Satellites that navigate by talking to each other slowly lose their sense of direction, because nothing in a purely Earth-orbiting network provides a fixed reference in space.

With the continuous advancement of spaceborne synthetic aperture radar (SAR) technology, L-band SAR has come to play an increasingly important role in Earth observation; however, its imaging, interferometric, and polarimetric performance are susceptible to ionospheric disturbances. In equatorial nighttime data, amplitude streak artifacts induced by equatorial plasma bubbles frequently occur and have traditionally been regarded as interference factors in SAR imaging. Nevertheless, these artifacts essentially carry critical information on ionospheric irregularities and can be exploited to retrieve physical parameters such as scintillation intensity and turbulence spectral indices. Existing methods either rely on comparisons between disturbed and undisturbed images or are limited to preliminary one-dimensional parameter estimation along the track, making it difficult to achieve two-dimensional fine-scale retrieval of scintillation structures, and lacking systematic validation of their spatial correlation with equatorial ionospheric anomalies. Therefore, how to extract the two-dimensional spatial distribution of ionospheric scintillation parameters from amplitude streak artifacts and reveal their intrinsic relationship with regions of enhanced electron density has become an important scientific question in the field of ionospheric remote sensing.

With the advancement of deep-space missions such as crewed lunar exploration and Mars exploration, artificial intelligence (AI) technologies are being increasingly embedded into aerospace engineering management processes, and managers' AI competency has become a critical factor affecting mission delivery performance and safety compliance. However, traditional project management competency models have been primarily constructed around process control and redundancy design within deterministic systems, failing to explicitly address governance challenges introduced by AI integration—including black-box opacity, accountability allocation, traceable review of AI outputs, and accountability mechanisms for assisted decision-making. Although existing NASA and ESA frameworks provide fundamental support for aerospace project delivery, AI-related competencies in aerospace management exhibit inherent interdependencies. Managers are required to continuously balance delivery schedules and cost pressures against mission assurance obligations such as verification and validation, configuration control, and compliance, making it impossible to simply represent AI competency as a linear list of attributes. Therefore, how to construct an AI competency assessment framework that captures governance priorities and multi-attribute interdependencies has become a critical issue urgently to be addressed in aerospace engineering management in the AI era.

Researchers analysing nearly 1,000 ice grains detected by NASA's Cassini spacecraft have uncovered unexpected chemical diversity in material originating from Enceladus' subsurface ocean. Laboratory experiments at ELSI show how salts can separate as ocean droplets slowly freeze. The researchers propose that these larger frozen droplets later shatter within the moon's icy vents, producing chemically distinct particles that are ejected into space.