### New Challenges and Related Issues When Using Double-Frequency Smartphones  
After the release of the first dual-frequency Xiaomi Mi8 smartphone in May 2018, new opportunities have appeared to use such devices for positioning, navigation, and geophysical studies. Optimistic researchers have focused on practical uses of low-cost devices, such as studying the ability to monitor geophysical parameters with GNSS reflectometry [9], estimating the tropospheric zenith total delay (ZTD) [10], and studying the ionospheric total electron content (TEC) [11]. In contrast, skeptics have preferred to focus on studying the quality of the signals emitted by these devices and their robustness and sensitivity estimates, knowing that measurements from such devices rely on small, embedded, linearly polarized antennas [12; 13; 14; 15; 16; 17]. Today, the use of low-cost devices is such a critical issue for geoscience that the IAG has established a working group called \"Reliability of Low-Cost & Android GNSS in Navigation and Geoscience.\" However, all previous studies had one thing in common: the authors emphasized that the performance of the devices should be investigated to identify the limiting factors before their use. Research on smartphone use for positioning is very intensive and covers a wide range of tasks. Measurements of the first dual-frequency smartphone, the Xiaomi Mi8, were immediately tested by [PERSON] et al. [18], who were followed by many others in much more extensive and specific studies [13; 18; 19; 20; 21; 22; 23; 24; 25; 26]. The effects of smartphone positioning in upright and prone positions were studied by [PERSON] et al. [27]. They showed that the choice of smartphone configuration can affect the positioning performance, even at a zero baseline setting, which might be a good starting point for the issue of signal polarization from interferences, i.e., jamming, and spoofing signals studied in [28]. [PERSON] et al. [14] highlighted that double-frequency smartphones can be affected by frequent cycle-slips, duty-cycling, random initial phase biases (IPBs), and a very noticeable divergence of carrier-phase biases between dual-frequency signals. All these issues relate to problems with noise and difficult ambiguity fixing. However, it is the limitations of smart devices and, at the same time, their utility that are the common starting point for researchers who are trying to improve device performance, both by developing new algorithms, as described in [12; 29], and by combining different measurements, as described in [7; 8]. Now we are witnessing the rapid improvement of vendor-specific chipsets embedded in smartphones, from the first Broadcom BCM4775 [31] to the third-generation BCM4778 [32], Qualcomm Snapdragon 855/855+, 860, 870, and 888 [33; 34], and Kirin 980, 990. Their integration into newer versions of smartphones offers a good basis for analyzing the improvements in efficiency for signal quality, positioning, and beyond. This also includes the question of how they react to intentional interference, which could vary due to the different construction of chipsets. For this reason, a fingerprint of smartphone behavior must be proved when using devices from different manufacturers, chipsets, and generations to better address the problem of jamming localization using a variety of GNSS-based devices. Researchers have already studied the effects of jammers and spoofers on some specific smartphones, where an inability to detect spoofing was the main research focus. [PERSON] et al. [30] were the first to investigate the question of whether Android GNSS raw measurements could be used to detect interference, especially jamming and spoofing, as soon as double-difference smartphones appeared on the market. Their first experiments were based on the Google Pixel 2 and the Huawei P10 receiver; the proposed solution was based on an ideal joint metric of CNR, inertial sensor data, pseudo-range residual metric, and automatic gain control (AGC) level for detecting strong radio-frequency interference (RFI).