(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 10, 2016 Editorial Preface From the Desk of Managing Editor…

It may be difficult to imagine that almost half a century ago we used computers far less sophisticated than current home desktop computers to put a man on the moon. In that 50 year span, the field of computer science has exploded.

Computer science has opened new avenues for thought and experimentation. What began as a way to simplify the calculation process has given birth to technology once only imagined by the human mind. The ability to communicate and share ideas even though collaborators are half a world away and exploration of not just the stars above but the internal workings of the human genome are some of the ways that this field has moved at an exponential pace.

At the International Journal of Advanced Computer Science and Applications it is our mission to provide an outlet for quality research. We want to promote universal access and opportunities for the international scientific community to share and disseminate scientific and technical information.

We believe in spreading knowledge of computer science and its applications to all classes of audiences. That is why we deliver up-to-date, authoritative coverage and offer open access of all our articles. Our archives have served as a place to provoke philosophical, theoretical, and empirical ideas from some of the finest minds in the field.

We utilize the talents and experience of editor and reviewers working at Universities and Institutions from around the world. We would like to express our gratitude to all authors, whose research results have been published in our journal, as well as our referees for their in-depth evaluations. Our high standards are maintained through a double blind review process.

We hope that this edition of IJACSA inspires and entices you to submit your own contributions in upcoming issues. Thank you for sharing wisdom.

Thank you for Sharing Wisdom!

Managing Editor IJACSA Volume 7 Issue 10 October 2016 ISSN 2156-5570 (Online) ISSN 2158-107X (Print) ©2013 The Science and Information (SAI) Organization

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Editorial Board

Editor-in-Chief

Dr. Kohei Arai - Saga University Domains of Research: Technology Trends, Computer Vision, Decision Making, Information Retrieval, Networking, Simulation

Associate Editors

Chao-Tung Yang Department of Computer Science, Tunghai University, Taiwan Domain of Research: Software Engineering and Quality, High Performance Computing, Parallel and Distributed Computing, Parallel Computing

Elena SCUTELNICU “Dunarea de Jos" University of Galati, Romania Domain of Research: e-Learning, e-Learning Tools, Simulation

Krassen Stefanov Professor at Sofia University St. Kliment Ohridski, Bulgaria Domains of Research: e-Learning, Agents and Multi-agent Systems, Artificial Intelligence, Big Data, Cloud Computing, Data Retrieval and Data Mining, Distributed Systems, e-Learning Organisational Issues, e-Learning Tools, Educational Systems Design, Human Computer Interaction, Internet Security, Knowledge Engineering and Mining, Knowledge Representation, Ontology Engineering, Social Computing, Web-based Learning Communities, Wireless/ Mobile Applications

Maria-Angeles Grado-Caffaro Scientific Consultant, Italy Domain of Research: Electronics, Sensing and Sensor Networks

Mohd Helmy Abd Wahab Universiti Tun Hussein Onn Malaysia Domain of Research: Intelligent Systems, Data Mining, Databases

T. V. Prasad Lingaya's University, India Domain of Research: Intelligent Systems, Bioinformatics, Image Processing, Knowledge Representation, Natural Language Processing, Robotics

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 Aamir Shaikh  Alexane Bouënard  Abbas Al-Ghaili Sensopia Mendeley  ALI ALWAN  Abbas Karimi International Islamic University Malaysia Islamic Azad University Arak Branch  Ali Ismail Awad  Abdelghni Lakehal Luleå University of Technology Université Abdelmalek Essaadi Faculté  Alicia Valdez Polydisciplinaire de Larache Route de Rabat, Km 2 -  Amin Shaqrah Larache BP. 745 - Larache 92004. Maroc. Taibah University  Abdul Razak  Amirrudin Kamsin  Abdul Karim ABED  Amitava Biswas  Abdur Rashid Khan Cisco Systems Gomal Unversity  Anand Nayyar  Abeer ELkorany KCL Institute of Management and Technology, Faculty of computers and information, Cairo Jalandhar  ADEMOLA ADESINA  Andi Wahju Rahardjo Emanuel University of the Western Cape Maranatha Christian University  Aderemi A. Atayero  Anews Samraj Covenant University Mahendra Engineering College  Adi Maaita  Anirban Sarkar ISRA UNIVERSITY National Institute of Technology, Durgapur  Adnan Ahmad  Anthony Isizoh  Adrian Branga Nnamdi Azikiwe University, Awka, Nigeria Department of Mathematics and Informatics,  Antonio Formisano Lucian Blaga University of Sibiu University of Naples Federico II  agana Becejski-Vujaklija  Anuj Gupta University of Belgrade, Faculty of organizational IKG Punjab Technical University  Ahmad Saifan  Anuranjan misra yarmouk university Bhagwant Institute of Technology, Ghaziabad, India  Ahmed Boutejdar  Appasami Govindasamy  Ahmed AL-Jumaily  Arash Habibi Lashkari Ahlia University University Technology Malaysia(UTM)  Ahmed Nabih Zaki Rashed  Aree Mohammed Menoufia University Directorate of IT/ University of Sulaimani  Ajantha Herath  ARINDAM SARKAR Stockton University Galloway University of Kalyani, DST INSPIRE Fellow  Akbar Hossain  Aris Skander  Akram Belghith Constantine 1 University University Of California, San Diego  Ashok Matani  Albert S Government College of Engg, Amravati Kongu Engineering College  Ashraf Owis  Alcinia Zita Sampaio Cairo University Technical University of Lisbon  Asoke Nath

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St. Xaviers College(Autonomous), 30 Park Street, University of New Brunswick Kolkata-700 016  C Venkateswarlu Sonagiri  Athanasios Koutras JNTU  Ayad Ismaeel  Chanashekhar Meshram Department of Information Systems Engineering- Chhattisgarh Swami Vivekananda Technical Technical Engineering College-Erbil Polytechnic University University, Erbil-Kurdistan Region- IRAQ  Chao Wang  Ayman Shehata  Chao-Tung Yang Department of Mathematics, Faculty of Science, Department of Computer Science, Tunghai Assiut University, Assiut 71516, Egypt. University  Ayman EL-SAYED  Charlie Obimbo Computer Science and Eng. Dept., Faculty of University of Guelph Electronic Engineering, Menofia University  Chee Hon Lew  Babatunde Opeoluwa Akinkunmi  Chien-Peng Ho University of Ibadan Information and Communications Research  Bae Bossoufi Laboratories, Industrial Technology Research University of Liege Institute of Taiwan  BALAMURUGAN RAJAMANICKAM  Chun-Kit (Ben) Ngan Anna university The Pennsylvania State University  Balasubramanie Palanisamy  Ciprian Dobre  BASANT VERMA University Politehnica of Bucharest RAJEEV GANDHI MEMORIAL COLLEGE,HYDERABAD  Constantin POPESCU  Basil Hamed Department of Mathematics and Computer Islamic University of Gaza Science, University of Oradea  Basil Hamed  Constantin Filote Islamic University of Gaza Stefan cel Mare University of Suceava  Bhanu Prasad Pinnamaneni  CORNELIA AURORA Gyorödi Rajalakshmi Engineering College; Matrix Vision University of Oradea GmbH  Cosmina Ivan  Bharti Waman Gawali  Cristina Turcu Department of Computer Science & information T  Dana PETCU  Bilian Song West University of Timisoara LinkedIn  Daniel Albuquerque  Binod Kumar  Dariusz Jakóbczak JSPM's Jayawant Technical Campus,Pune, India Technical University of Koszalin  Bogdan Belean  Deepak Garg  Bohumil Brtnik Thapar University University of Pardubice, Department of Electrical  Devena Prasad Engineering  DHAYA R  Bouchaib CHERRADI  Dheyaa Kadhim CRMEF University of Baghdad  Brahim Raouyane  Djilali IDOUGHI FSAC University A.. Mira of Bejaia  Branko Karan  Dong-Han Ham  Bright Keswani Chonnam National University Department of Computer Applications, Suresh Gyan  Dr. Arvind Sharma Vihar University, Jaipur (Rajasthan) INDIA  Brij Gupta (iv)

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Aryan College of Technology, Rajasthan Technology  Giacomo Veneri University, Kota University of Siena  Duck Hee Lee  Giri Babu Medical Engineering R&D Center/Asan Institute for Indian Space Research Organisation Life Sciences/Asan Medical Center  Govindarajulu Salendra  Elena SCUTELNICU  Grebenisan Gavril "Dunarea de Jos" University of Galati University of Oradea  Elena Camossi  Gufran Ahmad Ansari Joint Research Centre Qassim University  Eui Lee  Gunaseelan Devaraj Sangmyung University Jazan University, Kingdom of Saudi Arabia  Evgeny Nikulchev  GYÖRÖDI ROBERT STEFAN Moscow Technological Institute University of Oradea  Ezekiel OKIKE  Hadj Tadjine UNIVERSITY OF BOTSWANA, GABORONE IAV GmbH  Fahim Akhter  Haewon Byeon King Saud University Nambu University  FANGYONG HOU  Haiguang Chen School of IT, Deakin University ShangHai Normal University  Faris Al-Salem  Hamid Alinejad-Rokny GCET The University of New South Wales  Firkhan Ali Hamid Ali  Hamid AL-Asadi UTHM Department of Computer Science, Faculty of  Fokrul Alom Mazarbhuiya Education for Pure Science, Basra University King Khalid University  Hamid Mukhtar  Frank Ibikunle National University of Sciences and Technology Botswana Int’l University of Science & Technology  Hany Hassan (BIUST), Botswana EPF  Fu-Chien Kao  Harco Leslie Henic SPITS WARNARS Da-Y eh University Bina Nusantara University  Gamil Abdel Azim  Hariharan Shanmugasundaram Suez Canal University Associate Professor, SRM  Ganesh Sahoo  Harish Garg RMRIMS Thapar University Patiala  Gaurav Kumar  Hazem I. El Shekh Ahmed Manav Bharti University, Solan Himachal Pradesh Pure mathematics  George Pecherle  Hemalatha SenthilMahesh University of Oradea  Hesham Ibrahim  George Mastorakis Faculty of Marine Resources, Al-Mergheb Technological Educational Institute of Crete University  Georgios Galatas  Himanshu Aggarwal The University of Texas at Arlington Department of Computer Engineering  Gerard Dumancas  Hongda Mao Oklahoma Baptist University Hossam Faris  Ghalem Belalem  Huda K. AL-Jobori University of Oran 1, Ahmed Ben Bella Ahlia University  gherabi noreddine  Imed JABRI

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 iss EL OUADGHIRI  Khalid Mahmood  Iwan Setyawan IEEE Satya Wacana Christian University  Khalid Sattar Abdul  Jacek M. Czerniak Assistant Professor Casimir the Great University in Bydgoszcz  Khin Wee Lai  Jai Singh W Biomedical Engineering Department, University  JAMAIAH HAJI YAHAYA Malaya NORTHERN UNIVERSITY OF MALAYSIA (UUM)  Khurram Khurshid  James Coleman Institute of Space Technology Edge Hill University  KIRAN SREE POKKULURI  Jatinderkumar Saini Professor, Sri Vishnu Engineering College for Narmada College of Computer Application, Bharuch Women  Javed Sheikh  KITIMAPORN CHOOCHOTE University of Lahore, Pakistan Prince of Songkla University, Phuket Campus  Jayaram A  Krasimir Yordzhev Siddaganga Institute of Technology South-West University, Faculty of Mathematics and Natural Sciences, Blagoevgrad, Bulgaria  Ji Zhu  Krassen Stefanov University of Illinois at Urbana Champaign Professor at Sofia University St. Kliment Ohridski  Jia Uddin Jia  Labib Gergis Assistant Professor Misr Academy for Engineering and Technology  Jim Wang  LATHA RAJAGOPAL The State University of New York at Buffalo, Buffalo, NY  Lazar Stošic  John Sahlin College for professional studies educators Aleksinac, Serbia George Washington University  Leanos Maglaras  JOHN MANOHAR De Montfort University VTU, Belgaum  Leon Abdillah  JOSE PASTRANA Bina Darma University University of Malaga  Lijian Sun  Jui-Pin Yang Chinese Academy of Surveying and Shih Chien University  Ljubomir Jerinic  Jyoti Chaudhary University of Novi Sad, Faculty of Sciences, high performance computing research lab Department of Mathematics and Computer Science  K V.L.N.Acharyulu  Lokesh Sharma Bapatla Engineering college Indian Council of Medical Research  Ka-Chun Wong  Long Chen  Kamatchi R Qualcomm Incorporated  Kamran Kowsari  M. Reza Mashinchi The George Washington University Research Fellow  KANNADHASAN SURIIYAN  M. Tariq Banday  Kashif Nisar University of Kashmir Universiti Utara Malaysia  madjid khalilian  Kato Mivule  majzoob omer  Kayhan Zrar Ghafoor  Mallikarjuna Doodipala University Technology Malaysia Department of Engineering Mathematics, GITAM  Kennedy Okafor University, Hyderabad Campus, Telangana, INDIA Federal University of Technology, Owerri (vi)

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 Manas deep  Mohamed Najeh LAKHOUA Masters in Cyber Law & Information Security ESTI, University of Carthage  Manju Kaushik  Mohammad Ali Badamchizadeh  Manoharan P.S. University of Tabriz Associate Professor  Mohammad Jannati  Manoj Wadhwa  Mohammad Alomari Echelon Institute of Technology Faridabad Applied Science University  Manpreet Manna  Mohammad Haghighat Director, All India Council for Technical Education, University of Miami Ministry of HRD, Govt. of India  Mohammad Azzeh  Manuj Darbari Applied Science university BBD University  Mohammed Akour  Marcellin Julius Nkenlifack Yarmouk University University of Dschang  Mohammed Sadgal  Maria-Angeles Grado-Caffaro Cadi Ayyad University Scientific Consultant  Mohammed Al-shabi  Marwan Alseid Associate Professor Applied Science Private University  Mohammed Hussein  Mazin Al-Hakeem  Mohammed Kaiser LFU (Lebanese French University) - Erbil, IRAQ Institute of Information Technology  Md Islam  Mohammed Ali Hussain sikkim manipal university Sri Sai Madhavi Institute of Science & Technology  Md. Bhuiyan  Mohd Helmy Abd Wahab King Faisal University University Tun Hussein Onn Malaysia  Md. Zia Ur Rahman  Mokhtar Beldjehem Narasaraopeta Engg. College, Narasaraopeta University of Ottawa  Mehdi Bahrami  Mona Elshinawy University of California, Merced Howard University  Messaouda AZZOUZI  Mostafa Ezziyyani Ziane AChour University of Djelfa FSTT  Milena Bogdanovic  Mouhammd sharari alkasassbeh University of Nis, Teacher Training Faculty in Vranje  Mourad Amad  Miriampally Venkata Raghavendra Laboratory LAMOS, Bejaia University Adama Science & Technology University, Ethiopia  Mueen Uddin  Mirjana Popovic University Malaysia Pahang School of Electrical Engineering, Belgrade University  MUNTASIR AL-ASFOOR  Miroslav Baca University of Al-Qadisiyah University of Zagreb, Faculty of organization and  Murphy Choy informatics / Center for biometrics  Murthy Dasika  Moeiz Miraoui Geethanjali College of Engineering & Technology University of Gafsa  Mustapha OUJAOURA  Mohamed Eldosoky Faculty of Science and Technology Béni-Mellal  Mohamed Ali Mahjoub  MUTHUKUMAR SUBRAMANYAM Preparatory Institute of Engineer of Monastir DGCT, ANNA UNIVERSITY  Mohamed Kaloup  N.Ch. Iyengar  Mohamed El-Sayed VIT University Faculty of Science, Fayoum University, Egypt  Nagy Darwish (vii)

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Department of Computer and Information Sciences,  Ping Zhang Institute of Statistical Studies and Researches, Cairo IBM University  Poonam Garg  Najib Kofahi Institute of Management Technology, Ghaziabad Yarmouk University  Prabhat K Mahanti  Nan Wang UNIVERSITY OF NEW BRUNSWICK LinkedIn  PROF DURGA SHARMA ( PHD)  Natarajan Subramanyam AMUIT, MOEFDRE & External Consultant (IT) & PES Institute of Technology Technology Tansfer Research under ILO & UNDP,  Natheer Gharaibeh Academic Ambassador for Cloud Offering IBM-USA College of Computer Science & Engineering at  Purwanto Purwanto Yanbu - Taibah University Faculty of Computer Science, Dian Nuswantoro  Nazeeh Ghatasheh University The University of Jordan  Qifeng Qiao  Nazeeruddin Mohammad University of Virginia Prince Mohammad Bin Fahd University  Rachid Saadane  NEERAJ SHUKLA EE departement EHTP ITM UNiversity, Gurgaon, (Haryana) Inida  Radwan Tahboub  Neeraj Tiwari Palestine Polytechnic University  Nestor Velasco-Bermeo  raed Kanaan UPFIM, Mexican Society of Artificial Intelligence Amman Arab University  Nidhi Arora  Raghuraj Singh M.C.A. Institute, Ganpat University Harcourt Butler Technological Institute  Nilanjan Dey  Rahul Malik  Ning Cai  raja boddu Northwest University for Nationalities LENORA COLLEGE OF ENGINEERNG  Nithyanandam Subramanian  Raja Ramachandran Professor & Dean  Rajesh Kumar  Noura Aknin National University of Singapore University Abdelamlek Essaadi  Rakesh Dr.  Obaida Al-Hazaimeh Madan Mohan Malviya University of Technology Al- Balqa' Applied University (BAU)  Rakesh Balabantaray  Oliviu Matei IIIT Bhubaneswar Technical University of Cluj-Napoca  Ramani Kannan  Om Sangwan Universiti Teknologi PETRONAS, Bandar Seri  Omaima Al-Allaf Iskandar, 31750, Tronoh, Perak, Malaysia Asesstant Professor  Rashad Al-Jawfi  Osama Omer Ibb university Aswan University  Rashid Sheikh  Ouchtati Salim Shri Aurobindo Institute of Technology, Indore  Ousmane THIARE  Ravi Prakash Associate Professor University Gaston Berger of University of Mumbai Saint-Louis SENEGAL  RAVINA CHANGALA  Paresh V Virparia  Ravisankar Hari Sardar Patel University CENTRAL TOBACCO RESEARCH INSTITUE  Peng Xia  Rawya Rizk Microsoft Port Said University

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 Reshmy Krishnan  Shafiqul Abidin Muscat College affiliated to stirling University.U HMR Institute of Technology & Management  Ricardo Vardasca (Affiliated to G GS I P University), Hamidpur, Delhi - Faculty of Engineering of University of Porto 110036  Ritaban Dutta  Shahanawaj Ahamad ISSL, CSIRO, Tasmaniia, Australia The University of Al-Kharj  Rowayda Sadek  Shaidah Jusoh  Ruchika Malhotra  Shaiful Bakri Ismail Delhi Technoogical University  Shakir Khan  Rutvij Jhaveri Al-Imam Muhammad Ibn Saud Islamic University Gujarat  Shawki Al-Dubaee  SAADI Slami Assistant Professor University of Djelfa  Sherif Hussein  Sachin Kumar Agrawal Mansoura University University of Limerick  Shriram Vasudevan  Sagarmay Deb Amrita University Central Queensland Universiry, Australia  Siddhartha Jonnalagadda  Said Ghoniemy Mayo Clinic Taif University  Sim-Hui Tee  Sandeep Reddivari Multimedia University University of North Florida  Simon Ewedafe  Sanskruti Patel The University of the West Indies Charotar Univeristy of Science & Technology,  Siniša Opic Changa, Gujarat, India University of Zagreb, Faculty of Teacher Education  Santosh Kumar  Sivakumar Poruran Graphic Era University, Dehradun (UK) SKP ENGINEERING COLLEGE  Sasan Adibi  Slim BEN SAOUD Research In Motion (RIM) National Institute of Applied Sciences and  Satyena Singh Technology Professor  Sofien Mhatli  Sebastian Marius Rosu  sofyan Hayajneh Special Telecommunications Service  Sohail Jabbar  Seema Shah Bahria University Vidyalankar Institute of Technology Mumbai  Sri Devi Ravana  Seifedine Kadry University of Malaya American University of the Middle East  Sudarson Jena  Selem Charfi GITAM University, Hyderabad HD Technology  Suhail Sami Owais Owais  SENGOTTUVELAN P  Suhas J Manangi Anna University, Chennai Microsoft  Senol Piskin  SUKUMAR SENTHILKUMAR Istanbul Technical University, Informatics Institute Universiti Sains Malaysia  Sérgio Ferreira  Süleyman Eken School of Education and Psychology, Portuguese Kocaeli University Catholic University  Sumazly Sulaiman  Seyed Hamidreza Mohades Kasaei Institute of Space Science (ANGKASA), Universiti University of Isfahan Kebangsaan Malaysia

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 Sumit Goyal ANNA UNIVERSITY National Dairy Research Institute  Vinayak Bairagi  Suparerk Janjarasjitt AISSMS Institute of Information Technology, Pune Ubon Ratchathani University  Vishnu Mishra  Suresh Sankaranarayanan SVNIT, Surat Institut Teknologi Brunei  Vitus Lam  Susarla Sastry The University of Hong Kong JNTUK, Kakinada  VUDA SREENIVASARAO  Suseendran G PROFESSOR AND DEAN, St.Mary's Integrated Vels University, Chennai Campus, Hyderabad  Suxing Liu  Wali Mashwani Arkansas State University Kohat University of Science & Technology (KUST)  Syed Ali  Wei Wei SMI University Karachi Pakistan Xi’an Univ. of Tech.  T C.Manjunath  Wenbin Chen HKBK College of Engg 360Fly  T V Narayana rao Rao  Xi Zhang SNIST illinois Institute of Technology  T. V. Prasad  Xiaojing Xiang Lingaya's University AT&T Labs  Taiwo Ayodele  Xiaolong Wang Infonetmedia/University of Portsmouth University of Delaware  Talal Bonny  Yanping Huang Department of Electrical and Computer  Yao-Chin Wang Engineering, Sharjah University, UAE  Yasser Albagory  Tamara Zhukabayeva College of Computers and Information Technology,  Tarek Gharib Taif University, Saudi Arabia Ain Shams University  Yasser Alginahi  thabet slimani  Yi Fei Wang College of Computer Science and Information The University of British Columbia Technology  Yihong Yuan  Totok Biyanto University of California Santa Barbara Engineering Physics, ITS Surabaya  Yilun Shang  Touati Youcef Tongji University Computer sce Lab LIASD - University of Paris 8  Yu Qi  Tran Sang Mesh Capital LLC IT Faculty - Vinh University - Vietnam  Zacchaeus Omogbadegun  Tsvetanka Georgieva-Trifonova Covenant University University of Veliko Tarnovo  Zairi Rizman  Uchechukwu Awada Universiti Teknologi MARA Dalian University of Technology  Zarul Zaaba  Udai Pratap Rao Universiti Sains Malaysia  Urmila Shrawankar  Zenzo Ncube GHRCE, Nagpur, India North West University  Vaka MOHAN  Zhao Zhang TRR COLLEGE OF ENGINEERING Deptment of EE, City University of Hong Kong  VENKATESH JAGANATHAN  Zhihan Lv

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www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 10, 2016 Automatic Detection of Omega Signals Captured by the Poynting Flux Analyzer (PFX) on Board the Akebono Satellite

I Made Agus Dwi Suarjaya, Yoshiya Kasahara, and Yoshitaka Goto Graduate School of Natural Sci. and Tech. Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan

Abstract—The Akebono satellite was launched in 1989 to particular, a tomographic electron density profile could be observe the Earth’s magnetosphere and plasmasphere. Omega determined by calculating the Omega signal propagation path was a navigation system with 8 ground stations transmitter and using the ray tracing method. This method could estimate the had transmission pattern that repeats every 10 s. From 1989 to propagation path within one hour of single satellite 1997, the PFX on board the Akebono satellite received signals at observations [2]. The algorithm was further improved with a 10.2 kHz from these stations. Huge amounts of PFX data became flexible method and novel stochastic algorithm. This enabled valuable for studying the propagation characteristics of VLF estimates separate from the effects of the ionosphere and waves in the ionosphere and plasmasphere. In this study, we plasmasphere [3]. Goto et al. (2003) demonstrated that electron introduce a method for automatic detection of Omega signals density profile could be drastically changed day by day from the PFX data in a systematic way, it involves identifying a depending on magnetic activities during a magnetic storm. transmission station, calculating the delay time, and estimating the signal intensity. We show the reliability of the automatic Because the transmission pattern of frequency, time, and detection system where we able to detect the omega signal and the location of each station is known, we can easily distinguish confirmed its propagation to the opposite hemisphere along the the signal source. We can then determine many propagation Earth’s magnetic field lines. For more than three years (39 properties such as attenuation ratio, propagation direction, months), we detected 43,734 and 111,049 signals in the magnetic propagation time (delay time) from the transmission station, and electric field, respectively, and demonstrated that the and the observation point along the satellite trajectories. Such proposed method is powerful enough for the statistical analyses. parameters depend strongly on the plasma parameters along the Keywords—Auto-detection; Satellite; Signal processing; Wave propagation path. Therefore it is worth to analyze such Propagation; Plasmasphere propagation properties statistically using the long term observation data. I. INTRODUCTION Processing manually all of the data from 1989 to 1997, The Akebono (EXOS-D) satellite was launched at 23:30 however, will take a lot of time. Furthermore, more processes UT on February 21, 1989 to observe the Earth’s and analyses will be needed to see different results such as magnetosphere and plasmasphere. This satellite has onboard analysis based on magnetic local time, seasonal propagation VLF instruments and Poynting flux Analyzer (PFX) is one of analysis, and yearly propagation analysis. Our automatic the subsystems. The PFX is a waveform receiver that measures detection method makes all of the analysis processes simpler two components of electric fields and three components of and is able to produce most of the required result faster and magnetic fields with a band-width of 50 Hz in a frequency efficient. This study discusses the automatic detection methods range from 100 Hz to 12.75 kHz [1]. Omega was a navigation for faster analysis of huge amounts of PFX data to study the system with 8 ground stations transmitter and had transmission propagation characteristics of VLF waves which, in this case, pattern repeating every 10 s. Each station transmitted a is the Omega signal. An outline of the paper follows. In the different pattern of frequency but had a common frequency at second section, we present the technique we are using for 10.2 kHz. The Omega system was terminated in 1997 in favor automatic detection. It involves identifying a transmission of the GPS system. station, calculating the delay time, and estimating the signal intensity. In the third section, we discuss the result of this From 1989 to 1997, the PFX on board the Akebono analysis using an event study and statistical study for the satellite received signals at 10.2 kHz from the eight stations Norway station. In the final section, we present conclusions and huge amounts of PFX data became valuable to study the and summarize our research. propagation characteristics of VLF waves in the ionosphere and plasmasphere. Once the Omega signals are radiated in the II. DATA ANALYSIS TECHNIQUE plasmasphere permeating through the ionosphere, they propagate as a whistler mode wave in the plasma. The omega A. Omega Signal signal data captured by the PFX on board the Akebono has The Omega signal is very low frequency (VLF) signal been used to estimate global plasmaspheric electron density. In between 10 and 14 kHz transmitted by the Omega navigation

67 | P a g e www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 10, 2016 system that was operational in 1971. Before it shut down in the magnetic and/or electric field were occasionally saturated. 1997, its purpose was to provide a navigational aid for During the period of saturation, signal intensity is apparently domestic aviation and oceanic shipping. Omega receiver clipped and we cannot estimate absolute intensity. Then we determines location based on the phase of the signal from two define 'raise time' of Omega signal at the beginning point of the or more of the Omega stations [5]. This Omega signal was signal, while we use the data 0.5 seconds after the raise time transmitted from eight ground stations with each station when we evaluate 'absolute intensity' of the signals to exclude transmitting a unique pattern, based on which our analyzer the saturated data. The detailed detection algorithm to derive software could determine the source of the signal. The location raise time and intensity is described in the next section. of these Omega stations are Norway (NW), Liberia (LB), Hawaii (HW), North Dakota (ND), La Reunion Island (LR), Argentina (AZ), Australia (AS), and Japan (JP). There are four common frequencies of the Omega signal (10.2, 13.6, 11.33, and 11.05 kHz) with one unique frequency for each station. The transmission pattern at 10.2 kHz from each station is shown in Fig. 1. Each station transmit 10.2 kHz signal at different timing. There is an interval of 0.2 s separating each of the eight transmissions, with variations in the duration for each station.

Fig. 1. Transmission Pattern of the 10.2 kHz Omega Signal. There are four common frequencies of the Omega signal (10.2, 13.6, 11.33, and 11.05 kHz) with one unique frequency for each station. There is an interval of 0.2 s separating each of the eight transmissions, with variations in the duration for each station

B. PFX Subsystem of the Akebono Satellite The PFX subsystem of the Akebono satellite measures 3 components of magnetic fields (B1, B2, and B3) and 2 components of electric field (Ex and Ey). It’s comprised five- channel triple-super-heterodyne receivers with an output bandwidth of 50 Hz. It also had a local oscillator that could be stepped or fixed at a specific center frequency with a range Fig. 2. Raw PFX waveform at 18:05:29.503 UT on December 14, 1989. We from 100 Hz to 12.75 kHz and equipped with Wide Dynamic can observe the raised intensity from 18:05:33.500 UT to 18:05:34.900 UT, Range Amplifier (WIDA) hybrid IC to control the gain in the which in this case is the Omega signal from the Australia station. In the electric dynamic range for more than 80 dB [1]. WIDA hybrid IC will field components of EX and EY from 18:05:33.800 UT to 18:05:34.300 UT, we check automatically averaged signal level every 0.5 seconds can observe a 0.5 s saturated intensity signal caused by the WIDA IC and the gain of each channel is changed independently in 25 To measure Omega signals, we selected and analyzed the dB steps from 0 dB up to 75 dB. For our study, we use BX, BY, B , E and E in static coordinate system converted from B , PFX data when its center frequency was fixed at 10.2 kHz. The Z X Y 1 PFX data was recorded in Common Data Format (CDF) B2, B3, Ex and Ey obtained in the antenna coordinate system fixed to the spinning satellite as defined in Kimura et al. developed by the National Space Science Data Center (1990). The static coordinate system is referred to the direction (NSSDC) at NASA [4]. This ensured standardized read/write of the geomagnetic field (the geomagnetic field line is in the X- interfaces for multiple programming languages and software. Z plane and Y is perpendicular to the X-Z plane) and the The waveforms measured by the PFX were originally two direction of the sun (Z-axis). components of the electric field in the spin plane and three components of the magnetic field in the B1, B2 and B3 Fig. 2 shows the 10 s raw waveform of the PFX data at directions. These waveforms were orthogonal with respect to 18:05:29.503 UT on December 14, 1989. In the figure, we can each other but different from satellite coordinates. These observe the raised intensity from 18:05:33.500 UT to waveforms were calibrated, converted into static satellite 18:05:34.900 UT, which in this case is the Omega signal from coordinates, and stored in the CDF files. One month of PFX the Australia station. In the electric field components of EX and data represented 5–10 GB and one year of data consumed EY from 18:05:33.800 UT to 18:05:34.300 UT, the PFX approximately 60–90 GB. In total, the amount of data from receivers both for Ex and Ey were saturated as indicated by the 1989 to 1997 is approximately 570 GB. red arrows, because an intense Omega signal was captured. But the gain of these channels was immediately adopted adequately The PFX data is stored as waveforms sampled at rate of at 18:05:33.800 UT thanks to the WIDA IC. The WIDA IC 320 Hz. For FFT analysis, we used FFT size of 32. Therefore, worked independently for each five components. Therefore, all the time resolution was 100 ms and the frequency resolution channels are not necessarily saturated but some components of was 10 Hz. To improve the accuracy of delay time detection,

68 | P a g e www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 10, 2016 we applied an overlap-add FFT that moves over three sample After we calculated the EZ component, we calculated the points for a higher time resolution (~9.4 ms) when the first absolute intensity of the electric field |E| as shown in (2). In the signal was detected. Although PFX measured only two same way, we also calculated the absolute intensity of the components of the electric field, we could derive another magnetic field |B| using (3). We use dB (V/m) for the electric component (EZ) if we assumed the measured signal as a single field measurement unit and dB (T) for the magnetic field plane wave [5]. This is expressed using (1). measurement unit.

2 2 2 EX BX  EY BY (1) | E |  E  E  E (2) EZ   X Y Z BZ 2 2 2 | B |  BX  BY  BZ (3)

Fig. 3. Spectrum of the Omega signal start at 18:05:29.503 UT on December 14, 1989. We separated it into three frequency area bins (Pc, Pu, and Pl) to compare ambient noise and Omega signal intensity in the magnetic field (a) and in the electric field (b)

Based on extracted data from the analyzer, Fig. 4 compares III. DETECTION ALGORITHM ambient noise and Omega signal intensity in the magnetic field A. Detection of the Omega Signal at 18:05:29.503 UT on December 14, 1989. We can see the We first estimated the raise time of each signal by raised intensity level on Pc compared with Pu and Pl. This comparing the average intensity of specific time frame to the raised intensity level occurred within the expected transmission threshold level, expecting a sudden increase in intensity. time of the Australia station (from approximately 18:05:33.700 Second, we determined the transmission station by comparing UT to 18:05:34.900 UT) and the Japan station (from the raise time with the transmission patterns of the eight approximately 18:05:35.500 UT to 18:05:36.500 UT). Omega stations. At 0000 UT on January 1, 1972, the Omega and UTC scales were identical. However, we subsequently had to conduct a leap seconds calculation to synchronize the omega time and UTC because the Omega had no leap seconds like the UTC [5]. On December 31, 1989, the Omega time led the UTC by 14 s. The threshold level we use in the detection method, is based on the comparison of ambient noise level and Omega signal intensity. In Fig. 3, we show parameters for the comparison of ambient noise and Omega signal intensity visualized on a spectrogram of 10 s of PFX data in the magnetic field (a) and in the electric field (b) beginning at Fig. 4. Comparison of Omega signal intensity and ambient noise level in the 18:05:29.503 UT on December 14, 1989 when the Omega magnetic field. Raised intensity level occurred within the expected signal from Australia and Japan were expected to be received. transmission time of the Australia station (from approximately 18:05:33.700 The spectrogram in Fig. 3 consists of 16 bins in frequency (∆f UT to 18:05:34.900 UT) and the Japan station (from approximately = 10Hz/bin) and 1057 bins in time (∆t = ~9.4ms/bin). We 18:05:35.500 UT to 18:05:36.500 UT) separated it into three frequency area bins, where Pc denotes the center frequency (consisting of 5 bins in frequency and 1 A comparison of ambient noise and omega signal intensity in the electric field can be seen in Fig. 5, which is based on bin in time), Pu denotes the upper frequency (consisting of 5 extracted data from the analyzer at 18:05:29.503 UT on bins in frequency and 1 bin in time), and Pl denotes the lower frequency (consisting of 6 bins in frequency and 1 bin in time). December 14, 1989. In this case, the signal was saturated from

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approximately 18:05:33.800 UT to 18:05:34.300 UT because the WIDA IC was controlling the gain of receiver. In this case, we need to calculate the intensity of the saturated signal after 0.5 s. We recognized this saturation by calculating and comparing each each signals for any sudden change in the intensity of the constant duration. In this case, the WIDA IC will affect the next 0.5 s sample for increased gain when activated. This type of saturated signal could affect any of the 5 components measured by the PFX subsystem because the WIDA IC works independently for each component. B. Calculation of Signal Intensity and Delay Time Fig. 5. Comparison of Omega signal intensity and ambient noise level in the We calculated the delay time of the signal by detecting the electric field. The signal was saturated from approximately 18:05:33.800 UT to raise time of intensity and compare it with surrounding 18:05:34.300 UT because the WIDA IC was controlling the gain of receiver frequency from start time transmission during each station transmission duration as shown in (4).

Fig. 6. Parameters used in the delay time detection method. Delay time (td) calculated by detecting the raise time of intensity (Pn) and compare it with surrounding frequency (P_un and P_ln) from start time transmission (t0) during duration time of each station

tn t0 if P _ un  P _ ln / 2 TP  Pn and Pn  Pn1   TP (4) In the next step, we determined signal existence by td   0 otherwise comparing the intensity of the expected duration of the Omega signal with the surrounding intensity (higher and lower where TP denotes the intensity threshold (8 dB), td denotes frequency points of the center frequency). We determined delay time in seconds, Pn denotes intensity strength of center signal existence and derived the signal intensity (Pωs) by using frequency consists of 5 bins in frequency (∆f = 10Hz/bin) and (5) 20 bins in time (∆t = ~9.4ms/bin), P_un denotes intensity strength of upper frequency consists of 5 bins in frequency (∆f Ps if Pa  Pb / 2  TP  Ps and Pw  Tw  Ps (5) Pωs   = 10Hz/bin) and 20 bins in time (∆t = ~9.4ms/bin), P_ln 0 otherwise denotes intensity strength of lower frequency consists of 5 bins in frequency (∆f = 10Hz/bin) and 20 bins in time (∆t = where Pa denotes the intensity strength of the upper frequency bins in decibels, Pb denotes the intensity strength of ~9.4ms/bin), t0 denotes start time of the station’s transmission, lower frequency bins in decibels, Ps denotes the intensity tn denotes start time of Pn, and n denotes the iteration number for every 1 bin in time (∆t = ~9.4ms/bin). In Fig. 6, we show strength of center frequency bins in decibels. Pa, Pb, and Ps the parameters used for the delay time detection visualized on a consists of 5 bins in frequency (∆f = 10Hz/bin) and variation spectrogram of 10 s of PFX data in the magnetic field start at (128 bins for Australia and 106 bins for Japan) of bins in time 18:05:29.503 UT on December 14, 1989, when the Omega (∆t = ~9.4ms/bin) depends on the duration of the Omega signal signal from Australia and Japan were expected to be received. for each station. Pw denotes intensity strength of 16 bins in frequency (∆f = 10Hz/bin) and 1057 bins in time (∆t = ~9.4ms/bin) for every 10 s duration (1 window) in decibels.

Fig. 7. Parameters used for discrimination and intensity calculations. The signal existence determined by comparing the average intensity of the expected duration of the Omega signal (Ps) with the surrounding frequency (Pa and Pb) and the ambient noise of 10 seconds duration or 1 window (Pw)

In Fig. 7, we show the parameters used for the calculation frequency and 128 bins in time) because the Omega signal of the signal visualized on the same spectrogram as shown in from Australia station has 1.2 s in duration and we used Fig. 6. In Fig. 7, for example, Ps has total of 640 bins (5 bins in overlap-add FFT. We defined two intensity thresholds TP and

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TW, where TP had a fixed value of 8 dB and TW had a fixed geomagnetic and geographic maps of the Earth. Analyses of value of 5 dB. local time dependence are also available. An overview of the analyzer software is shown in Fig. 8. C. PFX Analyzer This analyzer connects to the Akebono orbit database and We developed software to analyze the Omega signal data can be used to manually analyze each signal in real time by measured by the Akebono from 1989 to 1997. The software manipulating the navigation panel. To automatically analyze was written in Java programming language. We can the CDF files for one month of data, we need 6–9 hours of interactively check the waveform measured by the PFX one by processing time using an Intel Quad Core with a 3.324 GHz one for an event study. It also enables us to detect Omega CPU and 4 GB of memory. The computational time depends signals automatically for several months or years and then on the number of available signal data and data size. show the results of electric and magnetic intensity and delay Depending on the computer specifications, it is possible to run time for specific locations of longitude, latitude, and altitude on multiple instances of the analyzer to speed up the process.

Fig. 8. Overview of the PFX Analyzer, written in Java programming language. It enables us to detect Omega signals automatically for several months or years and then show the results on geomagnetic and geographic maps of the Earth

D. Handling of Error Detection and Evaluation of the System and decide there is no signal detected for the entire 40 s. This For accurate results, we also applied error detection handling occurs in the background of the analyzer software and handling and a data smoothing process. It is possible for the the analyzer will still show a rectangle representing the analyzer to detect a high intensity noise as Omega signal. To detected signal. handle this type of error detection, we applied a function that We also applied a time smoothing algorithm to handle ignores a signal that is not detected continuously for a specific sudden peaks of delay time that may reflect false detection. duration of time. This is currently set at 40 s. For example, This was accomplished by using the average of every two when the signal is only detected for the first 30 s and then delay time value of detected signals. We expect to obtain more disappears in the next 10 s, the analyzer will ignore the signal reliable results using improved detection data.

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IV. RESULT OF ANALYSES A. Event Study

Fig. 9. Trajectory of Akebono, and observed Omega signal of Norway station from 08:29:39.802 UT to 09:55:33.842 UT on October 18, 1989. The PFX data is not available between latitude 49°N to 26°N and 2°N to 6°S and shown as dashed line. Left panels show trajectory line (a) (b) of the satellite. Right panels show (c) absolute intensities of Omega signal in the magnetic fields, (d) absolute intensities of Omega signal in the electric fields, (e) delay times of Omega signal, and (f) the approximate distance of the propagation path to the observation point

Fig. 9 (a) and 9 (b) shows 1.5 hours of trajectory of the field and electric field of the Omega signal is showing higher Akebono from the northern to southern hemisphere from intensity in the northern hemisphere where the Norway station 08:29:39.802 UT to 09:55:33.842 UT on October 18, 1989. was located. The Norway station was located at latitude During this period, Omega signals from the Norway station 56.42°N, and we can see higher signal intensity around −285 were continuously observed and 344 signals were detected in dB (T) for the magnetic field and approximately −125 dB the electric field and 63 signals were detected in the magnetic (V/m) for the electrical field. The intensity then becomes lower field. The location of the Norway station is also shown as a for the electric field or disappears for the magnetic field near small square in Fig. 9 (a) and (b). The PFX data is not available the equator at latitude 0°. The signal for the electric field then between latitude 49°N to 26°N and 2°N to 6°S. This increased at approximately −145 dB (V/m) near the southern unavailable of data is shown as dashed lines in Fig. 9 (a). hemisphere because the signal could propagate along the Earth’s magnetic field. The delay time around 0.2 s in the Fig. 9 (c) shows absolute intensities of Omega signal in the northern hemisphere where the Norway station was located and magnetic fields, Fig. 9 (d) shows absolute intensities of Omega then increased to more than 0.5 s as the trajectory of the signal in the electric fields, and Fig. 9 (e) shows delay time of Akebono satellite got closer to the southern hemisphere. This the Omega signals. The red curves indicate moving median occurred because propagation along the Earth’s magnetic field over 20 points (detected events). The intensity of the magnetic

72 | P a g e www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 10, 2016 required more time compared with a direct propagation path. along the Earth’s magnetic field. The calculated distance was Fig. 9 (f) shows the approximate distance based on a simple from the Norway station to the observation point. magnetic dipole model for the propagation path of the signal

Fig. 10. Sliced Center Frequency in the electric field from 08:29:46 UT to 09:55:36 UT on October 18, 1989. The transmission time of Norway station was 08:29:46 UT and repeated every 10 seconds. Signal from Norway expected to be 0.9 second in duration. Intensification at t=0.6 in a period from 08:29:46 UT to 08:57:46 UT was caused by the WIDA IC

For comparison, we calculated the sliced center frequency altitudinal axis consist of 20 bins of 637.1 Km for each bin. of each signal in the Electric Field from 08:29:46 UT to The rectangle on the map shows the location of the 09:55:36 UT on October 18, 1989. We plotted only 1 bin (∆f = transmission station at a longitude of 13.14°E and latitude of 10Hz/bin, ∆t = ~9.4ms/bin) of the center frequency spectrum 56.42°N. The gray color bin on the map shows the availability (~10.2 kHz) and arranged it as shown in the Fig. 10. The of PFX data in the area. However, no Omega signal was transmission time of Norway station start at 08:29:46 UT and detected by the analyzer. The white color bin on the map repeated every 10 seconds. We projected the red curves of shows that the PFX data is not available for that location. The trend line in Fig. 9 (e) to Fig. 10. This confirms that the meridian plane map shows the average intensity coverage of automatic detection of our analyzer produced quite reliable 10° to the east and 10° to the west from the station. From the results compared with manual analysis. Deviation occurred at figure, We can clearly demonstrate that Omega signal few parts because of false result by multiple high intensity propagated nearly along the geomagnetic field line to the noises and affected moving median of the trend line. Based on southern hemisphere. The intensity of the signal near the the Omega system transmission pattern, we expected signal equator shows low intensity at high altitude and no signal at from Norway should be 0.9 second in duration. Intensification lower altitude. We concluded that this unique propagation is at t=0.6 in a period from 08:29:46 UT to 08:57:46 UT was caused primarily by the location of the transmission station, the caused by the WIDA IC. Because of its weakness, WIDA IC Earth’s magnetic field, and global electron density. Previous may cause false intensification of signal as seen at t=2.05 in a study about global electron density had analyzed normal wave period from 08:57:46 UT to 09:36:06 UT. The effects of direction and delay time of the Omega signal [7][8] and also WIDA IC can be seen clearly in Fig. 6. Signal from Liberia deduced from whistlers [9][10][11]. For more than three years station was expected to be delayed and might be received after (39 months), we observed 43,734 detected signals in the t=1.0. In period from 09:36:06 UT to 09:55:36 UT, overlapped magnetic field and 111,049 detected signals in the electric signal between Norway and Liberia is possible. field.

B. Statistical Study V. CONCLUSIONS The results of more than three years data of Norway station In this study, we developed an advanced detection (October 1989 to December 1992) is shown in Fig. 11. The algorithm to continuously process large amounts of data longitudinal axis consists of 72 bins of 5° for each bin; the measured for several years by the PFX subsystem on board the latitudinal axis consist of 36 bins of 5° for each bin; and the Akebono satellite. The algorithm enables us to distinguish

73 | P a g e www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 10, 2016 noise and real omega signals and also detect errors in order to intervention in the process. We demonstrated that the proposed produce more accurate results. When compared with manual method is powerful enough for the statistical analyses and analysis, automatic detection can be accomplished with short further study of the propagation patterns of VLF waves with periods of processing time and does not require human regard to the Earth’s magnetic field and global electron density.

Fig. 11. Omega Signal Propagation of Norway Station from October 1989 to December 1992 in the magnetic field (a) and in the electric field (b). Omega signal is propagated to the southern hemisphere. The intensity of the signal near the equator shows low intensity at high altitude and no signal at lower altitude

[6] Yamamoto, M., Y. Ito, Y. Kishi, A. Sawada, I. Kimura, I. Nagano, E. ACKNOWLEDGMENT Kennai, T. Okada, K. Hashimoto, 1991. K vector measurements of VLF This research was partially supported by a Grant-in-Aid for signals by the satellite EXOS-D, Geophysical Research Letters, 18, pp. Scientific Research from the Japan Society for the Promotion 325-328 of Science (#24360159 and #16H01172). [7] Kimura, I., K. Tsunehara, A. Hikuma, Y. Z. Su,, Y. Kasahara, H. Oya, 1997. Global electron density distribution in the plasmasphere deduced REFERENCES from Akebono wave data and the IRI model, Journal of Atmospheric and Solar-Terrestrial Physics , 59, 1569–1586 [1] Kimura, I., K. Hashimoto, I. Nagano, T. Okada, M. Yamamoto, T. Yoshino, H. Matsumoto, M. Ejiri, K. Hayashi, 1990. VLF Observation [8] Sawada, A., T. Nobata, Y. Kishi, I. Kimura, 1993. Electron Density by the Akebono (EXOS-D) Satellite, Journal of Geomagnetism and Profile in the Magnetosphere Deduced From in Situ Electron Density Geoelectricity, 42, pp. 459-478 and Wave Normal Directions of Omega Signals Observed by the Akebono (EXOS D) Satellite, Journal of Geophysical Research, 98, A7, [2] Kimura, I., Y. Kasahara, H. Oya, 2001. Determination of global 11267-11274 plasmaspheric electron density profile by tomographic approach using Omega signals and ray tracing, Journal of [9] Tarcsai, G., P. Szemeredy, and L. Hegymegi, 1988. Average electron Atmospheric and Solar-Terrestrial Physics, 63, 1157–1170 density profiles in the plasmasphere between L = 1.4 and 3.2 deduced from whistlers, J. Atmos. Terr. Phys., 50(7), 07–611, doi:10.1016/0021- [3] Goto, Y., Y. Kasahara, T. Sato, 2003. Determination of plasmaspheric 9169(88)90058-X electron density profile by a stochastic approach, Radio Science, 38(3), 1060, doi: 10.1029/2002RS002603 [10] Collier, A. B., A. R. W. Hughes, J. Lichtenberger, and P. Steinbach, 2006. Seasonal and diurnal variation of lightning activity over southern [4] Common Data Format (CDF) Page. http://cdf.gsfc.nasa.gov/. Accessed Africa and correlation with European whistler observations, Ann. 21 May 2016 Geophys., 24, 529–542 [5] Morris, P. B., R. R. Gupta, R. S. Warren, P. M. Creamer, 1994. Omega [11] Lichtenberger, J., C. Ferencz, L. Bodnar, D. Hamar, and P. Steinbach, Navigation System Course Book, Vol. 1, National Technical 2008. Automatic whistler detector and analyzer system: Automatic Information Service, Springfield, Virginia, 434pp whistler detector, J. Geophys. Res., 113, A12201, doi:10.1029/2008JA013467

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