Biblio

This paper proposes a novel concept for an electric generator in which both ac windings and permanent magnets (PMs) are placed in the stator. Concentrated windings with a special pattern and phase coils placed in separate slots are employed. The PMs are positioned in a spoke-type field concentrating arrangement, which provides high flux intensification and enables the use of lower remanence and energy non-rare earth magnets. The rotor is exterior to the stator and has a simple and robust reluctance-type configuration without any active electromagnetic excitation components. The principle of operation is introduced based on the concept of virtual work with closed-form analytical airgap flux density distributions. Initial and parametric design studies were performed using electromagnetic FEA for a 3MW direct-drive wind turbine generator employing PMs of different magnetic remanence and specific energy. Results include indices for the goodness of excitation and the goodness of the electric machine designs; loss; and efficiency estimations, indicating that performance comparable to PM synchronous designs employing expensive and critical supply rare-earth PMs may be achieved with non-rare earth PMs using the proposed configuration.

Recent approaches have proven the effectiveness of local outlier factor-based outlier detection when applied over traffic flow probability distributions. However, these approaches used distance metrics based on the Bhattacharyya coefficient when calculating probability distribution similarity. Consequently, the limited expressiveness of the Bhattacharyya coefficient restricted the accuracy of the methods. The crucial deficiency of the Bhattacharyya distance metric is its inability to compare distributions with non-overlapping sample spaces over the domain of natural numbers. Traffic flow intensity varies greatly, which results in numerous non-overlapping sample spaces, rendering metrics based on the Bhattacharyya coefficient inappropriate. In this work, we address this issue by exploring alternative distance metrics and showing their applicability in a massive real-life traffic flow data set from 26 vital intersections in The Hague. The results on these data collected from 272 sensors for more than two years show various advantages of the Earth Mover's distance both in effectiveness and efficiency.

The Open Data Cube (ODC), created and facilitated by the Committee on Earth Observation Satellites (CEOS), is an open source software architecture that continues to gain global popularity through the integration of analysis-ready data (ARD) on cloud computing frameworks. In 2021, CEOS released a new ODC sandbox that provides global users with a free and open programming interface connected to Google Earth Engine datasets. The open source toolset allows users to run application algorithms using a Google Colab Python notebook environment. This tool demonstrates rapid creation of science products anywhere in the world without the need to download and process the satellite data. Basic operation of the tool will support many users but can also be scaled in size and scope to support enhanced user needs. The creation of the ODC sandbox was prompted by the migration of many CEOS ARD satellite datasets to the cloud. The combination of these datasets in an interoperable data cube framework will inspire the creation of many new application products and advance open science.

Recent work has quantified the degradations that occur in convolutional neural nets (CNN) deployed in harsh environments like space-based image or radio frequency (RF) processing applications. Such degradations yield a robust correlation and causality between single-event upset (SEU) induced errors in memory weights of on-orbit CNN implementations. However, minimal considerations have been given to how the resilience of CNNs can be improved algorithmically as opposed to via enhanced hardware. This paper focuses on RF-processing CNNs and performs an in-depth analysis of applying software-based error detection and correction mechanisms, which may subsequently be combined with protections of radiation-hardened processor platforms. These techniques are more accessible for low cost smallsat platforms than ruggedized hardware. Additionally, methods for minimizing the memory and computational complexity of the resulting resilience techniques are identified. Combined with periodic scrubbing, the resulting techniques are shown to improve expected lifetimes of CNN-based RF-processing algorithms by several orders of magnitude.

The Open Data Cube (ODC) initiative, with support from the Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has developed a state-of-the-art suite of software tools and products to facilitate the analysis of Earth Observation data. This paper presents a short summary of our novel architecture approach in a project related to the Open Data Cube (ODC) community that provides users with their own ODC sandbox environment. Users can have a sandbox environment all to themselves for the purpose of running Jupyter notebooks that leverage the ODC. This novel architecture layout will remove the necessity of hosting multiple users on a single Jupyter notebook server and provides better management tooling for handling resource usage. In this new layout each user will have their own credentials which will give them access to a personal Jupyter notebook server with access to a fully deployed ODC environment enabling exploration of solutions to problems that can be supported by Earth observation data.

Submarine optical-fiber cables are key components in the conveying of Internet data, and their failures have costly consequences. Currently, there are over a million km of such cables empowering the Internet. To carry the ever-growing Internet traffic, additional 100,000s of km of cables will be needed in the next few years. At an average cost of \$28,000 per km, this entails investments of billions of dollars. In current industry practice, cable paths are planned manually by experts. This paper surveys our recent work on cable path planning algorithms, where we use several methods to plan cable paths taking account of a range of cable risk factors in addition to cable costs. Two methods, namely, the fast marching method (FMM) and the Dijkstra's algorithm are applied here to long-haul cable path design in a new geographical region. A specific example is given to demonstrate the benefit of the FMM-based method in terms of the better path planning solutions over the Dijkstra's algorithm.

Earth provides natural resources, such as fossil fuels and minerals, that are vital for Europe's economy. As the global demand grows, especially for strategic metals, commodity prices rapidly rise and there is an identifiable risk of an increasing supply shortage of some metals, including those identified as critical to Europe's high technology sector. Hence a major element in any economy's long-term strategy must be to respond to the increasing pressure on natural resources to ensure security of supply for these strategic metals. In today's rapidly changing global economic landscape, mining in the deep sea, specifically at extinct hydrothermal vents and the vast areas covered by polymetallic nodules, has gone from a distant possibility to a likely reality within just a decade. The extremely hostile conditions found on the deep-ocean floor pose specific challenges, both technically and environmentally, which are demanding and entirely different from land-based mining. At present, European offshore industries and marine research institutions have significant experience and technology and are well positioned to develop engineering and knowledge-based solutions to resource exploitation in these challenging and sensitive environments. However, to keep this position there is a need to initiate pilot studies to develop breakthrough methodologies for the exploration, assessment and extraction of deep-sea minerals, as well as investigate the implications for economic and environmental sustainability. The Blue Mining project will address all aspects of the entire value chain in this field, from resource discovery to resource assessment, from exploitation technologies to the legal and regulatory framework.

In this work we design and develop Montage for real-time multi-user formation tracking and localization by off-the-shelf smartphones. Montage achieves submeter-level tracking accuracy by integrating temporal and spatial constraints from user movement vector estimation and distance measuring. In Montage we designed a suite of novel techniques to surmount a variety of challenges in real-time tracking, without infrastructure and fingerprints, and without any a priori user-specific (e.g., stride-length and phone-placement) or site-specific (e.g., digitalized map) knowledge. We implemented, deployed and evaluated Montage in both outdoor and indoor environment. Our experimental results (847 traces from 15 users) show that the stride-length estimated by Montage over all users has error within 9cm, and the moving-direction estimated by Montage is within 20°. For realtime tracking, Montage provides meter-second-level formation tracking accuracy with off-the-shelf mobile phones.

To deliver sample estimates provided with the necessary probability foundation to permit generalization from the sample data subset to the whole target population being sampled, probability sampling strategies are required to satisfy three necessary not sufficient conditions: 1) All inclusion probabilities be greater than zero in the target population to be sampled. If some sampling units have an inclusion probability of zero, then a map accuracy assessment does not represent the entire target region depicted in the map to be assessed. 2) The inclusion probabilities must be: a) knowable for nonsampled units and b) known for those units selected in the sample: since the inclusion probability determines the weight attached to each sampling unit in the accuracy estimation formulas, if the inclusion probabilities are unknown, so are the estimation weights. This original work presents a novel (to the best of these authors' knowledge, the first) probability sampling protocol for quality assessment and comparison of thematic maps generated from spaceborne/airborne very high resolution images, where: 1) an original Categorical Variable Pair Similarity Index (proposed in two different formulations) is estimated as a fuzzy degree of match between a reference and a test semantic vocabulary, which may not coincide, and 2) both symbolic pixel-based thematic quality indicators (TQIs) and sub-symbolic object-based spatial quality indicators (SQIs) are estimated with a degree of uncertainty in measurement in compliance with the well-known Quality Assurance Framework for Earth Observation (QA4EO) guidelines. Like a decision-tree, any protocol (guidelines for best practice) comprises a set of rules, equivalent to structural knowledge, and an order of presentation of the rule set, known as procedural knowledge. The combination of these two levels of knowledge makes an original protocol worth more than the sum of its parts. The several degrees of novelty of the proposed probability sampling protocol are highlighted in this paper, at the levels of understanding of both structural and procedural knowledge, in comparison with related multi-disciplinary works selected from the existing literature. In the experimental session, the proposed protocol is tested for accuracy validation of preliminary classification maps automatically generated by the Satellite Image Automatic Mapper (SIAM™) software product from two WorldView-2 images and one QuickBird-2 image provided by DigitalGlobe for testing purposes. In these experiments, collected TQIs and SQIs are statistically valid, statistically significant, consistent across maps, and in agreement with theoretical expectations, visual (qualitative) evidence and quantitative quality indexes of operativeness (OQIs) claimed for SIAM™ by related papers. As a subsidiary conclusion, the statistically consistent and statistically significant accuracy validation of the SIAM™ pre-classification maps proposed in this contribution, together with OQIs claimed for SIAM™ by related works, make the operational (automatic, accurate, near real-time, robust, scalable) SIAM™ software product eligible for opening up new inter-disciplinary research and market opportunities in accordance with the visionary goal of the Global Earth Observation System of Systems initiative and the QA4EO international guidelines.