dataesr/scientific-paragraphs-categorization huggingface dataset:
Attica VHR Dataset Starting with the Attica VHR dataset and the method in  [30] , we can observe in Table  2  that the proposed method outperforms the rest of the approaches both in building areas as well as in areas that include roads and fields. Giving some more details, the largest errors on the building landmark location points have occurred in the only A case, for all axes. As one can observe, the ds error is almost 5 pixels, which shows that even though the affine transformation has managed to bring the two image pairs closer, it still misses some local information, leading to inconsistencies in the matching process. In the only Φ case, the dx and ds errors are reduced by 1 pixel, while the dy error is slightly improved. When A & Φ are combined, the synergy among the two different types of transformations enhances the registration performance on dx and ds, while the dy error is not improved.
The fast inference is very important for remote sensing registration if one considers the large size of earth observation images. Version March 23, 2021 submitted to Remote Sens. 12 of 16 Unregistered onlyA  [30]  onlyΦ  [30]  A&Φ  [30]  Proposed the affine component was integrated. The proposed multi-step approach was the most convenient 314 method to fine tune, resulting in the most smooth alignments but not eliminating completely the curvy 315 parts. It should be mentioned here that this problem was not so difficult to control in the Attica VHR 316 dataset case in which the buildings are smaller and not so high. In addition, the data samples were 317 Visualized examples on the testing part of the Attica VHR dataset can be observed in Figures  3  and 4  for methods  [30, 36]  respectively.
ISPRS Ikonos Dataset Landmark location errors are also provided in Table  3  for the ISPRS Ikonos dataset. Here, the displacements of the unregistered image pairs are lower than the Attica VHR dataset, especially in the x axis. However, what is challenging in this dataset is that the patches depict larger and higher buildings, meaning that different viewpoints of the sensors alter significantly the appearance of the buildings. Once again, for this evaluation we provide errors separately for building areas and for areas that depict roads and fields. Beginning with the method in  [30]  on the building areas, we can notice that the largest errors occur in the only A case, while all errors are improved in the only Φ case. The combination of A & Φ enhances further the model's performance since the errors are even lower in all axes, which means that the integration of the affine transformation contributed to a more constructive registration process.
In the only A case the errors are much ameliorated while they are slightly improved in the A & Φ case where the affine transformation is applied as a preprocessing step. The A & Φ errors are similar with our proposed method, although the sum of all axis errors is lower when our method is applied. Specifically, as far as buildings are concerned, the sum of axis errors is 3. 35 for A & Φ [36] and 2. 72 for the proposed scheme. Regarding roads and fields, the sum of axis errors is 3. 80 in the case of A & Φ [36] and 2. 55 in the proposed framework. Lastly, like in the Attica VHR dataset, our approach provides better inference times. Some checkerboard visualizations from the testing part of the ISPRS Ikonos dataset are provided in Figure  5 , highlighting the advantages of our method. Examples from building areas as well as areas with roads and fields are demonstrated, proving the better image alignment that our method has achieved.
One issue that we can notice is that sometimes the methods that involve the deformable component result in curvy building boundaries. This deteriorated smoothness is inextricably related to the employed regularization term. For each method, we experimented with a wide range of regularization weight values, until the optimal result could be achieved. According to our experiments, the only Φ method was more difficult to control, while it was more easy to handle this issue when the affine component was integrated. The proposed multistep approach was the most convenient method to fine tune, resulting in the most smooth alignments but not eliminating completely the curvy parts. It should be mentioned here that this problem was not so difficult to control in the Attica VHR dataset case in which the buildings are smaller and not so high.
In addition, the data samples were much more rich, since we had almost 800 patches for training, compared to the Ikonos dataset where we had only 400. This smoothness issue is something that we want to further explore in the future, in an attempt to make the registration process even more robust. In Figure  6  we can also observe the training-validation loss curves for all the employed methods. The only A network produces the most noisy validation loss numbers, while after the 80th epoch there are even larger fluctuations and the losses do not further improve. In the A & Φ case, we notice that a less noisy graph has been produced. In addition, the network seems to learn until the last epoch since the training and validation losses are decreasing. Regarding the proposed framework as well as the only Φ approach, they result in more smooth loss curves.
Conclusions In this study we tackle the challenging problem of image registration by employing a multistep framework based on fully convolutional networks. Experiments on two datasets of different resolutions demonstrated the high potentials of our method which managed to align the given image pairs successfully with overall, less than 3 pixels error. Our method is completely unsupervised while it does not require any preprocessing steps, providing an end-to-end framework. This means that the user is able to omit the process of creating ground truth deformations, something which usually requires much time and attention. Additionally, the proposed scheme is independent of the employed deep architecture while it also provides fast inference times. In the future, we plan to further improve our method by investigating additional formulations that can be adapted to the optimization problem, e.
Quantitative evaluation on the ISPRS Ikonos dataset. Errors measured as average euclidean distances between 15 landmark locations. dx and dy represent the distances along the x and y axis, respectively. ds indicates the average error along all axes per pixel, while the last column indicates the time needed by the methods to register a pair of 192 × 192 images. Errors on building landmark locations (left) and errors related to points on roads and fields (right) are presented.
Thermodynamic modelling Thermodynamic modelling was used to calculate the thermodynamically stable phases in pure and wollastonite blended MKP cement pastes in the temperature range of 5-100 • C at 1 bar. The geochemical software GEMS-PSI software  [38, 39]  was used together with the PSI/ Nagra thermodynamic database  [40] , the cement-specific database Cemdata18 database  [41] , completed with data for M-S-H and the recently complied database of magnesium (potassium) phosphate and calcium phosphates  [11, 42] ; the Ca-and Mg-phosphate data used are summarised in Table  3. The thermodynamic data between 5 and 100 • C at 1 bar were calculated using the temperature dependence of the apparent Gibbs free energy of formation as detailed below: ∆ a G o T = ∆ f G o T0 -S o T0 (T -T 0 ) - ∫ T T0 ∫ T T0 C o p T dTdT = ∆ f G o T0 -S o T0 (T -T 0 ) -a 0 ( T ln T T 0 -T + T 0 ) -0.
1 and ~1. 1, mainly clustered into two zones. An Mg/P ratio of 0. 5 was arbitrarily selected as the boundary value in order to calculate the average Mg/P and K/P ratios of each zone of data points. The calculated average Mg/P and K/P ratios as given in Fig. 12A     EDS analyses of solids in the wollastonite blended paste (P4-W) after 7 days curing at 20 • C followed by a continuous cuing at 50 • C for 393 days. The grey datasets are from the areas dominated by K-struvite (see Fig. 11A ), and the orange datasets are from the areas dominated by the dissolution of wollastonite (see 2# and 3# in Fig. 11B ). In the areas dominated by K-struvite (see Fig. 11A ), the Ca/(Ca + Mg) ratios are between ≈0. 1 and 1 and the P/(Ca + Mg) ratios mostly below 1, indicating a mix of wollastonite, magnesium phosphate hydrates and calcium phosphate hydrates.
Acknowledgements The financial support of  Saint-Gobain Recherche, France , is gratefully acknowledged as well as the constructive suggestions of  Céline Cau Dit Coumes (CEA, France) ,  Andreas Leemann  ( Empa, Switzerland ),  Raül Leiva Muňoz  and  Nelly Brielles  ( Saint-Gobain Recherche, France ). Our thank is extended to  Boris Ingold (Empa)  for the SEM sample preparation, to  Daniel Rentsch  ( Empa ) for acquiring the 29 Si and 31 P MAS NMR spectra and to  Robin Pauer  ( Empa ) for the assistance with the SEM characterizations. The NMR hardware was partially granted by the  Swiss National Science Foundation  ( SNSF , grant no. 206021_150638/1 ).
No atrioesophageal fistulae, PV stenoses, myocardial infarctions, or deaths were observed in either arm. Of note, the control group reported that 6 subjects experienced an extended hospitalization compared with zero in the DTA. The clinical events committee adjudicated these events and further identified the events as renal disorder, post-operative hypotension, chest pain, pyrexia, pericardial effusion, and hemorrhage. All of the aforementioned events resolved with no further clinical sequelae. Steam pops were observed in 7 (3. 0%) DTA subjects and 5 (2. 1%) control subjects. Of the 12 subjects with a steam pop, all subjects completed the trial, and 11 subjects had no primary effectiveness endpoint failures. One subject in the control group did reach a primary effectiveness failure, but it was undetermined if the endpoint failure was associated or resulted from the intraprocedural steam pop given the data within the study.
The Orbiting Carbon Observatory 2 (OCO-2) satellite has been providing information to estimate carbon dioxide (CO 2 ) fluxes at global and regional scales since 2014 through the combination of CO 2 retrievals with top-down atmospheric inversion methods. Column average CO 2 dry-air mole fraction retrievals have been constantly improved. A bias correction has been applied in the OCO-2 version 9 retrievals compared to the previous OCO-2 version 7r improving data accuracy and coverage. We study an ensemble of 10 atmospheric inversions all characterized by different transport models, data assimilation algorithms, and prior fluxes using first OCO-2 v7 in 2015-2016 and then OCO-2 version 9 land observations for the longer period 2015-2018. Inversions assimilating in situ (IS) measurements have also been used to provide a baseline against which the satellite-driven results are compared.
The time series at different scales (going from global to regional scales) of the models emissions are analyzed and compared to each experiment using either OCO-2 or IS data. We then evaluate the inversion ensemble based on the dataset from the Total Carbon Column Observing Network (TCCON), aircraft, and in situ observations, all independent from assimilated data. While we find a similar constraint of global total carbon emissions between the ensemble spread using IS and both OCO-2 retrievals, differences between the two retrieval versions appear over regional scales and particularly in tropical Africa. A difference in the carbon budget between v7 and v9 is found over this region, which seems to show the impact.
However, past studies have also shown that top-down estimates can be sensitive to errors in the modeled atmospheric transport as well as in choices related to the optimization technique  (Chevallier et al. , 2010; Houwel-ing et al. , 2015; Basu et al. , 2018; Schuh et al. , 2019) , uncertainties which are difficult if not impossible to characterize formally in any one atmospheric inversion scheme. This shortcoming was the motivation for the OCO-2 Model Intercomparison Project (MIP), whose goal was to (1) study the impact of assimilating OCO-2 retrieval data into several atmospheric inversion models and (2) provide an overall ensemble spread of the model emissions characterizing most sources of known uncertainty. In addition to its primary goal of assimilating OCO-2 retrievals, MIP modelers also assimilate in situ data, a practice which has a long and documented history  (Enting and Newsam, 1990; Enting, 2002; Gurney et al.
, 2019). In some sense, the point of our paper is to update the  Crowell et al. (2019)  paper with the latest flux inversion results based on the longest and most recent set of in situ and satellite XCO 2. In particular, this study aims at evaluating whether (i) there is some change in the MIP CO 2 fluxes using OCO-2 v9 as compared to OCO-2 v7 data and, (ii) if there are some differences, what would the implications of using v9 be in the carbon cycle community regarding previous studies that have used v7? The paper is structured as follows. The MIP design as well as data used in the inversions (i. e. , the in situ data and OCO-2 v9 retrievals, as well as how v9 differs from v7) will be detailed in Sect. 2. In this same section the independent data used for evaluation will also be presented.
MIP design The MIP project, organized by the OCO-2 Science Team, is a collaboration of CO 2 modelers formed to study the impact of assimilating OCO-2 retrieval data into atmospheric inversion models. The project's goal is to create an ensemble of CO 2 surface flux estimates to understand how flux estimates using OCO-2 retrievals and in situ measurements depend on (i) transport, (ii) data assimilation methodology, (iii) prior flux and associated errors, and (iv) possible systematic errors in the OCO-2 retrievals, in particular across viewing modes, i. e. , ocean glint (OG), land nadir (LN), and land glint (LG). The OCO-2 MIP philosophically mimics past projects such as RECCAP (REgional Carbon Cycle Assessment and Processes) and TRANSCOM  (The Atmospheric Tracer Transport Model Intercomparison Project)  designed to analyze the uncertainty in inverse calculations of the global carbon budget resulting from errors in simulated atmospheric transport.
Despite the weak data constraint, LoFI is included below with the IS inversions because it depends on the annual, global growth rate determined from observations. Given the problems flux inversions have facing remotesensing retrieval biases  (O'Dell et al. , 2018)  and atmospheric transport errors  (Schuh et al. , 2019) , LoFI serves as a firstorder check on inversion skill. Times and places where a flux inversion outperforms or equals LoFI's skill suggest a nominally operating system, while significantly degraded skill suggests a problem, e. g. , in the prior, atmospheric transport, and/or ingested data. The modelers used NASA's operational bias-corrected OCO-2 L2 Lite XCO 2 product v9  (Kiel et al. , 2019 , https: //daac. gsfc. nasa. gov, last access: August 2019)  in the v9 version of the MIP.
The OCO-2 v9 dataset has an improved bias correction approach that results in reduced biases, particularly over areas of rough topography. While variations amongst inversion systems are considered beneficial for the purpose of characterizing flux uncertainty, some configurations needed to be standardized in order to avoid meaninglessly large differences in the ensemble spread. All inversion modelers were instructed to assimilate OCO-2 data from 6 September 2014 through 31 May 2019 and to submit estimated fluxes from 1 January 2015 through 31 December 2018 (to allow the flux estimate some time to spin up and down on either end). Fossil fuel emissions, which are typically not optimized in global top down studies  (Peylin et al. , 2013) , were standardized for the project. Similar to the experiments described in  Crowell et al.
(2019) , all modelers assumed the same monthly fossil fuel emissions from the Open-source Data Inventory for Anthropogenic CO 2 (ODIAC2019;  Oda and Maksyutov, 2011; Oda et al. , 2018) , modified with the TIMES diurnal and day-of-week scaling  (Nassar et al. , 2013). Though fossil fuel emissions are fixed, all other prior flux estimates were chosen independently by each modeling group. For instance, regarding fire emissions, most models used the Global Fire Emission Database -either version 3 (GFED3) or version 4 (GFED4). GFED3 and GFED4 mainly differ on burned area, where small fires are included in version 4  (Randerson et al. , 2012; Giglio et al. , 2013). The added information of small-fire burned area increases the burned area particularly over agricultural and peat land regions  (van der Werf et al.
6 and 2. 1 µm. OCO-2 provides spatially dense data with a narrow swath (no wider than 10 km) and with spatial footprints of a few square kilometers (less than 1. 25 km by 2. 2 km pro- The OCO-2 sensor provides observations in three modes. Nadir retrievals are those in which the satellite is looking at the Earth directly below, i. e. , at the sub-satellite point. These retrievals are only usable when the instrument is directly over land. Glint retrievals are from measurements occurring when the instrument is pointed (usually off-nadir) toward the solar glint spot. Glint is the primary mode for over-ocean retrievals, as the ocean surface is very dark in the SWIR spectral range, only reflecting sufficient solar radiation near the glint point. Target mode retrievals are obtained when the sensor points at a fixed location along the orbit to keep a particular point on the Earth's surface in view and is employed mainly to collect validation data over locations such as TCCON sites.
In situ CO 2 measurements The set of in situ CO 2 measurements used for assimilation and for evaluation is drawn from five collections in ObsPack  (Masarie et al. , 2014, and   2. obspack_co2_1_NRT_v5. 0_2019-08-13 (NOAA Carbon Cycle Group ObsPack Team, 2019). This near realtime ObsPack distribution is intended to provide data records after the end of the GLOBALVIEW+ v5. 0 product, for laboratories and datasets that can provide measurement data outside of an annual update cycle. These data are provisional and generally have not undergone final quality control. 3. obspack_co2_1_AirCore_v2. 0_2018-11-13. The balloon-borne AirCore instrument samples almost the entire atmospheric column. This early release collected all available profiles between  2014 and 2018. 4. obspack_co2_1_INPE_RESTRICTED_v2.
The MDM represents the expected statistical model residual from a measured value, and the current scheme estimates MDM values that vary by geographic location, month of year, local solar time of day, and distance from the Earth surface. These values are developed using model performance from previous simulations, by computing a climatology of expected model errors for a given dataset. These model errors are driven both by faults in simulated atmospheric transport and by incorrect upstream fluxes, but it is only the first of these that we attempt to represent with MDM. At many sites, model performance is dominated by a conspicuous cycle of seasonal error, attributed mostly to high ambient variability of CO 2 in the local growing season. Exploratory analysis has demonstrated that model-to-model differences in performance are significantly smaller than the other sources of variability in MDM like this annual cycle of model error.
The OCO-2 LGv7 ensemble spread does not deviate from the prior spread, showing the large impact of v9 corrections on the retrievals. We can also observe that the IS ensemble spread does not deviate from the prior spread. Neither IS nor LNLGv9, for each model, follows their priors in the tropics during the period of study (not shown here). The in situ Obspack dataset used for this study has been updated and includes more data per site, contrary to the in situ dataset used in  Crowell et al. (2019)  for the v7 MIP. We then have stronger sources observed with ISv9 than with ISv7 in 2015 and 2016. In addition, we can observe with both IS and LNLGv9 two clearly distinguished periods in terms of annual mean flux: the El Niño period for 2015 and 2016, with sources between 1. 5 and 2 Pg C yr -1 , and the recovery period, with median values between -0.
Gloor et al. (2018)  did a similar study as  Liu et al. (2017) , but instead of comparing 2015 El Niño to 2011 La Niña, they compared the 2015 El Niño to the 1998 El Niño. Additionally, they did not assimilate satellite retrievals but rather in situ data from the NOAA surface station network. While their conclusions for southern tropical America and southern tropical Asia were similar to what  Palmer et al. (2019)  and  Liu et al. (2017)  found, they were surprised by their results over tropical Africa. Contrary to their expectations,  Gloor et al. (2018)  found hot conditions in the Congo Basin in February 2016 suggesting a release of carbon. Their results for the Congo Basin (with source of carbon) were in opposition with the previous papers, which found sinks of carbon during the El Niño period of 2015-2016.
We find at the global scale a good agreement overall between fluxes inferred from the v7, v9, and in situ data. However, differences are found at smaller scales over northern latitudes and particularly over the tropics. While seasonality in the tropics differs significantly between the OCO-2 v7 and v9 compared to in situ results, the annual emissions show better agreement between the in situ data, LNv7, and v9, except over northern tropical Africa and northern tropical Asia. As was observed with v7, v9 also shows a stronger source of carbon over northern tropical Africa than that observed with in situ data. This weaker source given by the in situ data seems to be balanced over northern tropical Asia with a larger outgassing during the El Niño period that is not observed with the OCO-2 retrievals, as they show only sinks of carbon there for the whole period.
It is difficult to conclude where these sources come from, as there are few in situ observations over this region. Finally, we see, as previously mentioned in several studies, a carbon uptake (of around -0. 25 Pg C yr -1 ) over southern tropical Africa using the v7 data but a carbon release there using the v9 data of around 0. 25 Pg C yr -1 during the El Niño period. The in situ data also suggest a carbon release. This difference between v7 and v9 over southern tropical Africa seems to show the impact of retrieval bias corrections on the regional CO 2 fluxes, particularly in the tropics. This contradiction in the carbon budget conclusion between the two sets of OCO-2 inversions requires further investigation over this African region. Evaluation with the withheld data, ATom aircraft measurements, and TCCON retrievals suggests similarities in biases between the in situ data and LNLGv9 data, with negative bias in the v9 OCO-2 data for almost all latitudes, particularly large in the Southern Hemisphere and slightly negative the tropics, where few evaluation data are available.
B7 LoFI While the OCO-2 MIP v7  (Crowell et al. , 2019)  was composed of models assimilating OCO-2 retrieval v7 or in situ data, the OCO-2 MIP v9 also has the model Low-order Flu Inversion (LoFI;  Weir et al. , 2021) , which is an integrated Earth system model with data assimilation capabilities. LoFI  (Weir et al. , 2021)  reproduces atmospheric and oceanic growth rates from a group of remote-sensing carbon fluxes. The model used is the Heracles 4. 0 Goddard Earth Observing System (GEOS) general circulation model (GCM) including MERRA-2 meteorological inputs and a 0. 5 • ×0. 625 • resolution grid with 72 vertical levels. Biospheric fluxes have a 3hourly time step, while all other fluxes have a daily time step. Biospheric and biofuel priors come from the CASA-GFED3. Biomass burning priors are based on the Quick Fire Emissions Dataset (QFED;  Darmenov and Silva, 2015).
Data availability. The surface gridded flux are available from https://www. esrl. noaa. gov/gmd/ccgg/OCO2_v9mip/ (NOAA, 2019a). The in situ measurements are available from https://www. esrl. noaa. gov/gmd/ccgg/obspack/ (NOAA, 2019b). The TCCON data were obtained from the TCCON Data Archive hosted by CaltechDATA at https://tccondata. org (TCCON, 2019).