dataesr/scientific-paragraphs-categorization huggingface dataset:
TRD modeling over complex Earth surface As reviewed in Section 4, most of the existing TRD models show high performance for pure pixels on flat ground. The topographic effect and pixel mixture are not widely discussed although the rugged terrain covers approximately 24% of the Earth land surface  (Wen et al. , 2018)  and about 65% of the 1 km scale pixels of the Earth land surface are mixed  (Yu et al. , 2018). Topography may change the TRD pattern of mountain forest canopies significantly. Lipton and Ward (1997)  simulated the surface temperatures using a mesoscale numerical weather prediction model with high-resolution digital elevation data (30 m) from the USGS Digital Elevation Map datasets. The TRD effect reached as much as 9 K while assuming a satellite sensor resolution of 14 km. Even larger biases were found with finer sensor resolutions.
Two ways for developing dynamic TRD model Multi-angle datasets can be organized by removing cloudy day data from several day observations in the VNIR band  (Zeng et al. , 2016). This approach is based on the assumption that the land surface reflectance properties remain unchanged within a time frame  (Jiao et al. , 2014). However, for the land surface TIR properties, this is certainly not true, especially for the LST. Compared to the VNIR region that shows more inertia in the signal variability, rapid TIR temporal variations represent a major difficulty to correctly include the directionality issue in the satellite-derived LST/LSE products. The full potential of the TIR satellite-based network is limited by the close coupling that exists between the angular dependence and time dependence. It is almost impossible to exploit the angular information without dealing with the temporal issue.
, 2014). Then, the angular anisotropy of LST can be explained by the view fraction variation of components. The TRD and DTC effects have the potential to be successfully separated. Therefore, for vegetation canopies with two components (soil and leaf), only the eight parameters of LSCT DTC models are unknown. These can be easily estimated using available observations within a given time period, no matter the viewing time and angle differences, which makes the second way simpler and more practical. However, for other land surfaces, such as urban canopies, at least six components have to be considered (sunlit/shaded roof, road and wall), and the subsequent estimation of 24 unknown LSCT DTC parameters remains challenging. On the other hand, the error of data pre-processing (geolocation, calibration) will make the solution of dynamic TIR satellite network very difficult.
Designing simultaneous multi-angle multi-band satellite sensors Current multi-angle observations of a satellite network are constrained by differences in viewing time because the LST has significant temporal variability. The coupling of angular change and temporal variation makes a high-quality multi-angle dataset very difficult to obtain and further limits TRD related applications at the satellite scale. Ideally, there should be a TIR satellite sensor with simultaneous multiviewing angles and high revisit frequency. TIR measurements usually change rapidly at the landscape scale. This is in favor for having highresolution pixels clearly. Thus, ideally a constellation of polar orbiting satellites would appear to be the best solution to meet these requirements, which is potentially able to achieve with the onboard ECOST-RESS, GF-5, foreseen TRISHNA  (Lagouarde et al.
, 2018)  and LSTM  (Koetz et al. , 2018)  missions. A geostationary satellite constellation will allow to supply high quality multi-angle multi-band observations with moderate spatial solution. In this regard, parametric models may represent suitable tools to devise optimal angular configuration  (Hu et al. , 2016b; Ren et al. , 2015b). For the minimum angle configuration,  Ren et al. (2015b)  suggested a protocol with three values (0, +30, -50) based on the 4SAIL generated dataset and ROSS-LI parametric model with three kernel coefficients to be estimated. Hu et al. (2016b)  further discussed the best angle configuration using the same dataset and parametric model. They found that a five angle (0, ± 30, ± 50) configuration is preferable compared to the three angle (0, +30 and -50) or even seven angle (such as 0, ± 10, ± 30 and ± 50) configurations.
Lessons learned remain nevertheless too limited to decide on a design, as the 4SAIL model is only suitable for depicting a homogenous scene. Besides, the ROSS-LI model is a direct extension of the VNIR model with limited accuracy in the representativeness of TRD simulation  (Liu et al. , 2018). Furthermore, the analysis of  Hu et al. (2016b)  does not consider the FOV effect of the linear array imaging mode, which is the mainstream solution for satellite sensors. For the scope of developing the best configuration of spectral bands,  Sobrino and Jiménez-Muñoz (2014)  proved there exists a minimum number of TIR bands required to separate the LST and LSE without any prior knowledge about the land surface. It should be at least 8. 9, 10. 6 and 12. 0 μm. Beyond the NDVI dependent bands, also the water vapor dependent bands, the fire detecting bands and the cloud identifying bands should be included to ensure a high-quality dataset.
Establishing multi-angle datasets from ground and UAV observations Multi-angle datasets over different underlying surfaces are important for evaluating TRD models. However, the amount of accurate datasets reported in the literature is limited. Most of them were obtained from airborne observations supported by comprehensive outdoor experiments which face two main difficulties: 1) they are financially expensive and therefore challenging for the community; 2) The time acquired for TIR images collection impacts the quality of multi-angle datasets. Thus, most of the existing TIR multi-angle datasets consist of spatio-temporal averaged values. In recent years, UAV platforms provide new solutions to study the land surface thermal environments. Such platforms have been widelyused to identify crop frost, to analyze crop water stress index of different vegetated species, etc.
Even the thermal point cloud of 3D tree canopies has been captured using UAV based photogrammetry  (Webster et al. , 2018). However, no TRD study based on UAV observations has been published, although UAV based solutions have the advantages of being low cost and not time-consuming, when compared with traditional airborne multi-angle observations. More efforts should be conducted to generate multi-angle datasets over different underlying surfaces by using UAV platforms in the future. As a more common approach, ground-based and tower crane-based observations can acquire TIR multi-angle datasets. However, the footprint area and the experimental location are limited, which makes this approach difficult to be performed over tree canopies, urban canopies and sea surfaces. In addition, ground measurements are also influenced by both the LST temporal drift and the FOV effect.
Multi-angle observations have the potential to determine more component temperatures. However, multiangle datasets from TIR satellite-based virtual network is limited by geolocation and radiometric calibration uncertainties and the close coupling between angular dependence and time dependence. It is almost impossible to exploit the angular information without dealing with the temporal issue. This is the main difference between the BRDF in the VNIR spectral domain and the TRD in the TIR spectral domain. Therefore, the parametric TRD model is a potential solution for LST angular correction and SULR accurate estimation since the exact temperature distribution is not required. More works need to be conducted to develop comprehensive physical TRD models for complex land surfaces, and then some simplifications need to be done to propose a better parametric model as a tradeoff between physical accuracy and operational use.
Multi-angle TIR satellite sensor after optimizing the angle and spectral configurations will surely benefit TRD models and applications in the future. Some near surface, airborne and especially UAV experiments should be carried out to evaluate the sensor parameters at the regional scale and thus establish multi-angle datasets as an important support to future researches on TRD. The global LST, LSCT, SULR satellite products considered the TRD effect are widely expected by the TIR community and also the follow-up application areas. We can draw the following three conclusions. (1) The TRD effect is evidenced with laboratory, ground, airborne and satellite observations. It seriously limits further improvements of LST accuracy. A true harmonization of LST products is significantly required and more cooperation between the TRD modeling and LST estimation communities should be carried out to achieve this goal.
The TRD modeling is the most potential solution, and it is time to leave out the hypothesis of isothermal surface in LST estimation. (2) Among physical models, existing models of water bodies, ice, sea, snow and vegetation are relatively more mature than those of barren lands and urban scenes. In addition, parametric TRD models are not as mature as physical TRD models although they have greater potential to achieve LST harmonization. Complex surface modeling, dynamic modeling and parametric modeling are three important directions for TRD model developments. (3) It has been proved that multi-angle TIR observations permit to normalize the LST angular effect, to separate the LSCT and estimate SULR more accurately. More time series of ground-based and airborne multi-angle datasets over different surfaces are valuable to improve the TRD models.
Acknowledgements This work was supported in part by the  National Key Research and Development Program of China  under Grant  2018YFA0605503 , and in part by the  National Natural Science of Foundation of China  under Grant  41871258 , and in part by the  Strategic Priority Research Program of Chinese Academy of Sciences  under Grant  XDA15012400 , and in part by the  National Natural Science of Foundation of China  under Grant  41501366 , Grant  41571329 , Grant  41571357 , Grant  41571359 , and Grant  41671366 , and in part by the  Director Foundation of the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences  under Grant  Y5SJ1300CX , and in part by the  China Scholarship Council  project  201704910069 , and in part by the  PIFI project of Chinese Academy of Sciences  under Grant  2017VCA0034 ,  2017VCC0011  and  2019DC0020.
Senescence-Associated (Epi-)genetic and Gene Expression Changes The features listed above are associated with changes in gene expression, determined by transcriptional regulation of coding and non-coding RNAs, which can be exploited for senescence detection. Here, we discuss such major alterations and describe a novel database that can aid the identification of genes associated with senescence, termed SeneQuest (http://Senequest. net) (see Supplementary Information and Table  S1 ). Chromatin Landscape Epigenetic modifications occur during senescence but are mostly context dependent  (Cheng et al. , 2017). For example, replicative senescence has been correlated with global loss of DNA methylation at CpG sites  (Cheng et al. , 2017). In addition to the global loss of DNA methylation, cellular senescence entails focal increases in DNA methylation at certain CpG islands  (Cruickshanks et al.
ACKNOWLEDGMENTS We would like to thank  Nikolaos Kastrinakis ,  Panagiotis V. S. Vasileiou ,  Gkikas Magiorkinis ,  Eleni Fitsiou , and  Michela Borghesan  for their valuable support to this work. We apologize in advance that, for reason of space, we have omitted the citations of relevant papers and reviews. M. D. is funded by the  Dutch Cancer Foundation ,  Netherlands  (grant ID  10989 ). V. G. , K. E. , and K. V. were financially supported by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grants agreement no. 722729  (SYNTRAIN); the  Welfare Foundation for Social & Cultural Sciences (KIKPE) , Greece; the  KIKPE Foundation, Athens, Greece; Pentagon Biotechnology, UK; DeepMed IO, UK ; grant no. 775  from the  Hellenic Foundation for Research and Innovation (HFRI) ; and  NKUA-SARG  grants  70/3/9816 ,  70/3/12128 , and  70/3/15603.
In conjunction with this event, the drone-vs-bird detection challenge has been launched to address one of the main issues arising in the described context. Indeed, given their characteristics, drones can be easily confused with birds, which makes the surveillance tasks even more challenging especially in maritime areas where bird populations may be massive. The use of video analytics can solve the issue, but effective algorithms are needed able to operate also under unfavorable conditions, namely weak contrast, long range, low visibility, etc. The challenge was aimed at attracting research efforts to identify novel solutions to the problem outlined above, i. e. , discrimination between birds and drones, by providing an annotated video dataset recorded at shore areas in different conditions.
Participation and best proposed algorithms The challenge has attracted remarkable interest, with about 20 different research groups requesting access to the dataset for participation to the competition. The worldwide distribution of the research institutions that have been interested in the challenge is shown in Fig. 4. It is also worth noticing that none of the participants is a member of the SafeShore consortium, nor a research partner/collaborator. Target'bound. 'box'(24'pixels)' Algorithm'bound. 'box'(28'pixels)' 5'x'9'='45'' >>'penalty'for'this'frame'45/24'='1. 875' 8'x'12'='96'' >>'penalty'for'this'frame'96/24'='4' At a glance, the prominent ingredient of the solutions that have been proposed is the use of neural networks and deep learning approaches, coupled with additional processing blocks and ideas.
g. by averaging, resizing, or contrast reduction. Neurons in the last layers are fully connected, to mimic high-level reasoning where all possible paths are considered. In the following the most successful algorithms developed for the drone-vs-bird challenge are briefly described. Aker and Kalkan from KOVAN Research Lab. , Computer Engineering, Middle East Technical University Ankara, Turkey  [3] , have used an end-to-end object detection method based on CNNs to predict the location of the drone in the video frames. In order to be able to train the network, the authors created an artificial dataset by combining real drone and bird images with different background videos. The results show that the variance and the scale of the dataset make it possible to perform well on drone detection problem.
Saqib et al. from University of Technology Sydney, Australia, and Makkah Technology Valley, Kingdom of Saudi Arabia  [4] , have considered Faster R-CNN  [5]  with Caffe deep learning library. The Caffe-based pre-trained models are publicly available for most of the object detectors. There are too few images in the dataset to learn a deep model from scratch. Therefore, to take full advantage of network architectures, the authors have used transfer learning from ImageNet to fine-tune the models. The fine-tuning process helps the system to converge faster and perform better. Various network architectures have been tested such as ZF  [6] , VGG16 and VGG M 1024  [7]  to train the system (see details in the paper) and evaluate the performance on the test dataset. ZF is a 8 layered architecture containing 5 convolutional layers and 3 fully-connected layers.
To reduce the high number of false alarms, a CNN classifier is also used. In general, to classify UAVs in real world data is a challenging task due to varying object dimensions (in the range of less than ten to hundreds of pixels), large variety of existing UAVs, and often lack of training data. Furthermore, the classification is impeded by varying illumination conditions, differing backgrounds, and localization errors of the detector. To address the various object dimensions, in  [8]  it is proposed to use a small network that is optimized to handle low resolution objects such as UAVs at large distances. A proprietary dataset is used to train the CNN classifier. The dataset is composed of crawled and self-acquired UAV images, bird images of a publicly available dataset and crawled background images to account for the large variety of existing UAVs, other distracting flying objects, and varying illumination conditions and backgrounds.
Results The computation of the per-frame penalty has been performed based on the metric described in Sec. 2. Multiple bounding boxes are counted as additional penalties for the same frame. A final score is calculated to obtain a ranking for the average behavior on the whole test video; in particular it is computed as square root of the mean squared penalty across frames. Results are listed in Table  1 , only for the algorithms for which result data have been provided by the deadline; up to three different settings for the same algorithm were allowed, so as to test different solutions under the same approach. Interestingly, the results spontaneously grouped, irrespective of the specific setting; thus, for this specific outcome, it seems that the algorithm is more important than the finetuning of its parameters.
This arrangement allowed us to use it in parallel to an optical interferometer of Mach-Zehnder type, which was used to provide complementary data about the plasma dynamics, looking at the plasma electron density  8. The x-ray spectra were recorded on a fluorescent detector, the TR Fujifilm Image Plate. Here the main target is irradiated by a pulsed laser (0. 6 ns / 1054 nm / 40 J) and then the created plasma jet hits the solid obstacle that is used to generate accretion shocks. A magnetic field, generated by a Helmholtz coil  6  , is applied uniformly everywhere to collimate the plasma flow  4, 6. The targets could be shifted compared to the central transverse observation hole managed in the middle of the coil system in order to observe all the expansion dynamics of the jet  4, 6. Optical and x-ray measurements (the Focusing Spectrometer with Spectral Resolution, or FSSR) are provided in the transverse direction using that observation hole.
The satellites are initiated here by dielectronic recombination from the doubly excited states of ions which are situated very close to the corresponding resonance line (Ly α in this case). They proved  15, 16  to be an effective method of plasma diagnostics, since the satellite-to-resonance-line intensity ratio is essentially dependent on the temperature and in some cases on the electron density. In a previous experiment conducted with identical parameters of laser and target  17  , the temperature ~ 300 eV was measured at the target surface in a steady-state plasma approach. Calculations to support inferring such temperature were performed using the "zero-dimensional" program code FLYCHK  12  which features a collisional-radiative model. Second, we note that the ion emission of the expanding plasma has a recombination behavior  14  in the regions that are remote from the target surface.
4. In the optically thin case, the ratios of the corresponding spectral lines do obviously not depend on the plasma thickness. Fig. 4  gives the simulated ratio of the intensities of the spectral lines He β and He γ in a plasma at T e ~ 100 eV and having a variable density, corresponding to typical values recorded in the experiment in the case of a recombining plasma, i. e. far from the main target (see Fig. 3. d)  17. These calculations are made for various lateral plasma size, using FLYCHK code and in the cases of optical thin (dashed lines) and thick plasmas (solid lines). On top of this, the experimentally measured range for the considered ratio is shown. In the experiment, the optical measurements allows  4, 6  us to assess that the lateral size of the recombining plasma is about 1 mm for a density ~ 10 18 cm -3.
ACKNOWLEDGEMENTS X-ray data measurement, modeling and analysis are made by  JIHT RAS team  under financial support of  Russian Science Foundation  (project # 17-72-20272 ). The authors thank the entire staff of the  ELFIE laser facility at LULI  for their support during the experimental preparation and execution. This work was supported by  ANR Blanc  Grant No. 12-BS09-025-01   SILAMPA. It has received funding from the  European Union 's  Horizon 2020 research and innovation programme  through the  European Research Council  ( ERC , grant agreement No  787539 ). It was partly done within the  LABEX Plas@Par project , which is supported by Grant No. 11-IDEX-0004-02  from  Agence Nationale de la Recherche. The research leading to these results is supported by  Extreme Light Infrastructure Nuclear Physics (ELI-NP) Phase I , a project co-financed by the  Romanian Government and European Union  through the  European Regional Development Fund.
A combination of NDSI and BR (for deep shadows) information is used to identify the glacier outlines. By means of high-resolution satellite imagery (Google Earth) from the respective years, the threshold values for the NDSI of 0. 8 and the BR of 1. 7 for LANDSAT 5 TM data and 1. 5 for LANDSAT 8 OLI data are carefully selected. The resulting binary masks are transferred into polygons and cropped into individual glacier basins using the ice flowshed delineations of the RGI. Afterward, the glacier inventories are then visually inspected and misclassified areas, such as proglacial lakes and snow patches, are removed by means of high-resolution satellite data (Google Earth). The surface area (S ) of the final inventory and the individual glaciers is calculated in UTM 19S projection. Additionally, the topographic parameters (minimum, maximum and median elevation, slope and aspect) of the inventoried glaciers are calculated based on the void fill SRTM Version 3 digital elevation model (DEM) (NASA JPL, 2013, see also next section) according to RGI6.
Elevation changes and mass balance In this study, multiple DEMs derived from Interferometric Synthetic Aperture Radar (InSAR) data are used to determine glacier surface elevation changes throughout 2000-2016. The Shuttle Radar Topography Mission (SRTM) of the National Aeronautics and Space Administration (NASA) and the German Aerospace Center (DLR) acquired bi-static C-band InSAR data of landmasses between 60°N and 54°S from 11 to 22 February 2000. The goal of SRTM was to facilitate the generation of a nearly global, high-resolution and consistent DEM. Several DEM products were derived from SRTM data. In this study, the void-filled LP DAAC NASA Version 3 SRTM DEM (1 arcsec ground resolution) (NASA JPL, 2013), reprojected to UTM projection, is used. Further DEMs are generated using data sets from the TanDEM-X (TDX) mission of the DLR.
The TDX mission has been acquiring bi-static X-band InSAR data since 2010  (Zink and others, 2011). A nearly complete coverage of the study site by TDX data is available for 2013 and 2016. In order to reduce the biases on the elevation change calculation caused by seasonal surface elevation changes and SAR signal penetration (see Section 5. 3), TDX data within the same season as the SRTM are selected for 2013. Unfortunately, in 2016, TDX data are only available for the period September to November. The potential bias caused by this seasonal offset is discussed in Section 5. 3. The footprints of the TDX acquisitions used are shown in Figure  1  and the main characteristics are given in Table  S1. The TDX DEMs are processed according to  Seehaus and others (2016)  and  Malz and others (2018).