The LightSail 2 Controlled Solar Sailing Demonstration Mission

The LightSail 2 mission is the culmination of a decade-long program sponsored by The Planetary Society to advance solar sailing technology. The objective of LightSail 2 is to demonstrate controlled solar sailing in Earth orbit using a CubeSat platform. The LightSail 2 attitude is controlled using a single-axis momentum wheel and magnetic torque rods. During solar sailing operations, two 90 degree slews are performed each orbit to harness momentum from solar photons. Flight data show that LightSail 2 is successfully controlling its orientation relative to the Sun, and the controlled thrust from solar radiation pressure is measurably reducing the rate of orbital decay. The Planetary Society declared LightSail 2 mission success on July 31, 2019. This paper provides an overview of the LightSail 2 mission implementation, including the design of the flight system and flight software, and the pre-launch testing program. A summary of LightSail 2 mission operations is provided, including a description of the ground system. Solar sailing performance is presented, and anomalies encountered during the mission are discussed. The flight team continues to refine solar sailing performance and conduct on-orbit imaging for engineering purposes and to engage public interest. The LightSail program is entirely donor-funded, with over 50,000 contributors around the globe

Snooping Around: Observation Planning for the Signals of Opportunity P-Band Investigation (SNOOPI)

Launching October 2022, the SigNals Of Opportunity P-band Investigation (SNOOPI) is a 6U CubeSat dedicated to demonstrating spaceborne remote sensing of root zone soil moisture and snow water equivalent using signals of opportunity. P-band (240-500 MHz) frequencies are required to penetrate dense vegetation or snow and into the top 200 cm of soil, but this band is heavily subscribed. Rather than transmitting its own signal SNOOPI will observe reflected signals from the U.S. Navy’s Mobile User Objective System satellites. This makes planning observations challenging. The point of reflection is a function of both the transmitter and receiver satellite positions as well as terrain. The direct signal must be observed simultaneously on the same antenna pattern with sufficient gain. Ionospheric delay must also be accounted for. To satisfy these requirements and maintain a cadence of one observation per day, the SNOOPI science operations center at Purdue University has developed custom software for scheduling activities onboard the satellite. The software is highly automated, involving the user only in the definition of observation targets, priorities, and giving final approval to the proposed schedule. Orbit, attitude, power, communication, memory, and observation constraints are handled by a combination of linear programming and pattern search optimization methods. The purpose of this paper is to describe the challenges of scheduling observations for a signals of opportunity mission and illustrate how they were solved for SNOOPI

CD14 Signaling Restrains Chronic Inflammation through Induction of p38-MAPK/SOCS-Dependent Tolerance

Current thinking emphasizes the primacy of CD14 in facilitating recognition of microbes by certain TLRs to initiate pro-inflammatory signaling events and the importance of p38-MAPK in augmenting such responses. Herein, this paradigm is challenged by demonstrating that recognition of live Borrelia burgdorferi not only triggers an inflammatory response in the absence of CD14, but one that is, in part, a consequence of altered PI3K/AKT/p38-MAPK signaling and impaired negative regulation of TLR2. CD14 deficiency results in increased localization of PI3K to lipid rafts, hyperphosphorylation of AKT, and reduced activation of p38. Such aberrant signaling leads to decreased negative regulation by SOCS1, SOCS3, and CIS, thereby compromising the induction of tolerance in macrophages and engendering more severe and persistent inflammatory responses to B. burgdorferi. Importantly, these altered signaling events and the higher cytokine production observed can be mimicked through shRNA and pharmacological inhibition of p38 activity in CD14-expressing macrophages. Perturbation of this CD14/p38-MAPK-dependent immune regulation may underlie development of infectious chronic inflammatory syndromes

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Adaptive Continuation Strategies for Indirect Trajectory Optimization

Optimal control theory provides a mathematical foundation for solving a wide range of trajectory optimization problems. Indirect optimization methods use optimal control theory to provide high-quality solutions, but they are often regarded as inferior to other methods due to the difficulty of providing a good initial guess. Continuation addresses this difficulty by solving a series of incrementally more difficult subproblems which ultimately lead to the desired solution. The continuation process is controlled by evolving one or more parameters related to the optimal control problem. Currently, the order in which the continuation parameters are evolved must be determined by the designer. If many parameters are used or solutions are difficult to compute, this may require significant designer effort. This thesis presents strategies of automating the continuation process by drawing inspiration from methods in artificial intelligence path finding. These strategies adapt to difficult regions of the continuation space and intelligently search for valid continuation paths. In addition to increasing autonomy, adaptive continuation strategies improve the practicality of indirect methods by providing higher robustness, faster run times, and the ability to identify multiple optimal solutions

Deep Learning Fault Protection Applied to Spacecraft Attitude Determination and Control

The increasing numbers and complexity of spacecraft is driving a growing need for automated fault detection, isolation, and recovery. Anomalies and failures are common occurrences during space flight operations, yet most spacecraft currently possess limited ability to detect them, diagnose their underlying cause, and enact an appropriate response. This leaves ground operators to interpret extensive telemetry and resolve faults manually, something that is impractical for large constellations of satellites and difficult to do in a timely fashion for missions in deep space. A traditional hurdle for achieving autonomy has been that effective fault detection, isolation, and recovery requires appreciating the wider context of telemetry information. Advances in machine learning are finally allowing computers to succeed at such tasks. This dissertation presents an architecture based on machine learning for detecting, diagnosing, and responding to faults in a spacecraft attitude determination and control system. Unlike previous approaches, the availability of faulty examples is not assumed. In the first level of the system, one-class support vector machines are trained from nominal data to flag anomalies in telemetry. Meanwhile, a spacecraft simulator is used to model the activation of anomaly flags under different fault conditions and train a long short-term memory neural network to convert time-dependent anomaly information into a diagnosis. Decision theory is then used to convert diagnoses into a recovery action. The overall technique is successfully validated on data from the LightSail 2 mission