dataesr/scientific-paragraphs-categorization huggingface dataset:
Today, almost 40 years (1982 on) of satellite cloud observations are available  [47]. Currently, four long-term climate data records (data sets) exist that are exclusively based on AVHRR data. One is a CM SAF Cloud, Albedo, and Surface Radiation data set from AVHRR data, second edition (CLARA-A2). It applies a hierarchical decision tree thresholding method to retrieve cloud properties  [74]   Arctic cloudiness is particularly challenging for climate models, causing major uncertainties and discrepancies in regional climate change projections. Most models project increasing low-level cloudiness in the region. Satellite observations confirm this tendency  [114]. The Arctic was found to be more cloudy in spring (the decadal trend from 1984-2004 is 2. 3% dec -1 ) and summer (0. 5% dec -1 ) but less cloudy in winter (-3.
4% dec -1 )  [33]. More recent studies [133], however, found extensive positive low-level cloud fraction trends over the Arctic sea ice. The strongest trends are found for October and November. Amplitudes of these trends exceed +10% dec -1. The estimations of the TOA forcing sensitivity give -0. 46 ± 0. 90 W m -2 per each percentage of cloud cover change for shortwave radiation and +0. 14 ± 0. 087 W m -2 per percentage for longwave radiation. The temperature responses to radiative changes vary from 0. 25 W m -2 K -1 in the CLARA A1 data to 0. 43 W m -2 K -1 in the CERES broadband planetary albedo data  [53]. Hwang et al. [134]  gave estimations of the radiative feedback using CERES/Terra data (2000-2014) of 1. 88 ± 0. 73 W m -2 K -1 and 2. 38 ± 0. 59 W m -2 K -1 for short-and long-wave radiation, respectively.
Satellite platforms are the main tool to monitor marine ecosystems, providing for the onset and peaks of the annual spring and summer algae blooms as well as for their extent and phenology, both in open waters and under sea ice. Fishery fleet activity can be also monitored. The combined use of SAR and AIS data will provide information on changes in the catch pattern of the fishing fleet in Arctic waters. The ESA contribution has been politically recognized as an essential basis to sustain fisheries in the Arctic Ocean  [158]. Impact on soils and permafrost. Following the amplification and land cover changes, warming begins to penetrate in active soil layers and permafrost  [159]. The changes in permafrost could be monitored from space using direct and indirect methods. Indirect methods utilize diverse signatures left on terrestrial morphology, hydrology, and biology  [29, 160].
It estimates permafrost distribution using an equilibrium state model for the temperature at the top of the permafrost (TTOP model) for the 2000-2016 period. The Copernicus Sentinel-1 and -2 missions provide information on changing topography (land surface slumps, erosion related to thawing permafrost, surface depressions, shrubification), whereas missions carrying thermal sensors (Sentinel-3) assess changes in the land surface temperature. Information on snow conditions and land cover can be used as a proxy for soil properties. Both snow and soil regulate heat transfer and thus determine the impact of the amplification on the frozen soil beneath. Park et al. [54]  inferred the extent of permafrost from satellite microwave data of the daily landscape freeze-thaw status over 30 years. The data set is presented in Kim et al.
Conclusions and Perspectives Responding to the global issue of anthropogenic climate change, the European Space Agency (ESA) has undertaken the Climate Change Initiative (CCI) to exploit the full potential of long-term global satellite observations. The ESA CCI essential climate variables (ECVs) cover more than 40 years of monitoring the earth from space and provide climaticquality data sets for the investigation of climate phenomena in development  [3]. Data sent by the ESA, NASA, and some other satellite platforms are utilized to create a variety of ECV records. Both the advantages and challenges of the ESA CCI projects are related to the need to fuse data information from different sensors working on different satellite platforms with different spatial, temporal, and spectral resolution.
They revealed the spatial relocation of the amplification core from the northern continents to the marginal sea ice zone (e. g. , the Barents-Kara Sea region) as soon as multiyear sea ice cover had disappeared. This synthesis draws a broadly consistent picture of the amplification and its impacts derived from the ESA CCI ECVs and other collections of climatic-quality remote-sensing data products. At the same time, we have to agree with a critical judgement of satellite observations: "While suitable for detecting overall change, the current capability [of satellite observations] is inadequate for systematic monitoring and for improving process-based and largescale understanding of the integrated components the cryosphere, biosphere, hydrosphere, and atmosphere"  [5]. There is future potential in multi-sensor/data and synergetic applications of satellite and in situ data to be used in combination with numerical modeling.
The other data set-the NOAA's Pathfinder Atmospheres, Extended program (PATMOS-x)-is based on a naïve Bayesian method [76]. The third is the Extended Advanced Very High-Resolution Radiometer (AVHRR) Polar Pathfinder (APP-x) [77]. The fourth is the ESA Cloud CCI (version 3) 1982-2017, which uses neural network and optimal estimation techniques to provide cloud property retrievals [128,129]. Satellite cloud data products do not fully agree with each other. A study of 16 cloud climatologies showed that the annual mean total cloud fraction in the region north of 60 o N is 0. 70 ± 0. 03 (over the ocean 0. 74 ± 0. 04; over land 0. 67 ± 0. 03) [130]. The average disagreement between MODIS and CALIOP over the whole Arctic reaches 13. 1% during daytime and 26. 7% during nighttime [131]. This MODIS-CALIOP disagreement has high seasonal dependence; it is the lowest in summer (showing a 10.
The result is reported in the inset in Fig. S1. The Au islands are in white. The area and circularity of the nanoparticles were extracted using the ImageJ automated analysis software. Circularity is given by 4π * Area/(P erimeter) 2. The nanoparticles were also fitted with ellipses, allowing to extract several parameters: major and minor axes, the angle of the major axis with respect to the horizontal (Angle), and the aspect ratio. The results are summarized in Tab. S1 and Fig. S1. From Tab. S1, emerges a simplified average shape of thin, ellipsoidal disks with major axis around 13 nm and minor axis around 7 nm, elongated in randomly oriented directions. By looking at the distributions of Fig. S1 , however, it is clear that the sizes and shapes of the nanostructures span a vast range of scales, thus making necessary a true-to-shape finite-element modelling, as reported in Fig.
Lifetime of Σ valley population To perform the fits of the Σ valley population, we built a fitting function that consists of a double exponential decay convolved with a gaussian instrument response function. This allowed us to isolate the intrinsic and interfacing-dependent timescales. We first considered a dataset measured for the WSe 2 bare surface in the same experimental conditions, but with a much wider delay range, up to 200 ps. Fitting the population of the Σ valley with the convolved double exponential (with fixed gaussian FWHM of 40 fs), returned two timescales: 1502±78 fs and 31±2 ps. The result is reported in Fig. S5. The first time-constant seems rather short with respect to the dark exciton lifetime or defect recombination times reported in literature  [11]. It thus might be attributable to dark exciton diffusion in the bulk, away from the probing depth of ARPES.
In the dataset discussed in the main text, the delay range is much smaller, so only the short timescale is relevant. It is evident from the data, however, that a second, shorter timescale arises in the heterostructure. We thus considered the curve of Au/WSe 2 at low temperature, showing the fastest dynamics. By fixing the longest timescale to be 1502 fs, we obtain the shortest to be 240±28 fs. The amplitude of the fast decay is 72±2% of the signal. These two timescales are sufficient to fit the other two curves in Fig. 3c  with high reliability by only changing the ratio between them. The one for Au/WSe 2 at RT is fitted by reducing the short timescale to the 36±8% of the signal, while the pure WSe 2 converges with only 2±10% of the short timescale, indicating that it is well fitted by the single exponential of 1.
Fluence dependence of Σ valley population We discuss here the procedure to extract data from the 1030 nm pump dataset. The measurement was performed at fixed delay of +400 fs, i. e. after the ultrafast charge transfer mechanisms have taken place, thus in the condition to observe the exciton population generated in WSe 2 by two photon processes. The fluence was changed from 0. 28 to 2. 34 mJ/cm 2. In these conditions, the electronic temperature is between 1500-2000 K and, while still lower than 5800 K (the minimum electron Schottky barrier, which also increases transiently), this is sufficient to produce a background signal from the Au electrons that needs to be removed in order to isolate the Σ valley population. The shape of the Au bands in the momentum region corresponding to the WSe 2 sigma valley (between the red lines in Fig.
Achieving even sparse in situ coverage of the 3D solar wind is unrealistic in the near-or medium-term timeframe with progress depending on serendipitous measurements for some time to come. However, our understanding of the global solar wind would be significantly enhanced if we could achieve full remotely sensed coverage of the global solar atmosphere. This would guarantee that a complete map of source characteristics could be used in comparison to the in-situ measurements and their variations around an orbit. With that information, even a handful of in-ecliptic and high latitude measurements of the solar wind would provide a dataset capable of constraining many theories of solar wind origin, acceleration, and transport. Firefly observations in EUV and white light from four spacecraft, two of which will be orbiting to high solar polar latitudes, in combination with in-ecliptic assets, will provide a full global view of the coronal source regions of the 3D solar wind outflow.
The study protocols were approved by the « Comitede Protection des Personnes Lyon, Sud-Est 1» N˚2015-A00655-44, Role of the ANS Dysregulation in the Persistence of Fatigue in Rheumatoid Arthritis Patients Treated with Anti-TNF (ANSRA) NCT02475486. Bone Microarchitecture Abatacept (BMA2) NCT02675218. Impact of the Persistence of Inflammation at Doppler Ultrasound Level on the Structural Evolution of Erosion in Rheumatoid Arthritis Treated with Biotherapy. NCT02531061.
Analytic performances of serum Col3-4 ELISA As shown in Fig  1 , the calibration curve of the ELISA ranged from 0 to 128 ng/ml. The main analytical performances of the assay are listed in Table  1. Intra-and inter-assay coefficients of variation (CV) were assessed by 16 determinations of 4 different serum samples. Dilution recovery data were determined using 8 different serum samples diluted to 1/2 and 1/4 in sample buffer. Spiking recovery was determined by the addition of known quantities of standard peptides (20-40 ng/ml) into 3 different human serum samples. The stability of Col3-4 peptide levels was tested in 5 serum exposed for several hours at RT or 4˚C, or subjected to freeze-thaw cycles.
Increased levels of MMP-9 measured by ELISA have also been reported in the serum and plasma of RA patients compared to healthy controls  [21, 24]. These data indicate that MMP-9 plays an important role in the physiopathology of RA and the remodeling of the synovial collagen matrix. Our immunohistochemistry experiments of RA synovial tissue showed that the Col3-4 antibody stained the extracellular matrix surrounding the synovial cells and blood vessels and confirmed the presence of MMP-9 in these localizations. In test-tubes studies, we also found that the digestion of human type III and type IV collagen by active recombinant MMP-9 efficiently generated Col3-4 immunoreactive fragments measured by ELISA. All together, these data suggest that Col3-4 immunoreactivity in the synovial tissue of patients with RA results from the degradation of type III and type IV collagens by MMP-9 -and possibly other proteinases-which is over-expressed in destructive fibroblasts of the synovial lining layer  [25].
The primary objective of this study was to develop an immunoassay for a novel biochemical marker based on qualified biological reagents, to validate its technical performances, to characterize its synovial tissue localization and to provide preliminary clinical data in patient with RA. The pre-analytical factors that may affect the circulating levels of Col3-4 will be investigated in further investigations. In subjects with low RA disease activity, although on average Col3-4 levels were increased, a significant proportion of subjects had values within the normal range. This indicates that this biochemical marker, like the other tissue remodelling markers investigated so far, has a modest diagnostic value in treated subjects with low disease activity. More likely the measurements of Col 3-4 in patients with early RA could be useful to identify individuals with high synovium activity who are more likely to progress (prognostic value) and/or respond to specific treatments (monitoring efficacy).
We also set aside mobile technologies that are not directly aimed at probing brain and behavioral functions, such as blood pressure monitor devices, glucometers, etc. (see  [1]  for a review). We present these data types in two groups according to the role that the user (e. g. , the patient) takes in acquiring the data: active vs. passive. In this context, we mainly describe typical applications, but we also point the readers to specific applications for which active or passive data can be used. For instance, vocal recordings can be actively collected by instructing the user to self-record (e. g. , when completing a survey), but a microphone may also passively and continuously record the sound environment without the user triggering it (e. g. , automatic handwashing recognition using the microphone of the Apple watch to detect water sound  [2] ).
g. , weight, heart rate, and blood pressure) or health-related behaviors (e. g. , treatment compliance). These connected systems make data collection more systematic and readily available to the clinical practitioner. They are automatically integrated into data management systems. For example, on a pre-specified schedule, the patient will measure his/her blood pressure with a so-called smart blood pressure monitor, which may provide reminders and record and transmit these measurements to his/her doctor. Active connected devices (which require the patient to participate in the data collection process) may also track behavior: a connected pillbox would allow monitoring that the patient takes the medication according to the prescribed schedule  [10]. In a subsequent part of this chapter, we will refer to passive connected medical devices (which perform measurements without the intervention of the user/patient), such as fall detection systems.
Accelerometers Accelerometers are sensors used to measure linear acceleration, viz. , change in velocity or speed per time interval of the object being measured along reference axes. Furthermore, one can obtain velocity information by integrating accelerometry data with respect to time. The measuring acceleration unit in the International System of Units (SI) is a meter per second squared (m/s 2 ). Since we can distinguish a static component in the accelerometer signal as the gravitational acceleration, it is also common to use the unit G-force (g) to distinguish the relative free-fall gravitational acceleration with a conventional standard value of 1 g = 9. 81 m/s 2. A simplistic representation of the accelerometer's operation principle is based on a suspended mass attached to a mechanical suspension system with respect to a reference inside a box, as shown in Fig.
Benchmark Databases In the context of HAR, there are several advantages to having access to inertial databases. The most obvious one is that it allows for comparing several solutions. Inertial databases can be used to rapidly focus on the development of signal processing and machine learning solutions before spending time on the development and deployment of hardware sensing solutions. In the past years, several public databases have appeared in the literature for smartphone and wearables studies. The list of the main databases of inertial sensors used in research work is gathered in Lima and colleague's review  [19]  and in Sprager and Juric's review  [24].
Symptom and Treatment Monitoring Monitoring day-to-day activities and the evolution of symptoms is impossible for health providers outside the clinic without automated detection of events of interest and deployment of mobile interventions. Much like apathy, described above, many other psychological constructs may be sensed from continuous monitoring of behavioral parameters, such as agitation or aberrant mobile behavior  [55, 56]. Sleeping is one activity that cannot be monitored in any other way than with passive data probing in an ecological manner. Monitoring sleep is relevant when studying sleep disturbance, a core diagnostic feature of depressive disorder, anxiety disorders, bipolar disorder, and schizophrenia spectrum disorder. In this sense, sleep patterns have been scored using light sensors in mobile devices and usage data, allowing digital phenotyping of the users that, compared to the average, go to bed and wake up later and more often.
Another issue is the open access to commercial devices. Most commercial devices (smartphones, smartwatches, and connected devices) offer the developers the opportunity to use their integrated sensors to develop applications using their platforms (i. e. , Android, iOS, Tizen, etc. ). Usually, the development of these commercial devices comes with certain restrictions. For instance, the developers do not have complete access to the device, and to modifications of the operating system, the programming language is usually restricted, and some pre-programmed tasks are usually impossible to modify.
Related to the Data Data collection consists of data acquisition, data labeling, and existing database improvement. It is one critical challenge in machine learning and often the most time-consuming step in an end-to-end machine learning application due to the time spent collecting the data, cleaning, labeling (for supervised learning), and visualizing it. The data required by the machine learning models can be experimental, retrospective, observational, and, in some cases, synthetic data. While retrospective data collection methods such as surveys and interviews are easy to deploy, they are subject to recall and to self-selection bias, and they might add tedious collection logistical issues if tools and programs in mobile devices are not deployed. Retrospective data collection is sometimes the only means to capture subjective experiences in daily life.
Data Collection The availability of large-scale, curated scientific datasets is crucial for developing helpful machine learning benchmarks for scientific problems  [76] , especially for supervised learning solutions where data volume and modality are relevant  [77]. Even though machine learning has been used in many domains, there is still a broad panel of applications and fields, such as neuroscience and psychiatry, with few or even inexistent training databases. This is the case for connected devices' and sensors' derived datasets for brain disorder research. In contrast, there are nowadays larger neuroimaging and biological databases available, e. g. , the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Allen Brain Atlas. Fortunately, following the moderate adoption of machine learning in the brain research field, a trend toward increasing sharing of resources has emerged, but for now, it is mainly in the neuroimaging field.
Database Validation and Signal Richness For applications in medicine and healthcare, the datasets used to train the ML models should undergo detailed examination because they are central to understanding the model's biases and pitfalls. Before adopting an openly available dataset or creating one, there are some considerations that we have to keep in mind. Firstly, ensure a minimal chance of sample selection biases in the database (for instance, data acquired with particular equipment or with a particular setting). Errors from sample selection biases become evident when the model is deployed in settings different from those used for training. Secondly, we must be aware of the class imbalance problem that often occurs in cases where the data is rare (for instance, in low samples associated with rare diseases), which could negatively affect models designed for prognosis and early diagnosis.
Health Privacy Using mobile devices, connected objects, and sensors to collect data for machine learning for health applications is a process that generates data from human lives. In this sense, privacy is a common concern with health data. The concept of privacy in health refers to the contextual rules around generated data or information: how it flows depending on the actors involved, what is the process by which it is accessed, the frequency of the access, and the purpose of the access  [81]. The machine learning community has generally valued and embraced the concept of openness. It is common for code and datasets to be publicly released and paper preprints to be available on dedicated archival services before an article is published (despite rejection). Therefore, regulatory bodies should encourage and enforce data holders to collect and provide data under clear legal protection.
To date, there is a limitation due to an underpowered number of available pilot datasets that make the comparability of studies difficult and therefore the adoption of these new technologies into the clinical field. Indeed, sensor datasets come mainly from actigraphy and are not as numerous as available neuroimaging, MEG, or EEG datasets. Opposite to the few large patient-generated evidence, the number of solutions for connected devices and sensors with added features continues to grow every year. This rapid development of technologies represents a challenge to clinicians who might perceive difficulty in the feasibility and scalability of real-word implementations within the clinical workflow, especially since it is noticeable that devices become obsolete, outdated, or no longer useful very quickly.
g. , virtual assistants and presence detectors). When these devices are used together, they represent an opportunity for data fusion allowing the joint analysis of multiple datasets that provide an enhanced complementary view of the phenomenon of interest (e. g. , detecting a compulsive behavior like handwashing by combining inertial and acoustic data from a smartwatch). Without a doubt, some brain disorders are better suited for sensor-based assessments, like PD, because of their prominent motor symptoms, unlike other brain disorders whose symptom assessment requires the combination of close behavior observation and access to mental insight (e. g. , mood disorders). In the second case, combining sensor data would reduce uncertainty in monitoring and diagnosing, especially when the samples are taken continuously in an ecological manner.