sci fi reality….vechicle flying car expansive view

t h e   w h o l e   v i e w

a vechicle expansion

By Henryk Szubinski

the side view is a very generalised format of what would function at every activation of the formats oin whatever situation of the environmental alterations that are infinietly variable in the progressions of similar vechicles with unrestricted unlimited infinite limits while maintaining the same basic values of motion in a comfortable zone of requirements,,

data processed into a very defined basis of projective data as is = to any data conceptual curvature into a basic generalisation of the whole vechicle sectioned data process as burn in space time parameters simulated within or in the exterior of smooth spacetime . Basics of the whole values in their redistributions = general data on how the whole data value is as similar to minimal values as the til sections and the data force of the general break of the mid section into the data on force by progress in closures of sectional repeat responses = modified values in caged responses made to the spacetime and its whole value as the data input assists of the value as altered from the primary value of the 3rd = externalisations but being on the underside of the nose section are then rotated in the whole process described by 1 degree of each descriptive line defined in the conceptuality = resultant top fase fold zone back into the 2 processess that began the axpansions..

what you would need to lift a car by anti matter or artificial gravity:using the Newtonial calculations but using a interval value of 2——>1/x

to be used on a differencial direct relations to a secondary set where:

1/x ———–>2y

so that the first set of 3 values is:

F ———–> ?

2————x

m———–>a

basic equationality : F.a/m

as regards the usage of a equation on the side as a similar equation that defines what is to be forced:

the second equations set of 3 is then;

Inetria——————>enthropy

1/x———————->2y

Force——————–>?

the equation is then:

enthropy . Force / Inertia =?

there are now 2 ? formats so that the values of the combined computations are:

2————->x

1/x———–>2y

on of the formats can be cancelled so that

4y/x =enthropy. Force/ Inertia

F.a/m is then the second similarity with the same 4y /x

so that it can be doubled on the left side as:the right side would be halved:

enthropy .Force / Inertia =( 1/2)

sci fi reality….vergancy in boyant relations

 

d a t a   m u l t i p l e   s p a c e t i m e  b o u y a n c y   v e r g a n c e s

By Henryk Szubinski

a vechicle type 1 flying car space ship

Given a probability space , a stochastic process (or random process) with state space X is a collection of X-valued random variables indexed by a set T (“time”). That is, a stochastic process F is a collection

where each F_t is an X-valued random variable.

A modification G of the process F is a stochastic process on the same state space, with the same parameter set T such that

[edit] Finite-dimensional distributions

Let F be an X-valued stochastic process. For every finite subset , we may write , where and the restriction is a random variable taking values in X^k. The distribution of this random variable is a probability measure on X^k. Such random variables are called the finite-dimensional distributions of F.

Under suitable topological restrictions, a suitably “consistent” collection of finite-dimensional distributions can be used to define a stochastic process (see Kolmogorov extension in the next section).

Activity-based costing (ABC) is a costing model that identifies activities in an organization and assigns the cost of each activity resource to all products and services according to the actual consumption by each: it assigns more indirect costs (overhead) into direct costs.

In this way an organization can precisely estimate the cost of its individual products and services for the purposes of identifying and eliminating those which are unprofitable and lowering the prices of those which are overpriced.

In a business organization, the ABC methodology assigns an organization’s resource costs through activities to the products and services provided to its customers. It is generally used as a tool for understanding product and customer cost and profitability. As such, ABC has predominantly been used to support strategic decisions such as pricing, outsourcing and identification and measurement of process improvement initiatives.

how to balance a functionof a sectional buoyancy in a relation to any space time or atmospheric usage by the processors that are responsible for the data on processess in their relative fold back rates of a data format based on the basics of the same value wavelength as the  output between the 2 formats on each side = to the ballast functions that are basically the value in a process to activate the sensitivity of the interactive value in a rotational value simulator of the dynmics process by which the general data on the force of usage = the 2 values inbetween the vergance of bouyancies such as from 2 to a internal 2 by the clipp in rotation between them to a 3 rd value interactive value =2.

as the volume of all intercies = 1/3 (3)

the volume functions of their relative volumes are equal in the promary relationship of their largest sizes down to the size of clipp transferrance of buoyancy = 2 /3 additional values..

Singular spectrum analysis (SSA) combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. Its roots lie in the classical Karhunen (1946)–Loève (1945, 1978) spectral decomposition of time series and random fields and in the Mañé (1981)–Takens (1981) embedding theorem.

what OCCURS AT THE DYNAMICS OPEN STATE RELATIONSHIPS OF A STATIC RELATIONSHIP: ( on the bouyancy packs)

In practice, SSA is a nonparametric spectral estimation method based on embedding a time series X(t): t = 1,N in a vector space of dimension M. SSA proceeds by diagonalizing the lag-covariance matrix of X(t) to obtain spectral information on the time series, assumed to be stationary in the weak sense. The matrix can be estimated directly from the data as a Toeplitz matrix with constant diagonals (Vautard and Ghil, 1989), i.e., its entries c_ij depend only on the lag | i − j | :

An alternative way to compute , is by using the “trajectory matrix” that is formed by M lag-shifted copies of X(t), which are N‘ = N − M + 1 long; then

The M eigenvectors of the lag-covariance matrix are called temporal empirical orthogonal functions (EOFs). The eigenvalues λ_k of account for the partial variance in the direction and the sum of the eigenvalues, i.e., the trace of , gives the total variance of the original time series X(t). The name of the method derives from the singular values of

SPACIO TEMPORAL GAP FILLING

VENTILE PROCESSESS OF 1/3 AS A VALUE REDUCTIONS BY hALVING INTO 3 SETS:

The gap-filling version of SSA can be used to analyze data sets that are unevenly sampled or contain missing data (Kondrashov and Ghil, 2006). For a univariate time series, the SSA gap filling procedure utilizes temporal correlations to fill in the missing points. For a multivariate data set, gap filling by M-SSA takes advantage of both spatial and temporal correlations. In either case: (i) estimates of missing data points are produced iteratively, and are then used to compute a self-consistent lag-covariance matrix and its EOFs ; and (ii) cross-validation is used to optimize the window width M and the number of leading SSA modes to fill the gaps with the iteratively estimated “signal,” while the noise is discarded.