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Numerous radio stations across the nation perform nonbroadcast services in areas ranging from aviation, forestry protection, and telephone maintenance to amateur and citizen radio. These services can be grouped in four general categories: (1) safety, (2) industry, (3) land transportation, and (4) miscellaneous purposes. This bulletin briefly describes some 50 radio services which comprise the Safety and Special Radio Services, and FCC (Federal Communications Commission) regulations for these stations. (SC)

Accurate precipitation monitoring is crucial for understanding climate change and rainfall-driven hazards at a local scale. However, the current suite of monitoring approaches, including weather radar and rain gauges, have different insufficiencies such as low spatial and temporal resolution and difficulty in accurately detecting potentially destructive precipitation events such as hailstorms. In this study, we develop an array-based method to monitor rainfall with seismic nodal stations, offering both high spatial and temporal resolution. We analyze seismic records from 1825 densely spaced, high-frequency seismometers in Oklahoma, and identify signals from nine precipitation events that occurred during the one-month station deployment in 2016. After removing anthropogenic noise and Earth structure response, the obtained precipitation spatial pattern mimics the one from a nearby operational weather radar, while offering higher spatial (~ 300 m) and temporal (

The morphology and motion of auroras have been widely studied due to their indications on magnetospheric processes. Here, we report a new kind of "auroral curls," which have wavelengths in the mesoscale (∼100 km) and propagate azimuthally. Utilizing data from the Chinese Antarctic Zhongshan Station (the all‐sky imager and the high‐frequency radar), the Active Magnetosphere and Planetary Electrodynamics Response Experiment and the Defense Meteorological Satellite Program, we analyze an event occurred on 23 April 2019. We find these curls are fine structures in the poleward boundary of multiple arcs. Corresponding field‐aligned currents manifest as a series of longitudinally arranged pairs, while ionospheric flow velocities nearby oscillate with periods in the Pc 5 band. Observational evidence suggests these curls are connected with ultra‐low frequency (ULF) waves, which opens the possibility of using auroras to globally image ULF waves. Plain Language Summary: Auroras caused by precipitation of magnetospheric particles contain information about physical processes happened in the magnetosphere. In this letter, we report a new kind of auroral dynamic forms observed in Antarctica. These structures present both spatial and temporal periodic characteristics, which have similar scales with those of magnetospheric ultra‐low frequency (ULF) waves. We propose these auroral forms are connected with ULF waves, which provides a potential method to globally image ULF waves by analyzing properties of these auroras. Key Points: Azimuthally propagating "auroral curls" with mesoscale wavelengths were observed in AntarcticaThese curls are fine structures in the poleward boundary of multiple arcs formed by longitudinal‐arranged field‐aligned current pairsIonospheric flow velocities nearby oscillate with periods in the Pc 5 band, indicating connections with ultra‐low frequency waves [ABSTRACT FROM AUTHOR]

A long-standing problem in meteor science has been the persistent presence of bias in the measured value of atmospheric scale heights obtained from radio meteor echoes. A common practice of fitting a linear function for bias correction follows the assumption that the systematic bias is devoid of seasonal asymmetry. This would be true if the mass and the velocity distribution of meteoroids remain invariant, both spatially and temporally. But so far no such convincing evidence has been published. On the contrary, fundamental arguments suggest that a universal mass function of radio meteoroids is counterintuitive. This parameter cannot remain invariant due to the intrinsic variability in the meteor response function resulting from the Earth's motion on the plane of ecliptic. In this paper, we show that an inverse relation exists between the width of meteor height distribution, expressed in unit of atmospheric scale height, and the exponent of the mass function. The overall mean of this exponent for the Sodankylä radar is |$1.91 \pm 0.02$|⁠ , modulated by a seasonal variation from the mean of the order of |$\sim \pm 0.1$|⁠. The stated inverse relation is applied to correct for the effect of mass distribution on the height distribution. Allowing for variable mass correction effectively removes the non-linear bias in the measured scale heights in the meteor ionization region. [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]

Earth observation satellite-04 or EOS-04 (Radar imaging satellite-1A or RISAT-1A) provides the opportunity for characterization of surface features using C-band fully-polarimetric, hybrid-polarimetric and dual-polarimetric radar data from same platform in high to moderate resolution (2–50 m depending on mode) and varied incidence angle (12°–55°). These unique EOS-04 radar datasets can be processed using Microwave Data Analysis Software (MIDAS) tool, to derive radar polarimetric parameters. MIDAS is capable of carrying out synthetic aperture radar (SAR) data processing comprising radiometric and majority of the polarimetric processing of EOS-04 datasets. In addition, MIDAS has the functionality to orthorectify and geocode EOS-04 polarimetric products, which is missing in other softwares. Further, MIDAS contains module for bulk-processing of timeseries EOS-04 products. We showcase results obtained from EOS-04 datasets and describe the scattering behaviour of various targets. Further, we also demonstrate the temporal variation in the backscatter values from different target features obtained from EOS-04 time-series stack, which has strong applicability in land use/land cover and agricultural applications. Thus, the radiometric and polarimetric products can be highly beneficial for characterization of physical properties of the scatterers based on their radar scattering behaviour and further classification of the surface features. [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 authors present the design, fabrication and testing of a W‐band monopulse antenna. This antenna consists of three main building blocks: an array with 48 × 48 slots, a tapered corporate‐feed network and a comparator. The comparator has four input ports to generate sum and difference patterns in orthogonal cardinal planes for monopulse operation. A novel octuple excitation is adopted for the corporate network to achieve a side lobe level lower than −20 dB for the sum patterns and simultaneously reduce drastically the manufacturing complexity. Diffusion bonding technology is used for fabrication. Twenty‐four etched copper sheets with a thickness of 0.2 mm are stacked to realise the prototype. The total size of the antenna is 40.76×40.76×1.51λ03 $40.76\times 40.76\times 1.51{\lambda }_{0}^{3}$, with λ0 being the wavelength at the centre frequency (94 GHz). The antenna presents an isolation better than 12 dB among the input ports in the 84–100 GHz band. The realised measured gain is 40.7 dBi at 94 GHz. The measured 3‐dB gain bandwidth is 17.4%. The difference patterns at 94.0 GHz present null depths of −24.0, −18.0, −17.5, and −17.4 dB, in the E‐, H‐, 45°‐, and 135°‐planes, respectively. [ABSTRACT FROM AUTHOR]

This paper summarizes over 60 years of radar system development at MIT Lincoln Laboratory, from early research on satellite tracking and planetary radar to the present ability to perform the centimeter-resolution imaging of resident space objects and future plans to extend this capability to geosynchronous range. [ABSTRACT FROM AUTHOR]

With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination. Then, this network is used to classify small-sample micro-Doppler images obtained from millimeter-wave radar as the data source for pose recognition. Experimental tests were conducted on pose estimation and image classification tasks. The results demonstrate significant detection and recognition performance, with an accuracy of 94.5%, accompanied by a 95% confidence interval. Additionally, on the open-source dataset DIAT-μRadHAR, which is specially processed to increase classification difficulty, the network achieves a classification accuracy of 85.9%. [ABSTRACT FROM AUTHOR]

Within the context of a smart home, detecting the operating status of appliances in the environment plays a pivotal role, estimating power consumption, issuing overuse reminders, and identifying faults. The traditional contact-based approaches require equipment updates such as incorporating smart sockets or high-precision electric meters. Non-constant approaches involve the use of technologies like laser and Ultra-Wideband (UWB) radar. The former can only monitor one appliance at a time, and the latter is unable to detect appliances with extremely tiny vibrations and tends to be susceptible to interference from human activities. To address these challenges, we introduce HomeOSD, an advanced appliance status-detection system that uses mmWave radar. This innovative solution simultaneously tracks multiple appliances without human activity interference by measuring their extremely tiny vibrations. To reduce interference from other moving objects, like people, we introduce a Vibration-Intensity Metric based on periodic signal characteristics. We present the Adaptive Weighted Minimum Distance Classifier (AWMDC) to counteract appliance vibration fluctuations. Finally, we develop a system using a common mmWave radar and carry out real-world experiments to evaluate HomeOSD's performance. The detection accuracy is 95.58%, and the promising results demonstrate the feasibility and reliability of our proposed system. [ABSTRACT FROM AUTHOR]

With the rapidly growing interest in using structural timber, a need exists to inspect and assess these structures using non-destructive testing (NDT). This review article summarizes NDT methods for wood inspection. After an overview of the most important NDT methods currently used, a detailed review of Ground Penetrating Radar (GPR) and Ultrasonic Testing (UST) is presented. These two techniques can be applied in situ and produce useful visual representations for quantitative assessments and damage detection. With its commercial availability and portability, GPR can help rapidly identify critical features such as moisture, voids, and metal connectors in wood structures. UST, which effectively detects deep cracks, delaminations, and variations in ultrasonic wave velocity related to moisture content, complements GPR's capabilities. The non-destructive nature of both techniques preserves the structural integrity of timber, enabling thorough assessments without compromising integrity and durability. Techniques such as the Synthetic Aperture Focusing Technique (SAFT) and Total Focusing Method (TFM) allow for reconstructing images that an inspector can readily interpret for quantitative assessment. The development of new sensors, instruments, and analysis techniques has continued to improve the application of GPR and UST on wood. However, due to the hon-homogeneous anisotropic properties of this complex material, challenges remain to quantify defects and characterize inclusions reliably and accurately. By integrating advanced imaging algorithms that consider the material's complex properties, combining measurements with simulations, and employing machine learning techniques, the implementation and application of GPR and UST imaging and damage detection for wood structures can be further advanced. [ABSTRACT FROM AUTHOR]

Short sprints are predominantly assessed using timing gates and analyzed through parameters of the mono-exponential equation, including estimated maximal sprinting speed ( M S S ) and relative acceleration ( T A U ), derived maximum acceleration (MAC), and relative propulsive maximal power ( P M A X ), further referred to as the No Correction model. However, the frequently recommended flying start technique introduces a bias during parameter estimation. To correct this, two additional models (Estimated TC and Estimated FD) were proposed. To estimate model precision and sensitivity to detect the change, 31 basketball players executed multiple 30 m sprints. Athlete performance was simultaneously measured by a laser gun and timing gates positioned at 5, 10, 20, and 30 m. Short sprint parameters were estimated using a laser gun, representing the criterion measure, and five different timing gate models, representing the practical measures. Only the MSS parameter demonstrated a high agreement between the laser gun and timing gate models, using the percent mean absolute difference ( % M A D ) estimator ( % M A D < 10%). The MSS parameter also showed the highest sensitivity, using the minimum detectable change estimator ( % M D C 95 ), with an estimated % M D C 95 < 17%. Interestingly, sensitivity was the highest for the No Correction model ( % M D C 95 < 7%). All other parameters and models demonstrated an unsatisfying level of sensitivity. Thus, sports practitioners should be cautious when using timing gates to estimate maximum acceleration indices and changes in their respective levels. [ABSTRACT FROM AUTHOR]

The article presents the analysis, design, and low-cost implementation of application-specific AD converters for M-sequence-based UWB applications to minimize and integrate the whole UWB sensor system. Therefore, the main goal of this article is to integrate the AD converter's own design with the UWB analog part into the system-in-package (SiP) or directly into the system-on-a-chip (SoC), which cannot be implemented with commercial AD converters, or which would be disproportionately expensive. Based on the current and used UWB sensor system requirements, to achieve the maximum possible bandwidth in the proposed semiconductor technology, a parallel converter structure is designed and presented in this article. Moreover, 5-bit and 4-bit parallel flash AD converters were initially designed as part of the research and design of UWB M-sequence radar systems for specific applications, and are briefly introduced in this article. The requirements of the newly proposed specific UWB M-sequence systems were established based on the knowledge gained from these initial designs. After thorough testing and evaluation of the concept of the early proposed AD converters for these specific UWB M-sequence systems, the design of a new AD converter was initiated. After confirming sufficient characteristics based on the requirements of UWB M-sequence systems for specific applications, a 7-bit AD converter in low-cost 0.35 µm SiGe BiCMOS technology from AMS was designed, fabricated, and presented in this article. The proposed 7-bit AD converter achieves the following parameters: ENOB = 6.4 bits, SINAD = 38 dB, SFDR = 42 dBc, INL = ±2-bit LSB, and DNL = ±1.5 LSB. The maximum sampling rate reaches 1.4 Gs/s, the power consumption at 20 Ms/s is 1050 mW, and at 1.4 Gs/s is 1290 mW, with a power supply of −3.3 V. [ABSTRACT FROM AUTHOR]

In this paper, a 3D conformal meta-lens designed for manipulating electromagnetic beams via height-to-phase control is proposed. The structure consists of a 40 × 20 array of tunable unit cells fabricated using 3D printing, enabling full 360° phase compensation. A novel automatic synthesizing method (ASM) with an integrated optimization process based on genetic algorithm (GA) is adopted here to create the meta-lens. Simulation using CST Microwave Studio and MATLAB reveals the antenna's beam deflection capability by adjusting phase compensations for each unit cell. Various beam scanning techniques are demonstrated, including single-beam, dual-beam generation, and orbital angular momentum (OAM) beam deflection at different angles of 0°, 10°, 15°, 25°, 30°, and 45°. A 3D-printed prototype of the dual-beam feature has been fabricated and measured for validation purposes, with good agreement between both simulation and measurement results, with small discrepancies due to 3D printing's low resolution and fabrication errors. This meta-lens shows promise for low-cost, high-gain beam deflection in mm-wave wireless communication systems, especially for sensing applications, with potential for wider 2D beam scanning and independent beam deflection enhancements. [ABSTRACT FROM AUTHOR]

Transient interference often submerges the actual targets when employing over-the-horizon radar (OTHR) to detect targets. In addition, modern OTHR needs to carry out multi-target detection from sea to air, resulting in the sparse sampling of echo data. The sparse OTHR signal will raise serious grating lobes using conventional methods and thus degrade target detection performance. This article proposes a modified Alternating Direction Method of Multipliers (ADMM)-Net to reconstruct the target and clutter spectrum of sparse OTHR signals so that target detection can be performed normally. Firstly, transient interferences are identified based on the sparse basis representation and then excised. Therefore, the processed signal can be seen as a sparse OTHR signal. By solving the Doppler sparsity-constrained optimization with the trained network, the complete Doppler spectrum is reconstructed effectively for target detection. Compared with traditional sparse solution methods, the presented approach can balance the efficiency and accuracy of OTHR signal spectrum reconstruction. Both simulation and real-measured OTHR data proved the proposed approach's performance. [ABSTRACT FROM AUTHOR]

This contribution focuses on the detection of tracer particles within non-homogeneous bulk media, aiming to enhance insights into particulate systems. Polarimetric radar measurements are employed, utilizing cross-polarizing channels in order to mitigate interference from bulk media reflections. To distinguish the tracer particle in the measurements, a resonant cross-polarizing structure is constructed, facilitating the isolation of frequency signatures from the surrounding bulk clutter. In addition to characterizing the bulk and tracer components, this study provides a detailed presentation and discussion of the measurement setup, along with the employed signal processing methods. The effectiveness of the proposed methods is demonstrated through comprehensive measurements, where a tracer particle is systematically positioned at various locations. The results affirm the feasibility and efficacy of the approach, highlighting its applicability for enhanced dynamic monitoring in particulate systems within industrial processes. [ABSTRACT FROM AUTHOR]

In this paper, we present the results of a special experimental study of quiet-time behavior of the mid-latitude ionosphere over Eastern Europe by synchronously operation of different ground-based facilities. For the first time we used data obtained from Kharkiv incoherent scatter (IS) radar, ionosonde and coherent Doppler HF sounding system to detect and investigate traveling ionospheric disturbances (TIDs). The periods close to winter solstice and autumn equinox in 2018 were analyzed. The dominant periods and horizontal phase velocities of registered TIDs were 45–80 min and 230–460 m s−1, respectively. The strongest signatures were observed in the solstice measurement and represented by large amplitudes. Based on results obtained by all three methods, we found the LS TIDs with the same interval of time and altitude of propagation and similar characteristics (the period of about 50 min, estimated vertical 80 m/s and horizontal 460 m/s velocity and horizontal spatial scale size about 1360 km) for winter measurement. Such observational findings confirm the reliability of these TID detection techniques. Possible sources of TIDs generation were considered including solar terminator. [ABSTRACT FROM AUTHOR]

In this paper, a novel microwave oscillator is developed at frequencies of 5.7 and 7.5 GHz through the application of Negative Resistance and Harmonic Balance theory. The design process involves leveraging the Agilent Advance Design System (ADS) tool, ensuring exceptional electromagnetic (EM) performance. The utilization of microstrip circuit elements enhances the overall performance of the oscillator structure. Following optimization and co-simulation of nonlinear models for the compact Planar Microwave Oscillator (62 x 38 mm²), highly satisfactory results are obtained. Quantitatively, the measured output powers at 5.7 and 7.5 GHz are determined to be 9.5 dBm and 7.05 dBm, respectively. These power levels are particularly relevant for C band applications spanning 4 to 8 GHz, including areas such as satellite communication, radar, and wireless networks. [ABSTRACT FROM AUTHOR]

This paper introduces a machine-learning-driven approach for automated Nocturnal Low-Level Jet (NLLJ) identification using observations of wind profiles from a Radar Wind Profiler (RWP). The work discussed here is an effort to lay the groundwork for a systematic study of the Mid-Atlantic NLLJ's formation mechanisms and their influence on nocturnal and diurnal air quality in major urban regions by establishing a general framework of NLLJ features and characteristics with an identification algorithm. Leveraging a comprehensive wind profile dataset maintained by the Maryland Department of Environment's RWP network, our methodology employs supervised machine learning techniques to isolate the features of the south-westerly NLLJ. This methodology was developed to illuminate spatiotemporal patterns and nuanced characteristics of NLLJ events, unveiling their significant role in shaping the planetary boundary layer. This paper discusses the construction of this methodology, its performance against known NLLJs in the current literature, intended usage, and a preliminary statistical analysis. First light results from this analysis have identified a total of 90 south-westerly NLLJs from May–September of 2017–2021 as captured by the RWP stationed in Beltsville, MD (39.05°, -76.87°, 135 m ASL). A composite of these 90 jets is presented to better illustrate many of the bulk parameters, such as core height, duration, and maximum wind speed, associated with the onset and decay of the Mid-Atlantic NLLJ. We hope our study equips researchers and policymakers with further means to monitor, predict, and address these nocturnal dynamics phenomena that frequently influence boundary layer composition and air quality in the U.S. Mid-Atlantic and Northeastern regions. [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]

Aircraft icing refers to the accumulation of ice on the surface and components of an aircraft when supercooled water droplets collide with the aircraft above freezing levels (at altitudes at which the temperature is below 0 °C), which requires vigilant monitoring to avert aviation accidents attributable to icing. In response to this imperative, the Weather Radar Center (WRC) of the Korea Meteorological Administration (KMA) has developed a real-time icing detection algorithm. We utilized 3D dual-polarimetric radar variables, 3D atmospheric variables, and aircraft icing data and statistically analyzed these variables within the icing areas determined by aircraft icing data from 2018–2022. An algorithm capable of detecting icing potential areas (icing potential) was formulated by applying these characteristics. Employing this detection algorithm enabled the classification of icing potential into three stages: precipitation, icing caution, and icing warning. The algorithm was validated, demonstrating a notable performance with a probability of detection value of 0.88. The algorithm was applied to three distinct icing cases under varying environmental conditions—frontal, stratiform, and cumuliform clouds—thereby offering real-time observable icing potential across the entire Korean Peninsula. [ABSTRACT FROM AUTHOR]

In the realm of short-range radar applications, the focus on detecting "low, slow, and small" (LSS) targets has escalated, marking a pivotal aspect of critical area defense. This study pioneers the use of one-dimensional convolutional neural networks (1D-CNNs) for direct slow-time dimension radar feature extraction, sidestepping the complexity tied to frequency and wavelet domain transformations. It innovatively employs a network architecture enriched with multi-frequency multi-scale deformable convolution (MFMSDC) layers for nuanced feature extraction, integrates attention modules to foster comprehensive feature connectivity, and leverages linear operations to curtail overfitting. Through comparative evaluations and ablation studies, our methodology not only simplifies the analytic process but also demonstrates superior classification capabilities. This establishes a new benchmark for efficiently classifying low-altitude entities, such as birds and unmanned aerial vehicles (UAVs), thereby enhancing the precision and operational efficiency of radar detection systems. [ABSTRACT FROM AUTHOR]

There is growing demand for the high-fidelity characterization of satellites in Geosynchronous Earth Orbit (GEO) to support Space Domain Awareness (SDA). This is particularly true for newly launched satellites, where it is necessary for satellite providers to ascertain whether components have deployed properly. Conventional wideband radar systems are capable of imaging satellites provided that (i) they have sufficient power aperture and bandwidth, and (ii) they observe enough target aspect change to generate a resolved image. While wideband radars are used routinely for characterizing satellites in Low-Earth Orbit (LEO), powerful radars with sensitivity sufficient for large GEO ranges (36,000 km or greater) are lacking. Thus, researchers often rely on more widely available high-power narrowband tracking radars for GEO characterization. In this paper, we present a novel range-Doppler-time (RDT) tensor processing technique for GEO characterization with narrowband radar. This technique encapsulates the strengths of previously proposed methods for narrowband-radar characterization at GEO, providing a generalized approach that can be applied in a variety of settings. The technique generates fully resolved 2D images of rotating GEO satellites in low-bandwidth scenarios. In cases where aspect change is limited, the technique provides detailed Doppler information for enhanced satellite status monitoring. This work presents a comprehensive quantitative analysis of the technique that considers the impact of key parameters on characterization performance. Simulated radar data, and radar data collected in a compact range on a scaled satellite model, are used to evaluate the technique. [ABSTRACT FROM AUTHOR]

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]

This paper details the design and implementation of a harmonic frequency-modulated continuous-wave (FMCW) radar system, specialized in detecting harmonic tags and achieving precise range estimation. Operating within the 2.4–2.5 GHz frequency range for the forward channel and 4.8–5.0 GHz for the backward channel, this study delves into the various challenges faced during the system's realization. These challenges include selecting appropriate components, calibrating the system, processing signals, and integrating the system components. In addition, we introduce a single-layer passive harmonic tag, developed specifically for assessing the system, and provide an in-depth theoretical analysis and simulation results. Notably, the system is characterized by its low power consumption, making it particularly suitable for short-range applications. The system's efficacy is further validated through experimental evaluations in a real-world indoor environment across multiple tag positions. Our measurements underscore the system's robust ranging accuracy and its ability to mitigate self-interference, showcasing its significant potential for applications in harmonic tag detection and ranging. [ABSTRACT FROM AUTHOR]

Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user's quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler effect, combining a two-stream one-dimensional convolutional neural network (1D-CNN) with a bidirectional gated recurrent unit (BiGRU). Initially, radar sensor data are used to generate information related to time and frequency responses using short-time Fourier transform (STFT). Subsequently, the magnitudes and phase values are calculated and fed into the 1D-CNN and Bi-GRU models to extract spatial and temporal features for subsequent model training and activity recognition. Additionally, we propose a simple cross-channel operation (CCO) to facilitate the exchange of magnitude and phase features between parallel convolutional layers. An open dataset collected through radar, named Rad-HAR, is employed for model training and performance evaluation. Experimental results demonstrate that the proposed 1D-CNN+CCO-BiGRU model demonstrated superior performance, achieving an impressive accuracy rate of 98.2%. This outperformance of existing systems with the radar sensor underscores the proposed model's potential applicability in real-world scenarios, marking a significant advancement in the field of HAR within the IoT framework. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach for wide angle monitoring of vital signs in smart home applications. The person is tracked using an indoor radar. Upon detecting the person to be static, the radar automatically focuses its beam on that location, and subsequently breathing and heart rates are extracted from the reflected signals using continuous wavelet transform ( C W T ) analysis. In this way, leveraging the radar's on-chip processor enables real-time monitoring of vital signs across varying angles. In our experiment, we employ a commercial multi-input multi-output (MIMO) millimeter-wave FMCW radar to monitor vital signs within a range of 1.15 to 2.3 m and an angular span of − 44.8 to + 44.8 deg. In the Bland–Altman plot, the measured results indicate the average difference of − 1.5 and 0.06 beats per minute (BPM) relative to the reference for heart rate and breathing rate, respectively. [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]

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

With global warming and intensified human activities, extreme convective precipitation has become one of the most frequent natural disasters. An accurate and reliable assessment of severe convective precipitation events can support social stability and economic development. In order to investigate the accuracy enhancement methods and data fusion strategies for the assessment of severe convective precipitation events, this study is driven by the horizontal reflectance factor (ZH) and differential reflectance (ZDR) of the dual-polarization radar. This research work utilizes microphysical information of convective storms provided by radar variables to construct the precipitation event assessment model. Considering the problems of high dimensionality of variable data and low computational efficiency, this study proposes a dual-polarization radar echo-data-layering strategy. Combined with the results of mutual information (MI), this study constructs Bayes–Kalman filter (KF) models (RF, SVR, GRU, LSTM) for the assessment of severe convective precipitation events. Finally, this study comparatively analyzes the evaluation effectiveness and computational efficiency of different models. The results show that the data-layering strategy is able to reduce the data dimensions of 256 × 256 × 34,978 to 5 × 2213, which greatly improves the computational efficiency. In addition, the correlation coefficient of interval III–V calibration period is increased to 0.9, and the overall assessment accuracy of the model is good. Among them, the Bayes–KF-LSTM model has the best assessment effect, and the Bayes–KF-RF has the highest computational efficiency. Further, five typical precipitation events are selected for validation in this study. The stratified precipitation dataset agrees well with the near-surface precipitation, and the model's assessment values are close to the observed values. This study completely utilizes the microphysical information offered by dual-polarized radar ZH and ZDR in precipitation event assessment, which provides a wide range of application possibilities for the assessment of severe convective precipitation events. [ABSTRACT FROM AUTHOR]

Featured Application: Object detection method based on three-dimensional GPR data of railroad tracks using AI, trained solely on simulation data. Ground-penetrating radar (GPR) is a non-invasive technology that uses electromagnetic pulses for subsurface exploration. In the railroad sector, it is crucial to assessing soil layers and infrastructure, offering insights into soil stratification and geological features and aiding in identifying subsurface hazards. However, the automation of radargram analysis is impeded by the lack of ground truth—accurate real-world data used to validate machine learning models—thus affecting the deployment of advanced algorithms. This study focuses on generating high-quality simulated data to address the shortage of real-world data in the context of object detection along railroad tracks and presents a fully automated pipeline that includes data generation, algorithm training, and validation using real-world data. By doing so, it paves the way for significantly easing the future task of object detection algorithms in the railway sector. A simulation environment, including the digital twin of a GPR antenna, was developed for artificial data generation. The process involves pre- and post-processing techniques to transform the three-dimensional data from the multichannel GPR system into two-dimensional datasets. This ensures minimal information loss and suitability for established two-dimensional object detection algorithms like the well-known YOLO (You Only Look Once) framework. Validation involved real-world measurements on a track with predefined buried objects. The entire pipeline, encompassing data generation, processing, training, and application, was automated for efficient algorithm testing and implementation. Artificial data show promise for better performance with increased training. Future AI and sensor advancements will enhance subsurface exploration, contributing to safer and more reliable railroad operations. [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]

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]