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

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

Downhill thunderstorms frequently occur in Beijing during the rainy seasons, leading to substantial precipitation. The accurate intensity prediction of these events remains a challenge, partly attributed to insufficient observational studies that unveil the thermodynamic and dynamic structures along the vertical direction. This study provides a comprehensive methodology for identifying both enhanced and dissipated downhill thunderstorms. In addition, a radar wind profiler (RWP) mesonet has been built in Beijing to characterize the pre-storm environment downstream to the thunderstorms at the mountain foot. This involves deriving vertical distributions of high-resolution horizontal divergence and vertical motion from the horizontal wind profiles measured by the RWP mesonet. A case of enhanced downhill thunderstorm on 28 September 2018 is carried out, which support the idea that the enhanced southerly flow and corresponding convergence detected by the RWP mesonet could favorably support the development of thunderstorms in the afternoon. The results also indicate that low-level convergence is an effective signal in accounting for convective maintenance. Statistical analysis based on radar reflectivity from April to September 2018–2021 have shown that a total of 63 thunderstorm events tend to be enhanced after moving into downhill and urban areas, accounting for about 66 % of the total number of downhill thunderstorm events. A critical region for intensified thunderstorms lies on the downslope side of the mountains west to Beijing. The evolution of the downhill storm is associated with the dynamic conditions over the plain compared to its initial morphology. The lifting induced by stronger westerly winds and vertical shear in the low and midlevel troposphere exerts a critical influence on the enhancement of convection. The findings underscore the significant role of RWP network in elucidating the evolution of downhill storm. [ABSTRACT FROM AUTHOR]

The risk level of the NIMBY (Not In My Back Yard) phenomenon is crucial for the safety and economy of transmission and transformation projects which is rarely studied, especially for site selection and the construction of transmission lines and substations. In order to effectively evaluate the risk level to solve the dilemma caused by the NIMBY phenomenon, an evaluation method for quantifying the level of the NIMBY phenomenon is proposed. In this paper, thirty-one evaluation criteria and a risk model are put forward according to relevant laws and regulations that should be followed in the transmission and transformation project in China, then the scores corresponding to these criteria are obtained by a questionnaire survey. The radar chart method and minimum area method are applied to determine the weights of the element and unit layers. Furthermore, the overall risk level is evaluated by the fuzzy comprehensive evaluation method. In addition, a transmission and transformation project in Xi'an City, China, is used as an example to verify the correction of the risk model and its evaluation method. The results show that the weaknesses in the transmission and transformation project are analyzed, and suggestions for decreasing the risk level are put forward to minimize losses due to the NIMBY phenomenon. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth review of deep learning techniques to address the challenges of odometry and global ego-localization using frequency modulated continuous wave (FMCW) radar sensors. In particular, we focus on the prediction of odometry, which involves the determination of the ego-motion of a system by external sensors, and loop closure detection, which concentrates on the determination of the ego-position typically on an existing map. We initially emphasize the significance of these tasks in the context of radar sensors and underscore the motivations behind them. The subsequent sections delve into the practical implementation of deep learning approaches, strategically designed to effectively address the aforementioned challenges. We primarily focus on spinning and automotive radar configurations within the domain of autonomous driving. Additionally, we introduce publicly available datasets that have been instrumental in addressing these challenges and analyze the importance and struggles of current methods used for radar based odometry and localization. In conclusion, this paper highlights the distinctions between the addressed tasks and other radar perception applications, while also discussing their differences from challenges posed by alternative sensor modalities. The findings contribute to the ongoing discourse on advancing radar sensor capabilities through the application of deep learning methodologies, particularly in the context of enhancing odometry and ego-localization for autonomous driving applications. [ABSTRACT FROM AUTHOR]

Global concern on extreme events is increasing the need for real timemonitoring of the wave fields in coastal areas. High Frequency (HF) radars, a remote sensing technology widely applied to measure near real time surface coastal currents with demonstrated accuracy, can also play a major role in the operational monitoring of waves height, period and direction. However, the ability of HF radar to measure waves can be jeopardized by specific ocean-meteorological and environmental conditions. Thus, a case-to-case analysis and parameterization is necessary to ensure the best data in each study area. In the southeastern (SE) Bay of Biscay, the EuskOOS HF radar network, composed by two compact HF radar stations provides hourly surface waves data in near real time. In this work, we analyze the effects of wind and noise levels on the radar skills for wave measurement, compared with existing in-situ data obtained by an offshore buoy. Then, the HF radar wave measurements for 2022 are analyzed with special focus on the most energetic observed wave events. The analysis performed versus in-situ data shows that both stations present reliable and accurate data for waves over 1.5 m, in agreement to what can be expected for a 4.46 MHz radar. The highest correlations are observed for waves > 4 m significant wave height, which demonstrates the capabilities for monitoring highly energetic events. Interference and noise detected on very precise time slots significantly reduced the availability and reliability of the measurements. Also, local winds blowing from land direction were found to affect the agreement between radar and in-situ measurements. Recommendations extracted from the analysis are provided, with the aim that they can be extended to other HF networks for more accurate wave monitoring. [ABSTRACT FROM AUTHOR]

The analysis of the state of the literature in the field of methods of perception and control of the movement of autonomous vehicles shows the possibilities of improving them by using an artificial neural network to generate domains of prohibited maneuvers of passing objects, contributing to increasing the safety of autonomous driving in various real conditions of the surrounding environment. This article concerns radar perception, which involves receiving information about the movement of many autonomous objects, then identifying and assigning them a collision risk and preparing a maneuvering response. In the identification process, each object is assigned a domain generated by a previously trained neural network. The size of the domain is proportional to the risk of collisions and distance changes during autonomous driving. Then, an optimal trajectory is determined from among the possible safe paths, ensuring control in a minimum of time. The presented solution to the radar perception task was illustrated with a computer simulation of autonomous driving in a situation of passing many objects. The main achievements presented in this article are the synthesis of a radar perception algorithm mapping the neural domains of autonomous objects characterizing their collision risk and the assessment of the degree of radar perception on the example of multi-object autonomous driving simulation. [ABSTRACT FROM AUTHOR]

Since Mars is far away from Earth, the propagation delay between Mars and Earth is very large. To ensure the effective use of the link transmission bandwidth, China's first Mars exploration mission has put forward a demand for data compression for all scientific payloads. The on-board mature algorithms for data compression are mainly focused on optical images and microwave imaging radar applications. No articles have been published on data compression methods that are applied to subsurface-penetrating radar. Based on the background of this application, this paper proposes a logarithmic lossy compression algorithm which can meet the mission requirements for high compression ratios of 4:1 and 2.5:1. Its compression error is less than that of the block adaptive quantization (BAQ) algorithm. The algorithm is not only easy to implement on field-programmable gate array (FPGA) platforms, but also offers simple ground decompression and fast imaging. The experimental results show that high compression ratios of 4:1 and 2.5:1 can be realized, even if the data in and between traces do not have a strong correlation. And its relative error is less than 2%, which is a new type of high-efficiency data compression method that can be implemented on-board to meet with the demand of subsurface penetrating radar. [ABSTRACT FROM AUTHOR]

Carbon nanotubes (CNTs), which have a diameter on the nanometer scale, are tube-like materials composed of carbon and graphite sheets. Carbon nanotubes play an important role in many fields such as nanotechnology, electronics, optics and material science due to their superior properties such as high elasticity, high thermal conductivity, low density and being more chemically inert. In this study, the use of carbon nanotubes in radar absorbing materials (RAM) to reduce the reflection of electromagnetic waves was investigated. In this sense, by using a polymeric composite structure reinforced with carbon nanotubes, a material with unique properties such as solid absorption, low weight/thickness and costeffectiveness has been produced, minimizing the reflection of electromagnetic waves. For this purpose, carbon nanotubes with different particle sizes of 8 nm, 30-50 nm and 48-78 nm were blended with polyester at the ratio of 1%. The resulting composites were characterized using a Network analyzer in the frequency range of 8-12 GHz. According to the results obtained, it was observed that radar absorption increased with increasing particle size. It has been determined that by changing the particle sizes of carbon nanotubes in composites, the absorption bands can be adjusted to suit different applications in different frequency bands. [ABSTRACT FROM AUTHOR]

Similar in strength to hurricanes, Extratropical Cyclones (ECs) are responsible for innavigable sea states, coastal inundation and erosion, and subsequent destruction to coastal infrastructure. Across modern operational wave models, there exists a known systematic underestimation of wave heights during these extreme events. Using a global database of EC storm tracks and 36 years of satellite altimeter data, we examine EC structure and assess model performance through storm centered composite analyses of significant wave height (${H_s}$ H s ) and U10 wind speed ($|U10|$ | U 10 | ). Through the collocation of satellite altimeter observations with a state-of-the-art reanalysis product (ERA5), we investigate model performance with respect to $|U10|$ | U 10 | and ${H_s}$ H s within ECs of varying intensities. By rotating our data reference frames, we align all storm directions to account for asymmetry in EC wind fields and subsequent increased wave growth This rotation results in the organization of the strongest wind speed and ${H_s}$ H s , as well as trends in the ERA5 performance. A characteristic EC radii is calculated and used to normalize data coordinates in the storm centered reference frame, which highlights the organization of EC structures. Performance metrics are then compared within different EC quadrants to explore the relationship between wind forcing accuracy and underestimation of ${H_s}$ H s . [ABSTRACT FROM AUTHOR]

The applicability of high-frequency (HF) radar systems for wave measurement in an estuary was explored by extracting the significant wave height (${H_{sr}}$ H sr ) using a traditional Barrick equation from the Doppler spectra observed by three radar systems installed in Ise Bay, Japan. The minimum value of ${H_{sr}}$ H sr estimated around each grid point was relatively consistent with the wave height observed with a wave gauge/buoy, except for a deterioration of wave measurement accuracy caused by a decrease in seawater conductivity from the freshwater inflow after flooding. Furthermore, the relationship between the accuracy and the signal-to-noise ratios for the first- and second-order peaks (SNR1 and SNR2, respectively) highlighted the difficulty in determining the threshold values of SNRs in the bay. Thus, we suggest the use of ${H_{sr}}$ H sr as a criterion for quality control when applying a nonlinear inversion method for estimating ocean wave spectra based on the Bayesian possibility theorem (BIM). Our suggestion is to select the appropriate Doppler spectra and increase the acquisition rates of wave data with low relative error compared to BIMs using SNR1 and SNR2. These results can promote the applicability of the nonlinear inversion in estuary regions. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for identifying and tracking PocketQube satellites: the MRC-100 satellite is a model, and this method is based on a resonant radar reflector. The resonant reflector’s basic concept is that the resonant reflector uses a VHF/UHF communication subsystem antenna; there is no radiated RF signal, which means the power consumption is only some Milliampere (mA). The continuous wave (CW) illuminator RF source is on the ground, and the onboard antenna receives the CW RF signal from the Earth. The microcontroller (uC) periodically switches PIN diode forming BPSK modulated signal reflection so that another Earth station can receive the backscattered Binary Phase Shift Keying (BPSK) modulated signal. Also, it can detect the satellite if the ground station receiver can use a matched filter like a correlation receiver. If the ground station receiver knows the BPSK code of the satellite, it can detect it. If not, there is no way to detect the satellite. This method is similar to Radio Frequency Identification (RFID) applications, but the reader is the ground station, and the tag is the satellite. [ABSTRACT FROM AUTHOR]

LPI radar signal recognition based on convolutional neural networks usually assumes that the signal to be recognized belongs to a closed set of known signal classes. In an open electromagnetic signal environment, this type of closed‐set recognition method will experience a drastic drop in performance due to the encounter with unknown types of signals. We propose an SCNN‐SVDD model based on a combination of a lightweight convolutional neural network and a support vector data description algorithm to achieve open‐set recognition of LPI radar signals under unknown signal conditions. In this approach, Choi‐William's time‐frequency distribution is used to obtain two‐dimensional time‐frequency images of the signal to be identified, and convolutional neural networks are used to achieve high‐precision classification of known signals and extract the corresponding feature vectors. Then, the feature vectors are used as input to the SVDD algorithm and a hypersphere is constructed to detect whether the signal to be identified belongs to a known class. Experimental results show that the proposed method can detect unknown signals while maintaining high recognition accuracy for known signals. [ABSTRACT FROM AUTHOR]

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

Purpose: This study aims to learn the dynamic radar cross-section (RCS) of a deflection air brake. Design/methodology/approach: The aircraft model with delta wing, V-shaped tail and blended wing body is designed, and high-precision unstructured grid technology is used to deal with the surface of air brake and fuselage. The calculation method based on multiple tracking and dynamic scattering is presented to calculate RCS. Findings: The fuselage has a low scattering level, and the opening air brake will bring obvious dynamic RCS effects to itself and the whole machine. The average indicator of air brake RCS can be lower than –0.6 dBm2 under the tail azimuth, while that of forward and lateral direction is lower. The mean RCS of fuselage is obviously higher than that of air brake, while the deflected air brake and its cabin can still provide strong scattering sources at some azimuths. When the air brake is opening, the change amplitude of the aircraft forward RCS can exceed 19.81 dBm2. Practical implications: This research has practical significance for the dynamic electromagnetic scattering analysis and stealth design of the air brake. Originality/value: The calculation method for aircraft RCS considering air brake dynamic deflection has been established. [ABSTRACT FROM AUTHOR]

To evaluate the impact of different momentum control variable (CV) schemes (CV5, the momentum control variable option with ψχ and CV7, the momentum control variable option with UV) on radar data assimilation (DA) in weather research and forecasting model data-assimilation (WRFDA) systems, a heavy snowfall in central and eastern regions of China, which started on 6 February 2022, was taken as a case in this study. The results of the wind-field increments from the single observation tests indicated that the wind-field increments had a larger range of influence when stream function and velocity potential (ψχ) were used as momentum control variables in CV5. Some spurious increments were also generated in the wind-field analysis, since CV5 tended to maintain the integrated value of the wind field. When U-wind and V-wind were used as control variables in CV7, the wind-field increments had a smaller impact range, and there was less dependence among different locations on the wind increments. For the heavy snow case, the CV7 schemes displayed some improvements in simulating the composite reflectivity compared to the other two experiments, since the composite reflectivity in the CV5 and control experiments were overestimated to some level. It was also found that the RMSEs were lower in the CV7 compared to those in the CV5 in the short-term forecasts during the data-assimilation cycles. Results also indicated that the CV7 had a more significant effect on the 6 h accumulated precipitation forecasts. Meanwhile, the experiment Exp_CV7 achieved the best ETS and FSS scores among the three groups of experiments, while Exp_CV5 appeared to be generally superior to the CTRL. In summary, the precipitation of Exp_CV7 yielded the rainfall intensity and location most close to the observation compared to those from both the CTRL and Exp_CV5 experiments. [ABSTRACT FROM AUTHOR]

Based on ground observed data, S-band dual-polarization radar data, and ERA-5 reanalysis data, the statistical characteristics of polarimetric parameters and the application of melting layer (ML) and hydrometeor classification (HCL) products during eight snowstorm events in Jiangsu Province from 2020 to 2022 were investigated. A heavy snowstorm that went through different phases of rain, sleet, and pure snow and that occurred on 29 December 2020 was also analyzed as a typical example. The results showed the following: During the phase transition between rain and snow in the Jiangsu region, the basic reflectivity factor ZH ≥ 27 dBZ, the zero-order lag correlation coefficient CC ≤ 0.93, and the differential reflectivity ZDR ≥ 1.0 dB were important indicators for judging the melting layer while the specific differential phase KDP changed slightly. The snowstorm event was well observed and recorded by the Yancheng dual-polarimetric radar, whose low value area of CC coincided mostly with the melting layer. The ML products and HCL products based on fuzzy-logic hydrometeor classification algorithms can help identify the melting layer and the properties of precipitation particles. ML products are more reliable when the melting layer is high and can better show the trends of melting layer decline. They can certainly serve as a reference for detecting and judging precipitation phase changes in winter in Jiangsu Province. [ABSTRACT FROM AUTHOR]

Stratiform rain and the overlying ice play crucial roles in Earth's climate system. From a microphysics standpoint, water mass flux primarily depends on two variables: particles' concentration and their mass. The Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement mission core satellite is a spaceborne instrument capable of estimating these two quantities through dual-wavelength measurements. In this study, we evaluate bulk statistics on the ice particle properties derived from dual-wavelength radar data in relation to the properties of rain underneath. Specifically, we focus on DPR observations over stratiform precipitation, characterized by columns exhibiting a prominent bright band, where the melting layer can be easily detected. Our analysis reveals a large increase in the retrieved mass flux as we transition from the ice to the rain phase in the official DPR product. This observation is in disagreement with our expectation that mass flux should remain relatively stable across the bright band in cold-rain conditions. To address these discrepancies, we propose an alternative retrieval algorithm that ensures a gradual transition of Dm (mean mass-weighted particle melted-equivalent diameter) and the precipitation rate across the melting zone. This approach also helps in estimating bulk ice density above the melting level. These findings demonstrate that DPR observations can not only quantify ice particle content and their size above stratiform rain regions but also estimate bulk density, provided uniform conditions that minimize uncertainties related to partial beam filling. [ABSTRACT FROM AUTHOR]

High performance computing (HPC) electromagnetic (EM) emulators are used to simulate real-time EM wave interactions between numerous radar targets. Radar Cross Section (RCS) data stores reflection profiles of radar targets as a table; however, the needed storage size increases with frequency sampling density, aspect angle sampling density, and number of target types. The large quantity of data often exceeds storage capability and limits manipulation and representation feasibility. The spherical harmonic based monostatic anisotropic point scatterer model is proposed for HPC EM simulations where scattering responses can be computed with finite impulse response (FIR) filters. An efficient algorithm constructing this model with large scale RCS data is discussed. The scatterer position and the reflection profile of each scatterer are solved using least squares methods and particle swarm optimization (PSO). In addition, the function evaluations in PSO are accelerated by taking advantage of the matrix structure, making the algorithm 22 times faster [ABSTRACT FROM AUTHOR]

With the development of electronic warfare, anti‐jamming measure becomes more and more complex. There have been certain research results on jamming strategies, but only a few research materials on anti‐jamming strategies. It is difficult to simulate the real jamming environment, and there is no appropriate anti‐jamming decision‐making model for research. Cognitive radar can perceive the environment and receive feedback, which provides the possibility to solve the problem of anti‐jamming decision‐making. This article regards the anti‐jamming measure as a kind of interaction behaviour and establishes the cognitive radar antagonistic environment model and uses the reinforcement learning algorithm to solve the problem of anti‐jamming decision‐making. Finally, this article verifies the feasibility of applying reinforcement learning theory on making anti‐jamming decision in the radar antagonistic environment model. The performance of different reinforcement learning algorithms is compared, and their advantages and disadvantages are discussed. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles, or drones, pose a significant threat to privacy and security. To understand and assess this threat, classification between different drone models and types is required. One way in which this has been demonstrated experimentally is through this use of micro‐Doppler information from radars. Classifiers capable of exploiting differences in micro‐Doppler spectra will require large amounts of data but obtaining such data experimentally is expensive and time consuming. The authors present the methodology and results of a drone micro‐Doppler simulation framework which uses accurate 3D models of drone components to yield detailed and realistic synthetic micro‐Doppler signatures. This is followed by the description of a purpose‐built validation radar that has been developed specifically to gather high‐fidelity experimental drone micro‐Doppler data with which is used to validate the simulation. Detailed comparisons between the experimental and simulated micro‐Doppler spectra from three models of drones with differently shaped propellers are given, showing very good agreement. The aim is to introduce the simulation methodology. Validation using single propeller micro‐Doppler is provided, although the simulation can be extended to multiple propellers. The simulation framework offers the potential to generate large quantities of realistic drone micro‐Doppler signatures for training classification algorithms. [ABSTRACT FROM AUTHOR]

The Integrated Side Lobe Ratio (ISLR) problem the authors consider here consists in finding optimal sequences of phase shifts in order to minimise the mean squared cross‐correlation side lobes of a transmitted radar signal and a mismatched replica. Currently, ISLR does not seem to be easier than the general polynomial unconstrained binary problem, which is NP‐hard. In their work, the authors aim to take advantage of the scalability of quantum computing to find new optima, by solving the ISLR problem on a quantum annealer. This quantum device is designed to solve quadratic optimisation problems with binary variables (QUBO). After proposing suitable formulation for different instances of the ISLR, the authors discuss the performances and the scalability of their approach on the D‐Wave quantum computer. More broadly, their work enlightens the limits and potential of the adiabatic quantum computation for the solving of large instances of combinatorial optimisation problems. [ABSTRACT FROM AUTHOR]

For the integration detection of near space hypersonic weak targets by high pulse repetition frequency (PRF) radar, a novel method named Multi‐Hypothesis Fuzzy‐Matching Radon transform (MHFM‐RT) is proposed for the near space hypersonic target detection and tracking. For remote hypersonic target detection, to avoid range ambiguity, current radars always use a low PRF mode, which limit the number of pulse accumulations. Using the high PRF mode, the fuzzy folding will appear in the target range measurements when target trajectory crosses range fuzzy intervals. Therefore, there is a contradiction between range ambiguity and energy accumulation. The proposed method is used to match the fuzzy measurements, so as to realise the correct integration in the condition of range ambiguity. Firstly, considering the need of range ambiguity resolution, the mode of staggered PRF is used. Secondly, the first frame measurements are periodically extended for multiple‐fuzzy hypothesis. Finally, the weak target track is accumulated in MHFM‐RT domain, and the signal integration and ambiguity resolution can be realised simultaneously. The proposed method expands the Variable‐Diameter‐Arc‐Helix Radon transform (VDAH‐RT) method to fuzzy folding conditions. Compared with the existing methods, for 7‐scan measurements non‐coherent integration, the detection sensitivity of the proposed method is about 0.5–1 dB higher than that of the IMM hybrid filter algorithm, and about 1 dB higher than that of the RHT‐TBD approach, and it needs less storage space and has higher detection probability. [ABSTRACT FROM AUTHOR]

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

This article describes a concept whereby future operational polarimetric phased array radars (PPAR) routinely monitor ice crystal alignment regions caused by thundercloud electric fields with volume scan updates (∼12 min−1) sufficient to resolve the temporal variation due to lightning and subsequent rapid electric field regeneration in nonsevere thunderstorms. Routine observations of crystal alignment regions may enhance thunderstorm nowcasting through comparison of their temporal and spatial structure with other polarimetric signatures, integration with lightning detection data, and assimilation into convection resolving numerical weather prediction models. If crystal alignment observations indicate strong electrification well in advance of the first lightning strike and likewise reliably indicate the decay of strong electric fields at the end of a storm, this capability may improve warning for lightning-sensitive activities such as airport ramp operations and space launch. Experimental observations of crystal alignment volumes in central Oklahoma severe storms and their relation to those storms' structures are presented and used to motivate discussion of possible PPAR architectures. In one case—a tornadic supercell—these observations illustrate an important limitation. Even the hypothesized 12 min−1 volume scan update rate would not resolve the temporal variation of the crystal alignment regions in such storms, suggesting that special, adaptive scanning methods may be appropriate for such storms. We describe how future operational phased array radars could support a crystal alignment measurement mode via parallel, time-multiplexed processing and discuss potential impacts on the radar's primary weather observation mission. We conclude by discussing research needed to better understand technical challenges and operational benefits. [ABSTRACT FROM AUTHOR]

Metasurface radome is crucial to protect the radiating antenna and control electromagnetic waves flexibly. However, current design methods for metasurface radome merely focus on the single propagation direction, with limited degrees of freedom. Here, a super radome is proposed with a directional scattering response based on Janus metasurface that can realize the phase modulation of y‐polarized waves and the amplitude modulation of x‐polarized waves with a judiciously designed structure. Both numerical and experimental results illustrate two cross‐polarized transmission windows with intensity exceeding 0.9 centered at 10 and 12.2 GHz and two out‐of‐band invisibility with considerable backward radar cross‐section reduction over −10 dB within 9.3–15.4 and 7.5–23.8 GHz when the radome is illuminated by y‐ and x‐polarized waves along negative and positive z directions, respectively. Moreover, the radar cross‐section reduction is maintained for off‐normal incidence even up to 45°. This method sets up a directional degree of freedom for advanced radome design and demonstrates its enormous potential in developing next‐generation stealth antennas. [ABSTRACT FROM AUTHOR]

Some studies have shown that any part machined by an outsourcer usually has several basic quality characteristics. When the outsourcer's process capabilities are insufficient, the defective rate of various quality characteristics of the product will increase, thereby raising the rework rate and scrap rate. As a result, maintenance costs will go up, economic value will decrease, and even carbon emissions can increase during the production process. In addition, the process capability index and the radar chart are widely used in engineering management and other fields. Since process indicators often contain unknown parameters, sample data are needed for evaluation. With the rapid development of the Internet of Things and big data analysis, many companies regard rapid response as a basic requirement for timeliness and cost consideration. Therefore, companies often have to evaluate the process quality of ten small samples and decide whether to make some improvements. In order to solve the above problems, this study proposed a fuzzy radar chart evaluation model for the process quality of multi-quality characteristic parts based on the process capability index. Using this model can help all parts manufacturers continue to improve the quality of their machined parts as well as reduce their rework and scrap rates. Meanwhile, carbon emissions can be lessened during the production process, and companies can fulfill their social responsibilities. This fuzzy radar chart evaluation model is based on confidence intervals. As the company's past experience is incorporated, the evaluation accuracy can be maintained even with a smaller sample size. Furthermore, the fuzzy radar evaluation chart can simultaneously evaluate the process capabilities of all quality characteristics of the part. In addition to making it easier for manufacturers to master all quality characteristics, quality process capability can also help them seize improvement opportunities. [ABSTRACT FROM AUTHOR]

A simple microwave photonic, reconfigurable, instantaneous frequency measurement system based on low-voltage thin-film lithium niobate on an insulator phase modulator is put forward and experimentally demonstrated. Changing the wavelength of the optical carrier can realize the flexibility of the frequency measurement range and accuracy, showing that during the ranges of 0–10 GHz, 3–15 GHz, and 12–18 GHz, the average measurement errors are 26.9 MHz, 44.57 MHz, and 13.6 MHz, respectively, thanks to the stacked integrated learning models. Moreover, this system is still able to respond to microwave signals of as low as −30 dBm with the frequency measurement error of 62.06 MHz, as that low half-wave voltage for the phase modulator effectively improves the sensitivity of the system. The general-purpose, miniaturized, reconfigurable, instantaneous frequency measurement modules have unlimited potential in areas such as radar detection and early warning reception. [ABSTRACT FROM AUTHOR]

A fully integrated 24-GHz radar transceiver with one transmitter (TX) and two receivers (RXs) for compact frequency modulated continuous wave (FMCW) radar applications is here presented. The FMCW synthesizer was realized using a fractional-N phase-locked loop (PLL) and programmable chirp generator, which are completely integrated in the proposed transceiver. The measured output phase noise of the synthesizer is −80 dBc/Hz at 100 kHz offset. The TX consists of a three-bit bridged t-type attenuator for gain control, a two-stage drive amplifier (DA) and a one-stage power amplifier (PA). The TX chain provides an output power of 13 dBm while achieving <0.5 dB output power variation within the range of 24 to 24.25 GHz. The RX with a direct conversion I-Q structure is composed of a two-stage low noise amplifier (LNA), I-Q generator, mixer, transimpedance amplifier (TIA), a two-stage biquad band pass filter (BPF), and a differential-to-single (DTS) amplifier. The TIA and the BPF employ a DC offset cancellation (DCOC) circuit to suppress the strong reflection signal and TX-RX leakage. The RX chain exhibits an overall gain of 100 dB. The proposed radar transceiver is fabricated using a 65 nm CMOS technology. The transceiver consumes 220 mW from a 1 V supply voltage and has 4.84 mm2 die size including all pads. The prototype FMCW radar is realized with the proposed transceiver and Yagi antenna to verify the radar functionality, such as the distance and angle of targets. [ABSTRACT FROM AUTHOR]

Numerous analytical radar-scattering laws have been published through the past decades to interpret planetary radar observations, such as Hagfors' law, which has been commonly used for the Moon, and the cosine law, which is commonly used in the shape modeling of asteroids. Many of the laws have not been numerically validated in terms of their interpretation and limitations. This paper evaluates radar-scattering laws for self-affine fractal surfaces using a numerical approach. Traditionally, the autocorrelation function and, more recently, the Hurst exponent, which describes the self-affinity, have been used to quantify the height correlation. Here, hundreds of three-dimensional synthetic surfaces parameterized using a root-mean-square (rms) height and a Hurst exponent were generated, and their backscattering coefficient functions were computed to evaluate their consistency with selected analytical models. The numerical results were also compared to empirical models for roughness and radar-scattering measurements of Hawaii lava flows and found consistent. The Gaussian law performed best at predicting the rms slope regardless of the Hurst exponent. Consistent with the literature, it was found to be the most reliable radar-scattering law for the inverse modeling of the rms slopes and the Fresnel reflection coefficient from the quasi-specular backscattering peak, when homogeneous statistical properties and a ray-optics approach can be assumed. The contribution of multiple scattering in the backscattered power increases as a function of rms slope up to about 20% of the backscattered power at normal incidence when the rms slope angle is 46°. [ABSTRACT FROM AUTHOR]

Detecting multiple human targets in indoor scenarios using ultra-wideband (UWB) radar usually involves false detection results caused by the secondary reflections, which might reduce the target detection accuracy and cause a more severe deterioration when the number of targets increases. This article proposed a two-step accuracy improvement method for multitarget detection in environments with multiple human targets of more than three and strong secondary reflections by the surroundings, especially the walls. Based on the rough detection results acquired by the modified CA-CFAR (MCA-CFAR) processing, the first step achieves the primary false alarm suppression using a short-window accumulation in the time domain. Then, the second step applies the decision confidence on the detection results from the first step to assess the reliability of results for improved accuracy. The two-step accuracy improvement could thus have a higher accuracy through cascading false alarm suppression. The effectiveness and accuracy of the proposed algorithm are verified based on the experimental results. [ABSTRACT FROM AUTHOR]

The Biomass Proxy is a new cloud-free vegetation monitoring product that offers timely and analysis-ready data indicative of above-ground crop biomass dynamics at 10m spatial resolution. The Biomass Proxy links the consistent and continuous temporal signal of the Sentinel-1 Cross Ratio (CR), a vegetation index derived from Synthetic Aperture Radar backscatter, with the spatial information of the Sentinel-2 Normalized Difference Vegetation Index (NDVI), a vegetation index derived from optical observations. A global scaling relationship between CR and NDVI forms the basis of a novel fusion methodology based on static and dynamic combinations of temporal and spatial responses of CR and NDVI at field level. The fusion process is used to mitigate the impact on product quality of low satellite revisit periods due to acquisition design or persistent cloud coverage, and to respond to rapid changes in a timely manner to detect environmental and management events. The resulting Biomass Proxy provides time series that are continuous, unhindered by clouds, and produced uniformly across all geographical regions and crops. The Biomass Proxy offers opportunities including improved crop growth monitoring, event detection, and phenology stage detection. [ABSTRACT FROM AUTHOR]

The planet Mars is the most probable among the terrestrial planets in our solar system to support human settlement or colonization in the future. The detection of water ice or liquid water on the shallow subsurface of Mars is a crucial scientific objective for both the Chinese Tianwen-1 and United States Mars 2020 missions, which were launched in 2020. Both missions were equipped with Rover-mounted ground-penetrating radar (GPR) instruments, specifically the RoPeR on the Zhurong rover and the RIMFAX radar on the Perseverance rover. The in situ radar provides unprecedented opportunities to study the distribution of shallow subsurface water ice on Mars with its unique penetrating capability. The presence of water ice on the shallow surface layers of Mars is one of the most significant indicators of habitability on the extraterrestrial planet. A considerable amount of evidence pointing to the existence of water ice on Mars has been gathered by previous researchers through remote sensing photography, radar, measurements by gamma ray spectroscopy and neutron spectrometers, soil analysis, etc. This paper aims to review the various approaches utilized in detecting shallow subsurface water ice on Mars to date and to sort out the past and current evidence for its presence. This paper also provides a comprehensive overview of the possible clues of shallow subsurface water ice in the landing area of the Perseverance rover, serving as a reference for the RIMFAX radar to detect water ice on Mars in the future. Finally, this paper proposes the future emphasis and direction of rover-mounted radar for water ice exploration on the Martian shallow subsurface. [ABSTRACT FROM AUTHOR]

Metamaterial-based coded aperture imaging (MCAI) is a forward-looking radar imaging technique based on wavefront modulation. The scattering coefficients of the target can resolve as an ill-posed inverse problem. Data-based deep-learning methods provide an efficient, but expensive, way for target reconstruction. To address the difficulty in collecting paired training data, an untrained deep radar-echo-prior-based MCAI (DMCAI) optimization model is proposed. DMCAI combines the MCAI model with a modified U-Net for predicting radar echo. A joint loss function based on deep-radar echo prior and total variation is utilized to optimize network weights through back-propagation. A target reconstruction strategy by alternatively using the imaginary and real part of the radar echo signal (STAIR) is proposed to solve the DMCAI. It makes the target reconstruction task turn into an estimation from an input image by the U-Net. Then, the optimized weights serve as a parametrization that bridges the input image and the target. The simulation and experimental results demonstrate the effectiveness of the proposed approach under different SNRs or compression measurements. [ABSTRACT FROM AUTHOR]

Employing different bands of radar to detect precipitation information in identical regions enables the acquisition of a more comprehensive precipitation cloud structure, thereby refining the continuity and completeness of precipitation measurements. This study first compared the coincident data from CloudSat W-band cloud profiling radar (CPR) and Global Precipitation Measurement Mission (GPM) Ka-band precipitation radar (KaPR) from 2014 to 2017, and then a synthesis of the radar reflectivity from CPR and KaPR was attempted to obtain a complete cloud and precipitation structure. The findings of the reflectivity comparisons indicated that the echo-top height identified by CPR is on average 3.6 to 4.2 km higher than that from KaPR, due to the higher sensitivity. Because of strong attenuation of CPR by liquid-phase particles, the reflectivity below the height of the melting layer usually shows an opposite gradient to KaPR with decreasing altitude. The difference in the near-surface rain rates of CPR and KaPR was found to be related to reflectivity gradients in the vertical direction, and the larger the reflectivity gradients, the greater the differences in near-surface rain rates. For better representing the complete vertical structure of precipitation clouds and improving the consistency of the reflectivity and precipitation rate, the radar reflectivity was weighted, synthesized from CPR and KaPR based on the gradient difference of the reflectivity from the two radars. We presented the synthesis results for a stratiform cloud and a deep convective case, and Spearman's rank correlation coefficient (rs) between the GPM combined radiometer precipitation rate and the radar reflectivity was utilized to analyze the performance of the synthesis. The consistency between synthesized reflectivity and precipitation rate in the non-liquid phase was improved compared with KaPR, and the rs of the ice and mixed phases was increased by about 12% and 10%, respectively. [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]

For clinically node positive breast cancer patients treated with neoadjuvant chemotherapy (NAC), targeted axillary dissection (TAD) can be used to stage the axilla. TAD removes the sentinel lymph node (SLN) and tagged positive nodes, which can be identified via radar reflector localization (RRL). As it can be challenging to localize a previously positive node after NAC, we evaluated RRL prior to NAC. We performed a retrospective chart review of breast cancer patients with node positive disease treated with NAC who underwent TAD with RRL. We compared retrieval of radar reflector and clip, timing of localization, and, if a node was positive, whether the radar reflector node or SLN was positive. Seventy-nine patients fulfilled inclusion criteria; 32 were placed pre-NAC (mean 187 d before surgery) and 47 were placed post-NAC (mean 7 d before surgery). For pre-NAC placement, 31 of 32 radar reflectors and 31 of 32 clips were retrieved. For post-NAC placement, 47 of 47 radar reflectors and 46 of 47 clips were retrieved. There was no significant difference in radar reflector or clip retrieval rates between pre-NAC and post-NAC groups (P = 0.41, P = 1, respectively). Thirty of 32 patients with pathologic complete response avoided an axillary lymph node dissection. Of 47 patients with a positive lymph node, 32 were both the SLN and radar reflector node, 11 were radar reflector alone, and four were the SLN. RRL systems are an effective way to guide TAD, and RRL makers can be safely placed prior to NAC. • Ninety seven percent of radar reflectors placed before neoadjuvant chemotherapy (NAC) identified intraoperatively. • In pre-NAC placement, radar reflectors placed 187 d preoperatively on average. • No difference in retrieval of radar reflector if placed before or after NAC. • Radar reflector localization can be effectively performed prior to NAC. [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]

To investigate a thin layer, we conducted borehole radar measurements at the GPR facility of Tohoku University, Japan. Both zero-offset profiling and tomography measurements were used to obtain water content distributions. Analyzing first arrival times with amplitudes, we found that a borehole radar can detect velocity changes caused by a high-water content, which acts as an electromagnetic waveguide. We conducted a 3D FDTD simulation and confirmed that the EM wave propagates as a guided wave in a thin layer related to antenna frequency. We determined a thin geological layer compared to an antenna frequency that cannot be detected by reflection measurement due to the poor radar resolution. By comparing the simulated data with measurement data, we could determine the geological boundary between host layers and a thin layer with high water content. In contrast, both the amplitude and the travel time of direct waves are affected by antenna positions and electrical parameters, including permittivity and conductivity. A vertical velocity profile was obtained by combining complementary zero-offset profiling and tomography data, which can be used to investigate water content distribution. The low-velocity thin layer was characterised by amplitude attenuation and late arrival time. [ABSTRACT FROM AUTHOR]

Metamaterial technology indeed holds great promise for enhancing wireless communication, especially in the development of advanced filters and devices. The use of metamaterials for filter design is an exciting and promising area of research, driven by their exceptional properties and potential for revolutionizing various technological applications. In fact, Metamaterials can be engineered to exhibit unique electromagnetic properties, allowing for precise control of the filtering characteristics. This enables designers to create filters with sharp passbands and stopbands, as well as customizable cutoff frequencies. Besides, Metamaterials can be used to miniaturize filters, making them suitable for compact and portable devices. This is especially valuable for applications with size constraints, such as mobile phones and wearable technology. Moreover, Metamaterials provide the flexibility to design filters with adjustable bandwidth. In this paper, a triple band pass printed filter using split-ring resonators is designed and simulated. The proposed filter is composed of two split-ring arrays loaded on the transmission line. It is printed on a Rogers RT/duroid 6010/6010LM (tm) substrate. The proposed BPF has a small size (14 × 16 mm²) and high selectivity. The simulation result exhibit three passbands centered at 3.15 GHz, 6.27 GHz and 9.31 GHz, respectively with required return loss and insertion loss characteristics. The simulation studies are carried out with HFSS software and its electrical equivalent circuit model (ECM) is designed using ADS tool. The results obtained using HFSS is in well agreement with ECM results. [ABSTRACT FROM AUTHOR]

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Low lAtitude long Range Ionospheric raDar (LARID), which consists of two high frequency (HF) radars looking toward the east and west of Hainan Island, respectively, has been developed and installed at Dongfang (19.2°N, 108.8°E, dip lat. 13.8°N), China. This paper describes the system design of LARID and its first observational results of equatorial plasma bubble (EPB) irregularities. The antenna array of LARID is composed of a west‐looking array and an east‐looking array. Each array consists of 20 log‐periodic antennas for transmission and reception and 4 log‐periodic antennas for interferometry, and has a beam steering capability in the azimuth angles of ±24° due the boresight pointing east (or west). Observational results show that the LARID is capable of detecting backscatter echoes from EPB irregularities and the ground, with a distance of 4,000 km or more away from Hainan Island. Multiple EPB structures were continuously observed on 17 April 2023, with eastward drifts ranging between 70 and 130 m/s. Based on ray tracing simulations, the backscatter echoes of EPB irregularities were due to the 0.5‐hop and 1.5‐hop propagation modes. The distances between the successive EPB structures were estimated ranging between 500 and 900 km in longitude. The LARID observations, together with other instruments in the East and Southeast Asian sector, provided a clear picture of longitudinal variation of EPBs in 90–125°E. It is expected that the LARID will provide an important tool to study the generation and evolution of EPBs and their short‐term prediction in East and Southeast Asia. Key Points: A Low lAtitude long Range Ionospheric raDar (LARID) operating at 8–22 MHz has been developed and installed at Hainan Island, ChinaFirst long‐range observations of equatorial plasma bubble (EPB) irregularities and their zonal drifts by the LARID were presentedThe LARID is expected to play an important role in the study of EPB and its short‐term forecasting in East and Southeast Asia [ABSTRACT FROM AUTHOR]

The safety enhancement of small fixed-wing UAVs regarding obstacle detection is addressed using optimization techniques to find the best sensor orientations of different multi-sensor configurations. Four types of sensors for obstacle detection are modeled, namely an ultrasonic sensor, laser rangefinder, LIDAR, and RADAR, using specifications from commercially available models. The simulation environment developed includes collision avoidance with the Potential Fields method. An optimization study is conducted using a genetic algorithm that identifies the best sensor sets and respective orientations relative to the UAV longitudinal axis for the highest obstacle avoidance success rate. The UAV performance is found to be critical for the solutions found, and its speed is considered in the range of 5–15 m/s with a turning rate limited to 45°/s. Forty collision scenarios with both stationary and moving obstacles are randomly generated. Among the combinations of the sensors studied, 12 sensor sets are presented. The ultrasonic sensors prove to be inadequate due to their very limited range, while the laser rangefinders benefit from extended range but have a narrow field of view. In contrast, LIDAR and RADAR emerge as promising options with significant ranges and wide field of views. The best configurations involve a front-facing LIDAR complemented with two laser rangefinders oriented at ±10° or two RADARs oriented at ±28°. [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]

Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This article presents mmHawkeye, a passive approach for non-cooperative UAV detection and identification with a commercial off-the-shelf millimeter wave (mmWave) radar. mmHawkeye does not require prior knowledge of the type, motions, and flight trajectory of the UAV, while exploiting the signal feature induced by the UAV's periodic micro-motion (PMM) for long-range accurate detection. The design is therefore effective in dealing with low signal-to-noise ratio and uncertain reflected signals from the UAV. After analyzing the theoretical model of the PMM feature, mmHawkeye can further track the UAV's position containing range, azimuth and altitude angle with dynamic programming and particle filtering and then identify it with a Long Short-Term Memory–based detector. We implement mmHawkeye on a commercial mmWave radar and evaluate its performance under varied settings. The experimental results show that mmHawkeye has a detection accuracy of 95.8% and can realize detection at a range up to 80 m. [ABSTRACT FROM AUTHOR]

This paper introduces a novel nothing-on-road (NOR) bridge weigh-in-motion (BWIM) approach with deep learning (DL) and non-invasive ground-based radar (GBR) time-series data. BWIMs allow site-specific structural health monitoring (SHM) but are usually difficult to attach and maintain. GBR measures the bridge deflection contactless. In this study, GBR and an unmanned aerial vehicle (UAV) monitor a two-span bridge in Germany to gather ground-truth data. Based on the UAV data, we determine vehicle type, lane, locus, speed, axle count, and axle spacing for single-presence vehicle crossings. Since displacement is a global response, using peak detection like conventional strain-based BWIMs is challenging. Therefore, we investigate data-driven machine learning approaches to extract the vehicle configurations directly from the displacement data. Despite a small and imbalanced real-world dataset, the proposed approaches classify, e.g., the axle count for trucks with a balanced accuracy of 76.7% satisfyingly. Additionally, we demonstrate that, for the selected bridge, high-frequency vibrations can coincide with axles crossing the junction between the street and the bridge. We evaluate whether filtering approaches via bandpass filtering or wavelet transform can be exploited for axle count and axle spacing identification. Overall, we can show that GBR is a serious contender for BWIM systems. [ABSTRACT FROM AUTHOR]

Pre-trained code generation models (PCGMs) have been widely applied in neural code generation, which can generate executable code from functional descriptions in natural languages, possibly together with signatures. Despite substantial performance improvement of PCGMs, the role of method names in neural code generation has not been thoroughly investigated. In this article, we study and demonstrate the potential of benefiting from method names to enhance the performance of PCGMs from a model robustness perspective. Specifically, we propose a novel approach, named neuRAl coDe generAtor Robustifier (RADAR). RADAR consists of two components: RADAR -Attack and RADAR -Defense. The former attacks a PCGM by generating adversarial method names as part of the input, which are semantic and visual similar to the original input but may trick the PCGM to generate completely unrelated code snippets. As a countermeasure to such attacks, RADAR -Defense synthesizes a new method name from the functional description and supplies it to the PCGM. Evaluation results show that RADAR -Attack can reduce the CodeBLEU of generated code by 19.72% to 38.74% in three state-of-the-art PCGMs (i.e., CodeGPT, PLBART, and CodeT5) in the fine-tuning code generation task and reduce the Pass@1 of generated code by 32.28% to 44.42% in three state-of-the-art PCGMs (i.e., Replit, CodeGen, and CodeT5+) in the zero-shot code generation task. Moreover, RADAR -Defense is able to reinstate the performance of PCGMs with synthesized method names. These results highlight the importance of good method names in neural code generation and implicate the benefits of studying model robustness in software engineering. [ABSTRACT FROM AUTHOR]

Terahertz radars based on leaky-wave antennas are promising for the realization of radar systems with high resolution over short ranges. This type of radar relies on spatial frequency mapping to realize a wide field of view without mechanical actuation or electrical beam steering. Previously, integrated leaky-wave antennas based on metallic wave confinement have been implemented, but the high ohmic losses limit the realized antenna gain, which is essential for extending the range of such a system when limited power is available. Here, we demonstrate an all-dielectric leaky-wave antenna fabricated monolithically from silicon and then apply leaky-wave radar techniques to realize a terahertz radar system capable of real-time data acquisition. Through this all-dielectric approach, we can avoid metallic losses, achieving an experimentally measured maximum realized gain of 25 dBi and a 34.3° 3-dB field-of-view while utilizing established and scalable fabrication techniques essential for the wide-spread adoption of terahertz technologies. We foresee this technique being applied to a variety of real-time radar applications, and here we demonstrate two potential use-cases: multi-object tracking and differentiation of liquids. [ABSTRACT FROM AUTHOR]