ALBERT

Description: DE: Der Datensatz enthält Transkripte der qualitativen, teilstrukturierten Experteninterviews mit Erwerbstätigen in Unternehmen, NGOs oder gesetzlichen Interessenvertretungen in Deutschland oder Österreich, zum Einsatz von algorithmischen Verfahren für das Risikomanagement ("Predictive Risk Intelligence" oder PRI) in zunehmend komplexen Wertschöpfungsnetzwerken, welche Ausfallwahrscheinlichkeiten von Maschinen oder Infrastruktur vorhersagen. Das Projekt untersucht, wie und mit welchen Konsequenzen für die betriebliche und überbetriebliche Mitbestimmung Predictive Risk Intelligence (PRI) von Unternehmen bereits eingesetzt wird. Zudem wird untersucht, wie algorithmische Vorhersagesysteme (ähnlich zu PRI oder auch neuartig) von Arbeitnehmer:innenvertretungen genutzt werden können, um Mitbestimmung in Zeiten der Entsolidarisierung weiterzuentwickeln. Hierzu wurden über einen Zeitraum von acht Monaten dreißig Interviewpartner:innen aus drei Stakeholdergruppen (Merchants, Customers und Audience) rund um PRI in Lieferketten mit leitfadengestützten Experteninterviews befragt. Schwerpunkte der Interviews waren die Einordung der eigenen Organisation im Kontext von globalen Lieferketten und Risikomanagement, Erfahrungen oder Einschätzungen zum Einsatz von PRI sowie die Beziehungen zu anderen Stakeholdergruppen. Ein sekundärer Untersuchungsgegenstand war zudem die Auswirkungen des in Deutschland eingeführten Lieferkettensorgfaltspflichtengesetz (LkSG) auf Praktiken des Risikomanagements. Die Gruppenzuweisung der Interviewten wird im Datensatz durch den Zusatz M (für Merchants), A (für Audience) und C (für Customers) kenntlich gemacht. Als Merchants gelten Vertreter:innen von Softwarelösungen für das Risikomanagement, zur Audience-Gruppe gehören Interessenvertretungen für Risikomanagement in Lieferketten, Customers stellen (potenzielle) Kund:innen von PRI-Anbietern dar, die überwiegend aus den Bereichen des Einkaufs und Supply-Chain-Managements stammen. Von den 30 Interviewteilnehmer:innen haben 18 der Nachnutzung ihrer Daten zugestimmt. EN: The dataset consists of qualitative, semi-structured expert interviews with professionals in companies, NGOs or or legal interest groups in Germany and Austria about the use of algorithmic methods for risk management ("Predictive Risk Intelligence" or PRI) in increasingly complex value networks that predict the failure probabilities of machinery or infrastructure. This project examines how Predictive Risk Intelligence (PRI) is already being used by companies and its consequences for corporate and inter-corporate participation. Furthermore, it explores how algorithmic prediction systems (similar to PRI or new ones) can be utilized by employee representatives to further develop participation in times of decreasing solidarity. For a period of eight months, thirty interviewees from three stakeholder groups (Merchants, Customers and Audience) were questioned about PRI in supply chains through guided expert interviews. These interviews focused on assessing the interviewees' own organizations in the context of global supply chains and risk management, their experiences or expectations of PRI implementation, as well as their relationships with other stakeholder groups. Another incidental subject of investigation was the impact on risk management practices triggered by the introduction of the Supply Chain Due Diligence Act (LkSG) in Germany. Within the dataset, the assignment of the interviewees to the three groups are indicated by the letter M (for Merchants), A (for Audience), and C (for Customers). Merchants refer to representatives of software solutions for risk management, the Audience group includes interest groups for risk management in supply chains, Customers represent (potential) customers of PRI providers, mainly stemming from purchasing departments and supply chain management. Out of the 30 interview participants, 18 have agreed to the reuse of their data.

Description: Biomass is a spaceborn polarimetric P-band (435 MHz) synthetic aperture radar (SAR) in a dawn–dusk low Earth orbit. Its principal objective is to measure biomass content and change in all the Earth’s forests. The ionosphere introduces the Faraday rotation on every pulse emitted by low-frequency SAR and scintillations when the pulse traverses a region of plasma irregularities, consequently impacting the quality of the imaging. Some of these effects are due to total electron content (TEC) and its gradients along the propagation path. Therefore, an accurate assessment of the ionospheric morphology and dynamics is necessary to properly understand the impact on image quality, especially in the equatorial and tropical regions. To this scope, we have conducted an in-depth investigation of the significant noise budget introduced by the two crests of the equatorial ionospheric anomaly (EIA) over Brazil and Southeast Asia. This paper is characterized by a novel approach to conceive a SAR-oriented ionospheric assessment, aimed at detecting and identifying spatial and temporal TEC gradients, including scintillation effects and traveling ionospheric disturbances, by means of Global Navigation Satellite Systems ground-based monitoring stations. The novelty of this approach resides in the customization of the information about the impact of the ionosphere on SAR imaging as derived by local dense networks of ground instruments operating during the passes of Biomass spacecraft. The results identify the EIA crests as the regions hosting the bulk of irregularities potentially causing degradation on SAR imaging. Interesting insights about the local characteristics of low-latitudes ionosphere are also highlighted.

Description: Published

Description: 6412-6424

Description: 2A. Fisica dell'alta atmosfera

Description: 7SR AMBIENTE – Servizi e ricerca per la società

Description: 1IT. Reti di monitoraggio e sorveglianza

Description: JCR Journal