A Shallow Convolutional Neural Network for Onboard CubeSat Image Classification

The development of CubeSats dates back many years, and there has been much research on the payload\u27s ability to analyze satellite images. Due to the difficulty in downlinking photographs owing to their size and communication limitations, the majority of CubeSats are currently constructed with image processing capabilities. The majority of CubeSat payloads are created using embedded technology, which fits CubeSats\u27s size, weight, and power. However, the processing power of embedded devices is constrained. Different kinds of image classification models have been built based on fine-tuning previously trained models, which contain many parameters in this domain. While there has been some study towards creating lightweight models, it has not been extensively tested on microcontrollers or small application processors. We propose our research based on the Shallow Convolutional Neural Network, which is trained on a public dataset of satellite imagery (from CubeSatNet). The proposed model architecture combines image processing techniques and shallow neural architecture to enhance both efficiency with similar accuracy. Through careful experimentation and evaluation, our model achieved remarkable results, performing with comparable training accuracy and remarkable speed (at least an order of magnitude). Furthermore, the reduced parameter count of our model addresses the challenge of resource-intensive computations and memory requirements, making it more practical and cost-effective for deploying it on spaceborne microcontrollers

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Not AvailableMany often two surveys conducted independently with same or different objectives, may have some auxiliary variables in common. The first survey, which has small sample size, collects both variable of interest as well as auxiliary variables. The second survey, relatively larger in sample size, has only some auxiliary variables in common to the first survey. A small area predictor is proposed by combining data from these two surveys. Empirical results show that the proposed small area predictor can lead to efficiency gains when two surveys are combined.Not Availabl

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Not AvailableMany often two surveys conducted independently, may have some auxiliary variables in common along with a set of extra variables that are not common to both the surveys. One survey, which is small in sample size but collects both variable of interest as well as a set of auxiliary variables. The another survey which is relatively larger in sample size, does not collects variable of interest but collects a set of auxiliary variables, common to the small survey. In addition, the large survey collects multiple response variables as well as set of auxiliary variables not common to the small survey. A small area predictor for small domain (or area) means is proposed by combining data from these two surveys using multipurpose weights. Empirical results from model-based as well as design-based simulations indicate that the proposed small area predictor that incorporates the additional auxiliary variables of the large survey along with the common auxiliary variables, provide better efficiency gain.Not Availabl

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Not AvailableMany often two surveys conducted independently with same or different objectives, may have some auxiliary variables in common. The first survey, which has small sample size, collects both variable of interest as well as auxiliary variables. The second survey, relatively larger in sample size, has only some auxiliary variables in common to the first survey. A small area predictor is proposed by combining data from these two surveys. Empirical results show that the proposed small area predictor can lead to efficiency gains when two surveys are combined.Not Availabl

Contaminant Discharge From Outfalls and Subsequent Aquatic Ecological Risks in the River Systems in Dhaka City: Extent of Waste Load Contribution in Pollution

Dhaka, the capital city, which is the nerve center of Bangladesh, is crisscrossed by six different rivers. A network of peripheral rivers connects the city and functions as a natural drainage system for a massive amount of wastewater and sewage by the increased number of inhabitants impacting the overall environmental soundness and human health. This study intended to identify and characterize the outfalls along the peripheral rivers of Dhaka city with the assessment of different pollution indices such as comprehensive pollution index (CPI), organic pollution index (OPI), and ecological risk indices (ERI). The study evaluated the status of the pollution in the aquatic system in terms of ambient water quality parameters along the peripheral rivers due to discharge from outfalls with a particular focus on waste load contribution. Among the identified outfalls, the majority are industrial discharge (60%), and some are originated from municipal (30%), or domestic sewers (10%). Water quality parameters such as suspended solids (SS), 5-day biochemical oxygen demand (BOD5), and Ammoniacal Nitrogen (NH3-N) for most of the peripheral rivers deviated by as much as 40–50% from industrial discharge standards by the environment conservation rules, Bangladesh, 1997. Based on the CPI, the rivers Buriganga, Dhaleshwari, and Turag could be termed as severely polluted (CPI &amp;gt; 2.0), while the OPI indicated heavy organic pollutant (OPI &amp;gt; 4) contamination in the Dhaleshwari and Buriganga rivers. The associated pollution indices demonstrate a trend for each subsequent peripheral river with significant pollution toward the downstream areas. The demonstrated waste loading map from the outfalls identified sources of significant environmental contaminants in different rivers leading to subsequent ecological risks. The study outcomes emphasize the necessity of systematic investigation and monitoring while controlling the point and non-point urban pollution sources discharging into the peripheral rivers of Dhaka city.</jats:p

Data_Sheet_1_Contaminant Discharge From Outfalls and Subsequent Aquatic Ecological Risks in the River Systems in Dhaka City: Extent of Waste Load Contribution in Pollution.docx

Dhaka, the capital city, which is the nerve center of Bangladesh, is crisscrossed by six different rivers. A network of peripheral rivers connects the city and functions as a natural drainage system for a massive amount of wastewater and sewage by the increased number of inhabitants impacting the overall environmental soundness and human health. This study intended to identify and characterize the outfalls along the peripheral rivers of Dhaka city with the assessment of different pollution indices such as comprehensive pollution index (CPI), organic pollution index (OPI), and ecological risk indices (ERI). The study evaluated the status of the pollution in the aquatic system in terms of ambient water quality parameters along the peripheral rivers due to discharge from outfalls with a particular focus on waste load contribution. Among the identified outfalls, the majority are industrial discharge (60%), and some are originated from municipal (30%), or domestic sewers (10%). Water quality parameters such as suspended solids (SS), 5-day biochemical oxygen demand (BOD5), and Ammoniacal Nitrogen (NH3-N) for most of the peripheral rivers deviated by as much as 40–50% from industrial discharge standards by the environment conservation rules, Bangladesh, 1997. Based on the CPI, the rivers Buriganga, Dhaleshwari, and Turag could be termed as severely polluted (CPI > 2.0), while the OPI indicated heavy organic pollutant (OPI > 4) contamination in the Dhaleshwari and Buriganga rivers. The associated pollution indices demonstrate a trend for each subsequent peripheral river with significant pollution toward the downstream areas. The demonstrated waste loading map from the outfalls identified sources of significant environmental contaminants in different rivers leading to subsequent ecological risks. The study outcomes emphasize the necessity of systematic investigation and monitoring while controlling the point and non-point urban pollution sources discharging into the peripheral rivers of Dhaka city.</p

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Not Available: In this paper, we describe small area estimation (SAE) under a spatial dependent random effects model by combining data from two independent surveys. The spatial dependence is introduced using simultaneous autoregressive (SAR) structure in the random area effects part of the model. We use data from two independent surveys. The first survey, small in sample size, collects both variable of interest as well as auxiliary variables and the second survey, relatively larger in sample size, has some auxiliary variables common to the first survey. Our empirical results, based on simulation studies, show that proposed SAE method using the data from two surveys is efficient as compared to the one based on data from single survey. Use of spatial information further enhances the efficiency of the proposed estimato

Assessment of Physicochemical Properties and Comparative Pollution Status of the Dhaleshwari River in Bangladesh

The Dhaleshwari river which flows near Dhaka, the capital of Bangladesh, is currently under threat due to the recent relocation of the Hazaribagh tannery to the Savar area. This study investigated the physicochemical parameters of water quality along with the heavy metal levels in the Dhaleshwari river and performed a comparative analysis among the peripheral rivers around Dhaka City. Surface water quality parameters such as total dissolved solids (TDS), biochemical oxygen demand (BOD5), and chemical oxygen demand (COD) obtained for the Dhaleshwari river deviated by as much as 90% from World Health Organization (WHO) standards in certain instances due to direct discharge from untreated point sources. Concentrations of toxic metals such as chromium (Cr), cadmium (Cd), and nickel (Ni) were above the Food and Agriculture Organization (FAO) standards for heavy metals in surface waters. Strong correlations among the heavy metals indicated significant linear dependences. Based on the physicochemical and toxicity-based characterization, the river system in Dhaka city can be termed as severely polluted with respect to organic and solids discharge, while ecological risk indices (ERI) indicated disastrously high risk in the Dhaleshwari and Buriganga rivers. The study outcomes emphasize the necessity of frequent investigation while controlling the point and nonpoint urban pollution sources discharging into the peripheral rivers of Dhaka city