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Full Discussion: Connecting using SSH in WAN network
Operating Systems Linux Red Hat Connecting using SSH in WAN network Post 302993467 by gull04 on Friday 10th of March 2017 02:34:25 AM
Old 03-10-2017
Hi,

To connect to your friends computer in the USA should be quite simple, if your friend cooperates with you.

However there are a number of things that you must know and a number of things that you must setup at both ends of the link.

You need to know the IP Address or name of the computer that you are trying to connect to - you can generally find this by visiting "whatsmyip" or a similar service - please note it will be your friend that will have to provide you with this information.

Your friend will have also have to have a fixed IP Address or use one of the dynamic dns services as the target IP Address will likely change from time to time.

The remote end will also have to have the appropriate ports open (22 in the case of ssh) or port forwarding or some protocol like NAT (Network Address Translation) setup.

At your end you will also have to have the correct settings for your router, firewall etc.

Regards

Gull04
Last edited by gull04; 03-10-2017 at 03:35 AM.. Reason: Spelling Mistake
 

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LEARN ABOUT DEBIAN

tensor

tensor(2rheolef)						    rheolef-6.1 						  tensor(2rheolef)

NAME

       tensor - a N*N tensor, N=1,2,3

SYNOPSYS

       The tensor class defines a 3*3 tensor, as the value of a tensorial valued field. Basic algebra with scalars, vectors of R^3 (i.e. the point
       class) and tensor objects are supported.

IMPLEMENTATION

       template<class T>
       class tensor_basic {
	   public:

	       typedef size_t size_type;
	       typedef T  element_type;

       // allocators:

	       tensor_basic (const T& init_val = 0);
	       tensor_basic (T x[3][3]);
	       tensor_basic (const tensor_basic<T>& a);

       // affectation:

	       tensor_basic<T>& operator = (const tensor_basic<T>& a);
	       tensor_basic<T>& operator = (const T& val);

       // modifiers:

	       void fill (const T& init_val);
	       void reset ();
	       void set_row    (const point_basic<T>& r, size_t i, size_t d = 3);
	       void set_column (const point_basic<T>& c, size_t j, size_t d = 3);

       // accessors:

	       T& operator()(size_type i, size_type j);
	       T  operator()(size_type i, size_type j) const;
	       point_basic<T>  row(size_type i) const;
	       point_basic<T>  col(size_type i) const;
	       size_t nrow() const; // = 3, for template matrix compatibility
	       size_t ncol() const;

       // inputs/outputs:

	       std::ostream& put (std::ostream& s, size_type d = 3) const;
	       std::istream& get (std::istream&);

       // algebra:

	       bool operator== (const tensor_basic<T>&) const;
	       bool operator!= (const tensor_basic<T>& b) const { return ! operator== (b); }
	       template <class U>
	       friend tensor_basic<U> operator- (const tensor_basic<U>&);
	       template <class U>
	       friend tensor_basic<U> operator+ (const tensor_basic<U>&, const tensor_basic<U>&);
	       template <class U>
	       friend tensor_basic<U> operator- (const tensor_basic<U>&, const tensor_basic<U>&);
	       template <class U>
	       friend tensor_basic<U> operator* (int k, const tensor_basic<U>& a);
	       template <class U>
	       friend tensor_basic<U> operator* (const U& k, const tensor_basic<U>& a);
	       template <class U>
	       friend tensor_basic<U> operator* (const tensor_basic<U>& a, int k);
	       template <class U>
	       friend tensor_basic<U> operator* (const tensor_basic<U>& a, const U& k);
	       template <class U>
	       friend tensor_basic<U> operator/ (const tensor_basic<U>& a, int k);
	       template <class U>
	       friend tensor_basic<U> operator/ (const tensor_basic<U>& a, const U& k);
	       template <class U>
	       friend point_basic<U>  operator* (const tensor_basic<U>&, const point_basic<U>&);
	       template <class U>
	       friend point_basic<U>  operator* (const point_basic<U>& yt, const tensor_basic<U>& a);
		      point_basic<T>  trans_mult (const point_basic<T>& x) const;
	       template <class U>
	       friend tensor_basic<U> trans	 (const tensor_basic<U>& a, size_t d = 3);
	       template <class U>
	       friend tensor_basic<U> operator* (const tensor_basic<U>& a, const tensor_basic<U>& b);
	       template <class U>
	       friend void prod (const tensor_basic<U>& a, const tensor_basic<U>& b, tensor_basic<U>& result,
		       size_t di=3, size_t dj=3, size_t dk=3);
	       template <class U>
	       friend tensor_basic<U> inv	 (const tensor_basic<U>& a, size_t d = 3);
	       template <class U>
	       friend tensor_basic<U> diag (const point_basic<U>& d);
	       template <class U>
	       friend tensor_basic<U> identity (size_t d=3);
	       template <class U>
	       friend tensor_basic<U> dyadic (const point_basic<U>& u, const point_basic<U>& v, size_t d=3);

       // metric and geometric transformations:

	       template <class U>
	       friend U dotdot (const tensor_basic<U>&, const tensor_basic<U>&);
	       template <class U>
	       friend U norm2 (const tensor_basic<U>& a) { return dotdot(a,a); }
	       template <class U>
	       friend U dist2 (const tensor_basic<U>& a, const tensor_basic<U>& b) { return norm2(a-b); }
	       template <class U>
	       friend U norm  (const tensor_basic<U>& a) { return ::sqrt(norm2(a)); }
	       template <class U>
	       friend U dist  (const tensor_basic<U>& a, const tensor_basic<U>& b) { return norm(a-b); }
	       T determinant (size_type d = 3) const;
	       template <class U>
	       friend U determinant (const tensor_basic<U>& A, size_t d = 3);
	       template <class U>
	       friend bool invert_3x3 (const tensor_basic<U>& A, tensor_basic<U>& result);

       // spectral:
	       // eigenvalues & eigenvectors:
	       // a = q*d*q^T
	       // a may be symmetric
	       // where q=(q1,q2,q3) are eigenvectors in rows (othonormal matrix)
	       // and	d=(d1,d2,d3) are eigenvalues, sorted in decreasing order d1 >= d2 >= d3
	       // return d
	       point_basic<T> eig (tensor_basic<T>& q, size_t dim = 3) const;
	       point_basic<T> eig (size_t dim = 3) const;

	       // singular value decomposition:
	       // a = u*s*v^T
	       // a can be unsymmetric
	       // where u=(u1,u2,u3) are left pseudo-eigenvectors in rows (othonormal matrix)
	       //	v=(v1,v2,v3) are right pseudo-eigenvectors in rows (othonormal matrix)
	       // and	s=(s1,s2,s3) are eigenvalues, sorted in decreasing order s1 >= s2 >= s3
	       // return s
	       point_basic<T> svd (tensor_basic<T>& u, tensor_basic<T>& v, size_t dim = 3) const;

       // data:
	       T _x[3][3];
       };
       typedef tensor_basic<Float> tensor;

       // inputs/outputs:
       template<class T>
       inline
       std::istream& operator>> (std::istream& in, tensor_basic<T>& a)
       {
	   return a.get (in);
       }
       template<class T>
       inline
       std::ostream& operator<< (std::ostream& out, const tensor_basic<T>& a)
       {
	   return a.put (out);
       }
       // t = a otimes b
       template<class T>
       tensor_basic<T> otimes (const point_basic<T>& a, const point_basic<T>& b, size_t na = 3);
       // t += a otimes b
       template<class T>
       void cumul_otimes (tensor_basic<T>& t, const point_basic<T>& a, const point_basic<T>& b, size_t na = 3);
       template<class T>
       void cumul_otimes (tensor_basic<T>& t, const point_basic<T>& a, const point_basic<T>& b, size_t na, size_t nb);

rheolef-6.1							    rheolef-6.1 						  tensor(2rheolef)
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