OpenLLM-France/wikimedia huggingface dataset:
# Grand Theft Auto: San Andreas/Missions/Architectural Espionage
## Prerequisites

* Mission: Fender Ketchup
* Location: Four Dragons Casino, Las Venturas

## Walkthrough

If you have not got a camera with you, you need to "borrow" one. Head towards the red blip for a group of tourists. Kill the one taking pictures for his camera.
Drive to the planning authority building as indicated by the map blip. Make sure you have no weapons selected as indicated by the text on the screen, as there is a heavy security presence here. Talk to the receptionist, she will tell you where to go. Go up a few floors (the staircase is to the left of the lobby behind a swing door) and go into the first open door you see. Proceed over to the elderly aircon unit and break it, you may get away with a weapon here, since breaking it with a shovel will more than likely result in a burning CJ! A fire alarm will sound, everyone will be evacuated, and guards will be distracted. Go up to the top floor and photograph the plans in the room straight opposite the staircase exit, where you can see a white model on a table in the room.
Now it gets tricky, you need a powerful weapon like the M4 and it helps if your skill is high enough to allow you to move while in the aiming stance. Head back down the stairs through the smoke, picking off the guards/police as you go, there is one round almost every turn of the staircase and several in the building lobby. After clearing the building, you will face many cops outside. Simplest thing is to just get in a car, and race back to the back entrance of the Four Dragons Casino to end the mission.

## Tips

Plenty of ammo in (and practice with) the M4 or similar helps the escape.

## Rewards

* Respect+

## New Missions Available

* Key to her Heart
# GNU Health/Domiciliary Units
## Introduction to Domiciliary Units

A Domiciliary Unit (DU) represents a human dwelling. It is composed of intra (domiciliary) and extra (peridomiciliary) spaces. The DU is a physical entity that denotes the place where one or more people live regularly.
Since it is a physical location, it can always be geo-referenced by latitude and longitude, and many times it will have an associated address (street, ZIP code, city). This DU information should always be provided, since it is key determinant of health. Diseases like dengue fever and Chagas disease are intimately related to the DU condition (see Neglected Tropical Diseases for details).

## The Domiciliary Units Form

The Domiciliary Units form can be found at Health → Demographics → Domiciliary Units in the main navigation of GNU Health.
A domiciliary unit can be described by the following information:
* Code: A unique identifier of the dwelling. This field is required.
* Description: Short description of the DU.
* Address: Street, number, and other subdivisions (province, city, municipality, district, ... )
* Latitude and Longitude
* OSM Map URL: The OpenStreetMap URL is created automatically from the coordinates or the DU address. (If latitude and longitude are not provided, then the OSM Map URL will be generated based on the address components. However, latitude and longitude will usually give you a more accurate reference.)
* General Conditions: A summary of the living conditions. This field should be filled in all the times, since it's one of the most descriptive about the DU.
* Type of Dwelling: Single house, apartment, townhouse, ...
* Infrastructure: Electricity, gas, potable water, sewers, ...
* Operational Sector: The area that this DU belongs. Very important to know which operational area is responsible to take different health care actions (health promotion, healthcare area, ambulace action region, ...)
* Members: In this section a list with all inhabitants of a DU is shown. They may be or may not be family members. Each person should be associated to a DU.
← The Health Center · Individuals →
# GNU Health/Demographics

← Neglected Tropical Diseases · Epidemiology →
# GNU Health/Epidemiology

← Demographics · Installation →
# GNU Health/Countries

← Users · WebDAV →
# General Engineering Introduction/CDIO/Minimal Design
## Problem Statement

Design the water release section on the water ram. Creating where are goal for the water under pressure to reach.

## The Design Process
##### Requirements for each element or component derived from system level goals and requirements

The requirements for this project are to have pressurized water controled bu pressurized water valves create a flow of water against gravity using any tools you can.

##### Alternatives in design

This project has had some work on it before and im utilizing what has already been done and the parts that have been put in place for me to use.

##### The initial design

My design is using PVC pipe which holds an air take that pushes back against the pressured water causing to release the water until the pressure releases. The pressure valve re-closes when the pressure level goes back down. Were waiting to get a pressure valve so that we can put the piping together to test.

##### Experiment prototypes and tests need for design

The prototype we have now uses a user release valve in which the user determines when the pump closes for the pressure to be created.
# General Engineering Introduction/CDIO/Conceive

CONCEIVING AND ENGINEERING SYSTEMS
# General Engineering Introduction/CDIO/Tasks

Tasks are short statements negotiated between project managers/instructors and engineers/students that describe what is going to be done during a week. Typically the instructor types tasks into the teams page within the instructors user space. Students then repeat that task description in their weekly report pages. The same task might be worked on for four weeks. Or multiple tasks may be done in one week. The goal is to always have more tasks than time in case there are glitches, successes, materials problems, unplanned failures, etc.
Task activity is summarized in the students weekly report and detailed in a CDIO that may be shared with other team members or may be individually created/modified/reorganized.
Tasks have to be a subset of either a Conceive, Design, Implement or Operate CDIO document. Tasks thus have to come from this list:

### Conceive

* Decisions
* Estimation
* Find Starting Point
* Form/Function
* Goals/Requirement
* Knowledge Base
* Needs
* Problem Statement
* Research
* Risks
* Scale
* Scope
* Standards
* TradeOffs

### Design

* Analytical Analysis
* Brainstorm
* Build
* Calibration
* Decisions
* Drawings
* Failure Modes
* Material List
* Modeling
* Problem Statement
* Programming
* Reuse
* Reverse Engineer
* Safety
* Simulation
* Testing
* Theory of Operation
* Validation

### Implement

* Assembly
* Decisions
* Integration
* Problem Statement
* Programming
* Sourcing
* Staging
* Storage
* Testing
* WorkFlow

### Operation

* Decisions
* Disposables
* Disposal
* Improvement
* LifeCycle
* Logistics
* Operating Instructions
* Operation Manual
* Problem Statement
* Quality
* Reporting
# General Engineering Introduction/CDIO/Minimal Conceive

CONCEIVING AND ENGINEERING SYSTEMS

## Idea

To design a smart pet food dispenser that will not only allow for user friendly pet feeding but also ease the struggles of pet dieting (MT).

## Problem Statement

Design a pet food controller using RFID Tags and a mechanically operated food bowl rotator that will allow for all pet owners to feed their pets at any time of the time by simply dictating how much food to dispense and at what times feeding is to given (MT).

## Setting System Goals and Requirements
### Market/Customer needs

We had originally planned for a multi pet, food dispenser. After a few weekly meetings and looking into the design and implementation of a multi pet food storage system I knew that it would've been well beyond the scope of work. I decided that for a much more feasible and cost effective pet food controller I needed to alter my design for a single pet feeder (MT).

### Factors that set the context of the requirements

* Customer Needs (MT)
* Cost of Materials (MT)
* Time/Duration of Project (MT)
* Aesthetics (MT)
* Practicality (MT)
* Marketability (MT)
* Available Resources (MT)

##### Competitors and benchmarking information

When I was first tasked with this project I immediately researched all and every related projects that had been completed. Since this is quite a unique project I luckily found very little if any other projects that were similar to the pet food controller. However, I did find on Instructables that someone had created basically the same project that I was tasked to design. This was not a major problem for a couple of reasons; one of them being he had documented his design process fairly well and I was able to gain a decent amount of insight as to what I wanted to complete and the second was that I noticed he had done little to no marketing on his product, which meant that the market was still wide open (MT).

##### Ethical, social, environmental, legal and regulatory influences

In terms of ethics there are not many issues that I felt necessary to address except that end product needs to be 99% reliable because I could not in good conscious market a product that could potentially effect the health of an animal (MT).

### System goals and requirements
##### System performance metrics
## Defining Function, Concept and Architecture
### Necessary system functions

* Utilize RFID Tags to distribute food to specific animals (MT)
* Distribute proportioned amounts of food to maintain healthy animal diets (MT)
* Maintain a low cost budget (MT)
* Ease of troubleshooting mechanical, electrical, and cleanliness issues (MT)

### Trade-offs among concepts

* Multi-tube food storage tank vs. a single food storage tank for each individual pet (MT)
** Multi-tube food storage would require a common funnel to the food bowls with a mechanically operated rotary screw feeder for each tank (MT)
** Single food storage tank would allow for a simpler prototype which could then lead to improvements and ultimately a multi-tube storage tank (MT)
* One final product for multiple pets vs. one final product for each individual pet (MT)
# General Engineering Introduction/CDIO/Implement
## Software Implementing Process
##### The break down of high level components into module designs (including algorithms and data structures)
##### The low-level design (coding)

The low level design should contain the detail information about the code going to implement for a an application , software etc.It should be a detail design of the High level design document
# Engineering Analysis/Bessel Equation and Parseval Theorem

Bessel's equation relates the original function to the fourier coefficients a$_n$:
[Bessel's Equation]
    $\sum _{n=1}^{\infty }a_{n}^{2}\leq \|f(x)\|^{2}$
If the basis set is infinitely orthogonal, and if an infinite sum of the basis functions perfectly reproduces the function f(x), then the above equation will be an equality, known as Parseval's Theorem:
[Parseval's Theorem]
    $\sum _{n=1}^{\infty }a_{n}^{2}=\|f(x)\|^{2}$
Engineers may recognize this as a relationship between the energy of the signal, as represented in the time and frequency domains. However, parseval's rule applies not only to the classical Fourier series coefficients, but also to the generalized series coefficients as well.
# Engineering Analysis/Distributions
## Distributions

There are a number of common distributions, that are used in conjunction with random variables.

## Uniform Distribution

The uniform distribution is one of the easiest distributions to analyze. Also, uniform distributions of random numbers are easy to generate on computers, so they are typically used in computer software.
    $f_{X}(x)=\left\{{\begin{matrix}{\frac {1}{b-a}}&{\mbox{ if }}a<x<b\\0&{\mbox{ otherwise}}\end{matrix}}\right.$
    $F_{X}(x)=\left\{{\begin{matrix}0&{\mbox{ if }}x<a\\{\frac {x}{b-a}}&{\mbox{ if }}a<x<b\\1&{\mbox{ if }}x\geq b\end{matrix}}\right.$

## Gaussian Distribution

The gaussian distribution, or the "normal distribution" is one of the most common random distributions. A gaussian random variable is typically called a "normal" random variable.
    $f_{X}(x)={\mathcal {N}}(\mu ,\sigma ^{2})={\frac {1}{\sqrt {2\pi \sigma ^{2}}}}e^{-{\frac {(x-\mu )^{2}}{\sigma ^{2}}}}$
Where μ is the mean of the function, and σ$^2$ is the variance of the function. we will discuss both these terms later.
# Engineering Analysis/Cayley Hamilton Theorem

If the characteristic equation of matrix A is given by:
    $\Delta (\lambda )=|A-\lambda I|=(-1)^{n}(\lambda ^{n}+a_{n-1}\lambda ^{n-1}+\cdots +a_{0})=0$
Then the Cayley-Hamilton theorem states that the matrix A itself is also a valid solution to that equation:
    $\Delta (A)=(-1)^{n}(A^{n}+a_{n-1}A^{n-1}+\cdots +a_{0})=0$
Another theorem worth mentioning here (and by "worth mentioning", we really mean "fundamental for some later topics") is stated as:
If λ are the eigenvalues of matrix A, and if there is a function f that is defined as a linear combination of powers of λ:
    $f(\lambda )=\sum _{i=0}^{\infty }b_{i}\lambda ^{i}$
If this function has a radius of convergence S, and if all the eigenvectors of A have magnitudes less than S, then the matrix A itself is also a solution to that function:
    $f(A)=\sum _{i=0}^{\infty }b_{i}A^{i}$
# Engineering Analysis/Correlation
## Independence

Two random variables are called independent if changes in one do not affect, and are not affected by, changes in the other.

## Correlation

Two random variables are said to have correlation if they take the same values, or similar values, at the same point in time. Independence implies that two random variables will be uncorrelated, but two random variables being uncorrelated does not imply that they are independent.
# Engineering Analysis/Expectation and Entropy
## Expectation

The expectation operator of a random variable is defined as:
    $E[x]=\int _{-\infty }^{\infty }xf_{X}(x)dx$
This operator is very useful, and we can use it to derive the moments of the random variable.

## Moments

A moment is a value that contains some information about the random variable. The n-moment of a random variable is defined as:
    $E[x^{n}]=\int _{-\infty }^{\infty }x^{n}f_{X}(x)dx$

### Mean

The mean value, or the "average value" of a random variable is defined as the first moment of the random variable:
    $E[x]=\mu _{X}=\int _{-\infty }^{\infty }xf_{X}(x)dx$
We will use the Greek letter μ to denote the mean of a random variable.

## Central Moments

A central moment is similar to a moment, but it is also dependent on the mean of the random variable:
    $E[(x-\mu _{X})^{n}]=\int _{-\infty }^{\infty }(x-\mu _{X})^{n}f_{X}(x)dx$
The first central moment is always zero.

### Variance

The variance of a random variable is defined as the second central moment:
    $E[(x-\mu _{X})^{2}]=\sigma ^{2}$
The square-root of the variance, σ, is known as the standard-deviation of the random variable

### Mean and Variance

the mean and variance of a random variable can be related by:
    $\sigma ^{2}=\mu ^{2}+E[x^{2}]$
This is an important function, and we will use it later.

## Entropy

the entropy of a random variable $X$ is defined as:
    $H[X]=E\left[{\frac {1}{p(X)}}\right]$
# Engineering Analysis/Banach and Hilbert Spaces

There are some special spaces known as Banach spaces, and Hilbert spaces.

## Convergent Functions

Let's define the piece-wise function φ(x) as:
    $\phi _{n}(x)=\left\{{\begin{matrix}0&x\leq 0\\nx&0<x\leq {\frac {1}{n}}\\1&{\frac {1}{n}}<x\end{matrix}}\right.$
We can see that as we set $n\to \infty$, this function becomes the unit step function. We can say that as n approaches infinity, that this function converges to the unit step function. Notice that this function only converges in the L$_2$ space, because the unit step function does not exist in the C space (it is not continuous).

### Convergence

We can say that a function φ converges to a function φ$^*$ if:
    $\lim _{n\to \infty }\|\phi _{n}-\phi ^{*}\|=0$
We can call this sequences, and all such sequences that converge to a given function as n approaches infinity a cauchy sequence.

### Complete Function Spaces

A function space is called complete if all sequences in that space converge to another function in that space.

## Banach Space

A Banach Space is a complete normed function space.

## Hilbert Space

A Hilbert Space is a Banach Space with respect to a norm induced by the scalar product. That is, if there is a scalar product in the space X, then we can say the norm is induced by the scalar product if we can write:
    $\|f\|=g(\langle f,f\rangle )$
That is, that the norm can be written as a function of the scalar product. In the L$_2$ space, we can define the norm as:
    $\|f\|={\sqrt {\langle f,f\rangle }}$
If the scalar product space is a Banach Space, if the norm space is also a Banach space.
In a Hilbert Space, the Parallelogram rule holds for all members f and g in the function space:
    $\|f+g\|^{2}+\|f-g\|^{2}=2\|f\|^{2}+2\|g\|^{2}$
The L$_2$ space is a Hilbert Space. The C space, however, is not.
# Engineering Analysis/Error Estimation

Consider a scenario where the matrix representation of a system A differs from the actual implementation of the system by a factor of ΔA. In other words, our system uses the matrix:
    $A+\Delta A$
From the study of Control Systems, we know that the values of the eigenvectors can affect the stability of the system. For that reason, we would like to know how a small error in A will affect the eigenvalues.
First off, we assume that ΔA is a small shift. The definition of "small" in this sense is arbitrary, and will remained open. Keep in mind that the techniques discussed here are more accurate the smaller ΔA is.
If ΔA is the error in the matrix A, then Δλ is the error in the eigenvalues and Δv is the error in the eigenvectors. The characteristic equation becomes:
    $(A+\Delta A)(v+\Delta v)=(\lambda +\Delta \lambda )(v+\Delta v)$
We have an equation now with two unknowns: Δλ and Δv. In other words, we don't know how a small change in A will affect the eigenvalues and eigenvectors. If we multiply out both sides, we get:
    $Av+\Delta Av+A\Delta v+O(\Delta ^{2})=\lambda v+\Delta \lambda v+v\Delta \lambda +O(\Delta ^{2})$
This situation seems hopeless, until we multiply both sides by the corresponding left-eigenvector w from the left:
    $w^{T}Av+w^{T}\Delta Av+w^{T}v\Delta A=w^{T}\lambda v+w^{T}\Delta \lambda v+w^{T}v\Delta \lambda +O(\Delta ^{2})$
Terms where two Δs (which are known to be small, by definition) are multiplied together, we can say are negligible, and ignore them. Also, we know from our right-eigenvalue equation that:
    $w^{T}A=\lambda w^{T}$
Another fact is that the right-eigenvectors and left eigenvectors are orthogonal to each other, so the following result holds:
    $w^{T}v=0$
Substituting these results, where necessary, into our long equation above, we get the following simplification:
    $w^{T}\Delta Av=\Delta \lambda w^{T}\Delta v$
And solving for the change in the eigenvalue gives us:
    $\Delta \lambda ={\frac {w^{T}\Delta Av}{w^{T}\Delta v}}$
This approximate result is only good for small values of ΔA, and the result is less precise as the error increases.
# Engineering Analysis/Diagonalization
## Similarity

Matrices A and B are said to be similar to one another if there exists an invertable matrix T such that:
    $T^{-1}AT=B$
If there exists such a matrix T, the matrices are similar. Similar matrices have the same eigenvalues. If A has eigenvectors v$_1$, v$_2$ ..., then B has eigenvectors u given by:
    $u_{i}=Tv_{i}$

## Matrix Diagonalization

Some matricies are similar to diagonal matrices using a transition matrix, T. We will say that matrix A is diagonalizable if the following equation can be satisfied:
    $T^{-1}AT=D$
Where D is a diagonal matrix. An n × n square matrix is diagonalizable if and only if it has n linearly independent eigenvectors.

## Transition Matrix

If an n × n square matrix has n distinct eigenvalues λ, and therefore n distinct eigenvectors v, we can create a transition matrix T as:
    $T=[v_{1}v_{2}...v_{n}]$
And transforming matrix X gives us:
    $T^{-1}AT={\begin{bmatrix}\lambda _{1}&0&\cdots &0\\0&\lambda _{2}&\cdots &0\\\vdots &\vdots &\ddots &\vdots \\0&0&\cdots &\lambda _{n}\end{bmatrix}}$
Therefore, if the matrix has n distinct eigenvalues, the matrix is diagonalizable, and the diagonal entries of the diagonal matrix are the corresponding eigenvalues of the matrix.

## Complex Eigenvalues

Consider the situation where a matrix A has 1 or more complex conjugate eigenvalue pairs. The eigenvectors of A will also be complex. The resulting diagonal matrix D will have the complex eigenvalues as the diagonal entries. In engineering situations, it is often not a good idea to deal with complex matrices, so other matrix transformations can be used to create matrices that are "nearly diagonal".

## Generalized Eigenvectors

If the matrix A does not have a complete set of eigenvectors, that is, that they have d eigenvectors and n - d generalized eigenvectors, then the matrix A is not diagonalizable. However, the next best thing is acheived, and matrix A can be transformed into a Jordan Cannonical Matrix. Each set of generalized eigenvectors that are formed from a single eigenvector basis will create a jordan block. All the distinct eigenvectors that do not spawn any generalized eigenvectors will form a diagonal block in the Jordan matrix.
# Gambas/Sound

Back to Gambas

## Sound

There is no native sound class. You have to activate a component.
In the project properties menu, check a component like gb.sdl.sound or gb.sdl2.audio for using it.
After that, use the static class Music! Example:
# Gambas/String Manipulation
## String Concatenation

The concatenation operator represented by an ampersand symbol is used to join two strings together:

### The addition operator cannot be used to concatenate strings

In gambas, unlike traditional basic, the plus symbol cannot be used as concatenation operator. If an attempt is made to use a plus symbol for this purpose, a type mismatch error will occur when an attempt is made to run the program:

### Concatenation combination assignment operator

The concatenation combination assignment operator represented by a andequals symbol, can also be used to concatenate strings:

## Search and Replace

hello i have made a try to search in a text file -two function : 1- search by line and column 2-search by pos
-first function returns an array with results it will be for example like this ["1,12" , "2,1"] , the first element is the line the second is the column
-second function returns also an array but single one ex:[1,2,45,455] every element is the string pos
here you are ! :
# Gambas/Time

Back to Gambas

## Usar la Propiedad Time

Time returns the current time. Example:
You need 1 button:
Te daria algo asi: 22:13:00.998 (En ubuntu me da asi :P)
Por ejemplo si queremos hacer que a determinada hora nos avise, seria algo asi:
Necesitas 2 Textbox, 1 Boton, 1 Timer y un modulo..
En textbox1 escribimos la Hora que queremos que nos avise. En textbox2 escribimos el minuto que queremos que nos avise.
Within the module write:
Within form Fmain, Button1 write:

## Wait Command

The Syntax is
This command calls the event loop. If Delay is specified, it does not return until Delay seconds elapse. Delay is a floating point number. So, if you want to wait 100 ms, just do:
During the wait, no keyboard or mouse events are processed. Only drawing, timer and file descriptors events, like Process_Write are still running.
Example
You need a commandbutton and a textbox to get it going.