Search Results (318)

A Digital Elevation Model (DEM) provides accurate topographic data for planetary exploration (e.g., Moon and Mars), essential for tasks like lander navigation and path planning. This study proposes the first latent diffusion-based algorithm for DEM generation, leveraging a conditional decoder to enhance reconstruction accuracy from RGB satellite images. The algorithm performs the diffusion process in the latent space and uses a conditional decoder module to enhance the decoding accuracy of the DEM latent vectors. Experimental results show that the proposed algorithm outperforms the baseline algorithm in terms of reconstruction accuracy, providing a new technical approach to efficiently reconstruct DEMs for extraterrestrial planets. Full article

As a type of distinctive pit on Mars, skylights are entrances to subsurface lava caves. They are very important for studying volcanic activity and potential preserved water ice, and are also considered as potential sites for human extraterrestrial bases in the future. Most skylights are manually identified, which has low efficiency and is highly subjective. Although deep learning methods have recently been used to identify skylights, they face challenges of few effective samples and low identification accuracy. In this article, 151 positive samples and 920 negative samples based on the MRO-HiRISE image data was used to create an initial skylight dataset, which contained few positive samples. To augment the initial dataset, StyleGAN2-ADA was selected to synthesize some positive samples and generated an augmented dataset with 896 samples. On the basis of the augmented skylight dataset, we proposed YOLOv9-Skylight for skylight identification by incorporating Inner-EIoU loss and DySample to enhance localization accuracy and feature extracting ability. Compared with YOLOv9, the P, R, and the F1 of YOLOv9-Skylight were improved by about 9.1%, 2.8%, and 5.6%, respectively. Compared with other mainstream models such as YOLOv5, YOLOv10, Faster R-CNN, Mask R-CNN, and DETR, YOLOv9-Skylight achieved the highest accuracy (F1 = 92.5%), which shows a strong performance in skylight identification. Full article

The development of efficient and sustainable water recycling systems is essential for long-term human missions and the establishment of space habitats on the Moon, Mars, and beyond. Humidity condensate (HC) is a low-strength wastewater that is currently recycled on the International Space Station (ISS). The main contaminants in HC are primarily low-molecular-weight organics and ammonia. This has caused operational issues due to microbial growth in the Water Process Assembly (WPA) storage tank as well as failure of downstream systems. In addition, treatment of this wastewater primarily uses adsorptive and exchange media, which must be continually resupplied and represent a significant life-cycle cost. This study demonstrates the integration of a membrane-aerated biological reactor (MABR) for pretreatment and storage of HC, followed by brackish water reverse osmosis (BWRO). Two system configurations were tested: (1) periodic MABR fluid was sent to batch RO operating at 90% water recovery with the RO concentrate sent to a separate waste tank; and (2) periodic MABR fluid was sent to batch RO operating at 90% recovery with the RO concentrate returned to the MABR (accumulating salinity in the MABR). With an external recycle tank (configuration 2), the system produced 2160 L (i.e., 1080 crew-days) of near potable water (dissolved organic carbon (DOC) < 10 mg/L, total nitrogen (TN) < 12 mg/L, total dissolved solids (TDS) < 30 mg/L) with a single membrane (weight of 260 g). When the MABR was used as the RO recycle tank (configuration 1), 1100 L of permeate could be produced on a single membrane; RO permeate quality was slightly better but generally similar to the first configuration even though no brine was wasted during the run. The results suggest that this hybrid system has the potential to significantly enhance the self-sufficiency of space habitats, supporting sustainable extraterrestrial human habitation, as well as reducing current operational problems on the ISS. These systems may also apply to extreme locations such as remote/isolated terrestrial locations, especially in arid and semi-arid regions. Full article

Accurate estimation of potential evapotranspiration (PET) is of paramount importance for water resource management, especially in Mediterranean mountainous environments that are often data-scarce and highly sensitive to climate variability. This study evaluates the performance of four machine learning (ML) regression algorithms—Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and K-Nearest Neighbors (KNN)—in predicting daily PET using limited meteorological data from a high-altitude in Central Greece. The ML models were trained and tested using easily available meteorological inputs—temperature, relative humidity, and extraterrestrial solar radiation—on a dataset covering 11 years (2012–2023). Among the tested configurations, RFR showed the best performance (R² = 0.917, RMSE = 0.468 mm/d, MAPE = 0.119 mm/d) when all the above-mentioned input variables were included, closely approximating FAO56–PM outputs. Results bring to light the potential of machine learning models to reliably estimate PET in data-scarce conditions, with RFR outperforming others, whereas the inclusion of the easily estimated extraterrestrial radiation parameter in the ML models training enhances PET prediction accuracy. Full article

This study conducted a comprehensive geological background investigation of the Zhurong rover’s landing area in Utopia Planitia using 3.5 m/pixel DEM and 0.7 m/pixel DOM data and completed the compilation of a 1:250,000-scale geological map. A total of 17 geological structures were systematically identified within the landing area. Additionally, focusing on scientific questions regarding the evolution of troughs, cone units, and mesas, we theoretically designed an exploration route considering slope constraints by taking the Zhurong rover route design as a case study. This route, a conceptual design, starts from the hibernation location of the Zhurong rover and has a total length of 126 km. It can provide a reference for advancing detection strategies for volatile components (e.g., water and ice) and contribute to the design of the Tianwen-3 exploration route. Ultimately, this study aims to establish a general guideline for integrating geological mapping with rover mobility planning in future extraterrestrial exploration missions. Full article

Extraterrestrial exploration presents unique challenges for robotic systems, as traditional rigid rovers face limitations in stowage volume, traction on unpredictable terrain, and susceptibility to damage. Soft robotics offers promising solutions through bio-inspired designs that can mimic natural locomotion mechanisms. Here, we present an optimised, spider-inspired soft jumping robot for extraterrestrial exploration that addresses key challenges in soft robotics: actuation efficiency, controllability, and deployment. Drawing inspiration from spider physiology—particularly their hydraulic extension mechanism—we develop a lightweight limb capable of multi-modal behaviour with significantly reduced energy requirements. Our 3D-printed soft actuator leverages pressure-driven collapse for efficient retraction and pressure-enhanced rapid extension, achieving a power-to-weight ratio of 249 W/kg. The integration of a non-backdriveable clutch mechanism enables the system to hold positions with zero energy expenditure—a critical feature for space applications. Experimental characterisation and a subsequent optimisation methodology across various materials, dimensions, and pressures reveal that the robot can achieve jumping heights of up to 1.86 times its body length. The collapsible nature of the soft limb enables efficient stowage during spacecraft transit, while the integrated pumping system facilitates self-deployment upon arrival. This work demonstrates how biologically inspired design principles can be effectively applied to develop versatile robotic systems optimised for the unique constraints of extraterrestrial exploration. Full article

The Lunar Augmented Navigation Service (LANS) is the lunar equivalent of GNSS for future lunar explorations. It offers users accurate position, navigation, and timing (PNT) capabilities on and around the Moon. The Augmented Forward Signal (AFS) is a standardized signal structure for LANS, and its recommended standard was published online on 7 February 2025. This work presents software-defined radio (SDR) implementations of the LANS AFS simulator and receiver, which were rapidly developed within a month of the signal specification release. Based on open-source GNSS software, including GPS-SDR-SIM and Pocket SDR, our system provides a valuable platform for future algorithm research and hardware-in-the-loop testing. The receiver can operate on embedded platforms, such as the Raspberry Pi 5, in real-time. This feature makes it suitable for lunar surface applications, where conventional PC-based SDR systems are impractical due to their size, weight, and power requirements. Our approach demonstrates how open-source SDR frameworks can be rapidly applied to emerging satellite navigation signals, even for extraterrestrial PNT applications. Full article

The quest to establish permanent outposts in space, the Moon, and Mars requires growing plants for nutrition, water purification, and carbon/nutrient recycling, as well as the psychological well-being of crews and personnel on extra-terrestrial platforms/outposts. To achieve these essential goals, the safety, quality, and sustainability of plant material grown in space should be comparable to Earth-grown crops. In this study, radish plants were grown at 2500 ppm CO₂ in two successive grow-outs on the International Space Station and at similar CO₂ partial pressure at the Kennedy Space Center. An additional control experiment was performed at the University of Louisiana Lafayette laboratory, at ambient CO₂. Subsequent analyses of glucosinolate and sugar species and content showed that regardless of growth condition, glucoraphasatin, glucoraphenin, glucoerucin, glucobrassicin, 4-hydroxyglucobrassicin, 4-methoxyglucobrassicin, and three aliphatic GSLs tentatively assigned to 3-methylpentyl GSL, 4-methylpentyl GSL, and n-hexyl GSL were present in all examined plants. The most common sugars were fructose, glucose, and sucrose, but some plants also contained galactose, maltose, rhamnose, and trehalose. The variability of individual secondary metabolite abundances was not related to gravity conditions but appeared more sensitive to CO₂ concentration. No indication was found that radish cultivation in space resulted in stress(es) that increased glucosinolate secondary metabolism. Flavor and nutrient components in space-grown plants were comparable to cultivation on Earth. Full article

Knowledge of global radiation (Hg) is essential for regional economic development and can help guide public policies related to agricultural and energy potential. However, its availability in several Brazilian regions is still limited. This work evaluates the predictive capacity of two machine learning (ML) techniques, such as multi-layer perceptrons (MLPs) and support vector machines (SVMs), in the estimation of Hg in 20 meteorological stations with 40 different input combinations involving insolation, air temperature, air relative humidity, photoperiod, and extraterrestrial radiation. It is also compared with three empirical models based on insolation, temperature, and a hybrid combination. In general, the greater the number of input variables, the better the performance of ML techniques, especially in combinations involving insolation that reduced the dispersion of estimated Hg on days with high atmospheric transmissivity and air temperature on days with low atmospheric transmissivity. The performance of SVM was better when compared to MLP in all statistical indicators. ML techniques presented better results than empirical models, and in general, the ordering of the best models in the three locations is achieved using SVM, MLP, and empirical models. Therefore, due to their easy implementation and generation of good results, the use of SVM models is recommended to estimate daily global radiation in the Brazilian Amazon. Full article

The moment of inertia and tidal deformability of idealized stars with polytropic equations of state (EOSs) are numerically calculated under both Newtonian gravity and general relativity (GR). The results explicitly confirm that the relation between the moment of inertia and tidal deformability, parameterized by the star’s mass, exhibits variations up to $1\%$ and $10\%$ for different polytropic indices in Newtonian gravity and GR, respectively. This indicates a more robust I-Love universal relation in the Newtonian framework. The theoretically derived I-Love universal relation for polytropic stars is subsequently tested against observational data for the moment of inertia and tidal deformability of the eight planets and some moons in our solar system. The analysis reveals that the theoretical I-Love universal relation aligns well with the observational data, suggesting that it can serve as an empirical relation. Consequently, it enables the estimation of either the moment of inertia or the tidal deformability of an exoplanet if one of these quantities, along with the mass of the exoplanet, is known. Full article

Beneficial interactions between nitrogen-fixing soil bacteria and legumes offer a solution to increase crop yield on Earth and potentially in future Martian colonies. Paraburkholderia phymatum is a nitrogen-fixing beta-rhizobium, which enters symbiosis with more than 50 legumes and can survive in acidic or aluminium-rich soils. In a previous RNA-sequencing study, we showed that the beta-rhizobium P. phymatum grows well in simulated microgravity and identified phymabactin as the only siderophore produced by this strain. Here, the growth of the beta-rhizobium P. phymatum was assessed in Martian simulant soil using Enhanced Mojave Mars Simulant 2 (MMS-2), which contains a high amount of iron (18.4 percent by weight) and aluminium (13.1 percent by weight). While P. phymatum wild-type’s growth was not affected by exposure to MMS-2, a mutant strain impaired in siderophore biosynthesis (ΔphmJK) grew less than P. phymatum wild-type on gradient plates in the presence of a high concentration of MMS-2 or aluminium. This result suggests that the P. phymatum siderophore phymabactin alleviates aluminium-induced heavy metal stress. Ultra-high performance liquid chromatography–mass spectrometry (UHPLC-MS) showed that phymabactin can bind to aluminium more efficiently than iron. These results not only deepen our understanding of the behaviour of rhizobia in simulated extraterrestrial environments but also provide new insights into the potential use of P. phymatum for bioremediation of aluminium-rich soils and the multiple roles of the siderophore phymabactin. Full article

As the planet most similar to Earth in the solar system, Mars holds an important role in exploring significant scientific problems, such as the evolution of the solar system and the origins of life. Research on Mars mainly rely on planetary remote sensing technology, among which thermal infrared remote sensing is of great studying significance. This technology enables the recording of Martian thermal radiation properties. However, the current spatial resolution of Mars thermal infrared remote sensing images remains relatively low, limiting the detection of fine-scale thermal anomalies and the generation of higher-precision surface compositional maps. While updating extraterrestrial exploration satellites can help enhancing the spatial resolution of thermal infrared images, this method entails high cost and long update cycles, making improvement difficult to conduct in the short term. To address this issue, this paper proposes a super-resolution reconstruction method for Mars thermal infrared remote sensing images integrating multi-source data. First, based on the principle of domain adaptation, we introduced a method using highly correlated visible light images as auxiliary to enhance the spatial resolution of thermal infrared images. Then, a multi-sources data integration method is designed to constrain the thermal radiation flux of resulting images, ensuring the radiation distribution remains consistent with the original low-resolution thermal infrared images. Through both subjective and objective evaluations, our method is demonstrated to significantly enhance the spatial resolution of existing Mars thermal infrared images. It optimizes the quality of existing data, increasing the resolution of the original thermal infrared images by four times. In doing so, it not only recovers finer texture details to produce better visual effects than typical super-resolution methods, but also maintains the consistency of thermal radiation flux, with the error after applying the consistency constraint reduced by nearly tenfold, ensuring the applicability of the results for scientific research. Full article

This research examines the strategic integration of Artificial Intelligence (AI) into global net-zero emissions strategies, with a focus on both terrestrial and extraterrestrial sustainability. The objectives include quantifying AI’s impact on reducing greenhouse gas (GHG) emissions, improving energy efficiency, and optimizing resource utilization, a particularly critical but underexplored domain. A mixed-methods approach was employed, comprising a systematic literature review, a meta-analysis of quantitative data, and case study evaluations. Advanced mathematical models, including logistic growth and optimization equations, were applied to predict trends and assess the effectiveness of AI. The results reveal that AI-driven innovations achieve emissions reductions of 15–30% across energy, transportation, and manufacturing sectors, with predictive maintenance optimizing energy utilization by 20% and extending equipment lifespans. AI-enabled smart grids improved energy efficiency by 26.7%, surpassing the 20% benchmark in prior studies. Specific applications include optimized fuel usage and predictive modeling, which can cut emissions by up to 20%. Quantitative data demonstrated significant cost savings of 20% across sectors. Statistical tests confirmed results with p-values < 0.05, indicating strong significance. This study underscores AI’s transformative potential in achieving net-zero goals by extending sustainability frameworks. It provides actionable insights for policymakers, industry leaders, and researchers, advocating for the broader adoption of AI to address global environmental challenges. Full article

This review examines advancements in Mars habitation technologies, emphasizing Earth-based analog missions and closed-loop life support systems critical for long-duration human presence on the Red Planet. The paper categorizes major simulation projects—including Biosphere 2, Yuegong 1 (Lunar Palace 1), SAM, MaMBA, and CHAPEA—and analyzes their contributions to habitat design, psychological resilience, and environmental control. Technological domains such as in situ resource utilization (ISRU), habitat automation, and extraterrestrial health care are evaluated with respect to current limitations and future scalability. Additionally, the paper explores regulatory, economic, and international cooperation aspects, highlighting their significance in enabling sustainable settlement. By integrating empirical data from terrestrial experiments and recent space initiatives, this review offers a comprehensive assessment of readiness and gaps in Mars habitation strategies. Full article