dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulation protocol The presented all-atom simulation was performed using the newly developed GPU module  22  within the Tinker-HP package,  21  which is part of the Tinker 8 platform. 39 This newly developed module is able to efficiently exploit mixed precision  22  offering a strong acceleration of simulations using GPUs. The 98 694 atom initial structure of the fully solvated M pro dimer was extracted from the Protein Data Bank (PDB: 6LU7) and the AMOEBA PFF  33, 34, 36  was used to describe all atoms (protein and water). Periodic boundary conditions using a cubic box with side lengths of 100 Å were used. Langevin molecular dynamics simulations were performed using the BAOAB-RESPA1 integrator 40 using a 10 fs outer timestep, a preconditioned conjugate gradient polarization solver (with a 10 À5 convergence threshold), hydrogen-mass repartitioning (HMR) and random initial velocities.
Performance of the adaptive sampling exploration: comparisons with other available simulations As we mentioned in the method section, we use the PCA  28  as an intermediate quantity to orient the consecutive sampling iteration. However, it is also a good quantity to quickly assess the performance of the adaptive sampling scheme for the exploration of the conformational space. 3] [44]  PCA, being a dimensionality reduction algorithm that evaluates directions maximizing the variance of the dataset, is thus a revealer of a system conformational diversity. Therefore, it can be seen as a way to assess the amount of sampling and can also detect explicit "essential motions" otherwise not discernible using predened collective variables. Thus, it is interesting to compare the amount of sampling on the space of these reduced variables.
This is why we projected the RIKEN, the DESRES and the rst 2 ms Tinker-HP data set on the rst two PCA components of the rst 2 ms of the Tinker-HP data set (Fig. 1a  and b ). One can see that, in this space, the Tinker-HP adaptive scheme already captured the RIKEN and DESRES major main PCA features. It also appears that the RIKEN trajectory sampled a portion of conformational space close to the Tinker-HP data set while the DESRES trajectory seems to explore only the area that is most sampled by Tinker-HP. The same procedure was applied for the PCA components and associated data of the entire Tinker-HP data set (Fig. 1c  and d ) and it is striking that a much larger portion of conformational space has been sampled by our adaptive scheme. Additionally, we also projected the same data sets on the rst two principal components of the RIKEN trajectory which gives the same justication of the larger sampling obtained by our method (see Fig.
Markers of the structuring of the oxyanion hole To ensure the validity of our AMOEBA simulations, we compared our computed properties with available experimental data. Since the beginning of the COVID-19 pandemic various Xray structures have been released (PDB: 6Y84, 6LU7, 6Y2G,. ). 3, 47, 48  They provided important insight on specic interactions between residues as well as structural information about the active site. To be consistent with RIKEN simulations we used as reference the same PDB: 6LU7. 3  Note that DESRES used another PDB, 6Y84,  47  which we used as a reference in the computation of its properties. Crystal structures have been projected on the rst two PCA components of the Tinker-HP simulations (see Fig. 8  in the ESI †). Recently, Zhou et al. published an experimental study of the apo structure (PDB 1UJ1)  49  at physiological pH.
However, it has been shown to be useful since the initial studies on the SARS-CoV-1 main protease. 51 In practice, the absence of a well-structured oxyanion hole leads to the inhibition of the enzyme's activity. Experimentally, it is known that the M pro monomeric form is inactive while the active form is a homodimer containing two protomers. 53 In the holo state of SARS-CoV-1, the rst protomer is active while the second one is found inactive. 54 For SARS-CoV-2, a pH ¼ 6 crystal structure (PDB: 1UJ1)  53  predicted a strong asymmetry of the protomers with an inactive conformation for one of the protomers linked to a broken Glu166 and His172 interaction. However, the inactivity of one of the protomers is still a hypothesis as crystallographic studies of the dimer in the space group C2 encounter difficulties in capturing the details of each individual protomer.
The model provides an index ranging from 0 for a non-stacked interaction to 0. 6 for a perfect one. The Glu166 interactions and p-p stacking were thus calculated for both chains of all RIKEN, DESRES and Tinker-HP structures and then classied into histograms. Finally, each histogram has been unbiased (i. e. reweighted) and extrapolated using a univariate kernel density estimator. Final results are given in Fig. 9  of the ESI. † Furthermore a 6 ms adaptive sampling simulation was performed (on the Irene Joliot Curie Machine (TGCC, GENCI, France)) on the monomer species (PDB: 6LU7) and the same features as discussed below (p-p stacking between Phe140 and His163, and Glu166 interactions with both His172 and His163) were calculated. Since the monomer is known to be in an inactive conformation, it helps us to rationalize the behavior observed in our simulations.
Evaluation of the volumes of the enzyme cavities One way to measure some potential global differences between the different simulations is to measure the active site volume in each cluster and to depict the observed trend similarly to the pp stacking previously. Besides the main active site cavity, the main protease exhibits 2 other cavities: the distal site and the dimerization site. Represented in Fig. 5 , these cavities are considered as potential targets for drug inhibition. 60, 61 An accurate description of each of these cavities is essential to the estimation of efficient inhibitors. For each cluster of each dataset, we thus estimated those 3 cavity volumes. Volumes were calculated for each isolated cluster using POVME 3. 0 soware. 62 For each cavity, a 1. 0 Å grid spacing was chosen.
Agreement is also found with volumes obtained by Sztain et al. using a Gaussian accelerated MD (GaMD) enhanced sampling strategy coupled with AMBER ff14SB 8 which also match these results conrming the importance of simulating long enough in conventional MD. Overall, while Tinker-HP clusters 1 and 2 are in good agreement with RIKEN and DESRES clusters, our clusters 3, 4 and 5 appear to be different and specically highlight the importance of the PFF choice, i. e. these data are not obtained using enhanced sampling coupled with non-PFFs. 8 As we pointed out earlier, differences indeed occur between clusters and between different datasets, going in the same direction of the previous analysis of the p-p stacking between residues Phe140 and His163 in chains A and B. For Tinker-HP, we observed a contraction for the three cavities in cluster 3 while in cluster 4 and especially cluster 5, we observed a strong difference with a non-negligible increase of the cavity volumes.
Analysis of the local uctuations: high exibility of the Cterminal region Finally, it is also possible to study local uctuations in the structural dynamics of the M pro dimer system to uncover other types of difference between datasets. We calculated the uctuation of residues in each cluster on the same 1000 previously randomly chosen structures per cluster using the Root Mean Square Fluctuation (RMSF). These were calculated on the 5 clusters from Tinker-HP (AMOEBA), the 3 clusters from RIKEN (AMBER) and the single cluster from DESRES (DES-AMBER). Results are depicted in Fig. 7. The most interesting uctuation as well as the main differences between clusters originates from a different spatial rearrangement of the C-terminal region of the protein (e. g. residues 300 to 306 on chains A and B of the dimer).
Acknowledgements This work has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no. 810367 ), project  EMC2  (JPP). FC acknowledges funding from the  French  state funds managed by the  CalSimLab LABEX  and the  ANR  within the  Investissements d'Avenir program  (reference  ANR11-IDEX-0004-02 ) and support from the  Direction Génerale de l'Armement (DGA) Maîtrise NRBC of the French Ministry of Defense. DEA  acknowledges funding from the  Lebanese National Council for Scientic Research, CNRS-L. Adaptive sampling computations were performed at  GENCI  thanks to a COVID19 emergency allocation on the  Jean Zay machine (IDRIS, Orsay, France)  under grant no. A0070707671  and on the Irene Joliot Curie machine thanks to a  PRACE COVID-19 emergency grant  (project  COVID-HP ).
Furthermore, this modality provides a good state of DD accuracy rate and also is more appropriate than percentage-of-eye-closure (PERCLOS)  [25]  indicator in the evaluation of driver drowsiness. Thanks to its high temporal resolution, portability, and inexpensive cost, the Emotiv EPOC + (https://www. emotiv. com/epoc/ (accessed on 1 June 2020) headset  [26]  is considered one of the most commonly used among the EEG-based technologies. The neurotechnology headset is a brain measuring data hardware that enables to record brain activity using fourteen electrodes placed on the participant's scalp. In this paper, we focus on an EEGbased DD system using the Emotiv EPOC + headset to record brain activity by analyzing the drowsy or awake states. Over this decade, many EEG-based research works related to machine learning (ML)  [27] [28] [29] [30]  have been suggested in medical diagnosis, in particular for classification-based drowsiness detection tasks.
Nevertheless, some limitations appear in ML applications such as the need for a massive dataset to train, limitation predictions in return, the need of an intermediary step for feature representation and drawing conclusions to detect anomalies. In addition, deep learning (DL) researches  [31, 32]  have recently shown notable progress in biomedical signal analysis especially classification-based anomaly detection. However, DL  [33]  is now the fastest sub-field of ML technology  [34]  based on the artificial neural networks (ANNs)  [35]. Interestingly, DL networks offer great potential for biomedical signals analysis through the simplification of raw input signals (i. e. , through various steps including feature extraction, denoising, and feature selection) and the improvement of the classification results.
Signal Collection The signal collection step is developed by two processes, which are the hardware and the software parts  [55]. The Emotiv EPOC + hardware is a non-invasive brain-computer interface (BCI) used for the development of the human brain and contextual research. Figure  2  illustrates the various Emotiv EPOC + helmet components used in the experimental step consisting of a headset, a fourteen-sensors box, a USB key with cable for battery recharging that ensures the connection between the headset and the Emotiv Pro software, and a saline solution  [56]  that ensures impedance and contact with the cortex. Compared to medical gel  [57] , the saline solution is easy to use and maintains effective contact with the scalp of men and women. The Emotiv EPOC + headset provides excellent access to professional-level brain data.
As shown in Figure  3 , this helmet contains fourteen active electrodes with two reference electrodes, which are Driven Right Leg (DRL) and Common Mode Sense (CMS). The electrodes are mounted around the participant's scalp in the structures of the following zones: frontal and anterior parietal (AF3, AF4, F3, F4, F7, F8, FC5, FC6), temporal (T7, T8), and occipital-parietal (O1, O2, P7, P8). Table  1  presents some of the main characteristics of the Emotiv EPOC + helmet. The EmotivPRO software allows visualizing the data streams in real-time including all data sources. This program configures the vertical scaling of the EEG Graphics with the multi-channel and single-channel display mode. Subsequently, the raw EEG data are exported in European Data Format (EDF) or Comma-Separated Values (CSV) formats that are considered as the input of the data preprocessing step.
Data Augmentation In the recent year, DA  [77]  has been shown to achieve significant performance for DL with increasing accuracy and stability and reducing over-fitting. As developed in  [46] , DA is a process in which new data are artificially created from the current data on the training phase. In  [78] , the need for developing a DA technique contributes to avoiding over-fitting, improves classification accuracy and stability  [47, 79]  then better generalizes on new data and enhances performance in imbalanced class issues  [80]. Furthermore, DA allows improving the efficiency of CNN in the BCI field by overcoming the problems of small datasets. DA effectiveness varied considerably across techniques. However, sampling methods, noise addition, windows sliding, and Fourier transform are considered as the classic examples in signal classification tasks.
Dataset Our EEG signal collection contains forty-two records of six men and eight women aged between fourteen and sixty-four with normal mental health. For each person, we made three recordings lasting sixteen minutes over the day: in the morning, afternoon, and evening. For each recording, the total number of rows of data is equal to 123,648. In order to identify the state of each participant, we divided the EEG signal into windows of 3. 75 s. In this sense, we split each EEG recording into 256 different sets (segments) and the length of each segment is equal to 483. Based on the proposed data annotation step in our method, a deeper analysis of the brain is the preliminary phase in the detection of each participant's state. In this regard, we categorized the different participants according to drowsy and awake states.
Additionally, the cross validation method is used in order to evaluate our model with seven electrodes. Table  10  presents all the experimental results with different folds. Additionally, we compare our work with recent DD systems in the literature. In  [54] , the authors propose a system based on the EEG signal processing image, which converts the EEG signal into an image-like signal 2-D function map and then transfers them to the CNN model for DD. This architecture is composed of two convolutional and pooling layers with one fully connected layer. The total accuracy in the prediction imbalanced dataset result is equal to 71. 15%. In  [40] , the authors suggest a DD system based on a DL model. Using spectrograms from the channels of EEG signals, the proposed system is developed to the ULg Multimodality Drowsiness Database.
In fact, these waves reflect the state between sleep and wholeness. Therefore, comparative behavioral testing of alpha and theta waves can be beneficial for effective DD. The proposed DD system is divided into two steps as data acquisition and model analysis. The first step contains three steps, signal collection, data annotation, and data augmentation (DA). An Emotiv EPOC + headset is used for signal collection. Subsequently, we have annotated our dataset according to the amplitudes of alpha and theta waves. By incorporating the DA step to improve performance, we have done two experimental tests: with and without DA. For model analysis, we have built a CNN model in which implementation is done using the Keras framework. The average values of the accuracy, F1-score, precision, and recall showed a high classification rate using seven electrodes, in comparison to other competing methods.
Conclusions and Future Work This paper proposes a new DD system based on EEG signals using a CNN architecture. An Emotiv EPOC + headset is used for signal collection. Furthermore, our EEG data has been annotated to detect drowsiness based on the analysis of alpha and theta waves from the occipital and temporal area. A study has been conducted to select the most suitable number of electrodes. Obtained results are coherent with the state-of-the-art. In this context, we proposed a system for DD using only seven electrodes. The proposed system achieves an average classification accuracy of 90. 14%. In future work, EEG can be considered with other physiological assessment tools, such as EOG, ECG and Near-Infrared Spectroscopy (NIRS)  [120, 121] , which help to improve accuracy rate. We will also consider validating our system on larger datasets, especially collected under real driving conditions.
Unlike the profound advantages of the aforementioned time-slotted solutions in terms of reduced packet collisions, they can barely be used for bulk data collection because of the absence of a scheduler to allocate slots to the nodes or because they follow a slotted-Aloha approach which does not eliminate collisions. Since in bulk data collection, the nodes tend to use a high data rate to quickly expedite all transmissions, non collision-free approaches may suffer for long data collection times.
Requirements In order to achieve bulk data collection, as the gateway cannot be available at all times, a mechanism to (almost) simultaneously wake up the nodes once the gateway becomes available must exist. A simple way to achieve that is to schedule the data collection process at predefined times. Apparently, due to the clock drift of the nodes, some of them will wake up earlier than others. Taking into account experimental data from our previous work  [32] , the maximum clock drift does not exceed 100ppm, which is translated to a few seconds of time assuming that the data collection is executed once per day. However, having in mind that the data collection may take several hours to be completed (see experimental results in Section 6), the waking up waiting cost is negligible compared to the duration of the data collection.
The gateway can initiate the data collection process after this short period of time. In the case of non-coordinated transmissions, such as in an Aloha approach, this can happen by simply sending an initialisation packet. However, if the transmissions are time-synchronised, the gateway needs to compute the schedule and send it to the nodes before starting the data collection. As we already mentioned, the computation and the transmission of the schedule are two critical operations that may considerably affect the reliability of the system as well as the data collection time. These two operations are the two main research subjects of the current paper. Moreover, since the gateway is expected to be online for multiple hours per day, new nodes can periodically wake up and register in the gateway.
If the gateway is not present at that time, the node can return to sleep mode and wake up at a future time to try to register again. Since a gateway system consists of multiple transceivers and the registration takes place at a different frequency, registrations do not affect the data collection process. As a consequence, even if a joining node tries to register during the data collection phase, the latter would not be interrupted/interfered by the registration activity. Registrations can also get accelerated by considering a similar to LoRaWAN's personalised activation (ABP). In this case, the gateway is aware of the new nodes while the corresponding DevAddr as well as the security keys can be stored in the devices.
Data Collection Using a Pure & Slotted Aloha MAC In this subsection, we formulate a data collection time minimisation problem, as a function of the minimum required transmission rate and a given success probability of delivery. We show that reliable bulk data collection in Aloha (Pure and Slotted) cannot be achieved in a short amount of time even for a low number of nodes. At this point, we define a set of notations that are needed for the rest of the text. These notations as well as their meaning are summarised in Table  1. Aloha-based MAC protocols, such as the LoRaWAN, do not apply any mechanism to regulate packet transmissions. Indeed, each node transmits its packets whenever data are available and the duty cycle is obeyed. In this case, some of the packets are delivered while others are lost due to collisions.
Specifically, the collision rate depends on this transmission rate. For example, allowing the nodes to transmit in sparse intervals decreases the collisions but prolongs the data collection time. Assuming that all nodes transmit the same amount of packets , the average data collection time is then equal to / , where  is the packet transmission rate. In order to model an Aloha MAC behaviour for the bulk data collection scenario, we assume that all nodes' packets are equal in length and their transmissions follow a Poisson distribution with intensity  packets/sec. Assuming the best-case scenario of no inter-SF collisions, transmissions only collide when they overlap in SF, time, and power  [7]. Given the analysis provided in  [34] , if a node at distance  from the gateway transmits a packet with SF  , the transmission will be successful if it does not overlap with any other packets in the network having the same SF within the transmission time.
( 1 ) The shorter the distance  to the gateway, the higher the probability   () of success delivery due to less interferers. The worst case situation appears for nodes that are on the border of the deployment range (i. e. ,  → ). In this case, all nodes with the same SF are potential interferers and the minimum success probability    is then equal to 𝑃 𝑚𝑖𝑛 𝑓 = 𝑒 -2𝑇 𝑓 𝜃 𝑁 𝑓. As the transmission rate of the nodes decreases, the success probability increases, but so does the data collection time. This means that there is a certain (maximum) transmission rate   that satisfies a minimum success probability for all the nodes in the network. The question that arises is how long is the data collection time given this rate and the desired minimum allowed success probability? In order to answer this question, we formulate the following optimisation problem: max 𝜃, (2)    : 𝑝𝑘𝑡 𝑘=𝜌 𝑝𝑘𝑡 𝑘 𝑃 𝑚𝑖𝑛 𝑓 𝑘 1 -𝑃 𝑚𝑖𝑛 𝑓 𝑝𝑘𝑡 -𝑘 ≥ 𝑃 𝑔𝑖𝑣𝑒𝑛 , (3) 𝑃 𝑚𝑖𝑛 𝑓 = 𝑒 -2𝑇 𝑓 𝜃 𝑁 𝑓 , (4) 𝜃𝑇 𝑓 ≤ 𝐶.
9. All nodes use a 100-byte packet size, transmit 100 packets, and reach the gateway with SF7. We can observe that the data collection may take several hours even for low node numbers. This is because the data collection time depends on the packet rate  which is computed by solving the aforementioned optimization problem. As the number of nodes increases, the number of collisions increase as well. That means that  has to be increased considerably in order to make transmissions sparser and, thus, reduce the number of collisions. It is obvious from this analysis that an Aloha MAC (Pure or Slotted) cannot achieve a reliable bulk data collection in a reasonable period of time. We should mention that optimally assigning SFs such that the probability of delivery is maximised, can alleviate the problem of collisions  [34].
Apparently, the longer the frame, the sparser the transmission rate, and thus, the data collection time. As with all synchronised protocols, the nodes need to be periodically synchronised by the gateway according to a global clock. In order to accommodate the nodes' clock drift over time, guard times are added between successive time slots to ensure that adjacent transmissions do not overlap. Assuming the propagation delay negligible, the guard time depends on the crystal clock accuracy and on how often the gateway communicates with the nodes to synchronise their clocks. In TS-LoRa, this is performed using a novel mechanism called "SACK" (Synchronisation and ACKnowledgmemts) which merges sync and (optional) ack data into a single downlink packet. The frame size and the guard time are adjusted automatically according to the number of registered nodes.