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During digital transformation, companies integrate digital technologies creating large and complex digital infrastructures. These digital infrastructures hold combinatory affordances enabling companies to achieve outcomes that are more than the sum of their parts (i.e., the individual digital technologies). Owing to the complexity of digital infrastructures, these combinatory affordances are ever-present but hidden in the thicket of the infrastructure making their effective use a challenge. We report on an ethnographic study of a large German car manufacturer (Car Inc.) that increasingly integrated digital technologies into its infrastructure as part of its digital transformation strategy. With its infrastructure growing into a ‘digital jungle’, the combinatory affordances remained hidden impeding the infrastructure’s effective use. To facilitate effective use of their infrastructure, that is, support employees to navigate the digital jungle, Car Inc. created a digital compass as a tool to articulate the affordances hidden in the thickets of the digital jungle. nonPeerReviewed

The calibration system for XIPE is aimed at providing a way to check and correct possible variations of performance of the Gas Pixel Detector during the three years of operation in orbit (plus two years of possible extended operation), while facilitating the observation of the celestial sources. This will be performed by using a filter wheel with a large heritage having a set of positions for the calibration and the observation systems. In particular, it will allow for correcting possible gain variation, for measuring the modulation factor using a polarized source, for removing non interesting bright sources in the field of view and for observing very bright celestial sources. The on-board calibration system is composed of three filter wheels, one for each detector and it is expected to operate for a small number of times during the year. Moreover, since it operates once at a time, within the observation mode, it allows for simultaneous calibration and acquisition from celestial sources on different detectors. In this paper we present the scope and the requirements of the on-board calibration system, its design, and a description of its possible use in space.

In this paper, we describe a novel approach for representing state information for the purpose of intention recognition in cooperative human-robot environments. States are represented by a combination of spatial relationships in a Cartesian frame along with cardinal direction information. This approach is applied to a manufacturing kitting operation, where humans and robots are working together to develop kits. Based upon a set of predefined high-level states relationships that must be true for future actions to occur, a robot can use the detailed state information presented in this paper to infer the probability of subsequent actions occurring. This would enable the robot to better help the human with the operation or, at a minimum, better stay out of his or her way.

Digitalization offers new opportunities and changes how firms can explore and enter new markets. Current literature has deepened our understanding of the internationalization process of digital-based firms, but it provides very little guidance on how the specific characteristics of digital artifacts enable and accelerate internationalization or of the role of crossnational distance and cultural difference. We use a longitudinal single-case approach to explore how a Business-to-Business (B2B) platform provider internationalized its operations from inception. The case study illustrates that the ongoing development of the digital service and the integration with new devices played an important role in the firm’s internationalization and expansion into new markets. We also observed that cross-cultural distance and cultural differences played an unexpected role. Finally, we propose avenues for future research. peerReviewed

Designing and implementing efficient firewall strategies in the age of the Internet of Things (IoT) is far from trivial. This is because, as time proceeds, an increasing number of devices will be connected, accessed and controlled on the Internet. Additionally, an ever-increasingly amount of sensitive information will be stored on various networks. A good and effi- cient firewall strategy will attempt to secure this information, and to also manage the large amount of inevitable network traffic that these devices create. The goal of this paper is to propose a framework for designing optimized firewalls for the IoT. This paper deals with two fundamental challenges/problems encountered in such firewalls. The first problem is associated with the so-called “Rule Matching” (RM) time problem. In this regard, we propose a simple condition for performing the swapping of the firewall’s rules, and by satisfying this condition, we can guarantee that apart from preserving the firewall’s consistency and integrity, we can also ensure a greedy reduction in the matching time. It turns out that though our proposed novel solution is relatively simple, it can be perceived to be a generalization of the algorithm proposed by Fulp [1]. However, as opposed to Fulp’s solution, our swapping condition considers rules that are not necessarily consecutive. It rather invokes a novel concept that we refer to as the “swapping window”. The second contribution of our paper is a novel “batch”- based traffic estimator that provides network statistics to the firewall placement optimizer. The traffic estimator is a subtle but modified batch-based embodiment of the Stochastic Learning Weak Estimator (SLWE) proposed by Oommen and Rueda [2]. The paper contains the formal properties of this estimator. Further, by performing a rigorous suite of experiments, we demonstrate that both algorithms are capable of optimizing the constraints imposed for obtaining an efficient firewall

The Atmosphere-Space Interaction Monitor (ASIM) is an observatory mounted outside the Columbus module on the International Space Station. It has been operational since April 13th, 2018. It contains two instruments: The Modular X- and Gamma-ray Sensor (MXGS) and The Modular Multispectral Imaging Array (MMIA). The objective of ASIM is to monitor thunderstorms and auroras, including lightning discharges, especially discharges upwards above thunderstorms. This paper presents the instrument package and some first results.

The first measurements of the Experiment with MultiMuon Array (EMMA) have been analyzed for the selection of the Extensive Air Showers (EAS). Test data were recorded with an underground muon tracking station and a satellite station separated laterally by 10 metres. Events with tracks distributed over all of the tracking detector area and even extending over to the satellite station are identified as EAS. The recorded multiplicity spectrum of the events is in general agreement with CORSIKA EAS simulation and demonstrates the array’s capability of EAS detection. peerReviewed

We present a deep learning model with temporal memory to detect clouds in image time series acquired by the Seviri imager mounted on the Meteosat Second Generation (MSG) satellite. The model provides pixel-level cloud maps with related confidence and propagates information in time via a recurrent neural network structure. With a single model, we are able to outline clouds along all year and during day and night with high accuracy.

In support of ESA's Earth Explorer 8 candidate mission FLEX (FLuorescence EXplorer), a Photosynthesis Study has been initiated to quantitatively link fluorescence to photosynthesis. This led to the development of A-SCOPE, a graphical user interface software package that integrates multiple biochemical models into the soil-vegetation-atmosphere-transfer model SCOPE. Its latest version (v1.53) has been successfully verified and was subsequently evaluated through a global sensitivity analysis. By using the method of Saltelli [4], the relative importance of each input variable to model outputs was quantified through first order and total effect sensitivity indices. Variations in leaf area index (LAI) and chlorophyll content are mostly impacting the reflectance and fluorescence signal. Non-driving variables that can be safely set to default values have been identified and will facilitate consolidating SCOPE into an operational and invertible model.