JuanCastillo29/space-mission-intelligence-data huggingface dataset:
The spectral response describes how the instrument responds to a monochromatic flux of a given wavelength. The key parameter quantifying the spectral response is the FWHM, also called spectral width. As for previous hyperspectral imaging instruments6, the characterization of the spectral response of the instrument is based on a least-squares fitting using a Gaussian function:
"
#$%!%!
!"
𝐺(𝜆) = 𝑎𝑒
(1)
& '
where a is the height of the curve’s peak, λ0 is the wavelength corresponding to the center of the peak used for the absolute spectral calibration, and σ is the standard deviation characterizing the curve’s width used to assess the spectral FWHM. The FWHM is linked to the σ parameter (Eq. 1) by the equation below:
𝜎= +𝐹𝑊𝐻𝑀
2 ln 2            (2) To enhance the SNR and mitigate the effects of inoperable detector pixels, the spectrum data points were constructed by taking the median value of 40 physical pixels in the spatial direction around the target position. Background subtraction was also applied to the data to eliminate influences from the instrument and calibration setup that are uncorrelated with the monochromatic source. This correction involved subtracting a frame captured under identical conditions (integration time, operative modes), but with the light source switched off. In instances where background data were unavailable, background information obtained from another monochromator scan was utilized. Subsequently, a Least-Square (LSQ) algorithm was employed to fit this background-subtracted signal to a Gaussian function, enabling the extraction of the FWHM.

[Figure: Fig. 6 shows examples of comparison between scan profiles and their best fits. Some discrepancies can be observed, especially near the maximum of the response. However, the difference between the fitted FWHM and the data FWHM is negligible in most cases, meaning that a Gaussian function is appropriate to represent the spectral response function. Using such a function provides a more accurate estimate of the actual width if a few spurious values are present in the data. We estimate that this algorithm provides the FWHM with an accuracy better than 0.2 nm.]

[Figure: Fig. 6. Example of a Gaussian fit of the OP1 data for the response of a physical spectel at the FOV position 200 to successive monochromatic measurements. (A) VISNIR physical spectel #502 and (B) IR physical spectel #119 (Due to a software error during the calibration campaign, the 2617 nm monochromatic measurement was skipped, and the 2618 nm measurement was repeated).]

The number of spectels for which their spectral response is retrieved depends on the spectral domain covered by the monochromatic scan, the step used for the scan, the spectral sampling of the instrument, and the operability of the spectels being tested. Fig. 7 shows an example of the Gaussian fits of a cluster of successive boresight spectels in the 1400-1425 nm range.

[Figure: Fig. 7. Gaussian fits of physical spectels response to a monochromatic scan of the 1400-1435 nm range at the FOV position row 200 (boresight) performed during the calibration campaign.]
In addition to the spectral response characterization, a second parameter of interest derived from the analysis of the monochromator scans is the Central WaveLength (CWL). It corresponds to the spectral position of the maximum of the Gaussian, i.e., the parameter λ0 of Eq. 1. The fitting algorithm gives low error margins on the determination of λ0 (< 0.1 nm), one order of magnitude below the precision of the monochromators. Each derived value of CWL provides a data point for the absolute spectral calibration based on other measurements since monochromator scan measurements are restricted to a few wavelengths and FOV positions.
a. Absolute spectral calibration The ICU’s didymium and polystyrene filters (see Section II.A.2) introduce several diagnostic absorption signatures on the VISNIR and IR source signals. The sequence of acquisitions with the ICU includes a series of dark acquisitions, background acquisitions, and source acquisitions. The same algorithm was used as the one used to match atmospheric signatures (See Section III.C.1), but in this case, the inputs are the ICU data and laboratory spectra of the filter samples. Thanks to the broad width of the signatures and the excellent SNR, more extensive wavelength ranges could successfully be matched by the optimization algorithm while maintaining low error margins and without falling into a local minimum. One example of the best-fit procedure for each channel is shown in Fig. 9.

[Figure: Fig. 9. Panel A: Example of a match between the MAJIS VISNIR ICU data and the reference laboratory signatures of the didymium sample (blue). Panel B: Same as Panel A but for the IR channel and a set of polystyrene signatures near 3.35 µm.]

b. Spectral response Data from the ICU can offer insights into the relative changes in the FWHM of the response. This is done by convoluting the reference characterization data (from didymium and polystyrene) with Gaussian functions of different FWHM values. Once a specific signature is chosen, its shape is compared to the data gathered by the ICU. The quality of the match between these data sets is assessed by calculating the SSD between the ICU data and the convoluted data. The FWHM of the instrument is then determined to be the value that minimizes this SSD. However, this approach is limited by the broad shape of the signatures to give an accurate absolute estimate of the FWHM. Moreover, eliminating the continuum can introduce a slight bias in absolute terms. Nevertheless, by processing the ICU data in the same way in every case, any relative changes in FWHM can be assessed.
In other words, while this method may not be highly accurate, its level of precision is sufficient to yield valuable insights into the evolution of the FWHM.
The same methodology as the one described for the atmospheric gas acquisitions (Section II.C.1) was applied to these images to extract the absolute spectral positions before deriving the performances of the instrument. Backgrounds acquired for the sample measurements are subtracted from the sample acquisitions. The WCS fitting results show that several signatures can be matched across a large part of the spectral domain (from 1100 nm to 2000 nm) with low error margins (< 0.5 nm). The calcite sample results provide relevant absolute spectral positions restricted to the lower wavelengths (< 3000 nm) of the IR channel (Fig. 10). This is due to the low signal emitted by the light source on OP5 compared to the thermal emission of the samples themselves (samples at ambient temperature, with high emissivity), which precludes the identifications of the spectral signatures at longer wavelengths.

[Figure: Fig. 10. Panel A: Example of the best match procedure with the WCS sample (orange) around 1.93 µm signatures. The reference absorption of the sample is shown in blue. Panel B: Same as Panel A but for the calcite sample in the IR channel.]
While the MAJIS optical bench, optical elements, and their relative mountings have been designed to maintain an isothermal profile in order to keep optical performances within the requirements, the OH spectral response is expected to manifest small changes with the temperature. Therefore, specific measurements under thermal stress (hot case OH and VISNIR detector at 137 K, IR detector at 97 K) were conducted to explore whether the performances of the instrument remained within the requirements (Section II.A.3).
The FWHM of the spectral response function has been evaluated at three wavelengths for the hot case (OH temperature 137 K) and compared to the nominal case (126 K). An example of a spectel FWHM response is shown in Fig. 24. The results from all available scans are summarized in Table X. For the VISNIR channel, we observe at short visible wavelengths (550 nm) a significant 9% increase of the FWHM with the OH temperature. This increase is reduced to 1% at 1400 nm, a difference that is not significant considering errors. For the IR channel, the increase is 3% at 2600 nm, a value that is again not significant considering error bars. Overall, the analysis thus suggests that the FWHM of the spectral response increases by about 10% from nominal case to hot case at visible wavelengths and may also increase slightly by a few % at other wavelengths. This effect is significant enough to be carefully considered for future in-flight acquisitions.

[Figure: Fig. 24. Example of FWHM degradation due at high OH temperature. The spectral response of the physical spectel N°39 of the VISNIR channel is displayed for the monochromatic scan starting around 565 nm.]

Table X. Spectral responses derived for the hot case.

| Scan l (nm) | OH T° (K) | Mean FWHM (nm) | Std. dev. FWHM (nm) |
| --- | --- | --- | --- |
| 550-585 | 126 137 | 4.64 5.08 | 0.19 0.29 |
| 1400-1435 | 126 137 | 3.54 3.58 | 0.25 0.32 |
| 2325-2375 | 110 150 | 6.72 7.23 | 0.05 0.19 |
| 2600-2650 | 126 137 | 5.41 5.57 | 0.29 0.27 |
The impact of the temperature on the smile was assessed thanks to the ICU acquisitions. Differences measured (if any) are below the accuracy thresholds of the methodology used. Fig. 25 illustrates the virtually identical smile distortions measured from two ICU signatures. We thus conclude that no correction of the smile effect is required to account for the variation of the OH temperature between the nominal case and the hot case.

[Figure: Fig. 25. Comparison between the derived smile correction using the ICU acquisitions for the nominal and hot cases.]
SUMMARY AND CONCLUSIONS

In this article, we have analyzed ground and flight calibration data to characterize the MAJIS spectral behavior.
Specifically, the spectral performances of the instrument were derived in terms of:
• Absolute calibration
• Spectral response Furthermore, distortions were characterized, and established corrections were provided for:
• the smile (2D maps for the two channels)
• the exploration of the OH temperature (no correction for the VISNIR channel; correction for + 2.1 nm ± 0.2 nm / 10 K for the IR channel) The spectral performances of the instrument are summarized in Table XI. The NECP data has indicated that the absolute spectral calibration has been shifted after launch by -3.8 nm and -5.4 nm for the VISNIR and IR channels, respectively.
Meanwhile, the spectral response and smile have not changed significantly after the launch.
Table XI. Spectral performances (nominal thermal case) against requirements summary

| Criteria | VISNIR Requirements (nm) | VISNIR Performances (nm) | IR Requirements (nm) | IR Performances (nm) | Comments |
| --- | --- | --- | --- | --- | --- |
| Spectral Range before launch | 500-2350 | 489-2349 (Option 1) 491-2358 (Option 2) | 2250-5540 | 2268-5556 2271-5555 |  |
| Spectral Range after launch | 500-2350 | 493-2353 | 2250-5540 | 2273-5561 |  |
| FWHM (oversampling) |  | 2.9/4.6 |  | 5.5/7 | No significant change was observed in-flight |
| FWHM (nominal spectral binning) | <5.5 | 3.5/5.6 | <10 | 6.6/8.5 |  |
| Spectral Sampling (oversampling) |  | 1.79/1.86 |  | 3.0/3.48 | No significant change was observed in-flight |
| Spectral Sampling (nominal spectral binning) | 3.65 ± 0.25 | 3.58/3.72 | 6.5 ± 0.60 | 6.0/6.96 |  |
| Smile | <3.65 | -1.2/2.1 | <7.10 | -1.9/3.1 | A slight distortion decrease was observed in-flight (IR) |
| Thermal Shift (CWL) |  | - |  | +1.8/10K |  |

In addition, all relevant spectral performances were adequately characterized for both channels and all binning modes used in-flight (oversampling, nominal, binned×2, and binned×4). Although distortions in terms of smile and OH temperature were 43 observed, corrections were derived to counteract their effects on flight acquisitions.

The results demonstrate that the imaging spectrometer meets spectral resolution, range, and smile requirements within the expected OH thermal range. This on-ground spectral calibration should allow the MAJIS scientific team to rapidly and accurately analyze the data from Jupiter and its Galilean Moons.
Alessandra Barbis: formal analysis (supporting); investigation (equal); resources (equal). John Carter: conceptualization (equal); funding acquisition (equal); investigation (equal); resources (equal). Cydalise Dumesnil: data curation (equal); project administration (equal); resources (equal). Gianrico Filacchione: conceptualization (equal); formal analysis (supporting);
methodology (supporting); supervision (supporting); writing – review editing (supporting). Pierre Guiot: resources (equal);
investigation (equal). Paolo Haffoud: formal analysis (lead); investigation (equal); methodology (lead); writing – original draft (lead); writing – review editing (lead). Yves Langevin: conceptualization (equal); formal analysis (supporting); funding acquisition (equal); methodology (supporting); supervision (supporting); writing – review editing (supporting). Benoit Lecomte: resources (equal); investigation (equal). Giuseppe Piccioni: conceptualization (equal); funding acquisition (equal);
supervision (supporting). Cédric Pilorget: investigation (equal); resources (equal); writing – review editing (supporting).
François Poulet: conceptualization (equal); data curation (equal); funding acquisition (equal); methodology (supporting);
project administration (equal); supervision (lead); writing – original draft (supporting); writing – review editing (lead).
Sébastien Rodriguez: investigation (equal); methodology (supporting). Stefani Stefania: investigation (equal); resources (equal). Leonardo Tommasi: conceptualization (equal); investigation (equal); resources (equal); supervision (supporting).
Federico Tosi: conceptualization (equal). Mathieu Vincendon: conceptualization (equal); data curation (equal); methodology (supporting); project administration (equal); supervision (lead); writing – original draft (supporting); writing – review editing (lead).
The data that support the findings of this study are available from the corresponding author upon reasonable request.
1 K.

Stephan, T.

Roatsch, F.

Tosi, K.D.

Matz, E.

Kersten, R.

Wagner, P.

Molyneux, P.

Palumbo, F.

Poulet, H.

Hussmann et al,
“Regions of interest on Ganymede’s and Callisto’s surfaces as potential targets for ESA’s JUICE mission,” Planetary and Space Science 208, 105324 (2021).
2 O.

Grasset, M.K.

Dougherty, A.

Coustenis, E.J.

Bunce, C.

Erd, D.

Titov, M.

Blanc, A.

Coates, P.

Drossart, L.N.

Fletcher et
al, “JUpiter ICy moons Explorer (JUICE): An ESA mission to orbit Ganymede and to characterise the Jupiter system,” Planetary and Space Science 78, 1–21 (2013).
3 O.

Witasse, N.

Altobelli, S.

Barabash, L.

Bruzzone, M.K.

Dougherty, C.

Erd, L.N.

Fletcher, R.

Gladstone, O.

Grasset, L.

Gurvits et al., in European Planetary Science Congress (2015), p. 564.
4 F.

Poulet, G.

Piccioni, Y.

Langevin, C.

Dumesnil, L.

Tommasi, V.

Carlier, G.

Filacchione et al., “Moons And Jupiter Imaging
Spectrometer (MAJIS) on Jupiter Icy Moons Explorer (JUICE),” Space Sci.

Rev. (submitted), (2023).
5 M.

Vincendon, P.

Guiot, B.

Lecomte, M.

Condamin, F.

Poulet, A.

Arondel, J.

Barbay, J.

Carter, S.

DeAngelis, C.

Dumesnil,
G.

Filacchione, P.

Haffoud, J.

Hansotte, Y.

Langevin, G.

Piccioni, C.

Pilorget, E.

Quirico, and S.

Rodriguez, “Calibration of MAJIS (Moons And Jupiter Imaging Spectrometer): I.

Setup description and conducted measurements,” Rev.

Sci.

Instrum. (to be submitted), (2023).
6 F.

Altieri, G.

Filacchione, F.

Capaccioni, C.

Carli, M.

Dami, L.

Tommasi, G.

Aroldi, D.

Borrelli, A.

Barbis, M.

Baroni, G.

Pastorini, I.

Veltroni, and R.

Mugnuolo, Rev.

Sci.

Instrum. 88, 094503 (2017). 7 G.

Bellucci, F.

Altieri, J.

Bibring, G.

Bonello, Y.

Langevin, B.

Gondet, and F.

Poulet, Planetary and Space Science 54, 675 (2006).
8 F.

Capaccioni, M.C.

DeSanctis, G.

Filacchione, G.

Piccioni, E.

Ammannito, L.

Tommasi, I.

F.

Veltroni, M.

Cosi, S.

Debei,
L.

Calamai et al., “VIS-NIR imaging spectroscopy of Mercury’s surface: SIMBIO-SYS/VIHI experiment onboard the BepiColombo mission,” IEEE Transactions on Geoscience and Remote Sensing 48(11), 3932–3940 (2010).
9 A.

Adriani, G.

Filacchione, T.

Di Iorio, D.

Turrini, R.

Noschese, A.

Cicchetti, D.

Grassi, A.

Mura, G.

Sindoni, and M.

Zambelli, “JIRAM, the Jovian infrared auroral mapper,” Space Science Reviews 213, 393–446 (2017).
10 G.

Filacchione, G.

Piccioni, L.

Tommasi, A.

Barbis, F.

Poulet, Y.

Langevin, P.

Haffoud, S.

DeAngelis, C.

Dumesnil, S.

Stefani, and F.

Tosi, “Calibration of MAJIS (Moons And Jupiter Imaging Spectrometer): IV.

Spatial Calibration,” Rev.

Sci.

Instrum. (to be submitted), (2023).
11 Y.
ovian infrared auroral mapper,” Space Science Reviews 213, 393–446 (2017).
10 G.

Filacchione, G.

Piccioni, L.

Tommasi, A.

Barbis, F.

Poulet, Y.

Langevin, P.

Haffoud, S.

DeAngelis, C.

Dumesnil, S.

Stefani, and F.

Tosi, “Calibration of MAJIS (Moons And Jupiter Imaging Spectrometer): IV.

Spatial Calibration,” Rev.

Sci.

Instrum. (to be submitted), (2023).
11 Y.

Langevin et al., “Calibration of MAJIS (Moons And Jupiter Imaging Spectrometer): II.

Radiometric Calibration,” Rev.

Sci.

Instrum. (to be submitted), (2023).
12 A.

Barbis, M.

Barilli, A.

Bini, M.

Dami, E.

Fossati, G.

Pilato, L.

Tommasi, G.

Filacchione, S.

DeAngelis, G.

Piccioni et al.,
in Space Telescopes and Instrumentation 2022: Optical, Infrared, and Millimeter Wave (SPIE, 2022), pp. 128–147.
13 F.

Poulet, J.

Rodriguez-Ferreira, A.

Arondel, K.

Dassas, P.

Eng, P.

Lami, Y.

Langevin, Y.

Longval, P.

Pradel, and M.

Dami,
“The pre-flight calibration setup of the instrument SIMBIO-SYS onboard the mission BepiColombo,” Planetary and Space Science 117, 82–95 (2015).
14 G.

Piccioni, L.

Tommasi, Y.

Langevin, G.

Filacchione, F.

Tosi, D.

Grassi, F.

Poulet, C.

Dumesnil, C.R.

Degalarreta, A.

Bini
et al., in 2019 IEEE 5th International Workshop on Metrology for AeroSpace (MetroAeroSpace) (IEEE, 2019), pp. 318–323.
15 I.E Gordon, L.

Rothman, R.J.

Hargreaves, R.

Hashemi, E.V.

Karlovets, F.M.

Skinner, E.K.

Conway, C.

Hill, R.V.

Kochanov, Y.

Tan et al., “The HITRAN2020 Molecular Spectroscopic Database,” J.

Quant.

Spectrosc.

Radiat.

Transf. 277, 107949 (2022). 16 S.

Stefania et al., “Calibration of MAJIS (Moons And Jupiter Imaging Spectrometer): VI.

ICU,” Rev.

Sci.

Instrum. (to be submitted), (2023). 17 S.

Rodriguez et al., “Calibration of MAJIS (Moons And Jupiter Imaging Spectrometer): V.

Samples,” Rev.

Sci.

Instrum. (to be submitted), (2023).
18 L.

Riu, J.P.

Bibring, C.

Pilorget, F.

Poulet, V.

Hamm et al., “The on-ground calibration performances of the hyperspectral microscope MicrOmega for the Hayabusa-2 mission,” Planet.

Space Sci. 152, 31–44 (2018). 19 P.

Haffoud, A.

Arondel, V.

Carlier, J.

Carter, J.P.

Dubois, C.

Dumesnil, G.

Filacchione, and L.

Gonnod, in Space Telescopes and Instrumentation 2022: Optical, Infrared, and Millimeter Wave (SPIE, 2022), pp. 1168–1183.
20 J.

Carter, P.

Haffoud, Y.

Langevin, C.

Ketchazo, P.

Guiot, C.

Dumesnil, C.R. de Galarreta Fanjul, A.

Arondel, and V.

Carlier,
in Space Telescopes and Instrumentation 2022: Optical, Infrared, and Millimeter Wave (SPIE, 2022), pp. 1184–1197.
21 International Neural Network Society (INNS), The Ieee Neural Network Council Cooperating Societies, R.

Battiti, and F.

Masulli, in International Neural Network Conference: July 9--13, 1990 Palais Des Congres—Paris—France (Springer, 1990), pp. 757–760.
Experiment as an operational component of the
Jupiter Icy Moons Explorer mission

Vidhya Pallichadath1*, Dominic Dirkx1, Marie S. Fayolle1, S´andor Frey2, Leonid I. Gurvits1,3, Paul Boven3,4, Giuseppe Cim`o3, Judit Fogasy2, Guifr´e Molera Calv´es5, Krisztina Perger2, N. Masdiana Md Said3, Bert L.A. Vermeersen1
1*Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, Delft, 2629 HS, The Netherlands. 2Konkoly Observatory, ELKH Research Centre for Astronomy and Earth Sciences, MTA Centre of Excellence, Konkoly Thege M. ´ut 15-17, Budapest, H–1121, Hungary. 3Joint Institute for VLBI ERIC, Oude Hoogeveensedijk 4, Dwingeloo, 7991 PD, The Netherlands. 4Leiden Observatory, Leiden University, Box 9513, Leiden, 2300 RA, The Netherlands. 5Physics discipline, School of Natural Sciences, University of Tasmania, Street, Hobart, TAS 7000, Tasmania, Australia.
*Corresponding author(s). E-mail(s): v.pallichadath@tudelft.nl, ORCID: 0000-0003-0678-4814; Contributing authors: (d.dirkx@tudelft.nl, ORCID: 0000-0003-2069-0603); (m.s.fayolle-chambe@tudelft.nl, ORCID: 0000-0003-4407-5031); (frey.sandor@csfk.org, ORCID: 0000-0003-3079-1889; lgurvits@jive.eu, ORCID: 0000-0002-0694-2459; boven@jive.eu, ORCID: ; (cimo@jive.eu, ORCID: 0000-0002-1167-7565); (fogasy.judit@csfk.org, ORCID: 0000-0003-0003-000X); (guifre.moleracalves@utas.edu.au, ORCID: 0000-0001-8819-0651); (perger.krisztina@csfk.org, ORCID: 0000-0002-6044-6069); (said@jive.eu, ORCID: 0000-0002-7147-7039);
(l.l.a.vermeersen@tudelft.nl, ORCID: 0000-0002-6329-5972);
‘scheduling’ is used for the more detailed procedure of deciding which ground stations, frequencies, phase calibrators, etc. to use. Many criteria must be considered before a PRIDE observation can be conducted, including the spacecraft transmission window, (in-beam) phase-referencing opportunities with nearby calibrators, and ground station availability. Planning an observation is significantly impacted by these considerations. This section outlines the steps for scheduling, planning, and preparing for a PRIDE observation. First, the impact of planning and scheduling on PRIDE VLBI data quality is discussed in Section 3.1. A basic explanation of operational procedures in planning and scheduling is provided in Section 3.2. The PRIDE operations for the cruise phase are covered in Section 3.3, highlighting opportunities where our experiments for methodological validation will also be applied for scientific purposes. In particular, it is discussed how our planning and scheduling tools could be used to improve the science return from these experiments, in preparation for maximizing the contribution to the mission of extended PRIDE operations in the Jovian system.