Temporal variations of thermospheric hydrogen derived from in situ measurements

Diurnal variations of atomic hydrogen in thermosphere derived from Explorer 32 data and correlated with solar cycle, solar rotation, and earth rotatio

Thermospheric hydrogen - Absolute densities and temporal variations deduced from in situ measurements

Thermospheric model based on Explorer 32 hydrogen ion density measurements including periodic variations and temperature factors due to local time, solar activity, and magnetic effect

Observation of hydrogen and helium ions during a period of rising solar activity

Latitudinal variations in H and He ion distribution in upper ionosphere observed by OGO 2 and 4 satellites during rising solar activit

Altitude variation of ion composition in the midlatitude trough region - Evidence for upward plasma flow

Altitude effect on ion concentration in midlatitude trough and plasmaspher

Direct measurements of helium and hydrogen ion concentration and total ion density to an altitude of 940 kilometers

Measurement of ion concentration and total ion density in exosphere using mass spectrometer and electrostatic prob

In situ measurements of plasma drift velocity and enhanced NO(+) in the auroral electroject by the Bennett spectrometer on AE-C

Simultaneous measurements of ion composition and plasma drift velocity by the Bennett mass spectrometer on the Atmosphere Explorer-C satellite reveal a direct correlation between enhancements in NO(+) concentration and ion drift velocity in the southern auroral oval. Low altitude (137 to 250 km) nighttime data reveal a region of westward plasma flow at velocities up to 1.3 km/s between 62 deg and 68 deg invariant latitude, with corresponding NO(+) enhancements of up to a factor of 25. A narrow region of reverse flow at approximately 0.9 km/s was also measured. These drift observations are consistent with convective flow patterns derived from electric field measurements, and their correlation with NO(+) appears to support the suggestion that NO(+) enhancements would be expected in regions of drift owing to the dependence on ion energy of the reaction O(+) + N2 yields NO(+) + N

A theoretical model of the ionosphere dynamics with interhemispheric coupling

Dynamic model for ionospheric plasma with interhemispheric couplin

Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific and important applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future work are discussed to further enhance the effectiveness and efficiency of FL in the context of CAV