In this paper, we introduce a non-invasive approach for monitoring bridge infrastructure with ground-based interferometric radar. This approach is called the mirror mode, since it utilises the flat surface of the bridge underside as a mirror to reflect the signal to a corner reflector on the ground placed opposite of the radar sensor. For proving the feasibility of this approach, a measurement campaign has been carried out at an exemplary bridge in Karlsruhe (Germany) including a radar sensor in mirror mode, a second radar sensor in the default mode and a laser profile scanner. We investigate the potential of this approach to monitor the bridge displacement in vertical direction and compare the results with the two other sensors. The derived results reveal the potential for monitoring bridge infrastructure. Finally, we propose further research aspects of this approach to analyse its capabilities and limitation in the context of non-invasive infrastructure monitoring.

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

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

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

In this work, we present preliminary results of radar observations of near-Earth Objects (NEOs) carried out by European radio telescopes in the framework of the European Space Agency (ESA) project “NEO observation concepts for radar systems”, which was aimed to derive the functional requirements of a radar system, evaluate the available European assets to perform NEO radar observations and carry out test radar campaigns. In the first part of the project, we carried out the performance analysis of a possible European planetary radar system. Instrumental features, as much as issues like the impact of weather conditions on signal propagation at different radio frequencies, were considered. This paper is focused on the test campaigns, performed in the years 2021-22 in collaboration with the Jet Propulsion Laboratory (JPL), which led to the observation of several asteroids including 2021 AF8, (4660) Nereus and 2005 LW3, which allowed us to derive astrometric measurements, as well as to measure physical properties, such as rotation periods, and detect an asteroid’s satellite. The obtained results demonstrate that European radio astronomical dishes, although employed only as receivers (in bistatic or multistatic configurations) and for a limited amount of time, are able to provide a significant contribution to the constitution of a European network to increase the opportunities for NEO monitoring and studies, if a transmitting antenna - equipped with a suitable high-power transmitter - were made available.

Global navigation satellite system (GNSS) array antenna receivers are widely used to suppress wideband interference in navigation countermeasures. However, existing array antenna receivers all adopt digital array structure and digital beamforming technique, and it has limited analog-front-end (AFE) dynamic range. In strong interference scenarios, AFE saturation will occur, which limits the maximum interference suppression ability of the array receiver. Aiming at this issue, this paper proposes a robust wideband interference suppression method for GNSS array antenna receivers based on hybrid beamforming technique. Firstly, a novel fully connected hybrid array receiver structure is proposed; Secondly, the corresponding hybrid beamforming method is proposed at the same time, and it realizes the complete elimination of strong interference by jointly suppression in analog domain and digital domain. After mathematical simulation experiments, it is verified that compared to the digital beamforming-based anti-jamming technique, the proposed method can effectively suppress strong wideband interference, and the maximum interference suppression ability is improved by 36 dB.

This study presents the synthesis and electrochemical evaluation of nitrogen-doped vanadium oxide (N-V2O3/C) as a cathode material for aqueous zinc-ion batteries (AZIBs), using a hydrothermal method. Compared to undoped V2O3/C, N-V2O3/C exhibits enhanced electrical conductivity, capacity, and electrochemical kinetics, attributed to the incorporation of pyridinic and pyrrolic nitrogen. Initial charge-discharge cycles indicate phase transitions to amorphous vanadium oxides, enhancing conductivity. N-V2O3/C shows a high specific capacity of 168.4 mAh g−1 at 10 A g−1 and remarkable reversibility, highlighted by the transient existence of intermediate species during cycling. Optimal electrochemical performance is achieved with a vanadium to nitrogen molar ratio of 2:3, indicating the significant impact of nitrogen doping concentration on the material’s efficiency. This work underscores the potential of N-V2O3/C as a superior cathode material for AZIBs.

Using density functional theory (DFT), the density of states of NH4V3O8 (NVO) was analyzed pre and post-oxygen defects (Od) formation. The findings revealed a reduced bandgap in NVO after Od introduction, emphasizing the role of Od in enhancing conductivity of the material, thus improving its electrochemical attributes. Through the water bath method, both NVO and its oxygen-deficient counterpart, (NH4)2V10O25⸱8H2O (NVOd), were synthesized as potential cathode materials for aqueous zinc-ion batteries (AZIBs). Experimental outcomes resonated with DFT predictions, highlighting the beneficial role of oxygen defects in boosting electrical conductivity. Notably, the refined material displayed a remarkable capacity of 479.3 mAh g-1 at 0.1 A g-1, underscoring its promise for advanced energy storage solutions.

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

The main goal of the research is to assess the effectiveness of aerial ground-penetrating radar (GPR) in delineating boundaries between disturbed clayey ground and rock formations along the old Wadi. The study also tackles technical challenges associated with drone-based GPR applications in urban open areas, considering geological heterogeneity, topographic variations, and environmental conditions. The potential of using drone-based GPR equipped with an unshielded dipole antenna as a transformative tool in geospatial analysis was demonstrated. The research encompasses academic insights and field borehole drilling experiments to validate the accuracy and reliability of drone-based GPR data in real-world scenarios. Anticipated contributions include advancements in understanding drone-based GPR technology for mapping disturbed soil boundaries in foundation engineering applications and other relevant areas and recommendations for optimizing its performance in challenging terrains.

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

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

Ecosystem physical structure, defined by the quantity and spatial distribution of biomass, influences a range of ecosystem functions. Remote sensing tools permit the non-destructive characterization of canopy and root features, potentially providing opportunities to link above- and belowground structure at fine spatial resolution in functionally meaningful ways. To test this possibility, we employed ground-based portable canopy lidar (PCL) and ground penetrating radar (GPR) along co-located transects in forested sites spanning multiple stages of ecosystem development and, consequently, of structural complexity. We examined canopy and root structural data for coherence at multiple spatial scales ≤ 10 m within each site using wavelet analysis. Forest sites varied substantially in vertical canopy and root structure, with leaf area index and root mass more evenly distributed by height and depth, respectively, as forests aged. In all sites, above- and belowground structure, characterized as mean maximum canopy height and root mass, exhibited significant coherence at a scale of 3.5-4 meters, and results suggest that the scale of coherence may increase with stand age. Our findings demonstrate that canopy and root structure are linked at characteristic spatial scales, which provides the basis to optimize scales of observation. Our study highlights the potential, and limitations, for fusing lidar and radar technologies to quantitatively couple above- and belowground ecosystem structure.

This paper deals with bistatic subsurface probing of a horizontally layered dielectric half-space by means of ultra-wideband electromagnetic pulses. A receiver collects reflections from the air-ground interface and from the gradients of dielectric permittivity in the half-space. This scenario is of interest for ground penetrating radar (GPR) applications. For the analytical description of the received signal, we developed and implemented a novel time-domain version of the coupled-wave Wentzel–Kramers–Brillouin approximation. Our solution is in very good agreement with finite-difference time-domain results, radically accelerates calculations, and effectively accounts for the protracted return signals observed in the lower part of the GPR spectrum. The paper includes results showing the application of the proposed technique to two case studies: in particular, the method was employed for the post-processing of experimental radargrams collected on Lake Chebarkul, in Russia, and to simulate GPR probing of the Moon surface, to detect smooth gradients of the dielectric permittivity in lunar regolith.

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

This study reports on the development of bio-based hydrophobic coatings for packaging papers through deposition of polyhydroxybutyrate (PHB) particles in combination with nanofibrillated cellulose (NFC) and plant wax. In a first approach, PHB particles in micrometer range (PHB-MP) were prepared through a phase-separation technique providing internally nanosized structures. The particles were transferred as a coating by dip-coating filter papers in the particle suspension, followed by sizing with a carnauba wax solution. This approach allowed partial to almost full surface coverage of PHB-MP over the paper surface resulting in static water contact angles of 105° to 122° and 129° to 144° after additional wax coating. In a second approach, PHB particles with submicron sizes (PHB-SP) were synthesized by an oil-in-emulsion (o/w) solvent evaporation method, and mixed in aqueous suspensions with 0 to 7 wt% NFC. After dip-coating filter papers in PHB-SP/NFC suspensions and sizing with a carnauba wax solution, static water contact angles of 112° to 152° were obtained. The intrinsic properties of the particles were analyzed by scanning electron microscopy, thermal analysis and infrared spectroscopy, indicating higher crystallinity for PHB-SP than PHB-MP. The chemical interactions between the more amorphous PHB-MP particles and paper fibers were identified as an esterification reaction, while the morphology of the NFC fibrillar network was playing a key role as binding agent in the retention of more crystalline PHB-SP at the paper surface, hence contributing to higher hydrophobicity.

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

Bridge load testing is crucial in evaluating bridge load-carrying capacity and construction quality. However, conventional techniques have many limitations such as the need for direct contact and low acquisition frequency. This study provides a comprehensive examination of the static and dynamic load tests of a multi-span cable-stayed bridge utilizing a ground-based radar inter-ferometer (GB-radar). The case study was conducted at the Fifth Nanjing Yangtze River Bridge (FNYRB), which is recognized as the world's first lightweight steel-concrete composite cable-stayed bridge, with two spans measuring 600 m. To enhance measurement accuracy, a method was proposed to detect and recover radar phase jumps caused by vehicle motion. Furthermore, precise geometry projection was employed to acquire vertical displacements from GB-radar which were taken along the line-of-sight (LOS) direction. Moreover, during the static load test, a continuous deformation was observed with a maximum pace of 0.31 mm/min, indicating a post-construction settlement caused by soft soil consolidation. This case study highlights the high sampling frequency, high measurement accuracy, and exceptional weatherproof ability of GB-radar, thereby demonstrating its potential to be an alternative to the structural health monitoring (SHM) system.

In this study, the shape and size of a combustion cavity with a fracture zone in the gasified coal seam was determined with use control boreholes and a ground-penetrating radar (BGPR) test. The underground coal gasification (UCG) field-scale experiment was performed in Carboniferous strata in coal seam 501 at a depth of approx. 460 m in the Wieczorek hard-coal mine in the Upper Silesian Coal Basin, Poland. After the termination of the UCG reactor, five coring boreholes were drilled to identify the geometry of the resulting combustion cavity and the impact of the UCG process on the surrounding rock mass. Borehole ground-penetrating radar measurements were performed using a 100 MHz antenna in three boreholes with a length of about 40-50 m. This enabled the identification of the boundaries of the combustion cavity and the fracture zone in the coal seam. The fracture zones of rock layers and lithological borders near the control borehole were also depicted. As a result, the cavity was estimated to have a length of around 32 m, a width of around 7 m and a height of around 5 m. The analyses performed with the control boreholes and the BGPR provided sufficient information to determine the geometry of the combustion cavity and the fracture zone.

This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset.

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

The active layer thickness (ALT) is affected by local soil material and surface vegetation coverage in Tibetan plateau permafrost region. The human activities and engineering construction along the Qinghai-Tibet Highway (QTH) destroyed the surface vegetation coverage, which have seriously influenced the ALT and heat exchange balance. Meanwhile, the changes of the detailed internal structure in the active layer will affect the stability of engineering infrastructure. Ground-penetrating radar (GPR) was used to detect the ALT in the permafrost region. However, researches on the detailed structure of the active layer and spatial variation information of ALT are rare, the main reason given for this is that the lack of advanced imaging techniques to enhance the GPR images. In this paper, the researchers focus on solving this problem by applying the reverse time migration (RTM) method to GPR field data acquired in the Beiluhe region. Analyses of the results led to conclusions as follows: a) The RTM algorithm is proved to be accurate for imaging the characteristics of the active layer through the dielectric constant model obtained by common midpoint and GPR data velocity analysis. b) The result of RTM profiles show the loose structure and graben-like fine internal structure. Combining with drill data and soil materials information, the researchers deduce the graben-like structure might be coarse gravel layer, which may be related to subgrade settlements in some local area. c) The 2D contour maps not only show the spatial variation feature of ALT at two sides of the highway, but also can compensate the defect of the research for single 2D survey line. The ALT is about 0.6~0.8 m in most areas, and about 0.8~0.9 m near the highway at west side of the QTH on the 2D contour maps. From the RTM interpretation profile, the closer to the plash, the deeper thawed active layer. The detailed internal structure of the active layer and 2D contour maps of ALT have great significance for the study of fine internal structure and the characteristic of ALT spatial variation in the Tibetan plateau under the background of climatic change.

The Double U-pipe ground heat exchanger, recognized for its straightforward process, cost-effectiveness, high heat exchange efficiency, and low thermal resistance, stands as the predominant ground heat exchanger type in today's shallow geothermal energy development and utilization. Recent years have seen significant research into the heat transfer influencing factors and heat exchange performance of Double U-pipe ground heat ex-changers via experimental testing methods. Yet, studies integrating numerical simulation with in-situ testing have been less frequent. Drawing on the cylindrical heat source model theory and the outcomes of regional in-situ thermal response tests, this paper develops a Double U-pipe ground heat transfer model by establishing physical, mathematical, and heat transfer geometric models. It assesses the impact of varying inlet temperatures, flow rates, and initial ground temperatures on heat exchange efficiency under heating conditions. The results validate the precision of the double U-pipe ground heat exchanger model based on in-situ testing. They show that enhancing the temperature differential between the inlet and initial temperatures, elevating the initial ground temperature, and moderately increasing the flow rate can boost the system's heat exchange efficiency.

This paper numerically investigates a 3D mesh independency study of a straight blade horizontal axis tidal turbine modelled using Computational Fluid Dynamics (CFD). The solution was produced by employing two turbulence models, the standard k-ε model and Shear Stress Transport (SST) in ANSYS CFX. Three parameters were investigated: mesh resolution, turbulence model, and power coefficient in the initial CFD, analysis. It was found that the mesh resolution and the turbulence model affect the power coefficient results. The power coefficients obtained from the standard k-ε model are 15% to 20% lower than the accuracy of the SST model. It can also be demonstrated that the torque coefficient increases with the increasing Tip Speed Ratio (TSR), but drops drastically after TSR = 5 and k-ε model failing to capture the non-linearity in the torque coefficient with the increasing TSR.

In this paper, carbon nanotubes modified screen-printed electrode (CNTs/SPE) was prepared and the CNTs/SPE was employed for the electrochemical determination of antioxidant substance Chlorogenic acids (CGAs). A pair of well-defined redox peak of CGA was observed at the CNTs/SPE in 0.10 mol∙L^-1 acetic acid-sodium acetate buffer (pH 6.2) and electrode process is adsorption-controlled. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) method for the determination of CGA were proposed based on the CNTs/SPE. Under the optimal conditions, the proposed method exhibited linear ranges from 4.73×10^-7 to 4.45×10^-5 mol∙L^-1, the linear regression equation was Ipa(µA) = 4.1993 C (mol∙L^-1)+1.1039 (r = 0.9976) and the detection limit for CGA could reach 3.25×10^-6 mol∙L^-1. The recovery of matrine was 94.74~106.65% (RSD = 2.92%) in coffe beans. The proposed method is quick, sensitive, reliable, and can be used for the determination of CGA.

Intrahepatic cholangiocarcinoma (ICCA) is a malignant epithelial neoplasm characterized by biliary differentiation within the liver. ICCA is molecularly heterogeneous and exhibits a broad spectrum of histopathological features. It is a highly aggressive carcinoma with high mortality and poor survival rates. ICCAs are classified into two main subtypes: small duct type and large duct types. These two tumor types have different cell origins and clinicopathological features. ICCAs are characterized by numerous molecular alterations, including mutations in KRAS, TP53, IDH1/2, ARID1A, BAP1, BRAF, SAMD4, and EGFR, and FGFR2 fusion. Two main molecular subtypes–inflammation and proliferation–have been proposed. Recent advances in high-throughput assays using next-generation sequencing have improved our understanding of ICCA pathogenesis and molecular genetics. The diagnosis of ICCA poses a significant challenge for pathologists because of its varied morphologies and phenotypes. Accurate diagnosis of ICCA is essential for effective patient management and prognostic determination. This article provides an updated overview of ICCA pathology, focusing particularly on molecular features, histological subtypes, and diagnostic approaches.

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

The Roman villa of Pisões (Beja, Portugal), was part of the Lusitanian colony of Pax Iulia. This place stands out for the predominance of the water element in several structures of the villa, highlighting the balneum and the large natatio, one of the largest known in Roman Hispania. The records of the initial excavations that took place since 1967 do not allow the establishment of clear functionalities of the villa. The University of Évora, owner of the site, conceived an action plan for the requalification and enhancement of the archaeological site. One of the tasks aims to investigate using Applied Geophysics. This work analyses the landscape directly related to the villa, given that it is in the flooded area of a river, with a Roman containment dam. It is uncertain whether the water supply comes from this structure or other nearby springs. The use of ground-penetrating radar, combined with unnamed aerial vehicles, all integrated in a geographic information system, allows us to know the location of underground water connections and create a topographic model with high resolution. Considering all the information, we propose a model for the water transport inside the villa and estimate the location of the water supply.

In this work, we studies the principle of ground electrode current field through ground communication technology, signal design, and optimal working mode of excitation source, so as to adapt the working mode, working frequency, and transmission medium condition of signal transmission. Through waveform design, energy is concentrated in the main conduction direction, which is beneficial for signal filtering at the receiving end and achieving high reliability data transmission; On this basis, on-site environmental testing was conducted to verify the detection and recognition technology of weak signals. Signal design and detection recognition are a very important part of the grounding electrode current field through ground communication technology. The grounding electrode current field through ground communication technology is mainly applied to solve the problem of communication between rescue personnel and trapped personnel before and during tunnel engineering collapse and rescue.

Brushless Synchronous Machines (BSM) are replacing conventional synchronous machines with static excitation in generation facilities due to the absence of sparking and lower maintenance. However, this excitation system makes challenging measuring electric parameters in the rotor. This fact highly difficult the detection of ground faults, which are the most common type of electrical fault in electric machines. In this paper, a ground fault detection method for BSM is proposed. It is based on an inductive AC/DC rotating current sensor installed in the shaft. In case of a ground fault in the rotating parts of the BSM, a fault current will flow through the rotor’s sensor, inducing voltage in its stator. Analyzing the frequency components of the induced voltage the detection of a ground fault in the rotating elements is possible. The ground faults detection method proposed covers the whole rotor and discern between DC and AC sides. To corroborate the detection method, experimental tests have been performed using a prototype of this sensor connected to laboratory synchronous machines achieving satisfactory results.

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

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

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

Background: Circulating rare cells participate in breast cancer evolution as systemic components of the disease and thus, are a source of theranostic information. Exploration of cancer-associated rare cells is in its infancy. We determined the validity of a rare-cell detection platform and demonstrated the usefulness of a certain rare cell subset as a novel approach to characterize the breast cancer system. Methods: Linearity of the Rarmax® platform was established using a spike-in approach. The platform includes red blood cell lysis, leukocyte depletion and high-resolution fluorescence image recording. Rare cell analysis was conducted on 28 samples (before and after surgery) from 14 patients with breast cancer, 20 healthy volunteers and 9 non-cancer control volunteers. In-depth identification of rare cells, including circulating tumor cells, endothelial-like cells, erythroblasts and inflammation-associated cells, was performed using a phenotype and morphology-based classification system. Results: The platform performed linearly over a range of 5-950 spiked cells, with an average recovery of 84.6%. Circulating epithelial and endothelial-like cell subsets have been demonstrated to be associated with or independent of cancer with tumor presence. Furthermore, certain cell patterns may be associated with treatment-related adverse effects. The sensitivity in detecting tumor-presence and cancer-associated abnormality before surgery was 50% and 85.7%, respectively and the specificity was 100% and 96.6%, respectively. Conclusion: The present study supports the idea of a cancer-associated rare cell abnormality to represent tumor entities as well as systemic cancer. The latter is independent of the apparent clinical cancer.

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

A new algorithm is developed in this study to automatically detect reinforced concrete structures' rebar locations and diameters using the ground penetrating radar technique. The method approximates the hyperbolic signatures using two-way travel time and bi-quadratic equations. The study includes formulating electromagnetic wave speed in reinforced concrete (RC) structures. The algorithm was used in a computer code to provide an automated analysis of ground-penetrating radar data collected from a pre-planned survey grid. Two RC slabs were fabricated and tested to validate the proposed method. The proposed method is efficient in signal processing and accurately and reliably determining the rebar information.

We scrutinize the reliability of multilooked interferograms for deformation analysis. Designing a simple approach in the evaluation of the accuracy of the estimated deformation signals, we reveal a prominent bias in the deformation velocity maps. The bias is the result of propagation of small phase error of multilooked interferograms through the time series and can sum up to 6.5 mm/yr in case of using the error prone short temporal baseline interferograms. We further discuss the role of the phase estimation algorithms in reduction of the bias and put recommend a unified intermediate InSAR product for achieving high-precision deformation monitoring.

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

Subsurface drainage systems remove excess water from the soil profile thereby improving crop yields in poorly drained farmland. Knowledge of the position of the buried drain lines is important: 1) to improve understanding of leaching and offsite release of nutrients and pesticides, and 2) for the installation of a new set of drain lines between the old ones for enhanced soil water removal efficiency. Traditional methods of drainage mapping involve the use of tile probes and trenching equipment. While these can be effective, they are also time-consuming, labor-intensive, and invasive, thereby entailing an inherent risk of damaging the drainpipes. Non-invasive geophysical soil sensors provide a potential alternative solution. Previous research has focused on the use of time-domain ground penetrating radar (GPR), with variable success depending on local soil and hydrological conditions and the central frequency of the specific equipment employed. The objectives of this study were 1) to test the use of a stepped-frequency continuous wave (SFCW) 3D-GPR (GeoScope Mk IV 3D-Radar with DXG1820 antenna array) for subsurface drainage mapping, and 2) to evaluate the performance of a 3D-GPR with the use of a single-frequency multi-receiver electromagnetic induction (EMI) sensor (DUALEM) in-combination. The 3D-GPR system offers more flexibility for application to different (sub)surface conditions due to the coverage of wide frequency bandwidth. The EMI sensor simultaneously provides information about the apparent electrical conductivity (EC_a) for different soil volumes, corresponding to different depths. This sensor combination was evaluated on twelve different study sites with various soil types with textures ranging from sand to clay till. While the 3-D GPR showed a high success rate in finding the drainpipes at five sites (sandy, sandy loam, loamy sand, and organic topsoils), the results at the other seven sites were less successful due to limited penetration depth (PD) of the 3D-GPR signal. The results suggest that the electrical conductivity estimates produced by the inversion of EC_a data measured by the DUALEM sensor could be a useful proxy to explain the success achieved by the 3D-GPR in finding the drain lines. The high attenuation of electromagnetic waves in highly conductive media limiting the PD of the 3D-GPR can explain the findings obtained in this research.

In this paper, inspired by Jleli and Samet [journal of inequalities and applications 38 (2014) 2 1–8] we introduce two new classes of auxiliary functions and utilize the same to define (q, y)R-weak 3 contractions. Utilizing (q, y)R-weak contractions, we prove some fixed point theorems in the setting 4 of relational metric spaces. We employ some examples to substantiate the utility of our newly proved 5 results. Finally, we apply one of our newly proved results to ensure the existence and uniqueness of 6 solution of a Volterra-type integral equation.

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

Predictive numerical models in the study of ground-borne vibrations generated by railway sys-tems have traditionally relied on the segmented approach of subsystems. In this approach, loads are individually applied, and the cumulative effect of rolling stock is obtained through superposi-tion. While this method serves to mitigate computational costs, it may not fully capture the com-plex interactions involved in ground-borne vibrations. Recent advancements in computational and software tools have enabled the development of more sophisticated vibrational pollution models. These advanced models encompass the entire dynamic system, from the rolling stock to the terrain, allowing for continuous simulations with a defined time step. Furthermore, the incorporation of computational contact tools between various ele-ments not only ensures temporal accuracy but also extends the analysis to the frequency domain. This paper explores the evolution of predictive numerical models in the context of ground-borne vibrations generated by trains on ballasted tracks. It highlights the shift from segmented models to continuous simulations, demonstrating the progressive advancement towards more comprehen-sive and accurate representations of railway-induced and generated vibrations in the track sur-roundings as an additional way of evaluate sustainability of infrastructures. In particular, this development of a numerical model from its inception aimed to expand upon ex-isting emission - prediction models by enriching the data with frequency-domain information. This approach has been driven by the aspiration to bridge the gap between numerical predictions and measured signals, striving for a higher level of accuracy in our obtained results. This model can predict the impact of a high-speed rail (HSR) vehicle passing on its own track. It possesses sufficient information in both the time and frequency domains to precisely characterize vibrations. This initiative marks a pivotal advancement, enabling us to more effectively capture the intricacies of ground-borne vibrations and their impact on the surrounding environment due to a deeper comprehension of the occurrences in the frequency domain, there's a heightened ca-pability to accurately characterize vibrations.

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

Ground-based infrared cameras can be used effectively and safely to provide quantitative information about small to moderate-sized volcanic eruptions. This study describes an infrared camera that has been used to measure emissions from Mt Etna and Stromboli (Sicily, Italy) volcanoes. The camera provides calibrated brightness temperature images in a broadband (8--14 µm) channel that is used to determine height, plume ascent rate and volcanic cloud/plume temperature and emissivity at temporal sampling rates of up to 1 Hz. The camera can be operated in the field using a portable battery and includes a microprocessor, data-storage and WiFi. The processing and analyses of the data are described with examples from the field experiments. The updraft speeds of the small eruptions at Stromboli are found to decay with a timescale of $\sim$10 min and the volcanic plumes reach thermal equilibrium within $\sim$2 min. A strong eruption of Mt Etna on 1 April 2021 was found to reach $\sim$9 km, with ascent speeds of 10--20 ms$^{-1}$. The plume, mostly composed of water vapour and SO$_2$ gas became bent over by the prevailing winds at high levels.

The study conducts a comparative analysis of solar radiation estimation methods, assessing the efficacy of the Heliosat-2 algorithm against ground measurements across seven distinct countries: Netherlands, Spain, Japan, Namibia, South Africa, Saudi Arabia, and India. To achieve this, it utilizes two distinct satellite data sources—Himawari-8 for Japan and Metosat Second Generation-MSG for the rest of the countries—spanning the years Jan 2022 to April 2024. A robust methodology for determining albedo parameters specific to Heliosat-2 was developed. During cloudy days, the estimates provided by Heliosat-2 generally exceeded the ground measurements in all the countries. Conversely, on clear days, there was a tendency for underestimation, as indicated by the median value of the mean bias (MB) across most of the countries. The Heliosat-2 model slightly underestimates daily radiation values, with a median MB ranging from −27.5 to +10.2 W.m⁻². Notably, the median root mean squared error (RMSE) on clear days is significantly lower, with values ranging from 24.8 to 108.7 W.m⁻², compared to cloudy days where RMSE values lie between 75.3 and 180.2 W.m⁻². In terms of R² values, both satellites show a strong correlation between the estimated and actual values, with a median value consistently above 0.86 on a monthly scale and over 92% of daily data points falling within ±2 standard deviation.

Gesture recognition technology has been quickly developed in the field of human-computer interaction. The multiple-input multiple-output (MIMO) radar has been widely adopted in gesture recognition because of its notable spatial resolution. In this work, a highly accurate MIMO radar-based hand gesture recognition algorithm with very low complexity is proposed. To make the proposed system applicable in the industry and work well even when the size of the training data is limited, we applied several low-complexity adaptive signal processing methods to extract features and reduce the noise effect. First, spectrum analysis is applied to range-Doppler maps (RDMs) and a cell-averaging constant false alarm rate (CA-CFAR) with mirror filters is applied to improve the robustness to noise. Afterward, the features related to the distance, speed, direction, and the elevation angle of the moving object are determined by the proposed adaptive signal analysis techniques and the random forest is applied for classification. The proposed system has the capability to precisely distinguish and identify eight motions, including waving, moving to the left or right, patting, pushing, pulling, and rotating clockwise or anticlockwise, with an accuracy of 95%. Experiments demonstrate the capability of the proposed hand gesture recognition system to classify different movements precisely.

The authors of the paper have made an attempt to detect the fibre content and fibre spacing in a steel fibre reinforced concrete (SFRC) industrial floor. Two non-destructive testing (NDT) methods: an electromagnetic induction technique and a radar-based technique were applied. The first method allowed to detect the spacing in subsequent layers located in the thickness of the slab. The result of the second method was a 3D visualization of the detected fibre in the volume of concrete slab. The conducted tests showed aptitude and limitations of the applied methods in estimating fibre volume and spacing. The two techniques also allowed to locate the areas with relatively low fibre concentration which are very likely to be characterized by low mechanical properties.

(1) Background: Myelodysplastic syndromes (MDS) are a group of diseases that affect the morphology and function of neutrophils. Rapid diagnosis of MDS is crucial for the initiation of treatment that can vastly improve disease outcome. (2) methods: In this work, we present a new approach for detecting morphological differences between neutrophils isolated from blood samples of high risk MDS patients and that for patients and blood bank donors (BBDs). First, using fluorescent flow cytometry CD66b positive cells were stained with 2′,7′-dichlorofluorescin diacetate (DCF) and Hoechst. Secondly, we imaged the cells using interferometric phase microscopy (IPM) without utilizing cell staining. (3) Results: flow cytometry results show MDS patients present different dispersion patterns compared to BBDs. BBDs present two distinct subpopulations (one of these are the H cells constituting 16% for neutrophils) whereas MDS patients present only 4% of this subpopulation. IPM results are consistent with this pattern: ~20% of BBDs neutrophils were characterized as H cells and whereas MDS patients present only ~4% of these cells. (5) Conclusion- IPM can be utilized for diagnosis of complex hematological pathologies such as MDS.

Structure from Motion with Multi-View Stereo photogrammetry (SfM) is increasingly utilised in geoscience investigations as a cost-effective method of acquiring high resolution (sub-meter) topographic data, but has not been thoroughly tested in gullied savanna systems. The aim of this study was to test the accuracy of topographic models derived from aerial (via an Unmanned Aerial Vehicle, ‘UAV’) and ground-based (via a handheld digital camera, ‘Ground’) SfM in modelling a hillslope gully system in dry-tropical savanna, and to assess the strengths and limitations of the approach at different scales. A UAV survey covered an entire hillslope gully system (0.715 km²), whereas a Ground survey covered a single gully within the broader system (650 m²). SfM topographic models, including Digital Surface Models (DSM) and dense point clouds, were compared against RTK-GPS point data and a pre-existing airborne LiDAR Digital Elevation Model (DEM). Results indicate UAV SfM can deliver topographic models with a resolution and accuracy suitable to define gully systems at a hillslope scale (e.g., 0.1 m resolution with ~ 0.5 – 1.3 m elevation error), while ground-based SfM is more capable of quantifying gully morphology (e.g., 0.01 m resolution with ~ 0.1 m elevation error). Key strengths of SfM for these applications include: the production of high resolution 3D topographic models and ortho-photo mosaics, low survey instrument costs (< $AUD 3,000); and rapid survey time (4 and 2 hours for UAV and Ground survey respectively). Current limitations of SfM include: difficulties in reconstructing vegetated surfaces; uncertainty as to optimal survey and processing designs; and high computational demands. Overall, this study has demonstrated great potential for SfM to be used as a cost-effective tool to aid in the mapping, modelling and management of hillslope gully systems at different scales, in tropical savanna landscapes and elsewhere.

Fog, haze, or smoke are usual atmospheric phenomena that dramatically compromise the overall visibility of any scene, critically affecting features such as illumination, contrast, and contour detection of objects. The decrease in visibility compromises the performance of computer vision algorithms such as pattern recognition and segmentation, some of them very relevant for decision-making for the security or autonomous vehicle industries. Several dehazing methods have been proposed, however, to the best of our knowledge, all proposed metrics compare the defogged image to its ground truth for evaluation of the defogging algorithms, or need to estimate parameters through physical models. This fact hinders progress in the field as obtaining proper ground truth images is costly and time-consuming, and physical parameters greatly depend on the scene conditions. This paper aims to tackle this issue by proposing a contour-based metric for image defogging evaluation that does not need a ground truth image. The proposed metric only requires the original hazy RGB image and the RGB image after the defogging procedure. A comparison of the proposed metric with metrics currently used in the NTIRE 2018 defogging challenge is performed to prove its effectiveness in a general situation, showing comparable results to conventional metrics.

The application of signal-to-noise ratio (SNR) observations from ground-based GNSS Reflectometry is becoming an operational tool for coastal sea-level altimetry. As in all data analyses, systematic influences must be reduced here too, to achieve reliable results. A prominent influence results from atmospheric refraction. Different approaches exist to describe or to correct for this influence. In our contribution we will revise the latest developments and suggest a simple atmospheric interferometric delay model that takes into account ray bending as well as along-path propagation delay. The model takes into account a spherical reflector and can therefore be applied for data from very low elevation angles, too. The findings are double-checked by numerical experiments based on a step-by-step raytracing procedure.

A portable Fourier Transform Spectrometer (B3M-IR) is built and used to measure atmospheric trace gases in the city of Beijing during Olympic Games in 2008. A short description of the instrument is first provided in this paper. A detailed spectral analysis is then presented. The total columns of ozone (O3), carbon monoxide (CO), methane (CH4) and nitrous oxide (N2O) are retrieved from the ground-based solar absorption spectra recorded by the B3M-IR during the Olympic Games. Lacking validation data, only the retrieved total column of O3 is compared with that retrieved by MAX-DOAS, which is deployed at the same station. The mean difference between the two methods of measurement is 6.5%, demonstrating the performance and reliability of B3M-IR.

Pakistan relies mostly on fossil fuel for fulfilling its energy demand which is expensive as 11 well as not friendly for the environment. The need of the hour is to shift from fossil fuels to renew-12 able energy resources like geothermal, wind, solar etc. to cater for global warming issues. Pakistan 13 has a lot of potential geothermal sites because of several fault lines and hot springs. A sound 14 knowledge of ground temperature is essential to use geothermal energy, which is obtained by drill-15 ing boreholes and putting sensors making it an expensive procedure. Thus, to avoid huge cost for 16 drilling bore holes, particularly for ground temperature analysis, a numerical approach has been 17 considered for determining ground temperature. Furthermore, correlation charts between air and 18 ground temperatures have been developed as there were no proper studies on the ground temper-19 ature of Pakistan. Then, with the help of a boreholes drilled in National University of Sciences and 20 Technology Islamabad Pakistan, the actual ground and numerically calculated temperatures have 21 been compared which shows an error margin in the range between 0.27% for higher depths to 7.3% 22 near surface. Thus, it is shown that the proposed method is easy to implement and better than large 23 scale testing methods for the depths at which geothermal energy is extracted.

Target recognition mainly includes three approaches: optical image-based, echo detection-based, and passive signal analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an "end-to-end" model that cascades a noise estimation network with a recognition network to identify working modes in noise environment. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signal, the recognition network is realized based on Multi-Scale Convolutional Attention Network (MSCANet). Firstly, the deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem that the conventional method is seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environments, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness.

The last decade the impulse-radio ultra-wideband (IR-UWB) radar sensors have tended to substitute the gold standard, polysomnography (PSG), for the acquisition of the primary vital signs of the human body. However, the main drawback of the radar sensor is its sensitivity to the environmental noise and body motion. In this work, we aim to investigate whether radar sensor recordings of the chest and heart motion can be accurate substitutes to the PSG chest and heartbeat signals measurements. In terms of this scientific issue, we develop an innovative pipeline of handling the radar-based recordings, which includes: (a) the motion detection, (b) the extraction of the respiration, heartbeat and activity vital signs and (c) the estimation of the respiratory and heartbeat rates, i.e., the RR and HR, respectively. Our experimental results, applying our proposed methodology to 5 subjects during their sleep, showed that the radar sensor’s measurements can be comparative to the those producted by the PSG. Moreover, the corresponding RR and HR frequencies were proven to have Pearson’s correlation, between the radar and the PSG, greater than 0.9 and 0.78, respectively. Finally, the relative errors between the PSG, and the radar-based RR and HR rates were proven to be less than 6% and 10%, respectively.

In this research paper, change detection based methods were considered to find collapsed and intact buildings using radar remote sensing data or radar imageries. Main task of this research paper is collection of most relevant scientific research in field of building damage assessment using radar remote sensing data. Several methods are selected and presented as best methods in present time, there are methods with using interferometric coherence, backscattering coefficients in different spatial resolution. In conclusion, methods are given in end, which show, which methods and radar remote sensing data give more accuracy and more available for building damage assessment. Low resolution Sentinel-1A/B radar remote sensing data are recomended as free available for monitoring of destruction degree in microdistrict level. Change detection and texture based method are used together to increase overall accuracy. Homogeneity and Dissimilarity GLCM texture parameters found as better for separation of a collapsed and intact buildings. Dual polarization (VV,VH) backscattering coefficients and coherence coefficients (before earthquake and coseismic) were fully utilized for this study. There were defined the better multi variable for supervised classification of none building, damaged and intact buildings features in urban areas. In this work, we were achieved overall accuracy 0.77, producer’s accuracy for none building is 0.84, for damaged building case 0.85, for intact building 0.64. Amatrice town was chosen as most damaged from 2016 Central Italy Earthquake.

Continuous in-home monitoring of older adults living alone aims to improve their quality of life and independence, by detecting early signs of illness and functional decline or emergency conditions. To meet requirements for technology acceptance by seniors (unobtrusiveness, non-intrusiveness, privacy-preservation), this study presents and discusses a new smart sensor system for the detection of abnormalities during daily activities, based on ultra-wideband radar providing rich, not privacy-sensitive, information useful for sensing both cardiorespiratory and body movements, regardless of ambient lighting conditions and physical obstructions (through-wall sensing). The radar sensing is a very promising technology, enabling the measurement of vital signs and body movements at a distance, and thus meeting both requirements of unobtrusiveness and accuracy. In particular, impulse-radio ultra-wideband radar has attracted considerable attention in recent years thanks to many properties that make it useful for assisted living purposes. The proposed sensing system, evaluated in meaningful assisted living scenarios by involving 30 participants, exhibited the ability to detect vital signs, to discriminate among dangerous situations and activities of daily living, and to accommodate individual physical characteristics and habits. The reported results show that vital signs can be detected also while carrying out daily activities or after a fall event (post-fall phase), with accuracy varying according to the level of movements, reaching up to 95% and 91% in detecting respiration and heart rates, respectively. Similarly, good results were achieved in fall detection by using the micro-motion signature and unsupervised learning, with sensitivity and specificity greater than 97% and 90%, respectively.

Accurate cloud quantification is essential in climate change research. In this work, we construct an automated computer vision framework by synergistically incorporating deep neural networks and finite sector clustering to achieve robust whole sky image-based cloud classification, adaptive segmentation, and recognition under intricate illumination dynamics. A bespoke YOLOv8 architecture attains over 95% categorical precision across four archetypal cloud varieties curated from extensive annual observations (2020) at a Tibetan highland station. Tailor-made segmentation strategies adapted to distinct cloud configurations, allied with illumination-invariant image enhancement algorithms, effectively eliminate solar interference and substantially boost quantitative performance even in illumination-adverse analysis scenarios. Compared with the traditional threshold analysis method, the cloud quantification accuracy calculated within the framework of this paper is significantly improved. Collectively, the methodological innovations provide an advanced solution to markedly escalate cloud quantification precision levels imperative for climate change research, while offering a paradigm for cloud analytics transferable to various meteorological stations.

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

Sentinel-1 satellite system continuously observes European countries in a relatively high revisit frequency of 6 days per orbital track. Given the Sentinel-1 configuration, most areas in Czechia are observed every 1–2 days by different tracks in a moderate resolution. This is attractive for various types of analyses by various research groups. The starting point for processing is an original data provided by ESA, for interferometry (InSAR) this level is a Single Look Complex (SLC) data. This work represents advantages of storing data augmented to a specifically corrected level of data, SLC-C. The presented database contains Czech nation-wide Sentinel-1 data stored in burst units that have been preprocessed to the state of a consistent well-coregistered dataset of SLC-C. These are resampled SLC data with their phase values reduced by a topographic phase signature, ready for fast interferometric analyses (an interferogram is generated by a complex conjugate between two stored SLC-C files). The data can be used directly into multitemporal interferometry techniques, e.g. Persistent Scatterers (PS) or Small Baseline (SB) techniques applied here. A further development of the nation-wide system utilising SLC-C data would lead into a dynamic state where every new pre-processed burst triggers a processing update to detect unexpected changes from InSAR time series and therefore provide a signal for early warning against a potential dangerous displacement, e.g. a landslide, instability of an engineering structure or a formation of a sinkhole. An update of the processing chain would also allow use of cross-polarised Sentinel-1 data, needed for polarimetric analyses. The current system is running at a national supercomputing centre IT4Innovations in interconnection to the Czech Copernicus Collaborative Ground Segment (CESNET), providing fast on-demand InSAR results over Czech territories. A full nation-wide PS processing using data over Czechia has been performed in 2017, discovering several areas of land deformation. Its downsampled version and basic findings are demonstrated within the article.

The increasing enhancement in the modulation accuracy of spatial light modulators has garnered significant attention towards real-time control technology for light fields based on these modulators. It has been empirically demonstrated that this technology possesses a remarkable capability to generate vector beams with arbitrary complex amplitude distributions. Nevertheless, past studies indicate that the generation of only one vector beam at a time has been observed. The simultaneous generation of numerous vector light fields can give rise to several challenges, including compromised picture quality, limited single mode operation, and intricate optical path configurations. In pursuit of this objective, we present a novel methodology that integrates the coding methodology of modified off-axis interferometric holography with the idea of optical superposition. This technique facilitates the concurrent generation of several vector beams. In this study, we present a demonstration of the simultaneous creation of twelve vector beams using a single spatial light modulator (SLM) as a proof of concept. Significantly, this technology has the ability to generate an unlimited quantity of vector light fields concurrently, under the assumption that the resolution of the SLM does not impose any limitations. The findings indicate that the imaging quality achieved by this technology is of a high standard. Furthermore, it is possible to separately control the beam waist radius, topological charge, polarization order, and extra phase of each beam.

The application of smartphone-based color evaluation of passive sampling devices for gases has only been sparsely reported. The present review aims to compile available publications with respect to the configuration of the passive samplers, conditions of smartphone photographing, analytical procedure for color detection and quantification (including calibration processes), and their application to different target gases. The performance of the methods - whenever available - is presented regarding the analytical specifications selectivity, sensitivity, and limit of detection in comparison with other color evaluation methods of passive samplers. Practical aspects like requirements of instrumentation and ease of use will be outlined in view of the potential employment in education and citizen science projects. In one section of the review the inconsistent terminology of passive and diffusive sampling is discussed in order to clarify the distinction of information obtained from the uptake of the passive samplers between gas-phase concentration and the accumulated deposition flux of gaseous analytes. Colorimetric gas sensors are included in the review when applied in passive sampling configurations and evaluation is performed with smartphone-based color evaluation. Differences in the analytical procedures employed after the passive sampling step and prior to the detection of the colored compounds are also presented.

With the continuous development of 3D city modeling, traditional close-range photogrammetry is limited by complex processing procedures and incomplete 3D depth information, making it unable to meet high-precision modeling requirements. In contrast, the integration of LiDAR and camera in mobile measurement systems provides a new and highly effective solution. The LiDAR can quickly and accurately acquire the 3D spatial coordinates of target objects, while optical imagery contains abundant color information. If the two can be integrated, they can play an important role in multiple fields such as streetscape modeling, archaeology and digital city, etc. Currently, integrated mobile measurement systems commonly require cameras, lasers, POS and IMU, thus the hardware cost is relatively expensive and the system integration is complex. Therefore, in this paper we propose a simple ground mobile measurement system composed of a LiDAR and a GoPro camera without a POS system, providing a more convenient and reliable way to automatically obtain 3D point cloud data with spectral information. The automatic point cloud coloring based on video images mainly includes four aspects: (1) Establishing models for radial distortion and tangential distortion to correct video images. (2) Establishing a registration method based on normalized Zernike moments to obtain the exterior orientation elements. Normalized Zernike moments are region-based shape descriptors that can reflect the features of images in multiple dimensions even for low-quality video images. The results show that registration based on normalized Zernike moments provides a good result, with an error accuracy of 0.5-1 pixel, which is far higher than registration based on a collinearity equation. (3) Establishing adjacent video image relative orientation based on essential matrix decomposition and nonlinear optimization. This involves uniformly using the SURF algorithm with distance restriction and RANSAC to select corresponding points, which can improve the reliability of the corresponding points. The results indicate that the accuracy of the relative orientation method is high. Moreover, this method can converge to good results for stereo image pairs with large rotation angles and displacement amounts. Therefore, relative orientation based on essential matrix decomposition and nonlinear optimization has good applicability. (4) The video imagery suffers from significant motion blur and boundary distortion. Therefore, a point cloud coloring method based on Gaussian distribution with central region restriction is adopted. Only pixels within the central region are considered valid for coloring. Then, the point cloud is colored based on the mean of the Gaussian distribution of the color set. Experimental results show that the coloring accuracy between the video imagery and point cloud data is high, meeting the accuracy requirements of applications such as tunnel detection, street-view modeling and 3D urban modeling.

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

The importance of radar-based human activity recognition increased significantly over the last two decades in safety and smart surveillance applications due to its superiority towards vision-based sensing in the presence of poor environmental conditions, like illumination, increased radiative heat, occlusion, and fog. An increased public sensitivity for privacy protection, and the progress of cost-effective manufacturing, led to a higher acceptance and distribution. Deep learning approaches proved that the manual feature extraction that relies heavily upon process knowledge can be avoided by its hierarchical, non-descriptive nature. On the other hand, ML techniques based on manual feature extraction provide a robust, yet empirical based approach where the computational effort is comparatively low. This review outlines the basics of classical ML- and DL-based human activity recognition and its advances while taking recent progress of both categories into regard. For every category, state-of-the-art methods are introduced, briefly explained and discussed in terms of their pros, cons and gaps. A comparative study is performed to evaluate the performance and computational effort based on a benchmarking data set to provide a common basis for the assessment of the techniques’ degree of suitability.

This study presents the algorithm ACTION, defined as Automatic Center detection of Tropical cyclone (TC) using Image processing based on the Operational radar Network. Based on the high visibility of weather radar imagery, the TC’s motion vector is calculated from the continuous image change using optical flow, producing its rotation center as the TC’s center. The algorithm’s performance was verified for typhoons (TCs in the Northwestern Pacific) affecting the Korean Peninsula from 2018–2021 and showed a high detection rate of 80.8% within an error distance of 40 km compared to the best track of the Korea Meteorological Administration (KMA). The detection rate was 100% for typhoons with temporally consistent morphological characteristics. ACTION automatically generates TC center information upon the TC’s initial entry inside the observation radius even in the absence of uniform radar data. ACTION easily calculates using Open Source Computer Vision, performs in real time, and can be directly applied to rapidly generated weather radar images; hence, it is currently being utilized by the KMA. The high-temporal-resolution TC center information calculated through ACTION is expected to improve the efficiency of TC forecasting.

Detecting, tracking, and classifying unmanned aerial vehicles (UAVs) in a swarm presents significant challenges due to their small and diverse radar cross-sections, multiple flight altitudes, velocities, and close trajectories. To overcome these challenges, adjustments of the radar parameters and/or position of the radar (for airborne platforms) are often required during runtime. The runtime adjustments help to overcome the anomalies in the detection, tracking, and classification of UAVs. The runtime adjustments are performed either manually or through fixed algorithms, each of which can have its limitations for complex and dynamic scenarios. In this work, we propose the use of multi-agent reinforcement learning (RL) to carry out the runtime adjustment of the radar parameters and position of the radar platform. The radar used in our work is a multibeam multifunction phased array radar (MMPAR) placed onboard UAVs. The simulations show the cognitive adjustment of the MMPAR parameters and position of the airborne platform using RL helps to overcome anomalies in the detection, tracking, and classification of UAVs in a swarm. A comparison with other artificial intelligence (AI) algorithms shows that RL performs better due to runtime learning of the environment through rewards.

The Stepped Frequency Waveform (SFW) is commonly employed in radar technology to synthesize wideband signals through the aggregation of narrow-band pulses, thereby achieving high-resolution range profiles without the need to increase the radar's instantaneous bandwidth. However, the inherent large time-bandwidth product of SFW introduces substantial ranging errors and energy dispersion, which significantly hinders its efficacy in detecting high-speed objects. This paper introduces a pioneering velocity estimation technique utilizing the fractional Fourier transform (FrFT) to address these limitations. Leveraging the characteristic of the Doppler signal from a moving target, which manifests as a chirp signal with a rate proportional to the target's velocity, the FrFT is utilized for precise velocity estimation. Subsequent to this, velocity compensation is applied using the deduced metrics, followed by the application of the inverse fast Fourier transform (iFFT) to pinpoint the target's exact location. To optimize the computational efficiency of determining the FrFT's optimal order, we propose an iterative algorithm founded on the golden section search method. The effectiveness of the proposed approach is verified by simulation data, and the results demonstrate that the proposed approach can accurately estimate the velocity and the range of the high-speed targets with a relatively low computational complexity.

An innovative wireless sensor network (WSN) based on Ultra-Wide Band (UWB) technology for 3D accurate superficial monitoring of ground deformations, as landslides and subsidence, is proposed. The system has been designed and developed as part of an European Life+ project, called Wi-GIM (Wireless Sensor Network for Ground Instability Monitoring). The details of the architecture, the localization via wireless technology and data processing protocols are described. The flexibility and accuracy achieved by the UWB two-way ranging technique is analysed and compared with the traditional systems, such as robotic total stations (RTSs), Ground-based Interferometric Synthetic Aperture Radar (GB-InSAR), highlighting the pros and cons of the UWB solution to detect the surface movements. An extensive field trial campaign allows the validation of the system and the analysis of its sensitivity to different factors (e.g., sensor nodes inter-visibility, effects of the temperature, etc.). The Wi-GIM system represents a promising solution for landslide monitoring and it can be adopted in conjunction with traditional systems or as an alternative in areas where the available resources are inadequate. The versatility, easy/fast deployment and cost-effectiveness, together with the good accuracy, make the Wi-GIM system a possible solution for municipalities that cannot afford expensive/complex systems to monitor potential landslides in their territory.

Global Positioning System (GPS) stations located along coastal areas have the ability to measure tide gauge (TG) records by reflected signal reception from the sea water surface. In this study we used the GPS signal-to-noise ratio (SNR) data from the SEPT station (44.63 ⁰N, 124.05 ⁰W) located at South Beach, Oregon, USA, to estimate the TG records using only a few measurements. First, we derived the TG record from a GPS station (GPS-TG) and used traditional TG data from the National Water Level Observation Network (NWLON) sentinel station (Station ID: 9435380) located in Oregon for validation purposes because it was closest to the SEPT station. Our results show that the GPS-TG and NWLON-TG correlate well with the correlation coefficient (CC) of 0.942 and the root mean square (RMS) of their residuals was about 12.90 cm. The corresponding TG prediction by autoregressive moving average (ARMA-TG) and singular spectrum analysis (SSA-TG) models are evaluated for their effectiveness over the station. The comparative analysis demonstrates that the GPS-TG has improved correlation with ARMA-TG (CC of ~0.981 CC, RMS of ~4.80 cm), and SSA-TG (CC of ~0.998 CC, RMS of ~ 0.88 cm) compared to the NWLON-TG (CC of ~0.942 CC, RMS of ~12.90 cm) values. We believe the outcomes from this study contribute to a better understanding of the numerical modeling of TG records as well as other measurements based on reflectometry techniques.

Undisturbed ground temperature (UGT), thermal conductivity (TC) and heat capacity (HC) are essential parameters for the design of borehole heat exchanger (BHE) and borehole thermal energy storage systems. However, field methods to assess the thermal state and properties of the sub-surface are costly and time consuming. Moreover, HC is often not evaluated but arbitrarily selected from literature considering the geological materials intercepted by boreholes. Therefore, this work aims at proposing a field heat tracing method to infer the thermal diffusivity (TD) and HC with assumption of natural transient heat conduction in the subsurface. Empirical equations were developed to reproduce a UGT profile measured along a BHE. Experimental coefficients are found with a non-linear least square solver optimization and used to calculate the damping depth and TD. Subsequently, the TD is used to evaluate HC considering TC obtained from a thermal response test (TRT). Results from this proposed heat tracing method were verified and validated against a set of TRT results and oscillatory TRT analysis using a field dual probe concept to infer HC. The example here described highlights the advantages and novelty of this fast and simple field method relying only on a single UGT profile measured before a TRT.

Abstract: We present a technological analysis of the architectonic complex associated with a firing structure (locally named fogón) on top of the Pirámide Mayor, the largest pyramid in Caral. This Archaic Period society flourished between ca 2,700 and 2,000 cal B.C. and was located near the Pacific Ocean on the north-central coast of present-day Peru. All approaches employed in this study demonstrated that the corridor with antechamber and duct on top of Pirámide Mayor of Caral was a sophisticated ventilation system capable of providing between 3,000 and 18,000 liters of air per minute for combustion in the attached fogón. The analysis indicates that ventilating the fogón must have been a major consideration in deciding a) the construction of the pyramid, b) locating the structure at the edge of a natural alluvial terrace, and c) orientating the pyramid towards the then contemporary prevailing winds. Results from the analysis of this fogón support practical decisions made by the builders and challenge the assumption that religion or politics were the only considerations in American pyramid building. It also indicates that a complex device, capable to delivering the considerable airflows reported in this study was within the reach of an archaic society in ancient Peru. The application of this structure to the production of a remarkably high thermal output and its implications on the knowledge of ancient Americans will be discussed in a following companion article.

CO2 emissions resulting from the production of cement is a major issue, but can be limited by the partial substitution of cement by low-carbon-impact additions. The aim of this study was the formulation of a ternary binder based on ordinary Portland cement (OPC), ground granulated blast-furnace slag (GGBS) and flash-calcined sediment (FCS), a dredged waste which was valorized after applying a new heat treatment: flash calcination. The used materials were physically, chemically and mineralogically characterized. The composition of the formulations was optimized using mixture designs. Five formulations, one reference formulation RM (100% OPC), one binary formulation (50% OPC/50% GGBS), and three ternary formulations with a variable FCS rate (10%, 15%, 20%), were selected and characterized fresh and hardened. Results showed that the incorporation of FCS reduced the workability and increased the density. In addition, a decrease in the initial setting time and the heat of hydration peak were observed. In the hardened state, the formulation containing 10% FCS showed 90-day mechanical strengths superior to that of RM. The use of FCS in ternary binders could reduce the environmental impact by reducing greenhouse gas emissions.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.

Aiming at the prediction of environmental vibrations induced by elevated high-speed railway, a machine-learning method is developed by combining random forest algorithm and Bayesian optimization, which using the dataset from on-site experiments . When it comes to achieving a rapid and effective prediction of environmental vibration, there is few research on com-parisons and verifications of different algorithms, and neither on parameter tuning and optimi-zation of machine learning algorithms. In this paper, a field experiment is firstly carried out to measure the ground vibrations caused by high-speed trains running on bridge, and then the en-vironmental vibration characteristics are analyzed in view of ground accelerations and weighted vibration levels. Subsequently, three machine-learning algorithms of linear regression, support vector machine and random forest are developed by using experimental database, and their prediction performance are discussed. Finally, two optimization models for the hyperparameter set of random forest algorithm are further compared. It turns out that the integrated random forest algorithm has higher accuracy in predicting environmental vibration than linear regression and support vector machine; the Bayesian optimization has excellent performance and high efficiency in achieving efficient and in-depth optimization of parameters, and can be combined with the RF machine learning algorithm to effectively predict the environmental vibrations induced by the high-speed railway.

Precision farming, a labor-saving and highly productive management, is gaining popularity as the number of farmers declines in contrast to the increasing global food demand. However, it requires more efficient crop phenology observation and growth monitoring. One measure is the Leaf Area Index (LAI), which is essential for estimating biomass and yield, but its validation requires destructive field measurements. Remote sensing offers a non-destructive and effective method for ground- and UAV-based field observations. Thus, in this study, a method for indirect estimation of LAI was investigated using ground and UAV observation data. A weekly plant survey was done to measure the plant height, above-ground biomass, and light intensity. Furthermore, images from ground-based and UAV-based cameras were acquired to generate NDVI and the canopy height (CH), respectively. Using the canopy photosynthetic model, derived from Lambert-Beer’s law, the daily biomass was estimated by applying the weekly estimated LAI using CH, and the observed light intensity data as input. The results demonstrated the possibility of quantitatively estimating the daily growth biomass of rice plants, including its spatial variation. The near-real-time estimation method of rice biomass by integrating observation data at fields with numerical models can be applied to the management of major crops.

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

X-band dual-polarization phased array radar (XPAR-D) possesses high temporal-spatial resolutions and plays a significant role in detecting meso- and micro-scale convective systems. However, the precipitation attenuation it endures necessitates an effective correction method. This study selected radar data from XPAR-D at the peak of Maofeng Mountain in Guangzhou during May 16-17, 2020 from three precipitation stages after quality control. Attenuation coefficients are calculated for different precipitation types through scattering simulations of raindrop size distribution (RSD) data. Drawing upon this, an attenuation correction algorithm (MZH-KDP method) is proposed for radar reflectivity factor (ZH) according to different raindrop types, and is compared to the ZH-KDP method currently in use. The results indicate that the attenuation amount of XPAR-D echoes depends on the attenuation path and echo intensity. When the attenuation path is shorter and the echo intensity is weaker, the amount of attenuation and correction is smaller. Difficulties arise when there are noticeable deviations in such a situation, which are challenging to solve via attenuation correction methods. Longer attenuation paths and stronger echoes highlight the advantages of the MZH-KDP method, while the ZH-KDP method tends to overcorrect the bias. The MZH-KDP method outperforms the ZH-KDP method for different precipitation types. The superior correction capability of the MZH-KDP method provides a significant advantage in improving the performance of XPAR-D for the detection of extreme weather.

Effectively mapping and monitoring rubber plantation is still changing. Previous studies have explored the potential of phenology features for rubber plantation mapping through a pixel-based approach (pixel-based phenology approach). However, in fragmented mountainous Xishuangbanna, it could lead to noises and low accuracy of resultant maps. In this study, we investigated the capability of an integrated approach by combining phenology information with an object-based approach (object-based phenology approach) to map rubber plantations in Xishuangbanna. Moderate Resolution Imaging Spectroradiometer (MODIS) data were firstly used to acquire the temporal profile and phenological features of rubber plantations and natural forests, which delineates the time windows of defoliation and foliation phases. Landsat images were then used to extract a phenology algorithm comparing three different approaches: pixel-based phenology, object-based phenology, and extended object-based phenology to separate rubber plantations and natural forests. The results showed that the two object-based approaches achieved higher accuracy than the pixel-based approach, having overall accuracies of 96.4%, 97.4%, and 95.5%, respectively. This study proved the reliability of a phenology-based rubber mapping in fragmented landscapes with a distinct dry/cool season using Landsat images. This study indicated that the object-based phenology approaches can effectively improve the accuracy of the resultant maps in fragmented landscapes.

The rise in both the frequency of natural disasters and the ubiquity of artificial intelligence has led to novel applications of new technologies in improving disaster response processes, such as the labor-intensive assessment of disaster damages. Assessment of residential and commercial structure damages, a precursory step to government agencies being able to provide most of their financial assistance, has benefited from aerial and satellite imagery-based computer vision models; however, limitations of using such imagery include the ground structures being obscured by clouds or smoke, as well as the lack of resolution to distinguish individual structures from others. Using a different data source, we propose a damage severity classification model using ground-level imagery, focusing on residential structures damaged by wildfires. This classifier, a Vision Transformer (ViT) model trained on over 18,000 professionally labeled images of damaged homes from the 2020-2022 California wildfires, has achieved an accuracy score of over 95%. Further, we have open sourced the training dataset–the first of its kind and scale–as well as built a publicly available web application prototype, which we demoed to and received feedback from disaster response officials, both of which further contribute to the broader literature beyond the proposed model.

Time series ground deformation monitoring and reservoir parameter inversion are crucial for the dynamic assessment of oilfield resources and sustainable exploitation in an oilfield. As one of the regions with the richest oil reserves in China, the oilfield areas in the western Qaidam Basin are selected as a typical study area. Firstly, we used SAR images collected by the Sentinel-1A satellite from January 2021 to December 2022 and applied the Multidimensional Small Baseline Subset (MSBAS) method to obtain vertical and east-west deformation measurements. On this basis, a nonlinear Bayesian inversion method was applied to model the shallow reservoir in a series of complex deformation areas, based on a single-source model and a multi-source model, respectively. As a result, the ground deformation monitoring results obtained by long time series InSAR clearly reflect the uneven ground deformation caused by the oil extraction and water injection operation process. There is slight subsidence in the Huatugou oilfield, while significant uplift deformation occurred in the Ganchaigou oilfield and the Youshashan oilfield, with a maximum uplift rate of 48 mm/year. Further analysis indicates that the introduction of the 2D deformation field helps to improve the robustness of oilfield reservoir parameter inversion. Moreover, the dual-source model is more suitable than the single-source model for inverting complex deformation reservoir parameters. This study not only fills the blank of InSAR deformation monitoring for the oilfields in the western Qaidam Basin but also provides a theoretical reference for model and method selection of reservoir parameter inversion in other oilfields.

The response of structures under pulse-like ground motions is characterized by the large amount of energy input in a few cycles. Consequently, structures with insufficient strength may suffer severe damage owing to excessive deformation. In a previous paper, the energy-based prediction procedure for the peak and cumulative response of a reinforced concrete (RC) frame building with steel damper columns was proposed (Fujii and Shioda, 2023). Although this procedure was verified by comparison to the nonlinear time-history analysis (NTHA) results, the performance of the proposed procedure with pulse-like ground motion records has not been verified yet. In this study, the accuracy of the energy-based prediction procedure for an RC frame building with steel damper columns was investigated by considering pulse-like ground motions. The numerical analysis results reveal that the accuracy of the predicted peak response is satisfactory, which agrees with the results of the author’s previous study. However, the accuracy of the predicted total input energy to the building model depends on the ratio of the pulse period of the ground motion to the effective fundamental period of the building model. The reasons for this underestimation of the total input energy are discussed in this paper.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in the alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more response in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique's potential to differentiate various red seaweed species.

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

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

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

Glioblastoma (GBM) is the most common malignant brain tumor in adults. Despite advancements in treatment, the prognosis for patients with GBM remains poor due to its aggressive nature and resistance to therapy. CRISPR-based genetic screening has emerged as a powerful tool for identifying genes crucial for tumor progression and treatment resistance, offering promising targets for tumor therapy. In this review, we provide an overview of the recent advancements in CRISPR-based genetic screening approaches and their applications in GBM. We highlight how these approaches have been used to uncover genetic determinants of GBM progression and susceptibility to various therapies. Furthermore, we discuss ongoing challenges and future directions of CRISPR-based screening methods in advancing GBM research.

In this article, a method for the moisture mapping of the soil surface of agrophytocenosis was proposed using neural network based on synchronized radar and multispectral optoelectronic data of Sentinel-1,2. To verify the developed method, data from two experimental plots were used. These plots were located two on irrigated soybean crops. The first of them was located on the right bank (1st plot) and the second one on the left bank (2nd plot) of the down part of Volga River. Two experimental soil moisture geo-datasets were done by measurements and geo-referencing points using gravimetric method (1st plot) and with proximal sensing method (2nd plot) using Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict reflection coefficient of an electromagnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated on the basis of Sentinel-1 data) to the index (calculated on the basis of data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). Such way calculated index reveals showed a significantly greater dependence on soil moisture than on vegetation height that was been used in previous studies. The retrieved values of soil moisture were compared with the soil moisture measured in-situ. The proposed method with a determination coefficient of 0.44-0.65 and a standard deviation of 2.4%-4.2% for the 1st plot as well as with and of the same metrics for the 2nd allows predicting the soil moisture of both a test plots covered by soybean plants, relative to soil moisture measured in-situ. The conducted research created the scientific basis for a new technology for remote sensing the moisture content of the soil surface of agrophytocenosis as an element of the precision farming system and agroecology.

Carotid atherosclerotic lesions are a major cause of cerebrovascular disease (CVD). Identification and quantification of carotid plaques are important for categorizing the vulnerability of plaques for rupture and assessing the impact of treatments. The irregularity of plaque surface is associated with previous plaque rupture and plays an important role in the risk of stroke. Thus, the aim of this study is to develop and validate novel vulnerability biomarkers from three-dimensional ultrasound (3DUS) images by analyzing the surface morphological characterization of carotid plaque using fractal geometry features. 3D box-counting and 3D blanket are the two types of 3D fractal dimension that were employed to describe the smoothness of plaques. This fractal dimension analysis tool was used to evaluate the effect of atorvastatin using 3DUS carotid images, which were acquired from 6 patients treated with atorvastatin with 80 mg daily and 5 patients with placebo. The Student's T Test results showed that those two fractal features were effective for detecting the statin-related changes in carotid atherosclerosis with p<0.0068 and p<0.015 respectively, suggesting that 3D fractal dimension measurements can be used effectively to analyze the surface characteristics of carotid plaques, especially for evaluating the impact of the treatment.

There is crescent demand for knowledge improvement of the integrated water vapor (IWV) distribution in regions affected by heat islands that are associated with extreme rainfall events such as in the metropolitan area of Rio de Janeiro (MARJ). This work assessed the suitability and distribution of IWV in the MARJ using products from the Global Navigation Satellite Systems (GNSS), MODerate Resolution Imaging Spectroradiometer (MODIS), and radiosonde. GNSS data were collected by the tracking station named RDJN, from the cooperation of the International GNSS Monitoring and Assessment System (iGMAS) and the National Observatory of Brazil (Observatório Nacional - ON), and the tracking stations ONRJ, RIOD, and RJCG belonging to the Brazilian Network for Continuous Monitoring (RBMC) in the period of January 2015–August 2018. High variability of the near surface air temperature (T) and relative humidity (RH) were observed among eight meteorological sites considered. The mean T differences between sites, up to 4.4 °C, led to mean differences as high as 3.1 K for weighted mean temperature (Tm) and hence 0.83 mm for IWV differences. The performance of the MODIS MOD07 and MYD07 products provided a reasonably good representation of the mean spatial distribution of IWV, especially during the daylight passages of the satellites TERRA and AQUA. Local grid points of MODIS IWV estimates had relatively good agreement with the GNSS-derived IWV, with mean differences from -2.4–1.1 mm considering only daytime passages of the satellites TERRA and AQUA. During nighttime, MODIS underestimated IWV (from -9–-3 mm) with respect to GNSS, due to attenuation of IR radiation by clouds. A contrasting behavior was found in the radiosonde IWV estimates compared with the estimates from GNSS. There were dry biases of 1.4 mm (3.7% lower than expected) by radiosonde IWV during the daytime considering that all other estimates were unbiased and the differences between IWV GNSS and IWV RADS were consistent. Based on the IWV comparisons between radiosonde and GNSS at nighttime, the atmosphere over the radiosonde site is about 1.2 (2.3) mm wetter than over RIOD (RDJN) station. The long time series of the comparisons between IWVRDJN and IWVRIOD showed that the highest values of IWV occurred from the afternoon to nocturnal hours. Further, the atmosphere over the site RIOD was consistently about 1 mm wetter than over RDJN. These results showed the feasibility of the iGMAS RDJN station data compared with the RBMC, MODIS, and radiosonde data to investigate IWV in a region with occurrence of heat islands, and the peculiar physiographic and meteorological characteristics as in the MARJ. This work recommended the usage of complete meteorological station data collocated near every GNSS receiver aiming improvements of local GNSS IWV estimates and serving as additional support for operational numerical assimilation, weather forecast, and nowcast of extreme rainfall events.

An efficient design method is proposed for a compact common-mode rejection (CMR) filter utilizing dumbbell-shaped defected ground (DS-DG) structures and gap-coupled stub (GCS) resonators. A CMR filter for differential lines helps to improve the signal integrity of high-speed digital signals on printed circuit boards. The proposed CMR filter design is based on the equivalent circuit models, while the previous designs depended heavily on the DS-DG structure optimization using the EM simulations. The proposed CMR filter effectively reject the common-mode components with minimally affecting the differential signals. To prove the simplified design approach, a 5th-order Chebyshev band-stop filter has been designed with three DS-DG structures and two GCS resonators. From the simulated and measured results, it is found that the proposed CMR filter provides ~90% fractional frequency bandwidth with more than 20 dB of common-mode rejection ratio and less than 0.6 dB of insertion loss of the differential signal.

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

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

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

The properties of the annual, semiannual and triennial oscillations (AO, SAO and TO) in the middle atmosphere have been investigated using the TIMED/SABER temperature data. The Lomb-Scargle and wavelet spectra were used to determine the dominant oscillations in the background temperature field. The AO is prominent at the mid-latitudes. The AO amplitudes present an asymmetry between the two Hemispheres, being larger in the mesosphere than in the stratosphere. The SAO dominates the tropical regions, with three amplitude maxima at altitudes of 45, 75, and 85 km. The SAOs in the upper mesosphere (75 km) are out of phase with those in the mesopause (85 km) in the tropical regions, which can generate an enhancement of 11 K at each equinox, contributing to the lower mesospheric inversion layer. The TO is significant in the tropical region, with amplitude being maximum at 35, 45 and 85 km. Result shows that there may be potential interaction by the TO with SAO at 85km at the equator. The relation between ENSO and TO has also been discussed. The ENSO signal may modulate the amplitude of the TO, mainly in the lower stratosphere. The real origin of the TO may lie in the wave-mean-flow interaction.

To ensure consumers can purchase high-quality rice, accurate identification of rice varieties is particularly important. Methods: This article conducts research based on machine learning algorithms from the perspectives of image, spectrum, and spectrogram fusion. Six types of hyperspectral image data of rice were preprocessed using convolutional smoothing (SG) and multiple scatter correction (MSC). Texture information of the images was extracted using gray-level co-occurrence matrix. Spectra, texture, and spectrogram data were fused into a new matrix. Spectral, texture, and spectrogram fusion data were used as inputs for the model, and support vector machines (SVM), logistic regression (LR), and K-nearest neighbors (KNN) classification models were constructed and compared. Results: From the classification results, the spectrogram fusion classification performance was better than classification models using only spectra or texture. Conclusion: The research results showed that the accuracy of SVM and LR classification models exceeded 90%, and the LR model performed the best, effectively classifying rice varieties.

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

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

Over the past few decades, aircraft Ground Handling Operations (GHO) have been investigated by numerous researchers. Some aspects of GHO have been more focused on than others due to their importance in the GH processes. For instance, GHO performance has been tackled from different perspectives while workers and vehicles allocation suffer from the lack of research in the area. This paper is a literary review of the research that has been conducted in different areas of GHO. It sheds light on GHO performance, resource allocation and scheduling, operations scheduling, vehicle routing, and workers and vehicles allocation.

Cloud-induced atmospheric extinction and occlusion have a major effect on the effectiveness and quality of telescope observations. Real-time cloud cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. At ground-based astronomical telescope sites, cloud cover distribution is currently analyzed using manual observation methods. However, the main disadvantages of manual observation methods are human subjective, heavy workloads and poor real-time performance. Therefore, a real-time automatic cloud images classification method is desperately needed. This paper presents a new cloud identification method, which is named PSO+XGBoost model by combining eXtreme Gradient Boosting (XGBoost) and Particle Swarm Optimization (PSO). The entire cloud image is divided into 37 sub-regions in order to more precisely identify the distribution of the clouds. 19 features are then extracted, including the sky background, star density, lighting conditions, and subregion grayscale values. The experimental results have shown that the overall classification accuracy of 96.91% and our model is able to outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.