High-Resolution Reconstruction of the Ionosphere for SAR Applications

Caused by ionosphere’s strong impact on radio signal propagation, high resolution and highly accurate reconstructions of the ionosphere’s electron density distribution are demanded for a large number of applications, e.g. to contribute to the mitigation of ionospheric effects on Synthetic Aperture Radar (SAR) measurements. As a new generation of remote sensing satellites the TanDEM-L radar mission is planned to improve the understanding and modelling ability of global environmental processes and ecosystem change. TanDEM-L will operate in L-band with a wavelength of approximately 24 cm enabling a stronger penetration capability compared to X-band (3 cm) or C-band (5 cm). But accompanied by the lower frequency of the TanDEM-L signals the influence of the ionosphere will increase. In particular small scale irregularities of the ionosphere might lead to electron density variations within the synthetic aperture length of the TanDEM-L satellite and in turn might result into blurring and azimuth pixel shifts. Hence the quality of the radar image worsens if the ionospheric effects are not mitigated. The Helmholtz Alliance project “Remote Sensing and Earth System Dynamics” (EDA) aims in the preparation of the HGF centres and the science community for the utilisation and integration of the TanDEM-L products into the study of the Earth’s system. One significant point thereby is to cope with the mentioned ionospheric effects. Therefore different strategies towards achieving this objective are pursued: the mitigation of the ionospheric effects based on the radar data itself, the mitigation based on external information like global Total Electron Content (TEC) maps or reconstructions of the ionosphere and the combination of external information and radar data. In this presentation we describe the geostatistical approach chosen to analyse the behaviour of the ionosphere and to provide a high resolution 3D electron density reconstruction. As first step the horizontal structure of the ionosphere is studied in space and time on the base of ground-based TEC measurements in the European region. In order to determine the correlation of measurements at different locations or points of time the TEC measurements are subtracted by a base model to define a stationary random field. We outline the application of the NeQuick model and the final IGS TEC maps as background and show first results regarding the distribution and the stationarity of the resulting residuals. Moreover, the occurred problems and questions are discussed and finally an outlook towards the next modelling steps is presented

Compositional data analysis approach to organizational culture and strategic alignment

This paper examines the research question about IT management culture characteristics and their contributions to the strategic alignment of the business. The study compares two fourquadrants models: Cameron and Quinn’s (2011) Competing Values Framework and Henderson and Venkatraman’s (1993) Strategic Alignment Model. For examining organizational culture, the first model attributes it to the four types of clan culture, adhocracy culture, market culture, and hierarchy culture with the dimensions of flexibility vs. stability and an internal vs. external focus. Similarly, the Strategic Alignment Model differs from four perspectives for business-IT alignment with functional integration into IT or business dimensions and the strategic fit, i.e., the internal vs. external orientation. These strategic alignment perspectives and their performance criteria equal a cost center, an investment center, a profit center, and a service center. In a survey, respondents had to divide 100 points between four options. For example, is the dominant IT management culture type that of a clan, adhocracy, market, or hierarchy? The corresponding ipsative scales originate from the Competing Values Framework and result in compositional data. After eliminating missing values and imputing zeroes, the analysis calculated a linear model with the ilr-transformed variables. Converting ilr coefficients into the clr space revealed a coefficient matrix with the weights of IT management culture and its alignment as the mapping of market culture to profit center at 0.125, adhocracy culture to investment center at 0.123, clan culture to cost center at 0.027, and hierarchy culture to a service center at 0.198

Reconstruction of the ionospheric electron density by geostatistical inversion

The ionosphere is the upper part of the atmosphere where sufficient free electrons exist to affect the propagation of radio waves. Typically, the ionosphere extends from about 50 - 1000 km and its morphology is mainly driven by solar radiation, particle precipitation and charge exchange. Due to the strong ionospheric impact on many applications dealing with trans-ionospheric signals such as Global Navigation Satellite Systems (GNSS) positioning, navigation and remote sensing, the demand for a highly accurate reconstruction of the electron density is ever increasing. Within the Helmholtz Alliance project “Remote Sensing and Earth System Dynamics” (EDA) the utilization of the upcoming radar mission TanDEM-L and its related products are prepared. The TanDEM-L mission will operate in L-band with a wavelength of approximately 24 cm and aims at an improved understanding of environmental processes and ecosystem change, e.g. earthquakes, volcanos, glaciers, soil moisture and carbon cycle. Since its lower frequency compared to the X-band (3 cm) and C-band (5 cm) radar missions, the influence of the ionosphere will increase and might lead to a significant degradation of the radar image quality if no correction is applied. Consequently, our interest is the reconstruction of the ionospheric electron density in order to mitigate the ionospheric delay. Following the ionosphere’s behaviour we establish a non-stationary and anisotropic spatial covariance model of the electron density separated into a vertical and horizontal component. In order to estimate the model’s parameters we chose a maximum likelihood approach. This approach incorporates GNSS total electron content measurements, representing integral measurements of the electron density between satellite to receiver ray paths, and the NeQuick model as a non-stationary trend. Based on a multivariate normal distribution the spatial covariance model parameters are optimized and afterwards the 3D electron density can be calculated by kriging for arbitrary points or grids of interest

High-Resolution Reconstruction of the Ionosphere for SAR Applications

Caused by ionosphere’s strong impact on radio signal propagation, high resolution and highly accurate reconstructions of the ionosphere’s electron density distribution are demanded for a large number of applications, e.g. to contribute to the mitigation of ionospheric effects on Synthetic Aperture Radar (SAR) measurements. As a new generation of remote sensing satellites the TanDEM-L radar mission is planned to improve the understanding and modelling ability of global environmental processes and ecosystem change. TanDEM-L will operate in L-band with a wavelength of approximately 24 cm enabling a stronger penetration capability compared to X-band (3 cm) or C-band (5 cm). But accompanied by the lower frequency of the TanDEM-L signals the influence of the ionosphere will increase. In particular small scale irregularities of the ionosphere might lead to electron density variations within the synthetic aperture length of the TanDEM-L satellite and in turn might result into blurring and azimuth pixel shifts. Hence the quality of the radar image worsens if the ionospheric effects are not mitigated. The Helmholtz Alliance project “Remote Sensing and Earth System Dynamics” (EDA) aims in the preparation of the HGF centres and the science community for the utilisation and integration of the TanDEM-L products into the study of the Earth’s system. One significant point thereby is to cope with the mentioned ionospheric effects. Therefore different strategies towards achieving this objective are pursued: the mitigation of the ionospheric effects based on the radar data itself, the mitigation based on external information like global Total Electron Content (TEC) maps or reconstructions of the ionosphere and the combination of external information and radar data. In this presentation we describe the geostatistical approach chosen to analyse the behaviour of the ionosphere and to provide a high resolution 3D electron density reconstruction. As first step the horizontal structure of the ionosphere is studied in space and time on the base of ground-based TEC measurements in the European region. In order to determine the correlation of measurements at different locations or points of time the TEC measurements are subtracted by a base model to define a stationary random field. We outline the application of the NeQuick model and the final IGS TEC maps as background and show first results regarding the distribution and the stationarity of the resulting residuals. Moreover, the occurred problems and questions are discussed and finally an outlook towards the next modelling steps is presented

Knowledge Graph Development as a Collaborative Process

<p>Establishing semantic data and knowledge graphs in scientific working groups is no easy feat. In most cases there is neither a user friendly tool chain nor experience with ontologies for the respective research field. But without a start, said experience can never be gained. The same is true for individuals that want to start into the field.</p><p>We thus see knowledge graph development not as a task of expert individuals that already know everything, but as a collaborative (learning) process of working groups and organisations. At the start of this process the right ontologies are not known and the individuals do not yet have experience with expressing information in knowledge graphs. Thus, a tool chain must provide basic knowledge to help newcomers to get started. It must also support the learning process and the selection of terms and ontologies, while users are already working with their own data and metadata. Additionally, the tool chain must support cooperation and lateral transfer of knowledge within organisations and working groups as well as between working groups world wide.</p><p>We therefore propose to establish a data infrastructure in every research organisation consisting of the following elements: An organisational knowledge graph, integration of (global) ID services, links to FAIR ontologies, policies, and a graph editing tool. This editing tool must support simultaneously the input of graph data, the extension of ontologies, the development of data structures, and finding and reusing existing ontologies and data structures not only from other persons inside the organisation but also from globally emerging metadata standards. While searching for a fitting term from a predefined set of ontologies, the tool would also allow for the creation of an internal term, when no fitting one is found. While trying to create a new term, fitting ones are automatically searched and proposed. The here proposed graph editing tool would provide the possibility to refactor existing data to newly selected ontologies, e.g. through replacing terms or whole structures, while keeping the original history in a git+GitLab like structure. This would also allow for access control and cooperation within the organisation and beyond. Such refactoring translations would also be described in terms of graph data and be published, so that others considering the same transition could use them without much effort.</p><p>We think that in the presented infrastructure users could establish processes that would foster harmonization and convergence of ontologies and data structures, while not impeding the collection of data and learning processes of individuals before harmonization is achieved.</p&gt

Bayes linear spaces

Linear spaces consisting of σ-finite probability measures and infinite measures (improper priors and likelihood functions) are defined. The commutative group operation, called perturbation, is the updating given by Bayes theorem; the inverse operation is the Radon-Nikodym derivative. Bayes spaces of measures are sets of classes of proportional measures. In this framework, basic notions of mathematical statistics get a simple algebraic interpretation. For example, exponential families appear as affine subspaces with their sufficient statistics as a basis. Bayesian statistics, in particular some well-known properties of conjugated priors and likelihood functions, are revisited and slightly extende