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We report, for the first time, the impact of an interplanetary coronal mass ejection (ICME) on the recently discovered O($^1$S) 557.7 nm dayglow emission in the Martian atmosphere. Although there are only a few studies on the seasonal variation are available in the literature, the impact of ICME on 557.7 nm dayglow emission has not been investigated so far. Using the instruments aboard ExoMars-TGO and MAVEN spacecrafts, we show that the primary emission peak (75-80 km) remains unaffected during the ICME event compared to quiet-times. However, a noticeable enhancement has been observed in the brightness of secondary emission peak (110-120 km) and the upper altitude region (140-180 km). The enhancement is attributed to the increased solar electrons and X-ray fluxes, augmenting the electron-impact process and causing the enhancement in the brightness. These analyses have an implication to comprehend the role of intense solar transients like ICME on the Martian dayglow emissions.

One of the main difficulties to analyze modern spectroscopic datasets is due to the large amount of data. For example, in atmospheric transmittance spectroscopy, the solar occultation channel (SO) of the NOMAD instrument onboard the ESA ExoMars2016 satellite called Trace Gas Orbiter (TGO) had produced $\sim$10 millions of spectra in 20000 acquisition sequences since the beginning of the mission in April 2018 until 15 January 2020. Other datasets are even larger with $\sim$billions of spectra for OMEGA onboard Mars Express or CRISM onboard Mars Reconnaissance Orbiter. Usually, new lines are discovered after a long iterative process of model fitting and manual residual analysis. Here we propose a new method based on unsupervised machine learning, to automatically detect new minor species. Although precise quantification is out of scope, this tool can also be used to quickly summarize the dataset, by giving few endmembers ("source") and their abundances. We approximate the dataset non-linearity by a linear mixture of abundance and source spectra (endmembers). We used unsupervised source separation in form of non-negative matrix factorization to estimate those quantities. Several methods are tested on synthetic and simulation data. Our approach is dedicated to detect minor species spectra rather than precisely quantifying them. On synthetic example, this approach is able to detect chemical compounds present in form of 100 hidden spectra out of $10^4$, at 1.5 times the noise level. Results on simulated spectra of NOMAD-SO targeting CH$_{4}$ show that detection limits goes in the range of 100-500 ppt in favorable conditions. Results on real martian data from NOMAD-SO show that CO$_{2}$ and H$_{2}$O are present, as expected, but CH$_{4}$ is absent. Nevertheless, we confirm a set of new unexpected lines in the database, attributed by ACS instrument Team to the CO$_{2}$ magnetic dipole., Comment: 26 pages, 10 figures

Observations of water ice clouds and aerosols on Mars can provide important insights into the complexity of the water cycle. Recent observations have indicated an important link between dust activity and the water cycle, as intense dust activity can significantly raise the hygropause, and subsequently increase the escape of water after dissociation in the upper atmosphere. Here present observations from NOMAD/TGO that investigate the variation of water ice clouds in the perihelion season of Mars Year 34 (April 2018-19), their diurnal and seasonal behavior, and the vertical structure and microphysical properties of water ice and dust. These observations reveal the recurrent presence of a layer of mesospheric water ice clouds subsequent to the 2018 Global Dust Storm. We show that this layer rose from 45 to 80 km in altitude on a timescale of days from heating in the lower atmosphere due to the storm. In addition, we demonstrate that there is a strong dawn dusk asymmetry in water ice abundance, related to nighttime nucleation and subsequent daytime sublimation. Water ice particle sizes are retrieved consistently and exhibit sharp vertical gradients (from 0.1 to 4.0 um), as well as mesospheric differences between the Global Dust Storm (<0.5 um) and the 2019 regional dust storm (1.0 um), which suggests differing water ice nucleation efficiencies. These results form the basis to advance our understanding of mesospheric water ice clouds on Mars, and further constrain the interactions between water ice and dust in the middle atmosphere., Comment: Submitted to Journal of Geophysical Research - Planets. 36 pages, 9 figures, 1 table

The Nadir and Occultation for MArs Discovery instrument (NOMAD), onboard the ExoMars Trace Gas Orbiter (TGO) spacecraft was conceived to observe Mars in solar occultation, nadir, and limb geometries, and will be able to produce an outstanding amount of diverse data, mostly focused on properties of the atmosphere. The infrared channels of the instrument operate by combining an echelle grating spectrometer with an Acousto-Optical Tunable Filter (AOTF). Using in-flight data, we characterized the instrument performance and parameterized its calibration. In particular: an accurate frequency calibration was achieved, together with its variability due to thermal effects on the grating. The AOTF properties and transfer function were also quantified, and we developed and tested a realistic method to compute the spectral continuum transmitted through the coupled grating + AOTF system. The calibration results enabled unprecedented insights into the important problem of the sensitivity of NOMAD to methane abundances in the atmosphere. We also deeply characterized its performance under realistic conditions of varying aerosol abundances, diverse albedos and changing illumination conditions as foreseen over the nominal mission. The results show that, in low aerosol conditions, NOMAD single spectrum, 1-sigma sensitivity to CH4 is around 0.33 ppbv at 20 km of altitude when performing solar occultations, and better than 1 ppbv below 30 km. In dusty conditions, we show that the sensitivity drops to 0 below 10 km. In Nadir geometry, results demonstrate that NOMAD will be able to produce seasonal maps of CH4 with a sensitivity around 5 ppbv over most of planet's surface with spatial integration over 5x5 degrees bins. Results show also that such numbers can be improved by a factor of 10 to 30 by data binning. Overall, our results quantify NOMAD's capability to address the variable aspects of Martian climate.

Generating sequential decision process from huge amounts of measured process data is a future research direction for collaborative factory automation, making full use of those online or offline process data to directly design flexible make decisions policy, and evaluate performance. The key challenges for the sequential decision process is to online generate sequential decision-making policy directly, and transferring knowledge across tasks domain. Most multi-task policy generating algorithms often suffer from insufficient generating cross-task sharing structure at discrete-time nonlinear systems with applications. This paper proposes the multi-task generative adversarial nets with shared memory for cross-domain coordination control, which can generate sequential decision policy directly from raw sensory input of all of tasks, and online evaluate performance of system actions in discrete-time nonlinear systems. Experiments have been undertaken using a professional flexible manufacturing testbed deployed within a smart factory of Weichai Power in China. Results on three groups of discrete-time nonlinear control tasks show that our proposed model can availably improve the performance of task with the help of other related tasks.

Cloud computing provides a great opportunity for scientists, as it enables large-scale experiments that cannot are too long to run on local desktop machines. Cloud-based computations can be highly parallel, long running and data-intensive, which is desirable for many kinds of scientific experiments. However, to unlock this power, we need a user-friendly interface and an easy-to-use methodology for conducting these experiments. For this reason, we introduce here a formal model of a cloud-based platform and the corresponding open-source implementation. The proposed solution allows to conduct experiments without having a deep technical understanding of cloud-computing, HPC, fault tolerance, or data management in order to leverage the benefits of cloud computing. In the current version, we have focused on biophysics and structural chemistry experiments, based on the analysis of big data from synchrotrons and atomic force microscopy. The domain experts noted the time savings for computing and data management, as well as user-friendly interface., Comment: Preprint. Accepted to the 11th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2016). Final version published by SCITEPRESS, http://www.scitepress.org

We are developing the Virtual Experiences (Vx)Lab, a research and research training infrastructure and capability platform for global collaboration. VxLab comprises labs with visualisation capabilities, including underpinning networking to global points of presence, videoconferencing and high-performance computation, simulation and rendering, and sensors and actuators such as robotic instruments locally and in connected remote labs. VxLab has been used for industry projects in industrial automation, experimental research in cloud deployment, workshops and remote capability demonstrations, teaching advanced-level courses in games development, and student software engineering projects. Our goal is for resources to become a "catalyst" for IT-driven research results both within the university and with external industry partners. Use cases include: multi-disciplinary collaboration, prototyping and troubleshooting requiring multiple viewpoints and architectures, dashboards and decision support for global remote planning and operations., Comment: This is a pre-print of an extended talk abstract accepted for eResearch Australasia, Brisbane, October 2015