Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Biogenic Oil Films near Aquaculture Sites Using Sentinel-1 and Sentinel-2 Satellite Images

Biogenic films are very thin surface oils, frequently observed near aquaculture farms, that affect the roughness and the optical properties of the sea surface, making them visible in SAR and multispectral images. The purpose of this study is to investigate the potential of satellite SAR and multispectral sensors in the detection of biogenic oil films near aquaculture farms. Sentinel-1 SAR and Sentinel-2 multispectral data were exploited to detect the films around three aquaculture sites. The study is divided in three stages: (a) preprocessing, (b) main process and (c) accuracy assessment. The preprocessing stage includes subset, filtering, land masking and image corrections. The main process was similar for both datasets, using an adaptive thresholding method to identify dark formations, extract and classify them. Finally, the performance of the algorithm was evaluated based on the estimation of standard classification error statistics. The evaluation of the results was based on empirical photointerpretation and in situ photos. The results are successful and promising, with overall accuracy over 70%, while both sensors are proved to be effective in the detection, with Sentinel-1 SAR presenting slightly better accuracy (81%) than Sentinel-2 MSI (70%). There is no evidence of these films causing stress to the aquaculture farms or the surrounding environment; however, our knowledge on their presence, amount and dissolution is limited and further knowledge could contribute to efficient feeding management and fish welfare.
Determination of Key Phenological Phases of Winter Wheat Based on the Time-Weighted Dynamic Time Warping Algorithm and MODIS Time-Series Data

Accurate determination of phenological information of crops is essential for field management and decision-making. Remote sensing time-series data are widely used for extracting phenological phases. Existing methods mainly extract phenological phases directly from individual remote sensing time-series, which are easily affected by clouds, noise, and mixed pixels. This paper proposes a novel method of phenological phase extraction based on the time-weighted dynamic time warping (TWDTW) algorithm using MODIS Normalized Difference Vegetation Index (NDVI) 5-day time-series data with a spatial resolution of 500 m. Firstly, based on the phenological differences between winter wheat and other land cover types, winter wheat distribution is extracted using the TWDTW classification method, and the results show that the overall classification accuracy and Kappa coefficient reach 94.74% and 0.90, respectively. Then, we extract the pure winter-wheat pixels using a method based on the coefficient of variation, and use these pixels to generate the average phenological curve. Next, the difference between each winter-wheat phenological curve and the average winter-wheat phenological curve is quantitatively calculated using the TWDTW algorithm. Finally, the key phenological phases of winter wheat in the study area, namely, the green-up date (GUD), heading date (HD), and maturity date (MD), are determined. The results show that the phenological phase extraction using the TWDTW algorithm has high accuracy. By verification using phenological station data from the Meteorological Data Sharing Service System of China, the root mean square errors (RMSEs) of the GUD, HD, and MD are found to be 9.76, 5.72, and 6.98 days, respectively. Additionally, the method proposed in this article is shown to have a better extraction performance compared with several other methods. Furthermore, it is shown that, in Hebei Province, the GUD, HD, and MD are mainly affected by latitude and accumulated temperature. As the latitude increases from south to north, the GUD, HD, and MD are delayed, and for each 1° increment in latitude, the GUD, HD, and MD are delayed by 4.84, 5.79, and 6.61 days, respectively. The higher the accumulated temperature, the earlier the phenological phases occur. However, latitude and accumulated temperature have little effect on the length of the phenological phases. Additionally, the lengths of time between GUD and HD, HD and MD, and GUD and MD are stable at 46, 41, and 87 days, respectively. Overall, the proposed TWDTW method can accurately determine the key phenological phases of winter wheat at a regional scale using remote sensing time-series data.
A Comparison and Interpretation of Machine Learning Algorithm for the Prediction of Online Purchase Conversion

Machine learning technology is recently being applied to various fields. However, in the field of online consumer conversion, research is limited despite the high possibility of machine learning application due to the availability of big data. In this context, we investigate the following three research questions. First, what is the suitable machine learning model for predicting online consumer behavior? Second, what is the good data sampling method for predicting online con-sumer behavior? Third, can we interpret machine learning’s online consumer behavior prediction results? We analyze 374,749 online consumer behavior data from Google Merchandise Store, an online shopping mall, and explore research questions. As a result of the empirical analysis, the performance of the ensemble model eXtreme Gradient Boosting model is most suitable for pre-dicting purchase conversion of online consumers, and oversampling is the best method to mitigate data imbalance bias. In addition, by applying explainable artificial intelligence methods to the context of retargeting advertisements, we investigate which consumers are effective in retargeting advertisements. This study theoretically contributes to the marketing and machine learning lit-erature by exploring and answering the problems that arise when applying machine learning models to predicting online consumer conversion. It also contributes to the online advertising literature by exploring consumer characteristics that are effective for retargeting advertisements.
Assessing the Impact of Light/Shallow Precipitation Retrievals from Satellite-Based Observations Using Surface Radar and Micro Rain Radar Observations

The accurate representation of precipitation across the Earth’s surface is crucial to furthering our knowledge and understanding of the Earth System and its component processes. Precipitation poses a number of challenges, particularly due to the variability of precipitation over time and space and whether it falls as snow or rain. While conventional measures of precipitation are reasonably good at the location of their measurement, their distribution across the Earth’s surface is uneven with some regions having no surface measurements. Spaceborne sensors have the capability of providing regular observations across the Earth’s surface that can provide estimates of precipitation. However, the estimation of precipitation from satellite observations is not necessarily straightforward. Visible and/or infrared techniques rely upon imprecise cloud-top to surface precipitation relationships, while the sensitivity of passive microwave techniques to different precipitation types is not consistent. Active microwave (radar) observations provide the most direct satellite measurements of precipitation but cannot provide estimates close to the surface and are generally not sufficiently sensitive to resolve light precipitation. This is particularly problematic at mid to high latitudes, where light and/or shallow precipitation dominates. This paper compares measurements made by ground-based weather radars, Micro Rain Radars and the spaceborne Dual-frequency Precipitation Radar to study both light precipitation intensity and shallow precipitation occurrence and to assess their impact on satellites retrievals of precipitation at the mid to high latitudes.
Monitoring the Spatiotemporal Dynamics of Aeolian Desertification Using Google Earth Engine

Northern China has been long threatened by aeolian desertification. In recent years, all levels of the Chinese government have performed a series of ecological protection and sand control projects. To grasp the implementation effects of these projects and adjust policies in time, it is necessary to understand the process of aeolian desertification quickly and accurately. Remote sensing technologies play an irreplaceable role in aeolian desertification monitoring. In this study, the Zhenglan Banner, which is in the hinterland of the Hunshandake Sandy Land, was considered as the research area. Based on unmanned aerial vehicle (UAV) images, ground survey data, and Landsat images called in Google Earth Engine (GEE), the aeolian desertified land (ADL) in 2000, 2004, 2010, 2015, and 2019 was extracted using spectral mixture analysis. A desertification index (DI) was constructed to evaluate the spatial and temporal dynamics of the ADL in the Zhenglan Banner. Finally, a residual analysis explored the driving forces of aeolian desertification. The results showed that (1) the ADL area in the Zhenglan Banner has been trending downwards over the past 20 years but rebounded from 2004 to 2010; (2) over the past 20 years, the area of slightly, moderately, and severely desertified land has decreased at annual rates of 0.4%, 2.7%, and 3.4%, respectively; (3) human activities had significantly positive and negative impacts on the aeolian desertification trend for 20.0% and 21.0% of the study area, respectively, but not for the rest. This paper explored new techniques for rapid aeolian desertification monitoring and is of great significance for controlling and managing aeolian desertification in this region.
Improved SinGAN Integrated with an Attentional Mechanism for Remote Sensing Image Classification

Deep learning is an important research method in the remote sensing field. However, samples of remote sensing images are relatively few in real life, and those with markers are scarce. Many neural networks represented by Generative Adversarial Networks (GANs) can learn from real samples to generate pseudosamples, rather than traditional methods that often require more time and man-power to obtain samples. However, the generated pseudosamples often have poor realism and cannot be reliably used as the basis for various analyses and applications in the field of remote sensing. To address the abovementioned problems, a pseudolabeled sample generation method is proposed in this work and applied to scene classification of remote sensing images. The improved unconditional generative model that can be learned from a single natural image (Improved SinGAN) with an attention mechanism can effectively generate enough pseudolabeled samples from a single remote sensing scene image sample. Pseudosamples generated by the improved SinGAN model have stronger realism and relatively less training time, and the extracted features are easily recognized in the classification network. The improved SinGAN can better identify sub-jects from images with complex ground scenes compared with the original network. This mechanism solves the problem of geographic errors of generated pseudosamples. This study incorporated the generated pseudosamples into training data for the classification experiment. The result showed that the SinGAN model with the integration of the attention mechanism can better guarantee feature extraction of the training data. Thus, the quality of the generated samples is improved and the classification accuracy and stability of the classification network are also enhanced.
An Improved Quadtree Sampling Method for InSAR Seismic Deformation Inversion

With the development of interferometric synthetic aperture radar (InSAR), the seismic deformation observation density increases sharply. Data down-sampling can effectively reduce the observation density and the computational cost for subsequent researches. Considering the saliency of the deformation field, we introduce a saliency-based quadtree algorithm for down-sampling (SQS). Three simulation experiments show that SQS can effectively distinguish the near-field and far-field deformation, as well as reduce the amount of observation, while keeping the detailed information of the main deformation near the fault. SQS can avoid the interference of far-field local deformation better than the traditional quadtree sampling algorithm (QS), thus obtaining better inversion results. We took the Dingri earthquake on 20 March 2020 as a case study to verify the advantages of SQS in dealing with real earthquake deformation. We obtained the co-seismic deformation from the ascending and descending Sentinel-1 for the Dingri earthquake, using QS and SQS for sampling and inversion separately. The results show the advantages of SQS in data volume reduction, observation distribution, anti-interference of local deformation, and inversion accuracy. Our preferred solution based on SQS shows that the Dingri earthquake was caused by a normal fault slip. The main slip area is 2–5.5 km deep with a maximum slip of 0.68 m. The estimated geodetic moment is 3.14 × 1017 Nm, corresponding to a magnitude of Mw5.63.
A Single Historical Painting Super-Resolution via a Reference-Based Zero-Shot Network

As an important part of human cultural heritage, many ancient paintings have suffered from various deteriorations that have led to texture blurring, color fading, etc . Single image super-resolution (SISR) which aims to recover a high-resolution (HR) version fromalow-resolution(LR)inputisactivelyengagedinthedigitalpreservationofculturalrelics.Currently,onlytraditionalsuper-resolutioniswidelystudiedandusedinculturalheritage,anditisdifficulttoapplylearning-basedSISRtouniquehistorical paintingsbecauseoftheabsenceofbothgroundtruthanddatasets.Fortunately,therecentlyproposedZSSRmethodsuggeststhatitisfeasibletogenerateasmalldatasetandextractself-supervisedinformationfromasingleimage.However,whenapplied tothepreservationsofhistoricalpaintings,theperformanceofZSSRishighlylimitedduetothelackofimageknowledge.Toaddresstheaboveissuesandtounleashthegreatpotentialoflearning-basedmethodsinheritageconservation,wepresentRef-ZSSR,whichisthefirstattempttocombinezero-shotandreference-basedmethodstoachieveSISR.Inourmodel,bothglobal andlocalmulti-scalesimilarinformationisfullyexploitedfromthepaintingitself.Inanend-to-endmanner,thisinformationprovidesconsistentartisticstyleimageknowledgeandhelpssynthesizeSRimageswithsharptexturedetails.Comparedwiththe ZSSRmethod,ourapproachshowsimprovementinbothprecision(approximately4.68dBforscale×2)andvisualperception. Itisworthmentioningthatallimageknowledgerequiredinourmethodcanbeextractedfromthepaintingitself,i.e.,externalexamplesarenotrequired.Therefore,thisapproachcanbeeasilygeneralizedtoanydamagedhistoricalpaintings,brokenmurals, noisyoldphotos,incompleteartworks,etc. © 2021 The Authors . Published by Atlantis Press B
DFT Treatment of ANTA and Some of its Tautomers

An insensitive explosive material, ANTA, and some of its prototropic tautomers have been considered within the constraints of density functional theory at the levels of B3LYP/6-311++G(d,p) and B3LYP/cc-PVTZ (partly). Various energetic, quantum chemical and spectral properties have been obtained and discussed. The NICS(0) values have been obtained and contemplated on them.
INSTRUCTIONAL SUPERVISION IN JAMAICA: PERSPECTIVES OF PRINCIPALS AND TEACHERS

INTRODUCTION: Crime retards all forms of social development, including educational advancement. The general crime problem in Jamaica is such that it is now the leading national problem followed by unemployment and education, and this is affecting the amount of time principals allot to its management compared to instructional supervision or leadership. OBJECTIVES: The objectives of the current study are 1) To evaluate the perceived usage of instructional supervision in non-traditional high school in Kingston and lower St. Andrew; 2) To determine factors that influence instructional supervision in non-traditional high school in Kingston and lower St. Andrew; 3) To examine disparities in instructional supervision by particular socio-demographic characteristics, and 4) To assess the effectiveness of principals’ supervisory practices in non-traditional high schools in Kingston and lower St. Andrew. METHODS: This study employed a positivistic theoretical framework which allowed for a survey research methodology that was based on 1) measurement and sampling, 2) questionnaire, and 3) statistical analyses. FINDINGS: Among the results of this study are 1) low instructional cultured leadership, ineffective supervisory practices, poor performance of students and direct association between instructional supervision and students’
Defects in olivine

Abstract. Olivine, a ferromagnesian orthosilicate, is the most abundant mineral in Earth's upper mantle and is stable down to the olivine–wadsleyite phase transition, which defines the 410 km depth mantle transition zone. Olivine also occurs in crustal environments in metamorphic and hydrothermal rocks and is expected to be the major mineral constituent of the Martian and Venusian mantles. The olivine atomic structure is also used in materials science to manufacture lithium batteries. Like any other crystalline solid, including minerals, olivine never occurs with a perfect crystalline structure: defects in various dimensions are ubiquitous, from point, line, and planar defects to three-dimensional (3-D) inclusions. In this contribution, I review the current state of the art of defects in olivine and several implications for key processes occurring in Earth's mantle. Intrinsic and extrinsic point defects are detailed, exemplifying the astonishing diversity of atomic impurities in mantle-derived olivine. Linear defects, one of the key defect types responsible for ductile deformation in crystalline solids, are examined in light of recent progress in 3-D transmission electron microscopy, which has revealed an important diversity of dislocation slip systems. I summarize the principal characteristics of interface defects in olivine: the free surface, grain and interface boundaries, and internal planar defects. As the least-studied defects to date, interface defects represent an important challenge for future studies and are the main application of numerical simulation methods in materials science. I provide an overview of melt, fluid, and mineral inclusions, which are widely studied in volcanology and igneous petrology. Special attention is given to new crystalline defects that act as deformation agents: disclinations (rotational defects) and the potential occurrence of disconnections in olivine, both of which are expected to occur along or near grain boundaries. Finally, I detail outstanding questions and research directions that will further our understanding of the crystalline specificities and paradoxes of olivine and olivine-rich rocks and ultimately their implications for the dynamics of Earth's upper mantle.
Stress as a facilitator? Territorial male impala have higher glucocorticoid levels than bachelors

Territoriality is a common behavioural adaptation, widespread among ungulates. Here, we tested the hypothesis that territorial individuals have higher glucocorticoid concentrations than non-territorial bachelors, in wild impala (Aepyceros melampus) in the Serengeti ecosystem. We also investigated how the relationship between territoriality and glucocorticoid levels is influenced by environmental context, specifically, food quality, population density (i.e., territory defence intensity), and herd size (i.e., mate defence effort). We collected 139 faecal samples over 4 years and analysed these for faecal glucocorticoid metabolites (FGMs). We used Normalised Difference Vegetation Index (NDVI) as a proxy for food quality, and population density was based on aerial surveys. Territorial males had, on average, higher FGM concentrations than bachelors. Increased food quality did not affect FGM levels in territorial males, but decreased FGM levels in bachelors by 78%. Greater population density increased FGM levels by 47%, but this effect was not different between territorial and bachelor males. Herd size did not affect FGM levels in territorial males. While elevated GC levels are often suggested to be repercussions of being territorial, our findings support the hypothesis that elevated GC levels may be beneficial and act as a facilitator of a male’s reproductive potential. The elevated GC levels may increase the ability of territorial males to maintain a territory by increasing energy mobilisation and metabolic rate, ultimately increasing their reproductive fitness. Appreciating that long-term increases in GC levels are not simply costly but may have an adaptive, potentially facilitating role in an animal’s life history is key to understanding HPA-axis reactivity and its potential in eco-physiological studies.
Effect of amendment sources in sodic soil under subsurface drainage system on yield and nutrient uptake of maize

The present study was conducted at Post Graduate Institute, Research Farm, Department of Soil Science and Agricultural Chemistry, MPKV, Rahuri, during Kharif 2019. The experiment was laid out in a randomized block design with three replication and twelve treatments. The treatment comprised of T1: Absolute control, T2: Gypsum as per 100% GR, T3: Elemental sulphur as per 1/5th of GR, T4: Zeolite @ 600 kg ha, T5: Gypsum as per 100% GR + zeolite @ 600 kg ha, T6: Gypsum as per 50% GR + zeolite @ 600 kg ha, T7: Gypsum as per 100% GR + zeolite @ 300 kg ha, T8: Gypsum as per 50% GR + zeolite @ 300 kg ha, T9: Elemental sulphur as per 1/5th of GR + zeolite @ 600 kg ha, T10: Elemental sulphur as per 50% of 1/5th GR + zeolite @ 600 kg ha, T11: Elemental sulphur as per 1/5th of GR + zeolite @ 300 kg ha and T12: Elemental sulphur as per 50% of 1/5th GR + zeolite @ 300 kg ha. The biomass production in maize crop grown on sodic soils under SSD (Subsurface drainage) field in terms of their fresh and dry matter yield mainly depends on uptake of nutrient and micronutrient by maize crop. Total uptake of nitrogen, phosphorus and potassium by maize crop were found to be higher (132.77, 27.14 and 134.46 kg ha, respectively) in treatment Gypsum as per 100% GR + zeolite @ 600 kg ha. Total uptake of iron, zinc, manganese and copper were found to be significantly higher (2034, 1134, 1443 and 805 g ha, respectively) in treatment Gypsum as per 100% GR + zeolite @ 600 kg ha. The grain and stover yield of maize was significantly highest recorded in treatment Gypsum as per 100% GR + zeolite @ 600 kg ha (53.8 and 62.44 q ha). Therefore, based on above findings, application of Gypsum as per 100% GR + zeolite @ 600 kg ha-1 along with the FYM and general recommended dose of nutrients (10 t ha FYM + 120:60:40, N:P2O5:K2O kg ha) to maize in SSD field was found beneficial for reclamation of sodic soil, increase in availability of nutrients in soil, total uptake of macro and micronutrients and higher grain and stover yield of maize grown in sodic soil under SSD system.
Formic Acid Modified Co3O4-CeO2 Catalysts for CO Oxidation

A formic acid modified catalyst, Co3O4-CeO2, was prepared via facile urea-hydrothermal method and applied in CO oxidation. The Co3O4-CeO2-0.5 catalyst, treated by formic acid at 0.5 mol/L, performed better in CO oxidation with T50 obtained at 69.5 °C and T100 obtained at 150 °C, respectively. The characterization results indicate that after treating with formic acid, there is a more porous structure within the Co3O4-CeO2 catalyst; meanwhile, despite of the slightly decreased content of Co, there are more adsorption sites exposed by acid treatment, as suggested by CO-TPD and H2-TPD, which explains the improvement of catalytic performance.
Overview of solutions used in rehabilitation devices using muscles and pneumatic actuators

Article history Received: 8 February 2021 Received in revised form: 18 February 2021 Accepted: 20 February 2021 Available: 21 February 2021 Abstract The article presents selected rehabilitation devices with the use of classic actuators and pneumatic muscles. Manipulators supporting the movements of the lower and upper limbs of humans are presented. Moreover, the construction of actuators and pneumatic muscles is described.