Current newspapers are full of horrific tales of "cyber-attackers" threatening our energy systems. And, if not for the notorious "evil state"-actor, it is the ongoing digitization necessary to enable increasing renewable and volatile energy generation that threatens our energy supply and thus the stability of our society. And while the main approach seems to be to patch-up the detected vulnerabilities of protocols, software and controller devices, our approach is to research and develop the means to systematically design and test systems that are structurally resilient against failures and attackers alike. Security in cyber-systems mostly should be concerned with establishing asymetric control in favour of the operator of a system. In order to achieve this on a structural level at design time, reproducible benchmark tests are required.

In recent years, there has been an increasing interest in extending traditional stream processing engines with logical, rule-based, reasoning capabilities. This poses significant theoretical and practical challenges since rules can derive new information and propagate it both towards past and future time points; as a result, streamed query answers can depend on data that has not yet been received, as well as on data that arrived far in the past. Stream reasoning algorithms, however, must be able to stream out query answers as soon as possible, and can only keep a limited number of previous input facts in memory. In this paper, we propose novel reasoning problems to deal with these challenges, and study their computational properties on Datalog extended with a temporal sort and the successor function (a core rule-based language for stream reasoning applications).

Oxygen on-board future space missions will be made from the recycled breath of astronauts. The Advanced Closed Loop System (ACLS) has been built by the European Space Agency (ESA) and is now being installed on-board the orbiting spacecraft. The apparatus recycles half the carbon dioxide (CO2) exhaled by astronauts and converts it into oxygen. Scientists have heralded the invention as an important step towards long-term missions to mars and beyond. ESA astronaut Alexander Gerst poses with the ACLS life-support rack, newly installed on the International Space Station.

Contemporary smart power grids are being challenged by rapid voltage fluctuations, due to large-scale deployment of renewable generation, electric vehicles, and demand response programs. In this context, monitoring the grid's operating conditions in real time becomes increasingly critical. With the emergent large scale and nonconvexity however, past optimization based power system state estimation (PSSE) schemes are computationally expensive or yield suboptimal performance. To bypass these hurdles, this paper advocates deep neural networks (DNNs) for real-time power system monitoring. By unrolling a state-of-the-art prox-linear SE solver, a novel modelspecific DNN is developed for real-time PSSE, which entails a minimal tuning effort, and is easy to train. To further enable system awareness even ahead of the time horizon, as well as to endow the DNN-based estimator with resilience, deep recurrent neural networks (RNNs) are pursued for power system state forecasting. Deep RNNs exploit the long-term nonlinear dependencies present in the historical voltage time series to enable forecasting, and they are easy to implement. Numerical tests showcase improved performance of the proposed DNN-based estimation and forecasting approaches compared with existing alternatives. Empirically, the novel model-specific DNN-based PSSE offers nearly an order of magnitude improvement in performance over competing alternatives, including the widely adopted Gauss-Newton PSSE solver, in our tests using real load data on the IEEE 118-bus benchmark system.

Semantic segmentation of land cover classes is fundamental for agricultural and economic development work, from sustainable forestry to urban planning, yet existing training datasets have significant limitations. To generate an open and comprehensive training library of high resolution Earth imagery and high quality land cover classifications, public Sentinel-2 data at 10 m spatial resolution was matched with accurate GlobeLand30 labels from 2010, which were filtered by agreement with an intermediary Sentinel-2 classification at 20 m produced during atmospheric correction. Scene-level classifications were predicted by Random Forests trained on valid reflectance data and the filtered labels, and achieved over 80% model accuracy for a variety of locations. Further work is required to aggregate individual scene classifications for annual labels and to test the approach in more locations, before crowdsourcing human validation. The goal is to create a sustained community-wide effort to generate image labels not only for land cover, but also very specific images for major agriculture crops across the world and other thematic categories of interest to the global development community.

The performance of an organic photovoltaic device is intricately connected to its active layer morphology. This connection between the active layer and device performance is very expensive to evaluate, either experimentally or computationally. Hence, designing morphologies to achieve higher performances is non-trivial and often intractable. To solve this, we first introduce a deep convolutional neural network (CNN) architecture that can serve as a fast and robust surrogate for the complex structure-property map. Several tests were performed to gain trust in this trained model. Then, we utilize this fast framework to perform robust microstructural design to enhance device performance.

In this work, sequence-to-sequence (seq2seq) models, originally developed for language translation, are used to predict the temporal evolution of complex, multi-physics computer simulations. The predictive performance of seq2seq models is compared to state transition models for datasets generated with multiphysics codes with varying levels of complexity-from simple 1D diffusion calculations to simulations of inertial confinement fusion implosions. Seq2seq models demonstrate the ability to accurately emulate complex systems, enabling the rapid estimation of the evolution of quantities of interest in computationally expensive simulations. Keywords: recurrent neural network, sequence-to-sequence, multiphysics simulation, radiation hydrodynamics 1. Introduction Computer simulations of detailed multi-physics systems often take several hours to run, making exploration throughout a vast design space prohibitively expensive. A common method for mapping design spaces of large computer codes is to train a machine learning model to emulate the code in a region of the design space [1, 2, 3]. The machine learning model, often called a "surrogate", D. Humbird) Preprint submitted to Elsevier November 15, 2018 learns to accurately interpolate between a set of simulations spread throughout the reduced design space, such that it can rapidly predict quantities of interest anywhere within that space without requiring additional expensive simulations.

The growing integration of distributed energy resources (DERs) in urban distribution grids raises various reliability issues due to DER's uncertain and complex behaviors. With a large-scale DER penetration, traditional outage detection methods, which rely on customers making phone calls and smart meters' "last gasp" signals, will have limited performance, because the renewable generators can supply powers after line outages and many urban grids are mesh so line outages do not affect power supply. To address these drawbacks, we propose a data-driven outage monitoring approach based on the stochastic time series analysis from micro phasor measurement unit ($\mu$PMU). Specifically, we prove via power flow analysis that the dependency of time-series voltage measurements exhibits significant statistical changes after line outages. This makes the theory on optimal change-point detection suitable to identify line outages via $\mu$PMUs with fast and accurate sampling. However, existing change point detection methods require post-outage voltage distribution unknown in distribution systems. Therefore, we design a maximum likelihood-based method to directly learn the distribution parameters from $\mu$PMU data. We prove that the estimated parameters-based detection still achieves the optimal performance, making it extremely useful for distribution grid outage identifications. Simulation results show highly accurate outage identification in eight distribution grids with 14 configurations with and without DERs using $\mu$PMU data.

The whole idea behind drones is that they fly free. Unattached to the traffic-clogged, obstacle-riddled surface, they promise to change the way we move our stuff and even ourselves. So it's strange to hear that one startup thinks the best way to fly drones is by tying them to the ground. In that tether, CyPhy Works sees a different sort of liberty: freedom from short-lived batteries. The typical commercial drone can stay aloft for 20 to 30 minutes.

The upstream oil and gas industry could save up to $75 billion a year by 2023 with the deployment of so-called "big data," new research by consultancy Wood Mackenzie reveals. Embracing advances in analytics, machine learning and artificial intelligence could pay big dividends for the industry. These savings can be realized at every stage of the upstream lifecycle, Kallanish Energy learns. "Digitalization offers multiple prizes in exploration. The biggest would be to uncover new resources … but anyone with a competitive advantage in exploration would have a material advantage in licensing or M&A," Greig Aitken, principal analyst in Wood Mackenzie's corporate analysis team, said Monday.