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27/01/ · In this frame, spectral Mixture Analysis (SMA), Object-based oriented classification (Segmentation), and Change Vector Analysis are recently much 10/11/ · Analysis of spectral mixtures is important in remote sensing imaging spectroscopy, because essentially the spectrum of any pixel of a natural scene is a mixture. 31/12/ · Spectral Mixture Analysis for Ground-Cover Mapping Authors: Michael Schmidt German Aerospace Center (DLR) Peter Scarth The University of Queensland Abstract and Our free Spectral Analysis app makes it easy to incorporate spectroscopy into your biology and chemistry labs. Using the app, students can collect a full spectrum and explore topics such as Spectral Mixture Analysis for Monitoring and Mapping Desertification Processes in Semi-arid Areas in North Kordofan State, Sudan The thesis is submitted for the degree of Doctor of ... read more
The range of interpretation, it looks cleaner. Table 2 lists statistical results fractions for the three endmembers has been rescaled from [0, 1] percentage for the three endmembers as derived with the to [0, ]. It can be seen from the Veg, Soil and Impervious LDA. The accuracies in either average accuracy, AA or overall images for the areas in the rectangle in Fig. This is Fig. This indicates that mapping urban major because the number of final classes has only four. For the RMS error image Fig. Their means for the 9 spectral Fraction Relative Fraction Relative Fraction Relative bands each accounting for a corresponding endmember are error error error presented in Fig. Values LSS It can be seen from the error image that the error is assessed the effects of the number of hidden-layer nodes on RE relatively low. The average RMS errors are shown in Table 2. calculated from test samples Fig.
When the hidden-layer An average of 0. The low RMS error indicates that lowest RE was 0. with 30 nodes. When the computation time is considered, Nevertheless, the RE levels 0. This may be although it has a little bit of high RE value 0. Therefore, we caused by high confusions between Soil and two impervious fixed the ANN structure with a node hidden layer and the surfaces. Use of ANN With the same training data sets three sets as used to derive M in LSS, Fig. To train and test the ANN for spectral unmixing, input DNs For the purpose of display, we used the same rescaling as in were first normalized to the range of [0, 1].
The nine nodes in the Fig. The distribution of Veg fraction generated by ANN has a input layer correspond to DNs from three VNIR bands and six good agreement with that on the LULC map. The Impervious SWIR bands the SWIR bands having been resampled to m surface merged from Himp and Limp fractions is also better pixel size. The output layer had 4 nodes corresponding to 4 than the derived by LSS when compared to the LULC map. This endmembers: Veg, Soil, Himp and Limp. They correspond to the situation is very different from Fig. To find a better ANN structure, we subtle spectral difference between the two endmembers Gong tested various combinations of learning rate η , momentum et al. We used that may be favorable to separate the two endmembers of Soil training samples and test samples. When fixing and Himp whose spectra are much overlapped Fig. number of nodes 10 at one hidden layer, a series of test results The values of RE-train and RE-test with ANN, averaged from 12 combinations of η and α values η varied from 0.
All REs from test compared to the LSS REs, are considerably low around 0. When using the Table 2. This indicates that the ANN is more effective than the Fig. A close look at the spectral unmixing results and detailed comparison among the AISA airborne imagery, LULC map and ASTER spectral unmixing results. Letters V, S, Hi and Li in the figure represent the endmember names: Veg, Soil, Himp and Limp in the text. This conclusion calculated by averaging pixel-based unmixing results respectively is coincident with that by Pu et al. The relative error for each endmember fraction 4. Comparison and validation was computed for each method using LULC map values as the reference true values e. Therefore, from the table, and ANN, for spectral unmixing, on the one hand, we conducted a the three fractions estimated by the ANN have the lowest relative general comparison between unmixed results by LSS and ANN; error among the three methods: LSS, ANN and LDA except Veg on the other hand, we also took a close look at both sets of results.
If focusing classification results were used as a reference. However, if we ANN, LDA and LULC map restrained in LULC cover area emphasize on the urban ecosystem study and on local individual only. In the table, the fractions of the three components were pixels, the unmixed results may be more relevant. a , c and e are for Vegetation, Soil and Impervious fractions estimated with the ANN, respectively, while b , d and f are corresponding three fractions estimated with the LSS. For this case, LDA results were not used. The illustrates a close comparison between LSS, ANN and the LULC comparison result of the two spectral mixture analysis methods map. The AISA imagery can serve as a detailed reference. In for unmixing ASTER VNIR and SWIR data: LSS and ANN, Fig. Referring to ASTER VNIR image and the RE values of endmember fractions and more reasonable spatial AISA airborne image, obviously, the Veg image V from the distribution of fractions than those by LSS.
Such conclusions ANN is very similar to that from LULC map, thus better than that agree well with our previous work Pu et al. In this from LSS. For Soil component S , its distribution from ANN analysis, the ANN capable of handling spectral confusions result is less than those from both LSS and LULC map, but between the Soil and the two impervious surfaces is due to its compared to the image S of AISA and ASTER VNIR, it seems nonlinear nature that may account for the possible phenomenon more reasonable than that from LSS. For the two impervious of nonlinear mixture of more than one endmember in a pixel endmember images Hi: high albedo impervious surface and Li: space. The second reason to explain its generating satisfactory low albedo impervious surface in contrast with the results is that ANN can efficiently make use of subtle spectral corresponding AISA image, ASTER image and LULC map, differences in fraction estimation for those endmembers with the ANN results seem also better than those by LSS.
Therefore, similar spectral properties e. surfaces, their spectral differences still exist across most Fig. Such two reasons may help explain between fractions estimated by spectral unmixing methods and why the ANN algorithm is more capable of solving the spectral fractions interpreted from AISA image and those calculated mixture problem than the least square solution. from LULC map. The fractions from AISA interpretation and LULC map are used as actual values. Since the fractions derived 5. Conclusions from LULC map may not really reflect the spectral properties of ground surface types, the result of AISA interpretation is helpful. As the remote sensing technology advances, more and more For example, the farm land in the city area this phenomenon is data from new sensors will be available for urban environmental not usual to see in other countries, but very normal in city area in studies.
Among them, the Advanced Spaceborne Thermal Japan has varying spectral signature in different seasons e. Because the structure studies. AISA image possesses spectral characteristics that are compat- Based on our experimental results, we conclude that 1 ible with those of the ASTER imagery e. The scatter plots Fig. Acknowledgements Although the distribution of the 87 points for Soil endmember abundance is not ideal apparently, a lack of medium abundance The research was partially supported by the grant kzcx values of the endmember , the agreement between actual values from the Chinese Academy of Sciences, China, and the grant and estimated by ANN see Fig. LSS see Fig. This can be proven from the correlation level R2 and the degree of closeness to the diagonal References line. In addition, there exists a systematically biased low with the fractions of the three endmembers estimated by LSS when Adams, J.
Simple models for complex compared to those from AISA-LULC data. natural surfaces: A strategy for the hyperspectral era of remote sensing. The preliminary validation results demonstrate that the Proceedings of the International Geoscience and Remote Sensing Symposium, Vancouver, B. ASTER VNIR and SWIR image data can be used to efficiently Adams, J. Spectral mixture modeling: estimate abundance of urban surface components, especially A new analysis of rock and soil types at the Viking Lander1 site. Journal of those generated by the ANN method. Impervious surface coverage: The Liang, S. Simultaneous inversion of subpixel emergence of a key environmental indicator. Journal of the American proportions and signatures of mixed pixels: two components.
for Photogrammetry and Remote Sensing Fall Convention, Atlanta, Georgia Regional aerolian dynamics and sand mixing in the Gran Desierto: Evidence pp. from Landsat Thematic Mapper image. Journal of Geophysical Research, Lu, D. examining the relationship between urban thermal features and biophysical Bowers, T. Remote mineralogic and lithologic descriptors in Indianapolis, Indiana, USA. AVIRIS data. analysis, neural networks and logistic regression for predicting species Chavez, P. An improved dark-object subtraction technique for distributions: A case study with a Himalayan river bird. Ecological atmospheric scattering correction of multispectral data. Maselli, F. Multiclass spectral decomposition of remotely sensed scenes Chen, X.
Remote sensing by selective pixel unmixing. McGwire, K. Hyperspectral mixture Clapham, W. Continuum-based classification of remotely sensed modeling for quantifying sparse vegetation cover in arid environments. imagery to describe urban sprawl on a watershed scale. McKinney, M. Urbanization, biodiversity, and conservation. Ferrier, G. to mapping alteration zone associated with gold mineralization in southern Muchoney, D. Piexl- and site-based calibration and Spain. validation methods for evaluating supervised classification of remotely Flanagan, M. Subpixel impervious surface sensed data. Proceedings of American Society for Photogrammetry and Pao, Y. Adaptive pattern recognition and neural networks. New York: Remote Sensing Annual Convention, St. Louis, MO pp. Addison and Wesley. Foody, G. Relating the land-cover composition of mixed pixels to Pu, R. Determination of burnt scars using logistic regression artificial neural network classification. Gong, P. Integrated analysis of spatial data from multiple sources: Using Pu, R.
Assessment of multi- evidential reasoning and an artificial neural network for geological mapping. resolution and multi-sensor data for urban surface temperature retrieval. Image processing methods, in Remote Sensing of Human Pu, R. Oakwood crown closure estimation by Settlements edited by M. Ridd of the Manual of Remote Sensing, 3rd unmixing Landsat TM data. International Journal of Remote Sensing, 24, Edition, Volume 5, Chief Editor, A. Photogrammetry and Remote Sensing, Bethesda, Maryland, pp. Ridd, M. Exploring a V-I-S vegetation-impervious surface-soil model Gong, P. Land-use classification of SPOT HRV data for urban ecosystem analysis through remote sensing: Comparative anatomy using a cover-frequency method. International Journal of Remote Sensing, for cities.
Rumelhart, D. Learning internal Gong, P. Frequency-based contextual classification representations by error propagation. Parallel distributed processing— and grey-level vector reduction for land-use identification. Photogram- Explorations in the microstructure of cognition, Vol. Cambridge, MA: MIT Press. Spectral SAS Institute Inc. Release 6. Cary, decomposition of Landsat TM data for urban land-cover mapping. Canadian Symposium on Remote Sensing pp. Sasaki, K. Constrained nonlinear method for Gong, P. Forest canopy closure from estimating component spectra from multicomponent mixtures. application to an open canopy. IEEE Transactions on Geoscience and Settle, J. cover proportions. Mapping ecological land systems and Small, C. Estimation of urban vegetation abundance by spectral mixture classification uncertainty from digital elevation and forest cover data using analysis.
neural networks. Photogrammetric Engineering and Remote Sensing, 62 11 , Small, C. Multitemporal analysis of urban reflectance. Conifer species recognition: An exploratory Small, C. Estimation and vicarious validation of urban analysis of in situ hyperspectral data. Remote Sensing of Environment, 62, vegetation abundance by spectral mixture analysis. Noise effect on linear spectral unmixing. Geo- Smith, M. deserts: I. A regional measure of abundance from multispectral images. Green, A. for ordering multispectral data in terms of image quality with implications Sohn, Y. Mapping desert shrub rangeland using for noise removal. IEEE Transactions on Geoscience and Remote Sensing, spectral unmixing and modeling spectral mixtures with TM data. Hung, M. A subpixel classifier for urban land-cover Song, C. Spectral mixture analysis for subpixel vegetation fractions in mapping based on a maximum-likelihood approach and export system rules. the urban environment: How to incorporate endmember variability?
Lee, S. Sub-pixel estimation of urban land cover Tompkins, S. components with linear mixture model analysis and Landsat Thematic Optimization of endmembers for spectral mixture analysis. Remote Sensing Mapper imagery. Thermal remote sensing of urban climates. Yamaguchi, Y. Overview of Advanced Spaceborne Thermal Emission and Reflection Wang, X. The study on decompositing AVHRR mixed Radiometer ASTER. IEEE Transactions on Geoscience and Remote pixels by means of neural network model. Yang, L. Urban lad-cover change Wu, C. Remote Sensing of Environment, 93, data. Yu, Q. Object-based detailed Wu, C.
Estimating impervious surface distribution by vegetation mapping using high spatial resolution imagery. Photogrammetric spectral mixture analysis. Xian, G. Assessment of urban growth in the Tampa Bay Zhang, L. Study of the spectral mixture watershed using remote sensing data. Remote Sensing of Environment, 97, model of soil and vegetation in Poyang Lake area, China. An analysis of urban thermal characteristics and association land cover in Tampa Bay and Las Vegas using Landsat satellite data. RELATED PAPERS. Geoderma Detecting soil erosion in semi-arid Mediterranean environments using simulated EnMAP data. International Journal of Digital Earth Mapping alteration minerals using sub-pixel unmixing of ASTER data in the Sarduiyeh area, SE Kerman, Iran. Theories, Methods, and Applications Remote Sensing and GIS Integration. Analysis of the effectiveness of spectral mixture analysis and Markov random field based super resolution mapping over an urban environment.
International Journal of Applied Earth Observation and Geoinformation Land cover mapping at Alkali Flat and Lake Lucero, White Sands, New Mexico, USA using multi-temporal and multi-spectral remote sensing data. Remote Sensing of Environment Evaluating Hyperion capability for land cover mapping in a fragmented ecosystem: Pollino National Park, Italy. Surveys in Geophysics Synergies of Spaceborne Imaging Spectroscopy with Other Remote Sensing Approaches. Remote Sensing Quantification and Analysis of Impervious Surface Area in the Metropolitan Region of São Paulo, Brazil. ISPRS Journal of Photogrammetry and Remote Sensing Hyperspectral shape-based unmixing to improve intra- and interclass variability for forest and agro-ecosystem monitoring. Multi-scale standardized spectral mixture models. Geoderma Applying imaging spectroscopy techniques to map saline soils with ASTER images.
Remote Sensing Applications: Society and Environment Growth of Dehradun city: An application of linear spectral unmixing LSU technique using multi-temporal landsat satellite data sets. Remote Sensing Spectral Unmixing of Forest Crown Components at Close Range, Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale. A hybrid method combining neighborhood information from satellite data with modeled diurnal temperature cycles over consecutive days. Sensors Hyperspectral Sensor Data Capability for Retrieving Complex Urban Land Cover in Comparison with Multispectral Data: Venice City Case Study Italy. Remote Sensing Combining Optical and Radar Satellite Imagery to Investigate the Surface Properties and Evolution of the Lordsburg Playa, New Mexico, USA.
Disaggregation of remotely sensed land surface temperature: Literature survey, taxonomy, issues, and caveats. Applications and Challenges of Geospatial Technology Applications and Challenges of Geospatial Technology: Potential and Future Trends. Mapping Lithological and Mineralogical Units Using Hyperspectral Imagery. International Journal of Applied Earth Observation and Geoinformation Multi- and hyperspectral geologic remote sensing: A review. Journal of Information Engineering and Applications A Developed Algorithm for Automating the Multiple Bands Multiple Endmember Selection of Hyperion data Applied on Central of Cairo, Egypt.
Egyptian Journal of Soil Science Remote Estimation of Vegetation Parameters using Narrowband Sensor for Precision Agriculture in Arid Environment. International Soil and Water Conservation Research The assessment of water-borne erosion at catchment level using GIS-based RUSLE and remote sensing: A review. Oil Spill Mapping Using Hyperspectral Methods and Techniques. Remote Sensing Capability of Spaceborne Hyperspectral EnMAP Mission for Mapping Fractional Cover for Soil Erosion Modeling. Article Capability of Spaceborne Hyperspectral EnMAP Mission for Mapping Fractional Cover for Soil Erosion Modeling. Spatial and temporal dust source variability in northern China identified using advanced remote sensing analysis.
ANR HYEP ANR CEHyperspectral imagery forEnvironmental urban Planning HyepProgramme Mobilité et systèmes urbains Remote Sensing - Applications Mapping Soil Salinization of Agricultural Coastal Areas in Southeast Spain. An evaluation of Multiple Endmember Spectral Mixture Analysis applied to Landsat 8 OLI images for mapping land cover in southern Africa's Savanna. remote sensing The Lava Flow Field at Holuhraun, Iceland: Using Airborne Hyperspectral Remote Sensing for Discriminating the Lava Surface. RELATED TOPICS. Remote Sensing Geomatic Engineering Spectral Mixture Analysis High Spatial Resolution Artificial Neural Network Region of Interest Feature Space Land Use Land Cover.
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Vibration analysis is a process that monitors the levels and patterns of vibration signals within a component, machinery or structure, to detect abnormal vibration events and to evaluate the overall condition of the test object. Vibration analysis is a process that monitors vibration levels and investigates the patterns in vibration signals. It is commonly conducted both on the time waveforms of the vibration signal directly, as well as on the frequency spectrum, which is obtained by applying Fourier Transform on the time waveform. The time domain analysis, on chronologically recorded vibration waveforms, reveals when and how severe the abnormal vibration events occur, by extracting and studying parameters including but not limited to root-mean-square RMS , standard deviation, peak amplitude, kurtosis, crest factor, skewness and many others.
Time domain analysis is capable of evaluating the overall condition of the targets being monitored. In real world applications, especially in rotating machinery, it is highly desirable to incorporate the frequency spectrum analysis in addition to time domain analysis. A complex machine with many components will generate a mixture of vibrations, which is a combination of vibrations from each rotating components. Therefore, it is difficult to use only time waveforms to examine the condition of the critical components such as gears, bearings and shafts in a large rotating equipment. Frequency analysis decomposes time waveforms and describes the repetitiveness of vibration patterns, so that the frequency components corresponding to each components can be investigated.
Additionally, the well-established Fast Fourier Transform FFT technique facilitates fast and efficient frequency analysis, as well as the design of various digital noise filters. Vibration is a physical phenomenon that presents itself in operational rotating machineries and moving structures, regardless of the condition of their health. Vibration can be induced by various sources, including rotating shafts, meshing gear-teeth, rolling bearing elements, rotating electric field, fluid flows, combustion events, structural resonance and angular rotations. Because of its ubiquity, vibration is highly applicable for investigating the operational conditions and status of rotating machinery and structures. Vibrations can be represented in different forms, including displacement, velocity and acceleration.
Displacement describes the distance that the measuring point has moved; velocity describes how fast the movement is; and acceleration is self-explanatory. The three types are all widely used, specifically acceleration, which offers the widest frequency range and is extensively applied for dynamic fault analysis. Vibration can be measured through various types of sensors. Based on different types of vibrations, there are sensors designed to measure displacement, velocity and acceleration, with different measuring technologies, such as piezoelectric PZT sensors, microelectromechanical sensors MEMS , proximity probes, laser Doppler vibrometer and many others.
PZT sensors, the most commonly used sensor, generate voltages when deformed. The voltage signals can be digitalised and translated to represent the vibrations. Sensor installation is critical for ensuring that high quality data is recorded. The recommended method for installing sensors is to stud mount the sensor on a flat and clean surface on the machine. This ensures that a broad and smooth frequency spectrum is captured. When stud mount is not applicable, magnet holders, wax or glue can be adopted as substitutions with vibration levels and frequencies considered.
Vibration signals are usually below 20 kHz, except for certain vibration resonances that can reach beyond that. In practice, the sampling rate should be carefully chosen, to make sure that the bandwidth containing frequencies of interest are captured. Additionally, the recording length for one measurement should be at least several periods of the lowest speed of the machines. Vibrations can be described both in intensity by amplitude and in periodicity by frequency. Figure 1 shows the vibration time waveform captured from a moving mechanism. The time waveform is complicated by its speed-varying movement. The peak amplitude can be observed to be approximately 0.
Figure 2 demonstrates the frequency spectrum of the same signal. The dominant frequency is 30 Hz, which means the majority part of the mechanism movement vibrated 30 times per second. Figure 1. The vibration time waveform captured from a moving mechanism. Time domain vibration analysis is able to monitor vibration levels. Acceptable operation vibration limits can be pre-defined either through long-term operation and maintenance history or through referring to established standards. If the limit is breached, this could be that the overall health condition of the machine is deteriorating and defects have developed. Frequency domain vibration analysis excels at detecting abnormal vibrating patterns. For instance, a crack that has developed on a roller bearing outer race will lead to periodic collisions with bearing rollers.
In time waveform, this information is usually hidden and masked by the vibration from other sources. By studying the frequency spectrum, the periodicity of the collisions can be discovered and thus detect the presence of bearing faults. A vibration monitoring system is a complete system that is capable of acquiring vibration signals according to pre-determined parameters such as sampling frequency, vibration level, recording length, recording intervals and frequency bandwidths. The system should be able to process the recorded vibration and translate the information to intuitive indications for the machine operators, maintenance staff or asset managers. The system should not interfere the normal operation of the machines or structures that are being monitored and the benefits of the system should be higher than the cost of implementing the system.
Vibration analysis is predominantly applied for the condition monitoring on machineries and their key rotating parts, including but not limited to:. Vibration analysis has also been employed in structural health monitoring, including but not limited to:. A recently completed collaborative project focusing on the use of vibrational analysis for remote condition monitoring VA-RCM , and part-funded by the Technology Strategy Board and the Rail Safety and Standards Board, has successfully developed a system to detect wear and defects in train door machinery before breakdown occurs. Read more. Vibration-induced fatigue is one of the most common causes of failure in process piping systems. The resulting unexpected hydrocarbon release may lead to financial losses and impact both health and safety and the environment. TWI played a key role in an EU project that has developed an advanced condition monitoring system CMS and methods of continuously monitoring rotating parts in wind turbines.
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What is Vibration Analysis and What is it Used For? Facebook Twitter LinkedIn YouTube Instagram Print. Contents How Does Vibration Analysis Work? What is Vibration and What are the Types of Vibration? How Do You Measure Vibration? What Are the Characteristics of Vibration? What Can Vibration Analysis Detect? What is a Vibration Monitoring System? What Are Some Industrial Applications of Vibration Analysis? Advantages Limitations Insights Click here to see our latest technical engineering podcasts on YouTube. How Does Vibration Analysis Work? Figure 2.
The frequency spectrum of the same signal. Intelligent Vibration Analysis of Train Doors A recently completed collaborative project focusing on the use of vibrational analysis for remote condition monitoring VA-RCM , and part-funded by the Technology Strategy Board and the Rail Safety and Standards Board, has successfully developed a system to detect wear and defects in train door machinery before breakdown occurs. Vibration Analysis of Process Piping Vibration-induced fatigue is one of the most common causes of failure in process piping systems. Advanced Condition Monitoring of Rotating Wind Turbine Components TWI played a key role in an EU project that has developed an advanced condition monitoring system CMS and methods of continuously monitoring rotating parts in wind turbines.
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03/09/ · Understanding the processes governing soil community size and composition and the functional implications of biodiversity is challenging. Many microorganisms and soil animals utilize the same carbon (C) sources and mineralize at least a portion of that C to carbon dioxide (CO 2), but within any soil ecosystem, specialized organisms break down lignin, transform EBU R, K-system meter. RMS, true peak level, and clipping detector. SPAN is a free real-time “fast Fourier transform” audio spectrum analyzer AAX, AudioUnit, and VST plugin for professional sound and music production applications. SPAN provides you with a very flexible “mode” system which you can use to setup your spectrum analyzer Spectrum Analyzer. This audio spectrum analyzer enables you to see the frequencies present in audio recordings. The spectrum analyzer above gives us a graph of all the frequencies that are present in a sound recording at a given time. The resulting graph is known as a spectrogram. The darker areas are those where the frequencies have very low Spectral Mixture Analysis for Monitoring and Mapping Desertification Processes in Semi-arid Areas in North Kordofan State, Sudan The thesis is submitted for the degree of Doctor of 11/06/ · A Genetic Algorithm based Spectral Mixture Analysis Method for Hyperspectral Data 27/01/ · In this frame, spectral Mixture Analysis (SMA), Object-based oriented classification (Segmentation), and Change Vector Analysis are recently much ... read more
They correspond to the situation is very different from Fig. Capture screenshots of your desktop screen. Tanya Jane Pinsoy. If the limit is breached, this could be that the overall health condition of the machine is deteriorating and defects have developed. the network with greater between the probability of occurrence of each ES and the indepen- 2 Rpred between the probability of presence of a given ecological site dent validation points was analyzed using the Hosmer—Lemeshow and predicted outputs. The time waveform is complicated by its speed-varying movement.
Mac: fixed mouse wheel that required large movements to change parameter values. Remote Sensing - Applications Mapping Soil Salinization of Agricultural Coastal Areas in Southeast Spain. Dennison, P. Due to the lack of ground truth, few studies used it e. Maselli, F.
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