bluuebunny/crossref_metadata_2025 huggingface dataset:
Accumulation of PHA in the Microalgae Scenedesmus sp. under Nutrient-Deficient Conditions
Authors: García Gabriela , Sosa-Hernández Juan Eduardo , Rodas-Zuluaga Laura Isabel , Castillo-Zacarías Carlos , Iqbal Hafiz , Parra-Saldívar Roberto
Published: December 2020

Traditional plastics have undoubted utility and convenience for everyday life; but when they are derived from petroleum and are non-biodegradable, they contribute to two major crises today’s world is facing: fossil resources depletion and environmental degradation. Polyhydroxyalkanoates are a promising alternative to replace them, being biodegradable and suitable for a wide variety of applications. This biopolymer accumulates as energy and carbon storage material in various microorganisms, including microalgae. This study investigated the influence of glucose, N, P, Fe, and salinity over the production of polyhydroxyalkanoate (PHA) by Scenedesmus sp., a freshwater microalga strain not previously explored for this purpose. To assess the effect of the variables, a fractional Taguchi experimental design involving 16 experimental runs was planned and executed. Biopolymer was obtained in all the experiments in a wide range of concentrations (0.83–29.92%, w/w DW), and identified as polyhydroxybutyrate (PHB) by FTIR analysis. The statistical analysis of the response was carried out using Minitab 16, where phosphorus, glucose, and iron were identified as significant factors, together with the P-Fe and glucose-N interactions. The presence of other relevant macromolecules was also quantified. Doing this, this work contributes to the understanding of the critical factors that control PHA production and present Scenedesmus sp. as a promising species to produce bio-resources in commercial systems.
Cellulose Derived Graphene/Polyaniline Nanocomposite Anode for Energy Generation and Bioremediation of Toxic Metals via Benthic Microbial Fuel Cells
Authors: Yaqoob Asim Ali , Mohamad Ibrahim Mohamad Nasir , Umar Khalid , Bhawani Showkat Ahmad , Khan Anish , Asiri Abdullah M , Khan Mohammad Rizwan , Azam Mohammad , AlAmmari Ahmad Moid
Published: December 2020

Benthic microbial fuel cells (BMFCs) are considered to be one of the eco-friendly bioelectrochemical cell approaches nowadays. The utilization of waste materials in BMFCs is to generate energy and concurrently bioremediate the toxic metals from synthetic wastewater, which is an ideal approach. The use of novel electrode material and natural organic waste material as substrates can minimize the present challenges of the BMFCs. The present study is focused on cellulosic derived graphene-polyaniline (GO-PANI) composite anode fabrication in order to improve the electron transfer rate. Several electrochemical and physicochemical techniques are used to characterize the performance of anodes in BMFCs. The maximum current density during polarization behavior was found to be 87.71 mA/m2 in the presence of the GO-PANI anode with sweet potato as an organic substrate in BMFCs, while the GO-PANI offered 15.13 mA/m2 current density under the close circuit conditions in the presence of 1000 Ω external resistance. The modified graphene anode showed four times higher performance than the unmodified anode. Similarly, the remediation efficiency of GO-PANI was 65.51% for Cd (II) and 60.33% for Pb (II), which is also higher than the unmodified graphene anode. Furthermore, multiple parameters (pH, temperature, organic substrate) were optimized to validate the efficiency of the fabricated anode in different environmental atmospheres via BMFCs. In order to ensure the practice of BMFCs at industrial level, some present challenges and future perspectives are also considered briefly.
Modeling Injection Molding of High-Density Polyethylene with Crystallization in Open-Source Software
Authors: Krebelj Kristjan , Krebelj Anton , Halilovič Miroslav , Mole Nikolaj
Published: December 2020

This work investigates crystallization modeling by modifying an open-source computational fluid dynamics code OpenFOAM. The crystallization behavior of high-density polyethylene (HDPE) is implemented according to theoretical and experimental literature. A number of physical interdependencies are included. The cavity is modeled as deformable. The heat transfer coefficient in the thermal contact towards the mold depends on contact pressure. The thermal conductivity is pressure- and crystallinity-dependent. Specific heat depends on temperature and crystallinity. Latent heat is released according to the crystallization progress and temperature. Deviatoric elastic stress is evolved in the solidified material. The prediction of the cavity pressure evolution is used for the assessment of the solution quality because it is experimentally available and governs the residual stress development. Insight into the thermomechanical conditions is provided with through-thickness plots of pressure, temperature and cooling rate at different levels of crystallinity. The code and simulation setup are made openly available to further the research on the topic.
Influence of Humidity on Fatigue Performance of CFRP: A Molecular Simulation
Authors: Li Bowen , Chen Jianzhong , Lv Yong , Huang Li , Zhang Xiaoyu
Published: December 2020

The study on durability of carbon fiber reinforced plastics (CFRP) in complex environments is critical because of its wide applications. Herein, mechanical behavior of carbon fiber reinforced epoxy composites in the fatigue process were investigated under different humidity via molecular dynamics (MD) simulation method. Transversely isotropic atom based models were established to simulate the structure of CFRP at the atomistic level. Owing to the weak performance in vertical fiber direction, mechanical behavior in a 90° orientation was investigated. Mean stress and energy were both employed to describe the evolution of mechanical performance while mean squared displacement (MSD), radius of gyration (Rg), and free volume were performed to describe the evolution of structural change during the fatigue process. The results show that the humidity led to a weakened interfacial adhesive performance. Free volume became larger under cyclic load, which caused the water molecules to diffuse into the inside of epoxy resin. The distance between the matrix and fiber became larger in the dry system while it reduced because of the diffusion of water molecules in wet system. The rate of performance degradation decreased with the increase in humidity because of poor initial performance at high humidity.
Microfiltration Membranes Modified with Composition of Titanium Oxide and Silver Oxide by Magnetron Sputtering
Authors: Kacprzyńska-Gołacka Joanna , Łożyńska Monika , Barszcz Wioletta , Sowa Sylwia , Wieciński Piotr , Woskowicz Ewa
Published: December 2020

In this work, the authors present the possibility of modification of polymer membranes by TiO2 + AgO coating created by the magnetron sputtering method. The two-component TiO2 + AgO coating can improve and shape new functional properties such as bactericidal and photocatalytic properties. The influence of magnetron power changes on the structure of the membrane was investigated as well. The structure and elemental composition of TiO2 + AgO coatings were analyzed using SEM and EDS technique. All deposited coatings caused a total inhibition of the growth of two investigated colonies of Escherichia coli and Bacillus subtilis on the surface. The photocatalytic properties for membranes covered with oxide coatings were tested under UV irradiation and visible light. The filtration result show that polymer membranes covered with two-component TiO2 + AgO coatings have a permeate flux similar to the non-coated membranes.
Click Chemistry Enabling Covalent and Non-Covalent Modifications of Graphene with (Poly)saccharides
Authors: Li Hu , Papadakis Raffaello
Published: December 2020

Graphene is a material with outstanding properties and numerous potential applications in a wide range of research and technology areas, spanning from electronics, energy materials, sensors, and actuators to life-science and many more. However, the insolubility and poor dispersibility of graphene are two major problems hampering its use in certain applications. Tethering mono-, di-, or even poly-saccharides on graphene through click-chemistry is gaining more and more attention as a key modification approach leading to new graphene-based materials (GBM) with improved hydrophilicity and substantial dispersibility in polar solvents, e.g., water. The attachment of (poly)saccharides on graphene further renders the final GBMs biocompatible and could open new routes to novel biomedical and environmental applications. In this review, recent modifications of graphene and other carbon rich materials (CRMs) through click chemistry are reviewed.
An Additive Manufacturing Method Using Large-Scale Wood Inspired by Laminated Object Manufacturing and Plywood Technology
Authors: Tao Yubo , Yin Qing , Li Peng
Published: December 2020

Wood-based materials in current additive manufacturing (AM) feedstocks are primarily restricted to the micron scale. Utilizing large-scale wood in existing AM techniques remains a challenge. This paper proposes an AM method—laser-cut veneer lamination (LcVL)—for wood-based product fabrication. Inspired by laminated object manufacturing (LOM) and plywood technology, LcVL bonds wood veneers in a layer-upon-layer manner. As demonstrated by printed samples, LcVL was able to retain the advantageous qualities of AM, specifically, the ability to manufacture products with complex geometries which would otherwise be impossible using subtractive manufacturing techniques. Furthermore, LcVL-product structures designed through adjusting internal voids and wood-texture directionality could serve as material templates or matrices for functional wood-based materials. Numerical analyses established relations between the processing resolution of LcVL and proportional veneer thickness (layer height). LcVL could serve as a basis for the further development of large-scale wood usage in AM.
Nano-Intermediate of Magnetite Nanoparticles Supported on Activated Carbon from Spent Coffee Grounds for Treatment of Wastewater from Oil Industry and Energy Production
Authors: Acosta Laura , Galeano-Caro Dahiana , Medina Oscar E. , Cortés Farid B. , Franco Camilo A.
Published: December 2020

This work focused on evaluating the adsorptive removal of crude oil using a nano-intermediate based on magnetite nanoparticles supported on activated carbon synthesized from spent coffee grounds and the subsequent catalytic oil decomposition to recover by-products and regenerate the support material. The magnetite nanoparticles were synthesized by the co-precipitation method and were used as active phases on prepared activated carbon. The amount of crude oil adsorbed was determined by adsorption isotherms. In addition, dynamic tests were performed on a packed bed to evaluate the efficiency of the removal process. Thermogravimetric analysis and mass spectrometry were used to evaluate the catalytic powder and the quantification of by-products. Contrasting the results with commercial carbon, the one synthesized from the coffee residue showed a greater affinity for the oil. Likewise, the adsorption capacity increased by doping activated carbon with magnetite nanoparticles, obtaining an efficiency greater than 10%. The crude oil decomposition was carried out successfully by thermal cracking, obtaining a 100% removal. The gas produced after decomposition contains light hydrocarbons such as C2H4 and CH4 and shows a decrease in polluting species such as CO and CO2, leading to greater environmental sustainability of the process.
Analysis and Optimization of Two Film-Coated Tablet Production Processes by Computer Simulation: A Case Study
Authors: Hering Stefanie , Schäuble Nico , Buck Thomas M. , Loretz Brigitta , Rillmann Thomas , Stieneker Frank , Lehr Claus-Michael
Published: December 2020

Increasing regulatory demands are forcing the pharmaceutical industry to invest its available resources carefully. This is especially challenging for small- and middle-sized companies. Computer simulation software like FlexSim allows one to explore variations in production processes without the need to interrupt the running process. Here, we applied a discrete-event simulation to two approved film-coated tablet production processes. The simulations were performed with FlexSim (FlexSim Deutschland—Ingenieurbüro für Simulationsdienstleistung Ralf Gruber, Kirchlengern, Germany). Process visualization was done using Cmap Tools (Florida Institute for Human and Machine Cognition, Pensacola, FL, USA), and statistical analysis used MiniTab® (Minitab GmbH, Munich, Germany). The most critical elements identified during model building were the model logic, operating schedule, and processing times. These factors were graphically and statistically verified. To optimize the utilization of employees, three different shift systems were simulated, thereby revealing the advantages of two-shift and one-and-a-half-shift systems compared to a one-shift system. Without the need to interrupt any currently running production processes, we found that changing the shift system could save 50–53% of the campaign duration and 9–14% of the labor costs. In summary, we demonstrated that FlexSim, which is mainly used in logistics, can also be advantageously implemented for modeling and optimizing pharmaceutical production processes.
Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development
Authors: Salo-Ahen Outi M. H. , Alanko Ida , Bhadane Rajendra , Bonvin Alexandre M. J. J. , Honorato Rodrigo Vargas , Hossain Shakhawath , Juffer André H. , Kabedev Aleksei , Lahtela-Kakkonen Maija , Larsen Anders Støttrup , Lescrinier Eveline , Marimuthu Parthiban , Mirza Muhammad Usman , Mustafa Ghulam , Nunes-Alves Ariane , Pantsar Tatu , Saadabadi Atefeh , Singaravelu Kalaimathy , Vanmeert Michiel
Published: December 2020

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.
Encapsulation of Lactoferrin for Sustained Release Using Particles from Gas-Saturated Solutions
Authors: Ono Kento , Sakai Hiroki , Tokunaga Shinichi , Sharmin Tanjina , Aida Taku Michael , Mishima Kenji
Published: December 2020

The particles from gas saturated solutions (PGSS) process were performed to encapsulate lactofer-rin, an iron-binding milk glycoprotein, using supercritical carbon dioxide (scCO2). A natural en-teric polymer, shellac, was used as a coating material of lactoferrin carried out by the PGSS pro-cess. Conditions were optimized by applying different temperatures (20–50 °C) and pressures (8–10 MPa) and the particles were evaluated for particle shape and size, lactoferrin encapsulation ef-ficiency, Fourier transform infrared (FTIR) spectroscopy to confirm lactoferrin entrapment and in vitro dissolution studies at different pH values. Particles with an average diameter of 75.5 ± 7 μm were produced with encapsulation efficiency up to 71 ± 2%. Furthermore, particles that showed high stability in low pH (pH 1.2) and a sustained release over time (t2h = 75%) in higher pH (pH 7.4) suggested an effective encapsulation process for the protection of lactoferrin from gastric di-gestion.
Using Neural Networks to Obtain Indirect Information about the State Variables in an Alcoholic Fermentation Process
Authors: Sipos Anca , Florea Adrian , Arsin Maria , Fiore Ugo
Published: December 2020

This work provides a manual design space exploration regarding the structure, type, and inputs of a multilayer neural network (NN) to obtain indirect information about the state variables in the alcoholic fermentation process. The main benefit of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. The novelty of this research is the flexibility of the developed application, the use of a great number of variables, and the comparative presentation of the results obtained with different NNs (feedback vs. feed-forward) and different learning algorithms (Back-Propagation vs. Levenberg–Marquardt). The simulation results show that the feedback neural network outperformed the feed-forward neural network. The NN configuration is relatively flexible (with hidden layers and a number of nodes on each of them), but the number of input and output nodes depends on the fermentation process parameters. After laborious simulations, we determined that using pH and CO2 as inputs reduces the prediction errors of the NN. Thus, besides the most commonly used process parameters like fermentation temperature, time, the initial concentration of the substrate, the substrate concentration, and the biomass concentration, by adding pH and CO2, we obtained the optimum number of input nodes for the network. The optimal configuration in our case was obtained after 1500 iterations using a NN with one hidden layer and 12 neurons on it, seven neurons on the input layer, and one neuron as the output. If properly trained and validated, this model can be used in future research to accurately predict steady-state and dynamic alcoholic fermentation process behaviour and thereby improve process control performance.
A Feasibility Study of a Robotic Approach for the Gluing Process in the Footwear Industry
Authors: Castelli Kevin , Zaki Ahmed Magdy Ahmed , Dmytriyev Yevheniy , Carnevale Marco , Giberti Hermes
Published: December 2020

Manufacturing processes in the shoe industry are still characterized to a large extent by human labour, especially in small and medium craft enterprises. Even when machinery is adopted to support manufacturing operations, in most cases an operator has to supervise or carry out the task. On the other hand, craft footwear industries are called to respond to continuous challenges to face the globalization effects, so that a rapid adaptability to customer needs is required. The industry 4.0 paradigms, which are taking place in the industrial environments, represent an excellent opportunity to improve the efficiency and quality of production, and a way to face international competitors. This paper analyses and proposes a robotic cell to automatize the process of glue deposition on shoe upper, which exploits a new means of depositing the glue compared to State-of-Art applications. While the latter mainly adopt glue gun spraying systems or pneumatic syringes, the proposed robotic cell is based on an extrusion system for the deposition of molten material originally in the form of a filament, similar to all extent to those adopted for Fused Deposition Modeling (FDM). Two cell solutions are designed and tested. In the former the extruder is the robot end effector and the shoe upper is grounded to the cell frame. In the second, being the reciprocal, the shoe last is clamped to the robot wrist and the extruder is fixed to the cell frame. The peculiarities of the two solutions are pointed out and compared in terms of cell layout, hardware, programming software and possibility to develop collaborative applications. A self developed slicing software allows designing the trajectories for glue deposition based on the CAD model of the shoe upper, also allowing driving the inclination of the extruder nozzle with respect to the vectors normal to the upper surface. Both the proposed cell layouts permit to achieve good quality and production times. The solution with the mobile extruder is able to deposit glue at highest end-effector speed (up to 200 mm/s). On the other hand, the solution with the mobile shoe upper and fixed extruder seems to be more appropriate to enhance collaborative applications.
On the Potential of Improving WRF Model Forecasts by Assimilation of High-Resolution GPS-Derived Water-Vapor Maps Augmented with METEOSAT-11 Data
Authors: Leontiev Anton , Rostkier-Edelstein Dorita , Reuveni Yuval
Published: December 2020

Improving the accuracy of numerical weather predictions remains a challenging task. The absence of sufficiently detailed temporal and spatial real-time in-situ measurements poses a critical gap regarding the proper representation of atmospheric moisture fields, such as water vapor distribution, which are highly imperative for improving weather predictions accuracy. The estimated amount of the total vertically integrated water vapor (IWV), which can be derived from the attenuation of global positioning systems (GPS) signals, can support various atmospheric models at global, regional, and local scales. Currently, several existing atmospheric numerical models can estimate the IWV amount. However, they do not provide accurate results compared with in-situ measurements such as radiosondes. Here, we present a new strategy for assimilating 2D IWV regional maps estimations, derived from combined GPS and METEOSAT satellite imagery data, to improve Weather Research and Forecast (WRF) model predictions accuracy in Israel and surrounding areas. As opposed to previous studies, which used point measurements of IWV in the assimilation procedure, in the current study, we assimilate quasi-continuous 2D GPS IWV maps, combined with METEOSAT-11 data. Using the suggested methodology, our results indicate an improvement of more than 30% in the root mean square error (RMSE) of WRF forecasts after assimilation relative standalone WRF, when both are compared to the radiosonde measured data near the Mediterranean coast. Moreover, significant improvements along the Jordan Rift Valley and Dead Sea Valley areas are obtained when compared to 2D IWV regional maps estimations. Improvements in these areas suggest the impact of the assimilated high resolution IWV maps, with initialization times which coincide with the Mediterranean Sea Breeze propagation from the coastline to highland stations, as the distance to the Mediterranean Sea shore, along with other features, dictates its arrival times.
Sea-Level Change along the Emilia-Romagna Coast from Tide Gauge and Satellite Altimetry
Authors: Meli Matteo , Olivieri Marco , Romagnoli Claudia
Published: December 2020

Coastal flooding and retreat are markedly enhanced by sea-level rise. Thus, it is crucial to determine the sea-level variation at the local scale to support coastal hazard assessment and related management policies. In this work we focus on sea-level change along the Emilia-Romagna coast, a highly urbanized, 130 km-long belt facing the northern Adriatic Sea, by analyzing data from three tide gauges (with data records in the last 25–10 years) and related closest grid points from CMEMS monthly gridded satellite altimetry. The results reveal that the rate of sea-level rise observed by altimetry is coherent along the coast (2.8 ± 0.5 mm/year) for the period 1993–2019 and that a negative acceleration of −0.3 ± 0.1 mm/year is present, in contrast with the global scale. Rates resulting from tide gauge time series analysis diverge from these values mainly as a consequence of a large and heterogeneous rate of subsidence in the region. Over the common timespan, altimetry and tide gauge data show very high correlation, although their comparison suffers from the short overlapping period between the two data sets. Nevertheless, their combined use allows assessment of the recent (last 25 years) sea-level change along the Emilia-Romagna coast and to discuss the role of different interacting processes in the determination of the local sea level.
Analysis and Prediction of Gap Dynamics in a Secondary Deciduous Broadleaf Forest of Central Japan Using Airborne Multi-LiDAR Observations
Authors: Araki Kazuho , Awaya Yoshio
Published: December 2020

Gaps are important for growth of vegetation on the forest floor. However, monitoring of gaps in large areas is difficult. Airborne light detection and ranging (LiDAR) data make precise gap mapping possible. We formulated a method to describe changes in gaps by time-series tracking of gap area changes using three digital canopy height models (DCHMs) based on LiDAR data collected in 2005, 2011, and 2016 over secondary deciduous broadleaf forest. We generated a mask that covered merging or splitting of gaps in the three DCHMs and allowed us to identify their spatiotemporal relationships. One-fifth of gaps merged with adjacent gaps or split into several gaps between 2005 and 2016. Gap shrinkage showed a strong linear correlation with gap area in 2005, via lateral growth of gap-edge trees between 2005 and 2016, as modeled by a linear regression analysis. New gaps that emerged between 2005 and 2011 shrank faster than gaps present in 2005. A statistical model to predict gap lifespan was developed and gap lifespan was mapped using data from 2005 and 2016. Predicted gap lifespan decreased greatly due to shrinkage and splitting of gaps between 2005 and 2016.
Classification-Aided SAR and AIS Data Fusion for Space-Based Maritime Surveillance
Authors: Rodger Maximilian , Guida Raffaella
Published: December 2020

A wide range of research activities exploit spaceborne Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) for applications that contribute to maritime safety and security. An important requirement of SAR and AIS data fusion is accurate data association (or correlation), which is the process of linking SAR ship detections and AIS observations considered to be of a common origin. The data association is particularly difficult in dense shipping environments, where ships detected in SAR imagery can be wrongly associated with AIS observations. This often results in an erroneous and/or inaccurate maritime picture. Therefore, a classification-aided data association technique is proposed which uses a transfer learning method to classify ship types in SAR imagery. Specifically, a ship classification model is first trained on AIS data and then transferred to make predictions on SAR ship detections. These predictions are subsequently used in the data association which uses a rank-ordered assignment technique to provide a robust match between the data. Two case studies in the UK are used to evaluate the performance of the classification-aided data association technique based on the types of SAR product used for maritime surveillance: wide-area and large-scale data association in the English Channel and focused data association in the Solent. Results show a high level of correspondence between the data that is robust to dense shipping or high traffic, and the confidence in the data association is improved when using class (i.e., ship type) information.
A Spatial-Channel Collaborative Attention Network for Enhancement of Multiresolution Classification
Authors: Ma Wenping , Zhao Jiliang , Zhu Hao , Shen Jianchao , Jiao Licheng , Wu Yue , Hou Biao
Published: December 2020

Recently, with the popularity of space-borne earth satellites, the resolution of high-resolution panchromatic (PAN) and multispectral (MS) remote sensing images is also increasing year by year, multiresolution remote sensing classification has become a research hotspot. In this paper, from the perspective of deep learning, we design a dual-branch interactive spatial-channel collaborative attention enhancement network (SCCA-net) for multiresolution classification. It aims to combine sample enhancement and feature enhancement to improve classification accuracy. In the part of sample enhancement, we propose an adaptive neighbourhood transfer sampling strategy (ANTSS). Different from the traditional pixel-centric sampling strategy with orthogonal sampling angle, our algorithm allows each patch to adaptively transfer the neighbourhood range by finding the homogeneous region of the pixel to be classified. And it also adaptively adjust the sampling angle according to the texture distribution of the homogeneous region to capture neighbourhood information that is more conducive for classification. Moreover, in the part of feature enhancement part, we design a local spatial attention module (LSA-module) for PAN data to highlight the spatial resolution advantages and a global channel attention module (GCA-module) for MS data to improve the multi-channel representation. It not only highlights the spatial resolution advantage of PAN data and the multi-channel advantage of MS data, but also improves the difference between features through the interaction between the two modules. Quantitative and qualitative experimental results verify the robustness and effectiveness of the method.
Validation and Trend Analysis of Stratospheric Ozone Data from Ground-Based Observations at Lauder, New Zealand
Authors: Bernet Leonie , Boyd Ian , Nedoluha Gerald , Querel Richard , Swart Daan , Hocke Klemens
Published: December 2020

Changes in stratospheric ozone have to be assessed continuously to evaluate the effectiveness of the Montreal Protocol. In the southern hemisphere, few ground-based observational datasets exist, making measurements at the Network for the Detection of Atmospheric Composition Change (NDACC) station at Lauder, New Zealand invaluable. Investigating these datasets in detail is essential to derive realistic ozone trends. We compared lidar data and microwave radiometer data with collocated Aura Microwave Limb sounder (MLS) satellite data and ERA5 reanalysis data. The detailed comparison makes it possible to assess inhomogeneities in the data. We find good agreement between the datasets but also some possible biases, especially in the ERA5 data. The data uncertainties and the inhomogeneities were then considered when deriving trends. Using two regression models from the Long-term Ozone Trends and Uncertainties in the Stratosphere (LOTUS) project and from the Karlsruhe Institute of Technology (KIT), we estimated resulting ozone trends. Further, we assessed how trends are affected by data uncertainties and inhomogeneities. We find positive ozone trends throughout the stratosphere between 0% and 5% per decade and show that considering data uncertainties and inhomogeneities in the regression affects the resulting trends.
Climatology of the Combined ASTER MODIS Emissivity over Land (CAMEL) Version 2
Authors: Loveless Michelle , Borbas E. Eva , Knuteson Robert , Cawse-Nicholson Kerry , Hulley Glynn , Hook Simon
Published: December 2020

The Combined ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) MODIS (Moderate Resolution Imaging Spectroradiometer) Emissivity over Land (CAMEL) Version 2 (V002) has been available since March 2019 from the NASA LP DAAC (Land Processes Distributed Active Archive Center) and provides global, monthly infrared land surface emissivity and uncertainty at 0.05 degrees (~5 km) resolution. A climatology of the CAMEL V002 product is now available at the same spatial, temporal, and spectral resolution, covering the CAMEL record from 2000 to 2016. Characterization of the climatology over case sites and IGBP (International Geosphere-Biosphere Programme) land cover categories shows the climatology is a stable representation of the monthly CAMEL emissivity. Time series of the monthly CAMEL V002 product show realistic seasonal changes but also reveal subtle artifacts known to be from calibration and processing errors in the MODIS MxD11 emissivity. The use of the CAMEL V002 climatology mitigates many of these time dependent errors by providing an emissivity estimate which represents the complete 16-year record. The CAMEL V002 climatology’s integration into RTTOV (Radiative Transfer for TOVS) v12 is demonstrated through the simulation of IASI (Infrared Atmospheric Sounding Interferometer) radiances. Improved stability in CAMEL Version 3 is expected in the future with the incorporation of the new MxD21 and VIIRS VNP21 emissivity products in MODIS Collection 6.1.
Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data
Authors: Collados-Lara Antonio-Juan , Fassnacht Steven R. , Pardo-Igúzquiza Eulogio , Pulido-Velazquez David
Published: December 2020

There is necessity of considering air temperature to simulate the hydrology and management within water resources systems. In many cases, a big issue is considering the scarcity of data due to poor accessibility and limited funds. This paper proposes a methodology to obtain high resolution air temperature fields by combining scarce point measurements with elevation data and land surface temperature (LST) data from remote sensing. The available station data (SNOTEL stations) are sparse at Rocky Mountain National Park, necessitating the inclusion of correlated and well-sampled variables to assess the spatial variability of air temperature. Different geostatistical approaches and weighted solutions thereof were employed to obtain air temperature fields. These estimates were compared with two relatively direct solutions, the LST (MODIS) and a lapse rate-based interpolation technique. The methodology was evaluated using data from different seasons. The performance of the techniques was assessed through a cross validation experiment. In both cases, the weighted kriging with external drift solution (considering LST and elevation) showed the best results, with a mean squared error of 3.7 and 3.6 °C2 for the application and validation, respectively.
An SVM-Based Nested Sliding Window Approach for Spectral–Spatial Classification of Hyperspectral Images
Authors: Ren Jiansi , Wang Ruoxiang , Liu Gang , Wang Yuanni , Wu Wei
Published: December 2020

This paper proposes a Nested Sliding Window (NSW) method based on the correlation between pixel vectors, which can extract spatial information from the hyperspectral image (HSI) and reconstruct the original data. In the NSW method, the neighbourhood window constructed with the target pixel as the centre contains relevant pixels that are spatially adjacent to the target pixel. In the neighbourhood window, a nested sliding sub-window contains the target pixel and a part of the relevant pixels. The optimal sub-window position is determined according to the average value of the Pearson correlation coefficients of the target pixel and the relevant pixels, and the target pixel can be reconstructed by using the pixels and the corresponding correlation coefficients in the optimal sub-window. By combining NSW with Principal Component Analysis (PCA) and Support Vector Machine (SVM), a classification model, namely NSW-PCA-SVM, is obtained. This paper conducts experiments on three public datasets, and verifies the effectiveness of the proposed model by comparing with two basic models, i.e., SVM and PCA-SVM, and six state-of-the-art models, i.e., CDCT-WF-SVM, CDCT-2DCT-SVM, SDWT-2DWT-SVM, SDWT-WF-SVM, SDWT-2DCT-SVM and Two-Stage. The proposed approach has the following advantages in overall accuracy (OA)—take the experimental results on the Indian Pines dataset as an example: (1) Compared with SVM (OA = 53.29%) and PCA-SVM (OA = 58.44%), NSW-PCA-SVM (OA = 91.40%) effectively utilizes the spatial information of HSI and improves the classification accuracy. (2) The performance of the proposed model is mainly determined by two parameters, i.e., the window size in NSW and the number of principal components in PCA. The two parameters can be adjusted independently, making parameter adjustment more convenient. (3) When the sample size of the training set is small (20 samples per class), the proposed NSW-PCA-SVM approach achieves 2.38–18.40% advantages in OA over the six state-of-the-art models.
Multispectral Remote Sensing as a Tool to Support Organic Crop Certification: Assessment of the Discrimination Level between Organic and Conventional Maize
Authors: Denis Antoine , Desclee Baudouin , Migdall Silke , Hansen Herbert , Bach Heike , Ott Pierre , Kouadio Amani Louis , Tychon Bernard
Published: December 2020

The annual certification of organic agriculture products includes an in situ inspection of the fields declared organic. This inspection is more difficult, time-consuming, and costly for large farms or in production regions located in remote areas. The global objective of this research is to assess how spatial remote sensing may support the organic crop certification process by developing a method that would enable certification bodies to target for priority in situ control crop fields declared as organic but that would show on satellite imagery an appearance closer to conventional fields. For this purpose, the ability of multispectral satellite images to discriminate between organic and conventional maize fields was assessed through the use of a set of four satellite images of different spatial and spectral resolutions acquired at different crop growth stages over a large number of maize fields (32) that are part of an operational farm in Germany. In support of this main objective, a set of in situ measurements (leaf hyperspectral reflectance, chlorophyll, and nitrogen content and dry matter percentage, crop canopy cover, height, wet biomass and dry matter percentage, soil chemical composition) was conducted to characterize the nature of the biochemical and biophysical differences between organic and conventional maize fields. The results of this research showed that highly significant biochemical and biophysical differences between a large number of organic and conventional maize fields may exist at identified crop growth stages and that these differences may be sufficiently pronounced to enable the complete discrimination between crop management modes using satellite images issued from quite common multispectral satellite sensors through the use of spectral or spatial heterogeneity indices. These results are very encouraging and suggest, for the first time, that satellite images could effectively support the organic maize certification process.
Combining Deep Semantic Segmentation Network and Graph Convolutional Neural Network for Semantic Segmentation of Remote Sensing Imagery
Authors: Ouyang Song , Li Yansheng
Published: December 2020

Although the deep semantic segmentation network (DSSN) has been widely used in remote sensing (RS) image semantic segmentation, it still does not fully mind the spatial relationship cues between objects when extracting deep visual features through convolutional filters and pooling layers. In fact, the spatial distribution between objects from different classes has a strong correlation characteristic. For example, buildings tend to be close to roads. In view of the strong appearance extraction ability of DSSN and the powerful topological relationship modeling capability of the graph convolutional neural network (GCN), a DSSN-GCN framework, which combines the advantages of DSSN and GCN, is proposed in this paper for RS image semantic segmentation. To lift the appearance extraction ability, this paper proposes a new DSSN called the attention residual U-shaped network (AttResUNet), which leverages residual blocks to encode feature maps and the attention module to refine the features. As far as GCN, the graph is built, where graph nodes are denoted by the superpixels and the graph weight is calculated by considering the spectral information and spatial information of the nodes. The AttResUNet is trained to extract the high-level features to initialize the graph nodes. Then the GCN combines features and spatial relationships between nodes to conduct classification. It is worth noting that the usage of spatial relationship knowledge boosts the performance and robustness of the classification module. In addition, benefiting from modeling GCN on the superpixel level, the boundaries of objects are restored to a certain extent and there are less pixel-level noises in the final classification result. Extensive experiments on two publicly open datasets show that DSSN-GCN model outperforms the competitive baseline (i.e., the DSSN model) and the DSSN-GCN when adopting AttResUNet achieves the best performance, which demonstrates the advance of our method.
In-Orbit Validation of the FMPL-2 Instrument—The GNSS-R and L-Band Microwave Radiometer Payload of the FSSCat Mission
Authors: Munoz-Martin Joan Francesc , Fernandez Lara , Perez Adrian , Ruiz-de-Azua Joan Adrià , Park Hyuk , Camps Adriano , Domínguez Bernardo Carnicero , Pastena Massimiliano
Published: December 2020

The Flexible Microwave Payload-2 is the GNSS-R and L-band Microwave Radiometer Payload on board 3Cat-5/A, one of the two 6-unit CubeSats of the FSSCat mission, which were successfully launched on 3 September 2020 on Vega flight VV16. The instrument occupies nearly a single unit of the CubeSat, and its goal is to provide sea-ice extension and thickness over the poles, and soil moisture maps at low-moderate resolution over land, which will be downscaled using data from Cosine Hyperscout-2 on board 3Cat-5/B. The spacecrafts are in a 97.5° inclination Sun-synchronous orbit, and both the reflectometer and the radiometer have been successfully executed and validated over both the North and the South poles. This manuscript presents the results and validation of the first data sets collected by the instrument during the first two months of the mission. The results of the validation are showing a radiometric accuracy better than 2 K, and a sensitivity lower than the Kelvin. For the reflectometer, the results are showing that the sea-ice transition can be estimated even at short integration times (40 ms). The presented results shows the potential for Earth Observation missions based on CubeSats, which temporal and spatial resolution can be further increased by means of CubeSat constellations.
Intrinsic Identification and Mitigation of Multipath for Enhanced GNSS Positioning
Authors: Li Qianxia , Xia Linyuan , Chan Ting On , Xia Jingchao , Geng Jijun , Zhu Hongyu , Cai Yuezhen
Published: December 2020

In global navigation satellite system (GNSS)-based positioning and applications, multipath is by far the most obstinate impact. To overcome paradoxical issues faced by current processing approaches for multipath, this paper employs an intrinsic method to identify and mitigate multipath based on empirical mode decomposition (EMD) and Hilbert–Huang transform (HHT). Frequency spectrum and power spectrum are comprehensively employed to identify and extract multipath from complex data series composed by combined GNSS observations. To systematically inspect the multipath from both code range and carrier phase, typical kinds of combinations of the GNSS observations including the code minus phase (CMP), differential correction (DC), and double differential (DD) carrier phase are selected for the suggested intrinsic approach to recognize and mitigate multipath under typical positioning modes. Compared with other current processing algorithms, the proposed methodology can deal with multipath under normal positioning modes without recourse to the conditions that satellite orbits are accurately repeated and surrounding environments of observing sites remain intact. The method can adaptively extract and eliminate multipath from solely the GNSS observations using intrinsic decomposition mechanism. From theoretical discussions and validating tests, it is found that both code and carrier phase multipath can be identified and distinguished from ionospheric delay and other impacts using the EMD based techniques. The resultant positioning accuracy is therefore improved to an obvious extent after the removal of the multipath. Overall, the proposed method can form an extensive and sound technical frame to enhance localization accuracy under typical GNSS positioning modes and harsh multipath environments.
Real-Time Detection for Wheat Head Applying Deep Neural Network
Authors: Gong Bo , Ergu Daji , Cai Ying , Ma Bo
Published: December 2020

Wheat head detection can estimate various wheat traits, such as density, health, and the presence of wheat head. However, traditional detection methods have a huge array of problems, including low efficiency, strong subjectivity, and poor accuracy. In this paper, a method of wheat-head detection based on a deep neural network is proposed to enhance the speed and accuracy of detection. The YOLOv4 is taken as the basic network. The backbone part in the basic network is enhanced by adding dual spatial pyramid pooling (SPP) networks to improve the ability of feature learning and increase the receptive field of the convolutional network. Multilevel features are obtained by a multipath neck part using a top-down to bottom-up strategy. Finally, YOLOv3′s head structures are used to predict the boxes of wheat heads. For training images, some data augmentation technologies are used. The experimental results demonstrate that the proposed method has a significant advantage in accuracy and speed. The mean average precision of our method is 94.5%, and the detection speed is 71 FPS that can achieve the effect of real-time detection.
Characteristics of Very Low Frequency Sound Propagation in Full Waveguides of Shallow Water
Authors: Li Nansong , Zhu Hanhao , Wang Xiaohan , Xiao Rui , Xue Yangyang , Zheng Guangxue
Published: December 2020

This work is concerned with the characteristics of very low frequency sound propagation (VLF, ≤100 Hz) in the shallow marine environment. Under these conditions, the classical hypothesis of considering the sea bottom as a fluid environment is no longer appropriate, and the sound propagation characteristics at the sea bottom should be also considered. Hence, based on the finite element method (FEM), and setting the sea bottom as an elastic medium, a proposed model which unifies the sea water and sea bottom is established, and the propagation characteristics in full waveguides of shallow water can be synchronously discussed. Using this model, the effects of the sea bottom topography and the various geoacoustic parameters on VLF sound propagation and its corresponding mechanisms are investigated through numerical examples and acoustic theory. The simulation results demonstrate the adaptability of the proposed model to complex shallow water waveguides and the accuracy of the calculated acoustic field. For the sea bottom topography, the greater the inclination angle of an up-sloping sea bottom, the stronger the leak of acoustic energy to the sea bottom, and the more rapid the attenuation of the acoustic energy in sea water. The effect of a down-sloping sea bottom on acoustic energy is the opposite. Moreover, the greater the pressure wave (P-wave) speed in the sea bottom, the more acoustic energy remains in the water rather than leaking into the bottom; the influence laws of the density and the shear wave (S-wave) speed in the sea bottom are opposite.