Journal articles: 'In situ sensing'

1

Carin, Lawrence, Dehong Liu, and Bin Guo. "In situ compressive sensing." Inverse Problems 24, no. 1 (January 25, 2008): 015023. http://dx.doi.org/10.1088/0266-5611/24/1/015023.

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Knobelspies, S., B. Bierer, J. Wöllenstein, J. Kneer, and S. Palzer. "Towards In-situ Biogas Sensing." Procedia Engineering 120 (2015): 269–74. http://dx.doi.org/10.1016/j.proeng.2015.08.606.

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Wu, Xiaopei, Qingsi Wang, and Mingyan Liu. "In-situ Soil Moisture Sensing." ACM Transactions on Sensor Networks 11, no. 2 (March 2, 2015): 1–29. http://dx.doi.org/10.1145/2629439.

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Wu, Xiaopei, Mingyan Liu, and Yue Wu. "In-situ soil moisture sensing." ACM Transactions on Sensor Networks 8, no. 4 (September 2012): 1–30. http://dx.doi.org/10.1145/2240116.2240122.

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Bühl, J., S. Alexander, S. Crewell, A. Heymsfield, H. Kalesse, A. Khain, M. Maahn, K. Van Tricht, and M. Wendisch. "Remote Sensing." Meteorological Monographs 58 (January 1, 2017): 10.1–10.21. http://dx.doi.org/10.1175/amsmonographs-d-16-0015.1.

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Abstract State-of-the-art remote sensing techniques applicable to the investigation of ice formation and evolution are described. Ground-based and spaceborne measurements with lidar, radar, and radiometric techniques are discussed together with a global view on past and ongoing remote sensing measurement campaigns concerned with the study of ice formation and evolution. This chapter has the intention of a literature study and should illustrate the major efforts that are currently taken in the field of remote sensing of atmospheric ice. Since other chapters of this monograph mainly focus on aircraft in situ measurements, special emphasis is put on active remote sensing instruments and synergies between aircraft in situ measurements and passive remote sensing methods. The chapter concentrates on homogeneous and heterogeneous ice formation in the troposphere because this is a major topic of this monograph. Furthermore, methods that deliver direct, process-level information about ice formation are elaborated with a special emphasis on active remote sensing methods. Passive remote sensing methods are also dealt with but only in the context of synergy with aircraft in situ measurements.

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Maser, Kenneth R. "Automated Interpretation for Sensing in Situ Conditions." Journal of Computing in Civil Engineering 2, no. 3 (July 1988): 215–38. http://dx.doi.org/10.1061/(asce)0887-3801(1988)2:3(215).

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Wang, Fushun, Baoguo Chen, Lei Wu, Qiuhua Zhao, and Lidong Zhang. "In Situ Swelling-Gated Chemical Sensing Actuator." Cell Reports Physical Science 1, no. 2 (February 2020): 100011. http://dx.doi.org/10.1016/j.xcrp.2019.100011.

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Mangi, Aijaz Ahmed, Syed Salman Shahid, and Sikander Hayat Mirza. "In-situ Blockage Monitoring of Sensing Line." Nuclear Engineering and Technology 48, no. 1 (February 2016): 98–113. http://dx.doi.org/10.1016/j.net.2015.08.009.

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Karimzadeh, Sadra, and Masashi Matsuoka. "Development of Nationwide Road Quality Map: Remote Sensing Meets Field Sensing." Sensors 21, no. 6 (March 23, 2021): 2251. http://dx.doi.org/10.3390/s21062251.

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In this study, we measured the in situ international roughness index (IRI) for first-degree roads spanning more than 1300 km in East Azerbaijan Province, Iran, using a quarter car (QC). Since road quality mapping with in situ measurements is a costly and time-consuming task, we also developed new equations for constructing a road quality proxy map (RQPM) using discriminant analysis and multispectral information from high-resolution Sentinel-2 images, which we calibrated using the in situ data on the basis of geographic information system (GIS) data. The developed equations using optimum index factor (OIF) and norm R provide a valuable tool for creating proxy maps and mitigating hazards at the network scale, not only for primary roads but also for secondary roads, and for reducing the costs of road quality monitoring. The overall accuracy and kappa coefficient of the norm R equation for road classification in East Azerbaijan province are 65.0% and 0.59, respectively.

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Ochoa, Maicol A., and Michael Zwolak. "Optimal in situ electromechanical sensing of molecular species." Journal of Chemical Physics 152, no. 3 (January 21, 2020): 034109. http://dx.doi.org/10.1063/1.5132581.

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Moltyaner, G. L., and C. A. Wills. "Characterization of aquifer heterogeneity by in situ sensing." Water Resources Research 29, no. 10 (October 1993): 3417–31. http://dx.doi.org/10.1029/93wr01275.

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Griffiths, Arron, Aleksandr Dikarev, Pete R. Green, Barry Lennox, Xavier Poteau, and Simon Watson. "AVEXIS—Aqua Vehicle Explorer for In-Situ Sensing." IEEE Robotics and Automation Letters 1, no. 1 (January 2016): 282–87. http://dx.doi.org/10.1109/lra.2016.2519947.

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Rajendra, C. Viven, and Purushottam Kulkarni. "Road traffic estimation using in-situ acoustic sensing." ACM SIGCOMM Computer Communication Review 40, no. 4 (August 16, 2010): 431–32. http://dx.doi.org/10.1145/1851275.1851247.

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Wijewardane, Nuwan K., Sarah Hetrick, Jason Ackerson, Cristine L. S. Morgan, and Yufeng Ge. "VisNIR integrated multi-sensing penetrometer for in situ high-resolution vertical soil sensing." Soil and Tillage Research 199 (May 2020): 104604. http://dx.doi.org/10.1016/j.still.2020.104604.

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Isa, Muhammad, Dwiky Pobri Cesarian, Ismail Ahmad Abir, Elin Yusibani, Muhammad Syukri Surbakti, and Muksin Umar. "Remote Sensing Satellite Imagery and In-Situ Data for Identifying Geothermal Potential Sites: Jaboi, Indonesia." International Journal of Renewable Energy Development 9, no. 2 (February 19, 2020): 237–45. http://dx.doi.org/10.14710/ijred.9.2.237-245.

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Remote sensing makes it possible to map potential geothermal site for a large area effectively using thermal infrared. The purpose of the present research is to overlay ground temperature, resistivity and satellite retrieved temperature in identifying geothermal potential site in Jaboi, Sabang-Indonesia. The data of acquisition of the DEM imagery was January 3rd, 2009 and the Landsat 8 imagery is July 18th, 2017. The satellite data were applied to extract the land surface temperature and land classification across. Two supporting data in situ were used to validate the results from remote sensing. First dataset was ground temperature measurements with total 114 points and second dataset was vertical electrical sounding (VES) with total of 51 points. Satellite, VES and ground temperature data were processed and analysed using the Envi 5.3, PCI Geomatica 2016 and ArcMap 10.4. The results from each data were integrated to produce a map shows geothermal potential. Its integration produced four areas which were considered to have high geothermal potential. However, these areas vary in term of the clustering of the features of interest, for example lineament and drainage density of the area, high temperature in the surface area, fault existence and low resistivity subsurface. All the features must take into consideration to rank potential area which has higher potential. Finally, a map of geothermal potential across were successfully created as an insight for future reference. ©2020. CBIORE-IJRED. All rights reserved

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McCaul, Margaret, Jack Barland, John Cleary, Conor Cahalane, Tim McCarthy, and Dermot Diamond. "Combining Remote Temperature Sensing with in-Situ Sensing to Track Marine/Freshwater Mixing Dynamics." Sensors 16, no. 9 (August 31, 2016): 1402. http://dx.doi.org/10.3390/s16091402.

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Gu, Kaizhi, Wanshan Qiu, Zhiqian Guo, Chenxu Yan, Shiqin Zhu, Defan Yao, Ping Shi, He Tian, and Wei-Hong Zhu. "An enzyme-activatable probe liberating AIEgens: on-site sensing and long-term tracking of β-galactosidase in ovarian cancer cells." Chemical Science 10, no. 2 (2019): 398–405. http://dx.doi.org/10.1039/c8sc04266g.

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Malik, Shoaib, Liwei Wang, Paul Curtis, and Gerard Fernando. "Self-Sensing Composites: In-Situ Detection of Fibre Fracture." Sensors 16, no. 5 (April 28, 2016): 615. http://dx.doi.org/10.3390/s16050615.

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Godin, Michel, Olivier Laroche, Vincent Tabard-Cossa, L. Y. Beaulieu, Peter Grütter, and P. J. Williams. "Combined in situ micromechanical cantilever-based sensing and ellipsometry." Review of Scientific Instruments 74, no. 11 (November 2003): 4902–7. http://dx.doi.org/10.1063/1.1614859.

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Hwang, Joengmin, Tian He, and Yongdae Kim. "Exploring In-Situ Sensing Irregularity in Wireless Sensor Networks." IEEE Transactions on Parallel and Distributed Systems 21, no. 4 (April 2010): 547–61. http://dx.doi.org/10.1109/tpds.2009.92.

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Nurzaman, Surya G., Utku Culha, Luzius Brodbeck, Liyu Wang, and Fumiya Iida. "Active Sensing System with In Situ Adjustable Sensor Morphology." PLoS ONE 8, no. 12 (December 26, 2013): e84090. http://dx.doi.org/10.1371/journal.pone.0084090.

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Takahashi, Yasufumi, Yuanshu Zhou, and Takeshi Fukuma. "In situ chemical sensing by using scanning probe microscope." Folia Pharmacologica Japonica 153, no. 6 (2019): 267–72. http://dx.doi.org/10.1254/fpj.153.267.

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Jing, Wuming, and David Cappelleri. "A Magnetic Microrobot with in situ Force Sensing Capabilities." Robotics 3, no. 2 (April 8, 2014): 106–19. http://dx.doi.org/10.3390/robotics3020106.

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Mishra, Rahul Kumar, Indra Neel Pulidindi, Eihab Kabha, and Aharon Gedanken. "In situ formation of carbon dots aids ampicillin sensing." Anal. Methods 8, no. 11 (2016): 2441–47. http://dx.doi.org/10.1039/c6ay00413j.

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Koecher, Michael C., John H. Pande, Scott Merkley, Sam Henderson, David T. Fullwood, and Anton E. Bowden. "Piezoresistive in-situ strain sensing of composite laminate structures." Composites Part B: Engineering 69 (February 2015): 534–41. http://dx.doi.org/10.1016/j.compositesb.2014.09.029.

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Guo, Yongming, and Wei Zhao. "In situ formed nanomaterials for colorimetric and fluorescent sensing." Coordination Chemistry Reviews 387 (May 2019): 249–61. http://dx.doi.org/10.1016/j.ccr.2019.02.019.

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Chauvigné, Aurélien, Karine Sellegri, Maxime Hervo, Nadège Montoux, Patrick Freville, and Philippe Goloub. "Comparison of the aerosol optical properties and size distribution retrieved by sun photometer with in situ measurements at midlatitude." Atmospheric Measurement Techniques 9, no. 9 (September 16, 2016): 4569–85. http://dx.doi.org/10.5194/amt-9-4569-2016.

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Abstract. Aerosols influence the Earth radiative budget through scattering and absorption of solar radiation. Several methods are used to investigate aerosol properties and thus quantify their direct and indirect impacts on climate. At the Puy de Dôme station, continuous high-altitude near-surface in situ measurements and low-altitude ground-based remote sensing atmospheric column measurements give the opportunity to compare the aerosol extinction measured with both methods over a 1-year period. To our knowledge, it is the first time that such a comparison is realised with continuous measurements of a high-altitude site during a long-term period. This comparison addresses to which extent near-surface in situ measurements are representative of the whole atmospheric column, the aerosol mixing layer (ML) or the free troposphere (FT). In particular, the impact of multi-aerosol layers events detected using lidar backscatter profiles is analysed. A good correlation between in situ aerosol extinction coefficient and aerosol optical depth (AOD) measured by the Aerosol Robotic Network (AERONET) sun photometer is observed with a correlation coefficient around 0.80, indicating that the in situ measurements station is representative of the overall atmospheric column. After filtering for multilayer cases and correcting for each layer optical contribution (ML and FT), the atmospheric structure seems to be the main factor influencing the comparison between the two measurement techniques. When the site lies in the ML, the in situ extinction represents 45 % of the sun photometer ML extinction while when the site lies within the FT, the in situ extinction is more than 2 times higher than the FT sun photometer extinction. Moreover, the assumption of a decreasing linear vertical aerosol profile in the whole atmosphere has been tested, significantly improving the instrumental agreement. Remote sensing retrievals of the aerosol particle size distributions (PSDs) from the sun photometer observations are then compared to the near-surface in situ measurements, at dry and at ambient relative humidities. When in situ measurements are considered at dry state, the in situ fine mode diameters are 44 % higher than the sun-photometer-retrieved diameters and in situ volume concentrations are 20 % lower than those of the sun-photometer-retrieved fine mode concentration. Using a parameterised hygroscopic growth factor applied to aerosol diameters, the difference between in situ and retrieved diameters grows larger. Coarse mode in situ diameters and concentrations show a good correlation with retrieved PSDs from remote sensing.

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Homolová, Lucie, Růžena Janoutová, Petr Lukeš, Jan Hanuš, Jan Novotný, Olga Brovkina, and Rolling Richard Loayza Fernandez. "In situ data supporting remote sensing estimation of spruce forest parameters at the ecosystem station Bílý Kříž." Beskydy 10, no. 1-2 (2017): 75–86. http://dx.doi.org/10.11118/beskyd201710010075.

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Remote sensing offers an effective way of mapping vegetation parameters in a spatially continuous manner, at larger spatial scales and repeatedly in time compared to traditional in situ mapping approaches that are typically accurate, but limited to a few distributed location and few repetitions. In case of forest ecosystems, remote sensing allow to assess quantitative parameters or indicators related to forest health status such as leaf area index, leaf pigment content, chlorophyll fluorescence, etc. Development, calibration and validation of remote sensing-based methods, however, still rely on supportive in situ data. The aim of this contribution is to introduce the individual in situ components in the framework for the retrieval of forest quantitative parameters from airborne imaging spectroscopy data. All measurements were acquired during an extensive in situ/flight campaign that took place at the Norway spruce dominated study site Bílý Kříž (Moravian-Silesian Beskydy Mts., Czech Republic) during August 2016. In addition to airborne remote sensing data acquisition, the in situ activities included terrestrial laser scanning for tree 3D modelling, measurements of needle biochemical and optical properties, leaf area index measurements and spectral measurements of various natural and artificial surfaces. Leaf pigments varied between 25.2 and 49.1 µg cm-2 for chlorophyll a+b content, 4.9 – 10.6 µg cm-2 for carotenoid content depending on needle age and its adaptation to sun illumination, whereas ratio between the two pigments was stable around 4.6 – 5. 3. Specific leaf area of spruce needles varied between 49.3 and 105.8 cm2 g-1, being the highest for the shade adapted needles of the current year. Leaf area index of spruce stands of various age and densities varied between 5.3 and 9. 3.

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Zhang, Han, Yi Liu, Emiliano Bilotti, and Ton Peijs. "In-Situ Monitoring of Interlaminar Shear Damage in Carbon Fibre Composites." Advanced Composites Letters 24, no. 4 (July 2015): 096369351502400. http://dx.doi.org/10.1177/096369351502400405.

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The in-situ damage sensing of carbon/epoxy composites during interlaminar shear testing is investigated. Next to direct monitoring of woven fabric carbon/epoxy reference laminates, the introduction of carbon nanotubes (CNTs) onto these carbon fibre fabrics via a spray coating technique for damage sensing is evaluated. We observed very different sensing behaviour compared to previous studies, which is believed to be more useful for real applications. Through-thickness measurements showed for both reference and CNT modified specimens a continuous increase in electrical resistivity, due to reduced contact areas and conductive pathways. The effect of the introduced CNT network at the interfacial region is also been compared and analysed.

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Wang, Chao, Xinyan Zhuo, Pengfei Li, Nengcheng Chen, Wei Wang, and Zeqiang Chen. "An Ontology-Based Framework for Integrating Remote Sensing Imagery, Image Products, and In Situ Observations." Journal of Sensors 2020 (November 25, 2020): 1–12. http://dx.doi.org/10.1155/2020/6912820.

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Along with the rapid development of remote sensing satellites and sensor network technology, vast amounts of remote sensing imagery and in situ observations have been accumulated. Further, various researchers and agencies have released a variety of thematic image products. These heterogeneous observations are therefore difficult to utilize comprehensively. In this study, an ontology-based framework for integrating remote sensing imagery, image products, and in situ observations was developed. It was extended based on the Semantic Sensor Network (SSN) ontology in the Web Ontology Language (OWL). The detailed process of ontology construction and rule establishment was demonstrated. Combined with some actual remote sensing imagery, image products, and in situ observations, semantic queries based on DL Query and SPARQL were conducted to establish the rationality and feasibility of the ontology and framework.

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Blazkiewicz, P., K. Blazkiewicz, A. Verhaege, Y. G. Anissimov, M. S. Roberts, and A. V. Zvyagin. "Dialysis-assisted fiber optic spectroscopy for in situ biomedical sensing." Journal of Biomedical Optics 11, no. 1 (2006): 014033. http://dx.doi.org/10.1117/1.2166086.

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Zhang, Qian, Qian Wang, Xuan-Xuan Chen, Peng Zhang, Cai-Feng Ding, Zhao Li, and Yun-Bao Jiang. "Developing the spectral sensing scheme with in situ generated chromophores." TrAC Trends in Analytical Chemistry 109 (December 2018): 32–42. http://dx.doi.org/10.1016/j.trac.2018.09.020.

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Wu, Yuting, Carmine D'Agostino, Daniel J. Holland, and Lynn F. Gladden. "In situ study of reaction kinetics using compressed sensing NMR." Chem. Commun. 50, no. 91 (2014): 14137–40. http://dx.doi.org/10.1039/c4cc06051b.

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Barde, Rajesh, Kailash Nemade, and Sandeep Waghuley. "Ex-Situ Synthesis of Polythiophene/CeO2Composite for Carbon Dioxide Sensing." Macromolecular Symposia 361, no. 1 (March 2016): 149–51. http://dx.doi.org/10.1002/masy.201400223.

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Etzold, B. J. M., M. Schmidt, T. G. Euser, P. S. J. Russell, P. Wasserscheid, and A. M. Cubillas. "Photonic Crystal Fiber Microreactor for In Situ Sensing and Photocatalysis." Chemie Ingenieur Technik 86, no. 9 (August 28, 2014): 1615. http://dx.doi.org/10.1002/cite.201450116.

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Debieu, Sylvain, and Anthony Romieu. "In situ formation of pyronin dyes for fluorescence protease sensing." Organic & Biomolecular Chemistry 15, no. 12 (2017): 2575–84. http://dx.doi.org/10.1039/c7ob00370f.

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A cutting-edge strategy for fluorogenic sensing of proteases (leucine aminopeptidase for the proof of concept) and based on the “covalent-assembly” principle is reported. Non-fluorescent mixed bis-aryl ethers are readily converted into a fluorescent pyronin through a domino process triggered by the peptide bond cleavage event caused by the targeted enzyme.

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Ritchey, Joseph D. "Electronic Sensing Devices Used for In Situ Ground Water Monitoring." Groundwater Monitoring & Remediation 6, no. 2 (March 1986): 108–13. http://dx.doi.org/10.1111/j.1745-6592.1986.tb01249.x.

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Qiu, Liangsheng, Sufen Dong, Xun Yu, and Baoguo Han. "Self-sensing ultra-high performance concrete for in-situ monitoring." Sensors and Actuators A: Physical 331 (November 2021): 113049. http://dx.doi.org/10.1016/j.sna.2021.113049.

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Wang, Yuqiu, Maoze Guo, Bingfang He, and Bingbing Gao. "Intelligent Patches for Wound Management: In Situ Sensing and Treatment." Analytical Chemistry 93, no. 11 (March 14, 2021): 4687–96. http://dx.doi.org/10.1021/acs.analchem.0c04956.

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Ewing, Jordan, Thomas Oommen, Paramsothy Jayakumar, and Russell Alger. "Characterizing Soil Stiffness Using Thermal Remote Sensing and Machine Learning." Remote Sensing 13, no. 12 (June 12, 2021): 2306. http://dx.doi.org/10.3390/rs13122306.

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Soil strength characterization is essential for any problem that deals with geomechanics, including terramechanics/terrain mobility. Presently, the primary method of collecting soil strength parameters through in situ measurements but sending a team of people out to a site to collect data this has significant cost implications and accessing the location with the necessary equipment can be difficult. Remote sensing provides an alternate approach to in situ measurements. In this lab study, we compare the use of Apparent Thermal Inertia (ATI) against a GeoGauge for the direct testing of soil stiffness. ATI correlates with stiffness, so it allows one to predict the soil strength remotely using machine-learning algorithms. The best performing regression algorithm among the ones tested with different predictor variable combinations was found to be KNN with an R2 of 0.824 and a RMSE of 0.141. This study demonstrates the potential for using remote sensing to acquire thermal images that characterize terrain strength for mobility utilizing different machine-learning algorithms.

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Teillet, P. M., R. P. Gauthier, A. Chichagov, and G. Fedosejevs. "Towards integrated Earth sensing: Advanced technologies for in situ sensing in the context of Earth observation." Canadian Journal of Remote Sensing 28, no. 6 (December 2002): 713–18. http://dx.doi.org/10.5589/m02-068.

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Leifer, Ira, Dar Roberts, Jack Margolis, and Frank Kinnaman. "In situ sensing of methane emissions from natural marine hydrocarbon seeps: A potential remote sensing technology." Earth and Planetary Science Letters 245, no. 3-4 (May 2006): 509–22. http://dx.doi.org/10.1016/j.epsl.2006.01.047.

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Liu, Yansui, Md Anisul Islam, and Jay Gao. "Quantification of shallow water quality parameters by means of remote sensing." Progress in Physical Geography: Earth and Environment 27, no. 1 (March 2003): 24–43. http://dx.doi.org/10.1191/0309133303pp357ra.

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Quantification of quality parameters of inland and near shore waters by means of remote sensing has encountered varying degrees of success in spite of the high variability of the parameters under consideration and limitations of remote sensors themselves. This paper comprehensively evaluates the quantification of four types of water quality parameters: inorganic sediment particles, phytoplankton pigments, coloured dissolved organic material and Secchi disk depth. It concentrates on quantification requirements, as well as the options in selecting the most appropriate sensor data for the purpose. Relevant factors, such as quantification implementation and validation of the quantified results are also extensively discussed. This review reveals that the relationship between in situ samples and their corresponding remotely sensed data can be linear or nonlinear, but are nearly always site-specific. The quantification has been attempted from terrestrial satellite data largely for suspended sediments and chlorophyll concentrations. The quantification has been implemented through integration of remotely sensed imagery data, in situ water samples and ancillary data in a geographic information system (GIS). The introduction of GIS makes the quantification feasible for more variables at an increasingly higher accuracy. Affected by the number and quality of in situ samples, accuracy of quantification has been reported in different ways and varies widely.

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He, Y., S. C. Wang, F. C. Walsh, W. S. Li, L. He, and P. A. S. Reed. "The monitoring of coating health by in situ luminescent layers." RSC Advances 5, no. 53 (2015): 42965–70. http://dx.doi.org/10.1039/c5ra04475h.

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Balsamo, Gianpaolo, Anna Agusti-Panareda, Clement Albergel, Gabriele Arduini, Anton Beljaars, Jean Bidlot, Eleanor Blyth, et al. "Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review." Remote Sensing 10, no. 12 (December 14, 2018): 2038. http://dx.doi.org/10.3390/rs10122038.

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In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

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Ortega, María A., Júlia Rodríguez-Comas, Ozlem Yavas, Ferran Velasco-Mallorquí, Jordina Balaguer-Trias, Victor Parra, Anna Novials, Joan M. Servitja, Romain Quidant, and Javier Ramón-Azcón. "In Situ LSPR Sensing of Secreted Insulin in Organ-on-Chip." Biosensors 11, no. 5 (April 28, 2021): 138. http://dx.doi.org/10.3390/bios11050138.

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Organ-on-a-chip (OOC) devices offer new approaches for metabolic disease modeling and drug discovery by providing biologically relevant models of tissues and organs in vitro with a high degree of control over experimental variables for high-content screening applications. Yet, to fully exploit the potential of these platforms, there is a need to interface them with integrated non-labeled sensing modules, capable of monitoring, in situ, their biochemical response to external stimuli, such as stress or drugs. In order to meet this need, we aim here to develop an integrated technology based on coupling a localized surface plasmon resonance (LSPR) sensing module to an OOC device to monitor the insulin in situ secretion in pancreatic islets, a key physiological event that is usually perturbed in metabolic diseases such as type 2 diabetes (T2D). As a proof of concept, we developed a biomimetic islet-on-a-chip (IOC) device composed of mouse pancreatic islets hosted in a cellulose-based scaffold as a novel approach. The IOC was interfaced with a state-of-the-art on-chip LSPR sensing platform to monitor the in situ insulin secretion. The developed platform offers a powerful tool to enable the in situ response study of microtissues to external stimuli for applications such as a drug-screening platform for human models, bypassing animal testing.

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Schwenk, M., A. Katzir, and B. Mizaikoff. "In situ monitoring of additives during CO2 gas hydrate formation." Analytical Methods 8, no. 30 (2016): 5897–905. http://dx.doi.org/10.1039/c6ay00733c.

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Li, Sijia, Yulong Li, Xubo Liu, Xuewen Li, Tao Ding, and Hua Ouyang. "An In-Situ Electroplating Fabricated Fabry-Perot Interferometric Sensor and Its Temperature Sensing Characteristics." Coatings 10, no. 12 (November 30, 2020): 1174. http://dx.doi.org/10.3390/coatings10121174.

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In this study, in-situ electroplating method was used to fabricate a metal joint fixed extrinsic Fabry-Perot interferometric (EFPI) sensor. Specifically, optical fibers were firstly chemical plated with a very thin conductive nickel layer and then electroplated with nickel coating. After that, in-situ electroplating method was used to fix the metallized optical fibers and the capillary steel tube, the reflection spectra changes of the EFPI sensors during the in-situ electroplating process were recorded in real time, and the temperature sensing characteristics of the EFPI sensors were studied assisted by the temperature sensing system. Results show that: (i) optical fibers are well protected by the nickel layer; (ii) the reflection spectra of the EFPI sensors are clear and complete in the whole in-situ electroplating process, it is feasible to fabricate a EFPI sensor with the in-situ electroplating method; (iii) with the increases of temperature, the peak numbers of the reflection spectra of the EFPI sensors increase gradually; (iv) the EFPI sensors with different cavity length based on the in-situ electroplating method show excellent sensing characteristics, the temperature sensitivities reach up to about 700, 600, and 400 pm/°C from room temperature to 400 °C, respectively.

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Gao, F., S. Stanič, K. Bergant, T. Bolte, F. Coren, T. Y. He, A. Hrabar, et al. "Monitoring presence and streaming patterns of Icelandic volcanic ash during its arrival to Slovenia." Biogeosciences Discussions 8, no. 2 (April 14, 2011): 3863–98. http://dx.doi.org/10.5194/bgd-8-3863-2011.

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Abstract. The eruption of the Eyjafjallajökull volcano starting on 14 April 2010 resulted in the spreading of volcanic ash over most parts of Europe. In Slovenia, the presence of volcanic ash was monitored using ground-based in-situ measurements, lidar-based remote sensing and airborne in-situ measurements. Volcanic origin of the detected aerosols was confirmed by subsequent spectral and chemical analysis of the collected samples. The initial arrival of volcanic ash to Slovenia was detected at ground level using in-situ measurements during the night of 17 April 2010, but was not observed via lidar-based remote sensing due to the presence of clouds at lower altitudes while the streaming height of ash-loaded air masses was above 5 km a.s.l. The second arrival of volcanic ash on 20 April 2010 was detected by both lidar-based remote sensing and airborne in-situ measurement, revealing two or more elevated atmospheric aerosol layers above Slovenia. Identification of samples from ground-based in-situ and airborne in-situ measurements based on energy-dispersive X-ray spectroscopy confirmed that a fraction of particles was volcanic ash from the Eyjafjallajökull eruption. We performed simulations of airflow trajectories to explain the arrival of the air masses containing volcanic ash to Slovenia.

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Ma, Xiao, Xiaotian Du, Lei Li, Caroline Ladegard, Xuanhong Cheng, and James C. M. Hwang. "Broadband Electrical Sensing of a Live Biological Cell with In Situ Single-Connection Calibration." Sensors 20, no. 14 (July 9, 2020): 3844. http://dx.doi.org/10.3390/s20143844.

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Single-connection in situ calibration using biocompatible solutions is demonstrated in single-cell sensing from 0.5 to 9 GHz. The sensing is based on quickly trapping and releasing a live cell by dielectrophoresis on a coplanar transmission line with a little protrusion in one of its ground electrodes. The same transmission line is used as the calibration standard when covered by various solutions of known permittivities. The results show that the calibration technique may be precise enough to differentiate cells of different nucleus sizes, despite the measured difference being less than 0.01 dB in the deembedded scattering parameters. With better accuracy and throughput, the calibration technique may allow broadband electrical sensing of live cells in a high-throughput cytometer.

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Journal articles on the topic 'In situ sensing'

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