dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction The 3D numerisation of the environment using new handheld sensors is a rather vast subject around which new forms of technology have emerged such as smartphone LiDAR (Light Detection And Ranging) and handheld scanners. Despite the many difficulties that these sensors face today, particularly in terms of the accuracy and quality of the data returned, they are nevertheless new tools that seem more suitable in terms of use, particularly in the numerisation of buildings. Most research dealing with interior modelling or, more recently, the creation of as-built Building Information Modelling (BIM)  [1]  models, uses Terrestrial Laser Scanners (TLS) to collect datasets in the form of point clouds before converting them to Computer-Aided Design (CAD) models  [2]. Indeed, laser scanning technologies allow the collection of large amounts of accurate 3D data.
However, the whole system is still non-ergonomic to use, and the acquisition range is still quite low with a small precision. By testing the measurement accuracy on handheld scanners, Luetzenburg et al. [26]  made measurements on targets of simple geometric shapes with the LiDAR of the iPhone 12 Pro Max (as part of the numerisation of surfaces in geoscience) to estimate the squared error and the edge effect that can emerge respectively from its sensors in a controlled environment. An analysis of the geolocation, photo-video, and LiDAR sensor systems  [27]  shows the usefulness of using the iPhone 12 Pro in digitising geological ground surfaces while demonstrating the accuracy shortcomings they can have in geolocation. In recent works, the LiDAR sensors of the iPad Pro  [28]  were tested at six different resolutions on building facades in order to evaluate the metrics of the datasets.
Comparing these datasets with a static scan in a field environment (open space), the results showed the data from these sensors (processed and high resolution). The same comparison process with terrestrial laser scanners is used to study the point clouds captured by a HoloLens helmet  [29]  to scan the interior of buildings. The results show a higher coverage of HoloLens compared to the laser scan with however less precision. However, the comparison protocols are only studied in the field environment and do not include studies in the controlled environment. In a recent work, Teppati and al. [30]  have evaluated the accuracy and precision of the point cloud of an Apple LiDAR by testing them on different iOS applications (SiteScape, EveryPoint, and 3D Scanner App). Different materials were used depending on the natural sunlight and the results had to show the level of noise and the mode of acquisition with the iOS application.
Experiments in Controlled Environment In our first experimental protocol, we will analyse in 3 phases the different factors that can affect the LiDAR smartphone datasets. The first phase (Phase A): we evaluate the behaviour of the data (point clouds) as a function of distance, time, and brightness emitted by a digitised surface based on the work of Lachat et al. [12]. In phase B, to study the effect and the interaction between a light source and the acquisition surface of a digitised object, a design of the experiment was carried out to highlight the interactions between light source and material surface during the numerisation with these new LiDAR sensors. Finally, in phase C, some tests were carried out on different geometries and dimensions to evaluate the behaviour of the digitised data from the LiDAR smartphone.
The digitised surface is recov in FORMAT XYZ RGB point cloud data and received under CloudCompare software. scanned surface samples are fitted with planar primitives in CloudCompare. Plane p itives fitted to the point cloud are a good indicator of the noise level on the point clo Once fitted, the normal distance between all sample points in the cloud and the p primitive can be calculated. The standard deviation of all normal distances character the dispersion of the measured points around the plane primitive, which is a good i cator of the overall noise accuracy  [12]. The number of points present in each surface s ple is also estimated for the calculation of the mean density. For knowing the opt scanning time  [12]  to obtain good quality raw data, we studied the time factor in our.
The digitised surface is recovered in FORMAT XYZ RGB point cloud data and received under CloudCompare software. The scanned surface samples are fitted with planar primitives in CloudCompare. Plane primitives fitted to the point cloud are a good indicator of the noise level on the point cloud. Once fitted, the normal distance between all sample points in the cloud and the plane primitive can be calculated. The standard deviation of all normal distances characterises the dispersion of the measured points around the plane primitive, which is a good indicator of the overall noise accuracy  [12]. The number of points present in each surface sample is also estimated for the calculation of the mean density. For knowing the optimal scanning time  [12]  to obtain good quality raw data, we studied the time factor in our experiments.
In our experimental design, we want to study the effects and interactions between the incidence of a light source and the scanned material surface while considering other factors that can potentially affect the results during scanning. This explains why we have placed the surface and the light incidence in the first two columns of our table  (Table 1 ) and the other three factors (distances, time, and angle of incidence) in the other columns. In the third column, we placed the angle of incidence because of its major impact on the datasets compared to the distance  [13]. L1 S1 = Wood I1 = 0 • A1 = 0 • D1 = 1 m T1 = 2 s L2 S1 = Wood I1 = 0 • A2 = 25 • D2 = 2 m T2 = 5 s L3 S1 = Wood I1 = 0 • A3 = 45 • D3 = 3 m T3 = 8 s L4 S1 = Wood I2 = 40 • A1 = 0 • D1 = 1 m T2 = 5 s L5 S1 = Wood I2 = 40 • A2 = 25 • D2 = 2 m T3 = 8 s L6 S1 = Wood I2 = 40 • A3 = 45 • D3 = 3 m T1 = 2 s L7 S1 = Wood I3 = 80 • A1 = 0 • D2 = 2 m T1 = 2 s L8 S1 = Wood I3 = 80 • A2 = 25 • D3 = 3 m T2 = 5 s L9 S1 = Wood I3 = 80 • A3 = 45 • D1 = 1 m T3 = 8 s L10 S2 = Aluminium I1 = 0 • A1 = 0 • D3 = 3 m T3 = 8 s L11 S2 = Aluminium I1 = 0 • A2 = 25 • D1 = 1 m T1 = 2 s L12 S2 = Aluminium I1 = 0 • A3 = 45 • D2 = 2 m T2 = 5 s L13 S2 = Aluminium I2 = 40 • A1 = 0 • D2 = 2 m T1 = 2 s L14 S2 = Aluminium I2 = 40 • A2 = 25 • D3 = 3 m T2 = 5 s L15 S2 = Aluminium I2 = 40 • A3 = 45 • D1 = 1 m T3 = 8 s L16 S2 = Aluminium I3 = 80 • A1 = 0 • D3 = 3 m T2 = 5 s L17 S2 = Aluminium I3 = 80 • A2 = 25 • D1 = 1 m T3 = 8 s L18 S2 = Aluminium I3 = 80 • A3 = 45 • D2 = 2 m T1 = 2 s Sensors 2023, 23, 1967 10 of 26   For our experimental set-up, we chose two surfaces that reproduce well the two types of light reflection that can be observed in materials: wood-diffuse reflection, and aluminium-specular reflection, on a sample surface of 40 cm × 32.
At each turn, we performed three trials. This will allow us to evaluate how the dataset behaves when scanning the same surface or volume repeatedly. A root mean square error (for a series of three tests performed) has been calculated and results in values of 8-8. 12-8. 44 and 10. 74 mm for the one-turn-two-turn-three-turn-four-turn scans. The results show that the LiDAR smartphone provides data that degrade as the scanning is repeated over the same area or volume (especially beyond two repetitions). Acquisition of a cylindrical shape: For this study, two cylindrically shaped PVC objects were scanned by moving our LiDAR smartphone around at an almost fixed distance of 1 m. The first cylinder (C1) has a bigger diameter of 40 cm for a height of 50 cm and the second (C2) (Figure  12c, d ) has a smallest diameter of 20 cm for a height of 130 cm.
8 mm for C1 and 10 mm for C2, with a general mean distance point to primitive fitted (Cloud to primitive) at 25 mm and 25. 3 mm, respectively, for C1 and C2. Acquisition of a spherical shape: For this study, a plastic ball with a spherical shape (Sp) was scanned by moving our LiDAR smartphone at 1 m. The sphere is 55 cm (27. 5 cm) in diameter (see Figure  12e ). Using the free point cloud processing software CloudCompare as in phases A and B, a spherical primitive was fitted into the sphere point clouds. For the sphere, five tests were performed; the calculated radius of the fitted primitive varies between 28. 1 cm and 29 cm, which represents a root mean square error of the deviations of 1. 4 mm from the real dimensions of the object (27. 5 cm). The noise dispersion calculated from the primitive gives a standard deviation of about 6 mm, with a mean distance point to primitive of 13.
7 mm. Effect of measurement acquisition repetition: For further analysis on the aspect of measurement repeatability, we scanned the P1 card by performing a one-turn, two-turn, three-turn, and then four-turn displacement relative to the object. At each turn, we performed three trials. This will allow us to evaluate how the dataset behaves when scanning the same surface or volume repeatedly. A root mean square error (for a series of three tests performed) has been calculated and results in values of 8-8. 12-8. 44 and 10. 74 mm for the one-turn-two-turn-three-turn-four-turn scans. The results show that the LiDAR smartphone provides data that degrade as the scanning is repeated over the same area or volume (especially beyond two repetitions).
In Situ Experiments Acquisition of a cylindrical shape: For this study, two cylindrically shaped PVC objects were scanned by moving our LiDAR smartphone around at an almost fixed distance of 1 m. The first cylinder (C1) has a bigger diameter of 40 cm for a height of 50 cm and the second (C2) (Figure  12c, d ) has a smallest diameter of 20 cm for a height of 130 cm. Using the opensource point cloud processing software CloudCompare as in phases 1 and 2, we created cylindrical primitives to fit into the point clouds of the scanned cylinders. For cylinders C1 and C2, five tests were performed; the calculated radius of the fitted primitive varies between 20. 2 and 20. 5 cm for C1 and between 10. 6 and 10. 9 cm for C2, which represents a root mean square error of 4. 42 mm and 8. 63 mm compared to the real dimensions of the object.
Error precision increases much more in the presence of a low-dimensional cylinder radius and the aberrations (see Figure  12c, d ) are not negligible for these types of geometry. Moreover, a visual analysis allows us to evaluate an important edge effect on the end of the cylinders. The noise dispersion calculated from the primitive gives a standard deviation of about 5. 8 mm for C1 and 10 mm for C2, with a general mean distance point to primitive fitted (Cloud to primitive) at 25 mm and 25. 3 mm, respectively, for C1 and C2. Acquisition of a spherical shape: For this study, a plastic ball with a spherical shape (Sp) was scanned by moving our LiDAR smartphone at 1 m. The sphere is 55 cm (27. 5 cm) in diameter (see Figure  12e ). Using the free point cloud processing software CloudCompare as in phases A and B, a spherical primitive was fitted into the sphere point clouds.
From the test observed in the first part, the results allowed us to show that the FARO Focus X130 sensors are the most accurate and present the results closest to the exact data. This will allow a comparison of the two datasets to estimate the degree of accuracy of the smartphone LiDAR compared to the FARO. In this workflow, specific zones of the LiDAR smartphone point cloud with a significant deviation from the FARO reference cloud will be referred to as the critical zone. This zone will correspond to points located on a deviation of more than 5 cm (industrial specification). Overlapping of surfaces or two passes over the same surface after a long waiting time generates overlapping layer effects. The device and the acquisition environment allowed us to perform a single acquisition for the LiDAR smartphone for the FARO dynamic scanner.
The acquisition was carried out using two to three stations depending on the space and the difficulty of accessing certain surfaces. The results of these experiments are presented in the Figure  15 , where the colours represent the deviation scales obtained between the smartphone LiDAR cloud and the FARO cloud considered as the reference cloud. From the test observed in the first part, the results allowed us to show that the FARO Focus X130 sensors are the most accurate and present the results closest to the exact data. This will allow a comparison of the two datasets to estimate the degree of accuracy of the smartphone LiDAR compared to the FARO.
ACKNOWLEDGEMENTS We would like to thank two anonymous reviewers of the journal for their useful comments and suggestions on previous versions of this paper. Francesca Poggiolesi 's work was carried out in the context of the  ANR - IBS  project ( ANR-18-CE27-0012-01 ) hosted by  IHPST ,  UMR 8590. Massimiliano Carrara 's work was partially supported by the project  CARIPARO Padova  ( CARR-ECCE20-01 ): Polarization of irrational collective beliefs in post-truth societies (PolPost). Open Access Funding provided by  Universita degli Studi di Padova  within the  CRUI-CARE Agreement.
It can be concluded that the silica gels help to buffer the relative humidity, but are not efficient enough to dehumidify the atmosphere inside the showcase down to 30-40%. The observed temperature values are connected to museum opening times. Indeed, as shown in Figure  6 , temperature and humidity change within a day. During closed days around Christmas and New Year, such daily variation disappeared. A likely explanation for that phenomenon is the lack of heat production from lighting and other electronics in the exhibition during those days. This is highlighted by the slightly higher temperature difference inside the showcase, which is caused by the internal lighting. Judging from this dataset, the pilot and spacesuits are displayed in an environment that can be characterized as climate class AA, according to the Canadian Conservation Institute.
Acknowledgments: The authors would like to thank the curators for the space exhibition  Matthias Knopp ,  Robert Kluge , and  Andreas Hempfer. Furthermore, we thank  Lisa Young  and  Marcus Herdin  for consultations on the preventive conservation as well as  Sven Arlt  for background information on the showcase and  Sigrid Feulner  on the climate regulation at the  Deutsches Museum.
Costs measure We considered all direct and indirect healthcare costs in relation with care management during the follow-up. Resource use data were retrieved from two sources: hospital-based administrative databases and patients' self-reported measures. Resource use for each collaborative hospital was retrieved for all randomized patients. To measure ambulatory and community care, and at the margin to supplement hospital registry data, interview grids were built to assist participants in reporting individual and prospective resource use, based on previous studies of service use among people with mental illness  (Latimer et al. , 2017; Loubière et al. , 2020). Resource utilization included those relating to the intervention, including training of peer workers and time spent for PAD support, visits to the emergency department (ED), psychiatric hospital admissions and total number of days at hospital, as well as outpatient care.
These latter costs were assessed through consultations with general practitioners, referring psychiatrists and other specialists. Indirect costs were investigated based on the number of days of work absenteeism and compared between groups where relevant. Unit costs for hospital resources were estimated using data from the French National Hospital Database (https://www. atih. sante. fr/). The training of peer workers consisted in two half-day sessions per week for 6 weeks, with an estimated cost of €1,215. We observed the real cost of recruiting a peer worker at each site, either a full-time or part-time contract over the duration of the study. The average salary scales at the participating facilities were used to estimate the monthly salary of the peer workers. No overhead costs were charged to the intervention: the office room and equipment (i.
e. , computer and telephone) were already present and shared with the care/administrative team. In the city of Paris (i. e. , for two health facilities), transportation costs were taken into account (intra-city transportation card). For outpatient and community care costs, national tariffs were used (Source: National databases for medical and paramedical acts). All resources were valued in 2019 euros (see Supplementary Table  S1 ) and discounting was not applied.
Statistical analyses An intention-to-treat analysis was conducted in the present study and included all participants randomized in each group whether or not they received the intervention or were lost to follow-up. To detect a reduction of 30% in the rate of compulsory admissions to psychiatric hospitals during the 12-month follow-up, the planned sample was 200 per group, i. e. , 400 in total. This number of subjects allowed for a minimum cost difference of €320 with a standard deviation of 1,000, at a statistical power of 90%. Missing data were addressed using multiple imputations  (van Buuren, 2007) , under the assumption of missing at random  (Ware et al. , 2012). Markov chain Monte Carlo multiple imputation was used, which creates multiple 'complete' datasets by predictions for each missing value.
Fifty imputed datasets were implemented using Multivariate Imputation by Chained Equations and mitools R packages. We ran a sensitivity analysis for only the observed data (excluding missing data). A mixed model for longitudinal utility values was used to control for potential bias due to intra-patient correlated data and the existence of co-variates influencing quality of life. Betweengroup differences in service use and total costs were estimated using generalized estimating equations (GENLIN function) applying a Poisson distribution with a link log due to skewness. Mean differences and beta coefficients with 95% CI were provided. We used non-parametric bootstrapping (with 5,000 replications) to resample observations. The bootstrap results were combined to calculate the mean values for costs and utilities, and the SEs for the imputed values were used to calculate 95% CIs.
Values in italic indicate a statistically significant difference from the group variable (PW-PAD vs. control groups)    (21,498-24,436) 26,382 (25,695-27,981) -4,286 (-4,711  to -4,020) ICER -99,977  (-113,378 to -91,035)  PW-PAD: peer worker-facilitated psychiatric advance directive; QALY: quality-adjusted lifeyears; CI: confidence interval. The graph contains axes that represent the incremental cost (y-axis) and incremental effectiveness (x-axis). Each point in the graph represents the incremental cost and incremental effectiveness values (PW-PAD vs. control) from a single recalculation from the database. The willingness to pay (WTP), or ICER threshold, is used as the slope of a line intersecting the origin of the plot. The WTP line in the graph intersects points having the specified ICER value, and the region below and to the right of the line includes points where the intervention/PW-PAD is more cost-effective than the usual care/control.
Sensitivity analyses Sensitivity analyses with complete cases yielded similar results to imputed datasets (Table  S3 ). The tornado diagram (Fig. 2 ) indicated that cost-effectiveness was most strongly affected by utility values altered over their 95% CI and inpatient costs varying from ±30%. This was followed by the mortality rates in the PW-PAD and control groups. Regardless of the change in these parameters, the higher effectiveness and cost savings were maintained for PW-PAD. In addition, increasing or decreasing the costs of the intervention by 50% had less effects on the ICER. Taking sampling uncertainty into consideration, the costeffectiveness acceptability curve for the base-case analysis (Figure  S3 ) shows a 100% probability that PW-PAD was costeffective by comparison with usual care at the threshold of €1,000 WTP per QALY gained.
Our approach is in accordance with international guidelines and avoids the double counting of resource use in both the denominator and numerator of the ICER  (Neumann et al. , 2016). Third, resource use was based on the data collected from hospital-based databases that captured the entire hospital pathway associated with the management of mental illness. This trial had several limitations. First among them is the short follow-up period. Given the short-term nature of PAD fulfilment, the uncertainty surrounding long-term use and long-term consequences on health utility of PADs and the limited impact of PADs on survival, the 12-month randomized controlled trial timeline was deemed appropriate to capture most of the relevant costs and benefits associated with the intervention. Most of the incremental cost of PW-PAD is spent in the first 3 months, so this intervention might become even more cost-effective after 1 year if participants receiving the intervention continue to improve more than those receiving usual care without additional costs.
Financial support. support This work was supported by an institutional grant from the French 2017 National Program of Health Services Research (Programme de Recherche sur la Performance du système de Soins, PREPS-17-0575, FINESS number 130786049). Competing interest. None. Ethical standards. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2000.
Introduction: Freud proposed that slips of the tongue, including apparently simple ones, always have a sense and constitute « a half-success and a half-failure » compromise resulting from defensive mechanisms. Material and methods: A total of 55 subjects participated in a French adaptation of the Spoonerisms of Laboratory Induced Predisposition or SLIP-technique including 32 "neutral" and 32 taboo spoonerisms and measures of defensiveness. In accordance with a psychoanalytical and empirically supported distinction, we considered two kinds of defenses: elaborative or primary process and inhibitory or secondary process defenses, which were operationalized with the GeoCat and the Phonological-Nothing (PN) WordList, respectively. The GeoCat is a validated measure of primary process mentation and the PN WordList was shown to measure the defensive avoidance of language ambiguity.
Acknowledgments We are heartily thankful to  Mr. Robert Berry  and the  International Psychoanalytical Association (IPA)  for financial support. AB is thankful for the decisive mentoring received from  Howard Shevrin in Ann Arbor  (2003)(2004)(2005) and beyond. We are also thankful for  Laurine Sanchez  and  Yadamjav Chunag Balson  for their contribution in the parallel study on the subjective evaluations on the "tabooness" of the word pairs presented in the SLIP-task and to  Camille Demarque  for her contribution in the parallel study on the MCSD validation.