dataesr/scientific-paragraphs-categorization huggingface dataset:
Environmental and landscape variables We used environmental and landscape variables of ecological relevance for the studied organisms  (Cusimano, Massa & Morganti, 2016; Threlfall, Law & Banks, 2012b). We obtained climatic predictors including night temperature, precipitation levels and wind speed for Zurich (Meteo Schweiz database), Antwerp and Paris (Weather Underground, 2018). Landscape variables were calculated with different buffer radii from the focal sampling point: 50, 100, 350 and 500 m, in order to suit the dispersal abilities of insects  (Ropars, Dajoz, Fontaine, Muratet & Geslin, 2019)  and bats  (Hutson et al. , 2001). For each sampling site, we used the European Urban Atlas (EEA, 2012) for calculating the size (area of each patch), the Proximity Index, the edgeto-edge distance to the nearest UGA and the proportion of impervious surfaces (e.
g. urban fabric, roads). We estimated the distance to the nearest water body following  Krauel and LeBuhn (2016)  and  Price and Baker (2016). The land-use heterogeneity  (Matthies, R€ uter, Schaarschmidt & Prasse, 2017; McIntyre et al. , 2001)  was calculated as the Shannon Index of habitats per site, using the European Urban Atlas for the three cities and in Zurich also using a high-resolution land-cover map (Gruen Stadt Zuerich, 2010) (see Table  1 ). All the connectivity and landscape measurements were calculated in ESRI ArcMap 10. 4. 1. Moreover, for the city of Zurich we used Airborne Laser Scanning (ALS) metrics of woody vegetation (>1 m). These types of remote sensing data are surrogates of habitat amount and vertical heterogeneity of vegetation structure and have been shown to be good predictors of biodiversity in urban ecosystems  (Frey et al.
Statistical analysis All the analyses and statistical figures were done with the statistical computing software R v. 3. 5. 1 (R Core Team, 2018) and packages lme4 v. 1. 1-20, glmmLasso v. 1. 5. 1, car 3. 0-3, nparcomp v. 3. 0 and multcomp v. 1. 4. 14.
Acknowledgments We would like to thank all collaborators of the  BioVEINS  project (https://www. biodiversa. org/1012). We thank  H. Eggenberg  for designing the trap nests. A special thank goes to  A. Zanetta  for scientific advice,  T. Hallikma  and  K. Kilchhofer  for logistical support,  Ch. Ginzler  for preparing LiDAR data,  M. Noguera  and  C. Perez-Mon  for making fieldwork and data processing more pleasant and  A. Groll  for his precious statistical help. We also acknowledge the  authorities of Antwerp, Paris and Zurich  for the permissions to sample their  UGA  and ' Gr€ un Stadt Z€ urich' (GSZ)  for the permission to use their high-resolution habitat map of the city of Zurich. This work was partially funded by the  European ERA Net BiodivERsA  project " Bio-VEINS: Connectivity of green and blue infrastructures: living veins for biodiverse and healthy cities " ( H2020 Biodi-vERsA32015104 ), the  French Research Agency  ( ANR-16-EBI3-0012 ) and WSL funds.
Trial Oversight The design was approved by the institutional review board of the Massachusetts Institute of Technology (MIT) with Massachusetts General Hospital (MGH), Yale and Harvard ceding authority to MIT IRB. Messages were produced by the research team and approved to run (without modification) after going through Facebook's internal policy review to ensure compliance with policies. Primary outcomes were registered on ClinicalTrials. gov. There was just one deviation from the pre-registration: we initially planned to construct the mobility outcome from fine-grained data. Since the publicly available mobility data is at the county level, we use county-level mobility data instead.
Trial Design, Eligibility, Randomization and Recruitment Eligibility for the trial and randomization strategy were determined by data availability and power considerations. Movement range data computed by Facebook is publicly available at the county-level. COVID-19 level data is available at the zip code level in some states. We thus randomized both at county and zip code level to have experimental variation for each level. The CONSORT diagram (Figure  1 ) describes the factorial design and the allocation of clusters to each arm. Before the Thanksgiving campaign, we selected 13 states where weekly COVID-19 case-counts data were available at the zip code level (see maps in Figure  S1a  and S1b) and selected counties within these states where this data was available. The research team randomly allocated counties to be "high-intensity" (H) or "low-intensity" (L) with probability ½ each.
Outcomes Our primary outcomes are county level mobility and zip code level COVID-19 infections reported to state health authorities, which we regularly retrieved from state websites beginning on November 12, 2020 (a list of the websites is provided in Supplement 1, Section B). The movement range data are produced by aggregating location information obtained from mobile devices of Facebook users that opted to share their precise information with Facebook, and adding some noise for privacy protection  (6, 7)  (see Supplement 1, Section B for further details). The change in movement metric is the percentage change in distance covered in a day compared to the same day of the week in the benchmark period of February 2-29, 2020. The mobility data describes the behavior throughout the day, for people who were in each county that morning.
County level mobility data Our mobility outcomes come from the publicly-available Facebook Movement Range dataset, which can be downloaded at https://data. humdata. org/dataset/movement-range-maps. The data are constructed from location information collected by Facebook from users who have opted into Location History sharing and are aggregated to the county level. The publicly released data is subjected to a differential privacy framework to maintain the privacy of individual Facebook users. First, regions with fewer than 300 users in a given data are omitted from the data set. Second, random noise is added during the construction of each metric to limit the risk of being able to identify individual users. We use both the Change in Movement metric and the Stay Put metric in our analysis. Both are calculated daily and cover the period from 8pm to 7:59pm local time.
Stay Put is calculated as the fraction of observed users in a given county who do not leave a single level-16 Bing tile for the whole day. Specifically, in constructing the public version of this metric, 5 steps are followed: 1) a binary indicator is calculated for each user based on whether they remained in a single level-16 Bing tile for the entire day; 2) the total number of users in each county staying put is generated; steps 3)-5) from the Change in Movement calculation are followed. When we use the Stay Put metric in our analysis, we instead create Leave Home = 1 -Stay Put so that larger values indicate more movement. The Facebook Movement Range data are described in further detail at https://research. fb. com/blog/2020/06/protecting-privacy-in-facebookmobility-data-during-the-covid-19-response/.
Zip Code level COVID-19 data The COVID-19 data was retrieved twice a week from the following State health websites. The data is reported by hospital or labs to the centralized State wide health department, which publishes the data we collected and used. Most states report positive cases based on PCR tests, but some (AZ, IL, MN) combine confirmed with probable cases. Different states have different formats to report their data: some had clean spreadsheets, others had spreadsheets that were reformatted, and others had pdfs, that had to be converted into spreadsheets and cleaned. The data was retrieved manually and organized. States reported the cumulative cases reported in each zip code. Cases are assigned to a zip code based on the address of the person who tested positive. Some zip codes were not listed on the states' websites.
AZ: https://www. azdhs. gov/covid19/data/index. php NC: https://covid19. ncdhhs. gov/dashboard OK: https://looker-dashboards. ok. gov/embed/dashboards/80 OR: https://govstatus. egov. com/OR-OHA-COVID-19 RI: https://ri-department-of-health-covid-19-data-rihealth. hub. arcgis. com/ VA: https://www. vdh. virginia. gov/coronavirus/covid-19-data-insights/ Section C. Regression Models Details AR: https://achi. net/covid19/ FL: https://experience. arcgis. com/experience/96dd742462124fa0b38ddedb9b25e429 IL: https://www. dph. illinois. gov/covid19/covid19-statistics IN: https://hub. mph. in. gov/dataset?q=COVID ME: https://www. maine. gov/dhhs/mecdc/infectious-disease/epi/airborne/coronavirus/data. shtml MD: https://coronavirus. maryland. gov/datasets/mdcovid19-master-zip-code-cases/data MN: https://www.
Métral, 2020, pers. comm. ). Here, we analysed ABFT stomach contents from the same sampling sets on the basis of two gene regions. The cytochrome c oxidase subunit I (COI;  Hebert et al. , 2003)  region presents the highest potential for dietary analyses, combining species-level discrimination for many metazoan groups and the availability of large curated databases  (Leray et al. , 2013b; Machida et al. , 2017). The 18S-V1V2 ribosomal region  (Machida & Knowlton, 2012)  allowed us to complete the inventory with a broader range of potential metazoan prey, including more invertebrates than are usually captured using COI. We then compared our results with the previously published morphological inventories of prey  (Van Beveren et al. , 2017)  obtained from fishes caught the same three consecutive years.
barcodes from GenBank  (Sayers et al. , 2019). An in silico test was performed allowing a maximum of three mismatches against the Midori-UNIQUE Database  (Machida et al. , 2017)  to optimize the specificity of this primer while avoiding blocking the amplification of potential prey sequences. Ten samples were included twice in the trial: once with and the second time without the defined blocking primer (which was then included at a concentration of ten times that of the amplification primers) to empirically test its efficiency and accuracy.
Bioinformatics Data were analysed following the bioinformatic pipeline described by  Brandt et al. (2021). The FASTQ files were first processed using Cudadapt  (Martin, 2011)  to remove all primers and leftover adapters. The stringent error correction algorithm implemented in the program DADA2  (Callahan et al. , 2016)  was then applied, after pre-filtering reads using a maximum expected error (MaxEE) of 5 and truncation length of 250 bp (with minimum quality; truncQ =2). The parameters used for fragment size selection were an expected total length of 250-350 base pairs (bp) for COI and 300-500 bp for 18S-V1V2 assembled fragments, and a chimaera removal step was included. The output was a list of unique sequences referred to as amplicon sequence variants (ASVs), along with the number of times (reads) they were encountered.
To avoid confounding intraspecific diversity and species diversity, particularly for metazoans, processed ASVs were clustered into operational taxonomic units (OTUs) using the program swarm2  (Mahé et al. , 2015)  with an iterative local threshold d (the maximum number of differences between two ASVs) of 6 for COI and 4 for 18S-V1V2. Taxonomic assignment was performed at the ASV level using reference databases: Silva release 132  (Quast et al. 2013 ) for 18S-V1V2 ribosomal sequences and Midori  (Machida et al. , 2017)  for COI. Assignments were performed using the RDP naive Bayesian classifier method  (Wang et al. , 2007). Possible cross-contaminants introduced during extraction, PCR, and sequencing were removed using the decontam R package (using the prevalence method with a threshold of 0.
5;  (Davis et al. , 2018) ), with information on the identity and number of reads found in the extract and PCR-negative controls. For all gene regions, the final OTU counts were adjusted using an R-based script  (Wangensteen et al. , 2018)  to account for potential tag switches that are to occur during library preparation  (Schnell, Bohmann & Gilbert, 2015). To remove the remaining spurious sequences and particularly possible nuclear-degenerated copies (numts), known to occur when amplifying COI with universal primers (e. g. ,  (Song et al. , 2008) ), the program LULU  (Frøslev et al. , 2017)  was applied to the COI data, with an identity of 0. 84 and a cooccurrence of 0. 9.
Analyses and statistics The definitive datasets for each marker contained OTUs that were taxonomically identified with an RDP bootstrap value exceeding 0. 7 at the phylum level, excluding all human and tuna (Thunnus sp. ) matches. COI was shown not to be diagnostic for all distinct tuna species  (Viñas & Tudela, 2009) ; therefore, any sequence assigned to Thunnus sp. was considered a host/predator. Furthermore, an additional, better-curated species list was constructed based on the COI results using OTU bootstrap assignment scores of 0. 99 RDP or higher. For the 18S dataset, composed of sequences from diverse phyla, including protists and fungi, only metazoan sequences were retained for downstream analysis. A dual treatment was then applied to the OTU tables. First, the diet was summarized in simplified tables according to the presence-absence of OTUs, including the assessment of the percentage of occurrence (POO) and the weighted percentage of occurrence (wPOO) of prey taxa, both with a minimum 1% occurrence threshold.
Second, to test the accuracy of the semi-quantitative information that the number of reads could deliver, RRA  (Deagle et al. , 2019; Mychek-Londer, Chaganti & Heath, 2020)  was estimated for each prey item. The data for all gene regions were analysed in R using the vegan package (R version 3. 4. 4; Oksanen et al. , 2019)  and rmarkdown  (Xie, Allaire & Grolemund, 2018)  to estimate the Sørensen index  (Sørensen, 1948;  based on presence-absence) and Euclidean distance (based on read abundance equalized per sample) to quantify the compositional dissimilarity among samples. The difference between the results obtained on the basis of COI and 18S-V1 was tested using the Mantel test. We applied analysis of similarities (ANOSIM) as a nonparametric statistical test for identifying parameters likely to be significantly related to differences in the diet composition (size, sex, and weight of the fish, year of sampling).
1%), Malacostraca (7 OTUs, RRA = 3. 5%) and one insect (Hemiptera, RRA > 0. 01). The second most diverse taxonomic group (but detected the most frequently) was vertebrates (see also Fig. 2 ), which mostly included fish according to both gene regions, with nearly all tuna containing fish DNA in their stomachs. Within predator individuals identified based on the COI region included 40 Engraulis encrasicolus (RRA = 43. 4%), 38 Sardina pilchardus (RRA = 30%), 32 Sprattus sprattus (RRA = 8%) and 21 Scomber colias (RRA = 7%). Vertebrates were also the most abundant taxonomic group detected with Full-size DOI: 10. 7717/peerj. 11757/fig-  2  18SV1 (RRA = 61. 6), but most sequence were assigned to Alosa alosa (allis shad), which is unlikely based on the limitations of species identification combined with the relatively high abundance of sequences of this particular species in reference databases.
The genetic approach also allowed the detection of soft-bodied potential prey items, with 15 different cnidarian taxa detected from the COI and 18S data (1. 8% and 6. 7%, respectively; see Table  2 ). Overall, one-third (RRA = 27. 1%) of the taxa detected from 18S sequences were soft-bodied metazoans (ctenophores, cnidarians, and tunicates), including four different OTUs of Ctenophores (all Tentaculata, RRA = 8. 7%) and 12 different OTUs of tunicates (salps and Appendicularia, RRA = 11. 7%). Molluscs were detected mostly using COI (RRA = 1. 4%), including 5 cephalopods detected in 7 samples with Octopoda, Sepiolida, and Teuthida (RRA = 1. 3%), 5 gastropods (RRA = 0. 7%) from the genus Creseis, and one Bivalvia (RAA > 0. 01%).
Comparison with morphological data The RRA (COI) calculated per year showed the same trends as the relative abundance (RA) based on the morphological data from Van Beveren et al. (2017, see Fig. 2 ). A t -test confirmed that no significant difference could be found between RRA and RA, either per year  (2011, 2012, 2013)  or overall. For example, both morphological and COI analyses indicated that fish were the dominant prey, with four common and clearly identified fish species being recorded (see Fig. 2 ). Among the three years sampled for comparison, both the morphological and molecular COI datasets ranked anchovy (39-76% and 40-58% for RA and RRA per year, respectively), sardine (12-42% and 23-30% for RA and RRA per year, respectively) and sprat (4-14% and 5-17% for RA and RRA per year, respectively) as the most consumed prey.
Blocking primer results To avoid the possible overdetection of predator DNA (ABFT sequences in this case), a blocking primer for the COI gene region of Thunnus thynnus (and other Thunnus spec. ) was developed and tested within this study. Nearly all samples analysed produced a high number of Thunnus sequences. However, in the 10 samples analysed with the blocking primer, no Thunnus DNA was found, showing the high efficiency of the primer (see Fig. 4 ). Regarding the specificity of the primer, in silico analyses suggested a low amount of (if any) undesired blocking (i. e. , for non-tuna species), as only 92 species (including all tuna species) among the tested 110,726 species in the Midori database could be blocked when accepting a blocking effect with up to three mismatches. The exact list, including the number of mismatches, is provided in Table  S6 /Appendix 6.
, 2017; Kodama et al. , 2020). Here, the applied molecular approach detected an even higher biodiversity of potential prey than the morphological inventory, providing additional support to the generalist behaviour hypothesis for tuna. Additional potential prey include fish species not detected in morphological assessments, tunicates, cnidarians, ctenophores and molluscs, and possibly even echinoderms. The anecdotal detection of chicken DNA (Gallus gallus; detected by COI 100% identity verified on Genbank) in only one of the ABFT stomachs, with 5% of reads, and only three other detected taxa (sprat, sardine, and shrimp; thus allowing us to favour real biological evidence over the hypothesis of possible contamination) most likely originated from left-over food from humans and could indicate either scavenging or even less target-oriented foraging then hitherto considered.
The importance of gelatinous taxa in the ABFT diet Here, we complemented the COI metabarcode data with 18S data (although the latter are not ideal for the most important vertebrate prey) to specifically target invertebrates  (Cowart et al. , 2015; Cowart et al. , 2020). Like COI, vertebrates showed the highest RRA for 18S (>60%), directly followed by gelatinous prey. In case of ctenophores, the rate of detection tended to increase with ABFT size, indicating increasing consumption. As validated here by comparing COI RRA with morphological RA for vertebrates, previous studies have shown a good relationship between the 18S read abundance and morphological inventories of ctenophores and cnidarians  (Günther et al. , 2018). The large numbers of cnidarians, ctenophores, and even salps that were detected (RRA 27% of metazoan 18S detection altogether) tend to confirm that gelatinous taxa (including both benthic and planktonic taxa) are usually found in the ABFT diet, as this has also been shown for other top marine predators  (Cardona et al.
ACKNOWLEDGEMENTS Stomach collection was performed through the  EcoPelGol  project funded by  France Filière Pêche  and the Data Collection Framework funded by the  European Union. We thank  Claire Saraux  for sharing these samples with us,  Nicolas Bierne  for brainstorming during the first steps of this work, and the GenSeq technical facilities of  ISEM, Institut des Sciences de l'Evolution de Montpellier and labex ''Centre Méditerranéen de l'Environnement et de la Biodiversité '' for their contribution to the preparation of metabarcode libraries and for their sequencing. We are grateful to  Lucie Milet  and  Blandine Brisset  for the selection and preparation of samples as well as the organization of the laboratory work,  Sandrine Vaz  for advice with statistical analyses. Finally, we thank the three referees,  Taketoshi Kodama ,  Justin Mychek-Londer  and  Owen Wangensteen , for their comprehensive reviews and useful suggestion on a previous version of this manuscript.
LoRaWAN Most connected devices use LPWAN networks, such as LoRaWAN, SigFox, and NB-IoT (more details about technical differences between these three technologies are available in  [3] ). With LoRaWAN small amounts of data can be transmitted through long distances, with low energy consumption. Three frequency bands are used: 868 MHz in Europe, Sensors 2021, 21, 5466 2 of 16 915 MHz in America, and 433 MHz in Asia with bandwidths 125,250 and 500 kHz. It uses two modulation types: frequency shift keying (FSK) and chirp spread spectrum (CCS); it offers data rates from 250 to 5. 5 kb/s with CCS and up to 50 kb/s with FSK, and allows a payload of 243 bytes. This technology allows up to 10 Km as a communication range in urban zones and 20 km in rural zones, and can save battery life up to 5 years.
Excellent frequency stability with pulse-to-pulse variations of about 10 MHz (root mean square) is evident for over more than 2 years of operations in space despite the permanent occurrence of short periods with significantly enhanced frequency noise (> 30 MHz). The latter were found to coincide with specific rotation speeds of the satellite's reaction wheels, suggesting that the root cause are micro-vibrations that deteriorate the laser stability on timescales of a few tens of seconds. Analysis of the Aeo-lus wind error with respect to European Centre for Medium-Range Weather Forecasts (ECMWF) model winds shows that the temporally degraded frequency stability of the ALADIN laser transmitter has only a minor influence on the wind data quality on a global scale, which is primarily due to the small percentage of wind measurements for which the frequency fluctuations are considerably enhanced.
Regarding the Gravity Recovery and Climate Experiment (GRACE) follow-on mission, the sensitivity of the laser ranging interferometer instrument at frequencies greater than 0. 2 Hz is limited by the frequency stability of the laser which was assessed by ground tests prior to the launch in 2018  (Abich et al. , 2019). Pre-cise quantification of the frequency stability is a challenging task and only possible for Aeolus thanks to its unprecedented spectrometers that enable assessment of this parameter in orbit. For these reasons, literature on laser frequency stability of space lasers and its impact on the data quality of the retrieved products is rather scarce and more related to the preparation of future space missions  (Hovis et al. , 2008). This research article aims to provide a comprehensive overview of the frequency stability of the Aeolus laser transmitters and to explore its influence on the quality of the Aeolus wind data.
3. 4). The fourth section addresses the question of to what extent the Aeolus data quality is diminished by the temporally increased frequency noise. After a short assessment regarding the number of affected data in different phases of the mission, the impact of the degraded frequency stability on the accuracy and precision of the wind results (Sect. 4. 1) as well as on the ground velocities (Sect. 4. 2) is evaluated. Finally, a summary and conclusion of the study are provided in Sect. 5 together with an outlook on possible improvements in future space lidar missions.
Methods and datasets This section provides a brief description of the ALADIN instrument and its operating principle. After summarizing the results from on-ground tests of the laser frequency stability, the approach to assess the frequency stability in orbit by using the Mie channel is explained. Afterwards, the periods of the Aeolus mission that were selected for analysis are presented in the context of the overall performance of the ALADIN laser transmitters over the course of the 2 years after launch in 2018.