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All Journal Buana Information Technology and Computer Sciences (BIT and CS) Journal of Soft Computing Exploration International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Aerospace Engineering Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Geosciences and Environmental Studies Jurnal Teknokes
Paneru, Bishwash
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Aerospace Engineering Astronomy Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Journal : Aerospace Engineering

 1 
Emergence in Space Technologies with Nanosatellites, Exploring the Applications of AI in Space Development, and Future Trends Paneru, Biplov; Paneru, Bishwash; Poudyal, Ramhari
Aerospace Engineering Vol. 1 No. 1 (2024): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/aero.v1i1.2434

Abstract

Since, the first satellite was launched years ago, more than 6000 have been flown into orbit. Space science is the most fascinating field of study and research in the twenty-first century. The satellites are objects that travel in an elliptical orbit around the planet. Artificial satellites are being produced in large numbers. One of the most important aspects of modernizing science and communication technology, including space technology, mobile and radio communication, is satellite communication. The focus of the research work is on nanosatellites like cube sats, which are becoming increasingly well-known because to their wide-ranging applications in the field of radio transmission. This review paper explains some recent developments and the importance of nanosatellites in advancing space technology, highlight the recent development of space technologies in developing Asian nations and presents the future prospects of satellites development. Also, study is done in the role of AI in space technologies enhancement and space industries development its applications and future trends are discussed in this paper.
Application of UAVs and Remote Sensing Technologies for Atmospheric CO2 Capturing: A Study Application of UAVs and Remote Sensing in CO2 Reductions Paneru, Biplov; Paneru, Bishwash; Poudyal, Ramhari; Poudyal, Khem
Aerospace Engineering Vol. 1 No. 2 (2024): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/aero.v1i2.2508

Abstract

Human activities are a major contributor to climate change, with rising levels of CO₂ in the atmosphere. To address this essential issue, several carbon capture and sequestration (CCS) methods have been developed. Unmanned Aerial Vehicles (UAVs) and remote sensing technologies are emerging as major improvements to the efficiency and effectiveness of atmospheric carbon capture initiatives. This research examines the use of UAVs and remote sensing technologies to monitor, quantify, and manage atmospheric CO₂ levels. Furthermore, the study explores the broader implications of integrating robotic-drone technology, emphasizing their ability to contribute to a sustainable future. These technologies, which incorporate modern data collection and analysis methodologies, provide promising answers for both climate change mitigation and long-term environmental sustainability.
Deep Learning-Based Classification of Remote Sensing Images: Challenges, Techniques, and Future Directions in Global Sustainability Paneru, Biplov; Paneru, Bishwash; Sapkota, Sanjog Chhetri
Aerospace Engineering Vol. 1 No. 3 (2024): July
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/aero.v1i3.2772

Abstract

With its high accuracy and efficiency, deep learning has greatly improved the classification of remote sensing (RS) photos. In order to categorize RS photos, this research analyzes the effectiveness of three cutting-edge deep learning models: ResNet-50, EfficientNetB2, and MobileNetV2. The models' accuracy on training and validation data were noted after they were trained and assessed on a dataset containing a variety of situations. Our findings illustrate each model's advantages and disadvantages and shed light on how well suited each is for various RS image categorization applications. The ResNet-50 model performed well in our study, achieving 74.41% training accuracy and 75.00% validation accuracy. With a training accuracy of 74.66% and a higher validation accuracy of 80.33%, the EfficientNetB2 model performed marginally better, demonstrating its strong generalization capabilities. On the other hand, the MobileNetV2 model had severe overfitting, as evidenced by its validation accuracy of 22.79%, which was much lower than its extraordinary high training accuracy of 99.21%. In order to achieve balanced performance between training and validation datasets in remote sensing image classification tasks, these results emphasize the significance of model architecture and regularization strategies. The proposed model can be utilized for sustainable remote sensing based applications in global water, environment and air health.
Water Sustainability Enhancement with UAV and AIoT: An Integrated Technology for Water Quality and Flood Hazard Monitoring using the Internet of Drones Paneru, Biplov; Paneru, Bishwash; Sapkota, Sanjog Chhetri; Shah, Krishna Bikram; Poudel, Yam Krishna
Aerospace Engineering Vol. 1 No. 4 (2024): October
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/aero.v1i4.2773

Abstract

Globally, there are challenges in minimizing the effects of water pollution and global warming everywhere in the world. In order to map the flood conditions, we want to apply a sensor network connected to a Esp32 and Tensorflow lite integrated system for drone-based water surface waste collection. Finally, a GSM sim 800L Module incorporated is used to send notifications to the user about the monitored conditions, such as trash level and other data. An ultrasonic sensor is utilized to detect the water level. The outcome shows that there is a high chance of tracking water levels and monitoring floods. By using this innovative technology, users can receive warnings and be warned remotely. The Inception-v3 model on clean and unclean water images obtained 97% accuracy on testing USING Inception-v3 and using the proposed circuit diagram a prototype is developed for possible deployment in such water resource region for possible operation and application is presented in the paper.
Co-Authors Adhikari, Pukar Bikram Shah, Krishna Neupne, Kabita Pandey, Nitish Paneru, Biplov Poudel, Yam Krishna Poudyal, Khem Poudyal, Khem Narayan Poudyal, Ramhari Sapkota, Sanjog Chhetri Shah, Krishna Bikram Shah, Krishna Bikram Bikram