dataesr/scientific-paragraphs-categorization huggingface dataset:
Objective The aim of generating this dataset is to deliver open and accessible measures of meteorological and physical data of a microgrid in operation. These data are collected in the scope of the « Micro Reseau Mafate » project. This project develops microgrid installation in an isolated and non-connected area, usually at more than 10 0 0 m altitude  [1]. Meteorological data represent a significant input to design a microgrid in this specific configuration  [2]. As electricity is produced, stored and dispatched in a local grid, this electricity must be stored or consumed when photovoltaic panels produce in order to use all the solar resources available. As the resource is low in these landlocked regions, the afternoons are often characterized by an overcast sky, battery saturation must be avoided during a sunny sky and low electrical consumption.
Data Description Open source datasets from the experimental research at "Roche plate" are shared with the community: 'Meteo_dataset' file, 'Solar_plant_dataset' file and 'Demand_house_dataset' files. For this latter, each house has a CSV file in the repository identified by its number. They are provided in CSV format. The starting date is the 1st of May 2020 at 0 0:0 0:0 0 and the finishing date is the 1st of May 2021 at 23:59:00. The first file is related to the meteorological data, the second one to the physical parameters from the production unit and the last one to the home loads sometimes by a cluster of electrical appliances ( Figs. 1  and 2  ). Meteorological data are important to qualify the resources. Solar irradiance, air temperature, ambient humidity, wind speed and wind direction in situ are measured to identify meteorological conditions (see Table  1  ).
All physical parameters from the solar plant are recorded as well: output power and voltage from the solar regulators, output current and voltage from the battery and its operational temperature too (see Table  2  ). Classical electrical consumption stations in dwellings are a TV set, a freezer, a fridge, a washing machine, radio stations, light bulbs, cell chargers, game consoles and sockets for other appliances. Sometimes appliances are monitored together by cluster (see Table  3  ). Thanks to smart meters technology, real-time measurements    The sampling rate of 20 s is for meteorological data and 1-minute intervals for the other ones. All the data is available in comma-separated format. A ReadMe TXT file is included in the deposit to give detail about the different datasets. As data illustrations, Fig.
An optimal mesh of the domestic electrical network has been generated minimizing the impact on the daily life of inhabitants. Measures depend on the location and clustering of appliances. Recorders were installed in the junction box if possible or in the electrical switchboard, see Fig. 6. It uses LoRaWAN to communicate with a central hub system. All data are then stored in the Reunion University server via communication gateway and GPRS transfer network. The data logger stores the weather measurements in CSV files. Python scripts have been developed to check the CSV files' sanity before being transferred to the remote database. From raw CSV files, the presence of reading for every column/variable at each timestamp is checked. In the case where excessive data gaps are read, a Python script sends mail notifications to request an operator intervention to check the issue.
Csv files are sliced into month periods to avoid having too heavy files in the remote database. However, the weather data shared with the community are combined in a unique 1-year-file. The processing demand and power facility data have their own online platform for storage and visualization. Studer Cloud for power utility data and Ewattch Cloud for individual demand measurement. Python scripts are developed to regularly use the API accesses to transfer data stored online to the University's remote database which ensures having duplicates. In addition, all data are stored in CSV files.
Data source location "Roche Plate", Cirque de Mafate Saint-Paul 97460, Reunion Island, France. Latitude and longitude for collected samples: -21. 06959, 55. 40631 Data accessibility Repository name: Zenodo Data identification number: 10. 5281/zenodo. 8186303 Direct URL to data: https://doi. org/10. 5281/zenodo. 8186303.
Our method outperforms existing state-of-the-art algorithms, and is more flexible than supervised deep-learning methods requiring ground truth datasets. The performance, the flexibility, and the ability to push the spatiotemporal resolution limit of sub-diffracted fluorescence microscopy techniques are demonstrated on experimental datasets acquired with various microscopy techniques from 3D spinning-disk confocal up to lattice light sheet microscopy. and readout noise) is usually modeled as a mixed Poisson-Gaussian distribution which is non-stationary and signal-dependent. In the last twenty years, many restoration methods have been investigated in order to "inverse" the model in 2D-3D fluorescence microscopy. The most popular restoration approaches are linear methods (e. g. , Wiener filtering).
e. , mitochondria, cytoskeleton elements and biomolecules associated to moving vesicles in living cells. We also developed CPU and GPU implementations which can be downloaded as open-source from the GitHub website (https://github. com/sylvainprigent/spitfire) under a AGPL-3. 0 license. Finally SPITFIR(e) is interfaced with napari for ease of use (https://www. napari-hub. org/plugins/napari-sdeconv).
Implementation details and code availability The input image is the raw (noisy and blurred) image. In the case of Lattice Light Sheet Microscopy, a necessary "deskew" operation is applied to raw data before to be supplied to SPITFIR(e). Images have been processed on classical workstation for CPU calculation (CPU Intel Xeon 2. 8GHz 4 threads). We use this setup since it is the most common setup in biology labs. For high speed processing we developed a GPU version of SPITFIR(e). We run the GPU on two types of GPU, a Nvidia Quadro M2000 that can be found in most workstations and a Nvidia Tesla K80 for computing grid. Typical computing times (CPU and GPU) for processing 2D-3D-4D images are reported in the Table  3. These computing times are small and take into account the full data processing steps (including data normalization and data copy to the GPU device).
Cells are kept at 37 • C in the presence of 5% CO2 in a humid environment. Cells were seeded on 25mm round coverglass for 12h in the same culture conditions. Cells were then transfected with TOM20-GFP DNA plasmid using X-tremeGENE HP DNA Transfection Reagent (Sigma). After 24 hours, cells are mounted and imaged. During the experiments cells were kept at 37 • C with 5% CO2 in a humid environment using a Tokai Hit heating system (INUBG2E-PPZI). Previously acquired datasets of M10 cells, stably expressing Langerin tagged with eYFP, or Rab11a tagged with mCherry 38 or transiently transfected with PhLuorin Transferrin-Receptor (TfR)  37  were also exploited in this study.
5× tube lens with a final pixel size of 104 nm. Lattice light-sheet imaging was performed using an excitation pattern of outer NA equal to 0. 55 and inner NA equal to 0. 493. Composite volumetric datasets were obtained using 10 ms exposure/slice/channel at a time resolution of 2 seconds per total cell volume (about 60 planes). Fifty-six time points were acquired within 3 to 4 minutes (total raw data, only 1:45 min were analyzed for the movie) for live cell acquisition. Ten time-points were acquired within 15 to 20 seconds for fixed cell acquisition. Acquired data were deskewed, a necessary step to realigned image frames, using LLSpy, a python library to facilitate lattice light sheet data processing (copyright to T. Lambert, Harvard Medical School, Boston, USA; https://github. com/tlambert03/LLSpy).
Deskewed images are then considered as Raw images. Napari, a multi-dimensional image viewer for Python (https://github. com/napari/napari), was used for 3D rendering. Maximum intensity projections were generated using ImageJ/Fiji 1. 53c  63. Intensity profiles were plotted using Matlab2019b. Related supplementary Videos S1 and S2 were generated using napari and napari-animation plugin (https://github. com/napari/napari-animation). 3D Richardson-Lucy deconvolution was performed using deconvlucy from MATLAB imaging processing toolbox. Fourier Image Resolution (FIRE) was estimated using Fourier Ring Correlation  32.
Acknowledgements This work was jointly supported by the  French National Research Agency (France-BioImaging   ANR-10-INBS-04-07  and  ANR-10-INBS-04-01 ;  DALLISH-ANR-16-CE23-0005 ;  LabEx Cell(n)Scale  ( ANR-11-LABX-0038 ) as part of the  Idex   PSL ANR-10-IDEX-0001-02 ) and Innopsys company. This work was also supported by  ITMO Cancer  ( 18CQ091 ). We would like to thank  M. Maurin  from  Inserm   U932  for his help with STED image acquisition, and the  PICT imaging platform of Institut Curie , member of the  France-BioImaging infrastructure  ( ANR-10-INBS-04-01 ) for maintaining spinningdisk and line-scanning confocal microscopes used in this study. We also thank  Dr. Z. Chen  from  Peking University  for providing us with mitochondrial fluorescent probes. Finally, we warmly thank  Dr.
Recent medical applications are largely dominated by the application of Machine Learning (ML) models to assist expert decisions, leading to disruptive innovations in radiology, pathology, genomics, and hence modern healthcare systems in general. Despite the profitable usage of AI-based algorithms, these data-driven methods are facing issues such as the scarcity and privacy of user data, as well as the difficulty of institutions exchanging medical information. With insufficient data, ML is prevented from reaching its full potential, which is only possible if the database consists of the full spectrum of possible anatomies, pathologies, and input data types. To solve these issues, Federated Learning (FL) appeared as a valuable approach in the medical field, allowing patient data to stay where it is generated.
Since an FL setting allows many clients to collaboratively train a model while keeping training data decentralized, it can protect privacy-sensitive medical data. However, FL is still unable to deliver all its promises and meets the more stringent requirements (e. g. , latency, security) of a healthcare system based on multiple Internet of Medical Things (IoMT). For example, although no data are shared among the participants by definition in FL systems, some security risks are still present and can be considered as vulnerabilities from multiple aspects. This paper sheds light upon the emerging deployment of FL, provides a broad overview of current approaches and existing challenges, and outlines several directions of future work that are relevant to solving existing problems in federated healthcare, with a particular focus on security and privacy issues.
The need to protect electronic health records is accentuated by several international regulatory policies, set to restrict data access and protect medical data privacy. For example, the Health Insurance Portability and Accountability Act (HIPAA) 1 in the USA and the General Data Protection Regulation (GDPR) in European Union 2 completely redefine the data management policy. Consequently, valuable data are often confined to individual hospitals and cannot be leveraged for analysis, hindering the application of DL in the healthcare context. Moreover, the increase in heterogeneity of data sources could decrease the performance of the model, bottleneck the whole network, and cause an extra computational cost for both storage and processing. To leverage the value of existing health datasets while protecting privacy-sensitive patients' data, Federated Learning (FL) appeared as a promising solution.
With FL, a global model is trained collaboratively by each agent of the system (e. g. , hospitals, IoMTs, or health care centers) over the decentralized network. This is done via local updates, without exchanging private data. FL replaces the sharing of medical data across medical institutions, which is most of the time prohibited. With the sharing of local trained model information (e. g. , parameters and gradients), it is possible to obtain sufficient knowledge for model training. However, although FL implicitly offers a certain degree of privacy since sensitive data is not exposed and traffic may be encrypted  [7] , additional effort is needed to ensure that the algorithm is proceeding 1 https://www. cdc. gov/phlp/publications/topic/hipaa. html 2 https://gdpr-info. eu/issues/data-protection-officer/ [1]  An overview of IoMT based remote monitoring systems, smart hospitals, mobile health, and IoMT based enhanced chronic disease treatment methods.
x *  [18]  A detailed a detailed study on FL and its application in cybersecurity and cybersecurity for FL. x  [19]  A comprehensive survey on the integration of blockchain and FL. *  [20]  A comprehensive survey on the use of FL for big data services and applications. * x  [21]  An overview about the integration of FL and blockchain for IoT applications. x , x, and * indicates that the topic is totally, not or partially covered respectively. optimally without compromising security or patient privacy. For example, even if data is anonymized, gathering just a few data attributes may allow patient re-identification  [8]. Moreover, FL aims to improve not only privacy but also training efficiency as it uses the computation power and data of potentially millions of IoMT devices/hospitals for training in parallel.
Similarly, Qadri et al. [13]  present a survey on the use of ML for Healthcare-IoT and other relevant technologies, including edge computing, blockchain, big data, and software-defined networks. However, it appears that these solutions based on ML/DL models are obstructed by the scarcity and privacy of user data as well as the difficulty of collecting the data in a central entity. This made FL a good alternative to enhance data confidentiality. As a result, several review papers present the application of FL in many existing domains, including healthcare. For example, Liu et al. [22]  introduce the application of FL to 6G networks, while  [23]  presents an FL approach to route packets in virtualized networks. Moreover, Brik et al. [24]  present the integration of FL and Unmanned Aerial Vehicles (UAVs)-enabled wireless networks.
With FL, training can occur in two different versions: centralized and decentralized. The former, which corresponds to the typical FL workflow, is characterized by the presence of an aggregation server. In this scenario, a federation of training nodes works locally on the available data; each node submits its partially trained model to a central server intermittently for aggregation and then, using the received global model, continues training on the consensus model returned by the server. Conversely, in a decentralized or peer-to-peer FL, the model aggregation does not require a central entity. Each training node exchanges its partially trained models with some or all of its peers, and each does its own aggregation. Independently of the FL training strategy, a recent analysis for the medical field has shown that a general FL-trained model can achieve performance levels comparable to the ones trained on centrally hosted datasets and superior to models that only see isolated single-institutional data  [27]   [28].
Their experimental results show that human activity recognition with semi-supervised FL is not affected by the non-IID data and can achieve comparable accuracy to that of the supervised FL. Another area of applicability for FL is within health industrial and collaborative research for companies that even compete with each other. One of the biggest initiatives in this context is the Melloddy project, which aims to apply multitask FL to the datasets of 10 pharmaceutical companies  [41]. By training a predictive model that allows conclusions to be drawn about how chemical compounds bind to proteins, the partners aim to optimize the drug discovery process without revealing their extremely valuable internal data. Although the efficiency of FL application for healthcare systems, the shared global model trained on the FL server fails in personalization due to the different characteristics or daily activity patterns of users; hence, it is important to have a fine-grained or personalized model.
e. , the FL server). To evaluate the performance of their solution, the authors used a public human activity recognition dataset called UCI Smartphone. The experiment results demonstrate that FedHealth can improve the classification performance compared to the non-federated model and the traditional machine learning model. In a similar work, Elayan et al. [44]  proposed a TFL framework using IoMT devices in order to detect skin diseases. The results demonstrate that the FTL outperforms the non-TFL approach in terms of the Area Under The Curve (AUC) metric and maintains the same accuracy. It also shows that the TFL framework increases the classification time. In addition, to tackle the heterogeneity in IoT environments, Wu et al. [45]  proposed a personalized FL using transfer learning, called PerFit.
[36] RNN Disease-Symptom Knowledge Database The authors exploited the FL and attention mechanism to train an RNN-based model collaboratively by learning a shared global model at the FL server. The model's complexity was not discussed. [37] DNN, Softmax The Heterogeneity Human Activity Recognition Dataset The authors find that FL could achieve comparable accuracy to centralized learning. Also, they demonstrate that FL requires less communication and compute resources when using less complex models, but at the cost of lower accuracy. Small scale experiment. [40] Autoencoder, LSTM Opp, DG, PAMAP2 The authors demonstrate that semi-supervised FL is not affected by the non-IID data and can achieve comparable accuracy to that supervised FL. The performance under the heterogeneity of devices constrains was not explored.
[43]  CNN UCI Smartphone The average accuracy of the FedHealth framework is 5. 3% and 4% better than non-federated learning and no transfer approaches, respectively. The model's performance under the IoMT devices' computing heterogeneity was not addressed. [44]  ResNet-50 Atlas Dermatology dataset The FTL framework outperforms the non-TFL approach in terms of the AUC (Area Under The Curve) metric and maintaining the same accuracy. The heterogeneity of IoMT devices was not studied. [45] CNN MobiAct dataset The authors show that transfer FL can outperform the classical FL in terms of accuracy as well as the centralized learning in terms of communication overhead. The model complexity was not discussed. [ The non-IID condition was not explored. Small scale experiment (i. e. , only four clients).
[51] DNN N/A The authors proposed a A 5G-enabled architecture for COVID-19 diagnosis using FL mechanism. The non-IID condition was not explored. [52]  VGG-16 COVID-19 CT segmentation dataset, chest ultrasound images The authors proposed a clustered FL framework in order to tackle the convergence issues of the classical FL model due to the diverse distribution of the data. The non-IID condition was not explored. [53] CNN,GAN DarkCOVID, ChestCOVID FedGAN trains a GAN in a collaborative way to generate realistic COVID-19 images to improve the COVID-19 detection. The performance in a heterogeneity environment was not tested. [54] GhostNet, ResNet-50, ResNet-101 Covid-chestxray-dataset, COVID-CT The proposed system consists of two important points which are client participation and client selection in order to reduce the training time, communication cost while maintaining the detection performance.
N/A The authors proposed a zero-watermarking scheme based on FL in order to solve the privacy and security issues of the teledermatology healthcare framework. The complexity of the model was not discussed. clustered FL framework in order to tackle the convergence issues of the classical FL model due to the diverse distribution of the data. Specifically, the proposed framework trains a multi-modal ML model in a collaborative way using both X-ray and Ultrasound imagery. Furthermore, since the non-IID and the imbalanced data can decrease the performance of FL, Nguyen et al. [53]  proposed FedGAN in order to address dataset limitation and imbalance data issues. FedGAN is a novel scheme for COVID-19 detection obtained by enabling the joint design of FL and a Generative Adversarial Network (GAN) in a federated way.
However, despite introducing a trade-off between accuracy and centralization, this could lead to significant communication overhead, especially for medical data. To rectify the non-IID training dataset while addressing the communication overhead, Jeong et al. [91]  proposed FAug, in which each device can generate the missing data samples locally using a generative model, i. e. , a conditional GAN. A central server trains the generator, while each device detects the labels missing in data samples and uploads a few seed data samples of these target labels to the server. The server performs oversampling of the uploaded seed data samples, e. g. , via Google image search for visual data, to train the GAN model. Once the trained generator of the GAN is downloaded, each device can replenish the target labels until an IID training dataset is achieved.
At each iteration, the FL server needs to decide how much data and energy each mobile device uses to train the model to minimize the total energy consumption and the training latency while meeting the requirements of the training tasks. The simulation results show that the proposed approach can minimize energy consumption and improve the training latency. In addition, data pre-processing is an important step to remove the redundancy in the training set as well as to find the relevant features to be used during the model training  [110] ,  [111].
VI. CONCLUSION While machine learning approaches, and especially deep learning, are becoming the de-facto knowledge discovery approach in digital healthcare, it appears that data-driven medicine aiming to improve patient care globally requires federated efforts. With the promise of powerful, accurate, safe, robust, and unbiased models, federated learning can neatly protect sensitive medical data while collaboratively training a learning model. Already today, FL is improving medical image analysis by providing clinicians with better diagnostic tools, helping find similar patients for true precision medicine, and optimizing the drug discovery process by reducing costs and time-to-market for pharmaceutical companies. However, the current settings of FL have not answered all the technical questions, and this paper outlined the state-of-the-art approaches and the limitations associated with them.
Metagenomic Assembly, Annotation, Differential Abundance Statistical Analysis, and Genome Reconstruction Paired-end shotgun reads were quality-filtered, assembled, and decontaminated using ATLAS  [46]. In short, the bbduk module quality-filtered, trimmed, and decontaminated for ϕX174 phage DNA, a common Illumina sequencing spike-in, and for all Illumina adapters. Metagenomic de novo assembly was performed using Megahit (k-mer 21-121, version 1. 1. 3)  [47]. We used >5 kbp contigs only for all downstream analysis; this included taxonomic and functional annotation, and metagenomic binning. Protein-coding open reading frames (ORFs) and RNA prediction were completed using Prokka  [48]. Eggnog-mapper (diamond mode, version 0. 8. 22. 84) was used to obtain updated KEGG (KO) numbers for MAGs and contigs  [49].