Search Results (158)

The lack of cultural spaces and the inadequate preservation of architectural heritage hinder the development of ecotourism in Ollantaytambo. This research aims to propose an architectural design for green infrastructure that supports the growth of ecotourism at the Ollantaytambo archeological site, located in the Urubamba Province, Peru. The study consists of three main phases: a literature review; a site analysis focusing on climate, flora, and fauna; and the development of a comprehensive architectural proposal. The process is supported by digital tools, including Google Earth Pro 2024, OpenStreetMap 2024, SketchUp 2024, Lumion 2024, Photoshop 2024, and 3D Sun-Path 2024. The resulting design includes the implementation of a sustainable cultural center, conceived to ensure seasonal thermal comfort through the use of green roofs and walls, efficient irrigation systems, and native vegetation. The proposal incorporates elements of Cusco’s vernacular architecture by combining traditional earth-based construction techniques, such as rammed earth, adobe, and quincha, with contemporary materials, such as bamboo and timber, in order to improve the energy and environmental performance of the built environment. Furthermore, the project integrates a rainwater-harvesting system and a photovoltaic lighting system. It includes 30 solar-powered luminaires with an estimated monthly output of 72 kWh, and 135 photovoltaic panels capable of generating approximately 2673 kWh per month. In conclusion, the proposed design blends naturally with the local environment and culture. It adheres to principles of sustainability and energy efficiency and aligns with Sustainable Development Goals (SDGs) 3, 6, 7, 11, and 15 by promoting heritage conservation, environmental regeneration, and responsible ecotourism. Full article

The cislunar space navigation satellite system is essential infrastructure for lunar exploration in the next phase. It relies on high-precision orbit determination to provide the reference of time and space. This paper focuses on constructing a navigation constellation using special orbital locations such as Earth–Moon libration points and distant retrograde orbits (DRO), and it discusses the simplification of planetary perturbation models for their autonomous orbit determination on board. The gravitational perturbations exerted by major solar system bodies on spacecraft are first analyzed. The minimum perturbation required to maintain a precision of 10 m during a 30-day orbit extrapolation is calculated, followed by a simulation analysis. The results indicate that considering only gravitational perturbations from the Moon, Sun, Venus, Saturn, and Jupiter is sufficient to maintain orbital prediction accuracy within 10 m over 30 days. Based on these findings, a method for simplifying the ephemeris is proposed, which employs Hermite interpolation for the positions of the Sun and Moon at fixed time intervals, replacing the traditional Chebyshev polynomial fitting used in the JPL DE ephemeris. Several simplified schemes with varying time intervals and orders are designed. The simulation results of the inter-satellite links show that, with a 6-day orbit arc length, a 1-day lunar interpolation interval, and a 5-day solar interpolation interval, the accuracy loss for cislunar space navigation satellites remains within the meter level, while memory usage is reduced by approximately 60%. Full article

This study investigates the production and economic feasibility of biochar for smallholder and family farms in Central Amazonia, with potential implications for other tropical regions. The costs of construction of a prototype mobile kiln and biochar production were evaluated, using small-sized biomass from acai (Euterpe oleracea Mart.) agro-industrial residues as feedstock. The biochar produced was characterised in terms of its liming capacity (calcium carbonate equivalence, CaCO_3eq), nutrient content via organic fertilisation methods, and ash analysis by ICP-OES. Field trials with cowpea assessed economic outcomes, as well scenarios of fractional biochar application and cost comparison between biochar production in the prototype kiln and a traditional earth-brick kiln. The prototype kiln showed production costs of USD 0.87–2.06 kg⁻¹, whereas traditional kiln significantly reduced costs (USD 0.03–0.08 kg⁻¹). Biochar application alone increased cowpea revenue by 34%, while combining biochar and lime raised cowpea revenues by up to 84.6%. Owing to high input costs and the low value of the crop, the control treatment generated greater net revenue compared to treatments using lime alone. Moreover, biochar produced in traditional kilns provided a 94% increase in net revenue compared to liming. The estimated externalities indicated that carbon credits represented the most significant potential source of income (USD 2217 ha⁻¹). Finally, fractional biochar application in ten years can retain over 97% of soil carbon content, demonstrating potential for sustainable agriculture and carbon sequestration and a potential further motivation for farmers if integrated into carbon markets. Public policies and technological adaptations are essential for facilitating biochar adoption by small-scale tropical farmers. Full article

Rammed earth (RE), a traditional material aligned with circular economy (CE) principles, has been gaining renewed interest in contemporary construction due to its low environmental impact and compatibility with sustainable building strategies. Though not a modern invention, it is being reintroduced in response to the increasingly strict European Union (EU) regulations on carbon footprint, life cycle performance, and thermal efficiency. RE walls offer multiple benefits, including humidity regulation, thermal mass, plasticity, and structural strength. This study also draws attention to their often-overlooked ability to mitigate indoor overheating. To preserve these advantages while enhancing thermal performance, this study explores insulation strategies that maintain the vapor-permeable nature of RE walls. A parametric analysis using Delphin 6.1 software was conducted to simulate heat and moisture transfer in two main configurations: (a) a ventilated system insulated with mineral wool (MW), wood wool (WW), hemp shives (HS), and cellulose fiber (CF), protected by a jute mat wind barrier and finished with wooden cladding; (b) a closed system using MW and WW panels finished with lime plaster. In both cases, clay plaster was applied on the interior side. The results reveal distinct hygrothermal behavior among the insulation types and confirm the potential of natural, low-processed materials to support thermal comfort, moisture buffering, and the alignment with CE objectives in energy-efficient construction. Full article

The Medium Earth Orbit (MEO) spaceborne Synthetic Aperture Radar (SAR) has great coverage ability, which can improve maritime ship target surveillance performance significantly. However, due to the huge computational load required for imaging processing and the severe defocusing caused by ship motions, traditional ship recognition conducted in focused image domains cannot process MEO SAR data efficiently. To address this issue, a multi-level focusing-classification strategy for MEO SAR ship recognition is proposed, which is applied to the range-compressed ship data domain. Firstly, global fast coarse-focusing is conducted to compensate for sailing motion errors. Then, a coarse-classification network is designed to realize major target category classification, based on which local region image slices are extracted. Next, fine-focusing is performed to correct high-order motion errors, followed by applying fine-classification applied to the image slices to realize final ship classification. Equivalent MEO SAR ship images generated by real LEO SAR data are utilized to construct training and testing datasets. Simulated MEO SAR ship data are also used to evaluate the generalization of the whole method. The experimental results demonstrate that the proposed method can achieve high classification precision. Since only local region slices are used during the second-level processing step, the complex computations induced by fine-focusing for the full image can be avoided, thereby significantly improving overall efficiency. Full article

The construction industry is a major contributor to global carbon emissions, primarily due to its reliance on cement-based materials. Simultaneously, plastic bottle waste presents a significant environmental challenge. This study aims to address both issues by exploring the integration of plastic bottle waste into cob–earth materials as a sustainable alternative to traditional concrete modules. The research involves testing various mixes with plastic bottles arranged in different patterns to assess their load-bearing capacity and distribution. The cob mix with bottles arranged in a modified pattern demonstrated the highest load resistance, bearing over 47.1 kN, making it suitable for prototype development. The study also investigates the potential of using cob as an exterior finishing layer, reducing the need for cement. The results show that using local earth materials significantly lowers embodied carbon, offering a more sustainable construction solution. This approach helps mitigate plastic waste and supports climate resilience by promoting low-carbon, locally sourced materials, aligning with Egypt’s national sustainability commitments. Full article

This paper focuses on the triple-junction gallium arsenide solar array of a MEO (Medium Earth Orbit) satellite that has been in orbit for 7 years. Through a combination of theoretical and data-driven methods, it conducts research on anti-radiation design verification and life prediction. This study integrates the Long Short-Term Memory (LSTM) algorithm into the full life cycle management of MEO satellite solar arrays, providing a solution that combines theory and engineering for the design of high-reliability energy systems. Based on semiconductor physics theory, this paper establishes an output current calculation model. By combining radiation attenuation factors obtained from ground experiments, it derives the theoretical current values for the initial orbit insertion and the end of life. Aiming at the limitations of traditional physical models in addressing solar performance degradation under complex radiation environments, this paper introduces an LSTM algorithm to deeply mine the high-density current telemetry data (approximately 30 min per point) accumulated over 7 years in orbit. By comparing the prediction accuracy of LSTM with traditional models such as Recurrent Neural Network (RNN) and Feedforward Neural Network (FNN), the significant advantage of LSTM in capturing the long-term attenuation trend of solar arrays is verified. This study integrates deep learning technology into the full life cycle management of solar arrays, constructs a closed-loop verification system of “theoretical modeling–data-driven intelligent prediction”, and provides a solution for the long-life and high-reliability operation of the energy system of MEO orbit satellites. Full article

Mechanically stabilized earth (MSE) retaining walls have become a favored substitute for traditional poured concrete walls due to their affordability, minimal site preparation needs, and practical construction advantages. However, using backfill material with too many small particles and poor drainage qualities may cause the wall to rotate and shift a lot or collapse completely, especially when water pressure is present. This study examines an MSE wall considering different variables, such as water pressure, the type of soil materials in the backfill materials, external load, and the type of analysis. To this aim, both PLAXIS V20 and SLOPE/W (GeoStudio 2019 Suite) software were employed, and after the verification, further investigations were carried out. These numerical analyses aligned with the real-world failure reported by previous researchers, departments, and companies. The findings suggest that the elevated presence of fine particles likely contributed to the wall’s excessive shift. Also, hydrostatic pressure behind a wall, especially in the rainy season, plays a crucial role in the factor of safety reduction by 45% and wall failure, which leads us to consider it an appropriate factor of safety for the MSE wall. Full article

This paper investigates the super-resolution reconstruction technology of coarse granular particle images for embankment construction in earth/rock dams based on Real-ESRGAN, aiming to improve the quality of low-resolution particle images and enhance the accuracy of particle shape analysis. The paper begins with a review of traditional image super-resolution methods, introducing Generative Adversarial Networks (GAN) and Real-ESRGAN, which effectively enhance image detail recovery through perceptual loss and adversarial training. To improve the generalization ability of the super-resolution model, the study expands the morphological database of earth/rock dam particles by employing a multi-modal data augmentation strategy, covering a variety of particle shapes. The paper utilizes a dual-stage degradation model to simulate the image degradation process in real-world environments, providing a diverse set of degraded images for training the super-resolution reconstruction model. Through wavelet transform methods, the paper analyzes the edge and texture features of particle images, further improving the precision of particle shape feature extraction. Experimental results show that Real-ESRGAN outperforms other traditional super-resolution algorithms in terms of edge clarity, detail recovery, and the preservation of morphological features of particle images, particularly under low-resolution conditions, with significant improvement in image reconstruction. In conclusion, Real-ESRGAN demonstrates excellent performance in the super-resolution reconstruction of coarse granular particle images for embankment construction in earth/rock dams. It can effectively restore the details and morphological features of particle images, providing more accurate technical support for particle shape analysis in civil engineering. Full article

Optimized earth-rock allocation is key in the construction of large-scale navigation channel projects. This paper analyzes the characteristics of a large-scale navigation channel project and establishes an earth-rock allocation system in phases and categories without a transit field. Based on the physical characteristics of the earthwork and stonework used to design a differentiated transport strategy, a synergistic optimization model is built with economic and ecological benefits. As a solution, this paper proposes a sustainable earth-rock allocation optimization method that integrates the improved ant colony algorithm and firefly algorithm, and establishes a two-stage hybrid optimization framework. The application of the Pinglu Canal Project shows that ant colony optimization via division and cooperation combined with the firefly algorithm reduces the transportation cost by 0.128% compared with traditional ant colony optimization; improves the stability by 57.46% (standard deviation) and 59.09% (coefficient of variation) compared with ant colony optimization through division and cooperation; and effectively solves the problems of precocious convergence and local optimization of large-scale earth-rock allocation. It is used to successfully construct an earth-rock allocation model that takes into account the efficiency of the project and the protection of the ecological system in a dynamic environment. Full article

This study investigates the deformation characteristics and base stability of a circular diaphragm wall support system (external diameter: 90 m, wall thickness: 1.5 m) with pit bottom reinforcement for the South Anchorage deep foundation pit of the Zhangjinggao Yangtze River Bridge, which uses layered and partitioned top-down excavation combined with lining construction. Through field monitoring (deep horizontal displacement of the diaphragm wall, vertical displacement at the wall top, and earth pressure) and numerical simulations (PLAXIS Strength Reduction Method), we systematically analyzed the deformation evolution and failure mechanisms during construction. The results indicate the following: (1) Under the synergistic effect of the circular diaphragm wall, lining, and pit bottom reinforcement, the maximum horizontal displacement at the wall top was less than 30 mm and the vertical displacement was 0.04%H, both significantly below code-specified thresholds, verifying the effectiveness of the support system and pit bottom reinforcement. (2) Earth pressure exhibited a “decrease-then-increase” trend during the excavation proceeds. High-pressure jet grouting pile reinforcement at the pit base significantly enhanced basal constraints, leading to earth pressure below the Rankine active limit during intermediate stages and converging toward theoretical values as deformation progressed. (3) Without reinforcement, hydraulic uplift failure manifested as sand layer suspension and soil shear. After reinforcement, failure modes shifted to basal uplift and wall-external soil sliding, demonstrating that high-pressure jet grouting pile reinforcement had positive contribution basal heave stability by improving soil shear strength. (4) Improved stability verification methods for anti-heave and anti-hydraulic-uplift were proposed, incorporating soil shear strength contributions to overcome the underestimation of reinforcement effects in traditional pressure equilibrium and Terzaghi bearing capacity models. This study provides theoretical and practical references for similar deep foundation pit projects and offers systematic solutions for the safety design and deformation characteristics of circular diaphragm walls with pit bottom reinforcement. Full article

Based on the Clouds and the Earth’s Radiant Energy System (CERES) satellite data from 2001 to 2023 and the climate indices from the National Oceanic and Atmospheric Administration (NOAA), this study analyzes the solar irradiance over mainland China and the impacts of clouds and aerosols on it and constructs monthly forecasting models to analyze the influence of climate indices on irradiance forecasts. The irradiance over mainland China shows a spatial distribution of being higher in the west and lower in the east. The influence of clouds on irradiance decreases from south to north, and the influence of aerosols is prominent in the east. The average explained variance of clouds on irradiance is 86.72%, which is much higher than that of aerosols on irradiance, 15.62%. Singular Value Decomposition (SVD) analysis shows a high correlation between the respective time series of irradiance and cloud influence, with the two fields having similar spatial patterns of opposite signs. The variation in solar irradiance can be attributed mainly to the influence of clouds. Empirical Orthogonal Function (EOF) analysis indicates that the variation in the first mode of irradiance is consistent in most parts of China, and its time coefficient is selected for monthly forecasting. Both the traditional multiple linear regression method and the Long Short-Term Memory (LSTM) network are used to construct monthly forecast models, with the preceding time coefficient of the first EOF mode and different climate indices as input. Compared with the multiple linear regression method, LSTM has a better forecasting skill. When the input length increases, the forecasting skill decreases. The inclusion of climate indices, such as the Indian Ocean Basin (IOB), El Nino–Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD), can enhance the forecasting skill. Among these three indices, IOB has the most significant improvement effect. The research provides a basis for accurate forecasting of solar irradiance over China on monthly time scale. Full article

The provision of food in pre-Inca/Inca cultures (1000 BC–≈1532 AD) in environments near Lake Titikaka (approximately 4000 m above sea level) was possible through an agricultural technique called “Waru-Waru”, which consists of filling the space (volume) between rows of land containing plants that are cultivated (a series of earth platforms surrounded by water canals) with water, using water as thermal energy storage to store energy during the day and to regulate the temperature of the soil and crop atmosphere at night. The problem is that these cultures left no evidence in written documents that have been preserved to this day indicating the mathematical models, the physics involved, and the experimental part they performed for the research, development, and innovation of the “Waru-Waru” technique. From a review of the existing literature, there is (1) bibliography that is devoted to descriptive research (about the geometry, dimensions, and shapes of the crop fields (and more based on archaeological remains that have survived to the present day) and (2) studies presenting complex mathematical models with many physical parameters measured only with recently developed instrumentation. The research objectives of this paper are as follows: (1) develop a mathematical model that uses finite differences in fluid mechanics, thermodynamics, and heat transfer to explain the experimental and theory principles of this pre-Inca/Inca technique; (2) the proposed mathematical model must be in accordance with the mathematical calculation tools available in pre-Inca/Inca cultures (yupana and quipu), which are mainly based on arithmetic operations such as addition, subtraction, and multiplication; (3) develop a mathematical model in a sequence of steps aimed at determining the best geometric form for thermal energy storage and plant cultivation and that has a simple design (easy to transmit between farmers); (4) consider the assumptions necessary for the development of the mathematical model from the point of view of research on the geometry of earth platforms and water channels and their implantation in each cultivation area; (5) transmit knowledge of the construction and maintenance of “Waru-Waru” agricultural technology to farmers who have cultivated these fields since pre-Hispanic times. The main conclusion is that, in the mathematical model developed, algebraic mathematical expressions based on addition and multiplication are obtained to predict and explain the evolution of soil and water temperatures in a specific crop field using crop field characterization parameters for which their values are experimentally determined in the crop area where a “Waru-Waru” is to be built. Therefore, the storage of thermal energy in water allows crops to survive nights with low temperatures, and indirectly, it allows the interpretation that the Inca culture possessed knowledge of mathematics (addition, subtraction, multiplication, finite differences, approximation methods, and the like), physics (fluids, thermodynamics, and heat transfer), and experimentation, with priority given to agricultural techniques (and in general, as observed in all archaeological evidence) that are in-depth, exact, practical, lasting, and easy to transmit. Understanding this sustainable energy storage technique can be useful in the current circumstances of global warming and climate change within the same growing areas and/or in similar climatic and environmental scenarios. This technique can help in reducing the use of fossil or traditional fuels and infrastructure (greenhouses) that generate heat, expanding the agricultural frontier. Full article

Buildings of historic neighborhoods of Santiago de Chile are protected by a coating system composed of different layers of earth-based mortars, as part of a building culture that has been neglected and forgotten since the introduction of industrialized materials but still exists in many buildings. This study presents preliminary results from ongoing research that explores the hygroscopic capacity of this vernacular coating system and the impact of incorporating recent finishing layers into traditional construction practices. The investigation focuses on identifying materials and techniques typical of traditional Chilean coatings, highlighting their role in enhancing the durability of historic buildings, improving user comfort, and promoting environmental sustainability. It contributes to the conservation of historic buildings and their reuse, as well as to the health of its inhabitants, due to its contribution to hygrometric regulation. This article focuses on this last purpose, through the identification and characterization of the coating system and its finishing layer materials, and a comparative sorption/desorption test of four case studies with these vernacular coatings. This study began with the sample extraction in situ, followed by its observation and cataloguing. Stratigraphic and stereo microscope analysis of the finishing layers were carried out to identify them. The characterization of the finishing materials was performed using FTIR-ATR and SEM-EDX tests. The sorption/desorption test was performed with a set of original complete samples of the four case studies. Subsequently, another set was prepared with the removal of the finishing layers in order to compare their influence on the hygroscopicity of the coating systems. The results elucidate the variety of materials employed on the finishing layer of these coatings, which are often superimposed, revealing renovations and reparations over time. The influence of these finishing materials on sorption properties of the coating system (the scratch and base coats) is exposed by comparing the samples with and without them. Full article

Efficient and adaptive mission planning for Earth Observation Satellites (EOSs) remains a challenging task due to the growing complexity of user demands, task constraints, and limited satellite resources. Traditional heuristic and metaheuristic approaches often struggle with scalability and adaptability in dynamic environments. To overcome these limitations, we introduce AEM-D3QN, a novel intelligent task scheduling framework that integrates Graph Neural Networks (GNNs) with an Adaptive Exploration Mechanism-enabled Double Dueling Deep Q-Network (D3QN). This framework constructs a Directed Acyclic Graph (DAG) atlas to represent task dependencies and constraints, leveraging GNNs to extract spatial–temporal task features. These features are then encoded into a reinforcement learning model that dynamically optimizes scheduling policies under multiple resource constraints. The adaptive exploration mechanism improves learning efficiency by balancing exploration and exploitation based on task urgency and satellite status. Extensive experiments conducted under both periodic and emergency planning scenarios demonstrate that AEM-D3QN outperforms state-of-the-art algorithms in scheduling efficiency, response time, and task completion rate. The proposed framework offers a scalable and robust solution for real-time satellite mission planning in complex and dynamic operational environments. Full article