dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions We present a new, simple method to detect and monitor green macroalgae blooms using freely available Landsat imagery and GEE. Due to their distinct spectral characteristics, the stranded macroalgae can be detected through EO. A first application to four beaches in Northern Brittany revealed the temporal and spatial dynamics of algae blooms between 1984 and 2019. We found that despite an observed decrease in nitrogen concentration levels, Ulva blooms were still going strong. This highlights the importance of continuous and long-term monitoring of macroalgae surface estimates, possibly with the addition of EO data collected by Landsat and Sentinel satellites. It also underscores the need for more ambitious targets in nitrogen concentration reduction. Additionally, our method could be transferred to other systems of interest.
Data Availability Statement: Data generated and used during the study has been uploaded on the 4TU. ResearchData repository and is available at https://data. 4tu. nl/articles/dataset/Dataset_on_ green_tide_surfaces_over_Brittany_using_Landsat_imagery_for_35_years_of_monitoring/13747231 /2 (accessed on 1 March 2021). Sample codes used for the analysis are available at https://github. com/louiseschreyers/Green_tides_Brittany. git (accessed on 8 February 2021).
METHODS We will perform a systematic review guided by the recommendations from the Cochrane Collaboration. 20 The protocol is written with guidance from the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols statement, with additional considerations specific for network meta-analyses,  21 22  and was registered in the PROSPERO database on 21 August 2020. Any important updates to the protocol will be amended to PROSPERO and/or described in the final report. The final report will follow the recommendations from the Preferred Reporting Items for Systematic Review and Meta-Analysis -Network Meta-Analysis (PRISMA-NMA) statement. 23.
Search strategy Preliminary searches in PubMed and CENTRAL were performed on 21 February 2020. We used search terms for blood pressure lowering treatment combined with several CVD terms in both databases. We restricted our search to randomised controlled trials, but applied no date or language restrictions. Full search strategy in PubMed was (("antihypertensive agents"[Pharmacological Action] OR "antihypertensive agents"[MeSH Terms] OR "antihypertensive"[All Fields] OR "blood pressure-lowering"[All Fields] OR "blood pressure lowering"[All Fields] OR "blood-pressure lowering"[All Fields]) AND ("cardiovascular"[All Fields] OR "myocardial"[All Fields] OR "stroke"[MeSH Terms] OR "stroke"[All Fields] OR "heart failure"[All Fields] OR "mortality"[All Fields]) AND (randomized controlled trial[Publication Type] OR (randomized[Title/Abstract] AND controlled[Title/Abstract] AND trial[Title/ Abstract])).
Full search strategy for CENTRAL was (antihypertensive near agents OR "blood pressure-lowering" OR "blood pressure lowering" OR "blood-pressure lowering") AND ("cardiovascular" OR "myocardial" OR "stroke" OR "heart failure" OR "mortality") in All Text. Major systematic reviews and meta-analyses were further scrutinised for individual trials possibly missed by the database searches. 3 5 10 11 14 29-33 ecords from PubMed and CENTRAL were combined using EndNote reference software, removing duplicate records. Titles were screened by one reviewer (HJ) to remove apparently irrelevant publications. Abstracts and full-text articles were assessed against eligibility criteria by two reviewers independently (HJ and BC). Discrepancies on study inclusion were resolved by discussion or thirdparty involvement (MB).
Blood pressure data Mean systolic and diastolic blood pressure level at baseline and during follow-up will be collected for each treatment arm in each trial. If baseline blood pressure levels are not presented for each treatment arm separately, values for the total study population will be extracted and assumed to be similar for randomised groups. For follow-up blood pressure values, we will aim to include mean levels during the follow-up period. If this is not reported, we will calculate mean levels for the full follow-up manually when several values are presented, or include values at a single time point (eg, last visit). Blood pressure differences between groups will be recorded as reported, or calculated manually from follow-up values when not reported separately.
Missing outcome data The number of participants lost to follow-up will be recorded for cardiovascular events and mortality outcomes, for each treatment arm, separately. It is common in cardiovascular trials to follow participants for mortality through national or regional registers even if participants drop out from clinical follow-up during the intervention. Loss to follow-up will be reported descriptively, and the impact of attrition on primary outcomes will be estimated in sensitivity analyses using different assumptions for the outcome of the participants lost to follow-up allowing for some uncertainty in these assumptions. 35 36 sk of bias assessment All eligible trials will be assessed for risk of bias using the Cochrane Collaboration's risk of bias assessment tool, second version (RoB2).
If the number of participants with missing outcome data are of the same magnitude as the number of events, this will raise some concerns, whereas high risk of bias will generally require asymmetric missingness of such magnitude it could plausibly impact effect estimates in a clinically important way. For the fourth domain, a blinded endpoint committee will be regarded as the appropriate way of assessing outcomes. Outcome data reported as adverse events have generally been assessed in an open fashion to allow safety monitoring. This gives rise to some concerns, but will not be judged as high risk of bias because cardiovascular outcomes are fairly objective, and the value of blinding in such circumstances is probably limited. 38 For the fifth domain, assessing potential bias from selection of reported results, deviations will be noted for specific outcomes.
Data synthesis Pairwise meta-analysis In the presence of two or more studies comparing the same pair of interventions, we will synthesise results using random effects pairwise meta-analysis. The clinical heterogeneity (ie, differences in study and participant characteristics) of the studies within the same treatment comparison will be evaluated prior to synthesis. We will estimate the between-study variance (τ 2 ) using the restricted maximum likelihood estimator. Inference on statistical heterogeneity will be based on τ 2 as well as on the I 2 statistic. Relative risks will be calculated for all outcomes in all trials according the intention-totreat principle, or the modified intention-to-treat principle, using complete cases, where follow-up data are not available for all randomised participants.
LC-ADCs utilize the time-domain sparsity of signals, such as, electrocardiogram (ECG) signals, which remain inactive for large time intervals. LC-ADCs produce asynchronous samples of data triggered by the threshold crossings of the analog signal. This reduces the sampling in low-activity regions of the signal, thus reducing the data rate and energy consumption on-chip. LC-ADCs have the advantage of built-in compression and a corresponding potential reduction in processing costs in comparison with Nyquist sampling ADCs. The energy efficiency of a LC-ADC and traditional successive-approximation (SAR) ADC using various biosignals was studied in  [9]. They concluded that LC-ADCs are more energy-efficient than SAR ADCs for low-to-medium resolution applications, while also producing less data for transmission.
This article builds on the work presented in  [23] , which evaluated several level-crossing ADC models for cardiac arrhythmia classification and compression performance. To the best of our knowledge, no other performance analysis of LC-ADC models for event-driven ECG classification is found in the literature. In this article, we propose a new design strategy for modeling LC-ADC parameters inspired by the work presented in  [3]  and apply it to low-power event-driven cardiac arrhythmia classification. In this context, the major contributions of this paper are as follows: a) a new design technique for modeling level-crossing ADC parameters is presented that improves sampling accuracy and reduces data stream rate from the LC-ADC, b) several LC-ADC models are simulated and their performance is analyzed in terms of signal quality and compression using the MIT-BIH Arrhythmia dataset  [24] , c) a deep learning model is proposed for eventdriven cardiac arrhythmia classification and, d) an open-source event-driven ECG database derived from MIT-BIH database using the proposed LC-ADC models with arrhythmia annotations is presented.
A. LC-ADC Architecture The LC-ADC architecture is presented in Fig. 1a. The ECG signal range is divided into a fixed number of quantization levels. The LC-ADC sampling processing is implemented by continuously comparing against an upper and lower threshold voltage, U QL and L QL respectively, each of which is quantized into LSBs each having size q Volts: q ≜ A F S 2 M (1) where A F S is the full-scale voltage range of the ECG signal, M is the resolution of the LC-ADC and 2 M is the total number of quantization levels. The dynamic range of 10mVpp is used as the full-scale voltage throughout this study, which ensures industry standard of ambulatory equipment  [3]  and matches the dynamic range of the MIT-BIH Arrhythmia dataset  [18]  used to evaluate models in this study. The difference between the upper and lower threshold voltages, ∆v, is usually a fixed integer number of LSBs: ∆v = k * q (2) where k ≥ 1 ∈ Z.
Thus the approach we adapt here is to estimate the CDF (Cumulative Distribution Function) of the ECG slopes and take the 99. 9% level as being a reliable estimate of the typical maximum slope. Consider the vector ⃗ x containing the magnitude of slopes in the entire MIT-BIH arrhythmia dataset. Through a cumulative distributive function, find the value, x o , in ⃗ x, for which the magnitude of slopes in the dataset are less than or equal to x o , 99. 9% of the time. Therefore, x o and corresponding F c can be calculated as: P dECG in dt ≤ x o ≃ 0. 999 (5) ⇒ F c ≥ x o q (6) Fig. 3  shows the cumulative distributive function of magnitude of slopes in the MIT-BIH dataset with x o shown at 99. 9%. Fig. 4  shows the value of F c obtained in this manner as a function of the LC-ADC resolution (M ) obtained by simulation over the entire MIT-BIH Arrhythmia dataset.
C. Evaluation of LC-ADC Models The LC-ADC design parameters and the respective design conditions they must meet are summarized in Fig. 2a. It also presents the LC-ADC evaluation criteria using the two performance metrics, compression ratio (CR) and signal-todistortion ratio (SDR), introduced in Section IIB. These can help designers choose the right LC-ADC model for their application. Fig  2b  shows the LC-ADC evaluation methodology used in this subsection. We converted the 48 recordings in  the MIT-BIH Arrhythmia dataset to event-driven data using the ten (M, Fc, N) value sets summarized in Table  I , then reconstructed the signals to Fc using linear interpolation and evaluated them using CR and SDR. The aim is to select LC-ADC models that maximize the SDR subject to a CR greater than 1. 5.
However, the most promising trade-off between CR and SDR can be observed at M = 7. To study this LC-ADC resolution further, we looked into varying the counter clock resolution (N). The MIT-BIH Arrhythmia dataset is originally sampled at 360Hz using an 11-bit ADC. In order, to sample close to the Fc of 2372. 12 Hz for M= 7 bits, we first upsample the entire database to 38160Hz. From this up-sampled database, we then sample at Fc = 2385Hz, which is 0. 54% greater than the minimum required Fc at this resolution. Then using M= 7 bits and Fc = 2385Hz, we varied N from 4 to 8-bits to search over the curve created with these values (see M=7 bits in Fig. 5 ). Beyond 8-bit counter clock resolution, the sbr starts increasing linearly and therefore, excluded from analysis here. The results are summarized in Table  II.
B. Classification Performance A global training and verification approach is applied for classification of arrhythmias in the MIT-BIH Arrhythmia dataset. The four paced records 102, 104, 107 and 217 are excluded from the analysis as recommended by the Association for the Advancement of Medical Instrumentation (AAMI)  [26]. All the beats from the remaining records are split into 65% training set, 10% validation set, and 25% test set. For a fair comparison, we have also tested the classifier on uniformly-sampled data using the MIT-BIH database as is, named MDL0 from here on. The MIT-BIH database is sampled at 360Hz using an 11-bit ADC. Since time information is implicit in uniformly sampled data, we only use the amplitudes at each sample as input to the 1D-CNN. The input feature vector for the uniform model is of the size 238x1 (features, depth).
D. Open-Source Event-Driven ECG Dataset To the best of our knowledge, there is no publicly available non-uniformly sampled ECG dataset with arrhythmia labels for researching event-driven data processing and classification. Therefore, in this study, we used the popular MIT-BIH arrhythmia database and different LC-ADC models to derive a nonsampled event-driven ECG dataset. The dataset is generated using a MATLAB script and can generate LC-ADC data at different LC-ADC resolutions, clock frequencies and counter clock resolutions. The event-driven ECG dataset with arrhythmia annotations and corresponding MATLAB scripts is open-sourced in authors' website  2  to enable further research on the topic.
Then, a new 1D-CNN model was presented for cardiac arrhythmia classification. The event-driven models and a uniformly sampled model was analyzed for classification performance the Then a thorough accuracy-complexity analysis was It was concluded that a 7-bit LC-ADC at 2385Hz counter clock frequency and a 6-bit counter clock resolution offers the best accuracy-compression-complexity trade off. With classification performance comparable to a Nyquist sampled ADC, this LC-ADC model offers 3x data compression while maintaining the signal-to-distortion ratio of more than 21dB. Furthermore, this model requires only 49% FLOPS for classification in comparison with a uniformly sampled model. Therefore, the new LC-ADC evaluation and design methodology presented in this paper is a good tool for designers to choose an appropriate LC-ADC model for their event-driven applications, which may not be limited to ECG data.
Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI).
Time series of NDVI, SAVI, TSAVI, and EVI calculated from Sentinel-2, Landsat-5, and Landsat-8 surface reflectance products were used to estimate the time series LAI. The latter ones were validated with two LAI data sets, including a time series of MODIS LAI data over the Apiacás area in Brazil and a global LAI dataset. The positive and negative soil adjustment factor performances were then compared based on the correlation coefficients of the linear regression model.
Data Our study uses two data sets: MODIS 4-Day global 500 m LAI product (MCD15A3H V6 level 4, provided by the National Aeronautics and Space Administration Land Processes Distributed Active Archive Center at the U. S. Geological Survey Earth Resources Observation and Science Center which is located outside Sioux Falls, South Dakota, USA) from time-series observation data and the global LAI product global leaf area index from field measurements (GLAIFM) from field measurements. MODIS LAI data were used to study a region, Apiacá, for which VIs were calculated from Sentinel-2 and Landsat-8 surface reflectance. For the global field-measured LAI dataset, the VIs were calculated with Landsat-5 surface reflectance products.
Local MODIS Time Series LAI Data Our study includes the entire Apiacás (Latitude: -7. 35 • --9. 82 • N, Longitude: 57. 04 • -58. 57 • W) in the northernmost of Mato Grosso, Brazil. It was chosen because of its dense vegetation (Figure  1 ). It contains part of the 19,582 square kilometers of Juruena National Park, one of the largest conservation units in Brazil. Its elevation is around 200 m  [25]. global 500 m LAI products (MCD15A3H V6 level 4). They were used to validate LAI estimation from VIs calculated from surface multispectral reflectance datasets computed with Sentinel-2 images from 4 February 2019, to 10 November 2020, and Landsat-8 Tier 1 images from 16 April 2013, to 21 October 2020. The use of multiple sources of data helped us to increase the sampling frequency of the time series.
Because of the similarity of satellite resolution of Sentinel-2 (10 m) and Landsat-8 (30 m), the VIs differences caused by the two sensors were neglected. First, the multispectral images were processed. All available Sentinel-2 and Landsat-8 Tier 1 surface reflectance data in GEE before 10 November 2020, were selected and then filtered by two rules: selecting images that cover the study area and an average cloud cover lower than 20%. Then, each remaining image was clipped to ensure that only the study area's pixels were retained. After that, two boundary masks were used in a preprocessing stage. The first was the cloud mask, and the second was the vegetation mask. GEE provides cloud masks of Sentinel-2 and Landsat-8. Pixels in the cloud mask were removed in the subsequent data processing. The vegetation mask of Sentinel-2 was created through the classification of the NDVI and simple ratio NIR/Green Ratio Vegetation Index (GRVI)  [29]  images using thresholds indicated in a technical report of Sentinel-2  [30] : pixels with (NDVI > 0.
40) or (0. 36 ≤ NDVI ≤ 0. 40 and GRVI > 2. 50) were marked as vegetation pixels. The same rule was implemented to create a Landsat-8 vegetation mask. VIs and reflectance values were calculated pixelwise, and the final VIs and reflectance of each satellite First, the multispectral images were processed. All available Sentinel-2 and Landsat-8 Tier 1 surface reflectance data in GEE before 10 November 2020, were selected and then filtered by two rules: selecting images that cover the study area and an average cloud cover lower than 20%. Then, each remaining image was clipped to ensure that only the study area's pixels were retained. After that, two boundary masks were used in a preprocessing stage. The first was the cloud mask, and the second was the vegetation mask. GEE provides cloud masks of Sentinel-2 and Landsat-8.
Pixels in the cloud mask were removed in the subsequent data processing. The vegetation mask of Sentinel-2 was created through the classification of the NDVI and simple ratio NIR/Green Ratio Vegetation Index (GRVI)  [29]  images using thresholds indicated in a technical report of Sentinel-2  [30] : pixels with (NDVI > 0. 40) or (0. 36 ≤ NDVI ≤ 0. 40 and GRVI > 2. 50) were marked as vegetation pixels. The same rule was implemented to create a Landsat-8 vegetation mask. VIs and reflectance values were calculated pixelwise, and the final VIs and reflectance of each satellite image were derived by aggregating VIs and reflectance calculated per pixel. In the next step, LAI was extracted for each corresponding satellite image. In this work, we used MODIS leaf area index 4-day global 500 m (MCD15A3H V6, https://doi.
org/10. 5067/MODIS/MCD12Q1. 006, accessed on 11 March 2021)  [31]. LAI data were searched for each surface reflectance image within eight days of the acquisition time of the surface reflectance image. If no LAI product was available, LAI data within 16-day intervals was searched instead. If no LAI data was available within 16 days, this particular surface reflectance image was ignored. After getting the LAI data in the time range of satellite images, we extracted pixels within both the study area boundary and reflectance image boundary, and a mean LAI value was derived by aggregating all pixels' LAI inside these two boundaries. Finally, for each multispectral image, we had a mean value of different VIs, a mean value of reflectance in various bands, and a mean value of LAI. Depending on the type and location of sensors, the study area could correspond to several remote sensing images on the same day.
Global Field Measured LAI Data We used global LAI data from the GLAIFM dataset  [32]. This dataset is compiled from around 1000 published estimates of LAI covering the period from 1932 to 2000. These historical LAI data include natural and seminatural (managed) ecosystems and some cultivated vegetation. It contains a wide range of LAI values of 15 biome/land cover classes, from 0. 46-2. 16 for deserts to 4. 4-13. 04 for tree plantations. We downloaded it from the website (https://daac. ornl. gov/cgi-bin/dsviewer. pl?ds_id=584, accessed on 11 March 2021). We extracted the Landsat-5 Tier 1 surface reflectance product for each LAI pixel using the LAI measurement date and geographical information (latitude and longitude). Then, we calculated VIs from these reflectance values. The specific steps are as follows: (1) Data cleaning: data with null values for latitude, longitude, or date of measured LAI were removed.
Also, data with LAI as a range value instead of a specific value were removed; data with LAI measurements spanning over one month were removed. (2) Screening of data corresponding to dense vegetation: data with LAI values less than 4. 0 were removed. (3) Screening of time range: data with a LAI measurement date range outside the Landsat-5 coverage time range were removed. (4) Removing duplicate data: some data had the same measurement time and geographical location with different LAI values. On closer examination, we found that some of these data were from measurements of different biomes. Therefore, these data were also removed as we could not establish a one-to-one relationship between LAI and VIs. (5) Matching LAI with satellite reflectance image: because the temporal accuracy of all measured LAI data is only one month, we searched all surface reflectance data for the month of Landsat-5 Tier 1 data and averaged them to obtain the final surface reflectance data.
Some reflectance data could not be retrieved due to cloud cover, so their corresponding LAI data were removed. Table  1  shows the LAI data and corresponding station information that remained after the above steps. These data were used for evaluating the LAI estimation accuracy of VIs. The LAI value ranges from 4. 06 to 10. 59, and the sites are mainly located in Canada, Japan, and the USA. = N -R N + R (1) where R and N are the atmospherically corrected surface reflectance in the red and NIR bands. Conversely to NDVI, SAVI and TSAVI have a soil adjustment factor designed to mitigate the soil noise by considering multiple soil scattering. Here, this factor is referred to as "X" with the VIs name as a suffix. SAVI is defined as: SAVI = (N -R)(1 + X SAVI ) N + R + X SAVI (2) where X SAVI is the soil adjustment factor of SAVI.