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Journal articles on the topic 'Deep space applications'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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1

Patterson, R. L., A. Hammoud, J. E. Dickman, S. Gerber, M. Elbuluk, and E. Overton. "Electronics for deep space cryogenic applications." Journal de Physique IV (Proceedings) 12, no. 3 (2002): 207–10. http://dx.doi.org/10.1051/jp420020068.

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2

Jachna, Timothy J., Y. Santo, and N. Schadewitz. "Deep Space." International Journal of Architectural Computing 5, no. 1 (2007): 145–60. http://dx.doi.org/10.1260/147807707780913010.

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3

Subramanyam, A. V. G., D. Siva Reddy, V. K. Hariharan, V. V. Srinivasan, and Ajay Chakrabarty. "High Power Combline Filter for Deep Space Applications." International Journal of Microwave Science and Technology 2014 (September 14, 2014): 1–11. http://dx.doi.org/10.1155/2014/396494.

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An S-band, compact, high power filter, for use in the Mars Orbiter Mission (MOM) of Indian Space Research Organization (ISRO), has been designed and tested for multipaction. The telemetry, tracking, and commanding (TT&C) transponder of MOM is required to handle continuous RF power of 200 W in the telemetry path besides simultaneously maintaining an isolation of greater than 145 dBc to its sensitive telecommand path. This is accomplished with the help of a complex diplexer, requiring high power, high rejection transmit path filter, and a low power receive path filter. To reduce the complexi

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4

Hemmati, Hamid, Abhijit Biswas, and Ivan B. Djordjevic. "Deep-Space Optical Communications: Future Perspectives and Applications." Proceedings of the IEEE 99, no. 11 (2011): 2020–39. http://dx.doi.org/10.1109/jproc.2011.2160609.

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5

Strangman, Gary E., Aenor Sawyer, Kristin M. Fabre, et al. "Deep-space applications for point-of-care technologies." Current Opinion in Biomedical Engineering 11 (September 2019): 45–50. http://dx.doi.org/10.1016/j.cobme.2019.08.014.

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6

Chaloner, C. P., B. A. H. Olivier, and J. Howieson. "Advanced Microsatellite Mission—deep space applications and constraints." Acta Astronautica 59, no. 8-11 (2006): 817–22. http://dx.doi.org/10.1016/j.actaastro.2005.07.062.

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7

Yoshimori, Atsushi, and Jürgen Bajorath. "Deep SAR matrix: SAR matrix expansion for advanced analog design using deep learning architectures." Future Drug Discovery 2, no. 2 (2020): FDD36. http://dx.doi.org/10.4155/fdd-2020-0005.

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Aim: Enhancing the structure–activity relationship matrix (SARM) methodology through integration of deep learning and expansion of chemical space coverage. Background: Analog design is of critical importance for medicinal chemistry. The SARM approach, which combines systematic structural organization of compound series with analog design, is put into scientific context. Methodology: The new DeepSARM concept is introduced. The architecture of SARM-integrated deep generative models is detailed and the workflow for advanced analog design and matrix expansion described. Exemplary application: The

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8

Grześ, Paweł, Maria Michalska, and Jacek Świderski. "Gain-switched seed laser for Deep Space communication applications." Photonics Letters of Poland 10, no. 2 (2018): 45. http://dx.doi.org/10.4302/plp.v10i2.818.

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Deep Space (DS) communication plays an important role in space exploration programs, especially in interplanetary flights projects. To resolve limitations of a well-known microwave link, an optical communication is considered. In the article a gain-switched seed laser for high power transmitter in a Master Oscillator Power Amplifier (MOPA) architecture is presented. This optical signal source is able to generate picosecond pulses on demand and is suitable for high speed data link over a long range. The laser is dedicated to the pulse position modulation (PPM) scheme for low power consuming, hi

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9

Schulze, Ron, Robert E. Wallis, Robert K. Stilwell, and Weilun Cheng. "Enabling Antenna Systems for Extreme Deep-Space Mission Applications." Proceedings of the IEEE 95, no. 10 (2007): 1976–85. http://dx.doi.org/10.1109/jproc.2007.905068.

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10

Chen, Po-Ting, Bonan Zhou, Jason L. Speyer, David S. Bayard, Walid A. Majid, and Lincoln J. Wood. "Aspects of Pulsar Navigation for Deep Space Mission Applications." Journal of the Astronautical Sciences 67, no. 2 (2020): 704–39. http://dx.doi.org/10.1007/s40295-019-00209-9.

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11

Hall, J. R., R. C. Hastrup, and D. J. Bell. "Microwave systems applications in deep space telecommunications and navigation: Space Exploration Initiative architectures." IEEE Transactions on Microwave Theory and Techniques 40, no. 6 (1992): 1171–78. http://dx.doi.org/10.1109/22.141349.

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12

Bozzi, M., M. Cametti, M. Fornaroli, et al. "Future Architectures for European Space Agency Deep-Space Ground Stations [Antenna Applications Corner]." IEEE Antennas and Propagation Magazine 54, no. 1 (2012): 254–63. http://dx.doi.org/10.1109/map.2012.6202560.

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13

Clauss, G. F., S. Hoog, M. Vannahme, H. Gerber, F. Gasparoni, and D. Calore. "MODUS: Space Shuttle for Deep-Water Operations." Marine Technology and SNAME News 40, no. 03 (2003): 218–28. http://dx.doi.org/10.5957/mt1.2003.40.3.218.

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This paper presents MObile Docker for Underwater Sciences (MODUS), a specialized remotely operated vehicle (ROV) for deployment, recovery, and servicing of heavy sea bottom stations, for example, scientific observatories for deep-water application (depth rated 4,000 m). Developed, built, and tested as part of the EC-funded project GEophysical and Oceanographic STation for Abyssal Research (GEOSTAR 2001), the new ROV system includes the operating vessel with winch and umbilical and the versatile "space shuttle" MODUS for handling the bottom station with modularized payload. The paper focuses on

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14

Cong, Bo, Nanping Mao, and Xiaohong Tao. "A Comparative Study of Ranging Techniques in Deep Space Applications." Journal of Physics: Conference Series 679 (February 29, 2016): 012011. http://dx.doi.org/10.1088/1742-6596/679/1/012011.

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15

Praveena, P. "Implementation of LOCO-I Lossless Image Compression Algorithm for Deep Space Applications." International Journal of Reconfigurable and Embedded Systems (IJRES) 3, no. 1 (2014): 25. http://dx.doi.org/10.11591/ijres.v3.i1.pp25-30.

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<p>Present emerging trend in space science applications is to explore and utilize the deep space. Image coding in deep space communications play vital role in deep space missions. Lossless image compression has been recommended for space science exploration missions to retain the quality of image. On-board memory and bandwidth requirement is reduced by image compression. Programmable logic like field programmable gate array (FPGA) offers an attractive solution for performance and flexibility required by real time image compression algorithms. The powerful feature of FPGA is parallel proc

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16

Praveena, P. "Implementation of LOCO-I Lossless Image Compression Algorithm for Deep Space Applications." International Journal of Reconfigurable and Embedded Systems (IJRES) 3, no. 3 (2014): 98. http://dx.doi.org/10.11591/ijres.v3.i3.pp98-103.

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<p>Present emerging trend in space science applications is to explore and utilize the deep space. Image coding in deep space communications play vital role in deep space missions. Lossless image compression has been recommended for space science exploration missions to retain the quality of image. On-board memory and bandwidth requirement is reduced by image compression. Programmable logic like field programmable gate array (FPGA) offers an attractive solution for performance and flexibility required by real time image compression algorithms. The powerful feature of FPGA is parallel proc

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17

Backhaus, Scott, Michael Petach, and Emanuel Tward. "A small‐scale, thermoacoustic‐Stirling electric generator for deep‐space applications." Journal of the Acoustical Society of America 115, no. 5 (2004): 2380. http://dx.doi.org/10.1121/1.4780220.

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18

Hong, Seung-Ho, Gil-Bok Choi, Rock-Hyun Baek, Hee-Sung Kang, Sung-Woo Jung, and Yoon-Ha Jeong. "Low-Temperature Performance of Nanoscale MOSFET for Deep-Space RF Applications." IEEE Electron Device Letters 29, no. 7 (2008): 775–77. http://dx.doi.org/10.1109/led.2008.2000614.

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19

Andrews, Kenneth S., Dariush Divsalar, Sam Dolinar, Jon Hamkins, Christopher R. Jones, and Fabrizio Pollara. "The Development of Turbo and LDPC Codes for Deep-Space Applications." Proceedings of the IEEE 95, no. 11 (2007): 2142–56. http://dx.doi.org/10.1109/jproc.2007.905132.

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20

Hollington, D., J. T. Baird, T. J. Sumner, and P. J. Wass. "Characterising and testing deep UV LEDs for use in space applications." Classical and Quantum Gravity 32, no. 23 (2015): 235020. http://dx.doi.org/10.1088/0264-9381/32/23/235020.

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21

Tang, Jifei, Lanhua Xia, and Rabi Mahapatra. "An open-loop system design for deep space signal processing applications." Acta Astronautica 147 (June 2018): 259–72. http://dx.doi.org/10.1016/j.actaastro.2018.04.015.

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22

Zhou, Jian, Meina Song, Junde Song, Xian-wei Zhou, and Liyan Sun. "Autonomic Group Key Management in Deep Space DTN." Wireless Personal Communications 77, no. 1 (2013): 269–87. http://dx.doi.org/10.1007/s11277-013-1505-1.

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23

Wang, Xiao Yu, Jia Cai Hong, and Kai Hu. "A Study on Same-Beam Interferometry in Deep Space." Advanced Materials Research 989-994 (July 2014): 4014–18. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4014.

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Same-beam interferometry (SBI) has a wide range of applications and prospects in collocate geostationary satellites, space rendezvous and docking, landers and rovers positioning. The paper introduces the basic principles, underlying processing algorithms, ambiguity resolution solving, process flow comprehensively and systematically. Then use FX-related processing algorithms and integer ambiguity solution method step by step to process signals, draw process flowchart.

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24

Cooper, G. "Book reviews: Deep space telecommunications systems engineering." IEEE Communications Magazine 23, no. 9 (1985): 58. http://dx.doi.org/10.1109/mcom.1985.1092646.

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25

Han, Yoseo, Leonard Sunwoo, and Jong Chul Ye. "${k}$ -Space Deep Learning for Accelerated MRI." IEEE Transactions on Medical Imaging 39, no. 2 (2020): 377–86. http://dx.doi.org/10.1109/tmi.2019.2927101.

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26

Jayakumari, J., and J. Jane Sofia. "FPGA IMPLEMENTATION OF CONCATENATED LT AND LDPC CODER FOR DEEP SPACE APPLICATIONS." Far East Journal of Electronics and Communications 17, no. 2 (2017): 409–22. http://dx.doi.org/10.17654/ec017020409.

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27

Ma, Guixiang, Nesreen K. Ahmed, Theodore L. Willke, and Philip S. Yu. "Deep graph similarity learning: a survey." Data Mining and Knowledge Discovery 35, no. 3 (2021): 688–725. http://dx.doi.org/10.1007/s10618-020-00733-5.

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AbstractIn many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search. Recently, there has been an increasing interest in deep graph similarity learning, where the key idea is to learn a deep learning model that maps input graphs to a target space such that the distance in the target space approximates the structural distance in the input space. Here, we provide a comprehensive review of the existing literature of deep gra

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28

Lowe, S. T., and R. N. Treuhaft. "Applications of Few-Hundred Microarcsecond VLBI Astrometry: Planetary Relativistic Deflection, PPN Gamma Determination and Deep-Space Tracking." Symposium - International Astronomical Union 156 (1993): 145–49. http://dx.doi.org/10.1017/s0074180900173127.

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This paper presents several applications of a few hundred microarcsecond (μas) astrometric technique which has been developed and demonstrated using differential very long baseline interferometry (VLBI). A brief description of the technique along with several applications will be discussed below. This technique was developed for high-accuracy deep-space tracking, but the first application tracked an extragalactic radio source in a measurement of Jovian relativistic deflection. Current work includes making a state-of-the-art solar deflection measurement, and thus, an improved determination of t

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29

Dąbkowski, Dawid, Paweł Tabaszewski, and Paweł Górecki. "Minimizing the deep coalescence cost." Journal of Bioinformatics and Computational Biology 16, no. 05 (2018): 1840021. http://dx.doi.org/10.1142/s0219720018400218.

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Metagenomic studies identify the species present in an environmental sample usually by using procedures that match molecular sequences, e.g. genes, with the species taxonomy. Here, we first formulate the problem of gene-species matching in the parsimony framework using binary phylogenetic gene and species trees under the deep coalescence cost and the assumption that each gene is paired uniquely with one species. In particular, we solve the problem in the cases when one of the trees is a caterpillar. Next, we propose a dynamic programming algorithm, which solves the problem exactly, however, it

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30

Zhaoyu Pei, 裴照宇, 侯军 Jun Hou, and 王琼 Qiong Wang. "Applications of optical technology in lunar and deep space exploration in China(Invited)." Infrared and Laser Engineering 49, no. 5 (2020): 20201002. http://dx.doi.org/10.3788/irla.2_invited-peizhaoyu.

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31

Scheffer, L. K. "A scheme for a high-power, low-cost transmitter for deep space applications." Radio Science 40, no. 5 (2005): n/a. http://dx.doi.org/10.1029/2005rs003243.

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32

Heivly, Michael L., and Michal Reed. "The Space between the Real and the Imagined: Microwave Sculpture in Deep Space." Leonardo 25, no. 1 (1992): 17. http://dx.doi.org/10.2307/1575614.

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33

TAI, ANN T., SAVIO N. CHAU, and LEON ALKALAI. "COTS-BASED FAULT TOLERANCE IN DEEP SPACE: A CASE STUDY ON IEEE 1394 APPLICATION." International Journal of Reliability, Quality and Safety Engineering 09, no. 01 (2002): 17–40. http://dx.doi.org/10.1142/s0218539302000640.

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Among the COTS applications in the X2000 architecture for deep-space missions, the use of commercial bus standards is the highest-payoff COTS application since a bus interface has a global impact on system cost and capability. While COTS bus standards enable significant cost reductions, it is a great challenge for us to deliver a highly-reliable long-term survivable system employing COTS standards that are not developed for mission-critical applications. The essence of our solution to the problem is to exploit the pertinent standard features of a COTS product to circumvent its shortcomings, in

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34

Zhao, Dong-Di, Fan Li, Kashif Sharif, Guang-Min Xia, and Yu Wang. "Space Efficient Quantization for Deep Convolutional Neural Networks." Journal of Computer Science and Technology 34, no. 2 (2019): 305–17. http://dx.doi.org/10.1007/s11390-019-1912-1.

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35

Bocanegra-Bahamón, T. M., G. Molera Calvés, L. I. Gurvits, et al. "Planetary Radio Interferometry and Doppler Experiment (PRIDE) technique: A test case of the Mars Express Phobos Flyby." Astronomy & Astrophysics 609 (January 2018): A59. http://dx.doi.org/10.1051/0004-6361/201731524.

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Context. Closed-loop Doppler data obtained by deep space tracking networks, such as the NASA Deep Space Network (DSN) and the ESA tracking station network (Estrack), are routinely used for navigation and science applications. By shadow tracking the spacecraft signal, Earth-based radio telescopes involved in the Planetary Radio Interferometry and Doppler Experiment (PRIDE) can provide open-loop Doppler tracking data only when the dedicated deep space tracking facilities are operating in closed-loop mode. Aims. We explain the data processing pipeline in detail and discuss the capabilities of the

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36

Akan, O. B., H. Fang, and I. F. Akyildiz. "Performance of TCP protocols in deep space communication networks." IEEE Communications Letters 6, no. 11 (2002): 478–80. http://dx.doi.org/10.1109/lcomm.2002.805549.

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37

Choudhary, Anirudh, Li Tong, Yuanda Zhu, and May D. Wang. "Advancing Medical Imaging Informatics by Deep Learning-Based Domain Adaptation." Yearbook of Medical Informatics 29, no. 01 (2020): 129–38. http://dx.doi.org/10.1055/s-0040-1702009.

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Introduction: There has been a rapid development of deep learning (DL) models for medical imaging. However, DL requires a large labeled dataset for training the models. Getting large-scale labeled data remains a challenge, and multi-center datasets suffer from heterogeneity due to patient diversity and varying imaging protocols. Domain adaptation (DA) has been developed to transfer the knowledge from a labeled data domain to a related but unlabeled domain in either image space or feature space. DA is a type of transfer learning (TL) that can improve the performance of models when applied to mu

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38

Avanesov, G. A., B. S. Zhukov, P. S. Smetanin, and M. V. Mikhailov. "Testing the technology of deep space spacecraft autonomous navigation at the International Space Station." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 17, no. 7 (2020): 41–49. http://dx.doi.org/10.21046/2070-7401-2020-17-7-41-49.

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39

Mysoor, N. R., J. D. Perret, and A. W. Kermode. "An X-band spacecraft transponder for deep space applications-design concepts and breadboard performance." IEEE Transactions on Microwave Theory and Techniques 40, no. 6 (1992): 1192–98. http://dx.doi.org/10.1109/22.141351.

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40

P. RATNA, KAMALA, and R. V. S. Satyanarayana. "Robust Reed Solomon code Analysis for use in Deep Space and Mobile Communication Applications." i-manager's Journal on Information Technology 6, no. 2 (2017): 22. http://dx.doi.org/10.26634/jit.6.2.13573.

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41

Zhang, Burt, Melora Larson, and Jose Rodriguez. "Passive coolers for pre-cooling of JT loops for deep space infrared imaging applications." Cryogenics 50, no. 9 (2010): 628–32. http://dx.doi.org/10.1016/j.cryogenics.2010.02.019.

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42

Arkhypov, Oleksiy V., Maxim L. Khodachenko, Helmut Lammer, Manuel Güdel, Theresa Lüftinger, and Colin P. Johnstone. "DEEP MIXING IN STELLAR VARIABILITY: IMPROVED METHOD, STATISTICS, AND APPLICATIONS." Astrophysical Journal 826, no. 1 (2016): 35. http://dx.doi.org/10.3847/0004-637x/826/1/35.

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43

Wu, Kailiang, and Dongbin Xiu. "Data-driven deep learning of partial differential equations in modal space." Journal of Computational Physics 408 (May 2020): 109307. http://dx.doi.org/10.1016/j.jcp.2020.109307.

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44

Jian, Zhou, Sun Liyan, Duan Kaiyu, and Wu Yue. "Research on Self-Adaptive Group Key Management in Deep Space Networks." Wireless Personal Communications 114, no. 4 (2020): 3435–56. http://dx.doi.org/10.1007/s11277-020-07540-7.

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45

Idris, Sadiq, Hossam Selmy, and Waslon Terllizzie A. Lopes. "Performance analysis of hybrid MPAPM technique for deep‐space optical communications." IET Communications 15, no. 13 (2021): 1700–1709. http://dx.doi.org/10.1049/cmu2.12182.

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46

James, Mark L., Andrew A. Shapiro, Paul L. Springer, and Hans P. Zima. "Adaptive Fault Tolerance for Scalable Cluster Computing in Space." International Journal of High Performance Computing Applications 23, no. 3 (2009): 227–41. http://dx.doi.org/10.1177/1094342009106190.

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Future missions of deep-space exploration face the challenge of building more capable autonomous spacecraft and planetary rovers. Given the communication latencies and bandwidth limitations for such missions, the need for increased autonomy becomes mandatory, along with the requirement for enhanced on-board computational capabilities while in deep-space or time-critical situations. This will result in dramatic changes in the way missions are conducted and supported by on-board computing systems. Specifically, the traditional approach of relying exclusively on radiation-hardened hardware and mo

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47

Sabogal, Sebastian, Alan George, and Gary Crum. "Reconfigurable Framework for Resilient Semantic Segmentation for Space Applications." ACM Transactions on Reconfigurable Technology and Systems 14, no. 4 (2021): 1–32. http://dx.doi.org/10.1145/3472770.

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Deep learning (DL) presents new opportunities for enabling spacecraft autonomy, onboard analysis, and intelligent applications for space missions. However, DL applications are computationally intensive and often infeasible to deploy on radiation-hardened (rad-hard) processors, which traditionally harness a fraction of the computational capability of their commercial-off-the-shelf counterparts. Commercial FPGAs and system-on-chips present numerous architectural advantages and provide the computation capabilities to enable onboard DL applications; however, these devices are highly susceptible to

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48

Lee, Xian Yeow, Sambit Ghadai, Kai Liang Tan, Chinmay Hegde, and Soumik Sarkar. "Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4577–84. http://dx.doi.org/10.1609/aaai.v34i04.5887.

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Robustness of Deep Reinforcement Learning (DRL) algorithms towards adversarial attacks in real world applications such as those deployed in cyber-physical systems (CPS) are of increasing concern. Numerous studies have investigated the mechanisms of attacks on the RL agent's state space. Nonetheless, attacks on the RL agent's action space (corresponding to actuators in engineering systems) are equally perverse, but such attacks are relatively less studied in the ML literature. In this work, we first frame the problem as an optimization problem of minimizing the cumulative reward of an RL agent

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49

Smith, R. S., and F. Y. Hadaegh. "Distributed estimation, communication and control for deep space formations." IET Control Theory & Applications 1, no. 2 (2007): 445–51. http://dx.doi.org/10.1049/iet-cta:20050460.

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50

Cecchini, Micael A., Luiz A. T. Machado, Manfred Wendisch, et al. "Illustration of microphysical processes in Amazonian deep convective clouds in the gamma phase space: introduction and potential applications." Atmospheric Chemistry and Physics 17, no. 23 (2017): 14727–46. http://dx.doi.org/10.5194/acp-17-14727-2017.

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Abstract. The behavior of tropical clouds remains a major open scientific question, resulting in poor representation by models. One challenge is to realistically reproduce cloud droplet size distributions (DSDs) and their evolution over time and space. Many applications, not limited to models, use the gamma function to represent DSDs. However, even though the statistical characteristics of the gamma parameters have been widely studied, there is almost no study dedicated to understanding the phase space of this function and the associated physics. This phase space can be defined by the three pa

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