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For improved spectrum utilization, the key technique for acquiring spectrum situational awareness (SSA) — spectrum sensing — is greatly improved by cooperation among the active spectrum users, as network size increases. However, the many cooperative spectrum sensing (CSS) schemes that have been proposed are based on the assumptions of accurate noise power estimates, characterizable variation in noise level and absence of false or malicious users. As part of a series of SSA research projects, in this research work, we propose a novel scheme for minimizing the effects of noise power estimation error (NPEE) and received signal power falsification (RSPF) by energy-based reliability evaluation. The scheme adopts the Voting rule for fusing multiple spectrum sensing data. Based on simulation results, the proposed scheme yields significant improvement, 68.2—88.8%, over the conventional CSS schemes, when compared on the basis of the schemes’ stability to uncertainties in noise and signal power. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.