Replace missing datapoints with the average of 2 other distant observations when there are multiple missing observations

I have a dataset of net hourly animal movements but there are several occasions where observers were periodically absent. I wish to replace the missing datapoints (in a new column) with the average of the same time period 24 hours before and after the missing datapoint.

Example data:

#Data Creation
Day1<- rep(1,24)
Day2<- rep(2,24)
Day3<- rep(3,24)
Day<- c(Day1,Day2,Day3)
Hour<- rep(0:23,3)
Net <- round(rnorm(length(Day),mean = 2))
Dat<- data.frame(Day= Day,Hour= Hour,Net= Net)
#Populate missing observations
Dat[27,3]<- NA
Dat[31,3]<- NA
Dat

I initially applied a function (below) that would locate a single missing value and then index the missing datapoint to locate and take the average of the rows 24 hours before and after the missing point.

Dat$new.net<- sapply(Dat[,3],function(x)  
   if_else(is.na(x), mean(c(Dat[which(is.na(Dat),arr.ind = T)[1]-24,3],Dat[which(is.na(Dat),arr.ind = T)[1]+24,3])),x)) 

I cannot find a way to make the function I used for 1 missing value work for multiple missing occasions, producing a unique average for each missing value. Currently the code only uses the average for the first missing value due to the "Dat[which(is.na(Dat),arr.ind = T)[1]"

How can I alter my code to work for multiple missing values, or is there a more elegant solution?

PS. I know I will have issues if there are missing values in the first or final 23 hours. I will cross that bridge when I get there.

Any help will be greatly appreciated!

We could get the index of NA values and then subtract 24, add 24, to each of the elements, get the rowMeans after cbinding and assign it to missing index

ind <- which(is.na(Dat[[3]]))
ind_minus <- ind - 24
ind_minus[ind_minus < 1] <- NA
ind_plus <- ind + 24
nd_plus[ind_plus > nrow(Dat)] <- NA
Dat[[3]][ind] <- rowMeans(cbind(Dat[[3]][ind_minus], Dat[[3]][ind_plus]),
     na.rm = TRUE)