Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery

Plant biodiversity is an important feature of grassland ecosystems, as it is related to the provision of many ecosystem services crucial for the human economy and well-being. Given the importance of grasslands, research has been carried out in recent years on the potential to monitor them with novel remote sensing techniques. In this study, the optical diversity (also called spectral diversity) approach was adopted to check the potential of using high-resolution hyperspectral images to estimate α-diversity in grassland ecosystems. In 2018 and 2019, grassland species composition was surveyed and canopy hyperspectral data were acquired at two grassland sites: Monte Bondone (IT-MBo; species-rich semi-natural grasslands) and an experimental farm of the University of Padova, Legnaro, Padua, Italy (IT-PD; artificially established grassland plots with a species-poor mixture). The relationship between biodiversity (species richness, Shannon’s, species evenness, and Simpson’s indices) and optical diversity metrics (coefficient of variation-CV and standard deviation-SD) was not consistent across the investigated grassland plant communities. Species richness could be estimated by optical diversity metrics with an R = 0.87 at the IT-PD species-poor site. In the more complex and species-rich grasslands at IT-MBo, the estimation of biodiversity indices was more difficult and the optical diversity metrics failed to estimate biodiversity as accurately as in IT-PD probably due to the higher number of species and the strong canopy spatial heterogeneity. Therefore, the results of the study confirmed the ability of spectral proxies to detect grassland α-diversity in man-made grassland ecosystems but highlighted the limitations of the spectral diversity approach to estimate biodiversity when natural grasslands are observed. Nevertheless, at IT-MBo, the optical diversity metric SD calculated from post-processed hyperspectral images and transformed spectra showed, in the red part of the spectrum, a significant correlation (up to R = 0.56, p = 0.004) with biodiversity indices. Spatial resampling highlighted that for the IT-PD sward the optimal optical pixel size was 1 cm, while for the IT-MBo natural grassland it was 1 mm. The random pixel extraction did not improve the performance of the optical diversity metrics at both study sites. Further research is needed to fully understand the links between α-diversity and spectral and biochemical heterogeneity in complex heterogeneous ecosystems, and to assess whether the optical diversity approach can be adopted at the spatial scale to detect β-diversity. Such insights will provide more robust information on the mechanisms linking grassland diversity and optical heterogeneity.
Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images

Deep learning models have brought great breakthroughs in building extraction from high-resolution optical remote-sensing images. Among recent research, the self-attention module has called up a storm in many fields, including building extraction. However, most current deep learning models loading with the self-attention module still lose sight of the reconstruction bias’s effectiveness. Through tipping the balance between the abilities of encoding and decoding, i.e., making the decoding network be much more complex than the encoding network, the semantic segmentation ability will be reinforced. To remedy the research weakness in combing self-attention and reconstruction-bias modules for building extraction, this paper presents a U-Net architecture that combines self-attention and reconstruction-bias modules. In the encoding part, a self-attention module is added to learn the attention weights of the inputs. Through the self-attention module, the network will pay more attention to positions where there may be salient regions. In the decoding part, multiple large convolutional up-sampling operations are used for increasing the reconstruction ability. We test our model on two open available datasets: the WHU and Massachusetts Building datasets. We achieve IoU scores of 89.39% and 73.49% for the WHU and Massachusetts Building datasets, respectively. Compared with several recently famous semantic segmentation methods and representative building extraction methods, our method’s results are satisfactory.
Ordering state in orthopyroxene as determined by precession electron diffraction

Abstract Fe2+ and Mg distribution on octahedral M1 and M2 sites of the orthopyroxene structure is an indicator of the cooling rate and closure temperature of the mineral. It is generally obtained by single-crystal X‑ray diffraction, which is limited in spatial resolution. In this work, we determine the cationic distribution at a submicrometer scale in a transmission electron microscope using precession electron diffraction. Two orthopyroxene samples coming from the same metamorphic rock are studied, a naturally ordered one and a disordered one. The latter was obtained from the ordered sample by annealing at high temperature and rapid quenching. Both samples have been first studied in X‑ray diffraction and then in precession electron diffraction. Intensities recorded in zone-axis precession electron diffraction experiments have been quantitatively analyzed and compared to simulations, taking into account dynamical interactions between diffracted beams. Our structure refinement results are in good agreement with those obtained by single-crystal X‑ray diffraction. They enable to distinguish between the ordered sample and the disordered one in terms of the observed molar fractions of Fe at M1 and M2 sites. We discuss the sensitivity of the method as a function of experimental parameters. The larger dispersion of the results obtained on the ordered specimen is attributed to structural heterogeneities inherent to the sample.
Heterogeneous catalytic modified process in the production of biodiesel from sunflower oil, waste cooking oil and olive oil by transesterification method.

Using transesterifiction of sunflower oil, olive oil and waste cooking oil with methanol and heterogeneous catalyst made by clinoptilolite, biodiesel was obtained. With molar ratio of alcohol to oil 1:7 and mentioned catalyst (3 wt%), yield of biodiesel from sunflower oil, cooking waste oil and olive oil were: 97%, 95% & 92% respectively. SEM images showed the average particle sizeof 342.44 nm. FTIR spectrum showedno impurity in produced biodiesel. The products were compared to the consuming Petro-dieselin Tehran according ASTM standard cloud point, pour point, freezing point, cetane number, iodine value, saponification number, heating value, acid value, viscosity, density andflash point.
Infrared and Visible Image Object Detection via Focused Feature Enhancement and Cascaded Semantic Extension

Infrared and visible images (multi-sensor or multi-band images) have many complementary features which can effectively boost the performance of object detection. Recently, convolutional neural networks (CNNs) have seen frequent use to perform object detection in multi-band images. However, it is very difficult for CNNs to extract complementary features from infrared and visible images. In order to solve this problem, a difference maximum loss function is proposed in this paper. The loss function can guide the learning directions of two base CNNs and maximize the difference between features from the two base CNNs, so as to extract complementary and diverse features. In addition, we design a focused feature-enhancement module to make features in the shallow convolutional layer more significant. In this way, the detection performance of small objects can be effectively improved while not increasing the computational cost in the testing stage. Furthermore, since the actual receptive field is usually much smaller than the theoretical receptive field, the deep convolutional layer would not have sufficient semantic features for accurate detection of large objects. To overcome this drawback, a cascaded semantic extension module is added to the deep layer. Through simple multi-branch convolutional layers and dilated convolutions with different dilation rates, the cascaded semantic extension module can effectively enlarge the actual receptive field and increase the detection accuracy of large objects. We compare our detection network with five other state-of-the-art infrared and visible image object detection networks. Qualitative and quantitative experimental results prove the superiority of the proposed detection network.
Satellite-Based Aerosol Classification for Capital Cities in Asia Using a Random Forest Model

Aerosol types in Asian capital cities were classified using a random forest (RF) satellite-based aerosol classification model during 2018–2020 in an investigation of the contributions of aerosol types, with or without Aerosol Robotic Network (AERONET) observations. In this study, we used the recently developed RF aerosol classification model to detect and classify aerosols into four types: pure dust, dust-dominated aerosols, strongly absorbing aerosols, and non-absorbing aerosols. Aerosol optical and microphysical properties for each aerosol type detected by the RF model were found to be reasonably consistent with those for typical aerosol types. In Asian capital cities, pollution-sourced aerosols, especially non-absorbing aerosols, were found to predominate, although Asian cities also tend to be seasonally affected by natural dust aerosols, particularly in East Asia (March–May) and South Asia (March–August). No specific seasonal effects on aerosol type were detected in Southeast Asia, where there was a predominance of non-absorbing aerosols. The aerosol types detected by the RF model were compared with those identified by other aerosol classification models. This study indicates that the satellite-based RF model may be used as an alternative in the absence of AERONET sites or observations.
Modified Linear Scaling and Quantile Mapping Mean Bias Correction of MODIS Land Surface Temperature for Surface Air Temperature Estimation for the Lowland Areas of Peninsular Malaysia

MODIS land surface temperature data (MODIS Ts) products are quantified from the earth surface’s reflected thermal infrared signal via sensors onboard the Terra and Aqua satellites. MODIS Ts products are a great value to many environmental applications but often subject to discrepancies when compared to the air temperature (Ta) data that represent the temperature measured at 2 m above the ground surface. Although they are different in their nature, the relationship between Ts and Ta has been established by many researchers. Further validation and correction on the relationship between these two has enabled the estimation of Ta from MODIS Ts products in order to overcome the limitation of Ta that can only provide data in a point form with a very limited area coverage. Therefore, this study was conducted with the objective to assess the accuracy of MODIS Ts products, i.e., MOD11A1, MOD11A2, MYD11A1, and MYD11A2 against Ta and to identify the performance of a modified Linear Scaling using a constant and monthly correction factor (LS-MBC), and Quantile Mapping Mean Bias Correction (QM-MBC) methods for lowland area of Peninsular Malaysia. Furthermore, the correction factor (CF) values for each MBC were adjusted according to the condition set depending on the different bias levels. Then, the performance of the pre- and post-MBC correction for by stations and regions analysis were evaluated through root mean square error (RMSE), percentage bias (PBIAS), mean absolute error (MAE), and correlation coefficient (r). The region dataset is obtained by stacking the air temperature (Ta_r) and surface temperature (Ts_r) data corresponding to the number of stations within the identified regions. The assessment of pre-MBC data for both 36 stations and 5 regions demonstrated poor correspondence with high average errors and percentage biases, i.e., RMSE = 3.33–5.42 °C, PBIAS = 1.36–12.07%, MAE = 2.88–4.89 °C, and r = 0.16–0.29. The application of the MBCs has successfully reduced the errors and bias percentages, and slightly increased the r values for all MODIS Ts products. All post-MBC depicted good average accuracies (RMSE and MAE < 3 °C and PBIAS between ±5%) and r between 0.18 and 0.31. In detail, for the station analysis, the LS-MBC using monthly CF recorded better performance than the LS-MBC using constant CF or the QM-MBC. For the regional study, the QM-MBC outperformed the others. This study illustrated that the proposed LS-MBC, in spite of its simplicity, managed to perform well in reducing the error and bias terms of MODIS Ts as much as the performance of the more complex QM-MBC method.
Characterizing Off-Highway Road Use with Remote-Sensing, Social Media and Crowd-Sourced Data: An Application to Grizzly Bear (Ursus Arctos) Habitat

Characterizing roads is important for conservation since the relationship between road use and ecological impact can vary across species. However, road use is challenging to monitor due to limited data and high spatial-temporal variability, especially for unpaved roads, which often coincide with critical habitats. In this study, we developed and evaluated two methods to characterize off-highway road use across a large management area of grizzly bear (Ursus arctos) habitat using: (1) a ‘network-based’ approach to connect human activity hotspots identified from social media posts and remotely detected disturbances and (2) an ‘image-based’ approach, in which we modeled road surface conditions and travel speed from high spatial resolution satellite imagery trained with crowd-sourced smartphone data. To assess the differences between these approaches and their utility for characterizing roads in the context of habitat integrity, we evaluated how behavioural patterns of global positioning system (GPS)-collared grizzly bears were related to road use characterized by these methods compared to (a) assuming all roads have equal human activity and (b) using a ‘reference’ road classification from a government database. The network- and image-based methods showed similar patterns of road use and grizzly bear response compared to the reference, and all three revealed nocturnal behaviour near high-use roads and better predicted grizzly bear habitat selection compared to assuming all roads had equal human activity. The network- and image-based methods show promise as cost-effective approaches to characterize road use for conservation applications where data is not available.
Multi-level spatial attention network for image data segmentation

Deep learning models for semantic image segmentation are limited in their hierarchical architectures to extract features, which results in losing contextual and spatial information. In this paper, a new attention-based network, MSANet, which applies an encoder-decoder structure, is proposed for image data segmentation to aggregate contextual features from different levels and reconstruct spatial characteristics efficiently. To model long-range spatial dependencies among features, the multi-level spatial attention module (MSAM) is presented to process multi-level features in the encoder network and capture global contextual information. In this way, our model learns multi-level spatial dependencies between features by the MSAM and hierarchical representations of the input image by the stacked convolutional layers, which means the model is more capable of producing accurate segmentation results. The proposed network is evaluated on the PASCAL VOC 2012 and Cityscapes datasets. Results show that our model achieves excellent performance compared with U-net, FCNs, and DeepLabv3.
Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

Nighttime lights (NTL) are a popular type of data for evaluating economic performance of regions and economic impacts of various shocks and interventions. Several validation studies use traditional statistics on economic activity like national or regional Gross Domestic Product (GDP) as a benchmark to evaluate the usefulness of NTL data. Many of these studies rely on dated and imprecise Defence Meteorological Satellite Program (DMSP) data and use aggregated units such as nation-states or the first sub-national level. Yet applied researchers who draw support from validation studies to justify their use of NTL data as a proxy for economic activity increasingly focus on smaller and lower level spatial units. This study uses a 2001-19 time-series of GDP for over 3100 US counties as a benchmark to examine the usefulness of the recently released version 2 VIIRS nighttime lights (V.2 VNL) products as proxies for local economic activity. Contrasts are made between cross-sectional predictions for GDP differences between areas and time-series predictions of GDP changes within areas. Disaggregated GDP data for various industries are used to examine what types of economic activity are best proxied by NTL data and comparisons are also made with the predictive performance of earlier NTL data products.
Satellite-Observed Multi-Scale Variability of Sea Surface Chlorophyll-a Concentration along the South Coast of the Sumatra-Java Islands

The southern coast of Java is known as one of the most productive fishing grounds for tuna, feeding by nutrient-rich water along the coast caused by the subsurface water upwelling. This primary productivity can be evidenced by the high sea surface chlorophyll-a concentration (SSC). Based on satellite remote sensing products, we investigate the multi-scale variability in SSC along the Sumatra-Java coast. The results show that seasonal variability of SSCs is primarily due to monsoon-driven upwelling and rainfall in the Indian Ocean and Indonesian seas sides of the Sumatra and Java Islands, respectively. Local Ekman pumping plays a secondary role, while rainfall input to the ocean has little effect. Coastally trapped Kelvin waves and mesoscale eddies are responsible for the intraseasonal SSC anomalies in regions along the south coast of Java and off the Sunda and Lombok Straits, respectively. The interannual variability in SSC is caused by the anomalous upwelling related to the Indian Ocean Dipole. There was a weak increasing trend of ~0.1–0.2 mg/m3 per decade, above the global averaged trend, which may be related to enhanced local Ekman pumping. These analyses provide an overall description of SSC variations based on satellite observations; however, further investigations based on in situ observations are needed to achieve better quantification.
Intelligent Recognition Method of Low-Altitude Squint Optical Ship Target Fused with Simulation Samples

To address the problem of intelligent recognition of optical ship targets under low-altitude squint detection, we propose an intelligent recognition method based on simulation samples. This method comprehensively considers geometric and spectral characteristics of ship targets and ocean background and performs full link modeling combined with the squint detection atmospheric transmission model. It also generates and expands squint multi-angle imaging simulation samples of ship targets in the visible light band using the expanded sample type to perform feature analysis and modification on SqueezeNet. Shallow and deeper features are combined to improve the accuracy of feature recognition. The experimental results demonstrate that using simulation samples to expand the training set can improve the performance of the traditional k-nearest neighbors algorithm and modified SqueezeNet. For the classification of specific ship target types, a mixed-scene dataset expanded with simulation samples was used for training. The classification accuracy of the modified SqueezeNet was 91.85%. These results verify the effectiveness of the proposed method.
An efficient multi-energy vector power flow modeling and solution approach with unified loop-free formulation for heat and gas hydraulic equations

Integrated energy networks will be key to develop low-carbon multi-energy systems. Steady-state multi-energy vector power flow (MEVPF) analysis is the cornerstone of integrated network studies. However, despite a wide range of recently developed models, there are still significant challenges that have not been fully addressed, including applicability of different MEVPF formulations to different network topologies and node types, flexibility in adapting new connections and components, and appropriate convergence behavior for different loading levels and initial conditions. This paper analyzes different formulations and their relevant applicability and convergence properties for the steady-state MEVPF problem and proposes a new unified, loop-free formulation for heat and gas hydraulic equations. Both theoretical and numerical analyses conducted on a canonical system show the superiority of the proposed approach over the other available formulations analysed in terms of flexibility of applications and convergence performance.
Python Scripting for DIgSILENT PowerFactory: Enhancing Dynamic Modelling of Cascading Failures

The potential risk of cascading failure has been investigated by both industry and academia. With the introduction of new technologies, there is increasing uncertainty in power system operation which leads to greater needs for dynamic simulations to fully capture the behaviour and evolution of the system. This paper presents a new dynamic cascading failure simulation platform implemented in DIgSILENT PowerFactory via the Python Application Programming Interface (API). It automatically develops cascading mechanisms, simulates sets of failure scenarios and processes results, and also has good scalability such that it can be easily applied to any power system model. The proposed method overcomes the limitations of traditional manual simulation methods when performing a large number of repetitive modelling, simulation and data processing tasks, and greatly improves modelling efficiency. Case studies on 39-bus and 2000-bus systems are provided to illustrate the functions of various cascading mechanisms and to provide the probability distribution of blackout size based on N-2 contingency analysis.
Enhanced Bad Data Processing for Hybrid Block-Orthogonal State Estimators

This paper introduces a new strategy for bad data processing in connection with hybrid block-orthogonal state estimators in order to cope with the significant performance degradation exhibited by the conventional $J\left( {\hat x} \right)$-test when applied to large networks. The proposed method advocates the partitioning of the phasor measurement set and the underlying network into disjoint clusters. This allows that independent $J\left( {\hat x} \right)$-tests be performed on each network cluster, what improves the sensitivity of the detection test to bad data occurring in the phasor measurement set. A number of case studies conducted on the IEEE 300-Bus benchmark system are employed to evaluate the effectiveness of the enhanced strategy.
Dynamics of rapid extinction in a lumped system with Arrhenius chemistry

Abstract Reacting systems with Arrhenius chemistry can exhibit multiple steady states for γ > 4(1 + β)/β where γ is activation energy and β is the heat of reaction. We give a detailed analysis of the extremely rapid jump which takes place as catalyst activity slowly decays through criticality. Previous analyses are asymptotic in the limit γ → ∞; here we relax the large γ assumption and only require that passage through criticality is slow.