## 1 Introduction
In this contribution, the ionospheric observable was retrieved from the code and carrier-phase measurements using the carrier-to-code leveling technique and a new carrier-to-noise weighting strategy instead of an elevation weighting strategy. The slant TEC derived from the Xiaomi Mi8 was then compared to the slant TEC derived from a geodetic receiver as the reference. According to the results, applying smart device-level GNSS observations in ionospheric studies is feasible. [PERSON] et al. [27] proposed an inertial measurement unit (IMU)-aided uncombined PPP coupled mathematical model, suitable for smartphone positioning. The proposed PPP/INS-coupled model integrated the dual-frequency GNSS observations and IMU data from smartphones with C/N0-dependent stochastic model and robust Kalman filter (RKF) model to improve the positioning performance under GNSS-degraded environments. Experimental results indicated that the proposed PPP/INS method could effectively improve the smartphone positioning performance compared with the conventional smartphone PPP method. [PERSON] et al. [28] presented a novel sensor fusion technique using PPP and the inertial sensors in smartphones, combined with a single- and dual-frequency (SFDF) optimisation scheme for smartphones. Using several vehicle experiments, a significant improvement in the final solutions has been confirmed in the case of multi-GNSS PPP/IMU integration, providing consistent horizontal positioning accuracy of <2 m RMS in real-world driving scenarios. Among the contributions in the field of differential/relative positioning, we can also refer to the following. [PERSON] et al. [29] investigated the effect of L1 and L5 frequencies on the positioning accuracy of the pseudo-range differential GNSS (DGPS) using data from two Huawei P30 Pro devices. The results showed a better positioning accuracy employing the P(L5) code compared to the P(L1) code. [PERSON] et al. [30] proposed a combined RTK/fifth generation (5G) mobile communication technology positioning model by combining global positioning system-RTK with 5G time-of-arrival observations to improve the positioning accuracy under medium and long baselines. The results indicated that good positioningresults could be achieved in the case of combined RTK/5G positioning model, even while some satellites are occluded. [PERSON] et al. [31] presented a method for mitigating the multipath effect in order to improve the accuracy and robustness of GNSS RTK positioning from Android smartphones. The main idea is to weigh GNSS observations of each piece of satellite data considering the proposed parameter MDP (multipath detection parameter) and signal noise ratio (SNR) values. It assigns lower weights to the unreliable observations potentially affected by the multipath error (and vice versa). [PERSON] et al. [32] proposed a single-frequency RTK robust adaptive Kalman filtering algorithm applied to smartphone GNSS positioning. It is based on the quartile method to dynamically determine the threshold value and eliminate the gross error of observation. The effectiveness of the proposed quartile robust RTK algorithm has been validated using the simulated and real dynamic experiments. According to the results, the proposed method could significantly eliminate large gross errors, and reasonably allocate weights to different observations according to the innovation vector. As a result, the overall positioning accuracies have been improved. [PERSON] et al. [33] proposed a method to detect and correct the non-line-of-sight (NLOS) signals, which is an important issue in urban environments. This method is based on a convolutional neural network constructed using the original observations of smartphones providing the detection accuracy of more than 95%. The detected NLOS signals were decomposed using the variational mode decomposition method to eliminate the NLOS part and improve the data quality. They then evaluated the efficiency of the proposed method in both static and dynamic modes in an urban environment using the RTK method.