Prof. Pierre Tattevin, Infectious Diseases and Intensive Care Unit, Pontchaillou University Hospital, 2, rue Henri Le Guilloux, 35033 Rennes Cedex, France. E-mail: pierre.tattevin@chu-rennes.fr A c c e p t e d M a n u s c r i p t Among the 'big three' central nervous system (CNS) infections, brain abscess has long been neglected, in terms of research priority. Indeed, an unrestricted 'pubmed search' performed in December, 25 th , 2021 retrieved 129,403 papers with the keyword 'meningitis', 72,336 with the keyword 'encephalitis', and 12,011 with the keywords 'brain abscess'. Not only the numbers of clinical research papers dealing with brain abscesses were small (10 times smaller than the number of papers dealing with meningitis), but they were mostly single-center and retrospective, so that their findings were often limited, and not generalizable. The reasons why brain abscesses have not stimulated much research efforts, until recently, remain unclear, but it may involve the followings: i) its low incidence, estimated at less than 1 per 100,000 inhabitants per year in developed countries (although close to that of encephalitis, which stimulated 6 times more papers); ii) the broad spectrum of micro-organisms potentially involved -including bacteria, fungi, and parasites; iii) its management by several medical specialties (i.e. infectious diseases, neurosurgery, microbiology, radiology, intensive care medicine, neurology, rehabilitation), with limited opportunities for trans-disciplinary research collaboration [START_REF] Sonneville | An update on bacterial brain abscess in immunocompetent patients[END_REF].

Fortunately, this situation has improved over recent years, under the leadership of colleagues from Northern Europe. A few years ago, Matthijs Brouwer et al. performed a systematic literature review and metanalysis of almost 10,000 cases of brain abscess, to provide a large overview of clinical characteristics and outcome [START_REF] Brouwer | Clinical characteristics and outcome of brain abscess: systematic review and meta-analysis[END_REF]. Thereafter, several landmark studies have been performed in Denmark, by a multidisciplinary consortium of researchers, to decipher the pathophysiology, the characteristics, and the outcome of brain abscess. These researchers took the opportunity of national registries with robust data, collected prospectively during several decades, about the health and the medical management of all people residing in the country. The combination of different nationwide prospective registries translated into reliable findings, based on clinically relevant scientific questions, well-designed statistical analyses, and large study population (5.8 million residents in Denmark). These invaluable tools were previously applied successfully for other severe infectious diseases, such as Staphylococcus aureus bacteremia [START_REF] Oestergaard | Familial Clustering of Staphylococcus aureus Bacteremia in First-Degree Relatives: A Danish Nationwide Cohort Study[END_REF]. For brain abscess, Jacob Bodilsen et al. provided the most robust data on outcomes available to date, with one-year mortality rate estimated at 21%, and a A c c e p t e d M a n u s c r i p t prevalence of de novo epilepsy estimated at 32% in survivors [START_REF] Bodilsen | Long-term Mortality and Epilepsy in Patients After Brain Abscess: A Nationwide Population-Based Matched Cohort Study[END_REF]. They also identified the major risk factors for brain abscess through a large case-control study based on these prospective Danish registries, including data on 1,384 patients with brain abscess and 13,839 controls, and quantified the relative contribution of these risk factors. Accordingly, main contributors to the occurrence of brain abscess are neurosurgery (12%), solid cancer (11%), ear, nose, and throat infections (7%), and immunomodulating treatments (5%) [START_REF] Bodilsen | Risk Factors for Brain Abscess: A Nationwide, Population-Based, Nested Case-Control Study[END_REF].

In this issue of Clinical Infectious Diseases, the same consortium aimed to explore the link between oral health and brain abscess, and to evaluate the potential impact of invasive dental procedure on the risk of brain abscess (Bodilsen J, Helweg-Larsen J, Otto Jarløv J et al. Dentist's visits and risk of brain abscess: A nationwide, population-based case control study. Clin Infect Dis 2022). This time, they restricted the definition of cases to patients with culture-proven brain-abscess caused by oral cavity bacteria during years 1989-2016 (n=362), and selected 10 population controls per case, matched on age, sex, and residential area (n=3,257). They extracted data from four nationwide registries: i) the National Patients Registry, for information on all hospital admissions; ii) the Civil Registration System, for information on vital and migration status for all Danish residents; iii) the Danish National Health Service Register, for information on primary healthcare services, including dentist's visits; and iv) the healthcare records of all departments of clinical microbiology providing services to neurosurgical departments in Denmark, for information on the microbiology of brain abscess. They focused on two main exposures: i) invasive dental procedures performed within 6 months before the diagnosis of brain abscess, to estimate the risk associated with these procedures; ii) regular dentist's visits within one and three years before brain abscess diagnosis, as a proxy for good dental health.

The characteristics of patients with brain abscess included in this study is in line with literature data, with a median age of 53 years, and a male predominance (61%). Most common bacteria cultured from brain abscess samples were Streptococcus spp., Fusobacterium spp., Haemophilus spp., Actinomyces spp., Prevotella spp., and Aggregatibacter spp., all residents of the oral cavity, which confirms the validity of their inclusion criteria. However, they found no difference A c c e p t e d M a n u s c r i p t in the proportion of invasive dental procedures during the six months before study inclusion between patients with brain abscess (21/362, 6%) and their matched population controls (179/3,257, 5%).

Likewise, they found no difference in the frequency, and the patterns of dentist's visits within the three years prior to study inclusion. Several sensitivity and pre-specified subgroup analyses found no significant difference as well.

Where are we left with these unexpected findings? Firstly, these results should not be interpreted as a demonstration that dental health, and invasive dental procedures, have no role in the pathophysiology of brain abscess. Indeed, the study was observationalhence with potential biases, and may have been underpowered, despite its large scale, given the limited number of patients with culture-proven brain abscesses caused by oral cavity bacteria who underwent recent invasive dental procedures (n=21). Indeed, in their previous study on risk factors for brain abscess, with larger samples as it was not restricted to culture-positive brain abscesses, the same consortium found that previous in-hospital dental surgery was a risk factor for brain abscess, with an adjusted odds ratio of 2.57 (95% CI 1.71-3.84). Of note, this exposure was reported in only 2% of patients with brain abscess (22/1,384), and 0.3% of controls (40/13,839). Secondly, as elegantly suggested by the authors, both exposures analyzed may be associated with the risk of brain abscess in two opposite directions: Indeed, invasive dental procedures, or the number of dentist's visits, may both reflect either good care regarding dental health, or failure to prevent teeth diseases, which in turn would request more dentist's visits, and invasive dental procedures. These opposite associations may neutralize each other, with a final result suggesting no association. Thirdly, we may be grateful to the authors, and the editorial board of Clinical Infectious Diseases, for their commitment to publish these negative findings, despite our academic modern world tends to magnify positive results, and discard negative findings. Although the main findings of this study are negative, this paper is an important step ahead on our knowledge on brain abscesses. As the two other major CNS infections, brain abscesses are associated with a high burden of mortality, neurological sequels, and impaired quality of life. Robust data that will improve our understanding on the pathophysiology of this disease may allow to adjust preventive strategies.

A c c e p t e d M a n u s c r i p t

How can we reconciliate the facts that oral cavity bacteria are the most frequent microorganisms identified in brain abscess samples [START_REF] Sonneville | An update on bacterial brain abscess in immunocompetent patients[END_REF][2], while only 6% of patients with brain-abscess caused by oral cavity bacteria underwent recent invasive dental procedures, and no association was found between proxy for dental health, and the risk of brain abscess? The primary reason may be that the risks of brain abscess related to everyday bacteremia in patients with healthy teeth, including bacteremia following toothbrushing [START_REF] Lockhart | Bacteremia associated with toothbrushing and dental extraction[END_REF], outweigh the risks associated with invasive dental procedures, and poor dental health. Hence, large population-based studies may not be adequate to identify the pathophysiology of brain abscess in these subgroups. Anyway, healthy teeth are obviously beneficial for the quality of life, and for the prevention of diseases much more common than brain abscess. The failure to demonstrate that it also decreases the risk of brain abscess should not decrease public health efforts to promote oral hygiene.

A

  c c e p t e d M a n u s c r i p t High-quality large-scale clinical studies on brain abscess : Finally ! Pierre Tattevin 1,2 * 1 Infectious Diseases and Intensive Care Unit, Pontchaillou University Hospital, Rennes, France 2 European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Study Group for Infectious Diseases of the Brain (ESGIB) * Corresponding Author:

  s c r i p t


S Mazouffre

L Grimaud

Characteristics and performances of a 100 W Hall thruster for microspacecraft

Keywords: Hall thruster, low-power, microspacecraft, performances

In this contribution, we examine the characteristics of the ISCT100 thruster, a miniature xenon-fueled 100 W permanent magnet Hall thruster suited for microspacecraft propulsion. The impact of the thruster design upon discharge behavior, ion beam properties and performances is examined as for two discharge channel geometries as well as two values of the magnetic field strength. Thrust, specific impulse and efficiency obtained between 50 W and 200 W are compared to the ones of other low-power Hall thrusters.

I. INTRODUCTION

Over the last several decades, electronic and mechanical devices have drastically gained in terms of size, performance and reliability while the cost continuously decreased. Among other things, those miniaturization processes and cost reductions have led to the emergence of small satellites able to accomplish most of tasks so far assigned to large-size and heavy satellites [START_REF] Wekerle | Status and trends of smallsats and their launch vehicles -An up-to-date review[END_REF]. The observed trend on the satellite market should certainly accelerate as, in addition to the swift development of miniature platforms, small dedicated launchers will soon be available.

The mass is a relevant and useful criterion for categorization of spacecraft as it has a direct link to cost. Here we will use a classification in which the term "small spacecraft" covers spacecraft with masses below 500 kg. Four distinct groups are commonly considered:

-Minispacecraft: masses in the 100-500 kg range, -Microspacecraft: masses in the 10-100 kg range, -Nanospacecraft: masses in the 1-10 kg range, -Picospacecraft: masses < 1 kg. Note that CubeSats fit into the second and third groups. In this work we solely focus on microspacecraft as they represent a good option in terms of operational capabilities-to-cost ratio against heavy satellites. Microsatellites are well-suited for various types of missions such as Earth observation, communication, remote sensing and science with both civilian and military applications. As a matter of fact, they offer substantial advantages compared to heavy platforms: simplicity, low cost, flexibility and turnover rate.

The ability to achieve a large variety of missions with microspacecraft requires, however, the use of an appropriate propulsion system. Although the last decade has seen significant advances in the miniaturization of propulsion units [START_REF] Wright | Electric micropropulsion systems[END_REF]- [START_REF] Lemmer | Propulsion for Cubesats[END_REF], satellite designers still lack high efficiency space qualified devices that can cover a broad range of operating S. Mazouffre and L. Grimaud are with the EP team of the CNRS-ICARE laboratory, Orléans, France. (e-mail: stephane.mazouffre@cnrs-orleans.fr; lou.grimaud@cnrs-orleans.fr).

conditions. Electric propulsion is a suitable technology for microspacecraft as it offers a low propellant consumption, hence a low mass, long service life, large total impulse and flexibility [START_REF] Mazouffre | Electric propulsion for satellites and spacecraft: established technologies and novel approaches[END_REF]. There are two distinct strategies to construct an efficient electrical micropropulsion system. The first one involves the development of new technologies. Examples of thruster that fall into this category are solid-propellant Vacuum Arc Thrusters [START_REF] Keidar | Electric propulsion for small satellites[END_REF], liquid-propellant Field Emission Electric Propulsion devices [START_REF] Mitterauer | Micropropulsion for small spacecraft: a new challenge for field effect electric propulsion and microstructured liquid metal ion sources[END_REF], [START_REF] Reissner | mN-FEEP thruster module design and preliminary performance testing[END_REF], liquid-propellant electrospray thrusters [START_REF] Legge | Electrospray propulsion based on emitters microfabricated in porous metals[END_REF] and gaseous-propellant Electron Cyclotron Resonant thrusters [START_REF] Vialis | Geometry optimization and effect of gas propellant in an electron cyclotron resonance plasma thruster[END_REF]. The second approach consists in the miniaturization of existing technologies with flight heritage. This category includes, inter alia, gridded ion engines [START_REF] Goebel | Fundamentals of Electric Propulsion[END_REF]- [START_REF] Tsay | Micro radio-frequency ion propulsion system[END_REF] and Hall thrusters [START_REF] Boeuf | Tutorial: Physics and modeling of Hall thrusters[END_REF], [START_REF] Zhurin | Physics of closed drift thrusters[END_REF]. Hall thrusters, in addition to being simple and robust, deliver a large thrust-to-power ratio compared to ion engines with a specific impulse high enough to warrant substantial propellant mass saving [START_REF] Mazouffre | Electric propulsion for satellites and spacecraft: established technologies and novel approaches[END_REF]. Low-power Hall thrusters therefore appear as good candidate as propulsion unit for microspacecraft. This article deals with a xenon-fueled permanent magnet 100 W-class Hall thruster. First point, the thruster architecture is described. Second point, the plasma discharge as well as ion beam characteristics are reported for several configurations. Last point, thrust and performance level are given and compared with the ones of similar Hall thrusters.

II. HALL THRUSTER

A. Operation principle

A Hall thruster (HT) is an electrical propulsion device for spacecraft that uses a plasma discharge with magnetized electrons to ionize and accelerate a propellant gas [START_REF] Mazouffre | Electric propulsion for satellites and spacecraft: established technologies and novel approaches[END_REF], [START_REF] Goebel | Fundamentals of Electric Propulsion[END_REF], [START_REF] Boeuf | Tutorial: Physics and modeling of Hall thrusters[END_REF], [START_REF] Zhurin | Physics of closed drift thrusters[END_REF]. The operation principle of a HT relies upon a magnetic barrier and a low-pressure DC discharge generated between a hot cathode and an anode in such a way that a crossed electric and magnetic field discharge is created. The anode is located at the upstream end of a coaxial annular dielectric channel that confines the discharge. A large fraction of the electrons emitted by the thermionic cathode placed outside the channel flows downstream to neutralize the ion beam. The remaining part travels towards the anode to maintain the plasma discharge. The propellant gas, typically xenon, is introduced at the back of the channel either through the anode or using a dedicated injection system. Magnetizing coils or permanent magnets, incorporated into a magnetic circuit made of iron or iron-cobalt alloys, provide a radially directed magnetic field of which the strength is maximum in the vicinity of the channel exhaust. The magnetic field is chosen to be strong enough to make the electron Larmor radius much smaller than the discharge chamber characteristic dimensions, but weak enough not to affect ion trajectories. The electric potential drop is mostly concentrated in the final section of the channel owing to the low axial mobility of electrons that are magnetically confined in this restricted area. The electric field, which extends between the interior and the exterior of the channel, governs the propellant atoms ionization and the ion acceleration, namely the thrust and the specific impulse. The combination of the radial magnetic field with the axial electric field generates an E×B electron drift in the azimuthal direction, the so-called Hall current. The latter is responsible for the very efficient ionization of neutral atoms inside the channel.

Hall thrusters provide a moderate specific impulse level < 2000 s in contrast with gridded ion engines, but the thrustto-power ratio is typically 60 mN/kW and the thrust density is above 30 N/m 2 as there is no space-charge limitation on the ion current. In comparison, the two quantities amount to 30 mN/kW and about 5 N/m 2 respectively for ion engines. The thrust efficiency is about 50 % for moderate power Hall thrusters (∼1 kW), but it is in excess of 60 % for high-power devices, see e.g. [START_REF] Hall | High-power performance of a 100-kW class nested Hall thruster[END_REF] and references herein.

B. Low-power devices

A great challenge in developing low-power Hall thrusters is to achieve high thrust efficiency while keeping a long lifetime. Operation at low-power requires a decrease in size in order to maintain a sufficient ionization rate. Yet miniature Hall thrusters exhibit a large surface-to-volume ratio, which translates into large power losses, large channel wall erosion rate and overheating of magnetic circuit components. Many works have nevertheless been performed all around the world to develop, characterize and qualify 200 W-class Hall thrusters, which are well-adapted for drag compensation, trajectory changes, attitude control and end-of-life deorbiting of 100-200 kg satellites in Earth orbit. Such thrusters typically deliver 10 mN of thrust with an efficiency around 40 % despite small dimensions. Currently, the most advanced devices are, to the best of our knowledge, the BHT200 American HT [START_REF] Szabo | Performance evaluation of an iodine-vapor Hall thruster[END_REF], the SPT30 and Plas-40 Russian HTs [START_REF] Jacobson | Test results of a 200 W class Hall thruster[END_REF], [START_REF] Potapenko | PlaS-40 development status: New results[END_REF], the CAM200

Israeli HT [START_REF] Lev | CAM200 Hall Thruster -Development Overview[END_REF], the HT100 Italian HT [START_REF] Ducci | HT100D performance evaluation and endurance test results[END_REF], the KAIST Korean HT [START_REF] Lee | High efficiency compact annular Hall thruster with tailored magnetic field[END_REF] and the ISCT200 French HT [START_REF] Grimaud | Performance comparison between standard and magnetically shielded 200 W Hall thrusters with BN-SiO2 and graphite channel walls[END_REF]. The inclined reader can find additional information about low-power annular Hall thrusters in [START_REF] Grimaud | Performance comparison between standard and magnetically shielded 200 W Hall thrusters with BN-SiO2 and graphite channel walls[END_REF], [START_REF] Lev | The technological and commercial expansion of Electric Propulsion in the past 24 years[END_REF] and references herein.

Very few relevant and reliable studies have been carried out so far on Hall thrusters with input power below 200 W. In this contribution, the design of a 100 W-class permanent magnet Hall thruster operating with xenon is described and performances along with ion beam property measurements are given and discussed. On a fundamental viewpoint, this work furnishes low-power data to verify the validity of established scaling laws and to test computer simulations. On a more technological standpoint, this work is a necessary step to develop a robust low-cost EP system based on a 100 W Hall thruster able to produce a few mN of thrust at high specific impulse with a reasonable efficiency. Such a propulsion unit would be of interest for maneuvering large Cubesats and 50-100 kg satellites.

C. 100 W architecture

The ICARE Small Customizable Thruster 100, or ISCT100 in short, is a 100 W-class Hall thruster of which the architecture derives from the 200 W input power ISCT200 thruster series [START_REF] Grimaud | Performance comparison between standard and magnetically shielded 200 W Hall thrusters with BN-SiO2 and graphite channel walls[END_REF], [START_REF] Mazouffre | Ionization and acceleration processes in a small, variable channel width, permanent magnet Hall thruster[END_REF]. The annular channel walls of the ISCT100 are made of BN-SiO 2 ceramic. The channel width h can easily be modified while keeping the mean diameter d unchanged by means of various sets of ceramic rings. Works on scaling laws and HT design have shown that a large h leads to higher propellant utilization, thrust and anode efficiency [START_REF] Mazouffre | Ionization and acceleration processes in a small, variable channel width, permanent magnet Hall thruster[END_REF]. Higher performance level originates in the decrease of wall losses due to a more favorable surface-to-volume ratio. In this study, two geometrical configurations have been used, the so-called S 0 and 2S 0 configurations, by installing two sets of ceramic rings, such that the channel cross-section area is either S 0 or two times S 0 , wherein S 0 = πh 0 d 0 corresponds to the standard value of the well-known Russian SPT100. The channel length l is selected to maintain neutral density in the order of the critical density which ensures a high ionization degree [START_REF] Dannenmayer | Elementary scaling relations for Hall effect thrusters[END_REF]. The channel has also to be long enough to allow the ionization region to fully expand. The magnetic field is generated by way of small cylindrical Samarium-cobalt (SmCo) magnets brought together inside rings located on either side of the channel walls. SmCo magnets exhibit a high Curie point, which allows operation at relatively high temperature. Magnets are attractive in terms of power, volume and mass saving. But, contrary to magnetizing coils, instant adjustment of magnetic field magnitude and topology cannot be achieved. The choice between the two technological options depends on mission profile and constraints. A soft iron (ARMCO R ) magnetic circuit with a back gap permits to drive the magnetic flux for obtaining the desired topology. No magnetic screen is used. The magnetic field strength can be varied by simply changing the number of magnets. In the case of the ISCT100, the B field has a classical bell-shape distribution along the channel centerline with the highest intensity at the channel exit plane. Two magnetic field maximum magnitudes have been tested in this work, namely B 0 and 2B 0 with 2B 0 = 2 × B 0 . The field amplitude must be chosen according to three criteria. Firstly, electrons must be magnetized to generate the E × B electron azimuthal drift. Secondly, the electron gyroperiod in the magnetic barrier must be much shorter than the time between two consecutive electron-atom collisions to minimize electron diffusion losses. Lastly, the magnetic confinement of the electron fluid must enable a long residence time in order to maximize the ionization yield. The propellant gasxenon in this work -is injected homogeneously inside the channel using a mullite ring, high porosity ceramics, instead of a classical metal hollow gas injector. The mullite element is placed behind the channel back plate. A narrow annular gap in the back plate allows diffusion of the gas. The anode is a wide stainless-steel ring placed at the back of the channel against the internal surface of the outer dielectric wall. A central copper heat drain is employed to evacuate heat toward a radiator placed behind the thruster body. During operation, the thruster body is floating. A heated hollow cathode with a disk-shaped LaB 6 emitter was used to generate the electron current needed for discharge balance and ion beam neutralization [START_REF] Joussot | Examination of a 5 A class cathode with a LaB 6 flat disk emitter in the 2 A-20 A current range[END_REF]. The cathode is located outside the channel with its orifice in the vertical plane that contains the channel outlet. The cathode is electrically connected to the thruster anode and floating. High-purity xenon was used as the propellant gas for both the thruster discharge and the cathode discharge. The cathode has been operated with a constant xenon mass flow rate of 0.2 mg/s.

A photograph of the ISCT100 Hall thruster is shown in Fig. 1 without the external cathode. The visible copper ring is the radiator. The square copper plate area corresponds to a 1 U Cubesat cross-section area, i.e. 100 cm 2 . The total outside volume and mass of the ISCT100 thruster are 115 cm 3 and 360 g, respectively. The oversized copper heat sink represent 230 g of this total mass and could easily be made twice as light if any mass or thermal optimization were conducted. Figure 2 shows the ISCT100 Hall thruster in 2S 0 B 0 configuration firing with xenon at 100 W input power.

III. RESULTS

A. Discharge envelope

The four different configurations were tested first in the NExET vacuum chamber to assess their operating envelope and stability. The NExET chamber is a 0.7 × 1.5 m stainless steel cylinder with grounded walls fitted with both a cryogenic and turbomolecular pumping system. During measurements the background pressure was between 1 and 4×10 -5 mBar depending on mass flow rate.

Figure 3 shows the map of tested operating points. The shaded areas cover 90% of the discharge current oscillations. All the configurations were tested from extinction voltage up to either 200 W discharge power, the apparition of hot spots in the discharge channel or spontaneous shutdown (whichever came first). Spontaneous shutdown seemed to be mainly caused by very high oscillations. Before even looking at current profile it is interesting to note that thruster start up was made considerably harder as the magnetic field was increased and the channel width narrowed. The S 0 2B 0 case required high voltage (∼ 400 V) and high mass flow (∼ 1 mg/s) in order to ignite it. Identical trend was observed with the ISCT200 Hall thruster in previous works [START_REF] Mazouffre | Ionization and acceleration processes in a small, variable channel width, permanent magnet Hall thruster[END_REF].

Out of all those configurations only the 2S 0 2B 0 one is stable over a large number of operating points, see Fig. 3d. Both 2B 0 configurations have a discharge envelope resembling a classical Hall thruster such as the SPT100 [START_REF] Gascon | Wall material effects in stationary plasma thrusters. I. Parametric studies of an SPT-100[END_REF]. In the 2S 0 2B 0 case the oscillations are restrained to around 10% of the mean discharge current for most points. Above 150 V there is a steady moderate increase of discharge current as the voltage increase for a fixed mass flow rate. The oscillations also slightly increase with voltage but the strong oscillation regime sometime seen at higher voltage is not observed. As can be seen in Fig. 3b, the S 0 2B 0 case displays a local maximum of the discharge current between 150 and 200 V before settling into a nearly constant mean discharge current regime. As the voltage is increased a high oscillation "breathing mode"-like regime appears, with the dominant frequency in the 30-35 kHz range. This regime was only seen above 350 V for the SPT100 [START_REF] Gascon | Wall material effects in stationary plasma thrusters. I. Parametric studies of an SPT-100[END_REF]. In the case of the ISCT100 with the S 0 2B 0 configuration it appears from 225 V.

B. Plume divergence and characteristics

1) Plume current density profiles: For all configurations an ion current density angular profile was acquired across the plume. Those profiles were acquired with a 5.6 mm diameter planar probe fitted with a guard ring. The probe is swept in front of the thruster on a 180 degrees hemisphere with a 35 cm radius. The angular profiles measured for each configuration at 3 different operating conditions are displayed in Fig. 4.

The S 0 B 0 case clearly shows an unfocused beam with a significant portion of the ion current present at high angles. The S 0 2B 0 only has this Gaussian shaped current density distribution at 300 V and 0.5 mg/s. For both case it is correlated with very high current oscillation regimes. For the angular profiles corresponding to more stable operating points, the two peaks, coming from the two opposite sides of the discharge channel, are visible. The resolution of those two peaks is characteristic of a well focused ion beam.

2) Plume characteristics: From the angular distribution of the ion current density it is possible to calculate the total ion current by integrating the distribution over the whole hemisphere facing the thruster. This is generally expressed as the ion current fraction (η i ), i.e. the total ion current over the discharge current.

Knowing the xenon mass flow rate injected in the thruster channel it is also possible to compute how much of the propellant is ionized. The ratio of the ion mass flow rate to the atom mass flow rate is called the mass utilization fraction or the propellant utilization η prop . In the current calculation we assume that all collected ions are singly-charged ions, that means η prop is slightly overestimated. The impact is mainly noticeable at high voltage (> 300 V) when the multiplycharged ion fractions become relevant. Lastly the divergence of the beam (α d ) is defined as the half angle of the cone containing 90% of the ion current.

Some test chamber effects can affect the ion current profiles. Most notably the residual plasma well as scattering and charge exchange collisions between ions and atoms of the residual gas in the chamber can artificially increase the collected ions current [START_REF] Brown | Recommended practice for use of Faraday probes in electric propulsion testing[END_REF], [START_REF] Mazouffre | Evaluation of various probe designs for measuring the ion current density in a Hall thruster plume[END_REF]. Those effects are particularly strong at high angles where the background plasma density can be in the same order as the density of the plasma produced by the thruster. Moreover, the ion current density at high angles, although low, contributes significantly to the total current as they represent a bigger part of the integration hemisphere.

Correction methods can vary from very complex fitting of the experimental profiles to a pre-existing plume shape model to very simple background subtraction. Since all the plume data presented in the study is acquired in the same vacuum chamber with the same measuring instrument, angular profiles and associated quantities can be compared with no additional post processing. We however chose to remove the background plasma by subtracting a fixed offset corresponding to the average flux measured at +90 • and -90 • . This ensures that the data is at least somewhat comparable to data from other publications. Comparisons between various sets should be made with great care as chamber effects, probe design and data processing procedures can greatly influence the numerical results [START_REF] Mazouffre | Evaluation of various probe designs for measuring the ion current density in a Hall thruster plume[END_REF].

Table I shows plume data obtained in all four configurations for the 200 V 0.6 mg/s and the 300 V 0.5 mg/s points. Those respectively represent a discharge power close to 100 W and 140 W for all configurations. From this table it is apparent that the main parameter that influences the divergence angle is the channel width. The S 0 configurations all have higher divergence than the 2S 0 ones. Widening the discharge channel also seems to considerably increase the propellant ionization, in agreement with previous studies on a 200 W Hall thruster [START_REF] Mazouffre | Ionization and acceleration processes in a small, variable channel width, permanent magnet Hall thruster[END_REF]. The ion current fraction (η I ) is also increased with a wider channel but mainly for the high mass flow and low voltage operating points.

The magnetic field intensity does not greatly influence the plume divergence angle. The loss of one degree of divergence at higher field strength is not very conclusive. While the error bars of such measurement are complicated to estimate, they are almost certainly higher than one degree [START_REF] Mazouffre | Evaluation of various probe designs for measuring the ion current density in a Hall thruster plume[END_REF]. The ion beam current fraction shows an increase particularly significant at the highest voltage. This is consistent with the fact that HTs fitted with coils generally increase their magnetic field strength when operating at high voltages in order to maintain a low discharge current and a large efficiency. Finally the propellant utilization does not have any clear behavior tied to the magnetic field intensity.

C. Electric field

The electric field inside a thruster can be measured through Laser Induced Fluorescence (LIF) spectroscopy. This technique has been extensively used in our research team and details about the optical bench, detection branch and data treatment are available in the literature [START_REF] Mazouffre | Laser-induced fluorescence diagnostics of the cross-field discharge of Hall thrusters[END_REF]. LIF relies on the Doppler shift of the absorption spectra of metastable Xe + ions. The fluorescence signal collected is proportional in intensity to the density of metastable ions at the velocity probed. Assuming that the ratio of metastable to ground state ions is constant across all velocity groups at a given position, we can reconstruct the ion velocity distribution function (IVDF) at this position.

The most probable ion velocity in the laser beam direction can easily be inferred from the measured IVDF. Assuming a collsionless medium, the velocity profile can be transformed into a potential energy profile, which in turn is used to compute the electric field distribution [START_REF] Vaudolon | Indirect determination of the electric field in plasma discharges using laser-induced fluorescence spectroscopy[END_REF]. The anode potential is usually used as the reference. A second order centered difference differentiating scheme is used here to derive the electric field. This method was found to be a reasonable approximation when estimating the axial electric field [START_REF] Vaudolon | Indirect determination of the electric field in plasma discharges using laser-induced fluorescence spectroscopy[END_REF].

Measurements of the electric field where performed along the discharge channel centerline from 4 mm inside the discharge channel to 20 mm downstream of the exit plane. Only the best working configuration of 2S 0 2B 0 had its electric field measured. The thruster was operated at 200 V and 0.5 mg/s during the LIF measurements. Figure 5 shows a comparison between the electric fields of the 100 W thruster used in this work, of a 200 W HT with a similar 2S 0 channel configuration operated at 200 V and laboratory model of the 1.5 kW-class SPT100 thruster operated at 250 V. The three thrusters have BN-SiO 2 channel walls. It is interesting to note that across all those sizes, power and B field strength ranges the on-axis electric field profile does not change very much. Accounting for the higher discharge voltage in the case of the SPT100, all of the thrusters have a maximum electric field near the exit plane with a value of about 200 V/cm. The electric field distribution is slightly wider in the downstream region for the larger thrusters. This similarity is a further indication that no matter the size or magnetic field strength, Hall thrusters all operate near the same plasma conditions. It is for instance established that the current density is always in the order of 0.1 A/cm 2 for a Hall thruster that operates around its normal conditions [START_REF] Goebel | Fundamentals of Electric Propulsion[END_REF].

The ion axial kinetic energy behind the acceleration region can be inferred from the LIF data. The maximum ion energy is 155 eV, 151 eV and 211 eV for the ISCT100, ISCT200 and SPT100 Hall thrusters, respectively. Those values show energy losses are around 45 eV and they do not depend upon the thruster geometry and magnetic field strength provided that the wall material stays unchanged.

D. Performances

Thrust measurements were performed in the PIVOINE-2G test bench. The facility has been designed for testing thrusters between 1 kW and 20 kW input power. It consists in a 2.2 × 4 m cylindrical vacuum chamber fitted with a 200000 l/s cryogenic pumping system. Since the ISCT100 operates at very low power, and thus at very low mass flow rates, with respect to what the chamber is designed for, excellent background pressure were achieved. During testing all measurements were carried out with a residual gas pressure between 4 and 6×10 -6 mBar-Xe.

The thrust was recorded using a simple pendulum thrust stand. A capacitive sensors with a resolution of 0.2 µm measures the displacement of the moving platform supporting the thruster. The displacement is calibrated against the force exerted by known masses. Calibration is performed with the thruster off right after each test case in order to account for the thermal drift. The resulting thrust measurement has an accuracy in the order of ± 0.3 mN. The thrust uncertainty is the single most important contributor to the uncertainty on the specific impulse and the anode efficiency. Only the S 0 2B 0 and 2S 0 2B 0 configurations have been investigated as they offer the highest propellant utilization and the lowest divergence angle, see Tab. I. The S 0 2B 0 proved to be even harder to start in the PIVOINE-2G vacuum chamber than in the NExET chamber. This was probably due to the lower background pressure. As a result only two operating points were measured with theS 0 channel geometry. For the two tested configurations the discharge current was slightly lower in the PIVOINE-2G chamber than in the NExET one, due once again to the lower residual gas pressure, see reference [START_REF] Mazouffre | Evaluation of various probe designs for measuring the ion current density in a Hall thruster plume[END_REF].

As can be seen in Fig. 6a, a thrust between 4 and 9 mN was measured across the tested operating envelop, i.e. between 50 W and 250 W. At equivalent mass flow and discharge voltage the S 0 2B 0 configuration both had a lower thrust, a lower specific impulse and a lower anode efficiency, as can be seen in Fig. 6a, 6b and 6c. The 2S 0 2B 0 configuration achieves a specific impulse I sp between 850 s and 1300 s and an anode efficiency η between 25 and 32% for most of the design operating range. The two quantities I sp and η reads:

I sp = T ṁg 0 η = T 2 2 ṁU d I d . ( 1
)
where T is the thrust, ṁ the gas mass flow rate, g 0 is the standard gravity of the Earth and I d and U d the discharge current and applied voltage respectively. Thrust, Isp and efficiency increase with mass flow. A significant uncertainty (±3%) is present on the anode efficiency due to the limits in thrust stand , SPT-20 [START_REF] Loyan | Performance investigation of SPT-20M low power Hall effect thruster[END_REF], SPT-30 [START_REF] Jacobson | Test results of a 200 W class Hall thruster[END_REF], PSAC 100 [START_REF] Lim | Automated integrated robotic systems for diagnostics and test of electric and µ-propulsion thrusters[END_REF])

sensitivity. The best operating point was reached at 300 V and 0.7 mg/s that corresponds to a discharge power of 180 W: a thrust of 9 mN, an anode specific impulse of 1300 s and an anode efficiency of 32%.

IV. COMPARISON WITH EXISTING LOW POWER THRUSTERS

Performances of the ISCT100 Hall thruster in the 2S 0 2B 0 configuration are compared to performances of commercial and laboratory HT with similar sizes and input power in terms of anode efficiency, see Fig. 7. The selected thrusters are, in addition to the ISCT100: the BHT-100 American HT [START_REF] Szabo | Characterization of a one hundred Watt, long lifetime Hall effect thruster for small spacecraft[END_REF], the SPT-20 Ukrainian HT [START_REF] Loyan | Performance investigation of SPT-20M low power Hall effect thruster[END_REF], the SPT-30 Russian HT [START_REF] Jacobson | Test results of a 200 W class Hall thruster[END_REF] and the PSAC 100 [START_REF] Lim | Automated integrated robotic systems for diagnostics and test of electric and µ-propulsion thrusters[END_REF] from Singapore. The ISCT100 has a relatively flat performance curve over a wide range of operating conditions, i.e. between 50 W and 250 W in comparison with e.g. the SPT-20 and the SPT-30 thrusters. The performances of the ISCT100 are globally lower by 5% to 8% compared to the BHT-100 and SPT-20. This might be due to the wall material and/or to the magnetic field topology. Our TRL level is however lower than the one of these two Hall thrusters.

V. CONCLUSION

This study clearly confirms a Hall thruster can operate with xenon around 100 W input power with an efficiency of about 30 % while achieving a thrust-to-power ratio of about 50 mN/kW, close to the one of high power devices. This work also shows that the electric field distribution along the channel centerline does not depend much on the thruster sizes for a given discharge voltage. This fact suggests that the physical mechanisms that govern the magnetized plasma discharge of a Hall thruster are not much affected by sizes and power.

A second version of the ISCT100 Hall thruster is currently in preparation. This new version will be optimized in terms of gas injection system, geometry and magnetic field topology, aiming at higher thrust and an anode efficiency around 40 %. A dedicated low-current hot cathode will be developed in parallel.

Lou Grimaud received a MS in aerospace engineering from both ENSMA, France and the Illinois Institute of Technology (IIT), Chicago in 2014. Since 2015 he has joined the electric propulsion team in the ICARE CNRS laboratory in Orléans, France and is doing his Ph.D the supervision of Dr. Stéphane Mazouffre. His research interests include low erosion Hall thrusters (magnetic shielding and wall less), low power Hall thrusters and plasma diagnostics.

Fig. 1 .

 1 Fig. 1. Photograph of the 100 W-class permanent magnet ISCT100 Hall thruster developed at ICARE. The external cathode is not shown.

Fig. 2 .

 2 Fig. 2. ISCT100 Hall thruster in 2S0 B0 configuration firing with xenon at 100 W input power. The residual gas pressure in the vacuum chamber is 4 × 10 -5 mbar.

Fig. 3 .

 3 Fig. 3. ISCT100 Hall thruster discharge current as a function of anode mass flow rate and discharge voltage for different configurations.

Fig. 4 .

 4 Fig. 4. Current density profiles across the plume of each configurations at 200 V 0.5 mg/s, 300 V 0.5mg/s and 200 V 0.6 mg/s operating conditions. The dashed lines represent the computed divergence angle α d .

Fig. 5 .

 5 Fig. 5. Electric field measured by LIF at 200 V in the 100 W ISCT100 (2S 0 2B 0 ) and the 200 W ISCT200, and at 250 V in the 1.3 kW SPT100.

Fig. 6 .

 6 Fig. 6. a) Thrust versus power for different anode mass flow rates and for two thruster configurations. b) Anode specific impulse versus voltage. c) Anode efficiency versus input power.

Fig. 7 .

 7 Fig. 7. Comparison of the anode efficiency of 100 W class low power Hall thrusters (BHT-100[START_REF] Szabo | Characterization of a one hundred Watt, long lifetime Hall effect thruster for small spacecraft[END_REF], SPT-20[START_REF] Loyan | Performance investigation of SPT-20M low power Hall effect thruster[END_REF], SPT-30[START_REF] Jacobson | Test results of a 200 W class Hall thruster[END_REF], PSAC 100[START_REF] Lim | Automated integrated robotic systems for diagnostics and test of electric and µ-propulsion thrusters[END_REF])

TABLE I OVERVIEW

 I OF THE DATA DERIVED FROM PLUME MEASUREMENTS FOR THE 200 V 0.6 MG/S AND 300 V 0.5 MG/S CASES FOR ALL CONFIGURATIONS.

		V d (V)	ṁa (mg/s)	α d ( • )	S 0 η I	ηprop	α d ( • )	2S 0 η I	ηprop
	B 0	300 200	0.5 0.6	65 66	57% 53%	62% 59%	63 61	63% 71%	77% 82%
	2B 0	300 200	0.5 0.6	64 65	69% 64%	60% 62%	62 61	69% 72%	81% 76%

VI. ACKNOWLEDGMENTS

Lou Grimaud benefits from a CNES-Région Centre Ph.D. grant.


Halima Ramdani

Davy Monticolo

Armelle Brun

Eric Bonjour

Decision support system for online recruitment

Keywords: E-recruitment, Parsing, Matching, Machine learning, Decision support system

In the past, potential candidates for a job offer were in physical locations that could be reached through the major media that were available at the time, often strongly rooted in their local geographic space. Today, digital media replaced those traditional channels, offering advertisers a broader geographic reach. However digital channels are more and more numerous, making it difficult to target candidates on the web. Existing decision support system on e-recruitment in the literature does not identify the desired profile from a job offer (C1), the relevance of a resume (C2) or the changing environment of recruitment (C3). Thereby, the objective of our research is to optimize the e-recruitment process by designing a decision support system capable of targeting potential candidates at a lower cost and that addresses the challenges (C1), (C2) and (C3).

Introduction

The diversity of channels that broadcast job offers has expanded with the digital revolution (social networks, job boards, advertising sites). Each channel has a specific financial strategy and aims at targeting specific candidate profiles. As a result, online recruitment is becoming increasingly difficult for the recruiter. It is crucial to know precisely the characteristics of each channel. The broadcasting of job offers has a financing cost. For a company, the annual cost of recruitment can be very high. Consequently, it has become essential for recruiters to evaluate and analyze the various channels performances. The recruiter generally measures the performance of a recruitment campaign according to his objectives. Therefore, online recruitment requires analyzing massive data from multiple sources (career sites, recruitment sites, social network, etc.). As a result of these difficulties, some works in the literature have addressed e-recruitment optimization by proposing a recommendation system based on the content of the job offer [START_REF] Séguéla | On the predictive analysis of behavioral massive job data using embedded clustering and deep recurrent neural networks[END_REF] to estimate the performance of the channels. This work uses e-recruitment data containing interactions between the job offer and the candidates through the channels. These interactions generate events of clicks, applications sent etc. In this work, the conversion rate (the ratio between the number of resume received and the number of clicks) is used as an indicator, and the temporal dimension is not considered. However, it is evident that the moment when an offer is broadcasted on a channel influences its impact. The work of [16] has proved that taking temporality into account improves performance. Nevertheless, this system recommends channels based on the prediction of clicks over time. We have identified several weaknesses in the literature: (C1) The absence of the identification of the desired profile in job offers that would enable both to target more efficiently the most suitable candidates. A job offer can contain useless information that can create biases and ambiguities. Whereas the desired profile may be identified through the education, experience, skills etc. (C2) The exploitation of the numbers of clicks and the candidates' conversion rate. Recruiters use other indicators to define the performance of a recruitment campaign. For example, the cost per relevant candidate represents the cost of obtaining a resume that meets the desired profile in the advertisement. Although recruiters use this indicator, it is not taken into consideration in the literature. (C3) Several external factors (labor market, specific sector, etc.) or internal factors (more regular broadcasting on specific channels, human bias on the choice of channels) can influence clicks or the relevance of the resume received. These factors reflect a changing and uncertain environment. The uncertain nature of the environment is not considered in the literature. This research has one main objective:

(1) Propose a decision support system that can (1) recommend the recruiter the channels that meet his objectives (2) address the limitations listed above. The proposed model should address the research question "How to optimize the selection of recruitment channels based on the desired profile and the resume relevance in a changing and uncertain environment?". The paper is organized as follows. Section 2 explore the machine learning approaches used in the literature for job offer parsing (C1), resume and job matching (C2) and finally the machine learning algorithm that can consider unknown external factors and the changing environment (C3). Section 3 defines the proposed approach. Section 4 presents the research perspectives and the discussion.

Related work

Job offer parsing

The parsing aim is to identify and extract the desired profile from the offer by assigning the appropriate labels to the corresponding plain text: contract type, hard skills, required experience, etc. Several approaches have been studied in the literature to address this issue. The rule-based approach has been reviewed by [START_REF] Casagrande | Cerebra, un système de recommandation de candidats pour l'e-recrutement[END_REF] by combining regular expressions and dictionaries. Regular expressions that aim to identify strings, constructed with characters or meta-characters, were used to identify the postal code and the desired years of experience. The desired skills were identified using a dictionary containing the skills lexicon. This approach does indeed allow the extraction of years of experience and skills. However, its main limitation is that it cannot consider changes in the vocabulary (if a word is not represented in the dictionary of words or used in a regular expression, it cannot be parsed). Given the limitations of the rule-based, we turned to the machine learning approach. This approach is used in the process of knowledge extraction from data, and its objective is to extract information and exploit massive data. [START_REF] Kesler | E-gen : traitement automatique d'informations de ressources humaines[END_REF] propose an SVM-based classification. They use a vector representation for each text segment to assign a label to it. The SVM results do not provide the desired results due to various parameters (text written in natural language, uncertain splitting, varied delimiter). As a result, the analysis of the structure of the offer has made it possible to improve these results by considering the structure of the job offer. Nevertheless, we note two limitations to this work. The first is the choice of labels to be extracted (job, skills, salary, etc.) which is too limited to identify the desired profile. The second limitation is related to the use of the SVM, which does not exploit the plain text structure from the job offer. However, considering the structure could improve the parsing. The intuition behind our analysis is that recurrent neural networks can address this limitation as they are commonly used for plain text parsing in different domains. Due to the challenge of capturing dependencies between sequences in the long term, some works have favored LSTM (Long Short-Term Memory) recurrent neural networks that address the challenge of dependency between sequences. This approach can be applied to any textual document: natural language, structured, unstructured and semi-structured. Sequence labeling parsing considers a text as a sequence of sequences. The aim is to assign a label to each sequence. Our work's aim is to prove that this machine learning approach using sequence labeling associated with recurrent neural network can address the limitations of other approaches.

Job and resume matching

Numerous studies calculate the similarity between resume and job offers. The literature has examined rule-based methods, artificial learning, and knowledge management using ontologies. [START_REF] Golec | A fuzzy model for competency-based employee evaluation and selection[END_REF] proposes a fuzzy model for evaluating and selecting candidates based on skill. These skills are compared and ranked in comparison to the organization's objective. Only specific data (such as knowledge and skills) are used to sort the resume in [START_REF] Maheshwari | An approach to extract special skills to improve the performance of resume selection[END_REF]. All approaches tend to increase the effectiveness of recruiting applicants for jobs offers. Nonetheless, the full plain text of the job and resume is used. The plain texts, on the other hand, produce a significant amount of noise, resulting in poor precision and unsatisfactory rating performance. Certain approaches have favored an ontology-based approach. For instance, [START_REF] Annamalai | Towards an automated system for intelligent screening of candidates for recruitment using ontology mapping expert[END_REF] proposes a method for automatically mapping a job offer ontology to a resume ontology. The rating is based on a similarity function between the two ontologies properties. Although this approach overcomes the limitations of manual matching of candidates, creating an ontology requires the processing of a large number of documents. Additionally, manual work by an expert is used to cover new skills or careers. According to [START_REF] Maheshwary | Matching resumes to jobs via deep siamese network[END_REF] the matching problem is a component of a supervised learning framework in which Deep Neural Networks are used to identify the most qualified applicants for a job offer. The writers suggest that a convolutional neural network be adapted in a Siamese manner. The authors' method is based on a pairwise annotated dataset (label 0 if the job offer matches the resume, and 1 otherwise). Although the findings indicate that this technique performs well, we note a drawback with the annotated text pairs that need a considerable volume of annotated data to repeat these experiments. To address the annotation problem, [START_REF] Bhatia | End-to-end resume parsing and finding candidates for a job description using bert[END_REF] have developed a framework focused on deep learning that ranks job applicants according to their suitability for the job description. They accomplished this by using the BERT language representation model [START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF]. BERT was used to identify text segments and estimate the ranking of applicants for a work offer based on a similarity rate. Machine learning has significantly improved the matching of jobs and resumes. Present methods, on the other hand, are constrained by their syntactic structures and certain factors make reproducing the proposed approaches difficult. Recent study, which favors deep learning models, highlights the opacity of such systems. However, it is important to emphasize the recruiter's transparency about the matching system.

Machine learning algorithm applied on uncertain environment

Reinforcement learning (RL) is a machine learning technique that allows an agent to learn by trial and error in an immersive environment using input from its own behaviors and experiences. Though both supervised and reinforcement learning use mapping between input and output, reinforcement learning utilizes incentives and punishment as cues for good and negative behaviour, unlike supervised learning. Reinforcement learning differs from unsupervised learning in terms of objectives. The aim of unsupervised learning is to identify parallels and discrepancies between data points. Whereas, the goal of reinforcement learning is to find an appropriate behavior model that maximizes the agent's overall accumulated reward. The fundamental theory and components of a reinforcement learning paradigm are shown in the diagram below. The following steps can characterise reinforcement learning: [START_REF] Bhatia | End-to-end resume parsing and finding candidates for a job description using bert[END_REF] The agent observes an input state and choose an action; (2) The action is performed; (3) The agent receives an outcome based on its environment; (4) Information about the given result for this state or action is recorded and the agent chooses a new action based on the reward of past actions. This approach has never been used in the field of recruiting. Additionally, it has been studied in similar areas such as film recommendation or foreign and investment operation. To refine suggestions in uncertain environments, reinforcement learning was used. As a result, a comparison to the recruiting domain is possible. The state of this system, on the other hand, should correspond to our performance indicators (clicks, number of relevant resume, and cost), the action should be the selection of one or more channels, and the reward should be the function that maximizes the number of relevant resume while minimizing clicks and cost. However, it is worthwhile to investigate this method further because it does not include historical data. This first benefit removes the restriction on the use of flawed statistics. Unlike reinforcement learning, which generates data at each learning iteration, supervised learning is a machine learning task that involves learning a prediction function from existing annotated dataset [START_REF] Nasteski | An overview of the supervised machine learning methods[END_REF]. As a result, it requires a standardized, homogeneous, and annotated data corpus. In the case of recruiting, supervised learning has never been used. It will, however, allow the forecasting of performance indicators for a new job offer. These indicators' predictions will then be used to suggest channels that would increase the number of applications while reducing the number of clicks and cost. Supervised learning will overcome the first weakness identified in the state-of-the-art application by using these data as performance indicators.Furthermore, since this approach only considers historical data, it still discriminates against those channels, leaving out the factors that affect channel efficiency.

3 Proposed approach

The actors

The recruiter provides a job offer that includes a description of the desired profile. Different types of information can be found in the job offer to define the desired profile: profession, experience, education, hard skills, soft skills, missions, city, country, postal code, and contract type. It is important to define these labels. They are, in reality, critical for the job, resume parsing, and the matching of these two documents. The recruiter's company, its worth, and its size also reflect him. The recruiter also has a budget for e-recruitment, which we may refer to as financial constraints. The channel is represented by a type. It might be a social network like Facebook, Instagram, or a job board like Indeed, Glassdoor, or an advertising like Xander. The profile of users define also the characteristics of a channel. Indeed, different types of job boards may be used, each focusing on a particular contract type, number of years of experience, etc. The candidate is represented by his resume, which contains a huge amount of information. The same labels as the offers can be used to classify this information: occupation, experience, education, hard skills, soft skills, missions, city, country, and postal code.

Interaction between actors

The following describes the interactions between the various actors: (1) The recruiter sends job offers in a variety of formats to the system (xml, text, pdf etc.). Each offer or collection of offers is tied to a specific budget constraint. This data is saved in a database; (2) The decision support system (DSS) extracts data from the database and applies machine learning models to determine the optimal channel for each new job offer; (3) The recruiter is notified of the channel's ranking provided by the models; (4) The recruiter selects the channels and broadcast the job to the selected ones; [START_REF] Golec | A fuzzy model for competency-based employee evaluation and selection[END_REF] Once the jobs are posted on the channels, applicants can click to apply, submit their resume, or only display the job offer. [START_REF] Gower | Properties of euclidean and non-euclidean distance matrices[END_REF] The tracker maintains a record of these events; [START_REF] Kato | Embeddings of label components for sequence labeling: A case study of fine-grained named entity recognition[END_REF]The DSM updates the learning models to reflect new events in order to update the channel recommendation to the recruiter.

Decision support system

The proposed decision support system is composed of five components: 1. Data storage. The aim of this component is to retrieve and store data from various channels. It's made up of a tracker that pulls every event from the job board. When an applicant, for example, clicks on a job offer on Indeed at time t, a new event is generated in the database. 2. Data processing. Various types of data are processed in this component (Job offers and resume). The processing entails parsing the job offer and resumes to construct a standardized format for two purposes: (1) convert the plain text of the offer into an xml format (the channels only support this data structure), (2) do the job and resume matching. Two of our challenges (C1) and (C2) are addressed by this component: The parsing: For its ability to view a set of words as a sequence, and hence the semantic meaning of words, sequence labeling has long been of special interest for natural language processing, such as part-of-speech tagging or semantic annotation, etc. [START_REF] Ramponi | Biomedical event extraction as sequence labeling[END_REF][START_REF] Kato | Embeddings of label components for sequence labeling: A case study of fine-grained named entity recognition[END_REF]. The sequence labeling task entails assigning a categorical mark to each sequence using an algorithm that considers a text as a series of semantic words [START_REF] Lin | Enhanced sequence labeling based on latent variable conditional random fields[END_REF]. The parsed plain text is represented by the label/sequence pairs. The hypothesis of our work is that sequence labeling is an approach that can take advantage of plain text semi-structure while also considering the vocabulary evolution and ambiguity. The structure of the plain text (even if it isn't fixed) represents a collection of sequences and terms that can be used to capture meaning and thus consider vocabulary evolution. Our intuition is that sequence labeling could improve the parsing approaches used today to parse the job offers. The matching: Recent research on the matching has shown that machine learning methods can achieve a high level of accuracy. However, the recruiter cannot understand the results and implecations of machine learning algorithms such as classifications or supervised learning. As a result, our primary research will concentrate on a hybrid system that will first construct a semantic vectorization using BERT in order to understand the word's semantic context. This vectorization will be applied to the parsing-generated labels/sequences. Second, we can compute a distance between the vectorized job offer labels/sequences and the resume. The similarity of two vector representations is determined by the distance between them. As a measure of similarity, we can use the cosine (R Baeza Yates, 1999), the Euclidean distance [START_REF] Gower | Properties of euclidean and non-euclidean distance matrices[END_REF], or the Dice index [START_REF] Cabrera-Diego | How can we measure the similarity between résumés of selected candidates for a job?[END_REF]. These various techniques will be evaluated and only one will be kept. As a result, each label/sequence will have a distance score associated with it. This score would be more significant if the recruiter prioritizes this type of information over another. 3.Supervised learning: this component uses the output of the job parsing, the events stored in the database to predict the future actions in each channel for each job offer. A deep neural network is used to predict these values. 4. Filtering: is multi-objective function that aim to consider the objective of the recruiter. The first scenario is a single-objective optimization problem where the objective function is the maximization of the number of relevant resumes while adhering to aa cost constraint on each channel not to be exceeded. We then introduced an exponential smoothing technique, which is frequently used in time series research [START_REF] Liu | Trend following in financial time series with multi-objective optimization[END_REF] to consider past data. The implementation of historical data enables the following: (1) The model's convergence; (2) Prevent the removal of a diffusion channel whose output measures are suboptimal for the time span T and are affected by an unknown factor (vacations, economic crisis, changing labor market etc.). The theory of exponential smoothing is to give greater weight to a data set's most recent observations. 5. Reinforcement learning: Recall that reinforcement learning is characterized by the following steps defined in the state of the art the state of the art, steps that we instantiate on our problem:

(1) The agent observes an input state. In our context the DSS is the agent that observes the new events coming from the channels at t+T; (2) The reward represents the number of relevant resume received in each channel; (3) An action is determined by the agent. The action in our model represents keep or change the channel; (4) The the policy is the probability of taking action a for the next state for the context of the desired profile and the actual reward; (5) The action is performed; [START_REF] Gower | Properties of euclidean and non-euclidean distance matrices[END_REF]The proposed action for each channel/job offer is diplayed to the recruiter. This reinforcement learning module is re-launched periodically to re-evaluate the channels This reinforcement learning module is re-launched periodically to re-evaluate the channels chosen at initialization (we initially choose a period T=7 days, but the value of T can obviously be set to another value) to assess the relevance of the channels and choose new actions accordingly. Thus, every day, the performance indicators are retrieved to infer their value over the period T. The reiteration of the choice of channels by learning by reinforcement at each period t+T thus makes it possible to refine the results as the recruitment campaign progresses by learning from its actions/rewards and to adapt to the uncertain environment of the recruitment application context.

Research perspectives

In conclusion, the state-of-the-art on e-recruitment optimization allowed us to define the limits of existing work in the literature, which we defined ahead of time through challenges C1, C2 and C3. We improved the state-of-the-art work by proposing a parsing model for the desired profile identification and the candidate profile from respectively the job offer and resume (C1). Following that, we used this parsing to create a semantically-aware matching between offers and resumes that allows us to explain the results to the recruiter using a distance measure between sequences and labels (C2). Following that, we focused our research on the design of a decision support system, proposing a hybrid system that relies on an initialization based on supervised learning in order to provide a fixed first model of recommendation that uses historical events. Given the limitations of supervised learning, we've added reinforcement learning to help with adaptation to an unknown environment and to avoid discrimination in some channels and human bias in the data (C3). We've defined our first function goal, which is critical for determining the pertinence of channels. This role will become more refined as more experiments are carried out. In the next part of our work, the objective is to use data from Xtramile, a digital recruitment company, in order to validate our models and our hypotheses.