esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Application of Hourly PM\({}_{2.5}\) in Pollution Event Analysis  
In this study, we plotted the satellite estimations (Figure 7a-d,i-l) and the satellite-NAQPMS fused PM\({}_{2.5}\) spatial distribution (Figure 7e-h,m-p) from 09:00 to 16:00 on 19 April 2017. According to the spatial distribution, there are gaps in the satellite estimation PM\({}_{2.5}\) due to the influence of cloud coverage, which cannot reflect the pollution in some regions (Figure 7a-d,i-l). The full-coverage PM\({}_{2.5}\) spatial distribution was obtained after the fusion of satellite and NAQPMS data to make up for the shortcomings of satellite estimation results. At 09:00, the PM\({}_{2.5}\) high concentration area is mainly located in the junction of Meishan, Leshan, and Zigong; at 10:00, with the transit of southeast wind, pollutants migrate to Meishan and Chengdu, while the PM\({}_{2.5}\) concentration of some regions increases; at 11:00, with the transit of the northeast wind, pollutants of Ziyang and Deyang area migrate with the wind and pollutants accumulate in Meishan area, making PM\({}_{2.5}\) concentration higher; until 16:00, the northeast area of Sichuan pollutants diffuse and migrate with the wind, making the air quality better and PM\({}_{2.5}\) concentration lower. However, Leshan, Meishan, and Ya'an are located in the wind accumulation area and left of the mountainous area, making pollutants accumulate and PM\({}_{2.5}\) concentration relatively high.  
Figure 6: Scatterplot of the hourly density of PM\({}_{2.5}\) from satellite estimation and ground-based observation (09:00–16:00). The red and blue lines are 1:1 and fitted lines.  
In addition, we selected the PM\({}_{2.5}\) pollution process from 1 to 3 November 2018, as shown in Figure 8. According to the PM\({}_{2.5}\) concentration distribution and migration process, it can be seen that on November 1, PM\({}_{2.5}\) high concentration (>100 ug/m\({}^{3}\)) is mainly located in the junction of Suining, Ziyang, Chengdu, and Deyang at 09:00; until 16:00, pollutants gradually dissipated and high concentration region migrates around Mianyang. On 2 November, the high concentration area is located near Meishan and Chengdu at 09:00; with the diffusion of pollutants, PM\({}_{2.5}\) concentration gradually decreases and is mainly concentrated in Leshan at 16:00. On 3 November, pollutants migrate and gather near Yibin and Luzhou, and the whole pollution process ends by 16:00. Therefore, the results of spatial full coverage obtained by fusion can analyze the changes in pollutant migration, diffusion, and accumulation.  
Figure 7: PM\({}_{2.5}\) spatial distribution on 19 April 2017 09:00–16:00. (**a–d,i-l**) denote satellite estimation results; (**e–h,m–p**) denote model and satellite fusion results. The black arrows are wind direction.  
Figure 8: PM\({}_{2.5}\) spatial distribution on 1 to 3 November 2018 at 09:00–16:00. (**a1–a8**) denote 1 November; (**b1–b8**) denote 2 November; (**c1–c8**) denote 3 November.
# Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control
## 3 Methodology
### Hand Gesture Recognition (HGR)
#### 3.3.2 Hand Rotation Estimation Module  
The manipulation of the yaw axis is related to altering the orientation of the drone's perspective. This adjustment can be intuitively matched to the rotation of the hand as if operating the steering wheel of a vehicle. We utilize the finger joint information to measure the rotation angle of the hand, which is then used to determine the rotation direction of the yaw axis. First, we designate two points to establish a baseline. One is the origin set at the point of the wrist, and the other is an arbitrary point located far away in the verticaldirection above the origin. Specifically, the wrist point (landmark #0) serves as the origin, while the arbitrary point is selected as the middle finger point (landmark #9). Subsequently, the angle \(\theta\) of rotation of the hand is approximated by determining the angle between a straight line on the palm and the baseline, as shown in Figure 4.  
This straight line is defined by connecting two points in the current frame: the joint connecting the middle finger (landmark #9) and the wrist (landmark #0). To obtain the value of \(\theta\), we use np.arctan2. Specifically, the yaw factor can be determined using the following expression:  
\[Yaw=\begin{cases}\text{yaw\_left},&\theta>\theta_{1}\\ \text{yaw\_right},&\theta<\theta_{2},\\ \text{neutral},&\text{else}\end{cases} \tag{8}\]  
where \(\theta_{1}\) and \(\theta_{2}\) denote user-adaptable values and the condition is \(\theta_{2}<0<\theta_{1}\).
## 3 System Architecture
### Cloud Layer  
In this layer, each cloud controller is superior and functions to provide various services through the fog nodes, which act as a platform-as-a-service in the SAVP system. The main goals of the cloud layer are: to manage end-user or AV requests for parking and massive amounts of traffic data from the fog nodes; to monitor the fog operational activities; to continuously update the repository with information from the fog nodes; to offer long-term data storage, including a hash of transactions; to perform analytics on the massive amount of data that are continuously or from time to time collected from the fog nodes. In this layer, services such as the certificate authority (CA), key distribution center (KDC), and validation are also performed.
## 2 Hammerstein Model for DEAP Actuator
### ARX Model  
In this subsection, a linear dynamic ARX model is established to characterize the rate-dependent hysteresis of the DEAP actuator, which can be defined as  
\[A(z)y(k)=B(z)u(k)+d_{0}(k) \tag{10}\]  
where \(A(z):=1+a_{1}z^{-1}+a_{2}z^{-1}+\ldots+a_{n_{a}}z^{-n_{a}}\) and \(B(z):=b_{1}z^{-1}+b_{2}z^{-1}+\ldots+b_{n_{b}}z^{-n_{b}}\) are polynomials in the unit backward shift operator \(z^{-1}[z^{-1}y(k)=y(k-1)]\) with the known orders \(n_{a}\) and \(n_{b}\); \(y(k)\) is the system output sequence; \(u(k)\) is the input of the ARX model and also the output of the MPI model; \(d_{0}(k)\) is the external disturbance.  
Define the parameter vectors \(\zeta_{a}:=[a_{1},a_{2},\ldots,a_{n_{a}}]^{T}\in\Re^{n_{a}}\), \(\zeta_{b}:=[b_{1},b_{2},\ldots,b_{n_{b}}]^{T}\in\Re^{n_{b}}\) and \(\zeta:=[\zeta_{a},\zeta_{b}]^{T}\in\Re^{n_{a}+n_{b}}\). Define the information vectors \(\varphi_{a}:=[-y(k-1),-y(k-2),\ldots,-y(k-n_{a})]^{T}\in\Re^{n_{a}}\), \(\varphi_{b}:=[u(k-1),u(k-2),\ldots,u(k-n_{b})]^{T}\in\Re^{n_{b}}\) and \(\phi:=[\varphi_{a},\varphi_{b}]^{T}\in\Re^{n_{a}+n_{b}}\).  
Equation (10) can be turned into  
\[\begin{aligned} y(k)&=-\sum_{i=1}^{n_{a}}a_{i}y(k-i) +\sum_{i=1}^{n_{b}}b_{i}u(k-i)+d_{0}(k)\\ &=\varphi_{a}^{T}\zeta_{a}+\varphi_{b}^{T}\zeta_{b}+d_{0}(k)\\ &=\phi^{T}\zeta+d_{0}(k)\end{aligned} \tag{11}\]
## 3 Methods
### Vegetation Indices  
Vegetation indices (VIs) attempt to accentuate the vegetation signal, while diminishing soil background and solar irradiance contributions [28]. NDVI and Normalized Difference Water Index (NDWI) have been widely used in crop monitoring and crop mapping applications [7; 29; 30]. In this study, NDWI is used as defined by [PERSON] (1996), utilizing the SWIR and NIR parts of the electromagnetic spectrum to correlate to plants' water content [31]. SWIR is sensitive to dry matter content, leaf structure, and water content, while NIR only accounts for the first two; hence the water content property is set apart. VIs can be fully exploited in multi-temporal schemes, since crop types' unique phenology calendars enable accurate crop discrimination. In this regard the Plant Senescence Reflectance Index (PSRI), which is defined as (Red-Green)/NIR, can form dissimilar spectral signatures for the different crop types, being particularly sensitive to their senescence phase [28].
## 3 Results and Discussion
### Raman Spectroscopy  
Raman spectroscopic analyses were carried out to ratify the composite confirmation. Figure 2 compares the Raman spectra of HfO\({}_{2}\), graphene and graphene-HfO\({}_{2}\) nanocomposites. In Figure 2, the Raman peaks at 112, 142, 258, 502, and 576 cm\({}^{-1}\) could be attributed to the Ag modes of HfO\({}_{2}\), whereas the peaks at 330, 405,659, and 774 cm-1 are assigned to Bg modes of the HfO\({}_{2}\) monoclinic. These peaks are similar to reported values for monoclinic HfO\({}_{2}\)[53]. The unique peaks of graphene (Figure 2) can be found at 1357 and 1583 cm\({}^{-1}\), corresponding to D (where the D peak is a defect peak owing to intervallic scattering) and G (G refers to the graphene G peak) peaks [54]. In the Raman spectra of HfO\({}_{2}\)-graphene nanocomposite, it is worth noting that the typical graphene peaks were also observed in addition to the monoclinic of HfO\({}_{2}\) peaks (Figure 2). The appearance of the D & G bands corresponds to graphene. Since the wt% of graphene increases, the corresponding D & G bands also increased. The calculated relative amplitude ratios of the \"D\" and \"G\" bands were similar, and the values of graphene and graphene-HfO\({}_{2}\) (10, 30, 50 wt%) were 0.86 and 0.85, respectively. These results reveal the successive decoration of the HfO\({}_{2}\) nanoparticles on the graphene sheets.  
Figure 1: XRD pattern of (**a**) HfO\({}_{2}\) nanoparticles, (**b**) graphene, and (**c**-**e**) HfO\({}_{2}\)-graphene (10, 30, and 50 wt%) nanocomposites.  
Figure 2: Raman spectra of HfO\({}_{2}\) nanoparticles, graphene, HfO\({}_{2}\)-graphene (10, 30, and 50 wt%) nanocomposites.
## 4 Model Construction and Fit Evaluation
### Model Fit and Hypothesis Testing  
Model fit refers to the degree of consistency between the theoretical conceptual model and the actual statistical data model. Commonly used parameters for Structural Equation Modeling (SEM) include the Chi-square/degree of freedom ratio (CMIN/DF), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA) [39].  
To evaluate the constructed model, we selected four indicators based on the characteristics of the validation data, sample size, and hypothesis conditions: the Chi-square/degree of freedom ratio (CMIN/DF), goodness of fit index (GFI), adjusted goodness of fit index (AGFI), and root mean square error of approximation (RMSEA). A CMIN/DF value less than 3, a \(p\)-value smaller than 0.5, and an RMSEA value below 0.1 indicate that the model has an ideal fit. Additionally, the CFI and Comparative Fit Index (CFI) values should exceed the critical threshold of 0.8, ensuring a reasonable fit. The results of model fitting evaluation are shown in Table 4. Based on these model fit evaluation criteria, the overall model construction is deemed appropriate, and the results are reliable.
# Reversible Data Hiding for DICOM Image Using Lifting and Companding
## 5 Proposed Algorithm
### Data Decoding  
Step 1: Image transformation: Decompose the watermarked image (\(I_{w}\)) into 4-subbands, i.e., low-low (LL), high-low (HL), low-high (LH), and high-high (\(HH\)), using lifting.  
Step 2: Watermark information (bit) extraction: The watermark bits (\(W^{\prime}\)) are extracted by investigating the least significant bit (LSB) of \(HH\) sub-bands.  
Step 3: Generation of random binary matrix based on user-defined key: A binary pseudorandom matrix (R) is created depending on the secret key (K) supplied by the owner that was used at watermark embedding time.  
Step 4: Extraction of odd-even matrix pattern: Then odd-even matrix pattern (\(P^{\prime}\)) is generated based on the extracted watermark \(W^{\prime}\).  
\[P^{\prime}(x_{i},y_{i})=W^{\prime}(x_{i},y_{i})\oplus R(x_{i},y_{i}) \tag{15}\]  
Step 5: Generation of sign matrix: A sign matrix (S) is generated from \(HH\) sub-band, according to Equations (10) and (11).  
Step 6: Restoration of HH sub-band of watermarked image: The watermarked \(HH\) sub-band is restored to its original form according to the following rule.  
\[HH_{R}(x_{i},y_{i})=HH_{w}(x_{i},y_{i})+1\;if\;P^{\prime}(x_{i},y_{i})=odd\; \&W^{\prime}(x_{i},y_{i})=0 \tag{16}\]  
\[HH_{R}(x_{i},y_{i})=HH_{w}(x_{i},y_{i})-1\;if\;P^{\prime}(x_{i},y_{i})=even\; \&W^{\prime}(x_{i},y_{i})=1 \tag{17}\]  
\[HH_{R}(x_{i},y_{i})=HH_{w}(x_{i},y_{i})\;otherwise \tag{18}\]  
The symbol \(HH_{R}\) represents the restored \(HH\) sub-band. Then inverse lifting operation is done to reconstruct the original host image (\(I\)).
## 4 Conclusions  
The type of technology described herein was not designed to replace existing data collection and analysis techniques, but rather facilitate applications and education in geospatial sciences for a wider audience. Our results showed that the system is capable of multi-scale modeling applying key environmental variables regularly used for predicting species distributions. Just as important, we provide a user-friendly interface allowing new users to run models within a single day, and in some cases, the duration of a class period. This is far less time than it would take to install and master Geographic Information Systems and MaxEnt software, gather and format environmental datasets, and run a model.  
Currently the system only creates a single raster for the area bounded by the selected occurrence data and the system does not expose all of the options available for the MaxEnt modeling engine. The environmental layers are also currently limited to the continental United States. To extend this system, we plan to include consistent global environmental layers, add additional modeling techniques, provide more options for each modeling technique, and improve hardware performance. The ability to add data from directly from existing on-line databases, such as the Global Biodiversity Information Facility, is also being implemented through web services. A protocol is being developed specifically for accessing data for invasive species as part of the Global Invasive Species Information Network [28]. Future objectives also include developing training and educational materials to increase the user's understanding of modeling concepts, proper applications and, equally important, their limitations. We remain excited about the prospects these technologies offer to stakeholders, and hope our efforts provide new opportunities for resource management, environmental stewardship, education for all ages, and public participation
# Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model
###### Abstract  
This paper proposes the hybrid system model identified by a PWARX (piecewise affine autoregressive exogenous) model for modeling human driving behavior. In the proposed model, the mode segmentation is carried out automatically and the optimal number of modes is decided by a novel methodology based on consistent variable selection. In addition, model flexibility is added within the ARX (autoregressive exogenous) partitions in the form of statistical variable selection. The proposed method is able to capture both the decision-making and motion-control facets of the driving behavior. The resulting model is an optimal basal model which is not affected by the choice of data, where the explanatory variables are allowed to vary within each ARX region, thus, allowing a higher-level understanding of the motion-control aspect of the driving behavior, as well as explaining the driver's decision-making. The proposed model is applied to model the car-following driving task based on real-road driving data, as well as to ROS-CARLA-based car-following simulation and compared to [PERSON]'s driver model. Obtained results that show better performance both on prediction performance and mimicking actual real-road driving demonstrates and validates the usefulness of the model.  
driving behavior; driver model; hybrid system identification; statistical variable selection; consistent variable selection; PWARX model +
Footnote †: journal: Applied Sciences
## 3 Preliminaries
### Setup Phase
#### 4.1.2 Decision Making  
Depending on the neighborhood density, the sender node determines whether to run the clustering algorithm or calculate FR without using the FCM clustering method. Once a decision is made, the sender node executes the process and calculates FR. This FR value is then used as a threshold value to select the forwarder nodes.
## 5 Conclusions  
Through field measurements and theoretical analysis, the top plate force deformation characteristics of the underhand drift cut-and-fill stopping mining method were studied and the mechanical model of the filling body was constructed, and the following conclusions were mainly obtained:  
(1) With the increase in mining length, the load on the CPB roof tends to increase and then decrease and finally stabilize, while the stress-strain on the CPB roof tends to increase and then stabilize. The closer to the lower surface of the CPB roof, the higher the load and stress-strain.  
(2) As the span of the stope increases, the load on the CPB roof tends to increase linearly. As the length of the stope increases, the load on the CPB roof tends to increase rapidly and then stabilize. When the length of the stope exceeds 150 m, the load on the CPB roof reaches the extreme value.  
(3) The overlying load variation law of the CPB roof obtained based on the Classical Janssen pressure theory agrees with the field measurement, and the error with the measured value is less than 6%. The calculation results of the mechanical model of the filling body constructed by the thick plate theory have an error of no more than 5% with the field measurements.  
(4) Applying the research results to Jinchuan Group's third mine, the span of the stope can be expanded from 5 m to 6 m under the strength standard of the existing filling body, which results in 20% higher output, 11.4% lower mining cost and 16.3% higher mining efficiency. The research results are of great significance to the sustainable development of the mine.  
Writing--original draft preparation, Y.H.; writing--review and editing, B.H.; visualization, B.Z.; supervision, K.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (Grant No. 2018 YFC1900603).  
Not applicable.  
The data generated in this study are available upon request.  
Figure 9: Comparison of calculation and measurement values: (**a**) load, (**b**) stress.
# Shape Memory Alloy Reinforced Self-Healing Metal Matrix Composites
## 3 Synthesis of Shape Memory Alloy Reinforced Self-Healing Metal Matrix Composites
### The Impact of Reinforcement on the Solidification Process in SMA-Reinforced SHMMCs  
The impact of reinforcement on the solidification process in SMA-reinforced SHMMCs encompasses a range of factors, including how the wettability of the reinforcement affects the solidified microstructure of the composite [101; 102], how the reinforcement affects the viscosity and fluidity of the melts during nucleation and growth [103; 104; 105; 106], and how micro-segregation occurs during solidification [103]. When SMAs are presented in the liquid matrix, i.e., Sn-Bi, they can have several effects on the solidification of castings: acting as a solute and diffusion barrier, thereby altering the curvature of the solid-liquid interface; catalyzing heterogeneous nucleation of the solid phase from the melt on the SMA, which reduces the grain size; reducing the latent heat required for solidification, thereby increasing the solidification rate; decreasing the viscosity and fluidity of the melt and giving it thixotropic properties under certain conditions; restricting fluid convection because of the narrow interstices between the SMAs, which influences the solidification structure; affecting morphological instabilities such as planar-to-cellular dendritic solidification; influencing the dendrite structure; affecting grain size because of the heterogeneous nucleation or restricted spaces between the SMAs that restrict growth; and affecting micro-segregation in the matrix [102].  
Table 2 shows the synopsis of research on the synthesis of SMA-reinforced SHMMCs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year, Author** & **Matrix Alloy** & **Reinforcement (Pre-Strain)** & **Bonding and Special Distribution of SMA** \\ \hline
2007, Manuel [39] & Mg- 5.7 at\%Zn- 2.7 at.\%Al & 1\% continuous, uniaxially orientated & The wires were sputter coated with 5 nm of gold to increase the wettability of the wires. The wires were threaded through specially made clumps to hold them in tension during casting. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Synopsis of Research on Synthesis of Shape Memory Alloy Reinforced Self-Healing Metal Matrix Composites Showing the Composition of Matrix Alloys and Special Conditions of Reinforcement.
## 5 Conclusions  
In this paper, a two-channel and two-stage image deraining network based on a convolutional neural network is proposed for single image deraining. The proposed deraining network is based on DTDNet, which is our previous version for image dehazing. Specifically, a sub-pixel up-sampling module is introduced to expand the network channel to acquire more reasonable multi-scale features and improve the image resolution. Meanwhile, attention mechanism is combined with the multi-scale pooling layer of a pyramid to realize adaptive fusion of multi-scale features on the basis of preserving the global information. Finally, perceptual loss function, TV loss function, and MSE loss function are combined to effectively control the noise on the basis of improving the unstable output of MSE loss function due to outliers. Qualitative and quantitative experiments on both synthesized and real-world images demonstrated that the proposed model outperforms existing state-of-the-art CNN-based methods. Furthermore, additional ship detection experiments demonstrate that our model contributes to ship detection by enhancing images degraded under bad weather conditions.  
However, there are still some limitations for the network designed in this paper. Although previous research has shown that deraining networks trained on synthetic datasets can be effectively used in real-world image processing, a certain degree of rain streak residue is still present in the rain removal results of the real-world images compared to the synthetic images. Therefore, more reasonable datasets are needed for network model training. Besides, the network model structure needs to be further optimized to meet the practical applications. Finally, whether the number of parameters of the network meets the requirements of the hardware platform and the running time of the algorithm are also issues that need to be considered in practical applications including sea surface ship detection.  
Conceptualization, T.L.; Funding acquisition, T.L.; Investigation, L.N.; Methodology, B.Z.; Supervision, G.W.; Writing--original draft, T.L., P.L. and Y.Z.; Writing--review & editing, T.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by China Postdoctoral Science Foundation (2019M661076).  
Not applicable.  
Data is contained within the article.  
The authors declare no conflict of interest.