dataesr/scientific-paragraphs-categorization huggingface dataset:
2 and 2. 3). The architecture was then modified in the last part of the study for normalization parameter training, it is described on Fig. 5. In this approach, only the normalization parameters were tuned, meaning that all neural network parameters remained unchanged. In that sense, this approach only trained a total 42 parameters (because the x vectors represented in Fig. 3  have a size of 20, this means that both μ and σ represented in Fig. 5      were added as PyTorch parameters to the network class, but the LSTM and linear layer parameter training was disabled. That way, only the newly defined parameters were updated during training. All training was done using GPU acceleration available through Google Colab (a typical training took between 2 and 6 min depending on dataset size). The various models implemented in this work and the Na-ion dataset are available at https://gitlab.
Reference models In a first step, reference models were trained separately with each one of the different datasets, and will be referred to by their dataset name in the following (NASA dataset reference model becomes "NASA model" and so on). Note that the NASA model included temperature dependency at the final linear layer, while the others did not. NASA and MIT models achieved, respectively, 2. 40% and 1. 21% mean absolute error (respectively 4. 11% and 1. 08% in mean relative error, MRE) for the estimation of the SOH on their validation datasets, which are similar to the reference article average results (2. 46%). The Na-ion models, however, had trouble learning and had an average absolute error of 3. 79% (or 7. 66% MRE). This is likely due to the fact that the corresponding charge protocol was only composed of a CC step, meaning that the model had no way of extracting any information from the current (since it was always constant for each cycle).
Even if the actual value was different for some cycles and batteries, the dataset did not have enough variability for the model to learn from. ML models are not designed to work under conditions different from their training dataset. In particular, nothing guarantees that a model will be able to interpret data outside of the value ranges it was trained on -it is extremely likely that it will fail. In the following subsections, we study the feasibility of transferring a model from its training scope to new conditions. In practice, the approaches studied here are actually based on translating the data itself to the model training scope, so that they can be better interpreted, rather than the opposite. As it will become clear, nothing about the model is changed, only how the data is pre-treated before being fed into it.
In this work we propose that, if the data distribution (in this case, the distribution of the (I, V) data) is sufficiently close from one setting to the next (one battery chemistry to another, or eventually one cycle protocol to the other), we should be able to effectively translate the new situation to our original model, being able to use it, just by updating the normalization parameters (a procedure we will call hereinafter "renormalization"). The renormalization procedure we propose is to update the normalization parameters (the mean and standard deviation) according to the new incoming data. If the charging protocols are sufficiently similar between the training and new datasets, the data distribution should have a similar shape, but different values (like two normal distributions, each with a different mean and standard deviation).
If two distributions of the same type ("shape") are respectively standardized according to their own corresponding mean and standard deviation, the result are two identical distributions with a mean of 0 and standard deviation of 1. This means that normalizing the new dataset with the original parameters (μ v , σ v ) will yield a distribution that is out of the model scope, while normalizing the new data according to its own mean and standard deviation (μ v* , σ v* ) will most likely yield a distribution closer to it (since the model scope is a standardized distribution). This can be seen in Fig. 6d ), where the individually normalized distributions show a good overlap one with each other. We can see that the MIT and NASA datasets, which have the same type of charge protocol, have more similar distributionsthough NASA data have a longer "tail" (wider range of voltage), possibly due to the difference in SOH range observed in the datasets and/or the difference in SOC range in the data used for analysis of each dataset.
We can also see that the Na-ion dataset does not have a big voltage peak because its charge cycle does not include a CV step (although it has a little peak due to the asymptotic nature of the voltage evolution, as can be seen in Fig. 1 ). Despite that, the "hill" seen close to the value of 0 of normalized voltage is similar to the other distributions, which relates to the CC part. Note that this method does require data from the new dataset (we use the normalization parameters calculated for a dataset training set), in order to estimate these parameters, but it remains much less involving than performing a whole new training set and easier than training a new model. Figure  6  shows some of the results obtained with this approach as well as the individually normalized voltage distributions.
For the NASA and MIT target datasets, the predictions follow the SOH evolution relatively well, although they seem to have an offset. In particular, the Na-ion model fits the latter surprisingly well, with an average error close to MIT reference model (1. 23% MRE, against 1. 08%), which is better than NASA results (5. 91%). A similar tendency is seen for the Naion target dataset, although in this case the shape of the curves resembles the reference model better than the experimental data. These results show that the renormalization procedure could be used for reproducing variable evolution across different datasets. However, the offset prevents this approach from being more precise. The fact that the tendencies are well captured but that the results have an offset seem to indicate that the renormalization of the input variables was relatively successful, but that the mean value used for the SOH resulted in an underestimation of its true value.
Normalization parameter training In order to push further this approach and maximize the accuracy of the models by simply tweaking mean and standard deviation values, in a second step we have decided to include them into the model as trainable parameters. In this approach, we use the reference models as a base -serving as pre-trained networks -and optimize (fine-tune) solely the normalization parameters according to the targeted dataset (using its training set). The use of pre-trained models is a common Transfer Learning practice, and is often used in image processing applications. The idea is that the deepest layers learn how to extract features from the input, and the final, more shallow ones, are tasked with actually interpreting this information. That way, only retraining these last few layers is usually enough to change the scope of the model  [36].
For example, in the field of image recognition, this could correspond to taking a model trained to recognize dogs, and retraining its last layers to recognize cats instead. Our approach is relatively different because the parameters we retrain are the mean and standard deviation values used for normalization (which, of course, lose this statistical meaning in the process). This is interesting because the actual neural network parameters are kept untouched, and remain exactly the same as the reference model ones. The idea is to see just how far the renormalization procedure can enhance the models precision. Figure  7  shows this approach results after retraining each of the reference models normalization layers to the other datasets, as well as optimally normalized voltage distributions for the Na-ion model (each model has its own set of optimal normalization parameters).
It is immediately clear that the tuning of the normalization parameters solves the offset problem of MIT and Na-ion models transferred to the NASA dataset. The distributions seem to have less overlap when compared to the renormalized ones. This escapes our physical/statistical expectation and interpretation of the problem, but is not necessarily surprising because it is a result of a data-driven optimization of model error. Table  2  summarizes the models SOH average relative estimation error (MRE) for each set. Tables for the Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE) can be found in the Supplementary Information. The table shows results for the training sets for comparison, but the following analyses are done for the validation and test sets. For NASA target dataset, the MIT model came closer to the reference in terms of precision, achieving an MRE of 4.
74% (compared to 4. 11%). The Na-ion model got an average error of 5. 91%, which is extremely good considering the chemistry and protocol differences. Both results are also very good when we consider that those models did not include any temperature dependency, as opposed to the reference one. Looking into the mean results for each individual battery, we see that both models had mostly good results (under 2. 3% relative error) for batteries at high and room temperatures, and inconsistent ones -varying greatly from one battery to the other -for low temperatures. This behaviour is similar to the one presented by the reference model (despite its temperature dependency), and is expected since SOH estimation at low temperatures is usually considered as a difficult problem  [37]. Models had the best results when transferred to the MIT dataset, going as far as beating the reference model in terms of precision.
It is not fully understood why the transferred models end up having a better result. It could have to do with each model original training and variability (all batteries in MIT dataset had a very similar SOH evolution, and only one battery was actually used for training), or with the normalization parameters' flexibility in this approach. Transferring models to the Na-ion dataset had the worst results overall, with relative errors around 10%. A natural question that arises is why did the Na-ion reference model adapt better to the Li-ion datasets (even beating MIT reference model at its own dataset) than the opposite. It is possible that the first ten cycles of the Na-ion batteriestheir formation step -have a different electrical response than the other ones, which was overlooked and possibly hindered model transferring, as no formation step was present on the other datasets.
However, the Fig. 6. Selected results of the renormalization approach for the a) NASA; b) MIT; c) Na-ion target datasets. Panel d) shows the normalized voltage distribution for each training dataset. We can see for the NASA dataset that the MIT and Na-ion models estimate the evolution very well, but have an offset. This seems to indicate that the renormalization of the input variables was relatively successful, but that the mean value used for the SOH resulted in an underestimation of its true value. Na-ion model was still able to adapt well to the Li-ion ones, despite this difference. This behaviour is thought to be caused by protocol differences -and not due to the chemistry. Both Li-ion datasets have a CCCV charging protocol, meaning that their models are not capable of yielding a complete estimate for the SOH without CV step data -since they are trained on it.
Cross-Validation for the Na-ion dataset Considering the poor results observed for the Na-ion model, as well as the fact that the presence of formation cycles had been overlooked, a cross-validation technique was used for training a new reference model for the Na-ion dataset. A cross-validation method consists in devising a few different ways that the dataset can be split into the training and validation datasets, and training the model in each one of these combinations. Fig. 8  shows the splits used for this section. It must be noted that, due to the heterogeneity of the number of cycles available per battery, it was difficult to devise combinations of batteries that had a balanced data split, which is one of the reasons why this method wasn't originally employed. Batteries 4 and 12 are included in every split because they have the maximum and minimum SOH values observed throughout the whole dataset (meaning that no battery of the validation dataset would fall out of the training range).
The base model was trained different 5 times (to account for parameter initialization) for each one of the splits (a total of 50 times) using the original Na-ion dataset as well as a modified version which did not include the formation cycles. Results for the best model for each cross-validation run and dataset variant are shown in Table  3. For the models trained with the dataset containing the formation cycles, the best result was obtained using split 5 of the second cross-validation run -an average validation MRE of 6. 29%, with the best model (which is also in this split) achieving an MRE of 5. 61%. On the other hand, models trained with the modified dataset, without the formation cycles, had more trouble learning and the best one (using split 1 of the first cross-validation run) only got a 21.
Tests using practical capacity For the main results provided, the SOH was calculated according to Equation (1), using the battery nominal capacity in the definition. Another possible approach is to use the battery "practical" capacity, which is the capacity measured for the battery first cycle, as shown in Equation (  4 ): = × SOH Q Q 100% practical ( 4 ) The batteries used for training, validation and testing were the same as the ones used in the previous models, and results are shown in Table  4. This approach yielded slightly better results for the original Naion and MIT validation sets, greatly improved results for the modified  Na-ion dataset (i. e. the version without the formation cycles) but considerably worse results for the NASA dataset. This last case is attributed to the fact that the first cycle measured capacity isn't necessarily a good measurement of perfect battery health (SOH = 100%).
This is because the temperature plays a big role in the perceived (measured) capacity. Batteries cycled at lower temperatures tend to have a smaller perceived capacity. While it may seem that using the practical capacity would bring data from different temperatures to a more even playing field (values starting at 100%), it actually made it more difficult for the model to learn this influence. Basically, using the practical capacity made it so that heterogeneous behaviour is seen as equivalent, which in turn hinders model flexibility and performance. Results obtained for the MIT dataset were expected because it is very homogeneous in terms of SOH evolution. Despite yielding better results than the original approach for the validation dataset, results aren't that different, and are slightly worse for the test set.
The results observed for the Na-ion datasets were unexpected. It was thought that a similar result to NASA would be observed, due to the apparent heterogeneity of the dataset in terms of SOH evolution, but it looks as if the use of the practical capacity has had the exact opposite effect. In this case, bringing all batteries to a similar starting point was beneficial and likely acted as a "correction" of the data. The reason as to why this would have such an impact is not fully understood. The improvement is the most visible for the modified Na-ion dataset, as it achieves better results than the original one. Despite the impressive results achieved for the Na-ion dataset, the tendency observed for the NASA dataset is discouraging. We thus consider that this approach is only appropriate (and seems to yield better results) for datasets in which the batteries are all cycled at the same room temperature.
Conclusions This work explores two techniques for transferring SOH estimation models to scopes different from their training dataset, namely different chemistries and charging protocols. We have shown that updating and optimizing data normalization parameters can be enough to achieve this goal in some cases -achieving results comparable to reference models trained on the targeted datasets. Our main results led us to believe that battery chemistry does not have a big influence in the physical properties of this process but instead could be reinterpreted as a rescaling of its dynamical properties. However, protocol differences can hinder model performance. More tests need to be done in order to consolidate these results and conclusions. Particularly, applying this procedure to a dataset composed of Na-ion batteries charged with a CCCV protocol would be an ideal way of testing our hypothesis.
Finally, we believe that this approach to data transferring by updating model normalization parameters isn't strictly limited for battery SOH estimation, but could possibly used in other fields that use black-box modelling. These methods' limitations are not yet fully understood and will be the subject of a future study. Future research includes training models with mixed datasets and protocols, which could be a way of improving model flexibility and transferability. Also, these approaches still require data from the target dataset for calculating and training normalization parameters. Devising ways of estimating these parameters according to the protocol, system knowledge, resampling and data treatment procedures could be a simple way to decrease data dependency and simplify the transferring process even further, and could also be the subject of further research.
Acknowledgement We thank  Parth Desai ,  Hussein Hijazi ,  Sathiya Mariyappan  and  Jean-Marie Tarascon  ( Collège de France ) for providing us the Na-ion dataset and for fruitful discussions. We wish to acknowledge the support provided by Baalbek management and show our deep appreciation to its  CEO Tamara Leros  for her help. This work was supported by the  French National Research Agency (Labex STORE-EX , Grant No. ANR-10-LABX-0076 ).
We present the first results of a 21 cm H i line pilot observation carried out with the MeerKAT radio telescope in preparation for the ViCTORIA (Virgo Cluster multi Telescope Observations in Radio of Interacting galaxies and AGN) project, an untargeted survey of the Virgo galaxy cluster. The extraordinary quality of the data in terms of sensitivity and angular resolution (rms ∼ 0. 65 mJy beam -1 at ∼27 × 39 and 11 km s -1 resolution) allowed us to detect an extended (∼10 kpc projected length) low column density (N(HI) 2. 5 × 10 20 cm -2 ) H i gas tail associated with the dwarf (M star = 1. 6 × 10 9 M ) irregular galaxy NGC 4523 at the northern edge of the cluster. The morphology of the tail and of the stellar disc suggest that the galaxy is suffering a hydrodynamic interaction with the surrounding hot intracluster medium (ICM; ram pressure stripping).
MeerKAT observations and data reduction ViCTORIA pilot observations of the Virgo cluster were carried out using MeerKAT  (Camilo et al. 2018; Mauch et al. 2020)  on September 10 and 12, 2022. These observations were programmed to test the feasibility of a large untargeted radio survey at 20 cm covering the a large fraction of the Virgo cluster, planned to be carried out during 2023 (∼60 deg 2 , with a total of 125 h allocated; PI F. de Gasperin). The observations are part of the ViCTORIA project. The project includes data from MeerKAT and from the low-and high-band systems of LOFAR  (Edler et al. 2023) , and it will greatly increase the sensitivity and resolution of the wide-field coverage of Virgo in radio continuum between 42-1700 MHz and in the 21 cm line. Polarisation data from MeerKAT observations will also be obtained.
The present pilot observations concern ten partially overlapping fields covering an L-shaped pattern centred on M87 and stretching towards both the north and east. The data were taken with the MeerKAT 32 k mode, covering the frequency range 856-1712 MHz with 32 768 channels of width 26. 123 kHz. We splitted the ten fields in two groups of five. Each group was observed in a single ∼4. 5 hour-long MeerKAT observing session, during which we cycled nine times through the five fields integrating for 280 s on each field at each visit. We observed a phase calibrator for 2 min in between fivefield cycles (i. e. every ∼23 min) and observed the bandpass and polarisation calibrators for 10 min each at the end of the observing session. The total integration time was 42 min for each of our Virgo fields, which thanks to the excellent MeerKAT uv coverage and to our observing strategy ensured that all the relevant angular scales were fully covered for each field.
Multifrequency data The H i MeerKAT data presented in this work were combined with those available at other frequencies to study the perturbing mechanism and its effect on NGC 4523. These include narrowband Hα imaging, sensitive to the distribution of the young stellar population (age 10 Myr,  Kennicutt 1998; Boselli et al. 2009 ) and to the presence of diffuse ionised gas possibly removed during the interaction of the galaxy with the surrounding environment (e. g. ,  Gavazzi et al. 2001 Gavazzi et al. , 2018;; Yoshida et al. 2002 Yoshida et al. , 2012;; Yagi et al. 2007 Yagi et al. , 2010 Yagi et al. , 2017;; Sun et al. 2007 Sun et al. , 2010;; Fossati et al. 2012 Fossati et al. , 2018;; Boselli et al. 2016a Boselli et al. , 2018b;; Poggianti et al. 2017) , UV data, sensitive to the distribution of young stars (age ∼ 100 Myr,  Kennicutt 1998; Boselli et al.
This is consistent with a red-shifting ram-pressure wind caused by the galaxy's blue-shift relative to the ICM. Because of the orientation of the galaxy relative to the wind, the stripping process occurs mainly edge-on. The gas removed at the south-eastern edge of the disc remains gravitationally bound to the galaxy, while that at the south-western edge gets stripped, producing the extended tail along the LoS in the integrated profile. To derive the main kinematical parameters of the galaxy and quantify the impact of the external perturbation on the H i kinematics, we fitted the high-resolution H i velocity field of Fig. 5  following the 2D fitting method described in  Epinat et al. (2008). This method is based on the Levenberg-Marquardt nonlinear least-square algorithm and has been upgraded using the MocKinG software 2.
g. ,  Boissier et al. 2012; Villanueva et al. 2022). The completion of the MeerKAT survey of the Virgo cluster will certainly boost our understanding of this important physical process. Despite the presence of the bright radio galaxy M87 (Virgo A) in the field, the quality of the data is excellent in terms of sensitivity and angular resolution, and will be increased by a factor of ∼2 once the survey will be completed thanks to an increased overlap of pointings. ViCTORIA will provide us with critical information on the atomic gas content, the main gas component stripped during the interaction since loosely bound to the gravitational potential well of the galaxy. Combined with that of the other gas phases (molecular, VERTICO survey,  Brown et al. 2021; ionised, VESTIGE survey, Boselli et al.