dataesr/scientific-paragraphs-categorization huggingface dataset:
3. The aerosol lidar simulator uses a higher-resolution vertical grid than the cloud simulator: e. g. 320 vertical levels (typically 60 m) instead of 40 (typically 480 m). This is because the detailed vertical structure of aerosol is important for understanding aerosol mixing, transport and other physical processes, especially in the at-mospheric boundary layer. To be consistent with the CALIOP aerosol data product, we use the same vertical resolution. Note that for clouds the vertical resolution used in CFMIP experiments (dz = 480 m) results from a compromise between the wish to keep high horizontal resolution for sparse shallow clouds, the SNR of CALIOP data in daytime and the vertical resolution of CloudSat. Users can choose to run the new aerosol simulator alone, the standard cloud simulators alone (default), or both aerosol and cloud simulators.
Observations To facilitate fair comparisons between models and observations, we have created an observational dataset that is consistent with the simulator approach described in the previous section. The simulator outputs SR_observable and ATB_observable can be directly compared with the SR and ATB profiles above clouds observed by CALIOP. However, it should be noted that the total extinction profile (EXT_observable) cannot be observed directly by CALIOP, it is an output from the simulator that can only be used to interpret the difference between the observation and the model and simulator outputs. We use the CALIOP L1. 5 orbit file (NASA/LARC/S-D/ASDC, 2019) dataset that contains cloud-screened ATB profiles at 532 nm with 60 m vertical resolution and 20 km along-track and 90 m cross-track horizontal resolution.
The CALIOP L1. 5 data are built from the native L1 CALIOP data (1/3 km along-track horizontal resolution, 90 m crosstrack horizontal resolution and 30 m vertical resolution), but a cloud-screening procedure is applied so that the L1. 5 data only contain above-cloud measurements. The cloud screening is applied iteratively at different horizontal resolutions from 1/3 up to 80 km. When clouds are detected at a vertical level, all the data below the cloudy level are marked as Fill_Value and all the cloud-free and above-cloud profiles are retained below the altitude of 8 km. Then, these cloud-free and above-cloud profiles are averaged horizontally over the along-track 20 km grid. Since each L1. 5 20 km profile represents an averaged signal over the cloud-free profiles over use of a lower aerosol detection threshold in both observations and simulations, and is then able to detect optically thin aerosol layers at the 20 km spatial scale  (Ma et al.
, 2018). In this study, we created an example gridded data product from CALIPSO that is consistent with the GCM grid, so that the translation from the model to the simulator results can be more easily understood by the reader, in relation to how it can affect the interpretation of a comparison to the CALIOP observation profile. This dataset was created by averaging all the L1. 5 ATB cloud-screened profiles over a 1 • × 1 • latitude-longitude grid at a given date. It is worth noting that since CALIPSO is a polar-orbiting satellite with a relatively narrow swath, the number of profiles at high latitudes is larger than that in the tropics, and that not all the grid boxes contain a satellite observation in any single day. Similarly, we build the gridded product for SR from the orbit L1. 5 ATB dataset.
We find that the relative difference between these SR values and those obtained by using the full CALIOP data is highest in covered regions, but it never exceeds 15 % (not shown). This gives us confidence about the robustness of our results re- trieved by CALIOP over a 3-month period. An alternative approach would be to extend the analysis to cover multiple years, but the results would then be affected by the interannual variability of aerosol. We can thus conclude that 1. the SR maxima retrieved by CALIOP over 3 months are robust and 2. the method of comparing modeled and retrieved SR is robust, although the modeled and observed clouds show large differences. Therefore, the differences between observed and simulated SR values should be attributed to the representation of aerosol in the model.
For future comparisons between CALIOP data and other GCMs, or for model-to-model comparisons, one might find it useful to use one single optics module to eliminate aerosol optics as a potential source of discrepancy in the comparisons. This is beyond the scope of this study and requires future investigation. To evaluate the representation of aerosol composition in the model, the NASA product providing aerosol types from CALIPSO data is of particular interest. Indeed, CALIOP level 2 data include seven aerosol classes: clean marine, dust, polluted continental, clean continental, polluted dust, smoke and other. This classification utilizes the depolarization ratio, integrated attenuated backscatter coefficient, altitude, and land vs. ocean  (Kim et al. , 2018). The aerosol subtypes of CALIOP measurements have been shown to be in good agreement with the daily aerosol types derived from AERONET level 2.
Note that for a multi-model comparison, it is necessary to use a standard vertical grid with a coarser vertical resolution than N = 320 levels and z = 60 m, as traditional climate models do not reach such a fine resolution. For the comparison of these models with CALIOP observations, data interpolation is needed on the same vertical coarser grid. Vertically averaging the CALIOP data would enhance the SNR and consequently would allow us to lower the aerosol detection threshold and make use of the more noisy CALIOP daily data. For each model it is important to check that the errors in the model clouds do not significantly impact the modelobservation aerosol comparison over the considered period. Since 2018, the Atmospheric Dynamics Mission Aeolus (ADM-Aeolus) has been operating the first high-spectralresolution lidar (HSRL) in space.
Although primarily dedicated to wind measurements, the HSRL capability in the UV allows the separation of the molecular and particulate contributions and enables the measurements of the particulate backscatter and extinction coefficients. These measurements provide new insight into very thin aerosol layers and can be very useful for the validation of models that directly compute these quantities. Later in 2023, the EarthCARE mission will also provide data from the HSRL lidar ATLID at 355 nm. The COSPv2 simulator can be easily adapted to other wavelengths, which opens the way to the determination of new diagnostics for cloud susceptibility, aerosol typing and aerosol-cloud proximity metrics.
Acknowledgements. We thank  Rodrigo Guzman  for his work on the development of the aerosol lidar simulator and on its interface with the COSPv2 infrastructure and  Steve Klein  for his inputs on the improvements of the vertical re-gridding within COSPv2. Po-Lun Ma was supported by the " Enabling Aerosol-cloud interactions at GLobal convection-permitting scalES (EAGLES) " project (no. 74358 ), funded by the  U. S. Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research, Earth System Model Development (ESMD)  program area. The  Pacific Northwest National Laboratory (PNNL)  is operated for  DOE  by  Battelle Memorial Institute  under contract  DE-AC05-76RL01830. Financial support. This research has been supported by the  Centre National d'Etudes Spatiales (EECLAT)  and the  U.
Code availability. The aerosol lidar simulator presented in this paper is available at https://doi. org/10. 5281/zenodo. 7418199  (Bonazzola and Chepfer, 2022)  and is incorporated in the COSPv2 infrastructure at https://github. com/CFMIP/COSPv2. 0 (last access: 9 December 2022). Data availability. The CALIPSO L1. 5 data are available at https://doi. org/10. 5067/CALIOP/CALIPSO/CAL_LID_L15-Standard-V1-01 (NASA/LARC/SD/ASDC, 2019). The processed gridded CALIOP ATB and SR data files used in this study are available at https://doi. org/10. 5281/zenodo. 7107232 (Bonazzola,  2022a)  and https://doi. org/10. 5281/zenodo. 7107162 (Bonazzola,  2022b).
km, this dataset cannot be used to study the horizontal heterogeneity of aerosol with a spatial scale smaller than 20 km. Nevertheless, this dataset has the advantage of a much higher SNR than the original L1 profile (1/3 km), which permits the https://doi. org/10. 5194/gmd-16-1359-2023Geosci. Model Dev. , 16, 1359-1377, 2023.
Description of MonEco The present manuscript proposes MonEco: a health monitoring solution that allows the user to measure its physiological parameters and exchange this information with a physician. In this way, the doctor can directly analyze the impact of treatment on the patient, check if it is well adapted to his pathology, and prevent certain risks of incompatibility or adverse effects. The MonEco ecosystem, as shown in Fig. 1 , is based on different e-health care solutions: • A web application, where each user could visualize the information collected by his smart devices; the website recalls all user's information from a dedicated SQL database. This information can be shared with a third user, typically a physician, to investigate the possibility of an eventual cure or follow the treatment.
• UpNEA: a connected glove that monitors the user's sleep  [10]. The smart glove acquires PPG, SpO 2 and 3-axis accelerometer data for this task. It sends them to a mobile application that redirects this information to a remote server (AeneA) for signal processing. • AeneA: the remote server collects data sent from the connected devices and then performs the signal processing task on acquired records storing results in a dedicated SQL database. AeneA also hosts a web application that can access the SQL database to allow the user to visualize his health tracking results. The authors of the present manuscript developed and conceived CareUp and UpNEA. The smartwatch technology and performances were discussed in  [9]. The UpNEA architecture instead was proposed in  [10]. The sleep monitoring algorithms embedded in Ae-neA were introduced in two papers:  [11]  for sleep apnea-related methods and  [12]  for breathing rate estimation processes.
eCardio: a cardiovascular disease risk forecasting application eCardio is a cardiovascular disease (CVD) risk prediction platform  [8]. This platform is available in two versions: one in the online user dashboard and another embedded in a floor standing tablet. The two versions work the same way and share the same code and user interface. When a new session is started, the application visualizes a series of questions about his habits and physiological parameters. The list of inputs introduced by the user is presented in Table  1. Once all this information is collected, it is recorded in the AeneA SQL database. Finally, a report containing the ten-year CVD risk prediction is sent to the user by email. The client can repeat the   Sex, age, weight, height, ethnicity, nationality, total cholesterol, HDL cholesterol, systolic blood pressure, anti-hypertension, diabetes, chronic kidney disease, rheumatoid arthritis, atrial fibrillation, smoke, precedents of cardiovascular diseases, family history involving people with CVD.
Every two years, these people answered detailed questions about their lifestyle. Throughout the study, researchers kept track of which participants experienced heart disease and which did not. It was possible to study the relations between disease and collected data in this way. In 1971, 5120 recruits (first generation cohort) were added to the study. From the 1990s, the study also included new persons (second-generation cohort)  [18]  and finally, in 2001, a third-generation cohort  [15]  was added. The Framingham algorithm embedded in eCardio, was transposed from the work in  [15].
Systematic coronary risk evaluation The Systematic Coronary Risk Evaluation (SCORE)  [16]  project assembled a pool of datasets from 12 European cohort studies, mainly carried out in general population settings. This project was supported by the European Society of Cardiology (ESC) through the European Association of Preventive Cardiology (EAPC). There were 205178 persons (88080 women and 117098 men) involved in the project, and a ten-year risk of fatal CVD was calculated for highrisk and low-risk regions of Europe. The risk charts are based on gender, age, total cholesterol, systolic blood pressure, and smoking status, with relative risk chart, qualifiers, and instructions. The SCORE algorithm embedded in eCardio, was transposed from the work in  [16].
The present manuscript unveils a cuff-less method for BP estimation that requires two PPG signals as input. The algorithm is embedded in the CareUp smartwatch  [9]  and is based on the time delay the blood volume (pulse) takes to travel from the heart to peripheral organs. This time delay is called pulse transit time (PTT)  [26] [27] [28]  and is inversely related to BP  [29]. The PTT can be estimated using two PPGs: one obtained close to the heart and one obtained at a distal place. CareUp is made up of two pulse oximeters: one on the back and one on the front. The acquisition of two PPGs is initiated by placing the index finger on the front oximeter. Fig. 3  depicts the BP estimation procedure. The PTT is then calculated by filtering and cross-correlating the signals. BP is affected by a variety of factors, including vasomotor tones, neural control, and HR  [30, 31].
AeneA web application A dedicated internet web page hosted by AeneA allows the user to access his data. In Fig. 5 , the web application shows the user the eCardio report based on his daily habits and physiological data. The customer can also navigate, for example, through the recorded nights and select the one to display. The server imports data from the SQL database and displays them using the Highcharts JavaScript charting engine from Highsoft. Fig. 6  shows a generic overnight plot with no clinical significance. The chart is made up of a zoomable time series, a range selector bar at the bottom, and a legend. The range selector only shows the overnight sleep stages, and a legend is an active tool that allows to show/hide information on the plot. HR, SpO 2 , BR signals, apnea and hypopnea detections, and tachycardia and bradycardia events are depicted in colored bands on the chart.
UpNEA validation 3. 3. 1. Methods for apnea and hypopnea detection and classification The methods were tested on 96 overnight signals from patients with sleep apnea/hypopnea syndrome and no collateral morbidity at the UZ Leuven hospital. Each record included a PPG and a SpO 2 signal sampled at 500 Hz, and the apnea-hypopnea index (AHI) was calculated using the AASM 2012 rules  [82] s. In the UZ Leuven hospital dataset, the average AHI is 31. 3, and 39 percent of the subjects had an AHI greater than 30; 53 percent had an AHI between 5 and 30, and the remaining 8 percent had an AHI less or equal to 5. The start and end times of central apnea (CA), central hypopnea (CH), obstructive apnea (OA), and obstructive hypopnea (OH) are all annotated. The total number of annotations in the database per sleep-disruptive breathing event (SDBE) category is shown in Table  6.
For overnight sleep apnea/hypopnea detection, each PPG recording was divided into one-minute segments. Then, 3-fold crossvalidation (CV) was performed: two-thirds of the patients in the database trained the detector parameters and one-third tested the trained detector at each fold. The 3-fold CV was performed on each patient separately, with no re-substitution. Because PPG features can be patient-specific, it was decided that each patient should be included in either the detection test set or the detection training set to ensure proper generalization to new subjects. The authors published the algorithms for apnea and hypopnea detection in  [11]  and embedded them in UpNEA  [10]. Those methods had a 75. 1 percent accuracy (Acc) for detecting apneic/hypopneic events in one-minute segments, with 76.
9 percent sensitivity (Se) and 73. 2 percent specificity (Sp). The detection Se, Sp, and Acc values of the different sleep-disordered breathing events are reported in Table  7. To build the table, a criterion was adopted to compute the metrics: e. g. , considering central apnea detection, a sleep-disruptive breathing event was considered FP when it did not correspond to a central apnea annotation, even if other apnea types were present. Furthermore, those algorithms were tested on patients with a low AHI index (AHI ≤ 5), and their results are shown in Table  8. When we compare the apnea and hypopnea detector results from the comprehensive database to those from patients with low AHI, we see no significant difference, confirming the detector's robustness. Finally, those methods produced comparable results to those found in the literature when tested on the same database,  [83] [84] [85].
In order to classify SDBEs in CA, OA, CH, and OH, 37 features were extracted from the PPG and SpO 2 signals corresponding to the SDBE time frame. Because of the heterogeneous distribution of SDBE types among patients, a dataset containing labeled extracted features was created without implementing a Leave One Subject Out (LOSO) procedure. A subsequent step involved balancing data so that the same amount of information represented each class. The dimensionality problem was then addressed by implementing a feature selection algorithm. A 10-fold CV was then used to train and test the Fine Gaussian Support Vector Machine model. Additionally, in  [11] , authors also published the classification method designed on the detected DAP events to discriminate between central and obstructive, apneic and hypopneic events.
The classification was performed with a Fine Gaussian SVM classifier and yielded 92. 6 percent Acc in distinguishing central from obstructive apnea, 83. 7 percent in distinguishing central apnea from central hypopnea, and 82. 7 percent in distinguishing obstructive apnea from obstructive hypopnea. Table  9  displays the True Positive (TPr = Se) and False Positive (FPr = 1 -Sp) rates, as well as the Acc and ROC area under the curve (AUC) obtained with the classification. In comparison, no other work on the same database was done to classify the SDBE subtypes. It should be noted that these results were obtained using signals acquired by devices available at the UZ Leuven clinic rather than from the proposed UpNEA device. To simulate the UpNEA acquisition, the database signals were downsampled at 100 Hz before processing.
Derive respiratory rate from PPG The public CAPNOBASE database  [86] , which contains 42 subjects (29 children and 13 adults) with simultaneous ECG and PPG recordings, was used to validate different respiratory rate estimation algorithms. For each subject, eight minutes of high-quality data were collected during elective surgery or routine anesthesia. The capnography waveforms were manually labeled by the study's Khreis developed the PPG breathing rate estimation algorithm  [63] , integrated into the proposed sleep monitoring solution  [10]. By segmenting the signal in 32 s windows, the absolute median error was 0. 5 breath per minute (0. 2-1. 1 interquartile range 25th-75th) and 0. 2 breath per minute (0. 1-0. 9) for the 64 s windowing. These results outperformed others proposed by Pimentel in 2016  [87] , Karlen in 2013  [69] , Flemming in 2007  [88] , Shelly in 2006  [89] , and Nilson in 2000  [90].
For an exhaustive comparison of the breathing rate estimation algorithms, the reader can find more information in  [12]. In the present manuscript, Table  10  reports the performances of methods tested on the same CAPNOBASE database and proposed by the following authors: Khreis in  [63] , Pimentel in  [91] , Karlen  [92] , Flemming  [88] , Shelly  [89]  and Nilson  [90]. The Table shows the absolute errors expressed as the median, mean, interquartile range (IQR), 25th (Q1), and 75th (Q3) percentile. These values are obtained by evaluating breathing segments from 0-0. 5 Hz. Finally, the best performing algorithm was implemented by Khreis et al. in  [63] , that tracks the respiration signal, using the Kalman smoother, to fuse modulation signals with the highest respiratory quality indices.
CareUp The smartwatch's BP estimation performance was validated at the Institut Coeur Paris Centre Turin (ICPC) using a sphygmomanometer on 44 subjects. During the validation, the CareUp measures were compared to those of two existing oscillometrybased devices on the market: Thuasne ® and Magnien ®. The CareUp results were comparable to oscillometry-based devices measurements in terms of accuracy; additionally, they met the American Association for the Advancement of Medical Instrumentation standard for non-automated sphygmomanometers. In this way, CareUp becomes an easy-to-use, wearable device for monitoring blood pressure in real-time by incorporating the BP estimation task into a smartwatch. The user can register HR, SpO 2 and BP at different times in a day, and all those values will be registered into the AeneA SQL database to be available for the user upon request.
Conclusion The manuscript proposes an ecosystem that integrates different connected devices. The authors developed and tested them in separate works. MonEco aims to transform the doctor-patient relationship, letting the patient become a player in his health, particularly in improving his lifestyle or monitoring illnesses. The proposed system presents a smartwatch for blood pressure estimation (CareUp,  [9] ), a smart glove for sleep monitoring (Up-NEA,  [10] ) and a cardiovascular risk prediction platform (eCardio,  [8] ). Performances of blood pressure estimation performed by the smartwatch have been evaluated in a clinical study and are comparable to those reported by commercially available devices  [9]. The presented sleep monitoring activity provides sleep apnea and hypopnea detection and classification tasks, heart rate variability assessment, and sleep stages recognition  [10].
Acknowledgement The presented work was conducted under the financial support of the  Laboratoire Traitement du Signal et de l'Image (LTSI) Univ Rennes, INSERM ,  LTSI -UMR 1099 , F-35000 Rennes, France; the  Doctoral School MATHStic, F-35000 Rennes, France  and the  Farasha Labs company, France.
Radiative transfer (RT) simulations have long been used to study relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean from which the ocean apparent optical properties (AOPs) can be calculated. For example, inverse models to estimate IOPs from ocean color radiometric measurements have been developed or validated using results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range from the near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development was the generation of the synthetic dataset of seawater IOPs which served as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by probability distributions of IOPs that are consistent with global distributions representative of vast areas of open ocean pelagic environments and coastal regions covering a broad range of optical water types.