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We consider the approximate recovery functions $f$ on $[0,1]^d$ from the sampled values $f(x^1), ..., f(x^n)$, by the linear sampling algorithm \\begin{equation} \\nonumber L_n(X_n,\\Phi_n,f) \\ := \\ \\sum_{j=1}^n f(x^j)\\varphi_j. \\end{equation} The error of sampling recovery is measured in the norm of the space $L_q([0,1]^d)$-norm or the energy norm of the isotropic Sobolev sapce $W^\\gamma_q([0,1]^d)$ for $0 < q \\le \\infty$ and $\\gamma > 0$. 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We consider the approximate recovery functions $f$ on $[0,1]^d$ from the sampled values $f(x^1), ..., f(x^n)$, by the linear sampling algorithm \\begin{equation} \\nonumber L_n(X_n,\\Phi_n,f) \\ := \\ \\sum_{j=1}^n f(x^j)\\varphi_j. \\end{equation} The error of sampling recovery is measured in the norm of the space $L_q([0,1]^d)$-norm or the energy norm of the isotropic Sobolev sapce $W^\\gamma_q([0,1]^d)$ for $0 < q \\le \\infty$ and $\\gamma > 0$. 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