dataesr/scientific-paragraphs-categorization huggingface dataset:
However, when applied for regulatory purposes for compiling a NORM inventory by regulators and industry operators, a graded approach that includes iterative process is advised in application of NORM registers. This can be achieved by starting with a simple conservative assessment that is a screening stage with making use of generic input data, under the assumption of cautious exposure scenario. Then, if needed, increasing the complexity and realism of the assessment as necessary, which can be done by using more detailed questions in registers concerning data for realistic exposure scenarios. In this iterative process, the close collaboration of regulators, who should provide the advice and information on required screening and characterisation steps, and industry operators, who should provide the operational data to reply to the regulators requests, is facilitated when use NORM registers.
Target groups of end-users for NORM registers include: • International researchers (in the RadoNorm project and wider) that may collect comprehensive data sets from different NORM exposure sites in order to explore various issues, • Regulators that may use registers for conducting NORM inventories, especially in national situations worldwide where initial inventory is necessary/planned, while neither a precise methodology nor tools for NORM inventory are available. Moreover, NORM registers are foreseen to be useful for characterisation and decision-making concerning process of remediation at NORM-affected legacy sites, • Industry operators that may use registers for an initial screening or a systematic radiological characterisation and impact assessment of their sites, and, • Academics and international experts doing training courses on NORM inventory, exposure characterisation, but also teaching about specific industrial activities involving NORM and radiation protection issues.
Structure of NORM registers For each of the NORM involving industry categories listed in Section 2, a register was developed in the form of a spreadsheet software (Microsoft Excel workbook 1 ). NORM registers are made with a level of flexibility as they are intended to be used as generic tools for collecting available general information and data on specific industrial sector or NORM exposure category identified on the level of a country, but also as site specific tools where data from a specific facility can be gathered, when available. Each register contains three parts: 1) descriptive data, 2) quantitative data, and 3) predefined information to support characterisation of technological or other processes of concern. 1 More information is available in the supplementary material for this paper.
Main topics addressed by NORM registers The type of information and data to include in the registers, to support radiation protection of workers, public and environment, following the identification of NORM industries and exposure situations by means of the developed tiered methodology  (Michalik et al. , 2023) , were chosen as a result of a comprehensive international joint literature review (e. g. EC, 2014; IAEA, 2014; ICRP, 2019) and expert discussions. It emerged that, for any given NORM involving industry or situation, it is important to obtain information about the following main issues: 1. Legislative aspects of NORM management and regulatory approaches in the country, 2. Occurrence of NOR in different materials in industries and existing exposure situations, 3. NOR mobility and transport in the environment, monitoring programmes, 4.
Presence of non-radioactive hazards, 5. Dose assessment for workers, public and non-human biota, 6. Activity concentration of relevant radionuclides ( 238 U and 232 Th and respective progeny and considering possible disequilibria in the series, 40 K) in different material categories, and, 7. Destination and treatment of NORM residues and wastes. In the following sub-sections, the importance of each of these topics for the registers, along with the categorisations used for data collection, are discussed.
Its performing is a legal requirement in Europe according to the Safety and Health Framework Directive 89/391/EEC  (EC, 1989 (EC, , 2021)) , which obliges employers in Member States to conduct a workplace risk assessment. However, still in practice, exposure to radiation in a NORM context, in this dynamic risk assessment in industries, is often neglected. This neglection may be a result of lack of knowledge and information about NORM of the different involved parties, but also due to missing a properly adapted assessment methodology that would be available and user friendly for the mentioned parties and stakeholder groups with interest in risk assessments. In the RadoNorm project, following the data collection by NORM registers at identified sites, the characterisation of exposure scenarios and steps towards development of an integrated impact assessment procedure, for a general wider use and for different purposes, are aimed for multiple hazards including NOR.
In these activities, the existing dynamic risk assessment methodology for other types of hazards will be considered. Having knowledge and understanding of the NRH would allow optimization of management approaches while complying with radiation protection, health, safety and environmental standards. Therefore, with respect to both research and regulatory aspects, questions that would allow collation of qualitative information about presence of other NRHs at different NORM exposure situations and description of existing protective measures, which will be followed by quantitative data collation at chosen sites, have been included in the NORM registers. Examples of non-radioactive hazardous substances, identified for some of NORM involving mineral industries and legacies present in Norway, are given in the Table  3  (extracted from the Norwegian Pollution Release and Transfer Register, Norwegian PRTR, 2022).
Activity concentration of relevant radionuclides For each industrial activity involving NORM and other NORM existing exposure situations such as legacy sites and HBRA, quantitative data on activity concentrations of natural radionuclides can be collected by NORM registers. To avoid significant uncertainty in dose evaluation, only parent radionuclides of the most abundant decay series, 238   228 Th and 40 K, are required for raw materials, main and byproducts, residues, waste and discharges, for different exposure situations when using different NORM registers. For each radionuclide, data regarding minimum, maximum, mean and median concentrations are requested. The selected radionuclides are those having a half-life long enough to enable them to behave in the process and environment independently from their parents or having physical properties enabling specific migration pathways such as radon.
Additionally, radionuclide 220 Rn, with a short half-life of 55. 8 s  (Bé, et al, 2004)  was included only in registers for HBRA, legacy sites, mining industry, geothermal energy production and groundwater filtration activity, as it was considered that it can be of concern in these specific conditions. The collected data are intended to provide a quantitative overview, which will be further used for different case studies and analyses of processes of accumulation, mobilisation and transport of NOR in selected industries and surrounding environments, consideration of reuse possibilities for NORM residues as well as input data to the impact and risk assessment and evaluation of exposure doses and related risk in.
Possible drop-down menu answers include non-hazardous landfill, hazardous landfill, inert landfill, dedicated NORM landfill, repository for radioactive waste, sewage system, water bodiesthe sea, rivers, lakes, atmosphere; recovery or recovery before disposal options include incineration, reuse, recycling, reprocessing. At this level of data collection, such information is of importance to achieving a collection of best national practices regarding management options for NORM residue, which will eventually lead to improved management and regulatory approaches to NORM residues and waste internationally. Quantitative data of NOR in residues are expected to allow exposure dose estimations for public and NHB due to effluents and discharge in specific developed exposure scenarios, while data concerning waste will be used to evaluation of exposure doses at different disposal options in different scenarios.
Moreover, the results that will be obtained by application of these tools should provide an update of NORM exposure situations throughout Europe and potentially, eventually worldwide. The current work can also be seen as a preparatory work for the development of an international database of NORM in the future and for the preparation of industry-unified dose calculators. Finally, the developed methodology and register tools could be also helpful for worldwide countries in their dealing with NORM issues. • Polish Minister of Science and Higher Education programme "PMW" (international co-found projects) under grant agreement No 5156/ H2020-Euratom/2020/2021/2.
• POPH and national funds (OE), through FCT-Fundação para a Ciência e a Tecnologia, I. P. , in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the Article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19. We acknowledge financial support to CESAM by FCT/MCTES (UIDP/ 50017/2020 + UIDB/50017/2020 + LA/P/0094/2020), through national funds. • Joana Lourenço was funded by national funds (OE), through FCT-Fundação para a Ciência e a Tecnologia, I. P. , in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19. CESAM is a research unit financially supported by FCT/MCTES (UIDP/50017/2020 and UIDB/50017/ 2020), through national funds.
Acknowledgements This work has been supported by the  European Research Council  through the  ERC Starting Grant  No. MINOS-258567. J. C. , M. G. R. and A. M. M. acknowledge financial support by  MCIN/AEI/10. 13039/501100011033  under  I+D+i  project No. PID2020-114687GB-I00  and under grant  IJC2020-043878-I  (also funded by " European Union NextGenerationEU/PRTR") , by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 101023609 , by the  Consejería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía (Spain)  and " ERDF-A Way of Making Europe"  under  PAIDI  2020 project No. P20_01247 , and by the  European Social Fund and Junta de Andalucía  ( PAIDI 2020 ) under grant number  DOC-01006. J. G. , F.
2021 ) applied to observations that do not suffer from projection effects. Such high-latitude observations, when used concurrently with data from existing observatories such as the Helioseismic and Magnetic Imager (HMI), can generate definitive constraints on the polar meridional flow. Such measurements open up the possibility of (stereoscopic) helioseismic imaging of the plasma flows at deeper layers with greater fidelity; the deep component of the meridional flow is thought to play a critical role in the equatorward propagation and latitude of emergence of sunspots  (Nandy and Choudhuri 2002) , but its structure and variability remain ill-constrained. Solar internal rotation measurementsone of the greatest successes of helioseismology, is quite noisy in the vicinity of the poles.
Globally Coordinated Multi-Vantage Point Observations of the Heliosphere: We note that existing missions such as Parker Solar Probe, Solar Orbiter, Solar Dynamics Observatory, ACE, WIND, DSCOVR and upcoming missions such as CNSA Advanced Space-based Solar Observatory, ISRO's Aditya-L1 and ESA's Vigil offer the possibility of globally coordinated observations in conjunction with a novel solar polar mission. We recommend that this be taken full advantage of by cementing mechanisms for data exchange across mission teams and leveraging modeling and data analysis expertise across borders. This synergy may catalyze more ambitious, multiagency international programs for system wide exploration of Heliophysics through the deployment of multiple spacecrafts to cover the whole 4π steradian of the heliosphere (e.
Methods Three datasets were used in this research, each one with its own particularities. They contain the variation of I and V with time for a large number of charge/discharge cycles, for a set of batteries. The three datasets mainly differ from each other by the devices' compositions: Two of them (which are respectively named NASA  [31]  and MIT  [20]  datasets in the following) correspond to Li-ion batteries, while the third one was recently obtained for Na-ion batteries. They also display different charging/discharging protocols as shown on Fig. 1. In addition, the NASA dataset provides batteries cycled at various temperatures. More details about each dataset are provided below. For all datasets the Q nominal value used to calculate the SOH (see equation (  1 )) was the nominal capacity informed by the dataset authors, and the same value was used for all batteries in a given dataset, regardless of the measured capacity at their first cycle/BoL.
NASA dataset This first dataset is composed of 34 Li-ion batteries cycled at different temperatures, provided by the Prognostics CoE at NASA Ames  [31]. Batteries 49, 50, 51 and 52 were removed from our analyses because the data was corrupted or extremely noisy, as warned by the original authors ("Experimental Control Software Crashed") -meaning only 30 batteries were used. Batteries were cycled at different temperature ranges: low, at 4 ∘ C; room, between 22 and 24 ∘ C; and high, between 43 and 44 ∘ C. According to the authors, batteries were cycled until their SOH reached either 80% or 70% (depending on the battery). However, many batteries were cycled below this threshold, in particular those at low temperatures. All batteries shared a Constant Current (CC) -Constant Voltage (CV) charging protocol, where a CC was maintained at 1.
5 A until the voltage reached 4. 2 V, which was then maintained until the current reached 20 mA. Note that CC steps are often characterized by the C-rate instead of the current. This quantity is defined as the current normalized by the theoretical current under which the battery would deliver its nominal capacity in one hour (e. g. C-rates of 0. 5 C, 1 C and 2 C respectively correspond to charging times of 2 h, 1 h and 30 min). A typical charge cycle is shown in Fig. 1a ). The discharge protocols were different for each battery group but, as will be described in section 2. 5, they weren't used by the model. Capacity measurements (Q) were only done at the end of discharge cycles, so the charge cycle immediately after it was used as the input for the SOH estimation. 1 This same procedure was used for the following datasets, for consistency.
MIT dataset This dataset is a fraction of the one developed for Severson et al. article on battery cycle life prediction  [20]. Specifically, only the batch "2017-06-30", composed of 48 Li-ion batteries, was used. This was named as the "MIT" dataset for simplicity, following the first author institution. In this dataset, the charging protocol varies for each battery. It is a fast-charging protocol that lasts between 20 and 40 min, composed of 1 or 2 CC steps, varying in current intensity and duration, run until the battery State of Charge (SOC) reaches 80%. After a rest period where no current is passed through the battery, a final CCCV stage is then performed, with a CC step of 1. 1 A until the voltage reaches 3. 6 V, maintained until the current reaches a cut-off value. The Q nominal value used was of 1.
1 Ah. Fig. 1b ) shows the raw data for a typical charge cycle. In the present work, we decided to consider only the CCCV step for analyses. That way, all of the batteries in the dataset would have a similar and comparable input, since this last stage does not vary from one battery to another. Furthermore, we were interested in seeing if disregarding the path taken by the charge curve before reaching 80% SOC would be enough for the model to correctly estimate battery SOH, meaning that partial cycle data could be used for training the model. As it will become apparent in the later sections, this was enough -which also means that we could have used, for example, only the first part of the charge cycle.
Na-ion dataset This dataset is composed of 33 sodium-ion (Na-ion) coin batteries made with varying electrolyte/electrode compositions  [32]  -each one is made up of a different combination of solvents, additives, have different electrode masses and coating. Each cell is cycled at 55 ∘ C. This dataset charging protocol differs from the others since there was no CV step, but only a CC one (mostly at a C-rate of 0. 2 C, although it varied for some batteries and cycles) until the voltage reached 4. 3 V. The Crate and Q nominal varied slightly since batteries had different electrode masses, which was accounted for in the next sections and for calculating the SOH, as previously mentioned. The Q nominal ∕mass ratio used was of 0. 1284 Ah/mg. Batteries have a formation step composed of 10 cycles before a rest period of one week, after which cycling resumes as normal -this is the reason why plots display a discontinuity after this period.
An example of the raw data is shown in Fig. 1c ). The MIT dataset has one or two CC steps until the battery reaches 80% SOC, after which they go through a rest period before a CC-CV protocol is maintained. In order to test the model capability and to favour its comparison with the NASA dataset, only this last part is taken into account in our model. Finally, the Na-ion dataset is charged with a single CC step. 1 Interestingly enough, picking the charge cycle immediately before the discharge capacity measurement resulted in a model with a delayed response (by exactly one cycle) showing how good the model was capable of generalizing the data.
Training and Validation set split The available data is split into a training and a validation dataset, the former being used for the model parameter optimization and the second one being used to assess the model generalization capabilities. Only validation results should be used for ranking a model performance, since models are prone to overfitting their parameters to the training set. Usually, around 60-80% of the total amount of data is reserved for the training set, while the rest is used for validation. However, it is important that the operating conditions used in the validation data agree with the ones used for training, since models can't be excepted to perform well in circumstances never seen before. Sampling the data randomly is usually a good solution, but may not be adequate for all applications.
In this work, this would imply sacrificing the results interpretability and visualization, since it would correspond to randomly "taking out" some of the battery cycles for training, and graphical results would have large amounts of gaps between cycles used for validation. Because of this, batteries (i. e. all of the battery cycles) were either in the training dataset or in the validation dataset. The training batteries for each dataset were picked in a way to maximize the operating conditions ranges (i. e. I, V, SOH and temperature), so that the validation dataset would not bring anything out of the trained scope. Preliminary tests were done with different battery combinations in order to check for model convergence. Table  1  shows the final number of batteries used for training and validation datasets, as well as the number of cycles available in total.
The MIT dataset only counts one battery for training because it was very homogeneous and we were interested in testing if using only one battery would be enough for training the model. In effect, training with only one battery or with several of them yielded similar results, and so we present the results that use only one battery for training to show how it can be effective with such a homogeneous dataset. We also state that the battery used for training was the one that covered the largest SOH range, meaning that no other battery had a SOH out-of-bounds. Not all available batteries were included in the validation dataset for more efficient computation time, although results on these batteries is assessed later as a "test" dataset, containing 33 batteries and a total of 14427 cycles.
Model While research on new Deep Learning architecture and features engineering for SOH estimation remains very active (see e. g Ref. [33]  and  [34]  and references therein), the purpose of the current work is not to provide a new NN architecture for SOH estimation. Instead, based on its good performance, we have used as a starting point the Recurrent Neural Network (RNN) architecture proposed by You et al. [28]  to study its application to different datasets. The choice of model and type of NN is completely dependent on the variables used for SOH estimation and how they are chosen to be treated, which can be illustrated by the use of an FFNN in a previous work of You et al. [27]  using a different approach. We note that our normalization parameter training approach for transferring model knowledge should nevertheless be model-agnostic since, as will become clear in section 3.
That is, instead of only using one I(k), V(k) couple per entry ( = x k I k V k ( ) [ ( ), ( )]), they would include I(k) through I(k + δ -1) and V(k) through V(k + δ -1). The full discretization process, as well as the input definitions, are shown in Fig. 3. In this work, we have used δ = 10. The sequence of x k ( ) vectors makes up matrix x, which is used as input for a single SOH estimation. To follow SOH evolution, many charge cycles are needed. From here, our approach differed from the original work. The charge time varies according to a) the battery; b) SOH; and c) charging protocol. In their article, You et al. have also worked with partial charging data, increasing this variability even more. [28]  That is not a problem for the RNN since it can handle a variable sequence length. However, this approach did not work well with the NASA dataset used for testing the model architecture.
The network did not learn or generalize well. Because of this, we have used a fixed sequence length (N = 150), ignoring the rest of the cycle data, which yielded a better agreement. In order to guarantee that all charge cycles would have at least 150 + δ points, the Δt value would be picked conservatively, in function of the training dataset mean charge cycle duration. In the end, the resampling time step Δt would still be larger than the data acquisition sampling rate, and the model was able to correctly estimate SOH with the partial cycle data (just the first 150 points). These are two advantages of this data-driven approach when compared to methods like Coulomb Counting, referenced earlier, since less measurements are required.
The arrowhead on top of z is a reminder that the result is still a vector (with the same length as any h k ( ) or h k ( )). The final model used had a total of 28851 parameters. = = z N h k h k 1 ( ) ( ) k N 0 (2) In You et al. study, this architecture was sufficient for handling data at different temperature conditions  [28]. However, when training the models for the reference dataset, a clear temperature bias arose: the SOH would always be underestimated for batteries at high temperatures, and overestimated for those at low temperatures, even though the dynamics of the SOH was accurately predicted. Because of this, we included the temperature at which the batteries were cycled as an input to the last linear layer, adding one single parameter to the model, which corrected the bias. The original version of the model was still used for the datasets where all batteries were cycled at the same temperature (MIT and Na-ion datasets, Sections 2.