dataesr/scientific-paragraphs-categorization huggingface dataset:
In Southern Scandinavia and Estonia, the cropland signals in LUH2 are opposite in comparison to the ESA POULTER derived signal with an increase in cropland in LUH2 and thus also in the LUCAS LUC dataset. This is also the case for Egypt. Here, ESA POULTER shows a decrease in cropland cover for the Central Nile Delta and along the Nile River and increases along the Edges of the Nile Delta whereas LUCAS LUC shows increases in this region. The increase along the delta edges can be attributed to cropland expansion and the decreases to urbanization  (Xu et al. , 2017). These small-scale land cover dynamics are not captured by LUH2. The decreasing trend in cropland is visible for all datasets in the three PRUDENCE regions EA, ME, and IP (Fig. 4a-c ). Only in MODIS, some years show less cropland compared to the year 2015.
(2016) , who used data from the Common Agricultural Policy Regionalized Impact (CAPRI) database. Just like the cropland changes, alterations of grassland fractions are weaker in LUCAS LUC compared to the pasture changes in LUH2 in the Middle East (Fig. 3g, h ). This can be partly attributed to the larger share of bare ground in this region in LUCAS LUC. The changes in grassland are quite small in ESA POULTER except for Southern Russia and Northwestern Kazakhstan, where a dual pattern of decrease and increase can be seen (Fig. 3i ). The decrease in grassland in this region results from a conversion into cropland (3c), which LUH2 does not capture. The time series of grassland changes for the three PRUDENCE regions show substantial differences between the datasets (Fig. 4d-f ). Since LUH2 does not provide grassland cover, pasture area changes are plotted instead.
Forest Forest changes in LUCAS LUC match the LUH2 changes closely (Fig. 3d, e ). Increases in forest fractions in mountainous areas (e. g. , Alps, Carpathians Mountains, Balkan Mountains, and Pyrenees), Great Britain, Ireland, and in Russia between 55 based datasets show an increase in forest cover during the 1990s and the 2000s for the Alps (e. g. ,  Schwaab et al. , 2015; Bebi et al. , 2017). Substantial forest fraction increases in Northern Russia, Northern Norway, and Northern Finland can be seen in ESA POULTER. In these regions, both LUCAS LUC and LUH2 show no change in forest fraction at all. In these areas forest cover increased due to forest growth instead of active afforestation  (Potapov et al. , 2015) , which seems to be captured by the satellite-based ESA POULTER dataset but not by LUH2.
Forest cover increases in both LUH2 and LUCAS LUC in the PRUDENCE regions EA, ME, and MD (Fig. 4a-c ). In EA, ESA POULTER also shows a increase in forest cover but with a smaller magnitude while MODIS forest cover shows both increase and decreases. The difference between LUH2 and LUCAS LUC compared to the two satellite-based datasets are more substantial in ME. Here, ESA POULTER shows a strong decrease while MODIS shows both strong increases and decreases for some years. Such year-to-year variations seem to be unrealistic. Hence, a detailed investigation of the MODIS dataset for this region is needed. In the IP region, LUCAS LUC, LUH2, and MODIS show an increase in forest cover while ESA POULTER shows a decrease.
Forest A strong forest cover increase is found for SSP1/RCP1. 9, SSP1/RCP2. 6 (Fig. 10a, b ) and to a lesser extent for SSP2/RCP45 (Fig. 10a, b ). These are the scenarios for which the added tree cover fraction files are used because the original LUH2 dataset underestimates the strong afforestation signal (Sect. 2. 3. 2). In the SSP1-based scenarios, the largest increase is found in Ireland, England, Northern France, and in Russia near the border to Ukraine. Also, a widespread increase is visible for most countries in Central and Eastern Europe, Southern Sweden and Finland, and Northern Spain. The forest increase is mainly compensated by a decrease in grassland (Fig. 9  and shrubland (not shown) and to a lesser extent by declining cropland (Fig. 7 ). The only region with substantial forest reduction is Western Russia.
Urban In contrast to the historical period, all scenarios show both decreases and increases in urban fraction between 2015 and 2100 (Fig. 10 ). Urban changes are largely driven by the SSP scenarios (i. e. population dynamics) resulting in almost identical changes in the LUCAS LUC dataset in scenarios based on the same SSP scenario. A widespread urbanization signal can be found in the SSP5-based scenarios for Europe except for Eastern Europe, which shows a decrease in urban fraction (Fig. 10d, h ). The increase in urban fractions is particularly strong in Great Britain. The SSP1-based scenarios show a increase with a smaller magnitude in West-and South Europe, respectively, as well as Scandinavia and a decrease in the eastern European countries including some parts of Germany (Fig. 11a, b ).
Discussion The newly generated LULCC dataset LUCAS LUC was tailored towards the requirements of future downscaling experiments within the FPS LUCAS and EURO-CORDEX. The need for high-resolution land cover input is met by employing the ESA-CCI LC dataset, which has a ∼300 m grid, as a basemap for the year 2015. Since most of the state-of-the-art LSM employ a PFT land cover classification the ESA-CCI LC was converted into PFTs. This step also helps dealing with mixed ESA-CCI LC land cover classes, which can be conveniently converted into classes with similar properties. The uncertainty introduced by the cross-walking procedure was investigated by  Georgievski and Hagemann (2019). Their results show that this uncertainty can be of high relevance, comparable to the uncertainties in historical changes for the global forest cover.
For the LUCAS LUC dataset a more detailed cross-walking procedure was applied taking into account the dependency of the composition of ESA-CCI LC land cover classes on the climatic conditions  (Reinhart et al. , submitted). This likely introduces more uncertainty, e. g. , due to the climate dataset used to compute the HLZs. For instance, in mountainous regions, it is important to have a high-resolution climate dataset in order to capture the highly varying climate zones. In addition, the boundaries between different HLZs can be shifted if the datasets show differences in temperature and rainfall amounts. Such As indented, LUCAS LUC land cover changes closely follow the transitions for cropland, urban, and forest provided by LUH2 with some exceptions discussed in the previous section. LUCAS LUC changes are generally slightly smaller for all three land cover types in comparison to the LUH2 changes but follow the temporal evolution of LUH2.
Ceccherini et al. (2020)  showed that averaged over Europe such 485 changes are small compared to forest harvesting but can be larger in regions with frequent forest fires (e. g. , Portugal). Also the different definition of forest in LUH2, which is based on a biomass density threshold, and ESA-CCI LC, based which is based on tree cover, could have a substantial impact on the forest transitions. A detailed analysis of the ESA POULTER time series is needed to investigate if these processes caused the discrepancies in forest changes LUH2/LUCAS LUC and ESA-CCI LC. Another possibility is the already mentioned uncertainty originating from the CWP. However, the impact of the CWP on the the climate  (Valmassoi et al. , 2019). Hence, there is a need for high-resolution European-wide dataset with information on the 510 distribution and the development of irrigation methods.
Po Valley and Carpathian Mountains). The large block-like features appearing in the cropland and grassland change signals in all scenarios might be attributed to the harmonization process within the LUH2 workflow. Annual changes in cropland, grazing land, and urban areas are computed and aggregated to a 2 • grid and subsequently disaggregated to the final 0. 25 • grid  (Hurtt et al. , 2020). It is therefore likely that the disaggregation step did not fully dissolve the grid structure of the coarse 2 • grid. For GCMs or ESMs with a typical resolution of around 1 • this might not have caused any issues. However, for RCMs with a typical resolution of about 0. 1 • , for which the LUCAS LUC dataset has been created, the impact of such structures in the LULCC needs to be carefully investigated.
Data availability The LUCAS LUC historical land use and land cover change dataset (Version 1. 0) and The LUCAS LUC future land use and land cover change dataset (Version 1. 0) are published with the Long Term Archiving Service (LTA) for large research datasets which are relevant for climate or Earth system research of the German Climate Computing Service (DKRZ). The DKRZ LTA is accredited as regular member of the World Data System. Both datasets are available within the LANDMATE project data at https://cera-www. dkrz. de/WDCC/ui/cerasearch/entry?acronym=LUC_hist_landCovChange_v1. 0  (Hoffmann et al. , 2021c)  and https://cera-www. dkrz. de/WDCC/ui/cerasearch/entry?acronym=LUC_fut_landCovChange_v1. 0  (Hoffmann et al. , 2021b). Within the LANDMATE project, a short documentation summarizes the technical information on the LANDMATE PFT and LUCAS LUC dataset.
Conclusions The need of the RCM community for a high-resolution LULCC dataset is met using high-resolution PFT maps based on the ESA-CCI LC dataset and land use change information from the LUH2 dataset that were translated into PFT changes using a newly developed LUT. The resulting LUCAS LUC dataset is tailored towards RCM requirements. Urbanization, which is mostly discarded by LUTs, is included as well as changes in irrigated cropland. For the historical period, also changes in the broad-/needleleaf forest ratio are considered employing an additional forest type dataset by  McGrath et al. (2015). The LUCAS LUC dataset enables RCM modellers to include historical and future annual LULCC into the next-generation downscaling experiments (e. g. , within FPS LUCAS and EURO-CORDEX) based on CMIP6 projections.
Acknowledgements. This work was financed within the framework of the  Helmholtz Institute for Climate Service Science (HICSS) , a cooperation between  Climate Service Center Germany (GERICS)  and  Universität Hamburg, Germany  and conducted as part of the project  LANDMATE  (Modelling human LAND surface modifications and its  feedbacks on local and regional cliMATE ). We acknowledge the support of  LUCAS  by  WCRP-CORDEX  as a  Flagship Pilot Study. We acknowledge the E-OBS dataset from the  EU-FP6  project  UERRA.
• and 60 • are found in both datasets. The increases in Northern Italy and Ireland are weaker in LUCAS LUC compared to LUH2. This is again caused by the difference in forest cover in LUCAS LUC and LUH2 in the year 2015. The LUT can only decrease as much forest fraction backward in time (i. e. increase for the comparison between 1992 and 2015) as there is available in 2015. Decreasing forest fraction can be seen in Russia (e. g. , east of Belarus and at the border to Georgia) and in Albania. As for cropland and grassland, forest changes in LUH2 and thus also in LUCAS LUC differ in many regions compared to ESA POULTER. The change signals tend to be reversed, with a decrease in mountainous areas (e. g. , Alps, Pyrenees, and Balkan Mountains) and a increase near the Russian border to Belarus. Especially the decrease in the Alps and Pyrenees seem to be not supported by recent assessments.
On the contrary, when the power produced by the solar field is low or zero and the average TES temperature is within an operating range close to the melting phase (TORC,on = 217 °C and TORC,off = 215 °C with hysteresis), the thermal energy of the TES can be used to run the ORC unit and assure its operation for a maximum of 4 hours with no sun. In particular, in OM5 only the PCM thermal energy storage supplies the ORC, while in OM6 both the LFR solar field and the TES supply the ORC unit. All the subsystems are connected each other by proportional valves which allow the operation of the system in the different OM while the control strategy developed so far aims at maximizing the electricity generation using a deterministic approach. The plant is modelled in MATLAB /Simulink [10] using MATLAB function blocks and, in order to take into account the transient operation of the real plant, dynamic models of some components have been included.
The advection equation is solved using the finite difference method coded in MATLAB with the explicit first order upwind scheme. Moreover, the code vectorization in the longitudinal direction allows drastically reducing the computational efforts required by the code. More details of the pipelines model can be found in  [15]. The same numerical scheme has been applied to make the Forristal model of the receiver tubes dynamic. In this case, only the thermal inertia of the diathermic oil has been considered while that of the metal parts and the glass tube neglected. Eventually, the ORC unit is modelled considering a quasi-steady state operation. The electric power generated by the ORC is calculated as: Pel = mf•[ηm•ηel•∆he-∆hp/(ηm•ηel)] (4) in which ∆he and ∆hp are the expander and pump enthalpy difference, ηm and ηel the mechanical and electrical efficiency assumed constant and equal to 0.
9 for both the expander and the pump. The turbine isentropic efficiency varies with the operating conditions and its value is based on the experimental data provided by the manufacturer ENOGIA  [16]. More precisely, in preliminary laboratory tests it reaches a maximum value of 0. 43 with an expansion ratio of 8. Regarding the pump, a gear pump is installed and despite its small dimensions the isentropic efficiency has been assumed constant and equal to 0. 7. The thermal power output, instead, is assessed as: Pth = mc•cp,c•(Tout-Tin) (5) where mc is the flow rate of the cooling water, cp,c its specific heat and Tin and Tout the inlet and outlet temperatures of the cooling water at the condenser. Heat exchangers have been modelled according to the ε-NTU method, assuming constant the overall heat transfer coefficient since the exact characterization of the plate heat exchangers was out of the scope of this work.
After completion of the reaction (monitored by TLC), the solvent was removed under vacuum and the residue was dissolved in hexane and passed through Celite. The solvent was evaporated under vacuum and the residue was purified using preparative silica-gel TLC plates by eluting with hexane/CH 2 Cl 2 (80:20) mixture that yielded green solid 1 (0. 010 g, 10%) and known red solids [(Cp*Co) 2 B 6 H 10 ] (0. 025 g, 24%), [(Cp*Co) 3 B 4 H 4 ] (0. 018 g, 19%), and a few compounds which could not be isolated. The known cobaltaboranes were characterized with reference to their spectroscopic data reported earlier.
Computational Details Gaussian 09 program package  [76]  was used for geometry optimizations of all model clusters. Computations were done with the Cp ligands in place of Cp* to reduce the computational effort (for optimized model clusters see Figures  S10-S13 ). Gas-phase optimizations without any solvent effect or symmetry constraints, were done with the hybrid Becke-Lee-Yang-Parr (B3LYP) functional  [77]  in combination with the LANL2DZ (Los Alamos National Laboratory 2 Double-Zeta) basis set  [78]  for all the atoms from the EMSL (Environmental Molecular Sciences Laboratory) Basis Set Exchange Library  [79]. Frequency calculations were carried out for all structures to check the nature of the stationary states and the absence of any imaginary frequency to confirm that the optimized geometries were genuine minima on the potential energy hypersurface.
Acknowledgments: K. P. thanks  IIT Madras  and C. N. thanks the  Department of Science and Technology, India, DST-INSPIRE  for research fellowships. The  Department of Chemistry, IIT Madras  is thankfully acknowledged for X-ray support. The authors thank  IIT Madras  for computational facilities and  Babu Varghese  and  Venkatachalam Ramkumar  for Single Crystal X-ray Diffraction data analyses.
Data Availability Statement: Accession codes: 2043679 (1), 2060786  (2). Crystallographic data can be acquired for free from the Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/ data_request/cif or by emailing data_request@ccdc. cam. ac. uk, or by contacting The Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK; Fax: +44-1223-336033.
The thickness of a supraglacial layer is critical to the magnitude and time frame of glacier melt. Field-based, short pulse, ground-penetrating radar (GPR) has successfully measured debris thickness during a glacier's melt season, when there is a strong return from the ice-debris interface, but profiling with GPR in the absence of a highly reflective ice interface has not been explored. We investigated the performance of 960 MHz signals over 2 km of transects on Changri Nup Glacier, Nepal, during the post-monsoon. We also performed laboratory experiments to interpret the field data and investigate electromagnetic wave propagation into dry rocky debris. Laboratory tests confirmed wave penetration into the glacier ice and suggest that the ice-debris interface return was missing in field data because of a weak dielectric contrast between solid ice and porous dry debris.
Our aim was to determine if a GPR system operating at a bandwidth relevant to remote airborne systems that radiate near 1 GHz can be used to determine glacial debris thickness. Airborne platforms such as drones and low-flying planes achieve extensive spatial coverage but remain relatively unexplored for measuring supraglacial debris. Our study took place on Changri Nup Glacier in the Nepal Himalaya and differs from previous ones because (1) our antennas were elevated slightly (about one free space wavelength); (2) we made direct simultaneous observations of debris thickness and lithology; (3) we collected data in the post-monsoon and did not detect a debris-ice interface with our GPR and (4) we performed laboratory simulations to aid and support our interpretations. The elevation of our antennas makes our conclusions relevant to a future debris-sensing approach on airborne platforms.
The successful penetration achieved in these studies suggests that our signals passed through the debris-ice interface without significant reflection, and we set up experimental tests to investigate this behavior. With the initial observation of no distinct, detected debris-ice boundary, we sought a technique to measure debris thickness from GPR data lacking an interface delineation. Backscatter from angular clasts is significant at 960 MHz; accordingly, we hypothesized that volumetric backscatter itself may indicate debris depth. No previous study has directly addressed volumetric scatter of larger-clast debris like that characterizing Changri Nup. To this end, we compared the depth of volumetric backscatter from 960 MHz GPR data with manual ground-truth measurements of debris thickness. We justify our indirect backscatter approach with experimental studies and propose a methodology for determining debris thickness from GPR data that, for any number of reasons, do not show a clear debris bottom.
Hereafter, the debris-ice interface is referred to as 'the interface'; the distance to the interface is equivalent to the debris thickness. During data collection, the antenna unit operator was always adjacent to the antennas (Fig. S2 ), but subsequent tests confirmed no interference from body reflections. The face of the antenna unit was kept in approximate alignment with the surface. Although the surface was not flat, most deviations were within ±30°from horizontal. Such deviations and associated changes in polarization relative to transect direction did not impact results because the antenna unit has a 70°3-dB two-way beamwidth in air regardless of polarization  (Arcone and others, 1986). We made detailed observations of ground debris, including 353 measurements of thickness, by digging to the interface or measuring the height of blocks situated on ice.
e. voltage). The Hilbert transform improved interpretability  (Zheng and others, 2016)  by amplifying the difference between debris and ice, a subtle difference in our raw data. In our subsequent analysis, we derived a threshold parameter to relate depth-integrated raw energy to debris thickness. Using our ground-truth measurements, we calculated the percent of integrated scattered energy that corresponded to debris depth. We selected this method after first noting the absence of a clear and persistent reflector and then observing that the radargrams had bright and dark areas but no associated, straightforward backscatter drop-off. Integrating under a positive-valued curve is a way to use backscatter as a metric even when scans vary. By employing the method of leave-one-out cross validation (LOOCV; Arlot and Celisse, 2010), we simultaneously calculated a backscatter drop-off threshold (τ) and assessed the statistics of applying that calculated threshold to the entire dataset (i.
The rocks were placed in a trough (Fig. 3 ) in three clast size groups, small (1-4 cm), medium (5-9 cm) and large (10-20 cm), over 4 cm thick dry pine boards (ϵ = 1. 9-2. 0) above densely packed pine shavings (ϵ = 1. 4). The intention was to construct an interface whose reflectivity (Γ = 0. 11 for pine-debris) was on the order of (i. e. very low) but greater than that on the glacier to investigate wave behavior at a weakly reflective interface. A greater reflectivity in our constructed experiment setup than on the glacier allowed us to observe processes that had occurred during data collection on Changri Nup but which were not detected (in other words, enhancing them for the purpose of our investigation). We used the rock box to simulate the field conditions of dry angular clasts above a low dielectric substrate (the setup described above) and then above perfectly reflecting aluminum foil to (a) prove rock penetration, (b) examine the 960 MHz GPR's direct coupling duration and (c) measure time delays through the clasts in order to determine debris permittivity.
This demonstrates the importance of frequency selection; when clast size is comparable to or larger than a radar wavelength, the radar may not penetrate the debris and receive returns for deriving a threshold τ. It follows from the 2. 6 GHz wave behavior that the 960 MHz signal would face similar difficulty penetrating debris clast sizes ≥18 cm. Fortunately, most clasts encountered on Changri Nup were <18 cm in maximum dimension (the larger sizes seen in Fig. 2  did not make up the majority of the debris). Field observations of this were corroborated by a comparison of spectra from the rock box and field data. Average reflected spectra from the rock box varied from ∼ 770 MHz for the large clasts to 1 GHz for the small ones. Reflected spectra from the field data ranged from 950 to 1000 MHz (Fig.
A wet interface gives a much stronger reflection than a dry one  (Gades and others, 2012). Given the surface heterogeneity of debris and the fact that debris thicknesses do not follow a normal distribution, the median and interquartile (IQ) range statistics represent the measurement and retrieval datasets better than the mean (μ) and standard deviation (σ); their consistency with the calculated retrievals supports our LOOCV-based threshold. A different threshold is given for transect B because the antenna unit was elevated 8 cm more during data acquisition than it was for the other four transects. A threshold trained using transects A and C was applied to A, C, D and E. The uncertainty ranges are determined by the mean RMS difference in measuredretrieval depths. (1) 'Total' includes measurements to frozen debris, but only the measurements to the interface were used to train the threshold values and compute the statistics in column 5.