in this part i mounted my XBee and airspeed sensor to the front head of my plane. in next part i will try to make a good place for APM

Take Care

Here is the part1. Yesterday I made a box for my video transmitter. i dont want to put it inside of airframe cuz its getting very hot; it need air cooling and this idea came up to me. it looks not cool but i think it will be ok when i paint airframe

here is the images;

I will close camera hole today so i can mouth pan/tilt system.

Hi fellows!

Here is the part1. Yesterday I made a box for my video transmitter. i dont want to put it inside of airframe cuz its getting very hot; it need air cooling and this idea came up to me. it looks not cool but i think it will be ok when i paint airframe

here is the images;

I will close camera hole today so i can mouth pan/tilt system.

ESC will be cooling with air hole. it’s very good i think cuz i have big motor:)

looks cool isn’t it ?

There is a question for you guys if i want to coloring it which type of paint do you suggest? its fiber glass covered with shrink film. i wanna make it black

Take care.

These steps reset the password for the “root” account to “password”. To change the password for a different account, or to set a different password, just edit the variables in single-quotes in step 4.

If you know your existing MySQL root password, steps 1-3 are not necessary.

You’ll use the same table you used in Part 1. The full list of records is:

mysql> SELECT * FROM fulltext_sample;
+---------------------------+
| copy                      |
+---------------------------+
| It appears good from here |
| The here and the past     |
| Why are we here           |
| An all-out alert          |
| All you need is love      |
| A good alert              |
+---------------------------+

To perform a boolean search, the IN BOOLEAN MODE modifier is used. The following query demonstrates the new syntax.

mysql> SELECT * FROM fulltext_sample
WHERE MATCH (copy) AGAINST ('love' IN BOOLEAN MODE);
+----------------------+
| copy                 |
+----------------------+
| All you need is love |
+----------------------+

An important difference between ordinary MATCH() AGAINST() queries and IN BOOLEAN MODE searches is that the latter does not apply the 50% limit (so the word can appear in more than half the rows).

There are a number of special operators you’ll need to know in order to make the most of the boolean search. If no symbol is specified, the appearance of this word causes the relevance of the row to be higher, similar to an ordinary MATCH() AGAINST().

Boolean Search Operators

Examples

Let’s see some of this action. The first example returns all rows containing the word ‘here’, but not the word ‘past’.

mysql> SELECT copy FROM fulltext_sample WHERE MATCH(copy)
AGAINST ('+here -past' IN BOOLEAN MODE);
+---------------------------+
| copy                      |
+---------------------------+
| It appears good from here |
| Why are we here           |
+---------------------------+

Compare the difference between the next two examples:

mysql> SELECT copy FROM fulltext_sample WHERE MATCH(copy)
AGAINST ('here past' IN BOOLEAN MODE);
+---------------------------+
| copy                      |
+---------------------------+
| It appears good from here |
| The here and the past     |
| Why are we here           |
+---------------------------+

mysql> SELECT copy FROM fulltext_sample WHERE MATCH(copy)
AGAINST ('here +past'IN BOOLEAN MODE);
+-----------------------+
| copy                  |
+-----------------------+
| The here and the past |
+-----------------------+

The first works in a similar way to an ordinary MATCH() AGAINST(), albeit without the 50% threshold. The word ‘past’ is mandatory in the second search (by default a word is optional), so the other two records are not returned.

The next example demonstrates a common mistake.

mysql> SELECT copy FROM fulltext_sample
WHERE MATCH(copy) AGAINST ('+are here' IN BOOLEAN MODE);
+---------------------------+
| copy                      |
+---------------------------+
| It appears good from here |
| The here and the past     |
| Why are we here           |
+---------------------------+

The results may appear surprising compared the previous example, but since ‘are’ contains three or less letters, it is excluded for purposes of the search and is not mandatory.

The next two examples demonstrate a powerful enhancement for searching purposes:

mysql> SELECT copy FROM fulltext_sample
WHERE MATCH(copy) AGAINST ('aler' IN BOOLEAN MODE);
Empty set (0.01 sec)

mysql> SELECT copy FROM fulltext_sample
WHERE MATCH(copy) AGAINST ('aler*' IN BOOLEAN MODE);
+------------------+
| copy             |
+------------------+
| An all-out alert |
| A good alert     |
+------------------+

By default only whole words are matched, unless the '*' operator is used.

The '<' and '>' symbols are less commonly used, but they allow a great degree of control for relevance. In the following examples, we return the relevance indicator to demonstrate the difference between the queries.
mysql> SELECT copy, MATCH(copy)
AGAINST ('appears good alert' IN BOOLEAN MODE)
AS m FROM fulltext_sample WHERE MATCH(copy)
AGAINST ('appears good alert' IN BOOLEAN MODE);
+---------------------------+------+
| copy                      | m    |
+---------------------------+------+
| It appears good from here |    2 |
| An all-out alert          |    1 |
| A good alert              |    2 |
+---------------------------+------+

mysql> SELECT copy, MATCH(copy)
AGAINST ('appears <good alert' IN BOOLEAN MODE)
AS m FROM fulltext_sample WHERE MATCH(copy)
AGAINST ('appears <good alert' IN BOOLEAN MODE);
+---------------------------+------------------+
| copy                      | m                |
+---------------------------+------------------+
| It appears good from here |  1.6666667461395 |
| An all-out alert          |                1 |
| A good alert              |  1.6666667461395 |
+---------------------------+------------------+
The '<' operator decreases the relevance of the word 'good', in this case by approximately 0.33.
mysql> SELECT copy, MATCH(copy)
AGAINST ('appears good >alert' IN BOOLEAN MODE)
AS m FROM fulltext_sample WHERE MATCH(copy)
AGAINST ('appears good >alert' IN BOOLEAN MODE);
+---------------------------+------+
| copy                      | m    |
+---------------------------+------+
| It appears good from here |    2 |
| An all-out alert          |  1.5 |
| A good alert              |  2.5 |
+---------------------------+------+
The '>' operator increases the relevance of the word 'alert', in this case by 0.5.
The parentheses group words into a subexpression. In the following example, the '+' symbol applies to the group of words, so that at least one of 'appears' or 'past' must appear.
mysql> SELECT copy, MATCH(copy)
AGAINST ('+(appears past)' IN BOOLEAN MODE)
AS m FROM fulltext_sample WHERE MATCH(copy)
AGAINST ('(appears past)' IN BOOLEAN MODE);
+---------------------------+------+
| copy                      | m    |
+---------------------------+------+
| It appears good from here |    1 |
| The here and the past     |    1 |
+---------------------------+------+
You can also apply the operators to words in the subexpression, as follows:
mysql> SELECT copy, MATCH(copy)
AGAINST ('+(>appears <past)' IN BOOLEAN MODE)
AS m FROM fulltext_sample WHERE MATCH(copy)
AGAINST ('+(>appears <past)' IN BOOLEAN MODE);
+---------------------------+------------------+
| copy                      | m                |
+---------------------------+------------------+
| It appears good from here |              1.5 |
| The here and the past     | 0.66666668653488 |
+---------------------------+------------------+
The '~' operator contributes negatively to the relevance, but does not bar the word from appearing, as the '-' operator does. It also does not do the same as the '<' operator, which is to make a reduced, yet still positive, difference to the relevance. These subtle difference allows for powerfully tuned searches, but can cause confusion. Look at the difference between the following. First, the word 'here' is compulsory, and the word 'past' optional:
mysql> SELECT copy, MATCH(copy)
AGAINST ('+here past' IN BOOLEAN MODE)
AS m FROM fulltext_sample WHERE MATCH(copy)
AGAINST ('+here past' IN BOOLEAN MODE);
+---------------------------+-----------------+
| copy                      | m               |
+---------------------------+-----------------+
| It appears good from here |               1 |
| The here and the past     | 1.3333333730698 |
| Why are we here           |               1 |
+---------------------------+-----------------+
Next, a reduced relevance for the word 'past'. The same results are returned, but the record with both words ('The here and the past') has less weighting, though still more than the other records.
mysql> SELECT copy, MATCH(copy)
AGAINST ('+here <past' IN BOOLEAN MODE)
AS m FROM fulltext_sample WHERE MATCH(copy)
AGAINST ('+here <past' IN BOOLEAN MODE);
+---------------------------+-----------------+
| copy                      | m               |
+---------------------------+-----------------+
| It appears good from here |               1 |
| The here and the past     | 1.2222222089767 |
| Why are we here           |               1 |
+---------------------------+-----------------+
Take note of the relevance in the next example:
mysql> SELECT copy, MATCH(copy)
AGAINST ('+here ~past' IN BOOLEAN MODE)
AS m FROM fulltext_sample WHERE MATCH(copy)
AGAINST ('+here ~past' IN BOOLEAN MODE);
+---------------------------+------------------+
| copy                      | m                |
+---------------------------+------------------+
| It appears good from here |                1 |
| The here and the past     | 0.83333331346512 |
| Why are we here           |                1 |
+---------------------------+------------------+
The important difference is that the relevance is negatively affected, and is now lower than the other two records. This means if you asked for only '~past', you'd get nothing back, as the relevance would be lower than 0 (and therefore not returned)
mysql> SELECT copy, MATCH(copy)
AGAINST ('~past' IN BOOLEAN MODE)
AS m FROM fulltext_sample WHERE MATCH(copy)
AGAINST ('~past' IN BOOLEAN MODE);
Empty set (0.00 sec)
Finally, '-' simply removes the second record from the result set, as we've seen before.
mysql> SELECT copy, MATCH(copy)
AGAINST ('+here -past' IN BOOLEAN MODE)
AS m FROM fulltext_sample WHERE MATCH(copy)
AGAINST ('+here -past' IN BOOLEAN MODE);
+---------------------------+------+
| copy                      | m    |
+---------------------------+------+
| It appears good from here |    1 |
| Why are we here           |    1 |
+---------------------------+------+
The final operator, the double quotes ("), is useful to group phrases together. So, if you were interested in returning 'good from here' but none of those words if they did not appear in the phrase, you'd use:
mysql> SELECT copy, MATCH(copy)
AGAINST ('"good from here"' IN BOOLEAN MODE)
AS m FROM fulltext_sample WHERE MATCH(copy)
AGAINST ('"good from here"' IN BOOLEAN MODE);
+---------------------------+------+
| copy                      | m    |
+---------------------------+------+
| It appears good from here |    1 |
+---------------------------+------+
For many applications, boolean searches are one of MySQL 4's most useful features. With careful use, you can save yourself substantial of development time, and add many useful enhancements. Good luck!

The simple form of usage (the MATCH() function) is available to all MySQL servers from version 3.23.23, while the more complex usage (the IN BOOLEAN MODE modifier) is available from version 4. The first part of this article looks at the former, and the second part at the latter.

A sample table

We’re going to use the following table throughout this tutorial.

CREATE TABLE fulltext_sample(copy TEXT,FULLTEXT(copy)) TYPE=MyISAM;
The TYPE=MyISAM clause isn’t necessary unless you’ve set the default table type to be something other than MyISAM (perhaps you use InnoDB tables to make use of MySQL’s transactional capabilities). Once you’ve created the table, populate it with some data, as follows:
INSERT INTO fulltext_sample VALUES
(‘It appears good from here’),
(‘The here and the past’),
(‘Why are we hear’),
(‘An all-out alert’),
(‘All you need is love’),
(‘A good alert’);
If you’d already created an existing table, you can add a FULLTEXT index with the ALTER TABLE statement (as well as the CREATE INDEX statement), for example:

ALTER TABLE fulltext_sample ADD FULLTEXT(copy)
Searching for text

The syntax of a FULLTEXT search is simple. You MATCH the field AGAINST the text you are searching for, for example:

mysql> SELECT * FROM fulltext_sample WHERE MATCH(copy) AGAINST(‘love’);
+———————-+
| copy |
+———————-+
| All you need is love |
+———————-+
Searches on a FULLTEXT index are performed case-insensitively (as are searches on TEXT and non-binary VARCHAR fields generally). So the following works as well:
mysql> SELECT * FROM fulltext_sample WHERE MATCH(copy) AGAINST(‘LOVE’);
+———————-+
| copy |
+———————-+
| All you need is love |
+———————-+
FULLTEXT indexes are most often used to search natural language text, such as through newspaper articles, web page contents and so on. For this reason MySQL has added a number of features to assist this kind of searching. MySQL does not index any words less than or equal to 3 characters in length, nor does it index any words that appear in more than 50% of the rows. This means that if your table contains 2 or less rows, a search on a FULLTEXT index will never return anything. In future, MySQL will make this behavior more flexible, but for now it should suit most natural language uses. If most fields in your database contain the word ‘music’, you probably don’t want these records returned, You can use the IN BOOLEAN MODE modifier to get around the 50% threshold, as you’ll see in Part 2 of this article.

Results are returned in order of relevance, from highest to lowest.

The main features

A list of the main features of a standard FULLTEXT search follows:

Excludes partial words
Excludes words less than 4 characters in length (3 or less)
Excludes words that appear in more than half the rows (meaning at least 3 rows are required)
Hyphenated words are treated as two words
Rows are returned in order of relevance, descending
Words in the stopword list (common words) are also excluded from the search results. The stopword list is based upon common English words, so if your data is used for a different purpose, you’ll probably want to change the list. Unfortunately, doing so at present is not easy. You’ll need to edit the file myisam/ft_static.c. recompile MySQL, and rebuild the indexes! To save you hunting through the source, or if you have a binary version of MySQL, here is a list of stopwords. Note that these can and do change with different versions. To be absolutely sure, you’ll have to check the specific list for your version.
Stopwords

“a”, “a’s”, “able”, “about”, “above”, “according”, “accordingly”, “across”, “actually”, “after”, “afterwards”, “again”, “against”, “ain’t”, “all”, “allow”, “allows”, “almost”, “alone”, “along”, “already”, “also”, “although”, “always”, “am”, “among”, “amongst”, “an”, “and”, “another”, “any”, “anybody”, “anyhow”, “anyone”, “anything”, “anyway”, “anyways”, “anywhere”, “apart”, “appear”, “appreciate”, “appropriate”, “are”, “aren’t”, “around”, “as”, “aside”, “ask”, “asking”, “associated”, “at”, “available”, “away”, “awfully”, “b”, “be”, “became”, “because”, “become”, “becomes”, “becoming”, “been”, “before”, “beforehand”, “behind”, “being”, “believe”, “below”, “beside”, “besides”, “best”, “better”, “between”, “beyond”, “both”, “brief”, “but”, “by”, “c”, “c’mon”, “c’s”, “came”, “can”, “can’t”, “cannot”, “cant”, “cause”, “causes”, “certain”, “certainly”, “changes”, “clearly”, “co”, “com”, “come”, “comes”, “concerning”, “consequently”, “consider”, “considering”, “contain”, “containing”, “contains”, “corresponding”, “could”, “couldn’t”, “course”, “currently”, “d”, “definitely”, “described”, “despite”, “did”, “didn’t”, “different”, “do”, “does”, “doesn’t”, “doing”, “don’t”, “done”, “down”, “downwards”, “during”, “e”, “each”, “edu”, “eg”, “eight”, “either”, “else”, “elsewhere”, “enough”, “entirely”, “especially”, “et”, “etc”, “even”, “ever”, “every”, “everybody”, “everyone”, “everything”, “everywhere”, “ex”, “exactly”, “example”, “except”, “f”, “far”, “few”, “fifth”, “first”, “five”, “followed”, “following”, “follows”, “for”, “former”, “formerly”, “forth”, “four”, “from”, “further”, “furthermore”, “g”, “get”, “gets”, “getting”, “given”, “gives”, “go”, “goes”, “going”, “gone”, “got”, “gotten”, “greetings”, “h”, “had”, “hadn’t”, “happens”, “hardly”, “has”, “hasn’t”, “have”, “haven’t”, “having”, “he”, “he’s”, “hello”, “help”, “hence”, “her”, “here”, “here’s”, “hereafter”, “hereby”, “herein”, “hereupon”, “hers”, “herself”, “hi”, “him”, “himself”, “his”, “hither”, “hopefully”, “how”, “howbeit”, “however”, “i”, “i’d”, “i’ll”, “i’m”, “i’ve”, “ie”, “if”, “ignored”, “immediate”, “in”, “inasmuch”, “inc”, “indeed”, “indicate”, “indicated”, “indicates”, “inner”, “insofar”, “instead”, “into”, “inward”, “is”, “isn’t”, “it”, “it’d”, “it’ll”, “it’s”, “its”, “itself”, “j”, “just”, “k”, “keep”, “keeps”, “kept”, “know”, “knows”, “known”, “l”, “last”, “lately”, “later”, “latter”, “latterly”, “least”, “less”, “lest”, “let”, “let’s”, “like”, “liked”, “likely”, “little”, “look”, “looking”, “looks”, “ltd”, “m”, “mainly”, “many”, “may”, “maybe”, “me”, “mean”, “meanwhile”, “merely”, “might”, “more”, “moreover”, “most”, “mostly”, “much”, “must”, “my”, “myself”, “n”, “name”, “namely”, “nd”, “near”, “nearly”, “necessary”, “need”, “needs”, “neither”, “never”, “nevertheless”, “new”, “next”, “nine”, “no”, “nobody”, “non”, “none”, “noone”, “nor”, “normally”, “not”, “nothing”, “novel”, “now”, “nowhere”, “o”, “obviously”, “of”, “off”, “often”, “oh”, “ok”, “okay”, “old”, “on”, “once”, “one”, “ones”, “only”, “onto”, “or”, “other”, “others”, “otherwise”, “ought”, “our”, “ours”, “ourselves”, “out”, “outside”, “over”, “overall”, “own”, “p”, “particular”, “particularly”, “per”, “perhaps”, “placed”, “please”, “plus”, “possible”, “presumably”, “probably”, “provides”, “q”, “que”, “quite”, “qv”, “r”, “rather”, “rd”, “re”, “really”, “reasonably”, “regarding”, “regardless”, “regards”, “relatively”, “respectively”, “right”, “s”, “said”, “same”, “saw”, “say”, “saying”, “says”, “second”, “secondly”, “see”, “seeing”, “seem”, “seemed”, “seeming”, “seems”, “seen”, “self”, “selves”, “sensible”, “sent”, “serious”, “seriously”, “seven”, “several”, “shall”, “she”, “should”, “shouldn’t”, “since”, “six”, “so”, “some”, “somebody”, “somehow”, “someone”, “something”, “sometime”, “sometimes”, “somewhat”, “somewhere”, “soon”, “sorry”, “specified”, “specify”, “specifying”, “still”, “sub”, “such”, “sup”, “sure”, “t”, “t’s”, “take”, “taken”, “tell”, “tends”, “th”, “than”, “thank”, “thanks”, “thanx”, “that”, “that’s”, “thats”, “the”, “their”, “theirs”, “them”, “themselves”, “then”, “thence”, “there”, “there’s”, “thereafter”, “thereby”, “therefore”, “therein”, “theres”, “thereupon”, “these”, “they”, “they’d”, “they’ll”, “they’re”, “they’ve”, “think”, “third”, “this”, “thorough”, “thoroughly”, “those”, “though”, “three”, “through”, “throughout”, “thru”, “thus”, “to”, “together”, “too”, “took”, “toward”, “towards”, “tried”, “tries”, “truly”, “try”, “trying”, “twice”, “two”, “u”, “un”, “under”, “unfortunately”, “unless”, “unlikely”, “until”, “unto”, “up”, “upon”, “us”, “use”, “used”, “useful”, “uses”, “using”, “usually”, “v”, “value”, “various”, “very”, “via”, “viz”, “vs”, “w”, “want”, “wants”, “was”, “wasn’t”, “way”, “we”, “we’d”, “we’ll”, “we’re”, “we’ve”, “welcome”, “well”, “went”, “were”, “weren’t”, “what”, “what’s”, “whatever”, “when”, “whence”, “whenever”, “where”, “where’s”, “whereafter”, “whereas”, “whereby”, “wherein”, “whereupon”, “wherever”, “whether”, “which”, “while”, “whither”, “who”, “who’s”, “whoever”, “whole”, “whom”, “whose”, “why”, “will”, “willing”, “wish”, “with”, “within”, “without”, “won’t”, “wonder”, “would”, “would”, “wouldn’t”, “x”, “y”, “yes”, “yet”, “you”, “you’d”, “you’ll”, “you’re”, “you’ve”, “your”, “yours”, “yourself”, “yourselves”, “z”, “zero”,

Let’s have a look at some of the consequences of this. If you were a bit lazy in your typing, and tried ‘to look for the word ‘love’, as follows:
mysql> SELECT * FROM fulltext_sample WHERE MATCH(copy) AGAINST(‘lov’);
Empty set (0.00 sec)
you’d get nothing back, as the FULLTEXT index only contains complete words, not partial words. You’d have to write the full word to get anything back, as you did in the first example.
As mentioned, hyphenated words are also excluded from the FULLTEXT index (they are indexed as separate words), so the following also returns nothing:

mysql> SELECT * FROM fulltext_sample WHERE MATCH(copy) AGAINST(‘all-out’);
Empty set (0.00 sec)
Unfortunately, both words are less than the required 4 letters, so they don’t appear on their own either, and cannot be found at all with an ordinary search. Part 2 of this tutorial looks at BOOLEAN MODE searches when you can search for partial or hyphenated words.
You can also search for more than one word at a time, by separating the words with commas. Try and return records containing the words ‘here’ and ‘appears’, as follows:

mysql> SELECT * FROM fulltext_sample WHERE MATCH(copy) AGAINST(‘here’);
Empty set (0.01 sec)
Unexpectedly this returns nothing. But, a more careful look at the stopword list shows this word is listed, so they are excluded from the index. Stopwords are a common cause of people complaining that the MySQL FULLTEXT index facility is not working properly. If your query does return a result, then the stopword list in your version of MySQL does not contain the word ‘here’.
Relevance

The following example shows how the records are returned in order of precedence:

mysql> SELECT * FROM fulltext_sample WHERE MATCH(copy) AGAINST(‘good,alert’);
+—————————+
| copy |
+—————————+
| A good alert |
| It appears good from here |
| An all-out alert |
+—————————+
The record ‘A good alert’ appears first, as it contains both words being searched for. You don’t have to believe me – just ask MySQL to display the precedence in the results. Simply repeat the MATCH() function in the field list, as follows:
mysql> SELECT copy,MATCH(copy) AGAINST(‘good,alert’) AS relevance
FROM fulltext_sample WHERE MATCH(copy) AGAINST(‘good,alert’);
+—————————+——————+
| copy | relevance |
+—————————+——————+
| A good alert | 1.3551264824316 |
| An all-out alert | 0.68526663197496 |
| It appears good from hear | 0.67003110026735 |
+—————————+——————+
The relevance calculation is fairly complex and is based upon the number of words in the index, the number of unique words in that row, the total number of words in both the index and the result, as well as the weight of the word (for the average English sentence, the word ‘cool’ will be weighted less, and therefore have a lower relevance, than the word ‘dandy’, though trends can change!). The figures may differ in your version of MySQL, as MySQL does occasionally tweak the calculation algorithms.

While the standard FULLTEXT search is fairly useful and sufficient for many, MySQL 4 takes it much further. Part 2 of this article looks at BOOLEAN FULLTEXT searches, which offer a lot more functionality.

There is no simple command available to find out the largest files/directories on a Linux/UNIX/BSD filesystem. However, combination of following three commands (using pipes) you can easily find out list of largest files:

du : Estimate file space usage
sort : Sort lines of text files or given input data
head : Output the first part of files i.e. to display first 10 largest file
Here is what you need to type at shell prompt to find out top 10 largest file/directories is taking up the most space in a /var directory/file system:
# du -a /var | sort -n -r | head -n 10

Output:

1008372 /var
313236 /var/www
253964 /var/log
192544 /var/lib
152628 /var/spool
152508 /var/spool/squid
136524 /var/spool/squid/00
95736 /var/log/mrtg.log
74688 /var/log/squid
62544 /var/cache
If you want more human readable output try:

# du -ks /var | sort -n -r | head -n 10

Where,

-a : Include all files, not just directories (du command)
-h : Human readable format
-n : Numeric sort (sort command)
-r : Reverse the result of comparisons (sort command)
-n 10 : Display 10 largest file. If you want 20 largest file replace 10 with 20. (head command)