Journal of Aeronautics and Space Technologies <p><iframe src="" width="100%" height="180" frameborder="0" marginwidth="0" marginheight="0" scrolling="no"></iframe></p> en-US <p>The manuscript with title and authors is being submitted for publication in Journal of Aeronautics and Space Technologies. This article or a major portion of it was not published, not accepted and not submitted for publication elsewhere. If accepted for publication, I hereby grant the unlimited and all copyright privileges to Journal of Aeronautics and Space Technologies.<br><br>I declare that I am the responsible writer on behalf of all authors.&nbsp;</p> (Journal of Aeronautics and Space Technologies) (Fatma KUTLU GÜNDOĞDU) Fri, 28 Jul 2023 23:13:20 +0300 OJS 60 Predicting Subjective Well-being from a Psychological Perspective <p>Recent advances in pervasive computing enable the collection of personal health-related data using various sensors in daily life. Subjective well-being (SWB) considers how people experience and evaluate their lives and specific domains and activities in their lives. Human behavior modeling and analysis, particularly the quantification of SWB, is still challenging, as there are variations in its definition and measurement. Psychology literature defines different perspectives for SWB, such as hedonic, eudaimonic, or psychologic. In this paper, we propose a model for predicting an individual’s SWB from a psychological perspective. We use the data of The NetHealth study, a heterogeneous data set of 577 student participants from the University of Notre Dame. It contains data about individuals’ daily activities collected via smart wristbands, social relationships monitored through smartphones, and personality traits obtained from surveys. We develop a multi-class classifier based on commonly accepted machine learning algorithms. The results enable us to predict an individual’s well-being almost with an accuracy of 80%. We show that such a pervasive application can be offered as a personalized well-being assistant.</p> Nail Şenbaş, Sadettin Emre Alptekin Copyright (c) 2023 Journal of Aeronautics and Space Technologies Fri, 28 Jul 2023 00:00:00 +0300 Deep Learning-Based Airspeed Estimation System for a Commercial Aircraft <p>Airspeed data is so important for an aircraft operation. This study is focused on the estimation of the airspeed data without any additional measurement source such as hardware redundancy. The flight data obtained from a commercial aircraft is processed with a deep learning algorithm, particularly LSTM recurrent neural networks that are developed based on Matrix Laboratory (MATLAB). Correlation analysis is carried out for related data according to a 95% confidence interval for each coefficient in the study to show strong predictor candidates. Data related to the airspeed are processed using Holdout Cross-Validation Technique. According to the results, the designed model achieved an R-squared of 0.9999, a root-mean-squared error of 0.8303 knots, and a standard error of 0.0092 knots. These results show that it is possible to accurately estimate aircraft airspeed data using LSTM recurrent neural network in case of the airspeed data cannot be provided to the flight crew.</p> Uğur Kılıç Copyright (c) 2023 Journal of Aeronautics and Space Technologies Fri, 28 Jul 2023 00:00:00 +0300 Detection of Current Attacks in Active Directory Environment with Log Correlation Methods <p>Active Directory is a directory service that provides control and integrity with a centralized management and identity management to cyber structures that expand over time and increase the number of devices. Protecting user credentials, corporate systems and sensitive data from unauthorized access is one of the basic principles of information security. Security monitoring of active directory environments is usually performed using signature-based detection rules. However, these rules are not always effective and sufficient, especially for attacks that resemble legitimate activities in terms of control. In this study, log correlation techniques are applied to detect lateral movement and kerberoasting attacks. Based on features from the Windows Event Log, various machine learning algorithms were used and evaluated on data from a real active directory environment. It has been implemented as detection rules for practical use on the Splunk platform, which is a Security Information and Event Management (SIEM) software. In the experimental comparison with signature-based approaches, it is observed that the proposed solution improves the detection capabilities and also reduces the number of false alarms for both attack techniques considered.</p> Mehmet Sabri Elmastaş, Can Eyüpoğlu Copyright (c) 2023 Journal of Aeronautics and Space Technologies Fri, 28 Jul 2023 00:00:00 +0300 The Effects of Gallium Metal Reinforcement on the Crushing Performance of Re-entrant Honeycombs at Different Temperatures <p>This study proposes to investigate the crushing performance of auxetic honeycombs by combining liquid and solid phase materials in one structure. The multiphase structures were produced by injecting a liquid Gallium metal into the hierarchical voids of the proposed metamaterials and tested at different temperatures (22 and 35 0C), where the melting temperature of Gallium metal is 29.4 0C. The deformation mechanism, compressive strength, absorbed energy, and Poisson’s ratio (PR) of the samples was compared at different strains and at different temperatures. Results show that multi-material samples with Gallium metal lead to a progressive deformation mechanism and stabilize the fracture mechanism; however, they only exhibit larger compressive strength in a few cases. In addition, the effects of the phase change of Gallium from solid to liquid when the temperature increase from 22 0C to 35 0C stand out in the percent change of mechanical response. Overall, the presented samples in this work can be recommended to manipulate the collapse mechanism with a multi-material approach of liquid and solid constituents.</p> Fatih Usta Copyright (c) 2023 Journal of Aeronautics and Space Technologies Fri, 28 Jul 2023 00:00:00 +0300