Ionospheric signatures of ultralow frequency (ULF) wave in the Pc3‐5 band (1.7–40.0 mHz) were surveyed using ∼6‐s resolution data from Super Dual Auroral Radar Network (SuperDARN) radars in the Northern Hemisphere from 2010 to 2016. Numerical experiments were conducted to derive wave period‐dependent thresholds for automated detection of ULF waves using the Lomb‐Scargle periodogram technique. The spatial occurrence distribution, frequency characteristics, seasonal effects, solar wind condition, and geomagnetic activity level dependence have been studied. Pc5 wave events were found to dominate at high and polar latitudes with a most probable frequency of 2.08 ± 0.07 mHz, while Pc3‐4 waves were relatively more common at midlatitudes on the nightside with a most probable frequency of 11.39 ± 0.14 mHz. At high latitudes, the occurrence rate of Pc4‐5 waves maximizes in the dusk sector and during winter. These events tend to occur during low geomagnetic activity and northward interplanetary magnetic field. For the category of radially bounded but longitudinally extended Pc4 events in the duskside ionosphere, an internal driving source is suggested. At midlatitudes, the poloidal Pc3‐4 occurrence rate maximizes premidnight and during equinox. This tendency becomes more prominent with increasing auroral electrojet (AE) index and during southward interplanetary magnetic field, which suggests that many of these events are Pi2 and Pc3‐4 pulsations associated with magnetotail dynamics during active geomagnetic intervals. The overall occurrence rate of Pc3‐5 wave events is lowest in summer, which suggests that the ionospheric conductivity plays a role in controlling ULF wave occurrence.

Dusty (or complex) plasmas in the Earth’s middle and upper atmosphere ultimately result in exotic phenomena that are currently forefront research issues in the space science community. This paper presents some of the basic criteria and fundamental physical processes associated with the creation, evolution and dynamics of dusty plasmas in the near-Earth space environment. Recent remote sensing techniques to probe naturally created dusty plasma regions are also discussed. These include ground-based experiments employing high-power radio wave interaction. Some characteristics of the dusty plasmas that are actively produced by space-borne aerosol release experiments are discussed. Basic models that may be used to investigate the characteristics of such dusty plasma regions are presented.

Observations of secondary radiation, stimulated electromagnetic emission (SEE), produced during ionospheric modification experiments using ground-based, high-power, high-frequency (HF) radio waves are considered. The High Frequency Active Auroral Research Program (HAARP) facility is capable of generating narrowband SEE in the form of stimulated Brillouin scatter (SBS) and stimulated ion Bernstein scatter (SIBS) in the SEE spectrum. Such narrowband SEE spectral lines have not been reported using the European Incoherent Scatter (EISCAT) heater facility before. This work reports the first EISCAT results of narrowband SEE spectra and compares them to SEE previously observed at HAARP during electron gyro-harmonic heating. An analysis of experimental SEE data shows observations of emission lines within 100 Hz of the pump frequency, interpreted as SBS, during the 2012 July EISCAT campaign. Experimental results indicate that SBS strengthens as the pump frequency approaches the third electron gyro-harmonic. Also, for different heater antenna beam angles, the CUTLASS radar backscatter induced by HF radio pumping is suppressed near electron gyro-harmonics, whereas electron temperature enhancement weakens as measured by EISCAT/UHF radar. The main features of these new narrowband EISCAT observations are generally consistent with previous SBS measurements at HAARP.

Recently, experimental observations have shown that Polar Mesospheric Summer Echoes (PMSE) may be modulated by radio wave heating the irregularity source region with a ground‐based ionospheric heating facility. It is clear from these past investigations that the temporal behavior of PMSE during ionospheric heating shows promise as a diagnostic for the associated dust layer. To investigate the temporal behavior of plasma irregularities thought to produce PMSE, this work describes a new model that incorporates both finite diffusion time effects as well as dust charging. The hybrid model utilizes fluid ions described by continuity and momentum equations, electrons whose behavior is determined from quasi‐neutrality, and charged dust described by the standard Particle‐In‐Cell (PIC) method. The model has been used to investigate the temporal behavior of charged dust associated electron irregularities during electron temperature enhancement associated with radio wave heating. The model predicts that the temporal behavior of the irregularities depends on the ratio of the electron‐ion ambipolar diffusion time to the dust particle charging time τ_dif/τ_chg. The results indicate that typically for τ_dif/τ_chg ≪ 1, an enhancement in electron irregularity amplitude occurs for a period after turn‐off of the radio wave heating. The work also predicts that for τ_dif/τ_chg ≫ 1, an enhancement in electron irregularity amplitude occurs for a time period after the turn‐on of the radio wave heating. Owing to the dependence of τ_dif on irregularity scale size, these results have important implications for observations of PMSE modification at different radar frequencies. Therefore new possibilities may exist for diagnosing the dust layer with radio wave heating which are discussed.

Advances in machine learning provide the ability to leverage data from expensive simulations of high-energy-density experiments to significantly cut down on computational time and costs associated with the search for optimal target designs. This study presents an application of cutting-edge Bayesian optimization methods to the one-dimensional (1D) design optimization of double shell graded layer targets for inertial confinement fusion experiments. This investigation attempts to reduce hydrodynamic instabilities while retaining high yields for future NIF experiments. Machine learning methods can use predictive physics simulations to identify graded layer designs from within the vast design space that demonstrate high predicted performance, including novel designs with high uncertainty in performance that may hold unexpected promise. By applying machine learning tools to the simulation design, we map the trade-off between 1D yield and instability, specifically isolating parameter ranges, which maintain high performance while showing significantly improved Rayleigh–Taylor stability over the point design. The groundwork laid in this study will be a useful design tool for future NIF experiments with graded layer targets.