dataesr/scientific-paragraphs-categorization huggingface dataset:
Planar imaging Planar anterior and posterior images were generated by forward projection of the SPECT images at appropriate angles  [13]  using an in-house Python script. To mimic attenuation, the Hounsfield units measured in the CT images were converted into a µ-map showing the 140 keV 99m Tc gamma-ray attenuation coefficient value at each point (x, y, z) of the emission volume. Subsequently, the reconstructed emission images were projected through the µ-map, namely with an exponential attenuation of the integral of µ. This method allowed us to produce high-count planar images that were comparable to those obtained by a traditional planar acquisition  [14].
The lung segmentation in the V/Q SPECT/CT datasets was performed in two ways, namely an automated way and a manual way. The automated method was conducted with AutoLung3D, which is the AI-based supervised deep-learning tool developed by Siemens. An example of AutoLung3D segmentation is shown in Fig. 2. These data came from the same patient whose planar scintigraphy data are shown in Fig. 1. The manual method, which can serve as the reference method because it precisely describes the actual fixation in each lobe, involved manual segmentation of each lobe by an experienced nuclear physician. To facilitate this segmentation, we developed a home-made Python routine to convert the SPECT/CT data into a DICOM format that could be read by the contouring-dedicated module of the treatment planning system Eclipse (Varian Medical Systems, Palo Alto, California, USA).
This reflects the fact that Eclipse is not developed to work with SPECT images; consequently, these images must be converted into the fictive PET modality in the DICOM header. Our method also involved creating one DICOM file per slice to mimic PET files, whereas all slices are initially gathered in the same DICOM file in the SPECT images. Once the lobar segmentation was performed manually on the CT dataset in Eclipse, the structures were copied to the V/Q SPECT datasets and the counts inside each lobe were determined. An example of this manual segmentation performed in Eclipse is shown in Fig. 3. Again, these data came from the same patient whose planar and automated data are shown in Figs. 1  and 2.
Statistical analyses For each of the 43 patients, the relative distribution (%) of the radioactive tracer in each lobe in the left and right lung was obtained with the three quantification methods. These data were expressed as mean and 95% confidence intervals and ranges. The relative distributions in each total lung were also expressed in the same way. The three methods were compared in terms of the relative distributions in each lobe and each total lung by using a one-way analysis of variance (ANOVA) employing a Welch's test with an alpha risk α = 5%. These analyses were conducted separately for the ventilation and perfusion datasets. The interobserver variability data of each method were presented as the relative standard deviation of the results  [15].
001) lobes and higher relative distributions in the middle right lobe (31. 2% vs. 9. 5% and 9. 7%; p < 0. 001). The perfusion data showed very similar trends. By contrast, the automated and manual SPECT methods yielded similar results for all lobes and lungs for ventilation (Table  1  and Fig. 4 ) and perfusion (Table  2  and Fig. 5 ). This was also true for the 21 patients with lung parenchymal changes due to emphysema. Although these changes tended to obscure the fissures on the CT dataset and therefore required even more time-consuming manual delineation, they had little impact on the Autolung3D results (Table  3 ). Thus, automated segmentation remains accurate in such cases. However, the AutoLung3D software could not manage a patient who had had a lobectomy before imaging: It produced a five-lobe segmentation and did not detect the missing lobe.
This deficiency may reflect the training dataset used in the deep-learning process, which probably consisted of patients who had not undergone a lobectomy. Although lobectomy cases are rare in clinical practice, our finding suggests that AutoLung3D cannot accurately manage cases of gross anatomic change and should not be used in such cases. and 7 , respectively, summarize the V/Q quantification interoperator variability associated with the three quantification methods. In general, the planar method demonstrated the greatest variability in lobe estimates, particularly the inferior right lobe: The greatest average relative standard deviation in all estimates was 22. 8% (vs. 2. 8-6. 1% for the other methods). The exception was the middle right lobe; here, the manual method showed the highest variability (greatest average relative standard deviation = 18.
9% vs. 5. 6% for the planar method and 5. 4% for the automated method). This result may be explained by the horizontal fissure that delimitates the middle right lobe, which can be very difficult to distinguish on the CT dataset in some cases (emphysema for instance). The planar and manual methods were associated with similar variability in  the whole lungs. The automated method consistently showed the least variability of all three methods, including in the whole lungs; indeed, the greatest average relative standard deviation in all automated measurements was 5. 4% (vs. 22. 8% for the planar method and 18. 9% for the manual method).
Discussion Clinical V/Q estimates are generally obtained by conventional planar scintigraphy and segmentation of the lungs generated by imposing a six-ROI template on the images. This approach is both practical and fast. An alternative approach is to manually segment each lobe of each lung on the CT dataset, copy these segmentations on the SPECT ventilation or perfusion images, and then calculate the relative distribution values. This approach is much more laborious and time-consuming than the planar method because before export to the SPECT dataset, multiple steps must be performed:  (1)  the SPECT data have to be converted into a format that can be read by Eclipse, (2) the volumes of interest (lungs, lobes, and fissures) have to be created, (3) the fissures have to be segmented by the nuclear physicians, and (4) the lobe structures must undergo Boolean extraction.
These steps together take approximately 2 h for each patient, which is incompatible with a clinical workflow. Nonetheless, this manual approach precisely describes the actual fixation in each lobe and was therefore considered the reference method in this study. This study showed that while the planar and manual estimates for the whole lungs and the left lung lobes were quite similar, marked differences were observed for all three lobes in the right lung. Specifically, compared to the manual estimates, the planar estimates were much lower for the inferior and superior right lobes and much higher for the middle lobe. These results are in line with previous studies; specifically, marked right-lobe differences were observed between planar and SPECT/CT V/Q quantifications when manual segmentation  [8]  or an equivalent semi-automated lung lobe segmentation software (Hermes Lung Lobe Quantification; Hermes Medical Solutions, Stockholm, Sweden  [7] ) was employed.
The difference between planar and SPECT/CT quantifications can be explained by the right horizontal and oblique fissure positions: Since the conventional planar estimation involves dividing the right lung into three equal thirds, these positions are not taken into account. By contrast, the SPECT/CT data allow each lobe to be precisely segmented by following these fissures, which are visible on the CT dataset. Thus, the manual method provides a much more accurate estimation of the actual tracer distribution. Moreover, planar images involve the overlap of anatomic segments  [16]  and the superimposition of detected counts degrades the quantification. The fact that the planar and manual methods did not differ markedly in terms of V/Q estimates for the left lung lobes reflects the simpler anatomy of the left lung.
First, the SPECT V/Q estimations were based on a free-breathing CT dataset, which may increase uncertainty regarding the lung and lobe segmentations. A respiratory-gated CT would allow more precise segmentations. Second, the angular sampling of 5. 6° chosen for SPECT acquisitions is debatable. A lower angular step would theoretically decrease the streak artifacts in the reconstructed image  [17]. However, this kind of artifact is strongly reduced with iterative reconstructions  [18]. In practice, we did not observe any difference on the OSEM reconstructed lung images, between 128 projections/2. 8° acquisitions and 64 projections/5. 6° step acquisitions. The third limitation was the sample size (n = 43), relatively small for a clinical validation study. This reflects the time-consuming nature of the manual delineation (~ 2 h/ patient).
Nonetheless, the sample size was sufficient to identify statistically significant differences between planar and SPECT quantifications and to show that the quantification values are very close between manual and Autolung SPECT delineations. Moreover, our sample size exceeds those used to compare planar scintigraphy with manual SPECT/ CT (n = 17  [8] ) or to validate the Hermes (n = 30  [7] ) or Q. Lung (n = 39  [9] ) tools mentioned above. The fourth limitation was that we only analyzed a single patient with gross structural changes, namely those due to lobectomy. This reflected the fact that the patients were randomly selected to represent our clinical practice population. We will assess the accuracy of the AutoLung3D software in such cases in a separate study. Nonetheless, it should be noted that our study population also included 21 patients with lung parenchymal changes (e.
g. , emphysema); which did not impact the Autolung3D results. Thus, automated segmentation remains accurate in such cases. The fifth and final limitation of this study is that is does not assess the robustness of the AI method in relation to various SPECT/CT scanners and their respective settings. Undoubtedly, the robustness of the AI method is correlated with the similarity between the images in the user's dataset and those employed to train the deep-learning algorithm. As manufacturers do not provide information concerning the composition of the training dataset, it is advisable to conduct a robustness evaluation for each individual SPECT/CT device and its unique acquisition and reconstruction settings. Nevertheless, given that these settings are optimized for the specific clinical task of lung CT imaging, substantial variations in image quality across different SPECT/CT scanners are not to be expected.
Conclusions Our study suggests that AutoLung3D may be a feasible alternative to planar scintigraphy for routine clinical V/Q estimations because of three significant advantages, as follows. First, because it is based on SPECT/CT 3D datasets, it provides accurate and realistic estimations of the relative distributions in each lung lobe. Second, it is rapid; this makes SPECT/CT usable in the clinical routine, which was not possible previously. Third, its semi-automated AI-based component greatly reduces interobserver variability, which is a guarantee of quality in patient care. Indeed, because of these advantages, this tool is now often used in our nuclear medicine department to provide relative lobar quantification in V/Q SPECT/CT. Nevertheless, users of AutoLung3D should be aware that it may not be useful in patients with gross anatomic changes to their lungs such as lobectomy.
Figures 6 6 Figures6 and 7, respectively, summarize the V/Q quantification interoperator variability associated with the three quantification methods. In general, the planar method demonstrated the greatest variability in lobe estimates, particularly the inferior right lobe: The greatest average relative standard deviation in all estimates was 22. 8% (vs. 2. 8-6. 1% for the other methods). The exception was the middle right lobe; here, the manual method showed the highest variability (greatest average relative standard deviation = 18. 9% vs. 5. 6% for the planar method and 5. 4% for the automated method). This result may be explained by the horizontal fissure that delimitates the middle right lobe, which can be very difficult to distinguish on the CT dataset in some cases (emphysema for instance).
, 2018). On the other hand, Pavement Management Systems (PMS) are tools, developed since the 1970s, and organizing the collection, analysis, and reporting of pavement inventory and condition, to assist pavement management, and in particular maintenance scheme and financial optimization  (Harvey et al. , 2014; Peraka and Biligiri, 2020). The heart of a PMS is thus a database, that can be assorted with software programs analyzing road condition data to plan maintenance, and potentially vizualization. PMS can also be used to support pavement LCA  (Harvey et al. , 2014). These PMSs allow to adopt different strategies in the design-build-maintain sequence related to a pavement life cycle, compared to a non-digitally helped pavement management, for instance relying on staff observation, as it is done on most of the pavement network in France for instance.
A catalogue of standard solutions has been made available  (Corte et al. , 1998) , while design and maintenance plans of strategic pavements are usually tailored using the match ME software "Alize". As they carry a substantial portion of the traffic (around 20%) on a short portion of the national road network (1%) (de Bortoli, 2018), high-traffic roads present strategic socioeconomic importance, and more design-build-maintain alternatives. Thus, this case study will focus on high-traffic roads. The objectives of this study are to quantify the carbon footprint of (1) the PMS function of an I-BIM based on field data, and (2) three designbuild-maintain alternatives for a high-traffic road in France over its entire life cycle, (3) to understand the consequence of using the PMS function of a BIM on the contribution of pavement construction and maintenance to climate change, and (4) discuss the environmental consequences of a multifunctional BIM.
This second approach requires to use a ME design software and may be selected by road concessionaires to optimize discounted cash flows and reduce traffic risk. This risk management practice is particularly attractive under an ever more uncertain future, due to climate change consequences on pavement conditions and changes in behaviors related to cultural standards evolutions, economic crises, and else. Then, two approaches allow for managing the maintenance of progressively-designed pavements: theoretical mechanical planning (scenario 2) or data-supported adaptative planning (scenario 3). The maintenance in scenario 2 is planned during the design process using a ME design software considering traffic forecast and can be further modified with traffic data. Scenario 3 consists of data-supported maintenance management: it relies on traffic data but also pavement condition data collected and monitored over time, stored, and analyzed in a PMS -e.
Pavement management and I-BIM 2. 2. 4. 1. Modeling scenarios. In scenarios 1 and 2, premature pavement failures are identified by regular patrolling, i. e. via visual surveying performed by trained staff, often from the pavement management company. As scenario 3 also comes with this patrolling, these operations will be removed from the system boundaries for all scenarios, following ISO 14044 guidelines (International Organization for  Standardization, 2006b). On the other hand, scenario 3 requires pavement monitoring by an equipped truck which measures the evolution of the bearing capacity of the highway pavement. This data is therefore collected and then stored in a database. A pavement asset management team will then analyze them to plan the maintenance operations. To model this PMS's features (illustrated in Fig.
4 ), a "SysML Use cases" diagram is used. this formalism describes use cases and external actors' interactions in a concise way. Tree external system users are considered: external data (that need to be collected), an external database (that is used to store data), and the user of the PMS (that is a member of the pavement asset management team). Four use cases are considered: collecting external data, storing internal data, running a local task, and transferring internal data. The last two use cases constitute the PMS's maintenance operation planning. To be more efficient, only the main use cases are represented, and the extend/include relationships are omitted. For example, collecting data may be extended to storing.
PMS carbon footprint. The Global Warming Potential (GWP) of a PMS for a pavement section, I section PMS , can be calculated using the generic equation (2): I section PMS = I data PMS + I storage PMS + I maintenance PMS (2) Where: -The section is defined by a length and a maintenance period -I data PMS is the GWP to collect the data related to the section -I storage PMS is the GWP to store the data related to the section -I maintenance PMS is the GWP to plan the maintenance operation related to the section 2. 2. 4. 3. Data collection of the pavement condition. In France, we use different types of deflectometer to measure pavement deflection. In this case study, a curviameter is used, i. e. an instrumented truck weighted with 13 tons on its rear axle to record the deformation under this axle.
According to ecoinvent's truck classification, this vehicle is considered as a 16-32 ton truck, and a EURO6 emissions standard is considered. French national roads are standardly monitored every three years in each direction  (Lorino et al. , 2006) , but some strategic roads can be monitored more often, such as once a year. The GWP of the data collection, I data PMS , is defined by equation (3): I data PMS = n round *P m *L section *F m (3) Where: -P m is the maintenance period in years -L section is the length of the studied section in km -n round is the number of monitoring rounds per year -F m is the emission factor of the vehicle used to collect road condition data in kg CO 2 eq per km We choose the worst-case scenario, in which the monitoring round is conducted every year. The emission factor of the instrumented truck, F m , is calculated from the 16-32 ton EURO6 Transport freight process from Ecoinvent v3.
6 (see Table  4 ). To estimate the carbon footprint of such a truck per kilometer traveled, we rescaled the freight transportation process based on the ton-kilometer functional unit by the average load considered in the process by ecoinventi. e. 5. 79 tons  (Spielmann et al. , 2007). 2. 2. 4. 4. Data storage. The PMS's database is composed of several pieces of hardware: 3 servers (ProLiant BL460c Gen10 and ProLiant BL460c Gen9 models), 1 storage bay (3PAR 8200 model), and 1 backup bay (StorOnce 5500 model). The GWP of the data storage related to the studied section, I storage PMS , is defined by equation (  4 ): I storage PMS = P m *L section L t *k database * ( I hard Δ hard + I yrun ) (4) Where: -L t is the total section's length informed in the database in km -k database is the allocation coefficient considering the proportion of the database used for the PMS -I hard is the GWP of the PMS's database hardware in kg CO2eq -Δ hard is the depreciation period of the hardware database in years -I yrun is t he GWP of a one-year run of the database in kg CO2eq In this case, the entire database is dedicated to PMS: k database = 1.
Δ hard and L t are given by an internal expert (see Table  3 ). I hard and I yrun are provided by the manufacturer (see Table  4 ). management team to plan the maintenance. The evolution of the bearing capacity of the subgrade is extrapolated from the pavement deflection. Then, the need for further structural reinforcements or delaying operations is estimated. The digital activities of the team are composed of local tasks, with a computer, and data transfer from the PMS's database. More specifically, two types of users, and thus two types of workstations are involved: one mobile workstation, that we characterized as "standard" (index "s"), and one workstation desktop, that we characterized as "advanced" (index "adv"). The GWP of the maintenance operation planning, I maintenance PMS , is defined by equation (  5 ): I maintenance PMS = n operation *P m *L section * ( k t *F transfer + F adv + F s ) (5) Where: -n operation is the number of maintenance planning during one year -k t is the data volume transferred per PMS planning operation per km and year -F transfer is the emission factor to transfer data -F s and F adv are respectively the emission factors of the amortization of the two types of workstations, the mobile one and the desktop According to an internal expert, maintenance operation planning is carried out every 15 years: n operation = 1/15.
This internal expert considers, in a worst-case scenario, that three maintenance planning conducted on a 100 km section over 50 years require 50 gigabytes (GB) of data transfer. Thus, k t is calculated from this estimation. F adv and F s are calculated with equation (  6 ): F adv = k adv * ( I adv device Δ adv device + 24*P adv device *F elec ) (6) Where: -k adv is the number of days spent on the PMS software on the advanced station, per PMS planning operation, per km, and per year -I adv device is the GWP of the advanced computing station's manufacturing in kg CO2eq -Δ adv device is the depreciation period of the advanced computing station -24 is the number of hours per a day -P adv device is the electrical power in watts, daily weighted -F elec is the emission factor of the electricity consumption The internal expert considers, in a worst-case scenario, that three maintenance operation planning conducted on a 100 km-long section for 50 years require 40 days on the PMS software on the standard station and 15 days on the PMS software on the advanced station.
This allows calculating k adv and k s (see Table  3 ). A mobile workstation is composed of a laptop and two display screens while a workstation desktop uses a computer desktop with two display screens, a pointing device, and a keyboard. This allows calculating I adv device and I s device from the ecoinvent database (see Table  3 ). Δ adv device and Δ s device correspond to 5 years, converted to 1825 days. We assume, in agreement with the internal expert, that a display screen operates for 8 h per day at 90 W and stays on stand-by at 1 W for the remaining time. A laptop also operates 8 h per day at 65 W but is turned off the rest of the time. Finally, a desktop computer operates all day, i. e. 24 h, at 300 W. Those assumptions allow us to determine P adv device and P s device (see Table  3 ).
PMS contributions All results, presented in Table  5  and Fig. 7 , relate to the 10 km-long section over 30 years. The GWP of the PMS is 520. 7 kg CO 2 eq. It is divided in 241. 1 kg CO 2 eq (46. 3%) for data storage, 277. 9 kg CO 2 eq (53. 4%) for data collection activities and 1. 7 kg CO 2 eq (0. 3%) for maintenance planning activities, e. g. the usage of the PMS by asset managers. Data collection is the biggest contributor to PMS's carbon footprint, with 53. 4% of the total impact. Data storage is almost equally contributing, with 46. 3% of the total GWP, while the PMS's usage itself is responsible for a negligible amount of GHGs (0. 3%). The GWP due to data storage is largely due to the database running, i. e. electricity consumption (97. 3%). The amortization of the database's hardware manufacturing represents only 2.