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Defining Mars-Forward Capabilities of the Lunar Gateway space Station 22

Defining Mars-Forward Capabilities of the Lunar Gateway Spac...

22nd IAA Symposium on Building Blocks for Future space Exploration and Development at the 75th International Astronautical Congress, IAC 2024

作者： Thornton, Aaron Kibe, Abhijeet Battegazzore, Alessandro Llewellyn, Ali Ales, Amanda Singh, Amit Kumar Smith, Amy Pejic, Anamarija Nathaniel, Ben Turneanu, Bianca Diana Pietrusiak, Damian Selvanathan, Dilani Marrucci, Elisabetta Maruyama, Emi Leteurtre, Etienne La Fazia, Fabiola Luna Domene, Francesc Morgese, Francesco Voldbirk, Frederik Abid, Hakim Benecken, Ian Luca Alkathery, Ibrahim Krasovitzky, Ignacio Fiore, Ilaria Pia Grewal, Iqbal Fettes, James Kerrie, Jeff Llamazares, Laura González Hou, Laurence Shirolkar, Leena Gurman, Lilach Romagnolo, Lucrezia Mathieu, Maëlle Schmidt, Malica Ventura, Marc Casanovas Uludag, Mehmet Sevket Alfarsi, Mohsin Duursma, Nadine Alahmadi, Najla Mirzoeva, Ola Raj, Remya Gomes, Ricardo Sterling, Robert William Gurevitch, Shay Alhakmani, Waleed Amur Filipecka, Zuzanna Space Studies Program International Space University France Australia Delft University of Technology Netherlands 3 Canadian Space Division Canada Indian Space Research Organisation India Copenhagen Suborbitals Denmark Wroclaw University of Science and Technology Poland EngTech United Kingdom Japan Conservatoire National des Arts et Métiers France University of Naples Italy Politecnico di Milano Italy Saga Space Architects Denmark University of Stuttgart Germany Saudi Space Agency Saudi Arabia Humans2Venus Foundation United States University of British Columbia Canada Hackensack University Medical Center United States UK Space Agency United Kingdom United Kingdom Netherlands Israel Aerospace Industries - MBT Space Israel Ministry of Defence Oman

ISBN: (纸本)9798331312251

The Lunar Gateway is an essential element of deep space infrastructure that provides a multi-purpose long-duration cislunar platform. As such, it also provides an opportunity for Mars-forward use and technology development. Building on an analysis of NASA's Moon to Mars (M2M) program objectives, architecture documentation, and Gateway's existing capabilities and requirements, this paper will recommend ways to utilize the Gateway as a testbed for future missions and operational concepts, and hardware to fill gaps in enabling future human Mars exploration. Areas of assessment include extending human life, enhancing quality of life on deep space missions, establishing a sustainable cislunar ecosystem, international partnership and commercial opportunities around the Moon. This paper is a condensed version of the final report produced by the Lunar Gateway Team Project for the International space University's space Studies program 2024. It aims to define the Gateway's role in advancing capabilities for sustainable lunar exploration while serving as a springboard for future missions to Mars. The results of this study will advise recommendations to NASA's Gateway program on leveraging the lunar outpost for Mars and beyond. Copyright © 2024 by the International Astronautical Federation (IAF). All rights reserved.

关键词： NASA

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Estimation of aboveground biomass of arboreal species in the semi-arid region of Brazil using SAR(synthetic aperture radar)images

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Journal of Arid Land 2023年第6期15卷 695-709页

作者： Janisson B de JESUS Tatiana M KUPLICH Íkaro D de C BARRETO Fernando L HILLEBRAND Cristiano N da ROSA Postgraduate Program in Remote Sensing Federal University of Rio Grande do SulCampus ValePorto Alegre 91501970Brazil National Institute for Space Research(INPE) COESU(Southern Spatial Coordination)Santa Maria 97105970Brazil Postgraduate Program Biometry and Applied Statistics Rural Federal University of PernambucoRecife 52171900Brazil Federal Institute of Education Science and Technology of Rio Grande do Sul(IFRS)Campus RolanteRolante 95690000Brazil Postgraduate Program in Remote Sensing Polar and Climate CenterFederal University of Rio Grande do SulPorto Alegre 91501970Brazil

The Caatinga biome is an important ecosystem in the semi-arid region of *** has significantly degraded due to human activities and is currently a region undergoing ***,monitoring the variation in the Caatinga biome has become essential for its sustainable ***,traditional methods for estimating aboveground biomass(AGB)are time-consuming and *** sensing,such as optical and radar imaging,can estimate and correlate with ***,radar imaging is still a novelty to be applied in estimating the AGB of this biome,which is an area with little ***,this study aimed to use Sentinel-1 images to estimate the AGB of the Caatinga biome in Sergipe State(northeastern Brazil)and to verify its influencing *** sample plots(30 m×30 m)were selected,and the stems of individuals with a circumference at breast height(1.3 m above the ground)equal to or greater than 6.0 cm were measured,and the AGB through an allometric equation was *** Sentinel-1 images from 3 different periods(green,intermediate,and dry periods)were used to consider the phenological conditions of the Caatinga *** the pre-processing and extraction of attributes(co-polarized VV(vertical transmit and vertical receive),cross-polarized VH(vertical transmit and horizontal receive),and band ratio VH/VV backscatter,radar vegetation index,dual polarization synthetic aperture radar(SAR)vegetation index(DPSVI),entropy(H),and alpha angle(α))were performed with Sentinel’s Application *** attributes were used to estimate the AGB through simple and multiple linear regressions and evaluated by the coefficients of determination(R2),correlation(r),and root mean squared error(RMSE).The results showed that the attributes individually had little ability to estimate the AGB of the Caatinga biome in the three *** with multiple regression,we found that the intermediate period presented the equation with the best

关键词： Caatinga tropical dry forest coherent and incoherent attributes C-band Sentinel-1

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A-STEP for AstroPix: Development and Test of a space-based payload using novel pixelated silicon for gamma-ray measurement 38

A-STEP for AstroPix: Development and Test of a space-based p...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Steinhebel, Amanda L. Striebig, Nicolas Jadhav, Manoj Violette, Daniel Durachka, David Boggs, Ryan Seo, Lindsey Hesh, Scott Suda, Yusuke Shin, Taylor K.-W. Metzler, Zachary Kierans, Carolyn Caputo, Regina Tajima, Hiroyasu Fukazawa, Yasushi Leys, Richard Peric, Ivan Metcalfe, Jessica Perkins, Jeremy S. NASA Postdoctoral Program Fellow United States NASA Goddard Space Flight Center GreenbeltMD United States Center for Research and Exploration in Space Science and Technology NASA GSFC GreenbeltMD United States University of Maryland College ParkMD United States Karlsruhe Institute of Technology Karlsruhe Germany Argonne National Laboratory LemontIL United States Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan Institute for Space-Environment Research Nagoya University Aichi Nagoya Japan NASA Wallops Flight Facility Wallops IslandVA United States University of Califormia Santa Cruz Santa CruzCA United States

Next-generation medium-energy gamma-ray instruments sensitive from 25 keV to 1 GeV must be able to measure and reconstruct incident photons in the photoabsorption, Compton, and pair-production regimes. Such robust functionality requires a tracker with high resolution, low noise, and low power. The monolithic CMOS active pixel silicon sensor AstroPix is a novel mission-enabling technology. AstroPix will provide the energy- and angular-resolution required of a medium-energy gamma-ray telescope while reducing noise with the dual detection-and-readout capabilities of an HVCMOS chip. Plans for A-STEP, a funded Astropix Sounding rocket technology dEmonstration Payload, will be presented. This roughly 2U-sized payload will include three layers of AstroPix quad-chips for cosmic ray and gamma ray detection and tracking. This technology demonstration mission also strengthens the design readiness of AMEGO-X, an MIDEX-sized MeV telescope mission concept. Additional supplementary development and testing of the AstroPix project will also be considered, including A-STEP simulation efforts for environment and detector response modeling. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmology

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POST-CLASSIFICATION ENHANCEMENT IN THE RESULT OF DEEP LEARNING LAND COVER CLASSIFICATION USING VERY-HIGH RESOLUTION SATELLITE IMAGERY 44

POST-CLASSIFICATION ENHANCEMENT IN THE RESULT OF DEEP LEARNI...

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44th Asian Conference on Remote Sensing, ACRS 2023

作者： Hakim, Yofri Furqani Tsai, Fuan International Ph.D. Program in Environmental Science and Technology National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan Center for Space and Remote Sensing Research National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan

ISBN: (纸本)9781713893646

Land cover is one of the fundamental data utilized in spatial analysis for a range of applications, including climate, environment, natural resources, agriculture, forestry, planning, health, and even social issues. The demand for land cover data is diverse, ranging from global scale, regional scale, and detailed scale. Furthermore, data updates also become a necessity for users. The current trend is the requirement for more accurate and up-to-date land cover data. The growing demand for accurate and up-to-date land cover data is in sync with improvements in satellite imagery data acquisition technology, which now offers improved spatial resolution and more effective satellite imagery data processing. Satellite imagery data processing technology is also growing rapidly with the advance of artificial intelligence for semantic classification or segmentation. This research uses a deep learning approach to classify land cover on Pleiades very high-resolution satellite imagery. A post-classification enhancement is also carried out to improve the consistency and accuracy of the deep learning classification results. The preliminary research results show that post-classification enhancement with the algorithms proposed in this study can increase the accuracy of classification results using deep learning-based approaches by approximately 2%. The original Overall Accuracy and Kappa of the deep learning classification results were 0.84 and 0.79. After the post-classification enhancement process, the Overall Accuracy and Kappa values increased to 0.86 and 0.81. © 2023 ACRS. All Rights Reserved.

关键词： Deep Learning Image Morphology Land Cover Classification Post-Classification Enhancement Semantic Segmentation U-Net

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Development of an HV-CMOS active pixel sensor "AstroPix" for all-sky medium-energy gamma-ray telescopes 38

Development of an HV-CMOS active pixel sensor "AstroPix" for...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Suda, Yusuke Caputo, Regina Steinhebel, Amanda L. Fleischhack, Henrike Striebig, Nicolas Jadhav, Manoj Luz, Ricardo Violette, Daniel Kierans, Carolyn Tajima, Hiroyasu Fukazawa, Yasushi Leys, Richard Peric, Ivan Metcalfe, Jessica Negro, Michela Perkins, Jeremy S. Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan NASA Goddard Space Flight Center GreenbeltMD United States Catholic University of America 620 Michigan Ave NE WashingtonDC20064 United States Center for Research and Exploration in Space Science and Technology NASA/GSFC GreenbeltMD20771 United States ASIC and Detector Laboratory Karlsruhe Institute of Technology Karlsruhe Germany Argonne National Laboratory LemontIL60440 United States Institute for Space Earth Environmental Research Kobayashi-Maskawa Institute for The Origin of Particles and the Universe Nagoya University Japan

All-sky medium-energy gamma-ray observations are essential to deepen our understanding of physics in extreme astronomical objects such as gamma-ray bursts and active galactic nuclei. Those observations are also highly needed to further develop multi-messenger astronomy. Next-generation all-sky MeV gamma-ray telescopes must have a large area detector and keep high sensitivities even in the energies in which Compton scattering is dominant. Having both a silicon tracker (scatterer) and a calorimeter (absorber) as the main detector is a promising configuration for such a space mission. In order to fulfill the requirements such as a large sensitive area, low noise, high energy/positional resolution, and low power, we have been developing an HV-CMOS active pixel sensor, AstroPix. In this contribution, we report performance evaluations of AstroPix such as I-V and noise, energy calibration/resolution/threshold, and depletion depth measurements. Current achievements as a sensor for next-generation all-sky MeV gamma-ray telescopes and future development will be discussed. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： CMOS integrated circuits

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Impacts of land use and land cover changes on hydrological processes and sediment yield determined using the SWAT model

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International Journal of Sediment Research 2022年第1期37卷 54-69页

作者： Edivaldo Afonso de Oliveira Serr?o Madson Tavares Silva Thomás Rocha Ferreira Lorena Concei??o Paiva de Ataide Cleber Assis dos Santos Aline Maria Meiguins de Lima Vicente de Paulo Rodrigues da Silva Francisco de Assis Salviano de Sousa Denis José Cardoso Gomes Academic Unit of Atmospheric Sciences Federal University of Campina Grande Institute of Technology Civil Engineering Graduate Program Federal University of Pará Earth System Science Center National Institute of Space Research Institute of Geosciences Federal University of Pará Department of Physics Center for Natural and Exact Sciences Federal University of Santa Maria

Land use and land cover (LULC) changes strongly affect local hydrology and sediment *** current study focused on a basin in the Brazilian Amazon and had the following three objectives:(1) to perform an effective diagnosis of flow and sediment yield,(2) to evaluate the impacts of LULC changes over the last 40 years on the hydro-sedimentological variables,and (3) to investigate the impacts of the possible trends or breaking points in the flow,surface runoff,and sediment yield *** Soil and Water Assessment Tool (SWAT) model validation showed that the simulated results were consistent with the data measured in the dynamic reproduction of flow ***,changes in LULC altered surface runoff,sediment yield,and flow according to the Mann-Kendall and Pettitt non-parametric *** was also observed that the sub-basins in which pastureland is predominant are more susceptible to increased surface runoff and sediment *** to trend and homogeneity tests,these sub-basins had break points in the time series of these *** the other hand,in the sub-basins whose land cover is predominantly forest,the time series is homogeneous and *** current study highlighted the impacts that 40 years of LULC changes in an Amazonian basin had on hydro-sedimentological variables,which,in turn,can play an important role in changing the hydrological cycle,consequently these changes can also impact diverse human activities in the region,such as agribusiness,livestock,energy production,food security,and public water ***,it is concluded that the SWAT model is a good estimator of hydro-sedimentological processes in Amazonian basins and can be used by decision makers in the management of water and environmental resources.

关键词： Hydrological modeling Sediment yield Amazon basin SWAT model

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Correction: Comparative study on formic acid sensing properties of flame-made Zn2SnO4 nanoparticles and its parent metal oxides

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Physical Chemistry Chemical Physics 2023年第27期25卷 18497-18498页

作者： Matawee Punginsang Kanittha Inyawilert Mameaseng Siriwalai Anurat Wisitsoraat Adisorn Tuantranont Chaikarn Liewhiran Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand PhD Program in Nanoscience and Nanotechnology (International Program/Interdisciplinary) Faculty of Science Chiang Mai University Chiang Mai Thailand Graduate School Chiang Mai University Chiang Mai Thailand Center of Excellence in Materials Science and Technology Chiang Mai University Chiang Mai 50200 Thailand National Security and Dual-Use Technology Center National Science and Technology Development Agency (NSTDA) Klong Luang Phathum Thani 12120 Thailand

Correction for ‘Comparative study on formic acid sensing properties of flame-made Zn2SnO4 nanoparticles and its parent metal oxides’ by Matawee Punginsang et al., Phys. Chem. Chem. Phys., 2023, 25, 15407–15421, htt...

Correction for ‘Comparative study on formic acid sensing properties of flame-made Zn₂SnO₄ nanoparticles and its parent metal oxides’ by Matawee Punginsang et al., Phys. Chem. Chem. Phys., 2023, 25, 15407–15421, https://***/10.1039/D3CP00845B.

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INTEGRATING SENTINEL-1 SYNTHETIC APERTURE RADAR AND CHIRPS DATA FOR UNDERSTANDING HEAVY PRECIPITATION AND FLOODING IN AN GIANG PROVINCE, VIET NAM 44

INTEGRATING SENTINEL-1 SYNTHETIC APERTURE RADAR AND CHIRPS D...

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44th Asian Conference on Remote Sensing, ACRS 2023

作者： Minh, Ton Binh Chiang, Shou-Hao International Ph.D Program in Environmental Science and Technology University System of Taiwan No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan Center for Space and Remote Sensing Research National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan

ISBN: (纸本)9781713893646

Understanding the mechanisms of heavy precipitation and flooding is essential for effective climate change adaptation and response. The cloud-penetrating ability of microwaves makes Synthetic Aperture Radar (SAR) an ideal tool for providing weather-independent images for flood mapping. Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a land-only rainfall dataset that incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for various analysis applications. This study employs intensity images from Sentinel-1 satellites to generate flood maps and utilizes CHIRPS to derive heavy rainfall frequency. The segmentation technique is applied to distinguish between pre-existing standing water, recently flooded areas, and non-flooded regions within the study area. By combining SAR and CHIRPS data, this research yields valuable insights into the relationship between heavy rainfall and flood occurrences in An Giang province, Viet Nam. The escalating frequency of heavy precipitation correlates with an augmentation in local rainfall amounts, and a positive correlation emerges between heavy rainfall and the extension of flood-prone areas. While recognizing that flooding influences water flow from the upstream of the Mekong River, flooding discharges from neighboring areas, and tidal levels, the accelerated trend of urbanization has also played a significant role. The expansion of impervious surfaces contributes to the severity of deep floods and elevates flood potential in the region. Furthermore, this research affirms the effectiveness of SAR intensity image mapping for flood detection, and the integration of SAR and CHIRPS data furthers our understanding of heavy precipitation mechanisms. As the implications of climate change persist, the findings are crucial for developing sustainable flood management strategies, as well as enhancing resilience to climate-induced challenges in the Mekong River Del

关键词： CHIRPS climate change Flood Mekong River Delta Sentinel-1

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Generation of Detailed Classification Maps using High-Resolution Satellite Images At Country-wide Scale

Generation of Detailed Classification Maps using High-Resolu...

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2021 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2021

作者： Manandhar, Prajowal Jalil, Ahmad Zehi, Sultan Fareaa, Sanad Marpu, Prashanth Reddy Alaydaroos, Fatima Uae University National Space Science and Technology Center Al Ain United Arab Emirates Space Program Abu Dhabi United Arab Emirates Uae Space Agency Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665442497

A generation of detailed classification maps requires high-resolution satellite images classified into numerous classes depending upon the region of interest. This paper addresses the challenges encountered towards generating such classification maps at a large scale. While using a high-resolution satellite image at a large scale, one could face challenges during the pre-processing phase, starting with mosaicking satellite images, dealing with sample collection, projection issues, or even using classification algorithms to develop classification maps. Hence, this article provides details on such tasks performed at a larger scale. A CNN-based supervised method is used to train the model for the classification from the approximately equal size of samples of the interested land cover land use (LCLU) classes. In addition, few land-use classes such as parks and airports are used from Volunteered Geographic Information (VGI) sources. © 2021 IEEE.

关键词： Parks

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Unveiling Graph Power: SegmentAnything and GCN Synergy for Instance Segmentation and Classification

Unveiling Graph Power: SegmentAnything and GCN Synergy for I...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Vincenzo Mariano Scarrica Antonino Staiano National PhD Program in AI – Agrifood and Environment University of Naples Federico II Corso Umberto I 40 Naples Italy Department of Science and Technology Centro Direzionale Isola C4 University of Naples Parthenope Naples Italy

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

In this paper, we propose a novel approach to improve instance segmentation and classification tasks by incorporating SegmentAnything [1] before using graph neural network frameworks. Before experimentation, we ensure comprehensive data labeling using SegmentAnything for instance segmentation and Grounding DINO [2] for class annotation. Our study aims to evaluate the effectiveness of this integration by conducting a comparative analysis with the state-of-the-art Vision Transformer model [3]. Surprisingly, our experiments reveal that while the vision transformer has demonstrated remarkable performance in various tasks, it underperforms compared to our proposed approach on the same dataset. Our findings underscore the efficacy of integrating SegmentAnything with graph neural networks for instance segmentation and classification tasks, emphasizing the pivotal of those networks in advancing computer vision methodologies.

关键词： Instance segmentation Computer vision Analytical models Grounding Annotations Pipelines Transformers

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