Rayleigh-Taylor Instability in a Relativistic Shock

This Letter presents an analysis of near-field acoustic data collected on Space Launch System's Mobile Launcher tower during the Artemis I mission. Twelve pressure sensors located two and four effective nozzle diameters ( De) from the vehicle centerline recorded maximum overall sound pressure levels ranging from ∼ 162 dB to more than 170 dB, originating ∼ 10  De downstream of the nozzle exit plane. Frequency-dependent characteristics are also discussed. The peak noise is radiated over a broader frequency range than in the far field. Low-frequency noise locations match other rockets, but high-frequency locations diverge, falling between prior measurements of undeflected and deflected plumes.

Transfer learning (TL) is used to predict source-receiver range in a laboratory tank with varying water temperature. The input data are single-hydrophone spectral levels from linear chirps over the 50–100 kHz band recorded at different ranges. Data measured in room temperature water are used to train one-dimensional convolutional neural networks. When the trained models are applied to data measured in warmer water, a bias is introduced. TL with a small dataset improves the generalization results at the new temperature, demonstrating the potential of TL to improve performance under variable environmental conditions.

Crowds at collegiate basketball games react acoustically to events on the court in many ways, including applauding, chanting, cheering, and making distracting noises. Acoustic features can be extracted from recordings of crowds at basketball games to train machine learning models to classify crowd reactions. Such models may help identify crowd mood, which could help players secure fair contracts, venues refine fan experience, and safety personnel improve emergency response services or to minimize conflict in policing. By exposing the key features in these models, feature selection highlights physical insights about crowd noise, reduces computational costs, and often improves model performance. Feature selection is performed using random forests and least absolute shrinkage and selection operator logistic regression to identify the most useful acoustic features for identifying and classifying crowd reactions. The importance of including short-term feature temporal histories in the feature vector is also evaluated. Features related to specific 1/3-octave band shapes, sound level, and tonality are highly relevant for classifying crowd reactions. Additionally, the inclusion of feature temporal histories can increase classifier accuracies by up to 12%. Interestingly, some features are better predictors of future crowd reactions than current reactions. Reduced feature sets are human-interpretable on a case-by-case basis for the crowd reactions they predict.

Particulate contamination requires dust mitigation techniques to provide low-scatter surfaces on sensitive instrumentation in space. We have shown that poly(olefin sulfone)s photodegrade under spacelike conditions: in vacuum and with UV light exposure. We now demonstrate that photodegradable polymers can reduce dust accumulation on optical surfaces for space applications. This investigation shows that the dissociative degradation of poly(olefin sulfone)s significantly decreased the number of dust particles on a dust-coated surface. These results suggest a powerful way to mitigate the collection of extraterrestrial dust on optical surfaces in space, enabling passive removal of particulate contamination without any direct human intervention.

Load modeling is a primary activity in deriving verifiable models of power systems. It is often argued that the uncertainty in load models exceeds that of other components by a wide margin. The problem is intrinsically challenging, as the acceptable solution consists of many heterogeneous and even disparate physical components. The number of parameters needed to describe a composite dynamic load captures one quantitative aspect of model simplification. This paper uses information geometry as the main tool in a two-step process–model simplification followed by parameter determination. The method offers global results in parameter estimation and quantifies the common challenges in fitting standard models to measurement data. We use a very detailed WECC composite load model embedded in the real world 441-bus benchmark system to illustrate the procedure and provide recommendations.

We present a combined magnetometry, muon spin-relaxation (𝜇⁢SR), and neutron-scattering study of the insulating spin glass Zn_0.5Mn_0.5Te, for which magnetic Mn²⁺ and nonmagnetic Zn2+ ions are randomly distributed on a face-centered cubic lattice. The magnetometry and 𝜇SR results confirm a spin freezing transition around 𝑇𝑓≈23 K, with the spin-fluctuation rate decreasing gradually and somewhat inhomogeneously through the sample volume as the temperature decreases toward 𝑇𝑓. Characteristic spin-correlation times well above 𝑇𝑓 are on the order of 10−10 s, much slower than typically observed in canonical spin glasses but in line with expectations for a cluster spin glass. Using magnetic pair distribution function (mPDF) analysis and reverse Monte Carlo (RMC) modeling of the magnetic diffuse neutron-scattering data, we show that the spin-glass ground state consists of clusters of spins exhibiting short-range-ordered type-III antiferromagnetic correlations with a locally ordered moment of 3.1(1)𝜇_B between nearest-neighbor spins. The type-III correlations decay exponentially as a function of spin separation distance with a correlation length of approximately 5 Å. The diffuse magnetic scattering and corresponding mPDF show no significant changes across 𝑇𝑓, indicating that the dynamically fluctuating short-range spin correlations in the paramagnetic state retain the same basic type-III configuration that characterizes the spin-glass state; the only change apparent from the neutron-scattering data is a gradual reduction of the correlation length and locally ordered moment with increasing temperature. Taken together, these results paint a unique and detailed picture of the local magnetic structure and dynamics in Zn_0.5Mn_0.5Te and provide strong evidence that this material is best described as a cluster spin glass. In addition, this work showcases a statistical method for extracting diffuse scattering signals from neutron powder diffraction data, which we developed to facilitate the mPDF and RMC analysis of the neutron data. This method has the potential to be broadly useful for neutron powder diffraction experiments on a variety of materials with short-range atomic or magnetic order.