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Trans-Neptunian objects (TNOs) are considered to be among the most primitive objects in our Solar System. Knowledge of their primary physical properties is essential for understanding their origin and the evolution of the outer Solar System. We predicted a stellar occultation by this TNO for 2020 October 23 UT and ran a specific campaign to investigate this event. We derived the projected profile shape and size from the occultation observations by means of an elliptical fit to the occultation chords. We also performed photometric observations of (143707) 2003 UY117 to obtain the absolute magnitude and the rotational period from the observed rotational light curve. Finally, we combined these results to derive the three-dimensional shape, volume-equivalent diameter, and geometric albedo for this TNO. From the stellar occultation, we obtained a projected ellipse with axes of $(282 \pm 18) \times (184 \pm 32)$ km. The area-equivalent diameter for this ellipse is $D_\textrm{eq,A} = 228 \pm 21$ km. From our photometric $R$ band observations, we derived an absolute magnitude of $H_V = 5.97 \pm 0.07$ mag using $V-R = 0.46 \pm 0.07$ mag, which was derived from a $V$ band subset of these data. The rotational light curve has a peak-to-valley amplitude of $\Delta m = 0.36 \pm 0.13$ mag. We find the most likely rotation period to be $P = 12.376 \pm 0.0033$ hours. By combining the occultation with the rotational light curve results and assuming a triaxial ellipsoid, we derived axes of $a \times b \times c = (332 \pm 24)$ km $\times$ $(216 \pm 24)$ km $\times$ $(180\substack{+28\\-24})$ km for this ellipsoid, and therefore a volume-equivalent diameter of $D_\textrm{eq,V} = 235 \pm 25$ km. Finally, the values for the absolute magnitude and for the area-equivalent diameter yield a geometric albedo of $p_V = 0.139 \pm 0.027$., Comment: 9 pages, 7 figures, accepted for publication in Astronomy and Astrophysics on Sept 13, 2024

It is now widely recognized that surface and interfacial defects exhibit distinct behavior compared to bulk defects in metal halide perovskites. However, the transition from bulk to surface behavior and the spatial extent of the surface's influence are not well understood. To address this, we conducted first-principles calculations on iodine vacancies and interstitial defects in methylammonium lead iodide and cesium lead iodide at various depths from the surface, enabling us to map out depth-dependent behavior. We find that the defect formation energy follows a saturating exponential curve as the defect moves away from the surface to the bulk. Using first-principles calculated defect formation energies, we quantify the extent of the surface's influence by calculating the decay length associated with each defect. The difference between the surface and bulk defect formation energy is found to be as high as 1.12 eV for the negatively charged iodine vacancy in methylammonium lead iodide, leading to the enrichment of the surface with defects. Through analysis of defective structures, we find that the differences in the bulk and surface defect properties are a consequence of different bond lengths and in some cases, even changes in bonding and coordination environments. Finally, we determine how the defect transition levels change as a function of the layer index, which could contribute to increased non-radiative recombination. Our findings pave the way for a systematic treatment of non-radiative losses in perovskite solar cells that incorporate spatially dependent defect densities and transition levels., Comment: 28 pages + 10 pages of supporting information

The main benefit of model predictive control (MPC) is its ability to steer the system to a given reference without violating the constraints while minimizing some objective. Furthermore, a suitably designed MPC controller guarantees asymptotic stability of the closed-loop system to the given reference as long as its optimization problem is feasible at the initial state of the system. Therefore, one of the limitations of classical MPC is that changing the reference may lead to an unfeasible MPC problem. Furthermore, due to a lack of deep knowledge of the system, it is possible for the user to provide a desired reference that is unfeasible or non-attainable for the MPC controller, leading to the same problem. This chapter summarizes MPC formulations recently proposed that have been designed to address these issues. In particular, thanks to the addition of an artificial reference as decision variable, the formulations achieve asymptotic stability and recursive feasibility guarantees regardless of the reference provided by the user, even if it is changed online or if it violates the system constraints. We show a recent formulation which extends this idea, achieving better performance and larger domains of attraction when working with small prediction horizons. Additional benefits of these formulations, when compared to classical MPC, are also discussed and highlighted with illustrative examples., Comment: (21 pages, 8 figures)

This paper provides a comprehensive tutorial on a family of Model Predictive Control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization problem. These formulations have several benefits with respect to the classical MPC formulations, including guaranteed recursive feasibility under online reference changes, as well as asymptotic stability and an increased domain of attraction. This tutorial paper introduces the concept of using an artificial reference in MPC, presenting the benefits and theoretical guarantees obtained by its use. We then provide a survey of the main advances and extensions of the original linear MPC for tracking, including its non-linear extension. Additionally, we discuss its application to learning-based MPC, and discuss optimization aspects related to its implementation., Comment: (15 pages, 1 figure)

Centaurs, distinguished by their volatile-rich compositions, play a pivotal role in understanding the formation and evolution of the early solar system, as they represent remnants of the primordial material that populated the outer regions. Stellar occultations offer a means to investigate their physical properties, including shape, rotational state, or the potential presence of satellites and rings. This work aims to conduct a detailed study of the centaur (54598) Bienor through stellar occultations and rotational light curves from photometric data collected during recent years. We successfully predicted three stellar occultations by Bienor, which were observed from Japan, Eastern Europe, and the USA. In addition, we organized observational campaigns from Spain to obtain rotational light curves. At the same time, we develop software to generate synthetic light curves from three-dimensional shape models, enabling us to validate the outcomes through computer simulations. We resolve Bienor's projected ellipse for December 26, 2022, determine a prograde sense of rotation, and confirm an asymmetric rotational light curve. We also retrieve the axes of its triaxial ellipsoid shape as a = (127 $\pm$ 5) km, b = (55 $\pm$ 4) km, and c = (45 $\pm$ 4) km. Moreover, we refine the rotation period to 9.1736 $\pm$ 0.0002 hours and determine a geometric albedo of (6.5 $\pm$ 0.5) %, higher than previously determined by other methods. Finally, by comparing our findings with previous results and simulated rotational light curves, we analyze whether an irregular or contact-binary shape, the presence of an additional element such as a satellite, or significant albedo variations on Bienor's surface, may be present.

A Model Predictive Controller for Tracking is introduced for rendezvous with non-cooperative tumbling targets in active debris removal applications. The target's three-dimensional non-periodic rotational dynamics as well as other state and control constraints are considered. The approach is based on applying an intermediate coordinate transformation that eliminates the time-dependency due to rotations in the constraints. The control law is then found as the solution to a QP problem with linear constraints and dynamics, as derived from the HCW equations, that provides feasibility and stability guarantees by means of a terminal LQR and dead-beat region. The proposed control algorithm performs well in a realistic simulation scenario, namely a near rendezvous with the Envisat spacecraft., Comment: extended version of paper submitted to CDC'24

Model Predictive Control (MPC) is a popular control approach due to its ability to consider constraints, including input and state restrictions, while minimizing a cost function. However, in practice, these constraints can result in feasibility issues, either because the system model is not accurate or due to the existence of external disturbances. To mitigate this problem, a solution adopted by the MPC community is the use of soft constraints. In this article, we consider a not-so-typical methodology to encode soft constraints in a particular MPC formulation known as MPC for Tracking (MPCT), which has several advantages when compared to standard MPC formulations. The motivation behind the proposed encoding is to maintain the semi-banded structure of the ingredients of a recently proposed solver for the considered MPCT formulation, thus providing an efficient and fast solver when compared to alternative approaches from the literature. We show numerical results highlighting the benefits of the formulation and the computational efficiency of the solver.

The main objective of tracking control is to steer the tracking error, that is the difference between the reference and the output, to zero while the plant's operation limits are satisfied. This requires that some assumptions on the evolution of the future values of the reference must be taken into account. Typically a simple evolution of the reference is considered, such as step, ramp, or parabolic reference signals. It is important to notice that the tracking problem considers possible variations in the reference to be tracked, such as steps or slope variations of the ramps. Then the tracking control problem is inherently uncertain, since the reference may differ from what is expected. If the value of the reference is changed, then there is no guarantee that the feasibility and stability properties of the resulting control law hold. This report presents the MPC for tracking (MPCT) approach, which ensures recursive feasibility and asymptotic stability of the setpoint when the value of the reference is changed.

Model Predictive Control (MPC) for tracking formulation presents numerous advantages compared to standard MPC, such as a larger domain of attraction and recursive feasibility even when abrupt changes in the reference are produced. As a drawback, it includes some extra decision variables in its related optimization problem, leading to a semi-banded structure that differs from the banded structure encountered in standard MPC. This semi-banded structure prevents the direct use of the efficient algorithms available for banded problems. To address this issue, we present an algorithm based on the alternating direction method of multipliers that explicitly takes advantage of the underlying semi-banded structure of the MPC for tracking.

This work proposes a nonlinear model predictive control-based guidance strategy for unmanned surface vehicles, focused on path following. The application of this strategy, in addition to overcome drawbacks of previous line-of-sight-based guidance laws, intends to enable the application of predictive strategies also to the low-level control, responsible for tracking the references provided by the guidance strategy. The stability and robustness of the proposed strategy are theoretically discussed. Furthermore, given the non-negligible computational cost of such nonlinear predictive guidance strategy, a practical nonlinear model predictive control strategy is also applied in order to reduce the computational cost to a great extent. The effectiveness and advantages of both proposed strategies over other nonlinear guidance laws are illustrated through a complete set of simulations., Comment: 21 pages, 15 figures. Postprint of the final published work

Alkali metal anodes paired with solid ion conductors offer promising avenues for enhancing battery energy density and safety. To facilitate rapid ion transport crucial for fast charging and discharging, it is essential to understand point defects within these conductors. In this study, we investigate the heterogeneity of defect distribution in Li$_3$OCl solid ion conductor, quantifying the defect formation energy (DFE) of lithium vacancies and interstitials as a function of distance from the surface through first-principles simulations. Our results reveal that the surface DFE is consistently lower than bulk except for one surface termination, indicating significant defect aggregation at surfaces. This difference can cause the defect density to be up to 14 orders of magnitude higher at surfaces compared to the bulk. Moreover, we unveil the transition in DFE when moving from the surface to the bulk through the DFE function, which exhibits an exponentially decaying relationship. Incorporating this exponential trend, we develop a revised model for the average behavior of defects that offers a more accurate description of the influence of grain size. Surface effects dominate for grain sizes $\lesssim$ 1 $\mu$m, highlighting the importance of surface defect engineering and the DFE function for accurately capturing ion transport in devices. We further explore the kinetics of defect redistribution by calculating the migration barriers for defect movement between bulk and surfaces. We find a highly asymmetric energy landscape for the lithium vacancies, exhibiting lower migration barriers for movement towards the surface compared to the bulk, while interstitial defects exhibit comparable kinetics between surface and bulk regions. These insights underscore the importance of considering both thermodynamic and kinetic factors in the design of solid ion conductors., Comment: 29 pages of main text + 11 pages of Supporting Information; 8 figures and 2 tables

Harmonic model predictive control (HMPC) is a recent model predictive control (MPC) formulation for tracking piece-wise constant references that includes a parameterized artificial harmonic reference as a decision variable, resulting in an increased performance and domain of attraction with respect to other MPC formulations. This article presents an extension of the HMPC formulation to track periodic harmonic references and discusses its use to track arbitrary references. The proposed formulation inherits the benefits of its predecessor, namely its good performance and large domain of attraction when using small prediction horizons, and that the complexity of its optimization problem does not depend on the period of the periodic reference. We show closed-loop results discussing its performance and comparing it to other MPC formulations., Comment: (11 pages, 14 figures)

In the realm of maritime transportation, autonomous vessel navigation in natural inland waterways faces persistent challenges due to unpredictable natural factors. Existing scheduling algorithms fall short in handling these uncertainties, compromising both safety and efficiency. Moreover, these algorithms are primarily designed for non-autonomous vessels, leading to labor-intensive operations vulnerable to human error. To address these issues, this study proposes a risk-aware motion control approach for vessels that accounts for the dynamic and uncertain nature of tide islands in a distributionally robust manner. Specifically, a model predictive control method is employed to follow the reference trajectory in the time-space map while incorporating a risk constraint to prevent grounding accidents. To address uncertainties in tide islands, a novel modeling technique represents them as stochastic polytopes. Additionally, potential inaccuracies in waterway depth are addressed through a risk constraint that considers the worst-case uncertainty distribution within a Wasserstein ambiguity set around the empirical distribution. Using sensor data collected in the Guadalquivir River, we empirically demonstrate the performance of the proposed method through simulations on a vessel. As a result, the vessel successfully navigates the waterway while avoiding grounding accidents, even with a limited dataset of observations. This stands in contrast to existing non-robust controllers, highlighting the robustness and practical applicability of the proposed approach.

In this paper, we present the periodic modifier-adaptation formulation of the dynamic real time optimization. The proposed formulation uses gradient information to update the problem with affine modifiers so that, upon convergence, its solution matches the optimal steady periodic trajectory. Unlike other state of the art modifier-adaptation techniques, the proposed approach is able to converge not only to optimal steady states, but also to optimal periodic trajectories. The full control scheme to take the system from its current state to the optimal periodic trajectory is detailed. The convergence of the computed reference to the optimal periodic behaviour is shown by means of a periodic version of the quadruple tank benchmark.

Model Predictive Control (MPC) is typically characterized for being computationally demanding, as it requires solving optimization problems online; a particularly relevant point when considering its implementation in embedded systems. To reduce the computational burden of the optimization algorithm, most solvers perform as many offline operations as possible, typically performing the computation and factorization of its expensive matrices offline and then storing them in the embedded system. This improves the efficiency of the solver, with the disadvantage that online changes on some of the ingredients of the MPC formulation require performing these expensive computations online. This article presents an efficient algorithm for the factorization of the key matrix used in several first-order optimization methods applied to linear MPC formulations, allowing its prediction model and cost function matrices to be updated online at the expense of a small computational cost. We show results comparing the proposed approach with other solvers from the literature applied to a linear time-varying system., Comment: (6 pages, 2 figures)

We could accurately predict the shadow path and successfully observe an occultation of a bright star by Chiron on 2022 December 15. The Kottamia Astronomical Observatory in Egypt did not detect the occultation by the solid body, but we detected three extinction features in the light curve that had symmetrical counterparts with respect to the central time of the occultation. One of the features is broad and shallow, whereas the other two features are sharper with a maximum extinction of $\sim$25$\%$ at the achieved spatial resolution of 19 km per data point. From the Wise observatory in Israel, we detected the occultation caused by the main body and several extinction features surrounding the body. When all the secondary features are plotted in the sky plane we find that they can be caused by a broad $\sim$580 km disk with concentrations at radii of 325 \pm 16 km and 423 \pm 11 km surrounding Chiron. At least one of these structures appears to be outside the Roche limit. The ecliptic coordinates of the pole of the disk are $\lambda$ = 151$^\circ~\pm$ 8$^\circ$ and $\beta$ = 18$^\circ~\pm$ 11$^\circ$, in agreement with previous results. We also show our long-term photometry indicating that Chiron had suffered a brightness outburst of at least 0.6 mag between March and September 2021 and that Chiron was still somewhat brighter at the occultation date than at its nominal pre-outburst phase. The outermost extinction features might be consistent with a bound or temporarily bound structure associated with the brightness increase. However, the nature of the brightness outburst is unclear, and it is also unclear whether the dust or ice released in the outburst could be feeding a putative ring structure or if it emanated from it., Comment: 6 pages, 4, figures

CMB-S4, the next-generation ground-based cosmic microwave background (CMB) observatory, will provide detailed maps of the CMB at millimeter wavelengths to dramatically advance our understanding of the origin and evolution of the universe. CMB-S4 will deploy large and small aperture telescopes with hundreds of thousands of detectors to observe the CMB at arcminute and degree resolutions at millimeter wavelengths. Inflationary science benefits from a deep delensing survey at arcminute resolutions capable of observing a large field of view at millimeter wavelengths. This kind of survey acts as a complement to a degree angular resolution survey. The delensing survey requires a nearly uniform distribution of cameras per frequency band across the focal plane. We present a large-throughput, large-aperture (5-meter diameter) freeform three-mirror anastigmatic telescope and an array of 85 cameras for CMB observations at arcminute resolutions, which meets the needs of the delensing survey of CMB-S4. A detailed prescription of this three-mirror telescope and cameras is provided, with a series of numerical calculations that indicate expected optical performance and mechanical tolerance.

Observations of the millimeter sky contain valuable information on a number of signals, including the blackbody cosmic microwave background (CMB), Galactic emissions, and the Compton-$y$ distortion due to the thermal Sunyaev-Zel'dovich (tSZ) effect. Extracting new insight into cosmological and astrophysical questions often requires combining multi-wavelength observations to spectrally isolate one component. In this work, we present a new arcminute-resolution Compton-$y$ map, which traces out the line-of-sight-integrated electron pressure, as well as maps of the CMB in intensity and E-mode polarization, across a third of the sky (around 13,000 sq.~deg.). We produce these through a joint analysis of data from the Atacama Cosmology Telescope (ACT) Data Release 4 and 6 at frequencies of roughly 93, 148, and 225 GHz, together with data from the \textit{Planck} satellite at frequencies between 30 GHz and 545 GHz. We present detailed verification of an internal linear combination pipeline implemented in a needlet frame that allows us to efficiently suppress Galactic contamination and account for spatial variations in the ACT instrument noise. These maps provide a significant advance, in noise levels and resolution, over the existing \textit{Planck} component-separated maps and will enable a host of science goals including studies of cluster and galaxy astrophysics, inferences of the cosmic velocity field, primordial non-Gaussianity searches, and gravitational lensing reconstruction of the CMB., Comment: The Compton-y map and associated products will be made publicly available upon publication of the paper. The CMB T and E mode maps will be made available when the DR6 maps are made public

We construct a field theory for dissipative systems using a hypercomplex ring formalism that reproduces naturally the effective doubling field formulation of dissipative systems of thermal field theory. The system is quantized by a noncanonical ansatz that gives the unitarity of the system is conserved. Asymptotically entangled states are constructed to introduce the study of the ergodicity of systems that undergo entanglement.

The comprehension and adoption of Artificial Intelligence (AI) are beset with practical and ethical problems. This article presents a 5-level AI Capability Assessment Model (AI-CAM) and a related AI Capabilities Matrix (AI-CM) to assist practitioners in AI comprehension and adoption. These practical tools were developed with business executives, technologists, and other organisational stakeholders in mind. They are founded on a comprehensive conception of AI compared to those in other AI adoption models and are also open-source artefacts. Thus, the AI-CAM and AI-CM present an accessible resource to help inform organisational decision-makers on the capability requirements for (1) AI-based data analytics use cases based on machine learning technologies; (2) Knowledge representation to engineer and represent data, information and knowledge using semantic technologies; and (3) AI-based solutions that seek to emulate human reasoning and decision-making. The AI-CAM covers the core capability dimensions (business, data, technology, organisation, AI skills, risks, and ethical considerations) required at the five capability maturity levels to achieve optimal use of AI in organisations.