Overview

Dataset statistics

Number of variables3
Number of observations93
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory25.4 B

Variable types

Categorical3

Alerts

time has a high cardinality: 93 distinct values High cardinality
story is highly correlated with title and 1 other fieldsHigh correlation
title is highly correlated with story and 1 other fieldsHigh correlation
time is highly correlated with story and 1 other fieldsHigh correlation
title is highly correlated with story and 1 other fieldsHigh correlation
story is highly correlated with title and 1 other fieldsHigh correlation
time is highly correlated with title and 1 other fieldsHigh correlation
time is uniformly distributed Uniform
time has unique values Unique

Reproduction

Analysis started2022-10-20 19:04:05.736377
Analysis finished2022-10-20 19:04:09.219235
Duration3.48 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

title
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct35
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Memory size872.0 B
International Sports Science Association (ISSA)
cerebrovascular disease
 
5
the collective nature of the text
 
4
New York City is the largest
 
4
speeches
 
4
Other values (30)
67 

Length

Max length5558
Median length65
Mean length258.2688172
Min length2

Characters and Unicode

Total characters24019
Distinct characters84
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)16.1%

Sample

1st rowelectromagnetic energy is absorbed or emitted in discrete packets
2nd rowspeeches
3rd rowcerebrovascular disease
4th rowantidepressant medications
5th rowScalp cooling

Common Values

ValueCountFrequency (%)
International Sports Science Association (ISSA)9
 
9.7%
cerebrovascular disease5
 
5.4%
the collective nature of the text4
 
4.3%
New York City is the largest4
 
4.3%
speeches4
 
4.3%
at any age and not just during or after menopause4
 
4.3%
provides the prescription drug4
 
4.3%
description, identification, nomenclature, and classification of organisms4
 
4.3%
Film4
 
4.3%
by4
 
4.3%
Other values (25)47
50.5%

Length

2022-10-20T19:04:09.299048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the285
 
7.5%
of127
 
3.4%
a105
 
2.8%
to91
 
2.4%
and75
 
2.0%
64
 
1.7%
star57
 
1.5%
for50
 
1.3%
time46
 
1.2%
in41
 
1.1%
Other values (1006)2850
75.2%

Most occurring characters

ValueCountFrequency (%)
3627
15.1%
e2303
 
9.6%
t1898
 
7.9%
a1698
 
7.1%
o1469
 
6.1%
r1425
 
5.9%
s1376
 
5.7%
i1331
 
5.5%
n1223
 
5.1%
l749
 
3.1%
Other values (74)6920
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter18705
77.9%
Space Separator3627
 
15.1%
Uppercase Letter640
 
2.7%
Other Punctuation433
 
1.8%
Decimal Number176
 
0.7%
Control156
 
0.6%
Math Symbol128
 
0.5%
Open Punctuation57
 
0.2%
Close Punctuation53
 
0.2%
Dash Punctuation41
 
0.2%
Other values (2)3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2303
12.3%
t1898
10.1%
a1698
 
9.1%
o1469
 
7.9%
r1425
 
7.6%
s1376
 
7.4%
i1331
 
7.1%
n1223
 
6.5%
l749
 
4.0%
c746
 
4.0%
Other values (19)4487
24.0%
Uppercase Letter
ValueCountFrequency (%)
S119
18.6%
I66
 
10.3%
T65
 
10.2%
A51
 
8.0%
C39
 
6.1%
N34
 
5.3%
P29
 
4.5%
B26
 
4.1%
R24
 
3.8%
M22
 
3.4%
Other values (14)165
25.8%
Decimal Number
ValueCountFrequency (%)
065
36.9%
136
20.5%
915
 
8.5%
212
 
6.8%
512
 
6.8%
410
 
5.7%
39
 
5.1%
69
 
5.1%
75
 
2.8%
83
 
1.7%
Other Punctuation
ValueCountFrequency (%)
,201
46.4%
.167
38.6%
"18
 
4.2%
'16
 
3.7%
/14
 
3.2%
:9
 
2.1%
;7
 
1.6%
*1
 
0.2%
Math Symbol
ValueCountFrequency (%)
=123
96.1%
+4
 
3.1%
−1
 
0.8%
Control
ValueCountFrequency (%)
142
91.0%
14
 
9.0%
Open Punctuation
ValueCountFrequency (%)
(55
96.5%
[2
 
3.5%
Close Punctuation
ValueCountFrequency (%)
)51
96.2%
]2
 
3.8%
Space Separator
ValueCountFrequency (%)
3627
100.0%
Dash Punctuation
ValueCountFrequency (%)
-41
100.0%
Modifier Letter
ValueCountFrequency (%)
ˈ2
100.0%
Other Symbol
ValueCountFrequency (%)
°1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19345
80.5%
Common4674
 
19.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2303
11.9%
t1898
 
9.8%
a1698
 
8.8%
o1469
 
7.6%
r1425
 
7.4%
s1376
 
7.1%
i1331
 
6.9%
n1223
 
6.3%
l749
 
3.9%
c746
 
3.9%
Other values (43)5127
26.5%
Common
ValueCountFrequency (%)
3627
77.6%
,201
 
4.3%
.167
 
3.6%
142
 
3.0%
=123
 
2.6%
065
 
1.4%
(55
 
1.2%
)51
 
1.1%
-41
 
0.9%
136
 
0.8%
Other values (21)166
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII24011
> 99.9%
IPA Ext4
 
< 0.1%
Modifier Letters2
 
< 0.1%
None1
 
< 0.1%
Math Operators1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3627
15.1%
e2303
 
9.6%
t1898
 
7.9%
a1698
 
7.1%
o1469
 
6.1%
r1425
 
5.9%
s1376
 
5.7%
i1331
 
5.5%
n1223
 
5.1%
l749
 
3.1%
Other values (68)6912
28.8%
Modifier Letters
ValueCountFrequency (%)
ˈ2
100.0%
IPA Ext
ValueCountFrequency (%)
ɔ2
50.0%
ə1
25.0%
Ê€1
25.0%
None
ValueCountFrequency (%)
°1
100.0%
Math Operators
ValueCountFrequency (%)
−1
100.0%

story
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size872.0 B
https://en.wikipedia.org/wiki/Physical_fitness
22 
https://en.wikipedia.org/wiki/Alzheimer%27s_disease
 
5
https://en.wikipedia.org/wiki/Star_tracker
 
4
https://en.wikipedia.org/wiki/Anthology
 
4
https://en.wikipedia.org/wiki/Cicero
 
4
Other values (21)
54 

Length

Max length74
Median length51
Mean length44.84946237
Min length35

Characters and Unicode

Total characters4171
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)7.5%

Sample

1st rowhttps://en.wikipedia.org/wiki/Quantum
2nd rowhttps://en.wikipedia.org/wiki/Cicero
3rd rowhttps://en.wikipedia.org/wiki/Alzheimer%27s_disease
4th rowhttps://en.wikipedia.org/wiki/Peripheral_neuropathy
5th rowhttps://en.wikipedia.org/wiki/Chemotherapy

Common Values

ValueCountFrequency (%)
https://en.wikipedia.org/wiki/Physical_fitness22
23.7%
https://en.wikipedia.org/wiki/Alzheimer%27s_disease5
 
5.4%
https://en.wikipedia.org/wiki/Star_tracker4
 
4.3%
https://en.wikipedia.org/wiki/Anthology4
 
4.3%
https://en.wikipedia.org/wiki/Cicero4
 
4.3%
https://en.wikipedia.org/wiki/Pharmacy4
 
4.3%
https://en.wikipedia.org/wiki/Taxonomy4
 
4.3%
https://en.wikipedia.org/wiki/Financial_services4
 
4.3%
https://en.wikipedia.org/wiki/Securitization4
 
4.3%
https://en.wikipedia.org/wiki/Medicina4
 
4.3%
Other values (16)34
36.6%

Length

2022-10-20T19:04:09.444965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://en.wikipedia.org/wiki/physical_fitness22
23.7%
https://en.wikipedia.org/wiki/alzheimer%27s_disease5
 
5.4%
https://en.wikipedia.org/wiki/securitization4
 
4.3%
https://en.wikipedia.org/wiki/peripheral_neuropathy4
 
4.3%
https://en.wikipedia.org/wiki/chemotherapy4
 
4.3%
https://en.wikipedia.org/wiki/death4
 
4.3%
https://en.wikipedia.org/wiki/medicina4
 
4.3%
https://en.wikipedia.org/wiki/national_commission_for_culture_and_the_arts4
 
4.3%
https://en.wikipedia.org/wiki/financial_services4
 
4.3%
https://en.wikipedia.org/wiki/taxonomy4
 
4.3%
Other values (16)34
36.6%

Most occurring characters

ValueCountFrequency (%)
i584
14.0%
/372
 
8.9%
e308
 
7.4%
t275
 
6.6%
a209
 
5.0%
p207
 
5.0%
s202
 
4.8%
k192
 
4.6%
.186
 
4.5%
w186
 
4.5%
Other values (39)1450
34.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3305
79.2%
Other Punctuation658
 
15.8%
Uppercase Letter113
 
2.7%
Connector Punctuation85
 
2.0%
Decimal Number10
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i584
17.7%
e308
 
9.3%
t275
 
8.3%
a209
 
6.3%
p207
 
6.3%
s202
 
6.1%
k192
 
5.8%
w186
 
5.6%
r175
 
5.3%
n170
 
5.1%
Other values (14)797
24.1%
Uppercase Letter
ValueCountFrequency (%)
P30
26.5%
A18
15.9%
C18
15.9%
S13
11.5%
N6
 
5.3%
T5
 
4.4%
F4
 
3.5%
M4
 
3.5%
D4
 
3.5%
W2
 
1.8%
Other values (7)9
 
8.0%
Other Punctuation
ValueCountFrequency (%)
/372
56.5%
.186
28.3%
:93
 
14.1%
%5
 
0.8%
,2
 
0.3%
Decimal Number
ValueCountFrequency (%)
75
50.0%
25
50.0%
Connector Punctuation
ValueCountFrequency (%)
_85
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3418
81.9%
Common753
 
18.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i584
17.1%
e308
 
9.0%
t275
 
8.0%
a209
 
6.1%
p207
 
6.1%
s202
 
5.9%
k192
 
5.6%
w186
 
5.4%
r175
 
5.1%
n170
 
5.0%
Other values (31)910
26.6%
Common
ValueCountFrequency (%)
/372
49.4%
.186
24.7%
:93
 
12.4%
_85
 
11.3%
75
 
0.7%
%5
 
0.7%
25
 
0.7%
,2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i584
14.0%
/372
 
8.9%
e308
 
7.4%
t275
 
6.6%
a209
 
5.0%
p207
 
5.0%
s202
 
4.8%
k192
 
4.6%
.186
 
4.5%
w186
 
4.5%
Other values (39)1450
34.8%

time
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct93
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size872.0 B
2022-10-12 01:47:58.485120
 
1
2022-10-12 02:42:11.480184
 
1
2022-10-12 02:43:19.284149
 
1
2022-10-12 02:43:07.107545
 
1
2022-10-12 02:42:58.815490
 
1
Other values (88)
88 

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters2418
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)100.0%

Sample

1st row2022-10-12 01:47:58.485120
2nd row2022-10-12 01:48:09.186213
3rd row2022-10-12 01:48:18.661961
4th row2022-10-12 01:48:25.728941
5th row2022-10-12 01:48:37.233833

Common Values

ValueCountFrequency (%)
2022-10-12 01:47:58.4851201
 
1.1%
2022-10-12 02:42:11.4801841
 
1.1%
2022-10-12 02:43:19.2841491
 
1.1%
2022-10-12 02:43:07.1075451
 
1.1%
2022-10-12 02:42:58.8154901
 
1.1%
2022-10-12 02:42:49.0803671
 
1.1%
2022-10-12 02:42:38.8779971
 
1.1%
2022-10-12 02:42:33.6205961
 
1.1%
2022-10-12 02:42:28.0301691
 
1.1%
2022-10-12 02:42:22.8745691
 
1.1%
Other values (83)83
89.2%

Length

2022-10-20T19:04:09.719696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-10-1278
41.9%
2022-10-197
 
3.8%
2022-10-144
 
2.2%
2022-10-172
 
1.1%
01:49:17.0234431
 
0.5%
01:48:37.2338331
 
0.5%
01:48:45.1582201
 
0.5%
01:48:48.7521571
 
0.5%
01:48:56.0008131
 
0.5%
01:49:00.6372051
 
0.5%
Other values (89)89
47.8%

Most occurring characters

ValueCountFrequency (%)
2526
21.8%
0376
15.6%
1345
14.3%
-186
 
7.7%
:186
 
7.7%
4105
 
4.3%
5105
 
4.3%
93
 
3.8%
.93
 
3.8%
392
 
3.8%
Other values (4)311
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1860
76.9%
Other Punctuation279
 
11.5%
Dash Punctuation186
 
7.7%
Space Separator93
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2526
28.3%
0376
20.2%
1345
18.5%
4105
 
5.6%
5105
 
5.6%
392
 
4.9%
985
 
4.6%
880
 
4.3%
676
 
4.1%
770
 
3.8%
Other Punctuation
ValueCountFrequency (%)
:186
66.7%
.93
33.3%
Dash Punctuation
ValueCountFrequency (%)
-186
100.0%
Space Separator
ValueCountFrequency (%)
93
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2526
21.8%
0376
15.6%
1345
14.3%
-186
 
7.7%
:186
 
7.7%
4105
 
4.3%
5105
 
4.3%
93
 
3.8%
.93
 
3.8%
392
 
3.8%
Other values (4)311
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2526
21.8%
0376
15.6%
1345
14.3%
-186
 
7.7%
:186
 
7.7%
4105
 
4.3%
5105
 
4.3%
93
 
3.8%
.93
 
3.8%
392
 
3.8%
Other values (4)311
12.9%

Correlations

2022-10-20T19:04:09.810411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-20T19:04:09.918498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-20T19:04:09.028678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-20T19:04:09.154989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

titlestorytime
0electromagnetic energy is absorbed or emitted in discrete packetshttps://en.wikipedia.org/wiki/Quantum2022-10-12 01:47:58.485120
1speecheshttps://en.wikipedia.org/wiki/Cicero2022-10-12 01:48:09.186213
2cerebrovascular diseasehttps://en.wikipedia.org/wiki/Alzheimer%27s_disease2022-10-12 01:48:18.661961
3antidepressant medicationshttps://en.wikipedia.org/wiki/Peripheral_neuropathy2022-10-12 01:48:25.728941
4Scalp coolinghttps://en.wikipedia.org/wiki/Chemotherapy2022-10-12 01:48:37.233833
5pro-aging trancehttps://en.wikipedia.org/wiki/Death2022-10-12 01:48:45.158220
6Radioastronomia di Bologna (Institute for Radio Astronomyhttps://en.wikipedia.org/wiki/Medicina2022-10-12 01:48:48.752157
7byhttps://en.wikipedia.org/wiki/Securitization2022-10-12 01:48:56.000813
8New York City is the largesthttps://en.wikipedia.org/wiki/Financial_services2022-10-12 01:49:00.637205
9the collective nature of the texthttps://en.wikipedia.org/wiki/Anthology2022-10-12 01:49:04.268440

Last rows

titlestorytime
83metadatahttps://en.wikipedia.org/wiki/Scalable_Vector_Graphics2022-10-17 15:42:07.764264
84Organized efforts to search for extraterrestrial intelligencehttps://en.wikipedia.org/wiki/Technology2022-10-17 15:42:25.557130
85== Function ===\nCalcium is an essential elementhttps://en.wikipedia.org/wiki/Calcium2022-10-18 15:55:02.907650
86A star trail is a type of photograph that uses long exposure times to capture diurnal circles, the apparent motion of stars in the night sky due to Earth's rotation. A star-trail photograph shows individual stars as streaks across the image, with longer exposures yielding longer arcs. The term is used for similar photos captured elsewhere, such as on board the International Space Station and on Mars.Typical shutter speeds for a star trail range from 15 minutes to several hours, requiring a "Bulb" setting on the camera to open the shutter for a period longer than usual. However, a more practiced technique is to blend a number of frames together to create the final star trail image.Star trails have been used by professional astronomers to measure the quality of observing locations for major telescopes.\n\n\n== Capture ==\n\nStar trail photographs are captured by placing a camera on a tripod, pointing the lens toward the night sky, and allowing the shutter to stay open for a long period of time. Star trails are considered relatively easy for amateur astrophotographers to create. Photographers generally make these images by using a DSLR or Mirrorless camera with its lens focus set to infinity. A cable release or intervalometer allows the photographer to hold the shutter open for the desired amount of time. Typical exposure times range from 15 minutes to many hours long, depending on the desired length of the star trail arcs for the image. Even though star trail pictures are created under low-light conditions, long exposure times allow fast films, such as ISO 200 and ISO 400. Wide-apertures, such as f/5.6 and f/4, are recommended for star trails.\n\nBecause exposure times for star trail photographs can be several hours long, camera batteries can be easily depleted. Mechanical cameras that do not require a battery to open and close the shutter have an advantage over more modern film and digital cameras that rely on battery power. On these cameras, the Bulb, or B, exposure setting keeps the shutter open. Another problem that digital cameras encounter is an increase in electronic noise with increasing exposure time. However, this can be avoided through the use of shorter exposure times that are then stacked in post production software. This avoids possible heat build up or digital noise caused from a single long exposure. \nAmerican astronaut Don Pettit recorded star trails with a digital camera from the International Space Station in Earth orbit between April and June, 2012. Pettit described his technique as follows: "My star trail images are made by taking a time exposure of about 10 to 15 minutes. However, with modern digital cameras, 30 seconds is about the longest exposure possible, due to electronic detector noise effectively snowing out the image. To achieve the longer exposures I do what many amateur astronomers do. I take multiple 30-second exposures, then 'stack' them using imaging software, thus producing the longer exposure."Star trail images have also been taken on Mars. The Spirit rover produced them while looking for meteors. Since the camera was limited to 60 second exposures the trails appear as dashed lines.\n\n\n== Earth's rotation ==\n\nStar trail photographs are possible because of the rotation of Earth about its axis. The apparent motion of the stars is recorded as mostly curved streaks on the film or detector. For observers in the Northern Hemisphere, aiming the camera northward creates an image with concentric circular arcs centered on the north celestial pole (very near Polaris). For those in the Southern Hemisphere, this same effect is achieved by aiming the camera southward. In this case, the arc streaks are centered on the south celestial pole (near Sigma Octantis). Aiming the camera eastward or westward shows straight streaks on the celestial equator, which is tilted at angle with respect to the horizon. The angular measure of this tilt depends on the photographer's latitude (L), and is equal to 90° − L.\n\n\n== Astronomical site testing ==\nStar trail photographs can be used by astronomers to determine the quality of a location for telescope observations. Star trail observations of Polaris have been used to measure the quality of seeing in the atmosphere, and the vibrations in telescope mounting systems. The first recorded suggestion of this technique is from E.S. Skinner's 1931 book A Manual of Celestial Photography.\n\n\n== Gallery ==\n\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n\n== References ==\n\n\n== External links ==\n\n4 Steps To Creating Star Trails Photos Using Stacking Software\nStar trail photography\nStarStaX free multi-platform star trail softwarehttps://en.wikipedia.org/wiki/Star_trail2022-10-19 00:58:13.680029
87E.S. Skinnerhttps://en.wikipedia.org/wiki/Star_trail2022-10-19 00:58:16.667325
88A star tracker is an optical device that measures the positions of stars using photocells or a camera.\nAs the positions of many stars have been measured by astronomers to a high degree of accuracy, a star tracker on a satellite or spacecraft may be used to determine the orientation (or attitude) of the spacecraft with respect to the stars. In order to do this, the star tracker must obtain an image of the stars, measure their apparent position in the reference frame of the spacecraft, and identify the stars so their position can be compared with their known absolute position from a star catalog. A star tracker may include a processor to identify stars by comparing the pattern of observed stars with the known pattern of stars in the sky.\n\n\n== History ==\nIn the 1950s and early 1960s, star trackers were an important part of early long-range ballistic missiles and cruise missiles, in the era when inertial navigation systems (INS) were not sufficiently accurate for intercontinental ranges.Consider a Cold War missile flying towards its target; it initially starts by flying northward, passes over the arctic, and then begins flying southward again. From the missile's perspective, stars behind it appear to move closer to the southern horizon while those in front are rising. Before flight, one can calculate the relative angle of a star based on where the missile should be at that instant if it is in the correct location. That can then be compared to the measured location to produce an "error off" signal that can be used to bring the missile back onto its correct trajectory.Due to the Earth's rotation, stars that are in a usable location change over the course of a day and the location of the target. Generally, a selection of several bright stars would be used and one would be selected at launch time. For guidance systems based solely on star tracking, some sort of recording mechanism, typically a magnetic tape, was pre-recorded with a signal that represented the angle of the star over the period of a day. At launch, the tape was forwarded to the appropriate time. During the flight, the signal on the tape was used to roughly position a telescope so it would point at the expected position of the star. At the telescope's focus was a photocell and some sort of signal-generator, typically a spinning disk known as a chopper. The chopper causes the image of the star to repeatedly appear and disappear on the photocell, producing a signal that was then smoothed to produce an alternating current output. The phase of that signal was compared to the one on the tape to produce a guidance signal.Star trackers were often combined with an INS. INS systems measure accelerations and integrate those over time to determine a velocity and, optionally, double-integrate to produce a location relative to its launch location. Even tiny measurement errors, when integrated, adds up to an appreciable error known as "drift". For instance, the N-1 navigation system developed for the SM-64 Navaho cruise missile drifted at a rate of 1 nautical mile per hour, meaning that after a two-hour flight the INS would be indicating a position 2 nautical miles (3.7 km; 2.3 mi) away from its actual location. This was outside the desired accuracy of about half a mile.\nIn the case of an INS, the magnetic tape can be removed and those signals instead provided by the INS. The rest of the system works as before; the signal from the INS roughly positions the star tracker, which then measures the actual location of the star and produces an error signal. This signal is then used to correct the position being generated from the INS, reducing the accumulated drift back to the limit of the accuracy of the tracker. These "stellar inertial" systems were especially common from the 1950s through the 1980s, although some systems use it to this day.\n\n\n== Current technology ==\nMany models are currently available. There also exist open projects designed to be used for the global CubeSat researchers and developers community.\nStar trackers, which require high sensitivity, may become confused by sunlight reflected from the spacecraft, or by exhaust gas plumes from the spacecraft thrusters (either sunlight reflection or contamination of the star tracker window). Star trackers are also susceptible to a variety of errors (low spatial frequency, high spatial frequency, temporal, ...) in addition to a variety of optical sources of error (spherical aberration, chromatic aberration, etc.). There are also many potential sources of confusion for the star identification algorithm (planets, comets, supernovae, the bimodal character of the point spread function for adjacent stars, other nearby satellites, point-source light pollution from large cities on Earth, ...). There are roughly 57 bright navigational stars in common use. However, for more complex missions, entire star field databases are used to determine spacecraft orientation. A typical star catalog for high-fidelity attitude determination is originated from a standard base catalog (for example from the United States Naval Observatory) and then filtered to remove problematic stars, for example due to apparent magnitude variability, color index uncertainty, or a location within the Hertzsprung-Russell diagram implying unreliability. These types of star catalogs can have thousands of stars stored in memory on board the spacecraft, or else processed using tools at the ground station and then uploaded.\n\n\n== See also ==\nCelestial navigation\nGoTo (telescopes)\nSun sensor\n\n\n== References ==https://en.wikipedia.org/wiki/Star_tracker2022-10-19 00:58:42.472490
89Celestial navigation\nGoTo (telescopes)\nSun sensorhttps://en.wikipedia.org/wiki/Star_tracker2022-10-19 00:58:45.548139
90A star tracker is an optical device that measures the positions of stars using photocells or a camera.\nAs the positions of many stars have been measured by astronomers to a high degree of accuracy, a star tracker on a satellite or spacecraft may be used to determine the orientation (or attitude) of the spacecraft with respect to the stars. In order to do this, the star tracker must obtain an image of the stars, measure their apparent position in the reference frame of the spacecraft, and identify the stars so their position can be compared with their known absolute position from a star catalog. A star tracker may include a processor to identify stars by comparing the pattern of observed stars with the known pattern of stars in the sky.\n\n\n== History ==\nIn the 1950s and early 1960s, star trackers were an important part of early long-range ballistic missiles and cruise missiles, in the era when inertial navigation systems (INS) were not sufficiently accurate for intercontinental ranges.Consider a Cold War missile flying towards its target; it initially starts by flying northward, passes over the arctic, and then begins flying southward again. From the missile's perspective, stars behind it appear to move closer to the southern horizon while those in front are rising. Before flight, one can calculate the relative angle of a star based on where the missile should be at that instant if it is in the correct location. That can then be compared to the measured location to produce an "error off" signal that can be used to bring the missile back onto its correct trajectory.Due to the Earth's rotation, stars that are in a usable location change over the course of a day and the location of the target. Generally, a selection of several bright stars would be used and one would be selected at launch time. For guidance systems based solely on star tracking, some sort of recording mechanism, typically a magnetic tape, was pre-recorded with a signal that represented the angle of the star over the period of a day. At launch, the tape was forwarded to the appropriate time. During the flight, the signal on the tape was used to roughly position a telescope so it would point at the expected position of the star. At the telescope's focus was a photocell and some sort of signal-generator, typically a spinning disk known as a chopper. The chopper causes the image of the star to repeatedly appear and disappear on the photocell, producing a signal that was then smoothed to produce an alternating current output. The phase of that signal was compared to the one on the tape to produce a guidance signal.Star trackers were often combined with an INS. INS systems measure accelerations and integrate those over time to determine a velocity and, optionally, double-integrate to produce a location relative to its launch location. Even tiny measurement errors, when integrated, adds up to an appreciable error known as "drift". For instance, the N-1 navigation system developed for the SM-64 Navaho cruise missile drifted at a rate of 1 nautical mile per hour, meaning that after a two-hour flight the INS would be indicating a position 2 nautical miles (3.7 km; 2.3 mi) away from its actual location. This was outside the desired accuracy of about half a mile.\nIn the case of an INS, the magnetic tape can be removed and those signals instead provided by the INS. The rest of the system works as before; the signal from the INS roughly positions the star tracker, which then measures the actual location of the star and produces an error signal. This signal is then used to correct the position being generated from the INS, reducing the accumulated drift back to the limit of the accuracy of the tracker. These "stellar inertial" systems were especially common from the 1950s through the 1980s, although some systems use it to this day.\n\n\n== Current technology ==\nMany models are currently available. There also exist open projects designed to be used for the global CubeSat researchers and developers community.\nStar trackers, which require high sensitivity, may become confused by sunlight reflected from the spacecraft, or by exhaust gas plumes from the spacecraft thrusters (either sunlight reflection or contamination of the star tracker window). Star trackers are also susceptible to a variety of errors (low spatial frequency, high spatial frequency, temporal, ...) in addition to a variety of optical sources of error (spherical aberration, chromatic aberration, etc.). There are also many potential sources of confusion for the star identification algorithm (planets, comets, supernovae, the bimodal character of the point spread function for adjacent stars, other nearby satellites, point-source light pollution from large cities on Earth, ...). There are roughly 57 bright navigational stars in common use. However, for more complex missions, entire star field databases are used to determine spacecraft orientation. A typical star catalog for high-fidelity attitude determination is originated from a standard base catalog (for example from the United States Naval Observatory) and then filtered to remove problematic stars, for example due to apparent magnitude variability, color index uncertainty, or a location within the Hertzsprung-Russell diagram implying unreliability. These types of star catalogs can have thousands of stars stored in memory on board the spacecraft, or else processed using tools at the ground station and then uploaded.\n\n\n== See also ==\nCelestial navigation\nGoTo (telescopes)\nSun sensor\n\n\n== References ==https://en.wikipedia.org/wiki/Star_tracker2022-10-19 03:03:35.059016
91Celestial navigation\nGoTo (telescopes)\nSun sensorhttps://en.wikipedia.org/wiki/Star_tracker2022-10-19 03:03:39.599730
92Filmhttps://en.wikipedia.org/wiki/National_Commission_for_Culture_and_the_Arts2022-10-19 03:05:22.704793