Biology as Destiny in Gender Inequality

Generally differentiating men and women in society as a gender study is itself increasing the discrimination. It is in the name of eliminating discrimination activists are uniting, registering, advocating the weaker section of the society for political benefit. The weaker section is being stronger to fight against the stronger section to intensify the conflict for political benefit. The recent accident of Rukum district is the product of conflict-led politics of modernity. A Dalit boy went to marry his non-Dalit love with some of his friends but her people made them jump on the Karnali River to save their lives but killed. The incident was highly politically motivated. Until we follow the philosophy of modernity, the same kind of incidents may repeat time and again. The study is to analyze biology as destiny in gender inequality. Methodologically the study is a critical analysis of modernism on the issues of gender inequality. Men are men and women are women with pride, no regression at all. No one can eliminate destiny or Prarabdha but we can neutralize it.

A Single-Difference Multipath Hemispherical Map for Multipath Mitigation in BDS-2/BDS-3 Short Baseline Positioning

The BeiDou Navigation Satellite System (BDS) features a heterogeneous constellation so that it is difficult to mitigate the multipath in the coordinate-domain. Therefore, mitigating the multipath in the observation-domain becomes more important. Sidereal filtering is commonly used for multipath mitigation, which needs to calculate the orbit repeat time of each satellite. However, that poses a computational challenge and damages the integrity at the end of the multipath model. Therefore, this paper proposes a single-difference model based on the multipath hemispherical map (SD-MHM) to mitigate the BDS-2/BDS-3 multipath in a short baseline. The proposed method is converted from double-difference residuals to single-difference residuals, which is not restricted by the pivot satellite transformation. Moreover, it takes the elevation and the azimuth angles of the satellite as the independent variables of the multipath model. The SD-MHM overcomes the unequal observation time of some satellites and does not require specific hardware. The experimental results show that the SD-MHM reduces the root mean square of the positioning errors by 56.4%, 63.9%, and 67.4% in the east, north, and vertical directions; moreover, it contributes to an increase in the baseline accuracy from 1.97 to 0.84 mm. The proposed SD-MHM has significant advantages in multipath mitigation compared with the advanced sidereal filtering method. Besides, the SD-MHM also features an excellent multipath correction capability for observation data with a period of more than seven days. Therefore, the SD-MHM provides a universal strategy for BDS multipath mitigation.

Fires Monitoring by Means of Spectral Indices from Sentinel-2 Data: the Case Study of the Vesuvius and Lattari Mountains, Campania (Italy)

Forest fires are part of a set of natural disasters that have always affected regions of the world typically characterized by a tropical climate with long periods of drought. However, due to climate change in recent years, other regions of our planet have also been affected by this phenomenon, never seen before. One of them is certainly the Italian peninsula, and especially the regions of southern Italy. For this reason, the scientific community, as well as remote sensing one, is highly concerned in developing reliable techniques to provide useful support to the competent authorities. In particular, three specific tasks have been carried out in this work: (i) fire risk prevention, (ii) active fire detection, and (iii) post-fire area assessment. To accomplish these analyses, the capability of a set of spectral indices, derived from spaceborne remote sensing (RS) data, is assessed to monitor the forest fires. The spectral indices are obtained from Sentinel-2 multispectral images of the European Space Agency (ESA), which are free of charge and openly accessible. Moreover, the twin Sentinel-2 sensors allow to overcome some restrictions on time delivery and observation repeat time. The performance of the proposed analyses were assessed experimentally to monitor the forest fires occurred in two specific study areas during the summer of 2017: the volcano Vesuvius, near Naples, and the Lattari mountains, near Sorrento (both in Campania, Italy).

Glacier Image Velocimetry: an open-source toolbox for easy and rapid calculation of high-resolution glacier-velocity fields

We present Glacier Image Velocimetry (GIV), an open-source and easy-to-use tool for rapidly calculating high spatial and temporal resolution glacier-velocity fields. Glaciers' velocity fields reveal their flow dynamics, stability, and thickness. Obtaining widespread glacier-velocity measurements in the field is challenging and labour intensive. Recent increases in the availability of high-resolution, short-repeat-time optical imagery improve this, as persistent irregularities on the ice surface allow us to use feature tracking – an accidental form of particle image velocimetry to obtain displacement fields, and hence, velocity over time. While these techniques have been used to calculate velocity fields for many glaciers, existing toolboxes can be expensive, complex or inflexible to use. GIV is fully parallelized, and automatically detects, filters, and extracts velocities from large datasets of images. Through this coupled toolchain and an easy-to-use GUI, GIV can rapidly analyse hundreds to thousands of image pairs on any modern laptop or desktop. We present four examples of how this model may be used: to complement a glaciology field campaign (Glaciar Perito Moreno, Argentina), calculate the velocity fields of small (Glacier d’Argentière, France) and very large (Vavilov ice cap, Russia) glaciers, and determine the ice volume present within a tropical ice cap (Volcán Chimborazo, Ecuador). Fully commented code and a standalone app for GIV are available from GitHub and Zenodo.

Comparison of Satellite Repeat Shift Time for GPS, BDS, and Galileo Navigation Systems by Three Methods

The characteristic of the satellite repeat shift time can reflect the status of the satellite operation, and is also one of the key factors of the sidereal filtering multipath correction. Although some methods have been developed to calculate the repeat shift time, few efforts have been made to analyze and compare the performance of this feature for the GPS (Global Positioning System), BDS (BeiDou System), and Galileo in depth. Hence, three methods used for calculating the repeat shift time are presented, and used to compare and analyze the three global systems in depth, named the broadcast ephemeris method (BEM), correlation coefficient method (CCM), and aspect repeat time method (ARTM). The experiment results show that the repeat shift time of each satellite is different. Also, the difference between the maximum and minimum varies from different systems. The maximum difference is about 25 s for the BDS IGSO (Inclined Geosynchronous Orbit) and the minimum is merely 10 s for the GPS system. Furthermore, for the same satellite, the shift time calculated by the three methods is almost identical, and the maximum difference is only about 7 s between the CCM and the ARTM method for the BDS MEO (Medium Earth Orbit) satellite. Although the repeat shift time is different daily for the same satellite and the same method, the changes are very small. Moreover, in terms of the STD (Standard Deviation) of the BS (between satellites) and MS (mean shift for the same satellite), the GPS system is the best, the performance of the BDS system is medium, and the Galileo performs slightly worse than the GPS and BDS.

Global downscaling of remotely sensed soil moisture using neural networks

Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

Global Downscaling of Remotely-Sensed Soil Moisture using Neural Networks

Characterizing soil moisture at spatio-temporal scales relevant to land surface processes (i.e. of the order of a kilometer) is necessary in order to quantify its role in regional feedbacks between land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3 days repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite estimates soil moisture at two different spatial scales of 36 km and 9 km since April 2015. In this study, we develop a neural networks-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses mean monthly Normalized Differenced Vegetation Index (NDVI) as an ancillary data to quantify sub-pixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.