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Expected spatio-temporal variation of groundwater deficit by integrating groundwater modeling, remote sensing, and GIS techniques

Relative shannon’s entropy approach for quantifying urban growth using remote sensing and gis: a case study of cuttack city, odisha, india, review of conceptual models of estimating the spatio-temporal variations of water depth using remote sensing and gis for the management of dams and reservoirs, temporal assessment of sedimentation in siruvani reservoir using remote sensing and gis, evaluating the groundwater potential of wadi al-jizi, sultanate of oman, by integrating remote sensing and gis techniques, diachronic study of land cover of the medjerda watershed and estimation of rusle-c factor using ndvi-based equation, remote sensing, and gis, slum categorization for efficient development plan—a case study of udhampur city, jammu and kashmir using remote sensing and gis, morphometric analysis of damodar river sub-watershed, jharkhand, india, using remote sensing and gis techniques, land use/land cover change detection and validation of swat model on vishow sub-basin using remote sensing and gis techniques, zoning groundwater potential recharge using remote sensing and gis technique in the red river delta plain.

Abstract The Red River delta plain is the second largest delta in Vietnam and is located in the North of the country with an area of 14,860 km2 and residing more than 22.5 million inhabitants. Groundwater is mainly exploited in Quaternary sedimentary aquifers with a total discharge of about 3 million m3/day. Some localities have shown signs of over-exploitation such as in Hanoi and in Nam Dinh, which may lead to related problems such as depletion, subsidence, saltwater intrusion, and water pollution. In order to be able to sustainably exploit groundwater, the groundwater potential recharge needs to be estimated. There have been many studies using different methods to estimate the groundwater recharge and to zone potential recharge. In the study area, there are several studies for groundwater recharge, but some are still uncertain because of using indirect methods, some are locally estimated in specific areas. Therefore, the objective of this study is to apply remote sensing and GIS to zone the groundwater potential recharge and its verification by using radioactive isotope 3H analysis in the Red River delta plain. Various types of satellite images have been used and interpreted to detect the different thematic layers which concern the groundwater potential recharge. GIS has been applied as a platform for analysis and integration of thematic layers for zonation, finally. Field trip and water sampling for chemical and radioactive 3H analysis were also conducted. Zones with low, moderate, and high groundwater potential recharge have been delineated with good agreement from the direct estimation of groundwater recharge by radioactive isotopes 3H.

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The role of satellite remote sensing in climate change studies

• Jun Yang 1 ,
• Peng Gong 1 , 2 , 3 ,
• Rong Fu 4 ,
• Minghua Zhang 5 ,
• Jingming Chen 6 , 7 ,
• Shunlin Liang 8 , 9 ,
• Bing Xu 8 , 10 ,
• Jiancheng Shi 2 &
• Robert Dickinson 4  

Nature Climate Change volume  3 ,  pages 875–883 ( 2013 ) Cite this article

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An Erratum to this article was published on 20 December 2013

A Corrigendum to this article was published on 29 October 2013

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Satellite remote sensing has provided major advances in understanding the climate system and its changes, by quantifying processes and spatio-temporal states of the atmosphere, land and oceans. In this Review, we highlight some important discoveries about the climate system that have not been detected by climate models and conventional observations; for example, the spatial pattern of sea-level rise and the cooling effects of increased stratospheric aerosols. New insights are made feasible by the unparalleled global- and fine-scale spatial coverage of satellite observations. Nevertheless, the short duration of observation series and their uncertainties still pose challenges for capturing the robust long-term trends of many climate variables. We point out the need for future work and future systems to make better use of remote sensing in climate change studies.

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Change history, 27 september 2013.

In the version of this Review Article originally published, on page 877, the mass balance of glaciers in the Karakarom region should have been 0.11±0.2 m yr −1 . On page 879, in the second paragraph of the ‘Aerosols’ section, the estimated value for the direct radiative forcing should have been −1.0±0.34 W m −2 . These errors have now been corrected in the HTML and PDF versions of the Review Article.

02 December 2013

In the version of this Review Article originally published, the temperature anomaly trends for RSS and UAH in Fig. 2b,c should have been positive values. These errors have now been corrected in the online versions of the Review Article. In the previous corrigendum, the mass balance of glaciers in the Karakarom region should have read 0.11±0.22 m yr -1 .

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Acknowledgements

This work was supported by the National High-tech Research and Development Program of China (Grant No. 2009AA12200101) and the National Key Basic Research Program of China (Grant No. 2010CB530300).

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Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, 100084, China

Jun Yang & Peng Gong

State Key Lab of Remote Sensing Science, jointly sponsored by the Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, and Beijing Normal University, Beijing, 100101, China

Peng Gong & Jiancheng Shi

Department of Environmental Science, Policy and Management, University of California, Berkeley, 94720, California, USA

Jackson School of Geosciences, University of Texas, Austin, 78712, Texas, USA

Rong Fu & Robert Dickinson

School of Marine and Atmospheric Sciences, State University of New York, Stony Brook, 11794, New York, USA

Minghua Zhang

International Institute for Earth System Science, Nanjing University, Nanjing, 210093, China

Jingming Chen

Department of Geography and City Planning, University of Toronto, Toronto, M5S 3G3, Ontario, Canada

College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China

Shunlin Liang & Bing Xu

Department of Geography, University of Maryland, College Park, 20742, Maryland, USA

Shunlin Liang

College of Environmental Science and Engineering, Tsinghua University, Beijing, 100084, China

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J.Y. and P.G. designed the framework of the Review. All authors contributed to writing.

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Yang, J., Gong, P., Fu, R. et al. The role of satellite remote sensing in climate change studies. Nature Clim Change 3 , 875–883 (2013). https://doi.org/10.1038/nclimate1908

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Received : 23 October 2012

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Published : 15 September 2013

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DOI : https://doi.org/10.1038/nclimate1908

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A review of research on spectrum sensing based on deep learning.

1. Introduction

• Traditional approaches to radio spectrum sensing are studied, and a detailed summary of the strengths and weaknesses of each method is provided.
• The applications of CNNs, long and short-term memory networks (LSTMs), combinatorial neural networks, and other types of neural networks in radio spectrum sensing are described. A comprehensive review of deep-learning-based spectrum-sensing algorithms for the period 2021 to 2023 is presented.
• The paper concludes with a summary of the applications, current state of research, and challenges faced by deep learning in cooperative spectrum sensing.

2. Spectrum-Sensing Methods

2.1. spectrum-sensing modeling and performance metrics.

• Detection probability ( P d ): This is the probability that a secondary user detects the presence of the primary user when the primary user is present, denoted by P d = P r H 1 ∣ H 1 .
• False alarm probability ( P f ): This is the probability that a secondary user incorrectly believes that the primary user exists when the primary user does not exist, denoted by f = P r H 1 ∣ H 0 .
• Missed detection probability ( P m ): This is the probability that a secondary user mistakenly believes that the primary user does not exist when the primary user exists, which can be expressed as P m = P r H 0 ∣ H 1 . The sum of the probability of having a detection and the probability of missing a detection is 1, which can be expressed as P m + P d = 1 .

2.2. Single-Node Spectrum Sensing

• Spectrum monitoring and management: Single-node spectrum sensing monitors and manages spectrum utilization in specific frequency bands. It uses techniques to determine the utilization, interference, and possible availability of free spectrum in that band.
• Spectrum sharing: Single-node spectrum sensing enables dynamic spectrum sharing. Nodes can monitor the available spectrum and share this information with other devices or networks to improve the efficient utilization of spectrum resources.
• Interference detection and elimination: Single-node spectrum awareness detects potential sources of interference by monitoring the surrounding spectrum environment and taking appropriate measures to eliminate interference. This improves the performance and reliability of the communication system.
• Traditional algorithms for single-node spectrum sensing mainly include energy detection algorithms, cyclostationary feature algorithms, and matched filter detection algorithms [ 25 ]. In the next section, these traditional methods are explained in detail, basic simulation experiments are performed, and finally, the advantages and disadvantages of each method are compared and summarized.

2.2.1. Energy Detection

2.2.2. cyclostationary feature detection, 2.2.3. matched filter detection.

• PU signal uncertainty: Under the current spectrum management mechanism, each PU has absolute priority to use its working frequency band, and if a local node or secondary user requires prior knowledge of a PU’s signal, such as modulation coding method or timing, the PU is not obliged to provide it. Therefore, it is very difficult for the SUs to perceive the signal uncertainty in the PUs in the real spectrum.
• Complexity of real environments: The spectrum sensing range of a single node is generally physically limited, as it may be influenced by propagation distance and large obstacles such as plateaus and trees. This limits the range and accuracy of the spectrum resources that nodes can perceive. In wide area communication systems, multiple nodes may need to be deployed to extend the sensing range.
• Spectrum resource competition: A single node performing spectrum sensing may compete with other devices for the same spectrum resources. This can lead to a decrease in the accuracy of the spectrum sensing and may require additional coordination mechanisms to manage the allocation of spectrum resources.

2.3. Cooperative Spectrum Sensing

2.3.1. centralized cooperative spectrum detection, 2.3.2. distributed cooperative spectrum detection, 2.3.3. relay-assisted cooperative spectrum detection, 2.4. summary of conventional spectrum-sensing methods, 3. deep-learning-based spectrum-sensing methods, 3.1. application of convolutional neural networks to spectrum sensing, 3.1.1. convolutional neural networks, 3.1.2. spectrum-sensing method based on convolutional neural network.

• A CNN-based spectrum-sensing method for signal time–frequency domain information

3.1.3. Residual Network

3.1.4. application of residual neural networks to spectrum sensing, 3.2. application of long short-term memory networks to spectrum sensing, 3.2.1. lstm, 3.2.2. lstm-based spectrum-sensing methods, 3.3. other neural networks in spectrum sensing, 4. deep-learning-based cooperative spectrum sensing, 4.1. applications of deep neural networks in cooperative spectrum sensing, 4.2. applications of deep reinforcement learning to cooperative spectrum sensing.

5. Performance Comparison of Conventional Methods and Deep-Learning-Based Spectrum Sensing

6. challenges and perspectives, 6.1. further improvement of detection performance and robustness at a low snr, 6.2. investigating the robustness of sensing systems under various malicious attacks, 6.3. study of spectrum sensing under small-sample conditions, 6.4. study of dynamic threshold settings, 6.5. further research on the application of self-encoders in spectrum sensing, 7. conclusions, author contributions, data availability statement, conflicts of interest.

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Zhang, Y.; Luo, Z. A Review of Research on Spectrum Sensing Based on Deep Learning. Electronics 2023 , 12 , 4514. https://doi.org/10.3390/electronics12214514

Zhang Y, Luo Z. A Review of Research on Spectrum Sensing Based on Deep Learning. Electronics . 2023; 12(21):4514. https://doi.org/10.3390/electronics12214514

Zhang, Yixuan, and Zhongqiang Luo. 2023. "A Review of Research on Spectrum Sensing Based on Deep Learning" Electronics 12, no. 21: 4514. https://doi.org/10.3390/electronics12214514

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