducing the number of spectral components, or deploying the code in a Graphics Processing Unit(GPU) environment. Theoretical solutions include using a sparse model, e.g., as presented by [PERSON] [78], or a stochastic gradient descent [79].
## 3 Proposed Method
### Proposed Temperature-Based Mean-Shift Tracking
#### 3.3.1 Temperature-Based Histogram Backprojection  
This section introduces the temperature-based histogram backprojection method and temperature histogram model \(T(x)\) conversion. First, a target ROI was determined in the initial frame, and a temperature histogram \(T(x)\) was obtained for the corresponding selected ROI, and the ROI was stored as an initial frame target ROI histogram model. In the next frame, the temperature-based histogram model was also calculated for the next target ROI. A temperature histogram was used for the continuous data, where the bin ranged from 0 to 150, while a bar chart was a plot of the temperature value corresponding to each pixel of the current frame ROI image. Secondly, the weight value \(W_{T}(x)\) was calculated by dividing the calculated \(H_{model}(T_{next}(x))\) and \(H_{next}(T_{next}(x))\) histogram models as Equation (7), \(W_{T}(x)\).  
\[W_{T}(x)=\sqrt{\frac{H_{model}(T_{next}(x))}{H_{next}(T_{next}(x))}},\quad 0<T(x )<150 \tag{7}\]  
\[K(x)=\begin{cases}c(1-\|x\|^{2}),&\|x\|\leq 1\\ 0,&\text{otherwise}.\end{cases} \tag{8}\]  
Equation (8) presents the kernel density function (KDF). The kernel function \(K(x)\) was used to reduce the influence of the background. The symmetric function had the highest value in the center of the window and a smaller value in the outline. In this paper, the Epanechnikov function, which is the most representative symmetric function, was used. Finally, the next ROI position was calculated corresponding to the weighted average value of the pixel coordinates in the current ROI area.
## 3 General Opportunities, Challenges, and Applications
### Opportunities
#### 3.1.4 Utilization of Edge Devices and Real-Time Learning  
Federated learning utilizes the computing capabilities of edge devices like smartphones and IoT tools, incorporating them into its learning structure. This method helps to maximize the resources and promotes the widespread use of machine learning, leading to a more inclusive concept [24]. In dynamic sectors like autonomous driving and emergency medical response, real-time data processing is critical. FL excels in these areas by enabling models to adapt rapidly to new data, avoiding the latency associated with traditional batch updates. This capability is vital when even slight delays can have significant consequences.
## 3 Results and Discussion
### Analysis of Factors Affecting the Model's Estimation of Soil Temperature
For each of the factors, there was a significant relationship between it and the estimated RMSE values of \(T_{s}\) (Figure 8b). Among these factors, \(U_{2}\) had the lowest correlation (R = 0.14), whereas RH had the highest correlation (R = \(-\)0.48) and soil water content had the second highest correlation (R = 0.38), indicating that the heat absorption capacity of water had a more obvious effect on \(T_{s}\) than other factors such as \(T_{mean}\), \(R_{s}\), and soil organic matter content. To summarize, soil organic matter, soil water content, T\({}_{\text{mean}}\), RH, R\({}_{\text{s}}\), and U\({}_{2}\) are the main factors that cause errors in estimating T\({}_{\text{s}}\), all of which would have, to some extent, a critical impact on the model estimation of T\({}_{\text{s}}\).
# Evolution towards Smart and Software-Defined Internet of Things
## 5 Evolution of IoT Architecture
### IoT Gateway  
[PERSON] et al. in [8] analyze three network architectures that can work as IoT gateways for transmission of data and information between the sensing and the network layer. They are (1) Personal Area Network (PAN), (2) Vehicle Network, and (3) Home Network. However, standardization of these gateways has not yet been achieved. As IoT is in a very primitive stage at this time, the gateways only enable communication between the mentioned layers and lack advanced integration due to heterogeneous capabilities. The authors in [8] propose a reference IoT gateway that addresses the problems of multiple interfaces, protocol conversion, and manageability. The gateway acts as a proxy between different networks to address integration problems. It acts as a bridge for communication between the sensing and the network layer. However, the challenges of power optimization, cost efficiency, and protocol lightweight still stand large in the sensing domain.  
[PERSON] et al. in [74] propose a four-layered architecture of IoT comprised of (1) Perception, (2) Heterogeneous Network Access, (3) Data Management, and (4) Intelligent Services Layer. This model aims to enhance the security of the system and the communication to strengthen the trust between heterogeneous entities. This is achieved through injecting security mechanisms and protocols into each of the layers of the four-layered IoT architecture. In [74] mechanisms to enforce security are discussed at each of the four-layers and their verification is justified. However, security is an evolving phenomenon that needs to be upgraded with the pace of the upgrades and the additions of complex structures to the IoT architecture.
# Hybrid Empirical and Variational Mode Decomposition of Vibratory Signals
## 3 Hybrid Methodology EMD-VMD  
The hybrid methodology integrates Empirical Mode Decomposition and Variational Mode Decomposition to enhance the estimation of components in signals with multiple harmonic vibrations, particularly those characterized by closely spaced, nonlinear, and nonstationary frequencies. This approach leverages the adaptability of Empirical Mode Decomposition in extracting signal features and the precision of Variational Mode Decomposition in separating components, effectively addressing mode mixing.
## 3 Results
### RPGE Activates NF-\(\kappa\)B and MAPK Signaling in RAW 264.7 Cells  
To identify the effect of RPGE on NF-\(\kappa\)B signaling, we performed a transcriptional activity assay using reporter gene constructs of 3 xNF-\(\kappa\)B-binding elements in RAW 264.7 cells. Treatment with RPGE-C at concentrations of 100 and 500 \(\upmu\)g/mL (Figure 5a) or RPGE-P at a concentration 500 \(\upmu\)g/mL (Figure 5c) led to the induction of luciferase activity, indicating that RPGE activates NF-\(\kappa\)B signaling. To understand the molecular mechanisms of the effect of RPGE on NF-\(\kappa\)B signaling, we examined the phosphorylation status of NF-\(\kappa\)B and IxB by Western blot. Our results showed that RPGE-C increased the levels of phospho-NF-\(\kappa\)B and phospho-IkB\(\alpha\) in a dose-dependent manner (Figure 5b). In the case of RPGE-P, phosphorylation levels of NF-\(\kappa\)B and IxB\(\alpha\) were also increased at the highest concentration (500 \(\upmu\)g/mL, Figure 5d). Moreover, the presence of an NF-\(\kappa\)B inhibitor (pyrrolidine dithiocarbamate, PDTC) with RPGE inhibited NO production, cytokine levels, and phosphorylation of NF-\(\kappa\)B (Figure S1). Therefore, our results indicated that RPGE can induce macrophage activation via NF-\(\kappa\)B signaling.  
Since NF-\(\kappa\)B activation is mediated via MAPK (p38 MAPK and JNK) signaling [17], we assessed whether RPGE induces MAPK activation in RAW 264.7 cells. Figure 6a,b (left) showed that the activation of p38 MAPK and JNK signaling were observed following treatment with 500 \(\upmu\)g/mL of RPGE-C or RPGE-P. Maximum levels of p38 MAPK and JNK phosphorylation appeared at 30 min post-treatment and decreased thereafter. Not only RPGE-C but also RPGE-P treatment was shown to increase the phosphorylation level of MAPK in a concentration-dependent manner (Figure 6a,b, right).  
Figure 5: Effect of RPGE on NF-\(\kappa\)B activation in RAW 264.7 cells. (**a**) RPGE-C; (**c**) RPGE-P. Luciferase activity was expressed as a relative value to that of the VC, which is set to 100%. The figures show the mean \(\pm\) SEM. * \(p\)\(<\) 0.05 and *** \(p\)\(<\) 0.001. (**b**) The cells were treated with 500 \(\upmu\)g/mL of RPGE-C for the indicated times or indicated concentrations for 60 min prior to Western blot analysis; (**d**) The cells were treated with 500 \(\upmu\)g/mL of RPGE-P for the indicated times or indicated concentrations prior to Western blot analysis.
## 1 Introduction
Since vehicle speed appears in the denominator of this model and its small angle assumption, it is not applicable to such conditions as stop-and-go and large turning angle. [PERSON] et al. have compared the motion planning performances using five different kinds of vehicle dynamics models and found that each one is only suitable for some types of driving scenarios [21]. Though some researchers have tried to extend the kinematic model [22] and the dynamics model [23] to a wider scope of work conditions by different discretization methods and parameters, these studies evaluated from the system performance under some special scenarios, and the accuracy and application range of model itself is not improved in essence. Moreover, the designed motion planner becomes more sensitive to disturbances under the extended conditions. With the increasing of the intelligent level of automatic driving systems, the motion planner should deal with a variety of driving scenarios [24]. Limited by the application range of each vehicle model, a practical way is to design multiple motion planners, which are scheduled according to the current type of driving condition [25; 26]. This will increase the complexity of automatic driving systems. Moreover, the discontinuity in system state and control command, and oscillation among different planners are also unsolved problems. A unified and fine model describing the vehicle behavior in all normal driving conditions is beneficial to the comprehensive performance of motion planning and the practical application of optimal control methods on the design of motion planner. In this study, a unified and simplified vehicle dynamics model (SDM) is proposed to design a practical motion planner in any normal driving scenarios. By considering the coupling effect in longitudinal and lateral directions, a coupled dynamics model (CDM) is set up, whose accuracy is validated by comparisons with the kinematic model and single-track model under a variety of normal driving conditions. CDM considers the tire cornering characteristic, meanwhile is also applicable to conditions with large turning angle. To reduce the complexity of CDM, the coupling effect is analyzed numerically according to the transfer functions obtained by linearizing at equilibrium points, and the weak part of the coupling effect is ignored to derive SDM. Then, the accuracy of SDM is verified by comparing with CDM, kinematic and single-track models, and applied to design the motion planner by NMPC to validate its contribution on the performance improvement. The rest paper is organized as follows: Section 2 introduces the modelling process of CDM, which is simplified by the coupling effect analysis in Section 3; In Section 4, SDM is adopted to design the motion planner by NMPC to show its contribution on the performance improvement and Section 5 concludes the paper.