dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions This study assessed L-band sensitivity to windthrows and insectrelated forests disturbances and demonstrated the potential of spaceborne L-band backscatter to identify such areas within a change-detection framework. The results suggest that disturbance events can be delineated using relatively simple thresholding approaches. An important outcome of this study was the demonstration of the potential to identify areas vulnerable to insect outbreaks, which might provide a distinct advantage (early warning) over the use of other sensor types. The methods presented here can be currently applied using L-band data from the ALOS-2 PALSAR-2 and from future L-band missions, such NASA-ISRO SAR. The classification accuracy for the areas by affected windthrows was high, but lower when the disturbance was caused by insect outbreaks.
A differentiated result was not unexpected because backscatter changes are more abrupt for windthrows (days) than for insect outbreaks (months or years). Overall, it seems that changes in backscatter owing to insect outbreaks or windthrows are too generic to identify the specific stressor. As other agents (e. g. , fire, logging) could result in similar backscatter change values, a differential diagnosis using SAR remotesensing data alone is limited. Therefore, ancillary information might be needed to pinpoint the exact disturbance agent, as shown in other studies. Overall, the analysis proved to be complex because of the multitude of factors affecting backscatter changes (bark beetle outbreaks, logging, environmental conditions affecting the vegetation water content, and soil surface moisture) and limitations due to the density of SAR observations.
Samples are weighed for different relative humidity conditions, ensured by salt solutions. For the vapour permeability, the most common measurement procedure is based on the standard (ISO 12572). It measures the mass variation of a material sample under a controlled difference of relative humidity between both sides. The vapour permeability is estimated for different relative humidity levels (dry and wet cup). For the liquid permeability, the sample is exposed to a liquid pressure gradient. The permeability is estimated by measuring the liquid flux at the equilibrium. Even if measurements of these properties are well established and largely reported in the literature, some discrepancies can appear when comparing experimental data from in-situ measurements to numerical model results. A material, with an initial moisture content w 0 , is submitted to an adsorption phase at relative humidity φ 1 and then to a desorption phase at relative humidity φ 2.
It is adaptive and embedded to estimate local error in time with little extra cost. The algorithm was implemented in the Matlab environment. It is also important to note that a unitless formulation of the problem was used, due to a number of good reasons. First of all, it enables to determine important scaling parameters (Biot, Luikov numbers for instance). Henceforth, solving one dimensionless problem is equivalent to solve a whole class of dimensional problems sharing the same scaling parameters. Then, dimensionless equations allow to estimate the relative magnitude of various terms, and thus, eventually to simplify the problem using asymptotic methods  [29]. Finally, the floating point arithmetics is designed such as the rounding errors are minimal if you manipulate the numbers of the same magnitude  [21].
Using the Matlab platform and the ODE45 function, the computation of the results for one value of the measurement plan takes 150 s with Intel i7 CPU and 16GB of RAM. Thus, the exploration of the whole measurement plan requires less than 1 h. The solution of the direct and sensitivity problems are obtained for a given a priori parameter P and, in this case, the validity of the OED depends on this knowledge. If there is no prior information, the methodology of the OED can be done using an outer loop on the parameter P sampled using, for instance, Latin hypercube or Halton or Sobol quasi-random samplings. Interested readers may refer to  [8]  for further details on the computation of sensitivity coefficients. Equation (2. 10) has been written for a relative humidity step at the boundary of the material.
Deriving InSAR Long-Term Ground Displacement To derive the long-term ground displacement in lava fields, we perform an InSAR time series analysis on a large amount of X band SAR dataset acquired by the German Space Agency TerraSAR-X/TanDEM-X (hereinafter named TSX/TDX) satellites constellation. The dataset includes 56 acquisitions from an ascending orbit, spanning from 7 March 2009 to 15 November 2014, and 34 acquisitions from a descending orbit spanning from 13 December 2008 to 6 October 2014 (Table  1 ). A small baseline (SB) method based on the Stanford Method for Persistent Scatterers (StaMPS;  [24] ) was adopted to implement the InSAR time series analysis. Interferograms with perpendicular baselines less than 500 m and temporal baselines less than 365 days were firstly processed using DORIS software.
The time series were temporally referenced to the first acquisition date and spatially referenced to non-displacement areas about 10 km away from volcanic craters. The non-negligible long-wavelength artifacts remained in the time series maps were properly modeled and subtracted using a correction method based on the principal component decomposition implemented in the Principal Component Analysis-based Inversion Method package (PCAIM;  [29] ). The correction method is demonstrated in detail by  [23] , and will not be repeated here in this paper. The InSAR ascending and descending measurements provide displacement along two different lines of sight. They can be combined to resolve a 2-dimensional (2D) displacement problem under an assumption that the third dimensional component is negligible.
Estimating Lava Thickness To evaluate the thickness of lava flows emplaced between 1998 and 2007 at Piton de la Fournaise, we used two available Digital Elevation Models (DEMs). One is a 25 m resolution DEM produced by IGN from aerial photogrammetry in 1997 and the other is measured via airborne LiDAR surveying in 2008 with a 5 m resolution. It is worth noting that no eruptive events generating surface displacement occurred between the acquisition time of the 1997 DEM and March 1998, and between April 2007 and the acquisition time of the 2008 DEM. The two DEMs were first downsampled to the same grid posting. We then used the vertical difference between the two DEMs to estimate topographic height change, which reflects the thickness of total deposits. Given that lava flow is the main product of Hawaiian eruptions and composes much of the deposits, as at Piton de la Fournaise, and no independent data providing information about the discrepancies between the total deposit thickness and the lava thickness is available, we used the difference between the two DEMs to approximately represent the thickness of the lava flows.
Conclusions We measured the long-term displacement in recent lava fields at Piton de la Fournaise volcano using TSX/TDX InSAR data between 2009 and 2014. The spatial pattern and temporal behavior of the displacement of each lava flow, and the relationship between displacement, lava thickness and age are investigated by a detailed statistical analysis. The subsidence behavior exhibits a significant difference between the lava fields outside and inside of the EFA (Eastern Flank Area). The former shows a clear linear relationship with lava thickness and it decays with lava age, which is attributed to the post-lava emplacement processes. The latter however displays an irregularity in the subsidence and the relationship between subsidence and thickness and age, which confirms that, in addition to the post-lava emplacement processes, other processes must have played a role in the observed subsidence on the Eastern Flank.
Acknowledgments: The authors greatly appreciate the financial support from the  National Natural Science Foundations of China  (No. 41730109 ,  41471356  and  41804013 ). We would like to acknowledge the support of the  Natural Science Foundation of Jiangsu Province, China  (No. BK20180666 ), and the Jiangsu dual creative talents and Jiangsu dual creative teams program projects of  Jiangsu Province, China , awarded in 2017. We would like to thank the  DLR  for providing TSX/TDX images through the  LAN 0237 project , and  CNES (TOSCA)  and  INSU (SNOV)  for supporting InSAR data acquisition at  Piton de la Fournaise. The TSX/TDX images have been acquired within the framework of the  French National Service   OI2. Part of the work exposed in this paper was also supported by the  ANR-16-CE04-0004-01  ( SlideVOLC)  project funded by the  Agence Nationale de la Recherche.
ACKNOWLEDGEMENTS We thank  Fanny Delille ,  José Dugay  and  Didier Thiebault  from  ESPCI , for their help in GC-MS measurements. This work has received funding from the  ANR  EquipEx  SENSE-CITY  project, the FUI 18 MIMESYS funded by  Region Ile-de-France  and the  European Union 's  H2020 Programme for research, technological development and demonstration  under grant agreement No  644852.
Materials and Methods A 3D SAW sensor model was built on a 36 • rotated Y-cut lithium tantalite piezoelectric substrate (LiTa0 3 ) through the COMSOL software. The device is a delay line with a pair of interdigital transducers (IDT) which serve to generate and receive the acoustic waves. Both the IDTs and the sensitive area, the zone separating the IDTs, are made of 100/238 pm Cr/Au thin layers on LiTaO3 substrate. To simulate an IIP-based SAW, the sensing area was subdivided in elementary blocks whose physical properties can be modified separately and whose dimensions are equal to that of a Pb 2+ ion. Three configurations have been considered: a non-imprinted polymer (NIP) where all the elementary blocks were filled with a polymeric material, IIP where some of blocks are filled with lead ions (IIP before extraction) or with vacuum (IIP after extraction).
Results A 8MHz SAW sensor was chosen for investigation in this paper. The thickness of the sensitive area is 338 pm. A transient analysis is performed through COMSOL software to obtain the displacements and output voltages versus time. Figure2 reports the 3D field displacements for NIP and IIP before and after extraction (images correspond to 3 snapshots at 9. 63 ps). Their further displacement's amplitudes in the three directions, versus time, are presented in Figure  3a-c. The same emplacement has been considered. Results indicate that in all considered cases, the in-plane horizontal displacements are more important than the vertical ones, confirming thus the shear-horizontal nature of the generated waves. Further calculations have concerned comparison between a NIP/SAW sensor and a nonextracted IIP/SAW one.
5 × 10 -5 to 0. 01 Å -1. Silica particle suspensions were prepared in an electrolyte solution of potassium nitrate (1 × 10 -2 M) consisting of milli-Q water and D 2 O in different volume ratios. Solid concentrations were adjusted to between 5 and 40 vol % in order to investigate whether there is any influence on the optimization of the H 2 O/D 2 O ratio. Once a suspension was evenly mixed, its pH value was adjusted to 10 using 1M and/or 0. 1 M KOH in D 2 O. The suspension was then mixed by magnetic stirring for 30 min. The suspension was then transferred to a flat cell for USANS measurements or a cylindrical cell (Couette geometry) for rheo-USANS measurements. Experimental rocking curves were reduced and normalized to absolute intensity scale using the standard procedure  [34]  adapted to Kookaburra using Python scripts on the Gumtree platform  [35].
This shear rate was selected as it is well above the minimum rate that ensured good particle dispersion during the measurement for comparison with the tumbling flat cell. The viscosity of the suspension was measured at increasing constant shear rates for a fixed period of time (peak hold test). The minimum shear rate to maintain a constant viscosity was determined and higher shear rate than the minimum was tested (500 s -1 ). It can be seen that there is a very good agreement, for the 5 vol % silica sample (Figure  4a ), between the shape of the scattering curves of the sample in the tumbling cell and in the rheo-cell at 500 s -1 , measured independently. The thickness of the rheo-cell during data reduction, which impacts on the overall intensity scale, was chosen to be 1 mm and the good agreement in the intensity to the data in tumbling mode imply that the curvature of the rheo-cell was not significant to increase the value of the effective sample thickness significantly.
Failure Mechanism of TBC Systems with a VPS-Pt Bond-Coating Despite a significant difference in lifetime between AM3-VPS-Pt and CMSX-4-VPS-Pt TBC systems, the uncertainty domains had to be taken into account. The lifetimes of both TBC systems tend to be different, but additional data would be necessary to confirm. Nevertheless, from the present data, it is reasonable to say that the superalloy may influence the cyclic oxidation behavior of TBC systems with a VPS-Pt coating. The S content of AM3 and CMSX-4 superalloys was measured by GDMS; 0. 28 ppmw in AM3 against 1. 4 ppmw in CMSX-4. The higher S content should have accelerated the degradation of the CMSX-4-VPS-Pt TBC systems but thermal barrier spallation occurred more rapidly with AM3 superalloy. Hf, contained in CMSX-4, is a reactive element known to mitigate the detrimental effect of S  [67, [77] [78] [79].
Acknowledgments: Safran Helicopter Engines is gratefully acknowledged for providing the samples. The authors wish to express their thanks to  Praxair St  which manufactured the Tribomet ® coatings, with and without Pt, Safran Aircraft Engines for grit-blasting the TBC systems, C. C. C. which made EB-PVD thermal barrier coatings, to  Christophe Verdy  (at the  LERMPS ) for the VPS coatings and to  John Rayment Nicholls  for the coatings obtained by sputtering and for the third batch of EB-PVD thermal barrier coatings.
In this second design stage, in addition to pipe diameters, PAT operation points (head and flow) are also variables to be defined. Variable speed operation is considered with a speed constraint of 60-100% of its nominal value, and a flow constraint up to 50% of its nominal value  [18]. As a result, it is expected that the best configuration of pipes and PATs will be found, maximizing the energy production. In all cases, the software EPANET  [35]  was used to model the network and to simulate the various scenarios to achieve flow and pressure data. Figure  5  summarizes the described TN design procedure, including PAT selection. Water 2018, 10, x 6 of 14 𝑉 𝑖 𝑘+1 = 𝜔 • 𝑉 𝑖 𝑘 + 𝑐 1 • 𝑟𝑎𝑛𝑑 1 • (𝑃 𝑖 𝑘 -𝑋 𝑖 𝑘 ) ∆𝑡 + 𝑐 2 • 𝑟𝑎𝑛𝑑 2 • (𝐺 -𝑋 𝑖 𝑘 ) ∆𝑡 (6) 𝑋 𝑖 𝑘+1 = 𝑋 𝑖 𝑘 + 𝑉 𝑖 𝑘+1 • ∆𝑡 (7) The TN design will follow a three-stage procedure.
Variable speed operation is considered with a speed constraint of 60-100% of its nominal value, and a flow constraint up to 50% of its nominal value  [18]. As a result, it is expected that the best configuration of pipes and PATs will be found, maximizing the energy production. In all cases, the software EPANET  [35]  was used to model the network and to simulate the various scenarios to achieve flow and pressure data. Figure  5  summarizes the described TN design procedure, including PAT selection.
Objectives of the present study The main objective of this study was to assess the influence of environmental impact indicators on prioritisation of 'dispatchable' technologies in the energy mix, and thus the environmental impact on climate change, and the infrastructure costs involved in that change of paradigm. The environmental impact of current technologies changes over time and therefore needs to be compared against the environmental impact of prospective technologies. This secondary objective was necessary to achieve the main one and represents an additional methodological contribution of the study. In the analysis, economic growth remained the basis of prospective computations run by POLES, while environmental criteria were taken into account in the optimisation process. Section 2 of this paper describes the models used to implement direct and indirect emissions into the decision-making process on 'dispatchable' technologies and the adapted regional environmental impact database for the production technologies present in the energy models.
3 in appendix). In order to characterise electricity production as an output, POLES considers exogenous parameters such as GDP alongside population changes and carbon constraints, which are included by setting a carbon price within the system. Furthermore, market data are included using resources constraints and the interdependencies between fuel prices and fuel demand. Other outputs are also available, such as greenhouse gas emissions, system prices 150 and energy consumption for each year and country within the 151 model's scope. However, POLES lacks a representation of de-152 velopments in the electricity grid and its components, such as 153 storage. As storage technologies will be crucial for large-scale 154 deployment of VRES, an optimisation tool that considers all 155 these aspects is needed  [9]    nologies that are in the pilot phase.
Therefore, the environmen-250 tal impact of these technologies is not known and must be esti-251 mated. For this, we used data assessing the full costs and bene-252 fits of the future energy system taken from the New Energy Ex-253 ternalities Development for Sustainability (NEEDS) database 254  [38]  database. The NEEDS model comprises five scenarios: 281 f i, j,k,l-ratio = F NEEDS-scenario F NEEDS-Today -1 (1) where f i, j,k,l-ratio is the ratio of a specific product's emis- 291 F i, j,k,l-Scenario = F i, j,k,l-Today × f i, j,k,l-ratio (2) where F i, j,k,l-Scenario is the LCI of all technologies for the prospec- 315 min E m,Europe-k = ∑ i, j,k,l,t F i, j,k,l × P i, j,t (3) where E m,Europe-k is the total yearly emissions for a given indi-.
Pessimistic Business as Usual (P-BAU), Pessimistic 440 ppm 256 (P-440ppm), Realistic-Optimistic 440 ppm (RO-440ppm), very 257 optimistic 440 ppm (VO-440ppm), very optimistic based on a 258 renewable energy electricity (VO-Renew). Each of the five sce-259 narios is detailed for the period until 2025 and 2050. The NEEDS 260 database is based on the EcoInvent v1. 3 database, which is an 261 obsolete version considering the different aggregation of prod-262 ucts. However, we left technologies that are still considered 263 prospective as described in the NEEDS database, while we re-264 moved incorrect products that are misplaced as outputs in the 265 technologies (e. g. tidal and wave energy, concentrated solar 266 power plants etc. ). 267 To overcome the changes between the different versions of 268 the EcoInvent database, we downgraded the EcoInvent database 269 for the current technologies to similar products that appear in 270 the NEEDS database (F i, j,k,l-Today → F NEEDS-Today ).
There-271 fore, products appearing in the latest version of the EcoInvent 272 database were not integrated into the system. This did not affect 273 the output for any of the indicators characterised except water 274 depletion, human toxicity and land occupation. The main rea-275 son why this was done was to avoid discrepancies between the 276 database of prospective technologies and that of current tech-277 nologies, while computing the prospective emissions of power 278 production technologies for every country. Thus for each tech-279 nology present in the NEEDS database, the ratio between the 280 current scenario and prospective scenarios was calculated as:.
sions between the current scenario and a given prospective sce-283 nario [%], F NEEDS-Today is the emissions for a specific prod-284 uct in the LCI of a technology for the current scenario, and 285 NEEDS-scenario is the emissions for the same specific product in 286 the LCI of a technology for the prospective scenario. 287 Once the ratio for each product is calculated, it is possible to 288 obtain, for each technology, country and prospective scenario, 289 a database that reproduces the emissions behaviour of the exist-290 ing technology in F i, j,k,l-Today.
Acknowledgments This is the final manuscript author version. ©2018. This manuscript version is made available under the  CC-BY-NC-ND 4. 0 license http://creativecommons. org/licenses/by-nc-nd/4. 0/ doi:10. 1016/j. energy. 2018. 08. 093Oulu, and a grant from the  ARC-4 Energies Région Auvergne Rhône-Alpes, France.
255 Pessimistic Business as Usual (P-BAU), Pessimistic 440 ppm 256 (P-440ppm), Realistic-Optimistic 440 ppm (RO-440ppm), very 257 optimistic 440 ppm (VO-440ppm), very optimistic based on a 258 renewable energy electricity (VO-Renew). Each of the five sce-259 narios is detailed for the period until 2025 and 2050. The NEEDS 260 database is based on the EcoInvent v1. 3 database, which is an 261 obsolete version considering the different aggregation of prod-262 ucts. However, we left technologies that are still considered 263 prospective as described in the NEEDS database, while we re-264 moved incorrect products that are misplaced as outputs in the 265 technologies (e. g. tidal and wave energy, concentrated solar 266 power plants etc. ). 267 To overcome the changes between the different versions of 268 the EcoInvent database, we downgraded the EcoInvent database 269 for the current technologies to similar products that appear in 270 the NEEDS database (F i, j,k,l-Today → F NEEDS-Today ).
282F sions between the current scenario and a given prospective sce-283 nario [%], F NEEDS-Today is the emissions for a specific prod-284 uct in the LCI of a technology for the current scenario, and 285 NEEDS-scenario is the emissions for the same specific product in 286 the LCI of a technology for the prospective scenario. 287 Once the ratio for each product is calculated, it is possible to 288 obtain, for each technology, country and prospective scenario, 289 a database that reproduces the emissions behaviour of the exist-290 ing technology in F i, j,k,l-Today.
A classical Kolsky processing was applied to the recorded incident, reflected and transmitted pulses on the input and output bars so the differential axial displacement of the sample and the input and output forces were deduced. The confinement level is measured thanks to the data of strain-gauges glued on the ring. The results of a dynamic PS-PTS test performed on dry R30A7 concrete are given in Fig. 7. The strain-rate was estimated around 80/s. As observed in quasi-static condition the shear stress increases until the peak and rapidly decreases after the peak. The maximum shear stress (45 MPa) is significantly higher compared to the value of shear strength noted under quasi-static loading condition (33 MPa). On the other hand, the confinement stress reached at the peak (27. 5 MPa) is very close to the confinement level noted under static loading condition (24.
Shear experiments were conducted on dry R30A7 concrete under quasi-static condition by using a hydraulic press and under dynamic condition by using a SHPB testing facility. Whereas classical measurement techniques (LVDT and force cell in static test, classical Kolsky processing in dynamic test) provide the displacement and the shear stress in the ligament, the confinement stress is deduced from data of strain gauges attached to the ring. Finally, the obtained results show higher shear strength of dry R30A7 concrete in quasistatic than in dynamic experiments. This work has been sponsored by CEA-Gramat (France). The support of this organization is gratefully acknowledged by the authors.