Coordinated observation of field line resonance in the mid-tail

Standing Alfvén waves of 1.1 mHz (~15 min in period) were observed by the Cluster satellites in the mid-tail during 06:00-07:00 UT on 8 August 2003. Pulsations with the same frequency were also observed at several ground stations near Cluster's footpoint. The standing wave properties were determined from the electric and magnetic field measurements of Cluster. Data from the ground magnetometers indicated a latitudinal amplitude and phase structure consistent with the driven field line resonance (FLR) at 1.1 mHz. Simultaneously, quasi-periodic oscillations at different frequencies were observed in the post-midnight/early morning sector by GOES 12 (<i>l</i><sub>0</sub>≈8.7), Polar (<i>l</i><sub>0</sub>≈11-14) and Geotail (<i>l</i><sub>0</sub>≈9.8). The 8 August 2003 event yields rare and interesting datasets. It provides, for the first time, coordinated in situ and ground-based observations of a very low frequency FLR in the mid-tail on stretched field lines

Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches

As cellular networks evolve towards the 6th generation, machine learning is seen as a key enabling technology to improve the capabilities of the network. Machine learning provides a methodology for predictive systems, which can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a specific quality of service requirement. With predictive quality of service, a wide variety of new use cases, both safety- and entertainment-related, are emerging, especially in the automotive sector. Therefore, in this work, we consider maximum throughput prediction enhancing, for example, streaming or high-definition mapping applications. We discuss the entire machine learning workflow highlighting less regarded aspects such as the detailed sampling procedures, the in-depth analysis of the dataset characteristics, the effects of splits in the provided results, and the data availability. Reliable machine learning models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on machine learning technologies by better understanding the underlying characteristics of the collected data. We discuss feature engineering and the effects of different splits for the training processes, showcasing that random splits might overestimate performance by more than twofold. Moreover, we investigate diverse sets of input features, where network information proved to be most effective, cutting the error by half. Part of our contribution is the validation of multiple machine learning models within diverse scenarios. We also use explainable AI to show that machine learning can learn underlying principles of wireless networks without being explicitly programmed. Our data is collected from a deployed network that was under full control of the measurement team and covered different vehicular scenarios and radio environments.Comment: 18 pages, 12 Figures. Accepted on IEEE Acces

Ground-based and satellite observations of high-latitude auroral activity in the dusk sector of the auroral oval

On 7 December 2000, during 13:30-15:30 UT the MIRACLE all-sky camera at Ny Alesund observed auroras at high-latitudes (MLAT similar to 76) simultaneously when the Cluster spacecraft were skimming the magnetopause in the same MLT sector (at similar to 16:00-18:00 MLT). The location of the auroras (near the ionospheric convection reversal boundary) and the clear correlation between their dynamics and IMF variations suggests their close relationship with R1 currents. Consequently, we can assume that the Cluster spacecraft were making observations in the magnetospheric region associated with the auroras, although exact magnetic conjugacy between the ground-based and satellite observations did not exist. The solar wind variations appeared to control both the behaviour of the auroras and the magnetopause dynamics. Auroral structures were observed at Ny Alesund especially during periods of negative IMF B-Z. In addition, the Cluster spacecraft experienced periodic (T similar to 4 - 6 min) encounters between magnetospheric and magnetosheath plasmas. These undulations of the boundary can be interpreted as a consequence of tailward propagating magnetopause surface waves. Simultaneous dusk sector ground-based observations show weak, but discernible magnetic pulsations (Pc 5) and occasionally periodic variations (T - 2 - 3 min) in the high-latitude auroras. In the dusk sector, Pc 5 activity was stronger and had characteristics that were consistent with a field line resonance type of activity. When IMF BZ stayed positive for a longer period, the auroras were dimmer and the spacecraft stayed at the outer edge of the magnetopause where they observed electromagnetic pulsations with T similar to 1 min. We find these observations interesting especially from the viewpoint of previously presented studies relating poleward-moving high-latitude auroras with pulsation activity and MHD waves propagating at the magnetospheric boundary layers

Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the onboarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.Comment: 5 pages, 6 figures. Accepted for presentation at IEEE conference VTC2023-Spring. Available dataset at https://ieee-dataport.org/open-access/berlin-v2

Implementing a system of quality-of-life diagnosis and therapy for breast cancer patients: results of an exploratory trial as a prerequisite for a subsequent RCT

A system for quality-of-life diagnosis and therapy (QoL system) was implemented for breast cancer patients. The system fulfilled the criteria for complex interventions (Medical Research Council). Following theory and modeling, this study contains the exploratory trial as a next step before the randomised clinical trial (RCT) answering three questions: (1) Are there differences between implementation sample and general population? (2) Which amount and type of disagreement exist between patient and coordinating practitioners (CPs) in assessed global QoL? (3) Are there empirical reasons for a cutoff of 50 points discriminating between healthy and diseased QoL? Implementation was successful: 74% of CPs worked along the care pathway. However, CPs showed preferences for selecting patients with lower age and UICC prognostic staging. Patients and CPs disagreed considerably in values of global QoL, despite education in QoL assessment by outreach visits, opinion leaders and CME: Zero values of QoL were only expressed by patients. Finally, the cutoff of 50 points was supported by the relationship between QoL in single items and global QoL: no patients with values above 50 dropped global QoL below 50, but values below 50 and especially at 0 points in single items, induced a dramatic fall of global QoL down to below 50. The exploratory trial was important for defining the complex intervention in the definitive RCT: control for age and prognostic stage grading, support for a QoL unit combining patient's and CP's assessment of QoL and support for the 50-point cutoff criterion between healthy and diseased QoL

The significance of trust in the political system and motivation for pupils' learning progress in politics lessons

Very little research has been conducted on the contribution of political education to learning progress in Germany. Hence, there is a need for intervention studies measuring performance against the theoretical background of a political competence model. This model comprises three constructs: subject knowledge, motivation and attitudes. According to this model, politics lessons should not only convey knowledge but also arouse subject interest, promote political attitudes and develop problem-solving skills. This study investigates how knowledge acquisition is influenced by intervention using theory-oriented teaching materials on the European Union, intervention using conventional textbooks on the European Union and politics lessons without any reference to the European Union. It further asks how the performance-related self-concept and subject interest in political issues impact political knowledge and whether civic virtue and trust in the system are related to it. The sample comprises 1071 pupils. Theory-oriented politics classes lead to greater growth of pupils’ knowledge than in the control group. As anticipated, this study proves that a positive subject-specific self-concept impacts knowledge. The examination of political attitudes reveals a positive correlation between civic virtue and knowledge. There is no connection between trust in the political system and knowledge