Ricardo Moura

As data becomes increasingly available, relying on quality datasets for algorithm analysis and development is essential. However, data gathering can be expensive and time-consuming, and this process must be optimized to allow others to reuse data with simplicity and accuracy. The Wolfset is an acoustic dataset gathered using a Bruel & Kjaer type 8104 hydrophone in an anechoic tank usually used for ships’ sonar calibration. The name Wolfset is inspired by the Seawolf submarine class, renowned for its advanced sound source detection and classification capabilities. Using an anechoic tank, we can obtain a high-quality dataset representing acoustic sources without undesired external perturbations. In many operating conditions, several outboard motors and an electric motor from a basic remotely controlled ship model were used as sound sources, usually called targets. Then, external transients and noise sources were added to approximate the dataset to the sounds present in real-world conditions. This dataset uses a systematic approach to demonstrate the diversity and accuracy needed for effective algorithm development.

Military technological advancements are crucial for meeting evolving operational needs. Submarines are essential naval assets due to their difficulty in detection. Selecting appropriate equipment for mission efficiency is essential, and automating these tasks with a Decision Support System (DSS) is vital. Air Independent propulsion (AIP) is critical for covert submarine operations, allowing propulsion underwater without surfacing for oxygen. Choosing the right AIP system is challenging because it requires balancing operational efficiency, economic cost-effectiveness, and environmental consequences, all of which entail complex factors that are difficult to quantify or compare. The developed DSS is based on a two-stage Fuzzy Mamdani Model (FMM), applied to perform inference over a submarine AIP. Three reference values were obtained in the first stage to aid operational, economic, and environmental decision-making. In the second stage, these three values create a recommendation value using a percentage system selection. To prove its effectiveness, the DSS was used to evaluate four types of AIPs to establish the level of recommendation and the values of the subcategories in the initial phase. Experts from the Portuguese Navy (PoN) assisted with the system development by defining decision-making rules. The FMM offers advantages over traditional decision-making models by effectively managing vague and qualitative data, enabling reasoning that reflects expert judgment instead of relying on deterministic models. This DSS not only enhances the accuracy and transparency of AIP selection but also provides a scalable and adaptable framework applicable to various complex decision-making scenarios in other engineering applications and fields.

The development and generation of synthetic data are becoming increasingly vital in the field of statistical disclosure control. The PSInference package provides tools to perform exact inferential analysis on singly imputed synthetic data generated through Plug-in Sampling assuming that the original dataset follows a multivariate normal distribution. Includes functions to test the synthetic data's covariance structure, covering aspects like generalized variance, sphericity, independence between subsets of variables, and regression of one set of variables on another. This package addresses the gap in the existing software by providing exact inferential methods suitable for cases where only a single synthetic dataset is released.

It is of the utmost importance for every country to monitor and control maritime pollution within its exclusive economic zone (EEZ). The European Maritime Safety Agency (EMSA) has developed and implemented the CleanSeaNet (CSN) satellite monitoring system to aid in the surveillance and control of hydrocarbon and hazardous substance spills in the ocean. This system’s primary objective is to alert European Union (EU) coastal states to potential spills within their EEZs, enabling them to take the necessary legal and operational actions. To reduce operational costs and increase response capability, the feasibility of implementing a national network (NN) of unmanned vehicles (UVs), both surface and aerial, was explored using a Portuguese case study. The following approach and analysis can be easily generalized to other case studies, bringing essential knowledge to the field. Analyzing oil spill alert events in the Portuguese EEZ between 2017 and 2021 and performing a strengths, weaknesses, opportunities, and threats (SWOT) analysis, essential information has been proposed for the optimal location of an NN of UVs. The study results demonstrate that integrating spill alerts at sea with UVs may significantly improve response time, costs, and personnel involvement, making maritime pollution combat actions more effective.

The difference-based partially linear model is an appropriate regression model when both linear and nonlinear predictors are present in the data. However, when we want to optimize the weights using the difference-based method, the problem of variable selection can be difficult since low-variance predictors present a challenge. Therefore, this study aims to establish a novel methodology based on marginal theory to tackle such mixed relationships successfully, emphasizing variable selection in low dimensions. We suggest using a marginalized LASSO estimator with a penalty term that is not as severe and related to the difference order. As part of our numerical analysis of small sample performance, we undertake comprehensive simulation experiments to numerically demonstrate the strength of our proposed technique in estimation and prediction compared to the LASSO under a low-dimensional setup. This is done so that we can numerically demonstrate the strength of our proposed method in estimation and prediction. The bootstrapped method is utilized to evaluate how well our proposed prediction method performs when examining the King House dataset.