CubeSats have gained significant attention due to their low cost and versatility, yet their low inertia makes them particularly vulnerable to disturbances, such as magnetic torques and residual atmospheric drag, that induce strong nonlinearities in their rotational dynamics. While nonlinear controllers can address these challenges, their high computational burden and intricate stability analyses often forces practitioners to rely on linearization-based techniques, which fail to capture the full extent of the system’s nonlinear behavior. Moreover, existing Koopman-based approaches for attitude control have not exploited the quaternion representation intrinsic to CubeSat dynamics, nor have they adequately addressed the impact of environmental disturbances and sensor noise, an important gap in current research. In this paper, we propose a neural Koopman framework that lifts the nonlinear dynamics, represented in quaternion form, into a higher-dimensional linear space. By training deep neural networks as lifting functions using a control-aware loss function, our method enables the design of efficient linear controllers such as LQR. Comparative results demonstrates that the Koopman model preserves the unit-norm property of quaternions under sensor noise and disturbances and delivers superior reference tracking performance compared to the conventional linearized model, which suffers from renormalization issues. These findings highlight the potential of the proposed framework for achieving robust and computationally efficient CubeSat attitude control.

This study leverages the YOLOv8m computer vision algorithm with Ground Penetrating Radar (GPR) for enhanced aquifer detection in simulated Qatari terrains. Using image processing techniques like the Canny operator, a detection accuracy of 72% was achieved. Different radar resolutions were tested, highlighting the future potential of Transfer Learning for improvement. While simulations show promise, real-world deployment remains an essential next step.

Post-Translational Modifications (PTMs) are changes that occur in proteins after synthesis, influencing their structure, function, and cellular behavior. PTMs are essential in cell biology; they regulate protein function and stability, are involved in various cellular processes, and are linked to numerous diseases. A particularly interesting class of PTMs are chemical modifications such as phosphorylation introduced on amino acid side chains because they can drastically alter the physicochemical properties of the peptides once they are present. One or more PTMs can be attached to each amino acid of the peptide sequence. The most commonly applied technique to detect PTMs on proteins is bottom-up Mass Spectrometry-based proteomics (MS), where proteins are digested into peptides and subsequently analyzed using Tandem Mass Spectrometry (MS/MS). While an increasing number of machine learning models are published focusing on MS/MS-related property prediction of unmodified peptides, high-quality reference data for modified peptides is missing, impeding model development for this important class of peptides. To enable researchers to train machine learning models that can accurately predict the properties of modified peptides, we introduce four high-quality labeled datasets for applying machine and deep learning to tasks in MS-based proteomics. The four datasets comprise several subgroups of peptides with 1.2 million unique modified peptide sequences and 30 unique pairs of (amino-acid, PTM), covering both experimentally introduced and naturally occurring modifications on various amino acids. We evaluate the utility and importance of the dataset by providing benchmarking results on models trained with and without modifications and highlighting the impact of including modified sequences on downstream tasks. We demonstrate that predicting the properties of modified peptides is more challenging but has a broad impact since they are often the core of protein functionality and its regulation, and they have a potential role as biomarkers in clinical applications. Our datasets contribute to applied machine learning in proteomics by enabling the research community to experiment with methods to encode PTMs as model inputs and to benchmark against reference data for model comparison. With a proper data split for three common tasks in proteomics, we provide a robust way to evaluate model performance and assess generalization on unseen modified sequences.

Proteomics is the interdisciplinary field focusing on the large-scale study of proteins. Proteins essentially organize and execute all functions within organisms. Today, the bottom-up analysis approach is the most commonly used workflow, where proteins are digested into peptides and subsequently analyzed using Tandem Mass Spectrometry (MS/MS). MS-based proteomics has transformed various fields in life sciences, such as drug discovery and biomarker identification. Today, proteomics is entering a phase where it is helpful for clinical decision-making. Computational methods are vital in turning large amounts of acquired raw MS data into information and, ultimately, knowledge. Deep learning has proved its success in multiple domains as a robust framework for supervised and unsupervised machine learning problems. In proteomics, scientists are increasingly leveraging the potential of deep learning to predict the properties of peptides based on their sequence to improve their confident identification. However, a reference dataset is missing, covering several proteomics tasks, enabling performance comparison, and evaluating reproducibility and generalization. Here, we present a large labeled proteomics dataset spanning several tasks in the domain to address this challenge. We focus on two common applications: peptide retention time and MS/MS spectrum prediction. We review existing methods and task formulations from a machine learning perspective and recommend suitable evaluation metrics and visualizations. With an accessible dataset, we aim to lower the entry barrier and enable faster development in machine learning for proteomics.