On-board image classification payload for a 3U CubeSat using machine learning for on-orbit cloud detection

CubeSats are giving the opportunity for educational institutes to participate in the space industry, develop new technologies and test out new ideas in outer space. CubeSat missions are developed to perform scientific research and demonstrate new space technologies with relatively cheap cost and limited resources. This category of satellites has many limitations such as the short development time, the power consumption and the limited time and capability of data downlink. Earth Observation from a Low Earth Orbit is one of the most appealing m applications of CubeSats developed by students or non-space faring countries. Investigating new technologies to improve image quality and studying ways to increase acquisition adequacy is very promising. This paper aims to introduce a mission hardware design and machine learning-based algorithm used within an Earth Observation (EO) CubeSat. The case study of this paper is Alainsat-1 project which is a 3U CubeSat developed with the support of IEEE Geo-science and Remote Sensing Society (GRSS) at the National Space Science and Technology Center, UAE. The satellite is planned to be launched by 2022. A low-resolution Commercial off-the-shelf (COTS) camera for EO is developed as a primary mission in this CubeSat. The compatible hardware design and software algorithm proposed is responsible for classifying the images captured by the camera into different categories based on cloud intensity detected in these images before downloading them to the ground station. A microcontroller-based architecture is developed for controlling the mission board; it is responsible for accessing the memory, reading the images, and running the cloud detection algorithm. The cloud detection algorithm is based on a U-net architecture while the algorithm is developed using a Tensor-flow library. This model is trained using a dataset of images taken from the Landsat 8 satellite project. Moreover, the SPARCS cloud assessment dataset is used to evaluate the developed model on a new set of images. The overall accuracy achieved by the model is around 85% in addition to the acceptable performance of the model observed on a set of low-resolution images. The plan is to make the design modular and optimize its performance to be used on-board CubeSats fulfilling the size constraint and overall power consumption limitation of an add-on module to a camera mission

Saudi Journal of Medical and Pharmaceutical Sciences Evaluation of Phytochemical and in-vivo Antihyperlipidemic Activity of Solanum spirale Roxb. Leaves

Abstract: The objective of the study was to evaluate the phytochemical and in-vivo antihyperlipidemic activity of Solanum spirale Roxb. leaves. The physiochemical standardization of the dried leaves powder was done with respect to ash values, foaming index, extractive values and moisture content. The dried leaves were extracted with petroleum ether, chloroform and water. The phytochemical analyses were carried out and the antihyperlipidemic activity of the chloroform and aqueous extracts were evaluated. The antihyperlipidemic study was carried out by inducing hyperlipidemia in rats by means of triton. The serum collected was analyzed for total cholesterol, triglyceride, low density lipoprotein, and high density lipoprotein. The result of the present study revealed that both the aqueous and chloroform extracts of leaves of Solanum spirale Roxb. possess antihyperlipidemic activity

Production of freeze-dried lactic acid bacteria starter culture for cassava fermentation into gari.

peer reviewedSixteen lactic acid bacteria, eight Lactobacillus plantarum, three L. pentosus, 2 Weissella paramesenteroides, two L. fermemtum and one Leuconostoc mesenteroides ssp. mesenteroides were previously isolated from cassava fermentation and selected on the basis of their biochemical properties with a view to selecting appropriate starter cultures during cassava fermentation for gari production. In this study, the potential of these pre-selected strains as suitable freeze-dried cultures was evaluated. Their ability to tolerate the freeze-drying process was assessed by dehydration in a glycerol solution of increasing concentration, followed by staining with two fluorescent markers: rhodamine 123 and propydium iodide. Twelve strains that recovered more than 50% of their population value after visualisation on an epi-fluorescent microscope were produced in a bioreactor and freeze-dried. The technological characteristics identified after the freeze-drying process, were a high cell concentration or high survival rate. The ability of the freeze-dried strains to recover their acidification activity was evaluated through the determination of the pH, titratable acidity (% lactic acid/g Dry Weight) and cell count over 24 h on MRS broth. Ultimately, the strains L. plantarum VE36, G2/25, L. fermentum G2/10 and W. paramesenteroides LC11 were selected to be developed as freeze-dried starter cultures for gari production