ATLID (ATmospheric LIDar) is the atmospheric backscatter LIDAR (Light Detection and Ranging) on board of the EarthCARE (Earth Cloud, Aerosol and Radiation Explorer) mission, the sixth Earth Explorer Mission of the ESA (European Space Agency) Living Planet Programme [1-5]. ATLID’s purpose is to provide vertical profiles of optically thin cloud and aerosol layers, as well as the altitude of cloud boundaries [6-10]. In order to achieve this objective ATLID emits short duration laser pulses in the UV, at a repetition rate of 51 Hz, while pointing in a near nadir direction along track of the satellite trajectory. The atmospheric backscatter signal is then collected by its 620 mm aperture telescope, filtered through the optics of the instrument focal plane assembly, in order to separate and measure the atmospheric Mie and Rayleigh scattering signals. With the completion of the full instrument assembly in 2019, ATLID has been subjected to an ambient performance test campaign, followed by a successful environmental qualification test campaign, including performance calibration and characterization in thermal vacuum conditions. In this paper the design and operational principle of ATLID is recalled and the major performance test results are presented, addressing the main key receiver and emitter characteristics. Finally, the estimated instrument, in-orbit, flight predictions are presented; these indicate compliance of the ALTID instrument performance against its specification and that it will meet its mission science objectives for the EarthCARE mission, to be launched in 2023.

In the context of the Atmospheric Observing System (AOS) international program, a new generation spaceborne lidar is expected to be in polar orbit for deriving new observations of aerosol and clouds. In this work, we analyze the added values of these new observations for characterizing aerosol vertical distribution. For this, synthetic observations are simulated using the BLISS lidar simulator in terms of backscatter coefficient at 532 nm. We consider two types of lidar instruments, an elastic backscatter lidar instrument and a high spectral resolution lidar (HSRL). These simulations are performed with atmospheric profiles from a Nature Run (NR) modeled by the MOCAGE Chemical Transport Model. In three case studies involving large events of different aerosol species, the added value of the HSRL channel for measuring aerosol backscatter profiles with respect to simple backscatter measurements is shown. Observations independent from an a-priori lidar Ratio assumption, as done typically for simple backscattering instruments, allows probing the vertical structure of aerosol layers without divergence, even in case of intense episodes. Relative error in the backscatter coefficient profiles are observed to lay between +40% and -40% for low abudancies, with mean biases between +5% and -5%. A 5-day study in the case of desert dust completes the study of the added value of the HSRL channel with relative mean bias from the NR of the order of 1.5%.

Archaeological topography identification from high-resolution DEMs is a current method that is used with high success in archaeological prospecting of wide areas. I present a methodology trough which burial mounds (tumuli) from LiDAR DEMS can be identified. This methodology uses geomorphometric and statistical methods to identify with high accuracy burial mound candidates. Peaks, defined as local elevation maxima are found as a first step. In the second step, local convexity watershed segments and their seeds are compared with positions of local peaks and the peaks that correspond or have in vicinity local convexity segments seeds are selected. The local convexity segments that correspond to these selected peaks are further feed to a Random Forest algorithm together with shape descriptors and descriptive statistics of geomorphometric variables in order to build a model for the classification. Multiple approaches to tune and selected the proper training dataset, settings and variables were tested. The validation of the model was performed on the full dataset where the training was performed and on an external dataset in order to test the usability of the method for other areas in a similar geomorphological and archaeological setting. The validation was performed against manually mapped and field checked burial mounds from two neighbor study areas of 100 km² each. The results show that by training the Random Forest on a dataset composed of between 75% to 100% of the segments corresponding to burial mounds and ten times more non-burial mounds segments selected using latin hypercube sampling, 93% of the burial mound segments from the external dataset are identified. There are 42 false positive cases that need to be checked, and there are two burial mound segments missed. The method shows great promise to be used for burial mound detection on wider areas by delineating a certain number of tumuli mounds for model training.

Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR) aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL) and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m) are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification.

The result of the multidisciplinary collaboration of researchers from different areas of knowledge to validate a solar radiation model is presented. The MAPsol is a 3D local-scale adaptive solar radiation model that allows to estimate direct, diffuse and reflected irradiance for clear sky conditions. The model includes the adaptation of the mesh to complex orography and albedo, and considers the shadows cast by the terrain and buildings. The surface mesh generation is based on surface refinement, smoothing and parameterisation techniques and allows the generation of high quality adapted meshes with a reasonable number of elements. Another key aspect of the paper is the generation of a high-resolution digital elevation model (DEM). This high-resolution DEM is constructed from LiDAR data and its resolution if two times more accurate than the publicly available DEMs. The validation process uses direct and global solar irradiance data obtained from pyranometers at the University of Salamanca located in an urban area affected by systematic shading from nearby buildings. This work provides an efficient protocol for studying solar resources, with particular emphasis on areas of complex orography and dense buildings where shadows can potentially make solar energy production facilities less efficient.

Impervious surface mapping with high-resolution remote sensing imagery has attracted increasing interest as it can provide detailed information for urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping performs better than using only high-resolution imagery. However, due to the high cost of the acquisition of LiDAR data, it is difficult to obtain the multi-sensor remote sensing data acquired at the same acquisition time for impervious surface mapping. Consequently, real landscape changes between multi-sensor remote sensing data at different acquisition times would lead to the error of misclassification in impervious surface mapping. This issue has mostly been neglected in previous works. Furthermore, the observation differences generated from multi-sensor data, including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images would also challenge the final mapping result in the fusion of LiDAR data and high-resolution images. In order to conquer these problems, we propose an improved impervious surface mapping method incorporating both LiDAR data and high-resolution imagery at different acquisition times in consideration of real landscape changes and observation differences. In the proposed method, a multi-sensor change detection by supervised multivariate alteration detection is employed to obtain changed areas and misregistration areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution imagery are extracted by independent classification yielded by its corresponding single sensor data. Finally, an object-based post-classification fusion is proposed to take advantage of independent classification results with single-sensor data and the joint classification result with stacked multi-sensor data. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and obviously improve the performance of impervious surface mapping.

This algorithm is designed to perform Discrete Fourier Transforms (DFT) to convert temporal data into spectral data. This algorithm obtains the Fourier Transforms by studying the Coefficient of Determination of a series of artificial sinusoidal functions with the temporal data, and normalizing the variance data into a high-resolution spectral representation of the time-domain data with a finite sampling rate. What is especially beneficial about this DFT algorithm is that it can produce spectral data at any user-defined resolution.

The sensitivity of exercise ECG is marginally sufficient for detection of mild reduction of coronary blood flow in patients with early coronary atherosclerosis. Here we describe the application of new technique of ECG registration/analysis – ultrahigh resolution ECG (UHR ECG) – for early detection of myocardial ischemia (MIS). The utility of UHR ECG vs. conventional ECG (C ECG) was tested in anesthetized rats and pigs. Transmural MIS was induced in rats by the ligation of the left coronary artery (CA). In pigs, subendocardial ischemia of variable extent was produced by stepwise inflation of balloon within the right CA causing 25-100% reduction of its lumen. In rats, a reduction in power spectral density (PSD) in high-frequency (HF) channel of UHR ECG was registered at 60 s after ischemia (power 0.81±0.14 vs. 1.25±0.12 mW at baseline, p<0.01). This was not accompanied by any ST segment elevation on C ECG. In pigs, PSD in HF channel of UHR ECG was significantly decreased at 25% reduction of CA lumen, while ST segment on C ECG remained unchanged. In conclusion, UHR ECG enabled earlier detection of transmural MIS compared to C ECG. PSD in HF-channel of UHR ECG demonstrated greater sensitivity in the settings of subendocardial ischemia.

Automated extraction of buildings from earth observation (EO) data is important for various applications, including updating of maps, risk assessment, urban planning, policy making. Combining data from different sensors such as high-resolution multispectral (HRI) and light detection and ranging (LiDAR) has shown great potential in building extraction. Deep learning (DL) is increasingly used in multimodal data fusion and urban object extraction. However, DL-based multimodal fusion networks may underperform due to insufficient learning of “joint features” from multiple sources and oversimplified approaches to fusing multimodal features. Recently, an hybrid attention-aware fusion network (HAFNet) has been proposed for building extraction from a dataset including co-located Very-High-Resolution (VHR) optical images and Light Detection And Ranging (LiDAR) joint data. The system reported good performances thanks to the adaptivity of the attention mechanism to the features of the information content of the three streams but suffered from model overparametrization, which inevitably leads to long training times and heavy computational load. In this paper the authors propose a restructuring of the scheme, which involved replacing VGG-16-like encoders with the recently proposed EfficientNet, whose advantages counteract exactly the issues found with the HAFNet scheme. The novel configuration was tested on multiple benchmark datasets, reporting great improvements in terms of processing times, and also in terms of accuracy. The new scheme, called HAFNetE (HAFNet with EfficientNet integration), appears indeed capable of achieving good results with less parameters, translating into better computational efficiency. Based on these findings, we can conclude that, given the current advancements in single-thread schemes, the classical multi-thread HAFNet scheme could be effectively transformed by the HAFNetE scheme by replacing VGG-16 with EfficientNet blocks on each single thread. The remarkable reduction achieved in computational requirements moves the system one step closer to on-board implementation in a possible, future “urban mapping” satellite constellation.

The salt marshes, lying at the land-sea temperate interface, furnish a plethora of ecosystems services such as biodiversity niche support, ocean-climate change regulation, ornithology recreo-tourism or plant gathering by hand. They undergo significant worldwide losses due to their conversion into crop fields and to their spatial compression between the rising sea-level and the armoring shoreline. Their monitoring however requires to use a suite of remote sensing sensors to embrace the regional scale while capturing the plant details. This research innovatively adopts a multiscale approach using a cascading spaceborne and airborne process, from the 10-m Sentinel-2, through the 3-m Dove, to the 0.03-m unmanned airborne vehicle (UAV) imageries. The high to very high temporal resolution of the Sentinel-2 and Dove enabled to cover twenties and tens of km2 over five and four years, respectively, in the form of normalized difference vegetation index (NDVI) classes, associated with microphytobenthos, low, medium and high salt marsh vegetation, including the opportunistic Elyma genus. The NDVI was then modelled at the UAV scale (a few km2) using a three-layered NN prediction, providing the final near-infrared (NIR), and the intermediate red, green and blue reflectance imageries, calibrated/validated/tested with the Dove reflectance imageries (R2NIR=0.98, R2red=0.88, R2green=0.84, and R2blue=0.90). The 100fold increase in pixel size allowed to detect the decimeter-scale objects of the tidal flats and salt marshes, to enlarge the NDVI class ranges, and hold great promise to model other spectral bands at the UAV scale for further deeply enhancing the salt marsh mapping.

The rapidly increasing adoption of high-resolution accurate-mass methods in analytical laboratories has fueled demand for instruments that combine high performance and reliability with small size and greater ease-of-use. This paper presents the major design principles that are driving the evolution of the hybrid quadrupole-Orbitrap instrument architecture to enable a greater range of applications and users. These principles may be summarized as follows: better usage of physical space and better access for service by means of size reduction of pumping and ion optics; expanded use of technologies from electronics in ion-optical design; flexibility in performance via modularity of design of the hardware and software components; and, harmonization of interfaces with other instruments to facilitate sharing and transferability of analytical workflows. The design of a novel family of hybrid mass spectrometers is described in detail, and performance evaluation is carried out on a wide variety of samples for its three representatives: the Orbitrap Exploris 120, Orbitrap Exploris 240 and Orbitrap Exploris 480 mass spectrometers.The new instrument family is shown to offer compelling potential not only for high-end proteomics and biopharmaceutical applications, but also for screening, trace, targeted and clinical analysis by liquid chromatography/mass spectrometry methods.

Topology optimization is a pioneering design method that can provide various candidates with high mechanical properties. However, the high-resolution for the optimum structures is highly desired, normally in turn leading to computationally intractable puzzle, especially for the famous Solid Isotropic Material with Penalization (SIMP) method. In this paper, an efficient and high-resolution topology optimization method is proposed based on the Super-Resolution Convolutional Neural Network (SRCNN) technique in the framework of SIMP. The SRCNN includes four processes, i.e. refining, path extraction & representation, non-linear mapping, and reconstruction. The high computational efficiency is achieved by a pooling strategy, which can balance the number of finite element analysis (FEA) and the output mesh in optimization process. To further reduce the high computational cost of 3D topology optimization problems, a combined treatment method using 2D SRCNN is built as another speeding-up strategy. A number of typical examples justify that the high-resolution topology optimization method adopting SRCNN has excellent applicability and high efficiency for 2D and 3D problems with arbitrary boundary conditions, any design domain shape, and varied load.

With the continual global increase of PV power systems and the inherent weather-induced volatility of their power output, understanding the underlying variability of solar radiation in both space and time is important for the planning and reliable operation of future power grids. This paper concisely reviews recent advances in the characterization of irradiance variability with an emphasis on small spatial and temporal scales (respectively less than about 10\,km and 1\,min), for which comprehensive data sets are time consuming and expensive to collect, and although needed have not been available until recently. Special attention is given to studies dealing with the quantification of variability using such unique data, the analysis and modeling of spatial smoothing, and the evaluation of temporal averaging.

This article presents results of the polarization laser studies of the optical and microphysical characteristics of the high-level clouds (HLC). The high-altitude matrix polarization lidar (HAMPL; Tomsk, Russia) is described. HAMPL measures vertical profiles of all elements of the backscattering phase matrix (BSPM) of the HLC. Based on the joint analysis of lidar and radiosonde observations it is shown that the spatial structure of the HLC containing oriented ice crystals is inhomogeneous in the horizontal wind direction. It includes local areas with oriented particles; the sizes of such areas are estimated together with the most probable meteorological conditions of their formation. The shortcomings of the radiosonde observations performed closest to the location of the HAMPL are described. The applicability of the ERA5 reanalysis data of the European Centre for Medium-Range Weather Forecasts for use as an alternative source of information on the vertical profiles of meteorological quantities for the interpretation of HLC lidar sensing data in Western Siberia was checked.

Semantic segmentation of high-resolution remote sensing images provides detailed and precise feature information, playing a pivotal role in decision-making and analysis in sectors such as water management, agriculture, military, and environmental protection. However, most methods merely combine features from various branches directly, without a mechanism for spatial location feature screening, and treat all extracted features as equally important. To overcome these limitations, we introduce a novel spatially adaptive interaction network (SAINet) for dynamic interaction across different features in remote sensing semantic segmentation task. Specifically, we propose a spatial refined module that leverages local context information to filter spatial locations and extract salient regions. Following this, we introduce an innovative spatial interaction module that uses a spatial adaptive modulation mechanism to dynamically select and allocate spatial position weights. This facilitates interaction between local salient areas and global information, allowing the network to focus more on relevant regions. This flexible approach enables the network to capture more informative features, thereby enhancing segmentation accuracy. Experiments conducted on the DeepGlobe, Vaihingen, and Potsdam public datasets confirm the effectiveness and capability of SAINet. Our code and models will be publicly available

Conclusions: HRCT is a valuable tool in preoperative assessment of cholesteatoma, helping in making surgical decisions. It can accurately predict the extent of disease and is helpful for detecting unapparent dangerous situations. However it is not very accurate in detecting Fallopian canal and stapes erosion.

This article investigates the potential for carbon reduction in urban parks in Shangqiu City using high-resolution remote sensing imagery. The aim is to guide modern urban carbon neutrality strategies. The carbon reduction potential is estimated based on the mitigation of the urban heat island effect by park greenery, which reduces energy consumption. The parks are regarded as cool island centers, and 100 cooling gradients are gradually formed outward until the temperature reaches that of impermeable urban surfaces. The energy saved by the parks in mitigating the heat island effect is statistically calculated as the carbon reduction potential of urban park greenery. Additionally, this article classifies the sample parks into different categories and selects 26 landscape metrics to analyze their relationship with carbon-saving potential and driving factors.

Plasma and serum are the most widely used blood-derived biofluids for metabolomics and lipidomics assays, but the isolation of these products from blood may introduce additional bias as indicated by the fact that many analytes that are present at high concentrations in blood cells cannot be measured and evaluated in those samples. Of particular concern, variable hemolysis during the pre-processing of blood products could compromise accurate and reproducible quantification. Compared with plasma or serum, whole blood may be a better alternative due to simplicity of processing. In this study, we provide a comprehensive method for quantification of the whole blood sphingolipidome and the concentrations were compared with those from plasma. Combining a single-phase extraction method with liquid-chromatography high resolution mass spectrometry (R=120, 000), assisted by alkaline hydrolysis, we were able to identify and simultaneously quantify more than 150 sphingolipids. Furthermore, most of sphingolipids remained stable after a freeze/thaw cycle. Whole blood contained a higher concentration of most sphingolipids than corresponding plasma. Moreover, individual variations in the levels of sphingolipids were lower for whole blood than plasma. These findings demonstrate that whole blood could be a better alternative to plasma, and potentially guide the evaluation of sphinglipidome for biomarker discovery.

This paper presents an advanced microstructural analysis of the AlSiMg, Ti64 and N700 powders used for additive manufacturing. The internal microstructure of the regular and irregular powder grains were characterized down to atomic resolution by using scanning electron microscopy and high resolution scanning transmission electron microscopy.The accretionary forms on top of the irregular AlSiMg powder grains exhibit a slightly coarse microstructure with a network of eutectic Si consisting of nano-crystallites, suggestinga slower cooling than the grain itself that contain a predominately amorphous Si network. A nm thin amorphous C layer on the surface of some Ti64 plasma atomized powder grains promoted the attachment of satellites and growth of envelopes. In case of gas atomized N700 powder grains, we identified thin oxide and carbon amorphous layers as well as metal segregations at the interface between the grain body and the accretionary forms.

Marine renewable energy technologies have a great potential in supplying clean electricity to millions of people across the globe, if technical and economic conditions are in right. So far, ocean energy projects are commonly started by SMEs or educational institutions with limited budgets. Therefore, any effort to reduce expenses is of great value. One of the areas involving substantial expenses are the inevitable seabed inspection prior to deployment of marine renewable energy device. Detailed seabed inspections can also reduce the risk of associated with deployment of structures on uneven seabed, especially marine renewable energy devices with gravity foundations. By reducing the costs and risks of such surveys prior and during the installation phases, the feasibility of marine renewable energy projects can be more favoured and competitive. In this perspective, this study proposes a cost and time effective technique for seabed surveys. The proposed technique involves the use of high precision and inexpensive sonar systems and underwater optical cameras integrated into a versatile and compact subsea monitoring platform. It also involves simple and practical data acquisition and processing protocols that do not requires hi expertise for operation. The results obtained showed that high resolution bathymetric maps and detailed seabed inspections imagery can be acquired. This study concludes that a simple and inexpensive subsea monitoring platform comprising a multibeam, dual beam and video cameras can be effective for high resolution seabed inspection and bathymetric measurements for marine energy applications.

Geologists employ high-spatial-resolution (HR) remote sensing (RS) data for many diverse applications as they effectively reflect detailed geological information, enabling high-quality and efficient geological surveys. Applications of HR RS data to geological and related fields have grown recently. By analyzing these applications, we can better understand the results of previous studies and more effectively use the latest data and methods to efficiently extract key geological information. HR optical remote sensing data are widely used in geological hazard assessment, seismic monitoring, mineral exploitation, glacier monitoring, and mineral information extraction due to high accuracy and clear object features. Compared with optical satellite images, synthetic-aperture radar (SAR) images are stereoscopic and exhibit clear relief, strong performance, and good detection of terrain, landforms, and other information. SAR images have been applied to seismic mechanism research, volcanic monitoring, topographic deformation, and fault analysis. Furthermore, a multi-standard maturity analysis of the geological applications of HR images using literature from the Science Citation Index reveals that optical remote sensing data are superior to radar data for mining, geological disaster, lithologic, and volcanic applications, but inferior for earthquake, glacial, and fault applications. Therefore, geological remote sensing research needs to be truly multidisciplinary or interdisciplinary, ensuring more detailed and efficient surveys through cross-linking with other disciplines. Moreover, the recent application of deep learning technology to remote sensing data extraction has improved automatic processing and data analysis capabilities.

Incident solar radiation (insolation) passing through the forest canopy to the ground surface is either absorbed or scattered. This phenomenon, known as radiation attenuation, is measured using the extinction coefficient (K). The amount of radiation at the ground surface of a given site is effectively controlled by the canopy’s surface and structure, determining its suitability for plant species.Menhinick’s and Simpson biodiversity indexes were selected as spatially explicit response variables for the regression equation using canopy structure metrics as predictors. Independent variables include modeled area solar radiation, LiDAR derived canopy height, effective leaf area index data derived from multi-spectral imagery, and canopy strata metrics derived from LiDAR point-cloud data. The results support the hypothesis that, 1.) canopy surface and strata variability may be associated with understory species diversity due to habitat partitioning and radiation attenuation, and that, 2.) such a model can predict both this relationship and biodiversity clustering.The study data yielded significant correlations between predictor and response variables and was used to produce a multiple-linear model comprising canopy relief, texture of heights, and vegetation density to predict understory plant diversity. When analyzed for spatial autocorrelation, the predicted biodiversity data exhibited non-random spatial continuity.

Accurate analysis of continuous-wave electron spin resonance (cw ESR) spectra of biological or organic free-radicals and paramagnetic metal complexes is key to understand their structure-function relationships and electrochemical properties. Current method of analysis based on simulations often fail to extract the spectral information accurately. In addition, such analyses are highly sensitive to spectral resolution and artifacts, users’ defined input parameters and spectral complexity. We introduce a simulation-independent spectral analysis approach that enables broader application of ESR. We use a wavelet packet transform-based method for extracting g values and hyperfine (A) constants directly from cw ESR spectra. We show that our method overcomes the challenges associated with simulation-based methods for analyzing poorly / partially resolved and unresolved spectra, which is a common in most cases. The accuracy and consistency of the method are demonstrated on a series of experimental spectra of organic radicals and copper-nitrogen complexes. We showed that for a two-component system, the method identifies their individual spectral features even at a relative concentration of 5% for the minor component.

Due to the lack of theoretical research on the amount of spatio-temporal information in the high-speed photography technologies, obtaining an optimized system with a best amount of spatio-temporal information remains a challenge, resulting in insufficient effective information and observation accuracy for ultrafast events. The paper presents an ultrafast raster imaging (URI) system with a large amount of spatio-temporal information based on the all-optical raster principle in single-shot. Specifically, we derive the optimal equation of spatial resolution and the expression for the maximum amount of spatio-temporal information that can achieve excellent performance for an URI system. It serves as a general guideline for getting a large amount of information design in the URI system. Compared to the existing URI systems, the advanced URI system exhibits an improvement of nearly 1 order of magnitude in the amount of spatio-temporal information, and more than twofold in spatial resolution. It shows a great potential for capturing intricate and non-repetitive ultrafast events on the femtosecond time scale.

Background: Children with anorectal malformation (ARM) require long-term follow-up, especially for fecal continence and main predictors of longer-term success are the type of ARM, associated anomalies and sacral integrity. Three-Dimensional High Resolution Anorectal Manometry (3D-HRAM) provides an image of the pressure profile of anal sphincter complex. Aim of study was to evaluate the anal sphincter function in ARM patients with 3D HRAM correlating this analysis to clinical outcome and to associated malformations. Methods: Forty ARM patients, were submitted to 3D HRAM: manometric, anatomical and clinical score were correlated to response to bowel management (BM). Results: A positive correlation between all scores and type of ARM was found: in high ARM and in patients with spinal anomalies (regardless to ARM type) lower scores were reported and even after BM they do not achieve good continence. Conclusions: 3D-HRAM provides information on the functional anatomy of the sphincter complex. Our study shows good correlation between the manometric results and clinical outcome, confirming spinal malformations and ARM type as most important prognostic risk factors for poor outcome. Specific sphincteric defects can be explored with manometry, allowing for tailored bowel management strategies.

Currently available nationwide prediction models for fine particulate matter (PM_2.5) lack prediction confidence intervals and usually do not describe cross validated model performance at different spatiotemporal resolutions and extents. We used 41 different spatiotemporal predictors, including data on land use, meteorology, aerosol optical density, emissions, wildfires, population, traffic, and spatiotemporal indicators to train a machine learning model to predict daily averages of PM_2.5 concentrations at 0.75 sq km resolution across the contiguous United States from 2000 through 2020. We utilized a generalized random forest model that allowed us to generate asymptotically-valid prediction confidence intervals and took advantage of its usefulness as an ensemble learner to quickly and cheaply characterize leave-one-location-out CV model performance for different temporal resolutions and geographic regions. Using a variable importance metric, we selected 8 predictors that were able to accurately predict daily PM_2.5, with an overall leave-one-location-out cross validated median absolute error of 1.20 ug/m³, an R² of 0.84, and confidence interval coverage fraction of 95%. When considering aggregated temporal windows, the model achieved leave-one-location-out cross validated median absolute errors of 0.99, 0.76, 0.63, and 0.60 ug/m³ for weekly, monthly, annual, and all-time exposure assessments, respectively. We further describe the model’s cross validated performance at different geographic regions in the United States, finding that it performs worse in the Western half of the country where there are less monitors. The code and data used to create this model are publicly available and we have developed software packages to be used for exposure assessment. This accurate exposure assessment model will be useful for epidemiologists seeking to study the health effects of PM across the continental United States, while possibly considering exposure estimation accuracy and uncertainty specific to the the spatiotemporal resolution and extent of their study design and population.

The aim of this study is to investigate, by means of experimental measurements and full-wave simulations, the dominant factors for the very high-resolution (VHR) radar image speckles from exponential correlated rough surfaces. A Ka-band radar system was used to collect the return signal from such a surface sample fabricated by 3D printing, and that signal was further processed into images at different resolution scales, where the image samples were obtained by horizontally turning around the surface sample. To cross-validate the results and to further discuss the VHR speckle properties, full wave simulations by full 3D Finite Difference Time Domain (FDTD) method were conducted with 1600 realizations for the speckle analysis. At the considered very high resolution, speckle statistics show divergence from the fully developed Rayleigh distribution. The factors that impact on the high-resolution speckle properties from exponential correlated rough surface, are analyzed in views of the equivalent number of scatterers theory and scattering scales, respectively. From the data results and extended discussions, it is evident that both of the above factors matter for VHR speckle of backscattering, from the exponential correlated rough surface as a good representative for the ground surface.

Karst rocky desertification (KRD) is a process where strong anthropogenic disturbances and exposure of carbonate bedrock occurs in fragile karst ecosystems. The fractional cover of rocky outcrops is a key indicator and mechanistic driver of KRD and can be accurately assessed using remote sensing technology. Nevertheless, rugged karst terrain relief can cause shadow effects on satellite imagery and combine with high heterogeneity of karst landscapes to prevent fractional cover retrievals. In this study, we explored the feasibility of applying multispectral high spatial resolution ALOS imagery for fractional cover extraction of rocky outcrops. We selected the dimidiate pixel model (DPM), which has been applied in previous studies, and spectral mixture analysis (SMA; including simple endmember spectral mixture analysis (SESMA) and multiple endmember spectral mixture analysis (MESMA)) to explore the feasibility of using remote images for KRD monitoring and improve accuracy for estimating fractions. Results from MESMA achieved high overall accuracy (76.4%) in monitoring percentage of rocky outcrop fraction in the study area. SESMA appears to underestimate percentage of rocky outcrop likely because the development of KRD was driven by complex factors (soil erosion, dissolution and anthropogenic disturbances). This results in spectral reflectance of rocky outcrop being variable in different settings. Predicted exposed bedrock coverage using SESMA and MESMA was similar in sun-lit and shaded areas although predictions from SESMA were smaller than reference data. DPM underestimated the fractional cover of rocky outcrops on south-facing slopes and overestimated it in shaded areas. Furthermore, SESMA and MESMA effectively reduced topographic effects. We conclude that it is better to extract percentage of rocky outcrop using MESMA in the karst region of southwestern China. Remote sensing is emerging as a feasible method to extract surface condition information in heterogeneous and rugged landscapes.

Mineral dust is one of the most important aerosol in mass over the world, affecting health and dynamics. This aerosol is mainly emitted over arid areas but may be long-range transported, impacting the local budget of air quality in many cities. While models were extensively used to study a single specific event, or making a global analysis at coarse resolution, the goal of our study is to simultaneously focus on several areas - Europe, North America, Central Asia, East China and the Caribbean area - for a one month period, March 2014, avoiding any parameter fitting to better simulate a given dust outbreak. The simulation is performed for the first time with the hemispheric version of the CHIMERE model, with a horizontal resolution of 10km. In this study, an overview of several simultaneous dust outbreaks over the north hemisphere is proposed to assess the capability of such modelling tools to predict dust pollution events. A quantitative and qualitative evaluation of the most striking episodes is presented with comparisons to satellite data, ground based Particulate Matter and calcium measurements. Despite some overestimation of dust concentrations far from emission areas, the model is able to simulate the timing of the arrival of dust outbreaks on observational sites. For instance, several spectacular dust storms in the US and China are perfectly captured by the models. The high resolution allows to better describe and understand the orographic effects and the long-range transport of dust plumes.

The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the past decade, particularly as DEMs are relatively cheaply acquired at very high resolutions (VHR; <1m spatial resolution). Here, we implement a multiscale framework and compare DEM-derived variables produced by Light Detection and Ranging (LiDAR) and stereo-photogrammetry (PHOTO) methods, with the aims of assessing their relevance and utility in species distribution modelling (SDM). Using a case study on the arctic-alpine plant Arabis alpina in two valleys in the western Swiss Alps, we show that both LiDAR and PHOTO technologies can be relevant for producing DEM-derived variables for use in SDMs. We demonstrate that PHOTO DEMs rivalled the accuracy of LiDAR, putting the current paradigm of LiDAR being the more accurate of the two methods into question. We obtained DEMs at spatial resolutions of 6.25cm-8m for PHOTO and 50cm-32m for LiDAR, where we determined that the optimal spatial resolutions of DEM-derived variables in SDM were between 1 and 32m, depending on the variable and site characteristics. We found that the reduced extent of PHOTO DEMs altered the calculations of all derived variables, which had particular consequences on their relevance at the site with heterogenous terrain. However, for the homogenous site, we found that SDMs based on PHOTO-derived variables generally had higher predictive powers than those derived from LiDAR at matching resolutions. From our results, we recommend carefully considering the required DEM extent to produce relevant derived variables. We also advocate implementing a multiscale framework to appropriately assess the ecological relevance of derived variables, where we caution against the use of VHR-DEMs finer than 50cm in such studies.

Windthrow (i.e., trees broken and uprooted by wind) is a major natural disturbance in the Amazon. Images from medium-resolution optical satellites (mostly Landsat) combined with extensive field data have allowed researchers to assess patterns of tree mortality and monitor forest recovery over decades of subsequent succession in different regions. Although satellites with high spatial-resolution have become available for the Amazon in the last decade, they have not yet been employed for the mapping and quantification of windthrow tree-mortality. Here, we address how increasing the spatial resolution of satellites affects plot-to-landscape estimates of windthrow tree-mortality. We combined forest inventory data with Landsat 8 (30 m pixel), Sentinel 2 (10 m), and WorldView 2 (2 m) imagery over an old-growth forest in the Central Amazon that was disturbed by a single windthrow event in November/2015. Remote sensing estimates of tree mortality were produced with Spectral Mixture Analysis and analyzed together with forest inventory data using Generalized Linear Models. Windthrow tree-mortality measured in 3 transects (30 subplots) crossing the entire disturbance gradient was 26.9 ± 11.1% (mean ± 95% CI). Based on this ground truth, the three satellites produced reliable and statistically similar estimates (from 26.5% to 30.3% windthrow tree-mortality, p&lt;0.001). The mean-associated uncertainties decreased systematically with increasing spatial resolution (i.e., from Landsat 8 to Sentinel 2 and WorldView 2). However, the overall quality of fit of models showed the opposite pattern, which may reflect the influence of crown damage not accounted for in our field study, and fast-growing regeneration of leaf area. Among the satellites studied, Landsat 8 most accurately captured field observations of variations in tree mortality across the disturbance gradient (i.e., lower under- and/or overestimation from undisturbed to extremely damaged forest). Although satellites with high spatial-resolution can refine estimates of windthrow severity by allowing the quantification of individual tree damage and mortality, our results validate the reliability of Landsat imagery for assessing patterns of windthrow tree-mortality in dense and heterogeneous tropical forests. Although high-resolution imagery may improve estimates of tree damage and mortality, these should be validated using field data at compatible scales.

Trichomonas vaginalis is an extracellular parasite protozoan that causes human trichomoniasis, a sexually transmitted infection (STI) that affects approximately 200 million people worldwide. The presence of T. vaginalis adhesion to inert substrates has been described in several reports. Still, very few studies on cluster formation and more detailed analyses of the contact regions between the membranes of the parasites in these aggregate formations have not been carried out. We analyzed the formation of parasite monolayers and clusters using high-resolution scanning electron microscopy, transmission electron microscopy, cytochemistry, TEM tomography, and dye injection. Here, we show the formation of a monolayer of tightly adherent cells, epithelium-like, when the parasites are in contact with an inert material. Based on this observation, we analyzed whether this monolayer behaves as an epithelium, analyzing cell junctions, cell communication, and ultrastructural aspects. We also analyzed the cluster formation and the morphological characteristics of parasite aggregation observed in the culture supernatant. We report that monolayer formation differs from cluster formation in many aspects. The monolayers form strong adhesion, whereas the clusters have fragile attachments. There is no fusion or passage of molecules between neighbor-attached cells; there is no need for different strains to form filopodia, cytonemes, and extracellular vesicles during cluster formation.

Cyanobacteria are microorganisms able to adapt to a wide variety of environmental conditions and abiotic stresses. They produce a very large number of metabolites that can participate in the adaptation of cyanobacteria to a large range of resources such as light, temperature, or nutrient. The metabolites variation is one way to understand the physiological status and adaptation of cells. In this study, we aim to understand how the diversity and the dynamics of the whole metabolome is dependent of the growth phases and under control of abiotic factors (e.g. light intensity and temperature). The cyanobacteria Aliinostoc sp. PMC 882.14 was selected for its large number of biosynthetic gene clusters. Metabolomes were analyzed by using mass spectrometry (qTOF-MS/MS) combined with untargeted analysis to investigate the metabolite dynamics. Significant variations were characterized between exponential and stationary phases, whatever the culture conditions (“control”, “higher light”, or “higher temperature”). ”Higher light” and “higher temperature” favored the synthesis of metabolites belonging to the same molecular families. Among highly regulated metabolites, we observe the presence of mycosporine-like amino acids (MAAs), and various variants of somamides, microginins, and microviridins. Through Aliinostoc sp. PMC 882.14, this study shows the importance of knowing the physiological state of cyanobacteria for comparative global metabolomics and questions the regulation processes involve into metabolite families production. Our results also open up new perspectives in the context of the production of targeted bioactive metabolites.

The detection of temporary color steel buildings and the analysis of their spatiotemporal patterns and characteristics are of great importance in many developing cities, because areas densely distributed with color steel buildings are usually problematic regions with high population density, sustainable development and risk level, which are challenging for local governments. Using the high-resolution satellite images of the Anning District, Lanzhou City, China as the case study, this paper first extracts the information of the color steel buildings for two different periods. Then the spatiotemporal distributional characteristics of the color steel buildings are analyzed, both at small scale (temporary buildings) and large scale (planthouses, warehouses, etc.). Finally, this paper utilizes kernel density estimation, Delaunay triangulation and Voronoi diagrams to examine the spatial distribution, aggregation and proximity characteristics of the color steel buildings. Our experiments show that the temporal and spatial differences of urban development, the phased characteristics and urban space are represented by various types of color steel buildings. Thus, there are some robust coupling relationship between color steel buildings, urban spatial form and urban development.

This review discusses a broad range of recent advances (2013-2017) of chemical imaging using electrochemical methods, with a particular focus on techniques that have been applied to study cellular processes, or techniques that show promise for use in this field in the future. Non-scanning techniques such as microelectrode arrays (MEAs) offer high time-resolution (< 10 ms) imaging, however at reduced spatial resolution. In contrast, scanning electrochemical probe microscopies (SEPMs) offer higher spatial resolution (as low as a few nm per pixel) imaging, with images collected typically over many minutes. Recent significant research efforts to improve the spatial resolution of SEPMs using nanoscale probes, and to improve the temporal resolution using fast scanning have resulted in movie (multiple frame) imaging with frame rates as low as a few seconds per image. Many SEPM techniques lack chemical specificity or have poor selectivity (defined by the choice of applied potential for redox-active species). This can be improved using multifunctional probes, ion-selective electrodes and tip-integrated biosensors, although additional effort may be required to preserve sensor performance after miniaturization of these probes. We discuss advances to the field of electrochemical imaging, and technological developments which are anticipated to extend the range of processes that can be studied. This includes imaging cellular processes with increased sensor selectivity and at much improved spatiotemporal resolution than has been previously customary.

Three-dimensional reconstruction plays an important role in assisting doctors and surgeons in diagnosing bone defects’ healing progress. Common three-dimensional reconstruction methods include surface and volume rendering. As the focus is on the shape of the bone, volume rendering is omitted. Many improvements have been made on surface rendering methods like Marching Cubes and Marching Tetrahedra, but not many on working towards real-time or near real-time surface rendering for large medical images, and studying the effects of different parameter settings for the improvements. Hence, in this study, an attempt towards near real-time surface rendering for large medical images is made. Different parameter values are experimented on to study their effect on reconstruction accuracy, reconstruction and rendering time, and the number of vertices and faces. The proposed improvement involving three-dimensional data smoothing with convolution kernel Gaussian size 0.5 and mesh simplification reduction factor of 0.1, is the best parameter value combination for achieving a good balance between high reconstruction accuracy, low total execution time, and a low number of vertices and faces. It has successfully increased the reconstruction accuracy by 0.0235%, decreased the total execution time by 69.81%, and decreased the number of vertices and faces by 86.57% and 86.61% respectively.

Green Stormwater Infrastructure (GSI), a sustainable engineering design approach for managing urban stormwater runoff, has long been recommended as an alternative to conventional conveyance-based stormwater management strategies to mitigate the adverse impact of sprawling urbanization. Hydrological and hydraulic simulations of small-scale GSI measures in densely urbanized micro watersheds require high-resolution spatial databases of urban land use, stormwater structures, and topography. This study presents a highly resolved Storm Water Management Model developed under considerable spatial data constraints. It evaluates the cumulative effect of the implementation of dispersed, retrofitted, small-scale GSI measures in a heavily urbanized micro watershed of Costa Rica. Our methodology includes a high-resolution digital elevation model based on Google Earth information, whose accuracy was sufficient to determine flow patterns and slopes, as well as to approximate the subsurface stormwater structures. The model produced satisfactory results in event-based calibration and validation, which ensured the reliability of the data collection procedure. Simulating the implementation of GSI shows that dispersed, retrofitted, small-scale measures could significantly reduce impermeable surface runoff (peak runoff reduction up to 40%) during frequent, less intense storm events and delay peak surface runoff 5-10 minutes. The presented approach can benefit stormwater practitioners and modelers conducting small scale hydrological simulation under spatial data constraint.

Mostly populous city like Chennai is subjected to frequent flooding due to its complex nature of natural and man-made activities. From the analysis of the past records of flood events of 1943,1976,1985,2005 and 2008,it has been observed Adayar watershed is subjected to cataclysmic flooding in low-lying areas of the city and its suburbs because of inoperativeness of the local drainage system, rainfall associated with cyclonic activity, topography of the terrain, encroachments along the floodplain, hugh upstream flow discharge into the river and the highly impervious area which blocked the runoff to flow into the storm water drainage. After looking into these problems of flooding, a study have been conducted on Adayar watershed to develop a 2D hydrodynamic model for the two scenarios of existing condition of storm water drainage network and revised conditions of storm water drainage network using high resolution Lidar DEM to assess the volume of runoff with respect to time and duration on flood peaks for the two flood events of 2005 and 2015.Secondly to develop a 1D flood model to predict the river stages during peak floods using MIKE 11 for the Adayar watershed. Thirdly to integrate the coupled 1D and 2D model using MIKEFLOOD for assessing the extent of inundation in the floodplain area of Adayar river. Finally results from the integrated model have been validated and the results found satisfactory. As a part of mitigation measures, two flood mitigation measures have been adopted. One measure such as revised storm water drainage system which enhances the flood carrying capacity of the drains and results in less inundated area which solves the problem of urban flooding and second measure such as regrading the river bed which reduces the floodplain inundation around the adjacent area of the river. After adopting these measures, the river is free to flow into the sea without any blockades.

The preparation of gold nanoparticles (AuNPs) involves a variety of chemical and physical methods. These methods use toxic and environmentally harmful chemicals. Consequently, the synthesis of AuNPs using green chemistry has been under investigation to develop eco-friendly nanoparticles. One method to achieve this is the use of plant-derived phytochemicals capable of reducing gold ions to produce AuNPs. The aim of this study was to implement a facile microtitre-plate method to screen a large number of aqueous plant extracts to determine the optimum concentration (OC) to bio-synthesize the AuNPs. Several AuNPs of different sizes and shapes were successfully synthesized and characterized from seventeen South African plants. The characterization was done using Ultra Violet-Visible Spectroscopy, Dynamic Light Scattering, High Resolution Transmission Electron Microscopy and Energy-Dispersive X-ray Spectroscopy. We also studied the effects of temperature on the synthesis of the nanoparticles and measured its effect on the particle size of the synthesized AuNPs and the data showed that changes in temperatures affect the size and dispersity of the generated AuNPs. Further, some of the synthesized AuNPs were stable upon incubation with different biological solutions in vitro.

The Chang’e-4 (CE-4) lunar rover, equipped with The Visible and Near-IR Imaging Spectrometer(VNIS) which based on acousto-optic tunable filter spectroscopy, was launched to the far side of the moon on December 8, 2018. The detection band of VNIS ranges from 0.45 to 2.4μm. Because of the weak reflection of infrared radiation from the lunar surface, a static electronic phase-locked acquisition method is adopted in the infrared channel for signal amplification. In this paper, full-link simulations and modeling are conducted of the infrared channel information flow of the instrument. The signal/noise characteristics of VNIS are analyzed in depth, and the signal-to-noise(SNR) ratio prediction and laboratory verification are presented. On January 4, 2019, the VNIS started working successfully and acquired high-resolution spectrum data of the far side of the moon for the first time. Through analysis, the SNR ratio is in line with predictions, and the data obtained by VNIS in orbit are consistent with the information model proposed in this paper.

Deep Learning is a recent and important addition to the computational toolbox available for image reconstruction in fluorescence microscopy. We review state-of-the-art applications such as image restoration, super-resolution, and light-field imaging, and discuss how the latest Deep Learning research can be applied to other image reconstruction tasks such as structured illumination, spectral deconvolution, and sample stabilisation. Despite its successes, Deep Learning also poses significant challenges, has often misunderstood capabilities, and overlooked limits. We will address key questions, such as: What are the challenges in obtaining training data? Can we discover structures not present in the training data? And, what is the danger of inferring unsubstantiated image details?

Phenotypic characterization of crop genotypes is an essential yet challenging aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agriculture research due to diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. Phenotypic traits of crop fresh biomass, dry biomass, and plant height estimated by CBM data had high correlation with ground truth manual measurements in wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.

Originally developed to image the shadow region of the central black hole in Sagittarius A* and in the nearby galaxy M87, the Event Horizon Telescope (EHT) provides deep, very high angular resolution data on other AGN sources too. The challenges of working with EHT data have spurred the development of new image reconstruction algorithms. This work briefly reviews the status of the EHT and its utility for observing AGN sources, with emphasis on novel imaging techniques that offer the promise of better reconstructions at 1.3 mm and other wavelengths.

Dawn of the omics revolution in biological sciences meant that we seek to understand more and in greater detail the molecular constituents of cells and biological systems. While we have gained significant insights from conventional omics tools, we now seek to understand the spatial dimensions of the data where subcellular localisation may impact on cellular physiology and phenotype. This review paper seeks to address current questions in the new field of image-based spatial proteomics as well as outline future challenges of the field. At first glance, spatial proteomics offers enormous potential to expand our understanding of different cell types in different disease and cell states. But limitations in types of fluorophores and issues with spectral overlap significantly hampers the practical implementation of the technique. On the other hand, while we have super-resolution microscopy techniques such as STED, PALM and STORM able to achieve 10 to 20 nm spatial resolution in single molecule localisation, problems with slow image acquisition limits high temporal resolution tracking of multiple protein targets in live cell imaging. Hence, the field of spatial proteomics is a mix of promises and challenges where we could image, in multi-colour, upwards of 10 well-chosen proteins that could inform on the molecular mechanisms of selected biological processes, but, at present, the method could not tackle larger scale questions. In essence, current implementation of image-based spatial proteomics is useful, but it is unable to fulfil the mission of large-scale projects such as the Human Protein Atlas or Human Cell Atlas. Future challenges in the field includes the development of more fluorophores (especially photoswitchable and photoactivable ones) for single molecule localisation microscopy, as well as seeking to improve temporal resolutions to the sub-millisecond range.

Artistic image completion is fundamental for the preservation and restoration of invaluable art paintings and it has experienced significant progress through the implementation of deep learning methodologies. Despite these advancements, challenges persist, even with sophisticated approaches like Generative Adversarial Networks (GANs), particularly in achieving optimal results for high-resolution paintings. Small-scale texture synthesis and the inference of missing information from distant contexts present persistent issues, leading to distortions in lines and unnatural colors, especially in art paintings with complicated structures and textures. Concurrently, patch-based image synthesis has evolved by incorporating global optimization on the image pyramid to enhance structural coherence and details. However, methods relying on gradient-based synthesis encounter obstacles related to directionality, inconsistency, and the heavy computational burdens associated with solving the Poisson equation in non-integrable gradient fields. This paper introduces a groundbreaking approach, integrating Weighted Similarity-Confidence Laplacian Synthesis, to comprehensively address these challenges and advance the field of artistic image completion. This proposal addresses challenges not only in high-resolution artistic image completion but also makes a significant contribution to the broader field of patch-based synthesis by utilizing the Laplacian pyramid for enhanced edge-aware correspondence search. Experimental result confirms the effectiveness of our approach, offering promising outcomes for the preservation and restoration of art paintings with complicated details and irregular missing regions.

The semantic segmentation of high-resolution remote sensing images (HR-RSI) is crucial for a wide range of applications, such as precision agriculture, urban planning, natural resource assessment, and ecological monitoring. However, accurately classifying pixels in HR-RSI faces challenges due to densely distributed small objects and scale variations. Existing techniques, including Convolutional Neural Networks (CNNs) and other methods for hierarchical feature extraction and fusion of remote sensing image, often do not achieve the desired accuracy. In this paper, we propose a novel approach called the Hierarchical Rich-scale Fusion Network (HRFNet) to address these challenges. HRFNet utilizes advanced information rating and image partition techniques to extract rich-scale features within image layers. This allows for the adaptive exploration of both local and global contextual information. Moreover, we introduce a structured intra-layer to inter-layer feature aggregation module, which enables the adaptive extraction of fine-grained details and high-level semantic information from multi-layer feature maps in a highly flexible manner. Extensive experimentation has been conducted to validate the effectiveness of our proposed method. Our results demonstrate that HRFNet outperforms existing techniques, achieving state-of-the-art (SOTA) results on benchmark datasets, specifically the ISPRS Potsdam and Vaihingen datasets.

Environmental nanoparticles have been shown to exist in most aquatic environments, and environmental nanoparticles in water distribution systems significantly affect water quality. However, little research has been conducted on the environmental nanoparticles in mineral water, which is a special water resource. Nanoparticle tracking analysis (NTA) and high-resolution transmission electron microscopy (HRTEM) were conducted. This study was the first to identify many environmental nanoparticles in natural mineral water in Zibo City, Shandong Province, China. The results of the NTA showed that the concentration of the particles was 5.5 ×106 particles/mL and the peak diameter of the size distribution was 180 nm. The HRTEM showed that the nanoparticles were granular, pinniform, rodlike, and flakey in shape, and some of the nanoparticles existed in aggregation. The energy-dispersive spectrometry results showed that most of the nanoparticles contained O, Mg, Ca, Si, Fe, Ti, and P, and some of them also contained F, V, S, and Mn. When combined with the characteristics of the selected area electron diffraction pattern, the nanoparticles were confirmed to be Ca-bearing nanoparticles, attapulgite nanorods, MnO2 nanosheets, and TiO2 nanoparticles. These results indicate a new form of elements occurring in mineral water. In addition, based on the chemical and physical characteristics of the nanoparticles and mineral water, these environmental nanoparticles in the mineral water are likely to be the weathering byproducts of minerals. These nanoparticles within mineral water provide a good opportunity for understanding the behavior of nanoparticles in varied systems. Moreover, environmental nanoparticle science is important for continuing efforts in water safety, treatment, and remediation.

Diarylethene is a prototypical molecular switch that can be reversibly photoisomerized between its open and closed forms. Ligands bpy-DAE-bpy, consisting of a phenyl-diarylethene-phenyl (DAE) central core and bipyridine (bpy) terminal substituents, are able to self-organize. They are investigated by scanning tunneling microscopy at the solid-liquid interface. Upon light irradiation, cooperative photochromic switching of the ligands is recognized down to the sub-molecular level. The closed isomers show different electron density of states (DOS) contrasts, attributed to the HOMO or LUMO molecular orbitals observed. More importantly, the LUMO images show remarkable differences between the open and closed isomers, attributed to combined topographic and electronic contrasts mainly on the DAE moieties. The electronic contrasts from multiple HOMO or LUMO distributions, combined with topographic distortion of the open or closed DAE, are interpreted by density functional theory (DFT) calculations.

In Acropora, the complex canals in a coral colony connect all polyps into a holistic network to collaborate in performing biological processes, while axial canal is the largest canal amongst the network and distributes at the center of a coral branch. However, previous studies indicated that, in the non-radial symmetry transport system of Acropora, axial canal do not play a major role in the transport of hydroplasm, and the action of axial canal in coral growth is still obscure. In this study, we reconstructed six Acropora muricata samples by high resolution micro-computed tomography to investigate the growth patterns of axial canals during the processes of new branch forming and truncated branch rebuilding. We found that the axial canal of a new branch is transformed from a calice and the polyps in the new branch are budded from the polyp in the axial canal. Meanwhile, the axial canal can transport the calcareous skeletons to rebuild the tip of a truncated branch, which represents as the change in the diameter of axial canal and calcareous deposition/reduction in it. This work indicate the regulation of axial canal in the growth processes including budding, branching, and mineralising of an Acropora colony.

Semantic image segmentation has recently witnessed considerable progress by training deep convolutional neural networks (CNNs). The core issue of this technique is the limited capacity of CNNs to depict visual objects. Existing approaches tend to utilize approximate inference in a discrete domain or additional aides and do not have a global optimum guarantee. We propose the use of the multi-label manifold ranking (MR) method in solving the linear objective energy function in a continuous domain to delineate visual objects and solve these problems. We present a novel embedded single stream optimization method based on the manifold ranking (MR) model to avoid approximations without sacrificing expressive power. In addition, we propose a novel network, which we refer to as dual multi-scale manifold ranking (\textbf{DMSMR}) network, that combines the dilated, multi-scale strategies with the single stream MR optimization method in the deep learning architecture to further improve the performance. Experiments on high resolution images, including close-range and remote sensing datasets, demonstrate that the proposed approach can achieve competitive accuracy without additional aides in an end-to-end manner.

We are concerned with the use of some classical spectral collocation methods as well as with the new software system Chebfun in order to compute high order (index) eigenpairs of singular as well as regular Schrodinger eigenproblems. We want to highlight both the qualities as well as the shortcomings of these methods and evaluate them vis-a-vis the usual ones. In order to resolve a boundary singularity we use Chebfun with the simple domain truncation technique. Although this method is equally easy to apply with spectral collocation, things are more nuanced in the case of these methods. A special technique to introduce boundary conditions as well as a coordinate transform which maps an unbounded domain to a nite one are the ingredients. A challenging set of "hard" benchmark problems, for which usual numerical methods (f. d., f. e. m., shooting etc.) fail, are analysed. In order to separate "good"and "bad"eigenvalues we estimate the drift of the set of eigenvalues of interest with respect to the order of approximation and/or scaling of domain parameter. It automatically provides us with a measure of the error within which the eigenvalues are computed and a hint on numerical stability. We pay a particular attention to problems with almost multiple eigenvalues as well as for problems with a mixed (continuous) spectrum. In the latter case we try to numerically highlight its existence. Special attention will be paid to the higher eigenpairs (the pair of eigenvalue and the corresponding eigenfunction approximated by an eigenvector spanning its nodal values).

Diabetic kidney disease (DKD) is a major cause of morbidity and mortality of type 2 diabetes mellitus (T2DM). Aim of this study was to investigate whether albumin structural alterations correlate with DKD severity and evaluate whether native and reduced albumin concentrations could complement the diagnosis of DKD. To this aim, one hundred and seventeen T2DM patients without (n=42) and with (n=75) DKD (DKD I-III upon KDIGO classification) were evaluated; total albumin concentration (tHA) was quantified by bromocresol green assay, while structural alterations were profiled assessed by liquid chromatography-high resolution mass spectrometry (LC-HRMS). The concentrations of albumin in native (eHA, effective albumin) and reduced (rHA) forms were then assessed. HRMS analyses showed a reduced relative amount of native albumin in DKD patients along with an increased abundance of altered forms, especially those bearing oxidative modifications. Accordingly, both eHA and rHA values varied along the stages of progressive renal failure, and alterations dose-dependently correlated with renal dysfunction. ROC curves analysis showed a significantly higher sensitivity and specificity of eHA and rHA with respect to tHA for the diagnosis of DKD. Importantly, at multivariable logistic regression analysis, eHA was identified as independent predictor of DKD.

Sustainable food systems imply the re-use of biowastes and water. In this paper we characterize 30 top soil improvers of anthropogenic, animal and green waste origin, and 11 irrigation waters from rivers, channels, and civil wastewater treatment plants (cWWTPs) for the presence of antimicrobials. Liquid chromatography coupled to hybrid High Resolution Mass Spectrometry (LC-HRMS/MS) was applied to determine 50 different drugs belonging to the classes of sulfonamides (11), tetracyclines (7), fluoroquinolones (10), macrolides (12), amphenicols (3), pleuromutilins (2), diaminopyrimidines (1), rifamycines (1) and lincosamides (1). Biowastes from cWWTPs and animal manure, slurry and litter revealed the highest loads for sulfonamides, tetracyclines, fluoroquinolones, and macrolides (80, 470, 885, and 4,487 ng/g wet weight, respectively) with nor- and cipro-floxacin as marker of anthropogenic sources. In composts and digestates antimicrobials were almost below the determination limits. Re-used waters for irrigation in open field lettuce production resulted contaminated in the range of 12-221 ng/L for sulfonamides, tetracyclines, and fluoroquinolones, against very few detects in channels and surface waters. Antimicrobials Hazard Index (HI) based on Predicted No Effect Concentration for Antimicrobial Resistance (PNEC_AMR) was largely >100 in contaminated top soil improvers from urban and animal sources. Accounting for worst-case inputs from top soil improvers and irrigation water and dilution factors of amended soil, fluoroquinolones only showed a HI around 1 in open fields for lettuce production. The origin of top soil improvers plays a pivotal role for safe and sustainable leafy vegetable productions, to abate the risk of AMR onset in food-borne diseases, as well the transfer of AMR elements to human gut flora.

High resolution melting (HRM) analysis is a powerful detection method for fast, high-throughput post-PCR analysis. A two-step HRM marker system was developed for identification of the N-, S-, R- and T-cytoplasms of onion. In the first step for identification of N-, S-, and R-cytoplasms, one forward primer was designed to the identical sequences of both cox1 and orf725 genes and two reverse primers specific to the polymorphic sequences of cox1 and orf725 genes were used. For the second step breeding lines with N-cytoplasm were evaluated with primers developed from the orfA501 sequence to distinguish between N- and T-cytoplasms. An amplicon with primers to the mitocondrial atp9 gene was used as an internal control. The two-step HRM marker system was tested using 246 onion plants. HRM analysis showed that the most common source of CMS, often used by Russian breeders, is S-cytoplasm, the rarest type of CMS is R-cytoplasm, and the proportion of T-cytoplasm among the analyzed breeding lines was 20.5%.

In Acropora, the complex canals in a coral colony connect all polyps into a holistic network to collaborate in performing biological processes. There are various types of canals, including calice, axial canals, and other internal canals, with structures that are dynamically altered during different coral growth states due to internal calcium transport. However, few studies have considered the regulation of calcium transport in Acropora. In this study, we investigated the morphological changes of the axial canal in six Acropora muricata samples by high resolution micro-computed tomography, observing the patterns of the axial canal during the processes of new branch formation and truncated branch rebuilding. We visualized the formation of a new branch from a calice and deposition of the iconic hexactin skeletons in the axial canal. Furthermore, the diameter and volume changes of the axial canal in truncated branches during rebuilding processes were calculated, revealing that the volume ratio of calcareous deposits in the axial canal exhibit significant increases within the first three weeks, returning to levels in the initial state in the following week. This work indicates that the axial canal can transport calcium to form hexactin skeletons in a new branch and rebuild the tip of a truncated branch. The calcium transport along canal network regulates various growth processes, including budding, branching, skeleton forming, and self-rebuilding of an Acropora colony. Understanding the changes in canal function under normal and extreme conditions will provide theoretical guidance for restoration and protection of coral reefs.

We report herein high-resolution x-ray diffraction measurements of basal plane bending of homoepitaxial single-crystal diamond (SCD). The results reveal that growth parameters such as temperature, growth time and basal plane bending of the substrate affect the basal plane bending of SCD. First, the basal plane bending of SCD depends mainly on the substrate itself. The basal plane bending of SCD becomes more severe with increasing basal plane bending of the substrate and this type of basal plane bending cannot be recovered. The SCD growth experiments show that the basal plane bending increases at high temperature and with increasing growth time. Finally, to understand the mechanism behind basal plane bending, we investigate the substrate-surface temperature distribution as a function of basal plane bending of SCD fabricated by chemical vapour deposition (CVD). This allows us to propose a bending model and understand the origin of basal plane bending. The results indicate that an uneven temperature distribution on the substrate surface is the main cause of the CVD diamond base-plane bending.

Low-frequency electron paramagnetic resonance (EPR) is used to extract the EPR parameter A-mid and support the approximate X-band value of g-mid for Ba(Co_yZn_1/3-yTa_2/3)O₃. Although cobalt hyperfine structure for the [+/−1/2> state is often unresolved at X-band or S-band, it is resolved in measurements on this compound. This allows for detailed analysis of the molecular orbital for the [+/−1/2> state, which is often the ground state. Moreover, this work shows that the EPR parameters for Co substituted into Zn compounds gives important insight into the properties of zinc binding sites.

Land-cover classification that uses very-high-resolution (VHR) remote sensing images is a topic of considerable interest. Although many classification methods have been developed, there is still room for improvements in the accuracy and usability of classification systems. In this paper, a novel post-processing approach based on a dual-adaptive majority voting strategy (D-AMVS) is proposed for improving the performance of initial classification maps. D-AMVS defines a strategy for refining each label of a classified map that is obtained by different classification methods from the same original image and fusing the different refined classification maps to generate a final classification result. The proposed D-AMVS contains three main blocks. 1) An adaptive region is generated by extending gradually the region around a central pixel based on two predefined parameters (T1 and T2) in order to utilize the spatial feature of ground targets in a VHR image. 2) For each classified map, the label of the central pixel is refined according to the majority voting rule within the adaptive region. This is defined as adaptive majority voting (AMV). Each initial classified map is refined in this manner pixel by pixel. 3) Finally, the refined classified maps are used to generate a final classification map, and the label of the central pixel in the final classification map is determined by applying AMV again. Each entire classified map is scanned and refined pixel by pixel based on the proposed D-AMVS. The accuracies of the proposed D-AMVS approach are investigated through two remote sensing images with high spatial resolutions of 1.0 and 1.3 m, respectively. Compared with the classical majority voting method and a relatively new post-processing method called general post-classification framework, the proposed D-AMVS can achieve a land-cover classification map with less noise and higher classification accuracies.

The high radio frequency polarization imaging of non-thermal emission from active galactic nuclei (AGN) is a direct way to probe the magnetic field strength and structure in the immediate vicinity of supermassive black holes (SMBHs) and is crucial in testing the jet-launching scenario. To explore the the magnetic field configuration at the base of jets in blazars, we took advantage of the full polarization capabilities of the GMVA (Global Millimeter VLBI Array). With an angular resolution of ~50 micro-arcseconds at 86 GHz, one could reach scales down to ~900 Schwarzschild radii (for a 10^9 solar mass black hole). We present here the preliminary results of our study on the blazar BL Lac. Our results imply that on sub-mas scales the core and central jet of BL Lac is significantly polarized with two distinct regions of polarized intensity. We also noted a great morphological similarity between the 7mm/3mm VLBI images at very similar angular resolution.

Recently, new semantic segmentation and object detection methods have been proposed for direct processing of three-dimensional (3D) LiDAR sensor point clouds. LiDAR can produce highly accurate and detailed 3D maps of natural and man-made environments and is used for sensing in many contexts due to its ability to capture more information and its robustness to dynamic changes in the environment. In addition, the cost of LiDAR sensors has decreased in recent years, which is an important factor for many application scenarios. The challenge with 3D LiDAR sensors like the Velodyne HDL-64E S2 is that they can output large 3D data at up to 1,300,000 points per second, which is difficult to process in real time when applying complex algorithms and models for efficient semantic segmentation. Most existing approaches are either only suitable for relatively small point clouds or rely on computationally intensive sampling techniques to reduce their size. As a result, most of these methods do not work in real-time in realistic field robotics application scenarios, making them unsuitable for practical applications. Systematic point selection is a possible solution to reduce the amount of data to be processed, but although it is memory and computationally efficient, it selects only a small subset of points, which may result in important features being missed. To address this problem, we propose a new approach to semantic segmentation in forestry that uses a systematic sampling method in which the local neighbors of each point are retained to preserve geometric details. Our approach has been shown to process up to 1 million points in a single pass. It outperforms the state of the art in efficient semantic segmentation in large datasets such as Semantic3D. We also present a preliminary study on the performance of LiDAR-only data, i.e., intensity values from LiDAR sensors without RGB values for semi-autonomous robot perception.

This paper investigated building data from multispectral and single-photon Lidar systems. The multispectral datasets from the individual channels and fused channels were explored. The multispectral and single-photon Lidar data were compared across multiple aspects: the data acquisition geometry, number of echoes, intensity, density, resolution, data defects, noise level, and the absolute and relative accuracy. In addition, we explored the performance of the multispectral and single-photon data for roof plane detection for eight complex/stylish buildings to investigate the suitability of these data for 3D building reconstruction. The building data from the single-photon and multispectral Lidar systems were evaluated with respect to the reference building vector data with an accuracy of better than 5 cm. The advantages and disadvantages of both technologies and their applications in the urban building environment are discussed.

In this paper, the technical aspects of a multi-lidar instrument, the long-range WindScanner system, will be presented accompanied by an overview of the results from several field campaigns. The long-range WindScanner system consists of three spatially separated coherent Doppler scanning lidars and a remote master computer that coordinates them. The lidars were carefully engineered to perform arbitrary and time controlled scanning trajectories. Their wireless coordination via the master computer allows achieving and maintaining lidars’ synchronization within ten milliseconds. As a whole, the long-range WindScanner system can measure an entire wind field by emitting and directing three laser beams to intersect, and then by moving the beam intersection over the points of interest. The long-range WindScanner system was developed to tackle the need for high-quality observations of wind fields from scales of modern wind turbine and wind farms. It has been in operation since 2013.

Spectral image analysis has the potential to replace traditional approaches for assessing plant responses to different types of stress including herbicide, through non-destructive and high-throughput screening (HTS). Therefore, this study was conducted to develop a rapid bioassay method using a multi-well plate and spectral image analysis for diagnosis of herbicide activity and mode of action. Crabgrass (Digitaria ciliaris) as a model weed was cultivated in multi-well plates and subsequently treated with 6 herbicides with different modes of action when they reached the 1-leaf stage, using only a quarter of the recommended dose. To detect plant response to herbicides, plant spectral images were acquired after herbicide treatment using RGB, infrared (IR) thermal, and chlorophyll fluorescence (CF) sensors and analyzed for diagnosing herbicide efficacy and mode of action. Principal Component Analysis (PCA) using all spectral data successfully distinguished herbicides and clustered depending on their modes of action. The performed experiments have shown that the multi-well plate assay combined with spectral image analysis can be successfully applied for herbicide bioassay. In addition, the use of spectral image sensors, especially in CF image, would facilitate HTS by enabling rapid observation of herbicide response, as early as 3 hours after herbicide treatment.

The high-resolution bistatic lidar developed at the Physikalisch-Technische Bundesanstalt (PTB) aims to overcome the limitations of conventional monostatic lidar technology which is widely used for wind velocity measurements in wind energy and meteorology applications. Due to the large measurement volume of a combined optical transmitter and receiver tilting in multiple directions, monostatic lidar generally has poor spatial and temporal resolution. It also exhibits large measurement uncertainty when operated in inhomogeneous flow, for instance, over complex terrain. In contrast, PTB’s bistatic lidar uses three dedicated receivers arranged around a central transmitter, resulting in an exceptionally small measurement volume. The coherent detection and modulation schemes used allow the detection of backscattered, Doppler shifted light down to the scale of single aerosols, realising the simultaneous measurement of all three wind velocity components. This paper outlines design details and the theory of operation of PTB’s bistatic lidar and provides an overview of selected comparative measurements. The results of these measurements have shown that the measurement uncertainty of PTB’s bistatic lidar is well within the measurement uncertainty of traditional cup anemometers, while being fully independent of its site and traceable to the SI units. This allows its use as a transfer standard for the calibration of other remote sensing devices. Overall, PTB’s bistatic lidar shows great potential to universally improve the capability and accuracy of wind velocity measurements, such as for the investigation of highly dynamic flow processes upstream and in the wake of wind turbines.

Event-based vision is an emerging field of computer vision that offers unique properties such as asynchronous visual output, high temporal resolutions, and dependence on brightness changes to generate data. These properties can enable robust high-temporal-resolution object detection and tracking when combined with frame-based vision. In this paper, we present a hybrid, high-temporal-resolution, object detection and tracking approach, that combines learned and classical methods using synchronized images and event data. Off-the-shelf frame-based object detectors are used for initial object detection and classification. Then, event masks, generated per each detection, are used to enable inter-frame tracking at varying temporal resolutions using the event data. Detections are associated across time using a simple low-cost association metric. Moreover, we collect and label a traffic dataset using the hybrid sensor DAVIS 240c. This dataset is utilized for quantitative evaluation using state-of-the-art detection and tracking metrics. We provide ground truth bounding boxes and object IDs for each vehicle annotation. Further, we generate high-temporal-resolution ground truth data to analyze the tracking performance at different temporal rates. Our approach shows promising results with minimal performance deterioration at higher temporal resolutions (48 – 384 Hz) when compared with the baseline frame-based performance at 24 Hz.

Multi-gap Resistive Plate Chamber has been attracting increasing attention and has been studied in recent decades because of its good efficiency, high time resolution and relatively low cost. It has become a new standard technology for the Time of Flight system in particle and high energy physics experiments. In this paper, we introduce the operation principle and structure of the MRPC detector and review the applications on the Time of Flight system in several famous experiments, such as STAR, CBM and SoLID. The performances including time resolution and particle identification are discussed in detail. Some recent advances and perspectives with regard to the future development of the next generation MRPC are also outlined.

Gwosch et al. (2020) and Balzarotti et al. (2017) purport MINFLUX as the next revolutionary fluorescence microscopy technique claiming a spatial resolution in the range of 1-3 nm in fixed and living cells. Though the claim of molecular resolution is attractive, I am concerned whether true 1 nm resolution has been attained. Here, I compare the performance with other super-resolution methods focussing particularly on spatial resolution claims, atypical image rendering, visualisation enhancement, subjective filtering of localizations, detection vs labelling efficiency and the possible limitations when imaging biological samples containing densely labelled structures. I hope the analysis and evaluation parameters presented here are not only useful for future research directions but also microscope users, developers and core facility managers when deciding on an investment for the next 'state-of-the-art' instrument.

Glycosyl inositol phospho ceramides (GIPCs) are the major sphingolipids on earth as they account for a considerable fraction of the total lipids in plants and fungi which in turn represent a large portion of the biomass on earth. Despite their obvious importance, GIPC analysis remains challenging due to the lack of commercial standards and automated annotation software. In this work, we introduce a novel GIPC glycolipidomics workflow based on reversed-phase ultra-high pressure liquid chromatography coupled to high-resolution mass spectrometry. For the first time, automated GIPC assignment was performed using the open-source software Lipid Data Analyzer based on platform-independent decision rules. Four different plant samples (salad, spinach, raspberry, strawberry) were analyzed and revealed 64 GIPCs based on accurate mass, characteristic MS2 fragments and matching retention times. Relative quantification using lactosyl ceramide for internal standardization revealed GIPC t18:1/h24:0 as the most abundant species in all plants. Depending on the plant sample, GIPCs contained mainly amine, N-acetylamine or hydroxyl residues. Most GIPCs revealed a Hex-HexA-IPC core and contained a ceramide part with a trihydroxylated t18:0 or t18:1 long chain base and hydroxylated fatty acid chains ranging from 16 to 26 carbon atoms in length (h16:0 – h26:0). Interestingly, six GIPCs containing t18:2 were observed in raspberry, which was not reported so far. The presented workflow supports the characterization of different plant samples by automatic GIPC assignment potentially leading to the identification of new GIPCs. For the first time, automated high‑throughput profiling of these complex glycolipids is possible by liquid chromatography-high-resolution mass spectrometry and subsequent automated glycolipid annotation based on decision rules.

This paper aims at trying to answer the question of whether a low cost drone equipped with a regular camera can serve as a simple device to estimate the size of objects. Ad-ditionally, it assesses if the accuracy of measurement is good enough for using a drone for measurements, relying solely on GPS position and camera. A review of the literature pertaining to ground object measurements was conducted. The availability of such in-struments was evaluated in terms of purchase and rental costs, the number of instru-ments on the market, and the time to obtain them. The most common measurement methods were considered and their unit costs were reviewed. The cost of professional surveying and measuring equipment was compared to the possibility of using low-cost, non-commercial drones to measure objects and surfaces. The quality of the images ob-tained was compared with high-resolution satellite systems. Is an ordinary drone with a standard camera suitable for length measurement without professional measuring equipment? Should the results be considered only in the category of size estimation?

The key to simulating soil erosion is to calculate the vegetation cover (C) factor. Methods that apply remote sensing to calculate C factor at regional scale cannot directly use the C factor formula. That is because the C factor formula is obtained by experiment, and needs the coverage ratio data of croplands, woodlands and grasslands at standard plot scale. In this paper, we present a C factor conversion method from a standard plot to a km-sized grid based on large sample theory and multi-scale remote sensing. Results show that: 1) Compared with the existing C factor formula, our method is based on the coverage ratio of croplands, woodlands and grasslands on a km-sized grid, takes the C factor formula obtained from the standard plot experiment and applies it to regional scale. This method improves the applicability of the C factor formula, and can satisfy the need to simulate soil erosion in large areas. 2) The vegetation coverage obtained by remote sensing interpretation is significantly consistent (paired samples t-test, t = −0.03, df = 0.12, 2-tail significance p < 0.05) and significantly correlated with the measured vegetation coverage. 3) The C factor of the study area is smaller in the middle, southern and northern regions, and larger in the eastern and western regions. The main reason for that is the distribution of woodlands, the Hunshandake and Horqin sandy lands and the valleys affected by human activities. 4) The method presented in this paper is more meticulous than the C factor method based on the vegetation index, improves the applicability of the C factor formula, and can be used to simulate soil erosion on large scale and provide strong support for regional soil and water conservation planning.

An innovative mobile Lidar device, developed to monitor volcanic plumes during explosive eruptions at Mt. Etna (Italy) and analyze the optical properties of volcanic particles was upgraded in October 2023 with the aim to improve volcanic plume retrievals. The new configuration of the lidar allows to obtain new data of both the optical and the microphysical properties of the atmospheric aerosol. In fact, after its upgrade, the lidar has the possibility to measure 3β, 2α and 2δ in a configuration defined as “state of the art lidar” where properties such as the particle size distribution and refractive index can be derived. During the lidar implementation we were able to to test the system performance through specific calibration measurements. In this work, the first measurement results are shown and compared with results obtained by other instruments aimed at proving the ability of the upgraded system to more precisely characterize the aerosol optical and microphysical properties.

Abstract：Objective: To compare the effects of fermentation on the chemical composition of Gastrodia Tuder Halimasch Powder, and to provide a basis for the processing, processing and clinical application of this medicinal material. Methods: UHPLC-Q-Orbitrap HRMS was used to identify compound structure by comparing excimer ions, characteristic fragment ions and reference substances, and referring to relevant literature and database search. The main differential components were screened by orthogonal partial least squares-discriminant analysis (OPLS-DA), and the differential components were quantitatively studied by high performance liquid chromatography, and the differences in the types and contents of chemical components before and after fermentation were compared. Results: A total of 77 compounds were identified or preliminarily derived after fermentation, including 21 terpenes, 18 organic acids, 9 flavonoids, 7 nucleosides, 6 amides, 4 amino acids, 2 pyrrolidone derivatives, 2 sterols, 2 alkaloids and 6 others.Through high performance liquid chromatography, it was found that the content of daidzein genistein and ergosterol was significantly increased after fermentation, and a new component ergosterol was produced. Compared with Gastrodia Tuder Halimasch Powder before fermentation, the content of genistin and other components was significantly reduced, and there may be a process of genistin conversion to genistein.Conclusion: The fermentation process will have a certain influence on the types and contents of the chemical components of Gastrodia Tuder Halimasch Powder, and ergosterol, genistein and .daidzein may be the main pharmacodynamic components, which can provide a useful reference for the clinical rational application, quality control and mechanism of action of Gastrodia Tuder Halimasch Powder.

Existing methods enhance mid-low resolution remote sensing ship detection by feeding super-resolved images to the detectors. Although these methods marginally improve the detection accuracy, the correlation between image super-resolution (SR) and ship detection is under-exploited. In this paper, we propose a simple but effective ship detection method called ShipSR-Det, in which both the output image and the intermediate features of the SR module are fed to the detection module. Using the super-resolved feature representation, the potential benefit introduced by image SR can be fully used for ship detection. We apply our method to the SSD and Faster-RCNN detectors and develop ShipSR-SSD and ShipSR-Faster-RCNN, respectively. Extensive ablation studies validate the effectiveness and generality of our method. Moreover, we compare ShipSR-Faster-RCNN with several state-of-the-art ship detection methods. Comparative results on the HRSC2016, DOTA and NWPU VHR-10 datasets demonstrate the superior performance of our proposed method.

Trio-based exome sequencing and high-resolution HLA typing were employed to analyze three patients with autoimmune adrenal insufficiency (AAI), including those with autoimmune polyglandular syndrome (APS) type 2, as well as their parents. Benign or likely benign variants of the AIRE gene were identified in all participants of the study. These variants, coupled with clinical data and the results of antibody studies to type I interferons, helped to exclude APS-1. Patients with APS-2, in contrast to patient with AAI, inherited distinct variants of unknown significance in the CLEC16A gene, which is associated with autoimmune diseases including AAI. Various risk alleles in other genes associated with autoimmunity were identified in all patients. HLA typing of second class loci revealed alleles related to APS types 2, 3, and 4. Nevertheless, the frequencies of the haplotypes identified are substantial in the healthy Russian population, precluding from regarding these alleles as genetic determinants linked to APS development. Immunological examination can detect antibody carriers and predict the risk of autoimmune disease development. In the future, to identify genetic predictors of autoimmune endocrinopathies, it is recommended to analyze the whole genome of patients and their relatives, examining clinically relevant variants in non-coding regions.

In the Quaternary paleosciences, the rationale behind analogical inference presupposes that former natural changes can be explained by causes operating now, although their intensity and rates can vary through time. In this paper we synthesise synthetize the results of different modern analog studies and discuss their value to obtain the best inferences from high resolution past records. This synthesis is based on the following: 1) The monthly monitoring of calcite precipitation reveals a strong connection with primary producers and between-years variability; this precipitation produces a seasonal signal with imprint on varve formation. 2) Clear pollen sedimentation peaks occur in spring/summer and fall/winter that coincide with temperature, precipitation, relative humidity and winds; this pattern converges with the two-layer coupled varves representing the same seasonality. 3) We assess the lake’s contemporary oxygenation dynamics over a three- year period; a combination of sedimentary REDOX proxies revealed different scenarios of oxic/anoxic shifts since 1500 CE. 4) We investigate presence of seasonality in the production/distribution of glycerol dialkyl glycerol tetraethers and derived temperature estimates in soils and particulate matter. Branched glycerol dialkyl glycerol tetraethers signatures and some derived temperature estimates proxies appear to mainly depend on the non-seasonal shifts in soil properties. 5) Currently we examine relationships and similarities between extant phytoplankton and derived pigments in water and traps, and their correspondence with subfossil pigments; some preliminary results are presented here.Keywords: high resolution, endogenic varves, calcite precipitation, pollen sedimentation, meromixis, freshwater glycerol dialkyl tetraether, subfossil pigments, long-term ecology.

Almond is worldwide consumed and renowned as a valuable healthy food. In spite of this, it is also a potent source of allergenic proteins able to trigger several mild to life-threatening immunoreactions. Food processing proved to alter biochemical characteristics of proteins, thus affecting the respective allergenicity. In this paper we investigated the effect of autoclaving, preceded or not by a hydration step, on the biochemical and immunological properties of almond proteins. Any variation in the stability and immunoreactivity of almond proteins extracted from the treated materials, were evaluated by total protein quantification, ELISA assay and protein profiling by electrophoresis-based separation (SDS-PAGE). The autoclaving alone was found to weakly affect almond proteins stability, despite what observed for the combination of hydration and autoclaving, which resulted in a loss of approximately 70% of total protein content compared to untreated sample, and in a final negligible immunoreactivity, as well. The final SDS-PAGE protein pattern recorded for almonds hydrated and autoclaved disclosed significant changes. In addition, the same samples were further submitted to in vitro simulated gastro-duodenal (GI) digestion to evaluate potential changes induced by these processing on allergens digestibility. Digestion products were identified by HPLC-HRMS/MS analysis followed by software-based data mining, and complementary information were provided by analyzing the proteolytic fragments lower that 6 kDa in size. The autoclave based treatment was found not to alter the allergens digestibility, whereas an increased susceptibility to proteolytic action of digestive enzymes was observed in almonds subjected to the combination of prehydration and autoclaving. Finally, the residual immunoreactivity of the GI resistant peptides was investigated in-silico by bioinformatic tools, confirming that by following both approaches, no epitopes survived the almond digestion, thus demonstrating the potential effectiveness of these treatments to reduce almond allergenicity.

The Capon algorithm can be applied to reprocess SAR images, resulting in super-resolution super-resolution reconstructed scenes with lower sidelobe levels. Base on this idea, we have proposed a processing chain of Super-Resolution Persistent Scatterer Interferometry (SR-PSI), to increase PS density. In this paper, we review the main aspect of SR-PSI. We also propose a revised robust Capon algorithm. The result of real-life Sentinel-1 (S-1) data is shown.

Efficient traffic management at signalized intersections is integral to urban infrastructure development, requiring accurate estimation of delay times to mitigate congestion and enhance overall transportation systems. Traditional methodologies, including empirical observations and microsimulation software, have been prevalent in assessing delay times; however, their limitations have prompted the exploration of novel technologies like LiDAR (Light Detection and Ranging) sensors. This research study investigates and compares the accuracy of delay time estimations obtained from LiDAR sensor technology with those derived from microsimulation in AIMSUN. LiDAR sensors, known for their high-resolution, real-time data collection capabilities, offer a promising avenue for precise measurement of delay times at signalized intersections. Nonetheless, challenges in sensor placement, environmental influences, and data processing complexities suggest the need for further development and validation. In parallel, microsimulation software, exemplified by AIMSUN, provides a virtual platform for scenario testing but relies on assumptions that may not always mirror real-world traffic dynamics accurately. The comparative analysis conducted in this study aims to critically examine the discrepancies and potential complementarity between delay times obtained from LiDAR sensor technology and those derived from microsimulation in AIMSUN. The research involves an in-depth evaluation of real-time, high-resolution data collected by LiDAR sensors, assessing their accuracy in capturing the intricate movements and behavior of vehicles at signalized intersections. Simultaneously, AIMSUN microsimulation delay time models are scrutinized for their ability to accurately replicate these observed delay times. The disparities identified serve as critical insights into the challenges of both methodologies, prompting the discussion on the prospects of integrating LiDAR-derived data and microsimulation calibration processes to enhance the precision and reliability of delay time estimations. Future traffic management strategies can significantly benefit from a more accurate understanding of delay times, and this study endeavors to contribute to the advancement of methodologies in traffic engineering for more effective urban transportation systems.The paper's findings illuminate the potential and limitations of both LiDAR sensor technology and microsimulation in estimating delay times at signalized intersections. The results highlighted that the LiDAR sensors could accurately calculate delay times at a signalized intersection. Furthermore, the calculated delay time differences by LiDAR and AIMSUN at three days with the highest vehicle volumes (counts) are always less than 6.5%.

Self-driving vehicles demand efficient and reliable depth sensing technologies. Lidar, with its capacity for long-distance, high-precision measurement, is a crucial component in this pursuit. However, conventional mechanical scanning implementations suffer from reliability, cost, and frame rate limitations. Solid-state lidar solutions have emerged as a promising alternative, but the vast amount of photon data processed and stored using conventional direct time-of-flight (dToF) prevents long-distance sensing unless power-intensive partial histogram approaches are used. In this paper, we introduce a groundbreaking ‘guided’ dToF approach, harnessing external guidance from other onboard sensors to narrow down the depth search space for a power and data-efficient solution. This approach centers around a dToF sensor in-which the exposed time widow of independent pixels can be dynamically adjusted. We utilize a 64-by-32 macropixel dToF sensor and a pair of vision cameras to provide the guiding depth estimates. Our demonstrator captures a dynamic outdoor scene at 3 fps with distances up 75 m. Compared to a conventional full histogram approach, on-chip data is reduced by over 25 times, while the total laser cycles in each frame are reduced by at least 6 times compared to any partial histogram approach. The capability of guided dToF to mitigate multipath reflections is also demonstrated. For self-driving vehicles where a wealth of sensor data is already available, guided dToF opens new possibilities for efficient solid-state lidar.

We discuss a remote sensing system that is used to simultaneously detect range-resolved differential absorption LIDAR (light detection and ranging; DIAL) signals and integrated path DIAL signals (IP-DIAL) from aerosol targets for ranges up to 22 km. The DIAL/IP-DIAL frequency converter consists of an OPO pumped at 1064 nm to produce light at 1.6 μm and operates at 100 Hz pulse repetition frequency. The probe light is free space coupled to a movable platform that contains one transmitter and two receiver telescopes. Hybrid photon counting/current systems increase the dynamic range for detection by two orders of magnitude. Range resolved and column integrated dry-air CO2 and CH4 mixing ratios are obtained from line shape fits of CO2 and CH4 centered at 1602.2 nm and 1645.5 nm, respectively and measured at 10 different frequencies over ≈ 1.5 cm-1 bandwidth. The signal-to-noise ratios (SNR) of the IP-DIAL returns from cloud aerosols approach 1000:1 and the uncertainties in the mixing ratios weighted according to the integrated counts over the cloud segments range from 0.1 % to 1 %. The range averaged DIAL mixing ratios are in good agreement with the IP-DIAL mixing ratios at the 1 % to 2 % level for both CO2 and CH4. These results can serve as a validation method for future active and passive satellite observational systems.

We performed the accuracy assessment of three different Normalized Difference Water Indices (NDWIs) in water bodies during April 2019, a period in which floods occurred in a large proportion of the Southwest of the Buenos Aires Province (Argentina). The accuracy of the estimations using spaceborne medium-resolution multi-spectral imaging, and the reliability of three NDWIs to highlight shallow water features in satellite images, was evaluated using a high resolution airbone imagery as ground-truth. It is shown that these indices computed using Landsat 8 and Sentinel-2 imagery are only loosely correlated to the actual flooded area in shallow waters. Indeed, NDWI values vary significantly depending on the satellite mission used and the type of index computed.

Continuous water resources monitoring is needed for sustainable urban water supply. Remote sensing techniques have proven useful for monitoring some water qualitative parameters with optical characteristics. The study area was the Marateca reservoir in central inland Portugal. The aims were the following: (1) to explore the water quality parameters at the monitoring points of the Marateca reservoir that may explain the event; (2) to validate optical water quality parameters with the monitoring points data; and (3) to model the reservoir water characteristics regarding its deepness, trophic state, and turbidity. The parameters total phosphorus, total nitrogen, and chlorophyll-a were used to compute a trophic level index. The Sentinel-2 imagery was used to compute spectral indices and bands image ratio; to obtain spectral signatures for the monitoring points, and to model water characteristics. The water parameters were above the recommended values at the reservoir entry point from the Ocreza River. The reservoir trophic level was Hypereutrophic and Eutrophic. The spectral signatures confirmed a Hypereutrophic pattern in the entry point. The Marateca reservoir’s water characteristics modeling forecasted problematic zones by contamination. The methodological approach developed can be easily applied to other reservoirs and is a key support tool for decision-makers.

With the development of hyperspectral technology, to establish an effective spectral data compressive reconstruction method that can improving data storage, transmission and maintaining spectral information is critical for quantitative remote sensing research and application in vegetation. By introducing the idea of compressive sensing in compressed reconstruction, the spectral adaptive grouping distributed compressive sensing algorithm is proposed, which enables a distributed compressed sensing reconstruction of plant hyperspectral data. The experimental results showed that comparing with orthogonal matching pursuit(OMP) and gradient projection reconstruction(GPSR), the proposed algorithm can significantly improve the visual effect of image reconstruction in the spatial domain. The PSNR in low sampling rate(sampling rate is lower than 0.2) increases by 13.72dB than OMP and 1.66dB than GPSR. In the spectral domain, the average normalized root mean square error、the mean absolute percentage error and the mean absolute error of the proposed algorithm is35.38%，31.83% and 33.33% lower than GPSR respectively.. Therefore, the proposed algorithm can achieve relatively high reconstructed efficiency.

We are introducing a highly efficient 48-channel ultrasound beamforming system ideal for ultrasound endoscopy applications. The system includes a transmitter and a receiver, which allow for low-area, high-resolution imaging acquisition. The transmitter uses a charge redistribution HV (high voltage) scheme to generate three-level pulses that actuate the transducer, implemented with the standard CMOS process for optimal cost and power savings. Meanwhile, the receiver features a sub-array structure and a delay generator that reduces area usage. To achieve high-resolution ultrasound imaging acquisition with low computational power, we developed the Shift Coherence Factor (SCF) algorithm, which is hardware-friendly. This approach delivers a lateral resolution over 20% better than the conventional delay and sum (DAS) algorithm, with a contrast ratio of over 30 dB. The system was implemented in a 180 nm standard CMOS process with an area of 24.98 mm2, power consumption of 8.23 mW per channel, achieving a delay resolution of 8.33 ns, and a low-area implementation of 0.52 mm2 per channel. The system offers high-quality imaging acquisition with minimal additional area and power consumption, which has great potential for 3D imaging or catheterized ultrasound systems.

Diffuse interstitial lung diseases (DILD) are a heterogeneous group of over 200 entities, some with dramatical evolution and poor prognostic. Because of their overlapping clinical, physiopathological and imagistic nature, successful management requires early detection and proper progression evaluation. This paper tests a complex networks (CN) algorithm for imagistic aided diagnosis fitness for the possibility of achieving relevant and novel DILD management data. 65 DILD and 31 normal high resolution computer tomography (HRCT) scans were selected and analyzed with the CN model. The algorithm is showcased in two case reports and then statistical analysis on the entire lot shows that a CN algorithm quantifies progression evaluation with a very fine accuracy, surpassing functional parameters’ variations. The CN algorithm can also be successfully used for early detection, mainly on the ground glass opacity Hounsfield Units band of the scan.

Floods are one of the most wide-spread, frequent, and devastating natural disasters that continue to increase in frequency and intensity. Remote sensing, specifically synthetic aperture radar (SAR), has been widely used to detect surface water inundation to provide retrospective and near-real time (NRT) information due to its high-spatial resolution, self-illumination, and low atmospheric attenuation. However, the efficacy of flood inundation mapping with SAR is susceptible to reflections and scattering from a variety of factors including dense vegetation and urban areas. In this study, the topographic dataset height above nearest drainage (HAND) was investigated as a potential supplement to Sentinel-1A C-Band SAR along with supervised machine learning to improve the detection of inundation in heterogeneous areas. Three machine learning classifiers were trained on two sets of features SAR only (VV & VH) and VV, VH & HAND to map inundated areas. Three study sites along the Neuse River in North Carolina, USA during the record flood of Hurricane Matthew in October 2016 were selected. The binary classification analysis (inundated as positive vs. non-inundated as negative) revealed significant improvements when incorporating HAND in several metrics including classification accuracy (ACC) (+37.1%), true positive rate (TPR) (+51.2%), and negative predictive value (NPV) (+23.7%), A marginal improvement of +1.4% was seen for positive predictive value (PPV), but true negative rate (TNR) fell -15.1%. By incorporating HAND, a significant number of areas with high SAR backscatter but low HAND values were detected as inundated which increased true positives. This in turn also increased the false positives detected but to a lesser extent as evident in the metrics. This study demonstrates that HAND could be considered a valuable feature to enhance SAR flood inundation mapping especially in areas with heterogeneous land covers with dense vegetation that interfere with SAR.

On the 6th September at 03:08AM local time, the Iburi-Hokkaido Earthquake, only 33 km deep triggers >5,000 co-seismic mass-movements in the hills in a 25 km radius from the epicenter. Although the majority of the mass-movements occurred in complex-geometry valley with the coalescence of deposits, a small sub-set of 59 events deposited on the semi-horizontal val-ley-floor generating separated deposits that were studied in the present contribution. The aim of the present contribution was to contribute to the existing databases of empirical relationships based on planform and vertical dataset, and to define the scalars of those relations that charac-terize the mass-movements of the Iburi-Hokkaido earthquake, with the overarching goal of generating predictors for hazard-mapping. To reach these objectives, the methodology relies on LiDAR data flown in the aftermath of the earthquake as well as aerial photographs. Using Geo-graphical Information Science (GIS) tools planform and vertical parameters were extracted to calculate the power-law relations between areas and volume, between the Fahrböschung and the volume of the deposits, as well as other geometric relationships. Results have shown that the relation S=k〖V_d〗^(2/3) where S is the surface area of a deposit and Vd the volume, and k a scalar that is function of S: k=2.1842 ln⁡(S)-10.167 with a R2 of 0.52, and this relation is improved for the open-slope mass-movements but not the valley-confined ones, that present more varia-bility. The Fahrböschung for events that started as valley-confined mass-movements was Fc = -0.043ln(D) + 0.7082 with a R2 of 0.5m while for open-slope mass-movements, the Fo = -0.046ln(D) + 0.7088 with a R2 of 0.52. These results contribute to the growing co-seismic land-slide database and they can also be the base to understand the role of the counter-slopes and complex topography on the spread and distance travelled by the mass-movement deposits.

Topographic mapping using stereo plotting is not effective because it takes much time and labor-intensive. Thus, this research was conducted to find the effective way to extract building footprint for mapping acceleration. Building extraction method in this process comprises four steps: ground / non-ground filtering, building classification, segmentation, and building extraction. Non-ground points from filtering process were classified as building with the algorithm based on multi-scale local dimensionality to separate points at the maximum separability plane. Segmentation using segment growing was used to separate each building, so edge detection could be conducted for each segment to create boundary of each building. Lastly, building extraction was conducted through three steps: edge points detection, building delineation, and building regularization. With 10 samples and step 0.5, classification resulted quality and miss factor of 0.597 and 0.524, respectively. The quality was improved by segmentation process to 0.604, while miss factor was getting worse to 0.561. Meanwhile, on average shape index value from extracted building had 0.02 difference and the number of errors was 30% for line segment comparison. Regarding positional accuracy using centroid accuracy assessment, this method could produce RMSE of 1.169 meters.

Monitoring crown closure evolution using multi-temporal Light Detection and Ranging (LiDAR) surveys is a method that we expect to be increasingly adopted given the availability of LiDAR sensors and the accumulating survey archives. However, little attention was devoted to comparing crown closure estimates from independent surveys. Although survey parameters cannot be modified after the data collection, we speculate that the error associated to crown closure estimates comparison can be reduced by selecting optimal post-survey parameters. In this study, we compared crown closure estimates of three airborne LiDAR surveys from 2018 (40 pt/m²) used as a reference, and two lower-density surveys from 2016 (4.5 pt/m²) and 2018 (2 pt/m²). We studied the effect of the height threshold used to separate canopy points and the grid resolution, using skewness and variance of lagged difference of crown closure. Crown closure estimates using low height thresholds were more different across surveys, resulting in higher root mean squared error (RMSE), bias and more different variograms. Results show that optimal height threshold was 3 m and grid resolution was 25 m, although there was room for decision (RMSE of 7% and 5%, and bias of 4% and 0% for 2016 and 2018 low-density surveys).

The identification of bloodstain is one of the most important approaches in obtaining evidence in criminalistics. A threshold method based on spectral co-efficient and interclass variance is proposed in this paper, it is a non-contact, non-destructive method for quickly identifying bloodstains. The spectra of bloodstains and other suspected substances were all extracted from their hyper-spectral image. Then calculate the correlation coefficients of these spectral and interclass variances, analyze the differences between substances. The best blood recognition threshold was determined as 0.9. After preprocessing for eliminating systematic errors, experiments with the threshold 0.9 are carried out to identify bloodstains on the calico and red T-shirt. The method can remarkably identify the bloodstain from other non-blood substances both quickly and efficiently. The blood extraction rate can reach to 93.35% and 89.19%, respectively. It is an important step toward the implementation of bloodstain non-contact and non-destructive identification in forensic casework.

The small satellite market continues to grow year after year. A compound annual growth rate of 17% is estimated during the period between 2020 and 2025. Low-cost satellites can send a vast amount of images to be post-processed at the ground to improve the quality and extract detailed information. In this domain lies the resolution enhancement task, where a low-resolution image is converted to a higher resolution automatically. Deep learning approaches to Super-Resolution (SR) reached the state-of-the-art in multiple benchmarks; however, most of them were studied in a single-frame fashion. With satellite imagery, multi-frame images can be obtained at different conditions giving the possibility to add more information per image and improve the final analysis. In this context, we developed and applied to the PROBA-V dataset of multi-frame satellite images a model that recently topped the European Space Agency’s Multi-frame Super Resolution (MFSR) competition. The model is based on proven methods that worked on 2D images tweaked to work on 3D: the Wide Activation Super Resolution (WDSR) family. We show that with a simple 3D CNN residual architecture with WDSR blocks and a frame permutation technique as data augmentation better scores can be achieved than with more complex models. Moreover, the model requires few hardware resources, both for training and evaluation, so it can be applied directly from a personal laptop.

Perilla frutescens leaves are expected to exhibit antioxidant activity and amyloid-β (Aβ) aggregation inhibitory activity due to the presence of polyphenol-type compounds, etc. These activities fluctuate daily; however, measuring the contents of constituents and functionalities is cumbersome and unsuitable for instantaneous measurements in the field. In this study, we developed a facile method to analyze the activity and content of the components in leaves on-site based on the fluorescence spectra obtained by irradiating the perilla leaves with excitation light. The constituent contents (Chlorophyl (Chl), Total polyphenol content (TPC), Total flavonoid content (TFC), rosmarinic acid (RA) and functional properties DPPH radical scavenging activity, ferric reducing antioxidant power (FRAP), oxygen radical absorbance capacity (ORAC), Aβ aggregation inhibitory activity) were evaluated using established analytical techniques. The correlation between the fluorescence spectrum and the contents and activities was calculated using normalized difference spectral index (NDSI), ratio spectral index (RSI), and difference spectral index (DSI) analyses. We identified a predictive model with a high coefficient of determination R² ≥ 0.57 for content of the constituents, and R² ≥ 0.49 for functional properties. This allows convenient, simultaneous, and non-destructive monitoring of the constituent contents and the functionalities of perilla leave and a simple harvest-time diagnosis. The model established using this method is simple and suggesting that it can simultaneously infer the constituents and functionalities of perilla leaves.

Atmospheric correction is a necessary step in image processing data and spectra recorded by spaceborne sensors for pure cloudless atmosphere, primarily in the visible and near-IR spectral range. We have present a fast and sufficiently accurate method of atmospheric correction based on the proposed analytical solutions describing with high accuracy the spectrum of outgoing radiation at the top boundary of the cloudless atmosphere. This technique includes the model of the atmosphere and its optical parameters that are important in terms of radiation transfer. The solution of the inverse problem for finding unknown parameters of the model is carried out by the method of non-linear least squares (Levenberg-Marquardt algorithm) for an individual selected pixel of the image (its spectrum), taking into account the adjacency effects. Using the found parameters of the atmosphere and the average surface albedo, assuming homogeneity of the atmosphere within a certain area of the hyperspectral image (or the whole frame), the spectral albedo at the Earth's surface is calculated for all other pixels. It is essential that the procedure of the numerical simulation with non-linear least squares of the direct transfer problem is based on using analytical solutions, which provides a very short calculation time of the atmospheric parameters (seconds or less) and the ability to perform atmospheric correction "on-fly." Testing methods of atmospheric correction was performed using the synthetic outgoing radiation spectra at the top of the atmosphere (TOA), obtained by numerical simulation in the LibRadTran code, as well as spectra of real space images of the Hyperion hyperspectrometer. A comparison with the results of atmospheric correction in module FLAASH of ENVI package has been performed. Finally, to validate our data obtained by the SHARK method, a comparative analysis with ground-based measurements of Radiometric Calibration Network (RadCalNet) was carried out.

Spectral calibration consists of the calibration of wavelengths and the measurement of the instrument's spectral response function (SRF). Unlike conventional slits, the absorbed aerosol sensors (AAS) are used as a slit homogenizer, in which the spectral response function (SRF) is not a conventional Gaussian curve. To be more precise, the SRF is the convolution of the slit function of the spectrometer, the line spread function of the optical system, and the detector response function. The SRF of the slit homogenizer is a flat-topped multi-Gaussian function. Considering the convenience of fitting, a super-Gaussian function, which looks like a similar distribution to the flat-topped multi-Gaussian function, is used to fit the measured data in a spectral calibration. According to the results, the SRF’s shapes, due to the Earth port, resemble a Gaussian curve with a flatted top could be concluded, which contains an FWHM of 1.78–1.82 nm for the AAS. The results show that the correlation coefficients are about 0.99, which proves that the fitting function could better characterize the SRF of the instrument.

Digital elevation models (DEMs) have a wide range of applications and play a crucial role in many studies. Numerous public DEMs, frequently acquired using radar and optical satellite imagery, are currently available; however, DEM datasets tend to exhibit elevation values influenced by vegetation height and coverage, which can compromise the accuracy of models in representing terrain elevation. In this study, we developed a digital terrain model for South America using a novel methodology to remove vegetation bias in the Copernicus DEM GLO-30 (COPDEM) model using machine learning, Global Ecosystem Dynamics Investigation (GEDI) elevation data, and multispectral remote sensing products. Our results indicate considerable improvements compared to COPDEM in representing terrain elevation, reducing average errors (BIAS) from 9.6 m to 1.5 m. Furthermore, we evaluated our product (ANADEM) by comparison with other global DEMs, obtaining more accurate results for different conditions of vegetation fraction cover and land use. As a publicly available and open-source dataset, ANADEM will play a crucial role in advancing studies that demand accurate terrain elevation representations at large scales.

In this paper, we propose a system to create high-precision maps using UAV-LiDAR and to determine the location of individual fruit trees (apple trees) on the maps. The system is based on a UAV-LiDAR system that flies over an actual orchard. A UAV was flown over an actual orchard, and the point cloud of the onboard LiDAR and the location information of RTK-GNSS were obtained. The system records the LiDAR point cloud and RTK-GNSS position information. Automated software processes point cloud data offline and Automated software processes point cloud data offline and automatically segments each apple tree in the map. The RTK-GNSS position information is used for the segmented trees. The positional information obtained from RTK-GNSS was georeferenced to the segmented trees without using ground evaluation points. As a sample, location information was obtained from trees using the Quasi-Zenith Satellite System (QZSS) MICHIBIKI. The positional accuracy of the trees was evaluated using the positional information obtained from the Quasi-Zenith Satellite System MICHIBIKI as a reference. As a result, the alignment accuracy was sufficient to identify individual fruit trees.

Throughout history, the unique Dacian landscape has aroused the imagination of many. For decades, researchers have been fascinated by the magnificent structures the Dacians built and how they altered the mountains to their advantage. Dacian sites, despite their grandeur, remain mostly unknown due to their position deep within Romania's vast forests, generally in remote regions and hidden from the naked eye. Ground exploration in densely forested mountain regions is extremely difficult, and even if such campaigns existed, they would be insufficient to provide a comprehensive picture of the Dacian world. The lack of high-resolution remote-sensing data for wide areas made big-scale assessments of the landscape impractical. This is about to change, as new large datasets of LiDAR-derived digital elevation models, covering the entire heart of Dacian world, are now freely available. This paper reports on one of the most recent freely available LiDAR-based high-resolution digital elevation models in Romania, its impact on Romanian mountain archaeology, and how this can shape future research directions in understanding the Dacian landscape.

Storm Ophelia made landfall over Ireland as an extra-tropical storm on the morning of the 16th of October 2017. The storm caused major power outages, lifted roofs, caused coastal flooding in Ireland, and resulted in the loss of three lives. A model’s capability to forecast extreme weather events such as Storm Ophelia is of utmost importance and now with a changing climate, it becomes more important to improve and enhance model forecasting capability. The Weather Research and Forecasting (WRF) model V3.9 has been configured for the Irish domain and this study presents a preliminary evaluation of the Model during Storm Ophelia. Simulated wind speed and direction were compared with hourly remote sensing (lidar) and in-situ (wind speed and wind direction at 10m) observations at the coastal site of Mace Head Atmospheric Research Station on the West coast of Ireland (53.33◦ N, 9.90 49 ◦ W). The model simulation has generally small biases in the simulated wind speed and wind direction during this case study. The model also realistically simulated the magnitude and geographical distribution of the wind speed and wind direction observed during Ophelia.

Questions: Elevation, biodiversity, and forest structure are commonly correlated, but their relationships near the positive extremes of biodiversity and elevation are unclear. We asked 1) How does forest structure vary with elevation in a high biodiversity, high topographic complexity region? 2) Does forest structure predict vascular plant biodiversity? 3) Is plant biodiversity more strongly related to elevation or to forest structure? Location: Great Smoky Mountains National Park, USAMethods: We used terrestrial LiDAR scanning (TLS) to characterize vegetation structure in 12 forest plots. We combined two new canopy structural complexity metrics with traditional TLS-derived forest structural metrics and vascular plant biodiversity data to investigate correlations among forest structure metrics, biodiversity, and elevation. Results: Forest structure varied widely across plots spanning the elevational range of GRSM. Our new measures of canopy density (Depth) and structural complexity (σDepth) were sensitive to structural variations and effectively summarized horizontal and vertical dimensions of structural complexity. Vascular plant biodiversity was negatively correlated with elevation, and more strongly positively correlated with vegetation structure variables. Conclusions: The strong correlations we observed between canopy structural complexity and biodiversity suggest that structural complexity metrics could be used to assay plant biodiversity over large areas in concert with airborne and spaceborne platforms.

Taiwan developing offshore wind power to promote green energy and self-electricity production. In this study, a Light Detection and Ranging (Lidar) was set up at Chang-Hua development zone one on the sea and 10km away from the seashore. At Lidar location, WRF (3.33km & 2km grid lengths) model and WAsP were used to simulate the wind speed at various elevations. Three days mean wind speed of simulated results were compared with Lidar data. From the four wind data sets, developed five different comparisons to find an error% and R-Squared values. Comparison between WAsP and Floating Lidar was shown good consistency. Lukang meteorological station 10 years wind observations at 5m height were used for wind farm energy predictions. The yearly variation of energy predictions of traditional and TGC wind farm layouts are compared under purely neutral and stable condition. The one-year cycle average surface heat flux over the Taiwan Strait is negative (-72.5 (W/m2) and 157.13 STD), which represents stable condition. At stable condition TGC (92.39%) and 600(92.44%), wind farms were shown higher efficiency. The Fuhai met mast wind data was used to estimate roughness length and power law exponent. The average roughness lengths are very small and unstable atmosphere.