The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding the performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving the computational efficiency. Finally, the simulation results demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods.

The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated using a through-the-wall (TTW) Frequency Modulated Continuous Wave (FMCW) C-Band radar system named Soprano. The objective of this analysis was to classify whether an individual was carrying an item in both hands or not using micro-Doppler information from a FMCW sensor. The radar was deployed at a standoff distance, of approximately 0.5 m, outside a residential building and used to detect multiple people walking within a room. Through the application of digital filtering, it was shown that significant suppression of the primary wall reflection is possible, significantly enhancing the target signal to clutter ratio. Singular Value Decomposition (SVD) signal processing techniques were then applied to the micro-Doppler signatures from different individuals. Features from the SVD information have been used to classify whether the person was carrying an item or walking free handed. Excellent performance of the classifier was achieved in this challenging scenario with accuracies up to 94%, suggesting that future through wall radar sensors may have the ability to reliably recognize many different types of activities in TTW scenarios using these techniques.

Observed sea surface Ka-band normalized radar backscatter cross section (NRCS) and Doppler velocity (DV) exhibit energy at low frequencies (LF) below the surface wave range. It is shown that non-linearity in NRCS-wave slope Modulation Transfer Function (MTF) and inherent NRCS averaging within the footprint account for the NRCS and DV LF variance with the exception of VV NRCS for which almost half of the LF variance is attributable to wind fluctuations. Although the distribution of radar DV is quasi-Gaussian suggesting virtually little impact of non-linearity, the LF DV variations arise due to footprint averaging of correlated local DV and non-linear NRCS. Numerical simulations demonstrate that MTF non-linearity weakly affects traditional linear MTF estimate (less than 10% for |MTF|< 20). Thus the linear MTF is a good approximation to evaluate the DV averaged over large footprints typical of satellite observations.

Airborne Frequency Diversity Array (FDA) radar operating at a high pulse repetition frequency encounters severe range-ambiguous clutter. The slight frequency increments introduced by the FDA result in angle and range coupling. Under these conditions, conventional space-time adaptive processing (STAP) often exhibits diminished performance or fails, complicating target detection. This paper proposes a method combining elevation oblique subspace projection with azimuth-Doppler STAP to suppress range-ambiguous clutter. By analyzing the relationship between elevation frequency and range, the proposed method compensates for quadratic range dependence. Through the construction of an elevation oblique subspace projection technique, an elevation adaptive filter is used to separate clutter from ambiguous regions. Residual clutter suppression is achieved through azimuth-Doppler STAP, enhancing target detection performance. Simulation results demonstrate that the proposed method effectively addresses range dependence and ambiguity issues, improving target detection performance in complex airborne FDA radar environments.

Hemicellulose is one of the most important natural polysaccharides in nature. Hemicellulose from different sources varies in chemical composition and structure, which in turn affects the modification effects and industrial applications. Grain and oil by-products (GOBP) are important raw materials for hemicellulose. This article reviews the modification methods of hemicellulose in GOBP. The effects of chemical and physical modification methods on the properties of hemicellulose biomaterials were evaluated. The potential applications of GOBP hemicellulose in films, hydrogel, Three-dimensional (3D) printing and adsorbent are discussed. Future research directions and recommendations are also proposed to provide theoretical foundations and technical support for the efficient utilization of these by-products.

In the context of all-digital radar systems, phase-modulated continuous wave (PMCW) based on pseudorandom binary sequences (PRBSs) appears a prominent candidate modulation scheme for applications such as autonomous driving. Among the reasons for that are its simplified transmitter architecture and lower linearity requirements, e.g., compared to orthogonal-frequency division multiplexing radars, as well as its high velocity unambiguity and multiple-input multiple-output operation capability that are characteristic of digital radars. For appropriate operation of a PMCW radar, choosing a PRBS whose periodic autocorrelation function (PACF) has low sidelobes and high robustness to Doppler shifts is paramount. In this sense, this article performs an analysis of Doppler shift tolerance of the PACFs of typically adopted PRBSs in PMCW radar systems supported by simulation and measurement results. To accurately measure the Doppler-shift-induced degradation of PACFs, peak power loss ratio (PPLR), peak sidelobe level ratio (PSLR), and integrated-sidelobe level ratio (ISLR) were used as metrics. Furthermore, to account for effects on targets whose ranges are not multiples of the range resolution, oversampled PACFs are analyzed.

Surface velocity is traditionally measured with in situ techniques such as velocity probes (in shallow rivers) or Acoustic Doppler Current Profilers (in deeper water). In the last years, researchers have developed remote sensing techniques, both optical (e.g., image-based velocimetry techniques) and microwave (e.g., Doppler radar). These techniques can be deployed from Unmanned Aerial Systems (UAS), which ensure fast and low-cost surveys also in remotely-accessible locations. We compare the results obtained with a UAS-borne Doppler radar and UAS-borne Particle Image Velocimetry (PIV) in different rivers, which presented different hydraulic–morphological conditions (width, slope, surface roughness and sediment material). The Doppler radar was a commercial 24 GHz instrument, developed for static deployment, adapted for UAS integration. PIV was applied with natural seeding (e.g., foam, debris) when possible, or with artificial seeding (woodchips) in the stream where the density of natural particles was insufficient. PIV reconstructed the velocity profile with high accuracy typically in the order of a few cm s⁻¹ and a coefficient of determination (R²) typically larger than 0.7 (in half of the cases larger than 0.85), when compared with acoustic Doppler current profiler (ADCP) or velocity probe, in all investigated rivers. However, UAS-borne Doppler radar measurements show low reliability because of UAS vibrations, large instrument sampling footprint, large required sampling time and difficult-to-interpret quality indicators suggesting that additional research is needed to measure surface velocity from UAS-borne Doppler radar.

We introduce the Symplectic Structure of Information Geometry based on Souriau’s Lie Group Thermodynamics model, with a covariant definition of Gibbs equilibrium via invariances through co-adjoint action of a group on its moment space, defining physical observables like energy, heat, and moment as pure geometrical objects. Using Geometric (Planck) Temperature of Souriau model and Symplectic cocycle notion, the Fisher metric is identified as a Souriau Geometric Heat Capacity. Souriau model is based on affine representation of Lie Group and Lie algebra that we compare with Koszul works on G/K homogeneous space and bijective correspondence between the set of G-invariant flat connections on G/K and the set of affine representations of the Lie algebra of G. In the framework of Lie Group Thermodynamics, an Euler-Poincaré equation is elaborated with respect to thermodynamic variables, and a new variational principal for thermodynamics is built through an invariant Poincaré-Cartan-Souriau integral. The Souriau-Fisher metric is linked to KKS (Kostant-Kirillov-Souriau) 2-form that associates a canonical homogeneous symplectic manifold to the co-adjoint orbits. We apply this model in the framework of Information Geometry for the action of an affine Group for exponentiel families, and provide some illustrations of use cases for multivariate Gaussian densities. Information Geometry is presented in the context of seminal work of Fréchet and his Clairaut-Legendre equation. Souriau model of Statistical Physics is validated as compatible with Balian gauge model of thermodynamics. We recall the precursor work of Casalis on affine group invariance for natural exponential families.

It has been shown that the Micro-Rain Radar (MRR) can be used to derive rainfall rates every 10 m over a depth of 1.28 km using the mean vertical air velocity corrected Doppler raindrop fall speed spectra. Furthermore, it has been shown that by assuming a reasonable advection velocity for the rain, these data can be analyzed to produce spatial radial power spectra often readily fit using a power function. Previous work has shown, however, that each spectrum applies only to each particular set of data and usually lacks the statistical qualifications necessary to be considered generally applicable. However, this limitation does not preclude the potential existence of other generalizations that can be used to explore the rainfall formation processes. The intent of this study, then, is to perform an initial look for such possible behaviors using time-height profiles of the rainfall rate. It is found that once the rainfall rate, R, exceeds about 20 mm h-1, there is, apparently, an associated flattening of the spectra with increasing R so that the smaller scales play an ever increasingly important role in such rain near the ground perhaps reflecting the increasing importance of such scales in the formation of pockets of more intense convective rain. The true generality of this finding needs additional scrutiny using more data particularly from two spatially separated MRR as is currently under preparation.

In order to correct attenuated millimeter-wavelength (Ka-band) radar data and address the problem of instability, an attenuation correction methodology (attenuation correction with variation trend constraint; VTC) was developed. Using synchronous observation conditions and multi-band radars, the VTC method adopts the variation trends of reflectivity in X-band radar data captured with wavelet transform as a constraint to adjust reflectivity factors of millimeter-wavelength radar. The correction was evaluated by comparing reflectivities obtained by millimeter-wavelength cloud radar and X-band weather radar. Experiments showed that attenuation was a major contributory factor in the different reflectivities of the two radars when relatively intense echoes exist, and the attenuation correction developed in this study significantly improved data quality for millimeter-wavelength radar. Reflectivity differences between the two radars were reduced and reflectivity correlations were enhanced. Errors caused by attenuation were eliminated, while variation details in the reflectivity factors were retained. The VTC method is superior to the bin-by-bin method in terms of correction amplitude and can be used for attenuation correction of shorter wavelength radar assisted by longer wavelength radar data.

In the summertime, convections occur frequently over the Tibetan Plateau (TP) because of the large dynamic and thermal effects of the landmass. Measurements of vertical air velocity in convective cloud are useful for advancing our understanding of the dynamic and microphysical mechanisms of clouds and can be used to improve the parameterization of current numerical models. This paper presents a technique for retrieving high-resolution vertical air velocity from convective cloud over the TP, by using Doppler spectra from a vertically pointing Ka-band cloud radar. The method is based on the development of a “small-particle-traced” idea and the necessary data processing and uses three modes of radar. Spectral broadening corrections, uncertainty estimations, and result merging are used to ensure accurate results. Qualitative analysis of two typical convective cases shows that the retrievals are reliable and agree with the expectant results inferred from other radar measurements. A quantitative retrieval of vertical air motion from a ground-based optical disdrometer is used to preliminarily validate our radar-derived results. The comparison illustrates that while the data trends from the two methods of retrieval are similar, with the updrafts and downdrafts coinciding, cloud radar has a much higher resolution and can reveal the small-scale variation of vertical air motion.

A problem with synthetic aperture radar (SAR) is that, due to the poor penetrating action of electromagnetic waves inside solid bodies, the capability to observe inside distributed targets is precluded. Under these conditions, imaging action is provided only on the surface of distributed targets. The present work describes an imaging method based on the analysis of micro-movements on the Khnum-Khufu Pyramid, which are usually generated by background seismic waves. The results obtained prove to be very promising, as high-resolution full 3D tomographic imaging of the pyramid's interior and subsurface was achieved. Khnum-Khufu becomes transparent like a crystal when observed in the micro-movement domain. Based on this novelty, we have completely reconstructed internal objects, observing and measuring structures that have never been discovered before. The experimental results are estimated by processing series of SAR images from the second-generation Italian COSMO-SkyMed satellite system, demonstrating the effectiveness of the proposed method.

Potential permanent magnet (Fe0.7Co0.3)2B has large saturation magnetization and high Curie temperature but a moderate magnetocrystalline anisotropy (MCA) and displays relatively low coercivity. One way to improve coercivity is to combine the contribution from magnetocrystalline- and magnetic shape anisotropy by preparing (Fe0.7Co0.3)2B nanowires. We study the effect of size, morphology, and surface defect on the hard magnetic properties of nanowires using micromagnetic simulation. The hard magnetic properties of (Fe0.7Co0.3)2B nanowire bonded magnet have been estimated, including the role of inter-wire magnetostatic interaction. By considering the existence of local reduction of MCA energy up to 30% on the surface layer of nanowires, the anisotropic bonded magnet with 65% vol. of (Fe0.7Co0.3)2B nanowires displays typical magnetic properties of remanence Br=8.4 kG, coercivity Hci=9.9 kOe, and maximum energy product (BH)m=17.8 MGOe. Developing effective technology for synthesizing nanowires and fabricating corresponding bonded magnets is promising for manufacturing practical magnets based on magnetic phase with relatively low or moderate MCA, such as (Fe0.7Co0.3)2B.

The Range–Doppler (R-D) model is extensively employed for the geometric processing of Synthetic Aperture Radar (SAR) images. Refining the sensor motion state and imaging parameters is the most common method for achieving high-precision geometric processing using the R-D model, comprising a process that involves numerous parameters and complex computations. In this study, we introduce a novel approach to improve the positioning accuracy of the R-D model, implementing a low-order polynomial to compensate for the original imaging errors without altering the initial positioning parameters. We also design low-order polynomial compensation models with different parameters. The models are evaluated on various SAR images from different platforms and bands, including spaceborne TerraSAR-X and GF3-C images, manned airborne SAR-X images, and unmanned aerial vehicle-mounted miniSAR-Ku images. Furthermore, image positioning experiments involving the use of different polynomial compensation models and various numbers and distributions of Ground Control Points (GCPs) are conducted. The experimental results demonstrate that geometric processing accuracy comparable to that of the classical rigorous positioning method can be achieved even when applying only an affine transformation model to the images. Compared to classical refinement models, however, the proposed image-compensated R-D model is much simpler and easy to implement. Thus, this study provides a convenient, robust, and widely applicable method for the geometric positioning processing of SAR images, offering a potential approach for the joint-positioning processing of multi-source SAR images.

This study introduces a data obfuscation technique, leveraging the exponential map associated with the generators of Lie groups. Originating from quantum machine learning frameworks, our method illustrates the practical application of quantum mechanics principles in data processing. Specifically, it employs the exponential map of a generator algebra to introduce controlled noise into the data, achieving obfuscated data while preserving its utility for machine learning tasks. This strategy is shown to safeguard privacy in sensitive datasets, such as discussed medical records, and to enhance dataset volume and diversity through augmentation. Our empirical analysis, benchmarked against standard machine learning approaches, demonstrates that our method can maintain or even improve the predictive accuracy of the original data. This research highlights the potential of Lie group theory for advancing data privacy in medicine, marking a significant contribution to machine learning methodologies by offering the dual benefits of data obfuscation and enrichment. Through this synthesis of algebraic structures and machine learning, we propose new pathways for the secure and effective use of data in sensitive areas.

κ Draconis is a binary system with a classical Be star as the primary component. Its emission-line spectrum consists of hydrogen lines, notably the Hα line with a peak intensity ratio (V/R) variations phase-locked with the orbital period P = 61.55 days. Among binaries demonstrating the Be phenomenon, κ Dra stands out as one of a few systems with a discernible mass of its secondary component. Based on more than 200 spectra obtained in 2014 − 2023, we verified the physical parameters and constructed the mass function. We used part of these data obtained in 2014 − 2021 to investigate regions in the circumstellar disk of the primary component that emit the Hα line using the Doppler tomography method. The results show that the disk has a non-uniform density distribution with a prominent enhancement at Vy≈ 99 km s−1 and Vx≈−6 km s−1 that corresponds to a cloud-like source of the double-peaked Hα line profile. We argue that this enhancement’s motion is responsible for the periodic variations of the Hα V/R ratio, which is synchronised in orbital phase with the radial velocity (RV) of absorption lines from the atmosphere of the primary component.

Rescaling of rainfall requires measurements of rainfall rates over many dimensions. This paper develops one approach using 10 m vertical spatial observations of the Doppler spectra of falling rain every 10 seconds over intervals varying from 15 up to 41minutes in two different locations and in two different years using two different Micro-Rain Radars (MRR). The transformation of the temporal domain into spatial observations uses the Taylor ‘frozen’ turbulence hypothesis to estimate an average advection speed over an entire observation interval. Thus, when no other advection estimates are possible, this paper offers a new approach for estimating the appropriate frozen turbulence advection speed by minimizing power spectral differences between the ensemble of purely spatial radial power spectra observed at all times in the vertical and those using the ensemble of temporal spectra at all heights to yield statistically reliable scaling relations. Thus, it is likely that, MRR and other vertically pointing Doppler radars may often help to obviate the need for expensive and immobile large networks of instruments in order to determine such scaling relations, but not the need of those radars for surveillance.

Abstract: Doppler renal resistive index (RRI) has been used for several decades as a potential indi-cator for renal disease and progression. In recent years, RRI has emerged as a useful prognostic indicator for a range of co-morbidities in patients who are acutely unwell and as a potential marker for sub-clinical acute kidney injury (AKI) and post procedural AKI risk. Despite this, the clinical and theoretical determinants of RRI are poorly understood and remain contentious. Method: A narrative review of studies exploring the theoretical, experimental, and clinical evidence for factors affecting RRI. No date restrictions were set. Findings: Both experimental and clinical data confirm that RRI is markedly affected by a range of systemic and renal determinants and is influenced closely by the underlying risk factors and sys-temic vascular changes that lead to progressive renal damage. Discussion: This paper provides an overview of the key haemodynamic determinants of renal re-sistive index and aims to clarify their relative importance. With reference to a simplified theoretical model, evidence from experimental and clinical studies is reviewed. Pathophysiological factors and patient characteristics that influence RRI are explored to provide a better understanding of why RRI values change in the presence of common or complex pathologies. Conclusion: By gaining a better understanding of the complex systemic and renal factors that influ-ence RRI, ultrasound users will be better prepared to understand and interpret changes in RRI across a range of patient presentations.

Many fields of human activity benefit from the ability to create images of obscured objects placed behind walls and to map their displacement in a noninvasive way. Usually, imaging devices like Synthetic Aperture Radars (SAR) and Ground Penetrating Radars (GPR) use expensive dedicated electronics which results in prohibitive prices. This paper presents the experimental implementation and the results obtained from an imaging system capable of performing SAR imaging and interferometric displacement mapping of targets located behind walls, as well as 3D GPR imaging using a low-cost general-purpose radar sensor. The proposed solution uses for the RF section of the system a K-band microwave radar sensor module implementing Frequency Modulated Continuous Wave operation. The low-cost sensor was originally intended for simple presence detection and ranging for domestic applications. The proposed system was tested in several scenarios and proved to operate as intended for a fraction of the cost of a commercial imaging device.

Tissue Doppler Imaging (TDI) is a non-invasive, echocardiographic imaging technique that measures myocardial motion velocity throughout the cardiac cycle using Doppler principles. While conventional Doppler techniques assess blood flow velocity by sensing high-frequency, low amplitude signals from small, fast-moving blood cells, TDI uses the same Doppler principles to instead measure high-amplitude, lower-velocity signals of myocardial tissue. Methods A literature review was conducted to survey and review studies investigating the limitations, strengths, physical principles, novel methods, applications in diseased states, and prognostic capabilities of TDI. These articles were further screened for inclusion, and those deemed ineligible or irrelevant to the scope of the review were discarded. In total, 19 studies were included in the qualitative synthesis. Results TDI is shown to be an effective method for detailed quantification of cardiac function. It provides an early means of diagnosing cardiac dysfunction and is a valid prognostic indicator for various forms of heart disease. TDI's versatility and precision allows clinicians to predict the clinical course of disease, leading to early intervention and the selection of targeted care management plans for many cardiac pathologies. Despite imaging limitations like angle dependence and incapacity for passive and active motion differentiation, further investigation continues to reveal novel TDI methodologies that advance the scope of this imaging technique.

Accurate Quantitative Precipitation Estimation (QPE) is fundamental for a large number of hydrometeorological applications, especially when addressing extreme rainfall phenomena. This paper presents a comprehensive comparison of various rainfall estimation methods, specifically those relying on weather radar data, rain gauge data, and their fusion. The study evaluates the accuracy and reliability of each method in estimating rainfall for a severe event that occurred in Tuscany, Italy. The obtained results indicate that merging radar and rain gauge data outperform both individual approaches by reducing errors and improving the overall reliability of precipitation estimates. The study highlights the importance of data fusion in enhancing the accuracy of QPE and also supports its application in operational contexts, providing evidence for the greater reliability of merging methods

This research proposes a near-infrared hyperspectral imaging/two-branch convolutional neural network (NIR-HSI/2B-CNN) algorithmic scheme to detect mango anthracnose of the species Colletotrichum asianum at the early stages of disease development. In the algorithmic model development, root mean square propagation was used as the solver to train the neural network, given 150 epochs. In addition, spectral raw data was preprocessed to transform it into an understandable and efficient format. The optimal classification model was the 2B-CNN model with 1st-derivative preprocessing, achieving an accuracy of 0.94 for the calibration set and 0.71 for the prediction set. The proposed NIR-HSI/2B-CNN scheme could detect anthracnose mangoes since the the first day of inoculation of the spore suspension (i.e., day 0) through to day 3, achieving a moderate classification accuracy. Meanwhile, the accuracy of conventional convolutional neural networks (CNN) were within a range of 0.66-0.67 for the calibration set and 0.55-0.57 for the prediction set. The results indicated that incorporating spatial features in the 2B-CNN modeling enhanced the prediction performance of the algorithm. The proposed NIR-HSI/2B-CNN algorithmic scheme needs refinements to be able to reliably sort mango fruits into those suitable for premium fresh consumption and export without anthracnose and those for domestic consumption or processing. The novelty of this research lies in the use of NIR-HSI and 2B-CNN algorithm to detect plant pathogens at the early stages of disease development. In addition, the new method of natural simulation to deposit the fungal spores onto the mango surface by spraying spore suspension onto the mango surface where the conidia penetrated unaided into the underneath of the mango peel is proposed.

Introductory courses regarding radar technologies are very popular in undergraduate curriculum of many electrical and electronics engineering departments. Hands-on experience is an essential part for understanding the theoretical concepts given in lectures. In most cases, it is not affordable for universities to acquire experimental radar systems, especially those in developing countries. This paper presents a detailed description of a cost-effective easy-to-deploy radar system laboratory sessions and measures the educational effectiveness of the proposed material. The provided radar models can be used in teaching undergraduate students the working principles of frequency modulated continuous wave (FMCW) radar systems, as well as assisting graduate students in their research activities. The effectiveness of the laboratory sessions is measured thoroughly via qualitative and quantitative methods based on the proposed learning process and students’ success. The results show that the lab sessions have increased the students' understanding of the topics covered within the course, and the students' general perception is positive.

The existence of low-cost and accurate tools that can equip the First Responders (FRs) to detect trapped victims and provide insights about their health condition in the aftermath of a disaster, is imperative. To address the problem of detecting trapped victims behind walls or debris, we have developed a tool that exploits an Ultra-Wide Band (UWB) radar sensor for data collection and Machine Learning (ML) algorithms for data analysis. To evaluate the efficacy of our approach, we collected data from nine humans, both in standing and lying down positions. Next, we applied various ML algorithms to the collected dataset for two discrete sub-tasks that are of interest from an FR’s perspective. The first task is the detection of the victim’s presence, where the algorithms attained more than 95% accuracy and F1-Score. The second task is the estimation of the distance between the radar sensor and the victim, where the tool showed an average error of less than 40 cm.

Stable isotope-assisted approaches can improve untargeted LC-HRMS metabolomics studies. Here, we demonstrate at the example of chemically stressed wheat that metabolome-wide internal standardization by globally 13C-labeled metabolite extract (GLMe-IS) of experimental-condition-matched biological samples can help to improve coverage of treatment-relevant metabolites and aid in the post-acquisition assessment of putative matrix effects in samples obtained upon different treatments. For this, native extracts of toxin- and mock-treated (control) wheat ears were standardized by the addition of uniformly-13C-labeled wheat ear extracts that were cultivated under similar experimental conditions (toxin-treatment and control) and measured with LC-HRMS. The results show that 996 wheat-derived metabolites were detected with the non-condition-matched 13C-labeled metabolite extract, while another 68 were only covered by the experimental-condition-matched GLMe-IS. Additional testing is performed with the assumption that GLMe-IS enables compensation for matrix effects. Although on average no severe matrix differences between both experimental conditions were found, individual metabolites may be affected as is demonstrated by wrong decisions with respect to the classification of significantly altered metabolites. When GLMe-IS was applied to compensate for matrix effects, 272 metabolites showed significantly altered levels between treated and control samples, 42 of which would not have been classified as such without GLMe-IS.

K-band radar waves have high penetration and low attenuation coefficients. However, the wavelength of this radar wave is relatively short; thus, designing and preparing both broadband and wide-angle radar wave absorbers in this band presents considerable challenges. In this study, a resin-based K-band radar wave absorber with a biomimetic lotus leaf structure was designed and formed by UV curing. Here, microscale lotus leaf papillae and antireflection structures were prepared using a DLP 3D printer, and the contact angle between the material and water droplets was increased from 56° to 130°. In addition, the influence of the geometric parameters of the lotus leaf antireflection structure on the electromagnetic absorption performance and mechanical strength was investigated. After simulation optimization, the maximum electromagnetic loss of the lotus leaf structure 3D printed sample was −32.3 dB, and the electromagnetic loss was below −10 dB in the 20.8–26.5 GHz frequency range. When the radar incidence angle was 60°, the maximum electromagnetic loss was still less than −10 dB. The designed lotus leaf structure has a higher mechanical energy absorption per unit volume (337.22 KJ/m3) and per unit mass (0.55 KJ/Kg) than commonly used honeycomb lightweight structures during the elastic deformation stage, and we expect that the designed structure can be used as an effective lightweight material for K-band radar stealth.

The radar sensor described realizes healthcare monitoring capable of detecting subject chest-wall movement caused by cardiopulmonary activities, and wirelessly estimating the respiration and heartbeat rates of the subject without attaching any devices to the body. Conventional single-tone Doppler radar can only capture Doppler signatures because of a lack of bandwidth information with noncontact sensors. In contrast, we take full advantage of impulse radio ultra-wideband (IR-UWB) radar to achieve low power consumption and convenient portability, with a flexible detection range and desirable accuracy. A noise reduction method based on improved ensemble empirical mode decomposition (EEMD) and a vital sign separation method based on the continuous-wavelet transform (CWT) are proposed jointly to improve the signal-to-noise ratio (SNR) in order to acquire accurate respiration and heartbeat rates. Experimental results illustrate that respiration and heartbeat signals can be extracted accurately under different conditions. This noncontact healthcare sensor system proves the commercial feasibility and considerable accessibility of using compact IR-UWB radar for emerging biomedical applications.

Pencil-beam Doppler scatterometers are a promising remote sensing tool for measuring ocean vector winds and currents from space. While several point designs exist in the literature, these designs have been constrained by the hardware they inherited, and the design is sub-optimal. Here, I present guidelines to optimize the design of these instruments starting from the basic sensitivity equations. Unlike conventional scatterometers or pencil-beam imagers, appropriate sampling of the Doppler spectrum and optimizing the radial velocity error lead naturally to a design that incorporates a pulse-to-pulse separation and pulse length that vary with scan angle. Including this variation can improve radial velocity performance significantly and the optimal selection of system timing and bandwidth is derived. Following this, optimization of the performance based on frequency, incidence angle, antenna length, and spatial sampling strategy are considered. It is shown that antenna length influences the performance most strongly, while the errors depend only on the square root of the transmit transmit power. Finally, a set of example designs and associated performance are presented.

This study explores the near-surface dispersion mechanisms of contaminants in coastal waters, leveraging a comprehensive method that includes using dye and drifters as tracers, coupled with diverse observational platforms like drones, satellites, in-situ sampling, and HF radar. The aim is to deepen our understanding of surface currents' impact on contaminant dispersion, thereby improving predictive models for environmental incident management, including pollutant releases. Rhodamine WT dye, chosen for its significant fluorescent properties and detectability, along with drifter data, allowed us to investigate the dynamics of near-surface physical phenomena such as the Ekman current, Stokes drift, and wind-driven currents. Our research emphasizes the importance of integrating scalar tracers and Lagrangian markers in experimental designs, revealing differential dispersion behaviors due to near-surface vertical shear caused by the Ekman current and Stokes drift. The elongation direction of the dye patch aligns with the Ekman current direction during slow current conditions. Analytical calculations of vertical shear, based on the Ekman current and Stokes drift, closely matched those derived from tracer observations. Over a 7-hour experiment, the vertical diffusivity of near-surface water was observed at 2×10-4 m2/s, and the horizontal eddy diffusivity of the dye patch and drifters reached the order of 1 m2/s and at a 1000m length scale. Particle tracking models demonstrate that while HF radar currents can effectively predict the trajectories of tracers near the surface, incorporating near-surface currents, including Ekman current, Stokes drift, and windage, is essential for a more accurate prediction of the fate of surface floats.

Porcine Circovirus 2 (PCV2) vaccines are based on either inactivated whole virion, or recombinant ORF2 capsid protein assembled into Virus-like Particles (VLPs). No data are available instead about the immunizing properties of free, non-assembled capsid protein. To investigate this issue, ORF2 of a reference PCV2b strain was expressed in a Baculovirus-based expression system without assembly into VLPs. The free purified protein was formulated into an oil vaccine at three distinct Ag payloads: 10.8 / 3.6 / 1.2 micrograms /dose. Each dose was injected intramuscularly into five, 37-day old piglets, carefully matched for maternally-derived antibody. Five control piglets were injected with sterile PBS in oil adjuvant. Twenty-eight days later, all the pigs were challenged intranasally with 200,000 TCID50 of PCV2b strain DV6503. After challenge infection, all the pigs remained in good clinical conditions. The recombinant vaccine did not induce significant antibody and PCV2-specific IFN-gamma responses. ELISPOT and lymphocyte proliferation data confirmed poor induction of cell-mediated immunity. In terms of PCV2 viremia, there was no significant difference between vaccinated and control animals. The histological data indicated the absence of a detectable viral load and of PCVAD lesions in both vaccinated and control animals, as well as of histiocytes and multi-nucleated giant cells. We conclude that free, non-assembled ORF2 capsid protein does not induce protective immunity.

This paper investigates the feasibility of a backend design for real-time, multiple-channel processing digital phased array system, particularly for high-performance embedded computing platforms constructed of using general purpose digital signal processors. Frist, we obtained the lab-scale backend performance benchmark from simulating beamforming, pulse compression, and Doppler filtering based on MicroTCA chassis using Serial RapidIO protocol in backplane communication. Next, a field-scale demonstrator of a multifunctional phased array radar is emulated by using the similar configuration. Interestingly, the performance of a barebone design is compared to that of emerging tools that systematically take advantage of parallelism and multicore capabilities, including Open Computing Language.

The Ituri-Epulu-Aru landscape (IEAL) is experiencing deforestation and forest degradation. This deforestation is at the root of many environmental disturbances in a region characterized by endemism in biodiversity. This article focuses on the triangulation of spatialized prospective scenarios in order to identify future trajectories based on the knowledge of historical dynamics through the diachronic analysis of three satellite images (2003-2010-2014-2016). The scenarios were de-signed in a supervised model implemented in the DINAMICA EGO platform. The three scenarios Business-As-Usual (BAU), Rapid Economic Growth (REG) and Sustainable Management of the Environment (SME), extrapolating current trends, show that by 2061 this landscape will always be dominated forests (+ 84%). Old-growth forests occupy 74.2% of the landscape area in the BAU scenario, 81.4% in the SEM scenario and 61.2% in the REG scenario. The SEM scenario gives hope that restoration and preservation of biodiversity priority habitats is still possible if policy makers become aware of it.

The relativistic Doppler effect comes from the fact that observers in different inertial reference frames experience space and time differently, while the speed of light remains always the same. Consequently, a wave packet of light exhibits different frequencies, wavelengths, and amplitudes. In this paper, we present a local approach to the relativistic Doppler effect based on relativity, spatial and time translational symmetries, and energy conservation. Afterward, we investigate the implications of the relativistic Doppler effect for the quantum state transformations of wave packets of light and show that a local photon is a local photon at the same point in the spacetime diagram in all inertial frames.

The article aims to present the possible use of the simple modified optical training kit as a low-cost, simple setup for high precision surface speed measurements. A measurement capability evaluation of the optical Doppler kit used for velocimetry of a fast-rotating reflecting surface is presented. To get high repeatability measurements, we modified a fibre optic interferometry training kit. By using signal processing and statistics a precision under repeatability conditions of measurements was evaluated. Expressed by the standard deviations (3σ’s) the surface velocity measurements precision below 0.2 m/s is shown. The Cg’s capability indices were also evaluated. We postulate, the electric circuit stability of the measurement system power supply is essential for a signal noise reducing process for a wide range of metrology systems. It is crucial for precision measurements.

We apply the Canny Edge algorithm to the Utqiaġvik coastal sea ice radar system (CSIRS) to quantify open water and sea ice (landfast or drifting) concentration. The radar-derived sea ice concentration is compared against the 25-km resolution NSIDC Climate Data Record (CDR, one pixel closest to the radar field of view) and the 1-km merged MODIS-AMSR2 sea ice concentrations in the ∼11 km field of view for the year 2022-2023, when improved image contrast was first implemented. The algorithm was first optimized using sea ice concentration from 14 different images and 10 ice analysts (140 analysis in total) covering a wide range of ice condition with landfast ice, drifting ice and open water. The algorithm is also validated quantitatively against high-resolution MODIS-Terra in the visible range. Results show a correlation coefficient and mean bias error between the optimized algorithm, the CDR and MODIS-AMSR2 daily SIC of 0.18 and 0.54, and ∼ -1 and 0.9%, respectively, with an averaged inter-analyst error of ± 3%. In general, the CDR captures the melt period correctly and overestimates the SIC during the winter and freeze-up period, while the merged MODIS-AMSR2 better captures the punctual break-out events in winter including those during the freeze-up events (reduction of in SIC). Remnant issues with the detection algorithm include the false detection of sea ice in the presence, fog or precipitation (up to 20%), quantified from the summer reconstruction with known open water conditions. Coastal marine radars provide prove very useful in bridging the gap between (lower) resolution satellite-derived and in-situ (visual) sea ice conditions, and to quantify land contamination from lower resolution satellite-derived SIC.

This paper presents a simple and streamlined compensation technique for improving the quality of Synthetic Aperture Radar (SAR) images based on the Range Doppler Algorithm (RDA). Incorrect Doppler estimation in space orbit, caused by unexpected radar motion errors, orbit mismatches, and other factors, can significantly degrade SAR image quality. These inaccuracies result in mismatches between the azimuth-matched filter and the received Doppler chirp signal. To address this issue, we propose a Doppler estimation method that leverages the Fractional Fourier Transform (FrFT) and cross-correlation techniques. The received signals are compared with the azimuth-matched filter based on the rotation angle in the FrFT domain, and the Doppler centroid is adjusted to achieve optimal alignment. This process ensures high correlation values and enhanced resolution in the final SAR image. The efficacy of the proposed technique is validated through experiments using real spaceborne SAR data from the practical satellite. The results demonstrate significant improvements in image quality and resolution compared to conventional algorithms, highlighting the advantages of our approach for various remote sensing applications.

Maxwell's equations accurately describe the propagation of electromagnetic (EM) waves. However, the conventional formulation implies invariance to observers, posing challenges in explaining how the wave is perceived by different observers. For example, Doppler effect shows that observers perceive the same EM wave differently.By employing mathematical transformations, we derive a general form of Maxwell’s wave equations by incorporating a "time scaling" factor to account for observer perception. The original form is shown to be a special case when static. The Doppler effect and Sagnac effect are directly derived from Maxwell’s equations. All the results are consistent with established experiments.Our findings offer a fresh perspective, promising a deeper understanding and unification of EM phenomena. We extend Maxwell’s equations to describe not only the propagation of an EM wave, but also how it will be measured by different observers, say, antennas.

P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For example, a second order polynomial that contains 3 free parameters was presumed based on in-situ SMP data. The polynomial is currently parameterized based on 3 backscatter observations provided by AirMOSS (i.e. one frequency at three polarizations of HH, VV and HV). In this paper, a more realistic, physically-based SMP model containing 3 free parameters is derived based on a solution to Richards’ equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy.

This review paper focuses on the joint toxicity and interaction of carbon-based nanomaterials (CNMs) with co-existing pollutants in aquatic environments. It explores the potential harmful effects of chemical mixtures on living organisms, emphasizing the importance of scientific mod-eling to predict mixed toxic effects. The study involved a systematic literature review to gather information on the joint toxicity and interaction between CNMs and various co-contaminants in aquatic settings. A total of 53 publications were chosen and analyzed, categorizing the studies based on the tested CNMs, types of co-contaminants, and the used species. Common test models included fish and microalgae, with zebrafish being the most studied species. The review under-scores the necessity of conducting mixture toxicity testing to assess whether the combined effects of CNMs and co-existing pollutants are additive, synergistic, or antagonistic. The development of in silico models based on the solid foundation of research data represents the best opportunity for joint toxicity prediction, eliminating the need in a great quantity of experimental studies.

MIMO (multiple-input multiple-output) radar provides much more flexibility than the traditional radar for its ability to realize far more observation channels than the actual number of T/R (transmit and receive) elements. Designing the array of MIMO imaging radar, the commonly used virtual array theory generally assumes that all elements are placed on the same line. However, due to the physical size of the antennas and coupling effect between T/R elements, a certain height difference between T/R arrays is essential, resulting in the defocusing of edge points of the scene. On the other hand, the virtual array theory implies far-field approximation, leading to inevitable high grating lobes in the imaging result of near-field edge points of the scene observed by common MIMO array. To tackle these problems, this paper derives the relationship between target’s PSF (point spread function) and pattern of T/R arrays, by which the design criterion of near-field imaging MIMO array is presented. Firstly, the proper height between T/R arrays is designed to focus the near-field edge points well. Secondly, the far-field array is modified to suppress the grating lobes in the near-field area. Finally, the validity of the proposed methods is verified by simulations and an experiment.

With the advancement of technology, signal modulation types are becoming increasingly diverse and complex. The phenomenon of signal time-frequency overlap during transmission poses significant challenges for the classification and recognition of mixed signals, including poor recognition capabilities and low generality. This paper presents a recognition model for fine-grained analysis of mixed signal characteristics, proposing a Geometry Coordinate attention mechanism and introducing a low-rank bilinear pooling module to more effectively extract signal features for classification. The model employs a residual neural network as its backbone architecture and utilizes the Geometry Coordinate attention mechanism for time-frequency weighted analysis based on information geometry theory. This analysis targets multiple-scale features within the architecture, producing time-frequency weighted features of the signal. These weighted features are further analyzed through a low-rank bilinear pooling module, combined with the backbone features, to achieve fine-grained feature fusion. This results in a fused feature vector for mixed signal classification. Experiments were conducted on a simulated dataset comprising 39,600 mixed signal time-frequency plots. The model was benchmarked against a baseline using a residual neural network. The experimental outcomes demonstrated an improvement of 9% in the exact match ratio and 5% in the Hamming score. These results indicate that the proposed model significantly enhances the recognition capability and generalizability of mixed signal classification.

BACKGROUNDBrain death in most countries requires supplementation of clinical diagnosis with ancillary tests such as EEG, cerebral angiography, or radionuclide imaging. Transcranial Doppler sonography (TCD) is one such non-invasive, real-time, bedside test that sidesteps the logistic hurdles associated with some of the tests mentioned above. OBJECTIVETo substantiate the utility of TCD as a reliable ancillary test to diagnose brain death with more certainty and reliability.METHODOLOGYMeta-analysis was performed with the use of statistical and analytical software to obtain data and estimate Sensitivity, Specificity, positive predictive value(PPV), diagnostic odds ratio (DOR), and relative risk with 95 percent confidence intervals to examine critical clinical outcomes(CI).RESULTSInitial dataset of 31 studies of which 16 studies, with a total of 1317 patients was used considering the reliability of the data. The pooled sensitivity and specificity of TCD were 0.91 (95% CI range: 0.89-0.93) and 0.99 (95% CI range: 0.97-0.99) respectively. The summary of the ROC curve showed an area under the curve of 0.9750 and an overall diagnostic odds ratio (DOR) was 323.26 with the Younden Index being 0.9. Summary of Fagan plot analysis of all studies showed a prior probability of 63% (1.7); a Positive Likelihood Ratio of 56, a post-test probability of 99% (96); a Negative likelihood ratio of 0.09, and a post-test probability of 13% (0.2).CONCLUSIONOur study emphasizes that clinical diagnosis is sufficiently adequate to declare a patient cerebrally dead. However, for ambiguous cases, TCD is a practical and fast method showing close correspondence to angiography and is superior to other bedside tests like EEG.

The dynamics of surface and sub-surface water events can lead to slope instability resulting in anomalies such as slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We have implemented a supervised Mahalanobis distance classification algorithm for the detection of slough slides on levees using complex polarimetric Synthetic Aperture Radar (polSAR) data. The classifier output was followed by a spatial majority filter post-processing step which improved the accuracy. The effectiveness of the algorithm is demonstrated using fully quad-polarimetric L-band Synthetic Aperture Radar (SAR) imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the southern USA. Slide detection accuracy of up to 98 percent was achieved, although the number of available slides examples was small.

The origin and climate impact of Arctic aerosols, like the Arctic Haze, are not fully understood. Therefore, long-term aerosol observations in the Arctic are performed. In this study we present a homogenised data set from sun and star photometer operated in the European Arctic, in Ny-Ålesund, Svalbard, from 2004 – 2023. Due to polar day and polar night it is crucial to use observations of both instruments. Their data is evaluated in the same way and follows the cloud-screening procedure of AERONET. Additionally, an improved method for the calibration of the star photometer is presented. We found out, that winter months are generally more polluted and have larger particles than summer. While the monthly median Aerosol Optical Depth (AOD) decreases in spring, the AOD increases significantly in autumn months. The Arctic Haze is not characterised by large particles and can not be distinguished from large aerosols in winter months. With autocorrelation analysis we found out, that AOD events usually occur with duration of several hours and are therefore caused by large-scale processes and may have a periodicity of several days. Local processes can be neglected in Ny-Ålesund but long-range transport plays a major role for the Arctic aerosol budget. We also compared AOD events with large-scale processes, like large-scale oscillation patterns, sea ice, weather conditions or wildfires on the Northern Hemisphere but did not find one single cause, which clearly determines the Arctic AOD.

Ocean surface currents and winds are tightly coupled essential climate variables, and, given their short time scales, observing them at the same time and resolution is of great interest. DopplerScatt is an airborne Ka-band scatterometer that has been developed under NASA's Instrument Incubator Program (IIP) to provide a proof of concept of the feasability of measuring these variables using pencil-beam scanning Doppler scatterometry. In the first half of this paper, we present the Doppler scatterometer measurement and processing principles, paying particular attention to deriving a complete measurement error budget. Although Doppler radars have been used for the estimation of surface currents, pencil-beam Doppler Scatterometry offers challenges and opportunities that require separate treatment. The calibration of the Doppler measurement to remove platform and instrument biases has been a traditional challenge for Doppler systems, and we introduce several new techniques to mitigate these errors when conical scanning is used. The use of Ka-band for airborne Doppler scatterometry measurements is also new, and, in the second half of the paper, we examine the phenomenology of the mapping from radar cross section and radial velocity measurements to winds and surface currents. To this end, we present new Ka-band Geophysical Model Functions (GMF's) for winds and surface currents obtained from multiple airborne campaigns. We find that the wind Ka-band GMF exhibits similar dependence to wind speed as that for Ku-band scatterometers, such as QuikSCAT, albeit with much greater upwind-crosswind modulation. The surface current GMF at Ka-band is significantly different from that at C-band, and, above 4.5 m/s has a weak dependence on wind speed, although still dependent on wind direction. We examine the effects of Bragg-wave modulation by long waves through a Modululation Transfer Function (MTF), and show that the observed surface current dependence on winds is consistent with past Ka-band MTF observations. Finally, we provide a preliminary validation of our geophysical retrievals, which will be expanded in subsequent publications. Our results indicate that Ka-band Doppler scatterometry could be a feasible method for wide-swath simultaneous measurements of winds and currents from space.

Relationship between the results of penile duplex doppler ultrasound (PDDU) and response to vardenafil was investigated in patients diagnosed with erectile dysfunction (ED). Data of 148 patients with ED were analysed retrospectively. Patients who did not respond to therapy were classified as Group I (n = 32), those responded partially were classified as Group II (n = 40) and complete responders were classified as Group III (n = 76). Age, comorbid diseases, vascular and penile pathology were compared among the three groups. While diabetes mellitus (DM) and dyslipedimia positivity adversely affect the response to treatment, the presence of hypertension (HT), peyronie's disease and priapism increase the therapeutic response to the treatment (p < 0.05). Arterial insufficiency was present in 20(30.3%), 25(37,9%) and 21(31.8%) of the patients in Group I, Group II and Group III, respectively (p = 0.001). Venous insufficiency was observed in 3(14.3%) patients in Group I and in 8(85.7%) patients in Group III (p = 0.001). Arterial/venous insufficiency was seen in 9(30%), 14(46.7%) and 7(23.3%) of the patients in Group I, Group II and Group III, respectively (p = 0.001). Response rate to treatment was highest in normal patients according to PDDU, followed by patients with venous insuffiency. Besides, it was found that DM decreased the response to treatment, whereas response was increased in cases with HT, priapism and Peyronie’s disease.

In the field of maneuvering target tracking, the combined observations of azimuth and Doppler may cause weak observation or non-observation in the application of traditional target tracking algorithms. Additionally, traditional target-tracking algorithms require pre-defined multiple mathematical models to accurately capture the complex motion states of targets, while model mismatch and unavoidable measurement noise lead to significant errors in target state prediction. To address those above challenges, in recent years, the target-tracking algorithms based on neural networks, such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and Transformer architectures, have been widely used for their unique advantages to achieve accurate predictions. To better model the nonlinear relationship between the observation time series and the target state time series, as well as the contextual relationship among time series points, we present a deep learning algorithm called recursive downsample-convolve-interact neural network (RDCINN) based on convolutional neural network (CNN) that downsamples time series into sub-sequences and extracts multi-resolution features to enable the modeling of complex relationships between time series, which overcomes the shortcomings of traditional target-tracking algorithms in using observation information inefficiently due to weak observation or non-observation. The experimental results show that our algorithm outperforms other existing algorithms in the scenario of strong maneuvering target tracking with the combined observations of azimuth and Doppler.

Bistatic radar experiments have been used to study surface characteristics of extra-terrestrial bodies in the Solar System, including the Moon, Venus, Mars, and Titan. This paper proposes a 3D model to characterize the scattered field of a Gaussian rough surface on an extra-terrestrial body for an orbital bistatic radar configuration. Specifically, this model will investigate how the variability of surface roughness impacts the spectral broadening of the received signal using physical optics approximations and ray tracing on a surface model using a facet approach with Gaussian properties. A linear relationship between spectral broadening of the signal and surface roughness was found. This relationship is in line with results obtained by commonly used analytical models for bistatic radar on planetary surfaces.

Continuous casting is the most common method for producing steel into semi-finished shapes like billets or slabs. Throughout this process, steel experiences mechanical and thermal stresses, which influence its mechanical properties. During continuous casting, decreased formability in steel components leads to crack formation and failure. One reason for this phenomenon is the appearance of the soft ferrite phase during cooling. However, it is unclear under which conditions this ferrite is detrimental to the formability. In the present research, we investigated what microstructural changes decrease the formability of microalloyed steels during continuous casting. We studied the hot compression behaviour of microalloyed steel over temperatures ranging from 650 °C to 1100 °C and strain rates of 0.1 s-1 to 0.001 s-1 using a Gleeble 3800® device. We examined microstructural changes at various deformation conditions using microscopy. Furthermore, we implemented a physically-based model to describe the deformation of austenite and ferrite. The model describes the work hardening and dynamic restoration mechanisms, i.e., discontinuous dynamic recrystallisation (dDRX) in austenite and dynamic recovery (DRV) in ferrite and austenite. The model considers the stress, strain, and strain rate distribution between phases by describing the dynamic phase transformation during the deformation in iso-work conditions. Increasing the strain rate below the transformation temperature improves hot ductility by reducing DRV and strain concentration in ferrite. Due to limited grain boundary sliding, the hot ductility improves at lower temperatures (&lt;750 °C). In the single-phase domain, DRX improves the hot ductility provided that fracture occurs at strains in which DRX advances. However, at very low strain rates, the ductility decreases due to prolonged time for grain boundary sliding and crack propagation.

Drones are being used more and more frequently and in various ways to attack people and critical infrastructures, which increases their level of danger and makes early detection and risk assessment even more difficult. There is no ultimate drone sensor. Rather, drones can only be effectively detected, if suitable sensor technologies are integrated within a system. Among the best suited sensorics for this purpose are radar and optical sensors, because the advantage of the radar, given by the long detection range, can be well combined with the additional visual-based information gained from a camera in the mid- and short-range, where usually the radar fails detecting micro drones. In this paper, a modular drone detection and assistance system MODEAS is presented, which can be used to automatically detect and track drones within a range up to some kilometers on the sky, but also in complex urban environments, where buildings and vegetation offer a perfect background for camouflage. The system provides an early detection of flying objects as potential drones, when those are not at all or only as small dots visible for the human eye. Main strengths of the system are the robust AI detection to differentiate such an object against clutter (birds, clouds) and its further classification by model and payload recognition, the latter aspect being considered as an indication of dangerousness. Moreover, once a drone is detected, the system follows it automatically, precisely and smoothly with a high-resolution pan-tilt-camera, measuring the distance to the sensor station with a laser rangefinder, compute the geocoordinates and visualize them live on a map. To improve the precision of the detection, a multisensorial fusion of all sensor detections is performed. Furthermore, alarms may be generated when a drone enters a restricted area.

This work presents a new scheme for autonomous control and obstacle detection of a catamaran unmanned surface vessel. Based on the Newtonian mechanics, hydrodynamics, and maneuvering modelling group modelling principles in the attached coordinate system, a three-degree-of-freedom motion model of the catamaran unmanned surface vessel is constructed. The fuzzy inference method is used to overcome the drawback that the traditional PID control cannot tune the PID parameters online, and the trajectory controller is designed. Meanwhile, 3D LIDAR is used to achieve obstacle detection. The results of the trajectory tracking simulations and experiments verify the accuracy of the model design and the effectiveness of the autonomous control design.

Precipitation is a crucial driver for many environmental processes and weather radars are capable of providing precipitation information with high spatial and temporal resolution. However, radar-based quantitative precipitation estimates (QPE) are also subject to various potential uncertainties. This study explores the development, uncertainties and potentials of the hourly operational German radar-based and gauge-adjusted QPE called RADOLAN and its reanalysed radar climatology dataset named RADKLIM in comparison to ground-truth rain gauge data. The precipitation datasets are statistically analysed across various time scales ranging from annual and seasonal aggregations to hourly rainfall intensities in regard to their capability to map long-term precipitation distribution, to detect low intensity rainfall and to capture heavy rainfall. Moreover, the impacts of season, orography and distance from the radar on long-term precipitation sums are examined in order to evaluate dataset performance and to describe inherent biases. Results revealed that both radar products tend to underestimate total precipitation sums and particularly high intensity rainfall. But our analyses also showed significant improvements throughout the RADOLAN time series as well as major advances through the climatologic reanalysis regarding the correction of typical radar artefacts, orographic and winter precipitation as well as range-dependent attenuation.

The possibility to test a radio frequency transceiver through the use of appropriate channel emulators allows to evaluate their performance under various operating conditions. Many systems are able to operate with a relatively limited instantaneous bandwidth by applying known statistical models. Sometimes it is necessary to evaluate the performance of a RF transceiver installed on high or very high speed platforms over predictable trajectories using optionally DEMs (Digital Elevation Models)data of the terrain to estimate the number of stimulated paths and their contributions during the flight. When the instantaneous bandwidth of the signal becomes high (over several hundred of MHz), one of the most important phenomenon to consider is the Doppler spread induced by the channel and traditional narrowband models become unuseful. In this paper there are presented some results when a time expansion is adopted in order to emulate the transceiver dynamic and the consequent Doppler spread with the aim to control the spectral purity of the emulated propagation channel.

Virtual validation of radar sensor models is becoming increasingly important for the safety validation of adf. Therefore, methods for quantitative comparison of radar measurements in the context of model validation need to be developed. This paper presents a novel methodology for accessing and quantifying validation measurements of radar sensor models. This method uses edf and the so-called dvm to effectively quantify deviations between distributions. By applying this metric, the study measures the consistency, reproducibility and repeatability of radar sensor measurements. Different interfaces and different levels of detail are investigated. By comparing the radar signals from real world experiments where different objects are present, valuable insights are gained into the performance of the sensor. In particular, the research extends to assessing the impact of varying rain intensities on the measurement results, providing a comprehensive understanding of the sensor’s behaviour under these conditions. This holistic approach significantly advances the evaluation of radar sensor capabilities and enables the quantification of the maximum required quality of radar simulation models.

COMPASS is a mission concept aiming to determine the recent climate history of planet Mars. Aboard this mission, there would be a dual mode radar instrument capable of acting both as a Synthetic Aperture Radar (SAR) and a Subsurface Sounder for mapping shallow ice. The COMPASS Radar Observer for Mars Exploration (CROME) instrument will help advance science and exploration. First, the COMPASS mission is described. Then, the CROME instrument concept and its implementation are presented. Finally, the procedure used to predict the performance is detailed for both the Side-Looking SAR and the Nadir-looking Sounder modes.

An exhaustive list of thirteen instances of reports confirming experimentally the relativistic Doppler relation, are examined. For those involving longitudinal Doppler, the non-relativistic relation is seen to be confirmed, within the reported experimental accuracies, to the same degree as the standard relativistic relation. Higher values of the speed of the emitter would be required to examine further the claimed confirmations. For those reports involving saturation spectroscopy, there is much confusion over the appropriate Doppler relation to be used, together with some serious analytical flaws. For the two cases that involve transverse Doppler, there are seen to be either serious faults in the theoretical part, or intrusion from the first order effect. Therefore, the reported conclusions - that the results for the experiments confirm the relativistic SR relation - cannot be justified by any of the experimental works.

The pathogenesis of coronavirus disease 2019 (COVID-19) is associated with a hyperinflammatory response. The mechanisms of SARS-CoV-2-induced inflammation are scantly known. Methylglyoxal (MG) is a glycolysis-derived by-product, endowed with a potent glycating action, leading to the formation of advanced glycation end products (AGEs), the major of which is MG-H1. MG-H1 exerts strong pro-inflammatory effects, frequently mediated by the receptor for AGEs (RAGE). Here, we investigated the involvement of MG-H1/RAGE axis as a potential novel mechanism in SARS-CoV-2-induced inflammation by resorting to human bronchial (BEAS-2B) and alveolar (A549) epithelial cells, expressing different levels of ACE2 receptor (R), exposed to SARS-CoV-2 spike protein 1 (S1). Interestingly, we found in BEAS-2B cells that do not express ACE2-R, that S1 exerted a pro-inflammatory action through a novel MG-H1/RAGE-based pathway. RAGE expression levels in nasopharyngeal swabs from SARS-Cov-2 positive and negative individuals, as well as glyoxalase 1 expression, the major scavenging enzyme of MG, seem to support the results obtained in vitro. Altogether, our findings reveal a novel mechanism involved in the inflammation triggered by S1, paving the way to the study of MG-H1/RAGE inflammatory axis in SARS-CoV-2 infection as a potential therapeutic target to mitigate COVID-19 associated pathogenic inflammation.

Late onset Pompe disease is a recessive lysosomal storage disease. Clinical features include skeletal muscles deficiency and diaphragm weakness. Clinical management relies on supportive treatment and mechanical ventilation in patents with chronic respiratory failure. M mode ultrasound and tissue Doppler imaging can be used to assess and to follow up diaphragm function.

In this paper, we dive into the exciting world of wireless communication, focusing on how millimeter-wave technology and Multiple-Input Multiple-Output phased array antennas are shaping the future of 5G and the upcoming 6G technologies. We cover the latest advancements in millimeter-wave and beam-forming technologies, emphasizing their role in enhancing network security and efficiency in automotive vehicles through dual radar communication. Our discussion spans the benefits, applications, challenges, and solutions of these technologies individually from millimeter-wave to beam-forming technology and joint radar communications, alongside a look at their theoretical and practical implementations. We emphasize the integration of beam-forming technology in joint radar communications for future automotive vehicles and its impact on automotive systems, smart cities, and the Internet of Things (IoT). Looking ahead, we discuss the potential of these technologies to transform future tech landscapes, while also addressing the security implications of merging communication and radar capabilities. This paper aims to provide a clear view of the advancements and prospects of millimeter-wave, beam-forming, and dual radar communication technologies.

Currently, electromagnetic and radar methods are the most common non-destructive tests for non-destructive rebar location in structures.. Both methods have some advantages, disadvantages and limitations. This article is an attempt to describe possibilities of particular methods with emphasizing their advantages and limitations. The first stage of tests included results from tests conducted on a lintel beam made of autoclaved aerated concrete (AAC) and reinforced with small-diameter rebars and rebars placed close to each other. The second stage consisted in testing nine lightweight concrete specimens, each with three bars having a diameter of 10, 16 and 20 mm at three different space arrangements, to determine the effect of bar diameter and spacing on the measurement accuracy. The reinforcement of lintels and specimens was tested with two different electromagnetic scanners and a ground-penetrating radar (GPR) device. Received results from direct tests were compared with results from non-destructive tests (NDT). NDT tests conducted in such an element were found to perform the correct assessment of the concrete cover, and at the same time to give ambiguous results with reference to shape and number of rebars.

In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask sparse representation based classification (MSRC). Thus, to characterize the targets in the radar images, we combine the scale-invariant feature transform (SIFT) and the saliency map. The goal of this combination is to reduce the SIFT keypoints and their time computing time by maintaining only those located in the target area (salient region). Then, we compute the feature vectors of the resulting salient SIFT keypoints (MSKD). This methodology is applied for both training and test images. The MSKD of the training images leads to construct the dictionary of a sparse convex optimization problem. To achieve the recognition, we adopt the MSRC taking into consideration each vector in the MSKD as a task. This classifier solves the sparse representation problem for each task over the dictionary and determines the class of the radar image according to all sparse reconstruction errors (residuals). The effectiveness of the proposed approach method has been demonstrated by a set of extensive empirical results on ISAR and SAR images databases. The results show the ability of our method to predict adequately the aircraft and the ground targets.

The difference in the Hubble-Lemaitre constant determined by different probes is a yet unexplained observation. This paper proposes the contention that the tension could be explained by the Doppler shift effect of Ia supernovae or Cepheids, driven by the rotational velocity of their host galaxies relative to the rotational velocity of the Milky Way. While the effect of the Doppler shift is expected to be mild, observations show that it can lead to systematic differences in the apparent brightness, and consequently the estimated distances. A simple experiment is done by repeating a previous analysis. When using the original set of supernovae, $H_o$ is 73.758$\pm$1.943 km/s/Mpc. When using a subset of supernovae such that the host galaxies rotate in the same direction as the Milky Way, $H_o$ drops sharply to 69.049$\pm$3.42 km/s/Mpc, showing a far milder tension with the $H_o$ determined by the CMB. When using a subset of Ia supernovae that rotate in the opposite direction, Ho does not decrease, but instead it increases the Ho tension to 74.182$\pm$3.2. Further analysis will be required to determine the link between Ho observed by using Ia supernovae or Cepheids and the rotational velocity of the host galaxies.

It is important to understand the statistical-physical structure of the rain in the vertical so that observations aloft can be translated meaningfully into what will occur at the surface. In order to achieve this understanding, it is necessary to gather high temporal and spatial resolution observations of rain in the vertical. This can only be accomplished using radars. It can be achieved by translating radar Doppler spectra into drop size distributions which can then be integrated to calculate variables such as the rain fall rate. A long-standing difficulty in using such measurements, however, is the problem of vertical air motion which can shift the Doppler spectra, and, therefore, significantly alter the deduced drop size distributions and integrated variables. In this work, we illustrate the improvement in measured rain structures using a new approach for removing the effect of mean vertical air motion. It is demonstrated that the new approach proposed here not only produces what appear to be better estimates of the rainfall rates, but, also as a consequence, provides estimates of the temporal and spatial regionally coherent updraft and downdrafts occurring in the precipitation. Furthermore, the technique is readily applicable to other radars especially those operating at non-attenuating frequencies.

The cucumber mosaic virus (CMV) 2b protein is a suppressor of plant defenses and a pathogenicity determinant. Amongst the 2b protein’s host targets is the RNA silencing factor Argonaute 1 (AGO1), which it binds to and inhibits. In Arabidopsis thaliana, if 2b-induced inhibition of AGO1 is too efficient it induces reinforcement of antiviral silencing by AGO2, and triggers increased resistance against aphids, CMV’s insect vectors. These effects would be deleterious to CMV replication and transmission, respectively, but are moderated by the CMV 1a protein by sequestering sufficient 2b protein molecules into P-bodies to prevent excessive inhibition of AGO1. Mutant 2b protein variants were generated and red and green fluorescent protein fusions used to investigate subcellular colocalization with AGO1 and the 1a protein, and the effects of mutations on complex formation with the 1a protein and AGO1 were investigated using bimolecular fluorescence complementation and co-immunoprecipitation assays. Although we found that residues 56-60 influenced the 2b protein’s interactions with the 1a protein and AGO1, it appears unlikely that any single residue or sequence domain is solely responsible. In silico predictions of intrinsic disorder within the 2b protein secondary structure were supported by circular dichroism (CD) but not by nuclear magnetic resonance (NMR) spectroscopy. Intrinsic disorder provides a plausible model to explain the 2b protein’s ability to interact with AGO1, the 1a protein and other factors. However, the reasons for the conflicting conclusions provided by CD and NMR must first be resolved.

In order to improve real-time performance of gesture recognition by micro-Doppler map of mmWave Radar, the point cloud based gesture recognition for mmWave Radar is proposed in this paper.Two steps are carried out for mmWave Radar based gesture recognition. The first step is to estimate the point cloud of the gestures by 3D-FFT and the peak grouping. The second step is to train the TRANS-CNN model by combining the multi-head self-attention and the 1D-convolutional network, so as to extract the features in the point cloud data at a deeper level to categorize the gestures. In the experiments, TI mmWave Radar sensor IWR1642 is used as benchmark to evaluate the feasibility of the proposed approach. The results show that the accuracy of the gesture recognition reaches 98.5%. In order to prove the effectiveness of our approach, a simply 2Tx2Rx Radar sensor is developed in our lab and the accuracy of recognition reaches 97.1%. The results show that our proposed gesture recognition approach achieves the best performance in real-time with limited training data, in comparison with the existing methods.

A method of direction-of-arrival (DoA) estimation for FMCW radars is presented. In addition to MUSIC and ESPRIT, which are well-known high-resolution DoA estimation algorithms, deep learning has recently emerged as a very promising alternative. It is proposed in this paper to use a 3D convolutional neural network (CNN) for DoA estimation. The 3D-CNN extracts from the radar data cube spectrum features of the region of interest (RoI) centered on the potential positions of the targets, thereby capturing the spectrum phase shift information, which corresponds to DoA, along the antenna axis. Finally, the results of simulations and field experiments are provided to demonstrate the superior performance, as well as the limitations, of the proposed 3D-CNN.

In this paper, a new millimeter wave 3D imaging radar is proposed. The user just needs to move the radar along a circular track, a high resolution 3D imaging can be generated. The proposed radar uses the movement of itself to synthesize a large aperture in both the azimuth and elevation directions. It can utilize inverse Radon transform to resolve 3D imaging. To improve the sensing result, compressed sensing approach is further investigated. The simulation and experimental result further illustrated the design. Because a single transceiver circuit is needed, a light, affordable and high resolution 3D mmWave imaging radar is illustrated in the paper.

Holographic subsurface radar (HSR) is currently not in widespread usage. This is due to an historical perspective in the ground penetrating radar (GPR) community that the high attenuation of electromagnetic waves in most media of interest, and the inability to apply time-varying gain to the continuous wave (CW) HSR signal precludes sufficient effective penetration depth. While it is true that the fundamental physics of HSR, with its use of a CW signal, does not allow amplification of later (i.e. deeper) arrivals in lossy media (as is possible with impulse subsurface radar — ISR), HSR has distinct some distinctive advantages. The most important of these is the ability to do shallow subsurface imaging with a resolution that is not possible with ISR. In addition, the design of an HSR system is simpler than for ISR due to the relatively low-tech transmitting and receiving antennae. This paper provides a review of the main principles of HSR through an optical analogy and describes possible algorithms for radar hologram reconstruction. We also present a review of the history of development of systems and applications for HSR of the “RASCAN” type which is possibly the only holographic subsurface radar that is produced in lots. Among the subsurface imaging and remote sensing applications considered are humanitarian demining, construction inspection, surveys of historic architecture and artworks, nondestructive testing of dielectric aerospace materials, security applications, paleontology, detection of wood-boring insect damage, and others. Each application is illustrated with relevant data acquired in laboratory and/or field experiments.

Holographic subsurface radar (HSR) is currently not in widespread usage. This is due to an historical perspective in the ground penetrating radar (GPR) community that the high attenuation of electromagnetic waves in most media of interest, and the inability to apply time-varying gain to the continuous wave (CW) HSR signal precludes sufficient effective penetration depth. While it is true that the fundamental physics of HSR, with its use of a CW signal, does not allow amplification of later (i.e. deeper) arrivals in lossy media (as is possible with impulse subsurface radar — ISR), HSR has distinct some distinctive advantages. The most important of these is the ability to do shallow subsurface imaging with a resolution that is not possible with ISR. In addition, the design of an HSR system is simpler than for ISR due to the relatively low-tech transmitting and receiving antennae. This paper provides a review of the main principles of HSR through an optical analogy and describes possible algorithms for radar hologram reconstruction. We also present a review of the history of development of systems and applications for HSR of the “RASCAN” type which is possibly the only holographic subsurface radar that is produced in lots. Among the subsurface imaging and remote sensing applications considered are humanitarian demining, construction inspection, surveys of historic architecture and artworks, nondestructive testing of dielectric aerospace materials, security applications, paleontology, detection of wood-boring insect damage, and others. Each application is illustrated with relevant data acquired in laboratory and/or field experiments.

We describe an efficient and cost effective data access mechanism for Sentinel-1 TOPS 1 mode bursts. Our data access mechanism enables burst-based data access and processing, thereby 2 eliminating ESA’s Sentinel-1 SLC data packaging conventions as a bottleneck to large scale processing. 3 Pipeline throughput is now determined by available compute resources and efficiency of the analysis 4 algorithms. For targeted infrastructure monitoring studies, we are able to generate coregistered, 5 geocoded stacks of SLCs for any AOI in the world in a few minutes. In addition, we describe our 6 global scale radar backscatter and interferometric products and associated pipeline design decisions 7 that ensure geolocation consistency across the suite of derived products from Sentinel-1 data. Finally, 8 we discuss the benefits and limitations of working with geocoded SAR SLC data.

Weather conditions appear to be undergoing significant deviations from the long-term average, marked by pronounced extremes of heat, prolonged droughts, and heightened rainfall occurring with greater frequency worldwide. Consequently, new patterns of extreme weather are emerging, like the unusual and unorganized tropical cyclone called "Yaku", that influenced the extreme amount of rain that between March 6th to 10th, 2023 hits more than 1000 districts in the northwestern of Peru. One of the district more affected was Íllimo, in the Lambayeque province, due to the river that pass through the city, so called “La Leche”, after the continuous and intensive rain overflow devastating his surrounding areas and causing victims and damage. This emergency provided and excellent opportunity to apply, on the aforementioned areas, the “change detection” technique, that allows identifying areas of change between data obtained before and after of emergency events. In this research, optical data (Sentinel-2, Landsat, MODIS Terra and Aqua, and PeruSAT-1) and synthetic aperture radar (SAR) data (Sentinel-1 and COSMO SkyMed) were analyzed. A benchmark could also be carried out between open-source and commercial spatial analysis tools. The results indicated that in an extreme rain event the optical data are obscured by clouds and do not allow their use, while SAR data overcome clouds, and can be used for this research. The benchmark indicated that using the Jaccard index score, the Sentinel-1A set showed a 33% of correlation, while COSMO SkyMed demonstrated a 38% of match between the maps. The results obtained could be used to sensitize the population and develop a better management of future emergency events (floods) in different areas of Peru.

In this paper, the concept of absolute airspace is further elaborated, and the time transform effect, the frequency transform relation of clock timing, and the principle of Doppler effect of special relativistic light are analyzed and demonstrated. Based on the concept of absolute airspace, a new and accurate Doppler effect relation of light (electromagnetic wave) is proposed. It is proved that there is a synchronous relationship between the time shift effect and the frequency change of the timing inside the clock. When the light (electromagnetic wave) source is in motion, the Doppler effect includes two physical mechanisms: the frequency change caused by time transformation effect and the frequency change caused by relative motion. When the light source is in motion, the Doppler effect of the light there are some differences in different directions due to the rotation of the earth. The relation of Doppler effect of special relativistic light is only a highly approximate relation, and its theoretical basis, special relativity, is flawed in principle.

Assessment of cardiac function is the leading parameter when evaluating the state of the cardiovascular system of patients undergoing chronic hemodialysis. The aim of the paper: to assess the state of the cardiovascular system of these patients using new sensitive echocardiography and Doppler techniques and thus advance the prevention of cardiovascular disease. Method: Twenty children with end-stage renal insufficiency on chronic hemodialysis and twenty healthy controls underwent echocardiographic monitoring using standard Doppler and tissue Doppler imaging. Structural and functional changes in the left ventricle were evaluated. Results: Patients on hemodialysis had significantly greater left ventricular mass indices compared to the controls (p < 0.001). The patients on hemodialysis had preserved systolic function—their fractional shortening, ejection fraction and Sm (systolic myocardial velocity) did not differ significantly compared to the controls (p > 0.05). Early diastolic function in children on hemodialysis was also preserved: the E/A and Em/Am ratio did not differ significantly from the control group (p > 0.05). Children on hemodialysis exhibited impaired late diastolic function (compliance index), that is, considerably higher E/Em compared to controls (p < 0.00). Myocardial Performance Index values showed statistically significant elevation in children on hemodialysis compared to the control group (p < 0.001). Conclusion: Tissue Doppler in tandem with conventional Pulsed Doppler can provide additional information on left ventricular filling pressures (E/Em) in children on hemodialysis. It is therefore recommended to perform routine measuring of Em waves and the E/Em ratio, not only in order to evaluate myocardial relaxation and ventricular filling pressures, but primarily to stratify risk and provide a prognosis.

The paper presents experimental studies on the geophysical survey of Almaty – Nur-Sultan highway sections, which located 100 km. from Almaty, Almaty region of the Republic of Kazakhstan. As the object of study selected highway sections in 3 places: on the highway section with a well-preserved coating to identify layers; on the highway section with obvious damage (with holes, potholes, horizontal and grid cracks); on the section of the highway with a drainage pipe. Since the formulation of the problem requires the use of a non-destructive, fast method that gives an idea of the layer structures with an accuracy of ± 0.1 m., experimental studies were conducted using the ground penetrating radar (GPR) «Loza B» using different antennas, with different profile steps for accuracy. The results of the geophysical survey of the structure of the underlying layers and asphalt for the detection of defects and their causes are presented. The results of this work can be used to develop compositions of asphalt concrete mixtures with high temperature stability, strength and durability, considering the climatic conditions in order to ensure the safety of road surfaces during operation.

Bello's stochastic linear time-varying system theory has been widely used in the wireless communications literature to characterize multipath fading channel statistics. In the context of radar backscatter, this formulation allows for statistical characterization of distributed radar targets in range and Doppler using wide-sense stationary uncorrelated scattering (WSSUS) models. WSSUS models separate the channel from the effect of the waveform and receive filter, making it an ideal formulation for waveform design problems. Of particular interest in the radar waveform design community is the ability to suppress unwanted backscatter from the earth's surface, known as clutter. Various methods for estimating WSSUS system functions have been studied in the literature, but to date, no analytic expressions for radar surface clutter range-Doppler scattering functions exist. In this work we derive a wideband generalization of the Jakes Doppler spectrum model, which is widely used in the wireless communications literature, adapt it for use in radar problems, and show how the maximum entropy method can be used to extend this model to account for internal clutter motion. Validation of the spectral and stationarity properties of the proposed model against a subset of the Australian Ingara sea clutter database is performed, and good agreement is shown.

In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view including drone and non-drone targets. The analysis of the received signals allows multiple targets to be distinguished because of the different reflection patterns. The proposed framework consists of four processes including signal processing, cloud points clustering, target tracking, and target recognition. Signal processing translates the raw signals into spare cloud points. These points are merged into several clusters, each representing a single target. Target tracking estimates the new locations of each detected target. A novel convolutional neural network model is developed for drone and/or non-drone targets feature extraction and recognition. For performance evaluation, a dataset collected with an IWR6843ISK mmWave sensor by Texas Instruments is used for training and testing the convolutional neural network. The proposed recognition model achieves an accuracy of 98.4% for one target and 98.1% for two targets.

In hepatocellular carcinoma (HCC), recurrence usually occurs after curative surgical resection. Currently, no approved adjuvant therapy has been shown to reduce recurrence rates. In this report, the in vivo mouse effect of sequential combination treatment with recombinant mouse interferon-alpha (rmIFN-α) and an anti-mouse-PD1 antibody on hepatitis B virus (HBV) clearance was evaluated. A phase I clinical trial was then conducted to assess the safety, tolerability, and inhibitory activity of sequential therapy with ropeginterferon alfa-2b and nivolumab in HCC recurrence in patients who have undergone curative surgery for HBV-related HCC. In animal modeling, HBV suppression was significantly greater with rmIFN-α and anti-PD1 sequential combination treatment than with their treatment alone. In the Phase I study, eleven patients completed the sequential therapy with ropeginterferon alfa-2b every two weeks at the dose of 450 µg for six doses, followed by three doses of nivolumab every two weeks up to 0.75 mg/kg. A notable decrease or clearance of HBV surface antigen was observed in two patients. Dose-limiting toxicity of grade 3 alanine transaminase and aspartate aminotransferase increases was observed in one patient. The maximum tolerated dose was determined. Currently no HCC recurrence has been observed. The treatment modality was well tolerated. The data support further clinical development of sequential combination therapy as a post-surgery prophylactic measure against the recurrence of HBV-related HCC.

In this study, an experiment was conducted on hoop pine (Araucaria cunninghamii), which is a common tree species in Taiwan. Holes with diameters of 4, 5, 7, 10, 12, and 14 cm were drilled in dry hoop pine timber with a diameter of 39 cm and a height of 57 cm, and an 800 MHz Ground Penetrating Radar (GPR) antenna were used to determine the minimum image resolution and the size of the holes in the wood. GPR images of the timber were subjected to a filtering process to obtain characteristic reflection curves of the damage to the timber. Curves with clear waveforms were observed in the GPR profiles of eccentric holes with a diameter of 4 cm. Profiles of the damage were obtained and profile images with the trunk radius at the centre were transformed into polar coordinate images. From these polar coordinate images, the location, size and diameter of the eccentric holes in the wood were identified, and the reflection curves were characterised by waveforms and irregular fluctuations. The risk levels of tree trunks were determined on the basis of their t/R (minimum normal wall thickness of the cross-section of the trunk/radius of the trunk) values and hole area ratios. A comparison between the GPR-based results and the actual diameters of the holes revealed that the filtered GPR profiles could be used for the efficient and accurate assessment of damage to hoop pine timber (average error rate ≤ 4%).

Tephra plumes can cause a significant hazard for surrounding towns, infrastructure, and air traffic. The current work presents the use of a small and compact X-band Multi-Parameter (X-MP) radar for the remote tephra detection and tracking of two eruptive events at Merapi Volcano, Indonesia, in May and June 2018. Tephra detection was done by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity. These parameters were used to cancel unwanted clutter and retrieve tephra properties, which are grain size and concentration. Real-time spatial and temporal forecasting of tephra dispersal was performed by applying an advection scheme (nowcasting) in the manner of Ensemble Prediction System (EPS). Cross-validation was done using field-survey data, radar observations, and Himawari-8 imagery. The nowcasting model computed both the displacement and growth and decaying rate of the plume based on the temporal changes in two-dimensional movement and tephra concentration, respectively. Our results with ground-based data, where the radar-based estimated grain size distribution fell within the range of in-situ data. The uncertainty of real-time forecasted tephra plume depends on the initial condition, which affects the growth-and decaying rate estimation. The EPS improves the predictability rate by reducing the number of missed and false forecasted events. Our findings and the method presented here are suitable for early warning of tephra fall hazard at the local scale.

Background: In this study, we aimed to evaluate the effect of s-FLT-1 and PlGF concentrations in women in the first trimester of pregnancy on the pulsatility index of the ductus venosus and the right and left uterine arteries. Methods: A total of 108 pregnant women in their first trimester were included in the study, with 87 assigned to the experimental group and 21 to the control group. Ultrasound examination were performed to measure the uterine artery pulsatility index, resistance index, ductus venosus pulsatility index, and blood sampling levels of biomarkers such as b-HCG; PAPP-A; s-FLT-1 and PlGF. Results: There was a significant negative relationship between PAPP-A concentration and the risk of fetal growth restriction, and a significant negative relationship between PlGF concentration and the risk of preeclampsia and fetal growth restriction in women in their first trimester of pregnancy. There was also a significant positive relationship between sFLT-1/PLGF ratio and the risk of developing preeclampsia. No significant relationships were found between the concentrations of biomarkers beta-HCG, PAPP-A, sFLT-1, PlGF, and any of the parameters PIUARt, PIUALt, Mean uterine arteries, and DVPI. Summary: In conclusion, this study did not find conclusive evidence to support the hypothesis that beta-HCG, PAPP-A, s-FLT-1, and PlGF concentrations in women in the first trimester of pregnancy have a significant effect on the Pulsatility Index of the ductus venosus and the Right and left Uterine Arteries. Further research is needed to investigate these potential associations.

In 2023, we proposed the modified Delay and Doppler Profiler (mDDP) as an inter-carrier interference (ICI) countermeasure for underwater acoustic OFDM mobile communications in a multipath environment. However, the performance improvement in computer simulation and pool experiments was not significant. In a subsequent study, the accuracy of the Channel Transfer Function (CTF), which is the input for the mDDP channel parameter estimation, was considered insufficient. Then a 2-step mDDP was devised. This paper presents simulations of underwater OFDM communications using 1- to 3-step mDDPs. The simulation conditions are a two-wave multipath environment where the receiving transducer moves at a speed of 0.3 m/sec and is subjected to a Doppler shift in the opposite direction. As NumCOL, the number of taps in the multi-tap equalizer which removes ICI, was increased, Bit Error Rate (BER) of 0.016 at NumCOL = 1 was significantly reduced by a factor of approximately 40 to BER of 0.00041 at NumCOL = 41 for the 2-step mDDP.

Among the sensors necessary to equip vehicles with an autonomous driving system, there is a tacit agreement that cameras and some type of radar would be essential. The ability of radar to spatially locate objects (pedestrians, other vehicles, street furniture and traffic signs) makes it the most economical complement to the cameras in the visible spectrum, in order to give the correct depth to scenes. From the echoes obtained by the radar, some data fusion algorithm will try to locate each object in its correct place within the space surrounding the vehicle. In any case, the usefulness of the radar will be determined by several performance parameters, such as its aver-age error in distance, the maximum errors and the number of echoes per second it can provide. In this work we have tested experimentally the AWR1843 MIMO radar from Texas Instruments to measure those parameters.

In high-traffic harbor waters, marine radar frequently encounters signal interference stemming from various obstructive elements, thereby presenting formidable obstacles in the precise identification of ships. To achieve precise pixel-level ship identification in the complex environments, a customized neural network-based ship segmentation algorithm named MrisNet is proposed. MrisNet employs a lightweight and efficient FasterYOLO network to extract features from radar images at different levels, capturing fine-grained edge information and deep semantic features of ship pixels. To address the limitation of deep features in the backbone network lacking detailed shape and structured information, an adaptive attention mechanism is introduced after the FasterYOLO network to enhance crucial ship features. To fully utilize the multi-dimensional feature outputs, MrisNet incorporates a Transformer structure to reconstruct the PANet feature fusion network, allowing for the fusion of contextual information and capturing more essential ship information and semantic correlations. In the prediction stage, MrisNet optimizes the target position loss using the EIoU function, enabling the algorithm to adapt to ship position deviations and size variations, thereby improving segmentation accuracy and convergence speed. Experimental results demonstrate that MrisNet achieves high recall and precision rates of 94.8% and 95.2%, respectively, in ship instance segmentation, outperforming various YOLO and other single-stage algorithms. Moreover, MrisNet has a model parameter size of 13.8M and real-time computational cost of 23.5G, demonstrating notable advantages in terms of convolutional efficiency. In conclusion, MrisNet accurately segments ships with different spot features and under diverse environmental conditions in marine radar images. It exhibits outstanding performance, particularly in extreme scenarios and challenging interference conditions, showcasing robustness and applicability.

This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 to 6 km, with major variations at 2.5–3 km; it also reproduces better the mesoscale belt and cell patterns of precipitation fields.

Environmental effects and climate change are lately representing an increasing strain of the coastal areas which topography strongly depends on these conditions. However, the processes by which weather and environmental phenomena influence the highly variable beach morphology are still unknown. A continuous monitoring of the beach environment is necessary to implement protection strategies. This paper presents the results of an innovative study performed on a coastal area using satellite remote sensing data with the aim of understanding how environmental phenomena affect beaches. Two-years of synthetic aperture radar (SAR) Sentinel-1 images are used over a test area in Noordwijk, the Netherlands. At the same time as the SAR acquisitions, information on tidal and weather conditions are collected and integrated from nearby meteorological stations. Dedicated codes are implemented in order to understand the relationship between the SAR amplitude and the considered phenomena: wind, precipitation, tidal conditions. Surface roughness is taken into account. The results indicate a strong correlation between the amplitude and the wind. No particular correlation or trend could be noticed in the relation with the precipitation. The analysis of the amplitude also shows a decreasing trend moving from the dry area of the beach towards the sea and the correlation coefficient between the amplitude and the tide level gets negative with the increase of the water content.

In a software-defined radar (SDR) system, most of the signal processing usually implemented in hardware is implemented by software, thus allowing for higher flexibility and modularity compared to conventional radar systems. However, the majority of SDR demonstrators and proofs-of-concept reported in the open literature so far have been based on simple antenna systems. As a result, the full potentialities of an SDR approach have not been completely exploited yet. In this work, we propose a flexilbe antenna module to be integrated into an active electronically scanning array (AESA) with controlled sidelobe level over a wide angular range, exhibiting polarization reconfigurability with low cross-polarization level and high isolation. For this purpose, analytical and numerically efficient techniques for the synthesis of the aperture distribution and the correct evaluation of the radiating features (e.g., beamwidth, pointing angle, sidelobe levels, etc.) are presented in order to grant a real-time control of the digital beamforming network. A sub-array module demonstrator is fabricated and measured to corroborate the concept.

Urban sprawl propelled by rapid population growth leads to the shrinkage of productive agricultural lands and pristine forests in the suburban areas and, in turn, substantially alters ecosystem services. Hence, the quantification of urban sprawl is crucial for effective urban planning, and environmental and ecosystem management. Like many megacities in fast growing developing countries, Chennai, the capital of Tamilnadu and one of the business hubs in India, has experienced extensive urban sprawl triggered by the doubling of total population over the past three decades. We employed the Random Forest (RF) classification on Landsat imageries from 1991, 2003, and 2016, and computed spatial metrics to quantify the extent of urban sprawl within a 10km suburban buffer of Chennai. The rate of urban sprawl was quantified using Renyi’s entropy, and the urban extent was predicted for 2027 using land-use and land-cover change modeling. A 70.35% increase in urban areas was observed for the suburban periphery of Chennai between 1991 and 2016. The Renyi’s entropy value for year 2016 was ≥ 0.9, exhibiting a two-fold rate of urban sprawl. The spatial metrics values indicate that the existing urban areas of Chennai became denser and the suburban agricultural, forests and barren lands were transformed into fragmented urban settlements. The forecasted urban growth for 2027 predicts a conversion of 13670.33ha (16.57 % of the total landscape) of existing forests and agricultural lands into urban areas with an associated increase in the entropy value of 1.7. Our findings are relevant for urban planning and environmental management in Chennai and provide quantitative measures for addressing the social-ecological consequences of urban sprawl and the protection of ecosystem services.

Weather radar plays a crucial role in the monitoring of severe convective weather. However, due to its limited detection range, it cannot conduct effective monitoring in remote offshore areas. Therefore, this paper utilized the Unet++ to establish a model for retrieving radar composite reflectivity based on Himawari-9 satellite datasets. In the process of comparative analysis, we found that both satellite and radar data exhibit significant diurnal cycles, but there are notable differences in their variation characteristics. To address this, we established 4 comparative models to test the influence of latitude and diurnal cycles on the inversion results. The results show that adding the distribution map of the minimum cloud-top brightness temperature at the corresponding time in the model can effectively reduce the root mean square error (RMSE) of the model and enhance the accuracy of severe convective weather monitoring. This conclusion can be applied in the future to enhance and improve satellite quantitative precipitation estimation.

During the night between 9 and 10 September 2017, multiple flash floods associated to a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours, associated with a return period higher than 200 years, caused all the largest streams of the Livorno municipality to flood several areas of the town. We used the limited-area Weather Research and Forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. By providing more accurate descriptions of the low-level flow and a better assessment of the atmospheric water vapour, the results demonstrate that assimilating radar data improved the quantitative precipitation forecasts.

Morphological parameters like cotton height, branches, Leaf Area Index and biomass are mainly affected by the vegetation water content (VWC). Periodical assessment of the VWC and crop parameters is required for timely management of the crop for maximizing yield. The study aimed at using both optical and microwave remotely sensed data to assess cotton crop condition based on the above mentioned traits. Vegetation indices (VI) derived from ground based measurements (5 narrow band and 2 broad band VIs) as well as satellite derived reflectance (2 broad band VIs) were assessed. Regression models were derived for estimating LAI, biomass and plant water content using the ground based indices and applied to the satellite derived spectral index (from LISS-III) map to estimate the respective parameters. HH and HV polarization from RISAT-1 were used to derive Radar Vegetation Index (RVI). The coefficient of determination of the model for estimating LAI, biomass and vegetation water content of cotton with optical vegetation index as input parameter were found to be 0.42, 0.51 and 0.52, respectively. The correlation between RVI and plant height, date of planting in terms of the age of the crop and vegetation water content were found to range between 0.4 to 0.6. The fresh biomass from RVI showed spatial variability from 100 gm-2 to 4000 gm-2 while the dry biomass map derived from NDVI showed spatial variability of 50 to 950 g m-2 for the study area. Plant water content in the district varied from 65 to 85%. The correlation between optical vegetation index and RVI was not significant. Hence a multiple linear regression model using both optical index (NDVI and LSWI) and SAR index (RVI) was developed to assess the LAI, biomass and plant water content. The model showed a R2 of 0.5 for LAI estimation but not significant for biomass and water content. This study show cased the use of combined optical and microwave (C band) remote sensing for cotton condition assessment.

Microscopic and ultra-microscopic vascular sutures are indispensable in the surgical procedures such as arm transplantation and finger reattachment. The state of the blood vessels after suturing, such as vascular patency, narrow, and blocked, determines the success rate of the operation. If we can grasp the golden window period after blood vessel suture and before muscle tissue suture to achieve accurate and objective assessment of blood vessel status, it can not only reduce medical costs but also improve social benefits. Doppler optical coherence tomography enables high-speed, high-resolution imaging of biological tissues, especially microscopic and ultra-microscopic blood vessels. Using Doppler optical coherence tomography to image the sutured blood vessels, not only the three-dimensional structure of the blood vessels, but also the blood flow information can be obtained. By extracting the contour of blood vessel wall and the contour of blood flow area, the three-dimensional shape of blood vessel can be reconstructed in three dimensions, which provides parameter support for the assessment of blood vessel status. In this work, we propose a neural network-based multi-classification deep learning model that can simultaneously and automatically extract blood vessel boundaries from Doppler OCT vessel intensity images and the contours of blood flow regions from corresponding Doppler OCT vessel phase images. Compared with the traditional random walk segmentation algorithm and cascade neural network method, the proposed model has better performance. This method is easier to realize system integration and has great potential for clinical evaluation. It is expected to be applied to the evaluation of microscopic and ultra-microscopic vascular status in microvascular anastomosis.

Ultrasonic based inspection of thin-walled structures often requires prior knowledge of their mechanical properties. Their accurate estimation could be achieved in a non-destructive manner employing, e.g., elastic guided waves. Such procedures require efficient approaches for experimental data extraction and processing, which is still a challenging task. An advanced automated technique for material properties identification of an elastic waveguide is proposed in this investigation. It relies on the information on dispersion characteristics of guided waves, which are extracted by applying the matrix pencil method to the measurements obtained via laser Doppler vibrometry. Two objective functions have been successfully tested, and the advantages of both approaches are discussed (accuracy vs computational costs). The numerical analysis employing the synthetic data generated via the mathematical model as well as experimental data shows that both approaches are stable and accurate. The influence of the presence of various modes in the extracted data is investigated. One can conclude that the influence of the corruptions related to the extraction of dispersion curves is not critical if the majority of guided waves propagating in the considered frequency range are presented. Possible extensions of the proposed technique for damaged and multi-layered structures are also discussed.

Digital-Radio-Frequency-Memory (DRFM) has emerged as an advanced technique to achieve diverse jamming signals, due to its capability of intercepting waveforms within a short time. Multiple-Input Multiple-Output (MIMO) radars can transmit agile orthogonal waveform sets for different pulses to combat the DRFM-based jamming, where any two groups of waveform sets are also orthogonal. In order to design multiple groups of orthogonal waveform sets at the same time, this article formulates a group orthogonal waveforms optimal design model, whose objec-tive function is the weighted sum of the intra- and inter-group orthogonal performances metrics. To solve this optimization problem, this article proposes a group orthogonal waveforms design algorithm. Based on a primal-dual type method and proper relaxations, the proposed algorithm transforms the original problem into a series of simple sub-problems. Numerical results demonstrate that the proposed algorithm has the ability to balance the intra- and inter-group orthogonal performances by adjusting the weighting factors, which is not available using other state-of-the-art orthogonal waveform design algorithms.

Radar observation data with high temporal and spatial resolution are used in the data assimilation experiment to improve precipitation forecast of a numerical model. The numerical model considered in this study is Weather Research and Forecasting (WRF) model with double-moment 6-class microphysics scheme (WDM6). We calculated radar equivalent reflectivity factor using higher resolution WRF and compared with radar observations in South Korea. To compare the precipitation forecast characteristics of three-dimensional variational (3D-Var) assimilation of radar data, four experiments are performed based on different precipitation types. Comparisons of the 24-h accumulated rainfall with Automatic Weather Station (AWS) data, Contoured Frequency by Altitude Diagram (CFAD), Time Height Cross Sections (THCS), and vertical hydrometeor profiles are used to evaluate and compare the accuracy. The model simulations are performed with and with-out 3D-VAR radar reflectivity, radial velocity and AWS assimilation for two mesoscale convective cases and two synoptic scale cases. The radar data assimilation experiment improved the location of precipitation area and rainfall intensity compared to the control run. Especially, for the two convective cases, simulating mesoscale convective system was greatly improved.

This paper considers the detection of fluctuating target in heavy-tailed clutter through the use of dynamic programming based on track-before-detect (DP-TBD) in radar systems. The clutter is modeled in terms of K-distribution, which can be widely used to describe non-Gaussian clutter received from high-resolution radars and radars working at small grazing angle. Swerling type 1 is considered to describe the target fluctuation between scans. Conventional TBD techniques suffer from significant performance loss in heavy-tailed environments due to the more frequent occurrences of target-like outliers. In this paper, we resort to DP-TBD algorithm based on prior information, which can enhance the detection performance by using the environment and target fluctuating information during the integration process of TBD. Under non-Gaussian background, the expressions of the likelihood ratio merit function for Swerling type 1 target are derived first. However, the closed analytical form of the merit function is difficult to be obtained. In order to reduce the complexity of evaluating the merit function and the computational load, an efficient approximation method as well as a two-stage detection approach is proposed and used in the integration process. Finally, several numerical simulations of the new strategy and the comparisons are presented to verify that the proposed algorithm can improve the detection performance, especially for fluctuating target in heavy-tailed clutter.

Ho Chi Minh City (HCMC), the most crowded city and economic hub of Viet Nam, has been experiencing land subsidence over the past decades. This effort aims to contribute the spatial distribution of subsidence in HCMC in its horizontal and vertical components using synthetic aperture radar interferometry (InSAR) time series. To this purpose, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) InSAR technique was applied to two European Space Agency (ESA) Sentinel-1 datasets consisting of 96 ascending and 202 descending images, acquired from 2014 to 2020 over the HCMC area. A time series of 33 COSMO-SkyMed ascending images was also used for comparison. The combination of ascending and descending satellite passes is used to decompose the light of sight velocities into horizontal east-west and vertical components. Taking into account the presence of east-west horizontal motion, our findings indicate that the accuracy of the decomposed vertical velocity can be improved by up to 3 mm/year for Sentinel-1 data. The obtained results revealed that subsidence is most pronounced in the areas along the Sai Gon River, in the northwest-southeast axis, and in the southwest of the city, with a maximum value of 80 mm/yr, which is in accordance with the findings of the literature. The amplitude of east-west horizontal velocities is relatively small and large-scale eastward movement can be observed in the west of the city at a rate of 3-5 mm/yr. This confirmed that the displacement in Ho Chi Minh City area is mainly vertical downward. Together, these results reinforced the remarkable suitability of ESA's SAR Sentinel-1 for subsidence applications, even for non-European countries such as Vietnam and Southeast Asia.

Floods are one of the most wide-spread, frequent, and devastating natural disasters that continue to increase in frequency and intensity. Remote sensing, specifically synthetic aperture radar (SAR), has been widely used to detect surface water inundation to provide retrospective and near-real time (NRT) information due to its high-spatial resolution, self-illumination, and low atmospheric attenuation. However, the efficacy of flood inundation mapping with SAR is susceptible to reflections and scattering from a variety of factors including dense vegetation and urban areas. In this study, the topographic dataset height above nearest drainage (HAND) was investigated as a potential supplement to Sentinel-1A C-Band SAR along with supervised machine learning to improve the detection of inundation in heterogeneous areas. Three machine learning classifiers were trained on two sets of features SAR only (VV & VH) and VV, VH & HAND to map inundated areas. Three study sites along the Neuse River in North Carolina, USA during the record flood of Hurricane Matthew in October 2016 were selected. The binary classification analysis (inundated as positive vs. non-inundated as negative) revealed significant improvements when incorporating HAND in several metrics including classification accuracy (ACC) (+37.1%), true positive rate (TPR) (+51.2%), and negative predictive value (NPV) (+23.7%), A marginal improvement of +1.4% was seen for positive predictive value (PPV), but true negative rate (TNR) fell -15.1%. By incorporating HAND, a significant number of areas with high SAR backscatter but low HAND values were detected as inundated which increased true positives. This in turn also increased the false positives detected but to a lesser extent as evident in the metrics. This study demonstrates that HAND could be considered a valuable feature to enhance SAR flood inundation mapping especially in areas with heterogeneous land covers with dense vegetation that interfere with SAR.

This study proposed a spatially and temporally improving methodology adopting the Regional Frequency Analysis with L-moments approach to estimate rainfall quantiles from 22787 grids of radar rainfall in Taiwan for a 24-hour duration. Due to limited radar coverage in the eastern region, significant discordant grids were found in the coastal area of the eastern region. A K-means cluster analysis using scaled at-site characteristics was used to group the QPESUMS grids in Taiwan into 22 clusters/sub-regions based on their characteristics. Spatially, homogeneous subregions with QPESUMS data produce more detailed homogeneous subregions with clear and continuous boundaries, especially in the mountain range area where the number of rain stations is still very limited. According to the results of z-values and L-moment ratio diagrams, the Wakeby (WAK), Generalized Extreme Value (GEV), and Generalized Pareto (GPA) distributions of rainfall extremes fitted well for the majority of subregions. The Wakeby distribution was the dominant best-fitted distribution, especially in the central and eastern regions. The east of the northern part and southern part of Taiwan had the highest extreme rainfall for each return period. Both areas were frequently struck by typhoons. By using grid-based (at-site) as the basis for assessing regional frequency analysis, the results show that the regional approach in determining extreme rainfall is very suitable for large-scale applications and even better for smaller scales such as watershed areas. The spatial investigation was performed by establishing regions of interest in small subregions across the northern part. It showed that regionalization was correct and consistent.

The Independent Component Analysis (ICA) has been recently introduced as a reliable alternative to identify canonical scattering mechanisms within Polarimetric Synthetic Aperture Radar (PolSAR) images. This manuscript addresses two important aspects when applying such methods on real data, namely speckle filtering and statistical classification with ICA. A novel PolSAR data processing framework is introduced by adjusting the Lee's sigma filter to the particular nature of the Touzi's polarimetric decomposition. In its current form, it allows the use of the ICA mixing matrix in the derived speckle filter. An extension of the Fromont at al. iterative segmentation is introduced, equally. This proposed framework is tested using P band airborne PolSAR data acquired for the ESA campaign TropiSAR campaign.

The modern era of polarimetric radar begins with radiowave propagation research starting in the early 1970s with applications to measurement and modeling of wave attenuation in rain and depolarization due to ice particles along satellite-earth links. While there is a rich history of radar in meteorology after World War II, the impetus provided by radiowave propagation requirements lead to high quality antennas and feeds. Our journey starts by describing the key institutions and personnel responsible for development of weather radar polarimetry. The early period was dominated by circularly polarized radars for propagation research and at S-band for hail detection. By the mid-to late 70s, a paradigm shift occurred which led to the dominance of linear polarizations with applications to slant path attenuation prediction as well as estimation of rain rates and inferences of precipitation physics. The period from early 1980s to 1995 can be considered as the “golden” period of rapid research that brought in meteorologists, cloud physicists and hydrologists. This article describes the evolution of this technology from the vantage point of the authors. Their personal reflections and “behind the scenes” descriptions offer a glimpse into the inner workings at several key institutions which cannot be found elsewhere.