Skip to main content

Advertisement

Springer Nature Link
Log in

Electromagnetic Spectrum Contribution in Astronomy, Health, Atmospheric, Geology and Environment Applications

  • Original Research
  • Published:
International Journal of Wireless Information Networks Aims and scope Submit manuscript

Abstract

Spectrum technologies are shaping the way our world connects, communicates, and functions. Radio nodes connect through a nearly ubiquitous wireless mesh of other nodes, access points, satellites, and base stations to support an ever-expanding panorama of applications, spanning communication, autonomous navigation and transportation, radar-based geo-sciences, soil-sciences, renewable energy, space surveillance, environment and healthcare, smart buildings and grids, precision agriculture, consumer and industrial Internet-of-Things (IoT), and other elements of the emerging smart world. This paper offers an overview on the impact of the current and future diverse applications on the radio spectrum. Specific applications to be addressed include astronomy, health, atmospheric, geosciences, and wildfire monitoring. These applications along with many other emerging applications highlight the critical need of implementation of Intelligent Radios and dynamic spectrum access techniques that enable efficient spectrum management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

source is the cosmic microwave background. Other sources are the interstellar medium within the Milky Way, which emits radiation through synchrotron and free-free emission (labeled ‘Galactic Synchrotron’ and ‘free-free’, respectively) at low frequencies, and emission of dust grains at high frequencies that has two components, a thermal emission component at high frequency (labeled ‘Galactic Dust (thermal)’) and a much lower intensity component peaking near 20 GHz (labeled ‘Galactic Dust (spinning)). Frequencies of some of the main atomic and molecular Milky Way emission lines are shown with small magenta vertical bars (labeled ‘H-21 cm’ for the 21 cm hydrogen line, and ‘CO rotational lines’ for transitions of the CO molecule); their vertical position is arbitrary. Similar emission processes in distant galaxies contribute a background of integrated emission from radio (labeled ‘Radio Galaxies’) and infrared dusty galaxies (labeled ‘Dust Galaxies’), the latter also known as the cosmic infrared background. Zodiacal emission arises from the thermal emission by interplanetary particles in the solar system. The integrated emissions from extragalactic carbon monoxide (CO) rotational lines and ionized carbon and nitrogen transitions, shown in purple, are a sub-dominant part of the continuum extragalactic emission in this frequency range

Fig. 2

adopted from The Planck Collaboration: Y. Akramy, et al. [7]

Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. Other organizations such as IEEE and NATO have other designations for this spectral range.

  2. Another review giving a different perspective is provided by a publication of the National Academy of Sciences, Engineering, and Medicine [8].

References

  1. K. Pahlavan, Indoor Geolocation Science and Technology At the Emergence of Smart World and IoT, Gistrup, River Publishers, Denmark, 2019.

    Google Scholar 

  2. T. Chen, S. Barbarossa, X. Wang, G. B. Giannakis and Z. Zhang, Learning and management for internet of things: Accounting for adaptivity and scalability”, vol. 107, Proceedings of the IEEE, Vol. 107, No. 4, pp. 778–796, 2019.

    Article  Google Scholar 

  3. S. R. Zekavat and R. M. Buehrer, Handbook of Position Location; Theory, Practice and Advances, Piscataway, Wiley-IEEE Press, NJ, 2019.

    Google Scholar 

  4. S. A. Zekavat and X. Li, Ultimate Dynamic Spectrum Allocation via User Central Wireless Systems, Journal of Communications, Vol. 1, No. 1, pp. 60–67, 2006.

    Article  Google Scholar 

  5. "Radio Regulations," International Telecommunication Union (ITU), 2020. [Online]. Available: https://www.itu.int/pub/R-REG-RR.

  6. D. J. Fixsen, The temperature of the cosmic microwave background, The Astrophysical Journal, Vol. 707, pp. 916–920, 2009.

    Article  Google Scholar 

  7. Y. Akramy et al., "LVII. Joint Planck LFI and HFI data processing," Astronomy and Astrophysics, vol. 643, no. A&A, p. A42, 2020.

  8. Handbook of Frequency Allocations and Spectrum Protection for Scientific Uses: Second Edition, Washington, DC: The National Academies Press, 2015.

  9. M. F. Morales and J. S. B. Wyithe, Reionization and Cosmology with 21-cm Fluctuations, Annual Review of Astronomy and Astrophysics, Vol. 48, pp. 127–171, 2020.

    Article  Google Scholar 

  10. J. Silvious and D. Tahmoush, "UHF measurement of breathing and heartbeat at a distance,” IEEE Radio & Wireless Sym, RWW 2010. 567 – 570, 2010.," in IEEE Radio and Wireless Symposium (RWS), New Orleans, LA, 2010.

  11. P. Siegel, THz Technology in Biology and Medicine, IEEE Trans. Microwave Theory and Tech., Vol. 52, No. 10, pp. 2438–2448, 2004.

    Article  Google Scholar 

  12. N. Hafner, W. Massagram, V. M. Lubecke, and O. Boric-Lubecke, "Underwater motion and physiological sensing using UHF doppler radar," in IEEE MTT-S International Microwave Symposium Digest, Atlanta, GA, 2008.

  13. X. Wang, C. Yang and S. Mao, Resilient Respiration Rate Monitoring With Realtime Bimodal CSI Data, IEEE Sensors Journal, Vol. 20, No. 17, pp. 10187–10198, 2020.

    Article  Google Scholar 

  14. L. Lubecke, K. Ishmael, Y Zheng, O. Boric-Lubecke, and V. Lubecke, "Identification of COVID-19 Type Respiratory Disorders Using Channel State Analysis of Wireless Communications," in IEEE Engineering in Medicine and Biology Society, 2021.

  15. A. D. Droitcour, O. Boric-Lubecke, V. M. Lubecke, J. Lin and G. T. A. Kovacs, Range Correlation & I/Q Performance Benefits in Single Chip Si. Doppler Radars for Non-Contact Cardiopulmonary Monitoring, IEEE Transactions on Microwave Theory and Techniques, Vol. 52, No. 3, pp. 838–848, 2004.

    Article  Google Scholar 

  16. C. Li, V. M. Lubecke, O. Boric-Lubecke and J. Lin, A Review on Recent Advances in Doppler Radar Sensors for Noncontact Healthcare Monitoring, IEEE Transactions on Microwave Theory and Techniques, Vol. 61, No. 5, pp. 2046–2060, 2013.

    Article  Google Scholar 

  17. C. Li, V. M. Lubecke, O. Boric-Lubecke and J. Lin, Sensing of Life Activities at the Human-Microwave Frontier, IEEE Journal of Microwaves, Vol. 1, No. 1, pp. 66–78, 2021.

    Article  Google Scholar 

  18. A. D. Droitcour, T. B. Seto, B.-K. Park, S. Yamada, A. Vergara, C. El Hourani, Tommy Shing, A. Yuen, V. M. Lubecke, and O. Boric-Lubecke, "Non-contact respiratory rate measurement validation for hospitalized patients," in Annu Int Conf IEEE Eng Med Biol Soc, Minneapolis, MN, 2009.

  19. W. Massagram, N. Hafner, V. Lubecke and O. Boric-Lubecke, Tidal Volume Measurement Through Non-Contact Doppler Radar With DC Reconstruction, IEEE Sensors Journal, Vol. 13, No. 9, pp. 3397–3404, 2013.

    Article  Google Scholar 

  20. M. Baboli, A. Singh, B. Soll, O. Boric-Lubecke and V. M. Lubecke, Wireless Sleep Apnea Detection Using Continuous Wave Quadrature Doppler Radar, IEEE Sensors Journal, Vol. 20, No. 1, pp. 538–545, 2020.

    Article  Google Scholar 

  21. S. M. M. Islam, O. Borić-Lubecke, Y. Zheng and V. M. Lubecke, Radar-Based Non-Contact Continuous Identity Authentication, Remote Sens., Vol. 12, No. 14, pp. 2279, 2020.

    Article  Google Scholar 

  22. S. M. M Islam, A. Rahman, N. Prasad, O. Boric-Lubecke, and V. Lubecke, "Identity authentication system using support vector machine on radar respiration measurement," in 93rd ARFTG Microwave Measurement Conference (ARFTG), Boston, MA, 2019.

  23. T. Okano, S. Izumi, H. Kawaguchi and M. Yoshimoto, “Non-contact biometric identification and authentication using microwave Doppler sensor,” in IEEE Biomedical Circuits and Systems Conference (BioCAS), Italy, Turin, 2017.

    Google Scholar 

  24. W. Wang, Y. Wang, M. Zhou, and W. Nie, "A Novel Vital Sign Sensing Algorithm for Multiple People Detection Based on FMCW Radar," in IEEE Asia-Pacific Microwave Conference (APMC), Hong Kong, Hong Kong, 2020.

  25. S. M. M. Islam, N. Motoyama, S. Pacheco, and V. M. Lubecke, "Non-Contact Vital Signs Monitoring for Multiple Subjects Using a Millimeter Wave FMCW Automotive Radar," in IEEE/MTT-S International Microwave Symposium (IMS), Los Angeles, CA, 2020.

  26. S. M. M. Islam, O. Boric-Lubecke and V. M. Lubekce, Concurrent Respiration Monitoring of Multiple Subjects by Phase-Comparison Monopulse Radar Using Independent Component Analysis (ICA) With JADE Algorithm and Direction of Arrival (DOA), IEEE Access, Vol. 8, pp. 73558–73569, 2020.

    Article  Google Scholar 

  27. J. Saluja, J. Casanova and J. Lin, A Supervised Machine Learning Algorithm for Heart-Rate Detection Using Doppler Motion-Sensing Radar, IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, Vol. 4, No. 1, pp. 45–51, 2020.

    Article  Google Scholar 

  28. F. Snigdha, S. M. M. Islam, O. Boric-Lubecke, and V. Lubecke, "Obstructive Sleep Apnea (OSA) Events Classification by Effective Radar Cross Section (ERCS) Method Using Microwave Doppler Radar and Machine Learning Classifier," in IEEE MTT-S International Microwave Biomedical Conference (IMBioC), Toulouse, France, 2020.

  29. S. M. Islam, A. Rahman, E. Yavari, M. Baboli, O. Boric-Lubecke, and V. M. Lubecke, "Identity Authentication of OSA Patients Using Microwave Doppler radar and Machine Learning Classifiers," in IEEE Radio and Wireless Symposium (RWS), San Antonio, TX, 2020.

  30. M. Azimi and S. A. Zekavat, "Cloud classification using support vector machines," Proceedings IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2000, vol. 2, pp. 669–671, 2000.

  31. B. A. Wielicki, B. R. Barkstrom, E. F. Harrison, R. B. Lee III., G. L. Smith and J. E. Cooper, Clouds and the Earth’s Radiant Energy System (CERES): an Earth observing system experiment, Bulletin of the American Meteorological Society, Vol. 77, No. 5, pp. 853–868, 1996.

    Article  Google Scholar 

  32. "Radio-Frequency Interference (RFI)," European Centre for Medium-Range Weather Forecasts (ECMWF), 2018. [Online]. Available: https://www.ecmwf.int/sites/default/files/elibrary/2019/19026-radio-frequency-interference-rfi-workshop-final-report.pdf.

  33. E. Saltikoff, K. Friedrich, J. Soderholm, K. Lengfeld, B. Nelson, A. Becker, R. Hollmann, B. Urban, M. Heistermann and C. Tassone, An overview of using weather radar for climatological studies: Successes, challenges, and potential, Bull. Amer. Meteorol. Soc., Vol. 100, pp. 1739–1752, 2019.

    Article  Google Scholar 

  34. A. Kashani, M. Olsen, C. Parrish and N. Wilson, A review of lidar radiometric processing: from AD HOC intensity correction to rigorous radiometric calibration, Sensors, Vol. 15, pp. 28099, 2015.

    Article  Google Scholar 

  35. A. Comerón, C. Muñoz-Porcar, F. Rocadenbosch, A. Rodríquez-Gómez, and M. Sicard,, "Current research in lidar technology used for the remote sensing of atmospheric aerosols," Sensors (Basel), vol. 17, p. 1450, 2017.

  36. L. Gimeno, Grand challenges in atmospheric science, Front. Earth Sci., Vol. 1, pp. 1–5, 2013.

    Article  Google Scholar 

  37. O. Dubovik, G. L. Schuster, F. Xu, Y. Hu, H. Bösch, J. Landgraf and Z. Li, Grand challenges in satellite remote sensing, Front. Remote Sens, Vol. 2, 619818, 2021.

    Article  Google Scholar 

  38. T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P. M. Midgley, IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press, 2013.

  39. A. Witze, Global 5G wireless deal threatens weather forecasts, Nature, Vol. 575, pp. 377, 2019.

    Article  Google Scholar 

  40. P. Ceppi, F. Brient, M. D. Zelinka and D. L. Hartmann, Cloud feedback mechanisms and their representation in global climate models, WIREs Clim. Change, Vol. 8, No. 4, e465, 2017.

    Article  Google Scholar 

  41. S. C. Sherwood et al., "An assessment of Earth's climate sensitivity using multiple lines of evidence," Rev. Geophys., vol. 58, p. e2019RG000678, 2020.

  42. R. L. Bankert, C. Mitrescu, S. D. Miller and R. H. Wade, Comparison of GOES cloud classification algorithm employing explicit and implicit physics, J. App. Meteorol. Climatol., Vol. 48, pp. 1411–1421, 2009.

    Article  Google Scholar 

  43. S. Mahajan and B. Fataniya, Cloud detection methodologies: Variants and development – a review, Compl. Intell. Sys., Vol. 6, pp. 251–261, 2020.

    Article  Google Scholar 

  44. B. Hall, Precipitation associated with lightning-ignited wildfires in Arizona and New Mexico, International Journal of Wildland Fire, Vol. 242–254, pp. 16, 2007.

    Google Scholar 

  45. "Wildland Fire in Ecosystems- Effects of Fire on Soil and Water," United States Department of Agriculture, Rocky Mountain Research Station, US Forest Service, Fort Collins, CO, 2005.

  46. A. Westerling, "Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring," Phil. Trans. R. Soc., vol. B 371, p. 20150178, 2016.

  47. A. Shamsoshoara, F. Afghah, E. Blasch, J. Ashdown and M. Bennis, UAV-assisted communication in remote disaster areas using imitation learning, IEEE Open Journal of the Communication Society, Vol. 2, pp. 738–753, 2021.

    Article  Google Scholar 

  48. A. Shamsoshoara, F. Afghah, A. Razi, L. Zheng, P. Fule and E. Blasch, Aerial imagery pile burn detection using deep learning, Computer Networks, Vol. 193, 108001, 2021.

    Article  Google Scholar 

  49. F. Afghah, A. Razi, J. Chakareski, and J. Ashdown, "Wildfire monitoring in remote areas using autonomous unmanned aerial vehicles," in IEEE Conference on Computer Communications Workshops, INFOCOM Wksps, Paris, France, 2019.

  50. S. Islam, F. Afghah, A. Razi, and P. Fule, "Fire frontline monitoring by enabling UAV-based virtual reality with adaptive imaging rate," in IEEE, 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, 2019.

  51. H. Wu, H. Li, A. Shamsoshoara, A. Razi, and F. Afghah, "Transfer learning for wildfire identification in UAV imagery," in IEEE, 54th Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, 2020.

  52. Q. Huang, A. Razi, F. Afghah, and P. Fule, "Wildfire spread modeling with aerial image processing," in IEEE 21st International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM), Cork, Ireland, 2020.

  53. M. Mozaffari, W. Saad, M. Bennis, Y.-H. Nam and M. Debbah, Tutorial on UAVs for wireless networks: Applications, challenges, and open problems, IEEE Communications Surveys & Tutorials, Vol. 21, No. 3, pp. 2334–2360, 2019.

    Article  Google Scholar 

  54. N. Hosseini, H. Jamal, J. Haque, T. Magesacher, and D. W. Matolak, "UAV command and control, navigation and surveillance: A reiew of potential 5G and satellite systems," in IEEE Aerospace Conference, Big Sky, MT, 2019.

  55. S. Henriksen, "Unmanned Aircraft System Control and ATC Communications Bandwidth Requirements," NASA Technical Report, 2018.

  56. "Characteristics of unmanned aircraft systems and spectrum requirements to support their safe operation in non-segregated airspace," International Telecommunication Union, 2009.

  57. J. A. Godoy, F. Cabrera, V. Araña, D. Sánchez, I. Alonso and N. Molina, “A new Approach of V2X Communications for Long Range Applications in UAVs,” in 2nd URSI Atlantic Radio Science Meeting (AT-RASC), Gran Canaria, Spain, 2018.

    Google Scholar 

  58. M. Beck, T. Moore, N. French, E. Kissel, and M. Swany, "Data Logistics: Toolkit and Applications," in Proceedings of the 5th EAI International Conference on Smart Objects and Technologies for Social Good, Valencia, Spain, 2019.

  59. M. McHenry, Y. Zhao, and O. Haddadin,, "Dynamic Spectrum Access radio performance for UAS ISR missions," in Milcom 2010 Military Communications Conference, San Jose, CA, 2010.

  60. J. Kakar and V. Marojevic, "Waveform and spectrum management for unmanned aerial systems beyond 2025," in IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, QC, Canada, 2017.

  61. J. Wang, C. Jiang, Z. Han, Y. Ren, R. G. Maunder and L. Hanzo, Taking Drones to the Next Level: Cooperative Distributed Unmanned-Aerial-Vehicular Networks for Small and Mini Drones, IEEE Vehicular Technology Magazine, Vol. 12, No. 3, pp. 73–82, 2017.

    Article  Google Scholar 

  62. T. X. Brown and S. Jaroonvanichkul, “Policy-based radios for UAS operations,” in IEEE Globecom Workshops, Anaheim, CA, 2012.

    Google Scholar 

  63. A. Shamsoshoara, F. Afghah, A. Razi, S. Mousavi, J. Ashdown and K. Turk, An Autonomous Spectrum Management Scheme for UAV Networks in Disaster Relief Operations, IEEE Access, Vol. 8, pp. 58064–58079, 2020.

    Article  Google Scholar 

  64. M. Zaeri Amirani, F. Afghah, S. Zeadally, "A Hierarchical Spectrum Access Scheme for TV White Space Coexistence in Heterogeneous Networks," IEEE Access, vol., vol. 6, no. 1, pp. 78992–79004, 2018.

  65. A. Shamsoshoara, M. Khaledi, F. Afghah, A. Razi, J. Ashdown, and K. Turck, "A Solution for Dynamic Spectrum Management in Mission-Critical UAV Networks," in 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Boston, MA, 2019.

  66. A. Shamsoshoara, F.Afghah, A. Razi, S. Mousavi, J. Ashdown, and K. Turk, 2020; M. Zaeri Amirani, F. Afghah, S. Zeadally, "Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learning," in IEEE Consumer Communications & Networking Conference (CCNC'19), Las Vegas, NV, 2019.

  67. F. Afghah, A. Shamsoshoara, L. Njilla, and C. Kamboua, "A reputation-based stackelberg game model to enhance secrecy rate in spectrum leasing to selfish IoT devices," in IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Honolulu, HI, 2018.

  68. M. N. Nabighian, Electromagnetic Methods in Applied Geophysics: Volume 2, Application, Parts A and B, Tulsa, OK: Society of Exploration Geophysicists, 1991.

  69. C. J. Zablocki, Applications of the VLF induction method for studying some volcanic processes of Kilauea volcano, Hawaii, Journal of Volcanology and Geothermal Research, Vol. 3, No. 1–2, pp. 155–195, 1978.

    Article  Google Scholar 

  70. C.-C. Chen, C.-S. Chen and C.-F. Shieh, Crustal Electrical Conductors, Crustal Fluids and 1999 Chi-Chi, Taiwan, Earthquake, Terrestrial Atmospheric and Oceanic Sciences, Vol. 13, No. 3, pp. 367–374, 2002.

    Article  Google Scholar 

  71. U. Martyn, New developments in conventional hydrocarbon exploration with electromagnetic methods, CSEG Recorder, Vol. 30, No. 4, pp. 34–38, 2005.

    Google Scholar 

  72. D. J. Daniels, Ground Penetrating Radar, 2 edn,The Institution of Electrical Engineers, London, 2004.

    Book  Google Scholar 

  73. A. D. Chave and A. G. Jones, The Magnetotelluric Method, Theory and Practice, Cambridge, Cambridge University Press, UK, 2012.

    Book  Google Scholar 

  74. V. F. Labson., A. Becker, H. F. Morrison, and U. Conti, "Geophysical exploration with audiofrequency natural magnetic fields," Geophysics, vol. 50, pp. 656–664, 1985.

  75. M. Lazaro, S. Alm, A. Tiedeman, C. Page, D. Meade, J. Shoffner and K. Bucher, Department of the Navy Geothermal Exploration on Naval Air Station Fallon (NASF) Managed Lands in Dixie Valley, Nevada, Geothermal Resources Council Transactions, Vol. 35, pp. 873–878, 2011.

    Google Scholar 

  76. E. Holtham and D. W. Oldenburg, Three-dimensional inversion of ZTEM data, Geophysical Journal International, Vol. 182, No. 1, pp. 168–182, 2010.

    Google Scholar 

  77. D. I. Gough, Electromagnetic exploration for fluids in the Earth’s crust, Earth-Science Reviews, Vol. 32, No. 1–2, pp. 3–18, 1992.

    Article  Google Scholar 

  78. J.-P. Schmoldt, A. G. Jones, C. Hogg, and O. Rosell, "PICASSO- Phase I: MT Investigation of the Betic-Rif mountain system. Comparison of actual robust processing algorithms," in IAGA WG 1.2 on Electromagnetic Induction in the Earth , Beijing, China, 2008.

  79. P. Denny, A magnetotelluric and magnetovariational analysis of Variscan - Caledonian southwest Ireland, GFZ Publication Database, Potsdam, Germany, 2000.

    Google Scholar 

  80. S. Uyeda, T. Nagao and M. Kamogawa, Short-term earthquake prediction: Current status of seismo-electromagnetics, Tectonophysics, Vol. 470, No. 3–4, pp. 205–213, 2009.

    Article  Google Scholar 

  81. A. Tzanis and F. Vallianatos, A critical review of Electric Earthquake Precursors, Annali di Geofisica, Vol. 44, No. 2, pp. 429–460, 2001.

    Google Scholar 

  82. G. Yu, K. M. Strack, H. Tulinius, I. M. Porbergsottir, L. Adam, Z. Z. Hu and Z. X. He, “Integrated MT/Gravity Geothermal Exploration in Hungary: A Success Story,” in 21st ASEG Conference and Exhibition, Australia, Sydney, 2010.

    Google Scholar 

  83. B. Tournerie and M. Chouteau, Analysis of magnetotelluric data along the Lithoprobe seismic line 21 in the Blake River Group, Abitibi, Canada, Earth, Planets and Space, Vol. 54, No. 5, pp. 575–589, 2002.

    Article  Google Scholar 

  84. J. McLeod, I. Ferguson, J. Craven, B. Roberts and B. Giroux, Pre-injection magnetotelluric surveys at the Aquistore CO2 sequestration site, Estevan, Saskatchewan, Canada, International Journal of Greenhouse Gas Control, Vol. 74, pp. 99–118, 2018.

    Article  Google Scholar 

  85. M. Unsworth, W. Soyer, V. Tuncer, A. Wagner and D. Barnes, Hydrogeologic assessment of the Amchitka Island nuclear test site (Alaska) with magnetotellurics, Geophysics, Vol. 72, No. 3, pp. B47–B57, 2007.

    Article  Google Scholar 

  86. M. Poddar, Very low-frequency electromagnetic response of a perfectly conducting half-plane in a layered half-space, Geophysics, Vol. 47, No. 7, pp. 1059–1067, 1982.

    Article  Google Scholar 

  87. P. Gnaneshwar, A. Shivaji, Y. Srinivas, P. Jettaiah and N. Sundararajan, Very-low-frequency electromagnetic (VLF-EM) measurements in the Schirmacheroasen area, East Antarctica, Polar Science, Vol. 5, No. 1, pp. 11–19, 2011.

    Article  Google Scholar 

  88. V. R. Babu, S. Ram, and N. Sundararajan, "Modeling and inversion of magnetic and VLF-EM data with an application to basement fractures: A case study from Raigarh, India," Geophysics, vol. 72, no. 5, pp. 1SO-Z83, 2007.

  89. S. P. Sharma, A. Biswas, and V. C. Baranwal, "Very Low-Frequency Electromagnetic Method: A Shallow Subsurface Investigation Technique for Geophysical Applications," in Recent Trends in Modelling of Environmental Contaminants, Springer Link, 2014, pp. 119–141.

  90. G. Paal, Ore prospecting based on VLF radio signals, Geoexploration, Vol. 3, pp. 139–147, 1965.

    Article  Google Scholar 

  91. N. Sundararajan, G. Nandakumar, M. Narsimha Chary, K. Ramam, and Y. Srinivas, "VES and VLF—an application to groundwater exploration, Khammam, India," The Leading Edge, vol. 26, no. 6, pp. 708–716, 2007.

  92. S. K. Park, M. J. S. Johnston, T. R. Madden, F. D. Morgan and H. F. Morrison, Electromagnetic precursors to earthquakes in the Ulf band: A review of observations and mechanisms, Reviews of Geophysics, Vol. 31, No. 2, pp. 117–132, 1993.

    Article  Google Scholar 

  93. M. Zhdanov, Foundations of Geophysical Electromagnetic Theory and Methods 2nd Edition, Amsterdam, Elsevier, The Netherland, 2017.

    Google Scholar 

  94. B. G. Williams and D. Hoey, The use of electromagnetic induction to detect the spatial variability of the salt and clay contents of soils, Australian Journal of Soil Research, Vol. 25, No. 1, pp. 21–27, 1987.

    Article  Google Scholar 

  95. J. M. Blonquist Jr. S. B.Jones and D. A. Robinson, "Precise irrigation scheduling for turfgrass using a subsurface electromagnetic soil moisture sensor," Agricultural Water Management, vol. 84, no. 1–2, pp. 153–165, 2006.

  96. N. Goldshleger, O. Shamir, U. Basson and E. Zaady, Frequency Domain Electromagnetic Method (FDEM) as a Tool to Study Contamination at the Sub-Soil Layer, Geosciences, Vol. 9, No. 9, pp. 382, 2019.

    Article  Google Scholar 

  97. M. R. Gadallah and R. Fisher, Exploration Geophysics, Springer, Berlin Heidelberg., 2009.

    Google Scholar 

  98. P. Shangguan, I. L. Al-Qadi and S. Lahouar, Pattern recognition algorithms for density estimation of asphalt pavement during compaction: A simulation study, Journal of Applied Geophysics, Vol. 107, pp. 8–15, 2014.

    Article  Google Scholar 

  99. H. El-Kaliouby, "GPR study of karst in a carbonate coastal area for evaluating its suitability for construction, Wadi Shab, Eastern Oman," in Fifth International Conference on Engineering Geophysics, Al Ain, UAE, 2015.

  100. K. Wu, G. A. Rodriguez, M. Zajc, E. Jacquemin, M. Clément, A. De Coster and S. Lambot, A new drone-borne GPR for soil moisture mapping, Remote Sensing of Environment, Vol. 235, No. 15, 111456, 2019.

    Article  Google Scholar 

  101. A. Capra, S. Gandolfi, L. Laurencich, F. Mancini, A. Minelli, C. Orsini and A. Rodríguez, Multidisciplinary approach for archeological survey: exploring GPS method in landscape archeology studies, Journal of Cultural Heritage, Vol. 3, pp. 93–99, 2002.

    Article  Google Scholar 

  102. V. Rodríguez, F. Gutiérrez, A. G. Green, D. Carbonel, H. Horstmeyer and C. Schmelzbach, Characterizing Sagging and Collapse Sinkholes in a Mantled Karst by Means of Ground Penetrating Radar (GPR), Environmental and Engineering Geoscience, Vol. 20, No. 2, pp. 109–132, 2014.

    Article  Google Scholar 

  103. T.-N. Wu and Y.-C. Huang, “Detection of Illegal Dump Deposit with GPR: Case Study,” Pract, Period. Hazard. Toxic Radioact. Waste Manage, Vol. 10, No. 3, pp. 144–149, 2006.

    Article  Google Scholar 

  104. L. Crocco, F. Soldovieri, T. Millington and N. J. Cassidy, Bistatic Tomographic GPR Imaging for Incipient Pipeline Leakage Evaluation, Progress In Electromagnetics Research, Vol. 101, pp. 307–321, 2010.

    Article  Google Scholar 

  105. P. M. Barone and C. Ferrara, A posteriori GPR Evaluation of Tree Stability: A Case Study in Rome (Italy), Remote Sensing, Vol. 11, No. 11, pp. 1301, 2019.

    Article  Google Scholar 

  106. M. S. Munkholm and E. Auken, Electromagnetic Noise Contamination on Transient Electromagnetic Soundings in Culturally Disturbed Environments, Journal of Engineering and Environmental Geophysics, Vol. 1, No. 2, pp. 89–157, 1996.

    Article  Google Scholar 

  107. M. A. Uman, The Lightning Discharge, Academic Press, New York, 1980.

    Google Scholar 

  108. L. Szarka, Geophysical aspects of man-made electromagnetic noise in the earth—a review, Surv. Geophys., Vol. 9, No. 3, pp. 287–318, 1988.

    Article  Google Scholar 

  109. A. Junge, Characterization of and correction for cultural noise, Surv. Geophys., Vol. 17, No. 4, pp. 361–391, 1996.

    Article  Google Scholar 

  110. I. J. Ferguson, “Instrumentation and field procedures,” in The magnetotelluricmethod—theory and practice, pp. 421–479, Cambridge University Press, Cambridge, 2012.

    Google Scholar 

  111. A. G. Nekut and P. A. Eaton, "Effects of pipelines on EM soundings," in SEG Technical Program Expanded Abstracts: 491–494, 1990.

  112. M. P. Miensopust, Application of 3-D Electromagnetic Inversionin Practice: Challenges, Pitfalls and Solution Approaches, Surv. Geophys., Vol. 38, pp. 869–933, 2017.

    Article  Google Scholar 

  113. S. Glisic and B. Lorenzo, Artificial Intelligence and Quantum Computing for Advanced Wireless Networks, John Wiley & Sons, 2021.

  114. J. A. Bazerque, G. Mateos and G. B. Giannakis, Group-lasso on splines for spectrum cartography, IEEE Transactions on Signal Processing, Vol. 59, No. 10, pp. 4648–4663, 2011.

    Article  MathSciNet  MATH  Google Scholar 

  115. E. Dall’Anese, S. Kim, and G. B. Giannakis, "Channel gain map tracking via distributed kriging," IEEE Transactions on Vehicular Technology, vol. 60, no. 3, pp. 1205–1211, 2011.

  116. "The 1st Workshop on New Paradigms in Intelligent Spectrum Management and Regulations: Future Directions, Technologies, Standards, and Applications," The Center for Broad Explorations on Spectrum Technologies for Navigation, Environment, Surveillance, and Transportation (BEST NEST), 3–4 December 2020. [Online]. Available: https://bestnest.wpi.edu/index.php/synapsis-2/.

  117. "The 2nd Workshop on New Paradigms in Intelligent Spectrum Management and Regulations: Future Directions, Technologies, Standards, and Applications," The Center for Broad Explorations on Spectrum Technologies for Navigation, Environment, Surveillance, and Transportation (BEST NEST), 11–12 Feburary 2021. [Online].

  118. M. Kratsios, "Research and Development Priorities for American Leadership in Wireless Communications," The Networking and Information Technology Research and Development (NITRD), 2019.

  119. X. Li and S. A. Zekavat, Spectrum Sharing across Multiple Service Providers via Cognitive Radio Nodes, IET Communications, Vol. 4, No. 5, pp. 551–561, 2010.

    Article  Google Scholar 

  120. X. Li and S. A. Zekavat, Cognitive Radio Based Spectrum Sharing: Evaluating Channel Availability via Traffic Pattern Prediction, Journal of Communications and Networks, Vol. 11, No. 2, pp. 104–114, 2009.

    Article  Google Scholar 

Download references

Acknowledgements

This work has been partially supported by the NSF SII-2037782. The work of Fatemeh Afghah is supported by the Air Force Office of Scientific Research, United States of America under award number FA9550-20-1-0090 and the National Science Foundation, United States of America under Grants Number CNS-2034218 and CNS-2039026. Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author information

Authors and Affiliations

  1. Worcester Polytechnic Institute, Worcester, MA, USA

    Seyed Zekavat

  2. Clemson University, Clemson, SC, USA

    Fatemeh Afghah

  3. Michigan Technological University, Houghton, MI, USA

    Roohollah Askari

  4. CNRS, Université de Paris and Université Paris Saclay, Gif-sur-Yvette, France

    Jacques Delabrouille

  5. Michigan Tech Research Institute, Ann Arbor, MI, USA

    Nancy French

  6. University of Oklahoma, Norman, OK, USA

    Jason C. Furtado

  7. University of Minnesota/Twin Cities, Minneapolis, MN, USA

    Shaul Hanany

  8. University of Hawaii at Manoa, Honolulu, HI, USA

    Victor Lubecke

Authors
  1. Seyed Zekavat
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Fatemeh Afghah
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Roohollah Askari
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jacques Delabrouille
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Nancy French
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Jason C. Furtado
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Shaul Hanany
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Victor Lubecke
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Seyed Zekavat.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zekavat, S., Afghah, F., Askari, R. et al. Electromagnetic Spectrum Contribution in Astronomy, Health, Atmospheric, Geology and Environment Applications. Int J Wireless Inf Networks 29, 281–302 (2022). https://doi.org/10.1007/s10776-022-00558-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10776-022-00558-7

Keywords