dataesr/scientific-paragraphs-categorization huggingface dataset:
Fig. 3  shows a comparison of the raw image (A, A'), the denoised image (B, B'), the deconvolution of the raw image (C, C') and the deconvolution of the denoised image (D, D'). When inspecting the fluorescence intensity profiles, denoising alone did not improve the resolution of the image (Fig. 3B, B' ), even if it improved considerably the SNR. We observed a smoothing of the fluorescence intensity profile (Fig. 3B', box ). We then compared the deconvoluted dataset (Fig. 3C, C', box ) with the denoised and deconvoluted image (Fig. 3D, D', box ). We observed 5 main peaks in the region of interest of the deconvoluted image (Fig. 3C' ). Denoising improved SNR of the restoration result. After denoising we were still able to distinguish 5 peaks (Fig. 3D , D' and box), even if the indentation was less prominent than in the non-denoised dataset (Fig.
3D-reconstruction of the meiotic spindle Having established all optimal parameters, we applied them to acquire 3D-stacks of triple stained mouse oocytes. We evaluated the overall aspect of the meiotic spindle resolution. We used alphatubulin coupled to FITC, to mark microtubules, Crest, a protein staining the kinetochore, which was revealed with a secondary antibody coupled to Cy3, and Hoechst to stain the chromosomes. Fig. 6  shows the 3D rendering of the raw data (A) and the deconvoluted data (B) of the meiotic spindle. We observed a clear improvement in resolution and much better separated microtubules (Fig. 6 B' , arrows) and the kinetochores (Fig. 6  B', arrowheads) as compared to the raw data (Fig. 6  A',, arrows and arrowheads). With these results, we provide evidence that our workflow, consisting of refractive index matching, optimized image acquisition and deconvolution improves greatly the overall resolution of the meiotic spindle image.
Acknowledgements The authors thank Jean-François Gilles and Nicolas Heck for helpful discussions and Marc Dos Santos for precious help with statistical analysis and Adobe Illustrator. Work on mouse oocytes was supported by an ANR Grant (ANR-12-BSV2-0005-01) to KW. Confocal work was carried out at the Institute of Biology Paris-Seine Imaging Facility that is significantly (or: which receives significant support from. ) supported by the "Conseil Regional Ile-de France", the French national research council (CNRS) and Sorbonne University, UPMC Univ Paris 06.
Cities play an important role in reaching local and global targets on energy efficiency and the reduction of greenhouse gas emissions. In order to determine the potential of energy efficiency in the building sector new planning instruments are required that allow depicting the complete building stock on the one hand and investigate detailed measures on the other hand. To pursue this objective, the ISO 13970:2008 monthly heating and cooling energy model is implemented using an open source based software architecture (CityBEM), in connection with data from 3D city models in the CityGML standard (LOD2). Input parameters such as the building geometry, typology and energy characteristics have been associated with the 3D data. The model has been applied to several urban districts with different numbers of buildings in the city of Karlsruhe.
Application of the ISO 13790:2008 standard ISO 13970:2008 standard provides a deterministic model to calculate heating and cooling energy needs  (ISO 2008). It is one of a series of calculation methods for the design and evaluation of energy performance of buildings. Energy needs for space heating and cooling of buildings are calculated on the basis of the heat balance of the building zone at three different temporal resolutions: hourly, monthly and seasonal. Many studies have applied this approach with different assumptions and contexts. Several national authorities have also adopted a performance-based method using a monthly energy balance, for example Romania  (Attia and Ana Muresan 2015)  or South  Korea (Kwak, Jo and Suh 2015). Vollaro et al. (2014)  calculated the monthly cooling and heating energy performance of an old building in central Italy using the ISO 13970 based software named MC11300.
The results were validated with the dynamic simulation software (TRNSYS). Kim, Yoon and Park (2013)  compared the simplified ISO 13970 method and dynamic (EnergyPlus software) simulation approach with regard to uncertainty. The different simulations are tested on a general five-storey office building in the South Korea. They highlighted the importance of calibration for unknown parameters (numerical parameters and time constants) in a local application of the ISO 13790 monthly method. Kristensen and Petersen (2016)  gave an overview of existing sensitivity analyses for building energy modelling  (Local, Morris and Sobol' methods). In this regard, they investigated the monthly and the hourly ISO 13790 methods considering a building zone test case located in Denmark. Kokogiannakis , Strachan and Clarke (2008)  carried out a comparative analysis of the hourly and monthly ISO 13790 method with simulation programs (e.
g. ESP-r and EnergyPlus). Several parameters of a typical office building are varied by considering different climate, internal heat gains, glazing areas, ventilation schedules, etc. Corrado and Fabrizio (2007)  proposed a useful implementation of the ISO 13790 monthly method, in particular to supply a formulation of the dynamic parameters and adapt them to the local climate. The simulation was performed on some real buildings in Italy, assuming different climatic conditions. The method is then validated with EnergyPlus. Within the framework of a EU project ENTRANZE  (Zangheri et al. 2014) , cooling and heating energy demand of four different building types across 10 European cities (Seville, Madrid, Rome, Milan, Bucharest, Vienna, Paris, Prague, Berlin and Helsinki) were performed by EnergyPlus simulation software.
The results were compared with the ISO 13790 hourly (implemented in spreadsheet) and ISO 13790 monthly (INVERT/EE-Lab program) method. They concluded that due to different calculation approaches and different degrees of complexities of the building and usage description in the three simulation tools, the energy needs varied significantly. Furthermore,  Sirén and Hasan (2007)  applied the hourly and monthly methods using a model office building in Finland. The results were validated with the IDA-ICE building energy software, considering it as a reference. Vartieres, Berescu and Damian (2013)  applied three different modelling approaches (e. g. ISO 13970 monthly, CODYBA and TRNSYS) to calculate the cooling energy demand of an office building in Bucharest. Then a comparative assessment on the modelling input parameters and results was performed.
Main objectives Considering the research gaps, the main objectives of this paper are to (a) implement the ISO method using the 3D city models to calculate the building heating and cooling energy needs on monthly basis, (b) develop the CityBEM model-considering the open source software implementation to carry out a quick and robust analysis, (c) consider 3D buildings (building geometry and attributes) at a district or city and use publicly available datasets, (d) perform a 3-step validation of the CityBEM model and (e) allow 3D visualization and perform statistical analysis. In Chapter 2, a short description of the ISO 13970 method is given. The implementation of the method i. e. required data, modelling steps, etc. is explained in Chapter 3. Then Chapter 4 explains the results and visualizes the main findings in 3D environment.
2015). Advantages are highlighted in several literatures e. g. (Kim et al. 2013 ), (ISO 2008) , etc. The monthly ISO model also produces good results compared to other software such as EnergyPlus  (Kristensen and Petersen 2016). It is also proved that this simplified quasi-steady state monthly method is able to predict year-round energy needs considering that the dynamic 1 Utilization factor is a function mainly of the heat-balance ratio and the thermal inertia of the building  (ISO 2008). (Corrado and Fabrizio 2007).
Required input data and data handling The ISO 13790:2008 monthly method mainly requires three categories of input data for modelling building monthly energy needs (Table  2 ). LOD2 CityGML data on building is imported into the Postgres/CityDB database with information about the geometry and the building coordinates. Then, using the python script, surface and volume of each building is calculated within the eclipse environment. Building typology data datasets are collected in excel or csv format. All these datasets are processed using python scripts and are stored as different tables in the PostgreSQL database (Figure  1 ).
Model performance The CityBEM monthly model is tested in several European and Asian urban cities, with varying number of buildings in both LOD1 and LOD2 data 6. The multiprocessing package of Python is exploited to improve the computational efficiency of handling of large 3D city models. The model proves very efficient and quick in displaying results in the virtual machine  7. For example, it takes around 3 minutes to run on about 4300 LOD2 buildings, 8 minutes on 12000 LOD2 buildings, 28 seconds on 600 LOD1 buildings, etc.
VALIDATION APPROACH A comprehensive validation of the CityBEM monthly model is performed to justifies its applicability and robustness. At first, a review of the comparative validation results obtained by the different studies implementing the ISO 13970 method is performed. Second, to ensure the correct implementation of the method in python programming interface, the input values and corresponding results suggested in the Annex J of ISO document (ISO 2008), have been considered in the CityBEM. Finally, the energy needs of an office and a residential building are validated with a dynamic simulation model TRNSYS.
Validation of ISO 13970 method in other studies ISO developers have internally carried out error estimation and validation of the monthly methods. They concluded that the monthly calculation gives correct results on an annual basis, but the results for individual months close to the beginning and end of the heating and cooling season can have large relative errors  (ISO 2008), p. 15). Recently, several studies as explained in Table  1 , carried out validation of ISO methods with widely used dynamic simulation software such as TRNSYS or EnergyPlus. Kristensen and Petersen (2016)  found out that the monthly quasi-steady-state model predicts a lower monthly energy need for space heating and higher monthly energy need for cooling compared to the dynamic model. Zangheri et al. (2014)  found that the simplified monthly method tends to over-estimate the energy needs for cooling, especially for office buildings located in a Mediterranean climate.
The results obtained by  (Vollaro et al. 2014 ) confirms that the monthly method tend to overestimate and underestimate of approximately 12-14% the cooling and heating energy demands respectively, compared to the results from the dynamic simulation with TRNSYS. Kim et al. (2013)  carried out a deterministic and a stochastic comparison of simple hourly method with the EnergyPlus 6. 0 software, with the inputs and boundary conditions for both approaches as close to each other as possible. They found that in the deterministic method, ISO 13970 predicts less annual heating and cooling energy demands than EnergyPlus. In the stochastic approach, EnergyPlus showed higher stochastic robustness than ISO 13790. However,  Vartieres et al. (2013)  found that the yearly cooling need obtained with the monthly method is almost three times higher than that of TRNSYS.
From May to October, the predicted cooling demands by ISO 13790 are smaller than EnergyPlus ones, while in the other months the predicted cooling demands are larger than EnergyPlus  (Kim et al. 2013). Earlier  Kokogiannakis et al. (2008)  confirmed that monthly method demonstrates higher cooling needs than the other methods. The EU study in the ENTRANZE project concluded that heating needs in ISO monthly and hourly are in line with EnergyPlus but the cooling needs are different in some climatic conditions  (Zangheri et al. 2014). Therefore, most of the studies found that the ISO method predicts less heating and more cooling needs, compared to the dynamic simulation software. CityBEM also shows similar patterns of cooling and heating needs (Chapter 4).
Validation with ISO 13970 reference In order to validate the proper implementation of ISO 13970 method, e. g. use of the model equations, the ISO authors have given some exemplary input data and corresponding results in the Annex J of the document. Therefore, the robust implementation of the CityBEM model is checked using the exactly the same input values as suggested. Figure  6. Comparison of energy needs with the assumptions explained in the ISO 13790:2008 (Annex J) for an office building room A test case consisting of an office building room with only one external wall facing west is considered. Other specific assumptions and simplifications (according to the suggestions) are made for the thermal heat flows and heat gains coefficients calculations. The results obtained by the CityBEM exactly match with the annex results (Figure  6 ).
Validation with TRNSYS TRNSYS software is used to simulate the behaviour of the transient system (http://www. trnsys. com). The simulations are focused on assessing the performance of thermal and electrical energy systems. In this study, TRNSYS is used to validate the results of the cooling and heating energy needs obtained by the CityBEM. Since TRNSYS cannot perform simulation on multiple buildings at a time, two individual buildings e. g. an office (built in 1975) and a residential building  (built in 1985)  in Karlsruhe are validated separately, with the same input data and assumptions as chosen in the CityBEM model.
Preparation of input data Several inputs are required in TRNSYS. At first, the two buildings are extracted from the CityGML dataset to calculate the building geometries e. g. surface area of walls, roofs, etc. (Figure  7 ). Then the building attributes and thermal characteristics (e. g. surfaces areas, thickness and composition of walls, roof and ground layers, etc. ) are given as inputs in TRNSYS. Calculation of internal gains are different in both models, therefore, careful attention has been given to ensure the same input of heat gains in both models. For this reason, the internal gain was set considering the table from ISO 7730 as suggested in TRNSYS. Then, the scale was adjusted by considering the monthly aggregated values of the internal heat gains in order to ensure exactly the same inputs in both TRNSYS and CityBEM.
CONCLUSION In this study, the CityBEM monthly model is developed through an open source implementation of the ISO 13970:2008 standard to calculate monthly heating and cooling energy need for a large number of building stocks in a city. It is based on the 3D city models of the CityGML standard and the required input parameter such as the building geometry, typology and energy characteristics have been associated with the 3D data. The model is applied in several urban districts with varying number of buildings stocks, using both LOD1 and LOD2 datasets. The robustness of the model is also tested using a 3-step validation approach. The model can help the decision makers or utility companies to determine the potential energy efficiency in the buildings and thus help in energy planning. The monthly ISO method has several limitations, which have also been identified in other studies.
It is based on steady-state conditions, the variation of parameters such as heat flows and heat transfer coefficient depends on the external temperature which is set to a constant value for each month  (Vollaro et al. 2014). Vartieres et al. (2013)  concluded that assuming constant temperature with fresh air introduced gives cooling loads during transition months, when introducing outdoor airflow could reduce the temperature. The ventilation airflow supplied from the outside with a smaller averaged external temperature gives heat losses by ventilation even during summer. Moreover, the users and their behaviour play an important role in calculating building energy needs. It was difficult to collect such exact data for each building. Regarding handling of 3D data, we observe geometrical and topological errors in the CityGML dataset which could not be completely imported into the postgresSQL database, resulting in exclusion of some buildings in analysis.
The model can, however, be further improved in future. The energy needs for heating and cooling can be used as an input for the energy balance of the heating and cooling systems and ventilation system in order to calculate the energy use. Hourly and seasonal energy can also be calculated. In this regard, the CityBEM monthly model can also be validated with the hourly method by aggregating the hourly values into months and years. The sensitivity of the critical model input parameters can also be tested in future. Building energy saving potential and further refurbishment priorities/scenarios can be simulated to identify further applications of the model. The model can also be adapted to support more detailed LOD3 or LOD4 datasets. For example, the exact window to wall ratio can be calculated from those datasets, which will improve the modelling results.
The 3DCityDB (http://www. 3dcitydb. org/3dcitydb) is a free open source package consisting of a database schema and a set of software tools to import, manage, analyze, visualize, and export virtual 3D city models according to the CityGML standard. This software includes a tool for importing CityGML data into a PostgreSQL database. The software FZKViewer (https://www. iai. kit. edu/1302. php) is used to visualize and inspect CityGML data. QGIS is an open source Geographic Information System (GIS) application that provides data viewing, editing, and analysis. This software supports connection to PostgreSQL database. Finally, ArcScene is used to visualize the monthly energy need results in the 3D environment. The Feature Manipulation Engine (FME) is used to translation of spatial data e. g. conversion between shapefile and CityGML.
Reduction factor for intermittent cooling () and heating ( ) 7. Calculation of cooling and heating need:For the cooling mode:  , =   -. ℎ  , , =  ,. , (3) For the heating mode:  , =  ℎ -. , , =  ,. , the continuous heating mode MJ Energy need for the intermittent cooling mode MJ Energy need for the intermittent heating mode The calculation steps and equations are also implemented in the Eclipse using python scripts, PostgreSQL and other related tools. Several scripts and functions are written in modular structure. They are divided into 10 packages, which allows inspection of intermediate results of the individual model components. The intermediate results and final outputs the monthly cooling and heating energy needs are also saved as tables in the database from where automatized graphs are prepared.
A soil moisture retrieval algorithm from SNR data was derived by  Chew et al. (2014)  for bare soil. In subsequent modeling studies  Chew et al. (2015)  showed that the vegetation canopies affected the SNR modulation pattern. They showed that vegetation growth tended to trigger a decrease in the SNR amplitude. Because the vegetation effects tended to perturb the soil moisture retrieval,  Chew et al. (2016)  proposed an improved algorithm for soil moisture retrieval in vegetated environments, which used the amplitude decrease extent to decide when vegetation influence was too large. They used a model database for the SNR of the L2C signal to remove the most significant vegetation effects for the sites they considered in western USA. Small et al. (2016)  further compared different algorithms of GNSS-IR soil moisture retrieval in the presence of vegetation.
The periodic signature of the multipath SNR data is visible. We only analyzed the modulation patterns in a valid segment for satellite configurations corresponding to low elevation angles, ranging from 5 to 20 •. This corresponded to a valid segment data recording of less than one hour (40 to 50 min). We excluded very low elevation angles (less than 5 • ) in order to avoid spurious effects from trees and artificial surfaces surrounding the field. Because the SNR signal amplitude was much reduced and the wave pattern was not visible at high elevations for our field observations, we excluded elevation angles larger than 20 •.
Among the remaining tracks we removed the slowly moving tracks whose maximum cos θ • dθ/dt was less than 9. 5 × 10 -5 rad s -1 (threshold value based on our field observations) of the valid segment (elevation angles ranging between 5 and 20 • ). This specific data sorting was only made for vegetation height retrieval (Sect. 2. 5). After this selection, the number of available satellite tracks was 37 per day. Provided the reflecting surface is stable, the a priori antenna height can be used to estimate the SNR frequency. The SNR frequency is used to calculate the multipath SNR phase, and then the SNR phase is used to estimate VSM (Sect. 2. 4). If the reflecting surface is changing in response to vegetation growth, relative vegetation height can be retrieved instead of VSM by directly estimating the dynamic SNR frequency/period with a wavelet analysis (Sect.
The wavelet analysis is well suited for analyzing time series with nonstationary power and frequency changes across time as illustrated by Fig. 1. Our wavelet analysis methodology is based on the WaveletComp R-package  (Roesch et al. , 2014). To analyze the period structure, we used a well-known Morlet mother function which comes from a combination of a Gaussian and a sinusoidal function (Fig. S3 ). Due to its shape, Morlet daughters allow detection of singularities on all scales/periods of the spectrum. Morlet wavelets are also well suited for environmental analysis  (Grinsted et al. , 2004). We calculate the Morlet wavelet transform of the multipath SNR and evaluate the power spectrum of the multipath SNR signal (see Eqs. S1-S4). Vegetation height can be retrieved using the dominant SNR period (T d ), which is the peak period of the average power spectrum derived from a wavelet analysis of SNR, from the multipath SNR segment at elevation angles from 5 to 20 •.
After obtaining T d time series, the relative antenna height (h) can be derived from Eq. (4) as the following: h = λ 2 cos θ E9 • dθ E9 dt • T d. ( 9 ) The T d value is used to represent the multipath SNR data in order to estimate h. Also, changes in the elevation angle (θ ) and in dθ/dt have to be accounted for. In this study, changes in h were surveyed across dates at an elevation angle of 9 • (e. g. , θ E9 = 9 • ; see Sect. 3. 2). Changes in relative antenna height (h) during vegetation growth are directly related to vegetation height increase by H = h 0 -h. (10) Similarly to the phase change estimates ( φ t in Sect. 2. 4), h 0 is the median value of the top 15 % h data during the whole wheat growth cycle for each track. The final retrieved vegetation height (H ) is based on the mean relative antenna height change from all available satellite tracks (N = 37), plus one wavelength H = N H N + λ.
0148 m 3 m -3 •-1 , with a mean standard deviation value of 0. 036 m 3 m -3. GPS retrievals based on the adjusted slope value S = 0. 0033 m 3 m -3 •-1 presents intermediate values (0. 008 m 3 m -3 ), together with those based on the scaled soil wetness index (0. 009 m 3 m -3 ) and with the ISBA simulations (0. 005 m 3 m -3 ). Figure  3  shows that the sub-daily variability of GPS VSM retrievals tends to increase during the last 10 days of the retrieval period. It must be noted that GPS data are missing on 8 and 9 February, and that the ISBA simulations indicate soil freezing (i. e. , the presence of ice in the top soil layer) from 4 to 9 February. This period was excluded from the comparison. In the end, there were 47 valid observation days for the statistical analysis of the retrieved surface VSM, among which 43 days could be compared with model simulations.