if $A\in \mathbb{R}^{m \times n}$, then SVD of A is $U\Sigma V^T$. I've seen different version of SVD. In the first one, $U\in \mathbb{R}^{m \times m}$, $\Sigma \in \mathbb{R}^{m\times n}$, and $V \in \mathbb{R}^{n\times n}$. In the second one $U\in \mathbb{R}^{m \times r}$, $\Sigma \in \mathbb{R}^{r\times r}$, and $V \in \mathbb{R}^{r\times n}$, where $r$ is the rank of $A$. Is there difference in these two forms or does one of them have an advantage over the other one? For instance when $U$ and $V$ are square matrices, then they are invertible, i.e, $U^TU=UU^T=I$. but when $U$ is not square only $U^TU=I$ is true.
Second question. if $A=U\Sigma V^T$, I've seen that $U^TU=I$. Can we deduce that $UU^T=I$, too?
First question
It follows from the definition of the SVD. The first one
$$ A_{m \times n} = U_{m \times m} \Sigma_{m \times n} V_{n \times n}^{T} \tag{1} $$
is the full SVD
the second one
$$ A_{m \times n} = U_{m \times r} \Sigma_{r \times r} V_{r \times n}^{T} \tag{2} $$
is the truncated SVD or reduced SVD where $A$ has rank $r$
visually we see the difference
Second question
It follows from the definition of the SVD. $U$ is orthogonal, which means that
$$ U^{T}U = UU^{T} = I \tag{3} $$
Note
In the SVD
$$ \sigma_{1} \geq \sigma_{2} \geq \cdots \sigma_{r} \geq 0 \tag{4} $$
if you multiply
$$ U_{m \times m} \cdot \Sigma_{m \times m} V_{m \times n}^{T}\tag{5} $$
and there are only $r$ singular values then it is $0$ after that. A diagonal matrix simply multiplies across. So we only care about
$$ U_{m \times r}\Sigma_{r \times r} V_{r \times n}^{T} \tag{6} $$