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The three Swarm satellites provide an optimum, low Earth orbit (LEO) and multi‐spacecraft platform, to explore for the first time the local correlation between field‐aligned currents (FACs), auroral electrojets, and magnetic perturbations at the Earth's surface. By combining Swarm and ground magnetic field data, one can investigate systematically the full correlation chain, whose final link controls the ground induced currents and related space weather effects. We introduce an integrated FAC product, the Sheet FAC (SFAC) index, as a convenient measure of the in‐situ FAC data, and explore the correlations SFAC‐AE, SFAC‐PEJ and SFAC‐dH, with AE the standard auroral electrojet index, PEJ the local, Swarm based, polar electrojet index, and dH the horizontal magnetic field perturbation at the Earth's surface. Given the good SFAC‐dH correlation, we also suggest an extension of SFAC to higher LEO satellites, which cannot observe any more the electrojet currents, but are fully capable to monitor SFAC. Plain Language Summary ‘Space weather’ resembles, to some extent, ordinary weather. Likewise, storms in space, termed ‘magnetic storms’, have common features with ordinary storms. Just like ordinary storms, magnetic storms can cause damage, and similar to ordinary weather, space weather needs to be monitored and, ideally, predicted. The SFAC index, introduced in the paper, is shown to be a potentially useful tool for such goals, able to capture local effects. This is essential for efficient monitoring. Moreover, the SFAC index can be extended to many satellites, which is important too. Just like for ordinary weather, space weather prediction requires measurements of key parameters that are used as input by specific models. The more and denser the measurements, the better the output, namely the prediction. Key Points The newly introduced SFAC index is shown to be robust and appropriate for space weather monitoring by low Earth orbit satellites SFAC appears to be able to capture local features related to magnetospheric dynamics, as driven, in particular, by bursty bulk flows While the introduction of SFAC takes advantage of Swarm features, the index can be extended to other low Earth orbit satellites

Field-aligned currents (FACs) in the magnetosphere–ionosphere (M–I) system exhibit a range of spatial and temporal scales that are linked to key dynamic coupling processes. To disentangle the scale dependence in magnetic field signatures of auroral FACs and to characterize their geometry and orientation, Bunescu et al. (2015) introduced the multiscale FAC analyzer framework based on minimum variance analysis (MVA) of magnetic time series segments. In the present report this approach is carried further to include in the analysis framework a FAC density scalogram, i.e., a multiscale representation of the FAC density time series. The new technique is validated and illustrated using synthetic data consisting of overlapping sheets of FACs at different scales. The method is applied to Swarm data showing both large-scale and quiet aurora as well as mesoscale FAC structures observed during more disturbed conditions. We show both planar and non-planar FAC structures as well as uniform and non-uniform FAC density structures. For both synthetic and Swarm data, the multiscale analysis is applied by two scale sampling schemes, namely the linear and logarithmic scanning of the FAC scale domain. The local FAC density is compared with the input FAC density for the synthetic data, whereas for the Swarm data we cross-check the results with well-established single- and dual-spacecraft techniques. All the multiscale information provides a new visualization tool for the complex FAC signatures that complements other FAC analysis tools.

Ten years of CHAMP magnetic field measurements are integrated into MFACE, a model of field-aligned currents (FACs) using empirical orthogonal functions (EOFs). EOF1 gives the basic Region-1/Region-2 pattern varying mainly with the interplanetary magnetic field Bz component. EOF2 captures separately the cusp current signature and By-related variability. Compared to existing models, MFACE yields significantly better spatial resolution, reproduces typically observed FAC thickness and intensity, improves on the magnetic local time (MLT) distribution, and gives the seasonal dependence of FAC latitudes and the NBZ current signature. MFACE further reveals systematic dependences on By, including 1) Region-1/Region-2 topology modifications around noon; 2) imbalance between upward and downward maximum current density; 3) MLT location of the Harang discontinuity. Furthermore, our procedure allows quantifying response times of FACs to solar wind driving at the bow shock nose: we obtain 20 minutes and 35-40 minutes lags for the FAC density and latitude, respectively.

In situ satellite exploration of the lower thermosphere–ionosphere system (LTI) as anticipated in the recent Daedalus mission proposal to ESA will be essential to advance the understanding of the interface between the Earth's atmosphere and its space environment. To address physical processes also below perigee, in situ measurements are to be extrapolated using models of the LTI. Motivated by the need for assessing how cost-critical mission elements such as perigee and apogee distances as well as the number of spacecraft affect the accuracy of scientific inference in the LTI, the Daedalus Ionospheric Profile Continuation (DIPCont) project is concerned with the attainable quality of in situ measurement extrapolation for different mission parameters and configurations. This report introduces the methodological framework of the DIPCont approach. Once an LTI model is chosen, ensembles of model parameters are created by means of Monte Carlo simulations using synthetic measurements based on model predictions and relative uncertainties as specified in the Daedalus Report for Assessment. The parameter ensembles give rise to ensembles of model altitude profiles for LTI variables of interest. Extrapolation quality is quantified by statistics derived from the altitude profile ensembles. The vertical extent of meaningful profile continuation is captured by the concept of extrapolation horizons defined as the boundaries of regions where the deviations remain below a prescribed error threshold. To demonstrate the methodology, the initial version of the DIPCont package presented in this paper contains a simplified LTI model with a small number of parameters. As a major source of variability, the pronounced change in temperature across the LTI is captured by self-consistent non-isothermal neutral-density and electron density profiles, constructed from scale height profiles that increase linearly with altitude. The resulting extrapolation horizons are presented for dual-satellite measurements at different inter-spacecraft distances but also for the single-satellite case to compare the two basic mission scenarios under consideration. DIPCont models and procedures are implemented in a collection of Python modules and Jupyter notebooks supplementing this report.

Due to sustainability requirements in Europe, the defense industry is under pressure to adopt a circular economy model instead of a linear one. This paper describes the conceptual plan for Circular Defense Economy (CDE) platform, a modular, data-driven ecosystem designed to support circular practices across the entire Soldier Protection Equipment (SPE) life cycle. By using emerging technologies such as Al-driven decision support systems, blockchain for life-cycle monitoring and bio-based materials and recycling technologies, the CDE supports the ecodesign, reuse, repair, remanufacturing and recycling of high-value defense composites. The platform aligns with the objectives of the European Commission under the Circular Economy Action Plan (CEAP) and the Incubation Forum for Circular Economy (IF CEED) initiative, providing replicable models for sustainability, innovation and crossborder cooperation.

The transport and logistics sector is undergoing profound changes driven by digital transformation, evolving from early computerized planning (1.0) to highly integrated, Al-powered systems (4.0). Existing literature outlines successive waves of digitalization: initial route optimization tools, sensor-based IoT and telematics for realtime monitoring, advanced geospatial tracking platforms for supply chain visibility, and, most recently, artificial intelligence and machine learning for predictive and adaptive decision-making. However, despite substantial research on individual technologies, fewer studies systematically map these phases as an integrated transformation continuum. This paper addresses that gap by reviewing the technological evolution of transport logistics through the 1.04.0 framework and analyzing how these stages build on one another toward autonomous systems. Methodologically, the paper employs a structured literature review combined with conceptual modeling to answer two research questions: (1) How can the historical evolution of transport logistics digitalization be categorized into coherent stages? (2) What are the implications of this staged transformation for operational efficiency, visibility, and strategic decision-making? The main results outline a four-stage model of digital transformation, demonstrating the cumulative integration of loT, geospatial data, and Al in creating intelligent, interconnected systems. The paper highlights implications for fleet management, predictive maintenance, demand forecasting, and dynamic routing, contributing to improved responsiveness and resilience in supply chains. This work contributes to the field by offering a clear, stage-based conceptual framework to understand digital transformation in transport logistics, supporting both academic research and practical planning for technology adoption in the sector.

Ten years of CHAMP magnetic field measurements are integrated into MFACE, a model of field-aligned currents (FACs) using empirical orthogonal functions (EOFs). EOF1 gives the basic Region-1/Region-2 pattern varying mainly with the interplanetary magnetic field Bz component. EOF2 captures separately the cusp current signature and By-related variability. Compared to existing models, MFACE yields significantly better spatial resolution, reproduces typically observed FAC thickness and intensity, improves on the magnetic local time (MLT) distribution, and gives the seasonal dependence of FAC latitudes and the NBZ current signature. MFACE further reveals systematic dependences on By, including 1) Region-1/Region-2 topology modifications around noon; 2) imbalance between upward and downward maximum current density; 3) MLT location of the Harang discontinuity. Furthermore, our procedure allows quantifying response times of FACs to solar wind driving at the bow shock nose: we obtain 20 minutes and 35-40 minutes lags for the FAC density and latitude, respectively.