dataesr/scientific-paragraphs-categorization huggingface dataset:
Geological framework The original geological model is based on a region in the Hamersley province (Western Australia). It was built using the map2loop algorithm  (Jessell et al. , 2021b)  to parse the raw data and the Geomodeller ® implicit modelling engine for geological interpolation  (Calcagno et al. , 2008; Guillen et al. , 2008)  to model the contacts, stratigraphy, and orientation data measurement in the area (see the geographical location in Fig. 2 ). Data used to generate the model comprise the 2016 1 : 500 000 Interpreted Bedrock Geology map of Western Australia (https://catalogue. data. wa. gov. au/dataset/ 1-500-000-state-interpreted-bedrock-geology-dmirs-016, last access: 2 December 2020) and the WAROX outcrop database (https://catalogue. nla. gov. au/Record/7429427, last access: 2 December 2020).
Geophysical simulations and model discretisation The core 3D model is discretised into N x ×N y ×N z = 103× 113 × 33 cells of dimensions equal to 999 × 996 × 745 m 3. For both gravity and magnetics, we generate one geophysical measurement per cell along the horizontal axis, leading to N x × N y = 11 639 data points for each method, and add 10 padding cells in each horizontal direction to limit numerical border effects in the forward calculation, leading to a total of 123 × 133 × 33 = 539 847 model cells. We simulate a ground gravity survey by locating the measurements 1 m above ground level, and aeromagnetic data acquired by a fixed-wing aircraft flying 100 m above surface. To test the robustness of our inversion code to noise content in the data, the geophysical data inverted here are contaminated by noise.
2D model simulation in a 3D world As mentioned above, we perform the inversion of geophysical data along a 2D profile for simplicity and to simulate the challenging case of 2D data acquired in a 3D geological setting, in a part of the model where subhorizontal or gently dipping features can be observed. The philosophy of the numerical study presented here is summarised in Fig. 5. The 2D geological Sect. considered is shown in Fig. 6  (the black line marked in Figs. 3  and 4 ). Geological certainty is estimated using a measure of the dispersion away from the perfectly uninformed case where the all rock units are equiprobable. In each model cell, this measure, which we write as σ ψ , is calculated as a function of the standard deviation σ ψ of the probability ψ of observing the different rock units as follows: σ ψ = 1/card -σ ψ , ( 18 ) where card is the geological cardinality of the model.
Future research includes the utilisation of implicit geological modelling (in the sense of  Calcagno et al. , 2008)  with Tomofast-x to define geological structures and rules that inversion will be encouraged to follow. It also comprises the incorporation of topological laws previously used a posteriori  (Giraud et al. , 2020)  directly into inversion. The electrical capacitance tomography component of Tomofast-x  (Martin et al. , 2018) , which we have not detailed here, can be extended to acoustic (seismic) or electromagnetic data inversions that rely on the resolution of similar non-linear inversion problems. It opens the door to more versatility in the code and can be applied to joint inversion in similar ways but on more than two physical domains. In addition, future developments comprise the collaborative and joint inversion of seismic and potential field data.
It is planned to develop an interface between Tomofast-x and Unisolver (not yet released as open source by its authors), which is an extension of Seimic_Cpml code  (Komatitsch and Martin, 2007; Martin et al. , 2010 Martin et al. , , 2019)) , where integrated seismic imaging solvers are implemented. Unisolver is a multi-purpose 2D and 3D seismic imaging platform based on high-order finite-difference and finite-volume discretisation and non-linear seismic data inversion procedures. Such an interface would allow performing collaborative or joint inversion of seismic and gravity or magnetic data and could obtain the resulting models on the same mesh while benefitting from Tomofast-x's various functionalities. This will be an easy way to provide Tomofast-x with separate seismic information on the fly like sensitivity kernels as another physical domain.
Conclusions We have introduced the open-source joint inversion platform Tomofast-x and demonstrated its capabilities with a realistic data set taken from the Hamersley region in central Western Australia. The geophysical theoretical background of Tomofast-x was explained in depth to guide users in understanding and using the modelling approach implemented in the source code. We leveraged the modularity of Tomofast-x to study the sensitivity of inversion to prior structural, geological, and petrophysical information; joint inversion; and the code's scalability. We tested a new combination of constraints incorporating geological structural information in the smoothness term and dynamic prior model definition using petrophysical knowledge (ADMM bound constraints), a feature usually not available to most inversion software.
A2 Jacobian matrix truncation Tomofast-x offers the possibility to use a moving sensitivity domain approach  ( Čuma et al. , 2012; Čuma and Zhdanov 2014) , limiting the sensitivity domain to a cylinder, the radius of which is chosen by the user, to reduce computational requirements (the option for a sphere is also present in the source code but commented in the current version). The underlying assumption is that cells beyond a given distance exert a negligible influence on the measurement. Generally speaking, this radius is provided by the users and should be chosen carefully.
Code and data availability. The source code for Tomofast-x as used in this paper can be found in Ogarko et al. (2021b,  https://doi. org/10. 5281/zenodo. 4454220). The latest version of Tomofast-x is available at https://github. com/TOMOFAST/ Tomofast-x (last access: 7 September 2021). The geological model, a description of the input data, and the geophysical models are given in Jessell et al. (2021a,  https://doi. org/10. 5281/zenodo. 4431796). It also contains a data set using the same model projected onto a finer mesh of approximately 4. 2 M cells and 80 000 geophysical data. The data sets are licensed under the Attribution-ShareAlike 4. 0 International (CC BY-SA 4. 0) license (see https://creativecommons. org/licenses/ by-sa/4. 0/legalcode, last access: 7 September 2021, for details). Tomofast-x's source code is licensed under the MIT License (https: //opensource.
The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the.
performance loss rate value. In this study, several high-quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually.
| INTRODUCTION The calculation of the evolution of a photovoltaic (PV) system's performance is crucial to (i) evaluate if a system is operating within the boundaries of long-term yield assessments and warranties and (ii) provide more accurate values to be used in yield assessments not only in terms of absolute value but also in terms of uncertainty. In order to be able to judge a system's performance, the performance loss rate (PLR), which is provided in units of %/a, or %/year, must be calculated in an accurate and well-documented way and with its uncertainty reported. The PLR does not just represent the irreversible physical degradation of PV modules; it also measures performancereducing events, which may be reversible or even preventable through good O&M practices. Data availability, accuracy, and temporal resolution have to be taken into account when choosing and carrying out the necessary steps to calculate PLR values.
Here, we present PLR results on 19 real-world datasets and four datasets from a simulated, or "digital," PV power plant with a predefined degradation rate. The contributors were asked to apply their preferred metrics and statistical methods resulting in the use of 11 data filtering approaches, eight performance metrics, and nine statistical modeling methods. Two additional tools from this comparative study were the development of an A to D grading system based on data quality metrics of percentage of outliers, missing datapoints and data gaps, and an R package for PLR determination using multiple metrics and methods for PV time series data. The results were then compared to identify the PLR that is the most commonly reported PLR value for that system, and we use this averaged PLR as the most likely approximation for the unknown "true" PLR value.
Our goal is not to determine a single highly performing filter + metric + correction + model approach, which is based on the results probably not existing. Instead, we want to provide tools to PV researchers and PV plant owners to assess the performance of all their systems, even ones with realistic, and partially severe, data quality issues as well as to enable a cross-comparison among systems. We also analyze uncertainties by comparing the standard deviation of the input datasets, and the standard errors and confidence intervals of the PLR results from multiple metrics and methods, to illuminate relative performance differences among these multistep PLR determination approaches.
| DESCRIPTION OF DATASETS FOR PLR BENCHMARK Nineteen datasets were made available to the interlab benchmarking participants. An overview together with the most important information can be found in Table  A1. The climate zone categorization used in Table  A1  is based on the well-known Köppen-Geiger classification,  [3] [4] [5] [6]  and we include the new KGPV classification which includes irradiance intensity (L-K) but has reduced weather distinctions (A-F). In the following, the PV systems are briefly introduced, and Figure  1  shows their geographical distribution. All datasets are publicly available at https://osf. io/vtr2s/.
Data resolution: The time interval of collected data. Typical values range from 1 to 15 min but can vary between 1 s to one or more hours, depending on the hardware and user settings. Typically, highresolution data are desired since they provide more detail on the PV performance and also improve the model fitting due to the larger amount of data. Time length: The total operating time of a system. For understanding the long-term performance of systems, the obvious choice of data would be from systems that have been operating for long periods of time. The absolute minimum of available data is 2 years, as a linear PLR describes a yearly change in performance. For reliable PLR evaluation, at least 4 to 5 years of data should be available. 7 ailable variables: Power (P), irradiance (G), and temperature (T) are the foundation of most PLR analyses, but they can be measured in a number of different ways.
Power can be recorded at the AC or DC side or represented as energy accumulation instead of a power reading. DC power is desired over AC power readings in order to remove inverter influences. Irradiance can be reported as global horizontal irradiance (GHI) or plane-of-array (POA) irradiance. Temperature generally refers to ambient or module temperature, typically measured by a thermocouple. Backsheet temperature measurements of single modules may not be representative of the whole array in given PV system, particularly in larger plants. Additional variables including wind speed and direction, current, voltage, rainfall, and air mass can all be used in certain types of PLR analysis, depending on availability. Lastly, metadata are also important, for example, module and inverter characteristics, location, scale, and orientation.
Collection quality: Events such as missing values or gaps, reading errors, or sensor drifting are all commonly observed in PV system data. This is why the proposed PV data quality grading is a useful measure to better understand why one system can be modeled eas- for some of them. However, PLR cannot be calculated reliably in data sets with large proportions of anomalous or missing data or large time gaps in the dataset. Regular maintenance of sensors, site performance, and observation of data collection can reduce the impact of these issues.
| EURAC PV system The EURAC PV system was installed at the airport of Bolzano/Italy (ABD) in 2010. The polycrystalline system has a nominal power of 4. 2 kWp. The system is ground mounted with a fixed tilt of 30 and an orientation of 8. 5 west of south. Additionally, a weather station is installed in close proximity to the test side. Various meteorological parameters are recorded such as POA irradiance, ambient temperature, and wind speed. On the rear side of the system, the module temperature is measured. The sensors are systematically cleaned and periodically calibrated in order to comply with the IEC 61724-1:2017 standard. 8 The weather data are recorded with a time interval of one minute. Since the electrical measurements are taken at time intervals of 15 min, all values are averaged to the same time interval.
| FOSS PV system The FOSS PV system was installed at the outdoor test facility of the University of Cyprus (UCY) in Nicosia, Cyprus, and was commissioned in May 2006. The climate in Nicosia, Cyprus, is characterized as hot semiarid. The PV system dataset used in this investigation is obtained from a ground-mounted monocrystalline silicon (mono-c-Si) system that is rated at 1025 Wp, as depicted from the manufacturer's datasheet. Furthermore, the PV system is installed in an open-field mounting arrangement due South and at the optimum inclination angle of 27. 5. The monitoring of this system started in June 2006, and both climatic data and operational measurements were acquired and stored. More specifically, the electrical performance of the system along with the prevailing irradiance and environmental conditions were recorded according to the requirements set by the IEC 61724-1 standard standard  8  and stored with the use of a robust measurement monitoring system.
| US DOE RTC baseline systems The US Department of Energy (DOE) Regional Test Center (RTC) Project has five sites. 9 At each site, there are two systems that are used as the "baseline systems. " This dataset is currently version 0. 2 and consists of a series of eight PV systems in four locations/climate zones, 3. 24-kW strings with the same module and inverter at each site. The data are 1-min time series inverter data with ground and satellite weather data. These systems have proved to be useful for research purposes given that they are nearly identical at each climate zone, including the same number and brand of modules and inverters.
| NREL systems Four PV systems from the US National Renewable Energy Laboratory (NREL) were included in the benchmark study, all located at the NREL main campus in Golden, Colorado. Short time interval 1-min data are collected for three systems, with 15-min data collected for the fourth and largest system. System #1 is similar to the RTC baseline systems described above using the same PV module type in one string of 10, total system size 2. 7 kWdc, 3 kWac, with data available from April 2016 until July 2019. System #2 is also a small research system using a string of five silicon heterojunction modules, 1 kWdc, 1. Systems #3 and #4 are larger building-mounted systems with lower quality silicon photodiode G POA irradiance measurement. A nearby weather station provides calibrated GHI, wind speed, and ambient temperature.
| Digital power plant The simulated "digital" power plant has been created with EDF R&D's tool "PV NOV. " It consists of a string of 10 PV modules in series, with the following characteristics: • From datasheet: P mpp = 180 W (+/-3%), I sc = 5:29 A, V oc = 44:8 V • From Flash simulated: P mpp = 182 W, I sc = 5:44 A, V oc = 44:8 V The string is connected to a 2kW inverter. The behavior of the plant is simulated with a Dymola/BuildSyspro Software, developed by EDF. 10, 11 The model for the PV modules is a two-diode model. Solar data were derived from HelioClim with a temporal resolution of 15 min. All in all, four PV systems have been created with the following settings: • 1-year weather data repeated for 5 years with and without known degradation; • 5-year weather dataset (real weather conditions given by Helioclim for a period of 4 years and an added fifth year which is the minimum value of each previous years) with and without known degradation.
The degradation of PV panels is simulated with a linear variation of parameters: short-circuit current (I sc , initial value: 5. 44 A, variation of -4%/a), series resistance per cell (R scell , initial value: 0. 00012 Ω. m 2 , variation of +7%/a), shunt resistance per cell (R shcell , initial value: 0. 14745 Ω. m 2 , variation of -6%/a), and the temperature coefficient of the I sc (α Isc , initial value: 0. 037%/K, variation of -1. 5%/a). Those values were chosen to induce an overall high degradation of the output power in a scenario that belongs to a bigger experiment including a wide range of degradation cases. The theoretical degradation for the simulated dataset with induced power loss was calculated from two viewpoints: • @STC the P mpp degradation is -4. 41% per year. • Absolute energy degradation is -4.
First, we have to understand which data are available and the format conditions of our raw data. A quality assessment of measured data is always recommended and will ensure a smooth application of the steps to follow. To characterize the quality of time series datasets, we use a grading scheme that assess a dataset for outliers, missing datapoints, and larger gaps in the data. Next, we apply filters to extract the essence of our data. This step is performed to get rid of outliers, measurement errors and nonrepresentative data. Usually irradiance, power, temperature, and performance ratio (PR) are considered. In cases where local weather data (irradiance and temperature) are not available, it is possible to use interpolated (weighted regression) data of different peered weather stations in relatively close proximity to the test side, satellite, or reanalysis-based datasets or clear-sky modeled data.
| Inverter saturation and curtailment Inverter saturation occurs in a PV system when the power output produced by the modules is higher than the allowed AC power output of the inverter. At this point, the inverter will be "saturated" and the power output will be maintained at this maximum value and will not be able to increase, even if the module DC power increases. Curtailment is commonly used to stabilize the power output of PV plants and increase the capacity factor, making the systems easier to integrate into existing grids, but proactive curtailment can lead to reduced availability. As such, inverter saturation is most commonly observed in larger scale commercial PV systems. Saturation poses a unique problem in PV data analysis as it occurs at higher irradiances, when systems are assumed to perform under ideal operating conditions.
| Performance metric IQR filters Power P and performance ratio PR are the most common performance metrics used. PR is a unit-less parameter, which describes the relationship between incoming irradiation and produced power by a PV system. Since power and irradiance follow a nearly linear trend over a wide range of irradiance, this relationship can be used to detect and STL7 and STL8 follow the exact same approach including filtering and metric. The only difference is that STL7 uses the STL function of the R stats package  24  implemented with the rstl package  25  in Python and STL8 the function directly implemented in the statsmodel package  26  of Python.
| PR models The PR at the DC side is calculated by 8 PR DC = Y a Y ref = E DC =P nom H POA =G STC , ð3Þ where Y a is the array yield, and Y r is the reference yield; E DC is the DC energy produced over a certain time t; P nom is the nominal power at STC; H POA is the POA irradiation over a certain time t; and G STC is the irradiance of 1000 W/m 2. We have decided to use DC values to eliminate losses due to DC/AC conversion. The PR can be corrected for temperature  8  using temperature coefficients as provided by the manufacturers or obtained from the time series data to better reflect the actual outdoor performance of the module (PR + temperature correction [PR Tcorr ]). The advantage of correcting temperature based on power data over the PV power plant's lifetime is the large range of available temperature, increasing the certainty of the power versus temperature trend.
) of the final PLR. This statistical method is suitable for time series with a seasonal behavior and where the data are of high quality. 1 Y: The YoY approach for PLR determination was first applied by Hasselbrink et al. 41  and is now available in the RdTools package  20  in Python and the PVplr package in R. 22  In YoY, the differences between one datapoint in a calendar year with the datapoint at the same position in the subsequent year are accumulated over a 1-year period. The median value of these multiple yearly PLR represents the overall system PLR. The PLR of the YoY method is normalized to the first year's median, though one can choose not to normalize. The confidence interval is calculated using a Monte Carlo or bootstrap resampling of the distribution. 20, 21, 35 R method: The VAR method gives degradation rates from one year to the next.
Therefore, this method is suitable for the analysis of distributed rooftop PV systems and the more highly instrumented and well-modeled centralized PV power plants. In addition, irradiance sensors can themselves be treated as a PV power signal source, allowing the automated analysis of sensor drift. Finally, the results based on the real PV power time series datasets are examined in Section 4. 6 including a discussion on the impact of data quality with respect to the magnitude and uncertainty of PLR.
STL is a locally weighted regression, which extracts a nonlinear trend from a dataset by excluding the remaining seasonality and the residuals. This nonlinear trend can then be fitted with a simple linear model using regression to determine the best fit linear trend line, visible in Figure  3F. The yearly aggregated gradient of the linear function, divided by the intercept, is the final, assumed linear, relative PLR of our system (see Equation  1 ). The intercept of the function represents the PR value at the starting time of the time series. For this system, a relative PLR of -0. 90%/a was calculated using the approach explained above. For the associated uncertainty of the PLR value, the residuals are added back into the STL trend component. The uncertainty between the STL combined component and the linearized trend is ±0.