dataesr/scientific-paragraphs-categorization huggingface dataset:
8 MHz) and a more stringent RFI mask to completely exclude band edges and RFI-afflicted regions was applied. Next,, the resultant frequencyaveraged 960 channels were regridded along the frequency axis to obtain a measurement set with 16 physically distinct spectral windows. Three rounds of phase-only and one round of amplitude and phase self-calibration were performed. For the wide-field (6k × 6k image; pixel size = 2 arcsec) broad-band imaging, the CASA task tclean with w-projection as the gridding algorithm, with 128 planes, in combination with Multi-scale Multi-term Multi-frequency synthesis ( MTMFS ) for deconvolution, with nterms = 2 and four pixel scales to model the extended emission, were used. The images were deconvolved down to 3 σ using masks generated through the Python Blob Detector and Source Finder ( pyBDSF 7  ).
e. that would hav e been observ ed had the FRB occurred on boresight), S o ( ν i ) is the observed integrated S/N in band i , φ φ φ is a two-dimensional vector that represents the directional offset of the FRB from the boresight of the beam and G ( φ φ φ, ν) is the beam response of the telescope. Assuming an ideal telescope receiver (i. e a flat response of the receiver across the entire band), the S t for a given frequency ν would scale as, S t ( ν i ) = S t ( ν 0 ) ν i ν 0 α , ( 7 ) where ν 0 is the highest frequency sub-band. The observed dynamic spectrum of FRB 20201123A can be approximated as a power law and hence that assumption is valid. In order to perform the analysis, we split the data for FRB 20201123A into four frequency sub-bands, and for each band, we computed the S/N of the detected burst.
2021 )  analysis for FRB 20201123A. From the r -band image, we used the PHOTUTILS package  (Bradley et al. 2021 )  to detect sources within the localization region. Table  3  lists all of these detected sources and their measured properties. Apparent magnitudes were calibrated using reference stars from the SkyMapper southern survey  (Onken et al. 2019 ). In order to assess whether an object is a galaxy, we run SOURCE EXTRACTOR  (Bertin & Arnouts 1996 )  and used the CLASS STAR parameter as our star/galaxy classifier. In the following, we restrict to sources with a star/galaxy classifier value lower than 0. 9 and assume the remainder are stars. Four of the sources detected by PHOTUTILS were not detected by SOURCE EXTRACTOR , and thus, these lack a star/galaxy classifier. In the following, we will conservatively assume that these are all galaxies.
K. M. R. acknowledges support from the  Vici research program  ' ARGO ' with project number  639. 043. 815 ,financed by the  Dutch Research Council (NWO). M. C. acknowledges support of an  Australian Research Council   Disco v ery Early Career Research Award  (project number  DE220100819 ) funded by the  Australian Go v ernment  and the  ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions  ( ASTRO 3D ), through project number  CE170100013. J. K. K acknowledges support from the  Swiss National Science Foundation  under grant  185692. Authors W. F. , C. K. , J. X. P. , and N. T. as members of the Fast and Fortunate for FRB Follo w-up team, ackno wledge support from  NSF  grants  AST-1911140  and  AST-1910471. N. T. and C. N. acknowledge support by  FONDECYT  grant  11191217. The authors w ould lik e to thank  Rene Breton  for invaluable discussions regarding the localization of FRB 20201123A using the Bayesian Framework.
Methods: We carried out a systematic mapping study by conducting searches of 3 databases (Association for Computing Machinery, PubMed, and Scopus). Eligible papers were published between 2010 and 2021, and they described HCPs' perspectives on using ePROs. The data on the included papers were extracted, a thematic meta-synthesis was performed, and 7 themes were summarized into 3 categories. Results: A total of 17 papers were included in the study. The HCPs' perceived barriers and facilitators of using ePROs can be summarized into 7 themes: clinical workflow, organization and infrastructure, value to patients, value to HCPs, digital health literacy, usability, and data visualization and perceived features. These themes can be further summarized into 3 categories: work environment, value to users, and suggested features.
ePRO is a software that allows patients to independently answer questions and report on their health using electronic devices, and HCPs can follow their patients' well-being and assessment of symptoms. ePRO provides decision support for HCPs by helping with symptom monitoring and improving patient-clinician communication  [8]. Although most physicians agree on the importance of collecting self-reported data of patients with cancer, engaging physicians in using PROs is a key challenge  [9]. Furthermore, nurses and physicians have various preferences regarding PROMs in clinical practice  [10]. Increasing the awareness of PRO solution providers regarding the barriers and facilitators to using PROs in clinical practice can help inform the design of these tools to support the enhancement of the quality of patient care  [11].
Data Screening Figure  1  illustrates the study selection process and shows the number of included and excluded papers. The papers were imported from 3 academic research databases to Covidence (Covidence. org). Covidence, a systematic review management tool, was used to remove duplicates and manage all the references included in the title and abstract screening, full-text review, and extraction. The searches from the databases were done from August 22, 2021, to September 29, 2021. A total of 152 papers were imported using agreed-upon search terms, and after careful screening, 17 papers remained. A considerable share of studies excluded during the title and abstract screening stage were studies that used PROs as data sources but did not assess the barriers and facilitators of using the system.
Data Extraction and Analysis The data on the included papers were extracted as follows: authors, title, year of publication, study objective, cancer type, study design, duration of data collection, study population, administered PROM, ePRO name, sole use of ePRO, barriers, and facilitators. Thematic meta-synthesis for the extracted data was performed by adapting the methods for thematic synthesis described by Thomas and Harden  [20]. The verbatim findings of these 17 studies were entered into the Excel (Microsoft Corp) file. Each line of text was coded according to its meaning and content. The authors of the paper independently reviewed the data and grouped them into themes. Themes were then discussed and merged. After several rounds of reviewing the coding, the final 7 descriptive themes for the barriers and facilitators were determined.
Barriers Barriers to value were captured by 14 comments identified in 6 papers  [22, 28, 33, [35] [36] [37]. Some HCPs expressed concern that patients may not see the point of using ePRO  [36, 37] ; it may be that it takes too much time  [22]  and that they end up feeling overburdened  [33]  or experience fatigue by the process  [36]. The lack of home access to ePROs  [22]  and the lack of feedback from physicians  [22]  were also mentioned. Patients' inability to complete the PRO can vary based on language  [36, 37] , literacy  [35] [36] [37] , health literacy  [33] , culture  [36] , and health status  [37]. There can be reporting bias; for example, reporting actual symptoms might not be comfortable for patients  [35]. One paper shows that patients might prefer to share their needs directly with HCPs rather than via ePROs  [28].
Conclusions In this study, we provided a broad overview of the barriers and facilitators affecting the use of ePROs. Our work focused on how the working culture and service integration affects the success of ePRO. A greater understanding of barriers and facilitators is useful to software developers and clinical research organizations to create smoother implementations. We found that there are multiple ways to develop ePROs and their working environments to meet the needs of HCPs. They can be summarized into 3 categories: work environment, value to users, and suggested features. The takeaway points detail the findings of this study.
Future Research Based on this study, there is still a lack of information on the national and international knowledge of ePROs. Since there are only a few studies on fully electronically completed PRO data, future research should explore the barriers and facilitators of using ePROs, specifically in organizations where users have sufficient experience using ePROs. As this study is limited by the literature currently available in the selected databases, further work may expand on the knowledge by including additional sources and terms. Future work could also focus on exploring how the implementation of ePROs may affect the patient's journey through the health care system. It would also be interesting to understand whether more usability and features are required of an ePRO than of a paper PROM, given that it is possible to implement features beyond those of paper versions.
This sparked the advent of the second generation of semi-active robots, where the surgeon performs the operation with the assistance of a robotic arm providing a haptic feedback and restriction within a pre-programmed spatial window when making the bone cuts. The industry offers a range of semi-active systems with the MAKO Robotic arm Interactive Orthopaedic (RIO) system (Stryker, USA) being the most commonly used  [28]. The latest software version (MAKO 4. 0) has introduced 'virtual range of motion', a tool that offers real-time feedback, allowing the surgeon to visualise the effect of altering implant position as well as being able to visualise impingement. Studies assessing the outcomes of semi-active robots have shown positive results, such as better-positioned acetabular cups, improved functional outcomes, reduced post-operative pain and a short learning curve  [29] [30] [31].
Pre-operative functional planning Three-dimensional reconstructions were used based on CT pelvis scans to create a functional, patient-tailored plan using the MAKO total hip system (Stryker Corp, Mako Surgical Corp, Ft. Lauderdale, FL). The following workflow was utilised to formulate a patient specific plan: First, the spinopelvic measurements obtained from sitting and standing lumbar spine lateral radiographs were imported into the robotic software. The starting point for acetabular cup orientation was 40° inclination and 20° anteversion. The cup position was subsequently evaluated in the coronal, sagittal and transverse plane to ensure sufficient bony coverage, restoration of the centre of rotation and to ascertain the posterior wall. In relation to stem positioning, the two-dimensional predicted post-operative appearance that is available with the robotic software was evaluated to ensure the appropriate stem size, offset and correct any varus or valgus malalignment.
these samples (5. 2%) over the same period. We analyzed the cycle threshold (Ct) values of the qPCR to gain further insights into the biology and epidemiology of the infections. Finally, we used these results to explore prospective scenarios regarding the dynamics of critical care unit (CCU) occupancy in France in 2022. This study was approved by the Institutional Review Board of the Centre Hospitalier Universitaire of Montpellier and is registered at ClinicalTrials. gov (identifier no. NCT04738331).
Cohort Description The variant screening tests were performed by Cerba Laboratory (Saint Ouen L'Aumône, France) on samples that originated from partner centers in mainland France and tested positive for SARS-CoV-2 with a generic qPCR assay. Most of the samples originated from the general population (Tables  1, 2 ). We did not have access to additional details about patient symptoms; however, according to an earlier study on a similar cohort, nearly all the samples originated from nasopharyngeal swab specimens, and the proportion of symptomatic and asymptomatic individuals were comparable among the positive tests  (14). To limit epidemiologic biases, we removed persons >80 years or <5 years of age from the dataset.
Whole-Genome Sequencing Next-generation sequencing (NGS) was performed by Cerba Laboratory for 16,973 samples with a Ct <30 using the CovidSeq amplicon-based NGS assay according to supplier recommendations (Illumina, https:// www. illumina. com) and after a Janus/Chemagic RNA extraction (Perkin Elmer) from the nasopharyngeal swab. All sequences obtained were submitted to the EMERGEN Consortium Database (Santé Publique France, https://www. santepubliquefrance. fr/dossiers/coronavirus-covid-19/consortium-emergen) and GISAID (https://www. gisaid. org).
r-project. org). We computed estimated marginal means for the Ct values associated with the screening tests results by using the emmeans function from the eponym R package. We plotted the fitted values from the linear model by using the predict function in R. The statistical methods are further described (Appendix 1, https://wwwnc. cdc. gov/EID/article/28/7/22-0033-App1. pdf), and raw data and R scripts are available online (https:// doi. org/10. 5281/zenodo. 6536220).
These values, like others, were informed from literature data, technical reports, and preliminary work. Having a mechanistic model enables us to explore prospective scenarios for CCU activity. We did so by formulating assumptions regarding the intensity of future control measures and incorporating our estimates of growth advantage and relative frequency of the variants into the model. In this study, the temporal reproduction number (Rt) corresponds to the average number of secondary infections caused by an infected person at date t and is estimated by using national hospital admission data (https://www. data. gouv. fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19) and the EpiEstim method  (25). We shifted the dates in the incidence time series to compute Rt, setting the median time between infection and CCU admission to 14 days  (21, 26).
85 (95% CI 0. 83-0. 88) per age unit (equal to 56 years in this study). We also detected strong temporal increases in most of the regions of France that had RRR >10 per day. In some regions, we detected a temporal increase of A0B1C1 tests, consistent with the Kappa variant. Finally, in our dataset, the (rare) A1B0C1 tests only showed a slight temporal increase in 2 regions (Bretagne and Hauts-de-France). We then estimated growth advantages of A0B0C0 over A0B0C1 infections during 21-day time windows. The advantage was adjusted for covariates and assumed to be constant over each window. In September 2021, A0B0C0 infections were spreading less rapidly than A0B0C1 (Figure  2 , panel B). This finding is consistent with the rapid increase of the Delta variant at the time  (18). The pattern shifted at the end of November, with a 50% growth advantage of A0B0C0 infections, which increased to 105% (95% CI 96.
Acknowledgments The authors thank the Experimental and Theoretical Evolution team from  Maladies Infectieuses et Vecteurs: Écologie, Génétique, Évolution et Contrôle, University of Montpellier , for discussion, as well as the EMERGEN consortium (complete member list in Appendix 2, https:// wwwnc. cdc. gov/EID/article/ 28/7/22-0033-App2. xlsx). This project was supported by the  Occitanie Region  and the  Agence Nationale de la Recherche  through a grant to the  PhyEpi  Project and by  Agence Nationale de la Recherche Maladies Infectieuses Émergentes  to the  MODVAR  project (grant no. ANRS0151 ).
Each of these additional aspects may lead to further partitioning the population. Unfortunately, concerns are typically intertwined in epidemiological models in both executable code and mathematical equations. At first sight at least, it seems difficult to separate these concerns since, when a concern is added to a model, it is precisely because it interferes with the core epidemiological concern to introduce some kind of heterogeneity. For example, in a spatial SIR model, the spatial concern impacts the epidemic concern at each region which assigns a specific value to an epidemic parameter. If the epidemic parameters had the same value on the whole space, there would be little point in introducing a spatial concern in the first place. The Kendrick approach  (Bui et al. , 2016)  consists in defining concerns (age, sex, spatial heterogeneity,.
SDN encompasses a set of network management-oriented techniques that focuses primarily on decoupling the actions of the control plane logic from the data plane. This principle allows a high level of flexibility in the system while the logical control of the network can easily be centralised in an external entity, the SDN controller. NFV virtualizes traditional network functions, typically deployed on physical equipment, into software that can be run on servers or devices with computing capabilities, such as Virtual Network Functions (VNFs) or container network functions. From an architectural point of view, IDDM is essential in addressing the scalability Fig. 2 : DEDICAT 6G framework for CEaaS and dynamism issues of managing computing resources in B5G/6G scenarios, which is particularly critical at the edge and domains.
A. Context Awareness DEDICAT 6G has developed a context awareness module for device discovery. A new type of enhanced Internet of Things (IoT) devices is proposed to estimate the number of users in a coverage area by short-range signal scanning techniques (e. g. , Bluetooth  [7]  or Wi-Fi signals  [8] ). These devices monitor what is happening and draw a heat map related to the number of users present at a given time at a designated point. This strategy works like a people counter  [9] , whose application is to detect a crowd at a given location. It can be used to detect a possible overloading of communication networks due to the presence of a high number of users. The people counter is performed by IoT devices through the knowledge of media access control (MAC) addresses detected and the received signal strength in the coverage area  [9].
To collect the data generated by each IoT device, the context awareness module uses a standard messaging MQTT (Message Queuing Telemetry Transport) protocol. It simplifies the collection of data from the sensors, the publication of the different values obtained and the remote configuration of the nodes. Depending on the data analysis, this context awareness module can explicitly request CEaaS, support CEDM/IDDM/NODM decisions and make MAP deployments more efficient. Bernardos et al. [10]  predicted that spectrum management and utilization will become more flexible and dynamic compared to today's static and conservative approaches. In the 5. 9 GHz ITS band, multiple RATs, such as 5G NR, LTE, and Wi-Fi, can be used for vehicle-based MAP deployment along with the incumbent V2X technologies. DEDICAT 6G proposes a knowledge-building process which is used to sense the traffic from co-located RATs.
D. Intelligence Distribution Decision Making The DEDICAT 6G project assumes as intelligence every software-based part that can consume computational resources available on the nodes of a B5G network scheme. Three different types of intelligence are considered in this context, DEDICAT 6G FC instances, VNFs based on ETSI's NFV principles, and Verticals apps to exploit the MEC capabilities of the 5G ecosystem. In CEaaS it is especially relevant to put the spotlight on the first two types of intelligence. First, it is essential to deploy concrete FCs along the network fabric to perform their corresponding functionalities in order to enable CEaaS. For this purpose, the DEDICAT 6G architecture relies on the IDDM FC to calculate the best locations to accommodate the intelligence taking into account the current state of the computational and network contexts.
DEDICAT 6G considers as one of its main pillars the research and development of novel intelligence distribution techniques to be applied in a dynamic and changing network ecosystem. And secondly, combination of control-data decoupling in network management and virtualisation-based orchestration techniques is essential for successful end-to-end connectivity and coverage extension in B5G networks. DEDICAT 6G investigates techniques to automate the correct configuration of a connection from the core platform in coverage extension operations. We focus on instructing NFV Orchestrator (NFV-O) to set up the service/network portion to enable ad-hoc connectivity when the connectivity area needs to be extended or moved. We also involve deploying lightweight VNFs at edge nodes or far edge nodes, such as drones or robots.
DEDICAT 6G supports the configuration of 5G/RAN components and enables runtime operations or post-installation reconfigurations. Last but not least, IDDM FC will not directly implement the changes in the system but will provide optimal recommendations. The Service Orchestrator FC is in charge of first translating those instructions into the correct form, and ultimately interacting with the external entities to implement the corresponding actions. Hence, DEDICAT 6G is investigating and developing concrete modules to support such interactions with some of the trending 5G-based control plane tools, such as ETSI OpenSource MANO or Kubernetes.
A. Knowledge Building An efficient spectrum sensing mechanism is required to enable dynamic spectrum management of 6G networks  [15]. In this section, we present the performance of the spectrum sensing TR model used for the environment knowledge building process. The TR model is trained and validated based on dataset collected from LTE, 5G NR and ITS-G5 technologies. In the data collection phase, samples of each signal type are received and collected at a sampling rate of 20 Msps and a center frequency of 5. 9 GHz with 10 MHz bandwidth. For each technology, the dataset and a description of the dataset collection are available as an open source 1. The dataset contains 7500 examples for each technology. Initially, the collected IQ samples are pre-processed by introducing Additive White Gaussian Noise (AWGN) with -15, -10, • • • , 1 https://gitlab.
ilabt. imec. be/mgirmay/dataset-LTE-5GNR-ITS-G5 30 dB SNR (Signal to Noise Ratio) levels. This pre-processing is introduced to evaluate the performance of the TR model on different channel conditions. After the AWGN is introduced, FFT (Fast Fourier Transform) of the IQ samples is used to train and validate a Convolutional Neural Network (CNN) based TR model. Using FFT of the IQ samples as an input leads to higher accuracy due to the more distinguishable frequency domain features of the signal. This makes the FFT preprocessing essential for ITS band spectrum sharing, where the high accuracy is required to identify and protect safety critical information transmitted by the V2X users. The structure of the CNN model and the implementation details are described in  [11]. The CNN in  [11]  was modified to take into account three distinct classes, namely LTE, 5G NR, and ITS-G5, and the new dataset was collected at the 5.
9 GHz ITS band. Figure  3  shows the TR performance in terms of confusion matrix and classification accuracy. In the performance evaluation, 70% of the total dataset are randomly selected and used for training while the remaining 30% samples are used to validate and test the CNN model. Figure  3a  shows the confusion matrix of the proposed TR model at 10 dB SNR, while Figure  3b  illustrates the classification accuracy of the TR model for various SNR values from -15 to 30 dB. For SNR values of 0 dB or higher, we can observe that the TR model can effectively distinguish the technologies, exceeding a classification accuracy of 97%. This TR-based knowledge building process can be used to enable efficient and dynamic spectrum sharing in future 6G networks.