These innovative pavings convert the kinetic energy of footsteps into clean electric energy. We walk here, we walk there, we walk everywhere. Maybe you're headed to work or to lunch in a busy city. You're expending energy, and the exercise is good for you. But what if, on top of that, we could recapture all that freely supplied energy and convert it to usable electricity?

The geodesy of irregularly shaped small bodies presents fundamental challenges for gravitational field modeling, particularly as deep space exploration missions increasingly target asteroids and comets. Traditional approaches suffer from critical limitations: spherical harmonics diverge within the Brillouin sphere where spacecraft typically operate, polyhedral models assume unrealistic homogeneous density distributions, and existing machine learning methods like GeodesyNets and Physics-Informed Neural Networks (PINN-GM) require extensive computational resources and training time. This work introduces Mascon-Cubes, a novel self-supervised learning approach that formulates gravity inversion as a direct optimization problem over a regular 3D grid of point masses (mascons). Unlike implicit neural representations, MasconCubes explicitly model mass distributions while leveraging known asteroid shape information to constrain the solution space. Comprehensive evaluation on diverse asteroid models including Bennu, Eros, Itokawa, and synthetic planetesimals demonstrates that MasconCubes achieve superior performance across multiple metrics. Most notably, MasconCubes demonstrate computational efficiency advantages with training times approximately 40 times faster than GeodesyNets while maintaining physical interpretability through explicit mass distributions. These results establish MasconCubes as a promising approach for mission-critical gravitational modeling applications requiring high accuracy, computational efficiency, and physical insight into internal mass distributions of irregular celestial bodies.

Dru Smith, Chief Geodesist of the National Geodetic Survey stands near a measurement device used to survey the height of the Washington Monument in 2017.Susan Walsh/AP This story was originally published by Wired and is reproduced here as part of the Climate Desk collaboration. Cuts made by the Trump administration are threatening the function of a tiny but crucial office within the National Oceanic and Atmospheric Administration that maintains the US framework of spatial information: latitudes, longitudes, vertical measurements like elevation, and even measurements of Earth's gravitational field. Staff losses at the National Geodetic Survey (NGS), the oldest scientific agency in the US, could further cripple its mission and activities, including a long-awaited project to update the accuracy of these measurements, former employees and experts say. As the world turns more and more toward operations that need precise coordinate systems like the ones NGS provides, the science that underpins this office's activities, these experts say, is becoming even more crucial. The work of NGS, says Tim Burch, the executive director of the National Society of Professional Surveyors, "is kind of like oxygen. You don't know you need it until it's not there."

Large language models (LLMs) have revolutionized the field of artificial intelligence, demonstrating text understanding and generation capabilities approaching human levels. However, despite impressive results, the internal functioning mechanisms of these models largely remain a "black box." As Amodei [1] notes in his essay "The Urgency of Interpretability," researchers have limited understanding of why LLMs generate specific responses and how they arrive at their conclusions. This lack of transparency becomes increasingly problematic as LLMs begin to play central roles in economics, technology, and national security. Of particular concern are phenomena such as unpredictable hallucinations, extreme sensitivity to query formulations, and puzzling patterns in the probability distributions of generated tokens. These phenomena not only limit the reliability of LLMs in critical applications but also point to fundamental gaps in our understanding of their operation.

This document describes the architecture and algorithms of a high fidelity fixed wing flight simulator intended to test and validate novel guidance, navigation, and control (GNC) algorithms for autonomous aircraft. It aims to replicate the influence of as many factors as possible on the aircraft performances, the Earth model, the physics of flight and the associated equations of motion, and in particular the behavior of the onboard sensors, limiting the assumptions to the bare minimum, and including multiple relatively minor effects not usually considered in simulation that may play a role in the GNC algorithms not performing as intended. The author releases the flight simulator C ++ implementation as open-source software. The simulator modular design enables the replacement of the standard GNC algorithms with the objective of evaluating their performances when subject to specific missions and meteorological conditions (atmospheric properties, wind field, air turbulence). The testing and evaluation is performed by means of Monte Carlo simulations, as most simulation modules (such as the aircraft mission, the meteorological conditions, the errors introduced by the sensors, and the initial conditions) are defined stochastically and hence vary in a pseudo-random way from one execution to the next according to certain user-defined input parameters, ensuring that the results are valid for a wide range of conditions. In addition to modeling the outputs of all sensors usually present onboard a fixed wing platform, such as accelerometers, gyroscopes, magnetometers, Pitot tube, air vanes, and a Global Navigation Satellite System (GNCC) receiver, the simulator is also capable of generating realistic images of the Earth surface that resemble what an onboard camera would record if following the resulting trajectory, enabling the use and evaluation of visual and visual inertial navigation systems.

We present a novel approach based on artificial neural networks, so-called geodesyNets, and present compelling evidence of their ability to serve as accurate geodetic models of highly irregular bodies using minimal prior information on the body. The approach does not rely on the body shape information but, if available, can harness it. GeodesyNets learn a three-dimensional, differentiable, function representing the body density, which we call neural density field. The body shape, as well as other geodetic properties, can easily be recovered. We investigate six different shapes including the bodies 101955 Bennu, 67P Churyumov-Gerasimenko, 433 Eros and 25143 Itokawa for which shape models developed during close proximity surveys are available. Both heterogeneous and homogeneous mass distributions are considered. The gravitational acceleration computed from the trained geodesyNets models, as well as the inferred body shape, show great accuracy in all cases with a relative error on the predicted acceleration smaller than 1\% even close to the asteroid surface. When the body shape information is available, geodesyNets can seamlessly exploit it and be trained to represent a high-fidelity neural density field able to give insights into the internal structure of the body. This work introduces a new unexplored approach to geodesy, adding a powerful tool to consolidated ones based on spherical harmonics, mascon models and polyhedral gravity.

You haven't seen The Matrix? Well, you should watch it. Here's the basic idea--some dude (Neo) finds out he's been living in a computer program. Since his world isn't "real," he is able to do some superhuman things--like dodge bullets and jump from one building to the next. Yes, this building jump is what I want to look at.

We consider a class of misspecified dynamical models where the governing term is only approximately known. Under the assumption that observations of the system's evolution are accessible for various initial conditions, our goal is to infer a non-parametric correction to the misspecified driving term such as to faithfully represent the system dynamics and devise system evolution predictions for unobserved initial conditions. We model the unknown correction term as a Gaussian Process and analyze the problem of efficient experimental design to find an optimal correction term under constraints such as a limited experimental budget. We suggest a novel formulation for experimental design for this Gaussian Process and show that approximately optimal (up to a constant factor) designs may be efficiently derived by utilizing results from the literature on submodular optimization. Our numerical experiments exemplify the effectiveness of these techniques.

Moon caves could provide shelter for astronauts exploring Earth's nearest neighbor, researchers say. A new analysis of data gathered by NASA's twin Gravity Recovery and Interior Laboratory (GRAIL) spacecraft, which mapped the moon's gravitational field in unprecedented detail, turned up a number of new candidates for lava tubes -- cave-like structures that could be large enough to house supplies and astronauts. Space is a harsh environment. Radiation from the sun, galactic cosmic rays and constantly falling micrometeorites all present a threat to human explorers. "A lava tube provides a safe haven from all these hazardous environmental conditions," study team member Rohan Sood, a graduate student at Purdue University in Indiana, told Space.com.