Non-averaged regularized formulations as an alternative to semi-analytical orbit propagation methods

This paper is concerned with the comparison of semi-analytical and non-averaged propagation methods for Earth satellite orbits. We analyse the total integration error for semi-analytical methods and propose a novel decomposition into dynamical, model truncation, short-periodic, and numerical error components. The first three are attributable to distinct approximations required by the method of averaging, which fundamentally limit the attainable accuracy. In contrast, numerical error, the only component present in non-averaged methods, can be significantly mitigated by employing adaptive numerical algorithms and regularized formulations of the equations of motion. We present a collection of non-averaged methods based on the integration of existing regularized formulations of the equations of motion through an adaptive solver. We implemented the collection in the orbit propagation code THALASSA, which we make publicly available, and we compared the non-averaged methods to the semi-analytical method implemented in the orbit propagation tool STELA through numerical tests involving long-term propagations (on the order of decades) of LEO, GTO, and high-altitude HEO orbits. For the test cases considered, regularized non-averaged methods were found to be up to two times slower than semi-analytical for the LEO orbit, to have comparable speed for the GTO, and to be ten times as fast for the HEO (for the same accuracy). We show for the first time that efficient implementations of non-averaged regularized formulations of the equations of motion, and especially of non-singular element methods, are attractive candidates for the long-term study of high-altitude and highly elliptical Earth satellite orbits.Comment: 33 pages, 10 figures, 7 tables. Part of the CMDA Topical Collection on "50 years of Celestial Mechanics and Dynamical Astronomy". Comments and feedback are encourage

RESPONSABILITA' PENALE DEGLI OPERATORI DI PROTEZIONE CIVILE PER LE ATTIVITA' DI PREVISIONE, VALUTAZIONE E GESTIONE DEL RISCHIO

La ricerca prende spunto dalla recente accresciuta attenzione, da parte della magistratura, all’attività di protezione civile in relazione alla previsione, valutazione e gestione del rischio. Ciò è agevolato dal fatto che l’ordinamento italiano, a fronte del verificarsi di eventi avversi, reagisce usualmente facendo ricorso al diritto penale. Si tratta però di una scelta non priva di conseguenze e che, come già avvenuto in settori affini, quale quello sanitario, rischia di produrre delle significative conseguenze negative, a discapito della stessa efficacia del Sistema di Protezione civile. La prima parte della tesi è così dedicata alla ricognizione del fenomeno della “criminalizzazione” dell’attività di protezione civile e delle conseguenze che ciò ha comportato. Si passa poi all’individuazione delle cause, sia sociologiche, sia giuridiche, che hanno condotto a questo recente, esasperato controllo giudiziale sulla Protezione civile. Nel terzo capitolo è svolta una disamina dei ruoli e compiti della Protezione civile, nonché di alcune sentenze particolarmente rappresentative dei vari orientamenti giurisprudenziali che si sono sviluppati in questo settore. L’ultima parte contiene una ricognizione delle criticità che ancora affliggono l’operato della Protezione civile e che inducono all’adozione di comportamenti difensivi, cui segue l’analisi del progetto di riforma della materia, nonché la formulazione di alcune ulteriori considerazioni personali.The research has been inspired by the recent increased judicial focus on Civil protection activities. This phenomenon depends on the fact that the Italian legal system reacts to the failure of Civil protection duties essentially by using criminal law. This kind of reaction, as it has already been demonstrated in other cases, such as medical malpractice, has some contraindications, because it leads to defensive behaviours. The first part of the thesis is thus dedicated to the recognition of the judicial focus on civil protection activities and the relevant consequences. Then are studied the factors, both sociological and juridical, which have led to this recent exaggerated judicial control over Civil protection. The third chapter concerns the roles and duties of Italian Civil protection and then are examined some leading cases in this matter. In the last part it is conducted a recognition of the crucial problems that still affects Civil protection and that lead to defensive behaviours. It follows an analysis of the reform project of Civil protection and at last some personal proposal to solve the problem of “defensive civil protection” are given

Density Estimation for Entry Guidance Problems using Deep Learning

This work presents a deep-learning approach to estimate atmospheric density profiles for use in planetary entry guidance problems. A long short-term memory (LSTM) neural network is trained to learn the mapping between measurements available onboard an entry vehicle and the density profile through which it is flying. Measurements include the spherical state representation, Cartesian sensed acceleration components, and a surface-pressure measurement. Training data for the network is initially generated by performing a Monte Carlo analysis of an entry mission at Mars using the fully numerical predictor-corrector guidance (FNPEG) algorithm that utilizes an exponential density model, while the truth density profiles are sampled from MarsGRAM. A curriculum learning procedure is developed to refine the LSTM network's predictions for integration within the FNPEG algorithm. The trained LSTM is capable of both predicting the density profile through which the vehicle will fly and reconstructing the density profile through which it has already flown. The performance of the FNPEG algorithm is assessed for three different density estimation techniques: an exponential model, an exponential model augmented with a first-order fading-memory filter, and the LSTM network. Results demonstrate that using the LSTM model results in superior terminal accuracy compared to the other two techniques when considering both noisy and noiseless measurements.Comment: Currently under revision for the AIAA Journal of Guidance Control and Dynamic

Pyrolysis atmosphere effect on biochar properties and PTEs behaviour

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Carbon Fluxes in Sustainable Tree Crops: Field, Ecosystem and Global Dimension

Carbon (C) budget at cropping systems has not only agronomic but also environmental relevance because of their contribution to both emissions and removals of greenhouse gases (GHGs). Ideally, sustainable orchards are expected to remove atmospheric CO2 at a rate greater than that of the emissions because of (i) optimized biology of the system and (ii) reduced on-site/offsite inputs sourced by the technosphere. However, such a computation might produce inconsistent results and in turn biased communication on sustainability of the cropping systems because C accounting framework(s) are used under unclear context. This study examined the sustainability of orchards in terms of impact on GHGs focusing its significance at the field, ecosystem and global dimension analyzing some operational aspects and limitations of existing frameworks (e.g., net ecosystem carbon balance (NECB), life cycle assessment (LCA)). Global relevance of sustainable orchard was also discussed considering the C sequestration at cropland as instructed by Intergovernmental Panel on Climate Change (IPCC). The uniqueness of olive tree lifespan duration and C sequestration is discussed within the Product Environmental Footprint of agrifood product. The paper also highlighted overlapping components among the NECB, LCA and IPCC frameworks and the need for an integrated C accounting scheme for a more comprehensive and detailed mapping of sustainability in agriculture

Detecting Images Generated by Diffusers

This paper explores the task of detecting images generated by text-to-image diffusion models. To evaluate this, we consider images generated from captions in the MSCOCO and Wikimedia datasets using two state-of-the-art models: Stable Diffusion and GLIDE. Our experiments show that it is possible to detect the generated images using simple Multi-Layer Perceptrons (MLPs), starting from features extracted by CLIP, or traditional Convolutional Neural Networks (CNNs). We also observe that models trained on images generated by Stable Diffusion can detect images generated by GLIDE relatively well, however, the reverse is not true. Lastly, we find that incorporating the associated textual information with the images rarely leads to significant improvement in detection results but that the type of subject depicted in the image can have a significant impact on performance. This work provides insights into the feasibility of detecting generated images, and has implications for security and privacy concerns in real-world applications. The code to reproduce our results is available at: https://github.com/davide-coccomini/Detecting-Images-Generated-by-Diffuser

Long Term Wind Turbine Performance Analysis Through SCADA Data: A Case Study

Performance monitoring of horizontal-axis wind turbines is a complex task because they operate under nonstationary conditions. Furthermore, in real-world applications, there can be data quality issues because the free stream wind speed is reconstructed through a nacelle transfer function from cup anemometers measurements collected behind the rotor span. Given these matters of fact, one of the objectives of the present work is applying an innovative method for correcting the nacelle wind speed measurements, which is based on the manufacturer power curve and statistical considerations. Three operating wind turbines, having 2 MW of rated power and owned by the ENGIE Italia company, are contemplated as test cases. Operation data spanning ten years (2011–2020) are studied: actually, this work aims as well at contributing to the methods for estimating the performance decline with age of wind turbines, basing on long term SCADA data analysis. The raw and corrected wind speed measurements are fed as input to a Support Vector Regression for the power curve: by selecting appropriately the training and validation data sets, it is possible to estimate the average yearly rate of performance decline. Using the corrected wind speed, the estimate obtained in this study is compatible with the most recent findings in the literature, which indicate a -0.17% decline per year