<table><caption>**Table 13** Required, and assessed through measurements, flight temperatures ranges (in K) for the different subsystems, in operational (Op) and non-operational (Non-Op) situations, including a restricted range for some subsystems compared to in-flight predictions. Steady-state is considered for these predictions and uncertainties are -5/0 K for all temperatures. The NECP corresponds to measured temperatures during Non-Op with the high-gain antenna used as a sunshield (Sun pointed steady-state satellite attitude). Slight variations during the NECP are observed according to the thermal configuration of the other instruments</caption><thead><tr><th rowspan="2">Item</th><th colspan="6">Temperature (K)</th><th colspan="2">Prediction from PFM</th><th rowspan="2">In-flight prediction<br>GCO500</th><th rowspan="2">Jupiter Tour</th><th rowspan="2">Cold Cruise</th><th rowspan="2">NECP (Non-Op)</th></tr><tr><th>Cold Non-Op</th><th>Cold Op</th><th>Target</th><th>Hot Op</th><th>Hot Non-Op</th><th>S/C TV test (Hot Op)</th><th>3</th><th>3</th></tr></thead><tbody><tr><td>Radiative Environment</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>100</td><td>68.6</td><td>3</td><td></td><td></td><td>3</td><td></td></tr><tr><td>I/F with S/C</td><td>220</td><td>223</td><td>N/A</td><td>256</td><td>298</td><td>244</td><td></td><td></td><td></td><td></td><td>237</td><td></td></tr><tr><td>OH</td><td>110</td><td>120</td><td>130</td><td>160</td><td>320</td><td>146.5</td><td>135.3</td><td>128</td><td>126.7</td><td>133÷135</td><td></td><td></td></tr><tr><td>IR FPA</td><td>68</td><td>71</td><td>90</td><td>90</td><td>320</td><td>114</td><td>95.7</td><td>87.8</td><td>83</td><td>87÷88</td><td></td><td></td></tr><tr><td>IR FPE</td><td>120</td><td>120</td><td></td><td>160</td><td>320</td><td>158</td><td>149.5</td><td>133.2</td><td>128.2</td><td>131</td><td></td><td></td></tr><tr><td>VISNIR FPA</td><td>110</td><td>116</td><td>130</td><td>140</td><td>320</td><td>152.5</td><td>144</td><td>127</td><td>127.6</td><td>134.5</td><td></td><td></td></tr><tr><td>VISNIR FPE</td><td>120</td><td>120</td><td></td><td>160</td><td>320</td><td>170</td><td>161.5</td><td>145</td><td>128.3</td><td>134.5</td><td></td><td></td></tr></tbody></table>
<table><caption>**Table 14** MAJIS system parameters. Additional spatial and spectral parameters are listed in Table 9 (*) Harnesses outside OH and ME not included</caption><thead><tr><th>Parameter</th><th>Value</th><th>Comments</th></tr></thead><tbody><tr><td>Mass</td><td>50.84 kg (*)</td><td>OH: 40.9 kg Harness: 4.8 kg ME: 8.2 kg</td></tr><tr><td>Power</td><td>25-26 W (science mode with 2 channels ON)</td><td>29 W with calibration mode</td></tr><tr><td>Aperture</td><td>56 × 85 mm²</td><td>56 mm spectrum × 85 mm space</td></tr><tr><td>Aperture equiv. diameter</td><td>75 mm²</td><td></td></tr><tr><td>Telescope focal length</td><td>240 mm</td><td></td></tr><tr><td>Nominal pixel</td><td>36 µm</td><td>after 2 × 2 18- µm pixel binning</td></tr><tr><td>Instantaneous FOV (pixel angular size)</td><td>150 µrad</td><td>300 µrad or 600 µrad when spatial binning x2 or x4 is applied</td></tr><tr><td>Number of pixels across FOV</td><td>400</td><td>Nominal for both channels<br>Spatial binning x2 or x4 by TC giving 200 or 100 pixels respectively</td></tr><tr><td>Spectral range VISNIR</td><td>0.49 – 2.35 µm</td><td></td></tr><tr><td>Spectral range IR</td><td>2.27 – 5.56 µm</td><td></td></tr><tr><td>Number of spectels</td><td>508</td><td>Both channels, up to 640 by TC, Spectral binning x2 or x4 by TC, Spectral editing, Spectral oversampling (see Fig. 28 for different combination)</td></tr><tr><td>Cleanliness</td><td>N/A</td><td>Anticontamination heaters switch on after launch to avoid water ice condensation<br>Decontamination heaters to be enabled whenever needed</td></tr></tbody></table>

In order to monitor the temperatures during the mission, a set of platinum resistant temperature detector's sensors acquired by the S/C have been implemented in the instrument, at the most relevant locations: IR FPA, both FPE's, and OH structure. These sensors are acquired via the JUICE main electronics, and can thus also be read during the Non-OP phases (MAJIS OFF). The acquisition frequency has been optimized for the MAJIS needs. They will also be used for the survival heating control of the FPE's and for the anticontamination control of the IR FPA and OH structure. The Non-Op temperatures monitored during the NECP are reported in Table 13. Even if additional thermal simulation is required for this specific case (close to the Sun and with a particular S/C attitude), they are at first glance in line with the thermal expectations.

When the instrument is operating, additional internal housekeeping sensors, located in the scan mechanism and in the detection chain (both FPA's and FPE's) are acquired by the MAJIS Main Electronic Unit (both FPA's and FPE's). The overshields of the thermal sensor and heater harnesses (anticontamination line) are made of stainless steel in order to minimize the thermal leaks between the coldest part of the instrument (IR FPA) and the hotter sub-units of the OH. The overshield is overwrapped with aluminized mylar for cleanliness purpose.

## 4.5 Instrument Budget

Key system parameters are given in Table 14.
# 5 OnBoard Software and Data Processing

The core onboard software on MAJIS include several electronic units (Sect. 4.2) and core functions that feature autonomy, data processing and S/C interface that are described in this section.

## 5.1 MAJIS Architecture for Process Control and Data Processing

A MAJIS operational sequence from switch-on to switch-off consists in preparing then acquiring one or several sets of data frames each corresponding to an observation with specific operational parameters (in particular the spatial and spectral dimension of each frame and the repetition time between frames, see Sect. 7.1). The functional architecture of MAJIS for achieving this objective is presented in Fig. 19.

The CPCU oversees all MAJIS activities from switch-on to switch-off (Sect. 4.2.2). The CPCU software is written in C following coding standards for flight software. It configures all units involved in the acquisition with the parameters relevant for the next observation before initiating the acquisition of data frames. If only one channel is used in the operational sequence, it is possible to switch on only the units (CU, PE, FPE and FPA) relevant to this channel so as to minimize power.

The CU software, which is the same for both channels, is written in C following coding standards for flight software. It is loaded by the CPU from the MRAM after switching-on the CU unit. A set of configuration parameters sent by the CPCU before each observation defines how to process PE-frames sent by the PE into a CU-frame, then how to format series of 1 to 8 CU frames into science data to be sent to the SSMM through the CPCU. The CU parameters have to be consistent with those of the corresponding PE as a specific number of PE-frames with the required dimensions have to be received by the CU before it can generate CU-frames. Each CU has also a retransmission role for messages between the CPCU and the PE of the same channel.

Each of the two PE's is a non-reprogrammable FPGA. It is configured by the CPCU before each observation by sending a set of parameters defining how to process the FPA-frames sent by the FPE into a PE-frame sent to the corresponding CU. The PE parameters have to be consistent with those of the corresponding FPE's as a specific number of FPA-frames with the required dimensions and data format (12 bits or 16 bits/pixel) have to be received by the PE so that it is able to generate a PE-frame. The PE has also a retransmission role for messages from the CU to the FPE of the same channel.

The AUX board is also equipped with a non-reprogrammable FPGA. It is configured by the CPCU by sending specific commands before and after the observation, in particular the scanner start position, velocity and acceleration (the scanner must be commanded to the zero position even if not used so as to remain stable at boresight). The CPCU sends to the AUX board a "start scan" command together with the first data frame request and a "return to 0" command after the observation if scanner motion is required. "Shutter close" then "shutter open" commands are sent by the CPCU to the AUX board whenever a dark current acquisition is required. Internal calibration sources are switched on then off by the CPCU during internal calibration sequences (typically once per month) after setting up the scanner in a direction looking at the ICU diffuse screen beyond the edge of the FOV. Before sending each new frame request, the CPCU sends a request to the AUX board for relevant status data (shutter status, scan position, status of internal calibration sources) so as to include this context information in the science data.

Each of the two FPE's is a SIDECAR ASIC that acquires frames from the H1RG detector then sends them to the corresponding PE (Sect. 4.1.3). The FPA is switched on together with
<table><thead><tr><th>ME unit</th><th>FPU (FPE)</th><th>PE</th><th>CU/DPU/ME</th><th>CPCU/DPU/ME</th></tr></thead><tbody><tr><th scope="row">Data format</th><td>Detector-frame</td><td>PE-frame</td><td>CU-frame</td><td>NSDP</td></tr><tr><th scope="row">Processing step(s)</th><td>Spatial windowing (multiple of 64 rows) to mitigate the DV and to adapt the integration time</td><td>CDS subtraction/Reference line pixel correction (see SPIE)<br>Spatial averaging (x2)<br>Spectral averaging(x2)/Spectral oversampling<br>Dark subtraction<br>Despiking</td><td>Frame averaging of PE-frames into a single CU-frame<br>Additional spectral editing, averaging (x2x4)<br>Additional spatial averaging (x2x4)<br>Browse formatting<br>NSDP formatting: bit-packing or 3D compression</td><td>Frame acquisition TM<br>Send Native Science Data Packet to SSMM<br>Each logical address defined the directory in the SSMM file</td></tr><tr><th scope="row">Data characteristics</th><td>Nominal size: 800x1024<br>Full Frame (calibration): 1024 x 1024</td><td>PE-frame nominal size: 400 pixels x 508 to 640 spectels<br>PE Full Frame (calibration): 1024 x 1024</td><td>CU-frame: data element which is compressed and formatted for downlink by the CU<br>400 pixels x 508 to 640 spectels<br>Spectral size: 127, 254, 508<br>Spatial size: 100, 200, 400</td><td>Compressed data<br>Segmented into consecutive NSDP</td></tr></tbody></table>

<figure><img src="image_35.png" /><figcaption>**Fig. 23** Overview of the data on-board pipeline as a function of the electronic units. The corresponding data products generated at each major step is illustrated. The gray area and the references pixels are masked. See text for acronyms</figcaption></figure>

the FPE. The FPE is programmable with a specific assembly language code ("microcode"). It is configured by the CPCU before each observation by loading one of two microcodes (one for each of two detector read-out speeds, 100 kHz and 1 MHz) including a set of five parameters specific to each observation. Similarly to the two CU's, the same microcode can be used for either the IR FPU or the VISNIR FPU. A "low power" microcode which resets all internal FPE units is loaded after the initial switch-on and between observations so as to minimize power. This procedure automatically implements a thus named "warm" reboot of the FPE after each observation, which is useful for Fault Detection Isolation Recovery (FDIR) matter.

An overview of the data processing tasks performed by the MAJIS onboard software to generate a CU-frame of a given spectral cube from multiple FPA-frames is shown in Fig. 23. The functionalities of each unit involved in this processing are presented in greater detail in the following sections.

## 5.2 CPCU Software Architecture

As indicated in Sect. 5.1, the CPCU oversees all MAJIS activities when the instrument is ON. After switch-on, it is controlled by a boot software provided by ESA in the framework of the "common DPU" project activities, with a few adjustments specific to MAJIS. The boot software is therefore able to handle all mandatory services required for interfacing with the JUICE system level. At this stage, the instrument is in "standby" mode. In this mode, it is possible to update the content of the MAJIS MRAM which stores two memory images of the software to be loaded in the programmable MAJIS functional units: CPCU Application Software (ASW), CU software (CUSW) and FPE microcodes ("low power", 100 kHz read-out, 1 MHz read-out). Upon receiving an "ASW load" command, or after a timeout of 30 s,
<figure><img src="image_36.png" /><figcaption>**Fig. 24** Sequence of MAJIS modes from switch-on to switch-off. See text for a definition of the modes ("standby", "safe", "service" and 'science')</figcaption></figure>

the boot software loads the ASW software from the MRAM then transfers controls to the ASW. This puts MAJIS in a "safe" mode (all subsystems other than the CPCU are off) (Fig. 24).

The MAJIS ASW design is based on a Time and Space Partitioning (TSP) architecture. At the lower level, an hypervisor (Xtratum provided by Fentiss) ensures the segregation of the execution of the different software functionalities enclosed into "partitions". On top of the hypervisor, a generic payload software called LVCUGEN, designed and provided by CNES, implements the basic and recurrent functions used by flight SW and is extended by several partitions:

- an "Input/Output" partition (IOS partition) used to access the SpaceWire bus developed by the ATOS under a CNES contract with specifications updated by IAS,

- a Command-Control partition (CCSW partition) implementing the instrument operability also developed by the ATOS under a CNES contract with specifications updated by IAS,

- a Mission partition (MSW partition) specific to MAJIS which is in charge of the sequence of actions required for performing the series of MAJIS science observations from switch-on to switch-off on the basis of the sequence of MAJIS private TC's in the timeline.

## 5.3 MAJIS Operational Sequences and Modes Controlled by the MSW

The activities required for science data acquisition, formatting and transmission to the outgoing data front-end modules are all controlled by the MSW. Once in "safe" mode, a first set of commands triggers the switch-on by the MSW of the two sets of sub-units which are required for acquisitions (CU, PE, FPE and FPA which are switched on together). Operating with both channels is nominal. For very specific operations, or for degraded operations if one of the channels is not working anymore, it is possible to switch-on only one acquisition chain.

The FPGA-based PE's is immediately operational. Once the CU's are switched-on, the MSW triggers the loading of the CUSW from the selected image in the MRAM. At this stage, the "low power" microcode is loaded in the FPE's so as to minimize power. Once the proper initialization messages have been received from the PE's and CU's (not the FPE's, which are in reset), the MSW transitions into the "service" mode, which is required for MAJIS to accept TC's triggering science observations (Fig. 24).

A science observation is initiated by a sequence of two TC's: the "configuration" TC and the "Start Science" TC. Below we describe the functionalities that are triggered by these two TCs and the activities at the end of an observation.
### 5.3.1 "Configuration" TC Description

The "configuration" TC provides all the parameters required for acquiring the next science data set. The two highest level parameters are the number of MAJIS data frames ("CU-frames" as they are built then formatted by the CU from one or several "PE-frames" coming out of the PE depending on a frame averaging parameter which is specific to each channel) and the repetition time between CU-frames (as the MSW is triggered every 25 ms, it has to be a multiple of 25 ms). There are two options for the number of MAJIS data frames (an unsigned 16 bits number): if lower than 65535, it is considered as such ("fixed number of frames" observation). If equal to 65535, it means that the number of acquisitions is not defined ("segment" observation). In that case, the acquisition of frames will be stopped by a specific "Stop Science" TC in the MAJIS timeline.

When a configuration TC is received, the MSW sends the parameters relevant to each of the switched-on CU's and PE's on a "need to know" basis. The main parameters for the CU are the number NPE of the PE-frames which are to be averaged to obtain a CU-frame, the size of the PE-frames (nominally 400 pixels covering the full MAJIS FOV × 508 spectels). Averaging parameters are also sent to the CU: a spatial averaging by 1, 2 or 4 can be applied to the PE-frame on top of the averaging performed by the PE. In the spectral direction, the CU can perform spectral editing (which consists in skipping a spectral range when formatting the data) and spectral averaging on top of that performed by the PE) selected independently for each of up to 16 spectral bands. Data formatting with or without compression is performed by the CU. The 3-D compression scheme is based on the CCSDS 123.0-B-1 lossless compression standard which is implemented on a set of 8 CU-frames (less for the last set if the total number of frames is not a multiple of 8). Lossless compression is implemented after shifting the values by 1 to 8 bits to the right, which reduces the number of bits required for coding each value while increasing the quantization interval, hence the quantization noise. MAJIS data sets can cover large signal variations along the spectrum by a factor of up to 20 for both the VISNIR and IR channels. Therefore, the shift parameter can be set at a different value for each of the CU spectral bands depending on the signal level (which defines the shot noise) as optimum data quality is obtained when the quantization interval does not exceed the noise level. Larger shift settings (hence higher data compression ratios) can be selected on the basis of a trade-off between data quality and data volume.

The PE parameters define how a PE-frame is built. The H1RG detector is read-out with correlated double sampling: a first detector frame is obtained by the FPE by reading out each detector row after resetting it, then a second detector frame is read-out after a time interval which corresponds to the integration time. The FPE only reads-out the 800 detector rows corresponding to the MAJIS FOV (less if a smaller window is requested). Subtracting the "reset" frame from the "read" frame generates the CDS (correlated double sampled) frame that provides the number of electrons collected by each detector pixel (18 µm × 18 µm) during the integration time. This subtraction is performed by the PE so as to obtain a "sub-frame". At this stage, the 8 reference pixels of each row (4 on each side of the detector) are used so as to provide the 0 level for the 1016 connected detector pixels, so that a full FOV sub-frame is stored in the PE RAM as a 800 rows × 1016 spectral values array. The PE-frame is built from several sub-frames so as to mitigate radiation noise ("de-spiking") (Langevin et al. 2020). De-spiking is performed by sorting for each detector pixel the N values from each sub-frame from lowest to highest, then averaging the M lowest values. For a given pixel and wavelength, this screens out acquisitions for which the detector pixel has been impacted by a high energy electron, which generates a large number of additional
electrons. Splitting the acquisition into sub-acquisitions with short integration times is also required for avoiding saturation with the VIS channel when observing icy moons or Jupiter and with the IR channel when observing Jupiter hot spots.

The PE needs to know the following parameters:

- the size of detector frames (800 detector rows × 1024 spectral values for a full FOV acquisition),

- the number N of sub-frames (from 1 to 8),

- the number M of low values to be averaged for de-spiking,

- the detector read-out mode as the data comes out either as a 12-bit integer (1 MHz read-out) or a 16-bit integer (100 kHz read-out).

A first stage of averaging is performed by the PE, averaging pairs of successive rows so that 800 rows (pixels) result in 400 MAJIS pixels for the full FOV. Nominal spectral averaging is also by pairs in the spectral direction, but up to 264 spectels in 4 spectral bands can be selected so that they are included in the PE frame without spectral averaging (“oversampling”). As a result, the full FOV PE-frame built by the PE from N × 2 detector frames sent by the FPE (each 800 × 1024 detector pixels in size) has a size ranging from 400 × 508 to 400 × 640 data elements in case of oversampling (see Fig. 23).

The MSW configures each of the two FPE's by loading into the CPCU RAM the microcode corresponding to the commanded read-out mode for this channel (1 MHz read-out or 100 kHz read-out, it can be different for the VISNIR channel and the IR channel) and overwriting the parameters zone with the FPE command parameters extracted from the configuration TC:

- number of sub-frames to be acquired,

- 1st row and number of rows in the selected window; the 1st row parameter can be selected at different values for the VISNIR and IR channels so as to cope with a small shift in position between the two channels,

- sub-integration time (in ms).

The modified microcode is then loaded to the FPE. If the upload is successful, the FPE is considered by the MSW as ready for accepting frame requests.

The MSW also configures the AUX board by sending three commands extracted from the configuration TC. For what concerns the scan mechanism, *MIRROR_position* defines the start position of the scanner (Sect. 4.1.2). It is enacted by the AUX board upon reception. *Mirror* defines the initial angular speed of the scanner. If set at 0, it means that the scanner will remain at the start position for the whole observation. *MirrorAcceleration* defines the way *Mirror_speed* will change over time, which is required for motion compensation on non-circular orbits (satellite flybys, elliptical orbital phases around Ganymede). Both *Mirror_speed* and *MirrorAcceleration* will only be enacted when the AUX board receives a “Start scan” command.

### 5.3.2 “Start Science” TC Task

The “Start Science” TC triggers the start of the observation if (and only if) all required sub-units have been successfully configured. If this is the case, the MSW sets MAJIS into “science” mode (Fig. 24). The first action consists in initiating a “dark before” CU-frame acquisition (if requested by the configuration TC): this requires closing the shutter, acquiring a dark CU-frame then re-opening the shutter (this takes ~300 ms, which is much longer than the time required by the CU for formatting the dark CU-frame). The specific characteristics
of dark CU-frame acquisitions will be discussed in Sect. 5.4. Once the “dark before” activity is complete, the MSW sends a “start scan” command to the scanner if a scanner motion is required. When receiving this command, the AUX board will initiate the motion of the scanner according to the *Mirror_speed* and *Mirror_Acceleration* it received from the MSW on the basis of the configuration TC. The MSW initiates the high-level cycle for acquiring CU-frames at the time intervals defined by the configuration TC. At the beginning of each CU-frame cycle, the MSW requests from the AUX board the current values of relevant parameters (scanner position, shutter status, status of internal calibration sources) and it sends them to the CU together with other relevant parameters (in particular the detector temperatures) so as to be included in the science data stream (Sect. 5.4).

### 5.3.3 End of Observation Processing and Output Data Product

End of observation activities are triggered when the required number of CU-frames have been acquired or when a “Stop science” TC is received (“segment” observations). In both cases, the MSW sends a message to the CU’s informing them that the on-going CU-frame cycle is the last one. When formatting of this CU-frame for both channels is complete, the MSW sends a “go to zero” command to the AUX board so as to return the scanner to boresight. If requested by the configuration TC, the MSW then initiates a “dark after” CU frame acquisition if requested by the configuration TC.

There are therefore at least one and at most three CU-frame acquisition sequences during an observation: the “dark before” CU-frame (if requested), the observation CU-frames and the “dark after” CU-frame (if requested). Formatting each of these acquisition sequence by the CU results in a string of “Native Science Data Packets” (NSDP) sent to the CPDU (see Sect. 5.4 and Fig. 23). After formatting the last CU-frame of a frame acquisition sequence (which is also the first CU-frame for “dark before” and “dark after” sequences), the CU sends a “last data packet” message to the CPDU. Reception of the last of the two messages indicates to the MSW that it has received all the NSDP packets corresponding to an acquisition sequence from the two CU’s. Therefore, the reception of the last of the two messages for the last acquisition sequence (observation, or “dark after” if requested) indicates to the MSW that it has received all the NSDP’s corresponding to an observation. As will be discussed in Sect. 5.4, the MSW checks every 25 ms the status of the outgoing buffers of MAJIS data (one for each channel), hence it knows when the last NSDP corresponding to an observation has been successfully transmitted to the SSMM. The MSW then sends two events which close the data files corresponding to an observation in MAJIS dedicated section of the SSMM, one for high priority data and one for low priority data (“browse” and “nominal”, see below and Sect. 5.4).

“Browse” data files (received within a few days through X-band) are used for selecting which of several “nominal” data files corresponding to an observation will be requested for downlink at a later stage through Ka-band (“selective downlink”). The high priority file can be empty if no browse data is requested, which is the case for observations whose downlink is considered mandatory, so that the “nominal” data file will always be flagged for transmission. The MSW then switches the active settings of high priority (HIPRI) and LOPRI NSDP headers to be used by the CU’s so that the next NSDP’s prepared by the CU’s will be sent to an alternate set of two data stores in the SSMM. This flip-flop approach with two sets of data stores in the SSMM used alternatively makes it possible to initiate a new MAJIS observation shortly after the end of the previous observation.

After triggering file closure in the MAJIS data stores, the MSW goes back to service mode, waiting either for the next pair of “configuration” TC followed by a “start science” TC
<figure><img src="image_37.png" /><figcaption>Fig. 25 Data flow from the FPA to the CU for a full FOV observation (400 MAJIS pixels derived from 800 detector rows) implementing a de-spiking strategy with 5 sub-frames (e.g averaging the 3 lowest values for each data element of the sub-frames that is assumed to be the despiking parameters required for GCO5000)</figcaption></figure>

triggering the start of a new observation or a sequence of switch-off TC's which sets MAJIS back to safe mode, respecting specifications for FPE/FPA switch-off ("graceful shutdown"). Once back in safe mode, MAJIS waits to be switched-off at system level.

## 5.4 Control Activities and Data Flow in Science Mode

On the basis of the commanding scheme presented in the previous sections, there are three nested cycles triggered by the CPCU during the acquisition of a science CU-frame:

- the sub-frame acquisition cycle, each sub-frame requiring two detector read-outs, one immediately after reset and the second one integration time later,

- the PE-frame acquisition cycle, generating a PE-frame from N sub-frames by averaging the lowest values among N for each detector element (18 µm × 18 µm), then applying the selected spatial and spectral averaging scheme,

- the CU-frame acquisition cycle, generating a CU-frame from NPE PE-frames (frame averaging).

These steps are detailed in Fig. 25 for a CU frame constructed from a single PE frame (no frame averaging), a despiking strategy averaging the lowest 3 values of 5 sub-frames (as example, 10 detector read-outs are here transferred from the FPE to the PE during the PE-frame repetition time) and dark subtraction (nominal for science observations).

### 5.4.1 Sub-Frame Cycle

The sub-frame acquisition cycle is initiated by the FPE after receiving a “PE-frame request” message from the MSW transmitted through the CU and PE. Before transferring the PE-frame request message to the FPE, the PE checks that it has already received the 2xN detector frames required for preparing the previous PE frame (otherwise, it sends back to the MSW an error message indicating that the frame request came in too early). During the sub-frame acquisition cycle, the PE activities are triggered by the reception of detector frames from the FPE. The time FPE takes for acquiring a frame is longer with 100 kHz read-out (529 ms for a full FOV, 800 rows) than with 1 MHz read-out (64 ms for a full FOV). The conversion of the “read” frame cannot be initiated before the end of the conversion of
<figure><img src="image_38.png" /><figcaption>**Fig. 26** Chronogram of a sub-frame cycle at 1 MHz (top) and 100 kHz (bottom). Each vertical dashed line defines the request of a read or reset image. The red ramps correspond to the conversion of successive rows for the “reset” image, while the green ramps correspond to the conversion of successive rows for the “read” image. As discussed in Langevin et al. (2022), with 1 MHz read-out, the sequence needs to be initiated by a “drop” read frame (which is not transmitted to the PE) so as to ensure that for a constant signal the first sub-frame is at the same level as the next ones, which is required for the validity of the de-spiking procedure. With 100 kHz, a “drop” sub-frame (reset, then read) is required, and the interval between the read and reset frame must be the same as that between the reset and read frame (integration time $t_{int}$). The duty cycle for collecting electrons (sum of the $t_{int}$ intervals) is larger than 50% with 1 MHz read-out even considering the drop “read” frame at the beginning of the cycle and the PE processing time ($t_{proc}$). It is smaller than 50% at 100 khz due to the stiffer penalty incurred for ensuring reliability</figcaption></figure>

the “reset” frame. Therefore, the minimum integration time for a full FOV is 64 ms with 1 MHz read-out and 529 ms with 100 kHz read-out. For short integration times, the 1 MHz read-out mode is mandatory. The 100 kHz read-out mode has a lower read-out noise than the 1 MHz read-out mode and a lower power consumption (Table 11). Even considering the smaller time fraction available for collecting electrons (Fig. 26), the 100 kHz read-out mode provides improved performances for very low signals (e.g. exospheres, rings) and long integration times.

The transmission of detector frames from the FPE to the PE is performed on a 12 bits parallel bus at a rate of 10 Mpixels/s for 100 kHz read-out (16 bits ADC conversion by the FPE). With 1 MHz read-out (12 bits ADC conversion by the FPE), only the 12 LSbits of the parallel bus are used and the rate is 20 Mpixel/s. The transmission time is 41 ms for a full FOV (800 rows) with both read-out modes. The chronograms of sub-frame cycles are presented in Fig. 26 for the two conversion rates.

### 5.4.2 PE-Frame Cycle

The reset image is stored as such in one of the 8 sub-frame buffers in the PE RAM. When each row of the “read” image is received, the PE subtracts the values from the corresponding “reset” row then the average difference for the 8 reference pixels (which provide a 0 reference as these pixels are not connected). The PE divides the result by 2 with 100 kHz read-out and multiplies it by 8 with 1 MHz read-out so as to obtain in both cases a signed integer with a 16-bit dynamic range, which is stored back in the sub-frame buffer. When all N sub-frames ($N \times 2$ detector frames) have been received, the PE prepares the PE-frame by performing first the selected de-spiking procedure (average of the lowest M values out of N for each of the detector pixels contributing to a PE-frame data element) then (2 or 4
depending on whether oversampling or nominal sampling is selected for this wavelength range) which has been defined on the basis of the local radiation flux (much higher for Europa than for Callisto) so that at most 1% of data elements are still impacted after de-spiking (Langevin et al. 2020).

If dark subtraction is requested, which is nominal for science observations, the PE subtracts the corresponding value of the dark frame which is stored in its RAM before sending the result to the CU over a SpW link operating at 40 Mhz (2 Mpixels/s). The dark frame must to be acquired with the same acquisition parameters (first row, number of rows, de-spiking strategy, read-out speed, integration time) and the same oversampling ranges as the observation itself for the dark subtraction to be legitimate. For a full FOV, the dark subtraction processing time ranges from 108 ms (minimum size PE-frame of 400 × 508, number of sub-frames < 4) to 250 ms (maximum size PE frame of 400 × 640, number of sub-frames = 8). A PE-frame must be completed before the 1st row corresponding to the next PE-frame is received by the PE leading to additional constraints on the minimal interval between PE-frame request.

The dark PE-frame is updated by a specific procedure in which the MSW sends 9 PE-frame requests to the FPE which are flagged as dark PE-frame requests for the PE. In that case, the FPE produces and the PE processes each series of sub-frames with exactly the same procedure as for observation PE-frames (de-spiking, then spatial averaging, oversampling or nominal spectral averaging) except that there is no dark subtraction (the shutter is closed, but the FPE and PE do not have this information). The PE adds the outcome for each data element to a "total" buffer and it compares it with the previous highest value for this data element stored in a "max" buffer so as to update it if higher. After processing the 9th dark frame, the PE subtracts the "max" buffer from the "total" buffer before dividing by 8, stores the result in the "dark" RAM buffer and sends it only then (after 9 PE-frame requests) to the CU as the "dark PE-frame". This provides an average of the 8 lowest de-spiked values out of 9. If the configuration TC does not request averaging by the CU, the de-spiking parameters have been selected so that at most 1% of data elements in a PE-frame are impacted after de-spiking for each of the contributing 9 PE-frame acquisitions. With this procedure, at most 0.34% of the data elements in the dark PE-frame remain impacted as this requires that at least 2 of the 9 values corresponding to the same data element in the PE-frames are impacted.

### 5.4.3 CU-Frame Cycle

**Frame-Binning** The activities of the CU during a CU-frame cycle are controlled by the end of the reception of PE-frames from the PE similarly to the FPE-PE relationship during a PE-frame acquisition. The CU works on two stacks of 8 CU-frames, one for storing incoming PE-frames and the other for formatting the previously received PE-frames as CU-frames, the two processes running in parallel. The procedure implemented by the CU is presented when compression is implemented as it is nominal for all MAJIS science acquisitions. Each data element of a PE-frame corresponds to 16 bits signed integer so that 2 data elements can be stored at a 32-bit address. If the CU-frame is built from a single PE-frame (parameter NPE = 1, no frame averaging), the PE-frame is stored as such in the next available slot of the acquisition stack. If frame averaging is requested (NPE ≠ 1), the first PE-frame is stored in a 32-bits buffer and the following ones are added to it. After the last PE-frame has been received, each data element in the 32- bits buffer is divided by NPE, providing an average of the values for the NPE contributing PE-frames which it is stored in the next available slot of the acquisition stack.
<figure><img src="image_39.png" /><figcaption>**Fig. 27** CU-frame cycle for a GCO5000 observation. The long CU-frame repetition time (7.8 s) is allocated to a single PE-frame for the IR channel, while 8 successive PE-frame requests are issued to the VISNIR channel at intervals of 975 ms (frame averaging by 8). The drop frame explains the additional one frame to be considered for the calculation of the CU-frame repetition time (Fig. 26). A request for HK (mirror position, shutter status, sources) is issued by the MSW to the AUX board immediately before sending the first PE-frame request of the CU-frame cycle (in that case, the scanner is fixed at boresight). As soon as the AUX board has sent back this information, the MSW sends it to the CU's together with the on-board time of the 1st PE-frame request and other relevant HK values</figcaption></figure>

As shown on Fig. 27 for an observation during the GCO5000, the way a CU-frame is built can be quite different for the VISNIR and IR channel. A de-spiking strategy averaging 3 sub-frames out of 5 (as presented in Fig. 25) is adequate for both channels in the Ganymede radiation environment. The dwell time is very long (7.8 s), so that the 100 kHz read-out mode can be implemented for the IR channel with a 650 ms integration time. However, the VISNIR channel would saturate, hence a much shorter integration time of 99 ms is selected (this requires implementing the 1 MHz read-out mode), with frame averaging of 8 PE-frames during the 7.8 s long CU-frame cycle.

**Spatial Averaging, Spectral Editing/Averaging** These tasks are defined by the “configuration” TC (Sect. 5.3.1) They are performed on sets of 8 successive CU-frames (called “bricks”) which are processed together. The first step when processing a brick consists in preparing a first data packet that contains, for each of the 8 CU-frames formatted in the brick, 1) the on-board time of the first PE-frame request and 2) the relevant HK values that are sent to the CU by the MSW after initiating a new CU-frame cycle. After that, the CU extracts the nominal data stream from the brick by “sub-bricks”, with a size of e.g. 50 pixels × 64 spectels × 8 CU-frames for full FOV frames (400 pixels) with the maximum number of spectral elements (640). Each row of 8 sub-bricks corresponds to one of the CU spectral ranges (up to 15) as defined in the “configuration” TC, with a spatial averaging parameter that is common to both channels and all spectral bands, while a spectral editing and a spectral averaging and a compression parameter (shift) are specific to each CU spectral band (Sect. 5.3.1).

If the CU spectral band is flagged as edited, the CU does not format it (i.e. the spectral band is discarded). For non-edited spectral bands, the CU can implement spectral averaging by 1, 2 or 4. Spectral averaging by the CU is inhibited for spectral ranges for which the PE has been instructed to implement oversampling (i.e. not implementing the spectral averaging by 2 which is nominal). The number of detector spectels contributing to a MAJIS data element is therefore 1 (PE oversampling), 2 (PE nominal averaging, no CU spectral averaging), 4 (CU spectral averaging by 2) or 8 (CU spectral averaging by 4). The size of the rectangle of 18 µm × 18 µm detector elements which are averaged into a MAJIS data element is presented in Fig. 28 as a function of the spatial averaging parameter and the spectral averaging parameter for a given spectral band.
<figure><img src="image_40.png" /><figcaption>**Fig. 28** Size of the rectangle of detector elements (18 × 18 µm) contributing to a MAJIS data element as a function of the averaging parameters. The spatial dimension is in green, the spectral dimension is in red. The nominal size is 2 × 2. The selection of the size will be adapted according to the science case. Up to 64 18 × 18 µm data elements are averaged so as to obtain a data element of a PE frame, more if frame averaging is implemented. This must be considered when defining the de-spiking strategy as the result is unreliable if one of the contributing values is still impacted</figcaption></figure>

**Browse Data Formatting** If browse data is requested by the configuration TC, a first row of sub-bricks is generated by extracting from the stack of PE-frames, which always cover the full wavelength range of the channel, up to 32 data elements corresponding to wavelengths defined by a browse table selected by the configuration TC out of 31 possible browse tables (0 meaning “no browse”) stored in the CPCU MRAM. There is one set of 31 browse tables for each channel, each defined for a specific target (icy moons, Jupiter, exospheres...). The browse table is loaded by the CPCU in the CU when configuring it for each observation. Additional spatial averaging can be applied to the browse row on top of that already applied to nominal data so as to reduce the browse data volume (a data volume lower than 5% of that of the nominal data set is targeted). The browse NSDP’s are tagged with the “high priority” logical address for storage in the SSMM.

**Dark Frame Processing** When receiving from the PE a “dark” PE-frame, the CU prepares a “dark” CU-frame by applying the same spatial averaging, spectral editing and spectral averaging parameters as for observation CU-frames. It also uses the same spatial and spectral division into sub-bricks for formatting it as for observation CU-frames. However, each “dark” CU-frame is formatted as a standalone without compression by filling up 32-bits words by 16-bit values for each data element when preparing the NSDP’s. A “browse” dark CU-frame is prepared if browse data is requested by using the same browse table as for observation CU-frames, and it is also formatted without compression with the high priority tag. For both nominal data and browse data, this makes it possible to recover the CU-frame values before dark subtraction in case the temperature of the detector (which is available for each CU-frame in the first NSDP of a brick) has significantly changed during the observation. One can then interpolate values from dark frames acquired before and after the observation on the basis of the detector temperature so as to subtract the dark level which is relevant for each CU-frame.

**Compression and NSDP Formatting** Formatting with compression is performed on a set of “bricks”, each consisting of 8 successive CU-frames which are processed together (3-D
<figure><img src="image_41.png" /><figcaption>**Fig. 29** Transfer of NSDP's from their build-up by the VISNIR and IR CU's to the MAJIS dedicated data stores in the SSMM. The output stacks (TX) and input stacks (RX) all provide 64 slots, as well as the internal buffer used by the two IOS partitions. The large NSDP stack managed by the MSW partition guarantees optimal usage of the bandpass and it mitigates the impact of short duration interruptions of the SpW link to the SSMM. For this observation, NSDP are stored in the MAJ1 (high priority) and MAJ3 (low priority) data stores of the SSMM. For the next observation, they will be stored in the alternate set of data stores, MAJ2 and MAJ4</figcaption></figure>

compression scheme). The number of CU-frames can be smaller for the last brick if the total number of CU-frames is not a multiple of 8. The CU compresses the brick by sub-bricks (see Sect. 5.4.3). As an example, if no spatial or spectral averaging by the CU is requested in the configuration TC, there are 10 rows of sub-bricks of 50 pixels × 64 spectels × 8 CU-frames if maximum oversampling is implemented for full FOV frames (400 pixels × 640 spectels). The shift parameter which defines the level of compression is also defined for each CU spectral band of the VISNIR and IR channels by the configuration TC. The target for MAJIS data compression is ~ 5 bits/data (starting with 16 bits/data) when averaged over the full spectral range. Note that the compression level of any browse can be set higher than for nominal data.

Reversible compression of each sub-brick results in a string of 32-bits words which are formatted as NSDP each accommodating up to 1026 32-bits words of compressed data. The nominal NSDP is tagged with the logical address LOPRI for storage in the SSMM. NSDP are also tagged with the row and rank in the row of the sub-brick being processed, so that the MSW can detect upon reception the start and end of a string of NSDP's corresponding to a sub-brick. When the CU receives the "last CU-frame" message from the CPCU/MSW, it formats the last set of CU-frames as a brick of 1 to 8 CU-frames, and after the last sub-brick of the nominal data stream has been formatted it puts a "last NSDP" message in the output stack so as to inform the MSW that formatting of the on-going CU-frame acquisition cycle is over.

**Transfer to the SSMM** The CPCU activities linked to the MAJIS data flow consist in providing to each CU the relevant HK values for each CU-frames and in making sure that all generated NSDP's are successfully transmitted to the JUICE SSMM. The NSDP transfer process from the CU to the SSMM is presented in Fig. 29. NSDP's are received in one of two input stacks, one for each CU. The NSDP stack is large enough to guarantee optimum
usage of the maximum useful data rate of 42 Mbits/s (64 × 20 NSDP's can be transferred in 1 s, each with 1026 32-bits words of compressed data). The margins for science data transmission are more than adequate as it is nominally compressed (target: 5 bits/data) so that the maximum expected data rate is ~ 10 Mbits/s. The maximum transmission rate is also adequate (with smaller margins) in the few cases when uncompressed data is sent when performing an internal calibration.

A sequence of observations between a MAJIS switch-on and a MAJIS switch-off results in successive sets of two data files, a “nominal” data file in a LOPRI MAJIS data store of the SSMM and a “browse” data file in a HIPRI data store. If no “browse” data is requested, the “browse” data file is still created, but it is empty after closure. The two sets of data stores (MAJ1 and MAJ2 for high priority, MAJ3 and MAJ4 for low priority) are used in alternance for each observation (MAJ1 and MAJ3, then MAJ3 and MAJ4, MAJ1 and MAJ3, and so on). All MAJIS browse data files (including the empty ones) are transferred within a few days to ground by X-band. In compliance with the selective downlink scheme selected for JUICE, the MAJIS team will flag for transmission by Ka-band all nominal data files for which the browse data file is empty, and a subset of the nominal data files with associated browse data on the basis of its evaluation of the browse data set.

# 6 Testing, Calibration, and Performance

## 6.1 Performed Measurements

The validation of the performances and functionalities is in line with the verification methods defined for other space-borne imaging spectrometers, including OMEGA (Observatoire pour la Minéralogie, l'Eau, les Glaces et l'Activité) on Mars Express (Bonello et al. 2004), VIRTIS (Visible and Infrared Thermal Imaging Spectrometer) on Rosetta (Ammannito et al. 2006), JIRAM on Juno (Adriani et al. 2017a), and SIMBIO-SYS/VIHI on BepiColombo (Filacchione et al. 2017). Specifically, the functional and performance verification strategy combined simulations and measurements from subsystems to the integrated system level during assembly and integration on the S/C according to five steps:

1. Calibrations and tests at the components level (focal planes, scan mirror mechanism...);

2. Calibration and tests at the subsystem level (OH, ME as separate units);

3. End-to-end functional tests at instrument integrated level;

4. Calibration and tests at instrument integrated level;

5. Assembly, Integration and Test/Verification (AIV/AIT) activities during MAJIS integration onboard the S/C.

The major test campaigns are listed in Table 15.

After launch, in-flight check-outs are performed on a routine basis to monitor the health and performance of the instrument (Sects. 6.6 and 7.2).

### 6.1.1 Calibrations and Tests at Component Level

Instrument components such FPAs, spectrometer, telescope, scanning mirror mechanism, shutter, internal calibration sources were independently tested prior to their integration (Table 15). Critical components were the MAJIS VISNIR and IR FPUs for which the following measurements at different temperatures and/or wavelengths were addressed (Carter et al. 2002a; Haffoud et al. 2022):
<table><caption>**Table 15** Summary of the main test campaigns dedicated to the assessment of MAJIS performance</caption><thead><tr><td>Component</td><td>Premises/Responsible</td></tr></thead><tbody><tr><td>VI FPU/FPA FM & FS characterization including filter FPU alignment</td><td>IASB/ROB</td></tr><tr><td>IR FPU/FPA FM & FS characterization</td><td>IAS</td></tr><tr><td>VI FPU filter performances</td><td>Safran</td></tr><tr><td>IR FPU FM & FS filter performances</td><td>Viavi, IAS</td></tr><tr><td>Sub-systems OH characterization</td><td>IAPS</td></tr><tr><td></td><td>LDO</td></tr><tr><td>E2E (End-to-End) functional test</td><td>LDO/IAS</td></tr><tr><td>OH characterization including thermal cycling</td><td>LDO</td></tr><tr><td>MAJIS calibration</td><td>IAS</td></tr><tr><td>Internal Alignment</td><td>LDO</td></tr><tr><td>Alignment on the S/C</td><td>Airbus (Toulouse)</td></tr><tr><td>Thermal Vacuum test (with STM)</td><td>ESTEC</td></tr><tr><td>Thermal Vacuum test (with PFM)</td><td>Airbus (Toulouse)</td></tr></tbody></table>

1) FPA linearity (signal level at different temperatures, integration times and incoming flux) and quantum efficiency;

2) offsets, background signal and saturation level;

3) spectral and radiometric photo-response and flat field nonuniformity vs. temperature;

4) cross-talk, ghost (residual) images;

5) dark current and dark current non-uniformity, read out noise;

6) location and evaluation of damaged pixel location (out of linearity, fixed output);

7) characterization of FPA windows and order sorting filters transmittances;

8) evaluation of radiation effects (modeling and testing with radiation sources, Guiot et al. 2020).

Some of the above tests were provided by the FPA and filters manufacturers, while others were performed by MAJIS team institutes as appropriate with the support of the PI institute and IAPS.

## 6.1.2 Calibrations and Tests at Subsystem Level

In this phase, the performance and functionalities of the subsystem units were verified by the suppliers (scientific institutes and industrial partners) before the final integration of the MAJIS instrument. The main activities planned at the subsystem level were:

- ME: onboard software tests; data compressor tests; data packet integrity; thermal and vibration cycles. A dedicated E2E test and the calibration campaign validated the ME functionalities.

- PE functionalities: fine-tuning of the biases; data read-out tests; radiation spikes removal algorithm tests; thermal and vibration cycling. The E2E test and the calibration campaign validated the PE functionalities.

- OH: fine tuning of the OH elements to optimize optical alignment and focus and minimize residual aberrations; reduced spectral, spatial, and geometric calibrations to verify optical response and performances; alignment of the internal calibration unit and reference signal optimization; alignment of the scan mirror mechanism and characterization of the optical boresight (Instrument Line of Sight) w.r.t. the external reference cube mirror (referred
<figure><img src="image_42.png" /><figcaption>**Fig. 30** Summary of the thermal exploration (in K) of the OH and the two channels during the FPU, OH and instrument characterization campaigns. Flight predictions for OH and IR FPA are shown for comparison (Sect. 4.4.2)</figcaption></figure>

to as UARF) w.r.t. the JUICE optical bench (URF); survival/anticontamination heaters tests; thermal and vibration cycling. During these activities, the instrument was operated through a dedicated Electrical Ground Segment Equipment test setup.

### 6.1.3 Calibrations and Tests at Instrument Integrated Level

Once fully integrated, an E2E functional test was performed at the MAJIS PFM system level (OH+ME) prior to the delivery of the ME to the S/C. The OH unit experiment has then underwent two calibration campaigns within planning constraints in order to characterize the instrument's spectral, spatial and radiometric performance as a function of various parameters that may evolve during MAJIS operations (Barbis et al. 2022; Vincendon et al. in preparation). The measurements were performed in a meaningful subset of operational modes. As a secondary goal, the ground calibration also provided data to help validate operational procedures, functional performance of the ME (including the data handling, telemetry flow, data compressor, and command and operational modes), and further scientific reduction algorithms.

During these tests, the PFM was maintained in mechanical and thermal environments simulating space conditions. The two successive phases were carried out:

1. in the LDO facilities where OH functional tests, thermal cycling and reduced calibration (spectral, spatial and radiometric) were performed. In this phase the instrument was operated through a dedicated test setup (Unit Tester) using the FPU-GSE;

2. in the facilities of the IAS where the absolute radiometric calibration and ME functional tests were completed. During this phase the fully-integrated instrument (OH-FM controlled by the ME-FS) was tested under operational conditions. Due to planning constraints at the instrument level, some measurements performed at FPU and OH levels were not fully reproduced at this instrument level.

### 6.1.4 Thermal Exploration

The tests described in the previous subsections explored various thermal configurations at the component and instrument levels (Fig. 30). Due to planning constraints, most of the tests at non-nominal temperatures (outside of the in-flight predictions thermal range) were limited.
## 6.2 Spectral

To distinguish spectrally similar species that have different implications for their formation environments (e.g. exogenic vs endogenic processes) or to deconvolve gaseous signatures that trace different atmospheric Jovian phenomena (Sect. 2), adequate spectral range, resolution and sampling are necessary. This requires a sufficiently large spectral range, a high-density spectral sampling, and a narrow FWHM of the instrument response in the spectral direction as shown in Table 9.

Dedicated measurements were made during the ground calibration to characterize these spectral performances (Sect. 6.1). Despite the limitations due to the planning constraint that restricted a detailed exploration of all spectels, several sources have been used to retrieve the spectral parameters and to cross-check the derived values. Additional details on the calibration data sets and the analyses are provided in Haffoud et al. (2024). A summary of the main spectral characteristics is given below.

The final calibration results in overall wavelength actual limits that comply for both channels (Table 14). There is a minor deviation at short wavelengths of the IR channel with a starting wavelength of 2.27 µm instead of the 2.25 µm specification. The MAJIS benchmark for discriminating spectral signatures translates into the following requirements: (1) < 5.5 nm (resp. 10 nm) FWHM₃₆ (nominal spectral binning) for the VISNIR (resp. IR) channel and (2) 3.65 nm ± 0.25 (resp. 6.50 nm ± 0.25) spectral sampling for the VISNIR (resp. IR) channel. The FWHM₃₆ in the center of the FOV of the VISNIR channel is minimal at ~ 1.5 µm where it reaches 4 nm and is maximal at the two spectral extremes where it is 5.5 nm. For the IR channel, the FWHM₃₆ was characterized at short wavelengths only and is lower than 7 nm. A better characterization at long wavelengths (> 5 µm) was done using the cryogenic spectral properties of the polystyrene filter that is mounted in the ICU. For this purpose, laboratory reference spectra of polystyrene acquired at cold operating temperatures were compared to the ICU measurements (Haffoud et al. 2024). Two different positions in the FOV were examined at the cold nominal temperature (~130 K) and no deviation was found. The spectral response for the VISNIR channel seems to be stable in the 110-150 K range in the center of the FOV, while a small temperature dependence is measured in the IR channel. Although very little data has been collected at these two extreme temperatures, the temperature effect of the spectral calibration is expected to be very small given the much smaller predicted thermal excursion during in-flight science operations (<10 K as reported in Table 13). The total magnitude of the smile (wavelength drift along the FOV) does not exceed one pixel, with stronger and more complex variation along the slit in the VISNIR requiring 2D interpolation.

All the previous results are defined for the nominal spectral capability (508 spectels). As presented in Sect. 5 and Fig. 28, MAJIS has the capability to perform spectral oversampling or additional spectral averaging, resulting in variable sampling and spectral response. Figure 31 illustrates the effect of the spectral averaging on the spectral response of a monochromatic light used during the instrument calibration. If the spectral response has still a Gaussian shape with spectral oversampling and in the nominal case, this is no longer true when additional averaging is performed by the CU (4 × 18 µm in green or 8 × 18 µm in red).

## 6.3 Spatial

Resolving small-scale deposits (<75 × 75 m²) on the surface of the icy moons is a key MAJIS requirement. This depends on the spatial response of MAJIS, which has been characterized by measuring of the image Pixel and Slit functions. These functions correspond
<figure><img src="image_43.png" /><figcaption>**Fig. 31** Evolution of the spectral response w.r.t. the applied spectral averaging (from oversampling to averaging by 8). The data have been acquired using a monochromatic flux sampled by step of 1 nm between 2600 and 2650 nm. A Gaussian fit (blue line) of the oversampled data is shown for comparison</figcaption></figure>

to the instrumental resolution along and across the spectrometer's slit direction respectively. The Pixel Function, $F_P(s)$, at a pixel located at sample (s) along the slit direction is given by the following convolution:

$$F_P(s) = V(s) \otimes \text{INST}(s) \qquad (1)$$

where $V(s)$ is the physical pixel spatial response, and $\text{INST}(s)$ is the instrument's response along the slit. Both terms are unitary step functions.

Similarly, the Slit Function, $F_S(l)$, for a pixel placed along the scan direction at line (l), is equal to a similar convolution between unitary functions:

$$F_S(l) = U(l) \otimes \text{TEL}(l) \qquad (2)$$

where $U(l)$ is the spectrometer's slit spatial response, and $\text{TEL}(l)$ is the telescope's response along the scan direction. The spatial response along the slit is equal to the FWHM of the image pixel function profile while the response across the slit is similarly equal to the slit function's FWHM.

An ideal imaging system would respond with unitary step-function responses where all photons coming from a collimated point source are collected at a given pixel with no residuals across neighboring pixels. In contrast, real imaging spectrometers do not have unitary telescope and slit's function responses. For this reason, a point source signal is spread over more than one pixel. The larger the spread, the worse the spectral performance.

MAJIS is designed to provide an angular FWHM$_{36}$ ≤ 225 µrad corresponding to a spatial FWHM$_{36}$ ≤ 54 µm across the detector (Table 9). These values correspond to 1.5 times the nominal IFOV (150 µrad or 36 µm considering the nominal pixel size).

During the pre-launch characterization campaign, the Pixel and Slit functions have been derived by taking multiple images of a test slit scanning along and across the slit direction (Sect. 6). For a given pixel under test, the variation of the test slit signal during the spatial scan and the FWHM$_{36}$ of the resulting near-gaussian profiles have been measured.

Since MAJIS optics are designed to observe targets at infinite distances, a collimator was used to collimate the beam from the test slit before entering in the instrument's telescope pupil. During the measurements the OH was placed in a Thermal Vacuum Chamber where it is thermally stabilized at operating temperature (130 K). Some measurements are repeated
<table><caption>**Table 16** VISNIR and IR channel pixel function measured at three positions of the FOV (boresight, or on-axis direction, negative and positive fields) and at several spectral bands (#470 corresponding to wavelength 1346 nm and #870 corresponding to 2079 nm for the VISNIR; #150 corresponding to wavelength 2710 nm and #550 corresponding to 3988 nm for the IR). The average FWHM<sub>36</sub> for two contiguous nominal pixels (sample<sub>1</sub> and sample<sub>2</sub>) is reported. Neg-field (resp. pos-field) corresponds to the left (resp. right) position of the detector</caption><thead><tr><th>FOV Position</th><th>Sample<sub>1</sub></th><th>Sample<sub>2</sub></th><th>Band</th><th>FWHM<sub>36</sub> (μm)</th></tr></thead><tbody><tr><td>VISNIR channel</td><td></td><td></td><td></td><td></td></tr><tr><td>neg-field</td><td>133</td><td>134</td><td>870</td><td>26.7</td></tr><tr><td>neg-field</td><td>133</td><td>134</td><td>470</td><td>26.4</td></tr><tr><td>on-axis</td><td>503</td><td>504</td><td>870</td><td>50.5</td></tr><tr><td>on-axis</td><td>503</td><td>504</td><td>470</td><td>50.5</td></tr><tr><td>pos-field</td><td>867</td><td>868</td><td>870</td><td>106.0</td></tr><tr><td>pos-field</td><td>867</td><td>868</td><td>470</td><td>89.2</td></tr><tr><td>IR channel</td><td></td><td></td><td></td><td></td></tr><tr><td>neg-field</td><td>125</td><td>126</td><td>150</td><td>26.6</td></tr><tr><td>neg-field</td><td>125</td><td>126</td><td>550</td><td>27.9</td></tr><tr><td>on-axis</td><td>494</td><td>495</td><td>150</td><td>48.9</td></tr><tr><td>on-axis</td><td>494</td><td>495</td><td>550</td><td>53.2</td></tr><tr><td>pos-field</td><td>864</td><td>865</td><td>150</td><td>36.4</td></tr><tr><td>pos-field</td><td>864</td><td>865</td><td>550</td><td>41.0</td></tr></tbody></table>

at the minimum (110 K) and maximum (150 K) operating temperatures to evaluate possible changes of the spatial responses with temperature.

A full description of the geometric calibration and performances of MAJIS is given in Filacchione et al. (2024). In Table 16 to Table 18, we report some results representative of the spatial performances of the VISNIR and IR channels for three positions of the FOV based on a Gaussian fit.

The MAJIS spatial response is within the specifications for the majority of the measurements and it is stable with the temperature of the OH structure. The keystone (spatial drift along a detector column) is very small and also fully within the specifications (< 1 IFOV i.e. 36 µm). The main deviation is observed on the image pixel function over the positive field (right position on the detector) of the VISNIR channel below ~1 µm where it is 2-3 times worse than the specification (Table 16). This effect is probably caused by a residual of the VISNIR channel detector alignment procedure that was not fully recovered during the AIV/AIT phases.

## 6.4 Radiometric

Radiometric calibration from digital numbers to radiance units involves FPA conversion efficiency, linearity correction and bias subtraction, background subtraction (dark current and thermal), correction of FPU and optical head artifacts, division by exposure time, and conversion of the result of these steps to radiance units by comparison with radiometric references. The radiometric calibration of the two channels was performed in two steps: detector characterization and instrument level calibration (Table 15). In a first step, the FM detectors were characterized before integration in the OH w/o and with their filter in their FPU and then in the OH. The measurements at the FPU level were performed using the
<table><caption>**Table 17** VISNIR and IR channel slit function measured at three positions of the FOV and at different spectral bands (band #80 corresponding to 2500 nm)</caption><thead><tr><th>FOV Position</th><th>Sample</th><th>Band</th><th>FWHM<sub>36</sub> (μm)</th></tr></thead><tbody><tr><td colspan="4">VISNIR channel</td></tr><tr><td>neg-field</td><td>146</td><td>870</td><td>47.7</td></tr><tr><td>neg-field</td><td>146</td><td>470</td><td>42.9</td></tr><tr><td>on-axis</td><td>504</td><td>870</td><td>60.4</td></tr><tr><td>on-axis</td><td>504</td><td>470</td><td>62.9</td></tr><tr><td>pos-field</td><td>872</td><td>870</td><td>48.4</td></tr><tr><td>pos-field</td><td>872</td><td>470</td><td>48.1</td></tr><tr><td colspan="4">IR channel</td></tr><tr><td>neg-field</td><td>118</td><td>80</td><td>68.0</td></tr><tr><td>neg-field</td><td>118</td><td>550</td><td>75.6</td></tr><tr><td>on-axis</td><td>485</td><td>80</td><td>49.0</td></tr><tr><td>on-axis</td><td>485</td><td>550</td><td>70.2</td></tr><tr><td>pos-field</td><td>854</td><td>80</td><td>43.0</td></tr><tr><td>pos-field</td><td>854</td><td>550</td><td>51.2</td></tr></tbody></table>

<table><caption>**Table 18** VISNIR and IR channel slit functions measured on-axis at several spectral bands (see Table 16 and Table 17 for wavelength definition) as a function of the operating temperature of the OH. The ΔFWHM corresponds to the difference of the FWHM<sub>36</sub> at min (110 K) and max (130 K) temperature cases with respect to the nominal case</caption><thead><tr><th>T<sub>op</sub> (K)</th><th>Sample</th><th>Band</th><th>FWHM<sub>36</sub> (μm)</th><th>ΔFWHM (μm)</th></tr></thead><tbody><tr><td colspan="5">VISNIR channel</td></tr><tr><td>Min</td><td>504</td><td>870</td><td>63.9</td><td>3.6</td></tr><tr><td>Min</td><td>504</td><td>470</td><td>64.0</td><td>1.1</td></tr><tr><td>Nom</td><td>504</td><td>870</td><td>60.4</td><td>=</td></tr><tr><td>Nom</td><td>504</td><td>470</td><td>62.9</td><td>=</td></tr><tr><td>Max</td><td>504</td><td>870</td><td>56.5</td><td>-3.9</td></tr><tr><td>Max</td><td>504</td><td>470</td><td>57.0</td><td>-5.9</td></tr><tr><td colspan="5">IR channel</td></tr><tr><td>Min</td><td>532</td><td>80</td><td>68.6</td><td>19.6</td></tr><tr><td>Min</td><td>532</td><td>550</td><td>61.9</td><td>-8.2</td></tr><tr><td>Nom</td><td>485</td><td>80</td><td>49.9</td><td>=</td></tr><tr><td>Nom</td><td>485</td><td>550</td><td>70.2</td><td>=</td></tr><tr><td>Max</td><td>540</td><td>80</td><td>59.7</td><td>10.7</td></tr><tr><td>Max</td><td>540</td><td>550</td><td>73.4</td><td>3.2</td></tr></tbody></table>

procedure presented in Langevin et al. (2022) to achieve both precision and accuracy when reading out the H1RG detectors. The conversion efficiency (in e-/DN) was determined for both readout modes at low signal levels (Table 19).

The evolution of the conversion efficiency with the signal level (linearity) was also investigated during the characterization. It is dominated by the nearly linear decrease of the transimpedance of HxRG detectors (Loose et al. 2007; Blank et al. 2012). Tables of cor-
<table><caption>**Table 19** Conversion efficiency for FM detectors</caption><thead><tr><th></th><th>VISNIR FM detector</th><th>IR FM detector</th></tr></thead><tbody><tr><td>100 kHz read-out</td><td>4.320 e-/DN</td><td>4.260 e-/DN</td></tr><tr><td>1 MHz read-out</td><td>4.195 e-/DN</td><td>4.1375 e-/DN</td></tr></tbody></table>

rection coefficients were determined for each FM detector, read-out mode, to determine the actual number of electrons collected as a function of the observed signal level in DN.

For the IR channel, the radiometric calibration was performed at instrument level using a black body source inside the vacuum chamber covering the FOV, avoiding the need to monitor the spectral transmission of the chamber window. Signals ranging from low values to saturation over the whole wavelength range were obtained with 8 blackbody temperatures from 193 K to 323 K and two integration times (100 ms and 800 ms). The results obtained at different temperatures and integration times were fully consistent after implementing the linearity correction defined at the characterization stage except for short wavelengths in the broadband filter range (2.28 – 2.85 µm) where a straylight contribution had to be corrected: for a blackbody at 273 K, there are 35 times more photons per spectral interval at 2.85 µm than at 2.30 µm, so that even a very small straylight component becomes significant. No significant straylight issue was expected for bluer sources (as will be the case for MAJIS observations, which are dominated by reflected sunlight in this wavelength range). This was confirmed by observations of a QTH with a color temperature of 2800 K illuminating an integrated sphere, with a very low signal level beyond the edges of the FOV (a signature of straylight issues, as will be discussed for the VISNIR channel). The resulting Instrument Transfer Function (ITF) is in line with model expectations, with variations of no more than 2% of the ITF across the FOV for operable pixels.

For the VISNIR channel, the radiometric calibration was performed at the instrument level using three light sources. The black body inside the vacuum chamber was used for wavelengths longer than 2 µm by setting it to the maximum temperature compatible with the maintenance of the vacuum chamber. An external blackbody was used from 1 µm to 2 µm. In this case, window transmission and reflections within the optical bench must be considered, so these measures were cross-calibrated with those of the internal blackbody at long wavelengths to evaluate the overall efficiency of the optical setup. In the lower part of the wavelength range (0.49 to 1 µm), observations of a calibrated QTH lamp illuminating an integrating sphere during the OH performance characterization were used as the reference for radiometric calibration of the legitimate contribution to the signal. The VISNIR instrument transfer function for the legitimate contribution is generally in agreement with model expectations except for a higher throughput at wavelengths between 1.5 to 2.2 µm. Additional analyses are presented in Langevin et al. (submitted).

A VISNIR straylight issue at short wavelengths (< 1.4 µm) was identified during the OH performance characterization. A hardware correction has been applied, which significantly reduces the level of straylight compared to the legitimate signal. However, a residual straylight still exists, as it was confirmed during the calibration of the integrated instrument due to photons coming from higher wavelengths (maximum weight ~ 2 µm). In contrast to the IR channel, where the broadband filter (for wavelengths 2.28 – 2.85 µm) or a narrow bandpass LVF (for wavelengths 2.85 µm – 5.54 µm) eliminates long wavelength photons (thermal background) as well as short-wavelength photons from higher orders of the grating, the signal at the VISNIR detector is filtered from short-wavelength photons by a high
<figure><img src="image_44.png" /><figcaption>**Fig. 32** (left) MAJIS VISNIR FOV (800 rows out of the 1016 connected rows) illuminated by an integrating sphere that provides a very homogeneous output beam in the spatial direction which extends beyond the FOV in both the along-slit and cross-slit directions. A straylight contribution for this extended source with a color temperature of ~2800 K is clearly visible outside the FOV. (right) This contribution reaches a few percent at long wavelengths (blue curve) and it is very significant (a few 10 percent) at wavelengths shorter than 1300 nm (red curve). On the IR detector the straylight is very small (green curve)</figcaption></figure>

pass LVF that accepts all photons below the cutoff wavelength (2.55 µm). This design approach was chosen for two reasons: the mechanical integration of a high pass LVF is much easier than that of a narrow LVF one and the thermal background with a spectrometer at 130-140 K is negligible at such wavelengths. The measured straylight contribution is larger for very red sources (e.g. black bodies at 353 K to 550 K) than for the QTH source (color temperature ~2800 K). At wavelengths from 0.49 to 1 µm, the level of the signal beyond the edges of the FOV is due only to straylight and can be monitored because there are 1016 connected columns with an 800 rows FOV (Fig. 32). It was used to subtract the straylight contribution from the signal near the edge of the FOV to evaluate the instrument transfer function relevant to the legitimate contribution.

## 6.5 SNR Simulations

As presented in the previous section, a comparison between the model-derived MAJIS ITF (from which top-level science requirements about SNR were derived, as summarized in Table 9) and ground-based measurements shows that the measured responsivity of the instrument is in line with expectations for the IR channel. For the VISNIR channel, the responsivity is even slightly better than expected up to 1700 nm, and better up to a factor of up to 2 from 1700 nm to 2230 nm, a spectral range with diagnostic signatures of salts and hydrated minerals.

A very detailed discussion of the radiometric performance is presented in Langevin et al. (submitted), but the knowledge of the instrument response already allows to estimate the SNR for reference observations. Figure 33 shows the SNR expected for the regional mapping of Europa (providing a spatial sampling of a few km/pixel) and a Jupiter disk scan. The SNR calculation considers the despiking step (e.g. 3 of summed low values over 6 subframes for the IR Europa mapping), which significantly reduces the duty cycle (~25%) during a CU-frame repetition time as well as the thermal background of the instrument (assumed here to be at 130K). Splitting the repetition time into sub-integrations also has the advantage of avoiding saturation, which is about 100 ms for the satellites in the VISNIR and for the
<figure><img src="image_45.png" /><figcaption>**Fig. 33** SNR expected for Europa (A) during the approach (at a few tens of 10000 km providing km-sampling) of a flyby (phase angle of 45°). (B) Same as (A) but for Jupiter during a disk scan observation. The radiance of Europa comes from B. Carlson (personal communication) and that of Jupiter from Li et al. (2018). The spectral sampling that can be selected per band by TC (Fig. 28 and Fig. 31) is indicated (oversampling, nominal, averaging x2). The IR FPA temperature is 90K</figcaption></figure>

Jupiter hot spot in the IR. The spectral sampling has been tailored to emphasize spectral regions of interest where oversampling can help in discriminate spectral features (e.g. water ice signature at 1.65 µm or hydration bands in the 1.9-2.0 µm), and to increase the SNR by spectral averaging (e.g. $\lambda > 4$ µm for the icy satellites and $3.0 < \lambda < 3.8$ µm in the opaque methane absorption for Jupiter). Finally, the thermal background from the optical head of the instrument impacts the SNR at long wavelengths of the IR channel. During scientific operations, this background is subtracted by using 1 or 2 acquisitions of dark and thermal background frame before and/or after the acquisition of a cube. Still the SNR calculation includes the photon noise due to this background in the simulation. The SNR is > 100 for Europa up to ~2.8 µm and > a few tens of seconds at longer wavelengths. This low SNR is inherent to the low radiance at these wavelengths. For Jupiter, the SNR is > several 100 s for wavelengths not affected by the methane absorption. Due to the despiking strategy that favors short integration times (<100 ms), the SNR is RON-limited in most cases, which is especially true for the smallest signals (ice bands of icy satellites, the wings of the methane bands or of auroral emissions).

The SNR simulation shown above does not take into account the straylight contribution of the target signal at short wavelengths, which will be added to the legitimate contribution
<figure><img src="image_46.png" /><figcaption>**Fig. 34** (A) MAJIS spectrum (green line) when illuminated by the VISNIR lamp of the ICU compared to the scaled ICU laboratory spectrum (red line, Stefani et al. in prep). The signatures below 1000 nm are due to the didymium filter integrated in the ICU. (B) MAJIS spectra of the ICU VSINIR lamp (in blue) and ICU IR source (in green). The polystyrene absorption bands measured in the laboratory are plotted in red for comparison</figcaption></figure>

during the scientific operations. Since the VISNIR straylight comes from long wavelengths, its relative weight at short wavelengths is expected to decrease for sunlight (color temperature: 5800 K) when compared to all sources used during the calibration campaigns as well as the ICU (color temperature of the VISNIR lamp roughly estimated to be 2800 K). A first evaluation of the impact of straylight issues in the VISNIR channel using the deep neodymium bands of the VISNIR internal calibration source showed straylight weights in the range of 5-10% range for sunlight in the center of the FOV. After straylight correction, both the position and band depth of the ICU spectral signatures measured by MAJIS are similar to those of the ICU reference spectrum (Fig. 34). Additional assessments of the effect of the straylight have been made from observations of mineral samples during the calibration and will be presented in a dedicated paper (Rodriguez et al. 2024).

For the IR channel, a similar comparison can be made using the polystyrene bands that are present in the IR ICU signal. Excellent agreement is found between the IR ICU signal observed by MAJIS and the laboratory spectrum of the IR ICU, confirming the spectral
<figure><img src="image_47.png" /><figcaption>**Fig. 35** Comparison of the VISNIR ICU source acquisitions between ground calibration (A) and NECP (B). VISNIR channel: 0-1023; IR channel: 1024-2047. The same DN scale is applied to both frames. (C) and (D) Same as left but with the IR source. The major difference is seen at long wavelength due to the higher thermal background (about +5000 DN at longest wavelengths) resulting from the OH temperature at 133 K during the NECP instead of 126.8 K during the ground calibration</figcaption></figure>

calibration of the IR channel (Fig. 34B). Note also that the ICU VISNIR lamp can be easily detected at shorter wavelengths of the IR channel with appropriate integration time.

Ultimately, the straylight assessment of the target signal needs to be consolidated with observations of the Moon, which should be performed during the first Earth flyby (August 2024). Observations beyond the edge of the FOV demonstrated that the straylight contribution is spectrally smooth except for a narrow spectral range between 0.6 and 0.7 µm. Therefore, beyond this spectral range, the main impact on MAJIS science results will be a 5-20% reduction in spectral contrast in straylight-influenced regions, with correction algorithms required to improve the photometric accuracy beyond this level of uncertainty. Observations beyond the edge of the FOV will also be key for major targets of interest (Jupiter, icy moons) so as to optimize the VISNIR straylight mitigation approach.

## 6.6 Near-Earth Commissioning Phase

During the NECP in June 2023, a complete functional check-out was performed, including in-flight performance evolution with the ICU. The aim was to verify all functionalities and potential variations of instrument performance after launch with respect to ground tests (Table 15), within the constraints given by the S/C configuration and the lack of an external extended source. The core of the operations consisted of performing full functional tests (FFT) with the nominal and redundant ME and a set of acquisitions with the ICU. A comparison of four acquisitions with the ICU shows a very similar data set between the ground calibration and the NECP (Fig. 35). Two profiles of the row 200 extracted from the VISNIR lamp acquisitions (Fig. 35A and B) show very little discrepancy (Fig. 36), which at first glance indicates the photometric response is stable. Of particular interest is the absence of the absorption features in the 3.0-3.5 µm wavelength range, indicating that the post-launch anti-contamination sequence was efficient enough to prevent the deposition of organics or water ice on the IR detector. Performance was also assessed by deriving the noise from an FFT run simulating a high-resolution observation during a flyby of Europa (Fig. 37). The noise model of the instrument reproduces the measurement very well, indicating no degradation of the performance since the instrument was delivered.
<figure><img src="image_48.png" /><figcaption>**Fig. 36** Spectral profile of the signal acquired with the ICU lamp during the NECP (in green) and compared to the calibration measurement (in red)</figcaption></figure>

<figure><img src="image_49.png" /><figcaption>**Fig. 37** Noise derived from a series of science acquisitions simulating an observation of Europa close to the closest approach. A comparison with a noise model including the RON, the shot noise (here the dark signal of the IR detector), the spectral and spatial binnings and the despiking process is plotted in red</figcaption></figure>

# 7 Instrument Operations

Uplink planning and data processing will involve the MAJIS Science Ground Segment (SGS) of the Integrated Data & Operation Center (IDOC) at IAS, ESA's Science Operation Center (SOC) in Villafranca (Spain), and ESA's Mission Operation Center (MOC) in Darmstadt, Germany. IDOC houses the facilities used for MAJIS observation planning and command sequence generation, as well as the hardware and software required for data processing, calibration, and archiving. Instrument commands derived from the science plan issued by the science team are planned by the SGS and are transferred to the JUICE MOC for uplink. Downlinked MAJIS data, along with S/C housekeeping and pointing information, will be forwarded to the JUICE SOC for raw HK and science data processing and archiving. These raw data products data will be then automatically sent to IDOC for further processing
including radiometric calibrations, quick-look data validation and archiving at IDOC and ESA's Planetary Science Archive (PSA). IDOC, with the support of LESIA, will manage predicted SPICE kernels for data acquisition planning, and generate MAJIS-specific kernels and geometric cube products for data analysis and archiving. IDOC will maintain online products for all observations, to provide a quick look and to select raw and/or calibrated data for more detailed analysis. All downlinked spectra (raw and calibrated) and geometric products will be processed into PDS4-format files. The SGS will maintain a time-ordered record of instrument commands and the resulting data, while the SOC will maintain the official archive of raw and calibrated data products. To support these activities, the MAJIS SGS is divided into the following tasks: 1) calibration, 2) planning of the scientific observations, 3) definition of instrument command sequences and monitoring of instrument status and performance, 4) scientific activity including processing of data into deliverable calibrated PDS4 products and 5) data archiving.

# 7.1 Science Observation Modes

## 7.1.1 Description of Observation Modes

This section describes the planned observation modes based on the science objectives described in Sect. 2, the current knowledge of the science targets, and the performance of the instrument and the S/C. The observational strategy will be adapted to take into account any scientific discovery and/or evolution of the instrument or S/C capabilities.

Apart from stellar occultations, most MAJIS data sets will be image cubes covering all wavelengths of interest of the target region (Jupiter, satellites, exospheres...) to be studied. These image cubes will be constructed by acquiring spatial-spectral frames as the LOS (Line-Of-Sight) moves in the direction perpendicular to the slit. As described in Sect. 3, the scientific FOV is 3.4° along the slit (along the $X_{ILS}$ direction), ±1.7° with respect to the optical boresight ($Z_{ILS}$ direction) with an IFOV of 150 µrad (31 arcsec, 400 pixels over the FOV). Without spatial binning, there are 400 pixels with an IFOV of 0.150 mrad covering the 60 mrad FOV along the slit. Binning by 2 or 4 can be applied, resulting in 200 binned pixels (0.3 mrad) or 100 binned pixels (0.6 mrad), depending on the observation requirements. In the cross-slit direction, the FOV is generated by acquiring successive frames either while moving the scan mirror (within ±2° limits due to vignetting, ±4° for the LOS, Sect. 4.1.2) or during an S/C slew, keeping the scan mirror at boresight (or at a fixed optical angle ranged in the ±4° range), so that the resulting image cube can have a larger extension in the cross-slit direction. MAJIS can only operate during close flybys and on GCO500 with optimized pushbroom pointing (No Yaw Steering (NYS)): $Z_{SC}$ axis pointing at nadir, $Y_{SC}$ axis in the orbit plane, $X_{SC}$ perpendicular to the ground track) to perform motion compensation. Otherwise, the acquisition times are limited to the dwell time, 42 msec for GCO500 (or 17 msec for GCO-200), while MAJIS requires a minimum integration time of ~500 msec to achieve the required SNR.

Optimal sampling is obtained when the Line-of-Sight (LOS) moves by one projected IFOV during one repetition time. The cross-slit LOS motion can be obtained in several ways:

- Use of the internal scan mirror. The LOS is rotated perpendicular to the slit by the MAJIS scanning mirror while the S/C remains in the same orientation. The constant angular velocity is defined by the interval between scanner steps, where each step moves the LOS by 54.8 µrad (0.365 IFOV without spatial binning, 0.1825 IFOV with spatial binning by 2).
- Use of the S/C slew. The scanning mirror is held in a fixed position (usually the boresight position) while the S/C is rotated to provide the appropriate angular velocity across the slit. Contrary to the scan mode, the angular motion is continuous

- Pushbroom (during the GCO5000 or during short time windows of close flybys). The scanning mirror is held at boresight, and the cross-scan motion is provided by the orbital velocity. The repetition time is defined by the dwell time (e.g., 8 s for 3 km, binning by 4). As in slew mode, the LOS motion is continuous over a pixel.

- Pushbroom with motion compensation (close flybys / GCO500). If the dwell time is too short, it can be increased by rotating the LOS in the opposite direction to the orbital motion with the scanning mirror. In contrast to the scan mode, many motor steps (e.g. 26 for GCO500) are required for each IFOV, so that the motion of the LOS across an IFOV can be considered as continuous. In special cases, motion compensation could also be considered during slews.

- For scenes with very large sub-pixel variability (e.g. thermal emission of the hot spots in the methane window of Jupiter), a special scan mode called "synchronized scan" (3 PE-frames during a given repetition time (e.g. 2.1 s for Jupiter disk scan), mirror steps positioned when no data is acquired) has been defined. This mode has the advantage of reducing the despiking jitter and increasing the SNR and it should be preferred for MAJIS observations with repetition times in the range of 2 s – 5 s range.

The way of performing observations is selected in synergy with the S/C attitude profiles (pointing, slewing, steering), resulting in the following list of possible observation geometry modes that shall fulfill the MAJIS observation implementation defined in Tables of Sect. 2:

1. Nadir scan mode: JUICE continuously points the target at nadir. MAJIS acquiring in scan mode. This mode is optimal for the observing of satellites (dayside) and Jupiter (both day and nightside) when the dwell time is relaxed. Repetition of the observations over a longer time interval allows the acquisition of movies.

2. Off-nadir scan mode: JUICE points off-nadir at a certain angle to the target. MAJIS acquiring in scan mode. This mode is needed whenever specific areas of the targets need be observed out of the nadir geometry and is optimal for the observing of satellites (dayside) and Jupiter (both day and nightside) when the dwell time is relaxed. Repetition of the observations over a longer time interval allows the acquisition of movies.

3. Track Landmark scan mode: JUICE points and stares at a specific landmark on the surface of the target as it rotates. MAJIS acquiring in scan mode. This mode allows to image a region of interest of known position and to follow it at different local times; in this way it should be possible to characterize photometric and thermal properties for specific areas of the satellite's surfaces and to follow dynamical processes in Jupiter's atmosphere.

4. Limb pointing tracking mode: JUICE points and stares at the target limb while maintaining the tangent/vertical orientation of the MAJIS slit. This mode is designed to study satellite exospheres and Jupiter's atmosphere at different altitudes.

5. Limb slew scan mode: JUICE scans through the target limb at a constant angular velocity while maintaining the tangent orientation of the MAJIS slit. This mode allows the measurement of satellite exospheres and Jupiter atmosphere vertical profiles at high spatial resolution.

6. Stellar occultation mode: inertial pointing of the JUICE at the position (RA-Dec) of a bright star to keep the MAJIS slit fixed on it. This mode is dedicated to acquire stellar occultations behind Jupiter's atmosphere, Jupiter's rings, and the exospheres of its satellites in order to obtain vertical transmission and optical depth vertical profiles.

7. Mosaicking mode: JUICE points and stares at a grid of stations equally spaced over the target. For each station MAJIS shall use the internal scan mechanism to acquire a single
<figure><img src="image_50.png" /><figcaption>**Fig. 38** Examples of simulated MAJIS observations of the icy satellites using MOS/GFINDER planning tool (Sect. 7.3). (A) 15 spectral cubes (~2100 lines and repetition time between 2 slits = 7.8 s for each cube) during 7 consecutive days of the GCO5000 phase (duration ~15 weeks); the swaths are limited to phase angles smaller than 70°. This series of acquisitions corresponds to the goal GD.2b of Table 2. The panel was done by combining MOS simulations and the SOC Planetary Coverage tool (Seignovert et al. 2023). (B) Spatial coverage obtained with 5 consecutive cubes acquired during the closest approach of one of the GEO5000 orbit. The S/C altitude varies from ~5000 to 2400 km providing medium resolution observations from 750 m down to 360 m corresponding to GD.3e. goal of Table 2. (C) Series of high spatial resolution simulations using the motion compensation mode (internal scan) obtained near the closest approach of the first Europa flyby. A close-up of one of the series taken ~4 min before the closest approach is shown in C2. The simulation performed with the MOS/GFINDER tool provides 69 lines and a spatial resolution of 190 m/px</figcaption></figure>

element of the mosaic. The positions of the stations shall be by an angle of about 3.3° from previous position to provide contiguous spatial coverage. The number, size and timing of the pointing grids depend on the observation geometry. This mode is optimal for a full disk coverage of satellites (dayside), Jupiter (both day and nightside) and rings. For example, the largest Jupiter mosaic is based on a 4 × 4 grid.

8. Nominal Pushbroom mode: MAJIS slit oriented in any direction w.r.t to the ground track (JUICE yaw steering active). Scan mirror is fixed to boresight. SC is aimed at the target in both nadir and off-nadir orientations. This mode can be used to observe satellites, in particular during satellite flybys (on dayside) when the dwell time is greater than 0.5 s (Fig. 38).

9. Optimized Pushbroom mode: MAJIS slit oriented perpendicular w.r.t to the ground track (JUICE yaw steering inactive). Scan mirror fixed to boresight. JUICE points to the target in both nadir and off-nadir orientations. This mode provides optimal spatial coverage
and image reconstruction for satellite observations, especially during Callisto and Europa flybys (on day side) when dwell time is stringent (< 2 sec).

10. Motion Compensation Pushbroom mode: MAJIS slit oriented perpendicular w.r.t to the ground track (JUICE yaw steering inactive). MAJIS compensates the S/C drift by moving the scan mirror in the opposite direction to the JUICE motion. This mode is specific for closest approaches during F/B and for GCO500 phases when the dwell time is critical (< 0.1 sec). In these cases, it is necessary to extend the integration time beyond the dwell time to achieve a sufficient SNR on restricted regions of interest. During this time, it is necessary to stop the JUICE yaw steering (Fig. 38).

11. Synchronized scan mode: The scanner is configured so as to perform 3 steps (~1.10 IFOV) during the acquisition of each CU frame. This acquisition is performed by binning 3 PE-frames, each of which is acquired when the scanner is stationary. This mode requires a minimum CU-frame repetition time of 1800 ms (Europa, Ganymede, Jupiter at close range) or 1500 ms (Callisto, Jupiter from outside Callisto's orbit). It is implemented in nadir or 3-axis pointing mode when scanning a target with high sub-pixel contrast (e.g. Jupiter hot spots in the IR). It is also preferred when the required minimum CU frame repetition time is available. With this mode, all the sub-frames contributing to each of the 3 PE-frames (e.g. 4 sub-frames for a Jupiter disk scan) can be legitimately compared because they are acquired when the scanner is stationary.

12. Disk slew mode: JUICE performs a continuous slew at a variable angular velocity; it is assumed that the JUICE SC has the Z axis intersection with satellite/planet's disk in a fixed position on the nominal latitude/longitude grid of the planet/satellite. The variable angular velocity is defined by the dwell time required by MAJIS. The scan mirror is maintained at the boresight position.

## 7.1.2 Observation Modes for the Satellites and Jupiter

From these different geometries, we built the main observation modes for the satellites and Jupiter observations (Table 20 and Table 21 respectively). The data rate is calculated for each CU frame (data element which will be compressed and formatted for downlink by the CU). The CU repetition time is equal to the dwell time, but for a short-given time window. In nominal pushbroom operation, the repetition time is defined by the dwell time. The repetition time is then selected by the MAJIS team to achieve the required SNR. The rotation speed is set so that the viewing direction rotates by one IFOV during the repetition time (considering spatial binning if applied). For distant investigations (Jupiter, far flyby phases), coordinated observations with other remote sensing instruments may result in a required rotation rate of the $Z_{ILS}$ (boresight) being implemented by a S/C slew without operating the scanning mirror. The repetition time is divided into shorter sub-integration times for despiking purposes and/or to avoid saturation (e.g. ~100 ms in the VISNIR for Europa).

## 7.1.3 Observation Modes for the Minor Moons

During the Jupiter tour, there will be 11 opportunities to observe Amalthea and 10 opportunities to observe Thebe at resolutions better than 100 km/px, down to a maximum resolution of 75 km/px for Amalthea and 67 km/px for Thebe, and in a wide range of phase angles (from 0.7° to 106.3° for Amalthea, from 10.7° to 102.2° for Thebe). Some of these opportunities fall within the time segments dedicated to ring observations. Within the constraints imposed by the operations (e.g. proximity to a peri-jove or a flyby of a Galilean moon), all these observations would require a limited amount of data and should be exploited as much
<table><caption>**Table 20** Most frequently observing modes for the icy satellites according to the major phases. Data rates and data volumes have been evaluated assuming 1140 spectral elements per frame (typical for satellite observations) and a target rate of 5 bits/data elements after compression. Actual data rates and data volumes will vary depending on the information content of the scene or the selection of a specific spectral table with a number of spectral elements different from 1140 (maximum number of spectral elements: 1280)</caption><thead><tr><th>Mission phase</th><th>JUICE Altitude (km)</th><th>Spatial binning</th><th>Spatial elements</th><th>Ground swath / ROI size (km)</th><th>Repetition time (s)</th><th>Duration</th><th>Data rate (Mbits/s)</th><th>Data Volume per line (Mbits)</th><th>S/C pointing / MAJIS science mode</th></tr></thead><tbody><tr><td>Flybys (Ganymede, Calisto, Europa)</td><td>40000</td><td>1</td><td>400</td><td>2400</td><td>2.1</td><td>2100 s</td><td>1.086</td><td>2280</td><td>Yaw steering or S/C slew / Synchronized scan or pushbroom</td></tr><tr><td></td><td>200 to 1000</td><td>1, 2, 4</td><td>400, 200, 100</td><td>12 × 4 to 60 × 80</td><td>0.6</td><td>15 s to 130 s</td><td>0.95, 1.9, 3.8</td><td>14 to 500</td><td>Pushbroom / Motion compensation</td></tr><tr><td>Satellite exospheres</td><td>10000 km</td><td>1</td><td>128</td><td>350 km Altitude range</td><td>2</td><td>600 s</td><td>0.36</td><td>110</td><td>X axis parallel to the limb, Limb slew / scan mode</td></tr><tr><td>GEO-1 1000 × 9200 km</td><td>2470 to 2722</td><td>1</td><td>400</td><td>148 to 175 km</td><td>0.6</td><td>616 s</td><td>3.8</td><td>2340</td><td>Yaw steering / pushbroom</td></tr><tr><td>GCO5000</td><td>5100</td><td>4</td><td>100</td><td>300 km</td><td>7.8</td><td>≤ 4.5 hr</td><td>0.073</td><td>≤ 1180</td><td>Yaw steering / Nominal pushbroom</td></tr><tr><td>GEO-2 600 × 2000 km</td><td>1900 to 2000</td><td>1</td><td>400</td><td>120 km</td><td>0.6</td><td>1000 s</td><td>3.8</td><td>3800</td><td>Pushbroom / Motion compensation</td></tr><tr><td>GCO500</td><td>500</td><td>1/2</td><td>400/200</td><td>30 × 7.5/30 × 15</td><td>0.6</td><td>60 s</td><td>3.8/1.9</td><td>228/114</td><td>Pushbroom / Motion compensation</td></tr></tbody></table>

<figure><img src="image_51.png" /></figure>
<table><caption>**Table 21** Observation modes for Jupiter. Data rates and data volumes have been evaluated assuming 636 spectral elements per frame (typical for Jupiter observations) and a target rate of 5 bits/data elements after compression. Actual data rates and data volumes will vary depending on the information content of the scene or the selection of a specific spectral table with a number of spectral elements different from 636</caption><thead><tr><th>Observation Nomenclature</th><th>Observations geometry modes</th><th>S/C orientation</th><th>Spatial elements</th><th>Repetition time (s)</th><th>Duration</th><th>Data Rate (Mbit/s)</th><th>Data Volume (Mbits)</th></tr></thead><tbody><tr><td>Jupiter Nadir/Offnadir scan</td><td>Synchronized scan mode</td><td>Yaw steering</td><td>400</td><td>2.1 s</td><td>2 to 3 × 26 min</td><td>0.6</td><td>1510 to 2270</td></tr><tr><td>High Temporal Frequency Mode</td><td>Disk JUICE slew mode</td><td>Yaw steering</td><td>400</td><td>2.1 s</td><td>10 × 80 s</td><td>0.6</td><td>480</td></tr><tr><td>Limb Scan</td><td>Limb slew scan mode</td><td>Limb Tangent</td><td>64</td><td>11 s</td><td>220 s</td><td>0.018</td><td>4</td></tr><tr><td>Stellar Occultation</td><td>Stellar occultation mode</td><td>Limb Tangent</td><td>32</td><td>11 s</td><td>10 min</td><td>0.009</td><td>5.4</td></tr><tr><td>Auroral Mapping</td><td>Nadir-Offnadir scan</td><td>Yaw steering</td><td>400</td><td>5 to 11 depending on radiation</td><td>37 min</td><td>0.115 to 0.25</td><td>255 to 555</td></tr></tbody></table>

<figure><img src="image_52.png" /></figure>
as possible to derive the global surface composition and properties of these small moons, highlighting the effects due to the extreme space weathering environment they experience.

MAJIS will ride along JANUS to acquire disk-integrated spectra of some irregular satellites with the goal of revealing their global surface composition. If the trajectory of JUICE cannot be adjusted for a close encounter with an irregular satellite on the way to Jupiter or during the first elongated orbit, the best opportunities in terms of largest object size combined with lowest distance and phase angle should be preferred. As for the small inner satellites, these observations also require a very small data volume, and must be properly planned by applying long exposure times and possibly spectral binning to increase the SNR as much as possible.

## 7.2 In-Flight Calibration and Health Check Monitoring

A full functional check-out (i.e. without external source) including the evolution of the in-flight performance with the MAJIS ICU will be done during the cruise phase and science phase in a manner similar to the tests performed during the NECP (Sect. 6.6). The aim is to verify all the functionalities and variations of the instrument performances after launch with respect to the ground, with the limitations given by the JUICE configuration. The core of these operations consists in running the full functional tests with the nominal and redundant ME. Telemetered status packets containing measurements of currents, voltages, and temperatures are converted to physical units using appropriate calibration files and maintained in a specific database. Python notebooks have been created to automatically generate each value over time, covering the observation slot. Data are plotted along with alarm limits to assess long-term changes in instrument behavior. They would lead to the creation, distribution, and archiving of improved versions of the calibration files.

When using the ICU, both channels (VISNIR and IR) are operated in parallel during the internal calibration sequence to reduce the execution time and to (ideally) maintain the same thermal environment as during scientific observations when the two FPUs are used simultaneously. An advantage of this operating mode is that one source can be used to check the stability of the signal if the other source fails (for example if the IR source fails we can check the stability of the IR detector by looking at the residual signal of the VIS lamp in the IR/thermal range by adjusting the integration time). The selection of the source fluxes and the optimal integration times were evaluated during the AIV phases and the calibration campaign at the instrument level. In order to avoid the saturation in hot conditions due to the signal increase resulting from the larger internal background (OH thermal contribution) and the larger dark current, two cases were selected according to the thermal configuration (cold and hot case).

## 7.3 Planning

During the operational phase of the mission, the JUICE science operations planning is typically divided into three cycles: Long-Term Planning (LTP), Medium-Term Planning (MTP) and Short-Term Planning (STP). These cycles are a stepwise, sequential process that translates a high-level science activity plan, developed in the Long-Term Planning cycle (LTP), into the final S/C pointing and payload operational requests. The LTP is done well in advance of the onboard operation execution. It results in a trajectory baseline and a high-level science activity plan (segmentation) for a given time period. The MTP is done closer to the time of the onboard operation execution, and for a shorter given time period. It results in a detailed conflict-free operation plan that incorporates input from the instrument teams and is
<figure><img src="image_53.png" /><figcaption>**Fig. 39** Schematic flow of the MAJIS uplink planning and commanding process. See text for description</figcaption></figure>

validated by the JUICE Science Operations Centre (SOC). The instrument team can request specific S/C pointing, provided that it does not conflict with the science priorities agreed at the LTP-level. The STP allows for a final refinement of the instrument commands just prior to the execution of the command files. After the STP, the final TC's can be delivered to the MOC for upload and execution in the S/C.

The MAJIS science team will be involved in these 3 cycles. The primary inputs for planning MAJIS observations are as follows: 1. the predicted S/C trajectory; 2. a list of regions of interest for targeted observations; 3. status of the allocated MAJIS Mass Memory directories space and downlink; 4. a radiometric model providing SNR for a given opportunity and how MAJIS's data will be compressed in each spectral band; 5. S/C roll, stability, downlink and power constraints; 6. a list of regions of interest for targeted observations (e.g. Stephan et al. 2021b).

The MAJIS planning tasks can be grouped into three broad high-level tasks that apply to a given mission phase or segment: 1. identify observation opportunities, 2. simulate observations using the full flexibility of the instrument in terms of spatial and spectral binning, spectral editing and compression factor to optimize both the SNR and the spatial coverage, 3. request observations. The process is iterative and includes harmonization step(s) with the other instruments (especially in terms of pointing) and the S/C resource limitations. To support these tasks, the SGS will use a MAJIS Operations Simulator (MOS) system, which is under development at the time of the writing of the paper. It will consist of a set of interrelated software tools and data that will be accessible and used by the MAJIS team. The diagram presented in Fig. 39 provides an overview of the main MOS components, external interfaces and data flow. This high-level view/architectural design will evolve as the ex-
ternal interfaces (JUICE SOC Interfaces) are developed. A key component of the MOS is GFINDER, whose main functions are to define observation geometry and geometric conditions, find times of observation opportunities, for a given phase or segment of the mission, compute observation opportunity geometry data, visualize observation opportunity geometry data, manage input and output application data to feed the various inputs required to generate the MAJIS TC timeline and the JUICE operational tools.

## 7.4 Commanding

For a given observation or set of observations, a command generator module generates the instrument timeline profiles from the inputs issued by the MOS. A typical operational sequence consists in a series of “configure science TC” that define what to do for the next observation followed by a “start science” TC that puts MAJIS in “science” mode. The number of frames to be acquired can be defined by the “configure science” TC, in which case MAJIS will return to “Service” mode after the requested number of CU-frames has been acquired. If the number of frames is set to 0xFFFF (“continuous science mode”), MAJIS will remain in “science” mode until a “stop science” TC is received. It triggers a return to service mode after the activities required for the on-going frames have been completed. The MAJIS timeline for a given interval from switch-on to switch-off will be checked by the MAJIS team on the ground using the ground support equipment (Sect. 7.6).

10 units presented in Sect. 4 can be configured by the DPU for executing a specific task in “science” mode:

- 4 units in the OH (scanning mirror, shutter, black body for internal calibration, led source for internal calibration).

- 3 units in the VISNIR acquisition chain: VISNIR FPE/FPA, VISNIR-PE, VISNIR CU;

- 3 units in the IR acquisition chain (IR FPE/FPA, IR PE, IR CU)

Commands shall be validated prior to uplink using a dedicated ground support equipment described in Sect. 7.6 which can compare them to a set of internal flight rules for instrument timing and configuration, and compare engineering settings to values that could trigger alarms.

## 7.5 Data Product Generation

The MAJIS SGS will first convert the telemetry packets into the PDS4 products (TM2RAW pipeline) and then check the consistency of the telecommand timeline and the generated data. The software also provides the event list and an error report. The S/C sends the packets and files to the ESA Ground Operation System’s Data Dissemination System (EDDS), which sorts them according to the generation time, and provides them to the instrument team in binary and XML format. The flow of data processing to RAW products and the relationship between different data products is shown in Fig. 40. In the standard situation, the LOPRI NSDP are addressed to either the 0 × 59 or 0 × 69 data stores in the SSMM. When a “browse” product is generated (HIPRI product) for a given observation (Sect. ), it is used for monitoring the science data quality in near real time. After assessment of the HIPRI product, selective downlink of the nominal LOPRI data files can be applied.

The RAW2CAL pipeline will be implemented during the cruise based on the data calibration analysis and the evolution of the in-flight performance. The priority is to provide products to the community at least in radiance and I/F units. Data validation will be an iterative process throughout the history of processing the MAJIS data set. Moreover, time history shows that calibration of imaging spectrometers continues throughout their operational lifetime and sometimes beyond. Due to its complexity, MAJIS will not be an exception so that
<figure><img src="image_54.png" /><figcaption>**Fig. 40** Sequential processing from RAW data to PDS4 data product. The specific tools are indicated in italics below the boxes. DB=Data Base</figcaption></figure>

a specific PDS4 identification number label will be included in the data product to identify
the version of the applied calibration pipeline used. The pipelines will be deployed within
SOC and IDOC, each of which will be able to execute them to respond to a possible unavail-
ability of the other party. But formally, the SOC is prime on the TM2RAW chunk (pipeline
provided and managed by IDOC), IDOC on the RAW2CAL chunk.

7.6 Electrical Ground Support Equipment

Units of the MAJIS instrument representative of the flight model are available to test and val-
idate any scientific or maintenance procedure to be performed during the flight operations.
This ground setup consists of the following components (Fig. 41):

- a S/C Interface Simulator (SIS) provided by the JUICE project with the power front-end (PFE) and software front-end (SFE)

- the Engineering Model (EM) of the main electronics

- a H2RG ROIC with its SIDECAR board with the PE-VISNIR

- a H2RG detector in a cryostat with its SIDECAR cryoasic board to simulate the IR channel

- the AUX dummy board

The other objectives of this on-ground setup is to validate any software updates before up-
load and investigate any potential failure that could occur during in-flight operations. Below
we detail the main characteristics and functionalities of the components of this simulator.

The SIS fulfils the following main functions:

- To power the ME module

- To switch On/Off the ME module

- To monitor the On/Off status

- To exchange TM/TC data with the ME through SpW

The ME module is an EM model that is fully representative of the FM model except
that there is no redundancy. Some conditioning of housekeeping measurement (such as sec-
ondary voltages and board temperatures) is partially different, but this is taken into account
<figure><img src="image_55.png" /><figcaption>**Fig. 41** Illustration of the interfaces between the different modules of the E2E JUICE-MAJIS simulator. See text for acronyms</figcaption></figure>

when retrieving the engineering values from calibration curves. The AUX dummy load is used to simulate the auxiliary units of the OH. The AUX board can thus control a simulated shutter unit (main and redundant motor coils), a simulated ICU and simulated temperature sensors. The AUX dummy contains only a partially simulated scan mirror unit that corresponds to a resolver impedance. Since the scan mirror unit mechanism cannot be fully simulated without a real resolver because the position loop cannot be closed, this means that the AUX board cannot supply power to a dummy motor.

The warm H2RG ROIC kit is used as a replacement for the VISNIR detector. A H2RG ROIC is controlled by a cryoASIC board connected to a VISNIR FPE using a VISNIR PE-OH harness built for specific tests during the development of the instrument. The kit is mounted on a dedicated mechanical stand. Similarly, the IR FPU is represented by a cold H1RG kit made of a “reject grade” H1RG Sensor Chip Assembly (SCA) controlled by a cryoASIC board and connected to an IR PE-OH harness.

# 8 Laboratory Support

A significant body of work exists in literature regarding laboratory experiments aimed at understanding the surface properties of Jovian icy satellites by means of spectroscopic techniques. Much laboratory work has been done with the purpose of supporting the interpretation of remote sensing orbital data from NIMS spectrometer or Earth-based telescopes. To help support interpretation and analysis of icy satellite spectra, a dedicated effort to collect extensive spectral libraries of various species acquired over the spectral range in which MAJIS operates shall be necessary.

## 8.1 Past Measurements

Most of previous laboratory measurements focused on the study of ices (water and other volatiles) and on heavy hydrated salts, believed to be the main non-ice constituents on the icy satellites' surfaces, which could trace different physico-chemical formation conditions.
We can divide these laboratory studies into temperature-dependent measurements and irradiation experiments.

**Temperature-Dependent Measurements** The role of temperature (at room down to cryogenic temperatures) in the modification of optical properties of compounds and their spectra in the VISNIR region is one of the major objectives to be achieved. Dalton et al. (2005, 2007, 2011, 2012) investigated hydrated Mg-sulfates at cryogenic temperatures, in particular hexahydrite (MgSO₄·6H₂O) and epsomite (MgSO₄·7H₂O) in the VISNIR range together with spectral modeling and retrieval of optical properties. Other authors also investigated hydrated Mg-sulfates in terms of variable physical conditions (Crowley 1991; Craig et al. 2006; Cloutis et al. 2006, 2007), and at cryogenic temperatures in the 0.5-4 µm range for different grain sizes (Mg-sulfates in De Angelis et al. 2017; Na-sulfates in De Angelis et al. 2021). Reflectance spectroscopy of sodium carbonates with different levels of hydration (natrite (Na₂CO₃), thermonatrite (Na₂CO₃·H₂O) and natron (Na₂CO₃·10H₂O)) and different grain sizes at various cryogenic temperatures have been acquired in the 0.8-4.2 µm range (Sect. 2.2.1). Other authors investigated the stability of some of these compounds in more restricted ranges (McCord et al. 2001; Harner and Gilmore 2014, 2015 in the VISNIR) or in the mid/far-IR (Brooker and Bates 1971; Meekes et al. 1986). A recent work by Fulvio et al. (2023) concerned the NIR spectroscopic analyses at low temperatures of several Na- and Mg-bearing hydrated salts mixed with water. A few studies also investigated chlorinated salts in the VISNIR at room or some cryogenic temperatures (Hanley et al. 2014; Bishop et al. Bishop et al. 2014a,b 2016; Thomas et al. 2017). Following the suggestion of the presence of sodium and magnesium chlorides on satellites' surfaces on the base of telescopic observations (Ligier et al. 2016, 2019; Trumbo et al. 2019), recent laboratory studies have also been conducted on such compounds (hydrated Mg-chlorides) in the 0.5-5 µm, for different grain sizes and at cryogenic temperatures (De Angelis et al. 2022). Finally, a series of ammonia-bearing minerals have been studied at low temperature to monitor their phase changes and measure their reversible spectral changes (Fastelli et al. 2022).

**Irradiation Experiments** A large number of experiments of the last decades have shown the pivotal role played by energetic ion and electron irradiation in driving the chemical and physical evolution of the surfaces of the icy Jovian satellites (for reviews see Strazzulla 2011; Bennett et al. 2013; Strazzulla et al. 2003). If water ice and hydrated materials (as e.g. sulfuric acid) are the most abundant species on the surfaces of icy satellites, minor amounts of other species (e.g. H₂O₂, SO₂, and CO₂ and organic compounds) are also observed raising the question of their origin i.e. are they endogenous or have they been produced on the surfaces by exogenous processes such as the implantation of magnetospheric ions? As an example, the synthesis of the hydrogen peroxide observed on the surfaces of Europa (Carlson et al. 1999c), Ganymede and Callisto (Hendrix et al. 1999) is attributed to radiolysis. Indeed, several laboratory results evidenced the formation of hydrogen peroxide by ion bombardment of water ice (Moore and Hudson 2000; Gomis et al. 2004; Loeffler et al. 2006).

Ion irradiation causes the formation of O₂ and O₃ that have been observed on Ganymede (Noll et al. 1996; Johnson and Jesser 1997). Laboratory experiments on ion (Teolis et al. 2006; Boduch et al. 2016) and electron (Jones et al. 2014) irradiation of mixtures water ice-O₂ or CO₂ confirmed the synthesis of ozone.

Finally, the projectile ion itself that is implanted can be included in newly formed species. As an example, carbon dioxide is formed after ion implantation of C ions in H₂O ice (e.g. Strazzulla et al. 2003; Lv et al. 2012) and sulfur dioxide is formed after S ion implantation
in CO and CO₂ ice samples (Lv et al. 2014). Particularly relevant is the case of Sⁿ⁺ ion (n = 1, 7, 9, 11) implantation that produces H₂SO₄ • nH₂O (n = 0, 1, 4), with yields that are enough to strongly support the hypothesis that magnetospheric sulfur ion implantation is the dominant source of the sulfuric acid present on the surfaces of these satellites (Ding et al. 2013).

## 8.2 Future Experimental Work

Up to now most laboratory investigations of analogous materials at cryogenic temperatures have been focused on the spectroscopic analyses of single components. Extracting optical constants at different temperatures from the laboratory spectra of pure components that are currently unavailable in the literature will be extremely useful in supporting the modeling of MAJIS spectra and retrieving the relative abundances of the components. However, commonly used scattering models have limitations in reproducing molecular mixtures at scales smaller than the wavelength. In this respect, it will be extremely useful in future laboratory works to analyze in more detail the spectral properties of relevant components (Mg, Na, Cl or H₂SO₄ salts, water ice, other types of ices or salts, silicates) intimately mixed at scales smaller than the MAJIS wavelengths. The effect of grain shape, porosity and location of non-ice constituents (either on grain boundaries or within the grains) should be tested, as it will reveal key information on the formation and emplacement of brines and ices at the surface.

Another crucial aspect to be emphasized is the spectral resolution of these measurements. Given that most hydrated compounds develop fine structures in absorption band complexes at low (<200 K) temperatures in the NIR range, future spectral measurements at such temperatures shall be conducted with high spectral resolution (very few nm) in order to better characterize the behavior and evolution of spectra. Finally, the stability of single compounds and mixtures should be investigated also as a function of high vacuum as much as possible.

For what concerns the effect of the irradiation, it is necessary to expand the number of targets including materials that have been suggested to be possibly present on the icy satellites. These include sulfur- and chlorine-bearing molecules and hydrated minerals. Irradiation experiments are already programmed on some salts (particularly NaCl) whose presence on Europa has been suggested (e.g. Trumbo et al. 2019). Others interesting materials to be considered are berthierine (an iron-rich silicate hydrate), and epsomite (MgSO₄•7H₂O). More implantation experiments using reactive ions (H⁺, C⁺, N⁺, O⁺, S⁺, etc.) should also be conducted, in order to enlarge our knowledge on what endogenous vs. exogenous processes can determine the molecular mixtures that will be observed by MAJIS at the Jovian satellites. Finally, the study of the ion induced chemical “mixing” at the interface mineral-water ice seems to be particularly interesting also in view of the results obtained after experiments conducted at the interfaces carbonaceous solid materials-ice that demonstrated the efficient formation of carbon dioxide (e.g. Gomis and Strazzulla 2005).

## 8.3 Spectral Libraries

### 8.3.1 Databases on Solids (Icy and Non-icy Species)

Due to the sensitivity of absorption bands to temperature (with possible phase changes), the availability of laboratory data under conditions relevant to Galilean satellites in electronic form through databases is required to make accurate comparisons between spectral observations and laboratory or synthetic surface reflectance spectra obtained by radiative transfer simulation.
There are a number of databases that provide visible and near infrared reflectance spectra of many different types of Earth minerals at room temperature (e.g. USGS 'Spectroscopy lab', PDS Geosciences Node 'Spectral Library'/RELAB, JPL 'ECOSTRESS Spectral Library', Univ. Winnipeg 'Sample database', CRISM Spectral Library,...) but relatively few (mostly the SSHADE database of solid spectroscopy, www.sshade.eu) which makes available VISNIR spectral data on ices or on salts and their hydrates at low temperatures expected at the surface of the icy Galilean satellites. In this database a complete set of absorption coefficient spectra of water ice in the near-infrared from 20 to 270 K, every 10 K (Grundy and Schmitt 1998), that proved useful to determine the temperature of the icy terrains by radiative transfer modeling (Grundy et al. 1999), can be found. In addition, reflectance spectra with varying grain sizes (70-1060 µm) and shape (Taffin et al. 2012; Stephan et al. 2021a) can be retrieved.

Most of the recent laboratory works on 'dry' and hydrated Mg, Na, Cl and ammonia salts at low temperature relevant to the Galilean satellites, typically down to 70-90 K (De Angelis et al. 2017, 2019, 2021, 2022; Fastelli et al. 2022) are available in SSHADE. The temperature range and database links of the most important ones are listed in Table 22.

### 8.3.2 Future Needs

There are still a number of published spectra relevant to the Galilean satellites that are not yet available through databases, in particular on salts (e.g. epsomite, hexahydrite: De Angelis et al. 2017; Dalton and Pitman 2012) and their mixture with water ice (e.g. Fulvio et al. 2023). In addition, optical constants of salts are very scarce in literature, while they are essential to use radiative transfer models. Future works should focus on this journal, which will require to handle the growth of salt crystals with a controlled thickness, composition and crystalline state and to collect measurements over a temperature range fitting Ganymede's surface conditions (80-160 K).

Optical constants and bidirectional reflectance spectra of pure water ice are well established and available in publications and in electronic forms (Grundy and Schmitt 1998; Mastrapa et al. 2008, 2009). However, several ice mixtures of primary importance still need to be investigated, for providing tracers of the physical state (e.g. temperature, clustering, crystalline state) and optical constants. It is the case of molecules present (or suspected) as trapped in water ice: CO₂, H₂O₂, H₂SO₄, SO₄. In the case of CO₂, the ν₃ band revealed by NIMS (McCord et al. 1998a) and confirmed by the James Webb Space Telescope (Bockelée-Morvan et al. 2023) is very sensitive to the dilution state, clustering or porosity (Ehrenfreund et al. 1999; He et al. 2018a).

Dark materials at the surface of Ganymede and Callisto are basically unknown, and usually described as a dark neutral material in spectral models (Ligier et al. 2019). Here, needs are optical constants of simple carbonaceous compounds (e.g. coals, plasma reactors byproducts, amorphous carbons: Quirico et al. 2016; He et al. 2018b) and opaque minerals (iron sulfides, metal, oxides). In the case of coals for instance, optical constants are available in old papers, but data are often inconsistent one with each other (e.g. Forster and Howarth 1968; Blokh and Burak 1972). More complex analogs should also be investigated, e.g. assemblages at the submicrometer scale as observed in the matrix of carbonaceous chondrites and cometary grains, which are highly plausible candidates (Poppe 2016). Note that in that case, bidirectional reflectance spectra are essential to simulate complex textures (e.g. hyperporous), which cannot be simulated by radiative transfer models (Sultana et al. 2021, 2023). Last, and not least, in the high latitude regions exposed to energetic ions and electrons from the Jovian magnetosphere, all surface materials are expected to be modified in their first tens of micrometers. These effects need to be investigated through irradiation experiments.
<table><caption>**Table 22** Compilation of spectral data at low temperatures for species of interest for the Jovian icy moons. A few other data sets can also be found in SSHADE and other databases at low temperature on other ices (CO₂, . . . ), minerals and tholins</caption><thead><tr><th>Compound</th><th>Formula</th><th>Temp.</th><th>paper reference</th><th>database link</th></tr></thead><tbody><tr><td>Water ice</td><td>H<sub>2</sub>O</td><td>20-293 K</td><td>Grundy and Schmitt 1998</td><td>Grundy and Schmitt 1998</td></tr><tr><td>Sulfur dioxide ice</td><td>SO<sub>2</sub></td><td>77-196 K</td><td>Taffin et al. 2012</td><td>Taffin et al. 2012</td></tr><tr><td>Hydrogen sulfide</td><td>H<sub>2</sub>S</td><td>70-220 K</td><td>Stephan et al. 2021a</td><td>Stephan et al. 2021a</td></tr><tr><td>Sodium chloride</td><td>NaCl</td><td>125 K</td><td>Schmitt 1994</td><td>Schmitt 1994</td></tr><tr><td>Sodium perchlorate</td><td>NaClO<sub>4</sub>·2H<sub>2</sub>O</td><td>55-130 K</td><td>Schmitt and Rodríguez 2003</td><td>Schmitt 1991</td></tr><tr><td>Ammonium carbonate</td><td>(NH<sub>4</sub>)<sub>2</sub>CO<sub>3</sub></td><td>80 K, 298 K</td><td>Hanley et al. 2014</td><td></td></tr><tr><td>Ammonium nitrate</td><td>(NH<sub>4</sub>)NO<sub>3</sub></td><td>80 K, 298 K</td><td>Hanley et al. 2014</td><td></td></tr><tr><td>Epsomite</td><td>MgSO<sub>4</sub>·7H<sub>2</sub>O</td><td>80-300 K</td><td>Fastelli et al. 2022</td><td>Fastelli et al. 2022</td></tr><tr><td>Hexahydrite</td><td>MgSO<sub>4</sub>·6H<sub>2</sub>O</td><td>93-298 K</td><td>Fastelli et al. 2022</td><td>Fastelli et al. 2022</td></tr><tr><td>Magnesium chloride dihydrate</td><td>MgCl<sub>2</sub>·2(H<sub>2</sub>O)</td><td>93-298 K</td><td>De Angelis et al. 2017</td><td></td></tr><tr><td>Magnesium chloride hexahydrate</td><td>MgCl<sub>2</sub>·6(H<sub>2</sub>O)</td><td>80-295 K</td><td>De Angelis et al. 2017</td><td></td></tr><tr><td>Mascagnite</td><td>(NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub></td><td>80-295 K</td><td>De Angelis et al. 2022</td><td></td></tr><tr><td>Mirabilite</td><td>Na<sub>2</sub>SO<sub>4</sub>·10H<sub>2</sub>O</td><td>80-300 K</td><td>De Angelis et al. 2022</td><td></td></tr><tr><td>Natrite</td><td>Na<sub>2</sub>CO<sub>3</sub></td><td>93-279 K</td><td>De Angelis et al. 2021</td><td></td></tr><tr><td>Natron</td><td>Na<sub>2</sub>CO<sub>3</sub>·10 H<sub>2</sub>O</td><td>93-279 K</td><td>De Angelis et al. 2019</td><td></td></tr><tr><td>Salammoniac</td><td>NH<sub>4</sub>Cl</td><td>93-279 K</td><td>De Angelis et al. 2019</td><td></td></tr><tr><td>Thermonatrite</td><td>Na<sub>2</sub>CO<sub>3</sub>·H<sub>2</sub>O</td><td>90-300 K</td><td>Fastelli et al. 2022</td><td></td></tr><tr><td>Thenardite</td><td>Na<sub>2</sub>SO<sub>4</sub></td><td>93-279 K</td><td>De Angelis et al. 2019</td><td></td></tr><tr><td>Tschermigite</td><td>NH<sub>4</sub>Al(SO<sub>4</sub>)<sub>2</sub>·12 H<sub>2</sub>O</td><td>62-300 K</td><td>Fastelli et al. 2022</td><td></td></tr></tbody></table>

<figure><img src="image_56.png" /></figure>
**Acknowledgements** This paper is dedicated to the memory of Pascal Eng (project manager) and Sergio Fonti (involved in the optical design) who suddenly passed away during the development phase of the instrument.

**Funding** French contribution to MAJIS has been technically supported and funded by CNES - CONTRACT CNES - CNRS n° 180 117. Italian contribution to MAJIS has been coordinated and funded by Italian Space Agency - CONTRACT N. 2021-18-I.0 and supported by the ASI-INAF agreement n. 2023-6-HH.0. Belgian contribution to MAJIS has been funded by the Belgian Science Policy (Belspo) agency in the frame of ESA - PRODEX-11 (Grant No. PEA 4000124255).

**Declarations**

**Competing Interests** The authors have no competing interests to declare that are relevant to the content of this article.

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## Authors and Affiliations

F. Poulet¹ · G. Piccioni² · Y. Langevin¹ · C. Dumesnil¹ · L. Tommasi³,² · V. Carlier¹ ·

G. Filacchione² · M. Amoroso⁴ · A. Arondel¹ · E. D'Aversa² · A. Barbis³ · A. Bini³ ·

D. Bolsée⁵ · P. Bousquet⁶ · C. Caprini³ · J. Carter¹ · J.-P. Dubois¹ · M. Condamin¹ ·

S. Couturier¹ · K. Dassas¹,⁷ · M. Dexet¹ · L. Fletcher⁸ · D. Grassi² · I. Guerri³ · P. Haffoud¹ ·

C. Larigauderie⁶ · M. Le Du⁶ · R. Mugnuolo⁴ · G. Pilato³ · M. Rossi³ · S. Stefani² · F. Tosi² ·

M. Vincendon¹ · M. Zambelli² · G. Arnold⁹,¹⁰ · J.-P. Bibring¹ · D. Biondi² · A. Boccaccini² ·

R. Brunetto¹ · A. Carapelle¹¹ · M. Cisneros González⁵ · C. Hannou¹ · O. Karatekin⁵ ·

J.-C. Le Cle'ch¹ · C. Leyrat¹² · A. Migliorini² · A. Nathues¹³ · S. Rodriguez¹⁴ · B. Saggin¹⁵ ·

A. Sanchez-Lavega¹⁶ · B. Schmitt¹⁷ · B. Seignovert¹⁸ · R. Sordini² · K. Stephan⁹ ·

G. Tobie¹⁸ · F. Zambon² · A. Adriani² · F. Altieri² · D. Bockelée¹² · F. Capaccioni² ·

S. De Angelis² · M.-C. De Sanctis² · P. Drossart¹⁹ · T. Fouchet¹² · J.-C. Gérard²⁰ ·

D. Grodent²⁰ · N. Ignatiev²¹ · P. Irwin⁸ · N. Ligier¹ · N. Manaud¹ · N. Mangold¹⁸ ·

A. Mura² · C. Pilorget¹,²² · E. Quirico¹⁷ · E. Renotte¹¹ · G. Strazzulla²³ · D. Turrini²⁴ ·

A.-C. Vandaele⁵ · C. Carli² · M. Ciarniello² · S. Guerlet¹²,²⁵ · E. Lellouch¹² ·

F. Mancarella²⁶ · A. Morbidelli²⁷ · S. Le Mouélic¹⁸ · A. Raponi² · G. Sindoni⁴ · M. Snels²⁸

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F. Poulet
francois.poulet@ias.u-psud.fr

1 Institut d'Astrophysique Spatiale, CNRS/Université Paris-Saclay, 91405 Orsay Cedex, France

2 Istituto Nazionale di Astrofisica – Istituto di Astrofisica e Planetologia Spaziali (INAF-IAPS), Via del Fosso del Cavaliere 100, I-00133, Roma, Italy

3 Leonardo SpA – Via delle Officine Galileo, 1 50013 Campi Bisenzio, Italy

4 ASI, Agenzia Spaziale Italiana, Via del Politecnico snc 00133 Roma, Italy

5 Royal Belgian Institute for Space Aeronomy, Av. Circulaire 3, 1180 Brussels, Belgium

6 CNES, Centre National d'Études Spatiales, 2 Place Maurice Quentin, 75 039 Paris Cedex 01, France

7 CNRS – Centre national de la recherche scientifique, CESBIO (Centre d'étude Spatiale de la Biosphère), Rond-Point du Professeur Francis Cambou, 31400 Toulouse, France

8 School of Physics and Astronomy, University of Leicester, University Road, Leicester, LE1 7RH, UK

9 Institute of Planetary Research, German Aerospace Center (DLR), Rutherfordstrasse 2, 12489 Berlin, Germany

10 Institute of Geoscience, University Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany

11 Centre Spatial de Liège – Université de Liège, Avenue du Pré-Aily Liège Science Park, 4031 Angleur-Liège, Belgium

12 LESIA, Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université de Paris, 5 place Jules Janssen, 92195 Meudon Cedex, France

13 MPI for Solar System Research, Justus-von-Liebig-Weg 3, 37077 Goettingen, Germany

14 Université Paris Cité, Institut de Physique du Globe de Paris (IPGP), CNRS, Paris, France

15 Mechanical Department, Polytechnic University of Milan, via La Masa 1, 20156 Milano, Italy

16 Universidad del País Vasco UPV/EHU, Escuela Ingenieria de Bilbao, Bilbao, Spain

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17 Université Grenoble Alpes, CNRS, IPAG, 38000 Grenoble, France

18 Laboratoire de Planétologie et Géosciences, UMR-CNRS 6112, Nantes Université, 2, rue de la Houssinière, 44322 Nantes cedex, France

19 Institut d'Astrophysique de Paris (IAP), 98bis Bd Arago, 75014 Paris, France

20 Laboratoire de Physique Atmosphérique et Planétaire, STAR Institute, Université de Liège, 4000 Liège, Belgium

21 Space Research Institute of Russian Academy of Sciences (IKI), Profsyuznaya 84/32, 117997 Moscow, Russian Federation

22 Institut Universitaire de France, Paris, France

23 Istituto Nazionale di Astrofisica – Osservatorio Astrofisico di Catania, Via Santa Sofia 78, 95123 Catania, Italy

24 Istituto Nazionale di Astrofisica – Osservatorio Astrofisico di Torino, Via Osservatorio 20, I-10025, Pino Torinese, Italy

25 Laboratoire de Météorologie Dynamique, CNRS, Sorbonne Université, Université PSL, Institut Polytechnique, Paris, France

26 Dipartimento di Matematica e Fisica “Ennio De Giorgi”, Università del Salento, Salento, Italy

27 Observatoire de la Côte d'Azur, Boulevard de l'Observatoire CS 34229, 06304 NICE Cedex 4, France

28 CNR-ISAC-CNR, Via fosso del cavaliere 100, 00133, Roma, Italy