dataesr/scientific-paragraphs-categorization huggingface dataset:
A. Appendix: Synchronisation Between RX and TX, SDR-based Servoloops, and Other Optimizations For direct analysis of the transmission heterodyne signal the SDR and GNURadio-based platform is very well suited as, in addition to saving data for post processing, it allows direct access to the multiple channels of information. However, it should be mentioned, that with no special care taken, the time offset and thus relative phase between the two RX channels is random (although constant over a given run of the program). In order to provide a minimal and reproducible phase offset between the two inputs of the SDR platform, it is necessary to synchronize their DDC chains. This is done simply in software at the beginning of the program, by using timed commands to set the two input channels' DDC chain parameters simultaneously.
The corresponding Python code is: now = s e l f. USRP_RX. g e t _ t i m e _ n o w ( ) cmd_time = now + uhd. t i m e _ s p e c ( 1 ) s e l f. USRP_RX. s e t _ c o m m a n d _ t i m e ( cmd_time ) s e l f. USRP_RX. s e t _ c e n t e r _ f r e q ( f r e q , 0 ) s e l f. USRP_RX. s e t _ c e n t e r _ f r e q ( f r e q , 1 ) s e l f. USRP_RX. c l e a r _ c o m m a n d _ t i m e ( ) where the USRP_RX object is instantiated in the flowgraph by a "UHD: USRP Source" block and freq is the demodulation frequency of the DDC chain of the Ettus X310. Similarly, in closed-loop implementations where the error signal that is produced by the multichannel heterodyning is used to generate a correction signal to be emitted by one of the TX channels, it is desirable to minimize the time of propagation of the signal between the RX and TX channels.
Furthermore, making this propagation time repeatable is also desirable as otherwise the in-loop servo parameters would need to be fine tuned at each run of the program for optimal performance (defined in particular as maximal gain and bandwidth while maintaining servo-loop stability). The GNURadio framework is not particularly well suited for this application as it is largely designed to optimize throughput, not latency. However, it is possible to implement a closed-loop application with acceptable performance, at least for testing and prototyping, provided special care is taken to force the scheduler to accommodate this non-standard requirement. By default, the GNURadio implementation we use (version 3. 8. 4. 0 compiled from the GitHub sources) sets a 100 ms delay time between the start of the RX digitization and that of the TX generation.
Note that it is possible to set the delay_time parameter to zero, by providing an extra delay block of fixed delay value in the flowgraph. This somewhat surprising behavior is related to specifics of the GNURadio framework and delay block programming, and may not necessarily be expected to be true for all versions. Once this is done, it then becomes useful to optimize the working of the flowgraph to allow decreasing the latency as much as available computing power allows. We use a computer equipped with an Intel Xeon W-2275 multi-core processor. We found it beneficial to de-activate hyperthreading and force the operating clock frequency of all cores to the maximum availability (4. 0 GHz in our case, using the Linux cpu-freq-set package). Additionally, using the isol-cpu Linux package, we isolate 12 out of the 14 cores, making them available only to the GNURadio blocks involved in the closed loop part of the flowgraph.
We have also found it beneficial to assign each GNURadio block within the servo-loop signal path to a specific processor core (whereas in normal operation, the Linux scheduler would be free to assign and re-assign dynamically the blocks' threads to the various cores at run-time). Utilizing this approach, with careful optimization of core use by each GNURadio block, we are able to achieve a 100 Hz feedback bandwidth with our complete multichannel flowgraph (10 optical frequency channels), consistent with the minimal value of 2 ms of the delay_time without significant package loss or late arrival. Future work in our experiment involves the implementation of a custom purpose-built FPGA system, in place of the GNU Radio/SDR system presented here, in order to reduce signal latency. We will soon implement this change in our setup where we expect the latency reduction to translate into a higher locking bandwidth in our spectral-hole-based ultra-stable laser.
Funding. The project has received financial support from Ville de Paris Emergence Program, the Région Ile de France DIM C'nano and SIRTEQ, the LABEX Cluster of Excellence FIRST-TF (ANR-10-LABX-48-01) within the Program "Investissement d'Avenir" operated by the French National Research Agency (ANR), and the 15SIB03 OC18 and 20FUN08 NEXTLASERS projects from the EMPIR program cofinanced by the Participating States and from the European Union's Horizon 2020 research and innovation program, and the UltraStabLaserViaSHB (GAP-101068547) from Marie Skłodowska-Curie Actions (HORIZON-TMA-MSCA-PF-EF) from the European Commission Horizon Europe Framework Programme (HORIZON).
URock 2023a is an open source diagnostic model dedicated to wind field calculation in urban setting. It is based on a quick method initially proposed by Röckle and already implemented in the proprietary software QUIC-URB. First, the model method is described as well as its implementation in the free and open source geographic information system called QGIS. Then it is evaluated against wind tunnel measurements and QUIC-URB simulations for four different building settings plus one case with an isolated tree. The correlation between URock and QUIC-URB is high and URock reproduces quite well the spatial variations of the wind speed observed in the wind tunnel experiments. Sources of improvements are highlighted, which are applicable both for URock and QUIC-URB. URock 2023a is available via the Urban Multiscale Environment Predictor (UMEP), a city-based climate service tool designed for researchers and service providers presented as a plugin for QGIS.
8 is met. E = nx i=1 ny j=1 nz k=1 |λ t+1 i,j,k -λ t i,j,k | < ϵ (8) where ϵ the threshold value to stop iterations (default 0. 0001) Last, the wind velocity field is updated using the final λ values (Eq. 9). Note that the wind speed orthogonal to the boundary of a solid cell should be zero ( ∂λ ∂n ) and at the inflow/outflow boundary, the initial wind profile should not be modified (λ = 0). u = u 0 + 1 2•α 2 1 • ∂λ ∂x v = v 0 + 1 2•α 2 1 • ∂λ ∂y w = w 0 + 1 2•α 2 2 • ∂λ ∂z (9) 3 Model implementation Currently, URock 2023a is openly available as a QGIS plugin in the Zenodo repository https://zenodo. org/record/7681245 (the tool development is currently performed on GitHub at https://github. com/j3r3m1/urock_processing and will be soon continue at https://github. com/UMEP-dev). It is mainly coded in Python and can be used as a standalone python library.
Most of the spatial analysis is performed using the H2GIS spatial database  (Bocher et al. , 2015). The wind solver is based on the Numba Python library to boost the calculations. In QGIS, the following minimal informations are needed: geographical informations: one GIS layer for buildings or one for vegetation, with at least a single attribute for roof or crown top height from ground respectively, wind conditions: wind speed and direction at a given height or a wind direction and a file containing a wind profile (csv file with height as first column, wind speed as second column), cell size: the vertical and the horizontal resolution used for the wind solver, output height: one or several height for which the wind field is needed. As output, URock 2023a can save the 3D wind field in a NetCDF file or wind information along one or several planes at a height defined by the user in two formats: a raster file containing the absolute wind speed or a vector file containing horizontal wind speed, vertical wind speed, absolute wind speed and wind direction.
Soon, URock 2023a will be integrated within the QGIS plugin called UMEP. Like any UMEP processor, URock comes with its own preprocessor called urock_prepare and its own postprocessor called urock_analyser (cf. workflow Fig. 16 ). The first is useful if the user has the building footprint (or vegetation) but without height attribute. If he has a Digital Surface Model (for building or vegetation) and a Digital Elevation Model, he can use urock_prepare to generate the building and vegetation file in the right format. The postprocessor is used once URock 2023a has been run and a NetCDF file saved to plot a section view of the wind along a line or a vertical wind profile averaging the wind within a polygon. These two modules are already available on GitHub (at https://github. com/j3r3m1/urock_prepare and https://github.
Computation time For each of the AIJ cases simulated using the URock model, the number of cells used for the calculation and the computation time are given Tab. 2. The calculations have been performed using a single processor (frequency of 2. 3 GHz) of a personal computer. The installed Random Access Memory of the computer is 16 GB. Note that the time presented also account for file loading (spatial information and wind conditions), initializing connection with the database used for spatial calculation and writing output files.
In this configuration where the street canyon concept is not quite applicable (due to a very limited street canyon length), the wind flow should be modified in order not to have a brutal change of wind direction. Wind tunnel experiments where the effect of length of the street canyon is investigated could be a good dataset for model improvements. In the first three cases (section 4. 3, 4. 4 and 4. 5), the agreement between the URock field and the wind tunnel data is quite good. Most of the differences observed might be attributed to the high wind speed values located in an along-wind ellipse starting from the upwind corner of the building. This zone is not defined as a Röckle zone while decreasing its wind speed at the initialization stage could solve most of the problems thanks to the mass-balance process: reduction of the final wind speed in this zone (Fig.
Acknowledgements. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  896069. We would like to thank the  Architectural Institute of Japan  for the data availability of their wind tunnel experiments. Thank you to  Yoshihide Tominaga  for his answers concerning the datasets. We would also like to thank the  Los Alamos laboratory  to allow us freely using.
Code and data availability. The comparison between model outputs (URock, QUIC-URB) and observation (AIJ wind-tunnel experiments) can be partially reproduced. The QUIC-URB model being a proprietary software, only its output wind fields can be shared. The corresponding files are permanently available on Zenodo at https://zenodo. org/record/7681245 along with the spatial data for each AIJ case (A, B, C, E and G), the URock 2023a software and all scripts needed for running the AIJ cases and comparing QUIC-URB, URock and AIJ wind fields. More information about the step by step procedure to reproduce the results can be found in the Readme file of the Zenodo repository.
Facebook Gaming is a live stream platform whose interaction with the audience is performed through the streamer's own page. In terms of numbers, according to the website cloutboost. com Cloutboost (2022), in 2021, Twitch captured more than 70 % of the game streaming market with a total of 5. 79 billion total hours watched, while Facebook Gaming captured 15. 7 % with a total of 1. 29 billion total hours watched, and YouTube Gaming ranked third with 13. 8 % with a total of 1. 13 billion total hours watched. These data highlight the relevance of Twitch to the world of video games. This relevance is also because Twitch was born as a streaming platform, while YouTube started as a pre-recorded video platform that later incorporated live streams. Facebook Gaming is a division created from the Facebook social network.
To date, very few studies have addressed Twitch's influence on the performance of video game companies. Johnson and Woodcock (2019)  analyzed the importance of twitch in the video game industry. They conclude that live streaming is a new force in the videogame industry, creating new links between developers and influencers that change our expectations of gameplay and game design. Oh and Kim (2022)  analyze how live-streaming viewership influences video game consumption. Specifically, they analyzed data from Twitch and Steam, the leading digital marketplace for video games. They found that the number of viewers during the prerelease of a new game is positively associated with the game's initial user base; that is, it contributes to the initial success of the new game. In addition, it is related to an increase in the likelihood of replaying the game during the decline stage of the product life cycle  (Oh and Kim, 2022).
Panel data fsQCA To analyze the joint effect exerted by Twitch and sustainability on the stock returns of video game companies, fsQCA was used. The fsQCA is a qualitative analysis method in which both the conditions and the outcome have a degree of membership in the set they represent, which is determined through calibration  (Schmitt et al. 2017). Calibration is the first of the four steps to be carried out (Fig. 1 ): calibration, construction of the truth table, establishment of the consistency cut-off that allows us to distinguish the causal combinations that are part of the outcome from those that are not, and generation of the solutions (complex, parsimonious, and intermediate)  (Ragin, 2008). Usually, fsQCA uses data that do not incorporate temporal effects; however, recently, some fsQCA analyses have been carried out to incorporate these effects  (Guedes et al, 2016; Beynon et al, 2020).
Sample and data Two types of data were used for the analysis: financial data from companies and data from Twitch. Financial data were collected from several websites that provide financial data, namely Marketwatch (2021), MorningStar (2021),  Macrotrends (2021) , Ycharts (2021), and GuruFocus (2021). Twitch data were collected from the SullyGnome. com database (SullyGnome, 2021), which is a statistics and analytics service for Twitch that uses the Twitch API to collect information. As the aim is to analyze whether Twitch's influence on companies has changed during COVID-19, data were collected for the years 2018, 2019, and 2020, obtaining a three-year panel database and sorting according to the different videogame developers or distributor companies. The following procedure was used to select companies included in the sample: First, we searched for videogames that were among the 100 most-viewed categories 1  of each year.
Definition of the outcome and causal conditions The outcome was the annual return of company i (RET). This variable indicates the annual return obtained by the company in the stock market, considering the distribution of dividends. These data were obtained directly from MorningStar. com. The interpretation of the outcome, given that all companies have returns, is that the presence of the outcome means that the annual return is high, and the absence means that it is low. The causal conditions used were VIECH, SG, ESG, ESGS, ESGM, ESGG, and PER. VIECH is the average number of viewers per channel for all video games belonging to company i. To calculate this, the annual number of viewers per channel of each video game belonging to the company was taken, and the annual average was calculated. SG is the number of streamers per videogame that streamed a company's set of videogames i.
For example, if PER is present in a causal combination, it is interpreted as high. The outcomes and conditions are continuous variables; therefore, they must be calibrated. If possible, the calibration criteria should be based on the theory and structure of the dataset. In the absence of previous research, empirical calibration using percentile divisions of the sample is recommended  (Crilly, 2010). In this case, the 10th and 90th percentiles were established as thresholds for full non-membership and full membership, respectively, while the 50th percentile was used as the crossover point, as described by  Miranda et al. (2018)   2021 ), among others. Calibration was performed using the QCA package in R software  (Thiem and Dusa, 2013). Table  2  shows the descriptive statistics for each condition and for the outcome, both on calibrated and uncalibrated scores and the calibration values.
This study has certain limitations. First, the gaming sector is an industry that has many companies, mostly small-and medium-sized and indie, which makes it very complicated to collect information. Therefore, the sample for the analysis was reduced to companies that had their games among the top 100 most-viewed games on Twitch each year and listed on the stock exchange to access the information. Another limitation, derived from a previous study, is the limited number of cases to be analyzed. To mitigate this limitation, the fsQCA methodology was used, which allows analysis with a reduced number of observations. This is the first step that opens up many future research directions. The next step could be to analyze a larger number of years or perform a quantitative analysis using daily data and include another series of variables that may be of interest, such as Tobin's Q or performance.
Episodic failures of ice-dammed lakes have produced some of the largest floods in history, with disastrous consequences for communities in high mountains  [1] [2] [3] [4] [5] [6] [7]. Yet, estimating changes in the activity of ice-dam failures through time remains controversial because of inconsistent regional flood databases. Here, by collating 1,569 ice-dam failures in six major mountain regions, we systematically assess trends in peak discharge, volume, annual timing and source elevation between 1900 and 2021. We show that extreme peak flows and volumes (10 per cent highest) have declined by about an order of magnitude over this period in five of the six regions, whereas median flood discharges have fallen less or have remained unchanged. Ice-dam floods worldwide today originate at higher elevations and happen about six weeks earlier in the year than in 1900.
Article High Mountain Asia, the Andes, Iceland, Scandinavia and the European Alps (Fig. 1 ). In reviewing 446 sources of information, we found a total of 1,569 dated outbursts from 186 ice-dammed lakes in the period 1900-2021 (Methods). In 64% of all cases, we had access to primary sources such as original research papers, official documentation from local agencies, reports in newspapers and social media platforms, and written correspondence with local eyewitnesses and experts. Secondary sources (36%) include review articles or data in previously published databases, for which we could not access the primary source directly. For each GLOF, we recorded the name of the source lake and its parent glacier; the year and, if available, the month and day of occurrence; the elevation of the lake surface; and any previously gauged or estimated values of outburst peak discharge Q p and flood volume V 0.
Increasing elevation of ice-dam failures Our database further shows that outbursts from ice-dammed lakes originated at progressively higher elevations over the past 12 decades (Fig. 4 ). The largest elevation shifts are in Scandinavia, Iceland and the Andes, where new sources of GLOFs moved 25-50 m higher each decade on average. Outlet glaciers in these regions retreated rapidly in past decades, eliminating dams at their toes  25, 30, 31. GLOFs in the European Alps had no credible change in elevation during our study period, although many large dams formed at lower elevations before 1900, when glaciers descended down to major valleys 2. These dams disappeared with warming and glacier retreat after the mid-nineteenth century  2. In contrast, High Mountain Asia has had more than 100 valley-blocking glacier advances to lower elevations in the past three decades  3, 32  , explaining the unchanged elevation trend of ice-dam failures in that region.
Implications for GLOF hazard In our study regions, >70% of all reported GLOFs since 1900 originated from ice-dammed lakes  24. However, their present-day number and sizes are small compared with meltwater lakes dammed by moraines or bedrock. In northwest North America, ice-dammed lakes account only for 6% of the total lake abundance  18  , and in High Mountain Asia, as few as ~0. 06% of total lake area is impounded by glaciers  42. However, given their high outburst activity, we infer that ice-dammed lakes are among the most hazardous of all meltwater lakes. Still, catastrophic damage and fatalities remain a rare consequence of ice-dammed failures: only 10% of all floods in our database have documented socio-economic impacts, mostly involving destroyed bridges, pipelines, roads and campgrounds buried in debris, or loss of farmland and cattle affecting local food supply.
We find that 90% of all outburst floods in our database originated below the elevation band where most glacier mass is stored today (Fig. 4a ). Elevations of historical ice-dam failures are largely within glacier ablation areas, where glaciers melt fastest and temporarily trap excess meltwater at their margins. This zone has been rapidly shifting upwards under ongoing atmospheric warming. In the European Alps and the Himalayas, for example, glaciers have been melting throughout their entire elevation range in the twenty-first century  45, 46  , and ice-dammed lakes are now forming even in the highest parts of ice-covered catchments (Fig. 4a  and Extended Data Fig. 8 ). Progressive retreat at the glacier terminus may thus push the accommodation space for new lakes towards the natural limit set by the remaining glacier mass close to the drainage divide.
Integrating newly detected and reported cases into a single database is thus key to quantifying and predicting climate-driven changes in GLOF magnitude, frequency and hazard  24. In summary, our analysis reveals a strong decay in the highest GLOF magnitudes and discharges, whereas GLOFs of median size have changed little in the past 12 decades. Given their steep hydrographs, floods caused by ice-dam failures will remain discharge extremes, even during glacier retreat. How and where the remaining glaciers will form We mapped lake areas (polygons) from satellite imagery immediately before the outburst using QGIS V3. 22. 6. Colours show the year of the GLOFs. Blue bubbles show the average percent change in lake area between the first and last reported GLOF for each lake. Orange bubbles show the mean annual elevation change of the glacier dam.
Black horizontal scale bar is 1 km in all panels in c. Data on lake and glacier elevation change are available at ref. 27. All background imagery are from 2019 Planet Labs Inc. , obtained under a basic license in the Education and Research programme. We generated all maps in c using QGIS V3. 22. 6. dams in the future will determine the extent to which GLOFs will feed the sediment cascade  12  , disturb ecological and geochemical cycles in mountain rivers  51, 52  , and locally inundate floodplains and terraces  7. To anticipate these consequences, future work will be required to refine the few available physical models that link glacier retreat and lake formation with the size and timing of outbursts  10, [34] [35] [36]. Recently released high-resolution data on elevation, glacier change, ice-flow velocities and climate will be key to understanding better the nexus between meltwater accumulation and hazard.