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Joint transceiver beamforming is a fundamental and crucial research task in the field of signal processing. Despite extensive efforts made in recent years, the joint transceiver beamforming of frequency diverse array (FDA)-based multiple-input and multiple-output (MIMO) radar has received relatively less attention and is confronted with some tricky challenges, such as range–angle decoupling and the interaction between multiple performance metrics. In this paper, we initially derive the generalized ambiguity function of the FDA-MIMO radar to explore the intrinsic correlation between its waveform design and resolution. Following that, the joint beamforming optimization is formulated as a nonconvex bivariate quadratic programming problem (NBQP) with the aim of maximizing the Signal-to-Interference-Noise Ratio (SINR) of the FDA-MIMO radar system. Building upon this, we introduce an innovative alternating manifold optimization with nested iteration (AMO-NI) algorithm to address the NBQP. By incorporating manifold optimization into iterative updates of transmit waveform and receiving filter, the AMO-NI algorithm considers the interdependencies among the optimization variables. The algorithm efficiently and expeditiously finds global optimum solutions within a finite number of iterations. Compared with other methods, our approach yields a superior beampattern and higher SINR. [ABSTRACT FROM AUTHOR]

The proposed "Guanlan" ocean science satellite, led by China's Laoshan Laboratory, includes an interferometric radar altimeter (IRA) as a key payload. As an integral part of its development, an airborne IRA experiment was conducted on 6 November 2021, with a flight path of approximately 90 km in the South China Sea. This study investigates the IRA's ability to observe ocean sea surface height (SSH) across scales ranging from meters to mesoscale. The sea surface height anomaly (SSHA) of the IRA is aligned with the SSHA of the AVISO at scales greater than 30 km, but also demonstrates the ability to capture small-scale SSHA changes in two dimensions. We analyzed wavenumber spectra of SSHA obtained from the airborne IRA, ICESat-2, and SARAL/AltiKa satellite for this region. The results show a good agreement in power spectral density (PSD) levels between ICESat-2, SARAL/AltiKa and IRA at scales larger than 30 km. Within the submesoscale range of 1–10 km, the IRA SSHA spectrum exhibits a distinctly negative slope and the lowest energy level. The minimum PSD level of the IRA fell in the range of 10−4–10−3 m2/cycle/km, at scales around 1 km, which is more than an order of magnitude lower than that of ICESat-2, forming a spectral gap that is in agreement with the theoretical expectation. Furthermore, IRA-derived wave direction and significant wave height matched well with the MFWAM wave data. The results of this study underscore the considerable potential of airborne IRA in capturing SSHA across a range of scales, from oceanic waves to submesoscale. [ABSTRACT FROM AUTHOR]

The large quantity of resident space objects orbiting Earth poses a threat to safety and efficient operations in space. Radar sensors are well suited to detecting objects in space including decommissioned satellites and debris, whereas the more commonly used optical sensors are limited by daylight and weather conditions. Observations of three non-operational satellites using a VHF radar system are presented in this paper in the form of micro Doppler signatures associated with rotational motion. Micro Doppler signatures are particularly useful for characterising resident space objects at VHF given the limited bandwidth resulting in poor range resolution. Electromagnetic simulations of the micro Doppler signatures of the defunct satellites are also presented using simple computer-aided design (CAD) models to assist with interpretation of the radar observations. The simulated micro Doppler results are verified using the VHF radar data and provide insight into the attitude and spin axis of the three resident space objects. As future work, this approach will be extended to a larger number of resident space objects which requires a automated processing. [ABSTRACT FROM AUTHOR]

The Yulong porphyry copper belt in eastern Tibet is located in the middle of Tethys–Himalayan metallogenic mega-province, which is one of the three major porphyry copper metallogenic mega-provinces. The Yulong copper belt belongs to the super porphyry copper belt and represents one of the most important copper mineralization prospecting areas in China. A significant quantity of research data shows that this study area belongs to the environment of intracontinental collision and compression, with a complex geological structure, magmatic rock development and excellent metallogenic geological background. However, because this area is located in an alpine and high-altitude area, it is difficult to carry out any traditional field geological surveys, and the existing studies of both prospecting and prediction are relatively weak. This study focused on information extraction for alteration minerals in the Yulong metallogenic belt and its surroundings based on multispectral data and hyperspectral data, establishing a spectral library of alteration minerals in this area. Based on Sentinel-1A radar data and Landsat-8 OLI color synthesis data, the linear structure of the study area was interpreted. On this basis, the information extraction results relating to alteration minerals obtained from multi-source remote sensing data, linear structure interpretation results and the geochemical exploration data of the study area were superimposed to comprehensively analyze the metallogenic geological conditions and mineralization characteristics in the area, establish remote sensing prospecting indicators there and optimize the potential areas for prospecting, providing technical support for the next step of prospecting and exploration in the area. [ABSTRACT FROM AUTHOR]

An exceptionally strong westward propagating quasi‐6‐day wave (Q6DW) with zonal wavenumber 1 in connection with the rare 2019 Southern Hemispheric Sudden Stratospheric Warming (SSW) is observed by two meteor radars at 30°S and is found to modulate and interact with the diurnal tide and gravity waves (GWs). The diurnal tide is amplified every 6 days and a prominent 21 hr child wave attributed to Q6DW‐diurnal tide nonlinear interaction occurs. Q6DW modulation on GWs is confirmed as the 4–5 day periodicity in GW variances. Simultaneously, the Q6DW appears to shift its period toward the periodicity of the modulated GW variances. Enhancement is also observed in the first results of meteor radar observed Q6DW Eliassen‐Palm flux, which may facilitate the global perturbation and persistence of this Q6DW. We conclude that the observed SSW triggered Q6DW‐tide and Q6DW‐GW interactions play an important role in coupling the lower atmospheric forcings to ionospheric variabilities. Plain Language Summary: Our work provides observational evidence for the 6‐day planetary wave‐tide and 6‐day planetary wave‐gravity wave interactions at the Earth's mesosphere and lower thermosphere. The results strongly support the theory that wave‐wave interactions are the primary mechanism coupling planetary waves to ionospheric variability and provide an additional mechanism as the 6‐day wave modulation on the gravity waves. We utilize measurements from two meteor radars to diagnose planetary wave characteristics and identify wave‐wave interactions, and compute the first‐time meteor radar observed Eliassen‐Palm flux. Enhancement is observed in the Eliassen‐Palm flux of 6‐day wave following the SSW maximum phase, which demonstrates that energy of the 6‐day wave is enhanced and therefore, facilitates the global perturbation and persistence of the 6‐day wave for an extended time period. While meteor radar observations are widely used to investigate planetary waves and tides, high meteor detection rate is required for further studying temperature perturbations and small scale waves (e.g., gravity waves). Thus, this work also highlights the capability of a modern multi‐static meteor radar system, Chilean Observation Network De meteOr Radars, in resolving oscillations of small spatial scales over a broad range of periods, and for calculating Eliassen‐Palm flux of planetary waves. Key Points: A dominating W1 Q6DW is observed at 30°S and its Eliassen‐Palm flux is enhanced during the 2019 SH SSWQ6DW amplifies the diurnal tide every 6 days and a strong 21 hr child wave is observedQ6DW modulates the gravity wave variances and its frequency appears to shift accordingly [ABSTRACT FROM AUTHOR]

Hailstorms, while fascinating from a meteorological perspective, pose significant risks to communities, agriculture, and infrastructure. In regions such as the Central Andes of Peru, the characteristics and frequency of these extreme weather events remain largely uncharted. This study fills this gap by investigating the historical frequency and vertical structure of hailstorms in this region. We analyzed historical hailstorm records dating back to 1958 alongside 4 years of observations (2017–2021) from the Parsivel2 disdrometer and a cloud-profiling radar MIRA35c. Our findings indicate a trend of decreasing hail frequency (- 0.5 events per decade). However, the p value of 0.07 suggests the need for further investigation, particularly in relation to environmental changes and reporting methods. The results show that hailstorms predominantly occur during the austral summer months, with peak frequency in December, and are most common during the afternoon and early evening hours. The analysis of radar variables such as reflectivity, radial velocity, spectral width, and linear depolarization ratio (LDR) reveals distinct vertical profiles for hail events. Two case studies highlight the diversity in the radar measurements of hailstorms, underscoring the complexity of accurate hail detection. This study suggests the need for refining the Parsivel2 algorithm and further understanding its classification of hydrometeors. Additionally, the limitations of conventional radar variables for hail detection are discussed, recommending the use of LDR and Doppler spectrum analysis for future research. Our findings lay the groundwork for the development of more efficient hail detection algorithms and improved understanding of hailstorms in the Central Andes of Peru. [ABSTRACT FROM AUTHOR]

Objective: Unusual clinical course. Background: Endometriosis is a common cause of chronic pelvic pain among women globally. Pharmacological therapy for endometriosis includes non-steroidal anti-inflammatory drugs (NSAIDs) and hormonal contraceptives, while surgical therapy often involves either laparoscopic excision and ablation of endometriosis implants or open surgery. Surgical therapy is one of the mainstays of treatment especially for extrapelvic endometriomas. However, little guidance exists for the treatment of non-palpable or intermittently palpable lesions of this nature. Case Report: A 33-year-old woman with a previous cesarean section presented with complaints of intermittent discomfort in the area between her umbilicus and the surgical incision, for the previous 7 years, that worsened during her menstrual cycle. A 3×3-cm area of fullness was only intermittently palpable during various clinic visits, but was visualizable on computed tomography and magnetic resonance imaging. Given the lesion's varying palpability, a Savi Scout radar localization device was placed into the lesion pre-operatively to aid with surgical resection. The mass was excised, pathologic examination revealed endometrial tissue, and the patient had an uncomplicated postoperative course with resolution of her symptoms. Conclusions: Surgical removal of extrapelvic endometrioma lesions can be made difficult by varying levels of palpability or localizability due to a patient's menstrual cycle. The Savi Scout, most commonly used in breast mass localization, is a useful tool in guiding surgical excision of non-palpable or intermittently palpable extrapelvic endometrioma lesions. [ABSTRACT FROM AUTHOR]

Vestibular evoked myogenic potentials (VEMPs) are commonly used noninvasive electrophysiological test methods in clinics. VEMPs are short-latency muscle reflex responses triggered by stimulation of peripheral otolith organs. The inhibitory myogenic response measured over the sternocleidomastoid muscle is cervical VEMP (cVEMP); the excitatory myogenic response measured over the extraocular muscles, the inferior oblique, is the ocular VEMP (oVEMP). cVEMP is characterized by a biphasic wave in the form of one positive peak (P1/P13) occurring at an average of 13th milliseconds and one negative peak (N1/N23) occurring at an average of 23rd milliseconds. oVEMP is characterized by a biphasic wave in the form of one negative peak (N1/N10) occurring at an average of 10th milliseconds and one positive peak (P1/P16) occurring at an average of 16th milliseconds. Stimulus type affects VEMP findings. Click stimulus was first used in VEMP studies. The stimulus that effectively stimulates the otolith organs and generates the highest amplitude waves is being investigated. 500 Hertz (Hz) tone burst stimulus is more effective than click stimulus in VEMP tests and its clinical use has become widespread. Compressed high-intensity radar pulse (CHIRP) is an acoustic stimulus that is effective in auditory electrophysiology. VEMP in responses to CHIRP stimulus was reported in recent studies. In this review, the findings of studies examining VEMP responses triggered by CHIRP stimuli are integrated. The aim of this review is to evaluate whether the CHIRP stimulus is an effective stimulus in VEMP tests. [ABSTRACT FROM AUTHOR]

Background: Pneumonia is the most common intensive care unit (ICU)-acquired infection and source of potential sepsis in ICU populations but can be difficult to diagnose in real-time. Despite limited data, rapid initiation of antibiotic agents is endorsed by society guidelines. We hypothesized that a post hoc analysis of a recent randomized pilot study would show no difference between two antibiotic initiation strategies. Patients and Methods: The recent Trial of Antibiotic Restraint in Presumed Pneumonia (TARPP) was a pragmatic cluster-randomized pilot of antibiotic initiation strategies for patients with suspected ICU-acquired pneumonia. Participating ICUs were cluster-randomized to either an immediate initiation protocol or a specimen-initiated protocol where a gram stain was required for initiation of antibiotics. Patients in the study were divided into one of seven mutually exclusive outcome rankings (desirability of outcome ranking; DOOR): (1) Survival, No Pneumonia, No adverse events; (2) Survival, Pneumonia, No adverse events; (3) Survival, No Pneumonia, ventilator-free-alive days ≤14; (4) Survival, Pneumonia, ventilator-free-alive days ≤14; (5) Survival, No Pneumonia, Subsequent episode of suspected pneumonia; (6) Survival, Pneumonia, Subsequent episode of suspected pneumonia; and (7) Death. These rankings were further refined using the duration of antibiotics prescribed for pneumonia (response adjusted for antibiotic risk; RADAR). Results: There were 186 patients enrolled in the study. After applying the DOOR analysis, a randomly selected patient was equally likely to have a better outcome in specimen-initiated arm as in the immediate initiation arm (DOOR probability: 50.8%; 95% confidence interval [CI], 42.7%–58.9%). Outcome probabilities were similar after applying the RADAR analysis (52.5%; 95% CI, 44.2%–60.6%; p = 0.31). Conclusions: We found that patients for whom antibiotic agents were withheld until there was objective evidence (specimen-initiated group) had similar outcome rankings to patients for whom antibiotic agents were started immediately. This supports the findings of the TARPP pilot trial and provides further evidence for equipoise between these two treatment strategies. [ABSTRACT FROM AUTHOR]

In this study, we investigate the seasonal and diurnal variations in cloud occurrence frequency, as well as cloud vertical structure (CVS) characteristics under different seasons and precipitation intensities over the Guangzhou region in South China, based on the analysis of millimeter-wave cloud radar (MMCR) and ground automatic weather station rainfall observations from May 2019 to August 2021. The results showed that the occurrence frequency of clouds exhibits a bimodal distribution throughout the year, with peaks in March to June and October, reaching its highest occurrence in May at approximately 80% and its lowest from December to February at around 40%. Additionally, there are distinct diurnal variations in occurrence frequency, with the lowest rates occurring around 0005 LST, rapidly increasing after 0006 LST, and peaking during the afternoon to evening hours. Cloud top height (CTH) shows bimodal distributions during the pre-flood and post-flood seasons. The most frequently occurring range of CTH during the pre-flood season is below 3 km, accounting for approximately 43%, while during the post-flood season, it ranges from 11 to 14 km, constituting about 37%. For precipitation clouds, CTH can extend beyond 12 km, with the radar reflectivity decreasing gradually with increasing height. The highest frequencies of radar echoes are observed below 2 km and between 4 and 7 km, exhibiting clear diurnal variations, with echoes mainly below 2 km and between 4 to 6 km during the early morning, intensifying and shifting to higher altitudes during the day and reaching their maximum below 4 km during the afternoon to nighttime hours, while both the frequency and intensity increase in the height range of 4 to 12 km. Vertical profiles of radar reflectivity and cloud ice/liquid water content (IWC/LWC) exhibit similar trends under different precipitation intensities. The main differences are observed below 4 km, where both radar reflectivity and IWC/LWC generally increase with increasing precipitation intensity. These findings contribute to a better understanding of cloud characteristics in the South China region, enhance the accuracy of model simulations, and provide a scientific basis for accurate forecasting and warning of meteorological disasters. [ABSTRACT FROM AUTHOR]

The classification of movement in space is one of the key tasks in environmental science. Various geospatial data such as rainfall or other weather data, data on animal movement or landslide data require a quantitative analysis of the probable movement in space to obtain information on potential risks, ecological developments or changes in future. Usually, machine-learning tools are applied for this task, as these approaches are able to classify large amounts of data. Yet, machine-learning approaches also have some drawbacks, e.g. the often required large training sets and the fact that the algorithms are often hard to interpret. We propose a classification approach for spatial data based on ordinal patterns. Ordinal patterns have the advantage that they are easily applicable, even to small data sets, are robust in the presence of certain changes in the time series and deliver interpretative results. They therefore do not only offer an alternative to machine-learning in the case of small data sets but might also be used in pre-processing for a meaningful feature selection. In this work, we introduce the basic concept of multivariate ordinal patterns and the corresponding limit theorem. A simulation study based on bootstrap demonstrates the validity of the results. The approach is then applied to two real-life data sets, namely rainfall radar data and the movement of a leopard. Both applications emphasize the meaningfulness of the approach. Clearly, certain patterns related to the atmosphere and environment occur significantly often, indicating a strong dependence of the movement on the environment. [ABSTRACT FROM AUTHOR]

The increasing number of space objects (SO), debris, and constellation of satellites in Low Earth Orbit poses a significant threat to the sustainability and safety of space operations, which must be carefully and efficiently addressed to avoid mutual collisions. The space situational awareness is currently addressed by an ensemble of radar and radio‐telescopes that detect and track SO. However, a large part of space debris is composed of very small and tiny metallic objects, very difficult to detect. The authors demonstrate the benefits of using deep learning (DL) architectures for small space object detection by radar observations. TIRA radio telescope has been simulated to generate range‐Doppler maps, then used as inputs for object detection exploiting You‐Only‐Look‐Once (YOLO) frameworks. The results demonstrate that the object detection by using YOLO algorithms outperform conventional target detection approaches, thus indicating the potential benefits of using DL techniques for space surveillance applications. [ABSTRACT FROM AUTHOR]

Detection of space objects is a key component of space situational awareness, which could help prevent and minimise space collisions. While there have been lots of radar systems designed to detect space objects, few of them have dealt with long baseline distributed bistatic pairs. The authors focus on the feasibility of long baseline bistatic radars, which can be extended for the multistatic case; and the performance of the multistatic system for a target at different altitudes assuming one transmitter over three different scenarios: a cluster of receivers, receivers spread throughout the world and the combination of the two previous cases. To analyse the performance the multiple‐input‐multiple‐output (MIMO) ambiguity function (AF) will be employed. The results of the MIMO AF show how the fusion of different bistatic pairs improves the detection capabilities. Moreover, when the different radar measurements are coherently summed in the MIMO AF, the uncertainty on the location of the target is reduced. [ABSTRACT FROM AUTHOR]

This paper presents a high gain diagonally-probe-fed multi-layered dielectric resonator antenna (DPF-ML-DRA) designed for 77 GHz automotive radar applications. A comparison with the conventional probe-fed ML-DRA demonstrates that the proposed DPF-ML-DRA achieves higher antenna gain by 1 dB. The sub-array utilizing the proposed DPF-ML-DRA is tailored to meet specific radar system requirements, including a broad impedance bandwidth (> 5 GHz), high antenna gain (> 12 dBi), and wide half-power beamwidth (> ± 60°). Simulated results validate that the sub-array performance meets the aforementioned antenna requirements. To attain high azimuthal and elevational angular detecting resolution, 3 sub-arrays with 12 DF-ML-DRA for the Tx channel and 4 sub-arrays with 10 DF-ML-DRA for the Rx channel were designed and simulated. The fabricated radar system underwent field testing, demonstrating a maximum range of up to 160 m and a field of view of 120° for 100 m. Remarkably, the proposed DPF-ML-DRA exhibits equivalent radar performance while featuring a smaller form-factor compared to commercially available state-of-the-art automotive radar systems. Consequently, the proposed DPF-ML-DRA proves to be well-suited for 77 GHz automotive radar applications. [ABSTRACT FROM AUTHOR]

Rainfall can reduce the radar signal through a radome. The effects of different water layers on the radome transmission are studied using quasi-optical measurement from 26 to 330 GHz including water permittivity characterization. The water is deposited as drops of different sizes or as a uniform layer while keeping a constant volume, highlighting the importance of the control of the total wet surface for radar applications. A compact model is proposed to predict the signal loss due to water on the radome depending on the rain flow. [ABSTRACT FROM AUTHOR]

NASA's Soil Moisture Active Passive (SMAP) was originally designed to combine high-resolution active (radar) and coarse-resolution but highly sensitive passive (radiometer) L-band observations to achieve unprecedented spatial resolution and accuracy for soil moisture retrievals. However, shortly after SMAP was put into orbit, the radar component failed, and the high-resolution capability was lost. In this paper, the integration of an alternative radar sensor with the SMAP radiometer is proposed to enhance soil moisture retrieval capabilities over vegetated areas in the absence of the original high-resolution radar in the SMAP mission. ESA's Sentinel-1A C-band radar was used in this study to enhance the spatial resolution of the SMAP L-band radiometer and to improve soil moisture retrieval accuracy. To achieve this purpose, we downscaled the 9 km radiometer data of the SMAP to 1 km utilizing the Smoothing Filter-based Intensity Modulation (SFIM) method. An Artificial Neural Network (ANN) was then trained to exploit the synergy between the Sentinel-1A radar, SMAP radiometer, and the in situ-measured soil moisture. An analysis of the data obtained for a plant growing season over the Mississippi Delta showed that the VH-polarized Sentinel-1A radar data can yield a coefficient of correlation of 0.81 and serve as a complimentary source to the SMAP radiometer for more accurate and enhanced soil moisture prediction over agricultural fields. [ABSTRACT FROM AUTHOR]

Through-wall radar human body pose recognition technology has broad applications in both military and civilian sectors. Identifying the current pose of targets behind walls and predicting subsequent pose changes are significant challenges. Conventional methods typically utilize radar information along with machine learning algorithms such as SVM and random forests to aid in recognition. However, these approaches have limitations, particularly in complex scenarios. In response to this challenge, this paper proposes a cross-modal supervised through-wall radar human body pose recognition method. By integrating information from both cameras and radar, a cross-modal dataset was constructed, and a corresponding deep learning network architecture was designed. During training, the network effectively learned the pose features of targets obscured by walls, enabling accurate pose recognition (e.g., standing, crouching) in scenarios with unknown wall obstructions. The experimental results demonstrated the superiority of the proposed method over traditional approaches, offering an effective and innovative solution for practical through-wall radar applications. The contribution of this study lies in the integration of deep learning with cross-modal supervision, providing new perspectives for enhancing the robustness and accuracy of target pose recognition. [ABSTRACT FROM AUTHOR]

In the realm of road safety and the evolution toward automated driving, Advanced Driver Assistance and Automated Driving (ADAS/AD) systems play a pivotal role. As the complexity of these systems grows, comprehensive testing becomes imperative, with virtual test environments becoming crucial, especially for handling diverse and challenging scenarios. Radar sensors are integral to ADAS/AD units and are known for their robust performance even in adverse conditions. However, accurately modeling the radar's perception, particularly the radar cross-section (RCS), proves challenging. This paper adopts a data-driven approach, using Gaussian mixture models (GMMs) to model the radar's perception for various vehicles and aspect angles. A Bayesian variational approach automatically infers model complexity. The model is expanded into a comprehensive radar sensor model based on object lists, incorporating occlusion effects and RCS-based detectability decisions. The model's effectiveness is demonstrated through accurate reproduction of the RCS behavior and scatter point distribution. The full capabilities of the sensor model are demonstrated in different scenarios. The flexible and modular framework has proven apt for modeling specific aspects and allows for an easy model extension. Simultaneously, alongside model extension, more extensive validation is proposed to refine accuracy and broaden the model's applicability. [ABSTRACT FROM AUTHOR]

Amplification of wideband high-frequency and microwave signals is a fundamental element within every high-frequency circuit and device. Ultra-wideband (UWB) sensor applications use circuits designed for their specific application. The article presents the analysis, design, and implementation of ultra-wideband differential amplifiers for M-sequence-based UWB applications. The designed differential amplifiers are based on the Cherry–Hooper structure and are implemented in a low-cost 0.35 µm SiGe BiCMOS semiconductor process. The article presents an analysis and realization of several designs focused on different modifications of the Cherry–Hooper amplifier structure. The proposed amplifier modifications are focused on achieving the best result in one main parameter's performance. Amplifier designs modified by capacitive peaking to achieve the largest bandwidth, amplifiers with the lowest possible noise figure, and designs focused on achieving the highest common mode rejection ratio (CMRR) are described. The layout of the differential amplifiers was created and the chip was manufactured and wire-bonded to the QFN package. For evaluation purposes, a high-frequency PCB board was designed. Schematic simulations, post-layout simulations, and measurements of the individual parameters of the designed amplifiers were performed. The designed and fabricated ultra-wideband differential amplifiers have the following parameters: a supply current of 100–160 mA at −3.3 V or 3.3 V, bandwidth from 6 to 12 GHz, gain (at 1 GHz) from 12 to 16 dB, noise figure from 7 to 13 dB, and a common mode rejection ratio of up to 70 dB. [ABSTRACT FROM AUTHOR]

The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value. [ABSTRACT FROM AUTHOR]

Photonic radar, a cornerstone in the innovative applications of microwave photonics, emerges as a pivotal technology for future Intelligent Transportation Systems (ITS). Offering enhanced accuracy and reliability, it stands at the forefront of target detection and recognition across varying weather conditions. Recent advancements have concentrated on augmenting radar performance through high-speed, wide-band signal processing–a direct benefit of modern photonics' attributes such as EMI immunity, minimal transmission loss, and wide bandwidth. Our work introduces a cutting-edge photonic radar system that employs Frequency Modulated Continuous Wave (FMCW) signals, synergized with Mode Division and Wavelength Division Multiplexing (MDM-WDM). This fusion not only enhances target detection and recognition capabilities across diverse weather scenarios, including various intensities of fog and solar scintillations, but also demonstrates substantial resilience against solar noise. Furthermore, we have integrated machine learning techniques, including Decision Tree, Extremely Randomized Trees (ERT), and Random Forest classifiers, to substantially enhance target recognition accuracy. The results are telling: an accuracy of 91.51%, high sensitivity (91.47%), specificity (97.17%), and an F1 Score of 91.46%. These metrics underscore the efficacy of our approach in refining ITS radar systems, illustrating how advancements in microwave photonics can revolutionize traditional methodologies and systems. [ABSTRACT FROM AUTHOR]

Detecting the concrete crack depth has always been a technical problem. The traditional detection methods，which are based on stress wave theory, have limitations such as low accuracy, complex operation, time-consuming and laborious procedures. This paper proposes a new method based on the combination of tracer and hand-held radar to better complete this task. Its main procedures include firstly spreading tracer diffusion to the bottom of the crack propagation, and then mapping the location of the air-tracer interface by radar waves, thus visualising the crack depth information. This paper elaborates the detection principle of this new method, carried out the feasibility numerical analysis and simulation verification. Also, the concrete standard crack specimens were made, together with relevant experimental researches. As to the feasibility of the method, it is verified by using common distilled water and saturated sodium chloride solution as tracers. The detection effects of these two tracers are compared and analysed by comparing the test data with the actual depth of the specimen. This research innovatively applies tracers to actively improve the electrical ability of concrete cracks, and then uses radar to detect the electrical distribution of cracks, realising the visual detection of crack depth. [ABSTRACT FROM AUTHOR]

Surface air pressure is one of the most important parameters used in Numerical Weather Prediction (NWP) models. Although it has been measured using weather stations on the ground for many decades, the numbers of measurements are sparse and concentrated on land. Few measurements from buoys and ships are available over ocean. Global measurements can only be achieved by using remote sensing from Space, which is challenging; however, a novel design using Differential Absorption Radar (DAR) can provide a potential solution. The technique relies on two facts: first the electromagnetic fields are absorbed mainly by oxygen and water vapor, and second that oxygen is well mixed. In this work we discuss a space‐borne concept, which aims at providing, over the ocean, consistent, and regular observations for determining surface air pressure from space by a design of a multi‐tone radar operating on the upper wing of the O2 absorption band with tones from 64 to 70 GHz. Simulations of radar vertical profiles based on the output of a state of‐the‐art microphysical retrievals applied to the A‐Train suite of sensors are exploited to establish the performance of such a system for surface pressure determination. In particular the identification and quantification of errors introduced by the presence of water vapor, cloud liquid water and rain water and the potential of a correction via the three‐tone method is discussed. Errors introduced by surface measurement noise and temperature profile uncertainties are discussed as well. Results show that accuracy between 2 and 5 hPa is at reach. Plain Language Summary: Pressure is an important atmospheric variable. A radar‐based remote sensing techinque to measure surface pressure is proposed. The methodology is based on the darkening of the surface return when moving closer to the center of the 60 GHz oxygen absorption line. Results demonstrate that measurements of surface pressure with errors of few hPa are feasible. Key Points: Differential absorption radars with multiple tones within the oxygen band to enable surface pressure measurements with few hPa accuracyCloud, rain and water vapor produce biases in the surface pressure that must be correctedErrors can be minimized by adopting tunable frequencies and frequency diversity [ABSTRACT FROM AUTHOR]

In this paper, we propose an innovative approach for transforming 2D human pose estimation into 3D models using Single Input–Single Output (SISO) Ultra-Wideband (UWB) radar technology. This method addresses the significant challenge of reconstructing 3D human poses from 1D radar signals, a task traditionally hindered by low spatial resolution and complex inverse problems. The difficulty is further exacerbated by the ambiguity in 3D pose reconstruction, as multiple 3D poses may correspond to similar 2D projections. Our solution, termed the Radar PoseLifter network, leverages the micro-Doppler signatures inherent in 1D radar echoes to effectively convert 2D pose information into 3D structures. The network is specifically designed to handle the long-range dependencies present in sequences of 2D poses. It employs a fully convolutional architecture, enhanced with a dilated temporal convolutions network, for efficient data processing. We rigorously evaluated the Radar PoseLifter network using the HPSUR dataset, which includes a diverse range of human movements. This dataset comprises data from five individuals with varying physical characteristics, performing a variety of actions. Our experimental results demonstrate the method's robustness and accuracy in estimating complex human poses, highlighting its effectiveness. This research contributes significantly to the advancement of human motion capture using radar technology. It presents a viable solution for applications where precision and reliability in motion capture are paramount. The study not only enhances the understanding of 3D pose estimation from radar data but also opens new avenues for practical applications in various fields. [ABSTRACT FROM AUTHOR]

There is increasing interest in space domain awareness worldwide, motivating investigation of the use of non-traditional sensors for space surveillance. One such class of sensor is VHF wind profiling radars, which have a low cost relative to other radars typically applied to this task. These radars are ubiquitous throughout the world and may potentially offer complementary space surveillance capabilities to the Space Surveillance Network. This paper updates an initial investigation on the use of Buckland Park VHF wind profiling radars for observing resident space objects in low Earth orbit to further investigate the space surveillance capabilities of the sensor class. The radar was operated during the Australian Defence "SpaceFest" 2019 activity, incorporating new beam scheduling and signal processing functionality that extend upon the capabilities described in the initial investigation. The beam scheduling capability used two-line element propagations to determine the appropriate beam direction to use to observe transiting satellites. The signal processing capabilities used a technique based on the Keystone transform to correct for range migration, allowing the development of new signal processing modes that allow the coherent integration time to be increased to improve the SNR of the observed targets, thereby increasing the detection rate. The results reveal that 5874 objects were detected over 10 days, with 2202 unique objects detected, representing a three-fold increase in detection rate over previous single-beam direction observations. The maximum detection height was 2975.4 km, indicating a capability to detect objects in medium Earth orbit. A minimum detectable RCS at 1000 km of −10.97 dBm2 (0.09 m2) was observed. The effects of Faraday rotation resulting from the use of linearly polarised antennae are demonstrated. The radar's utility for providing total electron content (TEC) measurements is investigated using a high-range resolution mode and high-precision ephemeris data. The short-term Fourier transform is applied to demonstrate the radar's ability to investigate satellite rotation characteristics and monitor ionospheric plasma waves and instabilities. [ABSTRACT FROM AUTHOR]

The polar regions of Mars, including the South and North Poles, are crucial for studying Martian climate and geological history, as they contain the largest reservoir of subsurface water ice. This study introduces a new approach for reflector detection, which includes radargram denoising to effectively enhance the signal of underground reflectors, peak detection to extract the positions of subsurface stratification from the radar echoes, and peak points connection to form continuous layers. The mapped enhancement denoising process involves a linear brightness adjustment and a fourth-order diffusion equation to enhance the signal of the subsurface layers for effective detection. The subsurface detection extracts the surface and subsurface peak points based on a peak detection algorithm, while using locally window-enhanced peak filtering and Kullback–Leibler (KL) divergence mapping to filter out non-stratified peak points. Finally, the layered connection process uses the proximity parameter to connect peak points in the same layer. Applied to multiple SHARAD (Shallow Radar) images at the Martian poles, this algorithm demonstrated a false detection rate below 5%. Compared to other methods, this method has a missed detection rate of less than 5% and, additionally, exhibits fewer discontinuities in layer connectivity. Therefore, this algorithm shows exceptional proficiency and applicability in analyzing the complex subsurface structures of the Martian polar regions. [ABSTRACT FROM AUTHOR]

Radar emitter signal sorting (RESS) is a crucial process in contemporary electronic battlefield situation awareness. Separating pulses belonging to the same radar emitter from interleaved radar pulse sequences with a lack of prior information, high density, strong overlap, and wide parameter distribution has attracted increasing attention. In order to improve the accuracy of RESS under scenarios with limited labeled samples, this paper proposes an RESS model called TR-RAGCN-AFF-RESS. This model transforms the RESS problem into a pulse-by-pulse classification task. Firstly, a novel weighted adjacency matrix construction method was proposed to characterize the structural relationships between pulse attribute parameters more accurately. Building upon this foundation, two networks were developed: a Transformer(TR)-based interleaved pulse sequence temporal feature extraction network and a residual attention graph convolutional network (RAGCN) for extracting the structural relationship features of attribute parameters. Finally, the attention feature fusion (AFF) algorithm was introduced to fully integrate the temporal features and attribute parameter structure relationship features, enhancing the richness of feature representation for the original pulses and achieving more accurate sorting results. Compared to existing deep learning-based RESS algorithms, this method does not require many labeled samples for training, making it better suited for scenarios with limited labeled sample availability. Experimental results and analysis confirm that even with only 10% of the training samples, this method achieves a sorting accuracy exceeding 93.91%, demonstrating high robustness against measurement errors, lost pulses, and spurious pulses in non-ideal conditions. [ABSTRACT FROM AUTHOR]

The Radar Cross Section (RCS) plays a crucial role in radar systems, serving to detect and characterize radar targets. Moreover, the need to penetrate foliage and capture images of man-made structures like roads and buildings has driven the development of very high frequency/ultra high frequency (VHF/UHF)-based radar systems. In this context, we propose a novel polyphase-coded spread spectrum (PCSS) radar system designed for RCS measurements and ISAR imaging over VHF/UHF radio channels in open environments. To provide a comprehensive understanding, we examined the open range, considering factors such as amplitude distribution function, root mean square (RMS) delay, and coherence bandwidth, followed by a link budget analysis. We also conducted a comparative study of RCS measurements to evaluate the performance of the proposed radar system, demonstrating its effectiveness through theoretical calculations. Furthermore, we assessed the radar imaging capabilities by applying the CLEAN algorithm to improve the quality of the generated images. [ABSTRACT FROM AUTHOR]

Radio Detection and Ranging (RADAR) equipment is a significant information and navigational system onboard vessels and a critical part of a ship's cyber space. It is an electronic system used not only for detecting surrounding objects, to indicate their positions, and tracking targets using radio waves, but also providing safe navigation by receiving and displaying data from other navigational devices. Therefore, it is concerning to see that marine RADAR systems have various cyber vulnerabilities, including data deletion and data relocation. These systems can be manipulated and penetrated via malicious software, unauthorised remote access, human error, or sabotage by internal and external attackers. This is critical to the cyber hygiene of the ship, which affects its reliability and safety. This study performs a cyber risk assessment using the CORAS framework for RADAR cyber security by developing case-based RADAR cyber scenarios in terms of both its specific information technology subsystems and the cyber security control measures. The output of this study includes a holistic and visual assessment of RADAR's cyber security for both its cyber vulnerabilities and cyber hygiene to better protect shipboard RADAR in the future. [ABSTRACT FROM AUTHOR]

The dynamic characteristics of large-scale civil structures are generally evaluated by performing operational modal analysis based on ambient vibration records measured by contact sensors, e.g., accelerometers. However, owing to accessibility restrictions, it is sometimes inconvenient to conduct dynamic response measurements by contact sensors. Therefore, there is a need to adopt noncontact measurement technologies for monitoring the dynamic performance of civil structures. On the other hand, only response records are available for operational modal analysis on account of the unknown nature of ambient excitations, which may lead to uncertainties in modal estimates, particularly damping estimates. In this regard, this paper adopts a combined method consisting of modal decoupling, natural excitation technology, and an eigensystem realization algorithm to perform high-accuracy modal identification with uncertainty quantification. Through numerical simulation study of a frame structure, the accuracy and effectiveness of the combined method for identifying structural modal parameters are verified. Then, taking advantage of noncontact simultaneous multipoint measurements by an interferometric radar system and the combined modal estimation scheme, the noncontact operational modal analysis strategy is utilized to evaluate structural dynamic characteristics of a supertall building after experiencing a sudden vibration event that attracted considerable concern from the public. The purpose of this study is to provide a practical and reliable means of conducting modal estimation with uncertainty quantification from a single measurement and suggest a noncontact modal analysis strategy for structures with difficulty installing contact sensors. [ABSTRACT FROM AUTHOR]

• Middle atmosphere dynamics and coupling using three decades of Indian MST radar and complimentary observations. • Highlighted recent results on waves, tides, planetary waves, tropopause, STE processes and low-latitude mesospheric dynamics. • Gap areas and scope for future study using other complementary techniques are mentioned. The genesis for Indian Mesosphere-Stratosphere-Troposphere (MST) radar came from the Indian Middle Atmospheric Programme (IMAP) to study the middle atmospheric dynamics and its underlying processes. During the early 1990s VHF radar operating at 53 MHz has been set up at Gadanki (13.5°N, 79.2°E), India, which started operating initially in the stratospheric and tropospheric (ST) mode. It is upgraded to MST mode in 1993. Since then, it has served various scientific experiments not only from India but under international collaboration. Numerous focused campaigns that have national and international importance were conducted. Using this unique data in the last 30 years along with complementary measurements, several stimulating results has been reported covering a wide range of topics that includes gravity wave characteristics, tides, planetary waves, oscillations, tropopause dynamics and stratosphere-troposphere exchange processes, monsoon dynamics and circulations, mesospheric structure and dynamics, vertical and lateral coupling and long-term trends. The present communication will highlight the unique results obtained using long-term observations of Indian MST radar and complementary techniques covering the complete middle atmosphere. Unique results include quantification of the momentum carried by the high-frequency waves generated in the troposphere up to the mesosphere, demarcating the complete tropical tropopause layer using MST radar observations alone, indication for the strengthening of the tropical easterly jet in the recent decade, unusual behavior of the Hadley circulation during drought and El Nino years, the revelation of the mechanisms for the occurrence of mesospheric echoes at the tropical latitudes, quantification of the turbulence, evidence for the role of anthropogenic changes on the dynamics of the mesosphere and generation of the empirical model by combing multi-instrument data sets. These data set also served in validating numerous ground-based and space-borne measurements including several re-analysis products over a tropical latitude. Issues remained unresolved and ways to tackle in future experiments are highlighted. [ABSTRACT FROM AUTHOR]

This two‐part study presents a comprehensive analysis of (1) the vortex structure, (2) the inner core planetary boundary layer (PBL) wind profile, and (3) the overland surface winds of Typhoon Faxai(2019) during landfall observationally (Part I) and in high‐resolution Weather Research and Forecasting Model (WRF) simulations (Part II). Part II presents a framework for evaluating the wind field in the WRF simulations of Typhoon Faxai (2019) by comparing them with the observations presented in Part I. First, WRF simulations with two different surface roughness z0$$ {z}_0 $$ tables are presented. The simulation with the default z0$$ {z}_0 $$ table in WRF is shown to largely overestimate the surface wind speed. The overestimation is largely mitigated by using larger z0$$ {z}_0 $$ based on the climatology for urban and forest areas in Japan. Next, using the modified z0$$ {z}_0 $$ table, WRF simulations with three different PBL parametrizations, namely, Mellor–Yamada–Janjić, Yonsei University, and Mellor–Yamada–Nakanishi–Niino schemes are presented, and their impacts on the simulated vortex structure, inner core PBL wind profile, and overland surface winds are evaluated. In particular, the simulated surface winds are in good agreement with the observations outside the inner core of Typhoon Faxai. However, during the passage of the eyewall, the maximum surface winds are underestimated in the simulations. We show that the underestimation of the inertial stability, associated with the excessively large inner core of Typhoon Faxai in the simulations, led to the overestimation of the eyewall PBL jet height. This, in turn, caused the underestimation of the peak surface winds, as based on the empirical model of the wind reduction factor F2 km$$ {F}_{2\;\mathrm{km}} $$ proposed in Part I. [ABSTRACT FROM AUTHOR]

Slopes of radar reflectivity below freezing height (FZH) is a critical parameter to estimate the correct rainfall near the surface. TRMM PR-based radar reflectivity slopes are presented here, during Indian and Austral summer monsoon by calculating them in the lower troposphere (< 4 km). Slopes are either positive or negative, which means that the radar reflectivity either decreases (positive slopes) or increases (negative slopes) towards the surface. In the majority of cases, slopes decrease towards the surface over land, but over oceans increase towards the surface. In general, the slopes in convective tropical precipitation are negative where the Arabian Ocean has the highest fraction (~89%) of negative slopes. However, Bay of Bengal has the highest fraction of positive slopes (~21%). Western Himalaya Foothills has the highest fraction of positive slopes in convective precipitation and shows that ~76% and ~83% of convective and stratiform profiles decrease towards the surface. During the Austral summer monsoon, the Maritime Continent has the highest fraction of negative slopes (~92%) in convective precipitation followed by the Equatorial Indian Ocean. Land vs ocean and regional differences in radar reflectivity slopes are higher in convective precipitation compared to stratiform precipitation. Vertical profiles with extreme positive (>1 dBZ/km) slopes have higher echo top height (ETH) in convective precipitation over the tropical ocean during both the seasons, whereas over land, higher ETHs are associated with negative slopes. [ABSTRACT FROM AUTHOR]

Convection‐generated gravity waves (CGWs) transport momentum and energy, and this momentum is a dominant driver of global features of Earth's atmosphere's general circulation (e.g., the quasi‐biennial oscillation, the pole‐to‐pole mesospheric circulation). As CGWs are not generally resolved by global weather and climate models, their effects on the circulation need to be parameterized. However, quality observations of GWs are spatiotemporally sparse, limiting understanding and preventing constraints on parameterizations. Convection‐permitting or ‐resolving simulations do generate CGWs, but validation is not possible as these simulations cannot reproduce the CGW‐forcing convection at correct times, locations, and intensities. Here, realistic convective diabatic heating, learned from full‐physics convection‐permitting Weather Research and Forecasting simulations, is predicted from weather radar observations using neural networks and a previously developed look‐up table. These heating rates are then used to force an idealized GW‐resolving dynamical model. Simulated CGWs forced in this way closely resembled those observed by the Atmospheric InfraRed Sounder in the upper stratosphere. CGW drag in these validated simulations extends 100s of kilometers away from the convective sources, highlighting errors in current gravity wave drag parameterizations due to the use of the ubiquitous single‐column approximation. Such validatable simulations have significant potential to be used to further basic understanding of CGWs, improve their parameterizations physically, and provide more restrictive constraints on tuning with confidence. Plain Language Summary: Thunderstorms generate waves in the atmosphere that can generate turbulence at commercial aircraft cruising altitudes and further aloft. At these higher altitudes, they eventually break, not only generating turbulence, but also exerting forces that affect the large‐scale flows in the middle atmosphere. While these waves have been known to be important since at least the 1980s, they are difficult to observe. They can be simulated, but weather models do not simulate thunderstorms in the correct locations at the right times, meaning the simulated waves cannot be directly compared against observations. Here, weather radar observations are used as input to a look‐up table and a neural network to force realistic thunderstorm motions and waves within a simplified weather model. This method was able to reproduce a satellite‐observed case with notable skill. In one of the first simulations of thunderstorm‐generated waves comparable to satellite observations, these waves travel 100s of kilometers away from the thunderstorms, conflicting with assumptions made in weather and climate models. Key Points: If realistic convective diabatic heating is supplied at the correct places and times, GW‐resolving models can reasonably reproduce Convection‐generated gravity waves (CGWs)While location and type of convective storm do influence latent heating, these difference are of second order importance for CGW forcingDrag due to convection‐generated GWs from a compact source region is spread over O(1,000) km due to lateral propagation [ABSTRACT FROM AUTHOR]

The article focuses on nowcasting systems in European National Meteorological Services (NMS), defining nowcasting and its unique characteristics, including methods, and applications. Topics include the EUMETNET Nowcasting program's phases, various types of nowcasting systems (object-oriented, field-oriented radar and satellite, NWP models), and seamless systems (deterministic and probabilistic), highlighting their development, methodologies, and applications across European NMSs.

The structure and distribution of sub-glacial water directly influences Antarctic ice mass loss by reducing or enhancing basal shear stress and accelerating grounding line retreat. A common technique for detecting sub-glacial water involves analyzing the spatial variation in reflectivity from an airborne radar echo sounding (RES) survey. Basic RES analysis exploits the high dielectric contrast between water and most other substrate materials, where a reflectivity increase ≥ 15 dB is frequently correlated with the presence of sub-glacial water. There are surprisingly few additional tools to further characterize the size, shape, or extent of hydrological systems beneath large ice masses. We adapted an existing radar backscattering simulator to model RES reflections from sub-glacial water structures using the University of Texas Institute for Geophysics (UTIG) Multifrequency Airborne Radar Sounder with Full-phase Assessment (MARFA) instrument. Our series of hypothetical simulation cases modeled water structures from 5 to 50 m wide, surrounded by bed materials of varying roughness. We compared the relative reflectivity from rounded Röthlisberger channels and specular flat canals, showing both types of channels exhibit a positive correlation between size and reflectivity. Large (> 20 m), flat canals can increase reflectivity by more than 20 dB , while equivalent Röthlisberger channels show only modest reflectivity gains of 8–13 dB. Changes in substrate roughness may also alter observed reflectivity by 3–6 dB. All of these results indicate that a sophisticated approach to RES interpretation can be useful in constraining the size and shape of sub-glacial water features. However, a highly nuanced treatment of the geometric context is necessary. Finally, we compared simulated outputs to actual reflectivity from a single RES flight line collected over Thwaites Glacier in 2022. The flight line crosses a previously proposed Röthlisberger channel route, with an obvious bright bed reflection in the radargram. Through multiple simulations comparing various water system geometries, such as canals and sub-glacial lakes, we demonstrated the important role that topography and water geometry can play in observed RES reflectivity. From the scenarios that we tested, we concluded the bright reflector from our RES flight line cannot be a Röthlisberger channel but could be consistent with a series of flat canals or a sub-glacial lake. However, we note our simulations were not exhaustive of all possible sub-glacial water configurations. The approach outlined here has broad applicability for studying the basal environment of large glaciers. We expect to apply this technique when constraining the geometry and extent of many sub-glacial hydrologic structures in the future. Further research may also include comprehensive investigations of the impact of sub-glacial roughness, substrate heterogeneity, and computational efficiencies enabling more complex and complete simulations. [ABSTRACT FROM AUTHOR]

The wideband absorption and transmission of frequency-selective rasorber (FSR) remain a persistent challenge in the application of radar devices. In this article, a novel high performance wideband FSR design based on cross bend resonators was proposed. The FSR consists of an upper absorption lossy layer, which offers broad absorption and transmission bands, and a lower bandpass frequency-selective surface that enables a highly selective transmission of incident electromagnetic wave. Full wave simulation results showed that this novel design achieves an absorption bandwidth of 83.7% with more than 90% absorptivity in the frequency range of 5.2–12.7 GHz. Furthermore, the passband's fractional bandwidth for the insertion loss (IL) less than −3 dB is 33.9%, ranging from 14.9 to 21 GHz, with the minimum IL recorded at 0.69 dB at 17.7 GHz. To further verify the proposed method, a prototype FSR with 10 × 10 units of 120 mm × 120 mm was fabricated and the performance of the FSR was tested. The experiment results were in good agreement with the simulated results, and it showed a significant monostatic radar cross-section reduction in the frequency range of 5.3 GHz to 18.3 GHz compared with a metallic plane of the same size. [ABSTRACT FROM AUTHOR]

The rapid identification of structural damage in pavements remains a challenging issue. This paper proposes an automatic identification method that combines nondestructive testing, the ground penetrating radar (GPR), and object detection (OD) models for pavement structural assessment. Radar wave image data were collected from a 40 km single-lane track road and processed to create the training data set of 2,000 labeled images with two types of structural distress. Four OD models, including Faster R-CNN, YOLOv5, YOLOv8, and DETR, were adapted and trained on the data set to recognize structural damage in the images, and their prediction performances were compared. The results demonstrated that YOLOv8 was the state-of-the-art (SOTA) model, which achieved precise identification of the location and type of pavement structural damage with a mean average precision (mAP) of 98.5%. This study contributes to the improvement of latest OD models in the non-destructive inspection, facilitating the development of real-time automated pavement assessment and maintenance decision systems. Practical Applications: This research addresses a common problem in civil engineering: how to quickly detect all hidden damage in pavement structures over long stretches of highway projects. Although there has been previous research in this area, finding a more reliable solution has proven to be a challenging goal. This study presents a method that utilizes the geological radar, a nondestructive testing technology, meaning it will not harm the road, along with artificial intelligence (AI) computer models to identify damage under the pavement surface. Think of it as teaching a computer to recognize cracks or looseness in the radar images of the road. Four different computer models were trained using 2,000 road images with known damage to teach them how to identify different damage features. The results were impressive—the computer could accurately identify the location and type of damage with a high level of confidence. In fact, the best model achieved a 98.5% score measured by mean average precision, which indicates its effectiveness. This research has significant practical applications as it streamlines the process of inspecting roads for structural problems, making it faster and more efficient. It can help with real-time evaluations and decisions about road maintenance. [ABSTRACT FROM AUTHOR]

This paper focuses on the intelligent recognition problem of radar detection targets. Aiming at the low accuracy and slow speed of millimeter-wave radar clustering point cloud information, a feature algorithm of millimeter-wave radar suitable for detecting targets is proposed. In the detection of targets by millimeter-wave radar, distance is the biggest factor affecting the number and degree of sparsity. A method that combines the feature information of the point cloud with the KD tree proximity search algorithm and the DBSCAN clustering algorithm is proposed, which can adapt to the problems of uneven target point cloud, small amount of data and slow clustering speed. The improved algorithm can use the KD tree to quickly find adjacent points and calculate the distance between adjacent points. The corresponding number of thresholds is set according to the distance where the target is located, and the radius of the target area reflected by the millimeter-wave radar plus the distance of the last threshold point is used as the neighborhood radius of the improved algorithm. Therefore, fast and adaptive parameter adjustment of the millimeter-wave radar can be realized. Simulation tests show that the improved clustering algorithm has better parameters. The accuracy of the improved algorithm is increased by 4.2%, and it also greatly improves the clustering speed. [ABSTRACT FROM AUTHOR]

Several observational precipitation products that provide high temporal (≤3 h) and spatial (≤0.25°) resolution gridded estimates are available, although no single product can be assumed worldwide to be closest to the (unknown) "reality." Here, we propose and apply a methodology to quantify the uncertainty of a set of precipitation products and to identify, at individual grid points, the products that are likely wrong (i.e., outliers). The methodology is applied over eastern North America for the 2015–2019 period for eight high‐resolution gridded precipitation products: CMORPH, ERA5, GSMaP, IMERG, MSWEP, PERSIANN, STAGE IV and TMPA. Four difference metrics are used to quantify discrepancies in different aspects of the precipitation time series, such as the total accumulation, two characteristics of the intensity‐frequency distribution, and the timing of precipitating events. Large regional and seasonal variations in the observational uncertainty are found across the ensemble. The observational uncertainty is higher in Canada than in the United States, reflecting large differences in the density of precipitation gauge measurements. In northern midlatitudes, the uncertainty is highest in winter, demonstrating the difficulties of satellite retrieval algorithms in identifying precipitation in snow‐covered areas. In southern midlatitudes, the uncertainty is highest in summer, probably due to the more discontinuous nature of precipitation. While the best product cannot be identified due to the lack of an absolute reference, our study is able to identify products that are likely wrong and that should be excluded depending on the specific application. [ABSTRACT FROM AUTHOR]

Turbulence in clouds is known to enhance particle collision rates, as widely demonstrated for warm rain formation. A similar impact on ice growth processes is expected but a solid observational basis is missing. A statistical analysis of a 15‐month data set of cloud radar observations allows for the first time to quantify the impact of turbulence on ice aggregation and riming in Arctic low‐level mixed‐phase clouds. Increasing eddy dissipation rate (EDR), from below 10−4 to above 10−3 m2 s−3, yields larger ice aggregates, and higher particle concentration, likely caused by increasing fragmentation. In conditions more favorable to riming, higher EDR is associated with dramatically higher particle fall velocities (by up to 125%), under similar liquid water paths, indicative of markedly higher degrees of riming. Our findings thus reveal the key role of turbulence for cold precipitation formation, and highlight the need for an improved understanding of turbulence‐hydrometeor interactions in cold clouds. Plain Language Summary: Liquid and frozen precipitation mainly forms by collision and subsequent aggregation of small particles. Collisions between cloud particles, such as droplets and ice crystals, are thought to be increased by turbulence. While this effect has been intensively studied for liquid‐only clouds, the impact of turbulence on ice‐ice collisional growth (aggregation) and ice‐liquid collisional growth (riming) is expected but has so far been poorly quantified. We study the effect of turbulence on aggregation and riming based on a long‐term remote‐sensing data set of low‐level clouds containing both ice and liquid particles, recorded at the Arctic site of Ny‐Ålesund, Svalbard. Cloud radar observations are used to retrieve the dissipation rate of turbulent kinetic energy (i.e., the eddy dissipation rate; EDR), which is the relevant quantity driving increases in collision rates, and to characterize ice particle properties. We find evidence that higher EDR regimes enhance the aggregation of particles, and are associated with signatures of increased ice particle concentration, possibly caused by the production of particle fragments upon collision. In temperature regimes more favorable to riming, turbulence dramatically enhances the particles' fall velocity, denoting higher degrees of riming. Our findings thus highlight a key role of turbulence for the formation of precipitable ice. Key Points: Relation between turbulence and ice growth investigated based on long‐term remote sensing data set of Arctic low‐level mixed‐phase cloudsHigher eddy dissipation rate (EDR) correlates with larger ice aggregates, and possibly higher degrees of fragmentationHigh EDR is an essential component needed for the formation of rimed particles [ABSTRACT FROM AUTHOR]

Precise characterization of geomaterials improves subsurface energy extraction and storage. Understanding geomaterial property, and the complexities between petrophysics and geomechanics, plays a key role in maintaining energy security and the transition to a net zero global carbon economy. Multiple sectors demand accurate and rapid characterization of geomaterial conditions, requiring the extraction of core plugs in the field for full-field characterization and analysis in the laboratory. We present a novel technique for the non-invasive characterization of geomaterials by using Frequency Modulated Continuous Wave (FMCW) radar in the K-band, representing a new application of microwave radar. We collect data through the delivery of FMCW wave interactions with geomaterials under static and dynamic conditions and show that FMCW can detect fluid presence, differentiate fluid type, indicate the presence of metallic inclusions and detect imminent failure in loaded sandstones by up to 15 s, allowing for greater control in loading up to a failure event. Such precursors have the potential to significantly enhance our understanding of, and ability to model, geomaterial dynamics. This low-cost sensing method is easily deployable, provides quicker and more accessible data than many state-of-the-art systems, and new insights into geomaterial behavior under dynamic conditions. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) is a rapid and non-destructive geophysical technique widely employed to detect and quantify subsurface structures and characteristics. Its capability for time lapse (TL) detection provides essential insights into subsurface hydrological dynamics, including lateral flow and soil water distribution. However, during TL-GPR surveys, field conditions often create discrepancies in surface geometry, which introduces mismatches across sequential TL-GPR images. These discrepancies may generate spurious signal variations that impede the accurate interpretation of TL-GPR data when assessing subsurface hydrological processes. In responding to this issue, this study introduces a TL-GPR image alignment method by employing the dynamic time warping (DTW) algorithm. The purpose of the proposed method, namely TLIAM–DTW, is to correct for geometric mismatch in TL-GPR images collected from the identical survey line in the field. We validated the efficacy of the TLIAM–DTW method using both synthetic data from gprMax V3.0 simulations and actual field data collected from a hilly, forested area post-infiltration experiment. Analyses of the aligned TL-GPR images revealed that the TLIAM–DTW method effectively eliminates the influence of geometric mismatch while preserving the integrity of signal variations due to actual subsurface hydrological processes. Quantitative assessments of the proposed methods, measured by mean absolute error (MAE) and root mean square error (RMSE), showed significant improvements. After performing the TLIAM–DTW method, the MAE and RMSE between processed TL-GPR images and background images were reduced by 96% and 78%, respectively, in simple simulation scenarios; in more complex simulations, MAE declined by 27–31% and RMSE by 17–43%. Field data yielded reductions in MAE and RMSE of >82% and 69%, respectively. With these substantial improvements, the processed TL-GPR images successfully depict the spatial and temporal transitions associated with subsurface lateral flows, thereby enhancing the accuracy of monitoring subsurface hydrological processes under field conditions. [ABSTRACT FROM AUTHOR]

Ice thickness has a significant effect on the physical and biogeochemical processes of a lake, and it is an integral focus of research in the field of ice engineering. The Qinghai–Tibetan Plateau, known as the Third Pole of the world, contains numerous lakes. Compared with some information, such as the area, water level, and ice phenology of its lakes, the ice thickness of these lakes remains poorly understood. In this study, we used an unmanned aerial vehicle (UAV) with a 400/900 MHz ice-penetrating radar to detect the ice thickness of Qinghai Lake and Gahai Lake. Two observation fields were established on the western side of Qinghai Lake and Gahai Lake in January 2019 and January 2021, respectively. Based on the in situ ice thickness and the propagation time of the radar, the accuracy of the ice thickness measurements of these two non-freshwater lakes was comprehensively assessed. The results indicate that pre-processed echo images from the UAV-borne ice-penetrating radar identified non-freshwater lake ice, and we were thus able to accurately calculate the propagation time of radar waves through the ice. The average dielectric constants of Qinghai Lake and Gahai Lake were 4.3 and 4.6, respectively. This means that the speed of the radar waves that propagated through the ice of the non-freshwater lake was lower than that of the radio waves that propagated through the freshwater lake. The antenna frequency of the radar also had an impact on the accuracy of ice thickness modeling. The RMSEs were 0.034 m using the 400 MHz radar and 0.010 m using the 900 MHz radar. The radar with a higher antenna frequency was shown to provide greater accuracy in ice thickness monitoring, but the control of the UAV's altitude and speed should be addressed. [ABSTRACT FROM AUTHOR]

Dual-polarization radar often detects columnar regions of enhanced differential reflectivity (ZDR) extending vertically above the environmental 0 °C level. Indicative of supercooled liquid drops and wet ice particles lofted by strong updrafts, these ZDR columns are increasingly understood to be of use in predicting extreme rainfall. With the aim of achieving practical application of ZDR column measurements, this paper focuses on the relationship between the height of ZDR columns and rainfall intensity near the ground. All the data on ZDR columns analyzed in this study was collected from weather radar stations in Japan. The height of each column and rainfall rates at low levels were analyzed using an automated algorithm. A regression analysis result reveals peak column height to be positively correlated with maximum rainfall rate near ground level, and that rainfall intensity on the ground is likely to exceed 50 mm h-1 when radar identifies a ZDR column. Furthermore, extreme rainfall with an intensity of 180 mm h-1 or more is likely associated with a column over 3 km tall from the 0 °C level. These findings suggest that surveillance of ZDR columns can contribute to the reliability of very short-range forecasts or nowcasts as well as assist with the issue of early warnings of extreme rainfall and flash floods. [ABSTRACT FROM AUTHOR]

Geostationary Operational Environmental Satellites (GOES) Radar Estimation via Machine Learning to Inform NWP (GREMLIN) is a machine learning model that outputs composite reflectivity using GOES-R Series Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) input data. GREMLIN is useful for observing severe weather and initializing convection for short-term forecasts, especially over regions without ground-based radars. This study expands the evaluation of GREMLIN's accuracy against the Multi-Radar Multi-Sensor (MRMS) System to the entire contiguous United States (CONUS) for the entire annual cycle. Regional and temporal variation of validation metrics are examined over CONUS by season, day of year, and time of day. Since GREMLIN was trained with data in spring and summer, root-mean-square difference (RMSD) and bias are lowest in the order of summer, spring, fall, and winter. In summer, diurnal patterns of RMSD follow those of precipitation occurrence. Winter has the highest RMSD because of cold surfaces mistaken as precipitating clouds, but some of these errors can be removed by applying the ABI clear-sky mask product and correcting biases using a lookup table. In GREMLIN, strong echoes are closely related to the existence of lightning and corresponding low brightness temperatures, which result in different error distributions over different regions of CONUS. This leads to negative biases in cold seasons over Washington State, lower 30-dBZ critical success index caused by high misses over the Northeast, and higher false alarms over Florida that are due to higher frequency of lightning. [ABSTRACT FROM AUTHOR]