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Constantine VII

Constantine VII Porphyrogenitus (Κωνσταντῖνος Πορφυρογέννητος, Kōnstantinos Porphyrogennētos; 17 May 905 – 9 November  959) was the fourth Byzantine emperor of the Macedonian dynasty, reigning from 6 June 913 to 9 November 959. He was the son of Emperor Leo VI and his fourth wife, Zoe Karbonopsina, and the nephew of his predecessor Alexander.

Most of his reign was dominated by co-regents: from 913 until 919 he was under the regency of his mother, while from 920 until 945 he shared the throne with Romanos Lekapenos, whose daughter Helena he married, and his sons. Constantine VII is best known for the Geoponika (τά γεοπονικά), an important agronomic treatise compiled during his reign, and three, perhaps four, books; De Administrando Imperio (bearing in Greek the heading Πρὸς τὸν ἴδιον υἱὸν Ῥωμανόν), De Ceremoniis (Περὶ τῆς Βασιλείου Τάξεως), De Thematibus (Περὶ θεμάτων Άνατολῆς καὶ Δύσεως), and Vita Basilii (Βίος Βασιλείου), though his authorship of the Vita Basilii is not certain.

The epithet porphyrogenitus alludes to the Purple chamber of the imperial palace, decorated with porphyry, where legitimate children of reigning emperors were normally born. Constantine was also born in this room, although his mother Zoe had not been married to Leo at that time. Nevertheless, the epithet allowed him to underline his position as the legitimate son, as opposed to all others, who claimed the throne during his lifetime. Sons born to a reigning Emperor held precedence in the Eastern Roman line of succession over elder sons not born "in the purple".

Regency
Constantine was born in Constantinople on 17/18 May 905, an illegitimate son of Leo VI before an uncanonical fourth marriage. To help legitimize him, his mother gave birth to him in the Purple Room of the imperial palace, hence his nickname Porphyrogennetos. He was elevated to the throne as a two-year-old child by his father and uncle on 15 May 908 (Whitsunday).

In early 913, as his uncle Alexander lay dying, he appointed a seven-man regency council for Constantine. It was headed by the Patriarch Nicholas Mystikos, the two magistroi John Eladas and Stephen, the rhaiktor John Lazanes, the otherwise obscure Euthymius and Alexander's henchmen Basilitzes and Gabrielopoulos. Following Alexander's death (6 June), the new and shaky regime survived the attempted usurpation of Constantine Doukas, and Patriarch Nicholas Mystikos quickly assumed a dominant position among the regents.

Patriarch Nicholas was presently forced to make peace with Tsar Simeon of Bulgaria, whom he reluctantly recognized as Bulgarian emperor. Because of this unpopular concession, Patriarch Nicholas was driven out of the regency by Constantine's mother Zoe. She was no more successful with the Bulgarians, who defeated her main supporter, the general Leo Phokas, in 917.

In March 919, she was replaced as regent by the admiral Romanos Lekapenos, who married his daughter Helena Lekapene to Constantine. Romanos used his position to advance to the ranks of basileopatōr in April 919, to kaisar (Caesar) on 24 September 920, and finally to co-emperor on 17 December 920. Thus, just short of reaching nominal majority, Constantine was eclipsed by a senior emperor.

Constantine's youth had been a sad one due to his unpleasant appearance, his taciturn nature, and his relegation to the third level of succession, behind Christopher Lekapenos, the eldest son of Romanos I Lekapenos. Nevertheless, he was a very intelligent young man with a large range of interests, and he dedicated those years to studying the court's ceremony.

Senior emperor

Romanos kept and maintained power until 16/20 December 944, when he was deposed by his sons, the co-emperors Stephen and Constantine. Romanos spent the last years of his life in exile on the Island of Prote as a monk and died on 15 June 948. With the help of his wife, Constantine VII succeeded in removing his brothers-in-law, and on 27 January 945, Constantine VII became sole emperor at the age of 39, after a life spent in the shadow. Several months later, on 6 April (Easter), Constantine VII crowned his own son Romanos II co-emperor. Having never exercised executive authority, Constantine remained primarily devoted to his scholarly pursuits and delegated his authority to bureaucrats and generals, as well as to his energetic wife Helena Lekapene.

In 947, Constantine VII ordered the immediate restitution of all peasant lands, without compensation; by the end of his reign, the condition of the landed peasantry, which formed the foundation of the whole economic and military strength of the Empire, was better off than it had been for a century.

In 949, Constantine launched a new fleet of 100 ships (20 dromons, 64 chelandia, and 10 galleys) against the Arab corsairs hiding in Crete, but like his father's attempt to retake the island in 911, this attempt also failed.

On the Eastern frontier things went better, even if with alternate success. In the same year, the Byzantines conquered Germanicea, repeatedly defeated the enemy armies, and in 952 they crossed the upper Euphrates. But in 953, the Hamdanid amir Sayf al-Dawla retook Germanicea and entered the imperial territory. The land in the east was eventually recovered by Nikephoros Phokas, who conquered Adata, in northern Syria, in 958, and by the general John Tzimiskes, who one year later captured Samosata, in northern Mesopotamia. An Arab fleet was also destroyed by Greek fire in 957.

Constantine had active diplomatic relationships with foreign courts, including those of the caliph of Cordoba Abd ar-Rahman III and of Otto I, Holy Roman Emperor. In the autumn of 957 Constantine was visited by Olga of Kiev, regent of the Kievan Rus'. The reasons for this voyage have never been clarified; but she was baptised a Christian with the name Helena, and sought Christian missionaries to encourage her people to adopt Christianity. According to legends, Constantine VII fell in love with Olga, but she found a way to refuse him by tricking him into becoming her godfather. When she was baptized, she said it was inappropriate for a godfather to marry his goddaughter.

Constantine VII died at Constantinople on 9 November 959 and was succeeded by his son Romanos II. It was rumored that he had been poisoned by his son or his daughter-in-law Theophano.

Literary activity

Constantine VII was recognized as a writer and scholar, surrounding himself with educated people of the Imperial Court. He wrote, or had commissioned, the works Geoponika ("On Agriculture", in Greek Τὰ γεωπονικά), a compilation of agronomic works from earlier Greek and Punic texts that are otherwise lost; De Ceremoniis ("On Ceremonies", in Greek, Περὶ τῆς βασιλείου τάξεως), describing the kinds of court ceremonies (also described later in a more negative light by Liutprand of Cremona); De Administrando Imperio ("On the Administration of the Empire", bearing in Greek the heading Πρὸς τὸν ἴδιον ὑιὸν Ρωμανόν), giving advice on running the Empire internally and on fighting  external enemies; a history of the Empire covering events following the death of the chronographer Theophanes the Confessor in 817; and Excerpta Historica ("Excerpts from the Histories"), a collection of excerpts from ancient historians (many of whose works are now lost) in four volumes (1. De legationibus. 2. De virtutibus et vitiis. 3. De insidiis. 4. De sententiis). In The Manuscript Tradition of Polybius, John Michael Moore (CUP, 1965) provides a useful summary of the commission by Porphyrogenitus of the Constantine Excerpts: He felt that the historical studies were being seriously neglected, mainly because of the bulk of the histories. He therefore decided that a selection under fifty-three titles should be made from all the important historians extant in Constantinople; thus he hoped to assemble in a more manageable compass the most valuable parts of each author. ... Of the fifty-three titles into which the excerpts were divided, only six have survived: de Virtutibus et Vitiis; de Sententiis; de Insidiis; de Strategematis; de Legationibus Gentium ad Romanos; de Legationibus Romanorum ad Gentes. The titles of only about half the remaining forty-seven sections are known.

Also amongst his historical works is a history eulogizing the reign and achievements of his grandfather, Basil I (Vita Basilii, Βίος Βασιλείου). These books are insightful and of interest to the historian, sociologist, and anthropologist as a source of information about nations neighbouring the Empire. They also offer a fine insight into the Emperor himself.

In his book, A Short History of Byzantium, John Julius Norwich refers to Constantine VII as "The Scholar Emperor". Norwich describes Constantine: He was, we are told, a passionate collector—not only of books and manuscripts but works of art of every kind; more remarkable still for a man of his class, he seems to have been an excellent painter. He was the most generous of patrons—to writers and scholars, artists and craftsmen. Finally, he was an excellent Emperor: a competent, conscientious and hard-working administrator and an inspired picker of men, whose appointments to military, naval, ecclesiastical, civil and academic posts were both imaginative and successful. He did much to develop higher education and took a special interest in the administration of justice.

Family
By his wife Helena Lekapene, the daughter of Emperor Romanos I, Constantine VII had several children:
 * Leo (c. 939–944), who died young.
 * Romanos II (939–963), who succeeded as emperor.
 * Zoe, sent to a convent.
 * Theodora, who married emperor John I Tzimiskes.
 * Agatha, sent to a convent.
 * Theophano, sent to a convent.
 * Anna, sent to a convent.

Legacy
Constantine VII is still remembered in the Eastern Orthodox liturgy when the transfer of the Holy Mandylion is celebrated on 16 August in the Julian calendar which corresponds to 31 August in the Gregorian calendar. This feast is part of the Nut Feast of the Saviour for Slavs.

title: Constantine VII


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Eurovision Young Dancers 1989

The Eurovision Young Dancers 1989 was the third edition of the Eurovision Young Dancers held at the Palais des Congrès in Paris, France on 28 June 1989. Organised by the European Broadcasting Union (EBU) and host broadcaster France Régions 3 (FR3), dancers from ten countries participated in the televised final. A total of seventeen countries took part in the competition. and made their debut at the contest.

The participant countries could send one or two dancers, male or female, that could not be older than 19. Each dancer was free to participate in any of these 2 categories: classical dancing or contemporary, modern or jazz dancing. The pas de deux performances could not be longer than ten minutes, while the variations could not be longer than five minutes. There were 4 awards that year: contemporary dancing prize, classical dancing prize and 2 jury's special prizes (for contemporary and classical dance).

The non-qualified countries were, , , , , and. Agnès Letestu of France won the contemporary dance prize, with Tetsuya Kumakawa of United Kingdom winning the classical ballet prize.

Location
The Palais des congrès de Paris, a concert venue, convention centre and shopping mall in the 17th arrondissement of Paris, France, was the host venue for the 1989 edition of the Eurovision Young Dancers.

The venue was built by French architect Guillaume Gillet, and was inaugurated in 1974. The venue hosted the Eurovision Song Contest 1978. Nearby the venue are Bois de Boulogne and the affluent neighbourhood of Neuilly-sur-Seine. The closest métro and RER stations are Porte Maillot and Neuilly – Porte Maillot, accessible via the lower levels of the building.

Format
The format consists of dancers who are non-professional and between the ages of 16–21, competing in a performance of dance routines of their choice, which they have prepared in advance of the competition. All the dancers then take part in a choreographed group dance during 'Young Dancers Week'.

Jury members of a professional aspect and representing the elements of ballet, contemporary, and modern dancing styles, score each of the competing individual and group dance routines. The overall winner upon completion of the final dances is chosen by the professional jury members.

The opening act was "Mon truc en plumes" performed by host Zizi Jeanmaire with her dancers and for the interval "Concerto en Ré" by L'École du Ballet de l'Ópera de Paris.

Results
Due to time restrictions, a semi-final was held to select the ten performers for the final.

Preliminary round
A total of sixteen countries took part in the preliminary semi-final round of the 1989 contest, of which ten qualified to the televised grand final. The following countries failed to qualify.

Jury members
The jury members consisted of the following:

 * – Roland Petit (Head of jury)
 * – Frank Andersen
 * – Paolo Bortoluzzi
 * – Oscar Araiz
 * – Igor Eisner
 * – John Neumeier
 * – Ekaterina Maximova
 * – Heinz Spoerli
 * – Vladimir Vasiliev

Broadcasting
The 1989 Young Dancers competition was broadcast in 19 countries including Jordan and Bulgaria.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: Eurovision Young Dancers 1989


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TV and FM DX

TV DX and FM DX is the active search for distant radio or television stations received during unusual atmospheric conditions. The term DX is an old telegraphic term meaning "long distance."

VHF/UHF television and radio signals are normally limited to a maximum "deep fringe" reception service area of approximately 40 - 100 mi in areas where the broadcast spectrum is congested, and about 50 percent farther in the absence of interference. However, providing favourable atmospheric conditions are present, television and radio signals sometimes can be received hundreds or even thousands of miles outside their intended coverage area. These signals are often received using a large outdoor antenna system connected to a sensitive TV or FM receiver, although this may not always be the case. Many times smaller antennas and receivers such as those in vehicles will receive stations farther than normal depending on how favourable conditions are.

While only a limited number of local stations can normally be received at satisfactory signal strengths in any given area, tuning into other channels may reveal weaker signals from adjacent areas. More consistently strong signals, especially those accentuated by unusual atmospheric conditions, can be achieved by improving the antenna system. The development of interest in TV-FM DX as a hobby can arise after more distant signals are either intentionally or accidentally discovered, leading to a serious interest in improving the listener's antenna and receiving installation for the purpose of actively seeking long-range television and radio reception. The TV-FM DX hobby is somewhat similar to other radio/electronic related hobbies such as amateur radio, Medium Wave DX, or short-wave radio, and organisations such as the Worldwide TV-FM DX Association have developed to coordinate and foster the further study and enjoyment of VHF/UHF television and FM broadcast DX.

History
After the Alexandra Palace, London 405-line BBC channel B1 TV service was introduced in 1936, it soon became apparent that television could be received well outside the original intended service area.

For example, in November 1938, engineers at the RCA Research Station, Riverhead, Long Island, accidentally received a 3,000-mile (4,800 km) transatlantic F2 broadcast of the London 45.0 MHz, 405-line BBC Television service.

The flickering black-and-white footage (characteristic of F2 propagation) included Jasmine Bligh, one of the original BBC announcers, and a brief shot of Elizabeth Cowell, who also shared announcing duties with Jasmine, an excerpt from an unknown period costume drama and the BBC's station identification logo transmitted at the beginning and end of the day's programmes.

This reception was recorded on 16 mm movie film, and is now considered to be the only surviving example of pre-war, live British television.

The BBC temporarily ceased transmissions on September 1, 1939 as World War II began. After the BBC Television Service recommenced in 1946, distant reception reports were received from various parts of the world, including Italy, South Africa, India, the Middle East, North America and the Caribbean.

In May 1940, the Federal Communications Commission (FCC), a U.S. government agency, formally allocated the 42 – 50 MHz band for FM radio broadcasting. It was soon apparent that distant FM signals from up to 1400 mi distance would often interfere with local stations during the summer months.

Because the 42 – 50 MHz FM signals were originally intended to only cover a relatively confined service area, the sporadic long-distance signal propagation was seen as a nuisance, especially by station management.

In February 1942, the first known published long-distance FM broadcast station reception report was reported by FM magazine. The report provided details of 45.1 MHz W51C Chicago, Illinois, received in Monterrey, Mexico: "Zenith Radio Corporation, operating W51C, has received a letter from a listener in Monterrey, Mexico, telling of daily reception of this station between 3:00 P.M. and 6:00 P.M. This is the greatest distance, 1,100 miles, from which consistent reception of the 50 [kW] transmitter has been reported."

In June 1945, the FCC decided that FM would have to move from the established 42 – 50 MHz pre-war band to a new band at 88 – 108 MHz. According to 1945 and 1946 FCC documents, the three major factors which the commission considered in its decision to place FM in the 88 – 108 MHz band were sporadic E co-channel interference, F2 layer interference, and extent of coverage.

During the 1950s to early 1960s, long-distance television reports started to circulate via popular U.S. electronics hobbyist periodicals such as DXing Horizons, Popular Electronics, Television Horizons, Radio Horizons, and Radio-Electronics. In January 1960, the TV DX interest was further promoted via Robert B. Cooper's regular DXing Horizons column.

In 1957, the world record for TV DX was extended to 10800 mi with the reception of Britain's channel BBC TV in various parts of Australia. Most notably, George Palmer in Melbourne, Victoria, received viewable pictures and audio of a news program from the BBC TV London station. This BBC F2 reception was recorded on movie film.

During the early 1960s, the U.K. magazine Practical Television first published a regular TV DX column edited by Charles Rafarel. By 1970, Rafarel's column had attracted considerable interest from TV DXers worldwide. After Rafarel's death in 1971, UK TV DXer Roger Bunney continued the monthly column, which continued to be published by Television Magazine. With the demise of Television Magazine in June 2008, Bunney's column finished after 36 years of publication. In addition to the monthly TV DX column, Bunney has also published several TV DX books, including Long Distance Television Reception (TV-DX) for the Enthusiast 1981 ISBN 0-900162-71-6, and A TV DXer's Handbook 1986 ISBN 0-85934-150-X.

Tropospheric propagation
Tropospheric propagation refers to the way radio signals travel through the lowest layer of the Earth's atmosphere, the troposphere, at altitudes up to about to 17 km (11 miles). Weather conditions in the lower atmosphere can produce radio propagation over greater ranges than normal. If a temperature inversion occurs, with upper air warmer than lower air, VHF and UHF radio waves can be refracted over the Earth's surface instead of following a straight-line path into space or into the ground. Such "tropospheric ducting" can carry signals for 800 km (500 miles) or more, far beyond usual range.

F2 propagation (F2-skip)
The F2 layer is found about 200 miles (320 km) above the Earth's surface and can reflect radio waves back toward the Earth. When the layer is particularly strong during periods of high sunspot activity, FM and TV reception can take place over 2000 miles (3000 km) or more, as the signal effectively "bounces" off the high atmospheric layer.

Sporadic E propagation (E-skip)
Sporadic E, also called E-skip, is the phenomenon of irregularly scattered patches of relatively dense ionization that develop seasonally within the E region of the ionosphere and reflect TV and FM frequencies, generally up to about 150 MHz. When frequencies reflect off multiple patches, it is referred to as multi-hop skip. E-skip allows radio waves to travel a thousand miles or even more beyond their intended area of reception. E-skip is unrelated to tropospheric ducting.

Television and FM signals received via Sporadic E can be extremely strong and range in strength over a short period from just detectable to overloading. Although polarisation shift can occur, single-hop Sporadic E signals tend to remain in the original transmitted polarisation. Long single-hop (900 - 1500 mi) Sporadic E television signals tend to be more stable and relatively free of multipath images. Shorter-skip (400 - 800 mi) signals tend to be reflected from more than one part of the Sporadic E layer, resulting in multiple images and ghosting, with phase reversal at times. Picture degradation and signal-strength attenuation increases with each subsequent Sporadic E hop.

Sporadic E usually affects the lower VHF band I (TV channels 2 – 6) and band II (88 – 108 MHz FM broadcast band). The typical expected distances are about 600 to 1400 mi. However, under exceptional circumstances, a highly ionized Es cloud can propagate band I VHF signals down to approximately 350 mi. When short-skip Es reception occurs, i.e., under 500 mi in band I, there is a greater possibility that the ionized Es cloud will be capable of reflecting a signal at a much higher frequency – i.e., a VHF band 3 channel – since a sharp reflection angle (short skip) favours low frequencies, a shallower reflection angle from the same ionized cloud will favour a higher frequency.

At polar latitudes, Sporadic E can accompany auroras and associated disturbed magnetic conditions and is called Auroral-E.

No conclusive theory has yet been formulated as to the origin of Sporadic E. Attempts to connect the incidence of Sporadic E with the eleven-year Sunspot cycle have provided tentative correlations. There seems to be a positive correlation between sunspot maximum and Es activity in Europe. Conversely, there seems to be a negative correlation between maximum sunspot activity and Es activity in Australasia.

Transequatorial propagation (TEP)
Discovered in 1947, transequatorial spread-F (TE) propagation makes it possible for reception of television and radio stations between 3000 - 5000 mi across the equator on frequencies as high as 432 MHz. Reception of lower frequencies in the 30 – 70 MHz range are most common. If sunspot activity is sufficiently high, signals up to 108 MHz are also possible. Reception of TEP signals above 220 MHz is extremely rare. Transmitting and receiving stations should be nearly equidistant from the geomagnetic equator.

The first large-scale VHF TEP communications occurred around 1957 – 58 during the peak of solar cycle 19. Around 1970, the peak of cycle 20, many TEP contacts were made between Australian and Japanese radio amateurs. With the rise of cycle 21 starting around 1977, amateur contacts were made between Greece/Italy and Southern Africa (both South Africa and Rhodesia/Zimbabwe), and between Central and South America by TEP.

"Afternoon" and "evening" are two distinctly different types of trans-equatorial propagation.

Afternoon TEP
Afternoon TEP peaks during the mid-afternoon and early evening hours and is generally limited to distances of 4000 - 5000 mi. Signals propagated by this mode are limited to approximately 60 MHz. Afternoon TEP signals tend to have high signal strength and suffer moderate distortion due to multipath reflections.

Evening TEP
The second type of TEP peaks in the evening around 1900 to 2300 hours local time. Signals are possible up to 220 MHz, and even very rarely on 432 MHz. Evening TEP is quenched by moderate to severe geomagnetic disturbances. The occurrence of evening TEP is more heavily dependent on high solar activity than is the afternoon type.

During late September 2001, from 2000 to 2400 local time, VHF television and radio signals from Japan and Korea up to 220 MHz were received via evening transequatorial propagation near Darwin, Northern Territory.

Earth – Moon – Earth (EME) propagation (Moonbounce)
Since 1953, radio amateurs have been experimenting with lunar communications by reflecting VHF and UHF signals off the Moon. Moonbounce allows communication on Earth between any two points that can observe the Moon at a common time.

Since the Moon's mean distance from Earth is 239000 mi, path losses are very high. It follows that a typical 240 dB total path loss places great demand on high-gain receiving antennas, high-power transmissions, and sensitive receiving systems. Even when all these factors are observed, the resulting signal level is often just above the noise.

Because of the low signal-to-noise ratio, as with amateur-radio practice, EME signals can generally only be detected using narrow-band receiving systems. This means that the only aspect of the TV signal that could be detected is the field scan modulation (AM vision carrier). FM broadcast signals also feature wide frequency modulation, hence EME reception is generally not possible. There are no published records of VHF/UHF EME amateur radio contacts using FM.

Notable Earth-Moon-Earth (EME) DX receptions
During the mid-1970s, John Yurek, K3PGP, using a home-constructed, 24-foot (7.3 m), 0.6-focal-diameter parabolic dish and UHF TV dipole feed-point tuned to channel 68, received KVST-68 Los Angeles (1200 kW ERP) and WBTB-68 Newark, New Jersey via moonbounce. At the time of the experiment there were only two known transmitters operating in the United States on UHF television channel 68, the main reason why this channel was selected for EME experiments.

For three nights in December 1978, astronomer Dr. Woodruff T. Sullivan III used the 305-metre Arecibo radio telescope to observe the Moon at a variety of frequencies. This experiment demonstrated that the lunar surface is capable of reflecting terrestrial band III (175 – 230 MHz) television signals back to Earth. While not yet confirmed, FM broadcast EME reception may also be possible using the Arecibo dish antenna.

In 2002, physicist Dr. Tony Mann demonstrated that a single high-gain UHF yagi antenna, low noise masthead preamplifier, VHF/UHF synthesised communications receiver, and personal computer with FFT spectrum analyser software could be used to successfully detect extremely weak UHF television carriers via EME.

Auroral propagation
An aurora is most likely to occur during periods of high solar activity when there is a high probability of a large solar flare. When such an eruption occurs, charged particles from the flare may spiral towards Earth arriving about a day later. This may or may not cause an aurora: if the interstellar magnetic field has same polarity, the particles do not get coupled to the geomagnetic field efficiently. Besides sunspot-related active solar surface areas, other solar phenomena that produce particles causing auroras, such as re-occurring coronal holes spraying out intense solar wind. These charged particles are affected and captured by the geomagnetic field and the various radiation belts surrounding earth. The aurora-producing relativistic electrons eventually precipitate towards Earth's magnetic poles, resulting in an aurora which disrupts short-wave communications (SID) due to ionospheric/magnetic storms in the D, E, and F layers. Various visual effects are also seen in the sky towards the north – aptly called the Northern Lights. The same effect occurs in the Southern Hemisphere, but the visual effects are towards the south. The auroral event starts by onset of geomagnetic storm, followed by number of sub-storms over the next day or so.

The aurora produces a reflecting sheet (or metric sized columns) which tends to lie in a vertical plane. The result of this vertical ionospheric "curtain" is reflection of signals well into the upper VHF band. The reflection is very aspect sensitive. Since the reflecting sheet lies towards the poles, it follows that reflected signals will arrive from that general direction. An active region or coronal hole may persist for some 27 days resulting in a second aurora when the Sun has rotated. There is a tendency for auroras to occur around the March/April, September/October equinox periods, when the geomagnetic field is at right angle to Sun for efficient charged particle coupling. Signals propagated by aurora have a characteristic hum effect, which makes video and audio reception difficult. Video carriers, as heard on a communications receiver, no longer can be heard as a pure tone.

A typical radio aurora occurs in the afternoon, which produces strong and distorted signals for few hours. The local midnight sub-storming usually produces weaker signals, but with less distortion by Doppler from gyrating electrons.

Frequencies up to 200 MHz can be affected by auroral propagation.

Meteor scatter propagation
Meteor scatter occurs when a signal bounces off a meteor's ionized trail.

When a meteor strikes earth's atmosphere, a cylindrical region of free electrons is formed at the height of the E layer. This slender, ionized column is relatively long, and when first formed is sufficiently dense to reflect and scatter television and radio signals, generally observable from 25 MHz upwards through UHF TV, back to earth. Consequently, an incident television or radio signal is capable of being reflected up to distances approaching that of conventional Sporadic E propagation, typically about 1500 km (1000 miles). A signal reflected by such meteor ionisation can vary in duration from fractions of a second up to several minutes for intensely ionized trails. The events are classified as overdense and underdense, depending on the electron line-density (related to used frequency) of the trail plasma. The signal from overdense trail has a longer signal decay associated with fading and is physically a reflection from the ionized cylinder surface, while an underdense trail gives a signal of short duration, which rises fast and decays exponentially and is scattered from individual electrons inside the trail.

Frequencies in the range of 50 to 80 MHz have been found to be optimum for meteor scatter propagation. The 88 – 108 MHz FM broadcast band is also highly suited for meteor scatter experiments. During the major meteor showers, with extremely intense trails, band III 175 – 220 MHz signal reception can occur.

Ionized trails generally reflect lower frequencies for longer periods (and produce stronger signals) compared to higher frequencies. For example, an 8-second burst on 45.25 MHz may only cause a 4-second burst at 90.5 MHz.

The effect of a typical visually seen single meteor (of size 0.5 mm) shows up as a sudden "burst" of signal of short duration at a point not normally reached by the transmitter. The combined effect of several meteors impinging on earth's atmosphere, while perhaps too weak to provide long-term ionisation, is thought to contribute to the existence of the night-time E layer.

The optimum time for receiving RF reflections off sporadic meteors is the early morning period, when the velocity of earth relative to the velocity of the particles is greatest which also increases the number of meteors occurring on the morning-side of the earth, but some sporadic meteor reflections can be received at any time of the day, least in the early evening.

The annual major meteor showers are detailed below:

 * January 3 – 4: Quadrantids
 * April 22 – 23: Lyrids
 * May 5 – 6: Eta Aquariids
 * June 9 – 10: Arietids & zeta-Perseids
 * August 12 – 13: Perseids
 * October 21 – 22: Orionids
 * November 3 – 5: Taurids
 * November 16 – 18: Leonids (Note: activity varies, outburst only at about 33 year interval)
 * December 13 – 14: Geminids
 * December 22 – 23: Ursids

For observing meteor shower-related radio signals, the shower's radiant must be above the (propagation mid path) horizon. Otherwise no meteor of the shower can hit the atmosphere along the propagation path and no reflections from the shower's meteor trails can be observed.

Satellite UHF TVRO DX
Although not by strict definition terrestrial TV DX, satellite UHF TVRO reception is related in certain aspects. For example, reception of satellite signals requires sensitive receiving systems and large outdoor antenna systems. However, unlike terrestrial TV DX, satellite UHF TV reception is far easier to predict. The geosynchronous satellite at 22375 mi height is a line of sight reception source. If the satellite is above the horizon, it can be generally received, if it is below the horizon, reception is not possible.

Notable Satellite UHF TVRO DX receptions

 * In December 1975, Stephen Birkill, Sheffield, England, was the first DXer to receive viewable pictures from the 860 MHz Indian ATS-6 satellite, which was in synchronous orbit over Central Africa, for the purpose of providing educational television programs to the Indian subcontinent.
 * In 1978, Ian Roberts, South Africa, received 714 MHz television pictures from the Soviet UHF Ekran-class Statsionar-T satellite.
 * In 2022, amateur radio operator Derek OK9SGC, Czech Republic, received one of the few remaining analog terrestrial transmissions from Turkmenistan, which is being periodically picked up and relayed by newer Russian Meridian satellites.

Digital modes
Digital radio and digital television can also be received; however, there is much greater difficulty with reception of weak signals due to the cliff effect, particularly with the ATSC TV standard mandated in the U.S. However, when the signal is strong enough to be decoded identification is much easier than with analog TV as the picture is guaranteed to be noise-free when present. For DVB-T, hierarchical modulation may allow a lower-definition signal to be received even if the details of the full signal cannot be decoded. In reality, though, it's actually much more difficult to get DVB-T E-skip reception as the lowest channel DVB-T transmissions operate on is channel E5 which is 178 MHz. A unique issue observed on analog TV at the end of the DTV transition in the United States was that very distant analog stations were viewable in the hours after the permanent shutdown of local analog transmitters in June 2009. This was particularly pronounced because June is one of the strongest months for DX reception on VHF, and most digital stations were assigned to UHF.

DXing Software
Over the last few years, FM DXing using computers became more popular then ever, people have started using different software for DXing based on their devices, as it provides more comfort and receivers can also be tuned remotely. This allows DXers to place their antennas in remote places without the need of being at the mentioned place while DXing.

Most popular programs among DXers are:
 * XDR-GTK by Konrad Kosmatka (suitable for Sony XDR devices and NXP TEF668x-based receivers)
 * FM-DX Webserver by Marek Farkaš (suitable for Sony XDR devices, NXP TEF668x based tuners and Software-defined radio)
 * SDRSharp by AirSpy (suitable for Software-defined radio)
 * SDR++ by Alexandre Rouma (suitable for Software-defined radio)

title: TV and FM DX


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Factor analysis

Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. For example, it is possible that variations in six observed variables mainly reflect the variations in two unobserved (underlying) variables. Factor analysis searches for such joint variations in response to unobserved latent variables. The observed variables are modelled as linear combinations of the potential factors plus "error" terms, hence factor analysis can be thought of as a special case of errors-in-variables models.

Simply put, the factor loading of a variable quantifies the extent to which the variable is related to a given factor.

A common rationale behind factor analytic methods is that the information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset. Factor analysis is commonly used in psychometrics, personality psychology, biology, marketing, product management, operations research, finance, and machine learning. It may help to deal with data sets where there are large numbers of observed variables that are thought to reflect a smaller number of underlying/latent variables. It is one of the most commonly used inter-dependency techniques and is used when the relevant set of variables shows a systematic inter-dependence and the objective is to find out the latent factors that create a commonality.

Definition
The model attempts to explain a set of $$p$$ observations in each of $$n$$ individuals with a set of $$k$$ common factors ($$f_{i,j}$$) where there are fewer factors per unit than observations per unit ($$k<p$$). Each individual has $$k$$ of their own common factors, and these are related to the observations via the factor loading matrix ($$L \in \mathbb{R}^{p \times k}$$), for a single observation, according to

 * $$x_{i,m} - \mu_{i} = l_{i,1} f_{1,m} + \dots + l_{i,k} f_{k,m} + \varepsilon_{i,m} $$

where
 * $$x_{i,m}$$ is the value of the $$i$$th observation of the $$m$$th individual,
 * $$\mu_i$$ is the observation mean for the $$i$$th observation,
 * $$l_{i,j}$$ is the loading for the $$i$$th observation of the $$j$$th factor,
 * $$f_{j,m}$$ is the value of the $$j$$th factor of the $$m$$th individual, and
 * $$\varepsilon_{i,m} $$ is the $$(i,m)$$th unobserved stochastic error term with mean zero and finite variance.

In matrix notation

 * $$X - \Mu = L F + \varepsilon$$

where observation matrix $$X \in \mathbb{R}^{p \times n}$$, loading matrix $$L \in \mathbb{R}^{p \times k}$$, factor matrix $$F \in \mathbb{R}^{k \times n}$$, error term matrix $$\varepsilon \in \mathbb{R}^{p \times n}$$ and mean matrix $$\Mu \in \mathbb{R}^{p \times n}$$ whereby the $$(i,m)$$th element is simply $$\Mu_{i,m}=\mu_i$$.

Also we will impose the following assumptions on $$F$$:

 * 1) $$F$$ and $$\varepsilon$$ are independent.
 * 2) $$\mathrm{E}(F) = 0$$; where $$\mathrm E$$  is Expectation
 * 3) $$\mathrm{Cov}(F)=I$$ where $$\mathrm{Cov}$$  is the covariance matrix, to make sure that the factors are uncorrelated, and $$I$$ is the identity matrix.

Suppose $$\mathrm{Cov}(X - \Mu)=\Sigma$$. Then

 * $$\Sigma=\mathrm{Cov}(X - \Mu)=\mathrm{Cov}(LF + \varepsilon),\,$$

and therefore, from conditions 1 and 2 imposed on $$F$$ above, $$E[LF]=LE[F]=0$$ and $$Cov(LF+\epsilon)=Cov(LF)+Cov(\epsilon)$$, giving

 * $$\Sigma = L \mathrm{Cov}(F) L^T + \mathrm{Cov}(\varepsilon),\,$$

or, setting $$\Psi:=\mathrm{Cov}(\varepsilon)$$,

 * $$\Sigma = LL^T + \Psi.\,$$

Note that for any orthogonal matrix $$Q$$, if we set $$L^\prime=\ LQ$$ and $$F^\prime=Q^T F$$, the criteria for being factors and factor loadings still hold. Hence a set of factors and factor loadings is unique only up to an orthogonal transformation.

Example
Suppose a psychologist has the hypothesis that there are two kinds of intelligence, "verbal intelligence" and "mathematical intelligence", neither of which is directly observed. Evidence for the hypothesis is sought in the examination scores from each of 10 different academic fields of 1000 students. If each student is chosen randomly from a large population, then each student's 10 scores are random variables. The psychologist's hypothesis may say that for each of the 10 academic fields, the score averaged over the group of all students who share some common pair of values for verbal and mathematical "intelligences" is some constant times their level of verbal intelligence plus another constant times their level of mathematical intelligence, i.e., it is a linear combination of those two "factors". The numbers for a particular subject, by which the two kinds of intelligence are multiplied to obtain the expected score, are posited by the hypothesis to be the same for all intelligence level pairs, and are called "factor loading" for this subject. For example, the hypothesis may hold that the predicted average student's aptitude in the field of astronomy is

 * {10 × the student's verbal intelligence} + {6 × the student's mathematical intelligence}.

The numbers 10 and 6 are the factor loadings associated with astronomy. Other academic subjects may have different factor loadings.

Two students assumed to have identical degrees of verbal and mathematical intelligence may have different measured aptitudes in astronomy because individual aptitudes differ from average aptitudes (predicted above) and because of measurement error itself. Such differences make up what is collectively called the "error" — a statistical term that means the amount by which an individual, as measured, differs from what is average for or predicted by his or her levels of intelligence (see errors and residuals in statistics).

The observable data that go into factor analysis would be 10 scores of each of the 1000 students, a total of 10,000 numbers. The factor loadings and levels of the two kinds of intelligence of each student must be inferred from the data.

Mathematical model of the same example
In the following, matrices will be indicated by indexed variables. "Subject" indices will be indicated using letters $$a$$,$$b$$ and $$c$$, with values running from $$1$$ to $$p$$ which is equal to $$10$$ in the above example. "Factor" indices will be indicated using letters $$p$$, $$q$$ and $$r$$, with values running from $$1$$ to $$k$$ which is equal to $$2$$ in the above example. "Instance" or "sample" indices will be indicated using letters $$i$$,$$j$$ and $$k$$, with values running from $$1$$ to $$N$$. In the example above, if a sample of $$N=1000$$ students participated in the $$p=10$$ exams, the $$i$$th student's score for the $$a$$th exam is given by $$x_{ai}$$. The purpose of factor analysis is to characterize the correlations between the variables $$x_a$$ of which the $$x_{ai}$$ are a particular instance, or set of observations. In order for the variables to be on equal footing, they are normalized into standard scores $$z$$:
 * $$z_{ai}=\frac{x_{ai}-\hat\mu_a}{\hat\sigma_a}$$

where the sample mean is:
 * $$\hat\mu_a=\tfrac{1}{N}\sum_i x_{ai}$$

and the sample variance is given by:
 * $$\hat\sigma_a^2=\tfrac{1}{N-1}\sum_i (x_{ai}-\hat\mu_a)^2$$

The factor analysis model for this particular sample is then:
 * $$\begin{matrix}z_{1,i} & = & \ell_{1,1}F_{1,i} & + & \ell_{1,2}F_{2,i} & + & \varepsilon_{1,i} \\

\vdots & & \vdots & & \vdots & & \vdots \\ z_{10,i} & = & \ell_{10,1}F_{1,i} & + & \ell_{10,2}F_{2,i} & + & \varepsilon_{10,i} \end{matrix}$$

or, more succinctly:

z_{ai}=\sum_p \ell_{ap}F_{pi}+\varepsilon_{ai} $$

where
 * $$F_{1i}$$ is the $$i$$th student's "verbal intelligence",
 * $$F_{2i}$$ is the $$i$$th student's "mathematical intelligence",
 * $$\ell_{ap}$$ are the factor loadings for the $$a$$th subject, for $$p=1,2$$.

In matrix notation, we have
 * $$Z=LF+\varepsilon$$

Observe that by doubling the scale on which "verbal intelligence"—the first component in each column of $$F$$—is measured, and simultaneously halving the factor loadings for verbal intelligence makes no difference to the model. Thus, no generality is lost by assuming that the standard deviation of the factors for verbal intelligence is $$1$$. Likewise for mathematical intelligence. Moreover, for similar reasons, no generality is lost by assuming the two factors are uncorrelated with each other. In other words:
 * $$\sum_i F_{pi}F_{qi}=\delta_{pq}$$

where $$\delta_{pq}$$ is the Kronecker delta ($$0$$ when $$p \ne q$$ and $$1$$ when $$p=q$$).The errors are assumed to be independent of the factors:
 * $$\sum_i F_{pi}\varepsilon_{ai}=0$$

Note that, since any rotation of a solution is also a solution, this makes interpreting the factors difficult. See disadvantages below. In this particular example, if we do not know beforehand that the two types of intelligence are uncorrelated, then we cannot interpret the two factors as the two different types of intelligence. Even if they are uncorrelated, we cannot tell which factor corresponds to verbal intelligence and which corresponds to mathematical intelligence without an outside argument.

The values of the loadings $$L$$, the averages $$\mu$$, and the variances of the "errors" $$\varepsilon$$ must be estimated given the observed data $$X$$ and $$F$$ (the assumption about the levels of the factors is fixed for a given $$F$$). The "fundamental theorem" may be derived from the above conditions:
 * $$\sum_i z_{ai}z_{bi}=\sum_j \ell_{aj}\ell_{bj}+\sum_i \varepsilon_{ai}\varepsilon_{bi}$$

The term on the left is the $$(a,b)$$-term of the correlation matrix (a $$p \times p$$ matrix derived as the product of the $$ p \times N$$ matrix of standardized observations with its transpose) of the observed data, and its $$p$$ diagonal elements will be $$1$$s. The second term on the right will be a diagonal matrix with terms less than unity. The first term on the right is the "reduced correlation matrix" and will be equal to the correlation matrix except for its diagonal values which will be less than unity. These diagonal elements of the reduced correlation matrix are called "communalities" (which represent the fraction of the variance in the observed variable that is accounted for by the factors):

h_a^2=1-\psi_a=\sum_j \ell_{aj}\ell_{aj} $$ The sample data $$z_{ai}$$ will not exactly obey the fundamental equation given above due to sampling errors, inadequacy of the model, etc. The goal of any analysis of the above model is to find the factors $$F_{pi}$$ and loadings $$\ell_{ap}$$ which give a "best fit" to the data. In factor analysis, the best fit is defined as the minimum of the mean square error in the off-diagonal residuals of the correlation matrix:
 * $$\varepsilon^2 = \sum_{a\ne b} \left[\sum_i z_{ai}z_{bi}-\sum_j \ell_{aj}\ell_{bj}\right]^2$$

This is equivalent to minimizing the off-diagonal components of the error covariance which, in the model equations have expected values of zero. This is to be contrasted with principal component analysis which seeks to minimize the mean square error of all residuals. Before the advent of high-speed computers, considerable effort was devoted to finding approximate solutions to the problem, particularly in estimating the communalities by other means, which then simplifies the problem considerably by yielding a known reduced correlation matrix. This was then used to estimate the factors and the loadings. With the advent of high-speed computers, the minimization problem can be solved iteratively with adequate speed, and the communalities are calculated in the process, rather than being needed beforehand. The MinRes algorithm is particularly suited to this problem, but is hardly the only iterative means of finding a solution.

If the solution factors are allowed to be correlated (as in 'oblimin' rotation, for example), then the corresponding mathematical model uses skew coordinates rather than orthogonal coordinates.

Geometric interpretation

The parameters and variables of factor analysis can be given a geometrical interpretation. The data ($$z_{ai}$$), the factors ($$F_{pi}$$) and the errors ($$\varepsilon_{ai}$$) can be viewed as vectors in an $$N$$-dimensional Euclidean space (sample space), represented as $$\mathbf{z}_a$$, $$\mathbf{F}_p$$ and $$\boldsymbol{\varepsilon}_a$$ respectively. Since the data are standardized, the data vectors are of unit length ($$||\mathbf{z}_a||=1$$). The factor vectors define an $$k$$-dimensional linear subspace (i.e. a hyperplane) in this space, upon which the data vectors are projected orthogonally. This follows from the model equation
 * $$\mathbf{z}_a=\sum_p \ell_{ap} \mathbf{F}_p+\boldsymbol{\varepsilon}_a$$

and the independence of the factors and the errors: $$\mathbf{F}_p\cdot\boldsymbol{\varepsilon}_a=0$$. In the above example, the hyperplane is just a 2-dimensional plane defined by the two factor vectors. The projection of the data vectors onto the hyperplane is given by
 * $$\hat{\mathbf{z}}_a=\sum_p \ell_{ap}\mathbf{F}_p$$

and the errors are vectors from that projected point to the data point and are perpendicular to the hyperplane. The goal of factor analysis is to find a hyperplane which is a "best fit" to the data in some sense, so it doesn't matter how the factor vectors which define this hyperplane are chosen, as long as they are independent and lie in the hyperplane. We are free to specify them as both orthogonal and normal ($$\mathbf{F}_p\cdot \mathbf{F}_q=\delta_{pq}$$) with no loss of generality. After a suitable set of factors are found, they may also be arbitrarily rotated within the hyperplane, so that any rotation of the factor vectors will define the same hyperplane, and also be a solution. As a result, in the above example, in which the fitting hyperplane is two dimensional, if we do not know beforehand that the two types of intelligence are uncorrelated, then we cannot interpret the two factors as the two different types of intelligence. Even if they are uncorrelated, we cannot tell which factor corresponds to verbal intelligence and which corresponds to mathematical intelligence, or whether the factors are linear combinations of both, without an outside argument.

The data vectors $$\mathbf{z}_a$$ have unit length. The entries of the correlation matrix for the data are given by $$r_{ab}=\mathbf{z}_a\cdot\mathbf{z}_b$$. The correlation matrix can be geometrically interpreted as the cosine of the angle between the two data vectors $$\mathbf{z}_a$$ and $$\mathbf{z}_b$$. The diagonal elements will clearly be $$1$$s and the off diagonal elements will have absolute values less than or equal to unity. The "reduced correlation matrix" is defined as
 * $$\hat{r}_{ab}=\hat{\mathbf{z}}_a\cdot\hat{\mathbf{z}}_b$$.

The goal of factor analysis is to choose the fitting hyperplane such that the reduced correlation matrix reproduces the correlation matrix as nearly as possible, except for the diagonal elements of the correlation matrix which are known to have unit value. In other words, the goal is to reproduce as accurately as possible the cross-correlations in the data. Specifically, for the fitting hyperplane, the mean square error in the off-diagonal components
 * $$\varepsilon^2=\sum_{a\ne b} \left(r_{ab}-\hat{r}_{ab}\right)^2$$

is to be minimized, and this is accomplished by minimizing it with respect to a set of orthonormal factor vectors. It can be seen that

r_{ab}-\hat{r}_{ab}= \boldsymbol{\varepsilon}_a\cdot\boldsymbol{\varepsilon}_b $$

The term on the right is just the covariance of the errors. In the model, the error covariance is stated to be a diagonal matrix and so the above minimization problem will in fact yield a "best fit" to the model: It will yield a sample estimate of the error covariance which has its off-diagonal components minimized in the mean square sense. It can be seen that since the $$\hat{z}_a$$ are orthogonal projections of the data vectors, their length will be less than or equal to the length of the projected data vector, which is unity. The square of these lengths are just the diagonal elements of the reduced correlation matrix. These diagonal elements of the reduced correlation matrix are known as "communalities":

{h_a}^2=||\hat{\mathbf{z}}_a||^2= \sum_p {\ell_{ap}}^2 $$

Large values of the communalities will indicate that the fitting hyperplane is rather accurately reproducing the correlation matrix. The mean values of the factors must also be constrained to be zero, from which it follows that the mean values of the errors will also be zero.

Exploratory factor analysis
Exploratory factor analysis (EFA) is used to identify complex interrelationships among items and group items that are part of unified concepts. The researcher makes no a priori assumptions about relationships among factors.

Confirmatory factor analysis
Confirmatory factor analysis (CFA) is a more complex approach that tests the hypothesis that the items are associated with specific factors. CFA uses structural equation modeling to test a measurement model whereby loading on the factors allows for evaluation of relationships between observed variables and unobserved variables. Structural equation modeling approaches can accommodate measurement error and are less restrictive than least-squares estimation. Hypothesized models are tested against actual data, and the analysis would demonstrate loadings of observed variables on the latent variables (factors), as well as the correlation between the latent variables.

Types of factor extraction
Principal component analysis (PCA) is a widely used method for factor extraction, which is the first phase of EFA. Factor weights are computed to extract the maximum possible variance, with successive factoring continuing until there is no further meaningful variance left. The factor model must then be rotated for analysis.

Canonical factor analysis, also called Rao's canonical factoring, is a different method of computing the same model as PCA, which uses the principal axis method. Canonical factor analysis seeks factors that have the highest canonical correlation with the observed variables. Canonical factor analysis is unaffected by arbitrary rescaling of the data.

Common factor analysis, also called principal factor analysis (PFA) or principal axis factoring (PAF), seeks the fewest factors which can account for the common variance (correlation) of a set of variables.

Image factoring is based on the correlation matrix of predicted variables rather than actual variables, where each variable is predicted from the others using multiple regression.

Alpha factoring is based on maximizing the reliability of factors, assuming variables are randomly sampled from a universe of variables. All other methods assume cases to be sampled and variables fixed.

Factor regression model is a combinatorial model of factor model and regression model; or alternatively, it can be viewed as the hybrid factor model, whose factors are partially known.

Terminology
Factor loadings: Communality is the square of the standardized outer loading of an item. Analogous to Pearson's r-squared, the squared factor loading is the percent of variance in that indicator variable explained by the factor. To get the percent of variance in all the variables accounted for by each factor, add the sum of the squared factor loadings for that factor (column) and divide by the number of variables. (Note the number of variables equals the sum of their variances as the variance of a standardized variable is 1.) This is the same as dividing the factor's eigenvalue by the number of variables. When interpreting, by one rule of thumb in confirmatory factor analysis, factor loadings should be .7 or higher to confirm that independent variables identified a priori are represented by a particular factor, on the rationale that the .7 level corresponds to about half of the variance in the indicator being explained by the factor. However, the .7 standard is a high one and real-life data may well not meet this criterion, which is why some researchers, particularly for exploratory purposes, will use a lower level such as .4 for the central factor and .25 for other factors. In any event, factor loadings must be interpreted in the light of theory, not by arbitrary cutoff levels. In oblique rotation, one may examine both a pattern matrix and a structure matrix. The structure matrix is simply the factor loading matrix as in orthogonal rotation, representing the variance in a measured variable explained by a factor on both a unique and common contributions basis. The pattern matrix, in contrast, contains coefficients which just represent unique contributions. The more factors, the lower the pattern coefficients as a rule since there will be more common contributions to variance explained. For oblique rotation, the researcher looks at both the structure and pattern coefficients when attributing a label to a factor. Principles of oblique rotation can be derived from both cross entropy and its dual entropy. Communality: The sum of the squared factor loadings for all factors for a given variable (row) is the variance in that variable accounted for by all the factors. The communality measures the percent of variance in a given variable explained by all the factors jointly and may be interpreted as the reliability of the indicator in the context of the factors being posited. Spurious solutions: If the communality exceeds 1.0, there is a spurious solution, which may reflect too small a sample or the choice to extract too many or too few factors. Uniqueness of a variable: The variability of a variable minus its communality. Eigenvalues/characteristic roots: Eigenvalues measure the amount of variation in the total sample accounted for by each factor. The ratio of eigenvalues is the ratio of explanatory importance of the factors with respect to the variables. If a factor has a low eigenvalue, then it is contributing little to the explanation of variances in the variables and may be ignored as less important than the factors with higher eigenvalues. Extraction sums of squared loadings: Initial eigenvalues and eigenvalues after extraction (listed by SPSS as "Extraction Sums of Squared Loadings") are the same for PCA extraction, but for other extraction methods, eigenvalues after extraction will be lower than their initial counterparts. SPSS also prints "Rotation Sums of Squared Loadings" and even for PCA, these eigenvalues will differ from initial and extraction eigenvalues, though their total will be the same. Factor scores: Component scores (in PCA): The scores of each case (row) on each factor (column). To compute the factor score for a given case for a given factor, one takes the case's standardized score on each variable, multiplies by the corresponding loadings of the variable for the given factor, and sums these products. Computing factor scores allows one to look for factor outliers. Also, factor scores may be used as variables in subsequent modeling.

Criteria for determining the number of factors
Researchers wish to avoid such subjective or arbitrary criteria for factor retention as "it made sense to me". A number of objective methods have been developed to solve this problem, allowing users to determine an appropriate range of solutions to investigate. However these different methods often disagree with one another as to the number of factors that ought to be retained. For instance, the parallel analysis may suggest 5 factors while Velicer's MAP suggests 6, so the researcher may request both 5 and 6-factor solutions and discuss each in terms of their relation to external data and theory.

Modern criteria
Horn's parallel analysis (PA): A Monte-Carlo based simulation method that compares the observed eigenvalues with those obtained from uncorrelated normal variables. A factor or component is retained if the associated eigenvalue is bigger than the 95th percentile of the distribution of eigenvalues derived from the random data. PA is among the more commonly recommended rules for determining the number of components to retain, but many programs fail to include this option (a notable exception being R). However, Formann provided both theoretical and empirical evidence that its application might not be appropriate in many cases since its performance is considerably influenced by sample size, item discrimination, and type of correlation coefficient.

Velicer's (1976) MAP test as described by Courtney (2013) “involves a complete principal components analysis followed by the examination of a series of matrices of partial correlations” (p. 397 (though note that this quote does not occur in Velicer (1976) and the cited page number is outside the pages of the citation). The squared correlation for Step “0” (see Figure 4) is the average squared off-diagonal correlation for the unpartialed correlation matrix. On Step 1, the first principal component and its associated items are partialed out. Thereafter, the average squared off-diagonal correlation for the subsequent correlation matrix is then computed for Step 1. On Step 2, the first two principal components are partialed out and the resultant average squared off-diagonal correlation is again computed. The computations are carried out for k minus one step (k representing the total number of variables in the matrix). Thereafter, all of the average squared correlations for each step are lined up and the step number in the analyses that resulted in the lowest average squared partial correlation determines the number of components or factors to retain. By this method, components are maintained as long as the variance in the correlation matrix represents systematic variance, as opposed to residual or error variance. Although methodologically akin to principal components analysis, the MAP technique has been shown to perform quite well in determining the number of factors to retain in multiple simulation studies. This procedure is made available through SPSS's user interface, as well as the psych package for the R programming language.

Older methods
Kaiser criterion: The Kaiser rule is to drop all components with eigenvalues under 1.0 – this being the eigenvalue equal to the information accounted for by an average single item. The Kaiser criterion is the default in SPSS and most statistical software but is not recommended when used as the sole cut-off criterion for estimating the number of factors as it tends to over-extract factors. A variation of this method has been created where a researcher calculates confidence intervals for each eigenvalue and retains only factors which have the entire confidence interval greater than 1.0.

Scree plot: The Cattell scree test plots the components as the X-axis and the corresponding eigenvalues as the Y-axis. As one moves to the right, toward later components, the eigenvalues drop. When the drop ceases and the curve makes an elbow toward less steep decline, Cattell's scree test says to drop all further components after the one starting at the elbow. This rule is sometimes criticised for being amenable to researcher-controlled "fudging". That is, as picking the "elbow" can be subjective because the curve has multiple elbows or is a smooth curve, the researcher may be tempted to set the cut-off at the number of factors desired by their research agenda.

Variance explained criteria: Some researchers simply use the rule of keeping enough factors to account for 90% (sometimes 80%) of the variation. Where the researcher's goal emphasizes parsimony (explaining variance with as few factors as possible), the criterion could be as low as 50%.

Bayesian methods
By placing a prior distribution over the number of latent factors and then applying Bayes' theorem, Bayesian models can return a probability distribution over the number of latent factors. This has been modeled using the Indian buffet process, but can be modeled more simply by placing any discrete prior (e.g. a negative binomial distribution) on the number of components.

Rotation methods
The output of PCA maximizes the variance accounted for by the first factor first, then the second factor, etc. A disadvantage of this procedure is that most items load on the early factors, while very few items load on later variables. This makes interpreting the factors by reading through a list of questions and loadings difficult, as every question is strongly correlated with the first few components, while very few questions are strongly correlated with the last few components.

Rotation serves to make the output easier to interpret. By choosing a different basis for the same principal components – that is, choosing different factors to express the same correlation structure – it is possible to create variables that are more easily interpretable.

Rotations can be orthogonal or oblique; oblique rotations allow the factors to correlate. This increased flexibility means that more rotations are possible, some of which may be better at achieving a specified goal. However, this can also make the factors more difficult to interpret, as some information is "double-counted" and included multiple times in different components; some factors may even appear to be near-duplicates of each other.

Orthogonal methods
Two broad classes of orthogonal rotations exist: those that look for sparse rows (where each row is a case, i.e. subject), and those that look for sparse columns (where each column is a variable).

 * Simple factors: these rotations try to explain all factors by using only a few important variables. This effect can be achieved by using Varimax (the most common rotation).
 * Simple variables: these rotations try to explain all variables using only a few important factors. This effect can be achieved using either Quartimax or the unrotated components of PCA.
 * Both: these rotations try to compromise between both of the above goals, but in the process, may achieve a fit that is poor at both tasks; as such, they are unpopular compared to the above methods. Equamax is one such rotation.

Problems with factor rotation
It can be difficult to interpret a factor structure when each variable is loading on multiple factors. Small changes in the data can sometimes tip a balance in the factor rotation criterion so that a completely different factor rotation is produced. This can make it difficult to compare the results of different experiments. This problem is illustrated by a comparison of different studies of world-wide cultural differences. Each study has used different measures of cultural variables and produced a differently rotated factor analysis result. The authors of each study believed that they had discovered something new, and invented new names for the factors they found. A later comparison of the studies found that the results were rather similar when the unrotated results were compared. The common practice of factor rotation has obscured the similarity between the results of the different studies.

Higher order factor analysis
Higher-order factor analysis is a statistical method consisting of repeating steps factor analysis – oblique rotation – factor analysis of rotated factors. Its merit is to enable the researcher to see the hierarchical structure of studied phenomena. To interpret the results, one proceeds either by post-multiplying the primary factor pattern matrix by the higher-order factor pattern matrices (Gorsuch, 1983) and perhaps applying a Varimax rotation to the result (Thompson, 1990) or by using a Schmid-Leiman solution (SLS, Schmid & Leiman, 1957, also known as Schmid-Leiman transformation) which attributes the variation from the primary factors to the second-order factors.

Exploratory factor analysis (EFA) versus principal components analysis (PCA)
Factor analysis is related to principal component analysis (PCA), but the two are not identical. There has been significant controversy in the field over differences between the two techniques. PCA can be considered as a more basic version of exploratory factor analysis (EFA) that was developed in the early days prior to the advent of high-speed computers. Both PCA and factor analysis aim to reduce the dimensionality of a set of data, but the approaches taken to do so are different for the two techniques. Factor analysis is clearly designed with the objective to identify certain unobservable factors from the observed variables, whereas PCA does not directly address this objective; at best, PCA provides an approximation to the required factors. From the point of view of exploratory analysis, the eigenvalues of PCA are inflated component loadings, i.e., contaminated with error variance.

Whilst EFA and PCA are treated as synonymous techniques in some fields of statistics, this has been criticised. Factor analysis "deals with the assumption of an underlying causal structure: [it] assumes that the covariation in the observed variables is due to the presence of one or more latent variables (factors) that exert causal influence on these observed variables". In contrast, PCA neither assumes nor depends on such an underlying causal relationship. Researchers have argued that the distinctions between the two techniques may mean that there are objective benefits for preferring one over the other based on the analytic goal. If the factor model is incorrectly formulated or the assumptions are not met, then factor analysis will give erroneous results. Factor analysis has been used successfully where adequate understanding of the system permits good initial model formulations. PCA employs a mathematical transformation to the original data with no assumptions about the form of the covariance matrix. The objective of PCA is to determine linear combinations of the original variables and select a few that can be used to summarize the data set without losing much information.

Arguments contrasting PCA and EFA
Fabrigar et al. (1999) address a number of reasons used to suggest that PCA is not equivalent to factor analysis:

 * 1) It is sometimes suggested that PCA is computationally quicker and requires fewer resources than factor analysis. Fabrigar et al. suggest that readily available computer resources have rendered this practical concern irrelevant.
 * 2) PCA and factor analysis can produce similar results. This point is also addressed by Fabrigar et al.; in certain cases, whereby the communalities are low (e.g. 0.4), the two techniques produce divergent results. In fact, Fabrigar et al. argue that in cases where the data correspond to assumptions of the common factor model, the results of PCA are inaccurate results.
 * 3) There are certain cases where factor analysis leads to 'Heywood cases'. These encompass situations whereby 100% or more of the variance in a measured variable is estimated to be accounted for by the model. Fabrigar et al. suggest that these cases are actually informative to the researcher, indicating an incorrectly specified model or a violation of the common factor model. The lack of Heywood cases in the PCA approach may mean that such issues pass unnoticed.
 * 4) Researchers gain extra information from a PCA approach, such as an individual's score on a certain component; such information is not yielded from factor analysis. However, as Fabrigar et al. contend, the typical aim of factor analysis – i.e. to determine the factors accounting for the structure of the correlations between measured variables – does not require knowledge of factor scores and thus this advantage is negated. It is also possible to compute factor scores from a factor analysis.

Variance versus covariance
Factor analysis takes into account the random error that is inherent in measurement, whereas PCA fails to do so. This point is exemplified by Brown (2009), who indicated that, in respect to the correlation matrices involved in the calculations:

"'In PCA, 1.00s are put in the diagonal meaning that all of the variance in the matrix is to be accounted for (including variance unique to each variable, variance common among variables, and error variance). That would, therefore, by definition, include all of the variance in the variables. In contrast, in EFA, the communalities are put in the diagonal meaning that only the variance shared with other variables is to be accounted for (excluding variance unique to each variable and error variance). That would, therefore, by definition, include only variance that is common among the variables.'"

For this reason, Brown (2009) recommends using factor analysis when theoretical ideas about relationships between variables exist, whereas PCA should be used if the goal of the researcher is to explore patterns in their data.

Differences in procedure and results
The differences between PCA and factor analysis (FA) are further illustrated by Suhr (2009):
 * PCA results in principal components that account for a maximal amount of variance for observed variables; FA accounts for common variance in the data.
 * PCA inserts ones on the diagonals of the correlation matrix; FA adjusts the diagonals of the correlation matrix with the unique factors.
 * PCA minimizes the sum of squared perpendicular distance to the component axis; FA estimates factors that influence responses on observed variables.
 * The component scores in PCA represent a linear combination of the observed variables weighted by eigenvectors; the observed variables in FA are linear combinations of the underlying and unique factors.
 * In PCA, the components yielded are uninterpretable, i.e. they do not represent underlying ‘constructs’; in FA, the underlying constructs can be labelled and readily interpreted, given an accurate model specification.

History
Charles Spearman was the first psychologist to discuss common factor analysis and did so in his 1904 paper. It provided few details about his methods and was concerned with single-factor models. He discovered that school children's scores on a wide variety of seemingly unrelated subjects were positively correlated, which led him to postulate that a single general mental ability, or g, underlies and shapes human cognitive performance.

The initial development of common factor analysis with multiple factors was given by Louis Thurstone in two papers in the early 1930s, summarized in his 1935 book, The Vector of Mind. Thurstone introduced several important factor analysis concepts, including communality, uniqueness, and rotation. He advocated for "simple structure", and developed methods of rotation that could be used as a way to achieve such structure.

In Q methodology, William Stephenson, a student of Spearman, distinguish between R factor analysis, oriented toward the study of inter-individual differences, and Q factor analysis oriented toward subjective intra-individual differences.

Raymond Cattell was a strong advocate of factor analysis and psychometrics and used Thurstone's multi-factor theory to explain intelligence. Cattell also developed the scree test and similarity coefficients.

Applications in psychology
Factor analysis is used to identify "factors" that explain a variety of results on different tests. For example, intelligence research found that people who get a high score on a test of verbal ability are also good on other tests that require verbal abilities. Researchers explained this by using factor analysis to isolate one factor, often called verbal intelligence, which represents the degree to which someone is able to solve problems involving verbal skills.

Factor analysis in psychology is most often associated with intelligence research. However, it also has been used to find factors in a broad range of domains such as personality, attitudes, beliefs, etc. It is linked to psychometrics, as it can assess the validity of an instrument by finding if the instrument indeed measures the postulated factors.

Advantages

 * Reduction of number of variables, by combining two or more variables into a single factor. For example, performance at running, ball throwing, batting, jumping and weight lifting could be combined into a single factor such as general athletic ability. Usually, in an item by people matrix, factors are selected by grouping related items. In the Q factor analysis technique, the matrix is transposed and factors are created by grouping related people. For example, liberals, libertarians, conservatives, and socialists might form into separate groups.
 * Identification of groups of inter-related variables, to see how they are related to each other. For example, Carroll used factor analysis to build his Three Stratum Theory. He found that a factor called "broad visual perception" relates to how good an individual is at visual tasks. He also found a "broad auditory perception" factor, relating to auditory task capability. Furthermore, he found a global factor, called "g" or general intelligence, that relates to both "broad visual perception" and "broad auditory perception". This means someone with a high "g" is likely to have both a high "visual perception" capability and a high "auditory perception" capability, and that "g" therefore explains a good part of why someone is good or bad in both of those domains.

Disadvantages

 * "...each orientation is equally acceptable mathematically. But different factorial theories proved to differ as much in terms of the orientations of factorial axes for a given solution as in terms of anything else, so that model fitting did not prove to be useful in distinguishing among theories." (Sternberg, 1977 ). This means all rotations represent different underlying processes, but all rotations are equally valid outcomes of standard factor analysis optimization. Therefore, it is impossible to pick the proper rotation using factor analysis alone.
 * Factor analysis can be only as good as the data allows. In psychology, where researchers often have to rely on less valid and reliable measures such as self-reports, this can be problematic.
 * Interpreting factor analysis is based on using a "heuristic", which is a solution that is "convenient even if not absolutely true". More than one interpretation can be made of the same data factored the same way, and factor analysis cannot identify causality.

In cross-cultural research
Factor analysis is a frequently used technique in cross-cultural research. It serves the purpose of extracting cultural dimensions. The best known cultural dimensions models are those elaborated by Geert Hofstede, Ronald Inglehart, Christian Welzel, Shalom Schwartz and Michael Minkov. A popular visualization is Inglehart and Welzel's cultural map of the world.

In political science
In an early 1965 study, political systems around the world are examined via factor analysis to construct related theoretical models and research, compare political systems, and create typological categories. For these purposes, in this study seven basic political dimensions are identified, which are related to a wide variety of political behaviour: these dimensions are Access, Differentiation, Consensus, Sectionalism, Legitimation, Interest, and Leadership Theory and Research.

Other political scientists explore the measurement of internal political efficacy using four new questions added to the 1988 National Election Study. Factor analysis is here used to find that these items measure a single concept distinct from external efficacy and political trust, and that these four questions provided the best measure of internal political efficacy up to that point in time.

In marketing
The basic steps are:
 * Identify the salient attributes consumers use to evaluate products in this category.
 * Use quantitative marketing research techniques (such as surveys) to collect data from a sample of potential customers concerning their ratings of all the product attributes.
 * Input the data into a statistical program and run the factor analysis procedure. The computer will yield a set of underlying attributes (or factors).
 * Use these factors to construct perceptual maps and other product positioning devices.

Information collection
The data collection stage is usually done by marketing research professionals. Survey questions ask the respondent to rate a product sample or descriptions of product concepts on a range of attributes. Anywhere from five to twenty attributes are chosen. They could include things like: ease of use, weight, accuracy, durability, colourfulness, price, or size. The attributes chosen will vary depending on the product being studied. The same question is asked about all the products in the study. The data for multiple products is coded and input into a statistical program such as R, SPSS, SAS, Stata, STATISTICA, JMP, and SYSTAT.

Analysis
The analysis will isolate the underlying factors that explain the data using a matrix of associations. Factor analysis is an interdependence technique. The complete set of interdependent relationships is examined. There is no specification of dependent variables, independent variables, or causality. Factor analysis assumes that all the rating data on different attributes can be reduced down to a few important dimensions. This reduction is possible because some attributes may be related to each other. The rating given to any one attribute is partially the result of the influence of other attributes. The statistical algorithm deconstructs the rating (called a raw score) into its various components and reconstructs the partial scores into underlying factor scores. The degree of correlation between the initial raw score and the final factor score is called a factor loading.

Advantages

 * Both objective and subjective attributes can be used provided the subjective attributes can be converted into scores.
 * Factor analysis can identify latent dimensions or constructs that direct analysis may not.
 * It is easy and inexpensive.

Disadvantages

 * Usefulness depends on the researchers' ability to collect a sufficient set of product attributes. If important attributes are excluded or neglected, the value of the procedure is reduced.
 * If sets of observed variables are highly similar to each other and distinct from other items, factor analysis will assign a single factor to them. This may obscure factors that represent more interesting relationships.
 * Naming factors may require knowledge of theory because seemingly dissimilar attributes can correlate strongly for unknown reasons.

In physical and biological sciences
Factor analysis has also been widely used in physical sciences such as geochemistry, hydrochemistry, astrophysics and cosmology, as well as biological sciences, such as ecology, molecular biology, neuroscience and biochemistry.

In groundwater quality management, it is important to relate the spatial distribution of different chemical parameters to different possible sources, which have different chemical signatures. For example, a sulfide mine is likely to be associated with high levels of acidity, dissolved sulfates and transition metals. These signatures can be identified as factors through R-mode factor analysis, and the location of possible sources can be suggested by contouring the factor scores.

In geochemistry, different factors can correspond to different mineral associations, and thus to mineralisation.

In microarray analysis
Factor analysis can be used for summarizing high-density oligonucleotide DNA microarrays data at probe level for Affymetrix GeneChips. In this case, the latent variable corresponds to the RNA concentration in a sample.

Implementation
Factor analysis has been implemented in several statistical analysis programs since the 1980s:
 * BMDP
 * JMP (statistical software)
 * Mplus (statistical software)]
 * Python: module scikit-learn
 * R (with the base function factanal or fa function in package psych). Rotations are implemented in the GPArotation R package.
 * SAS (using PROC FACTOR or PROC CALIS)
 * SPSS
 * Stata

Stand-alone

 * Factor - free factor analysis software developed by the Rovira i Virgili University

title: Factor analysis


text:
User talk:Agent Smith

Hello there Agent Smith, welcome to Wikipedia! I hope you like the place and decide to stay. If you ever need editing help visit How does one edit a page or how to format them visit our manual of style. Experiment at Sandbox. If you need pointers on how we title pages visit Naming conventions. If you have any other questions about the project then check out Help or add a question to the Village pump. MB 20:40 26 Jun 2003 (UTC)

Even though I am demonstrably lonely. Why don't you just lift your gear and move to somewhere where that is not an impediment0! (A. Smith) Question: why are people editing this?
 * Because this is where we get to tell you things directly, without expecting you to go and look for them in a public place like the Village pump.
 * By the way, you sign your name by typing this: ~ . -Smack 07:22 4 Jul 2003 (UTC)

Thanks! A. Smith 13:14 4 Jul 2003 (UTC)

AfD nomination of Son Goten
I have nominated Son Goten, an article you created, for deletion. I do not think that this article satisfies Wikipedia's criteria for inclusion, and have explained why at Articles for deletion/Son Goten. Your opinions on the matter are welcome at that same discussion page; also, you are welcome to edit the article to address these concerns. Thank you for your time. -- Collectonian  (talk · contribs) 15:38, 5 October 2008 (UTC)  --  Collectonian  (talk · contribs) 15:38, 5 October 2008 (UTC)

AfD nomination of Cell (Dragon Ball)
I have nominated Cell (Dragon Ball), an article you created, for deletion. I do not think that this article satisfies Wikipedia's criteria for inclusion, and have explained why at Articles for deletion/Cell (Dragon Ball)&. Your opinions on the matter are welcome at that same discussion page; also, you are welcome to edit the article to address these concerns. Thank you for your time. -- Collectonian  (talk · contribs) 15:42, 5 October 2008 (UTC)  --  Collectonian  (talk · contribs) 15:42, 5 October 2008 (UTC)

Proposed deletion of Earth (Dragon Ball)
A proposed deletion template has been added to the article Earth (Dragon Ball), suggesting that it be deleted according to the proposed deletion process. All contributions are appreciated, but this article may not satisfy Wikipedia's criteria for inclusion, and the deletion notice should explain why (see also "What Wikipedia is not" and Wikipedia's deletion policy). You may prevent the proposed deletion by removing the  notice, but please explain why you disagree with the proposed deletion in your edit summary or on its talk page.

Please consider improving the article to address the issues raised because even though removing the deletion notice will prevent deletion through the proposed deletion process, the article may still be deleted if it matches any of the speedy deletion criteria or it can be sent to Articles for Deletion, where it may be deleted if consensus to delete is reached. Judgesurreal777 (talk) 22:44, 23 October 2008 (UTC)

AfD nomination of Earth (Dragon Ball)
I have nominated Earth (Dragon Ball), an article that you created, for deletion. I do not think that this article satisfies Wikipedia's criteria for inclusion, and have explained why at Articles for deletion/Earth (Dragon Ball). Your opinions on the matter are welcome at that same discussion page; also, you are welcome to edit the article to address these concerns. Thank you for your time. Judgesurreal777 (talk) 22:12, 8 November 2008 (UTC)

title: User talk:Agent Smith


text: User talk:212.229.115.84

title: User talk:212.229.115.84


text:
User talk:Viz

Hello there Viz, welcome to Wikipedia! I hope you like the place and decide to stay. If you ever need editing help visit How does one edit a page or how to format them visit our manual of style. Experiment at Sandbox. If you need pointers on how we title pages visit Naming conventions. If you have any other questions about the project then check out Help or add a question to the Village pump. MB 20:44 26 Jun 2003 (UTC)

Blanking
Hi there, please don't just blank pages even if they are duplicates, just list them on WP:VFD. thanks, Dori | Talk 06:44, Jan 21, 2004 (UTC)

Article Licensing
Hi, I've started a drive to get users to multi-license all of their contributions that they've made to either (1) all U.S. state, county, and city articles or (2) all articles, using the Creative Commons Attribution-Share Alike (CC-by-sa) v1.0 and v2.0 Licenses or into the public domain if they prefer. The CC-by-sa license is a true free documentation license that is similar to Wikipedia's license, the GFDL, but it allows other projects, such as WikiTravel, to use our articles. Since you are among the top 2000 Wikipedians by edits, I was wondering if you would be willing to multi-license all of your contributions or at minimum those on the geographic articles. Over 90% of people asked have agreed. For More Information:
 * Multi-Licensing FAQ - Lots of questions answered
 * Multi-Licensing Guide
 * Free the Rambot Articles Project

To allow us to track those users who muli-license their contributions, many users copy and paste the " " template into their user page, but there are other options at Template messages/User namespace. The following examples could also copied and pasted into your user page:

 * Option 1
 * I agree to multi-license all my contributions, with the exception of my user pages, as described below:

OR
 * Option 2
 * I agree to multi-license all my contributions to any U.S. state, county, or city article as described below:

Or if you wanted to place your work into the public domain, you could replace " " with "  ". If you only prefer using the GFDL, I would like to know that too. Please let me know what you think at my talk page. It's important to know either way so no one keeps asking. -- Ram-Man (comment| talk)

Talk:Kuru (disease)
The article has been deleted and is being rewritten. I don't know exactly what the reason for deletion was but it had something to do with potential copyright violation. There are a couple issues with the permission you posted: I've been searching through archives and google and I'm having trouble finding an email address for Stacy Mcgrath. I'd be happy to take care of getting the required permission and establishing the record if you can provide contact information. Thanks --Jeremyb 19:19, 7 August 2007 (UTC)
 * the permission quoted on the talk page does not explicitly allow it's use under an acceptable license (or place it into the public domain)
 * there is no formal record of receipt of the permission such as is done using WP:OTRS.

ArbCom elections are now open!
MediaWiki message delivery (talk) 08:52, 23 November 2015 (UTC)

title: User talk:Viz


text:
User talk:Avir

Your contact info
Hi Avir: Hope all is well. You have not enabled your Wikipedia Email feature in your "tool box" on the left hand side of your user page. Sometimes editors overlook that when it's a useful way of staying in touch with other editors. Best wishes. Shabbat Shalom. IZAK 12:53, 23 November 2006 (UTC)

title: User talk:Avir