OpenLLM-France/wikimedia huggingface dataset:
# English in Use/Sentences Overview

This section will serve as a basic overview of sentences. Each topic will be discussed in more detail in subsequent chapters.

### Phrases

A phrase is a group of words which contains neither a subject nor a verb. (It may, however, contain a verbal form such as an infinitive, a participle, or a gerund.)

### Clauses

A clause is a group of words containing at least a subject and a verb (the baby ate), and frequently it lets its hair down by containing some kind of a complement as well (the baby ate the goldfish). There are two kinds of clauses: independent and dependent.

## Forms

There are three forms of a sentence: simple, compound, and complex, and one combined form: compound-complex.

### Compound-complex

It is a sentence which is made to by joining two or more simple sentences.

## Purposes

Sentences are created for four main reasons: to declare, to command, to question, and to exclaim.

### Exclamatory
## Sentence diagrams

Introduction · About :
| Words: | Articles · Nouns · Pronouns · Verbs · Adjectives · Adverbs · Prepositions, Conjunctions, and Interjections · Verbals |
| Sentences: | Sentences Overview · Basic Components · Phrases · Clauses · Fragments and Run-on Sentences |
| Usage: | Adjective and Adverb Usage · Pronoun Usage · Subject-verb Agreement · Verb Usage |
| Punctuation: | End Marks · Commas · Apostrophes · Quotations · Other Common Punctuation Marks · Less Common Typographical Marks |
| Other key topics: | Time and Date · Capitalization · Spelling · Figures of Syntax · Syntax · Recent Grammar Restructure Attempts |
| Appendices: | Glossary · Q&A · External Resources · Common errors · History · Dictionary · Thesaurus |
# Digital Signal Processing/Spectral Transforms

When designing a particular filter, it is common practice to first design a Low-pass filter, and then using a spectral transform to convert that lowpass filter equation into the equation of a different type of filter. This is done because many common values for butterworth, cheybyshev and elliptical low-pass filters are already extensively tabulated, and designing a filter in this manner rarely requires more effort then looking values up in a table, and applying the correct transformations. This page will enumerate some of the common transformations for filter design.
It is important to note that spectral transformations are not exclusively used for analog filters. There are digital variants of these transformations that can be applied directly to digital filters to transform them into a different type. This page will only talk about the analog filter transforms, and the digital filter transforms will be discussed elsewhere.

## Low-Pass to Low-Pass

This spectral transform is used to convert between two lowpass filters with different cutoff frequencies.
    $z^{-1}={\frac {1}{F({\hat {z}})}}=$
# Digital Signal Processing/Tunable Filters

We have high-pass and lowpass filters, and it turns out that we can create both filter effects using a single piece of hardware called a tunable filter. Tunable filters rely on a device called an allpass filter to create other types of filter outputs.

## Allpass Filters

A common, although unintuitive, tool in the DSP toolbox is the all-pass filter. As the name implies, the all-pass filter passes all frequency components without attenuation.

## Why Allpass?

The question comes up, if an allpass filter doesn't actually filter anything, why do we bother to study it? The answer is that even though the allpass filter doesn't affect the magnitude response of the system, it will affect the phase response of the system.
Allpass filters have the property that cascading an allpass filter with another IIR filter will produce a linear output for the whole system.
# Digital Signal Processing/Using MATLAB

Digital Signal Processing is a field of study that is a combination of both mathematical theory, and physical implementation. It makes no sense to consider a digital system without first asking "can this be constructed physically?" MATLAB is an excellent (although expensive) tool for simulating systems, and for creating the ever-valuable "proof of concept". This book will make several references to MATLAB, but don't get confused: This book will not teach how to program in MATLAB. If you would like to learn MATLAB, check out the book Programming:MATLAB.
Alternately, enterprising students might attempt to use GNU Octave, which is a free and Open Source alternative to MATLAB. Octave can be obtained from http://www.octave.org . It endeavors to be MATLAB-compatible and largely it is (a lot of MATLAB code can be run using Octave, and vice versa), though some functionality is missing. For more information on Octave, see the Wikibook Octave Programming. (There are other free alternatives to MATLAB, with varying degrees of code compatibility.)
# Digital Signal Processing/Windowing

Most digital signals are infinite, or sufficiently large that the dataset cannot be manipulated as a whole. Sufficiently large signals are also difficult to analyze statistically, because statistical calculations require all points to be available for analysis. In order to avoid these problems, engineers typically analyze small subsets of the total data, through a process called windowing.

## Windowing Introduction

Windowing is the process of taking a small subset of a larger dataset, for processing and analysis. A naive approach, the rectangular window, involves simply truncating the dataset before and after the window, while not modifying the contents of the window at all. However, as we will see, this is a poor method of windowing and causes power leakage.

## Applying Windows

Application of a window to a dataset will alter the spectral properties of that dataset. In a rectangular window, for instance, all the data points outside the window are truncated and therefore assumed to be zero. The cut-off points at the ends of the sample will introduce high-frequency components.
Consider the system H(z), with input X(z) and output Y(z). We model this as:
    $Y(z)=X(z)H(z)$
If we have a window with transfer function W(z), we can mathematically apply the window to our signal, X(z) as such:
    ${\hat {X}}(z)=X(z)W(z)$
Then, we can pass our windowed signal into our system, H(z) as usual:
    ${\hat {Y}}(z)={\hat {X}}(z)H(z)$

## Leakage

If our signal is a lowpass or passband signal, the application of a window will introduce high-frequency components. Power from the original signal will be diverted from the specified frequency band into the high-frequency areas. This redistribution of power from a target band to the upper frequencies is known as leakage.
If we look at a rectangular window, we know from duality that the frequency response of that window is a sinc function, which has non-zero values all the way out to both positive and negative infinity. Convolution of the sinc function with any narrow-band signal is going to cause a very spread-out spectrum.

## Types of Windows
### Hamming

function of hamming
0.54+0.46cos(2*pi*n/N-1)

### Blackman window

0.42+.5cos(2*pi*n/N-1)+.08cos(4*pi*n/N-1)
Greatest stop band attenuation of mentioned windowing techniques at the expense of a larger transition band.
The eq. for the symmetric 4-term Blackman Harris window of length N is....
w(n) = a0 - a1*cos(2*pi*n/N-1) + a2*cos(4*pi*n/N-1) - a3*cos(2*pi*n/N-1); 0 <= n <= N-1
a0 = 0.35875 a1 = 0.48829 a2 = 0.14128 a3 = 0.01168
# Digital Technology and Cultures

This Wikibook is meant to serve as a resource for students of digital cultures to explore theory. In the spirit of helping ourselves and others discover and appreciate the strengths of particular theories for helping us articulate the relationship between digital technologies and social values we (the students and faculty of Digital Technology and Cultures at Seattle University) are continually adding our own perspectives on theory. The book is meant to be a "living document" that grows with each new cohort of students, leaving behind a resource for future students.
The book is broken out into major traditions of theory. In each of these sections are individual pages that outline some background and key ideas of a respective tradition, while applying and discussing that theory to a given digital technology phenomenon.

## Table of Contents
### The Enlightenment

* Age of Enlightenment and Education Today

### Semiotics

* Understanding the Semiotics of Virtual Reality
* Semiotics in Video Games

### Rhetoric

* Rhetoric For Better Behavior

### Phenomenology

* Phenomenology and Autonomous Vehicles
* Phenomenology of Digital Networks

### Frankfurt School

* Critical Theory/The Frankfurt School
* Marxism in the Matrix - The Frankfurt School and Virtual Reality
* How Does Technology Affect Class Identity

### Birmingham School

* Birmingham School
* Interconnected Technology And Cultural Studies and Identity

### Pragmatism

* Pragmatism and Emoji as a Universal Language
# Digital Signal Processing/Software tool

Digital Signal Processing is a field of study that combines both mathematical theory and physical implementation. It makes no sense to consider a digital system without asking oneself how it will be implemented. In the design and analysis phase, some general-purpose signal processing tools are available.

## Matlab

MATLAB is an excellent (although expensive) tool for simulating systems, and for creating the ever-valuable "proof of concept". This book will make several references to MATLAB, but don't get confused: This book will not teach how to program in MATLAB. If you would like to learn MATLAB, check out the book MATLAB Programming.
There are other free alternatives to MATLAB, with varying degrees of code compatibility.

## Octave

GNU Octave is a free and Open Source alternative to MATLAB. Octave can be obtained from http://www.octave.org. It endeavors to be MATLAB-compatible and largely it is (a lot of MATLAB code can be run using Octave, and vice versa), though some functionality is missing. For more information on Octave, see the Wikibook Octave Programming.

## SciPy

SciPy is a Python-based set of libraries which allow to perform numeric calculations. As the preceding tools, it features a signal processing toolbox. Also, the python scripts can make use of matplotlib, a plotting library whose basic commands are very similar to MATLAB's.
# Digital Signal Processing/Sound Processing
## Digital Sound
### Sampling Frequency

Sound in the digital realm is stored in one or more arrays of discrete samples, with each array of samples correlating to a channel (e.g. stereo sound requires two channels, and thus two arrays of samples). The interval of time between each sample is a constant, and is determined by the type of data to be represented. Since we are interested in sound, and the extreme upper limit of human hearing is generally accepted as 20 kHz, the Nyquist-Shannon sampling theorem can be used to determine the interval between samples to accurately re-construct the signals we're interested in.
This theorem states that
Essentially what this means is a signal that is limited to a certain range (audible sound: ~20 Hz to 20 kHz) can be reconstructed without error if it is sampled at a rate that is greater than twice the bandwidth. The Red Book audio CD standard sets the sampling rate at 44,100 Hz. This frequency was chosen to leave ample overhead (as required by the Nyquist-Shannon theorem), but could support at least up to 22 kHz.
44.1 kHz is the general standard for sampling rates in digital audio on consumer level equipment, however 48 kHz is common when working with film or video. Also, many recording engineers prefer to record classical or otherwise complex music at 88.2 or 96 kHz—some claim to be able to perceive a difference.
When converting from 48 kHz to 44.1 kHz a sonic blurring effect can sometime occur, because the math is floating point, which is inherently imprecise on a computer. The conversion from 88.2 kHz to 44.1 kHz or 96 kHz to 48 kHz is simpler to perform, since the computer, or device, doing the conversion only has to disregard half the samples. To bypass this problem, a high-quality digital-to-analog converter can be used to bring, for example, a 48 kHz signal back to its analog form, and then is fed into another high-quality analog-to-digital converter to re-sample the signal at 44.1 kHz. This technique is common practice in recording studios where high-end equipment can be trusted to do the conversion flawlessly, however in other situations, the sonic distortion caused by converting the audio in software or hardware may be of little concern.

### Bits Per Sample

While sampling frequency determines the time component of an audio signal, the number of bits per sample is used to describe the amplitude. Red Book audio CDs store each sample as a 16 bit signed integer. This means that when an audio signal is converted for use on a CD, each sample's value is quantized as an integer to fit in the range -32768 to +32767.

## Wave Files

Wave files contain data which is a representation of audio sound. This format for storing data is an uncompressed format. This means the data can be sent to the digital-to-analog processor for playback without an added step of decompression. This also means that this format will consume a great deal of memory.
# Digital Signal Processing/Wiener Filters

A Wiener Filter is a filtering system that is an optimal solution to the statistical filtering problem.

## Statistical Filtering
### Statement of Problem

$E[x]=\sum xf[n]$
d[n] is the expected response value, or the value that we would like the input to approach.
    $\sigma _{d}^{2}=E[d[n]d^{*}[n]]$
e[n] is the estimation error, or the difference between the expected signal d[n] and the output of the FIR filter. We denote the FIR filter output with a hat:
    ${\hat {d}}[n]=\sum _{k=1}^{M}w_{k}u[n-k+1]$
Where the convolution operation applies the input signal, u[n], to the filter with impulse response w[n].
We can define a performance index J[w] which is a function of the tap weights of the FIR filter, w[n], and can be used to show how close the filter is to reaching the desired output. We define the performance index as:
    $J[w]=E[e[n]e^{*}[n]]$
J[w] is also known as the mean-squared error signal. The goal of a Wiener filter is to minimize J[w] so that the filter operates with the least error.

### Adaptive Filtering

Adaptive filtering is a situation where the coefficients of a filter change over time, typically in response to changes in the characteristics of the input signal.

### Wiener-Hopf Equations

    $Rw_{o}=p$
    $w_{o}=R^{-1}p$
Where R is the autocorrelation matrix. w$_o$ is the optimal set of filter coefficients, arranged in a vector, and p is the cross-correlation vector.

## Wiener Filters

Wiener Filters are typically implemented with FIR filter constructions. This is because the wiener filter coefficients change over time, and IIR filter can become unstable for certain coefficient values. To prevent this instability, we typically construct adaptive filters with FIR structures.
# Economic Sophisms/162

<pagequality level="3" user="Zoeannl" />style="background: #ececec; text-align: left; padding-left: 0.5em; font-weight: bold;" class="table-rh"is the general wealth of the community. The value of a house is not always in proportion to what it has cost, but likewise in proportion to the number and fortune of the tenants. Are two houses exactly similar necessarily of the same value? By no means, if the one is situated in Paris and the other in Lower Brittany. Never speak of price without taking into account collateral circumstances, and let it be remembered that no attempt is so bootless as to endeavour to found the prosperity of parts on the ruin of the whole. And yet this is what the policy of restriction pretends to do.
Consider what would have happened at Paris, for example, if this strife of interests had been attended with success.
Suppose that the first shoemaker who established himself in that city had succeeded in ejecting all others; that the first tailor, the first mason, the first printer, the first watchmaker, the first physician, the first baker, had been equally successful. Paris would at this moment have been still a village of 1200 or 1500 inhabitants. It has turned out very differently. The market of Paris has been open to all (excepting those whom you still keep out), and it is this freedom which has enlarged and aggrandized it. The struggles of competition have been bitter and long continued, and this is what has made Paris a city of a million of inhabitants. The general wealth has increased, no doubt; but has the individual wealth of the shoemakers and tailors been diminished? This is the question you have to ask. You may say that according as the number of competitors increased, the price of their products would go on falling. Has it done so? No; for if the supply, has been augmented, the demand has been enlarged.
The same thing will hold good of your commodity, cloth; let it enter freely. You will have more competitors in the trade, it is true; but you will have more customers, and, above all, richer customers. Is it possible you can never have thought of this, when you see nine-tenths of your fellow=citizens underclothed in winter, for want of the commodity which you manufacture?
If you wish to prosper, allow your customers to thrive. This is a lesson which you have been very long in learning. When it is thoroughly learnt, each man will seek his own interest in the general good; and then jealousies between man and man, town and town, province and province, nation and nation, will no longer trouble the world.
# Economic Sophisms/164

<pagequality level="3" user="Zoeannl" />style="background: #ececec; text-align: left; padding-left: 0.5em; font-weight: bold;" class="table-rh"And do they not act in the interest of the civil list, which profits most of all from the policy of protection?
You see, then, very clearly, that if we did not despise this war of disparagement, arms would not be wanting to carry it on.
But this is beside the question.
The question, and we must never lose sight of it, is this:
Whether is it better for the working classes to be free, or not to be free to purchase foreign commodities?
Workmen! they tell you that "If you are free to purchase from the foreigner those things which you now produce yourselves, you will cease to produce them; you will be without employment, without wages, and without bread; it is therefore for your own good to restrain your liberty."
This objection returns upon us under two forms:—They say, for example, "If we clothe ourselves with English cloth; if we make our ploughs of English iron; if we cut our bread with English knives; if we wipe our hands with English towels,—what will become of French workmen, what will become of national labour?"
Tell me, workmen! if a man should stand on the quay at Boulogne, and say to every Englishman who landed, "If you will give me these English boots, I will give you this French hat;" or, "If you will give me that English horse, I will give you this French tilbury;" or ask him, "Will you exchange that machine made at Birmingham, for this clock made at Paris?" or, again, "Can you arrange to barter this Newcastle coal against this champagne wine?" Tell me whether, assuming this man to make his proposals with discernment, any one would be justified in saying that our national labour, taken in the aggregate, would suffer in consequence?
Nor would it make the slightest difference in this respect were we to suppose twenty such offers to be made in place of one, or a million such barters to be effected in place of four; nor would it in any respect alter the case were we to assume the intervention of merchants and money, whereby such transactions would be greatly facilitated and multiplied.
Now, when one country buys from another wholesale, to sell again in retail, or buys in retail, to sell again in the lump, if we
trace the transaction to its ultimate results, we shall always
# Economic Sophisms/165

<pagequality level="3" user="Zoeannl" />style="background: #ececec; text-align: left; padding-left: 0.5em; font-weight: bold;" class="table-rh"find that commerce resolves itself into barter, products for products, services for services. If, then, barter does no injury to national labour, since it implies as much national labour given as foreign labour received, it follows that a hundred thousand millions of such acts of barter would do as little injury as one.
But who would profit? you will ask The profit consists in turning to most account the resources of each country, so that the same amount of labour shall yield everywhere a greater amount of satisfactions and enjoyments.
There are some who in your case have recourse to a singular system of tactics. They begin by admitting the superiority of the free to the prohibitive system, in order, doubtless, not to have the battle to fight on this ground.
Then they remark that the transition from one system to another is always attended with some displacement of labour.
Lastly, they enlarge on the sufferings, which, in their opinion, such displacements must always entail. They exaggerate these sufferings, they multiply them, they make them the principal subject of discussion, they present them as the exclusive and definitive result of reform, and in this way they endeavour to enlist you under the banners of monopoly.
This is just the system of tactics which has been employed to defend every system of abuse; and one thing I must plainly avow, that it is this system of tactics which constantly embarrasses those who advocate reforms, even those most useful to the people. You will soon see the reason of this.
When an abuse has once taken root, everything is arranged on the assumption of its continuance. Some men depend upon it for subsistence, others depend upon them, and so on, till a formidable edifice is erected.
Would you venture to pull it down? All cry out, and remark this—the men who bawl out appear always at first sight to be in the right, because it is far easier to show the derangements which must accompany a reform than the arrangements which must follow it.
The supporters of abuses cite particular instances of sufferings; they point out particular employers who, with their workmen, and the people who supply them with materials, are
about to be injured; and the poor reformer can only refer to
# Economic Sophisms/17

<pagequality level="4" user="Zoeannl" />
Which is best for man, and for society, abundance or scarcity?
What! you exclaim, can that be a question? Has any one ever asserted, or is it possible to maintain, that scarcity is at the foundation of human wellbeing?
Yes, this has been asserted, and is maintained every day; and I hesitate not to affirm that the theory of scarcity is much the most popular. It is the life of conversation, of the journals, of books, and of the tribune; and strange as it may seem, it is certain that Political Economy will have fulfilled its practical mission when it has established beyond question, and widely disseminated, this very simple proposition: "The wealth of men consists in the abundance of commodities."
Do we not hear it said every day, "The foreigner is about to inundate us with his products?" Then we fear abundance.
Did not M. Saint Cricq exclaim, "Production is excessive?" Then he feared abundance.
Do workmen break machines? Then they fear excess of production, or abundance.
Has not M. Bugeaud pronounced these words, "Let bread be dear, and agriculturists will get rich?" Now, bread cannot be dear but because it is scarce. Therefore M. Bugeaud extols scarcity.
Does not M. d'Argout urge as an argument against sugar-growing the very productiveness of that industry? Does he not say, "Beetroot has no future, and its culture cannot be extended, because a few acres devoted to its culture in each