## 4 Data Acquisition System for High Repetition Rate Performance
A summary of measured data losses for individual oscilloscopes is reported in Table 1.  
\begin{table}
\begin{tabular}{c  
Data transfer rate from the oscilloscope to the PC depends mainly on the acquisition rate and the number of samples per channel. The oscilloscopes' transfer buffer and internal memory are independent. In case of overlapping between data transfer time and triggering time, there is still a possibility to transfer newly acquired data. However, inability to transfer data between neighboring triggers causes delay accumulation and after few repetitions leads to the loss of data acquired by the oscilloscope. A stable operation of the DAS with three parallel oscilloscopes (four channels per oscilloscope) can be easily achieved for 1 Hz laser repetition rates up to 50,000 samples per channel. On the other hand, operation at 10 Hz shows data loss, even acquiring 1000 samples per channel. However, a still reasonable performance of the DASwith the used oscilloscopes can be achieved even for 10,000 samples per channel and 10 Hz repetition rate (the observed data loss is about 6.5%). To conclude, the chosen oscilloscopes demonstrate reasonable performance for data collection up to 3 Hz repetition rate. TOF diagnostics typically requires about 1000 samples to record all details of the ion beam spectrum. In case of high requirements to time-resolution (ultrafast ion signal), a relatively low repetition rate (around 1 Hz) is still acceptable in terms of overall data loss. Nevertheless, for application-based experiments, typically requiring a large number of shots, a compromise could be found by recording the ion spectra at a reduced repetition rate (e.g., an average signal acquired during 10 shots), even if the laser is operated at 10 Hz. PW-class laser systems available at the ELI-Beamlines facility are characterized by high-repetition operating capability (1-10 Hz) that, in combination with suitable target delivery systems and laser-plasma diagnostic devices, provide unique possibilities for investigating new experimental regimes with high statistics. However, such a new scenario makes manual processing of raw data practically impossible. This also applies to typical laser-accelerated ion signals from various diagnostics, e.g., TOF detectors or TPS. In fact, any online analysis of a signal sequence would permit control of, inter alia, the uniformity and stability of TOF spectra that can be a crucial parameter for several applications of the measured particle beams [18]. Therefore, a special routine was developed using Python to perform TOF spectral analyses in real-time during an experiment. The script allows obtaining an average TOF signal based on the last N shots and, at the same time, calculates the standard deviation of the set, plotting the corresponding results (see Figure 6). The spectral changes over a certain time can be seen with high accuracy, and the overall stability can be tracked. This enables monitoring of the progress of the experiment and control of the process for possible errors or system breakdowns. Real-time analysis of the obtained TPS images during an experiment was performed with specifically developed MATLAB-based graphical user interface (see Figure 7). At the initialization stage, it allows setting up the basic parameter of the used TPS geometry, such as the diameter of the pinhole, length of the electric and magnetic electrodes, distances between electrodes and camera, as well as particularly applied electrode fields strength, orientation, and scaling factor of the camera for the current experimental cycle. The routine can determine the appearance of the new experimental data continuously stored by TPS camera in the predefined folder, automatically start the procedure of their analysis, and save the calculated results. At the first stage, the code performs a simulated trajectory of the particles inside the TPS device from the collimator down to the detector plane, according to the specified parameters.