(84922) 2003 VS2 is a near-Earth asteroid that was discovered in 2003. It is categorized as a member of the Apollo group of asteroids, which are characterized by their orbits crossing Earth's orbit. As a near-Earth object (NEO), it has the potential to come close to Earth at certain times, although it does not pose a significant threat.
Fast ice refers to sea ice that is connected to the coast or to large ice formations such as ice shelves. It remains anchored and does not drift with ocean currents or winds. Fast ice typically forms in areas where the water is shallow enough and is subject to consistent freezing conditions, allowing it to remain stable over longer periods. This type of ice plays a crucial role in the Arctic and Antarctic ecosystems as it provides habitat for various marine species, protects coastal areas from wave action, and influences local climate patterns.
Grease ice is a type of ice that forms under specific conditions in cold environments, typically found in polar regions or near ice-covered bodies of water. It is characterized by a thin, slushy layer that consists of small ice crystals and water, often resembling a viscous, greasy surface. Grease ice can develop when small ice crystals begin to coalesce and are mixed with water, creating a surface that appears shiny and can have a somewhat liquid-like texture.
Piezoelectric materials are substances that exhibit the piezoelectric effect, where mechanical stress applied to the material generates an electric charge, and conversely, applying an electric field can induce mechanical deformation. These materials are used in various applications, such as sensors, actuators, transducers, and even energy harvesting devices. Here’s a list of commonly used piezoelectric materials: ### Natural Piezoelectric Materials 1.
Rutilated quartz is a type of gemstone that features needle-like inclusions of rutile, a mineral composed primarily of titanium dioxide (TiO2). The rutile needles can appear in various colors, including gold, red, brown, and silver, and they can vary in thickness and arrangement, creating unique and striking patterns within the clear or translucent quartz.
Bottled gas refers to various types of gases that are stored under pressure in containers, typically cylinders or bottles. It is commonly used for a variety of applications, including heating, cooking, and fuel for vehicles. The most common types of bottled gas include: 1. **Liquefied Petroleum Gas (LPG)**: A mixture of propane and butane, LPG is widely used for home heating, cooking, and hot water systems. It is also used in certain vehicles as an alternative fuel.
Inelastic scattering is a process in which particles (such as photons, electrons, or neutrons) collide with a target and transfer some of their energy to the target during the interaction. This results in a change in the energy, momentum, or state of the incoming particles, as well as a change in the target particles.
Ocean color refers to the color of the ocean as perceived by the human eye, which results from the absorption and scattering of sunlight by water and various substances in the water. The color can vary widely depending on several factors, including: 1. **Water Depth**: In deep water, colors tend to appear darker and bluer, while shallow water may appear greener or brownish due to the presence of sediments and algae.
An ultramicroscope is a specialized optical microscope that is used to observe objects that are smaller than the wavelength of visible light. This allows for the visualization of colloidal particles, bacteria, and other minute structures that cannot be effectively resolved with conventional light microscopy. The ultramicroscope operates on the principle of dark-field microscopy, where light is directed at an angle to the specimen, and only scattered light is observed.
An emission spectrum is a spectrum of the electromagnetic radiation emitted by a substance that has absorbed energy. When atoms or molecules absorb energy, they can become excited and move to higher energy levels. When these electrons return to their original (or ground) state, they release energy in the form of light. The wavelengths of this emitted light correspond to specific energies and are characteristic of the particular element or compound.
Let' see if there's anything in records/mx.xz.
mx.csv is 21GB.
They do have " in the files to escape commas so:
mx.py
import csv
import sys
writer = csv.writer(sys.stdout)
with open('mx.csv', 'r') as f:
    reader = csv.reader(f)
    for row in reader:
        writer.writerow([row[0], row[3]])
Would have been better with csvkit: stackoverflow.com/questions/36287982/bash-parse-csv-with-quotes-commas-and-newlines
then:
# uniq not amazing as there are often two or three slightly different records repeated on multiple timestamps, but down to 11 GB
python3 mx.py | uniq > mx-uniq.csv
sqlite3 mx.sqlite 'create table t(d text, m text)'
# 13 GB
time sqlite3 mx.sqlite ".import --csv --skip 1 'mx-uniq.csv' t"

# 41 GB
time sqlite3 mx.sqlite 'create index td on t(d)'
time sqlite3 mx.sqlite 'create index tm on t(m)'
time sqlite3 mx.sqlite 'create index tdm on t(d, m)'

# Remove dupes.
# Rows: 150m
time sqlite3 mx.sqlite <<EOF
delete from t
where rowid not in (
  select min(rowid)
  from t
  group by d, m
)
EOF

# 15 GB
time sqlite3 mx.sqlite vacuum
Let's see what the hits use:
awk -F, 'NR>1{ print $2 }' ../media/cia-2010-covert-communication-websites/hits.csv | xargs -I{} sqlite3 mx.sqlite "select distinct * from t where d = '{}'"
At around 267 total hits, only 84 have MX records, and from those that do, almost all of them have exactly:
smtp.secureserver.net
mailstore1.secureserver.net
with only three exceptions:
dailynewsandsports.com|dailynewsandsports.com
inews-today.com|mail.inews-today.com
just-kidding-news.com|just-kidding-news.com
We need to count out of the totals!
sqlite3 mx.sqlite "select count(*) from t where m = 'mailstore1.secureserver.net'"
which gives, ~18M, so nope, it is too much by itself...
Let's try to use that to reduce av.sqlite from 2013 DNS Census virtual host cleanup a bit further:
time sqlite3 mx.sqlite '.mode csv' "attach 'aiddcu.sqlite' as 'av'" '.load ./ip' "select ipi2s(av.t.i), av.t.d from av.t inner join t as mx on av.t.d = mx.d and mx.m = 'mailstore1.secureserver.net' order by av.t.i asc" > avm.csv
where avm stands for av with mx pruning. This leaves us with only ~500k entries left. With one more figerprint we could do a Wayback Machine CDX scanning scan.
Let's check that we still have most our hits in there:
grep -f <(awk -F, 'NR>1{print $2}' /home/ciro/bak/git/media/cia-2010-covert-communication-websites/hits.csv) avm.csv
At 267 hits we got 81, so all are still present.
secureserver is a hosting provider, we can see their blank page e.g. at: web.archive.org/web/20110128152204/http://emmano.com/. security.stackexchange.com/questions/12610/why-did-secureserver-net-godaddy-access-my-gmail-account/12616#12616 comments:
secureserver.net is the name GoDaddy use as the reverse DNS for IP addresses used for dedicated/virtual server hosting
ns.csv is 57 GB. This file is too massive, working with it is a pain.
We can also cut down the data a lot with stackoverflow.com/questions/1915636/is-there-a-way-to-uniq-by-column/76605540#76605540 and tld filtering:
awk -F, 'BEGIN{OFS=","} { if ($1 != last) { print $1, $3; last = $1; } }' ns.csv | grep -E '\.(com|net|info|org|biz),' > nsu.csv
This brings us down to a much more manageable 3.0 GB, 83 M rows.
Let's just scan it once real quick to start with, since likely nothing will come of this venue:
grep -f <(awk -F, 'NR>1{print $2}' ../media/cia-2010-covert-communication-websites/hits.csv) nsu.csv | tee nsu-hits.csv
cat nsu-hits.csv | csvcut -c 2 | sort | awk -F. '{OFS="."; print $(NF-1), $(NF)}' | sort | uniq -c | sort -k1 -n
As of 267 hits we get:
      1 a2hosting.com
      1 amerinoc.com
      1 ayns.net
      1 dailyrazor.com
      1 domainingdepot.com
      1 easydns.com
      1 frienddns.ru
      1 hostgator.com
      1 kolmic.com
      1 name-services.com
      1 namecity.com
      1 netnames.net
      1 tonsmovies.net
      1 webmailer.de
      2 cashparking.com
     55 worldnic.com
     86 domaincontrol.com
so yeah, most of those are likely going to be humongous just by looking at the names.
The smallest ones by far from the total are: frienddns.ru with only 487 hits, all others quite large or fake hits due to CSV. Did a quick Wayback Machine CDX scanning there but no luck alas.
Let's check the smaller ones:
inews-today.com,2013-08-12T03:14:01,ns1.frienddns.ru
source-commodities.net,2012-12-13T20:58:28,ns1.namecity.com -> fake hit due to grep e-commodities.net
dailynewsandsports.com,2013-08-13T08:36:28,ns3.a2hosting.com
just-kidding-news.com,2012-02-04T07:40:50,jns3.dailyrazor.com
fightwithoutrules.com,2012-11-09T01:17:40,sk.s2.ns1.ns92.kolmic.com
fightwithoutrules.com,2013-07-01T22:46:23,ns1625.ztomy.com
half-court.net,2012-09-10T09:49:15,sk.s2.ns1.ns92.kolmic.com
half-court.net,2013-07-07T00:31:12,ns1621.ztomy.com
Doubt anything will come out of this.
Let's do a bit of counting out of the total:
grep domaincontrol.com ns.csv | awk -F, '{print $1}' | uniq | wc
gives ~20M domain using domaincontrol. Let's see how many domains are in the first place:
awk -F, '{print $1}' ns.csv | uniq | wc
so it accounts for 1/4 of the total.
dnshistory.org contains historical domain -> mappings.
We have not managed to extract much from this source, they don't have as much data on the range of interest.
But they do have some unique data at least, perhaps we should try them a bit more often, e.g. they were the only source we've seen so far that made the association: headlines2day.com -> 212.209.74.126 which places it in the more plausible globalbaseballnews.com IP range.
TODO can it do IP to domain? Or just domain to IP? Asked on their Discord: discord.com/channels/698151879166918727/968586102493552731/1124254204257632377. Their banner suggests that yes:
With our new look website you can now find other domains hosted on the same IP address, your website neighbours and more even quicker than before.
Owner replied, you can't:
At the moment you can only do this for current not historical records
This is a shame, reverse IP here could be quite valuable.
In principle, we could obtain this data from search engines, but Google doesn't track that entire website well, e.g. no hits for site:dnshistory.org "62.22.60.48" presumably due to heavy IP throttling.
Homepage dnshistory.org/ gives date starting in 2009:
Here at DNS History we have been crawling DNS records since 2009, our database currently contains over 1 billion domains and over 12 billion DNS records.
and it is true that they do have some hits from that useful era.
So far, no new domains have been found with Common Crawl, nor have any existing known domains been found to be present in Common Crawl. Our working theory is that Common Crawl never reached the domains How did Alexa find the domains?
Let's try and do something with Common Crawl.
Unfortunately there's no IP data apparently: github.com/commoncrawl/cc-index-table/issues/30, so let's focus on the URLs.
Hello world:
select * from "ccindex"."ccindex" limit 100;
Data scanned: 11.75 MB
Sample first output line:
#                            2
url_surtkey                  org,whwheelers)/robots.txt
url                          https://whwheelers.org/robots.txt
url_host_name                whwheelers.org
url_host_tld                 org
url_host_2nd_last_part       whwheelers
url_host_3rd_last_part
url_host_4th_last_part
url_host_5th_last_part
url_host_registry_suffix     org
url_host_registered_domain   whwheelers.org
url_host_private_suffix      org
url_host_private_domain      whwheelers.org
url_host_name_reversed
url_protocol                 https
url_port
url_path                     /robots.txt
url_query
fetch_time                   2021-06-22 16:36:50.000
fetch_status                 301
fetch_redirect               https://www.whwheelers.org/robots.txt
content_digest               3I42H3S6NNFQ2MSVX7XZKYAYSCX5QBYJ
content_mime_type            text/html
content_mime_detected        text/html
content_charset
content_languages
content_truncated
warc_filename                crawl-data/CC-MAIN-2021-25/segments/1623488519183.85/robotstxt/CC-MAIN-20210622155328-20210622185328-00312.warc.gz
warc_record_offset           1854030
warc_record_length           639
warc_segment                 1623488519183.85
crawl                        CC-MAIN-2021-25
subset                       robotstxt
So url_host_3rd_last_part might be a winner for CGI comms fingerprinting!
Naive one for one index:
select * from "ccindex"."ccindex" where url_host_registered_domain = 'conquermstoday.com' limit 100;
have no results... data scanned: 5.73 GB
Let's see if they have any of the domain hits. Let's also restrict by date to try and reduce the data scanned:
select * from "ccindex"."ccindex" where
  fetch_time < TIMESTAMP '2014-01-01 00:00:00' AND
  url_host_registered_domain IN (
   'activegaminginfo.com',
   'altworldnews.com',
   ...
   'topbillingsite.com',
   'worldwildlifeadventure.com'
 )
Humm, data scanned: 60.59 GB and no hits... weird.
Sanity check:
select * from "ccindex"."ccindex" WHERE
  crawl = 'CC-MAIN-2013-20' AND
  subset = 'warc' AND
  url_host_registered_domain IN (
   'google.com',
   'amazon.com'
 )
has a bunch of hits of course. Data scanned: 212.88 MB, WHERE crawl and subset are a must! Should have read the article first.
Let's widen a bit more:
select * from "ccindex"."ccindex" WHERE
  crawl IN (
    'CC-MAIN-2013-20',
    'CC-MAIN-2013-48',
    'CC-MAIN-2014-10'
  ) AND
  subset = 'warc' AND
  url_host_registered_domain IN (
    'activegaminginfo.com',
    'altworldnews.com',
    ...
    'worldnewsandent.com',
    'worldwildlifeadventure.com'
 )
Still nothing found... they don't seem to have any of the URLs of interest?
Does not appear to have any reverse IP hits unfortunately: opendata.stackexchange.com/questions/1951/dataset-of-domain-names/21077#21077. Likely only has domains that were explicitly advertised.
We could not find anything useful in it so far, but there is great potential to use this tool to find new IP ranges based on properties of existing IP ranges. Part of the problem is that the dataset is huge, and is split by top 256 bytes. But it would be reasonable to at least explore ranges with pre-existing known hits...
We have started looking for patterns on 66.* and 208.*, both selected as two relatively far away ranges that have a number of pre-existing hits. 208 should likely have been 212 considering later finds that put several ranges in 212.
tcpip_fp:
  • 66.104.
    • 66.104.175.41: grubbersworldrugbynews.com: 1346397300 SCAN(V=6.01%E=4%D=1/12%OT=22%CT=443%CU=%PV=N%G=N%TM=387CAB9E%P=mipsel-openwrt-linux-gnu),ECN(R=N),T1(R=N),T2(R=N),T3(R=N),T4(R=N),T5(R=N),T6(R=N),T7(R=N),U1(R=N),IE(R=N)
    • 66.104.175.48: worlddispatch.net: 1346816700 SCAN(V=6.01%E=4%D=1/2%OT=22%CT=443%CU=%PV=N%DC=I%G=N%TM=1D5EA%P=mipsel-openwrt-linux-gnu),SEQ(SP=F8%GCD=3%ISR=109%TI=Z%TS=A),ECN(R=N),T1(R=Y%DF=Y%TG=40%S=O%A=S+%F=AS%RD=0%Q=),T1(R=N),T2(R=N),T3(R=N),T4(R=N),T5(R=Y%DF=Y%TG=40%W=0%S=Z%A=S+%F=AR%O=%RD=0%Q=),T6(R=N),T7(R=N),U1(R=N),IE(R=N)
    • 66.104.175.49: webworldsports.com: 1346692500 SCAN(V=6.01%E=4%D=9/3%OT=22%CT=443%CU=%PV=N%DC=I%G=N%TM=5044E96E%P=mipsel-openwrt-linux-gnu),SEQ(SP=105%GCD=1%ISR=108%TI=Z%TS=A),OPS(O1=M550ST11NW6%O2=M550ST11NW6%O3=M550NNT11NW6%O4=M550ST11NW6%O5=M550ST11NW6%O6=M550ST11),WIN(W1=1510%W2=1510%W3=1510%W4=1510%W5=1510%W6=1510),ECN(R=N),T1(R=Y%DF=Y%TG=40%S=O%A=S+%F=AS%RD=0%Q=),T1(R=N),T2(R=N),T3(R=N),T4(R=N),T5(R=Y%DF=Y%TG=40%W=0%S=Z%A=S+%F=AR%O=%RD=0%Q=),T6(R=N),T7(R=N),U1(R=N),IE(R=N)
    • 66.104.175.50: fly-bybirdies.com: 1346822100 SCAN(V=6.01%E=4%D=1/1%OT=22%CT=443%CU=%PV=N%DC=I%G=N%TM=14655%P=mipsel-openwrt-linux-gnu),SEQ(TI=Z%TS=A),ECN(R=N),T1(R=Y%DF=Y%TG=40%S=O%A=S+%F=AS%RD=0%Q=),T1(R=N),T2(R=N),T3(R=N),T4(R=N),T5(R=Y%DF=Y%TG=40%W=0%S=Z%A=S+%F=AR%O=%RD=0%Q=),T6(R=N),T7(R=N),U1(R=N),IE(R=N)
    • 66.104.175.53: info-ology.net: 1346712300 SCAN(V=6.01%E=4%D=9/4%OT=22%CT=443%CU=%PV=N%DC=I%G=N%TM=50453230%P=mipsel-openwrt-linux-gnu),SEQ(SP=FB%GCD=1%ISR=FF%TI=Z%TS=A),ECN(R=N),T1(R=Y%DF=Y%TG=40%S=O%A=S+%F=AS%RD=0%Q=),T1(R=N),T2(R=N),T3(R=N),T4(R=N),T5(R=Y%DF=Y%TG=40%W=0%S=Z%A=S+%F=AR%O=%RD=0%Q=),T6(R=N),T7(R=N),U1(R=N),IE(R=N)
  • 66.175.106
    • 66.175.106.150: noticiasmusica.net: 1340077500 SCAN(V=5.51%D=1/3%OT=22%CT=443%CU=%PV=N%G=N%TM=38707542%P=mipsel-openwrt-linux-gnu),ECN(R=N),T1(R=N),T2(R=N),T3(R=N),T4(R=N),T5(R=Y%DF=Y%TG=40%W=0%S=Z%A=S+%F=AR%O=%RD=0%Q=),T6(R=N),T7(R=N),U1(R=N),IE(R=N)
    • 66.175.106.155: atomworldnews.com: 1345562100 SCAN(V=5.51%D=8/21%OT=22%CT=443%CU=%PV=N%DC=I%G=N%TM=5033A5F2%P=mips-openwrt-linux-gnu),SEQ(SP=FB%GCD=1%ISR=FC%TI=Z%TS=A),ECN(R=Y%DF=Y%TG=40%W=1540%O=M550NNSNW6%CC=N%Q=),T1(R=Y%DF=Y%TG=40%S=O%A=S+%F=AS%RD=0%Q=),T2(R=N),T3(R=N),T4(R=N),T5(R=Y%DF=Y%TG=40%W=0%S=Z%A=S+%F=AR%O=%RD=0%Q=),T6(R=N),T7(R=N),U1(R=N),IE(R=N)
Hostprobes quick look on two ranges:
208.254.40:
... similar down

208.254.40.95	1334668500	down	no-response
208.254.40.95	1338270300	down	no-response
208.254.40.95	1338839100	down	no-response
208.254.40.95	1339361100	down	no-response
208.254.40.95	1346391900	down	no-response
208.254.40.96	1335806100	up	unknown
208.254.40.96	1336979700	up	unknown
208.254.40.96	1338840900	up	unknown
208.254.40.96	1339454700	up	unknown
208.254.40.96	1346778900	up	echo-reply (0.34s latency).
208.254.40.96	1346838300	up	echo-reply (0.30s latency).
208.254.40.97	1335840300	up	unknown
208.254.40.97	1338446700	up	unknown
208.254.40.97	1339334100	up	unknown
208.254.40.97	1346658300	up	echo-reply (0.26s latency).

... similar up

208.254.40.126	1335708900	up	unknown
208.254.40.126	1338446700	up	unknown
208.254.40.126	1339330500	up	unknown
208.254.40.126	1346494500	up	echo-reply (0.24s latency).
208.254.40.127	1335840300	up	unknown
208.254.40.127	1337793300	up	unknown
208.254.40.127	1338853500	up	unknown
208.254.40.127	1346454900	up	echo-reply (0.23s latency).

208.254.40.128	1335856500	up	unknown
208.254.40.128	1338200100	down	no-response
208.254.40.128	1338749100	down	no-response
208.254.40.128	1339334100	down	no-response
208.254.40.128	1346607900	down	net-unreach
208.254.40.129	1335699900	up	unknown

... similar down
Suggests exactly 127 - 96 + 1 = 31 IPs.
208.254.42:
... similar down

208.254.42.191	1334522700	down	no-response
208.254.42.191	1335276900	down	no-response
208.254.42.191	1335784500	down	no-response
208.254.42.191	1337845500	down	no-response
208.254.42.191	1338752700	down	no-response
208.254.42.191	1339332300	down	no-response
208.254.42.191	1346499900	down	net-unreach

208.254.42.192	1334668500	up	unknown
208.254.42.192	1336808700	up	unknown
208.254.42.192	1339334100	up	unknown
208.254.42.192	1346766300	up	echo-reply (0.40s latency).
208.254.42.193	1335770100	up	unknown
208.254.42.193	1338444900	up	unknown
208.254.42.193	1339334100	up	unknown

... similar up

208.254.42.221	1346517900	up	echo-reply (0.19s latency).
208.254.42.222	1335708900	up	unknown
208.254.42.222	1335708900	up	unknown
208.254.42.222	1338066900	up	unknown
208.254.42.222	1338747300	up	unknown
208.254.42.222	1346872500	up	echo-reply (0.27s latency).
208.254.42.223	1335773700	up	unknown
208.254.42.223	1336949100	up	unknown
208.254.42.223	1338750900	up	unknown
208.254.42.223	1339334100	up	unknown
208.254.42.223	1346854500	up	echo-reply (0.13s latency).

208.254.42.224	1335665700	down	no-response
208.254.42.224	1336567500	down	no-response
208.254.42.224	1338840900	down	no-response
208.254.42.224	1339425900	down	no-response
208.254.42.224	1346494500	down	time-exceeded

... similar down
Suggests exactly 223 - 192 + 1 = 31 IPs.
Let's have a look at the file 68: outcome: no clear hits like on 208. One wonders why.
It does appears that long sequences of ranges are a sort of fingerprint. The question is how unique it would be.
First:
n=208
time awk '$3=="up"{ print $1 }' $n | uniq -c | sed -r 's/^ +//;s/ /,/' | tee $n-up-uniq
t=$n-up-uniq.sqlite
rm -f $t
time sqlite3 $t 'create table tmp(cnt text, i text)'
time sqlite3 $t ".import --csv $n-up-uniq tmp"
time sqlite3 $t 'create table t (i integer)'
time sqlite3 $t '.load ./ip' 'insert into t select str2ipv4(i) from tmp'
time sqlite3 $t 'drop table tmp'
time sqlite3 $t 'create index ti on t(i)'
This reduces us to 2 million IP rows from the total possible 16 million IPs.
OK now just counting hits on fixed windows has way too many results:
sqlite3 208-up-uniq.sqlite "\
SELECT * FROM (
  SELECT min(i), COUNT(*) OVER (
    ORDER BY i RANGE BETWEEN 15 PRECEDING AND 15 FOLLOWING
  ) as c FROM t
) WHERE c > 20 and c < 30
"
Let's try instead consecutive ranges of length exactly 31 instead then:
sqlite3 208-up-uniq.sqlite <<EOF
SELECT f, t - f as c FROM (
  SELECT min(i) as f, max(i) as t
  FROM (SELECT i, ROW_NUMBER() OVER (ORDER BY i) - i as grp FROM t)
  GROUP BY grp
  ORDER BY i
) where c = 31
EOF
271. Hmm. A bit more than we'd like...
Another route is to also count the ups:
n=208
time awk '$3=="up"{ print $1 }' $n | uniq -c | sed -r 's/^ +//;s/ /,/' | tee $n-up-uniq-cnt
t=$n-up-uniq-cnt.sqlite
rm -f $t
time sqlite3 $t 'create table tmp(cnt text, i text)'
time sqlite3 $t ".import --csv $n-up-uniq-cnt tmp"
time sqlite3 $t 'create table t (cnt integer, i integer)'
time sqlite3 $t '.load ./ip' 'insert into t select cnt as integer, str2ipv4(i) from tmp'
time sqlite3 $t 'drop table tmp'
time sqlite3 $t 'create index ti on t(i)'
Let's see how many consecutives with counts:
sqlite3 208-up-uniq-cnt.sqlite <<EOF
SELECT f, t - f as c FROM (
  SELECT min(i) as f, max(i) as t
  FROM (SELECT i, ROW_NUMBER() OVER (ORDER BY i) - i as grp FROM t WHERE cnt >= 3)
  GROUP BY grp
  ORDER BY i
) where c > 28 and c < 32
EOF
Let's check on 66:
grep -e '66.45.179' -e '66.45.179' 66
not representative at all... e.g. several convfirmed hits are down:
66.45.179.215   1335305700      down    no-response
66.45.179.215   1337579100      down    no-response
66.45.179.215   1338765300      down    no-response
66.45.179.215   1340271900      down    no-response
66.45.179.215   1346813100      down    no-response
Let's check relevancy of known hits:
grep -e '208.254.40' -e '208.254.42' 208 | tee 208hits
Output:
208.254.40.95	1355564700	unreachable
208.254.40.95	1355622300	unreachable
208.254.40.96	1334537100	alive, 36342
208.254.40.96	1335269700	alive, 17586

..

208.254.40.127	1355562900	alive, 35023
208.254.40.127	1355593500	alive, 59866
208.254.40.128	1334609100	unreachable
208.254.40.128	1334708100	alive from 208.254.32.214, 43358
208.254.40.128	1336596300	unreachable
The rest of 208 is mostly unreachable.
208.254.42.191	1335294900	unreachable
...
208.254.42.191	1344737700	unreachable
208.254.42.191	1345574700	Icmp Error: 0,ICMP Network Unreachable, from 63.111.123.26
208.254.42.191	1346166900	unreachable
...
208.254.42.191	1355665500	unreachable
208.254.42.192	1334625300	alive, 6672
...
208.254.42.192	1355658300	alive, 57412
208.254.42.193	1334677500	alive, 28985
208.254.42.193	1336524300	unreachable
208.254.42.193	1344447900	alive, 8934
208.254.42.193	1344613500	alive, 24037
208.254.42.193	1344806100	alive, 20410
208.254.42.193	1345162500	alive, 10177
...
208.254.42.223	1336590900	alive, 23284
...
208.254.42.223	1355555700	alive, 58841
208.254.42.224	1334607300	Icmp Type: 11,ICMP Time Exceeded, from 65.214.56.142
208.254.42.224	1334681100	Icmp Type: 11,ICMP Time Exceeded, from 65.214.56.142
208.254.42.224	1336563900	Icmp Type: 11,ICMP Time Exceeded, from 65.214.56.142
208.254.42.224	1344451500	Icmp Type: 11,ICMP Time Exceeded, from 65.214.56.138
208.254.42.224	1344566700	unreachable
208.254.42.224	1344762900	unreachable
Let's try with 66. First there way too much data, 9 GB, let's cut it down:
n=66
time awk '$3~/^alive,/ { print $1 }' $n | uniq -c | sed -r 's/^ +//;s/ /,/' | tee $n-up-uniq-c
OK down to 45 MB, now we can work.
grep -e '66.45.179' -e '66.104.169' -e '66.104.173' -e '66.104.175' -e '66.175.106' '66-alive-uniq-c' | tee 66hits
Nah, it's full of holes:
4,66.45.179.187
12,66.45.179.188
2,66.45.179.197
1,66.45.179.202
2,66.45.179.205
2,66.45.179.206
1,66.45.179.207
won't be able to find new ranges here.
Domain list only, no IPs and no dates. We haven't been able to extract anything of interest from this source so far.
Domain hit count when we were at 69 hits: only 9, some of which had been since reused. Likely their data collection did not cover the dates of interest.

Pinned article: Introduction to the OurBigBook Project

Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
Everyone is welcome to create an account and play with the site: ourbigbook.com/go/register. We belive that students themselves can write amazing tutorials, but teachers are welcome too. You can write about anything you want, it doesn't have to be STEM or even educational. Silly test content is very welcome and you won't be penalized in any way. Just keep it legal!
We have two killer features:
  1. topics: topics group articles by different users with the same title, e.g. here is the topic for the "Fundamental Theorem of Calculus" ourbigbook.com/go/topic/fundamental-theorem-of-calculus
    Articles of different users are sorted by upvote within each article page. This feature is a bit like:
    • a Wikipedia where each user can have their own version of each article
    • a Q&A website like Stack Overflow, where multiple people can give their views on a given topic, and the best ones are sorted by upvote. Except you don't need to wait for someone to ask first, and any topic goes, no matter how narrow or broad
    This feature makes it possible for readers to find better explanations of any topic created by other writers. And it allows writers to create an explanation in a place that readers might actually find it.
    Figure 1.
    Screenshot of the "Derivative" topic page
    . View it live at: ourbigbook.com/go/topic/derivative
  2. local editing: you can store all your personal knowledge base content locally in a plaintext markup format that can be edited locally and published either:
    This way you can be sure that even if OurBigBook.com were to go down one day (which we have no plans to do as it is quite cheap to host!), your content will still be perfectly readable as a static site.
    Figure 2.
    You can publish local OurBigBook lightweight markup files to either https://OurBigBook.com or as a static website
    .
    Figure 3.
    Visual Studio Code extension installation
    .
    Figure 4.
    Visual Studio Code extension tree navigation
    .
    Figure 5.
    Web editor
    . You can also edit articles on the Web editor without installing anything locally.
    Video 3.
    Edit locally and publish demo
    . Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.
    Video 4.
    OurBigBook Visual Studio Code extension editing and navigation demo
    . Source.
  3. https://raw.githubusercontent.com/ourbigbook/ourbigbook-media/master/feature/x/hilbert-space-arrow.png
  4. Infinitely deep tables of contents:
    Figure 6.
    Dynamic article tree with infinitely deep table of contents
    .
    Descendant pages can also show up as toplevel e.g.: ourbigbook.com/cirosantilli/chordate-subclade
All our software is open source and hosted at: github.com/ourbigbook/ourbigbook
Further documentation can be found at: docs.ourbigbook.com
Feel free to reach our to us for any help or suggestions: docs.ourbigbook.com/#contact