## network: slow Nmap scanning in some IP ranges

I am a security rookie trying to scan a private network in the range 10.0.0.0 – 10.0.15.254 using nmap on Kali Linux.

The routing table:

``````route -n

Kernel IP routing table
Destination     Gateway         Genmask         Flags Metric Ref    Use Iface
0.0.0.0         10.0.2.2        0.0.0.0         UG    100    0        0 eth0
10.0.0.0        192.168.0.1     255.255.255.0   UG    1      0        0 tun0
10.0.0.0        192.168.0.1     255.255.252.0   UG    0      0        0 tun0
10.0.2.0        192.168.0.1     255.255.255.0   UG    1      0        0 tun0
10.0.2.0        0.0.0.0         255.255.255.0   U     100    0        0 eth0
10.0.4.0        192.168.0.1     255.255.252.0   UG    0      0        0 tun0
192.168.0.0     0.0.0.0         255.255.255.0   U     0      0        0 tun0
``````

I tried to scan the entire range of the private network at once, but this caused nmap to close after a period of time.

Then I tried to scan blocks of 256 IP addresses at once, starting with

``````nmap -sS 10.0.0.0/24
``````

Scans in the range 10.0.0.0 – 10.0.4.255 are completed quickly, but the scans in the range 10.0.5.0 – 10.0.15.254 are exponentially slower and generally do not complete.

Is there anything in the network routing that is affecting this? Is there something I am missing completely?

## How to generate ranges from two numbers in Java with while?

Generate Ranges from two numbers in Java with while

## command line – How to obtain IP ranges from ASN?

I saw in other articles related to the exchange of the stack how to obtain an IPS Range for an ASN.
Here is one specifically: How to find all the IP ranges that belong to a particular AS?

This is for TOT Public Company Limited (Thailand)

1) This command will return both IPv4 and IPv6 and will return 817 results.

`whois -h whois.radb.net -- "-i origin 38040" | awk '/^route/ {print \$2;}' | sort | uniq`

When I verify this, your included IP ranges include "Akamai technologies"In the US, I don't think a company in Thailand will include it in the previous whois. But according to this utility, https://ipinfo.io/AS38040 are related.

However, even this list only has 35 results and not 817.

2) When I try this suggested by a user of the stack:

`whois -h whois.radb.net -- '-i origin 38040' | grep -Eo "((0-9.)+){4}/(0-9)+" | head`

This one comes back 10 results

3) If I go to websites like `hackertarget` or `enjen` I get a completely different list.

https://api.hackertarget.com/aslookup/?q=AS38040
35 results

https://www.enjen.net/asn-blocklist/index.php?asn=38040&type=iplist
39 results

It seems to me like hackertarget, leaveor ipinfo.io they are a precise source of truth to obtain the data because the IP ranges returned seem more reasonable to me, but I am trying to understand why `whois` It is very far from the base.

Questions
1. Why is my first `whois` What do users recommend in stack exchanges that return so many results?
2. How are the places like `enjen` Y `hackertarget` presenting your smallest list and it is possible to imitate that with the CLI `whois` I send?

Thanks for any help!

## algebraic geometry ag: are the smooth coherent ranges of rank one always the same as the line beams?

Suppose $$mathcal {L}$$ finished $$mathbb {CP} ^ 2$$ is given by the following exact short sequence,

$$0 rightarrow mathcal {L} rightarrow mathcal {O} (4) oplus mathcal {O} (2) oplus mathcal {O} (8) oplus mathcal {O} (1) oplus mathcal {O} (1) rightarrow mathcal {O} (14) oplus mathcal {O} (8) oplus mathcal {O} (5) oplus mathcal {O} (2) rightarrow 0.$$

It is easy to verify that $$mathcal {E} xt ^ i ( mathcal {L}, mathcal {O}) = 0$$ for $$i ge1$$. So $$mathcal {L}$$ It is a coherent sheaf of rank one without problems. So I wait $$mathcal {L}$$ it should be a line pack but $$Ch_2 ( mathcal {L}) ne frac {1} {2} c_1 ( mathcal {L})$$, contrary to what happens with line packages!

I don't know what's wrong here …

## Can you put ranges in knowledge skills that are not class skills?

Can you put ranges in knowledge skills that are not class skills?

As a sorcerer, Knowledge Engineering is not a class skill, can I continue to qualify to be able to do Engineering controls after leveling up?

## Verify regexmatch in various ranges in Google Sheets

I have a sheet that is automatically filled with a subset of articles (each a row with a column that represents diverse information such as the title, year of publication, author's name, etc.) based on a set of labels ( which have their own column in G6: G). There is a master tab "All publications" and, using the filter function, I check each cell in the column & # 39; main label & # 39; (& # 39; All publications & # 39;! F6: F) and if it matches one of the cells in the & # 39; labels for the sheet & # 39; column of which item is included. Example: you might want all articles on & # 39; fruit rot & # 39; be included in a new sheet called & # 39; Produce & # 39 ;. I already have this functionality in place with the following filter:

``````=FILTER('All Publications'!A6:A,
REGEXMATCH(LOWER('All Publications'!F6:F), LOWER(TEXTJOIN("|", TRUE,G5:G))))
``````

G5: G is the column where the labels for the given sheet are stored. Now I have two additional columns called & # 39; secondary tab & # 39; (& # 39; All publications & # 39 ;! G6: G) and & # 39; tertiary tab & # 39; (& # 39; All publications & # 39 ;! H6: H) respectively. I want to be able to include them in the search. In other words: I want an article to fill out my sheet if a match is found for any of the labels in G5: G in the ranges & # 39; All publications & # 39 ;! F6: F O & # 39; All publications & # 39; G6: G O & # 39; All publications & # 39 ;! H6: H. How do I do this? It is important to know that these articles contain hyperlinks and so using the QUERY function is not an option.

## gn. general topology: Sobolev tensor spaces and finite ranges

Leave $$W2,2 ( Omega_i)$$, $$Omega_i = (-1,1)$$, $$i = 1, ldots, d$$ are the Sobolev spaces of integrable square functions twice weakly differentiable. Leave more $$otimes_a$$ denote the algebraic tensor product (in other words, the non-topological) and $$Omega = Omega_1 times ldots times Omega_d = (-1,1) ^ d$$.

All functions in space. $$mathcal {W}: = W ^ {2,2} ( Omega_1) otimes_a ldots otimes_a W ^ {2,2} ( Omega_d)$$ they are continuous, that is $$mathcal {W} subset C ^ 0 ( Omega)$$. The induced norm $$| cdot | _ { mathcal {W}}$$ is stronger than the norm $$| cdot | 2.2$$ in $$W2.2 ( Omega)$$.

1. Is $$mathcal {W}$$ dense in $$W2.2 ( Omega)$$ (with respect to the norm $$| cdot | 2.2$$) Therefore it is $$overline { mathcal {W}} ^ { | cdot | 2,2 = W2,2 ( Omega)$$?

2. It is the semi standard $$| cdot | 2.2$$, defined through $$| u | _ {2,2} ^ 2: = sum_ {| α | = 2} | D ^ { alpha} u | L 2 ^ 2$$, equivalent to $$| cdot | 2.2$$ in the quotient space of functions that only differ by a constant function?

3. Leave $$r in mathbb {N}$$, $$mathcal {W} _r: = bigcap _ { mu = 1} ^ {d-1} { phi in mathcal {W} mid exist g_i in W ^ {2,2} ( Omega_1) otimes_a ldots otimes_a W ^ {2,2} ( Omega_ mu), h_i exists in W ^ {2,2} ( Omega _ { mu + 1}) otimes_a ldots otimes_a W ^ {2,2} ( Omega_d): phi (x) = sum_ {i = 1} ^ r g_i (x_1, ldots, x_ mu) cdot h_i (x _ { mu + 1}, ldots, x_d), forall x in Omega }$$ the range space (tensor train) $$r$$ functions in $$mathcal {W}$$. Then this space $$mathcal {W} _r$$ is closed under $$| cdot | 2.2$$. Leave more $$p_i in Omega, i = 1, ldots, m$$for some $$m in mathbb {N}$$Y $$y in mathbb {R} ^ m$$.
Is the problem $$u ^ ast = mathrm {argmin} _ {u in mathcal {W} _r} sum_ {i = 1} ^ m (u (p_i) – y_i) ^ 2 + | u | 2, 2 ^ 2$$ therefore well defined (based on this property of $$mathcal {W} _r$$And is there such a minimizer (regardless of its uniqueness)?

I firmly believe that all three statements are true, but I don't know easy ways to prove it. As this is a bit off topic for me, I also have some problems judging what counts as a rigorous argument. I have seen variants of these questions, but I could not find an answer specific enough.

I found a comment about the validity of $$1.$$ in a tensor calculation book, but no evidence is provided. Point $$2.$$ it must be provided by Poincaré's inequality, although it must be applied on two levels, and the devil is a bit in the details. The first part of the article $$3.$$ it requires knowledge about tensor formats, so it can be difficult to answer, but under the assumption that $$mathcal {W} _r$$ It is closed, the second part is still of great interest to me. Of course, I appreciate any help regarding any of the three questions.

## Countifs between ranges

I'm struggling to pull under the conditions together. I would like to have a formula that counts values ​​between `one` Y `5` only when cell U13 contains the text `1-5`.

``````= COUNTRIES (U13, "1-5")
``````

``````= COUNTRIES (K2: K22, "> = 1", K2: K22, "<= 5")
``````

## seo – Why do my different product pages with similar content have different ranges?

We have a car website based on products where we publish content for cars and bicycles. This includes car images, videos, prices, comments, similar offers.

We want to improve our Google SEO rankings for these product pages. To my surprise, few of these pages of models have the rank 1, while others have the 5, although the quantity and quality of the content of these models is very identical. In addition, the classification pattern is stable for months, so it is not random. In addition, I compared the pages of competition models for the same product page and that is also identical. Now I wonder why Google is ranking us low for few products while higher for another product. Is there any way in which I can detect the differences due to which this classification difference exists?

I reviewed some things but I could not get a convincing answer:

1) Domain score: although the competition has better DA, but that does not explain why they have a lower rank for some models with identical content.

2) Lighthouse / Google page speed score: we have better scores and page usage time here compared to the competition.

3) Page score: I'm not sure if we can get this type of score for each model page.

4) Time used, bounce rate and exit rate: all these indicators that give information about user feedback have similar numbers for both models, that is, where we classify 1 versus where we classify 5

5) Images / videos are there with similar quality

6) The price information is available for both

7) Reviews are available for both

8) Similar offers are available for both

9) Both have a similar structured fragment

10) The title description URL has been formed with identical logic for all products

What should I see to detect this difference to see how I can improve the range of product pages with lower ranges?