Wind retrieval from temperature measurements from the Rover Environmental Monitoring Station/Mars Science Laboratory

We are grateful to the entire MSL Curiosity rover team and to the REMS instrument team, in particular, for their work on the wind data on Mars, without which this research could not have been performed. MPZ has been partially funded by the Spanish State Research Agency (AEI) Project No. MDM-2017-0737 Unidad de Excelencia “María de Maeztu”- Centro de Astrobiología (CSIC-INTA). The resources used for the simulations presented in this work were provided by the Graduate School of Space Technology of Luleå University of Technology. We give special thanks to Ricardo M. Fonseca for his useful comments and suggestions on this work that extended the horizons of this research from the beginning.Peer reviewedPublisher PD

Development of a wind retrieval method for low-speed low-pressure flows for ExoMars

Acknowledgements: The HABIT FM and EQM were manufactured by Omnisys Instruments AB, Sweden, in cooperation with the Luleå University of Technology (LTU). The HABIT project was funded by the Swedish National Space Agency (SNSA). We thank the ExoMars project team, European Space Agency (ESA), Roscosmos, Space Research Institute (IKI) and Omnisys Instruments AB for their hard work on the ExoMars 2022 mission. We acknowledge the Luleå University of Technology, the Wallenberg Foundation and the Kempe Foundation for support of the Mars research activities. ASS acknowledges the support of the LTU Graduate School of Space Technology. MPZ has been partially funded by the Spanish State Research Agency (AEI) Project No. MDM-2017-0737 Unidad de Excelencia “María de Maeztu”-Centro de Astrobiología (INTA-CSIC). We acknowledge the support of Mr. Jens Jacob Iversen and Dr. Jonathan P. Merrison from the Aarhus Wind Tunnel of the Aarhus University (Denmark).Peer reviewedPublisher PD

ATMO-Vent : an adapted breathing atmosphere for COVID-19 patients

Acknowledgements: The authors of the paper would like to acknowledge Andreas Nilsson from Department of Computer Science, Electrical and Space Engineering of the Luleå University of Technology (LTU), Sweden, for his support in procurement and EMC testing of the ATMO-Vent. The authors would also like to thank Luleå University of Technology for access to facilities during the development of ATMO-Vent. The authors would like to acknowledge Teknikens Hus for their support in machining operations. MPZ has been partially funded by the Spanish State Research Agency (AEI) Project No. MDM-2017-0737 Unidad de Excelencia “María de Maeztu”- Centro de Astrobiología (INTA-CSIC).Peer reviewedPublisher PD

The HABIT (HabitAbility: Brine Irradiation and Temperature) environmental instrument for the ExoMars 2022 Surface Platform

Acknowledgements HABIT is an instrument of the Luleå University of Technology (LTU), led by J. Martín-Torres (PI) and M-P. Zorzano (co-PI). The international list of Co-Is and collaborators of the science team of HABIT is given in (https://atmospheres.research.ltu.se/habit/pages/team.php). HABIT engineering team: A. Soria-Salinas, M. I. Nazarious, S. Konatham, T. Mathanlal and A. Vakkada Ramachandran. HABIT IT team: J. –A. Ramirez-Luque and R. Mantas-Nakhai. ASS acknowledges the support of the LTU Graduate School of Space. M-P. Z's contribution has been partially supported by the Spanish State Research Agency (AEI) Project No. MDM-2017-0737 Unidad de Excelencia “María de Maeztu” - Centro de Astrobiología (INTA-CSIC). The HABIT FM and EQM were fabricated by Omnisys Instruments AB, based in Gothenburg, Sweden, under advice of LTU as part of the HABIT project development and funded by the Swedish National Space Agency (SNSA). We thank the ExoMars project team, European Space Agency (ESA), Roscosmos, Space Research Institute (IKI) and Omnisys Instruments AB for their hard work on the ExoMars mission. We thank Petra Rettberg and Carina Fink from DLR for their planetary protection analysis of HABIT samples. We acknowledge the Luleå University of Technology, the Wallenberg Foundation and the Kempe Foundation for support of the Mars research activities. We thank the support of the Swedish Institute for Space Physics (IRF) for the TVAC tests. The Oxia Planum environmental conditions research was partially funded by the European Research Foundation. The SpaceQ chamber has been developed together with Kurt J. Lesker Company and was funded by the Kempe Foundation. CRediT authorship contribution statement Javier Martín-Torres: Conceptualization, Methodology, Supervision, Investigation, Writing - original draft, Funding acquisition, Resources, Project administration. María-Paz Zorzano: Conceptualization, Methodology, Supervision, Investigation, Writing - original draft, Funding acquisition, Resources, Project administration. Álvaro Soria-Salinas: Formal analysis, Investigation, Visualization, Writing - review & editing. Miracle Israel Nazarious: Formal analysis, Investigation, Visualization, Writing - review & editing. Samuel Konatham: Formal analysis, Investigation, Visualization, Writing - review & editing. Thasshwin Mathanlal: Formal analysis, Investigation, Visualization, Writing - review & editing. Abhilash Vakkada Ramachandran: Formal analysis, Investigation, Visualization, Writing - review & editing. Juan-Antonio Ramírez-Luque: Software, Writing - review & editing. Roberto Mantas-Nakhai: Software, Writing - review & editing.Peer reviewedPostprin

Statistical learning for train delays and influence of winter climate and atmospheric icing

This study investigated the climate effect under consecutive winters on the arrival delay of high-speed passenger trains. Inhomogeneous Markov chain model and stratified Cox model were adopted to account for the time-varying risks of train delays. The inhomogeneous Markov chain modelling used covariates weather variables, train operational direction, and findings from the primary delay analysis through stratified Cox model. The results showed that temperature, snow depth, ice/snow precipitation, and train operational direction significantly impacted the arrival delay. Further, by partitioning the train line into three segments as per transition intensity, the model identified that the middle segment had the highest chance of a transfer from punctuality to delay, and the last segment had the lowest probability of recovering from delayed state. The performance of the fitted inhomogeneous Markov chain model was evaluated by the walk-forward validation method, which indicated that approximately 9% of trains may be misclassified as having arrival delays by the fitted model at a measuring point on the train line. With the model performance, the fitted model could be beneficial for both travellers to plan their trips reasonably and railway operators to design more efficient and wiser train schedules as per weather condition.NoIC