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Lunar exploration missions require detailed and accurate planning to ensure their safety. Remote sensing data, such as optical satellite imagery acquired by lunar orbiters, are key for the identification of future landing and mission sites. Here robot- and astronaut-scale obstacles are the most relevant to resolve; however, the spatial resolution of the available image data is often insufficient, particularly in the poorly illuminated polar regions of the moon, leading to uncertainty. This work shows how a novel single-image superresolution (SISR) application, the Adversarial Network for Uncertainty-Based Image SR (ANUBIS), can enhance lunar surface imagery by improving the resolution by a factor of two, outperforming other approaches and benchmarks. The enhanced images improve the reliability and detail of lunar traverse planning and topographic reconstruction, while providing an estimate of the uncertainty associated with the enhancement process, vital to ensure mission planning integrity. This work demonstrates how machine-learning-driven processing can enhance existing data products to maximize their value for science and the exploration of the moon and other celestial bodies.

Jilin Province is an important commercial grain production base in northeast China, and it has seen significant transition in cultivated land use in recent years. This study constructed a measurement system for the recessive transition of cultivated land use in Jilin Province based on the perspective of “three-function synergy” (productive, living, and ecological functions). It discussed the temporal and spatial evolution characteristics of the recessive transition of cultivated land use from 1990 to 2020, identified the turning point of the cultivated land transition trend, and built a model of recessive transition of cultivated land in Jilin Province. After analyzing the results, we came to the following conclusions: (1) The turning point of the “three-function synergy” of the recessive morphology of cultivated land in Jilin Province occurred earlier than the mutation point of the recessive transition of cultivated land, and there was a certain temporality in the recessive transition of cultivated land compared with the functional change of cultivated land; (2) the degree of recessive transition of cultivated land in Jilin Province showed a spatial distribution characteristic of being higher in the west and lower in the east; (3) the recessive transition of cultivated land use in Jilin Province could be divided into transition stages characterized by “low stage slow rise period”, “middle stage significant increase period”, and “high stage steady growth period”; (4) Jilin Province should adopt differentiated and diversified management of cultivated land to achieve a comprehensive management model that emphasizes quantity, quality, and ecology.

The aim of this study is to systematize and improve the theoretical and methodological framework for implementing digital twin technology. The study focuses on digital twins in agriculture. This paper is designed to solve the scientific problem associated with the development of a methodological framework for the implementation of digital twins in the work of agricultural organizations. Using methods of analysis of socio-economic phenomena and processes on the basis of a set of scientific approaches, economic-statistical analysis, and others, the study considers the importance of digital twins of agricultural machinery and equipment, identifies trends in agriculture determined by digitalization, and suggests promising areas for digital twins of agricultural machinery and equipment. This paper also examines the theoretical basis for the implementation of digital twin technology in the agricultural sector of production. New research results complement the theoretical provisions on the essence of digital twin technology; develop the methodological provisions of digital twin technology, represented by the study of their significance, principles, and features of operation. The study may be seen as academically novel as it reveals the prerequisites for implementing digital technology in agriculture as well as clarifies and improves the theoretical and methodological provisions of the application of digital twin technology in various sectors of agriculture.

In recent times, there has been a resurgence of interest in not only revisiting the Moon but also establishing a lasting and sustainable human presence there. Numer- ous agencies and private corporations, gearing up for future lunar missions, have established key goals involve both scientific exploration and the emerging lunar economy. These objectives include carrying out scientific experiments, harvesting and utilizing lunar resources on-site, and examining potential methods of using the Moon as an efficient launchpad to go further into the Solar System. In facilitating these lunar missions, robotics emerges as the main disruptive technology. It can allow the execution of various mission-critical activities in harsh environments, ensuring mission success without jeopardizing human safety. This thesis addresses the challenge presented by the limited resolution of lu- nar data and the overwhelming amount of non-processed information provided by the different remote sensing missions. Robotic operations on the Moon require tremendous precision in the mission planning phase. For this purpose, the remote sensing data collected by various satellites must be processed and relayed to the mission planning teams in the highest possible resolution and in an easily under- standable and digested format. To address this issue, the research presented on this thesis adopts a Machine Learning (ML) approach to enhance and process lunar data gathered by different satellite sensors, providing more detailed and comprehensive insights into the lunar surface, which is crucial for future robotic mission planning. ML provides the necessary tools to efficiently handle and analyze vast volumes of data, a critical aspect in deriving meaningful results, reaching valid conclusions, and deepening our understanding of the subject under study. In this thesis, the author suggests two distinct methods for enhancing and increasing the resolution of lunar images to facilitate improved robot navigation on the lunar surface. The first method involves creating a training dataset using a digital analog environment and utilizing multiple frames of the same location for image enhancement. The second approach proposes a unique architecture that utilizes a single capture from the lunar surface for resolution upscaling, accompanied by an uncertainty estimation of the process. Lastly, a thermophysical analysis of the lunar surface is conducted, which involves processing lunar thermal data in the search for recent asteroid impacts on the lunar surface.

Natural Capital Accounting (NCA) is becoming a reference tool for an increasing number of organizations transitioning towards environmental impact neutrality. However, one NCA technique applicable to all types of actors (individual, community, company, etc.) is missing because of the lack of consensus on how to quantify both their environmental impacts and dependencies on ecosystems. A coupled systematic and non-systematic review of the grey and scientific literature is performed here to (i) make an extensive review of state-of-the-art NCA methods, identifying their current utilization and limitations, and (ii) discern prospects about the challenges of integrating an Ecosystem Service Accounting in Life Cycle Assessment (ESA-LCA). While NCA methods can extensively evaluate the supply of ES, they tend to disregard the quantification of environmental impacts that imply a demand for ES. The ESA-LCA approach is identified as a robust solution to balance supply and demand of ecosystem services in NCA, allowing private and public actors to quantify their distance from impact neutrality targets. A novel definition of NC(A) in LCA is also formulated to support these future efforts, promoting a Mitigation Hierarchy-based strategy to avoid, minimize, restore, and offset impacts, and outlining a roadmap for practitioners to apply ESA-LCA across multiple economic sectors.

The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the present research aims to connect the broad multidisciplinary existing knowledge to pave the way for a complete SA system for mobile robotics that we deem paramount for autonomy. To this aim, we define the principal components to structure a robotic SA and their area of competence. Accordingly, this paper investigates each aspect of SA, surveying the state-of-the-art robotics algorithms that cover them, and discusses their current limitations. Remarkably, essential aspects of SA are still immature since the current algorithmic development restricts their performance to only specific environments. Nevertheless, Artificial Intelligence (AI), particularly Deep Learning (DL), has brought new methods to bridge the gap that maintains these fields apart from the deployment to real-world scenarios. Furthermore, an opportunity has been discovered to interconnect the vastly fragmented space of robotic comprehension algorithms through the mechanism of Situational Graph (S-Graph), a generalization of the well-known scene graph. Therefore, we finally shape our vision for the future of robotic Situational Awareness by discussing interesting recent research directions. Comment: 22 pages, 8 figures, 5 Tables

Drought compromises edible vegetable production worldwide, including common bean (Phaseolus vulgaris L.) an economically important crop that is highly dependent on optimum rainfall or abundant irrigation. In the present study, phenotypic data of 26 Bulgarian common bean mutant lines and cultivars subjected to drought stress has been summarized, and drought stress reaction was evaluated by chlorophyll fluorescence and proteomics approaches. Several basic photosynthetic parameters were examined during treatment to evaluate the drought stress response, and the mutant lines showed different responses. Subsequently, a relationship was found between productivity and photosynthetic performance with the expression of ribulose-1,5-bisphosphate carboxylase through comparative 2D-gel based electrophoresis; accumulation of the well-known stress-related proteins markers dehydrins and small heat shock proteins was established as well. These findings support the further selection of drought tolerant common bean lines for a sustainable agriculture.