This paper explores the various parallels between music and language, arguing that music functions as a communicative system akin to language, primarily through its ability to convey emotional meaning and affect. Drawing on philosophical theories of meaning by Frege, Searle, and Wittgenstein, it investigates how music and language use shared structures such as syntax, rhythm, and delivery to convey ideas and emotions. The paper addresses key objections to the classification of music as a language, particularly the differences in medium and the focus of music on affect rather than propositional thought. It argues that despite these differences, music and language share significant similarities in their reliance on context, performance, and cultural fluency. The implications of understanding music as a language extend to both linguistic and musical analysis, offering new insights into how people can interpret and experience both forms of communication.
The characterization of mental illness as a form of social deviance has shifted over time. Accordingly, attitudes towards people living with mental illness and the state’s obligation towards them have evolved with the social movements and political stances characterizing each era. While a popular assumption might be a vastly positive change compared to earlier practices, stigma remains a barrier in the full integration of people with mental illness into contemporary society. This article reviews the literature on public attitudes, institutional policies, and social exclusion concerning people with mental illness in Western Europe and North America.
This work presents a study on quantum remote sensing applied to national defense using RADAR (Radio Detection and Ranging) and LIDAR (Light Detection and Ranging) technologies, which can be adopted in metrology and remote sensing processes, capable of detecting and intercepting targets in a given coverage area. In this context, the work presents a bibliographic research addressing concepts, principles of quantum mechanics, basic configurations, methods of photon entanglement, projects, experiments, and potential applications of RADAR and LIDAR systems, highlighting advantages and aspects, such as speed, greater precision, lower cost, greater security guarantee, higher sensitivity, and resolution of quantum remote sensing operation, as well as associated challenges: photon loss, technical difficulties, the phenomenon of decoherence, sensitivity to noise, and the need for integration of quantum remote sensing systems with quantum error correction modules.
The goal of this project is to create a reinforcement learning algorithm that locates shipwrecked individuals using a swarm of drones. A simulated environment was developed to train and visualize the outcome of the trained algorithm, considering the ocean's dynamic circumstances. This project does not discuss image recognition of shipwrecked people, since the true focus of this project is to optimize the search routine of a drone to find the target in the most efficient way possible. The implemented Reinforce algorithm takes into account a dynamic map of probabilities, representing the chances of a person being found, as well as the position of other agents. Outcomes include an open-source python package for the environment and the implementation of the reinforcement learning algorithm. The algorithm demonstrates superiority over the predefined approach, proving the advantages of reinforcement learning in efficiency and effectiveness.
This paper presents a qualitative value model approach for Project Portfolio Selection (PPS) applied to Brazilian Air Force (FAB) projects. It begins with a brief literature review on PPS and Value-Focused Thinking (VFT). The study identifies key objectives from FAB's strategic planning to support the development of a qualitative value model for PPS. It then demonstrates the model through its application to a subportfolio focused on Anti-Aircraft Systems within FAB. Finally, the paper discusses methods for executing the PPS process using the VFT model. This approach innovates by tailoring the value model to the specific decision context of each subportfolio, which enhances the assessment of potential outcomes. Furthermore, it outlines procedures for aggregating PPS results across subportfolios, accounting for interdependencies among projects from different subportfolios.
A informação de ângulo de chegada (AOA, Angle of Arrival) em sensores RWR (Radar Warning Receiver) que utilizam o método de comparação de amplitude é suscetível a distintas fontes de erro que podem afetar a acurácia e a precisão deste parâmetro. A variação da razão sinal-ruído (SNR), a qual é relacionada diretamente à amplitude do sinal obtido pelo receptor, é um dos fatores que podem comprometer a determinação de AOA. Neste artigo, apresenta-se um experimento conduzido em laboratório para se avaliar o erro na determinação de AOA proveniente da variação da SNR. Realiza-se uma análise teórica, baseada em um modelo em que a SNR é função do ângulo de detecção da ameaça e da resposta do receptor, incluindo o padrão e a posição das antenas. A análise experimental valida os resultados teóricos obtidos pelo modelo considerado. Por fim, destaca-se que os testes realizados demonstram a possibilidade de se avaliar o processamento de AOA de sensores RWR dispostos em cadeia reduzida por meio de testes conduzidos, sendo uma alternativa para ensaios de campo ou em câmara anecoica.
The doctrinal pillar "Combat Survival" is gaining increasing notoriety in the military environment. Recent conflicts, such as the Ukrainian War, as well as the clash between Azerbaijan and Armenia, indicate the increasing use of loitering munitions and UAVs (\textit{Unmanned Aerial Vehicles}) as aerial attack platforms, reducing human contact on the front lines. This work performs the radar prediction of the X-47B UAV using electromagnetic simulations based on its static RCS (\textit{Radar Cross Section}) in the VHF, L, S, C, and X frequency bands. A hypothetical radar evaluates the greatest detection ranges among the aforementioned bands. The results demonstrate that the VHF band obtained the greatest detection range, with a gain of 272.2$\%$ about the L band. Thus, the RCS analysis of an aerial platform with a delta-shaped flying wing geometry, such as the X-47B UAV, without electromagnetic absorbing materials, does not present stealth characteristics in the analyzed electromagnetic spectrum bands.
Aircraft engine reliability is critical, particularly in military operations where mission success and safety depend on optimal engine performance. The PT6 engines, used in Super Tucano aircraft by the Ecuadorian and Brazilian Air Forces, are renowned for their versatility and robustness. However, their operational demands necessitate advanced maintenance strategies to prevent failures, enhance safety, and minimize downtime. One of the challenges in developing such strategies lies in managing the uncertainties inherent in engine performance and degradation. Variations in operating conditions, environmental factors, and measurement noise introduce uncertainties that can complicate the prediction of failures and the estimation of Remaining Useful Life (RUL). This study addresses these challenges by incorporating a parametric analysis within the machine learning framework, specifically using Random Forests. This approach not only captures the complex relationships between operational parameters and engine degradation but also evaluates the sensitivity of predictions to variations in key inputs. By leveraging Industry 4.0 technologies, including Big Data Analytics and IoT, the study aims to enhance the robustness of predictive maintenance (PdM) models, ensuring operational readiness and cost-effectiveness in both military and civilian aviation contexts.
During model-based systems engineering or software engineering activities, diagrams representing use cases (sequence diagrams) and diagrams representing object behaviors (state machine diagrams or statecharts) can conflict with each other in what is called an inconsistency. Detecting these inconsistencies is crucial to check if a given specification is realizable through the behavior that was conceived to meet it. This paper provides a systematic literature review of inconsistency detection methods for UML state machine diagrams and sequence diagrams. The selection process is aided by an open-source machine-learning tool, and resulted in the qualitative synthesis of 27 works. The included publications offer methods to tackle the detection of horizontal-semantic behavior inconsistencies.
The increasing use of drones is notable in both military operations and various civilian activities. However, the difficulty in detecting these devices has become a concern when it comes to protecting sensitive areas from unauthorized drone flights. Compounding these challenges is the ability of drones to fly at night, adding an extra layer of difficulty to surveillance and information security efforts. This paper explores the use of an expanded CO₂ laser beam, in a laboratory setting, as an illuminator directed at a drone flying in a controlled environment, aiming to capture images in the long-wave infrared (LWIR) spectrum. The acquired images were used to train a convolutional neural network (CNN) using the YOLO (You Only Look Once) architecture. The results demonstrate the feasibility of using this approach to detect drones when illuminated by an energy source.
In modern aerial defense operation, the evaluation of potential threats is of paramount importance for effective response strategies, particularly when such assessment is performed in real-time. This study presents a comparative analysis of an algorithm developed by the authors, and referred to as DM, and a Markov chain-based approach (MC) in terms of prediction accuracy, execution time, and processing capacity. Notably, DM consistently achieved higher accuracy until simulation time 1350, despite both methods utilizing the same Artificial Neural Network architecture. Additionally, DM exhibited superior execution time and processing capacity, handling a maximum of 89 threats within a one-second timeframe, while MC processed 10 threats. Based on this, it can be asserted that DM meets the requirements for real-time threat evaluation. The results can be attributed to DM's simplified methodology, enabling more accurate and distinct predictions.}
Remote sensing imaging satellites play a vital role in Intelligence, Surveillance, and Reconnaissance (ISR) missions, as they enable the acquisition of information from virtually any location on the Earth’s surface. To ensure the reliability of the provided information, it is essential to calibrate the onboard sensors on these satellites. This paper aims to present a methodology for spectral and radiometric calibration of a Parrot Sequoia camera in laboratory settings. This camera features four monochromatic sensors and one RGB sensor, similar to those onboard orbital platforms. The methodology described employs equipment available at the Laboratory of Radiometry and Characterization of Electro-Optical Sensors (LaRaC) at the Institute for Advanced Studies (IEAv). The paper presents the Spectral Response Functions (SRFs) of the camera sensors, as well as the Radiometric Calibration data. It is worth noting that the proposed methodology can be replicated for any other orbital electro-optical imaging sensor.
The use of radars in the maritime environment has both civil and military applications. The effect of co-channel interference between radars deployed on different ships occurs frequently in several navies. This work proposes to develop a methodology to mitigate the effect of interference between radars operating at the same frequency. A hypothetical radar is used to simulate the effect of co-channel interference between ships, varying the azimuthal engagement angle and the distance between the ships. In the end, a topology of arrangement of ships in a column is obtained, with a maximum distance of 5.95 NM and an arrangement in line with a maximum value of 37.01 NM. This work shows that interference occurs between radars, being overly dependent on the angular condition of engagement due to the effect of the RCS (Radar Cross Section) of the interfering ship.
The 1833 painting The Titan’s Goblet by Thomas Cole stands out as an oddity from his portfolio; its subject matter is strange, and many different historical attempts have been made to understand its meaning. However, most of them have been utterly debunked by the art history academic community. By looking at Cole’s personal views and comparing with much of his portfolio, the monumental glass depicted in this odd painting may be more clearly interpreted as a signifier of the lasting power of the Christian God over pagan gods of the past. This recognition can further help us understand the use of symbols in the works of Cole as well as his contemporaries.
Mary Robinson was a writer who made important contributions to the earliest phases of the Romantic movement. Infamous in her own time for having been the mistress of the Prince of Wales, but soon forgotten after her death, her work is largely understudied. Seeking to understand the work of this writer, this paper explores the theme of justice in Robinson’s work through a close reading of her poetry and finds her understanding of this theme shifts through her career, but is always connected to identity and selfhood. Her earlier poems suggest injustice is exterior to one’s sense of self and does not inhibit one’s ability to autonomously actualize their identity, while her later views suggest that injustice is interior to the self and can permanently inhibit the formation and actualization of the victim’s identity. Further, this paper connects events in Robinson’s life during the earlier and later phases of her writing that might have resulted in this shift in her views. In light of this research, further investigation of Robinson’s writing is necessary to advance an understanding of her unique voice in particular and the Romantic movement as a whole.
