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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.

International audience; To preserve environment for a sustainable agriculture, we explore the development of a new autonomous robot, called I-Weed Robot (Intelligent Weed Robot), which aims at reducing herbicides in crop fields (maize, sunflower...). Using a high precision positioning signal (RTK) to locate the robot in the field, a Kaman filter and a proportional-integral-derivative controller (PID controller) allow adjusting the orientation of the robot depending on a predefined trajectory. As for the spraying system, a camera in front of the mobile platform detects weed plants thanks to an image processing based on a crop/weed discrimination algorithm (Hough Transform). At the back a spray boom triggers the right nozzle at the right time depending on the location of weeds. This article assesses the performance of the guidance and weed detection algorithms using numerical simulations (virtual trajectory, virtual field image). The robustness of the guidance algorithm is tested for different noisy signals (GPS, DGPS and RTK). The accuracy of the crop/weed discrimination algorithm is evaluated using a large data base of synthetic images generated by the 'SimAField' software. To evaluate the accuracy of the algorithm and understand the sources of misclassification errors, the results are summarized in a confusion matrix which indicates the number of correctly and incorrectly classified pixels (both weed and crop classes). Results indicate accuracy up to 90%. A significant number of weed pixels are always considered as crops so that crop detection is overestimated. The reason is that the bandwidth value of the crop row mask that is automatically generated overlaps weed plant close to the crop row. These simulations demonstrate that both algorithms are reliable. However, further research in field conditions is necessary to confirm the promising results.

With the objective to improve data quality in terms of cloud detection, absolute radiometric calibration and atmospheric correction, the PRoject for On-Board Autonomy-Vegetation (PROBA-V) data archive (October 2013 - June 2020) will be reprocessed to Collection 2 (C2). The product validation is organized in three phases and focuses on the intercomparison with PROBA-V Collection 1 (C1), but also consistency analysis with SPOT-VGT, Sentinel-3 SYN-VGT, Terra-MODIS and METOP-AVHRR is foreseen. First preliminary results show the better performance of cloud and snow/ice masking, and indicate that statistical consistency between PROBA-V C2 and C1 are in line with expectations. PROBA-V C2 data are to be released to the public in September 2021.

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

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.

International audience; Currently, spraying chemical products in agriculture became the main means of weed controlbecause this cultural practice is very fast, inexpensive and very effective. However, awarenessof their negative impacts (environment, human health) requires thinking about new technicaland agronomic solutions. New technologies are promising techniques for a site-specific weedmanagement through the development of localized spraying.Some sprayers (often small width) located on the row may be able to answer thoseexpectations but they are most often expensive. In this context, the objective of the project“PLEVOP” (development of spot sprayer in oilseed and protein crops), proposed byTerres-Inovia, is the operational development in arable crops of a spot sprayer located on therow through model crops: the rapeseed and sunflower. The aim is to complete the chemicalweeding on the row by mechanical weeding (hoeing) on inter-row. Working with amanufacturer, MARECHAL, the objectives of the project are to 1) explore the potential of thissystem, 2) define its use conditions (volume, nozzle type, nozzle height, speed of passage, highwork output ...) and the effectiveness of this technique, in order to provide quality advice tofarmers.Through the tests conducted we want to 1 / know the influence of the type of guidance (GPSRTK,tracer wheels, vision ...) on this type of spray and 2 / study the biological efficiency onweeds by comparing it to that full treatment.Finally, to qualify the environmental gain (reduction of use and impact) and to evaluate thepracticability and the economic balance will be undeniable assets to accompany the farmer innew agri-environmental practices.

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

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

Despite having proven successful in generating precise motions under dynamic conditions in highly deformable soft-bodied robots, model based techniques are also prone to robustness issues connected to the intrinsic uncertain nature of the dynamics of these systems. This letter aims at tackling this challenge, by extending the augmented rigid robot formulation to a stable representation of three dimensional motions of soft robots, under Piecewise Constant Curvature hypothesis. In turn, the equivalence between soft-bodied and rigid robots permits to derive effective adaptive controllers for soft-bodied robots, achieving perfect posture regulation under considerable errors in the knowledge of system parameters. The effectiveness of the proposed control design is demonstrated through extensive simulations.