Evolutionary Optimization for Active Debris Removal Mission Planning

Active debris removal missions require an accurate planning for maximizing mission payout, by reaching the maximum number of potential orbiting targets in a given region of space. Such a problem is known to be computationally demanding and the present paper provides a technique for preliminary mission planning based on a novel evolutionary optimization algorithm, which identifies the best sequence of debris to be captured and/or deorbited. A permutation-based encoding is introduced, which may handle multiple spacecraft trajectories. An original archipelago structure is also adopted for improving algorithm capabilities to explore the search space. As a further contribution, several crossover and mutation operators and migration schemes are tested in order to identify the best set of algorithm parameters for the considered class of optimization problems. The algorithm is numerically tested for a fictitious cloud of debris in the neighborhood of Sun-synchronous orbits, including cases with multiple chasers

Approximate estimates of orbit transfer cost for efficient mission analysis and design

Symbolic Regression is investigated as a tool for identifying analytical expressions which provide an estimate of orbit transfer cost, evaluated in terms of required veloc- ity increment, as a function of initial and target orbit geometry. Different approaches are considered to identify the best approach to sample the problem parameter space and the algorithm which performs better, in the framework of Genetic Programming. Each resulting method is tested for five different orbit transfer geometries between coplanar circular and elliptical orbits. Results demonstrate the viability of the ap- proach, although when the number of problem parameter increases, computational cost becomes sizeable. Also, local minima may be filtered by the regression

Preliminary Design of Multi-Chaser Active Debris Removal Missions with Evolutionary Algorithms

This paper deals with the preliminary design of an active debris removal mission in Earth's orbit, where multiple active spacecraft are used to remove all the space debris belonging to a given cluster. The problem is posed as a purely combinatorial optimization problem and dealt with a Genetic Algorithm (GA). A novel permutation-based encoding, specifically designed to allow the simultaneous optimization of the sequence of targets, their distribution among the chaser spacecraft, and the selection of the best rendezvous epochs, is presented. Special permutation-preserving operators are introduced to improve the convergence capabilities of GA. Numerical results are presented for a case study involving a few dozens of debris and up to four chasers

The Prevalence, Characteristics and Risk Factors of Persistent Symptoms in Non-Hospitalized and Hospitalized Children with SARS-CoV-2 Infection Followed-Up for up to 12 Months: A Prospective, Cohort Study in Rome, Italy

Previous studies assessing the prevalence of COVID-19 sequelae in children have included either a small number of children or a short follow-up period, or have only focused on hospitalized children. We investigated the prevalence of persistent symptoms amongst children and assessed the risk factors, including the impact of variants. A prospective cohort study included children (≤18 years old) with PCR-confirmed SARS-CoV-2 infection. The participants were assessed via telephone and face-to-face visits at 1–5, 6–9 and 12 or more months post-SARS-CoV-2 diagnosis using the ISARIC COVID-19 follow-up survey. Of the 679 children enrolled, 51% were female; 488 were infected during the wild virus wave, and 29 were infected with the Alpha, 42 with the Delta and 120 with the Omicron variants. Fatigue (19%), headache (12%), insomnia (7.5%), muscle pain (6.9%) and confusion with concentration issues (6.8%) were the most common persistent symptoms. Families reported an overall improvement over time, with 0.7% of parents interviewed at 12 months or more of the follow-up period reporting a poor recovery. Patients that had not recovered by 6–9 months had a lower probability of recovering during the next follow-up period. Children infected with a variant or the wild virus had an overall similar rate of persistent symptoms (although the pattern of reported symptoms differed significantly) and recovery rates. Conclusions: Recovery rates after SARS-CoV-2 infection improved as time passed from the initial infection, ranging from 4% of children having poor recovery at 1–5 months’ follow-up to 1.3% at 6–9 months and 0.7% at 12 months. The patterns of persistence changed according to the variants involved at the time of infection. This study reinforces that a subgroup of children develop long-lasting persistent symptoms and highlights the need for further studies investigating the reasons behind the development of PCC

Post-COVID Condition in Adults and Children Living in the Same Household in Italy: A Prospective Cohort Study Using the ISARIC Global Follow-Up Protocol

Background: Emerging evidence shows that both adults and children may develop post-acute sequelae of SARS-CoV-2 infection (PASC). The aim of this study is to characterise and compare long-term post-SARS-CoV-2 infection outcomes in adults and children in a defined region in Italy. Methods: A prospective cohort study including children (≤18 years old) with PCR-confirmed SARS-CoV-2 infection and their household members. Participants were assessed via telephone and face-to-face visits up to 12 months post-SARS-CoV-2 diagnosis of household index case, using the ISARIC COVID-19 follow-up survey. Results: Of 507 participants from 201 households, 56.4% (286/507) were children, 43.6% (221/507) adults. SARS-CoV-2 positivity was 87% (249/286) in children, and 78% (172/221) in adults. The mean age of PCR positive children was 10.4 (SD = 4.5) and of PCR positive adults was 44.5 years (SD = 9.5), similar to the PCR negative control groups [children 10.5 years (SD = 3.24), adults 42.3 years (SD = 9.06)]. Median follow-up post-SARS-CoV-2 diagnosis was 77 days (IQR 47-169). A significantly higher proportion of adults compared to children reported at least one persistent symptom (67%, 68/101 vs. 32%, 57/179, p < 0.001) at the first follow up. Adults had more frequently coexistence of several symptom categories at both follow-up time-points. Female gender was identified as a risk factor for PASC in adults (p 0.02 at 1-3 months and p 0.01 at 6-9 months follow up), but not in children. We found no significant correlation between adults and children symptoms. In the paediatric group, there was a significant difference in persisting symptoms between those with confirmed SARS-CoV-2 infection compared to controls at 1-3 months follow up, but not at 6-9 months. Conversely, positive adults had a higher frequency of persisting symptoms at both follow-up assessments. Conclusion: Our data highlights that children can experience persistent multisystemic symptoms months after diagnosis of mild acute SARS-CoV-2 infection, although less frequently and less severely than co-habitant adults. There was no correlation between symptoms experienced by adults and children living in the same household. Our data highlights an urgent need for studies to characterise PASC in whole populations and the wider impact on familie