Call Busts and Reverse Busts in CQ WW, 2012

This is the eighth in a series of posts on busts and reverse busts in the CQ WW contests. These posts are based on the augmented versions of the CQ WW public logs.

Prior posts in the series:

• 2005
• 2006 
• 2007
• 2008 
• 2009 
• 2010
• 2011 

2012 SSB -- Most Busts
Position	Call	QSOs	Busts	% Busts
1	EE1W	9,271	206	2.2
2	HK1NA	16,780	197	1.2
3	HG1S	11,702	164	1.4
4	CN3A	13,973	155	1.1
5	A73A	11,336	150	1.3
6	ZW5B	10,775	148	1.4
7	OT5A	12,900	139	1.1
8	PT2CM	6,090	137	2.2
9	RT6A	6,752	127	1.9
10	Z38N	12,240	124	1.0

2012 SSB -- Most Reverse Busts
Position	Call	QSOs	Reverse Busts	% Reverse Busts
1	CW90A	1,815	424	23.4
2	DF0HQ	13,662	303	2.2
3	JA5FDJ	8,924	212	2.4
4	HK1NA	16,780	200	1.2
5	HG1S	11,702	190	1.6
6	JA3YBK	7,740	180	2.3
7	S52ZW	6,536	157	2.4
8	VK2IM	2,387	153	6.4
9	LR3M	6,417	150	2.3
10	K3LR	13,913	145	1.0

2012 SSB -- Highest Percentage of Busts (≥100 QSOs)
Position	Call	QSOs	% Busts
1	YO7LYM	184	15.8
2	PU8TLS	108	13.9
3	WA3RGH	164	12.2
4	RK0SM	159	11.9
5	OH1TS	127	11.8
6	KJ7NL	119	11.8
7	KD8CWP	105	11.4
8	SP9IBJ	309	11.3
9	W2UDT	119	10.9
10	CO2RVA	167	10.8

2012 SSB -- Highest Percentage of Reverse Busts (≥100 QSOs)
Position	Call	QSOs	% Reverse Busts
1	TA2YA	508	26.8
2	CE6VMO	381	25.7
3	CW90A	1,815	23.4
4	BH1LKO	110	19.1
5	BH1MHI	163	15.3
6	BA8AG	1,011	12.2
7	SA5BUE	127	11.8
8	KF5CXG	254	10.6
9	VK4FAAS	227	10.6
10	K4GOP	141	9.9

2012 CW -- Most Busts
Position	Call	QSOs	Busts	% Busts
1	D4C	21,969	243	1.1
2	HG1S	8,814	219	2.5
3	PI4CC	7,857	191	2.4
4	F6KOP	8,514	191	2.2
5	PV8ADI	1,814	184	10.1
6	HK1NA	16,385	181	1.1
7	PT2CM	6,741	172	2.6
8	Z38N	14,022	158	1.1
9	PJ2T	12,322	157	1.3
10	C5A	18,645	152	0.8

2012 CW -- Most Reverse Busts
Position	Call	QSOs	Reverse Busts	% Reverse Busts
1	DF0HQ	10,812	345	3.2
2	JS3CTQ	3,330	326	9.8
3	IT9UCW	378	311	82.3
4	JF1NHD	3,072	254	8.3
5	6V7V	8,295	227	3.0
6	Z60WW	6,778	239	3.5
7	ED9Z	8,199	236	2.9
8	DR1A	12,569	231	1.8
9	PW7T	9,574	213	2.2
10	UA5B	4,259	208	4.9

In tables of reverse busts, one sometimes finds what seems like an unreasonable number of reverse busts (as, in this table, for IT9UCW). This is generally caused by a discrepancy between the call actually sent by the listed station and the one recorded as being sent in at least some QSOs in the station's log.

2012 CW -- Highest Percentage of Busts (≥100 QSOs)
Position	Call	QSOs	% Busts
1	YO7LYM	200	17.5
2	SP2AEK	330	16.2
3	K8CMO	119	16.0
4	EA2GC	279	15.4
5	W4RJC	191	15.2
6	W5LEW	102	14.7
7	UY5MR	354	14.7
8	UR4LIN	130	14.6
9	W6DAS	135	14.1
10	IV3DLW/P	146	13.7

2012 CW -- Highest Percentage of Reverse Busts (≥100 QSOs)
Position	Call	QSOs	% Reverse Busts
1	IT9UCW	378	82.3
2	BG8BSX	108	13.0
3	K4XDB	108	12.0
4	WE6EZ	151	11.3
5	KH2D	136	11.0
6	JS3CTQ	3,330	9.8
7	UY7IS	250	9.6
8	JS6RGY	1,427	9.3
9	W5LEW	102	8.8
10	IK2XRW	151	8.6

Why I Don't Use a Word Processor (4)

Prior posts in this series:

• (1)
• (2)
• (3)

Having discussed the separation of the tasks of writing a book and formatting the content, and the tools I use for writing, I now turn to the more complicated issue: formatting a book in a professional manner. Essentially, this is the realm of typography, which Wikipedia defines as: the art and technique of arranging type to make written language legible, readable, and appealing when displayed. The basic goal can be summarised by a simple principle: typography should never distract from the text; or, to put it slightly differently, unless he is specifically analysing it, the reader should never notice the typography. Wikipedia says essentially the same thing: Traditionally, text is composed to create a readable, coherent, and visually satisfying typeface that works invisibly, without the awareness of the reader. Even distribution of typeset material, with a minimum of distractions and anomalies, is aimed at producing clarity and transparency.

Typography should make the words and the sense of the text easy to discern: every time that the reader is forced to slow the movement of his ocular scan of a page -- or, even worse, if he is force to go back and re-read some text simply because of its layout -- the typography has failed.

(Some typography is simply inherently appalling: I stopped reading commercial e-books when, as I was reading a thriller from St. Martin's Press, I came across the word "you" -- typeset as "y-" on one page and "ou" on the next. It's hard to believe that such an insult to good taste was permitted by a supposedly competent commercial publishing house. How can anyone be expected not to give up in disgust when faced with such gratuitously atrocious typography? But I digress.)

Unsurprisingly, the single most important typographic decision is that of which typeface to use. As described in the colophon of most of my books, I generally use fonts from the Latin Modern family, which I find to be particularly beautiful and easy to read. Many people use fonts based on Times New Roman, but such fonts, designed as they were for the narrow columnar format of a newspaper, tend to lead to some confusion when used to print items such as novels. For example, the letter pair rn, which appears rather frequently in English, may be easily confused with the single letter m unless the reader is paying more attention than is comfortable when reading a fast-paced novel.

Almost all modern books are printed in a serif typeface. Books printed in sans serif typefaces are tiring and frustrating to read, and it is a mystery to me why any competent publishing house would ever print a book in such a way. (My best guess is that on the few occasions that it happens, it is because an enPetered editor with too much power wants to appear modern and gives no thought to the unnecessary difficulties he or she is foisting on the book's readers.) If you doubt the increase in blood pressure induced by reading a book printed in such a way, I suggest that you read Marcus Trescothick's otherwise wonderful autobiography about his life as quite possibly the best [cricket] batsman in the world as he battled with depression, Coming Back to Me. Some editor at HarperCollins decided to allow the book to be typeset in a sans serif font. That person cannot possibly have sat down and tried to read the resulting book.

Here is a sample page from Coming Back to Me (although this is far from doing justice to the experience of reading a page in the physical book):

I find that simply reading a page of this is slow work, probably because the shapes of the letters are so unusual that each word requires an abnormal amount of work for the brain to decode.  After a few pages of this, I also find myself experiencing odd optical effects, such as a waviness in the baselines.

It's not just commercial houses that have indulged in foisting such poorly-considered typography on customers; even such a formerly august publisher as the OUP has done so. The OUP publishers the oddly uneven Very Short Introduction series, and some of those books (strangely, in this series there is no consistent layout from book to book) are typeset with a sans serif typeface. Here is a sample from A Very Short Introduction to Logic:

(The layout of this page violates a number of long-standing typographical conventions, leading me to suspect that the reader is again being subjected to an editor who believes in being modern rather than effective.)

Digressing wildly, the OUP has the distinction of publishing what is, in my opinion, the worst book I have read in recent years: An Introduction to Quantum Computing. Rife with grammatical and factual errors, it is hard to believe that any editor ever did more than simply skim the content without bothering to try to understand it. In my mind the final straw is the figures, of which this page contains two typical examples:

Quantum circuits are drawn throughout the book with the lines that indicate connections displayed in such a light colour that they can barely be seen, even when one knows where they must be. (But , strangely, not all circuits are drawn that way; every now and then one comes across one that is actually legible at a glance.) Can you see the boxes surrounding the gates and the connections between them without peering carefully at Figure 5.3 above? And notice that the text mentions "dashed boxes in Figure 5.5". Here is the page containing that figure:

I'll have to take the authors' word for it that there are dashed boxes in Figure 5.5, as I can't see them. None of this is a trick of the reproduction process for making the pages available here -- in fact, here one can magnify the images and see detail that is effectively invisible to the naked eye on the original page. In the original, the lines are printed too thinly in a very light grey. Given that such quantum circuits are in a real sense the essence of the book, it is bizarre that no care was taken to ensure that they are clear and unambiguous. The conclusion I draw is that no one, simply no one -- none of the three authors, nor any editor -- bothered even to sample the book when it was in the galley stage. Or perhaps no one subscribed to the notion that figures are supposed to demonstrate ideas clearly, rather than leave the reader perplexed because important features are essentially invisible. At least there is some consistency here, since the descriptions of important concepts in the text too frequently include substantive errors, or are presented in language that is so full of grammatical errors as to leave at least this reader puzzled as to the details of what the authors are trying to convey.

I have strayed too far from the intended subject, so will end there. Next time we'll begin to look at the more subtle aspects of good typography, which all too often are ignored nowadays, to the reader's detriment.

Call Busts and Reverse Busts in CQ WW, 2011

This is the seventh in a series of posts on busts and reverse busts in the CQ WW contests. These posts are based on the augmented versions of the CQ WW public logs.

Prior posts in the series:

• 2005
• 2006 
• 2007
• 2008 
• 2009 
• 2010

2011 SSB -- Most Busts
Position	Call	QSOs	Busts	% Busts
1	EB1WW	10,210	222	2.2
2	LY7A	9,729	154	1.6
3	PJ2T	13,021	147	1.1
4	HG1S	8,226	146	1.8
5	OT5A	12,310	145	1.2
6	C5A	18,155	141	0.8
7	C37N	15,766	139	0.9
8	DP6T	8,332	137	1.6
9	EF8R	9,718	136	1.4
10	9A1P	14,403	136	0.9

2011 SSB -- Most Reverse Busts
Position	Call	QSOs	Reverse Busts	% Reverse Busts
1	DF0HQ	11,100	287	2.6
2	JA5FDJ	9,831	218	2.2
3	UN7E/7	254	214	84.3
4	SN3R	6,724	108	3.1
5	EE9Z	9,998	173	1.7
6	WE3C	8,675	171	2.0
7	C37N	15,766	167	1.1
8	HG1S	8,226	165	2.0
9	WW2Y	1,459	160	11.0
10	W2PV	7,507	146	1.9

In tables of reverse busts, one sometimes finds what seems like an unreasonable number of reverse busts (as, in this table, for UN7E/7). This is generally caused by a discrepancy between the call actually sent by the listed station and the one recorded as being sent in at least some QSOs in the station's log.

2011 SSB -- Highest Percentage of Busts (≥100 QSOs)
Position	Call	QSOs	% Busts
1	RK0SM	306	14.7
2	RK9FBO	160	14.4
3	K2JMY	188	13.3
4	SP9IBJ	307	12.1
5	PV8ADI	427	11.9
6	LA9QNA	110	11.8
7	LU6EBY	138	11.6
8	K8TS	183	11.5
9	W7EWB	117	11.1
10	N5VEZ	190	11.1

2011 SSB -- Highest Percentage of Reverse Busts (≥100 QSOs)
Position	Call	QSOs	% Reverse Busts
1	UN7E/7	254	84.3
2	KB2HSH	100	13.0
3	WW2Y	1,459	11.0
4	BD4GNV	118	10.2
5	SM0LIU	183	9.8
6	PU2KXM	147	9.5
7	CE3TKV	101	8.9
8	BD3MF	237	8.9
9	2W0BRR	302	8.3
10	OZ8KEL	112	8.0

2011 CW -- Most Busts
Position	Call	QSOs	Busts	% Busts
1	PV8ADI	2,125	248	11.7
2	OZ5E	8,386	204	2.4
3	EF8R	8,612	181	2.1
4	ZF1A	7,846	170	2.2
5	PJ2T	15,684	160	1.0
6	C5A	19,616	153	0.8
7	PI4DX	7,205	151	2.1
8	YE2S	3,072	150	4.9
9	HG1S	9,211	148	1.6
10	6V7V	4,000	143	3.6

2011 CW -- Most Reverse Busts
Position	Call	QSOs	Reverse Busts	% Reverse Busts
1	DF0HQ	12,780	391	3.1
2	IR4X	9,039	346	3.8
3	JS3CTQ	3,486	329	9.4
4	ED9M	8,355	311	3.7
5	DR1A	14,457	299	2.1
6	TO7A	7,044	239	3.4
7	PW7T	10,329	133	2.3
8	P40F	7,513	186	2.5
9	HG1S	9,211	182	2.0
10	C6AAW	13,483	179	1.3

2011 CW -- Highest Percentage of Busts (≥100 QSOs)
Position	Call	QSOs	% Busts
1	OH1FJ	159	35.8
2	W2GN	319	23.5
3	OM4DA	400	21.0
4	N9GUN	111	20.7
5	SM3AF	105	20.0
6	US7ZM	136	19.1
7	N8WS	160	18.1
8	PU1MMZ	249	18.1
9	EA2NA	306	18.0
10	JA3AHY	123	17.9

2011 CW -- Highest Percentage of Reverse Busts (≥100 QSOs)
Position	Call	QSOs	% Reverse Busts
1	IW0EYT	132	14.4
2	LZ1195IR	726	11.6
3	GI0RQK	1,506	9.6
4	JS3CTQ	3,486	9.4
5	F8CRH	1,698	9.4
6	IK1TWC	138	9.4
7	JA2HYD	133	9.0
8	SA6G	1,116	8.6
9	DJ1SL	201	8.5
10	IZ2OBS	142	8.5

Revised Versions of the LANL GPS Charged-Particle Dataset

In the most recent post on this subject, I described various versions of the LANL GPS charged-particle dataset designed to improve the quality of the dataset as compared to the quality of the original release. Here, I intended to continue that process, but, as I describe below, due to some invalid data in the original dataset, I have reprocessed the data and uploaded a new file containing the resultant records.

First, though, I should mention that I received confirmation via private communication with LANL that, as I suspected, the value of collection_interval is intended to be taken from the set { 24, 120, 240, 4608 }, thus supporting the processing performed last time for stage 4.

However, I also received notification that in the original file ns41_041226_v1.03.ascii, the day number is incorrect for all the data marked as being in 2005, except for the very first entry. That is, everything past line 2330 of the original file is invalid.

The easiest way to remove the bad data seems to be (unfortunately) to go back to the the beginning of the processing and simply remove all the lines past line 2330 in file ns41_041226_v1.03.ascii, since I can think of no easy, foolproof way to remove the erroneous lines from the files created in stage 4 (or, indeed, any earlier stage).

Accordingly, we'll go back to the beginning and create new stages as follows:

Stage 1: Remove all lines past line 2330 in the file ns41_041226_v1.03.ascii

This is easily accomplished simply by transferring all the data files to the stage-1 directory unchanged, and then applying the command:

  sed -i '2331,99999d' -i ns41_041226_v1.03.ascii 

We then execute several stages as before (except that what was stage n now becomes stage n+1) -- see the last post on this subject for details.

Stage 2: All the data for each satellite in a single file

Stage 3: Remove all records marked as bad

The next two stages are no longer necessary, as removing the tail of file  ns41_041226_v1.03.ascii removed the records with bad values of decimal_day or collection_interval from the dataset. For the sake of consistency, though, I have retained the stages, although if you are duplicating the dataset there should be no need to create them (simply use the stage 3 files instead):

Stage 4: Remove all records marked with invalid day of year

Stage 5: Correct time information

Here are the number of records for each satellite at the end of the processing for stage 5:

Satellite	Stage 5 Records
ns41	1,990,891
ns48	1,105,991
ns53	1,331,687
ns54	1,939,151
ns55	1,055,328
ns56	1,680,887
ns57	1,082,626
ns58	1,175,519
ns59	1,516,528
ns60	1,495,541
ns61	1,470,445
ns62	775,535
ns63	652,110
ns64	344,702
ns65	480,935
ns66	446,801
ns67	327,994
ns68	306,513
ns69	262,992
ns70	110,971
ns71	221,336
ns72	182,332
ns73	145,858

As a checkpoint, I have uploaded the new stage 5 dataset (I have also deleted the old stage 4 dataset). The MD5 checksum of this file is 774cc449fbd6fac836e17196cdaac363.

 

 

 

 

Call Busts and Reverse Busts in CQ WW, 2010

This is the sixth in a series of posts on busts and reverse busts in the CQ WW contests. These posts are based on the augmented versions of the CQ WW public logs.

Prior posts in the series:

• 2005
• 2006 
• 2007
• 2008 
• 2009

2010 SSB -- Most Busts
Position	Call	QSOs	Busts	% Busts
1	EF8R	12,741	170	1.3
2	A73A	10,884	151	1.4
3	HG1S	7,318	151	2.1
4	OT5A	8,687	149	1.7
5	TM1O	5,442	148	2.7
6	JA7YRR	4,822	144	3.0
7	LZ9W	10,350	126	1.2
8	PJ2T	14,892	125	0.8
9	GM0B	7,273	118	1.6
10	PW7T	9,182	110	1.2

2010 SSB -- Most Reverse Busts
Position	Call	QSOs	Reverse Busts	% Reverse Busts
1	ON46CQ	566	463	81.8
2	T70A	8,424	302	3.6
3	W3N0	251	231	92.0
4	UW2I	2,691	171	6.4
5	DF0HQ	11,290	169	1.5
6	JA3YBK	5,291	160	3.0
7	HG10P	3,850	154	4.0
8	SJ2W	5,002	150	3.0
9	RZ3AXX	4,193	144	3.4
10	S52ZW	5,015	118	2.4

In tables of reverse busts, one sometimes finds what seems like an unreasonable number of reverse busts (as, in this table, for ON46CQ and W3N0 [sic]). This is generally caused by a discrepancy between the call actually sent by the listed station and the one recorded as being sent in at least some QSOs in the station's log.

2010 SSB -- Highest Percentage of Busts (≥100 QSOs)
Position	Call	QSOs	% Busts
1	PU2TRX	123	14.6
2	EA7IPP	273	13.9
3	LZ1VVV	168	11.9
4	OM8KD	173	11.6
5	BD7KQT	231	11.3
6	OH1FJ	178	11.2
7	IK5ZQC	108	11.1
8	BH4RIC	110	10.9
9	E76EA	115	10.4
10	YO7LTQ	204	10.3

2010 SSB -- Highest Percentage of Reverse Busts (≥100 QSOs)
Position	Call	QSOs	% Reverse Busts
1	W3N0	251	92.0
2	ON46CQ	566	81.8
3	OM4JD	100	56.0
4	BD1EFO	125	16.0
5	KH6SP/W5	532	13.0
6	OK2SWD	133	11.3
7	UU1AZ	330	11.2
8	DX33CA	108	10.2
9	BA6QR	118	10.2
10	IW2MYH	131	9.9

2010 CW -- Most Busts
Position	Call	QSOs	Busts	% Busts
1	RC3W	3,910	163	4.2
2	M2A	2,399	139	5.8
3	HD2M	7,858	135	1.7
4	LY7A	7,750	129	1.7
5	9A1A	10,869	129	1.2
6	AN2A	4,866	122	2.5
7	PW7T	7,943	115	1.4
8	KP2M	7,722	112	1.5
9	OZ5E	6,358	110	1.7
10	C4N	7,138	108	1.5

2010 CW -- Most Reverse Busts
Position	Call	QSOs	Reverse Busts	% Reverse Busts
1	RZ3VO	1,020	892	87.5
2	ED9M	7,829	316	4.0
3	DF0HQ	11,035	283	2.6
4	GI0RQK	1,598	283	17.7
5	JS3CTQ	2,419	258	10.7
6	ES9C	7,188	213	3.0
7	CN3A	5,948	198	3.3
8	IR4X	7,928	184	2.3
9	PW7T	7,943	164	2.1
10	IU1A	3,843	160	4.2

2010 CW -- Highest Percentage of Busts (≥100 QSOs)
Position	Call	QSOs	% Busts
1	OH1FJ	211	26.5
2	YO7LYM	195	24.1
3	F5BTH	155	20.6
4	SP9IBJ	280	18.2
5	JO3AGQ	124	16.9
6	LA8TIA	108	16.7
7	JA6WW	127	16.5
8	KJ4AOM	187	15.5
9	OH2MO	280	15.4
10	GM0TTY	115	14.8

2010 CW -- Highest Percentage of Reverse Busts (≥100 QSOs)
Position	Call	QSOs	% Reverse Busts
1	RZ3VO	1,020	87.5
2	DL7VRG	148	24.3
3	GI0RQK	1,598	17.7
4	SO9R	159	15.1
5	KS2G	128	13.3
6	2E0BFJ	105	11.4
7	KD3TB	161	11.2
8	JS3CTQ	2,419	10.7
9	OE1BKA	115	10.4
10	BD8NBG	342	10.2

A Grid-Based Scatter Metric for the RBN

I recently posted about an attempt to find a reasonable measure of the scatter of the stations that post reports to the Reverse Beacon Network (RBN). In that post, I looked at a metric based on the direct measurement of distances between posting stations. In this post, I look at a quite different approach based simply on counting the occupancy of cells defined over the 2-sphere that closely models the surface of the Earth.

If we look at two geographic representations of the RBN as it was in 2009 and then again in 2016...

 ...what strikes me most is not anything to do (directly) with the distribution of distances between points, nor is it to do with the colours of the points, but rather the obvious facts that: (i) there are a couple of areas where relatively many points are clustered; (ii) the vast majority of the area has no points at all; (iii) some spreading and filling-in has occurred over the time-span covered by the two figures.

That being so, it seems plausible that a better metric of the spreading of the RBN could be obtained by dividing the terrestrial 2-sphere into cells and counting the cells that are occupied (regardless of the number of posting stations within, or the number of posts emanating from, each cell). Such a metric has the additional merit of being relatively simple to calculate compared to the approach that derived a metric from the distances between posting stations.

Defining the Cells

Ideally, one would define the cells so that they would have two properties: (i) they would all be the same size; and (ii) they would all be as compact as possible.

The second desideratum may not be obvious: however, consider the case in which all the cells are defined such that each cell comprises all the longitudes between pairs of latitude lines (while defining the latitude lines in such a way that the area of each cell is the same, of course). One could easily divide the 2-sphere in such a manner, but this would mean that, for example, the cells for all the latitudes that cover Europe would appear as being populated, which would make a mockery of any attempt to interpret the number of populated cells as any kind of metric for the distribution of RBN posting stations.

To meet both desired criteria simultaneously essentially requires that we solve our friend from last time, the Tammes problem. Indeed, unless we allow non-compact cells, the problem of covering the surface of the Earth with cells of equal size results in a non-trivial distribution of cells (see, for example, the HEALPix projection and the documentation for the HEALPix code).

To make reasonable progress, we take a slightly different approach and relax our criteria, so that the cells are merely both reasonably similar in size and reasonably compact (after all, what we really want is a system that allows us quickly to decide in which cell a given RBN station is located, with the condition that reasonably separated RBN stations will be placed in different cells, rather than a system that meets any precise mathematical requirement).

The simplest approach seems to be (assuming latitudinal symmetry) to move from a pole towards the equator in bands defined by parallels of latitude, and defining the (integral) number of cells around each band in such a way that the size of the cells varies by only a relatively small amount.

For simplicity, we will consider just one half of the Earth's surface (the southern hemisphere); the pertinent results for the entire terrestrial 2-sphere will follow immediately by symmetry.  

It's easy to show that the area ₁A₂ of that portion of a sphere of radius r between two co-latitudes λ₁ and λ₂ is:
$$_1A_2 = 2\pi r^2 (cos \lambda_1 - cos \lambda_2)$$ 
So, suppose that we divide the hemisphere into bands of latitude 10°, and wish to construct a grid with a total of 50 cells (i.e., 100 cells cover the entire 2-sphere) aligned along these bands. The resultant best-fit cells can be summarised as:

Cells in 10° bands
λ1(°)	λ2(°)	1A2	N100
0	10	0.015192247	1
10	20	0.0451151	2
20	30	0.736672	4
30	40	0.099981	5
40	50	0.1232568	6
50	60	0.1427876	7
60	70	0.1579799	8
70	80	0.168372	8
80	90	0.1736482	9

A similar table for latitude bands of 15° is similarly summarised:

Cells in 15° bands
λ1(°)	λ2(°)	1A2	N100
0	15	0.0340742	2
15	30	0.0999004	5
30	45	0.1589186	8
45	60	0.2071068	10
60	75	0.241181	12
75	90	0.258819	13

In these tables, we labelled the column that shows the number of cells in each latitude band with the subscript 100 rather than 50, because that is the number of cells over the entire 2-sphere.

Note that these are just two of many tables that could be created for various values of the widths of the latitude bands and the total number of cells desired. We can denote any particular table by using parameters, so that G(15, 100) denotes the second table above, defining a total of 100 grid-based cells over the 2-sphere, with 15° bands of latitude. G(15, 100) seems like a reasonable metric with which to begin.

The cells for any latitude band may, of course, start at any longitude, but it makes things easier if, for each latitude band, we place a boundary at the Greenwich meridian, and also require that cell boundaries lie exactly on a meridian that is an integral number of degrees.

This gives us the following table for the longitudes at which cell boundaries occur:

Longitudes of Cell Boundaries
N Cells	1	2	3	4	5	6	7	8	9	10	11	12	13
1	360
2	180	360
3	120	240	360
4	90	180	270	360
5	72	144	216	288	360
6	60	120	180	240	300	360
7	51	103	154	206	257	309	360
8	45	90	135	180	225	270	315	360
9	40	80	120	160	200	240	280	320	360
10	36	72	108	144	180	216	252	288	324	360
11	33	65	98	131	164	196	229	262	295	327	360
12	30	60	90	120	150	180	210	240	270	300	330	360
13	28	55	83	111	138	166	194	222	249	277	304	332	360

With these constraints, G(15, 100) defines 100 cells as shown on this map:

These look like reasonable cells; therefore we will proceed with G(15,100) as the basis for the grid-based scatter metric. The actual value of the scatter metric over some time-frame Δ will therefore be the number of cells in the above plot that contain at least one RBN poster within the time-frame.

Digression - How Many Cells?

I have not addressed the issue of the number of cells we should use. There is no obvious criterion for choosing a particular number, although the number should be sufficiently large that it reflects the fact that large parts of the Earth's surface have no RBN posting stations. On the other hand, the number should not be so large that essentially every RBN node is in a distinct cell. The 100 cells on the above map seem a reasonable number to me. It also naturally leads to an index whose value, at least in theory, runs from 0 to 100.

Results

We can now easily compare the results of using G(15, 100) and the previously-defined scatter metric that was calculated directly from the positions of the posting stations.

Simply plotting the values of the two metrics as a function of time, we obtain:

(Pearson correlation coefficient is 0.78; rate of increase is about 145 per year.)

(Pearson correlation coefficient is 0.91; rate of increase is about 2.2 per year.)

Plotting the values of the metrics as functions of the number of posting stations, we obtain:

(Pearson correlation coefficient is 0.75; rate of increase is about 4.8 per poster.)

(Pearson correlation coefficient is 0.94; rate of increase is about 0.078 per poster.)

From these plots, I conclude that there is likely no point to looking at the third category of possible scatter metric (described as "[m]etrics based indirectly on a distance metric" in the prior post on this subject). [By this phrase, I intended to indicate the use of more complicated measures of the distribution of the distance-based measurements, rather than just the mean separation: for example, looking at higher moments of the distribution, or seeing if anything useful could be gained by looking at the fractal dimensionality.] The G(15,100) scatter metric is easy to calculate, it seems to correspond closely to the intuitive understanding of "scatter", and it appears to represent at least as good a metric as a directly-calculated metric based on mean separation of posting stations.

Thus, in future postings that require the use of a scatter metric, I will use the G(15, 100) metric unless there appears to be a good reason to do otherwise.

Call Busts and Reverse Busts in CQ WW, 2009

This is the fifth in a series of posts on busts and reverse busts in the CQ WW contests. These posts are based on the augmented versions of the CQ WW public logs.

Prior posts in the series:

• 2005
• 2006 
• 2007
• 2008

2009 SSB -- Most Busts
Position	Call	QSOs	Busts	% Busts
1	AO8A	11,468	139	1.2
2	Z37M	7,164	139	1.9
3	OT5A	9,618	134	1.4
4	LS2D	3,939	119	3.0
5	EK8WA	5,032	110	2.4
6	9A1P	11,677	118	1.0
7	EB1WW	6,051	115	1.9
8	PJ2T	11,207	104	0.9
9	AM3SSB	6,000	102	1.7
10	HG1S	7,398	102	1.4

2009 SSB -- Most Reverse Busts
Position	Call	QSOs	Reverse Busts	% Reverse Busts
1	DF0HQ	12,372	178	1.4
2	AO8A	11,468	175	1.5
3	S52ZW	6,096	134	2.2
4	HB0/HB9AON	7,705	128	1.7
5	JR5VHU	5,769	125	2.2
6	TM2S	3,388	111	3.3
7	SE0X	2,020	108	5.3
8	UX2IO	3,555	108	3.0
9	EE9Z	9,385	107	1.1
10	DQ4W	5,677	101	1.8

2009 SSB -- Highest Percentage of Busts (≥100 QSOs)
Position	Call	QSOs	% Busts
1	SQ8MFB	129	17.8
2	OH1FJ	126	15.9
3	LS4DX	123	13.8
4	EW8BQ	273	12.1
5	EI4JZ	175	11.4
6	BD1HFP	115	11.3
7	NT4TS	171	11.1
8	UT7VR	198	10.6
9	OZ6EI	128	10.2
10	EA3HAN	119	10.1

2009 SSB -- Highest Percentage of Reverse Busts (≥100 QSOs)
Position	Call	QSOs	% Reverse Busts
1	LT0D	214	13.6
2	VE7RSV/P	132	11.4
3	4Z5TK	101	10.9
4	SM7TZK	310	10.0
5	BG4IKE	101	9.9
6	5P0O	152	9.9
7	K4GOP	115	9.6
8	YC1BYX	170	9.4
9	F1SMV	135	8.9
10	LU8EOT	187	8.6

2009 CW -- Most Busts
Position	Call	QSOs	Busts	% Busts
1	OZ5E	3,706	162	4.4
2	HG1S	6,127	124	2.0
3	EE2W	10,410	115	1.1
4	ZM4T	3,249	107	3.3
5	6Y1V	10,486	101	1.0
6	EA5CW	6,180	101	1.6
7	RC3W	2,623	98	3.7
8	YT2W	6,760	97	1.4
9	VQ5V	4,660	96	2.1
10	4M5IR	1,281	96	7.5

2009 CW -- Most Reverse Busts
Position	Call	QSOs	Reverse Busts	% Reverse Busts
1	EE2W	10,410	438	4.2
2	W0QG	420	352	83.8
3	DF0HQ	9,945	254	2.6
4	RRA3XCZ	277	252	91.0
5	JS3CTQ	1,926	220	11.4
6	JS6RGY	2,501	180	7.2
7	DR1A	10,163	166	1.6
8	IR4X	7,391	158	2.1
9	CN3A	4,559	154	3.4
10	EF8M	7,419	152	2.0

In tables of reverse busts, one sometimes finds what seems like an unreasonable number of reverse busts (as, in this table, for RRA3XCZ and W0QG). This is generally caused by a discrepancy between the call actually sent by the listed station and the one recorded as being sent in at least some QSOs in the log.

2009 CW -- Highest Percentage of Busts (≥100 QSOs)
Position	Call	QSOs	% Busts
1	CE6VMO	129	20.9
2	YO7LTQ	133	19.5
3	K9IA	101	18.8
4	N1IMW	138	18.1
5	N7TL	127	16.5
6	OH2MO	146	16.4
7	KB0R	123	15.4
8	W9IIX	138	14.5
9	PY3CAL	152	14.5
10	G4RYV	170	14.1

2008 CW -- Highest Percentage of Reverse Busts (≥100 QSOs)
Position	Call	QSOs	% Reverse Busts
1	RRA3XCZ	277	91.0
2	JI30GI	110	89.1
3	W0QG	420	83.8
4	EI4JZ	108	35.2
5	DL7VRG	172	22.1
6	KS2G	102	12.7
7	JS3CTQ	1,926	11.4
8	PA3ANN	290	10.3
9	PY2ASS	135	9.6
10	UX3HA	763	9.6

Band-by-Band Comparison of DX QSO Rates for CQ WW CW and SSB, 2005 to 2016

The availability of public logs for CQ WW CW and SSB contests spanning twelve years allows us to look at the variation in the rate at which DX QSOs are made throughout each contest over a relatively long period. (I here define DX QSOs as those between different zones; other definitions are obviously possible.)

In the plots, the number of QSOs is accumulated every ten minutes, so there are six points per hour.

I think that the results are rather interesting. I'll let you draw your own conclusions.

In regard to the 10m results, I should point out that the CW contest occurs roughly a month after the SSB one, and is therefore considerably closer to the winter solstice, with the concomitant decrease in the number of hours of common daylight between Europe and North America (particularly western NA). I have long wondered what would happen if CQ WW (and CQ WPX and ARRL DX) would switch CW and SSB weekends every year.