dataesr/scientific-paragraphs-categorization huggingface dataset:
4a ) with a R 2 of 0. 96 and a slope of 0. 84. Despite larger variability for very high turbidity, these results confirm that OBS is a good tool for continuous measurements of turbidity in turbid environments. The ratio of the side scattering versus the back scattering derived from OBS measurements has a particular interest as it can provide information on the size and properties of the particles; e. g. a higher ratio could be explained by larger particles  (Nechad et al. , 2016). In the HYPERMAQ dataset, this ratio (Fig. 4b ) displayed a very high variability in low turbidity environments and an increasing slope for high-turbidity environments (i. e. Pauillac) as also observed by  Nechad et al. (2016). The very high variability when turbidity is low is explained by the strong impact of uncertainty on low back scattering values in the ratio calculation.
In Fig. 4b , it can be observed that the side scattering versus back scattering ratio varies significantly between and within sampled sites. For instance, this ratio is higher in the Gironde estuary at Le Verdon than in the Spuikom lagoon. It seems also to be higher in the Río de la Plata and in the Gironde at Pauillac than in the Chascomús lake, though the Río de la Plata showed high variability. Finally, the median ratio of the whole dataset is 1. 77, which is close to the mean value of 1. 72 found in  Nechad et al. (2016)  in turbid waters.
Conclusions Coastal and inland waters strongly interact with human activities. Some of these activities, like fisheries or tourism, rely on a good ecological status whereas the same activities but also others like farming, industry, or urbanization tend to affect water quality. Hence, monitoring these waters is extremely important, and for that optical remote sensing is a valuable tool as it allows a large spatial and temporal coverage. However, it is still challenging to retrieve biogeochemical parameters in complex case 2 waters  (Odermatt et al. , 2012)  because the transfer of light in water is affected by temporally and spatially variable inputs of CDOM and terrestrial sediments. To help the scientific community to build comprehensive database for the development of algorithms, the HYPERMAQ dataset provides data for six different studies areas with SPM and Chl-a ranging from moderate to extremely turbid and productive, and located over three continents (i.
e. Europe, South America, and Asia). The HYPER-MAQ dataset includes big river estuaries characterized by high turbidity, inland lagoons with productivity ranging from moderate to extreme, and finally Belgian coastal waters in the North Sea characterized by the high spatio-temporal variability of optical properties  (Vantrepotte et al. , 2012). The parameters shared in the HYPERMAQ dataset include descriptors of biogeochemical conditions (i. e. Chl-a, SPM, turbidity), and AOPs (i. e. water reflectance) and IOPs (a nw and c nw ). Although this dataset does not aim to cover the whole variability of case 2 waters, it provides valuable information to describe turbid and even extremely turbid waters and has the potential to help the development of remote sensing algorithms. It can also contribute to the production of a larger optical database, based on in situ measurements for a comprehensive description of case 2 waters.
Acknowledgements. This work has been founded and promoted by the Research program for earth observation 580 STEREO III HYPERMAQ project (contract nr. SR/00/335). Flemish LifeWatch BE programme, funding by FWO, is thanked for its contribution to the water sampling in the Belgian Coastal Zone. We thank VLIZ for providing the Zeekat and ship time on the RV Simon Stevin and her crew for their support during sampling. Inland water sampling in Belgium was also founded by the Belspo PONDER (SR/00/325) project. The SAPIGH analytical platform of the "Institut de la Mer de Villefranche" (CNRS-France) is thanked for having performed the analysis of pigments in the Gironde. NASA, USGS, ESA, and EUMETSAT are thanked for offering free access to Landsat 8 and Sentinel 2 images. Financial support. This research has been supported by the Belgian Federal Science Policy Office (STEREO III (grant nos.
Author contributions. HL, AD, DD, FS, AC, XS, JIG, PRR, MB, QV and KR participated to one or more field campaigns. Data processing has been made by HL, AD and JIG (turbidity), DD (absorption), KR, MB, QV, PRR, AD (water reflectance), and AC, AD, DD, FS, KS (chlorophyll-a, pigments and SPM). HL, DD, AD have compiled data and created the final dataset. All the authors participated in manuscript redaction and revision. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Data availabilityData are available from Lavigne et al. (2022) , hosted at PANGAEA (http://www. pangaea. de, last access: 24 October 2022) under the https://doi. org/10. 1594/PANGAEA. 944313. Earth Syst. Sci. Data, 14, 4935-4947, 2022   https://doi. org/10. 5194/essd-14-4935-2022.
However, theoretical models were uncertain because of a lack of experimental data at low pressure, and the difficulty to characterize turbulence or attenuation in a closed environment. Here using Perseverance microphone recordings, we present the first characterization of Mars' acoustic environment and pressure fluctuations in the audible range and beyond, from 20 Hz to 50 kHz. We find that atmospheric sounds extend measurements of pressure variations down to 1,000 times smaller scales than ever observed before, revealing a dissipative regime extending over 5 orders of magnitude in energy. Using point sources of sound (Ingenuity rotorcraft, laser-induced sparks), we highlight two distinct values for the speed of sound that are ~10 m/s apart below and above 240 Hz, a unique characteristic of low-pressure CO 2 -dominated atmosphere.
Moreover, Perseverance carries a second microphone as part of the Entry, Descent, and Landing Camera (EDLCAM  15  ), which has a frequency response from 20 Hz to 20 kHz at a sampling rate of 48 kHz. The EDL microphone is mounted on the port side of the rover, 1 m above the ground. It was activated on Sol 2 and subsequently. Figure  1  provides an overview of sounds acquired by the SuperCam's microphone (see Methods. 2). Sol 38a is the quietest recording in our dataset. Later on that same day (Sol 38b), the power spectral density (PSD) rises above the quiet state at frequencies below 100 Hz. On Sol 117, we associate this rise of power to an increase in the turbulent activity, which extends up to 300 Hz; this is the situation we observe most often. The recording of Sol 148 is the most active one shown, with the same shape starting toward higher frequencies, but with a slope break near 200 Hz; turbulence is detected up to 600 Hz.
In Figure  2b  the same acoustic data are plotted in the frequency domain and combined with low frequency measurements of pressure and wind from MEDA, for a 51-minute time period of continuous data around the microphone acquisition. The large difference in slope between MEDA and microphone data is indicative of regime change. The transition from the likely shear-dominated regime  28  to the dissipation regime occurs in this case between 1 and 20 Hz.
As a result, at 0. 6 kPa, the relaxation frequency is about 240 Hz on Mars. The recording of pulsed waves generated in LIBS mode provides a unique opportunity to measure directly and repetitively the local speed of sound for acoustic waves above 2 kHz, and that is for f > f R (see Methods. 6). From the daytime measurements, sound speeds between 246 m/s and 257 m/s are obtained (Figure  3a ), with maximum values between 11:00 to 14:00 Local True Solar Time (LTST) and minimum values around 18:00. The 1σ-dispersion of the sound speed during the ~20 min of a target analysis with LIBS is at its maximum at noon (1. 5 %) and is A C C E L E R A T E D A R T I C L E P R E V I E W reduced down to 0. 5% at 18:00, which highlights the vanishing of the atmospheric turbulence at dusk. These measurements are compared to temperature-derived speeds of sound obtained from (i) the MEDA temperature datasets at the surface, at the height of 0.
85 m and 1. 45 m, and (ii) the temperature at the surface and at 2 m height given by the Mars Climate Database (MCD 32 ) (see Methods. 4), using γ ∞ = 1. 4 (since f > f R ). The agreement between MEDA and MCD predictions is excellent. SuperCam sound speeds are comparable with temperature-derived values at the level of MEDA's 0. 85 m temperature sensor or higher. This is consistent with the fact that the speed is integrated between a height of 2. 1 m and the surface, possibly biased toward the surface when the temperature gradient is larger. Ingenuity's blade passage frequency (BPF)  33  is close to a harmonic source centered around 84 Hz, and in that case for f < f R (see Methods. 3). This signal recorded by SuperCam's microphone is modulated by the variations of the distance range between the microphone and the helicopter.
Sound attenuation The most remarkable property of sound propagation on Mars is the magnitude of the attenuation at all frequencies, especially above 1 kHz. The decrease of the LIBS acoustic signal with distance is an opportunity to verify the theory in situ and to test two different attenuation models  3, 4  which suffer from a lack of field data at Mars conditions. As the spherical LIBS acoustic wave propagates, sound pressure decreases as 1/r where r is the distance between the target and the microphone. This decrease is scaled by a factor r -0. 698 to account for the variation of laser irradiance  8  , multiplied by e -αr where α = α(f) is the atmospheric attenuation coefficient as a function of frequency. The frequency spectrum of the LIBS acoustic signal is divided into three bands, which account for the three main lobes observed in Figure  1 : from 3 kHz to 6 kHz, from 6 kHz to 11 kHz and from 11 kHz to 15 kHz.
Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-022-04679-0. A C C E L E R A T E D A R T I C L E P R E V I E W A C C E L E R A T E D A R T I C L E P R E V I E W.
Methods This section starts with a reminder on (1) acoustics adapted to Mars. It provides details of the different datasets: (2) acoustic data from Super-Cam's and EDL's microphones; (3) artificial sounds from MOXIE and Ingenuity, in addition to the recording of LIBS shock waves and rover noises; (4) wind speed, temperature, and pressure data from MEDA, temperatures extracted from the Mars Climate Database. Processing methods are presented: (5) computation of power spectral densities; (6) analyses of LIBS shock wave time series; (7) extraction of the Doppler effect from Ingenuity. In the end, (8) supporting explanations on the attenuation with distance are given.
Acoustic dataset SuperCam's microphone data set used in this study extends from Sol 1 to Sol 216 when the first solar conjunction of the mission occurred. At this date, a total of 4 hours and 40 minutes of Martian sounds have been recorded, including atmospheric turbulence (46% of the total duration), the accompanying pressure waves of LIBS sparks (12%) and mechanical noises (e. g. MOXIE  43  , Ingenuity 33 , Perseverance's mast rotation, Mastcam-Z mechanisms, 42%). Over the same period, the EDL  15  microphone has recorded a total of 56 minutes of Martian sounds, mainly during rover operations (e. g. rover drive, arm motion). Extended Data Table  1  lists all acoustic files used or mentioned in this study, except for those shown in Figures  3a  and 4a  which are too many to cite individually (see below).
SuperCam's microphone records air pressure fluctuations from 20 Hz to 12. 5 kHz at a 25 kHz sampling frequency, and up to 50 kHz when the 100 kHz sampling mode is used. The analog signal from the microphone, ranging from 0 to 5 V, is digitized (12-bit depth) using one of 4 electronic gains to boost the sensitivity from 0. 6 to 21 V/Pa and the resolution from 2 to 0. 06 mPa. Gain 0 is used to record the accompanying sounds from LIBS sparks on calibration targets, gains 1, 2, and 3 to record the accompanying sounds from LIBS sparks at various distances, gain 3 for atmospheric recordings. Recordings of the atmosphere and mechanical noises are generally 167 s long. The EDL microphone can record 10 mn and longer time series, with a fixed gain amplifier followed by a 24 bit/44 kHz digitizer (key characteristics are summarized in Extended Data Table  2 ).
Dataset of artificial sounds Both EDL and SuperCam microphones are also used to record sounds produced by the rover. They help to inform operators about equipment health (e. g. rover driving, Mars oxygen in-situ resource utilization experiment -MOXIE) and provide sources of sound that are well localized in space and time (e. g. during Ingenuity flights or LIBS sparks). All recordings also pick up some perturbations such as intense single-frequency emissions at 195 Hz, 198. 75 Hz and the following harmonics at 780 Hz and 795 Hz that are due to the rover internal heat pump used for thermal management. Sounds of the rover's instruments or pumps propagate through both structural vibrations (microphonics) and acoustic propagation in the atmosphere. The EDL microphone  15  was used to record the rover drive on Sol 16 (Extended Data Fig.
MOXIE utilizes SuperCam's microphone recordings for independent diagnosis of compressor performance, including precise measurements of the motor rotation rates as indicated by the fundamental frequency of the observed comb of harmonics. Distinct transitions in Extended Data Fig. 1c , recorded during a nighttime run (Sol 81), correspond to commanded changes in motor speed from 50 to 58. 3 Hz. The loudest harmonics are near 500 Hz, where several additional frequencies are also excited. This range corresponds to resonant frequencies of the MOXIE instrument, as observed during dynamics testing. Even recorded during one of the quietest times of the day, the amplitude of the signal only reaches 1. 5 mPa. Recording LIBS sparks was the main rationale to develop SuperCam's microphone to infer physical properties of rock targets, such as their hardness  13.
Wind speed, temperature, and pressure data The wind, temperature and pressure data are recorded from the Mars Environmental Dynamics Analyzer (MEDA  21  ) instrument. The wind data are acquired up to 2 Hz, pressure at 1 Hz and temperature at 1 or 2 Hz. Wind speed and direction are independently acquired from two separate booms separated by 120° (termed boom 1 and boom 2) where one is preferred for a given wind direction (see accuracies and resolutions in Extended Data Table  2 ). The Mars Climate Database  32  (MCD) provides climate predictions derived from 3D simulations of Mars atmosphere performed with the Mars global climate model developed at the Laboratoire de Météorologie Dynamique (http://www-mars. lmd. jussieu. fr). The LMD Mars GCM is described in ref. 32  , but since then it has adopted more sophisticated and realistic modeling for the CO 2 , dust and water cycles, photochemistry, radiative transfer, etc.
Doppler effect (Ingenuity recording) Ingenuity flights #4 (Sol 69), #5 (Sol 76), #6 (Sol 91) and #8 (Sol 120) were recorded by SuperCam at 25 kHz sampling rate. We use Flight #4 data, since this flight came closer to Perseverance than any other flight SuperCam could record. During this flight, Ingenuity climbed to an altitude of 5 m, accelerated to 3. 5 m/s, traveled 130 m at constant height, decelerated, turned around, and returned to its base by the same route. Taking off at 76 m distance from Perseverance, it came as close as 69 m, and has moved as far as 123 m from Perseverance (Extended Data Fig. 3b, bottom ). On the PSD obtained over the whole recording, the Blade Passage Frequency (BPF, 2x the rotation rate for a 2-bladed rotor) at 84 Hz and its first harmonic at 168 Hz are clearly visible above the background, which itself is higher than that of Sol 38a, a very quiet recording on Mars (Extended Data Fig.
List of audio recordings used in this studyRecordings from SuperCam's (S) and EDL's (E) microphones, with the Local True Solar Time (LTST) of each acquisition. Origin and characteristics of dataset used in this study Audio recordings are provided by SuperCam and EDL microphones. Atmospheric data are provided by MEDA. Resolution and accuracy are defined as the 90%-confidence range of the statistical error of the pressure, temperature, or wind speed values 8, 21.
Acknowledgements Many other people helped with this project in addition to the co-authors, including hardware and operation teams, and we are most grateful for their support. This project was supported in the US by the  NASA   Mars Exploration Program , and in France is conducted under the authority of  CNES. The work of  A. Munguira  is supported by the grant  PID2019-109467GB-I00  funded by  MCIN/AEI/10. 13039/501100011033.
1 | 2 | 12 List of audio recordings used in this studyRecordings from SuperCam's (S) and EDL's (E) microphones, with the Local True Solar Time (LTST) of each acquisition. Origin and characteristics of dataset used in this study Audio recordings are provided by SuperCam and EDL microphones. Atmospheric data are provided by MEDA. Resolution and accuracy are defined as the 90%-confidence range of the statistical error of the pressure, temperature, or wind speed values 8, 21.
With the Covid-19 pandemic and the introduction of the WHO's Essential Diagnostics List (EDL), increasing global attention is focused on the crucial role of diagnostics in achieving universal health coverage. To create national EDLs and to aid health system planning, it is vital to understand the most common conditions with which people present at primary care health facilities. We undertook a systematic review of the most common reasons for primary care visits in low-and middle-income countries. Six databases were searched for articles published between January 2009 and December 2019, with the search updated on MED-LINE to January 2021. Data on the most common patient reasons for encounter (RFEs) and provider diagnoses were collected. 17 of 22,279 screened articles were included. Most studies used unvalidated diagnostic classification systems or presented provider diagnosis data grouped by organ system, rather than presenting specific diagnoses.
Studies with a data collection period of at least three months were included, though data collection did not have to occur continuously throughout the three months. Studies focusing only on specific types of visits (e. g. follow-up visits for acute conditions, referred visits, routine examinations) were excluded. Studies that selected the population based on particular symptoms (e. g. patients with fever, children with respiratory symptoms) were excluded. Studies that selected the population based on particular morbidities (e. g. HIV-positive patients, diabetic patients) or were conducted exclusively in specialized care settings (e. g. sexually transmitted infection clinics, specialized medical departments) were excluded. Studies in which data collection occurred before 1 st January, 2009 were excluded.
Data extraction Data were extracted using a standardised extraction form in Google Forms, which was piloted beforehand. Three reviewers (JB, EM and PA) conducted the data extraction, with the data from each paper independently extracted by a combination of two of the three reviewers. Extracted data were compared and any discrepancies were resolved through discussion between the reviewers. Extracted data included: country of study, data collection period, type of healthcare facility, type of healthcare providers, healthcare sector, patient demographics, source of outcome data, classification system used, total number of visits or patients, number of conditions reported, frequency of different conditions.
Studies were pooled if at least three studies using the same classification system presented data on diagnoses or RFEs. Data from studies using different classification systems were not pooled, as it is not possible to reliably convert between different classification systems without access to individual patient data. Studies using in-house classification systems were not pooled. Data were pooled via a rank sum system. For each study, provider diagnoses and patient RFEs were ranked from most to least common. The number of provider diagnoses ranked was determined by the study that reported the lowest number of provider diagnoses. For example, of the studies that used ICPC-2, the lowest number of provider diagnoses reported was 10 so the top 10 provider diagnoses in each study were ranked. The most common provider diagnosis in each study was assigned rank 10, the second most common chapter was assigned rank 9 and so on.
Study selection After deduplication, 22,279 records were identified. A total of 21,938 records were excluded after title and abstract screening. Of the remaining 341 studies, three full-texts could not be retrieved and a further 321 were excluded after full-text review. The top three reasons for fulltext exclusion were data collection that took place before January 1 st , 2009, studies reporting fewer than five diagnoses or reasons for visits and studies being of the wrong design (e. g. casecontrol studies) [  Fig 1]. 17 studies were included in the review. Detailed study characteristics are shown in Table  1. Included studies represented seven LMICs: Bangladesh (n = 1), Brazil (n = 3), Cambodia (n = 1), India (n = 7), Malaysia (n = 1), Nigeria (n = 2) and South Africa (n = 2). Each of these countries is a middle-income country (MIC)-no studies had data from low-income countries (LICs).
Where possible, future studies should aim to randomly sample primary care facilities from across their country of study, or to provide countrywide data from a national primary care database, if available. Ideally, studies should use ICPC-3 as it is designed for classifying primary care encounters, it allows reporting of both RFEs and specific diagnoses and unlike in-house classification systems, it is easily comparable between studies. ICPC-3 codes should be included with RFEs and specific diagnoses, with the numbers of included patients, visits, diagnoses and RFEs clearly identified. Data for each code should be presented separately, with no combining of codes. An electronic version of ICPC-3 is available for free on the ICPC-3 website  [40]. If ICPC-3 cannot be used, another validated classification system such as ICD-11 should be used instead.
, 2007). As we require a set of lenses allowing Â2 magnification and reduced depth of field, we used a 100 mm lens and an inverted 50 mm lens for close-ups (Figure  1c ). Both were fully opened (respectively 1:2. 8 and 1:1. 4). A light-ring was attached to the end of the lens as a light source, allowing diffuse and grazing illumination at a working distance of <1 cm, while not occluding the lens (Figure  1d ). The focal stack was acquired by connecting the motorized rail to a Helicon Remote for automating the method. Finally, we precisely aligned the photos using Enfuse software for a precise alignment, while limiting the degradation of the image.