esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Objectives  
We are specifically interested in whether the results of these past studies are replicable when applying a more advanced processing scheme that uses all available data and accounts for inter-annual variability in vegetation growth. To achieve this, we will firstly reproduce the past studies with a comparable methodology and data setup (although acknowledging that full reproducibility could not be obtained following the definition above; reasons are outlined in the previous subsection, e.g., the exclusion of Landsat MSS data). Secondly, we will replicate the results by applying a data-driven, state-of-the-art methodology to the complete Landsat data record that temporally overlaps with the reference studies. Based on this comparative study, we will answer the research question \"How do results of past and new time-series analysis approaches compare and what drives potential disagreement?\" Subsequently, we will apply lessons learned to arrive at a more nuanced and information-enriched long-term vegetation change assessment for semi-arid ecosystems by separating changes of woody and herbaceous vegetation.
# Architecture Exploration of a Backprojection Algorithm for Real-Time Video SAR
## 5 Conclusions  
This paper proposed architectures for backprojection based video SAR for a system equipped with multiple GPUs. We analyzed the video SAR frame rate for non-overlapped and overlapped aperture modes. Depending on the choice of processing aperture length and the parallel structure on GPU devices, six architectures of single-stream BP-VISAR pipeline were proposed and their performances evaluated in both non-overlapped and overlapped modes. The GPU parallelized architecture with sub-aperture processing were found to be the best architecture as a single-stream pipeline and its architecture was further extended for multiple streams and multiple GPUs to utilize the idle GPU resources further. The experimental results showed that the proposed BP-VISAR architectures with sub-aperture processing were scalable with GPU devices for high pixel resolution.  
The proposed BP-VISAR architectures can be used to generate video SAR frames of target cross-range resolution in real-time from the streaming phase history data of the radar sensor. The accelerated video SAR systems can be loaded on airborne or drone platforms for collaborative day-and-night surveillance on the video SAR sensor networks.  
Conceptualization, M.L. and W.L.; data curation, S.L. and I.B.; investigation, S.L. and Y.J.; methodology, M.L. and W.L.; software, S.L. and I.B.; validation, S.L.; visualization, Y.J.; writing--original draft, S.L. and M.L.; writing--review and editing, Y.J. and W.L. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the support from Next Generation SAR Research Laboratory at Korea Aerospace University, originally funded by Defense Acquisition Program Administration (DAPA) and Agency for Defense Development (ADD).  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{table}
\begin{tabular}{r c c c c c c c c}  & [20] & [23] & \multicolumn{3}{c}{**Arch. F3**} & \multicolumn{3}{c}{**Arch. S3**} \\ \hline
**SAR Mode** & **Image** & **Image** & **Video** & & & & \\
**Pixel-Resolution** & **4096 \(\times\) 4096** & **8965 \(\times\) 7065** & **4096 \(\times\) 4096** & & & **4096 \(\times\) 4096** & & \\ \hline aperture proc. & full & full & full & full & sub & sub & sub & sub \\ overlap ratio(\%) & 0 & 0 & 0 & 80 & 0 & 80 & 80 & 80 \\ NP & 300,000 & 8192 & 585 & 585 & 585 & 585 & 585 & 585 \\ \#GPU & 1 & 4 & 1 & 1 & 1 & 1 & 4 \\ \#stream/GPU & n/a & n/a & 1 & 1 & 1 & 3 & 3 \\ \#CUDA cores & 10,496 & 8192 & 10,496 & 10,496 & 10,496 & 10,496 & 41,984 \\ imaging time (s) & 180.000 & 26.100 & 0.236 & 0.212 & 0.227 & 0.053 & 0.044 & 0.014 \\ GPU clock(GHz) & 1.70 & 1.17 & 1.70 & 1.70 & 1.70 & 1.70 & 1.70 \\ normalized time & 1.49 & 1.13 & 1.00 & 0.90 & 0.97 & 0.22 & 0.18 & 0.23 \\ \end{tabular}
\end{table}
Table 9: Performance comparison with previous studies.
## 5 Results and Discussion  
### Results of Clearance Effect on Contact Force Fluctuation  
For relatively minute angular deviations, as illustrated in Figure 2, the range of angular values can be approximated to the ratio of the clearance to pinion length and half of the clearance over the pinion length. Specifically, when the clearances are set at 10 \(\upmu\)m, 20 \(\upmu\)m, and 30 \(\upmu\)m, respectively, they correspond to the angular variations of [0.001 rad, 0.002 rad], [0.002 rad, 0.004 rad], and [0.003 rad, 0.006 rad]. Within these bounds, the relationship between the contact force and contact depth is depicted in Figure 8. Post the establishment of a slight angular discrepancy due to the clearance, there is an overall reduction in the contact force. Additionally, as the clearance marginally increases within a certain range, there is a subtle rise in the contact force.
# The Mesoscopic Numerical Simulation of GAP/CL20/AP Composite Solid Propellant Based on MPM and FEM
## 3 The Modeling Process
### The Constitutive Model of Grains and Matrix  
Compared with polymer adhesives, the elastic modulus of AP and CL20 grains is very large, the constitutive model of their materials can be approximately considered as linear elasticity, and the plane stress CPE4 element is selected. The mechanical properties elastic modulus \(E\) and Poisson's ratio \(v\) of the CL20 and AP grains are shown in Table 2.  
Other small grains and the binder matrix GAP could be considered as a homogeneous hyperelastic mixture matrix, and the hyperelastic constitutive model above the second order has a high accuracy. Furthermore, the viscoelastic properties of a mixed matrix under dynamic loads such as large deformation, long-time loading, impact, or cyclic loading can not be ignored, so a viscoelastic and hyperelastic constitutive model should be established, and the stress expression of this model is as follows:  
\[\sigma=\sigma^{e}+\sigma^{o} \tag{11}\]  
where \(\sigma^{e}\) denotes the viscoelastic part and \(\sigma^{o}\) represents the hyperelastic part. The second-order simplified polynomial model is stable in all strain ranges and has high accuracy, so this paper chooses the second-order simplified polynomial model as the constitutive model of the hyperelastic part of a GAP adhesive. The form of the reduced polynomial strain energy potential is as follows:  
\[U=\sum_{i=1}^{N}C_{i0}(\overline{I}_{1}-3)^{i}+\sum_{i=1}^{N}\frac{1}{D_{i}}( J-1)^{2i} \tag{12}\]  
where \(U\) is the strain energy per unit of reference volume; \(N\) is a material parameter; \(C_{i0}\) and \(D_{i}\) are temperature-dependent material parameters; and \(\overline{I}_{1}\) is the first deviatoric strain invariant. The stress expression of the hyperelastic part is as follows:  
\[\sigma^{e}=\sum_{n=1}^{2}\frac{2\mu_{n}}{\alpha_{n}}\Big{(}\lambda^{a_{n}-1}- \lambda^{-a_{n}/2-1}\Big{)} \tag{13}\]  
The viscoelastic part is a second-order Prony model, and its stress expression is as follows:  
\[\sigma^{v}=\int_{0}^{t}\sum_{m=1}^{2}E_{m}\exp\left(-\frac{t-\tau}{\tau_{m}} \right)\frac{\partial\varepsilon}{\partial\tau}\mathrm{d}\tau \tag{14}\]  
In this paper, the parameters of second-order simplified polynomial hyperelastic and second-order viscoelastic materials were obtained according to Reference [2], as shown in Table 3  
\begin{table}
\begin{tabular}{c c c c} \hline
**Component** & **AP** & **CL20** & **Al, Plasticizer, etc.** \\ \hline Mass fraction/\% & 9.58 & 40.13 & 50.29 \\ Volume fraction/\% & 8.95 & 34.18 & 56.87 \\ Grain size/um & 50–280 & 20–90 & Classified as \\  & normal distribution & normal distribution & adhesive matrix \\ \hline \end{tabular}
\end{table}
Table 1: The content and size distribution of various components of the propellant.  
\begin{table}
\begin{tabular}{c c c} \hline
**Component** & **\(\mathbf{E(GPa)}\)** & \(\boldsymbol{v}\) \\ \hline CL20 grains & 17.1 & 0.2 \\ AP grains & 32,450 & 0.14 \\ \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of CL20 and AP grains [54; 55].
# Dynamic Modeling and Full-Process Simulation of the Core Engine Test Rig Main Test System
###### Abstract  
As a virtual digital model that can reflect physical entities or systems, digital twins are revolutionizing industry. The first prerequisite for the construction of digital twins is the establishment of high-precision and complex entities or system models. A 47-components numerical system is established for the core engine test rig main test system by using the finite volume modularization modeling method. A comprehensive solution to the system-level valve-spool/orifice throttling modeling, the key issue of the fluid pipeline system modeling, is presented, and the algorithms of throttling and mixing are deepened and expanded. The full-process simulation study on two tests of normal-temperature 1400 s and low-temperature 1240 s shows that the combined regulation of five regulator valves and the change of cold source directly decide dynamic change of the system in each stage; the simulation reveals the phenomena such as the gas cylinder cooling with deflation, the air cooling when expanding from main pipeline to two branch pipelines, shunting flow by branch pipeline, and the cold and hot gases mixing; the overall variation trends of the simulation curves are consistent with those of all the experimental curves of the test rig normal-temperature/low-temperature air supply lines, exhaust bypass, and engine main line in two operating conditions, and the maximum error between simulation curves and test curves of pressure, total pressure, and total temperature is less than 12%. The numerical system can be used for the construction of virtual models of digital twins, and the modeling method provides a feasible solution to the key technology of digital twins.  
digital twin; core engine test rig; complex system dynamic simulation; finite volume method; transient pipeline flow; valve throttling modeling +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Non-Contact Temperature Controlled Medical Laser System  
The experimental setup is composed of a laser (\(\lambda=1071\) nm, Ytterbium Fiber Laser, YLM-20 SC, IPG Photonics, Oxford, MA, USA), laser delivery optics designed for a converging beam with a Gaussian profile, providing a spot diameter of 3.5 mm (\(\
icefrac{{\
u}}{{\varepsilon}}\)) at the application distance (40 mm), and an IRt/c temperature measurement and laser control system as illustrated in Figure 1. We used a commercially available IRt/c sensor (MLX90614 ESF-BCC, Melexis NV, Leper, Belgium) with a spectral bandwidth of 5.5-12 um in our temperature measurement system; therefore, the system does not get affected by the laser irradiation. We also utilized a \(\
icefrac{{\
u}}{{\gamma}}\)\" Lens Tube and an AR coated (3-11 um) ZnSe lens (ZnSe10, Crystran Ltd., Dorset, UK) to house the IRt/c sensor and to convert the diverging measurement cone of the sensor into a converging measurement cone. We calibrated our temperature measurement system using a NIST certified blackbody source (BB702, Omega Engineering, Norwalk, CT, USA). The IRt/c system takes average surface temperature measurements from a center weighted 3.6 mm diameter spot at 41 mm focal distance. It operates at 57 ms refresh time with 1 \(\
icefrac{{\mathrm{{}^{\circ}}}}{{\mathrm{C}}}\) accuracy and 0.02 \(\
icefrac{{\mathrm{{}^{\circ}}}}{{\mathrm{C}}}\) resolution. Our previous work contains details on characterization methods for determining system specifications [26]. We assumed that the emissivity of the tissue is equal to the emissivity of water (\(\epsilon=0.96\)). The IRt/c sensor assembly is attached to the side of the laser delivery optics at an oblique angle to perfectly coincide laser application and temperature measurement spots without interfering with the laser beam as depicted in Figure 1. We have used an ON-OFF feedback control mechanism for ensuring fast response of the system. The laser exposure is modulated using a TTL signal generated by the TI MSP-EXP430F5529 LP microcontroller board, which also reads out the IRt/c sensor via an I\({}^{2}\)C interface. This microcontroller feeds the real-time temperature data and the laser control signal to a computer via a Bluetooth connection for display and storage.  
Figure 1: Schematic and photo of the experiment setup. The laser delivery optics (Gaussian profile, \(\diameter_{laser}=3.5\) mm @ \(d=40\) mm) and the temperature measurement system (\(\diameter_{measurement}=3.6\) mm @ \(f=41\) mm) are in an off-axis alignment configuration.
## 1 Introduction
Administrative unit is mostly used for social and economic issues, and its segmentation does not accord with the neighborhood characteristics of landslide data. As such, administrative unit is rarely used in geological hazard assessment. Grid unit is a popular mapping unit for susceptibility assessment since it is easily accessible, but it is not associated with geological environments. Slope unit is a relative new mapping unit for evaluating landslide susceptibility, which is generated according to hydrology theory and is the watershed area defined by drainage lines (valley lines) and water divide lines (ridge lines) [27]. It is the basic topographical unit of geological hazard occurrence. Slope unit has higher internal homogeneity and between-unit heterogeneity than grid unit. It is closely related to geological environment conditions. In this sense, slope unit provides an alternative solution for spatial proximity expression of the GWR model for landslide susceptibility assessment. Two other key issues of GWR model are the multicollinearity elimination and the kernel function establishment. Previous studies, e.g., [28], indicated that GWR is highly susceptible to the effects of multicollinearity between explanatory variables, and collinearity among pairs of explanatory variables or multicollinearity among more than two variables often lead to problems such as parameter estimate instability and unintuitive parameter signs. These problems remain significant owing to the complicated conditions of landslide posing a high possibility of correlations between explanatory variables. Kernel function is based on the distances between observations and calibration units to place emphasis on observations that are closer in space [28]. The selection of kernel function type and the determination of its bandwidth are crucial to the spatial proximity modeling of GWR. In this study, we attempt to propose a spatial proximity based on geographically weighted regression (S-GWR) model for landslide susceptibility assessment. The presented model resolves the spatial non-stationarity of landslide susceptibility assessment with GWR model. Firstly, we generate slope units to establish spatial adjacency. Then, variance inflation factor (VIF) method [29] and principal component analysis (PCA) method [30] were adopted to eliminate multi-collinearity, and kernel function was determined according to the characteristics of landslide data. Finally, we chose Qingchuan County, Sichuan Province, China, as the study area to validate the applicability of the model, and further compared the established model with the grid-unit GWR model and other evaluation models.
## 6 Movement Track Positioning Model
### Model Establishment  
The movement track positioning model is based on the positioning model and the interference data recognition model. First, the collected data is classified by the classification model, and divided into undisturbed data and disturbed data. The disturbed data are marked in red, and the normal data without disturbance are marked in green. The disturbed signal and the undisturbed signal are substituted into the two positioning models to realize the positioning of the target point; finally, the coordinates of the positioned target points are connected in sequence to obtain the movement trajectory of the target point so as to realize the target point under different conditions.  
Figure 14: Movement track positioning technology route.
# Efficient Reversible Data Hiding Scheme for AMBTC-Compressed Images
## 1 Introduction
With such an arrangement, [PERSON] et al. successfully shrank the size of the original quantization values of blocks and saved more space to embed secret bits [20]. To improve the embedding capacity and efficiency rate further while maintaining the reconstruction of a cover image after extracting a secret message, in this paper, we propose a new, reversible data hiding scheme for an absolute moment block truncation coding (AMBTC) image, which is a variant of the BTC-compressed image. In the proposed scheme, the high correlation of values in two mean tables of an AMBTC-compressed image is explored to embed a secret message. Experimental results demonstrated that the proposed scheme obtained a high embedding capacity and the same PSNRs as the traditional AMBTC algorithm. In addition, the proposed scheme achieved a higher embedding capacity and higher efficiency rate than those of some previous schemes while maintaining an acceptable bit rate. The remainder of the paper is organized as follows. Section 2 briefly reviews the original AMBTC compression algorithm and previous AMBTC-based data hiding scheme [4]. Section 3 describes details concerning the proposed scheme. Then, experimental results are analyzed and compared to previous schemes in Section 4. Our conclusions and discussion are provided in Section 5.
## 3 Concept and Methodology
### Machine Learning and Data
Before elaborating on the machine learning (ML) model results, it is important that we graphically show the data sets and some visible impact of concentration, temperature, wavelengths, and their correlation with changes in the index of refraction or distance d. The following key point is significant because it demonstrates changes in the input layer or data variables that assist the ML model in mapping inputs with outputs or distinguishing and learning from the provided data sets. Therefore, it can correlate inputs to outputs by generating weights and biases. This can be seen in Figures 5-8. Figure 5 represents and depicts the effect of water concentration (\(W_{water}\)), indicating that distance \(d\) is changing with water concentration and is more noticeable with higher incident angle values. Another factor that helps ML to predict and correlate inputs with outputs is temperature dependence, which is clearly shown in Figure 6 by indicating the change in distance \(d\) for different temperature values and different incident angles \(\theta_{i}\). Refractive index (pure water and salt water) effect on the distance \(d\) for different incident angles \(\theta_{i}\) is also shown in Figure 7. Finally, Figure 8 denotes changes in the distance \(d\) for different wavelengths. Figure 4: Water classification sensor flowchart. Figure 5: Effect of pure water concentration \(W_{water}\) on the distance \(d\) for different angle of incidence \(\theta_{i}\) at \(\lambda\) = 660 nm and \(T\) = 30 \({}^{\circ}\)C.  
Figure 6: Temperature effect of salt water on the distance \(d\) for different angle of incidence \(\theta_{i}\) at \(\lambda\) = 660 nm and \(W_{water}\) = 10 cm. Figure 7: Effect of the water sample nature on the distance \(d\) for different angle of incidence \(\theta_{i}\) at \(\lambda\) = 660 nm, \(T\) = 30 \({}^{\circ}\)C, and \(W_{water}\) = 5 cm.
## 2 Methodology for Parameter Estimation from SAR Data
### Hierarchical Models  
An external radiometric calibration campaign results in a pool of data samples (see [5] for an example), one per reference point target ERCS per SAR image. Depending on the research question, the data often needs to be grouped to estimate group-level parameters (for compensation) or to select group-level models (for validation). Typical questions are:* What is the best estimate of the calibration factor (and its respective confidence interval) if several types of reference point targets (_i.e._, transponders and corners of different sizes) with different ERCS' and stabilities are deployed? Solving this problem with classical (frequentist) statistics would require to estimate the population mean of each group, and deriving the calibration factor after ERCS compensation between groups. The information on the variance within each group is lost, and a reliable statement of the final uncertainty or confidence interval on the estimated calibration factor is difficult to achieve. With hierarchical Bayesian modeling though, the variance within each group (target type) and the variance across all target types can be derived simultaneously because group and total dispersion are handled within a joint probability model.
* Is there a significant systematic dependence on the chosen antenna beam (or near/far range, left/right looking geometries, or ascending/descending orbits) for radiometric measurements? Once again the same set of data samples as before should be grouped, but this time by antenna beam (or near/far range, left/right looking acquisitions, or ascending/descending orbits). For each group, a posterior distribution for the respective calibration factor can now be derived. Comparing the different posterior distributions allows to conclude if a significant radiometric inter-beam offset exists.
* For a check on plausibility: Is the ERCS of one of the reference point targets systematically different from the others? (Here repeated overpasses over the same set of targets is assumed.) In order to answer this question, the overpass-dependent effect of the SAR system and the atmosphere should be modeled out of the analysis. This can be done by grouping the samples according to overpass and target ID. All target samples of one overpass can be used to compensate for SAR system and atmospheric effects, and in a second step the group ERCS of each target can be determined.  
In fact, all questions previously raised can be conveniently answered by setting up a single _joint_ hierarchical model. Partial results contribute to the particular derivation of posterior distributions without loss of information. On the other hand, a classical (frequentist) approach would require independent analyses, and carrying over results from one model to the next would result in loss of information (due to the summarizing nature of the classical (frequentist) approach).  
Further details on hierarchical models can be found in [12].