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Simulated raw data have become an essential tool for testing and assessing system parameters and imaging performance due to the high cost and limited availability of real raw data from spaceborne synthetic aperture radar (SAR). However, with increasing resolution and higher orbit altitudes, existing simulation methods fail to generate SAR simulated raw data that closely resemble real raw data. This is due to approximations such as curved orbits, “stop-and-go” assumption, and Earth’s rotation, among other factors. To overcome these challenges, this paper presents an accurate range model with a “nonstop-and-go” configuration for raw data simulation based on existing time-domain simulation methods. We model the SAR echo signal and establish a precise space geometry for spaceborne SAR. Additionally, we precisely identify the target illumination area based on elliptical beams through space coordinate transformation. Finally, the SAR raw data were accurately simulated using high-precision time-domain simulation methods. The accuracy of the proposed model was validated by comparing it with the traditional hyperbolic model and the curved orbit model with “stop-and-go” assumption through image processing of the generated raw data. Through the analysis of point target quality parameters, the errors of various parameters in our distance model compared with the other two models are within 1%. Furthermore, this simulation method can be adapted to simulate raw data of other modes and satellite orbits by adjusting beam control and satellite orbit parameters, respectively. The proposed simulation method demonstrated high accuracy and versatility, thereby providing a valuable contribution to the development of remote sensing technology.

Airborne synthetic aperture radar (SAR) systems often encounter the threats of interceptors or electronic countermeasures (ECM) and suffer from motion measurement errors. In order to design and analyze SAR systems while considering such threats and errors, an integrated raw data simulator is proposed for airborne spotlight electronic counter-countermeasure (ECCM) SAR. The raw data for reflected echo signals and jamming signals are generated in arbitrary waveform to achieve pulse diversity. The echo signals are simulated based on the scene model computed through the inverse polar reformatting of the reflectivity map. The reflectivity map is generated by applying a noise-like speckle to an arbitrary grayscale optical image. The received jamming signals are generated by the jamming model, and their powers are determined by the jamming equivalent sigma zero (JESZ), a newly proposed quantitative measure for designing ECCM SAR systems. The phase errors due to the inaccuracy of the navigation system are also considered in the design of the proposed simulator, as navigation sensor errors were added in the motion measurement process, with the results used for the motion compensation. The validity and usefulness of the proposed simulator is verified through the simulation of autofocus algorithms, SAR jamming, and SAR ECCM with pulse diversity. Various types of autofocus algorithms were performed through the proposed simulator and, as a result, the performance trends were identified to be similar to those of the real data from actual flight tests. The simulation results of the SAR jamming and SAR ECCM indicate that the proposed JESZ is well-defined measure for quantifying the power requirements of ECCM SAR and SAR jammers.

As a direct consequence of the orbital height, the integration time in geosynchronous synthetic aperture radar（GEO SAR） with metric or decimetric azimuth resolutions is in the order of several hundreds or even thousands of seconds. With such long integration time, the compensation of residual tropospheric propagation terms poses one of the fundamental challenges associated with GEO SAR missions. In order to better characterise the impact of the propagation errors on GEO SAR imaging, we put forward a model for the simulation of the tropospheric delay appropriate for the accurate simulation of GEO SAR surveys. The suggested model, with a deterministic background component and a random turbulent one, incorporates some of the most recent meteorological data for the characterization of the troposphere. To illustrate the relevance of the derivation, the suggested model is used for performance estimation and raw data simulation of GEO SAR raw data. Substantial conclusion on the system impulse response and the associated calibration requirements is also drawn from the analysis.

The Synthetic Aperture Radar (SAR) raw data generator is required to the evaluation of focusing algorithms, moving target analysis, and hardware design. The time-domain SAR simulator can generate the accurate raw data but it needs much time. The frequency-domain simulator not only increases the efficiency but also considers the trajectory deviations of the radar. In addition, the raw signal of the extended scene included static and moving targets can be generated by some frequency-domain simulators. However, the existing simulators concentrate on the raw signal simulation of the static extended scene and moving targets at uniform speed mostly. As for the issue, the two-dimensional signal spectrum of moving targets with constant acceleration can be derived accurately based on the geometric model of a side-looking SAR and reversion of series. And a frequency-domain algorithm for SAR echo signal simulation is presented based on the two-dimensional signal spectrum. The raw data generated with proposed method is verified by several simulation experiments. In addition to reveal the efficiency of the presented frequency-domain SAR scene simulator, the computational complexity of the proposed method is compared with the time-domain approach using the complex multiplication. Numerical results demonstrate that the present method can reduce the computational time significantly without accuracy loss while simulating SAR raw data.

Is Chinese urbanization going to take a long time, or can its development goal be achieved by the government in a short time？ What is the highest stable urbanization level that China can reach？ When can China complete its urbanization？ To answer these questions, this paper presents a system dynamic（SD） model of Chinese urbanization, and its validity and simulation are justified by a stock-flow test and a sensitivity analysis using real data from 1998 to 2013. Setting the initial conditions of the simulation by referring to the real data of 2013, the multi-scenario analysis from 2013 to 2050 reveals that Chinese urbanization will reach a level higher than 70% in 2035 and then proceed to a slow urbanization stage regardless of the population policy and GDP growth rate settings; in 2050, Chinese urbanization levels will reach approximately 75%, which is a stable and equilibrium level for China. Thus, it can be argued that Chinese urbanization is a long social development process that will require approximately20 years to complete and that the ultimate urbanization level will be 75–80%, which means that in the distant future, 20–25% of China＇s population will still settle in rural regions of China.

Biomass gasification has obtained great interest over the last few decades as an effective and trustable technology to produce energy and fuels with net-zero carbon emissions. Moreover, using biomass waste as feedstock enables the recycling of organic wastes and contributing to circular economy goals, thus reducing the environmental impacts of waste management. Even though many studies have already been carried out, this kind of process must still be investigated and optimized, with the final aim of developing industrial plants for different applications, from hydrogen production to net-negative emission strategies. Modeling and development of process simulations became an important tool to investigate the chemical and physical behavior of plants, allowing raw optimization of the process and defining heat and material balances of plants, as well as defining optimal geometrical parameters with cost- and time-effective approaches. The present review paper focuses on the main literature models developed until now to describe the biomass gasification process, and in particular on kinetic models, thermodynamic models, and computational fluid dynamic models. The aim of this study is to point out the strengths and the weakness of those models, comparing them and indicating in which situation it is better to use one approach instead of another. Moreover, theoretical shortcut models and software simulations not explicitly addressed by prior reviews are taken into account. For researchers and designers, this review provides a detailed methodology characterization as a guide to develop innovative studies or projects.

To advance additive manufacturing (AM), a scalable architecture is needed to structure, curate and access the data from AM R&D projects that are conducted to evaluate new materials, processes and technologies. Effective project metadata management enables the sharing of AM domain knowledge. This work introduces an AM data modeling architecture to capture pedigree information from AM projects which enables the traceability of the material. This overall AM model includes five modules covering information about (1) project management, (2) feedstock materials, (3) AM building and post processing, (4) microstructure and properties measurements and (5) computer simulations. The objective of this design is to ease the integration of the heterogeneous datasets from different sources and allow for extensions, for example, to incorporate sub-models from other efforts. As a proof of concept, the material and process models defined in the paper capture the major metadata elements for laser powder bed fusion AM. To demonstrate the effectiveness of the architecture, the models are implemented using extensible markup language and preliminarily tested using the project data from America Makes. Additional data sub-models can be integrated in this architecture without affecting the existing structure.

By integrating statistical modeling and data analysis techniques, we systematically assess the carbon emission performance of the ceramic industry and propose targeted emission reduction pathways. Firstly, the entropy weight TOPSIS model is employed to quantitatively evaluate the carbon emission performance of the three major Chinese ceramic production areas: Foshan, Jingdezhen, and Zibo. Through data-driven quantitative analysis, it is disclosed that the carbon emission intensity in Foshan is significantly higher than that in the other two regions (with a relative closeness degree of 0.5185). The key issues identified include high energy consumption in the production process, a high reliance on raw coal, and insufficient investment in environmental protection. Furthermore, through the XGBoost-SHAP combined modeling, the key drivers of carbon emissions are precisely identified from multi-dimensional data. It is found that the elasticity coefficient of raw coal in the carbon emission proportion is as high as 25.84%, while the potential for substitution with natural gas is remarkable. Based on statistical prediction techniques, a carbon emission trend model under the scenario of energy structure optimization is constructed, predicting that after reaching a peak in 2017, Foshan’s carbon emissions will continue to decline, with the proportion of raw coal dropping to 48% and that of natural gas rising to 10%, thereby verifying the feasibility of the green transformation. Additionally, a multi-agent carbon trading simulation model is constructed to explore the emission reduction behaviors of enterprises under different carbon price scenarios. This study not only achieves precise quantitative analysis of carbon emissions through statistical method innovation but also verifies the feasible paths of low-carbon transformation through data modeling.

Synthetic Aperture Radar (SAR) raw data simulation is a fundamental problem in radar system design and imaging algorithm research. The growth of surveying swath and resolution results in a significant increase in data volume and simulation period, which can be considered to be a comprehensive data intensive and computing intensive issue. Although several high performance computing (HPC) methods have demonstrated their potential for accelerating simulation, the input/output (I/O) bottleneck of huge raw data has not been eased. In this paper, we propose a cloud computing based SAR raw data simulation algorithm, which employs the MapReduce model to accelerate the raw data computing and the Hadoop distributed file system (HDFS) for fast I/O access. The MapReduce model is designed for the irregular parallel accumulation of raw data simulation, which greatly reduces the parallel efficiency of graphics processing unit (GPU) based simulation methods. In addition, three kinds of optimization strategies are put forward from the aspects of programming model, HDFS configuration and scheduling. The experimental results show that the cloud computing based algorithm achieves 4_ speedup over the baseline serial approach in an 8-node cloud environment, and each optimization strategy can improve about 20%. This work proves that the proposed cloud algorithm is capable of solving the computing intensive and data intensive issues in SAR raw data simulation, and is easily extended to large scale computing to achieve higher acceleration.

Batatasin-III, a phenanthrene compound isolated from Bletilla striata, has demonstrated potential anti-inflammatory and immunomodulatory effects, yet its precise molecular mechanism against ulcerative colitis (UC) remains largely unexplored. This study integrates network pharmacology, molecular docking, ADMET profiling, and molecular dynamics (MD) simulations to systematically elucidate the multitarget therapeutic potential of Batatasin-III in UC treatment. Batatasin-III-related targets were retrieved from SwissTargetPrediction, while UC-associated genes were collected from GeneCards and OMIM databases. A total of 101 intersecting genes were identified and subjected to PPI network construction using STRING and topological analysis in Cytoscape. GO and KEGG enrichment analyses revealed significant involvement in key biological processes and pathways such as MAPK signaling, PI3K-Akt signaling, protein phosphorylation, and cytokine-mediated inflammation. Molecular docking showed strong binding affinities between Batatasin-III and core targets ALB (− 8.4 kcal/mol), MAPK3 (− 8.2 kcal/mol), ESR1 (− 7.7 kcal/mol), and HSP90AA1 (− 5.7 kcal/mol). ADMET evaluation via ADMETlab 3.0 predicted favorable drug-likeness, bioavailability, and low toxicity for Batatasin-III. Subsequent 100-ns MD simulations demonstrated high conformational stability (RMSD < 3.7 Å), sustained hydrogen bonding, and compact binding dynamics, particularly in ESR1-Batatasin-III and MAPK3-Batatasin-III complexes. MM/PBSA binding free energy analysis supported strong binding thermodynamics, with ALB-Batatasin-III exhibiting the most favorable ΔG_bind (− 29.65 kcal/mol). Residue energy decomposition highlighted critical contributions from TYR411, MET125, HIS524, and ASN171, among others. To validate these computational predictions, in vitro assays were conducted. A CCK-8 assay confirmed Batatasin-III was non-cytotoxic to RAW 264.7 macrophages. In an LPS-stimulated model, Batatasin-III significantly and dose-dependently inhibited the mRNA expression of key pro-inflammatory mediators, including TNF-α, IL-6, IL-1β, and NOS2. Overall, Batatasin-III may exert therapeutic effects against UC through multitarget modulation of inflammation, kinase regulation, and epithelial repair, primarily via the MAPK and PI3K-Akt pathways. This study provides a validated mechanistic foundation for Batatasin-III as a potential bioactive compound for UC intervention and supports further in vivo validation.