dataesr/scientific-paragraphs-categorization huggingface dataset:
TRN site: Nocturnal vertical gradients The Trainou 180-m tall tower atmospheric observatory is located about 80 km south of GIF (Fig  1 ). A description of the TRN site is available online at https://icos-atc. lsce. ipsl. fr/ panelboard/TRN. At the TRN tall tower, atmospheric COS was measured with the Aerodyne Research quantum cascade laser (mini-QCL) formerly deployed at the SAC station  [14]. The sampling lines, made of Synflex tubing, collected air at 4 heights (5 m, 50 m, 100 m and 180 m) sequentially, the total sequence lasting for 80 minutes. Because the TRN station is operated in the framework of ICOS European Infrastructure  [15] , the mini-QCL was synchronized with an ICOS analyzer measuring CO 2 , CH 4 , CO and H 2 O (Picarro Model G2401). A proxy of the ratio of nocturnal ecosystem fluxes (F COS /F CO2 ) was calculated from nighttime vertical gradients of COS and CO 2 (Grad COS /Grad CO2 from data collected at 5 m and 180 m height), assuming similarity of COS fluxes and gradients to CO 2 fluxes and gradients.
Data used to calculate the linear regression slope between Grad COS and Grad CO2 , were selected according to time of day as follows:  [21; 4]  (UTC) in spring and summer since we only aimed at examining the capacity of local agricultural ecosystems in exchanging COS with the atmosphere. The linear regression slope between Grad COS and Grad CO2 is forced through zero because vertical gradients tend to zero during windy nights. The relative vertical gradients of COS measured at TRN between 5 and 180 m can be used to assess local exchange rates provided that the highest inlet height remains within the nocturnal boundary layer (NBL) throughout the night. This necessary condition was achieved during the months of May through August of 2019 and 2020 as shown later from an analysis of the diurnal variations in monthly mean CO 2 profiles.
COS fluxes are computed as the product of COS/ 222 Rn slopes during nighttime inversion and 222 Rn local exhalation rates. The latter (i. e. , 52 Bq m -2 h -1 with a seasonal cycle amplitude of +25% in summer and -25% in winter driven by soil moisture) was taken from  [17]. The RTM allowed us to The small ellipse on the left panel of Fig  1  delineates the fields from which COS fluxes were assessed indirectly from downwind minus upwind horizontal concentration gradients along the wind direction (WD). First, wind speed (WS) and WD data from a weather station located on the LSCE roof top (Vaisala model WXT530) was used to approximate local meteorology. In the field, we used the facilities of a local meteorological station belonging to the Institut National de la Recherche Agronomique (INRA), equipped with a wind vane providing a continuous visualization at the local scale of the WD.
WS data from the INRA weather station was downloaded later. Horizontal COS concentration gradients were determined discontinuously in spring-summer of 2019, 2020 and 2021. They were documented between 9:00 and 11:00 AM (local time), during non-rainy days, from flask-air samples collected in pairs each upwind and downwind (2 < WS < 14 km h -1 , mean = 6 km h -1 , SD = 3. 6 km h -1 ) of selected crops fields surrounded by the ellipse in Fig 1A. Although the total duration of the crop surveys was 2 hr, each horizontal gradient was documented within 40 minutes. We ensured that the concentration gradient was not affected by the residual nocturnal boundary layer by comparing samples collected on an hourly basis at 7 m (GIF time series) with upwind flask samples collected at canopy height. The sampling device has been described by  [18].
Moreover, VOC emissions of winter wheat increased twofold during the senescence stage compared to the maturation stage  [27]. This is qualitatively consistent with Maseyk et al. (2014)'s observations over wheat fields  [8]  and ours over rapeseed crops. Although those datasets suggest rapeseed to be a stronger emitter of volatile compounds than wheat, comparison with Maseyk et al. [8]  suggest that rapeseed and wheat emit COS during the senescence phase at about the same rates. However, both approaches have estimated COS emission rates by senescent plants indirectly, i. e. , from the difference between EC and soil flux measurements  [8]  and from the difference between fluxes estimated by the RTM method and simulated using the ORCHIDEE model (this work). There is a lack of comparative studies of COS fluxes by wheat, rapeseed and potentially by other plants using dynamic chambers as for VOC fluxes  [26].
Acknowledgments SB expresses its special thanks to  Mark Zahniser  at  Aerodyne Research  for its unconditional and enthusiastic support during operation of the mini-QCL. We thank  Nicolas Vuichard  and  Tanguy Martinez  for preparing the ERA5 forcing files for the ORCHIDEE model and the ICOS data products displayed in the supplements, respectively. We wish to thank the two reviewers, including  Mary Whelan , for their helpful suggestions to improve the paper.
On the other hand, DISPERS requires interfacing with the PDMS devices mainly at two levels: for getting the user profile and for getting the local results for system queries. In turn, this calls for the standardization of the PDMS interface, e. g. , similar to a relational SQL database. It also requires interactions with the PDMS owner  [14]  that has to set the appropriate sharing rules and exposed user profile. We argue that all these issues, essential for a deployment, are not specific or more complex for DISPERS than for a centralized system, e. g. , like Prio  [18]  or SMCQL  [9]  which need to collect users data either offline  [9]  or at query time  [18]. Finally, once the data sharing policies are defined, the users themselves do not need to be online (i. e. , live interaction) unless they want to query the system.
The mechanisms employed to negate these attacks are based on redundancy at the storage and routing levels  [74]. DISPERS leverages the idea of imposed node location based on a trustworthy PKI certificate like in the aforementioned works. Routing and storage attacks are not directly addressed but the verifications added to the active attack protocol use collaborative probabilistic proofs to ensure a correct execution. Distributed indexes. Several works consider indexing documents, profiles or even databases on top of a DHT using a distributed version of inverted indexes  [67]  as for our concept index. Enhancements were proposed to reduce the number of lookup operations  [31] , to minimize the index size  [64]  or to index compact database summary  [28]. These proposals do not consider security issues and are closely related with the type of indexation (e.
g. , for keyword searches), needing further work for security and profile indexing. Security tools. DISPERS relies on classical security tools (see Appendix A), sometimes slightly adjusted to achieve better system efficiency or to better distribute the system load (e. g. , adapted onion routing). Also, message anonymization has been extensively used in secure distributed data aggregation protocols (e. g. ,  [53, 54] ). However, many works consider a trusted third party, i. e. , the anonymizer. To relieve our system from this (strong) assumption, we employ message forwarding through proxy node chains whenever anonymization is required, which is similar to onion routing in distributed systems  [58]. Finally, SSSS is frequently used in distributed systems to protect data-at-rest  [12, 27]. It provides a fully-secure solution to our distributed profile indexing problem with a better trade-off between efficiency and redundancy than alternative secrete sharing schemes  [56].
Limitations and Other Challenges This section discusses the limitations of this work in more details. Computation integrity. DISPERS does not consider the issues related to the correctness of the contributed data or, in the Active Attack threat model, of the computation process. Indeed, in the Tamper-proof and Passive attack threat models, PDMSs follow the protocols and cannot alter the computations input/output: they have the means to attest that a given computation was correctly performed  [33, 34]. With fully corrupted PDMSs, this problem requires a complementary in-depth study. While existing solutions, such as Prio  [18] , could be used to verify that nodes' contributions are within some predefined interval, it will not warrant correct results. Indeed, the computation is fully distributed and data processors may also be corrupted.
g. ,  [9] ) or secret sharing (e. g. ,  [27] ). In DIS-PERS, data aggregation is performed by randomly selected nodes which cannot reasonably apply the evoked MPC approaches given the limited resources of user devices. Furthermore, the architectural difference also implies a different threat model. MPC solutions typically consider an honestbut-curious attacker (e. g. , SMCQL or Conclave) or an honest querier (e. g. , Obscure). In DISPERS both the data aggregators and the querier can be corrupted and, even worse, colluding. However, for classical aggregate functions, more secure distributed aggregation protocols, adapted to the DIS-PERS architecture can be envisioned. This is the focus of our future work extending DISPERS (preliminary results in  [45] ). Improving the DHT overlay network latency.
This paper is based on previous studies [38, 39, 40] : in [40] , we showed that the execution of a P2P query can indeed rely exclusively on data processor nodes if and only if they are selected in a verifiable random way, which cannot be influenced by corrupted nodes. [39]  is a demonstration of DISPERS architecture and applications. [38]  is a PhD manuscript. It includes implementation details of the protocols proposed in this paper and describes a proof-of-concept implementation of the most advanced protocol into the Cozy Cloud product [19].
Liquid Chromatography and Tandem Mass Spectrometry Mass spectrometry (MS) data were acquired using an Orbitrap Fusion Tribrid (Q-OT-qIT) mass spectrometer (Thermo Fisher Scientific) equipped with a Dionex UltiMate 3000 RSLCnano system (Thermo Fisher Scientific). MS calibration was performed using the Pierce LTQ Velos ESI Positive Ion Calibration Solution (Thermo Fisher Scientific). MS data acquisition was performed using Xcalibur v. 3. 0. 63 software (Thermo Fisher Scientific). For details, see Supporting Information Materials and Methods. 2. 14. Database Searches, Protein Identification, and Label-Free Quantification Analysis LC-MS/MS data were processed using PEAKS software v. X (Bioinformatics Solutions, Waterloo, ON, Canada). The data were searched against the 17,449 "Mus musculus" Swiss-Prot database (release February 2019), to which the yeast enolase 1 (P00924) sequence was added.
Development and Characterisation of Physiactisome C2C12 cell lines were transfected with either pCMV-6-Entry-HSPD1 plasmid, which encodes the Hsp60 var1 mouse gene (Physiactisome), or with the pCMV-6-Entry plasmid (Control). Transfection efficiency was monitored using flow cytometry (data not shown) to assess the number of Myc-DDK-positive cell lines, which accounted for 7-8% of the total cell population. A confocal analysis confirmed the presence of the cell line expressing high levels of Hsp60. Cell lines transfected with pCMV-6-Entry-HSPD1 exhibited foci (Supporting Information Figure  S2 ). EVs created using FunRich. The list of total proteins identified in large EVs (A) and small EVs (B) was uploaded, and interaction analysis tool was used to obtain the diagrams. The red and green spheres indicate the input proteins (selected genes) and the interacting proteins (interacting genes), respectively.
Development and Characterisation of Physiactisome C2C12 cell lines were transfected with either pCMV-6-Entry-HSPD1 plasmid, which encodes the Hsp60 var1 mouse gene (Physiactisome), or with the pCMV-6-Entry plasmid (Control). Transfection efficiency was monitored using flow cytometry (data not shown) to assess the number of Myc-DDK-positive cell lines, which accounted for 7-8% of the total cell population. A confocal analysis confirmed the presence of the cell line expressing high levels of Hsp60. Cell lines transfected with pCMV-6-Entry-HSPD1 exhibited foci (Supporting Information Figure  S2 ).
How do users get involved in UI processes within e-health? Pursuing a bottom-up understanding of UI application in e-health through the five RQs, this study reports the results from a bibliometric analysis to reveal the current advancements in this field and propose a research agenda accordingly. Based on the methods advanced by  Cobo et al. (2011) , the analysis was performed over a representative, high-quality sample of existing research consisting of publications from the Web of Science (WoS) and Scopus databases, using R software and its package Bibliometrix. In addition, we conducted a thorough content analysis to shed light on the users' involvement in the innovation process in e-health and gain deep insights into the roles that users are currently playing and those they could engage with in the future.
Sample selection The sample publications for conducting this study were selected using a Boolean search within the WoS and Scopus databases, because they contain a broad range of high-quality peer-reviewed journals with relevant and timely publications on previous e-health research efforts  ( Cwiklicki et al. , 2020). As suggested by  David and Han (2004) , the selection criteria centered on getting a representative sample of the existing literature while exceeding some threshold on rigor and correctness. On the restrictive side, books, book chapters, conference papers and publications not written in English were excluded from the sample. On the unrestrictive side, no exclusion was made on the grounds of field, year of publication or journal quartile. An important step in this type of bibliometric analysis is the appropriate selection of the keywords based on previous research, a process that was completed through the following procedure.
(1) General scope: we applied 11 keywords including e-health, telemedicine, and digital health on the one side and end user, lead user, co-design, co-creation, and similar keywords on the other with the Boolean search string in the two databases separately. This process yielded 218 results in WoS and 98 results in Scopus. The sum of 316 articles resulted in 280 articles, once duplicates were removed, in a single dataset containing information about author(s), language, year of publication, type of research, abstract, source title, keywords and cited references of the publications included in the search. (2) Specific scope: we examined the titles and abstracts of the 280 articles to exclude the out-of-scope articles based on a few specific inclusion/exclusion criteria. Content analysis and coding procedure (bottom-to-up approach covering the pr ecised data set) (RQ3) Why are users getting engaged in UI in e-health? Content analysis and coding procedure (bottom-to-up approach covering the pr ecised data set) (RQ4) When do users participate in UI processes in e-health? Content analysis and coding procedure (bottom-to-up approach covering the pr ecised data set) based on the innovation process with users by At this phase, the 280 records were examined carefully by going over the title and abstract of each publication to ensure the relevancy of their theme to our research aim.
As a result, some out-of-scope entries were identified based on the application of exclusion criteria, such as (1) the use of e-health solutions (products, services and methods) by end users without user involvement as participants in the innovation process, (2) the application of technology in healthcare without any contribution to user engagement or co-creation in the process, (3) the consideration of user needs and values without letting them participate on their own in the process, (4) the mere investigation into user satisfaction and acceptance of health technologies and (5) the acknowledgment of technical issues associated with collaborative e-health environment (e. g. credentials security). This process down-scoped the final sample of publications to 169 records published in the WoS and Scopus databases, thus providing a high-quality, focused representation of the research done pertaining to the research questions addressed in this study.
Co-keyword analysis As proposed by  Callon et al. (1983) , we applied a co-keyword analysis to discover semantic clusters of different topics based on the strength of the associations between the existing information in textual data with the aid of the R software and its package Bibliometrix. To do so, the author keywords from the sample publications were introduced as the inputs for the analysis. The dataset of author keywords was previously screened to avoid deviant results, such as those derived from using indexing keywords (instead of original author keywords) and heterogeneous spelling across databases and publications (e. g. e-health and ehealth, mhealth and m-health, mobile apps and mobile applications). In the next step, we determined the similarities between the items based on the frequencies of keyword co-occurrences.
, 2020; Fleisher et al. , 2020; Abujarad et al. , 2021). Interestingly, this reason has two facets: one for care provision and caregivers and another for healthcare and health professionals. Finally, the health education reason is related to the themes of e-health, the Internet and trust. Such innovations include massive open online courses (MOOCs), patient educational materials and information databases  (Ferri et al. , 2020b; Badiu et al. , 2017; Dai et al. , 2019; Cheng et al. , 2020; Marko-Holguin et al. , 2019). 4. 4. 3 When users participate. The content analysis involved coding each publication according to the phases of the innovation process in which the users participated. Taking the process model advanced by  Tietz et al. (2005) , user participation falls into two stages and six phases: (1) idea generation (encountered problem, concept derivation, concept development) and (  2 ) idea realization (information gathering, development, testing).
To evaluate the convenience domain inside a probabilistic context, a virtual environment allowed us to transfer the approach into a software tool that can store and manage many parameters and provides an application interface for potential stakeholders who handle input and output values. The approach was implemented using AnyLogic 8. 7. 2 for University, a virtual environment that allowed us to recreate industrial scenarios in a realistic way, using the software's calculation and optimisation process to provide a tool to the end-user to estimate the economic convenience of introducing disassembly processes in an existing manufacturing plant, and to determine the suitable number of disassembly operators. In particular, the optimisation process used by Anylogic consists of repetitive simulations of a model with different parameters.
Using sophisticated algorithms, AnyLogic varies controllable parameters from simulation to simulation to find the optimal parameters for solving a problem. The implementation of the software involved the following phases: 1. Design of disassembly and assembly lines through the appropriate concatenation of logic blocks (Section 3. 1); 2. Tool parametrisation through the introduction and setting of input and output parameters and the formulas necessary for the calculations (Section 3. 2); 3. Setting the case study and launching the tool (Section 4).
Tool Design Following the schematisation of the logical model reported in Section 3, the tool was designed in the software system. The lines that consider all the resources (operators, workstations, power lines, etc. ), how they are physically connected to each other and how they transform the inputs into system outputs were schematised and realised in the Anylogic virtual environment. The disassembly department is logically represented in the software environment as constituted by a conveyor system that transports the EoL and connects the intermediate stations where the EoL product is broken down. Each station represents a decomposition phase from which the reusable, recyclable and disposable components are obtained. The relative probability is represented by the chosen probability distributions, which are functions of the reusability index and the age of the EoL product.
g. cleaning, painting), a unit cost of treatment is considered; however, if the component is new, the associated unit cost of purchase/production is taken into account. Therefore, the components enter a priority queue, which allows the components retrieved from the disassembly line to be assembled. At the end of each run, the product cost is recorded within a specific variable, i. e. the unit cost of product (see Equation (  3 )). Furthermore, the software tool records the percentage variation between the product costs with and without disassembly. The system performs various experiments by changing the number of operators in order to find, in a given discrete domain, the value that minimise the product cost with different EoL arrival rates, by exploiting the AnyLogic optimisation process.
Coffee Maker Application This section presents a use case of the tool to show the functioning of the system and the achievable output. The starting product is a typical coffee maker for domestic use. The disassembly sequence reported in Figure  4  is assumed, through which the layout of the department is modelled (Figure  5  shows, for each station, which components are separated); furthermore, for each phase, the relative calculation of the reference time (i. e. the time of the single disassembly phases) was carried out. Then, for every step, the relative reference time was used as mode for a triangular probability distribution that simulates the variability of the operation. Therefore, the reusability and recyclability indices for each component of the coffee maker were calculated (see Figure  6 ) to obtain information for completing the bill of materials that would populate the tool's internal databases.
2. Adding the disassembly depth optimization feature: optimising the disassembly depth is fundamental to establishing a point in the decomposition sequence beyond which it is disadvantageous to employ manpower, and this strongly depends on the value of the components and materials downstream of the sequence. From this perspective, it is also necessary to define the disassembly sequence to be used, which can easily be investigated within the software by developing an additional function that uses all the parameters and indices already introduced in this study. 3. Development of a virtual simulation tool: This step presupposes the evolution in a discrete event simulator through a further in-depth study of the user's actual plant to allow system validation. This last target will be followed by improvements in the experience of using the tool from the user's perspective, setting up automatic construction systems of the logical model according to the bill of materials of the product that must be disassembled, and automatic parameterisation using a database containing the average values usable for various components and materials.
Reuse data: This database stores data relating to recovery for a new assembly. Specifically, for each of the components, the following data are recorded: a) Coefficient of employment (α); b) Reusability index (I r ) [%]; c) Unit cost of treatment [€/pc]; d) Unit cost of purchase/production [€/pc]. 3. Recycling data: This database contains data relating to recovery for recycling. Specifically, for each of the materials, the following items were recorded: a) Recyclability index (I rec ) [%]; b) Cost of recycling per kg [€/kg]; c) Selling price per kg [€/kg]; d) Amount of CO 2 emitted to produce one kg of material from primary sources Y GWP [kg CO2-eq /kg]; e) Amount of CO 2 saved by producing one kg of material from recovery sources δ GWP [kg CO2-eq /kg]; 4. Component compositions: This database defines, for each of the components, the quantity in kg present for each material in the bill of materials.