Ato Kms Rate 2023

[Yiraika Daimaru]

OnSept. 16, 2020, the FCC announced the opening of a second funding year 2020 filing window to allow schools to request additional E-Rate funding specifically to address urgent on-campus bandwidth needs resulting from the heightened reliance on remote learning during the COVID-19 pandemic. The second filing window closed on October 16, 2020.

The FCC's E-Rate program makes telecommunications and information services more affordable for schools and libraries. With funding from the Universal Service Fund (fcc.gov/general/universal-service-fund), E-Rate provides discounts for telecommunications, Internet access, and internal connections to eligible schools and libraries.

The ongoing proliferation of innovative digital learning technologies and the need to connect students, teachers and consumers to jobs, life-long learning, and information have led to a steady rise in demand for bandwidth in schools and libraries. In recent years, the FCC refocused E-Rate from legacy telecommunications services to broadband, with a goal to significantly expand Wi-Fi access. These steps to modernize the program are helping E-Rate keep pace with the need for increased Internet access. (Learn more about modernization of the E-Rate program: fcc.gov/e-rate-update.)

Eligible schools and libraries may receive discounts on telecommunications, telecommunications services, and Internet access, as well as internal connections, managed internal broadband services and basic maintenance of internal connections.

Discounts range from 20 to 90 percent and are based on the poverty level of the schools. Rural schools and libraries may also receive a higher discount. Recipients must pay some portion of the service costs.

An eligible school or library (see eligibility definitions at usac.org/e-rate/applicant-process/before-you-begin/school-and-library-eligibility/) identifies goods or services it needs and submits a request for competitive bids to the Universal Service Administrative Company (USAC). USAC posts these requests on its website for vendors to bid on. After reviewing the vendors' bids, the school or library selects the most cost-effective eligible products and services using price as the primary factor. It then applies to USAC for approval for the desired purchases.

Next, USAC issues funding commitments to eligible applicants. When a vendor provides the selected services, either the vendor or the applicant submits requests to USAC for reimbursement of the approved discounts.

To find which schools and libraries in your area benefit from E-Rate, use USAC's search tools to view public E-rate data, including commitment and other funding tools, at usac.org/e-rate/resources/tools/.

The FCC's plan complements the efforts of states and localities to bring advanced telecommunications and information services to schools and libraries. When the E-Rate program was established in 1996, only 14 percent of the nation's K-12 classrooms had access to the Internet.

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GSA establishes the rates that federal agencies use to reimburse their employees for lodging and meals and incidental expenses incurred while on official travel within the continental United States (CONUS). A standard rate applies to most of CONUS. Individual rates apply to about 300 non-standard areas (NSAs). Most NSAs are a key city/primary destination and the surrounding county. Rates for the coming federal government fiscal year are typically announced in mid-August. Search the rates below or refer to the flat files, API, or trip calculator.

Per OMB Circular A-123, federal travelers "...must provide a tax exemption certificate to lodging vendors, when applicable, to exclude state and local taxes from their hotel bills." GSA's SmartPay team maintains the most current state tax information, including any applicable forms.

Traveler reimbursement is based on the location of the work activities and not the accommodations, unless lodging is not available at the work activity, then the agency may authorize the rate where lodging is obtained.

Unless otherwise specified, the per diem locality is defined as "all locations within, or entirely surrounded by, the corporate limits of the key city, including independent entities located within those boundaries."

Per diem localities with county definitions shall include"all locations within, or entirely surrounded by, the corporate limits of the key city as well as the boundaries of the listed counties, including independent entities located within the boundaries of the key city and the listed counties (unless otherwise listed separately)."

When a military installation or Government - related facility(whether or not specifically named) is located partially within more than one city or county boundary, the applicable per diem rate for the entire installation or facility is the higher of the rates which apply to the cities and / or counties, even though part(s) of such activities may be located outside the defined per diem locality.

absent(v instant-vector) returns an empty vector if the vector passed to ithas any elements (floats or native histograms) and a 1-element vector with thevalue 1 if the vector passed to it has no elements.

absent_over_time(v range-vector) returns an empty vector if the range vectorpassed to it has any elements (floats or native histograms) and a 1-elementvector with the value 1 if the range vector passed to it has no elements.

delta(v range-vector) calculates the difference between thefirst and last value of each time series element in a range vector v,returning an instant vector with the given deltas and equivalent labels.The delta is extrapolated to cover the full time range as specified inthe range vector selector, so that it is possible to get a non-integerresult even if the sample values are all integers.

delta acts on native histograms by calculating a new histogram where eachcomponent (sum and count of observations, buckets) is the difference betweenthe respective component in the first and last native histogram inv. However, each element in v that contains a mix of float and nativehistogram samples within the range, will be missing from the result vector.

deriv(v range-vector) calculates the per-second derivative of the time series in a rangevector v, using simple linear regression.The range vector must have at least two samples in order to perform the calculation. When +Inf or -Inf are found in the range vector, the slope and offset value calculated will be NaN.

histogram_avg(v instant-vector) returns the arithmetic average of observed values stored ina native histogram. Samples that are not native histograms are ignored and donot show up in the returned vector.

For a native histogram, histogram_fraction(lower scalar, upper scalar, vinstant-vector) returns the estimated fraction of observations between theprovided lower and upper values. Samples that are not native histograms areignored and do not show up in the returned vector.

+Inf and -Inf are valid boundary values. For example, if the histogram inthe expression above included negative observations (which shouldn't be thecase for request durations), the appropriate lower boundary to include allobservations less than or equal 0.2 would be -Inf rather than 0.

Whether the provided boundaries are inclusive or exclusive is only relevant ifthe provided boundaries are precisely aligned with bucket boundaries in theunderlying native histogram. In this case, the behavior depends on the schemadefinition of the histogram. The currently supported schemas all featureinclusive upper boundaries and exclusive lower boundaries for positive values(and vice versa for negative values). Without a precise alignment ofboundaries, the function uses linear interpolation to estimate thefraction. With the resulting uncertainty, it becomes irrelevant if theboundaries are inclusive or exclusive.

The float samples in b are considered the counts of observations in eachbucket of one or more classic histograms. Each float sample must have a labelle where the label value denotes the inclusive upper bound of the bucket.(Float samples without such a label are silently ignored.) The other labels andthe metric name are used to identify the buckets belonging to each classichistogram. The histogram metrictypeautomatically provides time series with the _bucket suffix and theappropriate labels.

Example: A histogram metric is called http_request_duration_seconds (andtherefore the metric name for the buckets of a classic histogram ishttp_request_duration_seconds_bucket). To calculate the 90th percentile of requestdurations over the last 10m, use the following expression in casehttp_request_duration_seconds is a classic histogram:

The quantile is calculated for each label combination inhttp_request_duration_seconds. To aggregate, use the sum() aggregatoraround the rate() function. Since the le label is required byhistogram_quantile() to deal with classic histograms, it has to beincluded in the by clause. The following expression aggregates the 90thpercentile by job for classic histograms:

The following is only relevant for classic histograms: If b containsfewer than two buckets, NaN is returned. The highest bucket must have anupper bound of +Inf. (Otherwise, NaN is returned.) If a quantile is locatedin the highest bucket, the upper bound of the second highest bucket isreturned. A lower limit of the lowest bucket is assumed to be 0 if the upperbound of that bucket is greater than0. In that case, the usual linear interpolation is applied within thatbucket. Otherwise, the upper bound of the lowest bucket is returned forquantiles located in the lowest bucket.

However, floating point precision issues (e.g. small discrepancies introduced by computing of bucketswith sum(rate(...))) or invalid data might violate these assumptions. In that case,histogram_quantile would be unable to return meaningful results. To mitigate the issue,histogram_quantile assumes that tiny relative differences between consecutive buckets are happeningbecause of floating point precision errors and ignores them. (The threshold to ignore a differencebetween two buckets is a trillionth (1e-12) of the sum of both buckets.) Furthermore, if there arenon-monotonic bucket counts even after this adjustment, they are increased to the value of theprevious buckets to enforce monotonicity. The latter is evidence for an actual issue with the inputdata and is therefore flagged with an informational annotation reading input to histogram_quantileneeded to be fixed for monotonicity. If you encounter this annotation, you should find and removethe source of the invalid data.

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