A hierarchical framework for mapping pollination ecosystem service potential at the local scale

Wild bees play a major role in the cultivation of crops for human use, in the reproduction of many wild plants and are a key component of biodiversity. Mainly due to human activities, wild bees, like other insects, face a rapid decline in Europe. Understanding species distribution can help to design efficient conservation measures. Species distribution can also be used to estimate pollination ecosystem service potential, which can benefit the production of crops relying on pollination and the reproduction of wild plant communities. The presence of pollinators depends on a combination of environmental and biotic factors, each playing a determining role at different spatial scales. We therefore developed a model composed as a hierarchical framework for environmental predictors: climatic data and Land Use/Land Cover (LULC) variables at the European scale and species-specific habitat information at the local scale. The model combines the advantages of two different existing approaches: pollinator species distribution predictions based on their environmental requirements and knowledge on bee species life-history traits and habitats. This paper presents the predicted distribution of twenty-five wild bee species of the Andrena genus in an agricultural region in Northern Germany. We used oilseed rape pollinators as a case study and compared the potential pollination services to the potential demand in the Case Study Area. The developed framework allows to determine the capacity of landscapes to support pollination ecosystem services from wild bees at the local scale, which can support the identification of vulnerable areas and the design of local scale measures for habitat improvement and for conservation. The hierarchical approach leaves potential for further adaptations in order to improve the prediction of wild bee species dynamics and factors influencing their spatial distribution. © 202

Erweiterte Analysemethoden zur Unterscheidung der Prüflingsemissionen von überlagerten Umgebungsstörungen bei in-situ Messungen

In Deutschland sind gegenwärtig knapp 30.000 Windkraftanlagen in Betrieb mit einem jährlichen Zuwachs von 743 Anlagen im Jahr 2018 [1]. Nicht zuletzt durch die steigende Bedeutung der Windenergie in der Stromerzeugung wachsen die Sorgen um den Einfluss der Anlagen auf die Umwelt. Ein entscheidender Teil dessen ist die elektromagnetische Verträglichkeit. Die Leistungselektronik für z.B. die Frequenzumsetzer in der Gondel oder in einem Gerätehaus am Boden sorgen für Störaussendungen bis in den hohen Megaherzbereich. Durch lange Kabel innerhalb des Turms, der Rotorblätter oder auch durch die Metallstruktur des Turms selbst werden diese intensiv abgestrahlt. Auf Grund der großen Dimensionen einer Windkraftanlage kann diese nicht in einer Absorberhalle vermessen werden. Es wird in-situ, also am Aufstellort des Windrades gemessen. Neben den Umweltbedingungen wie dem Wetter oder geografischen Gegebenheiten erschwert vor allem der Einfluss von elektromagnetischen Störungen aus der Umgebung die Messung. Im für die Messung von Windkraftanlagen zu berücksichtigenden Frequenzbereich von 150 kHz bis 1 GHz sind vielfältige Umweltstörer zu finden. Neben dem Mobilfunk wie GSM oder LTE im Bereich oberhalb von 700 MHz ist der analoge Rundfunk um 100 MHz, sowie der digitale Rundfunk um 200 MHz nahezu omnipräsent. Besonders der Rundfunk ist gefährdet durch Störemissionen verursacht durch die Windkraftanlage. Teilweise überschreiten die Umgebungsstörungen bereits den maximal durch den Standard erlaubten Pegel. Um eine Reproduzierbarkeit für eine verlässliche Messung zu erzielen, müssen diese Umweltstörungen bewertet werden können. Dieser Beitrag befasst sich mit Methoden, um genau diese Klassifikation durchzuführen

Hierarchical compressed sensing

Compressed sensing is a paradigm within signal processing that provides the means for recovering structured signals from linear measurements in a highly efficient manner. Originally devised for the recovery of sparse signals, it has become clear that a similar methodology would also carry over to a wealth of other classes of structured signals. In this work, we provide an overview over the theory of compressed sensing for a particularly rich family of such signals, namely those of hierarchically structured signals. Examples of such signals are constituted by blocked vectors, with only few non-vanishing sparse blocks. We present recovery algorithms based on efficient hierarchical hard-thresholding. The algorithms are guaranteed to converge, in a stable fashion both with respect to measurement noise as well as to model mismatches, to the correct solution provided the measurement map acts isometrically restricted to the signal class. We then provide a series of results establishing the required condition for large classes of measurement ensembles. Building upon this machinery, we sketch practical applications of this framework in machine-type communications and quantum tomography.Comment: This book chapter is a report on findings within the DFG-funded priority program `Compressed Sensing in Information Processing' (CoSIP

Approaching a person in a socially acceptable manner using expanding random trees

In real world scenarios for mobile robots, socially acceptable navigation is a key component to interact naturally with other persons. On the one hand this enables a robot to behave more human-like, and on the other hand it increases the acceptance of the user towards the robot as an interaction partner. As part of this research field, we present in this paper a strategy of approaching a person in a socially acceptable manner. Therefore, we use the theory of ”personal space” and present a method of modeling this space to enable a mobile robot to approach a person from the front. We use a standard Dynamic Window Approach to control the robot motion and, since the personal space model could not be used directly, a graph planner in configuration space, to plan an optimal path by expanding the graph with the use of the DWA’s update rule. Additionally, we give a proof of concept with first preliminary experiments

Analysis of UAV-acquired wetland orthomosaics using GIS, computer vision, computational topology and deep learning

Invasive blueberry species endanger the sensitive environment of wetlands and protection laws call for management measures. Therefore, methods are needed to identify blueberry bushes, locate them, and characterise their distribution and properties with a minimum of disturbance. UAVs (Unmanned Aerial Vehicles) and image analysis have become important tools for classification and detection approaches. In this study, techniques, such as GIS (Geographical Information Systems) and deep learning, were combined in order to detect invasive blueberry species in wetland environments. Images that were collected by UAV were used to produce orthomosaics, which were analysed to produce maps of blueberry location, distribution, and spread in each study site, as well as bush height and area information. Deep learning networks were used with transfer learning and unfrozen weights in order to automatically detect blueberry bushes reaching True Positive Values (TPV) of 93.83% and an Overall Accuracy (OA) of 98.83%. A refinement of the result masks reached a Dice of 0.624. This study provides an efficient and effective methodology to study wetlands while using different techniques. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This

Input Signal Generation for Barrier Bucket RF Systems at GSI

At the GSI facility in Darmstadt, Germany, Barrier Bucket RF systems are currently designed for the SIS 100 synchrotron (part of the future FAIR facility) and the Experimental Storage Ring (ESR). The purpose of these systems is to provide single sine voltage pulses at the cavity gap. Due to the high requirements regarding the gap signal quality, the calculation of the pre-distorted input signal plays a major role in the system development. A procedure to generate the input signal based on the dynamic properties in the linear region of the system has been developed and tested at a prototype system. It was shown that this method is able to generate single sine gap signals of high quality in a wide voltage range. As linearity can only be assumed up to a certain magnitude, nonlinear effects limit the quality of the output signal at very high input levels. An approach to overcome this limit is to extend the input signal calculation to a nonlinear model of the system. In this contribution, the current method to calculate the required input signal is presented and experimental results at a prototype system are shown. Additionally, first results in the nonlinear region are presented

Test Setup for Automated Barrier Bucket Signal Generation

For sophisticated beam manipulation several ring accelerators at FAIR and GSI like the main synchrotron SIS100 and the ESR will be equipped with barrier bucket systems. Hence, the associated LLRF has to be applicable to different RF systems, with respect to the cavity layout and the power amplifier used, as well as to variable repetition rates and amplitudes. Since already the first barrier bucket pulse of a long sequence has to meet certain minimum demands, an open-loop control on the basis of calibration data is foreseen. Closed-loop control is required to improve the signal quality during a sequence of pulses and to adapt to changing conditions like temperature drifts. A test setup was realized that allows controlling the signal generator, reading out the oscilloscope as well as processing the collected data. Frequency and time domain methods can be implemented to approach the dynamics of the RF system successively and under operating conditions, i.e. generating single sine pulses. The setup and first results from measurements are presented as a step towards automated acquisition of calibration data and iterative improvement of the same