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The Soft X-ray Imager (SXI) is part of the scientific payload of the Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) mission. SMILE is a joint science mission between the European Space Agency (ESA) and the Chinese Academy of Sciences (CAS) and is due for launch in 2025. SXI is a compact X-ray telescope with a wide field-of-view (FOV) capable of encompassing large portions of Earth’s magnetosphere from the vantage point of the SMILE orbit. SXI is sensitive to the soft X-rays produced by the Solar Wind Charge eXchange (SWCX) process produced when heavy ions of solar wind origin interact with neutral particles in Earth’s exosphere. SWCX provides a mechanism for boundary detection within the magnetosphere, such as the position of Earth’s magnetopause, because the solar wind heavy ions have a very low density in regions of closed magnetic field lines. The sensitivity of the SXI is such that it can potentially track movements of the magnetopause on timescales of a few minutes and the orbit of SMILE will enable such movements to be tracked for segments lasting many hours. SXI is led by the University of Leicester in the United Kingdom (UK) with collaborating organisations on hardware, software and science support within the UK, Europe, China and the United States.

The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90 d. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and “hotspots” of interaction between environmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean–atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question.

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month proof-of-concept trial run designed to illustrate the utility and feasibility of the ARTMIP project.

Passive stereo imaging is capable of producing dense 3D data, but image matching algorithms generally perform poorly on images with large regions of homogenous texture due to ambiguous match costs. Stereo systems can be augmented with an additional light source that can project some form of unique texture onto surfaces in the scene. Methods include structured light, laser projection through diffractive optical elements, data projectors and laser speckle. Pattern projection using lasers has the advantage of producing images with a high signal to noise ratio. We have investigated the use of a scanning visible-beam LIDAR to simultaneously provide enhanced texture within the scene and to provide additional opportunities for data fusion in unmatched regions. The use of a LIDAR rather than a laser alone allows us to generate highly accurate ground truth data sets by scanning the scene at high resolution. This is necessary for evaluating different pattern projection schemes. Results from LIDAR generated random dots are presented and compared to other texture projection techniques. Finally, we investigate the use of image texture analysis to intelligently project texture where it is required while exploiting the texture available in the ambient light image.

Stereo vision and LIDAR continue to dominate standoff 3D measurement techniques in photogrammetry although the two techniques are normally used in competition. Stereo matching algorithms generate dense 3D data, but perform poorly on low-texture image features. LIDAR measurements are accurate, but imaging requires scanning and produces sparse point clouds. Clearly the two techniques are complementary, but recent attempts to improve stereo matching performance on low-texture surfaces using data fusion have focused on the use of time-of-flight cameras, with comparatively little work involving LIDAR. A low-level data fusion method is shown, involving a scanning LIDAR system and a stereo camera pair. By directly imaging the LIDAR laser spot during a scan, unique stereo correspondences are obtained. These correspondences are used to seed a regiongrowing stereo matcher until the whole image is matched. The iterative nature of the acquisition process minimises the number of LIDAR points needed. This method also enables simple calibration of stereo cameras without the need for targets and trivial coregistration between the stereo and LIDAR point clouds. Examples of this data fusion technique are provided for a variety of scenes.

Fluorescence analysis, being a non-invasive technique, has become one of the most powerful and widely used techniques for microbiologists and chemists to study various types of sample from photosynthetic microbes to hydrocarbons. The work reported here focuses on experimental results of fluorescent features of photosynthetic microbial species (cyanobacteria) and also five different crude oil samples. The cyanobacteria samples were collected from the Baltic Sea at the end of July 2011 and were associated with cyanobacterial bloom events, and the crude oil samples were from various oil spill events. The aim of the study was to find fluorescent biosignatures of cyanobacteria (initially a species specific to the Baltic Sea) and the fingerprints of crude oil; oil spills can be difficult to differentiate from biogenic films when using Synthetic Aperture Radar (SAR) or sunglint contaminated optical imagery. All samples were measured using a Perkin Elmer LS55 Luminescence spectrometer over a broad range of excitation and emission wavelength from ultraviolet (UV) to near infrared (NIR). The results are presented in Excitation Emission Matrices (EEMs) that exhibit the fluorescent features of each sample. In the EEM of the seawater sample containing cyanobacteria, there is an intense emission peak from tryptophan with fluorescent excitation and emission peaks at 285 and 345 nm respectively. In addition, fluorescent signatures of phycocyanin and chlorophyll-a are present with excitation and emission centre wavelengths at 555 nm, 645 nm and 390 nm, 685 nm, respectively. Additionally, the fluorescence signatures of Polycyclic Aromatic Hydrocarbons (PAHs) are present in the EEMs of crude oil samples with excitation and emission peaks at 285 nm and 425 nm. This study underpins further research on how to distinguish cyanobacteria species by their fluorescence signatures and the potential role that PAHs play in detection of cyanobacteria fluorescence features.

Little has been published on the behaviour of polymer gears operating under lubricated conditions. An experimental and analytical programme was undertaken to classify the regimes of EHL under which polymer spur gears operate. In doing so theoretical film thicknesses were calculated and then used to classify the regime according to Johnson's Map. The effects of lubrication on the operating efficiencies of high-performance polymer gears were interpreted and from these results coefficients of friction were derived. In addition to this the effect of tooth geometry was investigated and the beneficial influence of high-pressure angle tooth geometry is demonstrated. At loads typically associated with polymer gears the operating regime is shown to be mixed film lubrication. When high-pressure angle gears were tested at high loads the operating regime became full film lubrication and relatively little tooth flank damage occurred.