dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the KQ feature E 2 is observed for the CODE and MPC databases. We found several interesting characteristics in the databases we used. First, in q zone #1, whereas for the two models a lack of comets with small ecliptic inclination was found, such orbits were common in the two databases. This is striking: as already discussed in Sect. 4. 1, comets in this zone should have large ecliptic inclinations in order to survive planetary scattering. An excess of comets with a moderate semimajor axis (smaller than 20 000 au), perihelion in the region of the planetary region, and a small ecliptic inclination may be explained by either observational biases or the models failing to reproduce the dynamical routes that lead to such final orbits. In fact, orbital data from observations must contain comets that are also coming from the scattering disk, and such longperiod comets would likely end in q zone #1 with small ecliptic inclination 6.
Perihelion distributions We next considered the direction of the perihelion. As discussed in Sect. 2. 3, its direction is well conserved when the perihelion distance reaches its minimum during its Galactic oscillation. Figure  4  shows the position of the perihelion direction of comets on the (l G , b G ) plane where l G and b G are the Galactic longitude and latitude, respectively. The panels are categorized into the three z zones. The ecliptic and EE planes are also plotted. At first glance, it seems that for the two databases and the two models, the directions of perihelion are denser in the ecliptic and EE neighborhood. 6 The conservation of the Tisserand parameter during a close encounter with a giant planet for prograde and very eccentric orbits implies that an increase in the orbital energy is related to a decrease in the ecliptic inclination.
In order to see if some features of interest are present, the distributions of | sin δ| and | sin δ * | are considered. The same methodology as for the Ω G p and cos i E p distributions was used. Thus, the difference between two clones of a database consists only in the populations of the z zones because the direction of the perihelion is not affected. Figure  5  shows the distributions of | sin δ| and | sin δ * | for the two databases and the two models for the three different z zones. As shown in Fig. 3 , patches indicate the feature of interest where the disk distributions highlight a typical and distinguishable EE or KQ features. These features are, for the KQ feature, a maximum of the |δ| distribution at zero for z zone #2. For the EE features, they are a maximum of the |δ * | distributions toward zero for z zones #1 and #2 and a maximum of the | sin δ| distributions in one in the same two zones.
In the present study, the same strategy was applied: we picked the comets from the databases that were discovered after January 1, 1998, with a total magnitude smaller than 11, and which passed less than 5 au from the Sun (that is, 1 au higher than in the 2017 study). The magnitudes were taken from the MPC. With the above filters, the numbers of comets is 53 (2 of which are hyperbolic) in the CODE database and it 69 (7 of which are hyperbolics) for the MPC database. In q zone #1 and #2, the number of comets is now 36 in the CODE database, and 39 for the MPC database, compared to 112 and 115 for the complete database, respectively; thus, the filtered samples contain only approximately one-third the comets contained in the full ones. This clearly have affected the statistical reliability of the results.
The distributions obtained using the filtered database are shown in Fig. 6. First, we consider the comet population in q zone #1 with small a ecliptic inclination. A shown in Fig. 3 , the unfiltered databases contained an excess of such comets. By using the filtered databases, this excess was significantly reduced. Thus, some observational bias should exist in the complete sample, meaning that the number of comets along the ecliptic plane is overestimated. However, this does not rule out the possibility that the databases must contain comets coming A104, page 8 of 12  Fouchard, M. , et al. : A&A proofs,. Position of the comets for the two databases and the two models (CODE, MPC, disk, and isotropic, from left to right) in the (l Go , sin b Go ) plane for the three z zones from top to bottom (see the main text for details).
The solid black line corresponds to the ecliptic plane, and the dotted black line corresponds to the EE plane. from the scattered disk, and this flux was not present in our models. Regarding the features of interest, except for the E * 5 EE feature, all the others are distinguishable on the distributions obtained using the filtered MPC database. Compared to Figs. 3  and 5 , the features are all weakly altered, but they are still distinguishable. The situation is different for the CODE database, in which many of the features are invisible when filtered data are adopted. This is particularly true for EE features. However, as shown in Fig. 6 , although the features are unclear, the distributions using the filtered CODE database were clearly more similar to the distributions obtained with the disk model than with the isotropic model.
This alteration in the results could be explained by considering the quality of the cometary orbits. The orbit quality index for the CODE catalog  (Królikowska & Dybczyński 2013)  is slightly different from that used for the MPC  (Marsden et al. 1978). Specifically, the best 1A class of the MPC database corresponds roughly to the 1a+, 1a and 1b class of the CODE database. The proportions of 1a+, 1a, and 1b quality in the full CODE database were approximately 60%, whereas the proportion of the 1A quality in the MPC database was 48%. We cannot directly compare these proportions because they depend on the manner in which the data are used. However, when we consider the filtered databases, the best quality orbits for the CODE catalogs reached 43%, whereas they were 65% for the MPC database. This indicates that, the filtered data for the CODE catalogs are of lower quality than those for the whole catalog, whereas the opposite is true for the MPC database.
Statistical tests To obtain a comprehensive picture of our results, the following experiments were performed. For each feature of interest, E 1-3 for the KQ ones and E * 1-5 for the EE ones, the number of distributions among the 200 ones (the nominal one plus the 199 clones) for which the maximum of the distribution coincides with the feature is counted. The proportion obtained here can be compared to the proportion that is expected from a uniform distribution, 0. 25. This value comes from the fact that for any distributions four bins are considered. The results obtained for each database, filtered and not, are shown in Table  2. We note, however, that with this method a distribution cannot have a maximum that coincides with both E 3 and E * 4. And 10 Actually, the initial MPC database contains all the comets, but we consider only comets with orbital energy z = -1/a < 10 -4 au -1.
The original orbital energies of the comets differed between the CODE and MPC databases. Therefore, some comets in our CODE database are not in our MPC database. A104, page 9 of 12 indeed, one notes that E 3 is almost always detected, whereas E * 4 is not. However, this does not imply the absence of this feature, just that the feature E 3 dominates. From Table  2  we can see that the CODE database matches all three KQ features for both the full database and the filtered database. Three EE features were matched using the full database but only one using the filtered one. The MPC database had slightly different results for all three KQ features using the full database but only two with the filtered database. Three EE features were detected in the full database and only two in the filtered database.
Conclusions In the present study, we attempted to detect such traces in the observational databases of cometary orbits. We used two databases of long-period comets that were constructed independently. From each database, we selected new long-period comets that had an original semimajor axis greater than 10 4 au or were on their original hyperbolic orbit, and with perihelion distances Notes. For each feature of interest (first column), gives the proportion of cases over the 200 clones of each database where the maximum of the distribution coincides with the feature. For each feature, the proportion is first given using the entire database and the second using the supposed complete subset of comets. smaller than 5 au. In addition, to cancel out the effect of observational biases, each database was filtered in order to obtain a sample of comets whose completeness is much higher than that of the entire database.
The second one, called B2, also consists of orbits that start in the initial disk; however, during propagation the eccentricity, Galactic inclination, and argument of perihelion make a full Galactic period, whereas the Galactic longitude of the ascending node slowly decreases until the perihelion where the KQ process occurs. A third dynamical route typical of the disk model was also identified to generate the EE features. This is very similar to route B2 except that the Galactic longitude of the ascending node is decreasing smoothly such that when the perihelion is back to the planetary region, the longitude of the ascending node has accomplished half a period. In this case, the final orbital plane is close to the EE plane. Our results show that both features (KQ and EE) were detectable in the distributions obtained from the cometary orbital databases.
Such a detection is of great importance for understanding the shape of the current Oort Cloud. Indeed, the detection of the KQ feature indicates that the B1 and B2 dynamical routes might be common for comets in the databases, and the detection of EE features indicates that the B3 dynamical route might also be common. Hence, a disk should be present in the current and initial Oort Cloud at the end of its formation process. This result confirms what was already proposed by  Fernández (2002) , who observed an excess of prograde comets among "new" long-period comets. The size of the disk can be A104, page 10 of 12  Fouchard, M. , et al. : A&A proofs,. Same as Figs. 3 and 5 but with the databases restricted to supposedly complete subsets of comets. estimated using the results obtained by  Fouchard et al.
2019) , and scattering caused by the stellar cluster where the Sun was born  (Fernández & Brunini 2000; Kaib & Quinn 2008; Brasser et al. 2012; Wiśniowski et al. 2022). The capture of comets from other stars may have also contributed to the formation of the Oort Cloud  (Levison et al. 2010 ). The estimated shape of the initial Oort Cloud differs according to the role assigned to each process in its formation. For instance,  Rickman et al. (2023)  show that even the orbit of the giant planets could be destabilized by scattering from the birth stellar cluster of the Sun. This highlights the importance of the presence of a disk in the Oort Cloud with regard to the process at work during its formation. Although some KQ and EE features were detected in the distributions obtained from the databases, the fit with the disk model was far from perfect.
This could have some implication for the distribution of other families of small bodies (e. g. , "old" long-period comets, Halley-type or Jupiter family comets, and Centaurs). Two other aspects of our study should also be improved. The first regards the Galactic tides environment on a long timescale, from which the Galactic tides model is built. This is crucial because the features observed are directly related to the model. With the help of Gaia releases (Gaia Collaboration 2021), this is now within reach. The second aspect concerns the completeness of the cometary databases. Indeed, some bias was present when the full databases were used. The filtered databases were more complete in the sense that they were less subject to bias. Unfortunately, the quality of the results was altered by the number of data points used and the quality of the orbits, which were globally worse than that of the full sample for the CODE database.
Acknowledgements. We are grateful to  Melaine Saillenfest ,  Vacheslav Emel'Yanenko  and  Giovanni Valsecchi  for helpful discussions that allowed to improve the quality of the present paper. We are also grateful to  Hans Rickman  and a second anonymous referee for their helpful comments. We acknowledge  NASA 's Astrophysics Data System Bibliographic Services. T. I. acknowledges the research support from the  JSPS Kakenhi Grant  ( JP18K03730/2018-2021 ). The cometary orbital elements retrieved from various databases were processed and reorganized for analysis at the  Center for Computational Astrophysics (CfCA) ,  National Astronomical Observatory of Japan. This work was supported by the  Programme de Planétologie (PNP) of CNRS/INSU , co-funded by  CNES.
Instrument guidance definition On Phobos QSOs and during Deimos fly-bys, MMX's distance and relative velocity with respect to the target will significantly evolve with time. Fig. 9  shows MIRS projected slits on Phobos with 1-min intervals, from a medium altitude QSO, for three different roll angles, with the MIRS scanner in fixed canonical position. We observe an increase of MIRS projected slit size with the roll angle absolute value, which is the result of an increase of the distance to the observed point coupled with an increase of the emission angle. We also observe a dependency on the subsatellite longitude: • the spatial sampling is larger for longitudes around 0 • /180 • , shorter for longitudes around -90 • /90 • • the MIRS projected slit is respectively smaller and bigger Consequently, the basic principle of push-broom acquisition does not ensure a regular spatial sampling and degrades either the resolution or the data volume.
When the target speed relative to the MIRS slit is close to 0 deg/s, Scanner Guidance is the only option: it should be the case during MMX descents to Phobos and stars observation. The development of Temporal or Scanner Guidance requires complex calculations with, among other things, intersections with an ellipsoid or a shape model, which cannot be performed with the capacities of the onboard processors used today for interplanetary missions. The guidance profiles therefore have to be calculated on-ground and transferred to MMX with the best compromise between accuracy and quantity of information. Future interplanetary missions will very probably attempt to introduce more on-board autonomy, which could allow more reactivity for opportunistic observations and reduce the uplink data volume. But even in that prospect, the versatility of ground commanded observations would still be an asset.
Errors on the line-of-sight guidance We distinguish the pointing error, which induces an offset with respect to the target and the stability error, which degrades the instrument modulated transfer function with unwanted high-frequency variation of the line of sight. The pointing error can be mitigated by adding margins on the zone to be observed but there is no simple way to cope with the stability error. On MIRS, the main contributor to the stability error is MMX attitude control. Phobos descent trajectories are under assessment but orbit control could also be a contributor in that case. The contributors to the pointing errors are MMX, the MIRS scanner, the orbit control accuracy, the polynomial approximations of MMX attitude and the MIRS Scanner Guidance profiles, the time quantization of MMX and MIRS flight software, and the precision of the target celestial body shape and orientation models.
The thermal properties of the LHCb Vertex Locator (VELO) are studied using the real-time detector alignment procedure. The variation of the position and orientation of the detector elements as a function of the operating temperature of the VELO is presented. This study uses a dataset collected by the LHCb experiment during a VELO temperature scan performed at the end of LHC Run 2 (October 2018). Significant shrinkage of the VELO modules is observed at the operating temperature of -30 • C compared to the laboratory measurements on a single module taken at a range of temperatures from +45 • C to -25 • C. The thermal shrinkage expected from the extrapolation of laboratory measurements to lower temperatures, and the results of this alignment study are in good agreement.
The tracking system reconstructs the particles' trajectories (tracks) and determines their momentum. The RICH (Ring Imaging Cherenkov) detectors provide particle identification using Cherenkov radiation. A dipole magnet curves the paths of charged particles and allows the momenta to be determined. Their energy is measured by an electromagnetic and hadronic calorimeter. The last sub-detector of the experiment is the muon system which detects muons and measures their properties. The trigger system of the experiment determines which events are recorded and selected to be analysed. From the recorded information, different variables for the decay particles can be calculated and used for further data selection and analysis. This paper presents the VELO module position stability as a function of the operational temperature variation.
The VELO alignment The performance of the LHCb VELO has ensured track reconstruction efficiency above 98%, a vertex resolution along the beam axis of about 70 µm, and a decay time resolution of about 45 fs  [3]. One of the key ingredients to achieve such performance is the alignment. The correct VELO alignment contributes to correctly distinguishing primary and secondary vertices and to improve the quality of the impact parameter. The alignment of the VELO is determined by a Kalman filter  [5, 6]  track model that is used in a minimum-χ 2 algorithm to determine the position of the detector elements (halves, modules and sensors) from a sample of reconstructed tracks. The position of each detector element is stored in a database that is accessed by the software for reconstruction. The alignment algorithm estimates the misplacement of a set of positions of detector elements with respect to their expected positions after closure of the VELO.
VELO Temperature Scan At the end of Run 2, a temperature scan of the VELO was performed to evaluate the temperature effect on the detector position. During one of the last fills of the LHC Run 2, in October 2018, the operating temperature of the detector was changed from -30 • C to -20 • C using two intermediate steps with temperatures -26 • C and -24 • C. Before reaching the stable temperature of -24 • C the high voltage was changed from 350 V to 250 V. To avoid effects due to the accuracy of the closing system the data were collected in the same fill. In order to reach thermal stability the data was collected 5 minutes after the temperature change. The data samples were saved at each temperature point, and the samples are used to study the effect on the detector performance. In the next section we present the study of the detector movements as a function of the temperature.
Conclusion The thermal properties of the VELO modules are studied using the alignment. Data samples were collected at four operating temperatures between -30 • C and -20 • C and the alignment procedure was used to evaluate the VELO alignment constants variations as a function of the temperature. Both VELO halves and modules have been aligned for translations and rotations at each temperature point. The alignment constants are found to vary linearly as function of temperature. The only significant variation is for the translations along the x axis. The modules variations have a nonlinear behaviour with small variations within 2 µm at the same temperature. The variations in T y and R z are not significant as they are within the alignment accuracy. These results are compared with the measurements performed on a single module in laboratory conditions.
The ANTARES neutrino telescope operated in the Mediterranean sea from 2006 until its decommissioning in 2022. During its livetime, neutrino data was collected and analysed in the form of events. These were recorded by the data acquisition system upon the detection of sets of causally connected hits on the optical modules (OM). The event hits were then fitted to models describing neutrino interactions in order to obtain the energy and direction of the interacting neutrinos. Due to the distance between OMs this procedure was efficient for energies higher than 10 GeV, leaving the lower energy range inaccessible. Despite these limitations, a novel approach has been developed to search for periodic patterns in the optical module counting rates. This analysis could potentially reveal fluxes of low energy neutrinos from periodic sources such as pulsars.
A Fast Fourier Transform (FFT) is applied to the time stream of the PMT counting rates and a significant excess in the Fourier power spectrum is searched for. This requires the use of data different from the standard events that are recorded by neutrino telescopes. While the PMT counting rates are typically used for detector monitoring purposes and to simulate the optical background in detector simulations, they have never been considered for this type of analysis before. For the ANTARES neutrino telescope, over 14 years of data remain unanalysed to this day despite their scientific potential. This analysis can in constrain the emission of low energy neutrinos from pulsars, as predicted in previous studies  [2]. This report is organised as follows: section 2 describes the available data. Section 3 describes the analysis strategy which includes a preprocessing of the data in order to address possible discontinuities in the time stream, non-uniformity of the background rates as well as variations on the data taking conditions.
Data description The ANTARES neutrino telescope was installed in the Mediterranean Sea, and its primary data consists of hits generated when the signal in the PMT anode surpasses a predefined threshold. Hits can originate from neutrino interactions, atmospheric muons penetrating underwater, and optical background sources. The optical background is mainly caused by bioluminescent activity in the The data processing involved dividing the incoming stream into time slices of 104 ms. The data filter stored the total hit counts recorded by each PMT during each triggered time slice, allowing for the extraction of the PMT counting rates with a sampling frequency of approximately 9. 5 Hz. Although there may be occasional data gaps due to trigger requirements, the ANTARES data filter configuration ensured a trigger rate above 10 Hz since 2009.
Removing gaps from the time series data The data aqcuisition for ANTARES was performed through data taking runs with irregular recording times. Within each run, the counting rates were evenly sampled, enabling accurate calculation of the Fourier spectrum for each run. To enhance the sensitivity of the FFT for a fixed signal-to-noise ratio, it is desirable to combine multiple runs into a single extended time series. However, due to dead time during run changes, the runs of ANTARES do not seamlessly connect to each other, resulting in gaps in the time series and an unevenly spaced dataset. To address this, data padding is applied by introducing replacement values, which are computed as the mean of the surrounding time series, to fill these gaps and create an evenly spaced time series  [5]. However, the implementation of ANTARES data collection presents an additional challenge.