What is meant by a continuous-time white noise process? What is meant by a continuous-time white noise process?
In a discussion following a question a few months ago, I stated that as an engineer, I am used to thinking 
of a continuous-time wide-sense-stationary white noise process 
$\{X(t) \colon -\infty < t < \infty\}$
as a zero-mean process having autocorrelation function $R_X(\tau) = E[X(t)X(t+\tau)] =  \sigma^2\delta(\tau)$ where $\delta(\tau)$ is the Dirac delta or impulse, and power spectral density $S_X(f) = \sigma^2, -\infty < f < \infty$. At that time, several people with very high reputation on Math.SE
assured me
that this was an unduly restrictive notion, and that no difficulties arise
if one takes the autocorrelation function to be
$$E[X(t)X(t+\tau)] = \begin{cases}\sigma^2, & \tau = 0,\\
0, & \tau \neq 0. \end{cases}$$
What engineers like to call a white noise process is a hypothetical
beast that is never observed directly in any physical system, but 
which can be used to account for
the fact that the output of a linear time-invariant system whose
input is thermal noise is well-modeled by a wide-sense-stationary
Gaussian process whose power spectral density is proportional to
$|H(f)|^2$ where $H(f)$ is the transfer function of the linear
system. Standard second-order random process theory says that
the input and output power spectral densities $S_X(f)$ nd $S_Y(f)$
are related as
$$S_Y(f) = S_X(f)|H(f)|^2.$$
Thus, pretending that thermal noise is a white Gaussian noise process in the
engineering sense and pretending that the second-order theory 
extends to white noise processes (even though their variance is 
not finite) allows us to get to the result that the output power
spectral density is proportional to $|H(f)|^2$.
My query about the definition of a white noise process
is occasioned by a more recent question regarding the variance of a random variable $Y$ defined as
$$Y = \int_0^T h(t)X(t)\ \mathrm dt$$
where $\{X(t)\}$ is a white Gaussian noise process.
The answer given by Nate Eldredge
leads to
$$\operatorname{var}(Y) = \sigma^2 \int_0^T |h(t)|^2\ \mathrm dt$$
(as I pointed out in a comment on the answer) if the autocorrelation
function is taken to be $R_X(\tau) = \sigma^2\delta(\tau)$
(the engineering definition).  However, the OP on that question
specified $R_X(0) = \sigma^2$, not $\sigma^2\delta(\tau)$,
that is, the definition accepted by mathematicians.  For
this autocorrelation function, the variance is
$$\int_0^T \int_0^T E[X(t)X(s)]h(t)h(s)\mathrm dt\mathrm ds = 0$$
since the integrand is nonzero only on a set of measure $0$.
So, what is the variance of the random variable $Y$? and what
do readers of Math.SE understand by the phrase white noise process?
Perhaps this question should be converted to a Community wiki?
 A: This is a bit late, but I see that the main points in this question have not been completely addressed. I'll set
\begin{equation}
\sigma = 1
\end{equation}
for this answer.
The definition of white noise may be context-dependent: How you define it depends on what you want to do with it. There's nothing inherently wrong with saying that white noise (indexed by a set $T$) is just the process of iid standard normal random variables indexed by $T$, i.e. $E[X(t)X(s)] = \begin{cases} 1 & t = s \\ 0 & t \neq s \end{cases}.$ However, as cardinal noted here, Example 1.2.5 of Kallianpur's text shows that this process is not measurable (as a function of $(t, \omega)$). This is why, as Did commented above, $Y$ is undefined (with this definition of $X$). Thus, this definition of white noise is not appropriate for defining objects like $Y$.
Rather, you want $X$ to have covariance given by the Dirac delta. But the $\delta$ function is not a function but rather a measure and the best context for understanding it is the theory of distributions (or generalized functions---these are not to be confused with "probability distributions"). Likewise, the appropriate context for white noise is the theory of random distributions.
Let's warm up with a heuristic explanation: We'll think of white noise as the "derivative" of Brownian motion: "$dB_t/dt = X_t$". So ignoring rigor for a moment, we could write
\begin{equation}
\int_0^T h(t) X(t) dt = \int_0^T h(t) \frac{dB_t}{dt} dt = \int_0^T h(t) dB_t.
\end{equation}
The reason this isn't rigorous is that Brownian motion is nowhere differentiable. However, the theory of distributions allows us to "differentiate" non-differentiable functions. First of all, a distribution is a linear functional (linear map taking values in the real numbers) on a space of "test functions" (usually smooth functions of compact support). A continuous function $F$ can be viewed as a distribution via the pairing
\begin{equation}
(F, f) = \int_0^\infty F(t) f(t) dt.
\end{equation}
The distributional derivative of $F$ is the distribution $F'$ whose pairing with a test function $f$ is defined by
\begin{equation}
(F', f) = -(F, f').
\end{equation}
Thinking of Brownian motion as a random function, we can define white noise $X$ as its distributional derivative. Thus, $X$ is a random distribution whose pairing with a test function $f$ is the random variable
\begin{equation}
(X, f) = -(B, f') = -\int_0^\infty B(t) f'(t) dt.
\end{equation}
By stochastic integration by parts,
\begin{equation}
(X, f) = \int_0^\infty f(t) dB_t;
\end{equation}
this is the Itô integral of $f$ with respect to $B$.
Now a well-known fact in stochastic calculus is that $M_T = \int_0^T f(t) dB_t$ is a martingale starting at $M_0 = 0$, so $E (X, f) = 0$. Moreover, by the Itô isometry,
\begin{equation}
\mathrm{Var}((X, f)) = E (X, f)^2 = \int_0^\infty f(t)^2 dt.
\end{equation}
It can also be verified that $(X, f)$ is Gaussian.
My main point is that a more appropriate definition of $Y$ might be
\begin{equation}
Y = \int_0^T h(t) dB_t.
\end{equation}
As a last note, because of the way $X$ was defined above, $X_t$ is not defined but $(X, f)$ is. That is, $X$ is a stochastic process but whose index set is given by $T = \{ \text{test functions} \}$ rather than $T = [0, \infty)$. Moreover, again by the Itô isometry,
\begin{equation}
E (X, f) (X, g) = \int_0^\infty f(t) g(t) dt.
\end{equation}
Abandoning rigor again, this becomes
\begin{equation}
E (X, f) (X, g) = \int_0^\infty \int_0^\infty f(s) \delta(s - t) g(t) ds dt
\end{equation}
and it is in this sense that the covariance of $X$ is the Dirac delta.
Edit: Note that we could leave the definition of $(X, f)$ in terms of the ordinary integral and do all the above calculations using Fubini's theorem and (ordinary) integration by parts (it's just a bit messier).
A: Actually X is the digital signal and Y is the average analog signal power generated in the time-domain. The variance shows how deviation goes if signal Y goes for a long time.
Moreover, you should learn more about the delta function (your function becomes one if x = 0 ) . The engineering and mathematical aspects to explain the autocorrelation function are also correct. There is no contracdiction between them.
