dataesr/scientific-paragraphs-categorization huggingface dataset:
The experiment of each vehicular density is performed for 400 s and repeated 10 times with different random seeds. The results with a 95% confidence interval are shown in Figures  7  and 8. The junction points of the curve are the average values of the metric in repeated simulations. The upper and lower error bar represents the maximum and the minimum, respectively. Sensors 2016, 16, 920 14 of 26 The data messages are generated by a fixed source node located at an appropriate intersection and are collected by all vehicles moving in the ROI. Similar traffic routes were created for two maps, where vehicles are distributed uniformly in the scenarios. The experiment of each vehicular density is performed for 400 s and repeated 10 times with different random seeds. The results with a 95% confidence interval are shown in Figures  7  and 8   Figure  7a  shows that the PDR of TrAD and UV-CAST increases rapidly from about 70% to over 90% when the vehicular density increases from 40 v/km 2 to 80 v/km 2 , and then continues to gradually reach close to 100%.
Figures  8c  and 10c  present that traffic route 2 lets the delay of all protocols increase compared with the one of traffic route 1. Especially, for AMD, its average delay of all vehicular densities increases 13. 117 s. The different between AMD and TrAD increases more than twice. In addition, TrAD obtains the lowest delay in both uniform and deviated traffic routes. These observations show that TrAD can handle the deviated traffic route in the Manhattan scenario. A deviated traffic route, i. e. , traffic route 2, indeed influences the performance of IVC protocols significantly. It requires IVC protocol to consume more transmissions to maintain data coverage with high delay. However, TrAD shows an outstanding capability to handle this issue. The experimental observations show that TrAD only consumes a moderate amount of transmissions to handle the disconnected networks to achieve high data coverage with low delay.
The reason lies in that TrAD has an efficient SCF mechanism that assigns the role of SCF-agent to the breaker and the coordinator. Furthermore, TrAD also uses a SCF-agent rebroadcast constraint technique to further suppress transmissions. For UV-CAST, the gift-wrapping algorithm triggers more transmissions, which also brings the extra delay. For AMD, although it could recognize the relatively regular Manhattan map, there is a drawback in its SCF mechanism. The SCF mechanism only triggers the transmission of stored data while the transition, i. e. , from tail to non-tail, happens, but, due to signal fading or interference, the stored data is not always received, especially in the scenarios with deviated traffic routes. In case a transmission fails, there is not another chance to trigger the SCF-agent to forward the stored data again.
Highway Scenarios To implement the highway map, a section of A711 highway located near Clermont-Ferrand airport (Aulnat, France) is modeled, as shown in Figure  11. It has two lanes in each direction and has 2 km length. The data message is created by the fixed source node at the west end of the highway and gathered by all vehicles driving in the ROI. To detect the traffic flow, induction loop detectors were implemented under each lane as highlighted by the red circles in  In the case of highway scenarios, AMD achieves high PDRs that are similar as those of TrAD, as shown in Figure  12a , due to the relatively simple two-dimensional road topology. The PDR of AMD exceeds 90% since 883 vph and finally reaches close to 100%. However, Figure  12a  also highlights that the PDR of DV-CAST fails to reach 70% finally.
AMD costs less transmissions than TrAD in dense networks, which is consistent with the result of experiments in  [7]. However, the delay of TrAD is lower than the one of AMD. This means that the extra transmissions of TrAD would come from the SCF mechanism in one-hop broadcasting, especially at the border of the ROI. TrAD always employs the breaker going out of the ROI to load data messages to the vehicles just coming into the ROI. That is why the PDR of TrAD gains the advantage over AMD at most of vehicular densities, e. g. , 443 vph, 1319 vph and 1764 vph. Although the number of transmissions and the delay of DV-CAST are the lowest, its overall performance is still the worst, because the low transmissions and the low delay exactly benefit from the low PDR. (a) In the case of highway scenarios, AMD achieves high PDRs that are similar as those of TrAD, as shown in Figure  12a , due to the relatively simple two-dimensional road topology.
The PDR of AMD exceeds 90% since 883 vph and finally reaches close to 100%. However, Figure  12a  also highlights that the PDR of DV-CAST fails to reach 70% finally. The reason lies in that DV-CAST does not have the rescue mechanism for failed transmissions. Indeed, the transmitter vehicles forward the data message only once, i. e. , one-shot deal. Theoretically, the operation principle of DV-CAST guarantees a high data coverage on the highway. However, if a transmission fails, there is no mechanism to trigger the retransmission again. It is possible that the consecutive transmissions would fail due to fading or collisions. Therefore, if a transmission is broken at the middle forwarder, the vehicles in the rear of this forwarder cannot receive the data message. In addition, TrAD uses the message list in the beacon to detect data messages needed by neighbors, so that the failed transmission can be rescued in the next beaconing process.
Since the PDR values of TrAD and AMD are close, it makes sense to compare their number of transmissions and delay, as shown in Figure  12b, c. AMD costs less transmissions than TrAD in dense networks, which is consistent with the result of experiments in  [7]. However, the delay of TrAD is lower than the one of AMD. This means that the extra transmissions of TrAD would come from the SCF mechanism in one-hop broadcasting, especially at the border of the ROI. TrAD always employs the breaker going out of the ROI to load data messages to the vehicles just coming into the ROI. That is why the PDR of TrAD gains the advantage over AMD at most of vehicular densities, e. g. , 443 vph, 1319 vph and 1764 vph. Although the number of transmissions and the delay of DV-CAST are the lowest, its overall performance is still the worst, because the low transmissions and the low delay exactly benefit from the low PDR.
In the case of highway scenarios, AMD achieves high PDRs that are similar as those of TrAD, as shown in Figure  12a , due to the relatively simple two-dimensional road topology. The PDR of AMD exceeds 90% since 883 vph and finally reaches close to 100%. However, Figure  12a  also highlights that the PDR of DV-CAST fails to reach 70% finally. The reason lies in that DV-CAST does not have the rescue mechanism for failed transmissions. Indeed, the transmitter vehicles forward the data message only once, i. e. , one-shot deal. Theoretically, the operation principle of DV-CAST guarantees a high data coverage on the highway. However, if a transmission fails, there is no mechanism to trigger the retransmission again. It is possible that the consecutive transmissions would fail due to fading or collisions.
3% taking 31 s after the data dissemination has been started. Second, we select the Clermont-Ferrand scenario to evaluate the robustness of TrAD, AMD and UV-CAST to the GPS drift, since the Clermont-Ferrand scenario has more influence on TrAD than the Manhattan one. Error deviations include 0 m, 50 m and 100 m, as shown in Figures  15 16 17. In Figure  15 , the data coverage of TrAD increases rapidly to 100% while the data coverage of UV-CAST climbs up much slower than TrAD and finally reaches 99. 1%. In Figure  16 , TrAD still keeps a good upward trend and reaches 100% data coverage. It is interesting that, in this case, the GPS drift limits the performance of UV-CAST and let its dissemination speed be lower than the one of AMD at most of time. Finally, UV-CAST and AMD achieve 86% and 83% data coverage, respectively.
In Figure  17 , although the large positioning deviation hurts TrAD very badly, the data coverage of TrAD still exceeds the other two protocols 17 s after data dissemination starts and achieves 100% at the end. In this simulation, the dissemination speed of UV-CAST is always better than AMD, which is not the case in Figure  16. Finally, UV-CAST and AMD achieve 92% and 75. 1% data coverage, respectively. The abovementioned observations reveal that the random GPS drift will cause unreliable results in the protocols with less robustness. Although, for TrAD, the increasing slope of its data dissemination speed gradually decreases with the increasing of error deviations, it always takes the shortest time to reach 100% data coverage. In Figure  15 , the data coverage of TrAD increases rapidly to 100% while the data coverage of UV-CAST climbs up much slower than TrAD and finally reaches 99.
1%. In Figure  16 , TrAD still keeps a good upward trend and reaches 100% data coverage. It is interesting that, in this case, the GPS drift limits the performance of UV-CAST and let its dissemination speed be lower than the one of AMD at most of time. Finally, UV-CAST and AMD achieve 86% and 83% data coverage, respectively. In Figure  17 , although the large positioning deviation hurts TrAD very badly, the data coverage of TrAD still exceeds the other two protocols 17 s after data dissemination starts and achieves 100% at the end. In this simulation, the dissemination speed of UV-CAST is always better than AMD, which is not the case in Figure  16. Finally, UV-CAST and AMD achieve 92% and 75. 1% data coverage, respectively. The abovementioned observations reveal that the random GPS drift will cause unreliable results in the protocols with less robustness.
Although, for TrAD, the increasing slope of its data dissemination speed gradually decreases with the increasing of error deviations, it always takes the shortest time to reach 100% data coverage. In Figure  15 , the data coverage of TrAD increases rapidly to 100% while the data coverage of UV-CAST climbs up much slower than TrAD and finally reaches 99. 1%. In Figure  16 , TrAD still keeps a good upward trend and reaches 100% data coverage. It is interesting that, in this case, the GPS drift limits the performance of UV-CAST and let its dissemination speed be lower than the one of AMD at most of time. Finally, UV-CAST and AMD achieve 86% and 83% data coverage, respectively. In Figure  17 , although the large positioning deviation hurts TrAD very badly, the data coverage of TrAD still exceeds the other two protocols 17 s after data dissemination starts and achieves 100% at the end.
In this simulation, the dissemination speed of UV-CAST is always better than AMD, which is not the case in Figure  16. Finally, UV-CAST and AMD achieve 92% and 75. 1% data coverage, respectively. The abovementioned observations reveal that the random GPS drift will cause unreliable results in the protocols with less robustness. Although, for TrAD, the increasing slope of its data dissemination speed gradually decreases with the increasing of error deviations, it always takes the shortest time to reach 100% data coverage.
Manhattan scenario due to the complex nature of the traffic environment. The 25 m, 50 m and 75 m error deviations impact the performance of TrAD, but the data dissemination speed of these deviations still achieves a moderate performance. The result of 100 m deviation is the worst, but its data coverage also reaches 98. 3% taking 31 s after the data dissemination has been started. Second, we select the Clermont-Ferrand scenario to evaluate the robustness of TrAD, AMD and UV-CAST to the GPS drift, since the Clermont-Ferrand scenario has more influence on TrAD than the Manhattan one. Error deviations include 0 m, 50 m and 100 m, as shown in.
Acknowledgments: This work has been sponsored by the  French government research program  " Investissements d'avenir " through the  IMobS3 Laboratory of Excellence  ( ANR-10-LABX-16-01 ), by the  European Union  through the program Regional competitiveness and employment 2007-2013 (ERDF-Auvergne region), and by the  Auvergne region. We would like to thank  Philippe Vaslin ,  Jianjin Li  and  Jean Connier  for revising the manuscript of this paper, and  Dongsheng Yan  and  Philip Dechant  for creating the realistic scenarios.
Sensors 2016, 16 , 920 22 of 26 the 1626 Manhattan scenario due to the complex nature of the traffic environment. The 25 m, 50 m and 75 m error deviations impact the performance of TrAD, but the data dissemination speed of these deviations still achieves a moderate performance. The result of 100 m deviation is the worst, but its data coverage also reaches 98. 3% taking 31 s after the data dissemination has been started. Second, we select the Clermont-Ferrand scenario to evaluate the robustness of TrAD, AMD and UV-CAST to the GPS drift, since the Clermont-Ferrand scenario has more influence on TrAD than the Manhattan one. Error deviations include 0 m, 50 m and 100 m, as shown in.
Monitoring near-surface soil freeze/thaw patterns is becoming essential under the context of global changes as it is more sensitive to climatic fluctuation compared with subsurface thermal characteristics and its 15 evolution could be an early warning of changes in near-surface permafrost. It requires continuous long term and stable data record for understanding hydrological, ecological and biogeochemical responses of permafrost to global climate change. AMSR2 (Advanced Microwave Scanning Radiometer 2) is designed as a successor of AMSR-E (Advanced Microwave Scanning Radiometer -Earth Observing System) to ensure continuity of such observation. In this study, a linear regression is used to inter-calibrate the AMSR-E and AMSR2 brightness temperatures. Then 20 discriminant function algorithm is adopted to produce a long term freeze/thaw data record.
It is compared with in situ air temperature measurements from both the temporal and spatial aspects. The results show that the accuracy is consistent between AMSR-E and AMSR2 with a value above 85%, according to the result of spatial distribution accuracy. Analysis is conducted with this data record to explore the spatial distribution of frost days, its changing trend and the frost probability of each pixel on a specific date. The mean annual frost days of high northern latitude 25 (HNL, >45°N) zone is 214. 2±69. 5 days and the trend of frost days indicates that the frost period is decreasing at a rate of -0. 0065 day/month in 27% of the domain which is defined by significance level of the F-test, and most of which are concentrated in the high latitude area specifically over the Northeast of Canada, Central and Eastern Russia and most part of Eastern Europe.
, 2011; Zhao et al. , 2011). However, the AMSR-E was switched off on 4 October 2011 because of rotation The Cryosphere Discuss. , doi  :10. 5194/tc-2016-115, 2016  Manuscript under review for journal The Cryosphere Published: 21 June 2016 c Author(s) 2016. CC-BY 3. 0 License. problems. The AMSR2 took over to continue the observations. China is planning a Water Cycle Observation Mission  (Shi et al. , 2014; Zhao et al. , 2015) to measure the key water cycle elements including the soil F/T with a sensor that has the similar configuration with AMSR-E and AMSR2. It becomes very essential to continue the monitoring of soil F/T with AMSR2 towards a long-term data record. The big challenge is how to obtain consistent measurement of F/T status. Thus, our work was initiated to assess the performances of AMSR-E and AMSR2 5 brightness temperature data, and remove the observed bias in order to ensure consistency between the two instruments.
This inter-calibration methodology based on brightness temperature inter-comparisons has been used for the generation of other consistent and continuous land surface parameter data record, such as soil moisture  (Mladenova et al. ; Parinussa et al. , 2015). In this study, inter-calibration of AMSR-E and AMSR2 is first conducted to enable the transfer of original AMSR-10 E F/T algorithm to AMSR2. A long-term globally F/T data record is then developed based on  AMSR-E (2002 AMSR-E ( -2011) )  and its successor of  AMSR2 (2012-present). This continuous F/T data record is validated with in situ measurements globally, and the agreement of accuracy between AMSR-E and AMSR2 is assessed and confirmed. With the last more than ten years  ' (2002-2014)  data, the average satellite frost days and its changing trends are calculated and analysed as a preliminary demonstration.
20 AMSR-E and its successor AMSR2 share almost the same physical parameters of instruments except slightly difference: AMSR2 has a bigger antenna with the diameter of 2. 0 meters while the AMSR-E has a 1. 6 meters antenna and a new 7. 3GHz channel to eliminate Radio Frequency Interference (RFI) at C-band. Both instruments are operating at an altitude of approximately 700 kilometers orbit that crosses the equator each day at 13:30 local time for ascending orbits and 01:30 for descending. AMSR-E observation has halted because of the antenna 25 problems in November 2011. On 5 December 2012, AMSR-E had been restarted and provided data in Slow Rotation Mode. It can be obtained from the Global Change Observation Mission 1-Water (GCOM-W1) Research Product Distribution Service (Version Release4). This data enables us to inter-calibrate AMSR-E with other radiometers such as AMSR2.
The brightness temperatures (Tb, Version 03) we used for AMSR-E are obtained from National Snow and Ice Data Center (NSIDC). For AMSR2, Level 3 standard products (Version 01) are obtained from 30 GCOM-W Data Providing Service. The precipitation product from AMSR-E and AMSR2 (Level 3 standard product) were also used when freeze/thaw status were determined. All the datasets are provided with the Equirectangular projection in the resolution of 0. 25 degree globally. AMSR-E slow rotation data are used for inter-calibration with AMSR2. But there is about a 10-month observation gap between the standard products of AMSR-E and AMSR2, which are adopted for the F/T detection.
Ground based measurements and ancillary data In general, near-surface F/T cycles responds consistently to forcing by air temperature on an inter-annual basis  (Brown et al. , 2000). Therefore, daily surface air temperature data from the World Meteorological Organization (WMO) weather stations are used to evaluate and compare the accuracy of F/T data record developed in this study. The temperature data are obtained from the National Climate Data Center (NCDC) as the Global Summary of the 5 Day (GSOD). Previous study has also successfully used these data to evaluate the accuracy of F/T state from SSM/I  (Kim et al. , 2011). It contains a large number of weather stations distributed widely over the globe, with the maximum temperature (Tmax) and minimum temperature (Tmin) for each day. For the F/T accuracy validation or evaluation in this study, observations of two whole years (2010 and 2013) are selected, as the data in 2010 are used for AMSR-E and the year 2013 is used for AMSR2 after the inter-calibration.
It should be noted that any stations 10 with temperature record on a particular day would be used for validation, so the number of validation stations used might vary slightly on a day-to-day basis with a value around 6000. During the F/T detection, ancillary data were used to improve the accuracy of the classification including the land cover types and precipitation, which has a significant effect on the microwave emission. The MODerate resolution Imaging Spectroradiometer (MODIS) IGBP (International Geosphere-Biosphere Programme) land cover To investigate the F/T trend changing we also calculated the global land temperature anomalies trend of the last 14 20 years (2001-2014) using the gridded mean temperature anomalies dataset namely the Global Historical Climatology Network-Monthly (GHCN-M, Version 3.
2. 1) temperature dataset. The gridded anomalies were produced by month on a 5° by 5° basis and each month consists of 2592 gridded data points (72 longitude × 36 latitude grid boxes) and also available from 1880 to the most recent month in degree Celsius. The anomalies that is departure from the baseline which is the long-term averages of the latest decade for the 30 years effectively normalizes the temperature 25 and can be compared and combined to more accurately represent temperature patterns with respect to what is normal for different places within a region.
Inter-calibration The AMSR-E Slow Rotation Mode data (Level 1 S) and AMSR2 standard data provide us a chance to extract the 30 concurrent observations at almost the same time to adjust the brightness temperature difference introduced by different calibration procedures. In this work, only the land part of Tbs are used as we are focusing on the landscape F/T classification. The period for inter-calibration is designed for one year from 1 August 2013 to 31 July 2014. Only the concurrent observations (±10 minutes between overpass times) at the same gird are matched for comparison. The instrument AMSR2's configurations are coincide with that of AMSR-E so the difference of sensor characters is assumed to be low. A robust statistical algorithm was used to eliminate the influence of outliers which may occur in the coastlines and regions with large water fraction.
Comparison with external data The F/T state data derived from the discriminant function algorithm (using both AMSR-E and AMSR2 data) are compared with the in situ air temperature derived from GSOD as the reference dataset. It is assumed that the satellite 25 overpass time (13:30 and 01:30) are corresponding to the level of daily maximum and minimum temperature respectively, although the air temperature generally varies from day to night. The F/T classification based on air temperature should rely on the threshold. In this comparison we use a cut off of 0 ℃, which is consistent with other studies  (Kim et al. , 2011; Zwieback et al. , 2015). And when more than one station is located in single grid cell of the satellite data, the average temperature of these stations is used to define the F/T state.
Inter-calibration between AMSR-E and AMSR2 The inter-calibration is conducted for all frequencies available with ASMSR-E and AMSR2 at a global scale (only 5 observations over land are used here). Only results of frequencies used in the F/T classification are presented as in Fig. 1. The data points are filtered by using the pre-defined statistical threshold to exclude some outliers. This is done to focus on the main difference of the majority observations. The inter-calibration coefficients (including the slope and intercept) were derived by linear regression and the coefficients of determination (R-square) are as high as 0. 98, which indicates the observations from AMSR-E and AMSR2 are quite close and the transfer of F/T 10 algorithm from AMSR-E to AMSR2 is feasible. But, the RMSD values of the two sensors' observation range from 3 K to 5.
5 K, which is not acceptable to elaborate a continuous data record of F/T classification. This difference is associated to physical system configurations or to the calibration procedures which should be taken into account when the same algorithms are applied to both instruments. It is confirmed that there is a consistent warm bias of about 0 K to 3 K with AMSR2 compared with AMSR-E, which is consistent with a preliminary research by Japan 15 Aerospace Exploration Agency (JAXA)  (Okuyama and Imaoka, 2015). These differences are then removed by applying regression models to the AMSR2 data. It should be noted these results might change if JAXA made any recalibrations to AMSR2 (Version 01 is used in this study). The correction models used are as follows: 𝑇𝑏 𝐴𝑀𝑆𝑅𝐸_18. 7ℎ is the 18. 7GHz horizontal-polarization brightness temperature observed by AMSR-E.