dataesr/scientific-paragraphs-categorization huggingface dataset:
In particular, running ML inference on MCUs allows to use Artificial Intelligence (AI) with several hardware devices reducing energy consumption, latency, processing power, memory and storage, without data living the device and thus preserving privacy. TensorFlow Lite for Microcontrollers is an example of tool found in the state of the art which makes possible to run ML models on MCUs with only KiloBytes (KB) of memory. TensorFlow Lite for Microcontrollers (TFLM) is an experimental port of TensorFlow Lite, a ML framework for embedded devices develped by Google, and it has been tested widely with the Arm Cortex-M Series architectures. [25]  In this article, we present different tiny neural networks architectures to predict the maximum releasable capacity of Li-Ion batteries, using the datasets made available by NASA  [15] , and comparing the models in terms of accuracy.
III. PROPOSED METHODOLOGY A. PROBLEM DESCRIPTION AND DATA PREPROCESSING The dataset chosen for the analysis application is one of those provided by NASA Ames Prognostics Center of Excellence (PCoE)  [15] , and it contains measurements of voltage, current, temperature, capacity and impedance of Li-Ion batteries, generated through charge, discharge and impedance operational profiles. Charge and discharge tests ended when the batteries reached the 30% fade in rated capacity (from 2 A h to 1. 4 A h). The data generation mode used fixed reference charge and discharge profiles, and the resulting dataset is particularly suited to the estimation of the remaining capacity, as evidenced by many studies we have based on, which used this dataset to estimate the remaining capacity  [7] ,  [14] ,  [17] ,  [37].
In order to compute battery releasable capacity, the discharge current is integrated over time starting from a fully charged battery  [6]. However, a full discharge may not be possible in real cases. Therefore, only some samples for each discharge cycle were selected, and form the dataset for the presented analysis. In particular, battery output current, terminal voltage, time difference between samples and temperature are the four features selected for each discharge cycle. The target of the estimation is the capacity of the considered cycle. Considering all the selected batteries, the total number of observations (discharge cycles and capacity values) was 1462. In order to create an independent test set for model performance evaluation, one battery per set, namely battery number 05 (set 1), 27 (set 2), 30 (set 3), and 46 (set 4) was inserted in the test set (22 % of the total data), while the rest was used as training set.
B. PREDICTIVE MODELS Before the NNs embedding in the MCU, the presented models were trained and tested using the sets described in Subsection III-A. The Adaptive Moment Estimation (Adam) optimization algorithm was used to train to networks due to its efficiency in terms of adaptive learning rate method and its suitability to non-stationary problems  [40]. The Mean Squared Error (MSE) is the default loss function for regression problems, and it was chosen as the function to minimize. Since the datasets used in the state of the art were different and had several limitations (as discussed in more detail in Section IV-A) which influenced the prediction accuracy, a direct comparison of the results obtained with those of the state of the art was not possible. Thus, a Random Forest (RF) model and an SVR model were evaluated using the aforementioned dataset, and then were used as a baseline for the developed architectures.
C. COMPLEXITY PROFILING The trained neural networks were embedded into ARM Cortex-M4 MCU, integrated on a SensorTile Wireless Industrial Node (STWIN). It was chosen since it is very suitable for industrial applications which need condition monitoring, being provided with several industrial IoT sensors, an ultralow-power MCU (STM32L4R9ZIJ6), modular architecture, and a 480 mA h Li-Po battery. In addition to this, BLE wireless and WiFi connectivity on-board are supported. The memory embedded in the core system MCU consists in 2 Mbytes of Flash and 640 Kbytes of SRAM. [44]  The tool used for the pre-trained architectures conversion was STM32Cube. AI software tool (version 5. 0. 0), which automatically converts the models into optimized ANSI C code for embedded MCUs, and allows their performances analysis, running the validation both on PC and on the chosen MCU.
IV. RESULTS AND EVALUATION A. DATA PREPROCESSING The choices made for the dataset aim to overcome the limits found in the datasets used in the state of the art. Among them, Wei et al. [14] , Xu et al. [17] , Li et al. [16]  used entire charge/discharge cycles. In these cases the estimation of capacity is simplified and does not reproduce real world conditions, in which a complete cycle may not be available. In the dataset used for this work, only some samples were inserted for each signal. Weng et al. [13] , Wei et al. [14] , Xu al. [17] , Choi et al. [7]  used datasets whose data were generated using the same load profile. Instead, the dataset used in this work has only the discharge cycles, due to the greater variability of their experimental conditions. Additionally, Xu et al. [17]  reduced the complexity of capacity prediction by using the same batteries in both the test and training phases.
In contrast, in the dataset used in this article the batteries inserted in the test set and in the training set are different from each other, increasing the prediction complexity and the similarity to a real use case. Finally, the satisfactory results obtained by Prakash and Vigneswaran  [20]  cannot be compared to the ones presented in this work, due to the different methodologies used for network input features extraction. In fact, while Prakash et al. collected data during a random charge/discharge load profile and then processed them to extract input features, implying large data storage and off-line data processing, in this article only some samples for each cycle were selected, and they were directly used as input features for the networks, making cheap real-time SOH estimation possible.
V. SUMMARY AND CONCLUSION In this article, we showed different NNs architectures (i. e. CNN, LSTM, CNN LSTM, GRU, and CNN GRU) that predict the maximum releasable capacity of Li-Ion batteries, from which SoH can be easily computed. Li-Ion batteries datasets made available by NASA  [15]  were used for both the training and the testing phase. The final dataset was created with the aim of overcoming the limits found in the state of the art (e. g. the application of the same load profile for data generation), as widely motivated in the Subsection IV-A. According to the results of the predictions, CNN GRU exceeded all other NNs, with maximum RMSE and MAE of 0. 0488 and 0. 0414, respectively, and all NNs gave better results than the baseline models (i. e. RF and SVM). Then, via STM32Cube. AI tool, the pre-trained models were incorporated into ARM Cortex-M4 MCU, and integrated on a STWIN, converting them into optimized ANSI C code.
Study design and interventions LANTERN was a phase 3, prospective, randomized, stratified, parallel-group, non-inferiority study comparing the efficacy and safety of lanreotide autogel 60, 90, or 120 mg with lanreotide 40 mg PR (ClinicalTrials. gov: NCT02493517; chinadrugtrials. org. cn: CTR20140698; Fig. 1 ). The study was conducted between 29 October 2014 and 16 February 2017. As the treatments differed in method and frequency of administration, the study was conducted on an open-label basis. There were 32 weeks of study treatment (all patients) and an 8-week extension phase without study treatment for further assessment of PK in a subgroup of patients receiving lanreotide autogel. The study was conducted in 10 specialist centres in China, including eight endocrinology sites and two neurosurgery sites.
Random GH levels were assessed at weeks 1 and 5, then at weeks 28 or 29 (as for IGF-1 levels) (pre-dose, except week 1). IGF-1 and GH levels were determined centrally from blood samples. Tumor volumes (solid component) were measured centrally by two independent readers (using prespecified methods; Bioclinica, Newark, California, and Ipsen Pharma, Les Ulis, France) from MRI scans obtained at screening and at the end-of-study/early withdrawal visit. Readers were blind to sequence of scans and patients' treatment allocation. The readers measured tumor volumes by drawing tumor contours on each MRI image where the adenoma was visible; the volume was then automatically calculated by Smart Study Direct (version 9) software (Bioclinica, Newark, California), taking contour areas and slice thickness into account.
Statistical analyses Statistical analyses were performed using SAS® version 9. 3 (SAS Institute Inc. , Cary, NC, USA). Non-inferiority was declared if the upper limit of the two-sided 95% confidence intervals (CI) for the treatment difference in the primary endpoint (lanreotide autogellanreotide 40 mg PR) was less than the non-inferiority margin (i. e. 0. 6 log [SDS] for least squares [LS] mean difference of log [base e] IGF-1 SDS values between treatment groups resulting from the generalized linear model [GLM]), and 50 patients per group were required for 81% power. Assuming that approximately 85% of the patients randomized were evaluated in the per protocol (PP) population, 59 patients per group were required to be randomized. Therefore to ensure at least 100 patients in the PP population, a total of 118 patients were needed for randomization (intention-totreat [ITT] population).
Acknowledgements The authors thank all patients involved in the study, as well as their care team, investigators, and research staff in participating institutions. The authors thank  Chris Deakin, PhD , and  Louise Prince, PhD , of  Watermeadow Medical , an  Ashfield Company , for providing medical writing support. The authors would also like to thank all the LANTERN study investigators:  West China Hospital, Sichuan University , Sichuan: Z. M. An,  Z. P. Gou ,  J. W. Li ,  H. W. Tan ,  Q. Peng ,  L. B. Liang ,  Y. J. Li ,  P. Feng ,  L. Zheng. Tongji Hospital , Tongji.
Yang's group from ShanghaiTech by co-crystallisation with a peptide-like inhibitor N-[(5 methylisoxazol-3-yl)carbonyl]alanyl-L-valyl-N~1-((1R,2Z)-4-(benzyloxy)-4-oxo-1-{[(3R)-2-oxopyrrolidin -3-yl]methyl}but-2-enyl)-L-leucinamide (N3 or PRD_002214)  [13]. The same inhibitor was co-crystallised with other human coronaviruses, such as HCoV-NL63 (PDB ID: 5gwy), HCoV-KU1 (PDB ID: 3d23), or SARS-CoV (PDB ID: 2amq). This enzyme naturally forms a dimer, each of whose monomer consists of the N-terminal catalytic region and a C-terminal region  [14]. Although 12 residues differ between both CoVs, only one, namely, S46 in SARS-CoV-2 (A46 in SARS-CoV), is located in the proximity of the entrance to the active site. However, such a small structural change would typically be not expected to substantially affect the binding of small molecules  [12].
Despite the structural differences in the active sites of both Mpro proteins, major issues involving plasticity and flexibility of the binding site could result in significant difficulties in inhibitor design for this molecular target. Indeed, an in silico attempt has already been made involving a massive virtual screening for Mpro inhibitors of SARS-CoV-2 using Deep Docking  [15]. Other recent attempts focused on virtual screening for putative inhibitors of the same main protease of SARS-CoV-2 on the basis of the clinically approved drugs  [16] [17] [18] [19] [20] [21] , and also on the basis of the compounds from different databases or libraries  [22] [23] [24]. However, none of such attempts is likely to lead to clinical advances in the fight against SARS-CoV-2 for reasons we elaborate below.
Crystal Structure Comparison, and Location of the Replaced Amino Acids Distal to the Active Site The first SARS-CoV-2 main protease's crystallographic structure was made publicly available through the Protein Data Bank (PDB)  [25]  as a complex with an N3 inhibitor (PDB ID: 6lu7)  [13]. Next, the structure without the inhibitor was also made available (PDB ID: 6y2e)  [26]. We refer to these structures as SARS-CoV-2 Mpro N3 and SARS-CoV-2 Mpro, respectively. We also used two structures of the SARS-CoV main protease: one, referred to as SARS-CoV Mpro N3 (PDB ID: 2amq), co-crystallised with the same N3 inhibitor, and the other without an inhibitor (PDB ID: 1q2w), which we refer to as SARS-CoV Mpro. The SARS-CoV-2 Mpro and SARS-CoV Mpro structures differ by only 12 amino acids located mostly on the proteins' surface (Figure  1A , Table  S1 ).
molecules' tracking approach to analyse the conformational changes in the binding site. The experiment setup and methodology workflow is presented in Figure  S1. Despite the structural differences in the active sites of both Mpro proteins, major issues involving plasticity and flexibility of the binding site could result in significant difficulties in inhibitor design for this molecular target. Indeed, an in silico attempt has already been made involving a massive virtual screening for Mpro inhibitors of SARS-CoV-2 using Deep Docking  [15]. Other recent attempts focused on virtual screening for putative inhibitors of the same main protease of SARS-CoV-2 on the basis of the clinically approved drugs  [16] [17] [18] [19] [20] [21] , and also on the basis of the compounds from different databases or libraries  [22] [23] [24].
The small molecules tracking approach was used to determine the accessibility of the active site pocket in both SARS-CoV and SARS-CoV-2 Mpros, and a local distribution approach was used to provide information about an overall distribution of solvent in the proteins' interior. To properly examine the flexibility of both active site cavities, we used the time-window mode implemented in AQUA-DUCT software  [30]  to analyse the water molecules' flow through the cavity in a 10 ns time step and combined that with the outer pocket calculations to examine the plasticity and maximal accessible volume (MAV) of the binding cavity. Surprisingly, despite their high similarity, the binding cavities of SARS-CoV and SARS-CoV-2 Mpros showed significantly different MAV (Wald test, z = 2597, p < 0. 05). Both proteins reduced their MAV upon inhibitor binding by approximately 20%, but the maximal volume of SARS-CoV was over 50% larger than those of SARS-CoV-2 (Figure  2  and Figure  S2 ).
The small molecules tracking approach was used to determine the accessibility of the active site pocket in both SARS-CoV and SARS-CoV-2 Mpros, and a local distribution approach was used to provide information about an overall distribution of solvent in the proteins' interior. To properly examine the flexibility of both active site cavities, we used the time-window mode implemented in AQUA-DUCT software  [30]  to analyse the water molecules' flow through the cavity in a 10 ns time step and combined that with the outer pocket calculations to examine the plasticity and maximal accessible volume (MAV) of the binding cavity. Surprisingly, despite their high similarity, the binding cavities of SARS-CoV and SARS-CoV-2 Mpros showed significantly different MAV (Wald test, z = 2597, p < 0. 05). Both proteins reduced their MAV upon inhibitor binding by approximately 20%, but the maximal volume of SARS-CoV was over 50% larger than those of SARS-CoV-2 (Figures  2  and S2 ).
Indeed, the analysis performed with Comulator software  [33]  showed that within viral Mpros, evolutionary-correlated residues are dispersed throughout the structure. This indirectly supports our previous findings that distant amino acid mutation can contribute substantially to the binding site plasticity. It is worth adding that among evolutionary-correlated residues, we identified also those that differ between SARS-CoV-2 and SARS-CoV Mpros, located on the C44-P52 loop (Figure  5 ) and the F185-T201 linker loop. The CMA analysis indicated that particular residues in both loops are evolutionary-correlated. The Q189 from the linker loop correlates with residues from the C44-P52 loop, whereas R188, A191, and A194 correlate with selected residues from all domains, but not with Figure  5. Localisation of the evolutionary-correlated residues of Mpros (black sticks).
Indeed, the analysis performed with Comulator software  [33]  showed that within viral Mpros, evolutionary-correlated residues are dispersed throughout the structure. This indirectly supports our previous findings that distant amino acid mutation can contribute substantially to the binding site plasticity. It is worth adding that among evolutionary-correlated residues, we identified also those that differ between SARS-CoV-2 and SARS-CoV Mpros, located on the C44-P52 loop (Figure  5 ) and the F185-T201 linker loop. The CMA analysis indicated that particular residues in both loops are evolutionary-correlated. The Q189 from the linker loop correlates with residues from the C44-P52 loop, whereas R188, A191, and A194 correlate with selected residues from all domains, but not with the C44-P52 loop.
Classical MD Simulations The H++ server  [47]  was used to protonate the SARS-CoV-2 (PDB IDs: 6lu7, and 6y2e) and SARS-CoV main proteases' structures (PDB IDs: 2amq, and 1q2w) using standard parameters at pH 7. 4. The missing 4-amino-acids-long loop of the 1q2w model was added using the corresponding loop of the 6lu7 model, and the quality of the loop refinement was confirmed by comparison with 2h2z structure of SARS-CoV (Figure  S8 ). Additionally, 4 and 3 Na + ions were added to the SARS-CoV-2 and the SARS-CoV, respectively. Water molecules were placed using the combination of 3D-RISM  [48]  and the Placevent algorithm  [49]. The AMBER 18 LEaP  [50]  was used to immerse models in a truncated octahedral box with 12 Å radius of TIP3P water molecules and prepare the systems for simulation using the ff14SB force field.
PMEMD CUDA package of AMBER 18 software  [50]  was used to run a total of 2 µs (10 replicas of 50 ns for each system) simulations of both SARS-CoV-2 and SARS-CoV Mpros systems using apo structures and structure with co-crystalised N3 inhibitor (removed before starting the simulations) to provide more starting points for simulations. To improve conformation sampling, the starting geometry for each system was kept but the initial vectors were randomly assigned. The minimisation procedure consisted of 2000 steps, involving 1000 steepest descent steps followed by 1000 steps of conjugate gradient energy minimisation, with decreasing constraints on the protein backbone (500, 125, and 25 kcal × mol -1 × Å -2 ) and a final minimisation with no constraints of conjugate gradient energy minimization.
Mixed-Solvent MD Simulations-Cosolvent Preparation Six different cosolvents: acetonitrile (ACN), benzene (BNZ), dimethylsulfoxide (DMSO), methanol (MEO), phenol (PHN), and urea (URE) were selected to perform the mixed-solvent MD simulations. The chemical structures of cosolvent molecules were downloaded from the ChemSpider database  [53] , and a dedicated set of parameters was prepared. Parameters for ACN were adopted from the work by Nikitin and Lyubartsev  [54] , and parameters for URE were modified using the 8Mureabox force field to obtain parameters for a single molecule. For the rest of the co-solvent molecules, parameters were prepared using Antechamber  [55]  with Gasteiger charges  [56]. The number of added water and cosolvents molecules is shown in Table  S5 , and the parameters for cosolvents are available in Tables S6-S11.
Mixed-Solvent MD Simulations-Initial Configuration The Packmol software  [57]  was used to build the initial systems, consisting of protein (protonated according to the previously described procedure), water, and particular cosolvent molecules. We added 4 and 3 Na + ions to the SARS-CoV-2 Mpro and the SARS-CoV Mpro, respectively. It was assumed that the percentage concentration of the cosolvent should not exceed 5% (in the case of ACN, DMSO, MEO, and URE), or should be about 1% in the case of BNZ and PHN phenol (see Table  S5 ). The mixed-solvent MD simulation procedures (minimization, equilibration, and production) carried out using the AMBER 18 package were identical to the classical MD simulations. Only the heating stage differed-it was extended up to 40 ps. PMEMD CUDA package of AMBER 18 software  [50]  was used to run two replicas of 50 ns for each cosolvent of both SARS-CoV-2 and SARS-CoV Mpro systems using apo structures and structure with co-crystalised N3 inhibitor (removed before starting the simulations), thus providing a total of 2.
Water and Cosolvent Molecule Tracking The AQUA-DUCT 1. 0 software  [30]  was used to track water and cosolvent molecules. Molecules of interest, which entered the so-called Object, defined as a 5 Å sphere around the centre of geometry of active site residues, namely, H41, C145, H164, and D187, were traced within the Scope region, defined as the interior of a convex hull of both COVID-19 Mpro and SARS Mpro Cα atoms. All visualisations were made in PyMol  [58]. AQUA-DUCT was used to analyse maximal accessible volume (MAV), defined as the outer pocket  [30]. Pockets were calculated by analysis of paths found during molecules tracking step in AQUA-DUCT. A regular grid was constructed, spanning all paths. The grid size was 1 Å. For each grid cell, the density of tracked molecules was calculated.
Obtaining SARS-CoV-2 Mpro Gene Sequence SARS-CoV-2 Mpro was downloaded from the PDB as a complex with an N3 inhibitor (PDB ID: 6lu7). Tblastn  [59]  was run on the basis of the protein amino acid sequence. We obtained 100% identity with 10055-10972 region of SARS-CoV-2 Mpro complete genome (Sequence ID: MN985262. 1). Blastx  [60]  calculations were run with the selected region, and orf1a polyprotein (NCBI reference sequence: YP_009725295. 1) amino acid sequence, identical with the previously downloaded SARS-CoV-2 Mpro, was received.
Energetic Effect of Amino Acid Substitutions FoldX software  [35]  was used to insert substitutions into the structures of SARS-CoV and SARS-CoV-2 Mpros. To analyse the changes in energetic contribution to the protein stability of the two structures, 12 single-point mutations were introduced to the SARS structure using the BuildModel module. The BuildModel module introduces substitution(s) of selected amino acid(s), optimizes the structure of a new variant, and calculates the difference in the Gibbs free energy of protein folding between the wild-type and mutant variant in kilocalories per mole. The lower the difference in energetical terms, the more stable the mutant variant should be. Each of the residues in SARS-CoV Mpro was mutated to the respective SARS-CoV-2 Mpro residue, and the difference in total energies between the wild-type SARS-CoV-2 Mpro and the mutant structures were calculated.
Comulator Calculations of Correlation Between Amino Acids SARS-CoV Mpro was downloaded from the PDB (PDB ID: 1q2w). Blast  [61]  was run on the basis of the amino acid sequence. As a result, 2643 sequences of viral main proteases similar to chain A SARS-CoV Mpro were obtained. Clustal Omega  [62]  was used to prepare an alignment of those sequences. Comulator  [33]  was then employed to calculate the correlation between amino acids and, on the basis of the results, groups of positions in SARS-CoV Mpro sequence were selected whose amino acid occurrences strongly depended on each other.