Human space exploration missions in the near future will inevitably demand beyond-Earth manufacturing capacity to develop critical subsystems utilising in situ resources. Therefore, to find an alternative solution to the logistics challenges of long-duration space missions, an on-site component fabrication process utilising indigenous resources on the Moon and Mars will be economical and play a crucial role in ensuring the expansion of succeeding missions to deep space. Additive manufacturing (AM) exhibits excellent potential to develop intricate components with functional and tailorable properties at various scales. To assess the potential of AM, an artificial Mars soil has been processed to formulate stable aqueous paste containing less organics (1.5% versus typically 30-40%) amenable to resource-efficient 3D printing. The formulated paste was utilised to fabricate a range of solid and porous designs of various shapes and sizes using a layer-wise material extrusion method for the first time. The additively manufactured components sintered at 1100 degrees C for 2 h exhibited an average relative permittivity (epsilon r) = 4.43, dielectric loss (tan delta) = 0.0014, quality factor (Q x f) = 7710 GHz and TCf = - 9. This work not only demonstrates progress in paste additive manufacturing but also illustrates the potential to formulate eco-friendly ink that can deliver components with functional properties to support long-term space exploration utilising local resources available on Mars.

Stubble burning is a conventional technique of residue management that has affected the physio-chemical properties of the soils. In soil sciences, dielectric properties of soils using radio and microwave-based remote sensing have huge applications. Thus, presented paper has studied the burning effects of stubble on soil's physical, chemical and dielectric properties ($\varepsilon {{\prime}} $epsilon ' and $\varepsilon {{\prime \prime}}$epsilon ''). Moreover, the experimentally observed soil's dielectric data has been explored with various classical Machine Learning (ML) and Neural Network (NN) based regression models. The soil samples were taken from the fields of Punjab, India, in the October-November months following a multistage soil sampling method. Then, Dak-12 open-ended coaxial probe (DOCP) has been used in alliance with a two-port Vector Network Analyzer (VNA) E5071C, Agilent Technologies, to investigate the dielectric properties of soil samples. The obtained results indicate that physio-chemical and dielectric properties have been strongly affected by burning as well as because of the presence of high concentrations of ash residues.$ \varepsilon {{\prime}} $epsilon ' and $\varepsilon {{\prime \prime}}$epsilon '' variations with depth indicate that ash residues can seep up to depths of 10 cm in a single burning process. Moreover, the continuous burning of stubble can have permanent effects on soil's properties. Among considered regression models, the Deep NN-based regression model has given the most accurate predictions of the regressor variables $\varepsilon {{\prime}} $epsilon ' and $\varepsilon {{\prime \prime}}$epsilon '', with a root-mean-square-error (RMSE) of 0.06 and 0.07, respectively. Stubble burning has visible effects on physical, chemical as well as dielectric properties of soil. The burning of stubble damages natural ecosystem and essential nutrients which decrease fertility of soil. Also, the resultant residue ash becomes permanent part of soil profile and alters basic properties of soil. Moreover, exploration of ML-based regression models suggests the tremendous applications of data-centric models in soil and material sciences.