dataesr/scientific-paragraphs-categorization huggingface dataset:
P-band synthetic aperture radar (SAR) is sensitive to above-ground biomass (AGB) but retrieval accuracy has been shown to deteriorate in topographic areas. In boreal forest, the signal penetrates through the canopy to interact with the ground producing variations in backscatter depending on ground topography, forest structure, and soil moisture. Tomographic processing of multiple SAR images Tomographic SAR (TomoSAR) provides information about the vertical backscatter distribution. This article evaluates the use of P-band TomoSAR data to improve AGB retrievals from backscattered intensity by suppressing the backscattered signal from the ground. This approach can be used even when the tomographic resolution is insufficient to resolve the vertical backscatter profile. The analysis is based on P-band data from two campaigns: BioSAR-1 (2007) in Remingstorp, southern Sweden, and BioSAR-2 (2008) in Krycklan (KR), northern Sweden.
BioSAR airborne data were also processed to correspond as closely as possible to future BIOMASS TomoSAR acquisitions, with BioSAR-2-based results shown. A power law AGB model using volumetric HV polarized backscatter performs best in KR, with training residual root mean-squared error (RMSE) of 30%-36% (27-33 t/ha) for airborne data and 38%-39% for simulated BIOMASS data. Airborne TomoSAR data suggest that both vertical and horizontal tomographic resolution are of importance and that it is possible to greatly reduce AGB retrieval bias when compared with airborne P-band SAR backscatter intensity-based retrievals. A lack of significant ground slopes in Remningstorp reduces the benefit of using TomoSAR data which performs similar to retrievals based solely on P-band SAR backscatter intensity.
II. EXPERIMENTAL DATA This analysis is based on the P-band SAR images acquired using DLRs airborne E-SAR system as part of the BioSAR-1 and BioSAR-2 campaigns. These were two of a set of campaigns designed to support future L-and P-band SAR satellite missions with regard to boreal forest measurements, with a focus on the successful selection of BIOMASS for ESAs seventh Earth Explorer mission. Both campaigns targeted wellcharacterized forestry test sites in Sweden and include radar data as well as extensive in situ measurements of forest parameters and high-resolution airborne lidar scans  [18] ,  [19]. The forest data used is summarized in Table  I  and represents a combination of all relevant data sets available to maximize the area covered. Some stands and plots have individual measurements and estimates of AGB, and are used for validation while other sets use AGB derived from a recalibrated stem volume map or a lidar-based AGB map.
A. BioSAR-1, 2007 Remningstorp (RE) is a forestry test site located in the south of Sweden (Lat. 58 • 28' N, Long. 13 • 38' E) in relatively flat terrain as seen in Fig. 1 (a). The hemiboreal forest represent a transition between the temperate to the boreal zones and consists mostly of well-managed stands of Norway spruce, Picea abies, Scots pine Pinus sylvestris, and some birch Betula spp. with smaller areas of other species. A number of stands and plots were visited throughout the BioSAR-1 campaign and in situ measurements were complemented by airborne lidar scanning (ALS) on April 24, 2007 covering a central strip of the area of interest running north to south. Fully polarimetric P-band radar data were acquired on three separate occasions in 2007 to capture temporal variations: March 9, April 2, and May 2.
B. BioSAR-2, 2008 The 2008 BioSAR-2 campaign targeted the KR forestry research site, located in the north of Sweden (Lat. 64 • 14' N, Long. 19 • 50' E). KR has a pronounced topography and is covered by boreal forest (mostly Norway spruce, Picea abies, and Scots pine Pinus sylvestris) of varying density. During BioSAR-2 a set of 31 forest stands were selected and stand estimates of forest AGB values were calculated from in situ measurements of tree diameter at breast height, tree height, age, and species. The data were collected from a regular grid of 10-m radius circular plots with about ten plots per stand, enabling a statistical estimate of the error in the resulting stand biomass values  [19] ,  [20]. About 26 of these stands provide stand-level AGB within the coverage of the radar images and  are shown in red in Fig.
AGB values used for model training are obtained by averaging biomass map values within each plot. BioSAR 2 radar images were acquired using the E-SAR system, with the P-band data used here coming from a single flight on the 14th of October. The flight followed a race track path, imaging a 3 km × 10 km scene in a left looking geometry while displacing each subsequent pass horizontally. The result was two sets of six fully polarimetric images, with the first comprised of north-east looking images from a flight heading of 134 • and the second looking south-west from a flight heading of 314 •  [12] ,  [19]. These data sets are in this article denoted NE and SW, respectively.
However, the reduced range resolution due to the low bandwidth of BIOMASS (6 MHz) will set a lower bound for δ z to 20 m, corresponding to a maximum useful baseline of roughly 4600 m  [17]. If the airborne acquisitions have a significantly better vertical resolution it is possible to reprocess the resulting tomograms to have a resolution cell size and orientation corresponding to the BIOMASS geometry and performance. This method can however not change the incidence angle dependence of the original airborne tomographic intensity. An example of simulated BIOMASS tomograms based on TropiSAR data is found in  [17]. For the BioSAR data sets, the vertical resolution is not sufficient to fully emulate the spaceborne geometries except for the near range region of the BioSAR 1 scene. The simulated BIOMASS TomoSAR data sets have the correct bandwidth (through filtering in the frequency domain) but exhibit a larger range of incidence angles and vertical resolution.
A. Overview of Backscatter Variations in KR Two regions of interest were selected to show the different spatial variations seen in the radar data and highlight the relative impact of vertical tomographic resolution, ground slope, and forest AGB on SAR and TomoSAR HV polarized backscatter intensity. The first region, seen in Fig. 5 , contains stand 2228 which is located in the middle of the swath resulting in similar vertical resolution and incidence angles for the two opposing flight headings. Backscatter intensities are mostly constant within The difference between the data from the two opposing headings are highlighted in the third row of Fig. 5  which shows the ratio of NE to SW γ 0 , I ground and I vol. An increase in NE backscatter and decrease in SW backscatter is found in two areas running along the longer stand edges more or less parallel to the flight tracks.
This region contains stands 3245 and 32 398 which have high and low AGB averages of 167. 1 and 27. 5 t/ha, respectively. The large incidence angles in the NE data set produce a strong response for areas with positive u LOS and AGB variations within the forested area are not well represented in NE backscatter data. Even though ground signal suppression for I vol is limited by the poor vertical resolution the impact of ground slope is still decreased when compared with I ground and γ 0. With lower incidence angles and mainly negative slopes the SW looking data is less affected by topography and backscatter variations are more representative of AGB, especially for the lower AGB forest in stand 32 398. The improved tomographic resolution results in a larger difference between I vol and I ground than for the NE looking data.
The divergence of the fitted model parameters and increasing error for values of δ z higher than 50-60 m motivated a restriction to some minimum resolution. Restricting the incidence angle to the 25 • -35 • range to match the planned BIOMASS acquisition geometry gives a worst-case resolution of 38 m and will thus also ensure that the tomographic resolution is good enough for meaningful TomoSAR performance with these observables. results in the AGB retrievals shown in Figs. 9, 10, and 11, with all fitted parameters and average errors displayed in Table  IV. The results in Fig. 10  use the full airborne resolution SAR and TomoSAR data, showing that TomoSAR I tot gives similar, if slightly larger, residuals as γ 0 while TomoSAR I vol shows an significant improvement. The NE data set consistently result in better training residuals and retrievals, however the number of forest stands in each region is 10 (NE) and 3 (SW) making statistically significant validation difficult for the SW data set.
This results in a distribution of  [53, 42, 31, 12]  circular plots for the NE data and [30, 39, 17, 8] circular plots for the SW data. AGB retrieval model training residual bias, standard deviation, and root mean squared error (RMSE) are plotted against bin average AGB in Fig. 9. SAR γ 0 and TomoSAR I tot show no significant differences while I vol has a consistently lower bias and RMSE, especially for the lowest and highest AGB values. The improvement is more pronounced for the NE looking data set.
V. DISCUSSION The relatively flat terrain in RE demonstrates the case when the backscattered power is not strongly modulated by  sloping ground. There is little difference in the distribution of volume and ground intensity and removing the strong ground level contribution increases noise and has a net negative effect on performance. There is a marked difference between the two opposing imaging directions in KR, with the NE data set producing the best retrievals for both SAR and TomoSAR data. The only difference in image acquisition geometries is the opposing headings suggesting that these deviations are caused by systematic variations in the scene. This is highlighted by the RMSE values seen in Fig. 8  (bottom right) where the largest difference between NE and SW is found in the middle bin, which corresponds to the mostly overlapping region in the center of the swath and close to identical illumination angles from the two headings.
The largest relative differences between the observables are seen for small AGB values which represent a small fraction of total AGB in KR but are important for determining the sensitivity of the AGB retrieval. Simulated BIOMASS I vol , while an clear improvement over BIOMASS γ 0 , result in errors close to that of E-SAR γ 0 rather than E-SAR I vol. As the vertical tomographic resolution is similar in this case due to the geometry this should be directly related to the reduction in horizontal resolution. This suggest that while inhomogeneities in the vegetation layer can be resolved in the high-resolution E-SAR data these variations do not contribute linearly toward AGB retrieval from P-band TomoSAR when the resolution is lowered. Further work is needed to determine the relative importance of vertical and horizontal resolution for tomographic data and to investigate whether improvements can be made through changes to the tomographic processing or AGB retrieval models.
VI. CONCLUSION While tomographic processing of P-band SAR data has shown promise with regard to AGB retrieval in tropical forest the case for boreal forest is challenging. The limited vertical extent of the forest, generally less than the bandwidth limited resolution that will be available for BIOMASS, limits the possibility to resolve the vertical forest structure. At the same time, the ground level reflection is relatively strong as well as highly sensitive to ground slope which conspire to mask the AGB signal in the measurements. Using tomographic processing to suppress the HV ground signal is effective for KR with its varying topography as long as the resolution is no worse than about twice the forest height. TomoSAR data representative of BIOMASS result in errors on par with high resolution airborne E-SAR data while the corresponding airborne TomoSAR data suggest that further improvements might be possible, especially regarding a reduction of AGB retrieval bias over the whole AGB range.
Switching dynamics with reset maps can also be captured by the framework of hybrid systems  [7]  and ISS characterizations via Lyapunov functions for hybrid systems have been developed in  [3, 4]. Relaxing certain assumptions in these works, along with some developments based on converse results, iISS characterizations via Lyapunov functions for hybrid systems appear in  [22]. However, these results do not explicitly work out the stability conditions for switched systems in terms of the data associated with individual subsystems. Combining the aforementioned two directions of research, it is rather natural to ask whether the Lyapunov characterizations for individual subsystems can be combined with ADT and AAT notions to obtain sufficient conditions for ISS/iISS of switched systems with inputs.
Remark 3 In papers  [3, 22] , the authors work with a single-valued version of  (16) , that is, F(ξ, d) = f (ξ, d) and G(ξ, d) = g(ξ, d). The statements in Lemma 3 are obvious extensions of those results. Additionally, we see a condition on system data in the results of  [3, 22] , which says that, for each ξ ∈ X and each 0, the set {f (ξ, d) | d ∈ R m ∩ B} is convex. However, this condition is only required for proving the converse implication that ISS/iISS implies the existence of an appropriate Lyapunov function, and is not needed for our purposes.
Acknowledgements The results presented in this article were obtained in the framework of the collaborative project  MARine INfrastructures EFFects (MARINEFF ), co-funded by the  European Regional Development Fund  through the European cross-border programme  INTERREG V A France (Manche) / England. A special thanks to the  MNHN of Dinard , partner of the project for the installation and the recovery of the samples during diving in Dinard bay. Many thanks to the  University of Caen  for allowing us to immerse the samples at the  CREC of Luc-sur-Mer  and for their help in the installation of the mesocosms. The authors would also like to thank the financers and all project partners for their support.
Islands: why are they so important? Islands comprise roughly 10 M km 2 of the approximately 150 M km 2 of emerged land in the world, thus contributing 6. 67% to the world's emerging land  (Sayre et al. , 2018). Nevertheless, they are estimated to harbor a disproportionate 20% of the world's biota, in addition to an extraordinary cultural and linguistic human diversity  (Kier et al. , 2009; Gavin and Sibanda, 2012; Tershy et al. , 2015). Islands are furthermore home to about 10% of the world's human population  (Global Island Database, 2020) , and one in four of the world's 195 nations are islands or archipelagos (Fig. 1 ); these island nations have large exclusive economic zones with huge territorial claims to surrounding oceans. Thus, island-dwelling people are stewards of one sixth of the Earth's surface, including many of its most endangered species and vulnerable ecosystems  (Whittaker et al.
Acknowledgments We would like to thank  Álvaro Castilla-Beltrán  for his invaluable help on the preparation of the figures. This is publication number 123 from the  School of Life Sciences, University of Hawaiʻi at Mānoa (US). S. J. N. was supported by the  European Research Council  under the  EU H2020 and Research and Innovation program  ( SAPPHIRE grant   818854 ).
published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Hepatitis C virus (HCV) incidence among men who have sex with men (MSM) living with HIV: results from the French Hospital Database on HIV (ANRS CO4-FHDH) cohort study, 2014 to 2017.
In this favourable context, it is interesting to assess the trend of HCV incidence to determine the impact of DAAs on the epidemic and to estimate the potential for improvement. In view of this and to better inform public health strategies, this study estimates the incidence of primary HCV infection among MSM living with HIV enrolled in the French Hospital Database on HIV (ANRS CO4-FHDH) between 2014 and 2017 (i. e. , the DAA era).
Study population We used data from the ANRS CO4-FHDH -a nationwide open cohort created in 1989 -to follow PLHIV receiving care in 175 public hospitals located throughout France. The ANRS CO4-FHDH cohort is representative of the PLHIV population receiving medical care in France  [18]. Individuals were included in the ANRS CO4-FHDH cohort if they had a documented HIV-1 or HIV-2 infection and were followed-up in a centre participating in the FHDH  [18]. Standardised variables are collected at each outpatient visit or hospital admission when a new clinical manifestation is diagnosed, a new treatment is prescribed, or a change in biological markers is noted at least once over 6 months. In the present study, incident HCV infection was assessed among men who met the following criteria: (i) recorded as having acquired HIV through sex between men (regardless of injecting drug use); (ii) at least one clinic visit between 2014 and 2017; (iii) with a baseline negative anti-HCV test in the 2 years preceding the date of inclusion in the analysis; and (iv) at least one subsequent HCV test between 2014 and 2017.
If the test interval was more than 6 months, we  The grey boxes depict MSM whose data were excluded from the analysis. assumed that the date of infection was the date of first positive HCV test minus 3 months (i. e. , half the recommended testing interval). HCV negative status was assigned to individuals who had at least one HCV negative test and who never tested HCV positive throughout follow-up, which ended on 31 December 2017. This strategy assumed that negative HCV tests were not systematically reported due to the nature of the data (i. e. , data were recorded in a hospital database). Therefore, we assumed that individuals were still at risk as long as they were regularly followed in the ANRS CO4-FHDH cohort. For patients LTFU, follow-up stopped 6 months after last clinic visit before LTFU.
In a meeting in December 2019, we informed Pradat et al. that our results differed from theirs and informed them of our intention to publish this article. Several studies have observed high reinfection incidence rates among MSM living with HIV  [21] [22] [23] [24]. We did not estimate HCV reinfections as we lacked reliable data regarding HCV treatment, although this important issue needs to be addressed as HCV reinfections could sustain the epidemic. Indeed, one cannot exclude that some HCV treatments were initiated in hepatology units without being registered in our database. However, the global incidence is mainly driven by the primary incidence. Likewise, our analysis did not include HIV-negative MSM, including those receiving preexposure prophylaxis (PrEP) for whom high incidence of HCV has been reported in France  [28, 29].
ANRS CO4-FHDH: French hospital database on HIV; CI: confidence interval; HCV: hepatitis C virus; LTFU: Lost to follow-up; py: person years. a Date of infection (in the main analysis) = midpoint between the last negative and the first positive HCV test when the interval was ≤ 6 months, or the date of first positive HCV test minus 3 months when > 6 months. b End of follow-up for LTFU patients (in the main analysis) = 6 months after last clinic visit before LTFU. c End of follow-up for HCV negative patients with continued follow-up in the cohort (in the main analysis) = 31 December 2017. • sensitivity analysis 6: end of follow-up = date of last negative HCV test. Supplementary Figures S1, S2 and S3 provide graphical illustrations of the assumptions used in the different analyses and their impact on follow-up time estimation.
Acknowledgements We thank all the participants and research assistants of the  ANRS   CO4-FHDH cohort. The members of the ANRS CO4-FHDH are listed in the supplementary material and at the following link: https://anrs-co4. fhdh. fr/groupe-clinico-epidemiologique-anrs-co4-fhdh/Funding statement: This study was supported by the  French Agency for Research on AIDS and Viral Hepatitis (ANRS) , grant number  95031. They  ANRS  had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The  ANRS   CO4 -French Hospital Database on HIV is supported by the  French Agency for Research on AIDS and Viral Hepatitis (ANRS) ,  Institut national de la santé et de la recherche médicale (INSERM) , and the  French Ministry of Health.
13 ) 13 ANRS CO4-FHDH: French hospital database on HIV; CI: confidence interval; HCV: hepatitis C virus; LTFU: Lost to follow-up; py: person years. a Date of infection (in the main analysis) = midpoint between the last negative and the first positive HCV test when the interval was ≤ 6 months, or the date of first positive HCV test minus 3 months when > 6 months. b End of follow-up for LTFU patients (in the main analysis) = 6 months after last clinic visit before LTFU. c End of follow-up for HCV negative patients with continued follow-up in the cohort (in the main analysis) = 31 December 2017. • sensitivity analysis 6: end of follow-up = date of last negative HCV test. Supplementary Figures S1, S2 and S3 provide graphical illustrations of the assumptions used in the different analyses and their impact on follow-up time estimation.
, 2016). With that said, it is possible that the cumulative slope of q = -0. 7 ± 0. 3 is more ambiguous than suggested by the error bars. First,  Spencer et al. (2020; Sp20 hereafter)  revised the slope determination as q = -0. 8 +0. 4  -0. 6. Although the nominal slope is almost the same as in S19, the statistical uncertainty is much larger than originally estimated and does not exclude a slope as steep as q = -1. 4. Second, the slope of the crater SFD at the smallest observable sizes may also be subject to systematic uncertainties. For instance, R17 (see their Fig. 11 ) report q = -1. 47 ± 0. 15 for the same terrain and crater size-range considered in S19, when using craters identified in what they call the "consensus database". The reason for the difference between the slopes in S19 and R17 has yet to be fully investigated.
6B  in that paper). It is possible the higher than expected crater spatial densities come from a crater-production SFD that is steeper than that estimated from Charon's record in the range 1 < D < 10 km, but it may also have a number of other causes (e. g. , different impact rates per unit surface on Arrokoth and Pluto/Charon; different relationships between projectile and crater sizes that are due to the vastly different gravities of the targets; potentially different surface ages). The Arrokoth crater dataset in Sp20 also presents another intriguing property. There are several craters observed that are slightly smaller than 1 km, none are found between 1 < D < 7 km, and then there is one large singular D > 7 km crater seen that has been named Maryland. The absence of craters between the km-sized ones and Maryland is not due to limitations in the images and could suggest a relatively steep crater SFD.
1, the same as that of the Jupiter Trojans  (Grav et al. , 2011; Emery et al. , 2015) , which are believed to be captured Kuiper belt objects  (Nesvorny et al. , 2013). In the discussion below, "cold" and "hot" are a measure of dynamical excitation, not temperature. Compared to the model used in  Greenstreet et al. (2015 Greenstreet et al. ( , 2019;; G15 and G19 hereafter) , our model has (i) significantly fewer objects in the cold classical sub-population of the Kuiper belt, consistent with  Fraser et al. (2014) , and (ii) more objects in both the hot classical sub-population of the Kuiper belt and scattered disk (see Table  1 , to be compared with Table  I of G15  and G19 ). Given that the two models are based on observations and related biases (although different datasets), the origin of these differences is not fully clear to us.