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Recent innovations in task-agnostic imagery featurization have lowered the computational costs of using machine learning to predict ground conditions from satellite imagery. These methods hold particular promise for the development of imagery-based monitoring systems in low-income regions, where data and computational resources can be limited. However, these relatively simple prediction pipelines have not been evaluated in developing-country contexts over time, limiting our understanding of their performance in practice. Here, we compute task-agnostic random convolutional features from satellite imagery and use linear ridge regression models to predict maize yields over space and time in Zambia, a country prone to severe droughts and crop failure. Leveraging Landsat and Sentinel 2 satellite constellations, in combination with district-level yield data, our model explains 83% of the out-of-sample maize yield variation from 2016 to 2021, slightly outperforming a model trained on Normalized Difference Vegetation Index (NDVI) features, a common remote sensing approach used by practitioners to monitor crop health. Our approach maintains an R2 score of 0.74 when predicting temporal variation alone, while the performance of the NDVI-based approach drops to an R2 of 0.39. Our findings imply that this task-agnostic featurization can be used to predict spatial and temporal variation in agricultural outcomes, even in contexts with limited ground truth data. More broadly, these results point to imagery-based monitoring as a promising tool for assisting agricultural planning and food security, even in contexts where computationally expensive methodologies remain out of reach.

The complex nature of cyber-physical energy systems (CPES) makes systematic testing of new technologies for these setups challenging. Co-simulation has been identified as an efficient and flexible test approach that allows consideration of interdisciplinary dynamic interactions. However, basic coupling of simulation models alone fails to account for many of the challenges of simulation-based multi-domain testing such as expert collaboration in test planning. This paper illustrates an extended CPES test environment based on the co-simulation framework mosaik. The environment contains capabilities for simulation planning, uncertainty quantification and the development of multi-agent systems. An application case involving virtual power plant control is used to demonstrate the platform’s features. Future extensibility of the highly modular test environment is outlined.

The transition to a low-carbon society will completely change the structure of energy systems from a standalone hierarchical centralised vision to cooperative and distributed Multi-Energy Systems. The analysis of these complex systems requires the collaboration of researchers from different disciplines in the energy, ICT, social, economic, and political sectors. Combining such disparate disciplines into a single tool for modeling and analyzing such a complex environment as a Multi-Energy System requires tremendous effort. Researchers have overcome this effort by using co-simulation techniques that give the possibility of integrating existing domain-specific simulators in a single environment. Co-simulation frameworks, such as Mosaik and HELICS, have been developed to ease such integration. In this context, an additional challenge is the different temporal and spatial scales that are involved in the real world and that must be addressed during co-simulation. In particular, the huge number of heterogeneous actors populating the system makes it difficult to represent the system as a whole. In this paper, we propose a comparison of the scalability performance of two major co-simulation frameworks (i.e. HELICS and Mosaik) and a particular implementation of a well-known multi-agent systems library (i.e. AIOMAS). After describing a generic co-simulation framework infrastructure and its related challenges in managing a distributed co-simulation environment, the three selected frameworks are introduced and compared with each other to highlight their principal structure. Then, the scalability problem of co-simulation frameworks is introduced presenting four benchmark configurations to test their ability to scale in terms of a number of running instances. To carry out this comparison, a simplified multi-model energy scenario was used as a common testing environment. This work helps to understand which of the three frameworks and four configurations to select depending on the scenario to analyse. Experimental results show that a Multi-processing configuration of HELICS reaches the best performance in terms of KPIs defined to assess the scalability among the co-simulation frameworks.

The term “dedifferentiation” was classically used in sarcoma pathology to refer to tumors in which a high-grade, undifferentiated sarcoma, or a high-grade sarcoma showing heterologous differentiation, arises from a pre-existing neoplasm of borderline or low-grade malignancy. The best recognized examples of this include dedifferentiated liposarcoma, arising from atypical lipomatous tumor/well-differentiated liposarcoma, and dedifferentiated chondrosarcoma, arising from grade 1 hyaline chondrosarcoma of bone. In the overwhelming majority of cases, this dedifferentiated, high-grade sarcoma presents as a macroscopically visible mass, adjacent to and clearly distinct from the pre-existing low-grade lesion. It is less well appreciated that dedifferentiation may also occur in a so-called “mosaic pattern,’ in which the high-grade component is intimately admixed with elements of the precursor lesion, forming only microscopically apparent foci. This mosaic or co-mingling pattern of dedifferentiation is also reflected in the MR imaging appearance. In contrast to the classic pattern of dedifferentiation in which there are two distinct juxtaposed masses with different signal intensities and enhancement patterns, such changes are not seen in mosaic dedifferentiation. The imaging features of this pattern of dedifferentiation have not been described. In this report we describe the imaging features of two patients with mosaic pattern dedifferentiation, one with liposarcoma and one with chondrosarcoma. In both cases the precursor lesion was correctly diagnosed by pre-biopsy imaging, but the presence of high-grade sarcoma was not recognized.

Video watermarking technique aims at resolving insecurity problems. Recently, many approaches have been proposed in order to satisfy the new constraints of video applications such as robustness to collusion attacks, high level of security and signature invisibility. In this paper, a new video watermarking approach based on feature regions is proposed. The originality of this approach is to use crowdsourcing technique in order to detect feature regions. First, video summary is generated. This summary is then used to detect the first type of feature regions based on crowdsourcing technique. On the other hand, mosaic is generated from original video to detect the second type of feature region browsed by the moving objects. Finally, the signature is embedded into the mosaic generated after merging these two types of feature regions using multi-frequential watermarking scheme. Experimental results have shown a high level of invisibility thanks to the efficient choice of the embedded target. Moreover, the proposed approach is robust against several attacks especially to collusion attacks.

A testbed for evaluating if and how process-aware monitoring may increase the security of decentralized SCADA networks in power grids is presented. The testbed builds on the co-simulation framework Mosaik , and co-simulates in an integrated way, the power distribution network on different voltage levels, as well as the control network (Modbus/TCP). The existing simulators were extended to allow topology changes, and a controller (RTU) simulator connected to a SCADA server enabling remote control was implemented. Using the developed testbed, a recently proposed local monitoring approach was investigated. The results show that for so-called interlocks the proposed monitoring approach prevents the execution of 33.3% of the commands, that would result in an unsafe state of the power distribution grid. Furthermore, it is shown that unsafe transformer tap positions can also be avoided. To illustrate the relevance and importance of the proposed testbed, a detailed comparison of related work on process-aware intrusion detection approaches and testbeds combining (parts of) the control network and the power grid is provided.

Multiple weather radars’ joint detection can enlarge the radar detection range and can improve the detection accuracy, which has become an important method to monitor large-scale weather effectively. In this paper, the methods of interpolating reflectivity volume scan data onto the grids in the Cartesian coordinate system and three-dimensional mosaic for gridded reflectivity data of several radars are studied. In order to retain the physical characteristics of the raw data, the smoothing parameters of adaptive Barnes interpolation are improved which based on the space structure of the raw reflectivity data. Through comparison of constant altitude plan position indication (CAPPI) images obtained by the commonly used interpolation schemes and the improved Adaptive Barnes interpolation, it was found that the latter can provide consecutive reflectivity fields and retain high-resolution structure comparable to the raw data. The mean deviation filter filtering erroneous grid data and the data are fused by various fusion methods. Result shows that Exponential Weighting is an excellent method which can provide continuous three-dimensional reflectivity mosaic data.

The paper presents results of research based on analysis of historical and present studies of the Arctic ice drift. Current information about Arctic ice drift comes from the scientific expedition organized by the Alfred-Wrgener-Institut Helmholtz Centre for Polar and Marine Research (AWI) from Bremerhaven (Germany) in the Arctic Ocean, as a part of the Multidiscipli-nary drifting Observatory for the Study of Arctic Climate (MOSAiC), coming from the deck of the icebreaker RV “Polarstern”. The main purpose of the article was to collect and illustrate information on the phenomenon of ice drift in the Arctic Ocean, considering data from ongoing research during the MOSAiC expedition. The average movement speed of the icebreaker RV “Polarstern” frozen in Arctic ice during the first three legs of the expedition was over 5 Nm/day, which is characteristic of the current data relating to the speed of the Arctic ice drift in the place of research. On the other hand, the article is popular science, and presents the overall characteristics of Arctic ice drift with an indication of the general directions, and speed of its movement. Ice drift speeds in the Arctic can reach exceptionally high values under favorable conditions. The drift of sea ice reaching at its intensity/intensity values close to the limit (dangerous criterion) in these extreme cases is called the “ice river”. The speed of “ice rivers” can reach up to 1–2 knots, however, in extreme conditions up to 9 knots. Based on data from the AWI, correlation points were identified between the speed of Arctic ice drift and the speed of winds and atmospheric pressure values.

In this article, we propose a new method of image stitching that computes, in a robust manner, the transformation model applied to creating a panorama that is close to reality. The random selection of matching points used in existing methods, using Random Sample Consensus (RANSAC) or the threshold of the execution process (iteration number) cannot generally provide sufficient precision. Our approach, in this regard, comes to solve this problem. The calculation of the transformation model is based on the VORONOI diagram that divides images into regions to be used in the matching instead of control points. In this case, the transformation estimation will be based on the regions seeds that provide the best correlation score. Among the advantages of our method is solving problems related to outliers that can, in existing methods, affect the reliability of the mosaic. The results obtained are satisfactory in terms of stability, quality, execution time and reduction of the computational complexity.

Mosaic reconstruction is a stitching process of multiple images, of a particular scene, in a single frame that provides a larger amount of information compared to the separate images. Nowadays, image mosaic is a key tool that has invaded different fields and disciplines such as photography, virtual environment, medicine, etc. In this work, we propose a new pre-processing approach of multi-scale images we have named MSIP (Multi-Scale Image Pre-processing), invariant to scale changes and based on the distance between the matched points detected by SIFT. Its main purpose is to correct the scale difference between images to reduce outliers and alignment errors. The experimentation and statistical analysis, on a real database, show the robustness of our approach by improving the quality of mosaic results.