The Conform process (Continuous Rotary Extrusion, CRE) enables the production of magnesium alloy wires like Mg-Zn-Al-Ca (ZAX210) with refined microstructures and tailored textures, but also introduces cross-sectional inhomogeneities that affect the final material response. In this work, a combined experimental and finite element (FE) simulation approach was used to analyse these inhomogeneities in terms of microstructure, texture, and deformation conditions. Optical and electron backscatter diffraction (EBSD) analyses revealed an overall fine grain structure (∼5.7 μm), with slightly smaller grains in the surface layers due to enhanced dynamic recrystallization. Texture development is governed by extrusion- and shear-induced deformation, resulting in a rotated B-fibre (basal plane parallel to shear plane) as the global texture component. Local variations include a B-fibre with weak C 1 -fibre component (c-axis is fibre axis first rotated 90° in shear direction, then 30° in shear plane direction) in the upper region, a strong B-fibre in the middle region, and C 1 /C 2 -fibres in the lower region caused by shear reversal. A fiber texture describes preferred crystallographic orientation, where most grains align a specific crystal direction with a common axis. Twinning activity reflects the heterogeneous deformation state, with ( tension twins dominating overall but being suppressed in high-shear regions. FE simulations confirmed the asymmetric shear stress distribution and complex flow near the abutment, providing a mechanistic link between local strain, recrystallization, and texture evolution. The middle region, representing ∼62 % of the cross-sectional area, was found to dominate the global microstructure and texture.

Abstract High-performance stainless steels have been utilized in reusable rockets to achieve significant reductions in manufacturing and maintenance costs. In this study, the effect of silver doping on the mechanical and tribological properties of 316L stainless steel under cryogenic conditions was experimentally investigated. Three silver mass concentrations of 5 wt%, 10 wt%, and 15 wt%, were selected, and the corresponding microstructural characteristics were analyzed using optical microscopy, energy-dispersive spectroscopy, and electron backscatter diffraction. The cryogenic environment of rocket turbopumps was simulated by immersing the specimens in liquid nitrogen. Rockwell hardness, impact fatigue strength, and tribological performance were subsequently evaluated under both room-temperature and low-temperature conditions, with additional tribological tests conducted under water lubrication for comparison. The results indicated that silver preferentially segregated at austenite grain boundaries, leading to grain refinement and the formation of ductile accommodation regions within the hardened matrix. Silver doping enhanced cryogenic ductility and impact fatigue resistance by promoting plastic deformation capability. Owing to the intrinsic lubricating properties of silver, a self-lubricating film was formed at the sliding interfaces, resulting in reduced friction coefficients and wear-rates. Although silver addition slightly reduced hardness and toughness, its grain-stabilizing effect and interfacial lubricity led to an overall improvement in the cryogenic performance of 316L stainless steel. This work provides useful insights for the development of durable and highly reliable materials for reusable rockets and other cryogenic engineering applications.

Accurate estimation of aboveground carbon stocks in tropical rainforests is fundamental for global climate change mitigation, sustainable forest management, and carbon accounting. Traditional field-based carbon assessment methods are often time-consuming, labour-intensive, and spatially limited, making them unsuitable for large-scale or high-frequency monitoring. Recent advances in remote sensing have provided access to diverse data sources such as optical imagery, radar backscatter, and LiDAR point clouds, yet integrating these heterogeneous datasets effectively remains a major challenge. This study proposes a deep learning framework using an auto-encoder architecture to estimate forest carbon stocks by fusing multi-source remote sensing data. The framework systematically optimizes activation functions, learning rates, and encoder-decoder configurations to enhance model accuracy, stability, and computational efficiency. Experiments were conducted using multi-sensor datasets from tropical rainforests in Borneo, incorporating field-measured biomass data as ground truth. A comprehensive evaluation was performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Index of Agreement (IOA) metrics. The results indicate that ReLU, LeakyReLU, and Softplus activation functions deliver consistent performance across experiments, while RMSprop and Adam optimizers achieve the best convergence stability. The optimized encoder-decoder configuration significantly improves prediction accuracy compared to baseline models. Compared to the baseline encoder-decoder configuration, the proposed optimized architecture achieves a 2.7−3.2% reduction in RMSE, indicating incremental but consistent improvements attributable to architectural balancing rather than increased depth alone. The findings underscore the importance of multi-sensor fusion and systematic hyperparameter optimization for large-area carbon estimation. The proposed framework is scalable, adaptable to different forest ecosystems, and reproducible for other regions worldwide. This research contributes to the advancement of AI-driven environmental monitoring and provides a robust tool to support policy implementation, carbon credit verification, and sustainable forest resource management under Malaysia’s and global climate action agendas.

Abstract. During the southwest monsoon, seasonal storms bring torrential rainfall to the South Asian subcontinent and the northern Indian Ocean. Dense cloud cover limits the amount of sunlight that reaches the ocean surface, and sediment-laden river runoff limits the depths to which light can penetrate. Changing light availability should affect phytoplankton primary productivity and its dependent biogeochemical processes, yet little is known about how subtropical weather is linked to ecosystem processes below the ocean’s surface. Here, using novel physical and bio-optical measurements from an array of free-drifting, autonomous systems in the Bay of Bengal, we show that the onset of cloudy conditions associated with “active” monsoon conditions led to >50 % reduction in gross chlorophyll productivity (GCP) near the subsurface chlorophyll maximum (SCM) relative to sunny “break” conditions. Optical backscatter measurements confirm chlorophyll fluorescence fluctuations correspond to biomass variability of a similar scale. Simultaneous bioacoustic measurements collected onboard the autonomous platforms suggest this intraseasonal variability in SCM chlorophyll and biomass generated a response in higher trophic levels. Long-term measurements from biogeochemical (BGC) Argo floats in the bay confirm the presence of intraseasonal oscillations in chlorophyll a concentration with days-to-weeks variability in magnitude similar to the regional annual cycle in the region. Our findings demonstrate that intraseasonal subtropical air-sea variability modulates important regional biogeochemical ocean processes in the Northern Indian Ocean with implications for the Indian Ocean carbon cycle.

We present a single-photon light detection and ranging (LiDAR) transceiver system, designed for rapid three-dimensional (3D) imaging in highly scattering underwater environments. The system is based on a silicon single-photon avalanche diode (SPAD) detector array fabricated using complementary metal-oxide semiconductor (CMOS) technology. The detector array features 64 × 32 macro-pixels, where each macro-pixel comprised 4 × 4 SPAD detectors. Each macro-pixel is equipped with its own multi-event time-to-digital converter (METDC), facilitating rapid and simultaneous time-tagged acquisition of multiple photon events across the entire macro-pixel via the time-correlated single-photon counting (TCSPC) method. The detector operates in a time-gated mode that enables single-photon detection within an individual pre-defined timing range or within a sequence of time-gates. This multi-event timing approach in gated mode is particularly suited to single-photon LiDAR in turbid underwater environments which necessarily results in very high levels of optical backscatter. The use of the alternative single event TDC approach means that if back-scattered photons are detected first, this will close the detection chain to incoming target return photons until a later reset, leading to sensor inefficiencies as the target return photons will have an increased likelihood of not being detected. The underwater imaging performance was assessed in a water tank containing controlled levels of a scattering agent, with targets placed at a stand-off distance of approximately 1.65 m. The single-photon imaging system achieved detection up to the equivalent of 6.2 attenuation lengths between transceiver and target with an exposure time of only 1 ms. These results show single-photon depth imaging in turbid underwater environments using the ME-TDC approach, demonstrating 3D image acquisition approximately 50 times more rapidly than previously published work based on a 192 × 128 pixel CMOS SPAD detector array incorporating per-pixel single event TDCs to achieve comparable imaging performance levels.

This study examines the through-thickness microstructure and crystallographic texture evolution in friction-stir-welded (FSWed) AA5052-H32 and AA6061-T651 aluminum alloys using a tri-flats threaded pin tool. Optical microscopy and electron backscatter diffraction (EBSD) were employed to characterize grain morphology, boundary misorientation, and texture components across the weld thickness. Both alloys exhibited progressive grain refinement and increased high-angle grain boundary fractions from the top to the bottom of the stir zone due to combined thermal and strain gradients. The FSWed AA5052 displayed dominant {111}<110> and Y + γ fiber components at the upper and mid regions, whereas AA6061 showed more randomized textures. At the bottom region, both alloys developed rotated Goss {011}<01-1> and weak A ({112}<110>) and α fiber components. These results clarify how alloy strengthening mechanisms—solid-solution versus precipitation hardening—govern texture evolution under different strain-path and heat input conditions. The findings contribute to optimizing process parameters and material selection for structural-scale FSW aluminum joints in industrial applications such as bridge decks, transportation panels, and marine structures.

ABSTRACT 7075 aluminum alloy is extensively employed in aerospace, automotive, and marine sectors due to its superior strength-to-weight ratio. Nevertheless, friction stir welded joints often suffer from abnormal grain growth (AGG) during T6 heat treatment. This study investigates the mechanism behind AGG in FSW joints and explores cold rolling as a mitigation strategy. Welding was carried out on 6.3-mm thick 7075-T651 aluminum alloy plates, using optimized parameters followed by cold rolling with 10–30% thickness reduction before undergoing T6 treatment. Optical macroscopy and electron backscatter diffraction (EBSD) showed severe AGG in joints without rolling, whereas cold-rolled samples exhibited finer grains and strong resistance to AGG. The improvement is attributed to increased dislocation density, texture randomization, and enhanced recrystallization potential induced by cold rolling. Vickers microhardness results showed improved hardness and its uniformity across the weld, with the highest value of 197 HV obtained at 30% reduction. Overall, cold rolling, particularly at 30% reduction, effectively stabilizes the microstructure of FSW 7075 aluminum alloy and improves its reliability in heat-treated conditions.

In this paper, we experimentally demonstrate the use of hollow-core fiber (HCF) technology in analog mobile fronthaul photonic links utilizing radio-over-fiber (RoF) and free-space optics (FSO) technology. We also compare the performance of standard silica single-mode fiber (SSMF) and antiresonant HCF in bidirectional RoF and radio over FSO (RoFSO) transmission system. To probe system capacity and limitations, we propose a full duplex transmission scheme, corresponding to the centralized radio access network architecture that uses carrier reuse with seamless antenna operation in a millimeter wave frequency band of 25 GHz. The performance of the system is assessed by analyzing the received radio signal in terms of error vector magnitude (EVM) when subject to the reflections, nonlinearities, and atmospheric turbulences that occur in the optical distribution network consisting of a 1 km SSMF/HCF link and FSO channel. The results indicate that both linear and nonlinear effects, particularly Rayleigh and Brillouin scatterings, significantly impact the SSMF link in the full duplex regime, where a single laser at the central office serves both the downlink (DL) and the uplink (UL). The severe limitation in SSMF-based carrier reuse DL transmission is optical back-scattering, which is highly dependent on UL optical power, controlled by an erbium-doped fiber amplifier (EDFA) at the remote site. Advantageously, the HCF exhibits stable behavior with negligible interference between DL and UL. For an UL optical power of 20 dBm, the counter-propagating 64-quadrature amplitude modulation (64-QAM) downlink signal in SSMF exhibits an EVM degradation of more than 2% compared to HCF. In addition, we examine a 10 km SSMF section, revealing that the DL EVM threshold for 64-QAM is exceeded when the UL EDFA power reaches as little as 14 dBm. We also evaluate the performance of seamless RoFSO links under varying turbulence conditions in the atmospheric chamber. The results indicate that seamless FSO links based on SSMF and HCF exhibit similar EVM performance with no significant difference in degradation due to turbulence.

Multimodal data integration plays a critical role in accurately mapping snow avalanche susceptibility, yet existing models struggle to capture the complex nonlinear interactions among optical imagery, SAR backscatter, snowpack measurements, and meteorological variables. Traditional statistical and shallow machine learning (ML) approaches often rely on handcrafted features and linear assumptions, while recent deep learning (DL) methods employ simplistic fusion strategies that overlook higher-order dependencies and cross-modal discrepancies. To address these limitations, this study introduces a novel multimodal DL framework combining a Covariance-Correlation Differential Transformer (CCDT) for second-order feature extraction with an Avalanche Differential Attention Network (ADA-Net) for targeted cross-modal fusion. CCDT constructs differential covariance correlation matrices to reveal subtle interdependencies across 17 topographic, snowpack, and meteorological inputs, and ADA-Net’s Siamese transformer architecture amplifies critical discrepancies between modalities. Evaluated on a curated dataset of 150 avalanche events along China’s G217 and G218 highways in the Central Tianshan Mountains, our CCDT-ADA-Net framework achieves superior predictive performance (ROC-AUC = 0.9903, F1 = 0.96) and spatial accuracy, successfully classifying 97.4% of historical avalanche release areas within its high-susceptibility zone. It significantly outperforms benchmark models, including Vision Transformer, ConvLSTM, RNN, HybridMLP, and TabNet. Permutation importance analysis confirms snow density, elevation, and snow depth as dominant risk factors. This scalable, interpretable approach offers practical utility for operational avalanche forecasting and infrastructure protection in high-mountain regions.

We present a high-speed underwater optical backscatter communication technique based on acousto-optic light steering. Our approach enables underwater assets to transmit data at rates potentially reaching hundreds of Mbps, vastly outperforming current state-of-the-art optical and underwater backscatter systems, which typically operate at only a few kbps. In our system, a base station illuminates the backscatter device with a pulsed laser and captures the retroreflected signal using an ultrafast photodetector. The backscatter device comprises a retroreflector and a 2 MHz ultrasound transducer. The transducer generates pressure waves that dynamically modulate the refractive index of the surrounding medium, steering the light either toward the photodetector (encoding bit 1) or away from it (encoding bit 0). Using a 3-bit redundancy scheme, our prototype achieves a communication rate of approximately 0.66 Mbps with an energy consumption of ≤ 1 μJ/bit, representing a 60× improvement over prior techniques. We validate its performance through extensive laboratory experiments in which remote underwater assets wirelessly transmit multimedia data to the base station under various environmental conditions.

Microstructural evolution of micaceous mylonites

The significant advancement in additive technologies has made it possible to manufacture metal components in diverse shapes and sizes. Despite this progress, numerous processes and phenomena, along with the implications of producing components layer by layer on their performance under stress, remain inadequately explored. These factors not only affect microstructure but subsequently also the mechanical properties. The positioning of objects within the 3D printer’s workspace can thus significantly play a crucial role in their operational functionality, reliability, and safety of the equipment in an application. This article studies anisotropic properties and the influence of the printing orientation of aluminum alloy (AlSi10Mg) cylindrical tensile samples fabricated through an additive approach on their mechanical properties under tensile loading. Tensile testing of specimens covering seven different spatial orientations in the workspace of a 3D printing machine was performed according to ISO 6892-1 international standard. Minimum and maximum tensile properties (yield and ultimate tensile strength) have been observed in Y-sample and X-sample series, respectively. In contrast, elastic modulus of the 3D printed specimens was minimal for X-sample series, and maximal for Y-sample series. Fracture surfaces of the samples in seven basic spatial orientations were evaluated in synergy with the mechanical testing results determined by optical, electron microscopy, and electron backscatter diffraction (EBSD) textural analysis to find correlation between the strength of the samples and the orientation of grains, their size and morphology. Furthermore, thermodynamic and Scheil–Gulliver simulation has been employed in order to explain the formation of intermetallic phases during additive manufacturing and further justifying observations in microstructure and mechanical properties. The disparity in texture intensity between these regions for samples Y(3) is likely responsible for localized mechanical incompatibilities and strain heterogeneity, resulting in preferential crack paths and reduced mechanical strength compared to the sample Z(3), which presented a more randomized orientation distribution with less distinguishable texture zones, enabling better strain accommodation and more uniform plastic deformation, which correlates with its higher tensile and yield strength.

This study investigates the influence of optical loss induced by the macro-bending of optical fibers on the signal quality of an optical frequency-domain reflectometry (OFDR) system. First, the finite element software COMSOL 5.3 was used to perform numerical simulations of the optical loss of single-mode fibers under different bending radii. The simulations revealed that when the bending radius is relatively small, the optical loss exhibits oscillation as the bending radius varies. Next, an optical backscatter reflectometer (OBR) was employed to measure the optical loss of the optical fiber under different bending radii and numbers of bending loops. The experimental results showed good consistency with the simulation results, and the variation law of optical loss under different bending radii and numbers of bending loops was clarified. An OFDR strain demodulator was used to demodulate the strain signals under loaded conditions with different fiber bending radii and numbers of bending loops. It was found that when the cumulative optical loss increases to a certain threshold, the demodulated signal quality degrades significantly—this confirms that macro-bending loss directly impacts the SNR of OFDR output signals. The findings of this study provide practical guidance for the bending-oriented deployment of optical fiber sensors, which was successfully validated through a real-world structural strain monitoring case.

Improving the efficiency of electric vehicle traction motors requires non-oriented silicon steels with low core loss and favorable magnetic induction. This study aims to clarify the influence of annealing temperature on the microstructure, texture, and magnetic properties of a 3.2%Si–0.9%Al steel, providing guidance for process optimization. Optical metallography, X-ray diffraction, and electron backscatter diffraction were employed to characterize the evolution. Recrystallization was completed between 620 °C and 720 °C, during which fine recrystallized grains replaced the deformed structure, accompanied by the nucleation of {111}<112> and {114}<481> grains. With further annealing from 850 °C to 1050 °C, grain growth occurred, resulting in an α*-fiber texture dominated by {114}<481>. The fraction of high-angle {114}<481> grains increased, while low-angle {111}<112> grains decreased. This microstructural evolution significantly influenced the magnetic properties of non-oriented electrical steel. The P1.5/50 and P1.0/400 core losses reached minimum values of 2.02 W/kg and 16.48 W/kg at 1010 °C and 930 °C, respectively, while B50 decreased slightly from 1.670 T to 1.652 T. These findings indicate that precise control of the annealing temperature is an effective strategy to tailor microstructure and texture, thereby optimizing the magnetic properties of non-oriented electrical steel.

This study investigates the localization of plastic strain near the weld seams of porthole die extruded Al-Mg-Si alloys with primarily unrecrystallized microstructures, relevant to post-forming or crash scenarios. A link was established between the crystallographic texture and the plastic response of the material at a local level. Optical metallography and electron backscatter diffraction (EBSD) were used to systematically characterize the microstructure and textures in the extrudates. It was found that a variation in bridge geometry produced very different patterns of textures near the weld seams. To determine the effect of the texture patterns, a slip system level polycrystal plasticity code was used to simulate the mechanical response for each region of similar texture. The predicted properties were then used to fit a Barlat YLD2004-18p anisotropic yield function, and finite element method (FEM) simulations were conducted using the yield functions as inputs. The results revealed a match between the simulated patterns of strain localization and the experimental strain patterns observed via a micro-scale digital image correlation (DIC) technique for each bridge case. These findings establish crystallographic texture as a primary factor affecting the mechanical behaviour of extruded profiles, opening the door to influencing the weld seam properties through the control of crystallographic texture using die bridge design.

The NSF Ocean Observatories Initiative (OOI) Coastal Endurance Washington Offshore Profiler Mooring (CE09OSPM) was first deployed in April 2014. The mooring is located on the Washington continental slope about 60 km west of Grays Harbor, WA at 46.8517°N, 124.982°W. This mooring includes a McLane® Moored Profiler (MMP), which carries energy-efficient instruments that simultaneously measure water temperature, conductivity, pressure, and dissolved oxygen, as well as photosynthetically active radiation, chlorophyll-a fluorescence, coloured dissolved organic matter, optical backscatter, and water velocity. Moving at about 25 cm/s, the MMP collects up to eight profiles per day between approximately 35 m and 510 m water depth. This data article describes a data set that consists of 3244 daily averaged temperature, practical salinity, potential density, and dissolved oxygen profiles collected between October 2014 and May 2025 that were processed using a MATLAB® toolbox that was specifically created to process OOI MMP data. The toolbox imports unpacked MMP data files, applies the necessary calibration coefficients and data corrections, including adjusting for thermal-lag, flow, and sensor time constant effects, and produces a final, 0.5-dbar binned data set. From the daily, gridded profiler data, we calculated seasonal cycles for each variable using a least squares fit of the annual, semi-annual, and triannual harmonics. These gridded profiler data, which are vital for advancing our understanding of subsurface oceanographic phenomena - including modulation of the California Undercurrent, water mass and upwelling source water variability, marine heat waves, ocean acidification, and the increasing prevalence and severity of seasonal hypoxia in the Northern California Upwelling System - are available via Zenodo at https://doi.org/10.5281/zenodo.15627742.

Abstract We report hydrographic and biogeochemical measurements from a meridional transect performed along 150°W, 30°S to 60°S in the Southern Ocean, plus Polar waters to the east. Both of these areas are sites of annual high‐reflectance features in ocean color remote sensing, which were heretofore never confirmed with in situ measurements. This study aimed to document factors driving phytoplankton productivity and coccolithophore calcification within the circumpolar coccolithophore‐rich band known as the Great Calcite Belt (GCB). We measured concentrations of particulate inorganic carbon (PIC) and biogenic silica (BSi), two common biominerals, sources of ballast for organic matter, and contributors to optical reflectance. Results demonstrated that integrated euphotic standing stocks of PIC were highest in the GCB and at the Polar Front south of 54°S. BSi concentrations were highest south of 54°S. Integrated calcification rates were highest near the Polar and Subantarctic Fronts, whereas peak photosynthesis rates were observed in Subantarctic waters of the GCB, near the site of Subantarctic Mode Water formation. South of ∼54°S, optical particulate backscattering (bbp) of BSi dominated over PIC bbp by 10×, while in the GCB, PIC bbp dominated over BSi bbp by a similar magnitude. The slope of the particle size distribution function became less negative with depth, a trend that reflects larger particles becoming more abundant relative to smaller particles. Moreover, typical relationships between the particle size distribution slope and beam attenuation were only observed in the top 50 m depth, suggesting a fundamental difference in particle composition and size for deeper suspensions in this region.

Abstract This paper constructs a fault prevention and control system for distribution networks by integrating multi-source data perception and intelligent algorithms. Through refined modeling and optimization using wavelet transforms, the positioning error is stably controlled within 200 meters. By combining the advantages of LSTM and BP algorithms, the prediction accuracy reaches 92.3%. Based on the principle of optical backscattering, the system enables real-time monitoring of line temperature for fault warnings. The paper also explains the fast isolation technology of solid-state circuit breakers, which can cut off fault lines within 85 milliseconds in coordination with monitoring devices. Simulation results using PSCAD/EMTDC and pilot tests in Guangdong demonstrate that this system significantly reduces medium-voltage line fault rates by 37.6%, improving power supply reliability.

The corrosion behavior of an additively manufactured Mg-1Zn alloy was investigated in both the transverse and longitudinal directions relative to the build direction, in the as-built condition and after annealing at 350 °C for 24 h under high vacuum. Microstructural characterization using XRD and SEM revealed the presence of magnesium oxide (MgO) and the absence of intermetallic second-phase particles. Optical microscopy (OM) images and Electron Backscatter Diffraction (EBSD) maps showed a highly complex grain morphology with anomalous, anisotropic shapes and a heterogeneous grain size distribution. The microstructure includes grains with a pronounced columnar morphology aligned along the build direction and is therefore characterized by a strong crystallographic texture. Electrochemical techniques, including PDP and EIS, along with gravimetric H2 collection, concluded that the transverse plane exhibited greater corrosion resistance compared to the longitudinal plane. Additionally, an increase in cathodic kinetics was observed when comparing as-built with heat-treated samples.

ABSTRACT Wetlands are vital ecosystems that support regional ecological balance and require efficient, accurate, and cost-effective monitoring approaches. This study enhances wetland mapping in the Sanjiang Plain using multi-source (Sentinel-1 SAR and Sentinel-2 optical) and multi-temporal (2018–2021) satellite data on the Google Earth Engine platform. A Random Forest classifier was applied with training samples from high-resolution imagery and field surveys. Input features included spectral bands, vegetation indices, and radar backscatter coefficients. Preprocessing involved cloud masking for Sentinel-2 and speckle filtering for Sentinel-1. Classification accuracy was evaluated using independent validation samples, overall accuracy, and Kappa coefficient. Results indicate October images provide the highest single-year mapping accuracy, with Sentinel-1 and Sentinel-2 data from October 2021 achieving 85.4% accuracy and a Kappa of 0.669. Multi-temporal data improved accuracy to 88.9% (Kappa = 0.749). Sentinel-1 showed greater annual variation in wetland distribution compared to the stable Sentinel-2. Precipitation impacted accuracy, reducing Sentinel-2 performance while variably affecting Sentinel-1. Combining radar and optical data with multi-temporal analysis enhances wetland monitoring, offering guidance for data acquisition in large-scale conservation. Challenges include misclassifications like water-shadow confusion in optical imagery and backscatter interference from vegetation in radar data, requiring further research.