The JamSail CubeSat contains several major payloads, each of which serve a distinct research or educational purpose. Of these, the primary operational payload is a GNSS interference detection payload, which will monitor radio interference regarding the GNSS bands from Earth-originating signals. This payload will operate throughout the lifespan of the satellite and will continually produce data where possible. Successful operations of this payload will demonstrate the ability to geolocate sources of GNSS interference from a small satellite, and the technology could be used to develop a dedicated constellation of similar satellites in the future. Towards the end of the targeted operational period of the GNSS payload, the solar sail payload will be deployed, aiming to demonstrate its ability to adjust the spacecraft’s attitude and orbital parameters via the solar radiation pressure (SRP) that is generated by its refractive membranes. If successful, this payload will prove the feasibility of the use of a compact refractive sail for the manoeuvring of a CubeSat, with the potential to significantly improve in-orbit lifespan without being constrained by fuel capacity, and in a debris-neutral manner. Finally, throughout the mission, external-facing cameras will be programmed to capture colour photographs and transmit these via amateur radio frequencies for reception across the world to promote space and radio activities in communities globally.
3.1. GNSS Payload
The GNSS payload on JamSail aims to monitor Earth-originating GNSS interference and produce temporally precise geolocated data of this interference. Two frequency bands will be monitored: the E1/L1 band centred at 1575.42 MHz and the E5a/L5 band centred at 1176.45 MHz. The headline unit specifications of the GNSS payload are provided in
Table 1.
3.1.1. Payload Overview
The GNSS payload consists of a multiple-channel RF Front-End with two active antenna inputs (nadir- and zenith-facing) and an interface board to connect the Front-End with FPGA processing board. The payload operates in two possible modes, output of raw data from the RF Front-End and a processed output from the FPGA after analysing the spectra of the received signals. The raw or processed data are then downlinked at the next availability to be studied on the ground.
Initial conditioning of the GNSS signal will be performed by the RF Front-End, embedded in the NT1065 FMC2 board. This board provides power to the amplifiers in the antenna via SMA connectors, which are also used to receive the amplified and filtered analogue signal from the antennas. The NT1065 chip on this Front-End board is responsible for down-converting the GNSS signals to an intermediate frequency while also filtering and digitising this analogue signal (sample rate of 53 MHz) to enable digital processing in software. Additionally, a custom AGC from the FPGA via SPI interface of the NT1065 has been implemented to account for any variations in the input signal or the gain of the LNA or the Front-End with temperature.
Figure 2 shows the block diagram of the GNSS payload of the JamSail CubeSat. This block diagram consists of three stages. The first stage consists of the two antennas. The zenith antenna receives the signals from the GNSS satellites and the other nadir antenna detects the Earth interference from different sources, such as intentional and unintentional interference sources. These antennas will operate in two frequency bands, L1/E1 and L5/E5a, to be connected to the Front-End device via an RF splitter. L1 and L5 frequency bands for GPS constellations. E1 and E5a frequency bands for the Galileo constellation. The NT1065 Front-End is used for the payload. NT1065 is a four-channel RF Front-End IC for the use of the GNSS signals (GPS, GLONASS, Galileo, BeiDou, NavIC, and QZSS) and also signals of satellite-based augmentation systems like OmniStar at all frequency bands in various combinations: L1, L2, L3, L5, E1, E5a, E5b, E6, B1, B2, and B3. The Front-End will have some features such as a single conversion superheterodyne receiver. Also, this Front-End has 4 independent configuration channels, each one has LNA, mixer, IF filter, IFA, 2-bit ADC, embedded temperature sensor, dual AGC (RF and IF), or programmable gain. Moreover, the Front-End has an SPI interface with easy-to-use register maps. This Front-End will be connected to FMC with an SPI interface. The output from the Front-End will be 8-bit digitised data of the raw GNSS data. Also, there is a clock signal that is intended for clocking all 2-bit ADCs as well as clocking the external correlator engine. It is generated from LO frequency from PLL “A” since channel #2 is used for this payload. The NT1065 features two independent PLLs (Phase-Locked Loops): PLL “A” and PLL “B”. PLL “A” supplies the LO (Local Oscillator) signal to channels #1 and #2, while PLL “B” is dedicated to channels #3 and #4. This dual PLL setup enables greater flexibility and precision in generating different LO signals for separate channels, which is particularly useful in high-precision GNSS applications. For specific applications requiring a common LO source across all channels, PLL “A” can be configured to provide the LO signal to all four channels. This setup enhances signal customization and performance for applications like goniometric positioning and driverless vehicle systems [
29]. CLK source and frequency can be customised by the procedure.The FMC is an ANSI standard that provides a standard mezzanine card form factor, connectors, and modular interface to an FPGA located on a baseboard. Decoupling the I/O interfaces from the FPGA simplifies the I/O interface module design while maximising carrier card reuse. This FMC will be connected to the FPGA board.
The second stage is the FPGA board. We are targeting Xilinx Zynq 7030 with two A9 cores inside. The FPGA stage contains five core parts, which are the SPI control, the inbuilt CPU, the interference characterisation module, and UART. SPI is one of the most widely used interfaces between microcontrollers and peripheral ICs such as sensors, ADCs, DACs, shift registers, SRAM, and others. The SPI control will control the communication between the inbuilt CPU inside the FPGA and the FMC. The inbuilt CPU inside the FPGA contains the real-time operating system (RTOS). This RTOS will contain four tasks, which are the communication task, the timer task, the SPI task, and the Front-End task. This built-in CPU part will communicate with the interference characterisation module through the AXI interface. This module will have three main operations, which are the distribution calculation, the noise calculation, and the FFT spectrum calculation. The FFT spectrum calculation part has been implemented using VHDL and FPGA (hardware implementation) and has been validated using the MATLAB simulation (SWRs) in the lab. In order to verify the results of the hardware implementation against the software simulation [
30], the output data (32 bits) from the three operations inside the interference characterisation module will be passed into the Front-End task in the inbuilt CPU.
The third stage is managing the memory of the GNSS JamSail payload. The output from the interference characterisation module will be stored in the management memory block and the output from the RF Front-End will be stored in the management memory block. Then, the output from the management memory will be used as input for the external DDR3 memory (1 GByte).
3.1.2. GNSS Interference Scenarios
The manner of obtaining the GNSS interference raw data through the GNSS simulator is shown in
Figure 3. The Skydel Advanced GNSS Simulator was used to simulate various passes of JamSail CubeSat over an interference transmitter. This simulator provides two types of raw data, which are RF signal and IQ data (baseband). The RF signal will be received by an RF Front-End device. Then, the digitised output data from the Front-End will be loaded into the GNSS payload hardware, which is the FPGA board, or the digitised output from the Front-End will be loaded into GNSS software receivers such as MATLAB SWRx. This refers to the ground tests, where two approaches—the one with real hardware and the one using advanced simulation with Matlab tools—have been pursued.
The passes from the simulator were conducted at an orbital height of 550 km and various orbital inclination angles relative to the location of the transmitter. The varying closeness of passes allowed the full range of realistic passes to be tested. Continuous wave (CW) interference signals, pulsed signals, and chirp signals were all tested. These are some of the most common types of civilian interference signals that will be encountered. Overall, there was no difference in the way each signal had to be analysed to isolate its Doppler shift and Doppler rate.
3.1.3. Design Decisions
To convert the received data from the time domain to the spectral domain, the FFT was used. Choosing the number of samples to be used in each FFT was an exercise of compromise between the time resolution, frequency resolution, and processing power that would be used. Using a larger number of samples would provide a smaller frequency bin width and thus a higher resolution in frequency. On the other hand, using a smaller number of samples would provide a better resolution in time and use less processing power. The final number of samples was chosen through a process of trial and error by analysing the results of a large number of simulations. The frequency resolution and time resolution both had to be high enough that the signal’s changes in frequency and power over time could be tracked accurately. The final number chosen was N = 2
15 = 32,768 samples, which yielded a frequency resolution of 381 Hz and a time resolution of 2.62 ms at the simulator’s 12.5 Msps sampling frequency. In the hardware process, we tend to use a high sample frequency to make it more flexible and configurable, while, in the post-processing (MATLAB R2023a), we tend to use a low sample frequency to limit the power level. The next FFT was commenced halfway through the current one, following the sequence where the first sample in the n
th FFT was number n∗2
15/2+1 in the total set of data samples. This doubled the time resolution to 1.31 ms. The effect of leakage on the resulting spectra was minimised by applying a window to the sampled data. To be able to identify the signal’s actual frequency, the window’s main lobe should have a minimal width to make the spectral peak as thin as possible. In this case, the size of the sidelobes was unimportant. Three different windows were tested: the Hamming window, the Hanning window, and the Blackman–Harris window. These windows were selected as they all had thin main lobes, thus being good fits for the needed application. The Blackman–Harris window was selected for its superior frequency resolution and its ability to minimise spectral leakage. This window function is known for its high attenuation of sidelobes, which makes it ideal for applications requiring precise frequency domain analysis, such as our task of cancelling the nearest side frequencies. The window’s tapering reduces the leakage of nearby frequency components into the main lobe, improving the accuracy of frequency detection and separation in the processed data. This is particularly important in our case, where minimising interference from adjacent frequency components is critical for achieving accurate results. The SNR of the spectra was increased using averaging. Non-coherent averaging was chosen over coherent averaging as it does not require the time phase of the signal in each set of samples to be the same, which would be impossible to guarantee in this circumstance. As the signal’s frequency moved between the discrete frequency bins over time, only a certain number of averages could be completed before the signal’s power was ‘blurred’ between multiple bins too much. The FFT period was low enough that this effect would only become noticeable if over several hundred FFTs were used per average. Averaging lengths that were powers of two were used as this would be the most efficient way to implement this process on the CubeSat’s FPGA. As shown in
Figure 4, averages using 64, 128, and 256 FFTs were tested on a simulated CW jamming signal. They were compared to the same signal that had not been averaged to find the increase in SNR each number of averages yielded. Using 64 averages yielded an increase in SNR of approximately 5 dB. With 128 FFTs, the noise floor decreased by a further
dB. Finally, using 256 FFTs, the SNR demonstrated a further increase of only
dB. It was clear that using 64 FFTs per average yielded the best increase in SNR (5 dB) for the expected increase in computational power, so this number of averaging was chosen.
3.1.4. Minimum Detectable Power Levels
A simulation of the CubeSat passing from the transmitter’s horizon to its zenith was carried out. The slant range between the interference source and the CubeSat over the pass was calculated. As shown in
Figure 5, the slant range decreased from a maximum of 2737 km at the horizon to a minimum of 550 km at the zenith.
The Free Space Path Loss (FSPL) over the pass was calculated. The interference’s frequency was assumed to be 1575.42 MHz. While an actual interference signal may have a different frequency to this, the difference would be so small that the change in FSPL would be negligible. This includes signals in the L5/E5 band. To obtain a basic overview of what Equivalent Isotropic Radiated Power (EIRP) a transmission would have to radiate to be visible to the CubeSat, the FSPL over the pass was taken away from several power levels. This was only a basic overview as the calculation did not take into account other sources of gain and loss, such as the CubeSat’s antenna gain or low noise amplifier noise figure (however, these two values will likely be similar and therefore nearly cancel each other out). The results of this, as well as the FSPL on its own, have been shown with reference to the noise floor in
Figure 6. Including the impact of the averaging, the noise floor has been placed at −147 dBm.
The lowest EIRP that was always above the noise floor was 18.1 dBm (64.6 mW). This meant that, if there was a line of sight between the two antennas, the CubeSat should always be able to identify an interference transmission of this power. The weakest detectable transmission had an EIRP of 4.2 dBm (2.6 mW). It would probably be impossible to geolocate a signal with this power as, on the rare occasion that the CubeSat passed close enough to be able to detect it, it would not be distinguishable from the noise for long enough for the geolocation algorithm to work. To determine the interference level, anechoic chamber tests, and measurement power, the knowledge of the antenna patterns will be needed; this will be determined experimentally during further testing the payload.
3.1.5. Tracking of Doppler
The results of an example simulation of a satellite passing over a source transmitting a CW signal are shown in
Figure 7. The signal’s frequency was set to 1575.42 MHz and its power was set to 10 dBm. A 20 s simulation was carried out, with the CubeSat crossing the interference transmitter’s zenith at approximately 10 s. The set of spectra resulting from converting the received signal to the frequency domain are shown in
Figure 7a and the equivalent waterfall diagram is shown in
Figure 7b.
The script to isolate the interference peak was run. Once this had occurred, the frequency of the largest frequency bin in each FFT was recorded. This allowed the discrete frequency Doppler shift to be quantified. The staircase-like graph showing the result of this appears in
Figure 8. The truth data (provided by the GNSS simulator) for the Doppler shift were also calculated to validate the accuracy of the geolocation. As the line is slightly unclear over the entire simulation, a two-second extract is shown to clarify its general trend. The discrete curve followed the truth curve very closely, indicating that the result of the calculation was accurate. The point that the CubeSat crossed the interference transmitter’s zenith can be observed at about 11 s. The signal underwent around 10 kHz of total Doppler shift, with 5 kHz of Doppler shift on each side of the zenithal point. Differentiation of the data to find the Doppler rate in this discrete form was not attempted as it would have led to an erroneous result.
After the discrete Doppler shift had been isolated, the next step is to model it as a cubic equation to approximate the continuous-frequency Doppler shift. The result of this is again compared against the truth data to validate the accuracy of the modelling process. This has been plotted in
Figure 9. The error between the true Doppler shift and the modelled Doppler shift is shown in
Figure 10. The result was very accurate. The modelled cubic equation lines up very closely with the truth data over the entire 20 s extent of the simulation.
The final step that was undertaken was to differentiate the cubic equation to find the interference signal’s Doppler rate. The result of this is shown in
Figure 11, where it has been validated against the truth data once more. Again, the result was very accurate, which is 0.13 Hz/s. The error between the modelled Doppler rate and the true Doppler rate was plotted in
Figure 12. The error remained very small over the entire simulation, reaching a maximum of approximately 0.35 Hz/s at the beginning of the simulation.
3.1.6. Payload Requirements
The power requirements for the GNSS payload are not only determined by the power consumption of the RF Front-End board but also by the power it provides to the two-patch antenna via the SMA connector.
Table 1 at the start of
Section 3.1 shows the power consumption and other key requirements of the payload. As shown, the payload has a total power consumption of approximately 0.7 W during data collection, excluding the power consumed by the FPGA to process these data.
3.1.7. Modes of Operation
The GNSS payload has two modes of operation: raw data and spectrum output. In both modes, the RF Front-End will receive signals from both antennas, which are downsampled and digitised before being transferred to the FPGA in real time. When operating in raw data mode, the FPGA will store these digitised signals directly in memory, ready to be downlinked when possible for data processing on the ground. While this mode may only run for a short period of time due to a high data generation rate of 25 MByte/s, it provides an opportunity for more complex signal processing on the ground, including more detailed analysis of interference, as well as navigation and positioning abilities.
Alternatively, when the FPGA first receives the digitised signal in spectrum output mode, the signal is processed in real time, producing a signal amplitude spectrum in the frequency domain once per second. This spectrum output discretises the input signal into 16,384 frequency bins, each with a 32-bit complex value in both real and imaginary planes, providing an effective data rate of 65.5 kBytes/s. This process will run continuously for the full duration of a measurement cycle, resulting in a number of spectra for a specific region. Once downlinked, the spectra can be used to identify the presence of interference as well as determine the type of jamming used in intentional interference.
3.1.8. Safety and Verification of OBDH
FPGAs have been used in space for more than a decade; however, there are some challenges for the reprogramming of the FPGAs due to their sensitivity to involuntary reconfiguration due to single-event upsets (SEU) caused by radiation. Reprogramming FPGA is becoming a very fundamental requirement in CubeSats. It can allow the programming to complete some bug fixing in the system in the last stages of development and also after the launch of the mission. The microchip can offer the CubeSat the highest level of In-Flight reconfiguration by fusing the adaptability of the radiation-tolerant FPGA structure with the effectiveness of the radiation-hardened or radiation-tolerant microcontrollers. We are using a suitable COTS FPGA Zynq 7030, which has been through some radiation testing and has flight heritage.
Microblaze processor that is used for processing the data of the payload provides fault detection feature using parameter (C_FAULT_TOLERANT). These features provide error detection for the internal block RAMs and error detection and correction (EEC) for LMB block RAMs. All the soft errors in block RAM will be detected and corrected once the fault tolerance is enabled. Therefore, this will help to mitigate the chance of failure in space. ECC is automatically enabled in the connected LMB block RAM Interface Controllers by the tools once the fault tolerance in MicroBlaze is enabled. Additionally, the Mircoblaze processor is designed to be implemented in a Triple Modular Redundancy (TMR) implementation. TMR is one of the commonly used techniques in programmable logic designs of the processor. TMR implements three typical designs and then takes the majority vote on the outputs. This voting reduces the potential for a single failure of one implementation [
31].
The current design uses a softcore processor to maintain flexibility in the FPGA used and includes a fault-tolerant processor. However, in the Zynq, we also have a dual-core ARM Cortex-A9 processor, which can provide significant processing capability for extended operations of the payload in orbit once the primary mission objectives have been fulfilled, as well as for further development iterations of the payload after the JamSail mission.
3.2. Solar Sail Payload
The solar sail payload can be partitioned into three elements: the sail membrane, the static assembly, and the moving assembly. The sail membrane is a
thick optical film composed of biaxially oriented polyethylene terephalate (BoPET), shown in its deployed state in
Figure 13. The majority of this film is unmetallised and embossed with a micro-prism pattern using UV-activated roll-to-roll nanoimprint lithography (R2R-NIL). The patterned moulds used during R2R-NIL were created via greyscale lithography followed by dry etching. The key property of this sail is its ability to generate SRP through the refraction of sunlight rather than the more conventional method of using reflection. This allows it to generate SRP with a significant component that acts parallel to the sail (idealised depiction provided in
Figure 14), particularly within a Sun-pointing attitude. In a dusk–dawn Sun-synchronous orbit (SSO) or similar, this would allow the sail to accelerate optimally while within both the maximum illumination and minimum drag configurations simultaneously. This is in contrast to conventional reflective sails, which must sacrifice both their illumination and their drag profiles through tacking in order to align their SRP vector with the desired direction of impulse.
Although attitude control is primarily executed via magnetorquer, it is supplemented by the solar sail. This is principally achieved via an alternatively patterned region around the sail perimeter, wherein the pattern comprises an array of self-stabilising optical elements derived from lightfoils [
32,
33] rather than micro-prisms. These geometries will cause the sail to naturally oscillate about the Sun-pointing attitude in the absence of active control and expedite the process of converging upon said attitude in its presence. Furthermore, a small section of sail at the anterior edge is metallised; here, SRP is generated normal to the sail through conventional reflective means in order to counteract the torque generated by the micro-prism regions as depicted in
Figure 14; this is necessary because SRP that acts parallel to the sail also acts perpendicular to any radial vector drawn from the satellite centre of mass. The effect of this reflective section is that the net torque generated by the accelerating sail is zero in a Sun-pointing attitude, meaning that there is less disturbance torque that the self-stabilising sail sections must counteract.
This membrane is stowed using an origami-inspired folding pattern based on the
Miura-ori flasher, as depicted in
Figure 15. This membrane is attached to the rest of the payload by way of four flat springs located within the deployment assembly. These are fastened to the moving assembly in turn, which is constrained by the static assembly.
This static assembly serves as the structural interface between the payload and the JamSail bus, houses the membrane, and prevents the moving assembly from being ejected during and after deployment. It is principally composed of a static hub (beige,
Figure 16) that is fastened to the satellite bus via screws located at the sail plate above and a reinforcement ring below (blue,
Figure 16). This hub also features a membrane cavity, within which the membrane and flat springs are stowed under tension. Furthermore, beneath the static hub lies the compression spring housing (green,
Figure 16). This constrains the moving assembly prior to deployment and serves to ‘sandwich’ the four compression springs, which are compressed between the spring dock of the static assembly and the spring moving dock of the moving assembly.
The core component of the moving assembly is the plunger (red,
Figure 16), within which the four coiled flat springs are fastened and upon which the membrane rests prior to deployment. This assembly is initially kept static by the aforementioned compression spring housing of the static assembly (constraining it in −
x) and by a temporary wire fastening that moors it to the static assembly (constraining it in
). At the start of deployment, these fastenings are severed by way of a thermocutter, causing the moving assembly to rise under the influence of the four compression springs. These act upon the plunger via the four moving spring docks (see
Figure 17a). After the flat springs atop the plunger are free of the static housing, they cease to be constrained in the
plane, allowing them to spring outwards and unfurl the folded membrane. The moving assembly is prevented from being ejected from the satellite by the sail plate, which partially overlaps with the (now extended) plunger at four locations that lie towards the rail attachment points (see
Figure 17b).
The moving assembly would be functional with these elements alone. However, several design stressors are placed upon it by the refractive sail membrane. The first is the need to maintain a high degree of sail flatness. A refractive sail has greater sensitivity to solar incidence angle than its reflective contemporaries [
25], so an exaggerated sensitivity to wrinkling and billowing may be inferred. Furthermore, unmetallised patterned polymer membranes are atypical of spaceflight, and their tensile performance—particularly under the effects of radiation and vacuum—are not well documented. Greater control over membrane tension is therefore desirable, particularly during deployment and the later stages of the JamSail mission. Both issues can be mitigated through the active control of sail tension. To achieve this, first the sail membrane is segmented into four membranes; during deployment, each membrane segment is only constrained by the single flat spring to which it is attached, allowing it to deploy with theoretically zero tension. In practice, some tension is needed to enable the folded sail to unfurl. The degree of tension that is supplied to the membrane is controlled via nylon rigging that is sewn through the unconstrained sail edge nearest to the flat spring of the neighbouring membrane. It should be noted that the ratio of tension supplied to the sail near the spring tip relative to the spring base cannot be controlled actively and must be determined prior to launch. This is because, despite having several attachment points spanning the flat spring and the neighbouring free membrane edge, each rig is principally coiled into a single coil of wire. It is only by merit of having different lengths that they are able to ‘fray’ into distinct wires with different attachment points. Conversely, the net tension can be controlled actively during a mission by way of a motorised spool. One such wire coil is attributed to each flat spring, and all four share this common spool. Said spool is attached to a single motor, which in turn is attached to the underside of the plunger and rises with it during deployment (see
Figure 17a,b). This is so that the rigging has zero motion relative to the moving assembly during deployment; otherwise, sail tension would spike at a critical moment. This motorised spool ensures that tension can be controlled at every stage of the mission and may be used to bring the membrane to failure once a timely deorbit has been guaranteed by the sail, thereby generating useful data regarding unmetallised sail membrane performance in space. However, this configuration comes at the cost of complexity and mass and may be unnecessary during future missions once the relevant literature has been expanded.
3.3. External Cameras
JamSail will include a number of external-facing cameras, including one each on the ±Z faces, pointing nadir and zenith. These cameras serve two main purposes: attitude verification and outreach and will be composed of low-cost, commercial off-the-shelf hardware. The images these cameras capture will be broadcast on amateur radio frequencies unencrypted to be received by anyone with suitable radio equipment.
The attitude verification functionality of these external cameras serves to provide infrequent manual visual confirmation of the ADCS’s ability to orient the spacecraft to the correct attitude. This process will use major identifiable objects and features in the captured images to calculate the visually observed attitude via post-processing on ground and confirm if this is consistent with the attitude reported by on-board sensors. As the ADCS will be aiming to maintain a fixed nadir–zenith orientation for the Z-axis for purposes of the GNSS payload, the images produced by the cameras on the ±Z faces will allow the team to confirm this orientation has been reached and is maintained throughout the operational lifetime of the GNSS payload. If the images produced indicate the spacecraft is oriented differently to the numerical sensor data, adjustments to the pointing algorithm can be uplinked to mitigate against any sensor errors that are identified. Such an attitude verification process is included as a redundant safety feature and is not expected to be used except in cases of suspected errors in sensor data, where anomalous or conflicting values are observed.
In addition, the external cameras will provide important functionality for outreach and education. The images captured by these cameras, both of Earth and of deep space, will be transmitted openly via the radio to amateur radio enthusiasts all over the world, strengthening ties with existing collaborative partners in the local area and building new ties with amateur radio communities elsewhere. Importantly, these images will be transmitted at a suitably low resolution such that a whole image may be received even during short passes where the spacecraft is only visible for a few minutes. By designing the payload in this way, it introduces an opportunity for the educational use of JamSail by schools all over the world, where collaboration with local amateur radio communities can allow schoolchildren to receive their own photos of Earth from space. In particular, specific outreach campaigns will be scheduled throughout the mission, in which specific images of interest are transmitted at pre-advertised times to encourage participation.
Neither of these camera functions requires the camera to be pointed in any specific direction. For attitude verification functionality, they will operate as engineering cameras, providing an independent source of (approximate) attitude detection and verification, which will produce useful data regardless of spacecraft attitude. For amateur radio activities, JamSail will be capturing images as it orbits, and, due to the nature of the activity, it is not designed for taking photos of specific regions. As such, the external camera payload does not produce any requirements or constraints regarding pointing or stability.