Please, Translate the following English into Korean:
So as I said, so the vector itself is a basic form, a basic form of the linear algebra area. And then the definition of the vector itself is that the object is that can be added together, added together and then multiplied by the scalar to produce another object of the same kind. So here, there are two important operations. The first one is the addition, right? And the second operation is multiplied by scalars. So these are the same kind of operations. Operations, right? Operations for the object itself. Then that kind of object is called vector. Then we actually know that, you know, I think you've already learned about the vector from this geometric vector form. So you know, in this figure, maybe this is the origin, zero comma zero, right? And then here, if this one is a two dimensional space, then we know that this is zero comma zero, right? And if this one is a three dimensional space, it's order and dimensional space, then we can also enlarge the number of the columns, kind of columns in this point. But anyway, so let's imagine that, let's assume that so this is a two dimensional space. Then from the origin, then we also denote some kind of the point as a vector, right? So this is X vector. And then there is another direction and then the some kind of length, right? Then we can also define another vector named as Y. Then using those two vectors, we can also create another vector by adding together, right? X plus Y. Then this is another vector, which is the same kind of object, right? And then we can produce this part. And then what are the other things? So, you know, so from this one, and this is X, right? And if we multiply two times, then the direction itself is the same. However, we can also increase the length of the vector, right? So such as these kinds of things to multiply X. Then we already know that this is also another kind of the object. I mean, another kind of the vector one, right? So using the multiplied by Scala, that's another operation. And then using that part here, the two is Scala, then we can also produce the another vector object. So using that, as I said, using these two operations, we can also create the same kind of the vector in a geometric vector part, right? And then the polynomial, you know, the polynomial, what's the polynomial? The polynomial is something like this. So maybe, so the X square, no, I'm sorry. Maybe for instance, A X square plus B X plus C, something like that, right? Or we can also say that maybe A X cube, B X square plus C X plus D, right? Those kind of the form itself is also called polynomial. And then as you know that, if this one is a vector and this one is another times of the vector, then if we add this one together, then maybe it's not good. How can I do that? Yeah, I exceed. So let's use this figure, all right? So here, there are how many things to here? So maybe the green one, this is the maybe A X square plus B X square plus the cube and then C X plus D. And then we also know that maybe this one, another form is that maybe, what about the other form? Maybe A one, B one, C one and D one. Then here, this is A two square plus B two X plus C two, something like that, right? And then if we add these two polynomials together, then we can also create another form of the polynomial. For instance, when we added those two together, then A one X square plus A two plus B one X square. Yeah, cube one, sorry. And then B two plus C one X plus C two X plus D one. Then we already know that this is also polynomial. So adding the two polynomials, we can also create another types of the polynomial. And then if we multiply the scalar in front of this polynomial, for instance, two, then you know that this is also another types of the polynomial one. So that's why, so we can also say that the polynomial itself is the vector one. And what about the other one, the elements of R to the N? So what's the meaning of the R to the N?