dataesr/scientific-paragraphs-categorization huggingface dataset:
The purpose of this architecture seems to be to enable secure and decentralized communication between these components while maintaining modularity and scalability  [45]. The App is the user-facing component of the system and can be accessed through various interfaces such as web, mobile, or standalone applications. To access the system's functionality, users need to be authenticated and authorized, which is handled by the Auth-server. The App-server is in charge of interacting with the App, obtaining information from the DB, and, if necessary, assigning tasks to the Auth-server  [9]. The App-server in turn serves as a go-between for the block-chain network and the Auth-server. By accessing the block-chain network, it authenticates the user and grants access to the database. It can also execute commands and alter the global state, but it is the only element that communicates directly with the block-chain network.
Programming languages To implement the above concept, we use the following programming languages and technologies: By utilizing Node. js with Express for the backend application and writing chain code in Node. js for the Hyperledger Fabric network, along with technologies such as Docker for containerization and Docker Swarm for orchestration, we can create a robust system. AWS Cloud provides reliable hosting for the Hyperledger Fabric network, while JSON Web Tokens (JWT) define user roles and permissions. Role-based access control (RBAC) libraries like Casbin manage user access, and federated authentication is achieved through Open ID Connect or OAuth 2. 0. Twofactor authentication (2FA) libraries like Speakeasy add an extra layer of security. OAuth 2. 0 client libraries like Passport. js authorize third-party applications, and an API Gateway such as Kong or Tyk handles authentication and authorization.
Hyperledger Fabric ensures transparency and tamper-proof transactions through block-chain-based distributed ledger technology, and smart contract development with Chain code in Node. js manages data access policies and user permissions. DevOps tools like Jenkins automate continuous integration and deployment, while logging and monitoring tools provide ongoing system monitoring and updates. This combination of programming languages and technologies enables the creation of a secure, scalable, and decentralized system where users can authenticate, authorize third-party applications, and access specific data on the Hyperledger Fabric network. By using the above programming languages and technologies, we can build a secure, scalable, and decentralized system that allows users to authenticate and authorize third-party applications to access specific data stored on the Hyperledger Fabric network.
Pseudo-code of the program 5. 1. Here we provide a pseudo code to create an OAuth 2. 0 server, define client IDs and secrets, and create OAuth 2. 0 scopes In the above pseudo-code, the client array contains the client IDs and secrets for each third-party application. scopes array contains the OAuth 2. 0 scopes to specify the data that third-party applications can access. The app. get and app. post functions define the routes for the OAuth 2. 0 server, which can be implemented in our preferred programming language and web framework.
5. 2. Pseudocode that demonstrates how to deploy a Hyperledger Fabric network 5. 3. Pseudo code for data access policies 5. 6. Here is a pseudo-code for authorizing third-party applications to access data stored on the fabric network and issuing access tokens using the OAuth 2. 0 server 5. 7. The separation of authentication and authorization processes can be implemented using pseudocode 5. 8. Pseudocode for using block-chain technology to ensure transparency and tamper-proof transactions 5. 9. Pseudocode for storing access policies and user permissions on the fabric network 5. 10. Here is a pseudocode example for continuously monitoring and updating a system.
Comparative analysis with state-of-the-art techniques The proposed identity and access management system that combines OAuth 2. 0 and Hyper-ledger Fabric provides several advantages over traditional techniques  [21, 44]. Firstly, it ensures the privacy, security, and scalability of user authentication and authorization processes by separating these processes and using block-chain technology to maintain tamper-proof transaction records. Secondly, using OAuth 2. 0 as an authentication server helps keep user credentials secure and not stored in the database, reducing the risk of unauthorized access. Thirdly, the integration of Hyper-ledger Fabric enables the development of block-chain-based applications with a modular architecture, allowing granular access control to sensitive information, such as transaction history or asset ownership records.
Challenges related to deployment and adoption in real-world settings While the proposed algorithm provides a secure and scalable solution for managing access to sensitive resources, there are still several issues with its implementation and acceptance in practical contexts. Among the main obstacles are: 1. Integration complexity: Integrating OAuth 2. 0 and Hyper-ledger Fabric requires expertise in both technologies and integrating them into existing systems can be complex and time-consuming. 2. User acceptance: Users may not be familiar with block-chain technology and may be hesitant to adopt the new system, especially if it involves changing their current authentication and authorization processes. 3. Cost: The suggested system's implementation can be costly because it calls for large expenditures on labour, hardware, and software.
0 and Hyper-ledger Fabric to create a system that provides granular access control to patient records stored on the Fabric network. 3. Develop the algorithm: To ensure the security and privacy of patient information, the system incorporates various measures. OAuth 2. 0 is employed as the authentication server, eliminating the storage of patient credentials in the database and minimizing the risk of unauthorized access. Additionally, trusted providers like Google, Facebook, or LinkedIn are allowed for authentication, providing an extra layer of security. The system leverages Hyperledger Fabric, a modular block-chain framework, to develop applications that prioritize privacy, security, and scalability. OAuth 2. 0 is also utilized for authorizing third-party applications to access specific patient data stored on the Fabric network, enabling granular access control to sensitive information.
Background and Objectives: The characterization of the vocal tract geometry during speech interests various research topics, including speech production modeling, motor control analysis, and speech therapy design. Real-time MRI is a reliable and non-invasive tool for this purpose. In most cases, it is necessary to know the contours of the individual articulators from the glottis to the lips. Several techniques have been proposed for segmenting vocal tract articulators, but most are limited to specific applications. Moreover, they often do not provide individualized contours for all soft-tissue articulators in a multi-speaker configuration. Methods: A Mask R-CNN network was trained to detect and segment the vocal tract articulator contours in two real-time MRI (RT-MRI) datasets with speech recordings of multiple speakers.
The disadvantages are https://doi. org/10. 1016/j. cmpb. 2023. 107907 Received 10 April 2023; Received in revised form 26 October 2023; Accepted 29 October 2023 the small number of sensors that can be used without disrupting speech and the inability to track articulators in the back of the vocal tract and the pharynx. The X-ray and EMA weaknesses explain the vast use of magnetic resonance imaging (MRI) in the speech community  [3, 13, 6]. MRI is safe for the subject, enabling the recording of large datasets and permitting the acquisition of high-resolution volumetric images to visualize the complete vocal tract shape. Static volumetric MRI was extensively used to record sustained phonemes; however, the long acquisition duration per volume limits their use for recording dynamic processes such as speech, even though attempts can be found in the literature  [14, 15].
Then, the oriented active shape model  [21]  recognizes and delineates the vocal tract shapes. However, the small dataset (25 images from five speakers) limits the significance of this work. Alternatively, Silva and Teixeira  [22]  proposed an unsupervised method based on a modified version of the active appearance model  [23]. The technique takes advantage of the low inter-frame differences and is based on 26 vocal tract landmarks manually marked per frame in a training database of 51 images. Moving towards machine learning algorithms, Labrunie et al. [24]  explored a large RT-MRI corpus in French for training three supervised segmentation methods: Multiple Linear Regression (using pixel intensities), a modified version of the Active Shape Model (mASM), and Shape Particle Filtering (using more elaborate image features).
Datasets The corpus for this research comprises two real-time MRI datasets of French speakers, ArtSpeech Database 1 (ASD1) and ArtSpeech Database 2 (ASD2). The ASD1 is a part of the database described in Isaieva et al. [38]. The ASD1 corpus included 77 sentences which were constructed to provide a good coverage of the phonetic contexts of French vowels. While the published database contains ten subjects, 1 https://gitlab. inria. fr /vsouzari /vt _tracker. only seven (denoted in this text as S1-S7) were used in this study because the larynx was not visible in the images of the other three. The ASD1 contains a total of 365 400 frames uniformly distributed between the subjects. The same protocol was used for ASD2. This dataset contains 320 000 frames of a single subject and covers a larger speech corpus, ensuring better coverage of the French phonetic context.
The subject participating in ASD2 acquisition was the last subject S7 from the dataset ASD1. To distinguish these two sets, the data of S7 from ASD1 is denoted as S7. 1, and its data from ASD2 is marked as S7. 2. Both datasets were collected using state-of-the-art protocols and recommendations at the Centre Hospitalier Régional Universitaire de Nancy, France. All participants provided written informed consent, and the data were recorded under the approved ethical protocol "METHOD-O" (ClinicalTrials. gov Identifier: NCT02887053). The study was approved by the institutional ethics review board (CPP EST-III, 08. 10. 01). The images were acquired with a Siemens Prisma 3T scanner (Siemens, Erlangen, Germany). The radial RF-spoiled FLASH sequence  [39]  was used, with the parameters listed in Table  1.
The films were recorded at a frame rate of 50 fps and reconstructed with the algorithm presented in Uecker et al. [39]. During the acquisitions, the speakers were asked to read out loud sentences that were projected to them. Each acquisition took about 80 seconds. Due to the difficulty of annotating all of the images in the datasets, samples with a good representation of variability were selected. Initially, this selection was conducted independently for datasets ASD1 and ASD2, by different co-authors of this study, using slightly different methodologies. Later the collected and annotated data were merged to increase the database. In Isaieva et al. [36] , it was shown that several hundreds images were sufficient to train a good tongue segmentation algorithm. Therefore, sample sizes of both datasets was selected to be of this order.
To ensure the best variability coverage, the images for manual annotation were selected with the 𝑘-means algorithm. For ASD1 a 𝑘 = 100 was selected. The 𝑘-means algorithms was applied independently for each subject, and only the closest to the cluster centers images were kept, resulting in 700 images in total. For ASD2 the algorithm was applied with 𝑘 = 10. The clusters were evenly sampled, resulting in 427 images. The two datasets were split into train, validation, and test as described in Table  2. The data was divided at the complete sequences level, so all samples from the same acquisition were placed at the same split. The reason is that adjacent images are very similar, and putting them in separate sets would introduce bias into the train-test scheme. The validation set for S7. 1 is empty because it is the same subject as S7.
Annotation procedure We performed semi-automatic annotations. Our previous segmentation system  [5]  was used to track the upper vocal tract cavity, including the two lips, tongue, soft palate, and pharynx. For the larynx articulators, which were not included in the previous study, we trained a Mask R-CNN network with the 427 samples from ASD2 described in Table  2  to produce a first guess of the contours for each articulator. The mod- els were then used to automatically annotate the unlabeled images in the dataset. The automatic annotations were then carefully reviewed and manually corrected. This semi-automatic procedure allowed us to complete the annotation protocol with limited resources. The image annotations were made as follows: • Arytenoid Cartilage: Through their vocal process, these cartilages are the siege of the vocal cord attachment to the posterior part of the larynx (represented by the cricoid cartilage).
The models were pre-trained on the COCO train2017 dataset  [44] , which contains RGB images of common objects. However, the MRI frames are grayscale. Therefore, we used the temporal dimension to build a more contextualized input. The network's inputs were formed by putting frames 𝑡 -1, 𝑡, and 𝑡 + 1 in the first, second, and third input channels, respectively.
Experimental design We aimed to develop a speaker-independent method to accurately and individually track non-rigid vocal tract articulators in RT-MRI movies. We also wanted to investigate how speaker adaptation could improve the method's performance for a new subject. We carried out two experiments. The first was a LOOCV protocol. We removed subject S7. 1/S7. 2 from the test phase in the LOOCV pipeline because they account for the most images in the database, but still kept it for training. Leaving S7. 1 and S7. 2 out would have resulted in a significant reduction in the training set, making it difficult to determine the cause of the performance improvement. From the remaining six subjects, we isolated one at a time and trained a model with the remaining subjects. We then tested the model on the test set of the left-out   subjects.
While both annotations could be considered as correct, the annotator of this image selected a more internal part of the tongue body, while the model delineated a more external contour. Probable explanation of this phenomenon is that the model privileged the tongue contour selected by other annotators (who worked on other images of the dataset). These discrepancies are usual in machine learning. The performance metrics are calculated in reference to a human annotator. However, even if different specialists provide their annotations for the same images, the annotations are likely to differ. This effect is known as inter-annotator agreement, a common phenomenon in image segmentation tasks  [50]. A slight deviation from the ground truth is acceptable, but the hypothetical inter-annotator agreement should constrain it.
Acknowledgements This research was supported by the  French ANR  project  Full3DTalk-ingHead  (grant number  ANR-20-CE23-0008 ). This work is co-funded by the  French State-Region  contract  CPER 2015-2020  ( IT2MP ) and by the  European Union  through the  European Regional Development Fund  " FEDER-FSE Lorraine et Massif des Vosges 2014-2020 ".
Conclusions In this study, we proposed and assessed a method for segmenting the vocal tract shape in real-time MRI using a deep learning approach. We also proposed a transfer learning method operating with a small dataset (in comparison to other deep learning applications). The method accurately estimated the shapes of nine non-rigid articulators that delimit the vocal tract tube and are essential for articulatory speech synthesis. We also showed that the model can generalize to new subjects. If the position of a new subject significantly differs from the previous set, the model may need to be adapted to the target subject. Nevertheless, we showed that only a small amount of data (about ten images) is required for a good adaptation. Our recent research in articulatory synthesis of speech has shown that the method is helpful for this task  [4, 5, 52].
We plan to address some of the method's limitations in future work, such as the difficulty of segmenting contact regions, especially the tongue tip. Also, our source code is available as a Python package that makes exploring RT-MRI data to investigate speech production easy. The package only covers the soft articulators, but we plan to extend it to rigid bodies to complete the vocal tract shape. Finally, ASD1 images are already available  [38]. We plan to publicly release the ASD2 dataset together with the manual and automatic annotations for both datasets. We hope this work will be helpful for other researchers interested in investigating speech production using RT-MRI data.
According to this classification, Nancy (France) is Cfb which corresponds to a temperate oceanic climate whereas Las Vegas (USA) is Bwk which stands for cold desert climate. Therefore, the two selected sites should lead to different results and interesting complementary analyses. The data for the US location are taken from the United States Environmental Protection Fig. 6. Soiling ratio simulated from Coello's model. The cleaning ratio is set to 0. 3 mm d - 1  for soiling ratio. This value is chosen according to the literature  [39]. Agency. 1 For France, the online portal Geodair was used. 2  The reason for having chosen these two very different locations can be seen in the representation of the precipitation and the simulated soiling ratio (see Figs. 5 to 6 ). In the Nevada region rainfall is very low and the desert environment favors the deposit of dust on the panel.
Time integration: estimation of the electrical production For time-integrated simulations, the objective is to obtain the energy produced by a photovoltaic panel over long periods of time without omitting events that appear dynamically in the energy balance. Then, the electrical production found by the current model is compared to the production predicted by the PVGIS tool. 3  This tool is capable of predicting the electrical production for a given location over a year with monthly details. Firstly, the current model was applied to June 2022 in Nancy using the local weather data whereas PVGIS uses satellite data. Secondly, the energy is estimated for a whole year (2019) using weather data from the NREL laboratory as inputs of the presented model. The electrical production (energy) is obtained by integrating the electrical power over time from 𝑡 start to 𝑡 end.
Electrical production for june 2022 June 2022 is chosen to compare the measured electrical power to the estimated energy productions from the current model and PVGIS tool. The data are collected from the local weather station and used as inputs for the model. The data shows that the measured DC energy production from the experimental platform is 51. 26 kWh. From the Monte Carlo simulations, the estimated energy without soiling is 51. 1 ± 0. 33 kWh and 50. 7 ± 0. 33 kWh with activation of the soiling. These results are also compared with those from the PVGIS tool. With the same location, tilt angle, azimuthal orientation and mounting configuration, PVGIS estimates the AC electrical production for a typical June month to be 40. 45 kWh. The DC/AC efficiency of the micro-inverter installed on the Cerema facilities is 97 % according to the datasheet.
Firstly, the input data are not the same. PVGIS uses satellite data, whereas in our June assessment, local data were used for the Monte Carlo model. Local data provide a more accurate estimate, as they are closer to actual weather conditions at PV panel level. This is reinforced by the particular architecture of the building on which the photovoltaic panel is installed. Indeed, PVGIS allows to choose the type of mounting as Building Integrated, but does not fully reflect the actual mounting. In addition, PVGIS does not estimate production on the DC side, but on the AC side of the installation. Even though the DC/AC conversion has been calculated based on the efficiency of the microinverter, this may introduce an additional bias into the comparison of the two results. Finally, the thermal model used by PVGIS to evaluate the PV panel temperature is a function of the irradiance, air temperature and wind speed and does not take into account the thermal capacity of the PV panel  [44, 45].
Annual production and monthly analysis The aim is to estimate the energy produced over a year by a 310 W PV panel located in Nancy (France) and the same PV panel near Las Vegas (Nevada, United States) where one of the biggest photovoltaic plants is installed. These two locations present very different weather and solar conditions and should provide very different results for the Fig. 13. Monthly photovoltaic production compared to monthly solar energy for Nancy and Nevada locations in 2019. Graphically, the photovoltaic production is above the incident solar energy for most months except July, August and September. This observation is characteristic of the negative effect of temperature on the electrical production during the summer period. electrical production. For this longer time period, the 2019 weather data from the NREL laboratory were used.
The global, diffuse and direct horizontal irradiances were extracted from the data file to calculate the plane of array (POA) irradiance of a 30°tilted and south-oriented PV panel. Air temperature and wind speed were also used from the NREL file. The soiling ratio was calculated using rainfall events measured from the local weather station. The long-wave radiation data were not available in the data file and Eq. (  34 ) was used to calculate the sky temperature from air temperature. Using the CDF described in Eq. (  52 ) and plotted in Fig. 7  to estimate the annual energy production in Nancy, only 10,000 realizations are necessary to achieve a standard deviation of 0. 89 kWh which means that the number of realizations has been reduced by a factor of 100 compared to the previous uniform PDF.
Electrical production over several years In this last section the electrical production of the panel is evaluated over an eight-year period from 2013 to 2020 in Nancy. The meteorological data presented in Fig. 15  come from Meteo-France and the particle data are extracted from the same source as in Section 3. 4. 2. It is possible to obtain an estimate of the electrical production with only 10,000 Monte Carlo realizations by integrating soiling and aging effects on the PV panel with a standard deviation around 10 kWh thanks to double randomization. With soiling and aging effects activated, the electrical production over the eight years is 3159. 8 kWh with a standard deviation equal to 10. 8 kWh. Without the aging effect, the electrical output for the same period is 3336. 9 kWh with a standard deviation equal to 11.
Acknowledgments The computational environment used in this study is based on the open source library Star-Engine developed by the start-up Meso-Star (https://www. meso-star. com/projects/misc/about-fr. html). This work was carried out using HPC resources from the  University of Lorraine 's EXPLOR Mesocentre (https://explor. univ-lorraine. fr/). The authors would like to thank the  Recom-Sillia and Enphase companies  for donating equipment for this study.