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.5 Sensor current (MA) 10 60 Lead lag time (ms) 1 20 Low data rate optimize “0” (If SF ≥ 7 and SF ≤ 10) “1” (IF SF > 10) Codding rate 1 4 Band width 125 500 Explicit header 0 1 Sensor on time (ms) 600 1,000 Tx cycle period (ms) 60 500 The output parameter is

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,” Renew. Energ. , vol. 78 , pp. 105 – 113 , 2015 . 10.1016/j.renene.2014.12.072 [13] Y. Zhang , S. Gao , and T. Gu , “ Prediction of I-V characteristics for a PV panel by combining single diode model and explicit analytical model ,” Solar Energ

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. & Stauffer, D., 1994. A description of the fifth generation Penn State/NCAR Mesoscale Model. NCAR Tech. Note NCAR/TN-398+STR. Hohenegger, C. et al., 2009. The soil moisture–precipitation feedback in simulations with explicit and

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Agrokémia és Talajtan
Authors: Hajnalka Breuer, Ferenc Ács, Borbála Laza, István Matyasovszky, Ákos Horváth, Tamás Weidinger, and Kálmán Rajkai

soils on the prediction of cumulus convective rainfall. Rev. Geophys. 39. 151–177. Reisner, J., Rasmussen, R. M. & Bruintjes, R. T., 1998. Explicit forecasting of supercooled liquid water in winter storms using the MM5

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251 263 Mölders, N. & Rühaak, W., 2002. On the impact of explicitly predicted runoff on the simulated atmospheric response to small-scale land-use changes — an integrative

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