EOX IT Services (Austria)

Operating in the polar regions is growing increasingly important due to expanding research needs, emerging economic opportunities and efforts to protect national sovereignty.Ships operating in the Arctic and Antarctic face heightened risks and more severe consequences in the event of an accident due to sea ice, icebergs, harsh sea states, low visibility and extreme temperatures. These hazards are compounded by sparse infrastructures and remoteness, with immediate assistances not readily available during emergencies.While climate change is quickly reducing the amount of sea ice, this does not necessarily translate to a reduction in risk in the short-term. On the contrary, uncertainly due to changing sea ice conditions, and an increase in icebergs due to melting glaciers, can increase risk.The operational sea ice community provides information to lessen risk to life, property and the environment and to improve operational efficiency. That information is used by ships to avoid or navigate through sea ice, by ship operators in planning polar voyages, and by policy makers to assess the impact of climate change on future decisions regarding polar operations. Beyond safety, these services also increase operational efficiency by enabling optimized routing, reduced fuel consumption, and shorter transit times.The information provided by the operational sea ice community comes from in-situ measurements, satellite earth observation data, and sophisticated models of the atmosphere, oceans, sea ice, and icebergs. These data streams have historically been assembled and interpreted by highly trained human analysts. Given the rapid increase in the amount of data that needs to be analyzed and the constraints on workforces due to government fiscal reductions, their work is increasingly being assisted by artificial intelligence. Furthermore, because sea ice is highly dynamic and can change within a matter of hours, automation and AI are indispensable for providing real-time and forecast information.The operational sea ice workflows have many of the attributes of an Earth System Digital Twin:Utilization of detailed digital models of relevant earth systems (atmosphere, ocean, sea ice),Continuous incorporation of near-real-time data from in-situ sensors and earth observation satellites,Use of artificial intelligence and machine learning,Generation of predictive models and forecasts, andProvision of advance analytics and decision support tools to allow end-users to optimize their choices.In fact, much of the information both used and generated by the operational sea ice community is available in Destination Earth, the initiative of the European Commission to develop a digital twin of the Earth.This presentation will examine the provision of operational sea ice information as an example of the application of digital twins to provide actionable insights on climate adaptation and disaster risk reduction. It will present current capabilities, AI-enabled workflows, and lessons learned from operational implementation, with a focus on supporting safe and sustainable polar shipping.

We read with great interest the study by Murin et al., “Uncovering Hidden Profiles: From Pain-Centric to Multi-Symptom Small Fiber Neuropathy.” The authors describe a cohort of 203 patients with small fiber neuropathy (SFN) with only 20% of the entire cohort having severe neuropathic pain, which was termed the “algesic” group. The other 80% of patients had significant fatigue, myalgia, and generalized weakness, and either no or mild neuropathic pain [1]. Importantly, most patients also experienced autonomic symptoms, with the most common being dry eyes, dry mouth, and GI dysmotility prior to undergoing a skin biopsy, compared to healthy controls [1]. Noteworthy is that patients in cluster 2 (termed “myalgic”) consisting of 60% of the cohort had especially severe fatigue compared to other symptoms—more intense than neuropathic symptoms. In this cluster, patients also experienced significantly more severe weakness and myalgias than burning pain [1]. Interestingly, autonomic symptoms were present in all three clusters [1]. It's important to note that traditionally, neuropathic pain was required for a diagnosis of SFN, and the absence of neuropathic pain as a feature often excluded neuropathy from the differential diagnoses in patients presenting with fatigue, myalgia, and generalized muscle weakness. The study by Murin et al. illustrates that neuropathic pain may be mild or absent, but SFN may still be present, as confirmed via reduced epidermal nerve fiber density [1]. We emphasize that at least 50% of patients with postural orthostatic tachycardia syndrome (POTS) and Long COVID and nearly a third of patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) were found to have SFN when assessed via skin biopsy, quantitative sudomotor axon reflex test, corneal microscopy or microneurography [2, 3]. However, many patients with these disorders do not experience severe—or any—neuropathic pain and, instead, typically present with fatigue, lightheadedness/dizziness, generalized weakness, and myalgia [1, 3]. Murin et al.'s study compellingly demonstrates that SFN can present predominantly with fatigue and weakness rather than pain, challenging conventional diagnostic assumptions [1]. The fact that patients with fatigue and generalized weakness may have SFN without exhibiting neuropathic pain or significant abnormalities on neurologic exam should be emphasized in neurology training, which we believe is essential to improving diagnostic accuracy and optimizing treatment outcomes. Finally, autoimmune conditions and abnormal autoimmune markers were common in this and other cohorts of patients with SFN [1, 3-5], suggesting an autoimmune and immune-mediated etiology, which is similarly identified as a major pathophysiologic mechanism in patients with POTS, Long COVID and ME/CFS. Additionally, dysautonomia more broadly is a common manifestation of a wide variety of systemic disorders, many of which are autoimmune, immune-mediated, inflammatory and post-acute infectious in nature [3, 4]. Currently, the mechanistic interplay between SFN and dysautonomia, and whether SFN is an underlying mechanism of dysautonomia or an associated comorbidity in the autoimmune and immune-mediated pathways that drive both conditions, is unknown. Regardless, immunotherapies for patients with SFN, POTS, ME/CFS, and Long COVID, especially when accompanied by comorbid autoimmune conditions or abnormal autoimmune or immunologic markers, may be considered, and carefully designed randomized controlled trials are needed to assess their efficacy in each disorder [5]. Svetlana Blitshteyn: conceptualization, writing – original draft, investigation, writing – review and editing. Michael Stingl: writing – review and editing. Jill R. Schofield: writing – review and editing. The authors have nothing to report. The authors have nothing to report. The authors declare no conflicts of interest. Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Abstract. The exponential growth of Earth Observation (EO) data generated by satellites demands scalable, efficient, and interoperable methods not only for storing and managing, but also for distributing data packages tailored to diverse use cases. The Open Container Initiative (OCI) registries, together with the OCI artifact specifications, present a promising framework for packaging, exchanging, and managing EO-derived and combined datasets. Originally developed for software containers, OCI registries offer key capabilities such as content-addressable storage, data integrity verification, cryptographic attestation, layered packaging, and version control. A notable advantage is their ability to act as access gateways—enforcing access control at the artifact level without requiring direct exposure of the underlying storage backends (e.g., S3, GCS, Azure Blob, NFS, Ceph, IPFS). The ubiquity of OCI registries—spanning public platforms, managed enterprise services, and open-source deployments—makes them a practical foundation for distributing EO data across heterogeneous environments without custom infrastructure. This paper investigates the applicability of today’s OCI registry ecosystem to EO data pipelines, evaluating both strengths and current limitations in handling large, complex, and dynamic datasets. We explore design conventions and layout strategies to align EO products with the OCI artifact model, with a focus on metadata representation, access efficiency, and storage reuse. By comparing self-contained data packages with modular, layered asset stores, we highlight trade-offs in retrieval performance, interoperability, and client complexity. Recent trends in machine learning model distribution further underscore the growing relevance of OCI-based artifacts for scientific and geospatial workflows. Ultimately, this research positions OCI artifacts as a viable foundation for scalable, standards-aligned, and interoperable EO data handling—paving the way toward more streamlined and resilient data supply chains in the EO domain.

The EarthCODE vision will provide a cloud-based, user-centric development environment which can be used to support ESA science activities and projects. Our solution is built around existing open-source solutions and building blocks, primarily the Open Science Catalogue, EOxHub and the Exploitation Platform Common Architecture (EOEPCA+). EarthCODE capability will be hosted on the Copernicus Data Space Ecosystem (CDSE). Because EarthCODE will adopt a federated approach, it will also facilitate processing on other platforms, and these may include Deep Earth System Data Lab (DeepESDL), ESA EURO Data Cube (EDC), Open EO Cloud / Open EO Platform and perhaps AIOPEN / AI4DTE. 1. GOALS EarthCODE will support FAIR Open Science. The main use cases will allow users to “Access Compute Resource” in thecloud and “Conduct Science” using their preferred approach to create reproducible workflows. “Manage Workflows” willallow users to execute workflows on a cloud platform of their choice and enable federated workflow and federated datasolutions. “Publish Results” will allow scientists to share experiments and data within the community and “Interact with Community” will enable a collaborative approach throughout this lifecycle. EarthCODE will allow scientists to concentrateon science and collaboration and hide the complexity of Managing and implementing Workflows when required. 2. OPEN SCIENCE PLATFORMSEarthCODE will provide users with a portal to the underlying platform services. Broadly speaking, this will include FAIR Open Science services that support the delivery of scientific workflows, Infrastructure Services that support the Execution ofworkflows and Community Services to support underlying collaboration and exploitation. 3. BUILDING BLOCKS EarthCODE will be created use existing building blocks. These will include EOEPCA+, EOxHub, Open Science Catalogue and other existing platform services. 4. PLATFORM FREEDOM EarthCODE will be designed using open standards to help facilitate platform freedom and platform interoperability. This means that more than one type of Development Tooling can be used to conduct science and more than one of IntegratedPlatform service can be used to Manage Workflows. 5. PLATFORM INTEGRATIONUsing the above approach, EarthCODE will facilitate the use of data and services from a wide variety of platforms as illustratedbelow. This shows how EarthCODE will promote integration with different platforms yet still provide freedom of platformimplementation. STAC will be used at the heart of the Science Catalogue to describe resources. Platform integration will also be supported by a DevOps best practice approach to facilitate integration. 6. WORKFLOW AND DATA INTEGRATION EarthCODE will prioritise the management of Workflow and Data modelling to ensure successful platform integration andsuccessful collaboration. This approach will further support the integration of platforms and data. FAIR Data principles will beapplied. ACKNOWLEDGEMENTS This work is funded by the European Space Agency.  

<p>ESA's Swarm mission continues to deliver excellent data providing insight into a wide range of geophysical phenomena. The mission is an important asset whose data are used within a number of critical resources, from geomagnetic field models to space weather services. As the product portfolio grows to better deliver on the mission's scientific goals, we face increasing complexity in accessing, processing, and visualising the data and models. ESA provides “VirES for Swarm” [1] (developed by EOX IT Services) to help solve this problem. VirES is a web-based data retrieval and visualisation tool where the majority of Swarm products are available. VirES has a graphical interface but also a machine-to-machine interface (API) for programmable use (a Python client is provided). The VirES API also provides access to geomagnetic ground observatory data, as well as forwards evaluation of geomagnetic field models to give data-model residuals. The "Virtual Research Environment" (VRE) adds utility to VirES with a free cloud-based JupyterLab interface allowing scientists to immediately program their own analysis of Swarm products using the Python ecosystem. We are augmenting this with a suite of Jupyter notebooks and dashboards, each targeting a specific use case, and seek community involvement to grow this resource.</p><p>[1] https://vires.services</p>

<p><span>VirES (Virtual Workspace for Earth Observation Scientists) is a highly interactive data manipulation and retrieval interface for specific ESA Earth Explorer mission products. It is under evolutionary/operational maintenance by EOX since 2016 for the Swarm geomagnetic mission and starting 2018 it has been extended for ESA's Earth Explorer Aeolus wind profiling mission.<br><br>The goal of this service is to provide users with intuitive and easy access to the mission’s level 1B, 2A, 2B and several auxiliary data products.<br>In order to be able to understand, manage, visualize and analyze the new and complex data produced by the satellite a close collaboration between project partners DLR and DoRIT and EOX as industry partner has been established.<br><br>VirES for Aeolus provides means for multi-dimensional visualization, interactive plotting and analysis and stands for a modern concept of extended access to Earth Observation (EO) data. It supports novel ways of data discovery, visualization, filtering, selection, analysis, snapshotting and downloading.<br><br>The service was designed with a focus on the following areas of application:<br>Quality analysis, calibration and validation, scientific exploitation, modeling and prediction.<br><br>Specialized solutions have been developed to allow visualization of the complex and large datasets in an interactive and intuitive way. The tool has been further refined and improved since the Aeolus launch August 2018 in close collaboration with its users and project partners. VirES for Aeolus  offers easy access to the mission data through ordinary web browsers  via </span><span> without the need for installing any specialized software.</span></p>

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.