Ground Facility Gaps
We can use the function:
ground_facility_gaps(orbp, args...; duration::Number = 86400, initial_time::Number = 0, kwargs...) -> DataFrameto compute the gaps between the accesses of ground facilities. The arguments and keywords are the same as the ones used in the function ground_facility_accesses (see Ground Facility Accesses).
Notice that the gap analysis starts in the orbit propagator epoch plus initial_time and lasts for duration [s].
This function returns a DataFrame with three columns:
access_beginning: Time of the access beginning [UTC] encoded usingDateTime.access_end: Time of the access end [UTC] encoded usingDateTime.duration: Duration of the access [s]. The unit of the columndurationis stored in theDataFrameusing metadata.
Examples
Let's compute the gaps of the Amazonia-1 satellite to the INPE's ground station at Cuiabá, MT, Brazil.
First, we need to define an orbit propagator. In this case, we will use the TLE obtained from CelesTrak when this documentation was written:
julia> tle_amz1 = tle""" AMAZONIA 1 1 47699U 21015A 24008.13079366 .00000299 00000+0 10693-3 0 9994 2 47699 98.4120 87.2350 0001570 92.1147 268.0222 14.40836963150331 """TLE: Name : AMAZONIA 1 Satellite number : 47699 International designator : 21015A Epoch (Year / Day) : 24 / 8.13079366 (2024-01-08T03:08:20.572) Element set number : 999 Eccentricity : 0.00015700 Inclination : 98.41200000 deg RAAN : 87.23500000 deg Argument of perigee : 92.11470000 deg Mean anomaly : 268.02220000 deg Mean motion (n) : 14.40836963 revs / day Revolution number : 15033 B* : 0.00010693 1 / er ṅ / 2 : 2.99e-06 rev / day² n̈ / 6 : 0 rev / day³julia> orbp = Propagators.init(Val(:SGP4), tle_amz1)OrbitPropagatorSgp4{Float64, Float64}: Propagator name : SGP4 Orbit Propagator Propagator epoch : 2024-01-08T03:08:20.572 Last propagation : 2024-01-08T03:08:20.572
Now, we can compute the gaps during one day considering the INPE's station at Cuiabá:
julia> ground_facility_gaps( orbp, [(-(15 + 33 / 60) |> deg2rad, -(56 + 04 / 60) |> deg2rad, 0)]; duration = 1 * 86400, minimum_elevation = 5 |> deg2rad, unit = :m )4×3 DataFrame Row │ gap_beginning gap_end duration │ DateTime DateTime Float64 ─────┼──────────────────────────────────────────────────────────── 1 │ 2024-01-08T03:09:16.311 2024-01-08T13:56:48.099 647.53 2 │ 2024-01-08T14:08:50.888 2024-01-08T15:39:28.281 90.6232 3 │ 2024-01-08T15:43:47.366 2024-01-09T02:18:02.879 634.259 4 │ 2024-01-09T02:30:08.933 2024-01-09T03:08:20.572 38.194
If we want to change the reference frames used in the analysis, we must provide a function f_eci_to_ecef(r_i, jd) that converts the vector r_i to the desired ECEF frame at the instant jd [Julian Day]. For example, let's use the more precise ITRF instead of the PEF (default):
julia> const eop = fetch_iers_eop(Val(:IAU1980));julia> function f_eci_to_ecef(r_teme, jd) D_itrf_teme = r_eci_to_ecef(TEME(), ITRF(), jd, eop) r_itrf = D_itrf_teme * r_teme return r_itrf endf_eci_to_ecef (generic function with 1 method)julia> ground_facility_gaps( orbp, [(-(15 + 33 / 60) |> deg2rad, -(56 + 04 / 60) |> deg2rad, 0)]; duration = 1 * 86400, f_eci_to_ecef = f_eci_to_ecef, minimum_elevation = 5 |> deg2rad, unit = :m )4×3 DataFrame Row │ gap_beginning gap_end duration │ DateTime DateTime Float64 ─────┼──────────────────────────────────────────────────────────── 1 │ 2024-01-08T03:09:16.312 2024-01-08T13:56:48.098 647.53 2 │ 2024-01-08T14:08:50.887 2024-01-08T15:39:28.281 90.6232 3 │ 2024-01-08T15:43:47.364 2024-01-09T02:18:02.880 634.259 4 │ 2024-01-09T02:30:08.935 2024-01-09T03:08:20.572 38.194
We can also perform analyses using multiple ground facilities. For example, let's find the accumulated gap if we consider the INPE's stations at Cuiabá, MT, Brazil, and Alcântara, MA, Brazil:
julia> ground_facility_gaps( orbp, [ (-(15 + 33 / 60) |> deg2rad, -(56 + 04 / 60) |> deg2rad, 0) (-( 2 + 20 / 60) |> deg2rad, -(44 + 24 / 60) |> deg2rad, 0) ]; duration = 1 * 86400, minimum_elevation = 5 |> deg2rad, unit = :m )6×3 DataFrame Row │ gap_beginning gap_end duration │ DateTime DateTime Float64 ─────┼──────────────────────────────────────────────────────────── 1 │ 2024-01-08T03:09:16.311 2024-01-08T12:16:55.777 547.658 2 │ 2024-01-08T12:23:20.782 2024-01-08T13:52:44.135 89.3892 3 │ 2024-01-08T14:08:50.888 2024-01-08T15:39:28.281 90.6232 4 │ 2024-01-08T15:43:47.366 2024-01-09T00:43:23.474 539.602 5 │ 2024-01-09T00:53:25.918 2024-01-09T02:18:02.879 84.616 6 │ 2024-01-09T02:32:05.994 2024-01-09T03:08:20.572 36.243
By default, the algorithm computes the accumulated gap, i.e., it considers the gap active if neither station has visibility to the satellite. We can change this logic by overloading the function in the keyword parameter reduction. For example, let's compute the gap when the satellite does not have visibility to both ground stations at the same time:
julia> ground_facility_gaps( orbp, [ (-(15 + 33 / 60) |> deg2rad, -(56 + 04 / 60) |> deg2rad, 0) (-( 2 + 20 / 60) |> deg2rad, -(44 + 24 / 60) |> deg2rad, 0) ]; duration = 1 * 86400, minimum_elevation = 5 |> deg2rad, reduction = v -> (&)(v...), unit = :m )3×3 DataFrame Row │ gap_beginning gap_end duration │ DateTime DateTime Float64 ─────┼──────────────────────────────────────────────────────────── 1 │ 2024-01-08T03:08:20.572 2024-01-08T13:56:48.099 648.459 2 │ 2024-01-08T14:04:29.601 2024-01-09T02:21:47.636 737.301 3 │ 2024-01-09T02:30:08.933 2024-01-09T03:08:20.572 38.194