As most of the related approaches, we assume a static dataset with mostly read-only accesses.
In both approaches, we assume equal energy allocation scheme, where the energy is divided equally across sensor nodes.
For the TOA-based approaches, we assume knowledge of the template s(n), and estimate τ i by cross-correlating the received signal with the clean template: τ ^ i = arg max τ i ∑ n = 0 N - 1 z i ( n ) s ( n - τ i ).
First, as with any training set dependent candidate gene ranking approaches, we assume that the OD causal genes we have yet to discover will be consistent with what is already known about an OD and/or its genetic basis which may not always be the case.
In our modeling approach, we assumed that porosity changes are only due to mineral volume changes and this leads to permeability changes that are positively correlated with porosity variations.
In our approach, we assumed an adversary which aims to retrieve for each protected query, both the content of the initial query and the identity of the associated user.
With this approach we assumed a tetrasomic pattern of inheritance, in which the different homolog chromosomes can be transmitted to gametes in all possible combinations.
In our approach we assumed multivariate normality, that is normality of all marginal and conditional distributions of the measurements and, related to this, linearity between the conditional mean expression of a gene and the expression levels of its neighboring genes (equation (2)).
In this approach, we assumed that cooling occurred if the temperature was higher than 24°C.
Following the WHO-CHOICE standardised approach, we assumed that interventions were performed optimally (that is, no undertreatment or overtreatment at the highest efficiency level).
