## 3 Experimental Methods and Results  
The tests were done at Observatorio do Parque Eolico de Vila Nova de Gaia, Miranda do Corvo, Portugal (Geodetic coordinates: 40.0453 N, 8.2764 W). This place is at 930 m of altitude and far from large cities, enabling observations with minimal light pollution. The breadboard was fixed on a tripod, which enabled the easy movement of the camera. Also, a laser pointer was attached to the breadboard to facilitate the camera's direction to the correct position in the sky. Figure 8 shows the device on the tripod, with the laser pointer, and plugged into the computer. The temperature controller was programmed to cool down the system until 15 \({}^{\circ}\)C. In other words, this is turned on only if the optical system's temperature exceeds 15 \({}^{\circ}\)C. This is the minimum temperature that the power bank can provide to the system. For lower temperatures, one would need a more powerful power supply. On the night of observation, the temperature measured at the sensor at the beginning was 13 \({}^{\circ}\)C and, at the end, 12 \({}^{\circ}\)C. Therefore, the cooling system was not necessary. Satellites and rocket bodies were observed to represent space debris. The software Heavens-Above [27] was used to plan the observations. This software provides information about the objects that will be visible on the desired night, enabling an accurate measurement of the time and position of passing. Thus, 77 images of sources, like the ISS, Starlink satellites, and some rocket bodies, were taken for an integration time of 3 s. This was the best integration time tested, which prevented the saturation signals and provided a complete streak of the object inside the _FOV_. A few of these images can be seen in Figure 9. The images are shown with a negative filter to facilitate the visualisation. Figure 8: Breadboard connected to the computer and positioned for the observations. * Image reduction  
For astronomical images, it is usually processed by a process called data reduction [20], which is a technique to remove inhomogeneities in the images using a flat field, a set of pictures of the homogeneous sky at sunset with an integrated time leading to counts about 1/3 lower than saturation, and a bias frame. This data reduction was executed as follows: the bias frames were combined in a method called stack to generate the master bias. After that, the flat images were stacked into a single image, and the subtraction with the master bias was done to create the master flat. Then, the master bias and master flat were applied to each image from the tests in order to produce calibrated images. The data reduction process is summarised in Figure 10.  
* Photometry In our context, photometry is the technique used to measure the magnitude of astronomical/sideral sources/objects. It is well-defined how to determine the brightness of  
Figure 10: Data reduction process of the images of the tests. Figure 9: Some detected objects. stars and other point-like sources through methods that calculate the flux received in the image sensor within concentric circles around the object/star. The flux makes it possible to determine the magnitude [28]. Meanwhile, this standard technique is mostly used for point-like sources, which is not the case for debris that do not move at sidereal rates. Photometry for debris follows the same procedure as for asteroids and meteoroids, which accounts for trailed sources that put the objects inside a more suitable geometry than a circle. The photometry was performed using Photutils [29], an open-source software for the photometry of astronomical sources. The photometry study was performed on the images shown in Figure 9. Each image was uploaded into Astrometry.net [30] for star identification on the _FOV_. After that, four identified stars were selected to be photometrically measured. The chosen stars are in line 'Object' in Table 3. Then the measured magnitude of each star was compared with its known tabled magnitude to find the calibrated magnitude.