Microsatellite Constellation for Mars Communication and Navigation

Exploration of Mars and establishment of human settlement have been of sharp interest for several decades. Since the turn of the century, efforts have been ramped up to make these a reality. With the execution of multiple robotic exploration missions and several more planned missions in the next two decades, as well as serious plans for human landing missions, a key need is the establishment of accurate, reliable, expansive, and cost-effective positioning and communication service for several users in the Mars environment. The Mars Communication and Navigation (MCN) mission is a multi-satellite constellation at Mars that shall provide data relay and positioning services for the identified possible users, that are orbiters, landers, ascenders, autonomous rovers, and human landing missions. The aim of MCN is to investigate and prototype key technologies for a Mars positioning and communication system using small satellites, in order to enable the development and operations of a wide range of Mars missions, providing a backbone Earth–Mars communication and navigation infrastructure. This work focuses on the critical architectural aspects of the MCN. The end-to-end (E2E) system architecture is presented, in order to provide an overview of the space and ground segments along with the operations concepts. Concerning the orbital configuration, the constellation and its deployment strategy are discussed. The MCN constellation baseline comprises 24 microsatellites operating in a Walker-like orbital configuration at Mars to provide service for more than 70 users potentially. Moreover, a Relay/Gateway link is utilized to serve as a communication bridge between Earth ground segment and the MCN constellation. Concerning the communication and navigation aspects, their architectures and possible solutions are highlighted, together with an overview of the related critical technologies required to achieve the mission objectives

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

SARS-CoV-2 B.1.617.2 Delta variant replication and immune evasion

Abstract: The B.1.617.2 (Delta) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in the state of Maharashtra in late 2020 and spread throughout India, outcompeting pre-existing lineages including B.1.617.1 (Kappa) and B.1.1.7 (Alpha)1. In vitro, B.1.617.2 is sixfold less sensitive to serum neutralizing antibodies from recovered individuals, and eightfold less sensitive to vaccine-elicited antibodies, compared with wild-type Wuhan-1 bearing D614G. Serum neutralizing titres against B.1.617.2 were lower in ChAdOx1 vaccinees than in BNT162b2 vaccinees. B.1.617.2 spike pseudotyped viruses exhibited compromised sensitivity to monoclonal antibodies to the receptor-binding domain and the amino-terminal domain. B.1.617.2 demonstrated higher replication efficiency than B.1.1.7 in both airway organoid and human airway epithelial systems, associated with B.1.617.2 spike being in a predominantly cleaved state compared with B.1.1.7 spike. The B.1.617.2 spike protein was able to mediate highly efficient syncytium formation that was less sensitive to inhibition by neutralizing antibody, compared with that of wild-type spike. We also observed that B.1.617.2 had higher replication and spike-mediated entry than B.1.617.1, potentially explaining the B.1.617.2 dominance. In an analysis of more than 130 SARS-CoV-2-infected health care workers across three centres in India during a period of mixed lineage circulation, we observed reduced ChAdOx1 vaccine effectiveness against B.1.617.2 relative to non-B.1.617.2, with the caveat of possible residual confounding. Compromised vaccine efficacy against the highly fit and immune-evasive B.1.617.2 Delta variant warrants continued infection control measures in the post-vaccination era

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Combined System-Trajectory Design for Geostationary Orbit Platforms on Hybrid Transfer

A novel methodology for a combined systems–trajectory optimization for a geostationary equatorial orbit (GEO) platform is proposed to obtain comprehensive design solutions. A combined chemical–electric propulsion system is used to execute hybrid high-thrust/low-thrust trajectory transfer to GEO, thereby balancing the overall system mass and transfer time. A systematic and payload-centric mission design provides a new set of design options to deliver tailored solutions to customized payloads. The hybrid trajectory characterization and spacecraft systems design find the required platform launch mass to deliver a GEO platform with a defined final mass and operational power. Elements of the system design are combined with those of multispiral low-thrust trajectory optimization as well as radiation absorption and solar array degradation to provide a comprehensive design solution. The result is a wide set of solutions to reach GEO, where fully chemical and fully electric transfers represent the boundaries of the hybrid transfer trade space. A payload throughput power of 20 kW entails a spacecraft mass in GEO between 4000 and 4550 kg, an initial thrust-to-mass ratio range of 1.7−2.3×10−4  m/s2, and a cover-glass thickness between 4 and 24 mils to guarantee a minimum end-of-life/beginning-of-life power ratio of 85%. In addition, all-electric solutions from different injection orbits yield transfers to GEO with a time of flight of 60–150 days and an initial mass for the platform of 4400–5500 kg

Combined Chemical–Electric Propulsion for a Stand-Alone Mars CubeSat

Stand-alone interplanetary CubeSats require primary propulsion systems for orbit maneuvering and precise trajectory control. The current work focuses on the design and performance characterization of the combined chemical–electric propulsion systems that shall enable a stand-alone 16U CubeSat mission on a hybrid high-thrust– low-thrust trajectory from a supersynchronous geostationary transfer orbit to a circular orbit about Mars. The highthrust chemical propulsion is used to escape Earth and to initiate stabilization at Mars. The low-thrust electric propulsion is used in heliocentric transfer, ballistic capture, and circularization. For chemical propulsion, design and performance characteristics of a monopropellant thruster and feed system using ADN-based FLP-106 propellant are presented. For electric propulsion, a performance model of an iodine-propelled inductively coupled miniature radiofrequency ion thruster is implemented to calculate the variation of thrust, specific impulse, and efficiency with input power. A power-constrained low-thrust trajectory optimization using the thruster performance model is pursued to calculate the transfer time, ΔV, and the required propellant mass for fuel-optimal and time-optimal transfers. Overall, the combined chemical–electric systems yield a feasible propulsion solution for stand-alone CubeSat missions to Mars that balances propellant mass and transfer time

Dynamic Mode Decomposition of Backward Facing Step Flow Simulation Data

Improved delayed detached eddy (IDDES) simulation results of a backward facing step flow are analyzed using dynamic mode decomposition (DMD). Different flow variables and the time-resolved skin friction coefficient are investigated and compared to a spectral analysis of the wall pressure fluctations. Although the flow field does not contain single dominant modes, two distinct flow features can be extracted and visualized using the DMD mode shapes. A low frequency flapping motion of the shear layer is found in the mode decomposition of the pressure, the wall-normal velocity and the skin friction coefficient. At higher frequencies, a wake mode similar to a von Karman vortex street is identified in the streamwise velocity, the pressure and the vorticity field