Experimental Data Acquisition Research Articles

QiandaoEar22: a high-quality noise dataset for identifying specific ship from multiple underwater acoustic targets using ship-radiated noise

Target identification of ship-radiated noise is a crucial area in underwater target recognition. However, there is currently a lack of multi-target ship datasets that accurately represent real-world underwater acoustic conditions. To tackle this issue, we conducted experimental data acquisition, resulting in the release of QiandaoEar22—a comprehensive underwater acoustic multi-target dataset. This dataset encompasses 9 h and 28 min of real-world ship-radiated noise data and 21 h and 58 min of background noise data. To demonstrate the availability of QiandaoEar22, we executed two experimental tasks. The first task focuses on assessing the presence of ship-radiated noise, while the second task involves identifying specific ships within the recognized targets in the multi-ship mixed data. In the latter task, we extracted eight features from the data and employed six deep learning networks for classification, aiming to evaluate and compare the performance of various features and networks. The experimental results reveal that ship-radiated noise can be identified from background noise in over 99% of cases. For the specific identification of individual ships, the optimal recognition accuracy achieves 99.56%. Finally, we found using spectrum and MFCC as feature inputs and DenseNet as classifier can achieve excellent recognition performance. Considering the computational efficiency, CRNN and ECAPA-TDNN are also good choices. Our work not only establishes a benchmark for algorithm evaluation but also inspires the development of innovative methods to enhance underwater acoustic target detection (UATD) and underwater acoustic target recognition (UATR).

Simulating the Cellular Context in Synthetic Datasets for Cryo-Electron Tomography.

Cryo-electron tomography (cryo-ET) allows to visualize the cellular context at macromolecular level. To date, the impossibility of obtaining a reliable ground truth is limiting the application of deep learning-based image processing algorithms in this field. As a consequence, there is a growing demand of realistic synthetic datasets for training deep learning algorithms. In addition, besides assisting the acquisition and interpretation of experimental data, synthetic tomograms are used as reference models for cellular organization analysis from cellular tomograms. Current simulators in cryo-ET focus on reproducing distortions from image acquisition and tomogram reconstruction, however, they can not generate many of the low order features present in cellular tomograms. Here we propose several geometric and organization models to simulate low order cellular structures imaged by cryo-ET. Specifically, clusters of any known cytosolic or membrane-bound macromolecules, membranes with different geometries as well as different filamentous structures such as microtubules or actin-like networks. Moreover, we use parametrizable stochastic models to generate a high diversity of geometries and organizations to simulate representative and generalized datasets, including very crowded environments like those observed in native cells. These models have been implemented in a multiplatform open-source Python package, including scripts to generate cryo-tomograms with adjustable sizes and resolutions. In addition, these scripts provide also distortion-free density maps besides the ground truth in different file formats for efficient access and advanced visualization. We show that such a realistic synthetic dataset can be readily used to train generalizable deep learning algorithms.

Implementation of an experimental set-up for insertion loss measurements of sonic crystal acoustic screens

The use of metamaterials for the implementation of noise control devices in the context of environmental acoustics is a topic that has gained more and more attention in recent years. The complex design of such devices and the difficulty of their construction in real-world applications complicates the acquisition of experimental data related to their acoustical performance. Numerical simulation tools are used to simulate the behaviour of these novel noise control devices in their design phase and help to validate the designs by comparing them with experimental data. This data, usually acquired under controlled conditions, holds significant importance in confirming the increasingly precise numerical models that have been developed over the years to simulate the behaviour of these physical systems. In this paper, an experimental set-up that enables acquisition of acoustical performance data for a particular metamaterial-based noise control device, consisting of networks of isolated scatterers, is presented. This prototype possesses several distinctive characteristics that render it particularly interesting, including its adaptability, ease of use, speed of obtaining results, and, particularly, its economic viability in relation to the tests that can be conducted in an anechoic chamber.

Virtual vibration test rig for fatigue analysis of dozer push arms

AbstractTo obtain accurate fatigue life results for construction machinery components, acquiring load spectra is crucial, as their authenticity and validity directly determine the precision of the analysis. In working conditions, component attitudes change continuously, but they remain static on the vibration test rig (VTR), so the acquired target signals should match with the actual component attitudes in the driving signal generation. This paper proposes an efficient and economical simulation‐based virtual VTR for fatigue analysis of dozers. First, the relationship between the push arm rotation angle and the cylinder stroke is established, since the cylinder strokes can be measured easily in data acquisition experiments. Second, load decomposition is used to determine the attitude relationship between virtual VTR conditions and actual conditions, and target signals are calculated based on this attitude relationship and measured data. According to the system's frequency response function, the driving signals are iterated until the system's response signals converge with the target signals. Finally, the iteratively obtained load spectra are utilized for fatigue life analysis. The results show that the virtual VTR can effectively and accurately obtain the results of fatigue analysis and has engineering application significance.

Mathematical analysis of FLASH effect models based on theoretical hypotheses.

Clinical applications of FLASH radiotherapy require formulas to describe how the FLASH radiation features and other related factors determine the FLASH effect. Mathematical analysis of the models can connect the theoretical hypotheses with the radiobiological effect, which provides the foundation for establishing clinical application models. Moreover, experimental and clinical data can be used to explore the key factors through mathematical analysis. 
Approach: We abstract the complex models of the oxygen depletion hypothesis and radical recombination-antioxidants hypothesis into concise mathematical equations. The equations are solved to analyze how the radiation features and other factors influence the FLASH effect. Then we propose methodologies for determining the parameters in the models and utilizing the models to predict the FLASH effect.
Main results: The formulas linking the physical, chemical and biological factors to the FLASH effect are obtained through mathematical derivation of the equation. The analysis indicates that the initial oxygen concentration, radiolytic oxygen consumption and oxygen recovery are key factors for the oxygen depletion hypothesis and that the level of antioxidants is the key factor for the radical recombination-antioxidants hypothesis. According to the model derivations and analysis, the methodologies for determining parameters and predicting the FLASH effect are proposed: the criteria for data filtration; the strategy of hybrid FLASH and conventional dose rate (CONV) irradiation to ensure the acquisition of effective experimental data across a wide dose range; pipelines of fitting parameters and predicting the FLASH effect.
Significance: This study establishes the quantitative relationship between the FLASH effect and key factors. The derived formulas can be used to calculate the FLASH effect in future clinical FLASH radiotherapy. The proposed methodologies guide to obtain sufficient high-quality datasets and utilize them to predict FLASH effect. Furthermore, this study indicates the key factors of FLASH effect and offers clues to further explore the FLASH mechanism.

Parameterization of H2SO4 and organic contributions to volatile PM in aircraft plumes at ground idle

ABSTRACT Volatile Particulate Matter (vPM) emissions are challenging to measure and quantify, since they are not present in the condensed form at the engine exit plane and they evolve to first form in the aircraft plume and then continue to grow and change as they mix and dilute in the ambient atmosphere. To better understand the issues associated with the initial formation and growth of vPM, a modeling study has been undertaken to examine several key parameters that affect the formation and properties of the vPM that is created in the initial cooling and dilution of the aircraft exhaust. A modeling tool (Aerosol Dynamic Simulation Code, ADSC) that was developed and enhanced over a series of past research projects supported by NASA, DoD’s SERDP/ESTCP, and FAA was used to perform a parametric analysis of vPM. The parameters of fuel sulfur content (FSC), emitted condensable hydrocarbon (HC) concentrations, and the species profile of the HCs were used to construct a computational matrix that framed a wide range of expected parameter values. This computational matrix was executed for two representative commercial aircraft engines at ground idle and results were obtained for distances of 250 m and 1000 m downstream. From prior results, the most significant vPM emissions occur at the lowest power settings, so an engine power condition of 7% rated thrust was used. A primary goal of the parametric study is to develop an updated vPM modeling methodology and also to help interpret data collected in experimental campaigns. The parameterization proposed here allows the vPM emission composition and particle numbers to be estimated in greater detail than current methods. The aim is to provide additional understanding on how the vPM properties vary with fuel and engine parameters to increase the utility of vPM predictions. Implications: Volatile Particulate Matter (vPM) is an important contribution to the total PM emitted by aviation engines. While vPM is not currently a part of engine emissions certification regulations, vPM is used in aviation environmental impact assessments and for air quality modeling in and around airports. Current methods in use, such as FOA, were developed before many recent advances in experimental data acquisition and in understanding of vPM processes. The parameterization proposed here allows the vPM emission composition and particle numbers to be estimated in greater detail than current methods. These estimates can be used to develop inventories and provide a better estimate of total emission for most aviation engines. Its use in international regulatory tools can inform possible future regulatory actions regarding vPM.

Photon and neutron dose evaluation at the Beam Test Facility of the INFN - National Laboratory of Frascati

Dose evaluation and direct measurements are fundamental for radiation protection in non-conventional accelerator facilities, both before and after the primary and secondary shielding. In this paper, we will report about the experimental setup, data acquisition and analysis, together with FLUKA modeling, of the dose measurements test carried out in the Beam Test Facility (BTF) of the INFN - Frascati’s National Laboratories (LNF), where an intense mixed field is produced and measured with thermoluminescent dosimeters. BTF is an extraction and transport line of DAΦNE LINAC (Buonomo et al. 2021; Mazzitelli et al. 2003). It is optimized for electrons and positrons production in a wide range of intensity, energy (30 MeV–800 MeV), beam spot dimensions and divergence, using both primary and secondary beam of the DAΦNE LINAC. Through the years, the BTF has gained an important role in particle detectors test and development with electron/positron beam. A small fraction of the BTF’s shifts have been dedicated to radiation damage test using LINAC electron primary beam up to 5×1010 e-/s. As radiation protection group of the LNF, we evaluated the dose when electrons impinging on a Pb target from: (i) photon Bremsstrahlung production; (ii) photoneutron production. Three dedicated tests with 503 MeV electrons impinging on a ∼16 cm thick Pb target have been carried out in February, June 2022 and in January 2023, using TLD700 and TLD600, measuring doses at several charge intervals. The aim of this study focuses on evaluating dosimetric quantities produced by the mixed field, air kerma for the photon component, and ambient dose equivalent for the neutron one, using thermoluminescence dosimeters calibrated with low-energy standards: Cs-137 and Am-Be. The approach adopted involves the use of Monte Carlo simulations of the experiment, both to benchmark against experimental measurements and to validate the results obtained for energies higher than those of calibration. The results of this comparison show excellent agreement between measured and simulated quantities in the forward direction, allowing us to conclude and confirm the validity of the calibrations themselves.

A Hybrid EEG-Based Stress State Classification Model Using Multi-Domain Transfer Entropy and PCANet.

This paper proposes a new hybrid model for classifying stress states using EEG signals, combining multi-domain transfer entropy (TrEn) with a two-dimensional PCANet (2D-PCANet) approach. The aim is to create an automated system for identifying stress levels, which is crucial for early intervention and mental health management. A major challenge in this field lies in extracting meaningful emotional information from the complex patterns observed in EEG. Our model addresses this by initially applying independent component analysis (ICA) to purify the EEG signals, enhancing the clarity for further analysis. We then leverage the adaptability of the fractional Fourier transform (FrFT) to represent the EEG data in time, frequency, and time-frequency domains. This multi-domain representation allows for a more nuanced understanding of the brain's activity in response to stress. The subsequent stage involves the deployment of a two-layer 2D-PCANet network designed to autonomously distill EEG features associated with stress. These features are then classified by a support vector machine (SVM) to determine the stress state. Moreover, stress induction and data acquisition experiments are designed. We employed two distinct tasks known to trigger stress responses. Other stress-inducing elements that enhance the stress response were included in the experimental design, such as time limits and performance feedback. The EEG data collected from 15 participants were retained. The proposed algorithm achieves an average accuracy of over 92% on this self-collected dataset, enabling stress state detection under different task-induced conditions.

Operando Scanning Small-/Wide-Angle X-ray Scattering for Polymer Electrolyte Fuel Cells: Investigation of Catalyst Layer Saturation and Membrane Hydration- Capabilities and Challenges.

Polymer electrolyte fuel cells are an essential technology for future local emission-free mobility. One of the critical challenges for thriving commercialization is water management in the cells. We propose small- and wide-angle X-ray scattering as a suitable diagnostic tool to quantify the liquid saturation in the catalyst layer and determine the hydration of the ion-conducting membrane in real operating conditions. The challenges that may occur in operando data collection are described in detail─separation of the anode and cathode, cell alignment to the beam, X-ray radiation damage, and the possibility of membrane swelling. A synergistic development of experimental setup, data acquisition, and data interpretation circumvents the major challenges and leads to practical and reliable insights.

High-speed 4-dimensional scanning transmission electron microscopy using compressive sensing techniques.

Here we show that compressive sensing allows 4-dimensional (4-D) STEM data to be obtained and accurately reconstructed with both high-speed and reduced electron fluence. The methodology needed to achieve these results compared to conventional 4-D approaches requires only that a random subset of probe locations is acquired from the typical regular scanning grid, which immediately generates both higher speed and the lower fluence experimentally. We also consider downsampling of the detector, showing that oversampling is inherent within convergent beam electron diffraction (CBED) patterns and that detector downsampling does not reduce precision but allows faster experimental data acquisition. Analysis of an experimental atomic resolution yttrium silicide dataset shows that it is possible to recover over 25 dB peak signal-to-noise ratio in the recovered phase using 0.3% of the totaldata. Lay abstract: Four-dimensional scanning transmission electron microscopy (4-D STEM) is a powerful technique for characterizing complex nanoscale structures. In this method, a convergent beam electron diffraction pattern (CBED) is acquired at each probe location during the scan of the sample. This means that a 2-dimensional signal is acquired at each 2-D probe location, equating to a 4-D dataset. Despite the recent development of fast direct electron detectors, some capable of 100kHz frame rates, the limiting factor for 4-D STEM is acquisition times in the majority of cases, where cameras will typically operate on the order of 2kHz. This means that a raster scan containing 256^2 probe locations can take on the order of 30s, approximately 100-1000 times longer than a conventional STEM imaging technique using monolithic radial detectors. As a result, 4-D STEM acquisitions can be subject to adverse effects such as drift, beam damage, and sample contamination. Recent advances in computational imaging techniques for STEM have allowed for faster acquisition speeds by way of acquiring only a random subset of probe locations from the field of view. By doing this, the acquisition time is significantly reduced, in some cases by a factor of 10-100 times. The acquired data is then processed to fill-in or inpaint the missing data, taking advantage of the inherently low-complex signals which can be linearly combined to recover the information. In this work, similar methods are demonstrated for the acquisition of 4-D STEM data, where only a random subset of CBED patterns are acquired over the raster scan. We simulate the compressive sensing acquisition method for 4-D STEM and present our findings for a variety of analysis techniques such as ptychography and differential phase contrast. Our results show that acquisition times can be significantly reduced on the order of 100-300 times, therefore improving existing frame rates, as well as further reducing the electron fluence beyond just using a faster camera.

Shedding Light on the Failure Factors of Subsea Critical Fastener Bolts

Offshore facilities, such as oil and gas rigs, wind turbines, and related subsea equipment, typically use flanges fastened using bolts and nuts as the main connectors. In this study, multidisciplinary parameters, namely the preload torque used to tighten bolts, simulated subsea water currents, water temperature, and impressed current cathodic protection, were applied to ASTM A193 B7 bolts. An experimental supervisory control and data acquisition system was designed to obtain measurements every 5 min throughout a 21-day experiment. Finite element analysis was performed to predict the structurally vulnerable areas of the bolts. A strong correlation was found between the reference electrode readings and the measured electrical current, tightening torque, and water temperature. As the water temperature rises during the day, the reference electrode reading becomes less negative and the electrical current decreases. Subsea water currents cause about a four-time increase in the bolt corrosion rate, with unprotected bolts suffering a nine-time-higher corrosion rate than protected bolts. A unique supply–demand interaction is observed; less protection is supplied to areas with lower corrosion rates (lower demand for protection). Finally, scanning electron microscopy examination reveals new insights into the failure mechanisms of subsea bolts.

A sonication-filtration method for acquisition of integrated bacterial communities from phycosphere

Bacteria in phycosphere engage in intricate interactions with microalgae by exchanging organic and inorganic matter. However, elucidating the primary roles of bacteria in phycosphere has been a big challenge, due to the lack of adequate methods for separating tightly associated bacteria from microalgal cells. In this study we evaluated several isolation methods including centrifugation, filtration, sonication combined with filtration, and tween lysis followed by sonication and filtration, aiming to efficiently acquire complete bacterial communities from phycosphere. The results demonstrated that the sonication-filtration approach maximally preserves the original characteristics of the bacterial communities. This method will facilitate the acquisition and further analysis of future experimental data.

Experimental study on the use of the ARM Cortex M7 processor for measuring far-field blast waves

AbstractThe ongoing study of blast waves and blast wave mitigation continues to play an essential role in protecting structures and personnel. The methodology, however, for capturing far-field blast waves in large-scale tests has remained largely unchanged for three decades, relying on large arrays of pressure transducers connected by hundreds of meters of cabling and requiring a considerable amount of time to set up. This paper evaluates the use of a modern low-cost microprocessor with high computational power to capture blast waves with sufficient fidelity to provide scientists and engineers with credible data. The system utilizes an ARM Cortex M7 processor as an experimental data acquisition (DAQ) system for measuring far-field blast waves in an open-air blast arena at sampling speeds of up to 1.8 Msps (megasamples per second). The experimental system’s performance was evaluated by comparing it to a traditional commercial system used for measuring blast waves. The comparison showed an average Spearman correlation coefficient r of 0.928 between the two systems, suggesting a low variance between the commercial and experimental DAQ systems. This suggests that, despite its simplicity, the experimental system is an effective and low-cost alternative for accurately measuring blast waves.

Distributed neural representations of conditioned threat in the human brain

Detecting and responding to threat engages several neural nodes including the amygdala, hippocampus, insular cortex, and medial prefrontal cortices. Recent propositions call for the integration of more distributed neural nodes that process sensory and cognitive facets related to threat. Integrative, sensitive, and reproducible distributed neural decoders for the detection and response to threat and safety have yet to be established. We combine functional MRI data across varying threat conditioning and negative affect paradigms from 1465 participants with multivariate pattern analysis to investigate distributed neural representations of threat and safety. The trained decoders sensitively and specifically distinguish between threat and safety cues across multiple datasets. We further show that many neural nodes dynamically shift representations between threat and safety. Our results establish reproducible decoders that integrate neural circuits, merging the well-characterized ‘threat circuit’ with sensory and cognitive nodes, discriminating threat from safety regardless of experimental designs or data acquisition parameters.

A Three-Dimensional Deformation Monitoring Method: Combining Optical Deformation Monitoring Based on Regression Models and GB-SAR Interferometry.

This paper introduces a method for quantifying the three-dimensional deformation of ground targets and outlines the associated process. Initially, ground-based synthetic aperture radar was employed to monitor the radial deformation of targets, and optical equipment monitored pixel-level deformation in the vertical plane of the line of sight. Subsequently, a regression model was established to transform pixel-level deformation into two-dimensional deformation based on a fundamental length unit, and the radar deformation monitoring data were merged with the optical deformation monitoring data. Finally, the fused data underwent deformation, resulting in a comprehensive three-dimensional deformation profile of the target. Through physical data acquisition experiments, the comprehensive three-dimensional deformation of targets was obtained and compared with the actual deformations. The experimental results show that the method has a relative error of less than 10%, and monitoring accuracy is achieved at the millimeter level.

Analysis of particle dispersion and cavity formation during bulk particle water entry using fully coupled CFD-DEM-VOF approach

This paper presents the development and validation of a fully coupled computational fluid dynamics—discrete element method—volume of fluid (CFD-DEM-VOF) model to simulate the complex behavior of particle-laden flows with free surfaces. The coupling between the fluid and particle phases is established through the implemented continuity, momentum, and alpha transport equation. The coupled particle forces such as drag, pressure gradient, dense virtual mass, viscous, and interface forces are also integrated, with drag and dense virtual mass forces being dependent on local porosity. The integrated conservative alpha transport equation ensures phase volume conservation during interactions between particles and water. Additionally, we have implemented a trilinear interpolation method designed to operate on unstructured hexahedral meshes. This method has been tested for its ability to properly resolve the coupling effects in the numerical simulations, particularly in cases with a relatively low cell-size ratio. The model is validated through three distinct test cases: single particle water entry, dam break with particles, and water entry of a group of particles case. The experimental setup is built to study the dynamics of the water entry of a group of particles, where three key flow features are analyzed: the evolution of average particle velocity, cavity shape, and particle dispersion cloud profiles in water. The tests involve four different scenarios, including two different water levels (16.1 and 20.1 cm) and two different particle densities (2650 and 4000 kg/m3). High-speed videometry and particle tracking velocimetry (using ImageJ/TrackMate) methods are employed for experimental data acquisition. It is demonstrated that numerical results are in excellent agreement with theoretical predictions and experimental data. The study highlights the significance of vortices in cavity shaping and particle dispersion. The validated CFD-DEM-VOF model constitutes a robust tool for simulating particle-laden flows, contributing valuable insights into the complex interplay between particles and fluids.

Development of a cost-effective compact diode-laser-based photoacoustic sensing instrument for breast tissue diagnosis.

The photoacoustic (PA) technique, a noninvasive pump-probe technique, has found interesting applications in biomedical tissue diagnosis over the last decade. To take it a step further to clinical applications, the PA technique needs to be designed as an instrument focusing on a compact design, reducing the cost, and quickly providing a quantitative diagnosis. This work presents a design and characterization of a cost-effective, compact PA sensing instrument for biomedical tissue diagnosis. A compact laser diode case design is developed to house several laser diodes for PA excitation, and a pulsed current supply unit is also developed in-house to power the laser diodes to generate a 25ns current pulse at a frequency of 20kHz. After PA experimental data acquisition, the signal's frequency spectra were calculated to characterize the tissue quantitatively and correlated with their mechanobiological properties. The corresponding dominant frequency peak in the PA spectral response (PASR) study was low in the fibrofatty normal breast tissue , compared to the dominant frequency peak of in the fibrocystic disease tissue, which had increased glandular and stromal elements, thereby increased tissue density. The histopathological findings correlated with the PASR results, and the fibrocystic breast disease tissue exhibited a higher dominant frequency peak and energy compared to the normal breast tissue. We experimented with an in vitro PASR study of fibrocystic human breast tissues and successfully differentiated different tissue types using quantitative spectral parameters peak frequency, mean frequency, and spectral energy. This gives the potential to take this technique further for cost-effective and quick clinical applications.

Treatment of multiple-beam X-ray diffraction in energy-dependent measurements.

During X-ray diffraction experiments on single crystals, the diffracted beam intensities may be affected by multiple-beam X-ray diffraction (MBD). This effect is particularly frequent at higher X-ray energies and for larger unit cells. The appearance of this so-called Renninger effect often impairs the interpretation of diffracted intensities. This applies in particular to energy spectra analysed in resonant experiments, since during scans of the incident photon energy these conditions are necessarily met for specific X-ray energies. This effect can be addressed by carefully avoiding multiple-beam reflection conditions at a given X-ray energy and a given position in reciprocal space. However, areas which are (nearly) free of MBD are not always available. This article presents a universal concept of data acquisition and post-processing for resonant X-ray diffraction experiments. Our concept facilitates the reliable determination of kinematic (MBD-free) resonant diffraction intensities even at relatively high energies which, in turn, enables the study of higher absorption edges. This way, the applicability of resonant diffraction, e.g. to reveal the local atomic and electronic structure or chemical environment, is extended for a vast majority of crystalline materials. The potential of this approach compared with conventional data reduction is demonstrated by the measurements of the Ta L3 edge of well studied lithium tantalate LiTaO3.

Synthetic IMU Datasets and Protocols Can Simplify Fall Detection Experiments and Optimize Sensor Configuration.

Falls represent a significant cause of injury among the elderly population. Extensive research has been devoted to the utilization of wearable IMU sensors in conjunction with machine learning techniques for fall detection. To address the challenge of acquiring costly training data, this paper presents a novel method that generates a substantial volume of synthetic IMU data with minimal actual fall experiments. First, unmarked 3D motion capture technology is employed to reconstruct human movements. Subsequently, utilizing the biomechanical simulation platform Opensim and forward kinematic methods, an ample amount of training data from various body segments can be custom generated. Synthetic IMU data was then used to train a machine learning model, achieving testing accuracies of 91.99% and 86.62% on two distinct datasets of actual fall-related IMU data. Building upon the simulation framework, this paper further optimized the single IMU attachment position and multiple IMU combinations on fall detection. The proposed method simplifies fall detection data acquisition experiments, provides novel venue for generating low cost synthetic data in scenario where acquiring data for machine learning is challenging and paves the way for customizing machine learning configurations.

Integration of Automatic Procedures for Acquisition, Processing, Storage, Analysis and Monitoring of Experimental Data in Physics Education

Integration of Automatic Procedures for Acquisition, Processing, Storage, Analysis and Monitoring of Experimental Data in Physics Education