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Human falls occur rarely; however, they can lead to severe consequences if not addressed immediately. With the rise of nuclear families, there has been a significant increase in the risk of unnoticed falls leading to even death. Therefore, an efficient fall monitoring system is essential for prompt assistance and minimizing fall risks. Several strategies have been proposed with technological advancements, and some devices are already available on the market. Among the proposed systems, radar technology has emerged as a state-of-the-art noninvasive technique due to its attractive and unique features, such as the ability to operate under occlusion, dirt, and fog without compromising the person's privacy. This paper presents a systematic review of a state-of-the-art fall detection system based on radar technology. While various reviews on fall-related technologies exist, none specifically focus on radar-based systems. To our knowledge, we are the first to provide a review in such detail of state-of-the-art radar sensor-based fall detection. We comprehensively outline the radar-based fall monitoring system and the corresponding data processing techniques. Finally, we conclude our paper by discussing current trends, future research direction and challenges in this research field. This paper aims to help researchers in the field of noninvasive fall detection and facilitate future research. [ABSTRACT FROM AUTHOR]

This article introduces a novel microstrip slot antenna array (SAA) configuration for radar applications. The proposed antenna is specifically designed for operation in the K-band, spanning from 23 to 24.3 GHz. The antenna structure comprises two substrates: the feed network and ground plane are on the bottom substrate, and the radiating slots are on the top layer of the first substrate. The incorporation of a unique grid feed configuration, featuring 50 Ohm center excitation for the first time, improves the feed mechanism of the microstrip SAA. This innovation contributes to achieving a compact size and high gain. To enhance the side lobe level, the design incorporates a substrate-integrated waveguide-backed cavity, which significantly reduces surface waves. The SAA consists of 25 radiating elements with a gain of 14 dBi. In the elevation and azimuth planes, the half-power beamwidths are measured at 12.1° and 69.1°, respectively. The proposed antenna array's measured impedance bandwidth ranges from 23.15 to 24.75 GHz, guaranteeing a reflection coefficient (S11) of less than − 10 dB. The suggested antenna's applicability for automotive multi-input multi-output radar has been validated. [ABSTRACT FROM AUTHOR]

Ship response in short-crested irregular waves is calculated as the product of the frequency response function of ship response in regular waves and the directional spectrum of waves. However, the commonly used IACS and JONSWAP spectra may not accurately reproduce detailed spectra by wave hindcasting data and wave radar measurements, leading to over- or underestimation of ship responses. This study focuses on the reproducibility of a detailed directional spectrum by standard directional spectrum based on physical considerations concerning the mean spreading angle. This approach facilitates discussion on the limitations of reproducing a detailed directional spectrum with a standard directional spectrum. Furthermore, a procedure is proposed for the use of a standard directional spectrum based on the mean spreading angle as an indicator. Analysis using 6 example ships demonstrates that employing the procedure enables the extraction of highly reproducible and valuable data for short-term prediction of the added resistance in short-crested irregular waves. [ABSTRACT FROM AUTHOR]

Purpose of Review: The radiologist's role is integral in the diagnosis and preoperative management of breast cancer patients. The pre-surgical localization of non-palpable breast masses has been achieved using wire-guided devices for decades. Despite being widely regarded as the gold standard for their proven effectiveness and cost-effectiveness, wire-guided localization (WGL) techniques have been associated with several drawbacks. These drawbacks include patient discomfort, the possibility of displacement prior to resection, and the requirement for placement on the same day as surgery. This review aims to outline non-wire localization (NWL) devices and techniques available as alternatives to WGL, including their benefits and limitations. Recent Findings: No significant differences have been found with surgical outcomes between WGL and NWL. Between 2013 and 2018, the use of WGL has decreased from 75 to 32%, and the use of radioactive seed localization has increased from 16 to 61%. Summary: A multidisciplinary approach that considers the patient, provider, and healthcare institution and current research on surgical outcomes must be utilized to determine the optimal course of action for preoperative localization of non-palpable breast masses. [ABSTRACT FROM AUTHOR]

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The physical characteristics of electromagnetic waves are combined with digital information in coding metasurfaces. Coding metasurfaces enable precise control of beams by flexibly designing coding sequences. However, achieving continuous multivariate modulation of electromagnetic waves on passive flexible coded metasurfaces remains a challenge. Previous passive coding metasurfaces have a fixed phase difference between adjacent coding units throughout the operating frequency band, and when the coding pattern is defined, the coded metasurface can only achieve a single electromagnetic function. Our proposed frequency coding metasurface units vary linearly in phase difference over the operating frequency band with different phase sensitivities. Frequency coding metarsurfaces enable a wide range of tunable and versatile electromagnetic energy radiation, without introducing any active devices and changing the coding pattern. As a demonstration of the concept, we have shown theoretically and numerically that frequency coding metasurface can achieve successive transformations of electromagnetic functions, including multi-beam generation, anomalous deflection and diffuse scattering. In addition, beam sweeping function is achieved by means of spatially non-periodically distributed frequency coding metasurface. When the frequency of the incident wave is changed, the deflection angle of the beam is also changed. In addition to the tunability of properties, research on coding metasurfaces has tended to be limited to rigid materials. Flexible coding metasurfaces have potential applications in microwave antennas, radar and aircraft. The passive flexible frequency coding metasurfaces provide a novel approach to manipulating electromagnetic waves with increased design flexibility. This promises applications in microwave antennas, radar, aircraft, and satellite communications. [ABSTRACT FROM AUTHOR]

This document introduces the RadIOCD, which is a dataset that contains sparse point cloud representations of indoor objects, collected by subjects wearing a commercial off-the-shelf mmWave radar. In particular, RadIOCD includes the recordings of 10 volunteers moving towards 5 different objects (i.e., backpack, chair, desk, human, and wall), placed in 3 different environments. RadIOCD includes sparse 3D point cloud data, together with their doppler velocity and intensity provided by the mmWave radar. A total of 5,776 files are available, with each one having an approximate duration of 8s. The scope of RadIOCD is the availability of data for the recognition of objects solely recorded by the mmWave radar, to be used in applications were the vision-based classification is cumbersome though critical (e.g., in search and rescue operation where there is smoke inside a building). Furthermore, we showcase that this dataset after being segmented into 76,821 samples contains enough data to apply Machine Learning-based techniques, ensuring that they could generalize in different environments and "unseen" subjects. [ABSTRACT FROM AUTHOR]

On August 8 and 9, 2022, a record-breaking rain rate of 142 mm h−1, with an accumulated rainfall of more than 500 mm, was observed in the Seoul metropolitan area, Republic of Korea. This study focuses on analyzing the concentration of lightning in southern Seoul, which occurred solely on August 8. It is worth noting that the daily rainfall of August 8 was approximately twice that of August 9 (381 mm on August 8 vs. 198 mm on August 9). The RKSG (located in Yongin, 40 km south of Seoul) Weather Surveillance Radar-1988 Doppler was used to explore the characteristics of cloud microphysics associated with lightning activity. Four major heavy rain periods on August 8 were grouped into three categories of lightning rate (e.g., intense, moderate, and none), and their polarimetric signatures were compared. Significant differences in the vertical distribution of graupel were found within the temperature range of 0 °C and − 20 °C, as indicated by radar reflectivity (ZH) > 40 dBZ and differential reflectivity (ZDR) < 0.5 dB. Although graupel was detected in all three categories at the relatively warm temperatures of 0 °C to − 10 °C, its presence extended into colder regions exclusively in the intense category. This observation preceded the appearance of lightning by approximately 6 min. At heights with temperature ≤ − 20 °C, a high concentration of vertically aligned ice crystals was observed in lightning-prone regions, leading to a decrease in differential phase (ΦDP). In summary, this study provides valuable insights into the microphysical characteristics of thunderstorms and their relationship to lightning activity in the Seoul metropolitan area. [ABSTRACT FROM AUTHOR]

Three high-frequency (HF) ocean radars were installed at the Soya/La Perouse Strait to monitor the surface current fields of the Soya Warm Current (SWC), which flows through the strait from the Sea of Japan to the Sea of Okhotsk. These ocean radars were operated for 19 years, from August 2003 to March 2022. In this paper, seasonal to interannual variations in the SWC were investigated using surface current fields obtained by the HF ocean radars combined with coastal tide gauge records and sea level anomaly from satellite altimetry. The HF ocean radars clearly captured the seasonal and interannual variations in the SWC. The alongshore surface velocity of SWC showed a high correlation with the alongshore sea level difference (SLD) between the Sea of Japan and the Sea of Okhotsk if seasonal variations were included. However, anomalies of the SLD and SWC alongshore velocity exhibited a lower correlation, especially in summer. This suggests that different mechanisms may be involved in summer and winter. The alongshore SLD between Wakkanai and Abashiri, which has been utilized in previous studies as an indicator of SWC intensity, may not be appropriate for representing interannual variations in the SWC intensity in summer, when the SWC reaches the maximum current velocity. Instead, it was demonstrated that the offshore SLD anomaly estimated using satellite altimetry with the coastal tide gauge record at Wakkanai could be a better index for representing the interannual variations throughout the year. [ABSTRACT FROM AUTHOR]

The Radar for Europa Assessment and Sounding: Ocean to Near-surface (REASON) is a dual-frequency ice-penetrating radar (9 and 60 MHz) onboard the Europa Clipper mission. REASON is designed to probe Europa from exosphere to subsurface ocean, contributing the third dimension to observations of this enigmatic world. The hypotheses REASON will test are that (1) the ice shell of Europa hosts liquid water, (2) the ice shell overlies an ocean and is subject to tidal flexing, and (3) the exosphere, near-surface, ice shell, and ocean participate in material exchange essential to the habitability of this moon. REASON will investigate processes governing this material exchange by characterizing the distribution of putative non-ice material (e.g., brines, salts) in the subsurface, searching for an ice–ocean interface, characterizing the ice shell's global structure, and constraining the amplitude of Europa's radial tidal deformations. REASON will accomplish these science objectives using a combination of radar measurement techniques including altimetry, reflectometry, sounding, interferometry, plasma characterization, and ranging. Building on a rich heritage from Earth, the moon, and Mars, REASON will be the first ice-penetrating radar to explore the outer solar system. Because these radars are untested for the icy worlds in the outer solar system, a novel approach to measurement quality assessment was developed to represent uncertainties in key properties of Europa that affect REASON performance and ensure robustness across a range of plausible parameters suggested for the icy moon. REASON will shed light on a never-before-seen dimension of Europa and – in concert with other instruments on Europa Clipper – help to investigate whether Europa is a habitable world. [ABSTRACT FROM AUTHOR]

The paper presents new detailed radar maps and polarimetric data covering the southern polar region of the near side of the Moon measuring 400 × 800 km with a spatial resolution of about 75 m. The data were obtained using the 64-meter antenna (TNA-1500) of the Satellite Communications Center Bear Lakes of the Special Design Bureau of the Moscow Power Engineering Institute and the 13.2-meter radio telescope (RT-13) of the Svetloe Observatory of the Institute of Applied Astronomy, Russian Academy of Sciences, at a wavelength of 4.2 cm. At this wavelength, radar signals penetrate the lunar regolith to depths of up to 1 m and are sensitive to surface and suspended rocks larger than 1 cm. The maps show 39% of the area of permanently shadowed regions not observable by optical Earth-based instruments, which may hide water ice deposits. Analysis of radar maps did not reveal any relationship between the polarization properties of the surface in these regions and the presence of solar illumination. The data obtained as a result of this work can be used to study the surface and subsurface regolith features of the lunar south polar region, including searching for ice deposits in permanently shadowed regions, as well as for planning future lunar missions. [ABSTRACT FROM AUTHOR]

In this paper, we propose an integrated sensing and communication (ISAC) base station (BS) system designed for applications by multiple users in complex offshore environments. We begin by modeling the communication and detection functions of the dual-function BS in the corresponding maritime channel environment, as well as the sea clutter influence of multiple-input-multiple-output (MIMO) radar in the marine environment. To evaluate the performance of our dual-function system, we measure the communication reachable rate and sensing signal-clutter-noise ratio (SCNR) of the offshore system separately for communication and radar functions, respectively. We then develop two optimization schemes to maximize the system's performance under power constraint of radar SCNR and communication rate constraint, respectively, and solve them using convex optimization methods such as the successive convex approximation (SCA) method. We also model the beamforming and provide simulation results that demonstrate the effectiveness of our proposed maritime integrated sensing and communication (MISAC) system. [ABSTRACT FROM AUTHOR]

Due to the alleviated spectral congestion through spectrum sharing, integrating radar and communication has raised increasing attention in both academic and industrial fields. This paper proposes several orthogonal frequency division multiplexing (OFDM) based waveform design approaches for joint cooperation of radar sensing and communication transmission. A mathematical model of comprehensive weight selection, communication modulation mode and phase coding sequence is proposed, aiming at the problems of high peak sidelobe and large envelope fluctuation in OFDM integrated waveform, two feasible schemes to achieve superior performance in terms of ambiguity function are investigated, including the iterative optimization of the peak sidelobe ratio (PSLR) and peak to average power ratio (PAPR) optimization method based on random phase coding, which can improve the range resolution without degrading Doppler resolution. Simulation results show that the proposed waveform optimization method can improve PSLR below −30dB and PAPR below 2, verifying the effectiveness of the designed integrated waveform in simultaneous radar and communication operations. [ABSTRACT FROM AUTHOR]

The anti-sorting transmission is one of the primary RF stealth signals, as well as radio frequency (RF) stealth is one of the greatest significant study areas in radar stealth technology. This study first examines the anti-sorting signal's design basis. Next, it is suggested to use a quadratic stochastic appropriate algorithm on the cosine exponent. Furthermore, utilizing the suggested nonlinear chaotic mapping, a more unpredictable wide-interval PRI signals is created while taking the sorting individual's allowable limits into account. Finally, evaluations of intricacy, equilibrium, and unpredictability confirm the effectiveness of the suggested chaotic mapping. The transmitter capacity for anti-sorting is then modeled. The outcomes demonstrate the intended signal's strong anti-sorting capabilities. [ABSTRACT FROM AUTHOR]

The frequency diverse array (FDA) is a very interesting technique in wireless communications. Many research papers are introduced in this field. A lot of them is concerned with linear point sources array. In this paper, a design of a time-modulated planar frequency diverse array architecture is proposed for tracking single or multi-target for the first time. The time-modulation array (TMA) and FDA are combined for detecting single and multi-target. The effectiveness of this combined scheme is demonstrated through simulation results that compared with a point source case to show its superior performance. Authors introduce planar FDA array instead of linear FDA array and realize the planar array using cylindrical dielectric resonator antenna array (CDRA). A planar array consists of 8 × 8 CDRA is used to enable the detection of single target/multi-target. The operating frequency of the antenna is set at 10 GHz, aligning with its applications in the FDA radar. The radiation characteristics of the CDRA antenna element are introduced. High maximum gain of 6 dBi is achieved with a remarkably high efficiency. A set of representative results is reported and analyzed to assess the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Background: This study presents a 4-year (January 2019–April 2023) X-band radar network-based bow echo observation over Great Bandung Indonesia. This study provides insight into the temporal and spatial variability of bow echo distribution and presents the atmospheric condition associated with the bow echo events. Temporal analysis is categorical into monthly, seasonally, and diurnal. The analysis was performed using X-band radar network and reanalysis data (ERA5). Result: At least 26 bow echoes were identified across the Bandung basin from X-band radar network during the study period. From this total number of bow echoes, the observation of initiation modes is primarily generated from a weakly organized cell, with few coming from the squall line. The bow echo mostly evolved from noon until afternoon. The rainy season (December–January–February) and transition season (March–April–May) is the most frequent period of bow echo occurrence, with March being the most active month. Nevertheless, this study also found bow echo occurrence in the dry season (June–July–August). For the spatial analysis, the studied area is divided into two regions representing the eastern and western part of Bandung basin. The eastern region recorded the most intense occurrences with 14 events. The movement of bow echo in this region covered a shorter distance (average distance only 4.56 km), with all initiation modes occurring inside the region. The atmospheric condition within this region has less moisture flux, with higher CAPE and slightly higher surface temperature. Meanwhile, in the western region have different characteristics with higher moisture flux, a slight effect of CAPE and CINH, with longer distance and zonal movement direction of bow echo displacement. Conclusion: These conditions indicate that local convection is the dominant mode of bow echo initiation mode in the eastern region of Bandung basin. Meanwhile, the monsoon effect influences the bow echo initiation mode in the western region. Given that the observed 4-year bow echo has different characteristics from previous studies of bow echo in mid latitudes, developing different criteria for bow echo detection in the tropics is crucial. [ABSTRACT FROM AUTHOR]

The integration of both sensing and communication functions is a crucial feature for future communication systems. This paper considers a novel scenario where a radar covers multiple small-cell base stations which operate in different spectra. Due to the limited spectra resource, the radar needs to reuse the spectra of one BS for tracking. To suppress interference and improve performance, we propose a framework including two stages and the corresponding communication and sensing algorithms. Specifically, at the detection stage, we aim to minimize the system transmit power while maintaining the performance of both radar and communication. At the tracking stage, our goal is to maximize the radar sensing performance under a given power budget, while ensuring communication quality. Due to the complexity of the original problem, we address it by introducing auxiliary variables and proposing a penalty dual decomposition-based algorithm, enabling us to solve a more tractable form of the problem. In the inner loop, we propose a concave–convex procedure-based algorithm to handle the optimization problem, while adopting the block coordinate descent algorithm to iteratively update the variables. In the outer loop, we update the penalty term or Lagrange multipliers. Furthermore, we propose a radar target parameter estimation algorithm based on successive interference cancellation, which aims to mitigate communication interference. Finally, numerical simulations demonstrate the effectiveness of the proposed system, and the superior performance of our proposed algorithms over benchmark algorithms. [ABSTRACT FROM AUTHOR]

Extremely severe cyclonic storm (ESCS) 'Fani' formed in the North Indian Ocean and crossed at Puri in Orissa State on the east coast of India on 03 May 2019. In this study, we examine the sensitivity of convection permitting WRF simulations (3 km) of 'Fani' to cloud microphysics (CMP) schemes using radar and multi-satellite data products. Five CMP schemes, namely Thompson, Goddard, WSM6, Morrison and Lin are tested in WRF. Results show that the changes in the CMP schemes primarily affect the simulated intensity and have lesser impact on the track predictions. Simulations with Thompson followed by Goddard produced the best predictions for both track and intensity estimates. Our analysis reveals significant variations in vertical motions associated with Fani across different CMP schemes; the WSM6, Goddard and Lin schemes produced relatively stronger vertical motions. The explicit WRF simulations could reproduce the wind profiler radar observed intense convective motions during the transit of Fani between 1 and 2 May 2019 at Gadanki station. Experiments with Thompson and Goddard schemes simulated the mean vertical velocities in lower, middle and upper layers in better agreement with radar data. The Lin, WSM6 and Goddard CMP predicted stronger updraft velocities (~ 0.35 m/s); Thompson produced moderate updraft velocities (~ 0.25 m/s) in the upper troposphere over a relatively wider area of high theta-e (385–390 K) indicating the simulation of a convectively stronger and warmer core compared to Morrison. Our analysis suggests that the differences in vertical motions in various CMP simulations are mainly due to the variations in the warming in simulations. It has been found that WSM6, Lin and Goddard produced a deeper core (up to 200 hPa) with a stronger diabatic heating of ~ 6° C followed by Thompson, which simulated a moderately deep core extending to ~ 250 hPa with moderate heating of ~ 5 °C whereas Morrison produced a relatively weak core with a heating of ~ 4 °C limited to 300 hPa. The stronger simulated diabatic heating in Lin, WSM6 and Goddard produces stronger inflow, moisture convergence in the lower levels and stronger outflow and divergence in the upper levels leading to stronger convection in the core region in these cases. The Lin, WSM6 and Goddard mixed phase schemes with more solid hydrometeors simulated stronger radar reflectivities, and stronger eyewalls, due to more latent heat release leading to the development of a strong warm core in the upper troposphere and thus a stronger TC. [ABSTRACT FROM AUTHOR]

A compact three-band symmetrical triangular linearly frequency-modulated (TLFM) continuous-wave radar based on the gain-switched DFB laser is proposed in this paper. By introducing the low central-frequency TLFM signal with high-power to modulate the DFB laser, the DFB laser would enter into the gain-switching state and a coherent and stable three-band broadband TLFM signals (S-/C-/X-band) with a bandwidth of 0.4*N GHz (N = 1, 2, 3) would be generated after the photodetector. Based on this condition, one three-band TLFM continuous-wave radar system can be realized. The system does not require an external modulator in multi-band generation, and uses a single-channel transceiver module to achieve simultaneous transmission and echo dechirp processing of multi-band radar, which greatly reduces the deployment difficulty of the multi-band radar system, so the cost, size, weight and other characteristics of the system are expected to be reduced. In the current proof-of-concept experiment, the range resolution of the three-band TLFM signal can reach 13.8 cm, compared with the single-band TLFM signal which cannot distinguish the targets. [ABSTRACT FROM AUTHOR]

Multi-sensor information fusion (MSIF) technology based on deep convolutional neural networks (CNN) has been widely used in UAV obstacle avoidance. However, detection efficiency needs to be improved in practice because of its high computational complexity and limited airborne hardware resources. A lightweight CNN-based fast detection method based on the fusion of millimeter-wave (MMW) radar and camera is proposed in this paper. In the data preprocessing stage, the input images to the network were preprocessed based on the radar and image data. A rough detection algorithm calculates and segments the saliency image to obtain regions of interest (ROI). The computational complexity of the network training and prediction was reduced by setting the image pixels outside the ROI. In the detection stage based on deep learning, a lightweight network structure based on ResNet18 was designed to fuse the saliency images at different convolution depths. In the post-decoding stage, the calibration points are fused for local non-maximum suppression (NMS). In contrast to typical detection methods, the proposed method improves the detection speed by removing redundant pixels and local NMS, increasing the detection accuracy by fusing the feature information of the radar and camera in multiple stages. The experimental results indicate that compared with the latest lightweight network (YOLOv8n), the detection accuracy is increased by 10.51%, and the FPS increased by 44.43%. Compared with the latest YOLOv8s and YOLOv9m models, the FPS is increased by 3.0X-4.4X. The field programmable gate array (FPGA) implementation achieves a performance of 60.0 FPS. This is an improvement compared with typical methods, demonstrating that the proposed method is more effective than state-of-the-art models. [ABSTRACT FROM AUTHOR]

Heart rate (HR) and respiration rate (RR) play an important role in the study of complex behaviors and their physiological correlations in non-human primates (NHPs). However, collecting HR and RR information is often challenging, involving either invasive implants or tedious behavioral training, and there are currently few established simple and non-invasive techniques for HR and RR measurement in NHPs owing to their stress response or indocility. In this study, we employed a frequency-modulated continuous wave (FMCW) radar to design a novel contactless HR and RR monitoring system. The designed system can estimate HR and RR in real time by placing the FMCW radar on the cage and facing the chest of both awake and anesthetized macaques, the NHP investigated in this study. Experimental results show that the proposed method outperforms existing methods, with averaged absolute errors between the reference monitor and radar estimates of 0.77 beats per minute (bpm) and 1.29 respirations per minute (rpm) for HR and RR, respectively. In summary, we believe that the proposed non-invasive and contactless estimation method could be generalized as a HR and RR monitoring tool for NHPs. Furthermore, after modifying the radar signal-processing algorithms, it also shows promise for applications in other experimental animals for animal welfare, behavioral, neurological, and ethological research. [ABSTRACT FROM AUTHOR]

Along the western coastal plain of the Red Sea, the discovery of lithic tools proved that the occurrence of archaeological remains belonged to the Stone Age era. Unfortunately, until recently, most of the Stone Age site plans in the western Red Sea periphery have been unknown because of change in climate in addition to covering these sites with a large layer of sand. Recently, remote sensing (RS) data have proven to be effective at identifying buried archaeological sites using radar satellite imagery due to their ability to penetrate the land surface. This paper aims to identify and reconstruct several potential Stone Age sites using radar satellite images in the Tokar region, North-Eastern Sudan, on the western plain of the Red Sea. Radar (Sentinel-1 SAR GRD: C-band) data were processed utilizing the advantages of the Google Earth Engine platform and Snap software. The results of this research showed that some potential settlements belonging to the Stone Age era (expectedly dating back to the end of the Pleistocene and/or the initial phase of the Holocene) have subcircular shapes with some interior subcircles. These results shed new light on the potential construction technique, size, and function of the settlement in the Stone Age era and can provide some initial insights into the ancient lifestyle in this region. Additionally, this study can aid in future archaeological surveys and excavation missions in this region. [ABSTRACT FROM AUTHOR]

The spatiotemporal distribution of the lightning activity radar characteristics and parameters for two supercells with a lifetime of more than 10 hours in the North Caucasus on August 19, 2015 is analyzed. It has been shown that despite similar radar characteristics of clouds, there were significant differences in the parameters of the supercell lightning activity. The data on variations in the types of lightnings, their polarity and current characteristics are interpreted in terms of their diagnostic and prognostic potential. The need for further studies on the spatiotemporal structure of the electrically active zones of deep convective clouds is considered. [ABSTRACT FROM AUTHOR]

A unipolar ground-based weather radar is a widely-used instrument for rainfall measurement. These radar measurements, however, need to be calibrated for more accurate rainfall estimation. This article introduces a new calibration approach using time-stepwise processing of reflectivity–rainfall rate () relationship. Based on the previous work utilizing the radar–rainfall relationship, this article hypothesizes that the rainfall measurement from a ground based radar are affected by the distance from radar, altitudes and rainfall time duration. Unipolar ground-based radar data sets for two consecutive years with 77 occurrences of rainfall from 39 stations in calibration window of three hours as well as the corresponding rainfall measured from registered rain gauges were used in this study. The results indicated that it is better to use the radar–rainfall relationship in view of the altitude effect () and empirical coefficient (), such that . The changes in the distance from the radar and duration of precipitation were evidently significant. For radar–rainfall relationship individually, the values of the determination coefficient made up from 0.88 to 0.97, and those for this relationship in view of the altitude effect () were from 0.70 to 0.97. It is therefore concluded that the use of other effective parameters (distance from radar, altitude and rainfall time duration) leads to optimum accuracy of the relationship. [ABSTRACT FROM AUTHOR]

Extensive studies have been carried out on the characteristics of quantum radar cross section (QRCS) of targets. However, one crucial question related to multi-photon quantum radar cross section (M-QRCS) for targets in the atmospheric medium has not been explored yet. Understanding this question is vital for target detection and identification of quantum radar. This paper presents a universal method to solve M-QRCS in a homogeneous atmospheric medium (HAM-QRCS). The process is based on the photon wave function in a homogeneous atmospheric medium and the interaction mechanism of multi-photon and multiple atoms. It is suitable for analyzing the HAM-QRCS characteristics of targets of arbitrary shapes. The simulation results show that the molecules, particles, and other factors in the atmospheric medium cause the signal photons' energy to decrease and the propagation direction to change, leading to a decrease in the target return responses. However, in a specific angle range, as the photon number increases, the main lobe and first side lobe structures of the bistatic HAM-QRCS response are enhanced. These findings can be utilized to design target detection strategies and optimize stealth target structures of the quantum radar in the atmospheric medium. [ABSTRACT FROM AUTHOR]

Terahertz imaging is one of the most promising approaches for non-destructive control. An interesting approach to having cost-effective systems is to use frequency-modulated continuous wave (FMCW) radars with a raster scan configuration. Nevertheless, current systems using linear stages or robotic arms have the disadvantage of being heavy, requiring a long scan and not allowing a direct visualization of the result being measured. In addition, it is complex to evaluate the position of the measuring point on the real object, particularly if it is not flat. Here, we propose to solve these previous challenges with a portable system combining an FMCW radar with an augmented reality (AR) interface using a smartphone. This system achieves two goals: (i) first is to achieve data acquisition in the AR environment and (ii) the second is to make possible the visualization of data, even after post-processing, in the AR environment. [ABSTRACT FROM AUTHOR]

In this study, a regional Parsivel OTT disdrometer network covering urban Zhengzhou and adjacent areas is employed to investigate the temporal–spatial variability of raindrop size distributions (DSDs) in the Zhengzhou extreme rainfall event on 20 July 2021. The rain rates observed by disdrometers and rain gauges from six operational sites are in good agreement, despite significant site-to-site variations of 24-h accumulated rainfall ranging from 198.3 to 624.1 mm. The Parsivel OTT observations show prominent temporal–spatial variations of DSDs, and the most drastic change was registered at Zhengzhou Station where the record-breaking hourly rainfall of 201.9 mm over 1500–1600 LST (local standard time) was reported. This hourly rainfall is characterized by fairly high concentrations of large raindrops, and the mass-weighted raindrop diameter generally increases with the rain rate before reaching the equilibrium state of DSDs with the rain rate of about 50 mm h−1. Besides, polarimetric radar observations show the highest differential phase shift (Kdp) and differential reflectivity (Zdr) near surface over Zhengzhou Station from 1500 to 1600 LST. In light of the remarkable temporal–spatial variability of DSDs, a reflectivity-grouped fitting approach is proposed to optimize the reflectivity–rain rate (Z–R) parameterization for radar quantitative precipitation estimation (QPE), and the rain gauge measurements are used for validation. The results show an increase of mean bias ratio from 0.57 to 0.79 and a decrease of root-mean-square error from 23.69 to 18.36 for the rainfall intensity above 20.0 mm h−1, as compared with the fixed Z–R parameterization. This study reveals the drastic temporal–spatial variations of rain microphysics during the Zhengzhou extreme rainfall event and warrants the promise of using reflectivity-grouped fitting Z–R relationships for radar QPE of such events. [ABSTRACT FROM AUTHOR]

Information on the spatial distribution of rainfall is required for many applications, including water and flood management. Weather radars can provide quantitative rainfall estimation over an area. Taking the example of an X-band radar installed at Mandhardev in the Western Ghats, we show that radar reflectivity data can have significant bias despite following standard calibration procedures. We report a technique to identify bias in the X-band radar reflectivity factor using collocated disdrometer observations and propose a methodology to correct it. Simultaneous data collected on 83 days during June–September of 2018 are used. Our results show that bias in the radar reflectivity factor reduced from –8.3 to –0.8 dB after correction. The estimated 83-day accumulated rainfall using uncorrected radar reflectivity data is 80% less compared to that of the disdrometer and the difference between the two reduces to 2% with correction. Bias in the estimated rainfall reduces from –30 to –0.7 mm day–1 and the RMSE reduces by 42%. There are days where daily rainfall from the two instruments agrees well with each other, while large differences exist on some days. We show that the sampling issue is one of the major sources of error, and its contribution depends on the nature/type of precipitating clouds, whereas uncertainties in the Z–R relationships account for about 15% difference. [ABSTRACT FROM AUTHOR]

Range-Doppler images represent one of the most informative radar data forms, providing range and frequency information. This study explores the performance of machine learning and deep learning techniques in classifying human activities behind walls using Range-Doppler images. Therefore, we input the HOG features of Range-Doppler images into various machine-learning approaches. Although the HOG feature enhances the performance of machine learning methods, we observe the superior performance of Convolutional Neural Network (CNN) architectures in a more complex scenario in which the number of target activity classes is higher. To obtain sufficient data for the CNN architecture, we combine the Uniform Linear Array (ULA) and stepped-frequency continuous-wave (SFCW) structures, enabling the acquisition of multi-channel data. The experimental results demonstrate both the improvement of the machine learning accuracy from 95.33% to 98.67% through the HOG + Range-Doppler approach and an approximately 6% enhancement in CNN performance achieved through the SFCW-ULA combination. [ABSTRACT FROM AUTHOR]

With the advent of Mobile Edge Computing (MEC), shifting data processing from cloud centers to the network edge presents an advanced computational paradigm for addressing latency-sensitive applications. Specifically, in radar systems, the real-time processing and prediction of radar echo data pose significant challenges in dynamic and resource-constrained environments. MEC, by processing data near its source, not only significantly reduces communication latency and enhances bandwidth utilization but also diminishes the necessity of transmitting large volumes of data to the cloud, which is crucial for improving the timeliness and efficiency of radar data processing. To meet this demand, this paper proposes a model that integrates a spatiotemporal Attention Module (STAM) with a Long Short-Term Memory Gated Recurrent Unit (ST-ConvLSGRU) to enhance the accuracy of radar echo prediction while leveraging the advantages of MEC. STAM, by extending the spatiotemporal receptive field of the prediction units, effectively captures key inter-frame motion information, while optimizations to the convolutional structure and loss function further boost the model's predictive performance. Experimental results demonstrate that our approach significantly improves the accuracy of short-term weather forecasting in a mobile edge computing environment, showcasing an efficient and practical solution for processing radar echo data under dynamic, resource-limited conditions. [ABSTRACT FROM AUTHOR]

Since 2021, a new surge in discrete aurora detections at Mars has been observed by the Emirates Mars Ultraviolet Spectrometer (EMUS) onboard the Emirates Mars Mission (EMM) Hope Orbiter as EMUS started to regularly obtain synoptic auroral images with a high sensitivity. Here we report on a fortuitous conjunction between EMM and Mars Express (MEX) using far ultraviolet (FUV) imaging of discrete aurora by EMM EMUS, in situ measurements of suprathermal electrons by the MEX Analyzer of Space Plasma and Energetic Atoms Electron Spectrometer (ELS), and topside radar sounding of the nightside ionosphere by the MEX Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS). In this event, EMM EMUS imaged a clear discrete aurora signature around moderately strong crustal magnetic fields on the nightside near the dusk terminator, 11 min before which MEX MARSIS measured a prominent local enhancement of the peak electron density in the nightside ionosphere and MEX ELS observed an in situ enhancement of suprathermal electrons at the corresponding location. A remarkable geographic agreement is found between the enhancements of the aurora, ionosphere, and suprathermal electrons, suggesting that the enhanced ionization and auroral emission are caused concurrently by precipitating suprathermal electrons. Subsequent images indicate that the discrete aurora slightly changed its shape in 15 min and mostly disappeared in a few hours. The MEX MARSIS measurements of the auroral ionosphere display overlapping ionospheric and surface echoes indicative of horizontal gradients of the peak electron density. Analysis of the overlapping echoes implies that the auroral ionosphere and electron precipitation could be highly structured with horizontal spatial scales on the order of several tens of km. MEX MARSIS also observed a non-auroral ionospheric enhancement with a wider spatial extent than the local auroral enhancement, suggesting alternative sources of the enhanced nightside ionosphere such as plasma transport. The comparison between the ionospheric structures measured by MEX MARSIS, suprathermal electron flux measured by MEX ELS, and discrete auroral emission imaged by EMM EMUS underscores the complexity of the auroral and non-auroral nightside ionospheres. This motivates further investigations of their sources, transport, and connections to the magnetotail dynamics of Mars. [ABSTRACT FROM AUTHOR]

The article considers issues related to measuring the wave impedance and complex reflection and transmission coefficients of devices operating at super high frequencies in coaxial waveguides. It is important to measure these parameters in the design and manufacture of communication and data transmission systems, as well as radar and radio navigation systems. In order to ensure measurement uniformity, reproduction, and dissemination of the units of wave impedance and complex reflection and transmission coefficients to measuring instruments with 1 mm coaxial connectors within the frequency range of 0.01–67 GHz, GET 75-2023 State primary standard of the unit of wave impedance in coaxial waveguides was approved. The system comprises means of reproducing the unit of wave impedance and complex reflection coefficients, as well as a comparator and transfer standards in a coaxial path having a cross-section of 1.0/0.434 mm with a 1 mm connector. Expressions for calculating the parameters of coaxial waveguides with bimetallic conductors are refined. The technical characteristics and results of metrological studies of GET 75-2023 are given. The study results confirmed the high accuracy of GET 75-2023, whose metrological characteristics are comparable with those of national metrological institutes in other countries. [ABSTRACT FROM AUTHOR]

Agriculture is considered as the important field which makes its huge contribution over the country's economic growth. The yield of food crops and the precise categorization of crops based on several characteristics are of primary importance in this agricultural industry. However, due to a lack of an effective classification method, this industry has significant issues correctly classifying the crops. In addition, classifying food crops using data mining is highly efficient as these techniques can deal with huge amounts of crop data. To this extent, this paper proposes an efficient classification model based on the cropland data extracted from the cropland images. Initially, the dataset is pre-processed based on data-mining techniques like data cleaning and data discretization. Then, the data are clustered based on their relevance using an Improved Density-based Spatial Clustering of Applications with Noise (IDBSCAN) clustering technique. Finally, classification is performed accurately using the Adaptive Capsule Transient Auto-Encoder (ACTAE). The experimental validation of a proposed approach proved its efficiency over the other existing models with an overall accuracy rate of 97% which is incomparable to the other crop classification models implemented over the cropland dataset. [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]

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]

Radar signal sorting is a vital component of electronic warfare reconnaissance, serving as the basis for identifying the source of radar signals. However, traditional radar signal sorting methods are increasingly inadequate and computationally complex in modern electromagnetic environments. To address this issue, this paper presents a novel machine-learning-based approach for radar signal sorting. Our method utilizes SemHybridNet, a Semantically Enhanced Hybrid CNN-Transformer Network, for the classification of semantic information in two-dimensional radar pulse images obtained by converting the original radar data. SemHybridNet incorporates two innovative modules: one for extracting period structure features, and the other for ensuring effective integration of local and global features. Notably, SemHybridNet adopts an end-to-end structure, eliminating the need for repetitive looping over the original sequence and reducing computational complexity. We evaluate the performance of our method through conducting comprehensive comparative experiments. The results demonstrate our method significantly outperforms the traditional methods, particularly in environments with high missing and noise pulse rates. Moreover, the ablation studies confirm the effectiveness of these two proposed modules in enhancing the performance of SemHybridNet. In conclusion, our method holds promise for enhancing electronic warfare reconnaissance capabilities and opens new avenues for future research in this field. [ABSTRACT FROM AUTHOR]

The paper describes an algorithm to differentiate leads from sea ice using the dual polarization synthetic aperture radar (SAR) data from the Sentinel-1 satellite in an extrawide swath mode. The algorithm uses the polarimetric features of the sea surface signal obtained in the SAR images: the ratio between co- and cross-polarization. A technique is proposed for classifying the SAR images to identify discontinuities (cracks, leads) in drifting sea ice using the ratio and difference of polarizations together with texture features and the neural network implementation. The method was tested using the satellite data obtained over the Arctic seas in the Russian Federation. [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]

Ground-penetrating radar is used to study defects in the form of internal layers in concrete structures. It is found that modulus of deflection coefficient of GPR signals changes as function of the layer thickness and electromagnetic properties of the material filling the layer (sand, wet sand or air). The experimental and numerical research used the method of peak-to-peak amplitude ratio, which enabled determining the Fresnel coefficients for the upper and lower boundaries of a layer. The minimal layer thickness recorded by GPR was 2 mm. [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]

This study presents the initial findings from analyzing the ocean surface current observations during 2018 from the recently installed high-frequency (HF) radars in the Gulf of Khambhat in the northeastern Arabian Sea, India. The research is structured into two main sections: firstly, the extraction of the major (M2, S2, N2, K1, and O1) and shallow-water (M4, MS4, M6, and M8) tidal currents in the gulf, and secondly, understanding the impact of seasonal riverine freshwater influxes on the M2 tidal currents. The HF radars accurately captured strongest currents of ~2.0 m/s within the gulf. Additionally, the circulation pattern in the western gulf is mostly characterized by zonal currents, in contrast to the eastern gulf, where meridional currents prevail. Based on the findings of the higher harmonic analysis, it is apparent that the M2 tidal currents exhibit the highest magnitude, followed by other semi-daily constituents such as S2 and N2, as well as diurnal tidal constituents including K1 and O1. The M4 tidal currents, which are one of the shallow-water tidal components, exhibit strengths that span from 3.15 to 16.50 cm/s. The enhancement of tidal currents in the nearshore areas (within approximately 50 m) can be attributed to their interaction with the bottom bathymetry and the general coastline geometry of the gulf. Notably, higher values of Richardson number ( R i ) and Brunt-Väisälä frequency ( N 2 ) indicated the presence of highly stratified upper layers, particularly during September. The signatures of higher stratification during September contribute to the highest amplitude (>1.50 m/s) of M2 tidal currents. [ABSTRACT FROM AUTHOR]

In the tuna purse seine fishery, seiners spend a considerable amount of time detecting objects such as seabirds, for which radar is a very efficient method. In this study, we present a radar image seabird identification method that can calculate the number of clusters, the area of seabird clusters, and the activity level of seabird clusters. We used a fishing vessel's radar to collect information on seabird groups within 29,632 km of the vessel and calculated a spatial clustering of the seabird-echoes. Generalized additive mixed models (GAMM) were used to investigate the relationship between drifting fish aggregating devices (FADs) and seabird dynamics in the Republic of Kiribati. The findings indicate that FAD variables affected seabird behavior. The random effects on cluster number, cluster area, and cluster ability were 3.27, 17.41, and −0.17, respectively. Then, we compared the radar image information that was calculated. The bird cluster around drifting FADs was found to be more concentrated and denser than in areas without FADs, with a lower level of activity observed. The longitude of 165°E had the highest number of bird clusters and the greatest area inhabited by birds, but these decreased to the east. However, model 3 showed that the minimum value of seabird cluster activity level occurred around 165°E and increased to the east. [ABSTRACT FROM AUTHOR]

The integration of sensor data is crucial in the field of robotics to take full advantage of the various sensors employed. One critical aspect of this integration is determining the extrinsic calibration parameters, such as the relative transformation, between each sensor. The use of data fusion between complementary sensors, such as radar and LiDAR, can provide significant benefits, particularly in harsh environments where accurate depth data is required. However, noise included in radar sensor data can make the estimation of extrinsic calibration challenging. To address this issue, we present a novel framework for the extrinsic calibration of radar and LiDAR sensors, utilizing CycleGAN as a method of image-to-image translation. Our proposed method employs translating radar bird-eye-view images into LiDAR-style images to estimate the 3-DOF extrinsic parameters. The use of image registration techniques, as well as deskewing based on sensor odometry and B-spline interpolation, is employed to address the rolling shutter effect commonly present in spinning sensors. Our method demonstrates a notable improvement in extrinsic calibration compared to filter-based methods using the MulRan dataset. [ABSTRACT FROM AUTHOR]

This study presents a first simultaneous observation of N 2 + Meinel (0,0) band (hereafter, N 2 + (M)) aurora by cutting-edge short-wavelength infrared imaging spectrograph (NIRAS-2) and monochromatic camera (NIRAC) installed at the Kjell Henriksen Observatory (78 ∘ N, 16 ∘ E). On January 21 2023, N 2 + (M) intensification that is associated with a band-shape aurora structure was observed by the NIRAS-2 and the NIRAC having temporal resolutions of 30 s and 20 s, respectively. In addition, the European incoherent scatter Svalbard Radar also observed electron density variations at the same time. Electron density measured at altitude range from 100 km 120 km shows similar variations as of N 2 + (M) intensity, which implies that a primary source of N 2 + (M) emissions is direct collisions of N 2 by precipitating electrons penetrating down to around 100 km altitude (up to 10 keV). However, the observation also demonstrated moderate correlations between N 2 + (M) intensity and electron density above 140 km, which implies that different N 2 + (M) generation process, N 2 charge exchange with O + , may work up to near 160 km and make a non-negligible contribution to N 2 + (M) emissions. This hypothesis would be verified with further radar observations or stereo imaging observations useful to estimate the vertical distribution of the emission layers. The N 2 + (M) is a very promising target wavelength for aurora observation because the quality of sensors is highly expected to improve further and further. Continuous observations with our new instruments will undoubtedly provide an important information of N 2 + (M) characteristics, for future missions of both balloon-borne and satellite-borne imaging. [ABSTRACT FROM AUTHOR]

This study presents a first simultaneous observation of N 2 + Meinel (0,0) band (hereafter, N 2 + (M)) aurora by cutting-edge short-wavelength infrared imaging spectrograph (NIRAS-2) and monochromatic camera (NIRAC) installed at the Kjell Henriksen Observatory (78 ∘ N, 16 ∘ E). On January 21 2023, N 2 + (M) intensification that is associated with a band-shape aurora structure was observed by the NIRAS-2 and the NIRAC having temporal resolutions of 30 s and 20 s, respectively. In addition, the European incoherent scatter Svalbard Radar also observed electron density variations at the same time. Electron density measured at altitude range from 100 km 120 km shows similar variations as of N 2 + (M) intensity, which implies that a primary source of N 2 + (M) emissions is direct collisions of N 2 by precipitating electrons penetrating down to around 100 km altitude (up to 10 keV). However, the observation also demonstrated moderate correlations between N 2 + (M) intensity and electron density above 140 km, which implies that different N 2 + (M) generation process, N 2 charge exchange with O + , may work up to near 160 km and make a non-negligible contribution to N 2 + (M) emissions. This hypothesis would be verified with further radar observations or stereo imaging observations useful to estimate the vertical distribution of the emission layers. The N 2 + (M) is a very promising target wavelength for aurora observation because the quality of sensors is highly expected to improve further and further. Continuous observations with our new instruments will undoubtedly provide an important information of N 2 + (M) characteristics, for future missions of both balloon-borne and satellite-borne imaging. [ABSTRACT FROM AUTHOR]

Applying deep learning techniques to solve radar signal processing problems such as phase-enhanced direction of arrival (DOA) estimation involves the acquisition of large-scale data, which are usually small and difficult to obtain. Hence, we develop a steering vector embedding complex-valued generative adversarial network (CVGAN) for radar data generation; this is an excellent technique for increasing the size of the dataset we can collect. First, a novel CVGAN model is proposed to mine real radar data features and generate fake data with similar distribution characteristics through game theory. To improve the interpretability of the data generated by CVGAN, a steering vector embedding technique is proposed, in which fake radar data with the desired DOA are generated by embedding the corresponding steering vector. The proposed CVGAN model can effectively reduce the dependence of the model on the amount of data we need to collect. The simulation and experimental results show that the proposed CVGAN model can fully explore the data features contained in a small number of samples and augment the datasets, and the phase enhancement learning model combined with the CVGAN has higher estimation accuracy and generalizability for DOA estimation. [ABSTRACT FROM AUTHOR]

In the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric refraction, equipment malfunctions, and other factors, resulting in diminished data quality. Traditional radar blockage correction methods, such as employing approximate radial information interpolation and supplementing missing data, often fail to effectively exploit potential patterns in massive radar data, for the large volume of data precludes a thorough analysis and understanding of the inherent complex patterns and dependencies through simple interpolation or supplementation techniques. Fortunately, edge computing possesses certain data processing capabilities and cloud center boasts substantial computational power, which together can collaboratively offer timely computation and storage for the correction of radar beam blockage. To this end, an edge-cloud collaborative driven deep learning model named DenMerD is proposed in this paper, which includes dense connection module and merge distribution (MD) unit. Compared to existing models such as RC-FCN, DenseNet, and VGG, this model greatly improves key performance metrics, with 30.7 % improvement in Critical Success Index (CSI), 30.1 % improvement in Probability of Detection (POD), and 3.1 % improvement in False Alarm Rate (FAR). It also performs well in the Structure Similarity Index Measure (SSIM) metrics compared to its counterparts. These findings underscore the efficacy of the design in improving feature propagation and beam blockage accuracy, and also highlights the potential and value of mobile edge computing in processing large-scale meteorological data. [ABSTRACT FROM AUTHOR]

Objective: To provide a comprehensive framework for value assessment of artificial intelligence (AI) in radiology. Methods: This paper presents the RADAR framework, which has been adapted from Fryback and Thornbury's imaging efficacy framework to facilitate the valuation of radiology AI from conception to local implementation. Local efficacy has been newly introduced to underscore the importance of appraising an AI technology within its local environment. Furthermore, the RADAR framework is illustrated through a myriad of study designs that help assess value. Results: RADAR presents a seven-level hierarchy, providing radiologists, researchers, and policymakers with a structured approach to the comprehensive assessment of value in radiology AI. RADAR is designed to be dynamic and meet the different valuation needs throughout the AI's lifecycle. Initial phases like technical and diagnostic efficacy (RADAR-1 and RADAR-2) are assessed pre-clinical deployment via in silico clinical trials and cross-sectional studies. Subsequent stages, spanning from diagnostic thinking to patient outcome efficacy (RADAR-3 to RADAR-5), require clinical integration and are explored via randomized controlled trials and cohort studies. Cost-effectiveness efficacy (RADAR-6) takes a societal perspective on financial feasibility, addressed via health-economic evaluations. The final level, RADAR-7, determines how prior valuations translate locally, evaluated through budget impact analysis, multi-criteria decision analyses, and prospective monitoring. Conclusion: The RADAR framework offers a comprehensive framework for valuing radiology AI. Its layered, hierarchical structure, combined with a focus on local relevance, aligns RADAR seamlessly with the principles of value-based radiology. Critical relevance statement: The RADAR framework advances artificial intelligence in radiology by delineating a much-needed framework for comprehensive valuation. Keypoints: • Radiology artificial intelligence lacks a comprehensive approach to value assessment. • The RADAR framework provides a dynamic, hierarchical method for thorough valuation of radiology AI. • RADAR advances clinical radiology by bridging the artificial intelligence implementation gap. [ABSTRACT FROM AUTHOR]

The results of analysis of spatiotemporal variability of submesoscale surface eddy structures from May to October of 2019–2022 in Lake Ladoga are summarized using more than 1000 Sentinel–1A/B high-resolution images as the original data. The wide occurrence of small eddy structures on the lake surface is revealed during the warm period of the year. In total, 496 surface eddies with an average diameter of 2 km are registered. More than 90% of the recorded eddies belong to the submesoscale variability range. Cyclonic rotation is typical of 84% of the structures. A seasonal maximum of eddy activity is registered during the stable stratification over most of Lake Ladoga. The eddies occur most frequently to the north and northeast of the Valaam Island in areas ~100 m deep but close to the seabed unevenness. During the spring thermocline, the eddies are often registered from the cold side of the frontal boundary indicating their important contribution to the water exchange and mixing processes. [ABSTRACT FROM AUTHOR]