dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors are grateful to  Saudi Wildlife Authority (SWA) officials in Farasan Islands National Park  for the support and facilities provided during the field work. We are indebted to staff in the  King Saud University museum of Arthropods (KSMA ;  King Saud University, Riyadh ) for insect identification. We also would like to thank our colleagues at the  Biology Department (Faculty of Science, Jazan University, Jizan ),  Dr. Remesch Moochikkal  for plant identification,  Dr. Maqbool Thaikkottathil  for photography,  Prof. Salah Abbutalb ,  Dr. Mustafa Massoud ,  Dr. Salama Zeidan , and  Dr. Mohamed Salah  for their support, without whose help in the field this work would never have been possible. We would like to express our appreciation to the  Dean  and  Vice Deans  as well as the  Head of the Department of Biology of the Faculty of Science, Jazan University , for providing all facilities and encouragement for accomplishing this work.
Moreover, an iterative channel estimation and analog beamforming algorithm has been investigated in  [53] -  [56] , where the coefficients at the transmitter and receiver sides are calculated to reach asymptotically singular value decomposition (SVD) elements. Unfortunately, these solutions suffer from hardware limitations due to the constant modulus constraint imposed by the implementation of the phase shifters, hence the signals can only be partially adjusted  [5] ,  [49]. Another drawback of the fully-analog architecture is that it supports only one data stream, which makes its usage for multiuser communications tricky. However, this architecture is still a good fit for some standards such as WirelessHD for instance.
V. CHANNEL ESTIMATION TECHNIQUES FOR HYBRID ARCHITECTURE SYSTEMS For the sake of attaining more precoding gains as well as to be able to precode simultaneously multiple data streams, numerous studies have been proposed to split the precoding process into analog and digital domains  [60] ,  [71]. Moreover, to overcome the limitations of analog-only beamforming mmWave systems, constrained by the analog phase shifters amplitude which has to be constant, and by the potentially low-resolution signal phase control, several works have adopted the joint analog-digital hybrid architecture. Some channel estimation methods with this hybrid architecture have been proposed taking into account the aforementioned constraints. Most of these works assume a frequency-flat narrowband mmWave channel model,  [62] ,  [71] ,  [104] ,  [127] -  [131].
NON-MMWAVE SYSTEMS Under different conditions, some works dealt with the acquisition of channel information with the assumption of low-resolution ADCs but not for mmWave systems and channels. Some of these works include,  [178]  where a joint channel-and-data estimation algorithm for low-precision ADCs system was proposed based on the optimal Bayes estimator, and  [195]  where the bilinear generalized approximate message passing (BiG-AMP) technique was employed. A recent paper  [196]  studied channel estimation for multi-user uplink massive MIMO systems with 1-bit spatial sigma-delta ADCs. The authors explain the benefits of using the sigmadelta ADCs in reducing the noise on the signal, where it was shown through simulation results that the sigma-delta modulation reduces the quantization error of the low-resolution ADCs.
OTHER ASPECTS OF FEW-BIT ADCS SYSTEMS Many papers studied the effects of low-resolution ADCs on mmWave systems for different aspects rather than channel estimation, including achievable throughput, achievable capacity, beamforming schemes and types, spectral and energy efficiency, codebook designs, and many more. One of these works is  [198] , where the authors studied the effects of low-resolution ADCs in two cases. First, they showed that the capacity of single-user SISO systems can be achieved by combining least-squares channel estimation with joint pilot and data symbols detection. Second, they showed that the use of maximum ratio combining jointly with least-squares channel estimation is adequate for the use of high-order constellation (example 16-QAM) in the case of multi-user uplink massive MIMO system.
• Graphical models: The channel estimation problem can be reformulated such as it can be represented in factor graph models which allows to employ the message passing algorithms as a powerful solution. This approach was exploited in the literature especially for the systems with few-bit ADCs where the GAMP is mainly used. It is promising to extend this effort to the other mmWave system architectures, and to other variations of message passing algorithms such as expectation-propagation algorithms and belief-propagation algorithms and their different approximations. • Machine learning: It is not complicated to collect a large amount of wireless communications data which makes worth investigating if machine learning tools are capable to inspect the structure of the received signal and hence to estimate the mmWave channel.
The disadvantage though is that it focuses only on specific risks and market segments and so does not allow for assessment of how macroprudential policies affect the stability of the financial system as a whole, which is the main objective of the macroprudential regulation. For instance,  Jiménez et al. [2017]  find that dynamic provisioning rules are useful in smoothing credit supply cycles for Spain. More importantly, they find evidence that such a countercyclical macroprudential policy can help to mitigate credit crunches during downturns by upholding the availability of credit to firms and the performance of firms during recessions. Using a large panel dataset of banks around the world,  Claessens et al. [2013]  assess how different macroprudential instruments affect the growth in leverage, asset and non-core to core liabilities.
, 2018; Carreras et al. , 2018). Akinci and Olmstead-Rumsey [2018]  use the IMF Global Macroprudential Policy Instruments (GMPI) survey and national sources to analyse how macroprudential policies evolve in 57 advanced and emerging economies over the period 2000:Q1-2013:Q4 looking at whether the prudential tools considered in the dataset were tightened or loosened in each given quarter. They produce a cumulative macroprudential policy stance indicator that shows the sum of tightenings net of easings since 2000 for each country, and they find that tightening is associated with lower growth in bank credit, housing credit, and house prices. Their findings also suggest that borrower-targeted macroprudential instruments tend to be more effective at curbing credit growth. Similar results are obtained by  Zhang and Zoli [2016]  for a sample of Asian economies.
A cointegration framework is used by  Carreras et al. [2018]  to reinvestigate this issue for the OECD countries. They use the database compiled by  Cerutti et al. [2017b]  and consider both the cumulative and quarter-by-quarter changes in macroprudential policy measures. Like  Akinci and Olmstead-Rumsey [2018] , they find a negative and significant relationship between the cumulative changes in macroprudential tools and the quarterly growth rate of housing prices and household credit. However, the relationship appears not to be statistically significant when they consider the quarterby-quarter changes. As argued by  Akinci and Olmstead-Rumsey [2018] , this result could arise because macroprudential policies may be delayed in their effect. Carreras et al. [2018]  also consider the stance of each macroprudential tool individually.
Furthermore, in contrast to the existing literature, our empirical analysis considers an extensive set of prudential tools to capture the overall stance of macroprudential policy. In this way, our paper fills a gap in the literature and provides the first formal answer to an extensive academic and policy debate. 3 Measuring the Stance of Macroprudential and Monetary Policies 3. 1 Measuring the stance of macroprudential policy. To analyse how effective macroprudential instruments are at curbing the credit cycle, we first need to assess the overall macroprudential policy stance. Efforts have been made recently in the academic literature to develop datasets that capture the use of macroprudential policies in a large sample of emerging and industrialised economies. Two types of dataset can be distinguished.
, 2017b; Akinci and Olmstead-Rumsey, 2018; Alam et al. , 2019). The main objective of these datasets is to use information on easing and tightening of different macroprudential policy instruments to reflect the policy direction. In this paper, we use the database provided by  Cerutti et al. [2017b] , which is one of the most comprehensive datasets on macroprudential policy actions. Using the same survey as  Cerutti et al. [2017a] ,  Cerutti et al. [2017b]  consider five types of prudential instrument across a sample of 64 countries over the period 2000Q1-2014Q4. The five types of instrument are capital buffers, interbank exposure limits, concentration limits, loan-to-value ratio limits, and reserve requirements. More precisely, capital buffers are divided into four sub-indexes: general capital requirements, specific capital buffers related to real estate credit, specific capital buffers related to consumer credit, and other specific capital buffers.
It can take three different values: -1, 0, +1. Formally, P ruC is defined as follows: P ruC i,t =            +1 if a x a,i,t > 0 0 if a x a,i,t = 0 -1 if a x a,i,t < 0 (1) where subscripts i and t refer respectively to country and time period, while the subscript a represents a given macroprudential instrument from among the nine recorded in the database. It is important though to note that the number of instruments considered can vary across countries depending on which instruments have or have not been adopted. As mentioned above, the absence of legislation authorising the use of a particular macroprudential instrument by policymakers is coded in the database as missing. x a,i,t reflects the orientation of instrument a in country i at time t. More precisely, for each instrument, it corresponds to the difference between the number of tightening actions and the number of easing actions.
In line with  Bogdanova and Hofmann [2012]  and using historical time series for each country in our sample, we estimate the following reaction function: i t = ρ i t-1 + (1 -ρ)[α + β π (π t ) + β y (y t -ȳt )] + ε t (7) where i t is the actual short-term policy rate of a given country, which is lagged one period on the right side of Equation (  7 ) to capture interest rate smoothing. As in the original Taylor rule, this assumes a gradual adjustment of policy rates to their benchmark level. π t is the contemporaneous inflation rate, y t -ȳ is the output gap, and ε t is the error term. 4 A positive relationship could be expected between the inflation rate, the output gap, and the policy rate, meaning β π > 0 and β y > 0. The central bank policy rates are taken from the database provided by the Bank for International Settlements (BIS).
As these data are collected on a monthly basis, we consider the end-of-quarter rates. The annual inflation rate comes from the International Monetary Fund's International Financial Statistics (IFS) database. Real GDP is taken from the OECD Statistics for the OECD countries, and from the IFS for the others. The inflation and GDP data series are seasonally adjusted using the US Census Bureau X-11-ARIMA method. Finally, the output gap corresponds to the difference between actual real GDP and its trend, computed using the traditional Hodrick-Prescott filter. Following  Clarida et al. [2000]  and the related literature, we estimate Equation (  7 ) using the Generalised Method of Moments (GMM) estimator to overcome the issue of potential endogeneity. Furthermore, the time period for estimating Equation (  7 ) covers the longest available data time span so as to give consistent estimates, and then differs across the countries in our sample.
Econometric approach Our empirical analysis proceeds in two steps. First we follow the existing literature by reinvestigating how our different measures of the macroprudential policy stance affect credit growth. To this end we consider two alternative measures of domestic credit, which are total credit to the private non-financial sector from banks and total credit to households and non-profit institutions serving households. These data are taken from the BIS. The baseline model that we estimate is the following: ∆Credit i,t = α + 4 k=1 β k M aP i,t-k + η X i,t-1 + θ Crisis t + µ i + i,t (8) where ∆Credit i,t is the yearly growth of our different measures of credit, and M aP i,t-k corresponds to our alternative macroprudential policy stance indexes, for which we include four lags (see, e. g.
,  Kuttner and Shim, 2016; Zhang and Tressel, 2017) , as some macroprudential actions may be delayed in their effect. X i,t-1 represents the vector of control variables. Following the existing literature, we consider two control variables, which are the annual GDP growth rate and the change in the nominal monetary policy rate. These two variables are lagged one period and are taken from the IFS database and the BIS respectively. A negative relationship should be expected between the change in policy rate and the growth of credit, while higher GDP growth should be associated with higher credit growth. The GDP growth rate is included to control for the procyclicality of credit  (Athanasoglou et al. , 2014) , and this then allows us to capture the part of credit that is not driven by real economic activity, which is excess credit growth.
Acknowledgments We would like to thank  Lawrence Christiano ,  Sebastian Edwards ,  Paul Hubert ,  Michele Lenza ,  Grégory Levieuge  and  Patrick Villieu  for their comments and suggestions. We also thank the participants of the 5th HenU/INFER Workshop on Applied Macroeconomics, the 21st  INFER Annual Conference , the 5th  International Conference on Applied Theory, Macro and Empirical Finance , the  International Conference TIMTED 2019 , the 23rd International Conference on Macroeconomic Analysis and  International Finance , and of the internal seminar at the  Bank of Lithuania  for their useful feedbacks.
To be enrolled, patients had to be at a high cardiovascular risk. If patients were ≥65 years old, no additional inclusion criteria were needed. Patients <65 years old required the presence of atherosclerosis involving at least two vascular beds or two or more additional risk factors: current smoker, diabetes, moderate chronic renal insufficiency, heart failure or non-lacunar ischaemic stroke (≥1 month previously; Figure  6 ). • CrCl ≤50 ml/min Patients were randomised in a 1:1:1 ratio to receive one of the following three regimens: • Rivaroxaban 2. 5 mg twice daily + aspirin 100 mg once daily, long term. • Rivaroxaban 5 mg twice daily, long term. • Aspirin 100 mg once daily (control). The study was prematurely stopped by the data safety monitoring board following interim analysis that showed a significant improvement in the primary composite endpoint, as well as all-cause death.
, 1993). Bursts of non-Io decametric radio emission have also been observed to occur contemporaneously with significant brightening of the X-ray aurora  (Dunn et al. , 2016). Through February and March 2007, NASA's New Horizons spacecraft was approaching Jupiter. At this time, a series of HST, Chandra, and XMM-Newton observations of Jupiter were conducted, while radio observations by Wind/Waves and the NDA (Nançay Decameter Array) were ongoing. The combination of these campaigns provides a rich multiwaveband data set. In this paper, we utilize these data to explore links between Jovian X-ray emissions, other aurora wavebands, and the solar wind. This is the second in a series of papers that include the Jovian X-ray data from 2007. The first paper  (Dunn et al. , 2020)  reported general trends in the equatorial and auroral X-ray emissions during solar minimum.
(e) New Horizons SWAP solar wind peak velocity upstream of Jupiter. mSWiM propagations from 1 AU of solar wind velocity (f), density (g), and mSWiM dynamic pressure driven  Joy et al. (2002)  model magnetopause stand-off distances (h). f, g, and h have been shifted so that the velocity discontinuity E2 in the SWAP and mSWiM velocities is aligned (vertical green arrow) and so that the arrival of shock ABC occurs a solar rotation later than observed by New Horizons at A3B3C3, which coincides with the UV auroral morphology change. As shown by  Nichols et al. (2009b)  and through auroral comparisons with New Horizons in situ data, the shift in the mSWiM data is not constant but varies for CIR ABC versus CIR DEF. We interpret the X-ray data assuming different shifts for each CIR. Unshifted data are shown in the supporting information.
Radio Observation Analysis Using the catalogue produced by  Marques et al. (2017)  and by surveying the Wind/Waves (1-15 MHz) measurements, we collated the non-Io decametric emissions from January to March 2007 (Table  2 ). We filtered out the DAM arcs produced by the Io-Jupiter interaction through ExPRES simulations (detailed in  Hess et al. , 2008; Louis et al. , 2017 Louis et al. , , 2019)). The Wind/Waves spectrograms and ExPRES simulations for Table 2 can be found in the supporting information. We disregarded the WIND/Waves data between 9. 5 and 15 MHz due to extensive radio frequency interference bands. Figure  4  shows an example of an interval that shows both types of emission on 27 January 2007. Figure  4b  shows that between 07:00-08:00 (DOY 27 2007) and 02:00-03:00 (DOY 28 2007), the decametric arcs observed at less than 9 MHz by Wind/Waves are a good fit to the simulated vertex early and vertex late Io arcs shown in Figure  4a.
ACKNOWLEDGMENTS We acknowledge all students who have contributed to EIRSAT-1. The EIRSAT-1 project is carried out with the support of the  Education Office of the European Space Agency  under the educational Fly Your Satellite!programme. SW acknowledges support from the  European Space Agency  under PRODEX contract number 4000120713. DM acknowledges support from the  Irish Research Council (IRC)  under grant  GOIPG/2014/453. JT acknowledges support from the  IRC  under grant  GOIPG/2014/684. MD acknowledges support from the  IRC  under grant  GOIP/2018/2564. JE, JR and RD acknowledges scholarships from the  UCD School of Physics. CO'T acknowledges support from the  IRC  under grant  GOIPG/2017/1031. LS acknowledges support from the  IRC  under grant  GOIPG/2017/1525. JM and AU acknowledge support from  Science Foundation Ireland  under grant number  17/CDA/4723.
Survey Design We conducted large online surveys in five countries (France, Germany, Italy, the United Kingdom, and the United States) to measure the acceptability of app-based contact tracing for COVID-19 before apps were introduced in any of the five countries. A complete description of the survey can be found in the Multimedia Appendix 1; here, we provide an overview. At the beginning, after collecting respondents' informed consent, we described the app, explaining how a general version would function as well as the purpose it would serve (Textbox 1). We abstracted from any details about centralized versus decentralized data storage procedures. Respondents had to pass a comprehension check to proceed further. We then asked respondents how likely they would be to install the app on their phone, if it became available to download voluntarily ("opt-in" installation policy).
Implications The lack of trust in government can have far-reaching implications. Our analysis shows that this factor has a negative effect on people's intention to install a contact-tracing app on their phones. Furthermore, supplementary analysis (Section C. 6 in Multimedia Appendix 1) also shows that people with lower trust in government are more in favor of an opt-in installation policy than an opt-out regime where the government asks mobile phone providers to automatically install the app on all phones. An opt-out regime is likely to translate into higher effective installation rates, for instance, by reducing the negative effects of procrastination or unawareness  [15]. However, our data suggests that only governments that enjoy a relatively high level of trust from their citizens may be able to resort to more paternalistic approaches.
Limitations Our study has some limitations that we tried to address in different ways. First, respondents recruited online may not be representative of the entire population. In particular, digital literacy and willingness to share data could be higher among such respondents. To ensure that our results do not hinge on our specific sample, we replicated an abridged version of the German survey with a different panel provider that randomly recruits its participants offline. Our results remain almost completely unchanged (Section B. 3 in Multimedia Appendix 1). Second, our survey asked hypothetical questions about future behavior. However, high levels of intended installations may not directly translate into actual installations. While studies often find good correlation between what people declare they would do in surveys and actual behavior  [18] [19] [20] [21] [22] , even in relation to app installations  [23] [24] [25] [26] , many things will have changed between the time of the surveys and when countries eventually introduce the app.
We present the activities and improvements of the International Geodynamics and Earth Tide Service (IGETS) over the last four years. IGETS collects, archives and distributes long time series from geodynamic sensor, in particular superconducting gravimeter data currently from more than 40 stations and 60 different sensors. In addition to the raw 1s and 1-min gravity and atmospheric pressure data (Level 1), IGETS produces end-user products on different levels. These include gravity and atmospheric pressure data corrected for major instrumental perturbations and ready for tidal analysis (Level 2). Since 2019, IGETS provides gravity residuals corrected for most geophysical contributions (Level 3) which can be used directly for geophysical applications without any expert knowledge in the processing of gravimetric time series.
Introduction The International Geodynamics and Earth Tide Service (IGETS) was established at the 2015 IUGG meeting in Prague as an official service of the IAG within the IGFS. Its main objective is to provide a service to monitor temporal variations of the Earth gravity field through long-term records from ground gravimeters, including superconducting gravimeters (SG) and other geodynamic sensors, such as tiltmeters, extensometers, etc. (Voigt et al. 2016). IGETS continues the activities of the Global Geodynamic Project  (Crossley and Hinderer 2009)  to provide support to geodetic and geophysical research activities using SG data within the context of an international network. IGETS also continues the activities of the International Center for Earth Tides, in particular, in collecting, archiving and distributing Earth tide records from long series of gravimeters, and other geodynamic sensors.
We present here the status and progress of IGETS and its different products after four years of existence for the support of geophysical research activities. We first document the observation network, with 42 existing stations equipped by 60 different instruments, including seven double-sphere SGs. We then describe the three levels of data products, from the raw 1-s or 1-min gravity and atmospheric pressure records (Level 1), to the pre-processed datasets ready for tidal analysis (Level 2) and the gravity residuals corrected for major geophysical contributions (Level 3).