## The reason behind IPv6 adoption rate dramatical drop in China according to Google measurements?

Google has an IPv6 measurement page that reports that their numbers report on the percentage of users that access Google over IPv6.

According to the report by Jan 2020 0.3% of users in China used IPv6 to access Google

However, looking at this metric in dynamic we see the substantial drop starting from June 2019.

I failed to find any solid news that may cause such behavior. I have two hypotheses in mind.

1. Also as it is a percentage metric, they can adjust their calculation on the total internet penetration rate in China.
2. Previously open discussions between netizens took place on Google Plus groups. In April 2019,
Chinese-language blogs, forums, and groups. For obvious reasons,
discussions must be hosted outside China, and posters must register under pseudonyms. So probably that caused the shift from Google services but I hardly believe that it may cause such plummet.

## Compact embedding of the room of signed radon measurements in the Sobolev room \$ W ^ {- 1, q} \$ from Evans paper; Does it work in a room dimension?

Background: I am working on a PDE problem where I have an approximate sequence of measurement functions and have to embed them compactly in a negative Sobolev space $$W ^ {- m, q}$$ on the limited interval in $$mathbb {R}$$. I am mainly interested in the rooms in which $$q = 2$$. I only found such an embedding in the one sentence from the paper:

Evans – Weak Convergence Methods for Nonlinear Partial Differential Equations, 1990.

Sentence 6 (Compactness for measures, page 7): Take the order $${ mu_k } _ {k = 1} ^ { infty}$$ is limited in $$mathcal {M} (U)$$, $$U subset mathbb {R} ^ n$$. Then $${ mu_k } _ {k = 1} ^ { infty}$$ is in precompact $$W ^ {- 1, q} (U)$$ for each $$1 leq q <1 ^ *$$.

Here $$mathcal {M} (U)$$ represents the space of the signed radon measurements $$U$$ with finite mass, $$U subset mathbb {R} ^ n$$ is an open, limited, smooth subset of $$mathbb {R} ^ n, n geq 2$$ and $$1 ^ * = frac {n} {n-1}$$ represents a Sobolev conjugate.

The identical sentence (Lemma 2.55, page 38) is given in the book: Malek, Necas, Rokyta, Ruzicka – Weak and measurable solutions for evolutionary PDEs, 1996, with one difference that instead of $$1 leq q <1 ^ *$$, There it says $$1 leq q < frac {n} {n-1}$$ (This is not explicitly written here $$n geq 2$$).

My question: does sentence 6 work in one dimension ($$n = 1$$)? That means we have a compact embedding of the room $$mathcal {M} (U)$$ in the room $$W ^ {- 1, q} (U)$$, Where $$U subset mathbb {R}$$?

• I assume that we have a compact embedding in $$W ^ {- 1, q} (U)$$, then we have it in the $$W ^ {- m, q} (U), m geq 1$$?
• Are there other measurement spaces (e.g. space of finite positive dimensions)? $$mathcal {M} _ +$$The space of probability measures with a finite first moment $$Pr_1$$etc.), which are compactly embedded in some negative Sobolev spaces $$W ^ {- m, q} (U)$$?

I think if we use the definition of the Sobolev conjugate: $$frac {1} {p ^ *} = frac {1} {p} – frac {1} {n}$$, we get for $$p = 1, n = 1$$ the $$frac {1} {1 ^ *} = frac {1} {1} – frac {1} {1} Rightarrow 1 ^ * = infty$$. So we would have sentence 6 (maybe) working for everyone $$1 leq q < infty$$ (and then for $$q = 2$$ Likewise)? If we use $$p ^ * = frac {np} {n-p}$$ we would have for $$n = 1,$$ $$p ^ * = frac {p} {1-p}$$ and we couldn't take here $$p = 1$$ and get $$p ^ *$$.

I don't usually deal with the measured and negative Sobolev spaces, so I don't know much about them. Help would be great and I definitely need it. And any additional reference besides the two above would be nice. Thanks in advance.

## dnd 3.5e – three-dimensional measurements

As usual in D & D 3.5e, I refer to "squares" when I really mean "cubes". Just take "square" as game jargon, which it is in this case.

Anyway, the "5-foot, 10-foot." is an approximation of the cost of diagonals $$1.5 times$$ the distance, which itself approximates the cost $$sqrt {2} times approx.1,414 times$$ (Pythagoras' theorem says a right angle $$a$$ because the legs have a hypotenuse of $$sqrt {2} times a$$).

A "double diagonal" is the hypotenuse of a right angle with legs of $$a$$ and $$sqrt {2} times a$$, this is how the hypotenuse will be $$sqrt {3} times a$$, so we need an approximation of $$sqrt {3} times approx.1,732 times$$. If we round it up $$1.75 times$$we need "5 feet, 10 feet, 10 feet, 10 feet". (So ​​moving four squares costs 35 feet of motion –$$1.75 times$$ the 20 ft. it would normally take.

Obviously "5 feet, 10 feet, 10 feet, 10 feet". is a pain, and it is also much more questionable to start with 5 feet on the first square than with the "5 feet, 10 feet". to plan. It's also less clear how to combine it with a "single diagonal" move on the same lap – you probably shouldn't be able to move a double diagonal square 5 feet and then another diagonal for another 5 feet.

The most accurate way to fix this is to imagine the "5 feet, 10 feet". Rule than actually "7.5 feet". every time – it's really "7.5 feet (rounded to 5 feet), 15 feet (rounded to 15 feet, 10 feet above the first)". For the double diagonals, consider 8.75 feet that are still rounded to 5 feet the first time, and then 17.5 feet (rounded to 15 feet total distance), 26.25 feet (25 feet), 35 feet (35 feet) ).

Perhaps easier to see in tabular form. Here, $$d$$ is the actual, unrounded distance, $$lfloor d rfloor$$ for the rounded distance and $$Delta lfloor d rfloor$$ for the cost of the last step. Each step should cost what is listed as $$Delta lfloor d rfloor$$.

$$begin {array} {c | c | c} { textbf {even} \ begin {array} {c c c} d & lfloor d rfloor & Delta lfloor d rfloor \ hline phantom {0} 5 & phantom {0} 5 & 5 \ 10 & 10 & 5 \ 15 & 15 & 5 \ 20 & 20 & 5 \ end {array} }} & { textbf {Single Diagonal} \ begin {array} {c c c} d & lfloor d rfloor & Delta lfloor d rfloor \ hline phantom {0} 7.5 & phantom {0} 5 & phantom {0} 5 \ 15 phantom {.0} & 15 & 10 \ 22.5 & 20 & Phantom {0} 5 \ 30 phantom {.0} & 30 & 10 \ end {array} }} & { textbf {double diagonal} \ begin {array} {c c c} d & lfloor d rfloor & Delta lfloor d rfloor \ hline phantom {0} 8,75 & phantom {0} 5 & phantom {0} 5 \ 17.5 Phantom {0} & 15 & 10 \ 26.25 & 25 & 10 \ 35 phantom {.00} & 35 & 10 \ end {array} }} end {array}$$

Combining single and double diagonals is then possible using these fractions – 7.5 feet. + 8.75 feet is 16.25 feet, so the second step when moving a single diagonal and a double diagonal is 10 feet, but the 1.25 feet "extra" is less than the 2.5 feet "extra" of two Double diagonal movements. By tracking this extra, you can track how far a character has actually moved.

And if you are actually dealing with this mess, I greet you because it is crazy. Unfortunately, this is the reality of 3D motion in D&D 3.5e. I strongly recommend a gentleman's approval to just keep things on the floor or fly abstract in some way – here's mine.

## GPS – How do you calculate the location long and long from the raw measurements on Android phones?

I am researching GNSS. I used GNSS logger to take measurements, but couldn't understand how these measurements became long and lat locations. The image below shows the measurement attributes that I collected with the GNSS logger.

## How can it be demonstrated that the space for non-negative radon measurements is complete?

To let $$mathcal {M} ^ {+} ( mathbb {R} _ {+})$$ There should be room for non-negative radon measurements $$mathbb {R} _ {+}$$ and define the metric $$rho$$ on $$mathcal {M} ^ {+} ( mathbb {R} _ {+})$$ how $$rho ( mu, nu) = sup left { int _ { mathbb {R} _ {+}} psi d ( mu – nu) ~ | ~ psi in C ^ {1} ( mathbb {R} _ {+}), | psi | _ { infty} le 1, | partielle_ {x} psi | _ { infty} le 1 correct }.$$ How to prove $$mathcal {M} ^ {+} ( mathbb {R} _ {+})$$ is completely w.r.t. $$rho$$, I know that $$lim_ {n to infty} rho ( mu_ {n}, mu) = 0 iff mu_ {n} to mu ~ text {eng for} ~ n to infty.$$
But how can over-equivalence help us prove completeness?

## postgresql – Database design suggestions for data that are currently available as CSV files and contain time series x location measurements for various conditions

I apologize if this is a pseudo question, as this is the first time I am trying to design a complex database.
I did an experiment to measure time along different points along a power line. The data was recorded in several CSV files. Each CSV file contains data on a specific measurement for a range of time values ​​(rows) and a range of distance values ​​(columns). These measurements were carried out for different power lines under different test conditions. Each category of line and test conditions is registered in the name of the CSV file and the corresponding type of measurement.
I don't need insertion efficiency since I'll do it one day, but I need an efficient way to build queries to display the data in different ways. I tried to migrate my data to TimeScaleDB, but I'm not sure if this is a good choice. I read in the TimeScaleDB documentation that there are two ways to model time series data: Wide Table or Narrow Table. In my case, however, I need a mix of these two types because I have a lot of measurements with the same timestamp, but there will be a lot of patterns of them that match every location, test condition, and power line type.
Do you have any suggestions on how this data should be modeled or which engine would be suitable in this case?

## networking – explanations for differences between Internet bandwidth measurements with MikroTik and local clients

Problem:

I use an internet provider that offers a symmetrical gigabit internet.
I recently noticed a noticeable slowdown in my Internet connection (up to 50 Mbit / s and up to 100 Mbit / s, LAN). A few months ago I reached speeds around 900/950 with exactly the same hardware / software setting.

My provider sent me a new fiber patch cable and a certified, gigabit-capable router for debugging.

I repeated the speed measurements according to the instructions of my provider:
https://www.init7.net/de/support/faq/speedtest/ with https://init7.speedtest.net/
with 3 Win10 clients and 1 Ubuntu 19.1 client. All firewalls and antivirus software were disabled during the test. All clients have gigabit-capable hardware. I still couldn't get better speeds.

For further debugging, my provider sent me a new fiber patch cable and a MikroTik router (CCR1009-8G-1S-1S +, RouterOS v6.39.2) and carried out remote speed measurements directly on the MikroTik router. They reported speeds of 900/900 Mbit / s directly on the MikroTik.

Now my provider states that my local customer is causing a slowdown in internet bandwidth.
I really doubt that something is wrong with my local customers.

Question:

Is there a possible explanation for the differences between the Internet bandwidth measured with the MikroTik and the local clients?

## Reference requirement – law of large numbers for random Dirac measurements

Accept $${X_1, … X_n }: Omega to mathbb {R} ^ p$$ i.i.d. Random vectors with general probability law / measure $$p$$i.e. $$Prob (X_i ^ {- 1} (E)) = p (E) forall E subset mathbb {R} ^ p$$ Borel measurable.

Consider the random Dirac measurements $$delta_ {X_i}$$and their average, which is a random measure of probability $$mathbb {R} ^ p$$, defined by $$frac {1} {n} sum_ {i = 1} ^ {n} delta_ {X_i}$$, I would like to know if $$frac {1} {n} sum_ {i = 1} ^ {n} delta_ {X_i}$$ weakly converges to the deterministic measure $$p$$.

i.e. any continuous, limited function $$f: mathbb {R} ^ p to mathbb {R}$$we have to have:

$$frac {1} {n} sum_ {i = 1} ^ {n} {f (X_i)} to int _ { mathbb {R} ^ p} f (x) dp (x)$$
as a convergence in probability a sequence of random variables $$frac {1} {n} sum_ {i = 1} ^ {n} {f (X_i)}$$?

In a related note, I would also like to know whether or not the following applies:

If a sequence of random measures converges in probability to a deterministic measure, is it equivalent to almost certainly have the same convergence? This question is motivated by the fact that if a sequence of random variables converges with a different probability, the convergence a.s.

P.S. I understand that many of you may find this question difficult to answer. Some references are therefore very much appreciated!

## Grafana – combine several SELECTs from different measurements

I'm totally new here, I tried to find a solution, and I found the following:
Influxdb and Grafana combine several SELECTs

However, this works fine if we have different fields from the same measurement, but this does not work (for me) when adding values ​​from different measurements. what am I doing wrong here? Could you please take a look?

``````SELECT ( sum("valFromA") + sum("valFromB") )
FROM (
SELECT mean("valueA") AS "valFromA"
FROM "DSA"
WHERE "hostname" = 'host' AND \$timeFilter GROUP BY time(\$__interval) fill(null)
),
(
SELECT mean("valueB") AS "valFromB"
FROM "DSB"
WHERE "hostname" = 'host' AND \$timeFilter GROUP BY time(\$__interval) fill(null)
)
GROUP BY time(\$__interval) fill(null)
``````

In this case, nothing is drawn.

By the way – if I divide these queries into separate ones – there is data back. The combination as above shows an empty diagram. e.g.

Single selection is ok:

``````SELECT ( sum("valFromA") )
FROM (
SELECT mean("valueA") AS "valFromA"
FROM "DSA"
WHERE "hostname" = 'host' AND \$timeFilter GROUP BY time(\$__interval) fill(null)
)
GROUP BY time(\$__interval) fill(null)
``````

Grafana 6.4.4 + Influx 5.1.0

Greetings,
Mike

## real analysis – order of Lebesgue measurements

To let $$mu_i$$ be the Lebesgue measure.
To let $$d mu_0 = dx$$ ; $$0 leq a leq b leq 1$$ and keep in mind:

$$mu_1 [a, b] = int_ {a} ^ {b} x d mu_0$$

$$mu_2 [a, b] = int_ {a} ^ {b} x d mu_1$$

And so on until:

$$mu_k [a, b] = int_ {a} ^ {b} xd mu_ {k-1}$$

What is then $$mu_k$$?

I have not studied any theorems with derivatives like Radon Nikodym, but my professor said that they are not necessary for it.

My Thought: I can prove and then evaluate the Fundamentals Theorem of Lebesgue Integrals $$mu_1$$ and the rest follows by induction.

Is this the right way ? If this is not the case, is there any indication of how to proceed?