dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, in order to measure wind speeds with an accuracy of 1 m s -1 , the required relative accuracy of the frequency measurement is on the order of 10 -8 , which poses stringent requirements on the frequency stability of the laser transmitter. This is especially true as the atmospheric backscatter from multiple outgoing laser pulses are accumulated on the ACCD prior to digitization and data downlink. This implies that a pulse-to-pulse normalization of the return signal frequency with the internal reference frequency is not possible. Instead, the determined frequency of the pulses averaged over one measurement is affected by inhomogeneous cirrus or broken clouds as only a subset of the 18 pulses may be detected in the atmospheric path. As a result, large frequency variations on the measurement level (over periods of 0.
Analyzed periods of in-orbit Aeolus datasets The laser frequency stability was analyzed for different periods of the Aeolus mission. Since the satellite is circling around the Earth with an orbit repeat cycle of 1 week, it was decided to study the performance over selected 7 d periods. This approach was also motivated by the observed correlation between the frequency stability and the satellite's geolocation (see Sect. 3. 2). After 1 week the maximum coverage of the globe is reached, and the operation timeline, particularly the attitude control sequence of the platform, approximately repeats. In total, 5 weeks between December 2018, when the mission was still in the commissioning phase after its launch on 22 August 2018, and October 2020 were chosen for investigation, as listed in Table  1.
2. 4). The performance from the week between 14 and 21 October 2019 (Fig. 4a ), based on the Mie response data from more than 27 million laser pulses, is illustrated in Fig. 6. Each dot in the two maps represents one observation, whereby the color and opacity indicate the frequency stability in terms of σ f (N = 540). The analysis revealed that the enhanced frequency noise does not occur randomly but is correlated with the location of the satellite over the Earth's surface. More strikingly, different linear and circular structures are evident for ascending and descending orbits, which makes clear that the frequency stability depends not only on the geolocation but also on the satellite's orientation along the orbit. For ascending orbits those observations with enhanced frequency fluctuations are accumulated in several bands wrapping around the globe, most prominently around East Asia and the Pacific Ocean.
The wheels are all mounted on isolation suspensions to reduce micro-vibrations. Based on the dataset from the week in October 2019, the frequency stability on the observation level, as depicted in Fig. 6 , was correlated with the rotational speed of the three active reaction wheels on board Aeolus (RWA 4 serves as a backup). The same procedure was performed for the FM-A period in May 2019 (see Table  1 ). The resulting six correla-tion plots, which can also be considered to be spectra in terms of the wheel rotation frequency (rotations per second, RPS), are shown in Fig. 8. Note that the plot includes data from both ascending and descending orbits and that RWA 1 and RWA 3 rotate counterclockwise, while RWA 2 rotates clockwise. For the sake of better comparability of the three spectra, the absolute wheel speeds are plotted in the figure, and the negative sign for the wheel speeds of RWA 1 and RWA 3 was omitted.
While the black data points represent the entire 1 h period, the green and red data points correspond to 20 min subperiods with comparatively low and high frequency noise, respectively. The Allan deviation provides further information on the frequency stability on those timescales that are relevant for the wind retrieval. Apart from the observation level (12 s, 540 pulses) the frequency variations on the measurement level (0. 4 s, 18 pulses; see Sect. 2. 1) are crucial for the wind data quality. This is due to the fact that the signal data from individual measure-ments are classified into "clear" and "cloudy" bins by using estimates of the backscatter ratio before the on-ground accumulation to so-called groups in the L2B processor  (Rennie et al. , 2020). This grouping algorithm enables the distinction between measurements that are better analyzed with the Rayleigh or Mie spectrometer, respectively, and hence to minimize the effect of crosstalk between the two receiver channels that is detrimental to the wind data quality.
Impact of the frequency fluctuations on the data quality After having identified the root cause of the temporally degraded frequency stability in orbit, the question is to what extent the Aeolus wind data quality are diminished during the periods of enhanced frequency noise. Before answering this question, it is meaningful to consider how often these periods occur and how long they last. For this purpose, the percentage of observations for which the frequency stability is significantly worse in comparison to the overall performance over a certain period can be regarded. This information is summarized in Table  4  for the 5 selected weeks of the mission that were already introduced in Sect. 2. 4 as being representative of different periods of the Aeolus mission. The table provides the standard deviation over the entire week and the portion of observations for which σ f (N = 540) is above 15, 20 and 25 MHz.
Accuracy and precision of the Aeolus Rayleigh and Mie winds The wind error assessment is based on the data from 2 weeks of FM-B operation in August and September/October 2020, as listed in Tables  1  and 4. These datasets already include the correction for the influence of the temperature variations across the primary telescope mirror on the wind results (M1 correction), which was implemented prior to the operational assimilation of the Aeolus wind data in NWP by various weather services  (Rennie and Isaksen, 2020; Weiler et al. , 2021b). The Rayleigh and Mie wind observations were extracted from the L2C product, which, in addition to a copy of the Aeolus L2B product, includes ECMWF model winds (analysis and background) provided on the same horizontal and vertical grid. Using the background model winds as the reference, the wind speed differences between observation and background (O-B) can be interpreted as the wind errors.
The median of the laser frequency stability is 6. 6 MHz for the two data subsets, while the mean values both account for 8. 2 MHz, which is comparable to the frequency stability on the observation level for the August 2020 period stated in Table  4. Although the frequency stability is comparable for the two different processor timescales when the mean over large datasets is considered, the fluctuations are significantly higher on the measurement level (> 30 MHz) than on the observation level in rare cases when the peak of a short high-noise period falls within one measurement. In a next step, the Mie and Rayleigh wind results were separated into those for which the frequency stability was better or worse than 10 MHz on the measurement level. The resulting probability density functions (PDFs) of the (O-B) horizontal line-of-sight (HLOS) wind speed differences are provided in the middle and right columns of Fig.
079 ± 0. 008) m s -1 for σ < 10 MHz to (0. 187 ± 0. 016) m s -1 for σ > 10 MHz, while the Rayleigh bias differs by only 0. 02 m s -1. The respective random errors are 4. 22 and 4. 28 m s -1 for the Mie and 7. 16 and 7. 17 m s -1 for the Rayleigh channel, respectively. In order to further evaluate the impact of the laser frequency noise on the Aeolus wind data quality, a variable frequency stability threshold was applied, and the bias and random error with respect to the ECMWF model background were calculated for those winds that were measured during periods with frequency fluctuations below the threshold. The statistical results are depicted in Fig. 13. The mean Mie and Rayleigh wind biases are plotted in the left column, while the respective random errors for the 2 investigated weeks in August and September/October 2020 are shown on the right.
Both values are presented relative to the statistical parameters that are obtained for a threshold of 8 MHz as this allows for a direct comparison of the two different datasets regardless of the absolute values, which differ among the analyzed periods and receiver channels due to other error contributions. The reference bias and random error values are indicated in the round boxes. A frequency stability of 8 MHz was chosen as a reference threshold since this value is close to the mean stability over the entire week and thus represents the average conditions in terms of laser frequency noise. The plots additionally present the number of winds that entered the statistics depending on the applied threshold. As can be seen from the figure, the number of wind results increases considerably when the threshold is relaxed from 8 to 15 MHz, whereas only few results are added at even higher thresholds.
Overall, the number of Rayleigh (≈ 1 000 000) and Mie winds (≈ 350 000) is comparable for the two periods. Regarding the bias, a noticeable impact of the frequency stability is only evident for the Mie winds, as seen before. Here, the bias increases by up to 0. 05 m s -1 when wind data from high-noise periods are included in the statistics. For the Rayleigh winds, the bias change is negligible (< 0. 01 m s -1 ). The same holds true for the random errors in both channels, which change by less than 0. 02 m s -1 when the frequency stability threshold is relaxed from 8 MHz to more than 20 MHz. In conclusion, the temporally degraded frequency stability of the ALADIN laser transmitter has only marginal influence on the wind data quality on a global scale. This is primarily due to the small percentage of measurements for which the frequency fluctuations are considerably enhanced.
The biggest impact is observed for the Mie wind bias, which is increased by more than 0. 3 m s -1 when considering only those phases with strong variations (> 20 MHz) on the measurement level. However, as these winds represent only about 4 % of all wind results from the studied weeks, the effect is hardly noticeable in the statistics derived from the entire global dataset. Nevertheless, this study helps to identify an optimal threshold for a quality control (QC) that filters out measurements for which the frequency noise is considerably enhanced. The according QC parameter, which describes the frequency stability in terms of the standard deviation on the measurement level, is already included in the L1B product. Since wind observations with enhanced frequency jitter occur over specific geolocations (see Fig.
6 ), it is interesting to study whether the wind data are significantly degraded in such affected locations. For this purpose, several areas where one or multiple critical reaction wheel speeds regularly occur, i. e. , in East Asia or central Africa, were analyzed with regard to the deviations from Aeolus observations from ECMWF model winds (O-B). This preliminary study revealed that, although the percentage of measurements with frequency stability worse than 15 MHz is increased by a factor of 2 to 3 compared to the global percentage, the change in wind bias and random error is below 0. 5 m s -1. However, as the number of wind results is drastically decreased by the restriction to rather small geographical boxes, these results lack statistical significance. Hence, additional studies using longer periods of wind data are required to verify this small influence on the wind data quality on a local scale.
Moreover, in contrast to the multitude of Rayleigh and Mie wind results, which are available from multiple range gates and all over the globe, ground return signals that are strong enough to be utilized for a potential ZWC of the Aeolus data are rare. Given the susceptibility to atmospheric contamination, i. e. , the detrimental impact of the broadband atmospheric return signal on the narrowband ground return signal, only ground velocities that were measured from surfaces with high albedo in the UV spectral range are considered valid, which drastically limits the number of available ZWC values. The useful signal thresholds applied in the L1B processor are 1000 ACCD counts and 1200 ACCD counts for the Mie and Rayleigh channel, respectively. For this reason, the ZWC data from the 2 weeks were combined in order to obtain meaningful statistics.
Since only data from high-albedo regions, i. e. , Antarctica, Arctic and sea ice and snow-covered land areas, entered the statistics, solely the frequency stability over these locations plays a role in the analysis. Here, the precision of the ground returns is impaired by blowing snow that leads to a narrowband Mie return with a potentially non-vanishing Doppler shift, thus acting as another source of random errors on the ground returns. Since the ice coverage was comparable in the 2 weeks in 2020, the studied locations and thus the influence of the wheel speeds on the respective frequency fluctuations were similar for the individual periods that contributed nearly the same number of ground velocity values (≈ 4000) to the combined dataset. Note also that the ZWC data are provided only on the observation level (12 s = 540 pulses) as there is no differentiation between clear and cloudy bins.
From these results it can be concluded that, like for the atmospheric winds, the enhanced frequency noise has only minor influence on the Mie and Rayleigh ground velocities. Although the impact is slightly larger, it is still hardly noticeable in the statistics derived from the complete ZWC dataset and certainly represents a smaller issue than, e. g. , blowing snow affecting the Mie and Rayleigh responses. Nevertheless, the approximately linear relationship between the frequency stability and the Mie mean ZWC value with a slope of about 0. 08 m s -1 MHz -1 (Fig. 14b ) demonstrates that a more severe degradation of the stability would lead to a significant change in the Mie ground velocities. Hence, assuming frequency fluctuations of 50 MHz on the observation level, the corresponding Mie ZWC values would deviate by more than 3 m s -1.
This hypothesis is supported by the fact that the laser stability is also degraded during thruster firings of the satellite, which introduce high vibration levels also at the location of the power laser head. The identified detrimental frequencies of the reaction wheels range between 14 and 28 RPS and are consistent among the three active wheels, although the relative impact on the two lasers is different. Owing to the dependency of the reaction wheel speeds on the magnetorquer control authority and the strong influence of the latter on the magnetometer readings, there is an indirect link and hence decent correlation between the frequency stability and the magnetic field measured by the onboard magnetometer. In the context of the enhanced frequency noise, the Aeolus wind error with respect to ECMWF model background winds was studied, pointing out that the temporally degraded stability of the ALADIN laser transmitter has only marginal influence on the global wind data quality.
For the Rayleigh winds, a QC based on the frequency stability is not considered useful since the error is largely dominated by shot noise. Hence, the discarding of any measurements, even with poor frequency stability, will rather diminish the Rayleigh wind precision given that it is primarily limited by shot noise. Concerning the use of Aeolus data on smaller geographical scales, e. g. , for its validation by on-ground or airborne instruments, the geolocational dependence of the laser frequency stability should be considered. Regions where the reaction wheels repeatedly rotate at their critical frequencies suffer from enhanced frequency noise during every overpass along Aeolus' ascending or descending orbit. Consequently, the percentage of wind observations with degraded stability is considerably higher than on a global scale and is expected to have a stronger impact on the wind accuracy, especially for the Mie channel.
As an alternative, active closed-loop vibration isolation systems based on sensing and counteracting the mechanical disturbances arising in the spacecraft show great promise in overcoming the limitations of passive systems. These can be implemented at the level of the source  (Preda et al. , 2018)  and/or the payload  (Sanfedino et al. , 2021)  and can significantly mitigate the micro-vibration environment in Aeolus follow-on and other future space lidar missions. Data availability. The presented work includes data of the Aeolus mission that is part of the European Space Agency (ESA) Earth Explorer program. This includes the L2C wind product (baseline 10) (de Kloe et al. , 2020, https://earth. esa. int/eogateway/documents/ 20142/37627/Aeolus-L2B-2C-Input-Output-DD-ICD. pdf, last access: 10 September 2021) that is publicly available and can be downloaded from the ESA Aeolus Online Dissemination System (http://aeolus-ds.
eo. esa. int/oads/access/collection/L2C_Wind_ Products, last access: 10 September 2021; ESA, 2021). The processor development, improvement and product reprocessing preparation are performed by the Aeolus DISC (Data, Innovation and Science Cluster), which involves the DLR, DoRIT, TROPOS, ECMWF, KNMI, CNRS, S&T, ABB and Serco, in close cooperation with the Aeolus PDGS (Payload Data Ground Segment). The presented analysis was performed in the framework of the Aeolus DISC. The raw Mie channel data used for the assessment of the laser frequency stability in the different analyzed periods are included in the Aeolus L1A product (baselines 02, 03, 06, 10), which is accessible for members of the DISC. The housekeeping telemetry data were obtained from the Aeolus MUST tool, which is also only accessible to members of the DISC.
The ground velocity data are part of the reprocessed Aeolus AUX_ZWC product and will become publicly available in October 2021. Author contributions. OLu analyzed the ALADIN laser frequency stability, its correlation with the reaction wheel speeds and its impact on the Aeolus wind data quality. CL, FW, TK, DW and OR supported the data analysis and interpretation of the results. GR and AH studied the platform parameters related to the Aeolus attitude and orbit control system. GR investigated the results from the on-ground micro-vibration tests with regard to the in-orbit instrument performance. OLe, PM and FF were involved in the on-ground micro-vibration tests of the ALADIN instrument and provided insight into the test procedure and findings at that time. PB provided information of the ALADIN MO cavity control scheme.
TP is the Aeolus mission manager. OR is the scientific coordinator of the Aeolus DISC. The paper was written by OLu with contributions from all co-authors. Competing interests. The authors declare that they have no conflict of interest. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Special issue statement. This article is part of the special issue "Aeolus data and their application (AMT/ACP/WCD inter-journal SI)". It is not associated with a conference. Acknowledgements. The Doppler wind lidar ALADIN was built by Airbus SAS in Toulouse, France; the satellite by Airbus Ltd in Stevenage, UK; and the laser transmitters by Leonardo S. p. A. in Florence and Pomezia, Italy. The authors acknowledge Anne Grete Straume (Aeolus mission scientist) and Jonas von Bismarck (Aeolus data quality manager) as well as the Aeolus Mission Advisory Group, the Aeolus Space and Ground Segment Operations teams, and the Aeolus DISC for their invaluable contributions.
The authors are also grateful for the insightful discussions with colleagues from the ESA's SWARM mission, particularly Nils Olsen, Anja Strømme, Rune Floberghagen, Laurent Maleville and Jerome Bouffard, regarding the interaction between the reaction wheels, magnetorquers and magnetometers on board Aeolus, which helped to rule out a direct influence of the Earth's magnetic field on the laser frequency stability. Financial support. This research has been supported by the European Space Agency (grant no. 4000126336/18/I-BG). The first author was partly funded by a young scientist grant by the ESA within the DRAGON 4 program (grant no. 4000121191/17/I-NB). The article processing charges for this open-access publication were covered by the German Aerospace Center (DLR). Review statement. This paper was edited by Ad Stoffelen and reviewed by Weibiao Chen and one anonymous referee.
Overview of the proteomic analysis To compare the diversity and abundance of cellular proteins that accumulate during incompatible and compatible interactions between grapevine and P. viticola at an early stage, total proteins were extracted at 12 hpi from P. viticola-inoculated (P) or mock-inoculated (M) grapevine leaves (LB12-P, LB12-M, PN12-P, PN12-M, respectively) and analyzed by iTRAQ. The workflow of the analysis is shown in Fig. 3A. In total, 296,872 spectra were obtained, representing 40,165 peptides, 29,605 unique peptides and 6612 proteins. The entire dataset is accessible through ProteomeXchange (PXD018845). Principal component analysis (PCA) of the normalized protein expression data set showed that the three replicates of each experimental condition were well grouped, indicating a high degree of correlation among the replicates (Fig.
Functional annotation and classification Among the 709 DEPs, 538 DEPs were annotated as hypothetical or unknown proteins in UniProt (http:// www. uniprot. org/). To gain more information about these proteins, BLAST (http://www. ncbi. nlm. nih. gov/BLAST/) was used to identify their homologous proteins in the NCBI nonredundant (nr) protein database. In addition, to gain a more detailed description, MapMan was used to conduct the functional annotation and classification of the DEPs  29. These 538 DEPs were classified into 34 functional MapMan bins, as shown in Fig. 4. In both LB and PN, the predominant proteins that showed alterations in abundance in response to downy mildew were assigned to photosynthesis, metabolism, stress, and redox categories.
Validation of DEPs by parallel reaction monitoring (PRM) PRM technology is an ion monitoring technique based on high-resolution and high-precision mass spectrometry. PRM initially uses the selective detection capability of a quadrupole mass analyzer to selectively detect the precursor ion information of the target peptide. Selected ion precursors are then fragmented by HCD in a collision cell and finally analyzed by a high-resolution and high-massaccuracy Orbitrap analyzer. This technology allows for an accurate and specific analysis of target proteins/peptides in complex samples  30. To validate the iTRAQ results, four differentially expressed proteins were analyzed using PRM in both varieties at 12 hpi. Data are available from Pro-teomeXchange (PXD018868). The correlation coefficient between the iTRAQ and PRM analyses was 0.
Mass spectrometry (MS) The peptide combination was loaded onto a reversedphase trap column connected to a C18 reversed-phase analytical column in Buffer A (0. 1% formic acid) and separated with a linear gradient of Buffer B (84% acetonitrile and 0. 1% formic acid) at a flow rate of 300 nL/min controlled by IntelliFlow technology. LC-MS/MS analysis was carried out through a Q Exactive mass spectrometer (Thermo Scientific), which functioned in positive ion pattern and was connected to an Easy nLC for 120 min. MS data were generated by adopting a data-dependent top ten approach, which conducts higher energy dissociation fragmentation by picking the rich precursor ions out from the survey scan (300-1800 m/z). The target automatic gain control (AGC) was adjusted to 3e6, the maximum injection duration was 10 ms, and the dynamic exclusion period was 40 s.