Miracle-dz/UniFAR-FTUs huggingface dataset:
A Climate Hyperspectral Infrared Radiance Product (CHIRP) Combining the AIRS and CrIS Satellite Sounding Record

A Climate Hyperspectral Infrared Radiance Product (CHIRP) is introduced combining data from the Atmospheric Infrared Sounder (AIRS) on NASA’s EOS-AQUA platform, the Cross-Track Infrared Sounder (CrIS) sounder on NASA’s SNPP platform, and continuing with CRIS sounders on the NOAA/NASA Joint Polar Satellite Series (JPSS) of polar satellites. The CHIRP product converts the parent instrument’s radiances to a common Spectral Response Function (SRF) and removes inter-satellite biases, providing a consistent inter-satellite radiance record. The CHIRP record starts in September 2002 with AIRS, followed by CrIS SNPP and the JPSS series of CrIS instruments. The CHIRP record should continue until the mid-2040’s as additional JPSS satellites are launched. These sensors, in CHIRP format, provide the climate community with a homogeneous sensor record covering much of the infrared. We give an overview of the conversion of AIRS and CrIS to CHIRP, and define the SRF for common CHIRP format. Considerable attention is paid to removing static bias offsets among these three sensors. The CrIS instrument on NASA’s SNPP satellite is used as the calibration standard. Simultaneous Nadir Overpasses (SNOs) as well as large statistical samplings of radiances from these three satellites are used to derive the instrument bias offsets and estimate the bias offset accuracy, which is ~0.03 K. In addition, possible scene-dependent calibration differences between CHIRP derived from AIRS and CHIRP derived from CrIS on the SNPP platform are presented.
Forest Road Detection Using LiDAR Data and Hybrid Classification

Knowledge about forest road networks is essential for sustainable forest management and fire management. The aim of this study was to assess the accuracy of a new hierarchical-hybrid classification tool (HyClass) for mapping paved and unpaved forest roads with LiDAR data. Bare-earth and low-lying vegetation were also identified. For this purpose, a rural landscape (area 70 ha) in northwestern Spain was selected for study, and a road network map was extracted from the cadastral maps as the ground truth data. The HyClass tool is based on a decision tree which integrates segmentation processes at local scale with decision rules. The proposed approach yielded an overall accuracy (OA) of 96.5%, with a confidence interval (CI) of 94.0–97.6%, representing an improvement over pixel-based classification (OA = 87.0%, CI = 83.7–89.8%) using Random Forest (RF). In addition, with the HyClass tool, the classification precision varied significantly after reducing the original point density from 8.7 to 1 point/m2. The proposed method can provide accurate road mapping to support forest management as an alternative to pixel-based RF classification when the LiDAR point density is higher than 1 point/m2.
The combinatorics of rim hook tableaux

We give a new combinatorial proof that the coefficient of the power symmetric function pμ in the Schur symmetric function sλ can be expressed in terms of rim hook tableaux (the Murnaghan-Nakayama rule). Two other known identities involving the coefficient of pμ in the homogeneous symmetric function hλ and the coefficient of pμ in the elementary symmetric function eλ are proved in similar ways.
INVESTIGATION OF FASTNESS PROPERTIES AND ANTIBACTERIAL EFFECT OF METALLOPHTHALOCYANINE (M: ZN) CONTAINING EUGENOL PRINTED ON COTTON FABRIC PAMUKLU KUMAŞ ÜZERİNE BASILMIŞ EUGENOL İÇEREN METALLOFTALOSİYANİNİN (M: ZN) ANTİBAKTERİYEL ETKİSİNİN VE HASLIK ÖZELLİKLERİNİN İNCELENMESİ

Main objective of this research is to obtain antibacterial effect of metallophthalocyanine (M: Zn) with four eugenol substituents on cotton fabric. Eugenol is the main component in the essential oil extracted from cloves (Eugenia Caryophillis) and is proved to be active against many pathogenic bacteria, fungi and viruses. Metal ions like zinc, copper, silver…etc. are used as antibacterial agents. It is expected that antibacterial properties of eugenol and zinc metal ion can be combined in this phthalocyanine pigment. A suspension of the phthalocyanine green pigment was prepared by of a convenient dispersant using zirconia ball milling method. Then its particle size distribution was characterized by a laser diffraction particle size analyzer. Cotton fabric was colored with pigment printing and then rubbing fastness and light fastness of fabrics were tested according to ISO 105×12 and ISO 105 B02 test methods respectively. Antibacterial properties of the fabric were investigated according to AATCC 147-1998 test method. The samples were tested against two types of bacteria: Staphylococcus Aureus (S. Aureus, Gram positive) and Klebsiella Pneumoniae (K. Pneumoniae, Gram negative). It was observed that it had an effective antibacterial action to S. Aureus bacteria but not effective on K. Pneumoniae bacteria. In contrast with pigment printing general properties, better wet and dry rubbing fastness results and less light fastness result were obtained.
Earth Observation-Informed Risk Maps of the Lyme Disease Vector Ixodes scapularis in Central and Eastern Canada

Climate change is facilitating the geographic range expansion of populations of the tick vector of Lyme disease Ixodes scapularis in Canada. Here, we characterize and map the spatio-temporal variability of environments suitable for I. scapularis using Earth observation (EO) data. A simple algorithm for I. scapularis occurrence (cumulative degree-days and forest: CSDF) was developed by combining cumulative annual surface degree-days above 0 °C and forest cover. To map the environmental risk of I. scapularis (risk of I. scapularis: RIS) in central and eastern Canada from 2000 to 2015, CSDF was adjusted using data from an I. scapularis population model. CSDF was validated using cumulative annual degree days >0 °C (CADD) from meteorological stations, and CSDF was strongly associated with CADD (n = 52, R2 > 0.86, p < 0.001). Data on field surveillance for I. scapularis ticks (2008 to 2018) were used to validate the risk maps. The presence of I. scapularis ticks was significantly associated with CSDF, and at a limit of 2800, sensitivity approached 100%. RIS increased over the study period, with the highest values in 2012 and the lowest in 2000. The RIS was on average higher in Ontario and Quebec compared to other provinces, and it was higher in the southern parts of the provinces. The proportion of the populated areas with a positive RIS increased on average in central and eastern Canada from 2000 to 2015. Predicted I. scapularis occurrence identifies areas with a more probable risk of tick bites, Lyme disease, and other I. scapularis-borne diseases, which can help guide targeted surveillance, prevention, and control interventions.
The Morphology of Evolved Urban Fabric around Farm Ponds

Resilience, water-based ecosystems, and regional urbanization are closely related. Ponds, as a part of water-based ecosystems, are subject to the indicators of urbanization. The farm ponds in Taoyuan, Taiwan, represent a sustainable fulfillment of irrigation demand and are a system that has been resilient to the topological change in plate elevation over time. The old system was developed in three stages and gradually replaced by canals and reservoirs, with lands abolished or demolished for other purposes. This study aimed to investigate the resilience of farm ponds based on a quantitative estimation of the morphologically evolved urban fabric. Based on five types of map resources, case studies were made on the ponds located near or away from the Green Line of the Taoyuan Rapid Transit System in order to explore their potential relationship with urbanization and the concept of transit-oriented development (TOD). The results show high dynamics of land-use changes while each one was surrounded by a specific urban fabric and contributed to the individual history, designated role, and major type of buildings developed. Quantitative estimation made from satellite images found the developing patterns of resilience in different convergence stages. Each of the four pond cases represents a different stage and application pattern to the integration of water and urban resilience and, in the meantime, maintains the old pond culture. The verification of the water surface area was compared between former 3D scans and satellite images.
Polymer flocculation of calcite: Relating the aggregate size to the settling rate

The initial hindered settling velocities of flocculated calcite suspensions have been related to the mean aggregate sizes and solid fraction under a range of flocculation conditions that are similar to those occurring within a mineral processing thickener. Calcite particles were flocculated in turbulent pipe flow and the aggregates sized with an in situ probe (Lasentec FBRM). The flocculated suspension then flowed into vertical column used to measure the hindered settling velocity. This allowed the investigation of key flocculation parameters — the flocculant dosage, the primary particle size, the suspension solid fraction and the fluid shear rate. The hindered settling velocity was related to the mean aggregate size using a relationship based on Richardson and Zaki's extension of Stokes' law. The effect of aggregate porosity was incorporated using fractal geometry, allowing the estimation of the fractal dimension (2.4) from experimental data. It is an important step toward the development of a link between the size of aggregates produced by flocculation, and their settling and dewatering characteristics. This is also an essential step in the development of robust process models describing the performance of gravity thickeners which are widely used throughout the minerals and other industries.
NOAA-20 and S-NPP VIIRS Thermal Emissive Bands On-Orbit Calibration Algorithm Update and Long-Term Performance Inter-Comparison

The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the National Oceanic and Atmospheric Administration-20 (NOAA-20) and the Suomi National Polar-orbiting Partnership Program (S-NPP) satellites were launched in late 2017 and 2011, respectively. This paper presents a recent update in the VIIRS thermal emissive bands (TEB) on-orbit calibration algorithm and inter-compares long-term instrument and TEB sensor data records (SDR) performances of the two VIIRS, to support user communities. The VIIRS TEB calibration algorithm was improved to mitigate calibration biases during the blackbody warm-up/cool-down (WUCD) events. Four WUCD bias correction methods were implemented in the NOAA operational processing in 2019: (1) the Nominal-F method, (2) the WUCD-C method, (3) the Ltrace method, and (4) the Ltrace-2 method. Our evaluation results indicate that the on-orbit performances of the two VIIRS instruments have been generally stable and comparable with each other, except that NOAA-20 VIIRS blackbody and instrument temperatures are lower than those of the S-NPP VIIRS. The degradations in the S-NPP TEB detector responsivities remain small after 9 years on-orbit. NOAA-20 detector responsivities have been generally stable after the longwave infrared degradation during its early mission was resolved by the mid-mission outgassing. NOAA-20 and S-NPP VIIRS TEB SDRs agree with co-located Cross-track Infrared Sounder observations, with daily averaged biases within 0.1 K at nadir. After the implementation of operational WUCD bias correction, residual TEB WUCD biases are similar for NOAA-20 and S-NPP, with daily averaged biases ~0.01 K in all bands.
A Novel Vegetation Index for Coffee Ripeness Monitoring Using Aerial Imagery

Coffee ripeness monitoring is a key indicator for defining the moment of starting the harvest, especially because the coffee quality is related to the fruit ripeness degree. The most used method to define the start of harvesting is by visual inspection, which is time-consuming, labor-intensive, and does not provide information on the entire area. There is a lack of new techniques or alternative methodologies to provide faster measurements that can support harvest planning. Based on that, this study aimed at developing a vegetation index (VI) for coffee ripeness monitoring using aerial imagery. For this, an experiment was set up in five arabica coffee fields in Minas Gerais State, Brazil. During the coffee ripeness stage, four flights were carried out to acquire spectral information on the crop canopy using two quadcopters, one equipped with a five-band multispectral camera and another with an RGB (Red, Green, Blue) camera. Prior to the flights, manual counts of the percentage of unripe fruits were carried out using irregular sampling grids on each day for validation purposes. After image acquisition, the coffee ripeness index (CRI) and other five VIs were obtained. The CRI was developed combining reflectance from the red band and from a ground-based red target placed on the study area. The effectiveness of the CRI was compared under different analyses with traditional VIs. The CRI showed a higher sensitivity to discriminate coffee plants ready for harvest from not-ready for harvest in all coffee fields. Furthermore, the highest R2 and lowest RMSE values for estimating the coffee ripeness were also presented by the CRI (R2: 0.70; 12.42%), whereas the other VIs showed R2 and RMSE values ranging from 0.22 to 0.67 and from 13.28 to 16.50, respectively. Finally, the study demonstrated that the time-consuming fieldwork can be replaced by the methodology based on VIs.
The Novel Microwave Temperature Vegetation Drought Index (MTVDI) Captures Canopy Seasonality across Amazonian Tropical Evergreen Forests

Despite its perennial canopy, the Amazonian tropical evergreen forest shows significant canopy growth seasonality, which has been represented by optical satellite-based observations. In this paper, a new Microwave Temperature–Vegetation Drought Index (MTVDI) based on Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) sensors was used to capture the canopy seasonality from 2003 to 2010 in comparison with four climatic dryness indicators (Palmer Drought Severity Index (PDSI), Climatological Water Deficit (CWD), Terrestrial Water Storage (TWS), Vapor Pressure Deficit (VPD)) and two photosynthesis proxies (Enhanced Vegetation Index (EVI) and Solar-Induced chlorophyll Fluorescence (SIF)), respectively. Our results suggest that the MTVDI shows opposite seasonal variability with two photosynthesis proxies and performs better than the four climatic dryness indicators in reflecting the canopy photosynthesis seasonality of tropical forests in the Amazon. Besides, the MTVDI captures wet regions that show green-up during the dry season with mean annual precipitation higher than 2000 mm per year. The MTVDI provides a new way for monitoring the canopy seasonality of tropical forests from microwave signals.
Gap Filling for Historical Landsat NDVI Time Series by Integrating Climate Data

High-quality Normalized Difference Vegetation Index (NDVI) time series are essential in studying vegetation phenology, dynamic monitoring, and global change. Gap filling is the most important issue in reconstructing NDVI time series from satellites with high spatial resolution, e.g., the Landsat series and Chinese GaoFen-1/6 series. Due to the sparse revisit frequencies of high-resolution satellites, traditional reconstruction approaches face the challenge of dealing with large gaps in raw NDVI time series data. In this paper, a climate incorporated gap-filling (CGF) method is proposed for the reconstruction of Landsat historical NDVI time series data. The CGF model considers the relationship of the NDVI time series and climate conditions between two adjacent years. Climate variables, including downward solar shortwave radiation, precipitation, and temperature, are used to characterize the constrain factors of vegetation growth. Radial basis function networks (RBFNs) are used to link the NDVI time series between two adjacent years with variabilities in climatic conditions. An RBFN predicted a background NDVI time series in the target year, and the observed NDVI values in this year were used to adjust the predicted NDVI time series. Finally, the NDVI time series were recursively reconstructed from 2018 to 1986. The experiments were performed in a heterogeneous region in the Qilian Mountains. The results demonstrate that the proposed method can accurately reconstruct and generate continuous 30 m 8-day NDVI time series using Landsat observations. The CGF method outperforms traditional time series reconstruction methods (e.g., the harmonic analysis of time series (HANTS) and Savitzky-Golay (SG) filter methods) when the raw time series is contaminated with large gaps, which widely exist in Landsat images.
A Novel LSTM Model with Interaction Dual Attention for Radar Echo Extrapolation

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.
Capture Sequencing to Explore and Map Rare Casein Variants in Goats

Genetic variations in the four casein genes CSN1S1, CSN2, CSN1S2, and CSN3 have obtained substantial attention since they affect the milk protein yield, milk composition, cheese processing properties, and digestibility as well as tolerance in human nutrition. Furthermore, milk protein variants are used for breed characterization, biodiversity, and phylogenetic studies. The current study aimed at the identification of casein protein variants in five domestic goat breeds from Sudan (Nubian, Desert, Nilotic, Taggar, and Saanen) and three wild goat species [Capra aegagrus aegagrus (Bezoar ibex), Capra nubiana (Nubian ibex), and Capra ibex (Alpine ibex)]. High-density capture sequencing of 33 goats identified in total 22 non-synonymous and 13 synonymous single nucleotide polymorphisms (SNPs), of which nine non-synonymous and seven synonymous SNPs are new. In the CSN1S1 gene, the new non-synonymous SNP ss7213522403 segregated in Alpine ibex. In the CSN2 gene, the new non-synonymous SNPs ss7213522526, ss7213522558, and ss7213522487 were found exclusively in Nubian and Alpine ibex. In the CSN1S2 gene, the new non-synonymous SNPs ss7213522477, ss7213522549, and ss7213522575 were found in Nubian ibex only. In the CSN3 gene, the non-synonymous SNPs ss7213522604 and ss7213522610 were found in Alpine ibex. The identified DNA sequence variants led to the detection of nine new casein protein variants. New variants were detected for alpha S1 casein in Saanen goats (CSN1S1∗C1), Bezoar ibex (CSN1S1∗J), and Alpine ibex (CSN1S1∗K), for beta and kappa caseins in Alpine ibex (CSN2∗F and CSN3∗X), and for alpha S2 casein in all domesticated and wild goats (CSN1S2∗H), in Nubian and Desert goats (CSN1S2∗I), or in Nubian ibex only (CSN1S2∗J and CSN1S2∗K). The results show that most novel SNPs and protein variants occur in the critically endangered Nubian ibex. This highlights the importance of the preservation of this endangered breed. Furthermore, we suggest validating and further characterizing the new casein protein variants.
Single Cell RNA-Seq and Machine Learning Reveal Novel Subpopulations in Low-Grade Inflammatory Monocytes With Unique Regulatory Circuits

Subclinical doses of LPS (SD-LPS) are known to cause low-grade inflammatory activation of monocytes, which could lead to inflammatory diseases including atherosclerosis and metabolic syndrome. Sodium 4-phenylbutyrate is a potential therapeutic compound which can reduce the inflammation caused by SD-LPS. To understand the gene regulatory networks of these processes, we have generated scRNA-seq data from mouse monocytes treated with these compounds and identified 11 novel cell clusters. We have developed a machine learning method to integrate scRNA-seq, ATAC-seq, and binding motifs to characterize gene regulatory networks underlying these cell clusters. Using guided regularized random forest and feature selection, our method achieved high performance and outperformed a traditional enrichment-based method in selecting candidate regulatory genes. Our method is particularly efficient in selecting a few candidate genes to explain observed expression pattern. In particular, among 531 candidate TFs, our method achieves an auROC of 0.961 with only 10 motifs. Finally, we found two novel subpopulations of monocyte cells in response to SD-LPS and we confirmed our analysis using independent flow cytometry experiments. Our results suggest that our new machine learning method can select candidate regulatory genes as potential targets for developing new therapeutics against low grade inflammation.