Spoken and Written Communication Needs of Aviation Management Trainees

The need to investigate the communication skills of specific participants usually arises upon demand from the stakeholders, including the employers, passengers, clients and others. The common reasons for stakeholders to consult course developers or language practitioners regarding developing a specific purpose course include poor language proficiency and lack of professional communication skills amongst personnel whose jobs require efficient communication. The needs analysis (NA) method is often used to explore the necessary communication skills at the workplace, including business, medical and aviation. In aviation, the aviation English program content is based on the stakeholders’ communication requirements. Having said that, there are two main objectives of this study. Firstly, this study aims to investigate the importance of English as a medium for communication amongst AVM personnel serving in local and foreign-based companies within Malaysia. Secondly, it aims to determine the spoken and written communication activities that aviation management personnel are frequently involved in the aviation industry. Based on the findings, the NA revealed that the English language is vital in developing professional communication skills among AVM trainees as they require English proficiency for speaking and writing activities.

A multi-sensor satellite-based archive of the largest SO₂ volcanic eruptions since 2006

We present a multi-sensor archive collecting spatial and temporal information about volcanic SO2 clouds generated by the 11 largest eruptions of this century. The detection and monitoring of volcanic clouds are an important topic for aviation management, climate issues and weather forecasts. Several studies focusing on single eruptive events exist, but no archive available at the moment combines quantitative data from as many instruments. We archived and collocated the SO2 vertical column density estimations from three different satellite instruments (AIRS, IASI and GOME-2), atmospheric parameters as vertical profiles from the Global Navigation Satellite Systems (GNSS) Radio Occultations (RO), and the cloud-top height and aerosol type from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Additionally, we provide information about the cloud-top height from three different algorithms and the atmospheric anomaly due to the presence of the cloud. The dataset is gathering 206 d of SO2 data, collocated with 44 180 backscatter profiles and 64 764 radio occultation profiles. The modular structure of the archive allows an easy collocation of the datasets according to the users' needs, and the cross-comparison of the datasets shows different consistency of the parameters estimated with different sensors and algorithms, according to the sensitivity and resolution of the instruments. The data described here are published with a DOI at https://doi.org/10.5880/fidgeo.2020.016 (Tournigand et al., 2020a).

A multi-sensor satellite-based archive of the largest SO₂ volcanic eruptions since 2006

We present a multi-sensor archive collecting spatial and temporal information about volcanic SO2 clouds generated by the eleven largest eruptions of this century. The detection and monitoring of volcanic clouds is an important topic for aviation management, climate issues and weather forecast. Several papers have been published focusing on single events, but not any archive is available at the moment to be used as background for future studies. We archived and collocated the SO2 vertical column density estimations from three different instruments (AIRS, IASI and GOME-2), the atmospheric parameters vertical profiles from the Global Navigation Satellite Systems (GNSS) Radio Occultations (RO) and the vertical backscatter from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP). We additionally provide information about the cloud top height from three different algorithms and the atmospheric anomaly due to the presence of the cloud. The dataset consists of 223 days monitored with SO2 clouds, collocated with 56675 backscatter profiles and 70126 radio occultation profiles. The modular structure of the archive allows an easy collocation of the different datasets according to the users’ needs and the cross-comparison of the datasets shows the high consistency of the parameters estimated with different sensors and algorithms. The data described here will be published with a DOI after final acceptance of this manuscript (Tournigand et al., 2020, http://doi.org/10.5880/fidgeo.2020.016). During the discussion period, the data are accessible via this temporary link: http://pmd.gfz-potsdam.de/panmetaworks/review/0f85d699707efcdc567765bd0dafaaadf94b6df5a531f310167f7e974ea803bf

Learning Difficulties in German as a Third Language Experienced by Turkish Undergraduate Students

The current study aims to investigate learning difficulties in L3 German experienced by the Turkish undergraduate students. The target group involved 29 students from three academic programs at a state university in Turkey: Travel Management and Tourist Guiding, Tourism and Hotel Management and Aviation Management. In the study, semi-structured interview form was designed by the researcher as data collection tool. In the analysis process of the data, the thematic analysis procedure was conducted. The results of the study indicated that the Turkish undergraduate students had learning difficulties in L3 German due to three main factors such as linguistic facts, effective factors and external factors. The linguistic facts involve the learning difficulties related to the linguistic characteristics of German language, linguistic habitus (effect of L2 English) and the effect of L1. In the context of effective factors, it was discussed that the lack of motivation, negative attitudes towards German language, the level of readiness for learning L3 German and the lack of self-confidence lead the students to experience negative learning process in L3 German. Finally, socio-cultural developments and the financial matters emerging in the learning environment were considered as external factors that lead the students to have difficulties in learning L3 German.

Deep Complex-valued Convolutional Neural Network for Drone Recognition Based on RF Fingerprinting

<div>Drones-aided ubiquitous applications play more and more important roles in our daily life. Accurate recognition of drones is required in aviation management due to their potential risks and even disasters.</div><div>Radio frequency (RF) fingerprinting-based recognition technology based on deep learning is considered as one of the effective approaches to extract hidden abstract features from RF data of drones. Existing deep learning-based methods are either a high computational burden or low accuracy.</div><div>In this paper, we propose a deep complex-valued convolutional neural network (DC-CNN) method based on RF fingerprinting for recognizing different drones.</div><div>Compared with existing recognition methods, the DC-CNN method has the advantages of high recognition accuracy, fast running time and small network complexity.</div><div>Nine algorithm models and two datasets are used to represent the superior performance of our system.</div><div>Experimental results show that our proposed DC-CNN can achieve recognition accuracy of 99.5\% and 74.1\% respectively on 4 and 8 classes of RF drone datasets.</div>