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Journal articles on the topic 'Flow sampling'

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Published: 4 June 2025
Last updated: 23 June 2025

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1

Williams, John G. "Sampling for Environmental Flow Assessments." Fisheries 35, no. 9 (2010): 434–43. http://dx.doi.org/10.1577/1548-8446-35.9.434.

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2

Wang, Xiaoming, Xiaoyong Li, and Dmitri Loguinov. "Modeling Residual-Geometric Flow Sampling." IEEE/ACM Transactions on Networking 21, no. 4 (2013): 1090–103. http://dx.doi.org/10.1109/tnet.2012.2231435.

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3

Liu, Yan. "Research of the Flow Sampling Algorithm." Advanced Materials Research 472-475 (February 2012): 2588–91. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.2588.

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This article presents a flow measurement sampling method based on the application group, which identify the received message and then trasmit the identified packets to the corresponding application packet sample space, after this sampling each space packet independently. Thus to ensure the network to restore the situation in the application of fine-grained has lower error rate, thereby reducing the distortion of the application distribution.
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4

Duffield, Nick. "Fair sampling across network flow measurements." ACM SIGMETRICS Performance Evaluation Review 40, no. 1 (2012): 367–78. http://dx.doi.org/10.1145/2318857.2254800.

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5

Duffield, Nick, Carsten Lund, and Mikkel Thorup. "Flow sampling under hard resource constraints." ACM SIGMETRICS Performance Evaluation Review 32, no. 1 (2004): 85–96. http://dx.doi.org/10.1145/1012888.1005699.

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6

Cai, Qiu-liang, Lei Tong, Ning Zhong, Jie Zheng, Meng-meng He, and Hang Xiao. "Sampling efficiency of flow-through air sampler: Effect of sampling rate." Journal of Chromatography A 1738 (December 2024): 465474. http://dx.doi.org/10.1016/j.chroma.2024.465474.

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7

Aldroubi, Akram, Karlheinz Gröchenig, Longxiu Huang, Philippe Jaming, Ilya Krishtal, and José Luis Romero. "Sampling the Flow of a Bandlimited Function." Journal of Geometric Analysis 31, no. 9 (2021): 9241–75. http://dx.doi.org/10.1007/s12220-021-00617-0.

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AbstractWe analyze the problem of reconstruction of a bandlimited function f from the space–time samples of its states $$f_t=\phi _t*f$$ f t = ϕ t ∗ f resulting from the convolution with a kernel $$\phi _t$$ ϕ t . It is well-known that, in natural phenomena, uniform space–time samples of f are not sufficient to reconstruct f in a stable way. To enable stable reconstruction, a space–time sampling with periodic nonuniformly spaced samples must be used as was shown by Lu and Vetterli. We show that the stability of reconstruction, as measured by a condition number, controls the maximal gap between the spacial samples. We provide a quantitative statement of this result. In addition, instead of irregular space–time samples, we show that uniform dynamical samples at sub-Nyquist spatial rate allow one to stably reconstruct the function $$\widehat{f}$$ f ^ away from certain, explicitly described blind spots. We also consider several classes of finite dimensional subsets of bandlimited functions in which the stable reconstruction is possible, even inside the blind spots. We obtain quantitative estimates for it using Remez-Turán type inequalities. En route, we obtain Remez-Turán inequality for prolate spheroidal wave functions. To illustrate our results, we present some numerics and explicit estimates for the heat flow problem.
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Choi, Beak-Young, Jaesung Park, and Zhi-Li Zhang. "Adaptive packet sampling for flow volume measurement." ACM SIGCOMM Computer Communication Review 32, no. 3 (2002): 9. http://dx.doi.org/10.1145/571697.571698.

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9

Miliauskas, John. "Lymph node sampling for flow cytometric analysis." Pathology 34, no. 5 (2002): 481–82. http://dx.doi.org/10.1080/0031302021000009478.

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10

R. A. Kohl and C. G. Carlson. "VOLUMETRIC SAMPLING OF SOILWATER FLOW USING WICKS." Transactions of the ASAE 40, no. 5 (1997): 1373–76. http://dx.doi.org/10.13031/2013.21396.

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11

Prior, João A. V., João L. M. Santos, and José L. F. C. Lima. "Sampling strategies exploiting multi-pumping flow systems." Analytical and Bioanalytical Chemistry 375, no. 8 (2003): 1234–39. http://dx.doi.org/10.1007/s00216-003-1813-5.

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12

Fullagar, Clive J., and E. Kevin Kelloway. "Flow at work: An experience sampling approach." Journal of Occupational and Organizational Psychology 82, no. 3 (2009): 595–615. http://dx.doi.org/10.1348/096317908x357903.

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13

Afek, Yehuda, Anat Bremler-Barr, Shir Landau Feibish, and Liron Schiff. "Sampling and Large Flow Detection in SDN." ACM SIGCOMM Computer Communication Review 45, no. 4 (2015): 345–46. http://dx.doi.org/10.1145/2829988.2790009.

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14

Risdianto, Aris Cahyadi, and Nuryani. "CDF-based Flow Detection for Network Flow Sampling and Packet Capturing." Jurnal Elektronika dan Telekomunikasi 19, no. 1 (2019): 26. http://dx.doi.org/10.14203/jet.v19.26-31.

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Providing an appropriate level of flow collection, relying on packet capturing or flow sampling method, is extremely hard due to various practical limitations and resources requirements. To address this challenge, this paper investigated a CDF (Cumulative Distribution Function)-based flow detection to decide between “known” and “unknown” flows. Therefore, a combined flow collection can be achieved to improve the collection’s efficiency by sampling only the known flows and capturing the remaining unknown flows. As a preliminary experiment, detecting known and unknown flows was conducted over a long period by calculating the empirical CDF distance between each flow’s rate and overall packet’s rate distribution, called as FPR (Flow-to-Packet Ratio), with a threshold (FPRmin) based on a significant level of observed data. The result shows that unknown flow is detected for most of the recommended significant level values.
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15

Swanson, C. J., and C. Wingard. "Digital sampling of low fluid flow: application to coronary flow measurements." Measurement Science and Technology 1, no. 3 (1990): 303–6. http://dx.doi.org/10.1088/0957-0233/1/3/017.

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16

HAMANO, Makoto, Hiroyuki FURUKAWA, and Takashi WATANABE. "3937 Numerical study of sampling flow information in Taylor vortex flow." Proceedings of the JSME annual meeting 2006.7 (2006): 155–56. http://dx.doi.org/10.1299/jsmemecjo.2006.7.0_155.

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17

Yu, Bo Lin, and Fa Hong Yu. "Sampling Flow Channel Design in Automobile Engine Intake Manifold." Advanced Materials Research 821-822 (September 2013): 1450–55. http://dx.doi.org/10.4028/www.scientific.net/amr.821-822.1450.

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As a result of turbulence and wide flow velocity range, flow velocity in engine intake manifold cannot be tested directly by using a thermal flow sensor. In this study, a sampling flow channel is designed according to the testing conditions and range of the thermal flow sensor. The flow velocity in the whole intake manifold was simulated by the finite element method software program. was found proportional to , and slope K was constant. ranged from 0 m/s to 4.8 m/s at the suitable outlet position of the sampling flow channel, which could be tested for the thermal flow sensor. The mass flow intake manifold was obtained by testing the mean flow velocity in the test section of the sampling flow channel .
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18

Sherwood, J. D. "Flow towards a guarded sampling probe: Modelling of a 2D flow cell." Journal of Petroleum Science and Engineering 66, no. 3-4 (2009): 133–42. http://dx.doi.org/10.1016/j.petrol.2009.02.002.

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19

Hu, Qihong Lorena Li, Patricia García-Caspueñas, Andrea Ianiro, and Stefano Discetti. "Galerkin Model For Time Super-Sampling Of PIV Measurements." Proceedings of the International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics 21 (July 8, 2024): 1–11. http://dx.doi.org/10.55037/lxlaser.21st.223.

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We propose a data-driven method for increasing the time resolution of snapshot Particle Image Velocimetry. The flow dynamics are reconstructed by integrating in time an empirical Galerkin model based on Proper Orthogonal Decomposition modes of the flow field, as developed by Noack et al. (2005). The main objective is to obtain a time-resolved description of the flow dynamics of an experimental non-time-resolved dataset. The proposed methodology is assessed both with numerical and experimental data of a wake and a jet flow. The results we obtained indicate that the method can effectively reconstruct the flow dynamics over a duration spanning several flow characteristic times.
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20

Wang, Bao Qing, Shu Yao, De Qing Wang, Zhi Peng Bai, and Xin Hua Wang. "Flow Analysis of a Shrouded Aerosol Sampling Inlet." Applied Mechanics and Materials 347-350 (August 2013): 3903–6. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3903.

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A shrouded aerosol sampling inlet has been designed from high-speed aircraft. The sampling inlet was conducted using the CFD to perform a flow simulation. The shroud diameter is 150 mm. The inlet is located 180mm from the shroud entrance plane. The shroud is 300 mm long. Results are presented graphically, showing the shrouds have provided significant improvements in flow characteristics. Straighten the streamlines of gas of sampling inlet for flow angles up to five degrees. It is suggested that CFD simulation can be useful for improving the optimum a shrouded aerosol sampling inlet.
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21

Ju, S. H., K. J. S. Kung, and C. S. Helling. "Simulating Impact of Funnel Flow on Contaminant Sampling." Soil Science Society of America Journal 61, no. 2 (1997): 427. http://dx.doi.org/10.2136/sssaj1997.03615995006100020009x.

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22

Akcoglu, Mustafa, Alexandra Bellow, Andr{és del Junco, and Roger L. Jones. "Divergence of averages obtained by sampling a flow." Proceedings of the American Mathematical Society 118, no. 2 (1993): 499. http://dx.doi.org/10.1090/s0002-9939-1993-1143221-1.

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23

Pontarelli, S., P. Reviriego, and J. A. Maestro. "Efficient Flow Sampling With Back-Annotated Cuckoo Hashing." IEEE Communications Letters 18, no. 10 (2014): 1695–98. http://dx.doi.org/10.1109/lcomm.2014.2347959.

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24

Yuanzhen, Liu, Huang Shurong, and Liu Jianzhao. "Flow Fair Sampling Based on Multistage Bloom Filters." TELKOMNIKA (Telecommunication Computing Electronics and Control) 14, no. 3 (2016): 1142. http://dx.doi.org/10.12928/telkomnika.v14i3.3648.

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25

Parsons, Anthony J., and Athol D. Abrahams. "A MINIATURE FLUME FOR SAMPLING INTERRILL OVERLAND FLOW." Physical Geography 10, no. 1 (1989): 85–94. http://dx.doi.org/10.1080/02723646.1989.10642369.

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26

Lamberti, Francesco, Camilla Luni, Alessandro Zambon, Pier Andrea Serra, Monica Giomo, and Nicola Elvassore. "Flow biosensing and sampling in indirect electrochemical detection." Biomicrofluidics 6, no. 2 (2012): 024114. http://dx.doi.org/10.1063/1.4705368.

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27

Saha-Shah, Anumita, Curtis M. Green, David H. Abraham, and Lane A. Baker. "Segmented flow sampling with push–pull theta pipettes." Analyst 141, no. 6 (2016): 1958–65. http://dx.doi.org/10.1039/c6an00028b.

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28

Zardawi, Ibrahim. "Re: Lymph node sampling for flow cytometric analysis." Pathology 35, no. 2 (2003): 183. http://dx.doi.org/10.1080/00313020307577.

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29

Bonald, T., and M. A. Tran. "Flow vs. time sampling for throughput performance evaluation." Performance Evaluation 64, no. 9-12 (2007): 1181–93. http://dx.doi.org/10.1016/j.peva.2007.06.010.

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30

Zardawi, Ibrahim M. "Re: Lymph node sampling for flow cytometric analysis." Pathology 35, no. 2 (2003): 183. http://dx.doi.org/10.1097/01268031-200335020-00019.

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31

Madhyastha, Harsha V., and Balachander Krishnamurthy. "A generic language for application-specific flow sampling." ACM SIGCOMM Computer Communication Review 38, no. 2 (2008): 5–16. http://dx.doi.org/10.1145/1355734.1355736.

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32

Bi-ke, Zheng, Chen Li-hong, Lin Ran, and Chang Xin-yu. "Gas sampling and analysis in high enthalpy flow." Heat Transfer—Asian Research 37, no. 1 (2007): 1–10. http://dx.doi.org/10.1002/htj.20190.

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33

Wang, Yidong, Deyi Hou, Shengqi Qi, David O'Connor, and Jian Luo. "High stress low-flow (HSLF) sampling: A newly proposed groundwater purge and sampling approach." Science of The Total Environment 664 (May 2019): 127–32. http://dx.doi.org/10.1016/j.scitotenv.2019.01.423.

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34

Cooper, MS, MBA, CIH, Casey, Jeremy Slagley, PhD, CIH, CSP, James Lohaus Jr, PhD, et al. "Comparison of high-volume air sampling equipment for viral aerosol sampling during emergency response." Journal of Emergency Management 12, no. 2 (2014): 161. http://dx.doi.org/10.5055/jem.2014.0170.

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Objective: This study compared the performance of two high-volume bioaerosol air samplers for viable virus to an accepted standard low-volume sampler. In typical bioaerosol emergency response scenarios, high-volume sampling is essential for the low infective concentrations and large air volumes involved.Design: Two high-volume air samplers (XMX/2LMIL and DFU-1000) were evaluated alongside a low-volume sample (BioSampler). Low and high concentrations (9.3-93.2 agent containing particles per liter of air [ACPLA]) of male-specific coliphage 2 (MS2) virus were released into a 12 m3 aerosol test chamber and collected using the air samplers. The collection media from the samplers were then processed and viable virus was assessed via plaque assay. Setting: Aerosol test chamber.Subjects, participants: None.Interventions: Collection media and flow rate were modified for the XMX/2L-MIL sampler for viable analysis.Main outcome measures: Concentration estimates in units of plaque forming units per liter of air (PFU/liter) assessed by the samplers as compared to the levels inside the chamber as evaluated with a slit to agar plate in units of ACPLA. Comparison was made via one-way analysis of variance. Results: Both the XMX/2L-MIL and DFU-1000 achieved collection effectiveness equal to or greater than the low-volume air sampler for the evaluated MS2 concentrations. The XMX/2L-MIL reliably collected quantifiable low concentrations of MS2, but the DFU-1000 was unable to do so.Conclusions: For emergency response to suspected bioaerosols, the evaluated high-volume samplers are as effective as the standard low-flow sampler and should be considered in conducting a health risk assessment. If low concentrations are expected, then high-flow samplers using liquid collection are preferred.
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35

Zhao, Li Feng, Xiang Bin Ji, and Yao Sun. "Fluid Dynamics Analysis of Hot Film Sensor in a Sampling Micro Flow Channel." Applied Mechanics and Materials 448-453 (October 2013): 3537–40. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3537.

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Thermal film air flow sensor has the characteristics of high precision and fast response. The intake-air velocity in the engine inlet manifold is over the measurement range of the sensor, it is necessary to design special sampling flow channel which can reduce the flow velocity and achieve a stable flow state. The micro flow channel flow field was simulated with CFD meathod, the results show that a well-designed sampling port can obtain stable flow field and effectively low the speed, which can improve the velocity uniformity for measurement in the sensor location. The sampling channel conduces to obtain higher measurement accuracy.
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36

Pan, Jiacai, Qingshan Jiang, and Zheping Shao. "Trajectory Clustering by Sampling and Density." Marine Technology Society Journal 48, no. 6 (2014): 74–85. http://dx.doi.org/10.4031/mtsj.48.6.8.

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AbstractThe trajectory data of moving objects contain huge amounts of information pertaining to traffic flow. It is incredibly important to extract valuable knowledge from this particular kind of data. Trajectory clustering is one of the most widely used approaches to complete this extraction. However, the current practice of trajectory clustering always groups similar subtrajectories that are partitioned from the trajectories; these methods would thus lose important information of the trajectory as a whole. To deal with this problem, this paper introduces a new trajectory-clustering algorithm based on sampling and density, which groups similar traffic movement tracks (car, ship, airplane, etc.) for further analysis of the characteristics of traffic flow. In particular, this paper proposes a novel technique of measuring distances between trajectories using point sampling. This distance measure does not divide the trajectory and thus conserves the integrated knowledge of these trajectories. This trajectory clustering approach is a new adaptation of a density-based clustering algorithm to the trajectories of moving objects. This paper then adopts the entropy theory as the heuristic for selecting the parameter values of this algorithm and the sum of the squared error method for measuring the clustering quality. Experiments on real ship trajectory data have shown that this algorithm is superior to the classical method TRACLUSS in the run time and that this method works well in discovering traffic flow patterns.
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37

Oleksiy, Klimenko. "DYNAMIC VARIABLE VOLUME SAMPLING METHOD FOR DETERMINING MASS EMISSIONS OF POLLUTING SUBSTANCES WITH EXHAUST GASES." Eastern-European Journal of Enterprise Technologies 3, no. 10 (105) (2020): 38–47. https://doi.org/10.15587/1729-4061.2020.206679.

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The results of the development and research of a promising full-flow dynamic method of variable volume sampling to determine in laboratory conditions the values of mass emissions of pollutants from the exhaust gases of automobile engines are presented. Emissions are determined by the test procedures for complete vehicles in driving cycles, or separately of their engines in motor test cycles. Current mass emissions of pollutants are calculated from time-synchronized instantaneous concentrations and instantaneous flow rates of the mixture of exhaust gases and air. In the mixing chamber, which serves the exhaust gases and air, also serves calibration gas mixture with a flow rate that is changed in accordance with the periodic function. This is used to determine the transfer functions of the inverse calculation of the instantaneous flow rate of the mixture of exhaust gases and air, and the current values of the concentrations of pollutants at the time of sampling. Mass emissions of gaseous pollutants are calculated as the difference between the total mass emissions of pollutants and the mass emissions of pollutants that are added with the flow of the calibration gas mixture. The performance of the dynamic full-flow variable-volume sampling method is proved by comparing the calculated (carbon balance method) and directly measured fuel consumption by cars in driving cycles. The difference between the directly measured and calculated (for certain mass emissions of pollutants) fuel consumption does not exceed±3.5 %. This is a satisfactory result, taking into account, in particular, the measurement uncertainty in the dynamics of rapidly changing concentrations of pollutants, diluted exhaust gas flow rate, fuel consumption, and the determination of carbon content in the fuel. Fundamentally new possibilities have been obtained for measuring the mass specific emissions of pollutants by forced-ignition engines and modern diesel engines (Euro-6 environmental class) and promising low-emission vehicles
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38

Tian, Ying, Guocheng Zhang, Yanqi Wu, et al. "Aerosol Sampling and Detection by Magnetic Nanoparticle-Based Cyclone Methods." Journal of Biomedical Nanotechnology 18, no. 5 (2022): 1397–404. http://dx.doi.org/10.1166/jbn.2022.3343.

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Bioaerosol transmission has received much attention as one of the significant ways of causing respiratory diseases. Therefore, studying bioaerosol sampling and detection is crucial. In this study, magnetic nanoparticles and cyclone sampling were combined to monitor pathogens in the form of bioaerosol at low concentrations in the environment. Magnetic nanoparticles had obvious advantages in extracting bacterial nucleic acid, which could be purified and enriched in a short time. The influencing factors on the performance of the high-flow cyclone sampler were also explored. The results showed that the high-flow cyclone sampler had good collection efficiency owing to high flow rate and small cutting size. Moreover, the sampling effect was better when the flow rate and the sampling time were set to 300 L/min and 20 min, which was probably due to desiccation effect, impact stress, embedding, and particle rebound. The experimental results indicated that the cyclone aerosol sampling technology based on magnetic nanoparticles could even detect low concentration bioaerosol, providing a reference for using high-flow cyclone samplers in different applications.
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39

SHIMADA, Jun. "Groundwater sampling technique for the macroscale groundwater flow investigation." Journal of Groundwater Hydrology 29, no. 3 (1987): 137–41. http://dx.doi.org/10.5917/jagh1987.29.137.

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40

Jadidi, Zahra, Vallipuram Muthukkumarasamy, Elankayer Sithirasenan, and Kalvinder Singh. "A probabilistic sampling method for efficient flow-based analysis." Journal of Communications and Networks 18, no. 5 (2016): 818–25. http://dx.doi.org/10.1109/jcn.2016.000110.

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41

Sevee, John E., Carol A. White, and David J. Maher. "An Analysis of Low-Flow Ground Water Sampling Methodology." Groundwater Monitoring & Remediation 20, no. 2 (2000): 87–93. http://dx.doi.org/10.1111/j.1745-6592.2000.tb00269.x.

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42

Toei, Jun'Ichi. "An Improved Zone Sampling Method for Flow Injection Analysis." Analytical Letters 21, no. 9 (1988): 1633–51. http://dx.doi.org/10.1080/00032718808066517.

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43

Karger, David R. "Random Sampling in Cut, Flow, and Network Design Problems." Mathematics of Operations Research 24, no. 2 (1999): 383–413. http://dx.doi.org/10.1287/moor.24.2.383.

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44

Androulidakis, G., and S. Papavassiliou. "Improving network anomaly detection via selective flow-based sampling." IET Communications 2, no. 3 (2008): 399. http://dx.doi.org/10.1049/iet-com:20070231.

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45

Mucciarone, David A., and Robert B. Dunbar. "Automated multiport flow-through water pumping and sampling system." HardwareX 8 (October 2020): e00147. http://dx.doi.org/10.1016/j.ohx.2020.e00147.

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46

Volný, Michael, Joelle Rolfs, Bejan Hakimi, et al. "Nanoliter Segmented-Flow Sampling Mass Spectrometry with Online Compartmentalization." Analytical Chemistry 86, no. 7 (2014): 3647–52. http://dx.doi.org/10.1021/ac500365r.

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47

Chen, Pei-Shih, and Chih-Shan Li. "Sampling Performance for Bioaerosols by Flow Cytometry with Fluorochrome." Aerosol Science and Technology 39, no. 3 (2005): 231–37. http://dx.doi.org/10.1080/027868290925534.

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48

Franken, Arnoud R. C., and Paul C. Ivey. "Enhancing Flow Field Measurements Through Adaptive Multidimensional Data Sampling." Journal of Engineering for Gas Turbines and Power 128, no. 3 (2005): 518–24. http://dx.doi.org/10.1115/1.2135822.

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A way to gain insight into the flow field conditions in turbomachinery is by carrying out a series of point measurements in a cross section of the flow, for example, with a miniature multihole pressure probe. A problem commonly encountered in situations like these is the selection of a suitable measurement grid layout and density for obtaining all essential information in a cost-effective and timely manner. In order to achieve the latter, a novel adaptive multidimensional data sampling technique has been developed at Cranfield University. This paper describes the underlying principles of this technique, the algorithms utilized, and the results obtained during its successful application to data sets of two different flow fields in a high-speed research compressor.
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49

Johnsen, Rainer, and B. K. Chatterjee. "Flow of binary gas mixtures through small sampling orifices." Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 29, no. 1 (2011): 011002. http://dx.doi.org/10.1116/1.3520637.

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50

Todd, M. S., and D. J. LeCaptain. "Segmented-Flow Direct Sampling Mass Spectrometry of Biodiesel Headspace." Applied Spectroscopy 61, no. 12 (2007): 1410–42. http://dx.doi.org/10.1366/000370207783292145.

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