Detection and clearance of landmines is a complex and risky activity that requires advanced remote sensing techniques to reduce the risk to operators in the field. In this article, we propose a novel synthetic dataset for buried landmine detection to provide researchers with a valuable resource to observe, measure, locate, and address issues in landmine detection. The dataset consists of 41 800 microwave holographic images (2-D) and their holographic inverted scans (3-D) of different types of buried objects, including landmines, clutter, and pottery objects, and is collected by means of a microwave holography radar. We evaluate the performance of several state-of-the-art deep learning models trained on our synthetic dataset for various classification tasks. While the results do not yield yet high performances, showing the difficulty of the proposed task, we believe that our dataset has significant potential to drive progress in the field of landmine detection; thanks to the accuracy and resolution obtainable using the holographic radars. To the best of the authors' knowledge, the dataset is the first of its kind and will help drive further research on computer vision methods to automatize mine detection, with the overall goal of reducing the risks and the costs of the demining process.
The study investigates the impact of motions of floating offshore wind turbine platforms on wake evolution and overall wind farm performance, employing large-eddy simulation (LES) and dynamic wake modeling method. First, the differences between wakes of floating and bottom-fixed wind turbines under forced motion are examined. Subsequently, a systematic comparative analysis is performed for four representative floating platform configurations—Spar, Semi-submersible, Tension-Leg Platform (TLP), and Monopile (Mnpl)—to assess wake dynamics and downstream turbine responses within tandem-arranged arrays. Results indicate that platform pitch motion, by inducing periodic variations in the rotor’s relative inflow angle, significantly enhances wake unsteadiness, accelerates kinetic energy recovery, and promotes vortex breakdown. Tandem-arrange turbines simulations further reveal that platform-dependent motion characteristics substantially influence wake center displacement, velocity deficit, downstream turbine thrust, and overall power fluctuations at the wind farm scale. Among the examined configurations, the Spar platform exhibits the most pronounced wake disturbance and the largest downstream load and power oscillations, with rotor torque and thrust increasing by 10.2% and 10.6%, respectively, compared to other designs. This study elucidates the coupled mechanisms among 6-DOFs (Six Degrees Of Freedom) motions, wake evolution, and power performance, providing critical insights for optimizing floating wind farm platform design and developing advanced cooperative control strategies.
The increasing demand for cost-effective floating offshore wind turbines (FOWTs) necessitates streamlined mass production and efficient assembly strategies. This research investigates the assembly and integration of 15 MW FOWT floaters, utilising a semi-submersible floater equipped with a 15 MW wind turbine. The infrastructure and existing port facilities of Taiwan are used as an example. The effectiveness of various assembly and integration strategies has been evaluated. The study outlines equipment and infrastructure requirements for on-quay floater and turbine assembly, comparing on-quay assembly to construction at remote locations and subsequent towing. Detailed analyses of port operations, crane specifications, and assembly procedures are presented, emphasising the critical role of crane selection and configuration. The findings indicate that on-quay assembly at one major port is feasible and cost-effective, provided that port infrastructure and operational logistics are optimised. This research offers insights and recommendations for implementing large-scale FOWT projects, contributing to advancing offshore wind energy deployment.
Production of electric energy or power. Powerplants. Central stations
Four types of undulated cylinders with streamwise undulation, transverse undulation, in-phase undulation and antiphase undulation are employed to investigate the undulation-axis effect on the structure of heat transfer around wavy cylinders. The flows around these undulated cylinders are numerically simulated by large eddy simulation at <i>Re</i> = 3000. The force coefficients and Nusselt numbers of the cylinders with transverse undulation and in-phase undulation are significantly influenced by wavelength and wave amplitude. On the other hand, the cylinders with streamwise undulation and antiphase undulation show a very weak dependence of the force coefficients and Nusselt numbers on the combinations of wavelength and wave amplitude. It is noted that the cylinder with antiphase undulation, under certain wavy conditions, provides about the same Nusselt number as the smooth cylinder, even though the force coefficients are considerably decreased. The thermal characteristics, according to the combination of wavy geometric parameters, are supported by the surface distribution of the Nusselt numbers. In addition, the isothermal distribution, which depends on the wake flow, explains the variation in the Nusselt numbers. The present results suggest that a proper modification of geometry can improve both heat transfer and aerodynamic performances.
Current land use classification models based on very high-resolution (VHR) remote sensing images often suffer from high sample dependence and poor transferability. To address these challenges, we propose an unsupervised multisource domain adaptation framework for cross-domain land use classification that eliminates the need for repeatedly using source domain data. Our method uses the Swin Transformer as the backbone of the source domain model to extract features from multiple source domain samples. The model is trained on source domain samples, and unlabeled target domain samples are then used for target domain model training. To minimize the feature discrepancies between the source and target domains, we use a weighted information maximization loss and self-supervised pseudolabels to alleviate cross-domain classification noise. We conducted experiments on four public scene datasets and four new land use scene datasets created from different VHR images in four Chinese cities. Results show that our method outperformed three existing single-source cross-domain methods (i.e., DANN, DeepCORAL, and DSAN) and four multisource cross-domain methods (i.e., M3SDA, PTMDA, MFSAN, and SHOT), achieving the highest average classification accuracy and strong stability. We conclude that our method has high potential for practical applications in cross-domain land use classification using VHR images.
Carbon dots have many new and interesting phenomena and the concentration‐dependent luminescence wavelength is an intriguing one. Luminescent carbon dots have recently gained interest due to their new and interesting phenomena such as its concentration‐dependent peak emission. While there have been reports and discussions on this phenomenon, the mechanism continues to be poorly understood. Herein, new observations on nitrogen–sulfur codoping carbon dots (NSCDs) diluted at different concentrations show that the high concentration of G‐NSCDs (0.2 mg mL−1) emits green fluorescence while the low concentration of B‐NSCDs (0.01 mg mL−1) emits blue fluorescence. It is found that the carbon dot size changes with the particle concentration: the high concentration of G‐NSCDs is about 37 nm in size while the low concentration of B‐NSCDs is only about 1.8 nm in size. Therefore, it is clearly demonstrated that the emission wavelength is related to the particle size. These interesting behaviors open a door for the exploration of new materials with novel potential.
In the process of ocean exploration, highly accurate and sensitive measurements of seawater temperature and pressure significantly impact the study of seawater’s physical, chemical, and biological processes. In this paper, three different package structures, V-shape, square-shape, and semicircle-shape, are designed and fabricated, and an optical microfiber coupler combined Sagnac loop (OMCSL) is encapsulated in these structures with polydimethylsiloxane (PDMS). Then, the temperature and pressure response characteristics of the OMCSL, under different package structures, are analyzed by simulation and experiment. The experimental results show that structural change hardly affects temperature sensitivity, and square-shape has the highest pressure sensitivity. In addition, with an input error of 1% F.S., temperature and pressure errors were calculated, which shows that a semicircle-shape structure can increase the angle between lines in the sensitivity matrix method (SMM), and reduce the effect of the input error, thus optimizing the ill-conditioned matrix. Finally, this paper shows that using the machine learning method (MLM) effectively improves demodulation accuracy. In conclusion, this paper proposes to optimize the ill-conditioned matrix problem in SMM demodulation by improving sensitivity with structural optimization, which essentially explains the cause of the large errors for multiparameter cross-sensitivity. In addition, this paper proposes to use the MLM to solve the problem of large errors in the SMM, which provides a new method to solve the problem of the ill-conditioned matrix in SMM demodulation. These have practical implications for engineering an all-optical sensor that can be used for detection in the ocean environment.
Air temperature at approximately 2 m above the ground (<inline-formula><tex-math notation="LaTeX">$T_{a}$</tex-math></inline-formula>) is one of the most important environmental and biophysical parameters to study various earth surface processes. <inline-formula><tex-math notation="LaTeX">$T_{a}$</tex-math></inline-formula> measured from meteorological stations is inadequate to study its spatio-temporal patterns since the stations are unevenly and sparsely distributed. Satellite-derived land surface temperature (LST) provides global coverage, and is generally utilized to estimate <inline-formula><tex-math notation="LaTeX">$T_{a}$</tex-math></inline-formula> due to the close relationship between LST and <inline-formula><tex-math notation="LaTeX">$T_{a}$</tex-math></inline-formula>. However, LST products are sensitive to cloud contamination, resulting in missing values in LST and leading to the estimated <inline-formula><tex-math notation="LaTeX">$T_{a}$</tex-math></inline-formula> being spatially incomplete. To solve the missing data problem, we propose a deep learning method to estimate spatially seamless <inline-formula><tex-math notation="LaTeX">$T_{a}$</tex-math></inline-formula> from LST that contains missing values. Experimental results on 5-year data of mainland China illustrate that the image-to-image training strategy alleviates the missing data problem and fills the gaps in LST implicitly. Plus, the strong linear relationships between observed daily mean <inline-formula><tex-math notation="LaTeX">$T_{a}$</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">$T_{\rm{mean}}$</tex-math></inline-formula>), daily minimum <inline-formula><tex-math notation="LaTeX">$T_{a}$</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">$T_{\min}$</tex-math></inline-formula>), and daily maximum <inline-formula><tex-math notation="LaTeX">$T_{a}$</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">$T_{\max}$</tex-math></inline-formula>) make the estimation of <inline-formula><tex-math notation="LaTeX">$T_{\rm{mean}}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$T_{\min}$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$T_{\max}$</tex-math></inline-formula> simultaneously possible. For mainland China, the proposed method achieves results with <inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> of 0.962, 0.953, 0.944, mean absolute error (MAE) of 1.793 <inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>C, 2.143 <inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>C, and 2.125 <inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>C, and root-mean-square error (RMSE) of 2.376 <inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>C, 2.808 <inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>C, and 2.823 <inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>C for <inline-formula><tex-math notation="LaTeX">$T_{\rm{\rm{mean}}}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$T_{\min}$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$T_{\max}$</tex-math></inline-formula>, respectively. Our study provides a new paradigm for estimating spatially seamless ground-level parameters from satellite products. Code and more results are available at <uri>https://github.com/cvvsu/LSTa</uri>.
Abstract Corrosion of petrochemical equipment is one of the reasons that affect the process safety and sustainability of the petrochemical industry. To understand the research status of corrosion in petrochemical equipment and explore the knowledge base and hot trends, 1,887 studies from 2000 to 2020 on the WOS retrieval platforms SCIE, SSCI, A & HCI and CPCI-S were used as data samples. Co-occurrence analysis, keyword mutation analysis, and co-citation analysis were used to analyze the current status of petrochemical equipment corrosion research using the method of informetrics. The number of papers published in China ranks first among countries/regions, and the Universidade de Lisboa ranks first among relevant institutions. Corrosion resistance structure analysis, corrosion loss model analysis and steel structure corrosion analysis are the basis of knowledge in the field of petrochemical equipment corrosion research, and scholars mostly take oil tankers as the object of analysis. Engineering Failure Analysis, Marine Structure, Ocean Engineering, Corrosion Science and Corrosion are the core journals in this field. At present, the basic theory and research systems in the field of petrochemical equipment corrosion have been largely completed, and many research directions and broad frontier branches are currently emerging. “Research on corrosion mechanism and anti-corrosion mechanism”, “microbial corrosion research” and “corrosion detection and monitoring research” are the three core development paths in this field. In the process of evolution, the research path is gradually becoming refined, and research directions are gradually changing to nano anti-corrosion materials. Simulation, biofuels and composites are the new research frontiers of research on corrosion of petrochemical equipment.
Nertjana Ustalli, Gerhard Krieger, Michelangelo Villano
This article addresses the design of a low-cost synthetic aperture radar (SAR) for a single dedicated application, namely illegal vessel detection, which can be implemented using a small satellite and is characterized by reduced transmit power and high resolution. Minimum requirements in terms of noise equivalent sigma zero and resolution that ensure acceptable detection performance are derived on the basis of ship statistics extracted from TerraSAR-X data. One peculiarity of the design is that a pulse repetition frequency much smaller than the nominal Doppler bandwidth is selected to increase the swath width beyond the classical SAR limitation without using digital beamforming, as the azimuth ambiguities can be tolerated for this specific application. Several design examples of SAR systems operating in <italic>X</italic>-band demonstrate the potential of this concept for small ship monitoring over swaths of 50–90 km with antennas smaller than 0.6 m<sup>2</sup> and very low average transmit powers comprised between 20 and 80 W.
Gregarious larval settlement represents an important window for chimera formation in reef corals, yet it remains largely unknown how aggregated settlement and early chimerism could modify the performance and responses of coral recruits under elevated temperature and pCO2. In this study, single and aggregated recruits of the broadcast spawning coral Acropora austera were exposed to contrasts of two temperatures (28 versus 30.5°C) and pCO2 levels (~500 versus 1000 μatm) for two weeks, and algal symbiont infection success, survivorship and growth were assessed. Results showed that symbiont infection success was mainly affected by temperature and recruit type, with reduced symbiont infection at increased temperature and consistently higher infection success in chimeric recruits compared to single recruits. Furthermore, although chimeric recruits with larger areal size had significantly higher survivorship in all treatments, the polyp-specific growth rates were considerably lower in chimeric entities than individual recruits. More importantly, the recruit type significantly influenced the responses of recruit polyp-specific growth rates to elevated temperature, with chimeras exhibiting lowered skeletal lateral growth under elevated temperature. These results demonstrate the benefits and costs associated with gregarious larval settlement for juvenile corals under ocean warming and acidification, and highlight the ecological role of larval settlement behavior in mediating the responses of coral recruits to climate change stressors.
Science, General. Including nature conservation, geographical distribution