Lucas Leverne

Anja Krieger-Liszkay
email: anja.krieger-liszkay@cea.fr

Moderate drought stress stabilizes the primary quinone acceptor QA and the secondary quinone acceptor QB in photosystem II

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Moderate drought stress stabilizes the primary quinone acceptor QA and the secondary quinone acceptor QB in photosystem II

Lucas Leverne, Anja Krieger-Liszkay

Introduction

Drought is one of the most important abiotic stress conditions leading to a loss of crop production. Upon drought, stomata keep closed, CO2 concentration in the mesophyll decreases and CO2 assimilation declines. When the CO2 concentration at the ribulose-1,5bisphosphate carboxylase/oxygenase (RuBisCO) is low, the oxygenase reaction becomes more important compared to the carboxylase reaction and photorespiration is enhanced. Photosynthetic electron transport is not considered as primary damage site under water deficit [START_REF] Kaiser | Effects of water deficit on photosynthetic capacity[END_REF][START_REF] Cornic | Photosynthetic carbon reduction and carbon oxidation cycles are the main electron sinks for photosystem II activity during a mild drought[END_REF]. The photosynthetic apparatus has been demonstrated to be highly resistant to drought as shown by an unaltered quantum yield of photosystem II (ΦPSII) under moderate drought stress and a reversible decrease in ΦPSII upon increasing drought stress (Cornic and Briantais 1992;[START_REF] Genty | Partitioning of photosynthetic electron flow between CO2 and O2 reduction in a C3 leaf (Phaseolus vulgaris L.) at different CO2 concentrations and during drought stress[END_REF]). It has been described that photosynthetic electron flow is down-regulated under drought stress protecting the photosynthetic apparatus against oxidative stress. A number of regulation mechanisms such as nonphotochemical quenching (NPQ), photosynthetic control and cyclic electron transport are operating under drought. Ott and co-workers (1999) reported under drought conditions a slowing down of the reduction of oxidised P700, the primary electron donor of photosystem I (PSI), and interpreted this effect as a pH-dependent regulation of electron transport, most likely at the level of the cytochrome b6f complex (photosynthetic control).

Cyclic electron flow was proposed to be responsible for this higher proton gradient leading to higher non-photochemical quenching and protecting thereby the photosynthetic apparatus [START_REF] Golding | Down-regulation of linear and activation of cyclic electron transport during drought[END_REF]. Chlororespiration employs the same pathways like cyclic electron flow leading to a reduction of the plastoquinone pool and a generation of a proton gradient in the dark [START_REF] Peltier | NDH-1 and NDH-2 plastoquinone reductases in oxygenic photosynthesis[END_REF]. Induction of cyclic/chlororespiratory pathways can be seen by thermoluminescence upon slow dehydration of leaves [START_REF] Bürling | Assessment of photosystem II thermoluminescence as a tool to investigate the effects of dehydration and rehydration on the cyclic/chlororespiratory electron pathways in wheat and barley leaves[END_REF].

Thermoluminescence can be used as a probe of the behaviour of PSII reaction centres, both in isolated systems and in whole leaves (for review, see [START_REF] Ducruet | Thermoluminescence: experimental[END_REF]. Using this method, samples are first cooled down and then illuminated with a given number of single turnover flashes at the lower temperature. Subsequent warming reveals several peaks of luminescence emission. These emission bands originate from recombination of different types of trapped charge pairs, which can be identified by their maximum emission temperature, Tm.

For example, recombination of the semiquinone, QB •-with the S2-or S3-state of the watersplitting complex yields a TL band centered around 30 °C, the so-called B-band (Rutherford et al. 1982). In whole leaves, at higher temperatures (40-46 °C at a 0.5°C s -1 heating rate) an additional TL band, the afterglow band, has been reported and ascribed to the afterglow luminescence emission (AG-band) induced by reverse electron flow [START_REF] Peeva | Thermoluminescence and P700 redox kinetics as complementary tools to investigate the cyclic/chlororespiratory electron pathways in stress conditions in barley leaves[END_REF][START_REF] Bürling | Assessment of photosystem II thermoluminescence as a tool to investigate the effects of dehydration and rehydration on the cyclic/chlororespiratory electron pathways in wheat and barley leaves[END_REF]. This AG-band is suppressed by an uncoupler or by freezing the sample.

Physiological conditions like drought that activate cyclic/chlororespiratory pathways cause a downshift in the maximum temperature of the AG-band [START_REF] Ducruet | Cyclic electron flow around PSI monitored by afterglow luminescence in leaves of maize inbred lines (Zea mays L.): correlation with chilling tolerance[END_REF][START_REF] Bürling | Assessment of photosystem II thermoluminescence as a tool to investigate the effects of dehydration and rehydration on the cyclic/chlororespiratory electron pathways in wheat and barley leaves[END_REF]. The intensity of the AG band depends on the illumination protocol and on the concentration of NADPH and ATP in the stroma, dependent on the dihydroxyacetone phosphate/3-phosphoglycerate ratio [START_REF] Krieger | Thermoluminescence studies on the facultative crassulacean-acid-metabolism plant Mesembryanthenum crystallinum L[END_REF].

Aim of this study was to show the different regulation mechanisms set in place upon drought stress using chlorophyll fluorescence, thermoluminescence and P700 absorption changes. We investigated whether, in addition to the regulation mechanisms described in the literature, another regulation mechanism at PSII is activated under drought. According to our hypothesis photorespiration protects the photosynthetic apparatus not only by evacuating excess electrons, thereby keeping the electron transport chain oxidized and protecting against PSI acceptor side limitation [START_REF] Eisenhut | Photorespiration is crucial for dynamic response of photosynthetic metabolism and stomatal movement to altered CO2 availability[END_REF] but also by acting directly on PSII. It has been recently shown in a photorespiratory mutant accumulating the photorespiratory metabolite glycolate that PSII electron transport is altered thereby protecting PSII against 1 O2 production and photo-oxidative damage [START_REF] Messant | Glycolate induces redox tuning of photosystem II in vivo: Study of a photorespiration mutant[END_REF]. Glycolate was supposed to exchange the bicarbonate ligated to the non-heme iron at the acceptor side of PSII and to shift the midpoint potential of the redox couple QA/QA •- [START_REF] Messant | Glycolate induces redox tuning of photosystem II in vivo: Study of a photorespiration mutant[END_REF]. Release of the bicarbonate ligated to the non-heme iron shift the midpoint potential of the redox couple QA/QA •-to a more positive potential [START_REF] Brinkert | Bicarbonate-induced redox tuning in Photosystem II for regulation and protection[END_REF]. Until now it remains an open question whether drought stress inducing photorespiratory conditions evokes a similar modification of the acceptor side of PSII in a wild-type plant as has been observed in the photorespiratory mutant.

To show alterations of photosynthetic electron transport and especially of PSII under drought stress, we measured chlorophyll fluorescence induction, decay, thermoluminescence and performed P700 absorption measurements.

Materials and Methods

Plant material. Spinach (Spinacia oleracea L.) was used for this study. Individual plants were grown in pots with a diameter of 13 cm in a greenhouse for six weeks and then moved for 1 week to controlled high light condition (8 h light, I=450 µmol quanta m -2 s -1 , 22°C, 16 h dark, 18°C; light source, fluorescent light tubes; Osram Dulux L 55W/840). Plants in control condition were watered every day while stressed plants were unwatered to attend 35% of total weight pot loss (3-4 days) in stress condition and kept afterwards at the same stress level during the following days by adding the lost water once a day.

Chlorophyll fluorescence. Chlorophyll fluorescence was measured on leaves attached to the plants at room temperature with a Dual-PAM-100 (Walz, Effeltrich, Germany). Fluorescence induction was measured during excitation with a multiple turnover flash (duration 300 ms).

Fluorescence decay was measured after excitation with a saturating single turnover flash with a sampling rate of 2.5 µs.

Thermoluminescence (TL) was measured using a homebuilt apparatus. Leaf discs were darkadapted for 5 min at 20°C. TL was charged by two single turnover flashes with a xenon flash lamp at 1°C or at -7°C when leaf disks frozen at 77K were used. The flashes were spaced with a 1s dark interval. The TL signal was recorded during warming of the sample to 70°C at a heating rate of 0.4°C s -1 . P700 absorption. The redox state of primary donor of photosystem I (PSI) P700 was monitored by following the changes in absorbance of 2 min dark-adapted leaves at 830 versus 875 nm using the Dual-PAM-100. Attached leaves were used. To probe the maximum extent of P700 oxidation, leaves were illuminated with far red light superimposed on the saturating pulse of red light. Oxidation of P700 was measured in leaves of WT and hpr1-1 as a function of the intensity of the actinic light. Y(NA) = (Pm-Pm')/Pm is a measure of the limitation of electron transport at the PSI acceptor side. Pm represents the maximum oxidation of P700 and Pm' the maximum oxidation at a given light intensity after application of a saturating light pulse. Y(ND) is a measure of the donor side limitation and it is calculated as Y(ND)=1-P700red, where P700red represents the fraction of reduced P700 at a given state (Klughammer and Schreiber, 2008).

Immunoblots Thylakoid membranes were prepared as in Shimakawa et al. (2020) and used for analysis by SDS-PAGE (12% acrylamide) and immunoblotting. Proteins were blotted onto nitrocellulose membrane. Labelling of the membranes with antibodies was carried out at room temperature in 1x TBS (50 mM Tris-HCl pH 7.6, 150 mM NaCl), 0.1% Tween 20 and 1% non-fat powder milk). Primary antibodies were purchased from Agrisera (Vännäs, Sweden) with the exception of the antibodies raised against PTOX, kindly provided by M. Kuntz (CEA Grenoble). After washing, bound antibodies were revealed with a peroxidase-linked secondary anti-rabbit antibody (Agrisera, Vännäs, Sweden) and visualized by enhanced chemiluminescence.

Results

Drought stress in spinach plants was achieved by withholding water for 3-4 days until 35% of the initial weight of the pots was lost. This moderate drought stress did not induce a decrease in the absolute quantum yield of PSII (Fv/Fm). Well-watered plants had an Fv/Fm value of 0.81±0.02 and stressed plants of 0.80±0.01. Also the water content of the stressed leaves remained unchanged. Prolonged drought stress induced a decrease in Fv/Fm. Since we are interested in studying a regulation mechanism and not an irreversible damage of the photosynthetic apparatus upon drought stress, mature leaves of moderately stressed plants (about 35% of loss of initial pot weight) were used for the following measurements.

Alterations of the PSII acceptor side in the drought-stressed plants compared to the control were followed by chlorophyll fluorescence measurements. As shown in Fig. 1, in a leaf from a stressed plant the different phases of the induction curve were flattened, and the area above the induction curve reflecting the number of available electron acceptors was smaller. Similar effects have been observed earlier under drought stress for other species and have been interpreted as reduced electron demand by PSI under these conditions [START_REF] Oukarroum | Drought stress effects on photosystem I content and photosystem II thermotolerance analyzed using Chl a fluorescence kinetics in barley varieties differing in their drought tolerance[END_REF]).

The kinetics of QA •-reoxidation can be followed by analysing the fluorescence decay (Fig. 2). According to de Wijn and van [START_REF] De Wijn | Kinetics of electron transfer from QA to QB in photosystem II[END_REF], different phases of QA •-reoxidation are assigned to electron transfer to QB (or QB •-) which is bound to the QB site at the time of the flash (fast and slower phases) and to electron transfer to QB but rate-limited by the exchange of a reduced quinone (QBH2) in the QB-site for a PQ from the pool (slow phase). We fitted here the decay curves by a sum of two exponential functions (time zero to 20 ms). The slow phase was not analysed since there was no longer a difference observed between the two conditions. As shown in Fig. 2 and Table 1, moderate drought stress led to an increase in the life-times of the fast phase (t1) and the slower phase (t2). The increase in the life-times indicates a slowdown of the electron transport between QA and QB/QB •-supporting the hypothesis that photorespiratory activity affects the PSII acceptor side shifting the midpoint potential of both quinone acceptors, QA and QB, to a more positive value.

Drought stress has been described to affect thermoluminescence emission in barley and wheat leaves favouring the appearance of the AG-band compared to well-watered leaves [START_REF] Peeva | Thermoluminescence and P700 redox kinetics as complementary tools to investigate the cyclic/chlororespiratory electron pathways in stress conditions in barley leaves[END_REF][START_REF] Bürling | Assessment of photosystem II thermoluminescence as a tool to investigate the effects of dehydration and rehydration on the cyclic/chlororespiratory electron pathways in wheat and barley leaves[END_REF]. This is indeed the case also for spinach. Fig. 3 shows representative TL curves after excitation of the samples by two flashes at 1°C. The wellwatered plant showed only a very small AG-band while a strong increase of the AG-band was observed in the stressed plant. A decrease in both, the maximum temperature of the B-band and the AG-band was found in stressed samples similar to previous reports [START_REF] Bürling | Assessment of photosystem II thermoluminescence as a tool to investigate the effects of dehydration and rehydration on the cyclic/chlororespiratory electron pathways in wheat and barley leaves[END_REF][START_REF] Peeva | Thermoluminescence and P700 redox kinetics as complementary tools to investigate the cyclic/chlororespiratory electron pathways in stress conditions in barley leaves[END_REF]. The downshift of the maximum temperature of the TL-bands under drought conditions is attributed to a larger proton gradient across the thylakoid membrane and a stimulation of cyclic electron transport according to Ducruet (Miranda and Ducruet 2005;[START_REF] Bürling | Assessment of photosystem II thermoluminescence as a tool to investigate the effects of dehydration and rehydration on the cyclic/chlororespiratory electron pathways in wheat and barley leaves[END_REF][START_REF] Peeva | Thermoluminescence and P700 redox kinetics as complementary tools to investigate the cyclic/chlororespiratory electron pathways in stress conditions in barley leaves[END_REF]. An increase in cyclic electron transport activity may depend on a biochemical activation of this pathway by, for example, redox regulation [START_REF] Courteille | Thioredoxin m4 controls photosynthetic alternative electron pathways in Arabidopsis[END_REF] or by an increase in the amount of the proteins implicated in cyclic electron transport. Western Blot analysis (Fig. 4) showed in thylakoids from stressed leaves a higher amount of proteins involved in the two pathways of cyclic electron flow via the NDH complex and the Ferredoxin Quinone Reductase (FQR) pathway mediated by the proteins PGR5/PGRL1. In addition, higher levels of the Plastid Terminal OXidase, PTOX, involved in chlororespiration, and of Early Light-Induced Proteins, ELIP, a stress-related protein [START_REF] Heddad | Differential expression and localization of early light-induced proteins in Arabidopsis[END_REF]), were found.

As detailed in the introduction, under drought accumulation of the photorespiratory metabolite glycolate may affect the acceptor side of PSII. A large ΔpH may overlay the effect of glycolate accumulating under drought on the midpoint potentials of the quinone acceptors QA and QB. To destroy the ΔpH leaves were frozen in liquid nitrogen prior to the TL measurement. Frozen leaves showed a B-band at a higher temperature in stressed leaves than in control leaves (Fig. 5). A temperature difference of 3°C was found between the samples (Table 2), indicating that the S2,3QB •-couple was stabilized in stressed leaves. It would have been interesting to study a possible shift of Tm of the Q-band (S2QA •-recombination) upon drought stress. This would give access to modifications of the midpoint potential of the redox couple QA/QA •-. However, to obtain a Q-band addition of DCMU is required that binds to the QB-binding site and inhibits electron transfer between QA and QB. We did not infiltrate leaves with DCMU because such an infiltration procedure could remove photorespiratory metabolites like glycolate from the non-heme iron at the acceptor side of PSII.

It has been reported previously that drought stress affects the redox state of P700 [START_REF] Ott | Regulation of the photosynthetic electron transport chain[END_REF]. As seen in Fig. 6, we observed a stronger donor-side limitation of PSI in stressed leaves compared to the control. A clear trend is shown in Fig. 6, although the differences are statistically not significant. No sign of acceptor side limitation was observed in the stressed plants showing that photorespiration activity was sufficient to keep the PSI acceptor side open.

Discussion

Photosynthetic electron transport is regulated at a number of different sites during moderate drought stress according to the data shown here and previously published work [START_REF] Ott | Regulation of the photosynthetic electron transport chain[END_REF][START_REF] Golding | Down-regulation of linear and activation of cyclic electron transport during drought[END_REF][START_REF] Peeva | Thermoluminescence and P700 redox kinetics as complementary tools to investigate the cyclic/chlororespiratory electron pathways in stress conditions in barley leaves[END_REF]Bührling et al. 2014). In Figure 7, we present a model showing four sites of regulation of photosynthetic electron transport: PSII, PTOX, cyclic flow and electron donation to PSI. As shown in Fig. 6, P700 is in a slightly higher oxidation state under steady state conditions in stressed leaves although this difference is statistically not significant. A higher oxidation state of P700 has been reported previously in different species [START_REF] Ott | Regulation of the photosynthetic electron transport chain[END_REF]Wada et al. 2019). Accumulation of P700 + protects PSI against photo-oxidative damage [START_REF] Shimakawa | Oxidation of P700 ensures robust photosynthesis[END_REF]Wada et al. 2019). Ott and coworkers (1999) reported a slowing down of the reduction of P700 + that can be explained either by a slower electron donation by plastocyanin or by a limitation of electron transport at the cytochrome b6f complex, the so-called photosynthetic control. Plastocyanin diffusion may become limited under severe drought conditions since the thylakoid lumen may shrink. This has been shown to be the case in desiccated cyanobacteria from a dessert crust [START_REF] Bar-Eyal | An easily reversible structural change underlies mechanisms enabling desert crust cyanobacteria to survive desiccation[END_REF]. In these cyanobacteria P700 + accumulates and acts as a quencher of excess energy, protecting the photosynthetic apparatus against oxidative stress. Under moderate drought conditions, however, limitation of plastocyanin seems to be very unlikely and instead photosynthetic control, i. e. electron transport limitation at the cyt b6f complex when the pH in the lumen drops below a certain threshold value, is the likely reason for slowing down electron transport towards PSI.

An increase in the proton gradient can be achieved by cyclic electron transport that has been shown to be enhanced under drought stress [START_REF] Golding | Down-regulation of linear and activation of cyclic electron transport during drought[END_REF]. Cyclic electron flow takes place via two different pathways: the ferredoxin quinone reductase (FQR) pathway which likely involves the proteins PGRL1 and PGR5 and via the NDH complex (for a recent review see [START_REF] Nawrocki | The mechanism of cyclic electron flow[END_REF]. During slow dehydration, [START_REF] Lehtimäki | Drought stress-induced upregulation of components involved in ferredoxin-dependent cyclic electron transfer[END_REF] reported an increased amount of PGR5 and PGRL1 proteins but not of NDH-H. [START_REF] Ibanez | Chlororespiration and tolerance to drought, heat and high illumination[END_REF] observed that PTOX and both, the FQR and NDH-dependent pathway of cyclic flow, were activated under a combination of drought, heat and high illumination stress. Under the moderate drought stress applied here, both PGR5 and NDH-B protein levels were increased (Fig. 4) supporting the upregulation of cyclic electron transport pathways upon drought stress.

The amount of PTOX is also clearly increased indicating activation not only of cyclic but also of chlororespiration. In stressed plants PTOX may also play a role as a safety valve during illumination. Thermoluminescence measurements revealed under drought stress an increase in the AG-band (Fig. 3) that has been attributed to an activation of cyclic/chlororespiratory flow [START_REF] Peeva | Thermoluminescence and P700 redox kinetics as complementary tools to investigate the cyclic/chlororespiratory electron pathways in stress conditions in barley leaves[END_REF][START_REF] Bürling | Assessment of photosystem II thermoluminescence as a tool to investigate the effects of dehydration and rehydration on the cyclic/chlororespiratory electron pathways in wheat and barley leaves[END_REF]. The lumen acidification leads to a downshift of both, the B-band and the AG-band (Fig. 3; see also [START_REF] Bürling | Assessment of photosystem II thermoluminescence as a tool to investigate the effects of dehydration and rehydration on the cyclic/chlororespiratory electron pathways in wheat and barley leaves[END_REF]. The peak temperature of a TL-band is indicative of the energy stored in the charge-separated state and is determined by redox potentials of both the recombination partners, in this case, S2/S3 and QB/QB •-. The QB/QB •-couple involves proton release when QB •-is reoxidized and is expected to follow Nernst behaviour, with the redox potential changing by -59 mV per pH unit [START_REF] Rutherford | Thermoluminescence as a probe of photosystem II. The redox and protonation states of the secondary acceptor quinone and the O2-evolving enzyme[END_REF]Vass and Inoue 1986;De Causmaeker et al. 2019).

In addition to these previously described regulation mechanisms activated upon drought stress and described above, we show here that the electron transport between QA and QB is slowed down (Figs 1,2). We propose that this modification is caused by a photorespiratory metabolite accumulating under drought stress, most likely glycolate that replaces the bicarbonate ligated to the non-heme iron situated between QA and QB at the acceptor side of PSII. As shown by [START_REF] Messant | Glycolate induces redox tuning of photosystem II in vivo: Study of a photorespiration mutant[END_REF], accumulation of glycolate affects the electron transport between QA and QB and protects PSII against 1 O2 generation. 1 O2 is highly toxic and able to damage PSII. [START_REF] Brinkert | Bicarbonate-induced redox tuning in Photosystem II for regulation and protection[END_REF] have shown that the midpoint potential of the redox couple QA/QA •-is shifted towards a more positive value in the absence of bicarbonate, allowing for a direct charge recombination pathway in PSII that does not lead to chlorophyll triplet formation and subsequent 1 O2 production. A protection mechanism at PSII avoiding triplet chlorophyll and 1 O2 generation is important to protect PSII against photoinhibition under conditions when the electron transport chain is highly reduced as it is the case under drought stress. In addition to QA the midpoint potential of the QB/QB •-couple seems to be stabilized under drought stress. As seen in the fluorescence decay, all phases are slowed down, including the slow phase that reflects according to de Wijn and van Gorkom (2001) the exchange of QBH2 in the QB-site for a PQ molecule from the PQ pool. The binding efficiency of PQ to the QB site might be affected by the ligand change at the non-heme iron, but is also influenced by the concentration of oxidized PQ. If enhanced cyclic electron flow reduces the PQ pool, the concentration of oxidized PQ is lowered, which limits its availability for binding to the QB site and contributes to the decreased rate of fluorescence decay in the ms phase. The rate of forward electron transfer between QA -and QB is determined by free energy difference between the QA/QA •-and QB/QB •-states. Upon drought stress, also the fast phase of the fluorescence decay is slower showing that the free energy difference between the quinone acceptors is decreased. This can arise either from stabilization of QA/QA •-or destabilization of QB/QB •-. Since QB/QB •-is stabilized according to the Tl data (Fig. 5), we assume that QA/QA •- is even more stabilized compared to QB/QB •-. Stabilization of the QB/QB •-couple has been described previously in Synechocystis PCC 6803 mutants, mutated in the D-de loop of the D2 protein, which showed an upshift of the B-band [START_REF] Kless | Protein modifications in the D2 protein of photosystem II affect properties of the QB/herbicide-binding environment[END_REF][START_REF] Mäenpää | A mutation in the D-de loop modifies the stability of the S2QAand the S2QB-states in photosystem II[END_REF] and in the Synechocystis PCC 6803 ΔPsbT strain. Stabilization of S2QB -has been correlated with increased light sensitivity [START_REF] Fagerlund | Stabilization of photosystem II by the PbsT protein impacts photodamage, repair and biogenesis[END_REF]. Further studies are required to show whether mild drought stress protects higher plants against photoinhibition or increases the susceptibility to photoinhibition. Fitted parameters for fluorescence decay of control and drought-stressed plants (Fig. 2). Lifetimes (t) were obtained after fitting the fluorescence decay curves by a sum of two exponential functions (n=9 Leave disks were frozen at 77K prior to the insertion into the thermoluminescence cuvette.

Samples were excited at -7°C by two single turnover flashes. Black: control, red: stressed leaf.

  Shimakawa G, Sétif P, Krieger-Liszkay A (2020) Near-infrared in vivo measurements of photosystem I and its luminal electron donors with a recently developed spectrophotometer. Photosynth Res 144:63-72 Vass I, Inoue Y (1986) pH dependent stabilization of S2QA -and S2QB -charge pairs studied by thermoluminescence. Photosynth Res 10:431-436 Wada S, Takagi D, Miyake C, Makino A, Suzuki Y (2019) Responses of the photosynthetic electron transport reactions stimulate the oxidation of the reaction center chlorophyll of photosystem I, P700, under drought and high temperatures in rice. Int J Mol Sci 20:2068 Table 1

Figure 1 .

 1 Figure 1. Chlorophyll fluorescence induction curves in control and drought-stressed leaves. A: representative curves (black: control, red: stressed); B: Area above the induction curves. Average values with standard deviation are shown. Statistically significant differences between the area above the induction curves of leaves of control and stressed plants were determined by Student's t-test (*p < 0.01; n=11).

Figure 2 .

 2 Figure 2. Chlorophyll fluorescence decay in control and drought-stressed leaves. Variable fluorescence was induced by saturating a single turnover flash. A: representative traces (black: control, red: stressed). B: half-times of the fast, middle and slow phase. Decays were fitted by the sum of two exponential decay functions (fast and middle phase) and a linear function (slow phase). Statistically significant differences between leaves of control and stressed plants were determined by Student's t-test (*p < 0.01; n≥9).

Figure 3 .

 3 Figure 3. Thermoluminescence bands in control and drought-stressed leaves. Samples were excited at 1°C by two single turnover flashes. Black: control, red: stressed leaf. Red line: Fit of the experimental curves by two Gaussian functions. The B-band and AG-band intensities are indicated by the green curves, respectively.

Figure 4 .

 4 Figure 4. Immunoblots of proteins involved in alternative electron transport in membrane fractions isolated from control and drought-stressed leaves. Immunoblots were conducted with antibodies directed against the plastid terminal oxidase (PTOX), PGR5, the subunit B of the NDH complex (NDH-B), Early Light-Induced Proteins (ELIP). RuBisCo large subunit (RbcL) was used as loading control. In addition, the Coomassie stained gel is shown. 2.5 µg chlorophyll was loaded for both samples.

Figure 5 .

 5 Figure 5. Thermoluminescence bands in control and drought-stressed leaves frozen at 77K.

Figure 6 .

 6 Figure6. The redox state of the PSI primary donor P700 was monitored through the changes in absorbance at 830 versus 875 nm. Following the initial determination of maximal oxidation of

Figure 7 .

 7 Figure 7. Model of the regulation of electron transport under stress conditions. Four different sites slow down electron transport and protect the photosynthetic electron transport. 1: the acceptor side of PSII is modified, most likely by binding of a photorespiratory metabolite at the non-heme iron between QA and QB. Dashed line shows the ligation of bicarbonate or glycolate to the non-heme iron. According to our model, this induces a stabilization of the midpoint potentials of the redox couples QA/QA •-and QB/QB •-and slows down forward electron transport and protects PSII against photooxidative damage by 1 O2. 2: PTOX activity acting as a safety valve to keep the redox poise of the plastoquinone pool. 3: cyclic electron transport via the NDH complex or via the FQR pathway involving PGR5/PGRL1 is stimulated under drought leading to an increase in the ΔpH. 4: a high ΔpH slows down the turnover of the cytochrome b6f complex and the electron donation to PSI. Accumulation of P700 + protects PSI against photooxidative damage.

  Figure 5

Table 2

 2 ). The difference between the life-times of control and droughtstressed plants are statistically different (*) according to Student's t-test (p < 0.05).Thermoluminescence measurements performed on leaf discs of control and drought-stressed plants. Leaf disks were frozen at 77K, excited at -7°C with 2 single turnover flashes. The maximum temperature of the B-band was determined (n=6). The difference between the Tm of control and drought-stressed plants are statistically different (*) according to Student's ttest (p < 0.05).

		Fluorescence decay (t1/2 in ms)
		t1	t2
	control	0.303 ± 0.019	4.17 ± 0.50
	drought stress	0.565 ± 0.051*	6.57 ± 0.94*
		TL
		Tmax B-band (°C)
	control	31.2 ± 1.9
	drought stress	34.8 ± 2.0*

Acknowledgements

We thank M. Messant (I2BC) and Ginga Shimakawa (Osaka University) for critical reading of the manuscript. This work was supported by the LabEx Saclay Plant Sciences-SPS (grant number ANR-17-EUR-0007) and the French Infrastructure for Integrated Structural Biology (FRISBI; grant number ANR-10-INSB-05).


Chunhui He

Qian Zhang

Tingwei Gao

Chenguang Liu

Zhenyu Chen

Cezhou Zhao

Chun Zhao

Richard J Nichols

Yannick J Dappe
email: yannick.dappe@cea.fr

Li Yang
email: li.yang@xjtlu.edu.cn

Cezhou Zhao

Chun Zhao

Charge transport in hybrid platinum/molecule/graphene single molecule junctions

come   L'archive ouverte pluridisciplinaire

Introduction

Since Aviram and Ratner originally proposed the imaginative concept of using single molecule as a molecular rectifier in 1974, 1 there have been huge significant developments in molecular electronics. These have included advances in understanding charge transport mechanisms in molecular junctions (MJs), 2 demonstration of how current across single molecules can be switched using electrochemistry or light, 3,4 comprehension of the effects of environment and inter-molecular interactions on molecular conductance 5 and exploitation of quantum interferences in molecular junctions. 6 A recent exciting development has been a commercialization of large area molecular junctions, which have been deployed as the central component in audio distortion circuits for guitar pedals. 7 These, any many other advances continue to play an important role in determining the future development of the field of molecular electronics.

A key factor for integrating molecules into large scale devices is achieving reliable and reproducible electrode-molecule-electrode junctions. Indeed, these so-called molecular junctions (MJs) are a cornerstone in molecular electronics. [8][9] Molecular junctions have also been downsized to the single-molecule level which represents the ultimate miniaturization limit for molecular devices. The majority of recent studies of single-molecule junctions have featured a "symmetrical" configuration. Such symmetrical configurations use the same electrode contact (Au, Cu, Ag, and Pt etc.) on each side of the molecular bridge, alongside a symmetrical molecular core and the same anchoring groups (thiol, amine, carboxylic acid, methyl sulfide etc.) at each end of the junction. [10][11][12][13][14] These studies have formed the classical testbed for understanding electron transport through molecular nano-junctions. However, breaking the symmetry of electrodes, molecular wire or anchoring groups can shift the orbital energies, change the distribution of the electronic states, and consequently generate new transport properties of MJs including rectification, molecular switching or negative differential resistance (NDR). It is pertinent to note here that the seminal publication of the molecular rectifier concept innovated by Aviram and Ratner feature such a non-symmetrical contact.

A number of studies have shown that breaking the symmetry of molecular junctions can impact the electrical response. Tamaki and co-worker 15 have reviewed and summarized various junction devices featuring a series of non-symmetric molecular structures and their underlying mechanism of rectification, NDR and switching. Recently charge transport was studied in highly non-symmetric molecules containing a 9'9-spirobifluorene tripodal platform. 16 The results showed that by making several contacts between the molecule and the electrode, the number of possible molecular junction configurations was reduced and the most apparent charge transport pathway was better defined. MJs containing non-symmetric dipyrimidinyl-diphenyl diblock molecules bridged between two gold electrodes were reported to achieve pronounced rectification behavior. These non-symmetric molecules featured larger currents than their symmetrical counterpart, and their studies highlighted the importance of the molecule-electrode coupling on achieving rectification. 17 A systematic study has been undertaken with the configuration A-bridge-A, A-bridge-B and B-bridge-B (A = thiol terminus and B = COOH). This study found that when symmetry is broken by using such non-identical anchoring groups (A-bridge-B) the single molecule conductance is smaller when compared with symmetrical molecules. 18 Kim and co-workers introduced a hybrid device structure with the amine-terminated oligophenyls sandwiched between a gold tip and a graphite substrate. Rectification behaviour was observed in these junctions and is directly explained by an asymmetry in the electronic coupling to the two contacting electrodes. 19 The use of non-identical contacting groups at each end of a molecular bridge clearly introduces electronic coupling differences at each contact which has been discussed in previous literature for sandwich type junctions featuring self-assembled monolayers. 20 Influential factors here include the contact resistance at the molecule-electrode interface, the barrier height for the electron transfer, the alignment at the interfaces of the lowest unoccupied molecular orbital (LUMO) and the highest occupied molecular orbital (HOMO) and the Fermi levels (EF) of the contacting electrodes. 21 To date, less concrete data exist in the literature regarding non-symmetric molecule-electrode contacting, where the length dependence is addressed in terms of type of chemical bonding at the contacts (i.e. physisorbed versus chemisorbed). In our earlier studies, we have shown for nonsymmetric hybrid Au-molecule-graphene junctions that the tunneling decay constant (𝜷 𝒏 ) is lower than the corresponding goldmolecule-gold junctions which are by contrast symmetric. For these studies, symmetry breaking was achieved with the contacting (Au/graphene electrode) and different anchoring groups were employed at each ends of the molecular bridge (dithiol, 22,23 diamine, 24 and dicarboxylic acid 25 ).

The present report extends our study to Pt/alkanedithiol/graphene non-symmetric junctions and highlights the attractive features of this pronounced symmetry breaking achieved through a physisorbed contact at the graphene interface and a chemisorbed contact (Pt-S) at the platinum side. The MJs formed by Pt electrodes have shown higher conductance compared to the equivalent junctions with Au electrodes. For example, Kiguchi et al. made the direct binding of benzene molecules to two Pt electrodes and the relevant junctions revealed 1 order larger conductance than the equivalent junctions featuring Au-Au electrode pairs. 26 Ko et al. compared the electronic coupling of thiolate on Au, Pd and Pt electrodes. Their results showed that junctions with Pt electrodes exhibit about 3.5 fold greater conductance than those of Au electrodes. 27 In line with these findings our junctions with non-symmetric Pt/graphene contacting also yields a higher conductance at longer lengths and features low decay constants, when compared to similar MJs with Au/Au and Pt/Pt electrodes. We attribute these findings to the pronounced coupling differences to the non-equivalent electrodes at either end of the molecular bridge. These conclusions are supported by detailed density functional theory (DFT) calculations.

Experimental methods

Materials. All chemicals were reagent grade and were used as received. All solutions were prepared in mesitylene (99%, Aladdin). Ethanedithiol (C2), butanedithiol (C4), hexanedithiol (C6), octanedithiol (C8), decanedithiol (C10) were purchased from Alfa Aesar. 0.25 mm platinum wire (99.99%) was purchased from Tianjing Lucheng Metal. A ready-to-use fewlayer graphene (10 mm ×10 mm) on top of a nickel layer was purchased from the Graphene Supermarket, US. Tip preparation. Pt tips were prepared using electrochemistry etching previously reported. 28 Briefly, the procedure was divided into two steps. In the first step, the Pt wire was ultrasonically cleaned in acetone for 10 mins. Then a piece of ~15 mm long Pt wire was cut and immersed into a calcium chloride electrolyte with a concentration of 30 wt. %. The amount of wire immersed into the solution was approximately 2-3 mm. An AC voltage of 35 V was applied between the platinum and graphite counter electrode. After 90 s, for the second stage the voltage was then reduced to 18 V for fine etching in order to sharpen the tips. The etching reaction stopped automatically when the platinum wire section immersed the electrolyte broke off.

Conductance measurement.

The compounds under study were prepared at 1 mM concentrations in mesitylene solutions and 1-2 drops of the solution was placed in a STM fluid cell. The graphene substrate was fixed onto the sample plate with silver gel and dried overnight to facilitate good electrical contact plate. The conductance of single molecular junctions was measured using the scanning tunnelling microscopy (STM) I(s) method (I = current, s = vertical distance), following the method described by Haiss et al. with appropriate modifications to our Bruker STM instrumentation. 29 Figure 1 shows the schematic diagram of the experimental setup. In the I(s) method the STM tip suffers no direct contact with the substrate, and hence damage to either the tip or the graphene substrate is obviated. The Pt tip was approached to be within electron tunnelling distance of the substrate. During this approach, when the separation was sufficiently close the preset threshold of the setpoint current was attained, and the feedback loop was then disconnected. The STM tip was then rapidly retracted vertically to the initial distance by around 4 nm. This process of extending and retracting the STM tip was repeated many times to find the characteristic plateaus in the recorded current-distance curves, which indicate the successful formation of MJs. The STM-I(s) Please do not adjust margins Please do not adjust margins measurements were made at a current setpoint of 1 nA and the ramp rate of 1.03 Hz. The tip bias for all the experiments was set at Vbias= + 0.3 V and typically over 10000 I(s) curves were gathered. More than 500 curves showing well-defined plateaus and low noise were selected for the statistical analysis. According to the position of the plateaus, the selected curves were segmented into 8 bins equally based on the current set point. The curves from bins with the largest and the second largest numbers were then converted into conductance values to obtain a one-dimensional (1D) electrical conductance histogram. A Gaussian fit was applied to the most dominant peak and the representative conductance value was obtained. Theoretical calculation. Density Functional Theory (DFT) has been used to model the molecular junctions and to determine the electronic transport properties. We have used the DFT code Fireball, which has very efficient localized-orbitals basis sets. 30 Basis sets of sp 3 d 5 numerical orbitals for Pt, sp 3 for C and S, and s for H have been used to determine the atomic configuration and the electronic properties of the junctions. The corresponding cut-off radii (in atomic units) are s = 4.6, p = 5.8, d = 4.2 (Pt), 31 s = 4.5, p = 4.5 (C), s = 3.1, p = 3.9 (S), and s = 4.1 (H). 32 Following the procedure developed for the study of gold tips, 22 we have used a Pt tip of 35 atoms, and a 5×5 graphene supercell in the XY plane. Fireball was then used to optimize all the configurations until the forces went under 0.05 eV/Å. Also, we have employed a Keldysh-Green formalism to compute the conductance of the molecular junctions at 0 K and zero bias. 22,

Results and discussion

To study the influence of the tip materials to the hybrid graphene molecular junctions, simple alkanedithiols with their saturated carbon atom chains (polymethylene bridge) have been used as model compounds. Polymethylene chains are amongst the most popular choices of model molecular bridges for investigating the molecule-electrode coupling and junction conductance. 18,22,23 Pertinent features include their stable chemical properties, alongside the large gap (several electron volts) between the HOMO and LUMO. In this work, the conductance measurements were performed for ethanedithiol (C2), butanedithiol (C4), hexanedithiol (C6), octanedithiol (C8), and decanedithiol (C10) molecular junctions. Figure 2 shows typical I(s) curves measured for all the molecular junctions in a liquid cell filled with pure mesitylene solvent. The formation of the molecular junction is readily identified in each curve by a characteristic plateau, followed by a sudden decay. In the absence of a molecule between the two electrodes, the conductance curve simply decays exponentially with no visible step observed (not shown here). In Figure 2, it is clear that for the junctions studied here, the conductance values are strongly dependent on the length of the molecular backbone. As such, for bridges with smaller numbers of methylene groups, the conductance is higher. For example, the conductance of the longer Pt/C6/Graphene junctions is located around 25-40 nS, while, by contrast, the conductance of the Pt/C2/Graphene junctions which are shorter falls around 85-100 nS. Notice that the plateau height shows some variation for the same molecular junction, indicating the stochastic nature of the junction formation and breaking events. To better illustrate the predominant molecular junction configuration, a large number of molecular junctions are repeatedly generated and a statistical analysis from at least 500 I(s) curves is applied to all the tested molecules. The plotted 1D histograms are shown in Figure 3 and a progression of conductance of 1,n-alkanedithiol (n=2, 4, 6, 8, 10) with molecular length is observed. Only a pronounced main peak was found for each histogram. After a Gaussian fit, the resulting conductance values of C2 (black), C4 (purple), C6 (red), C8 (green) and C10 (blue) are 99, 45, 29, 18, and 7.5 nS, respectively. The conductance values decrease with increasing molecular length, as is expected. Interestingly, the conductance values have a certain spread especially for shorter molecules, and this distribution is generally proportional to the magnitude of the experimental value. Shorter junctions present higher conductance values, so their relative distribution of conductance values is normally greater than their longer homologues. For example, Pt/C8/graphene junctions located between 40 nS to 70 nS, while Pt/C10/graphene junctions located between 5 nS to 10 nS. The conductance of alkanedithiol bound between graphene and Pt contacts is consistent with our previous report, 22 where we used graphene and Au electrodes, which established the reliability of the approach and the reproducible results obtained when using graphene as a bottom electrode. Figure 4 shows a comparative analysis of the length dependence of the electrical conductance of Pt/S-(CH2)n-SH/graphene (yellow) and Au/S-(CH2)n-SH/graphene (blue) junctions, respectively. Here the natural logarithm of the molecular conductance is plotted as a function of the molecular length expressed in the number of methylene groups. The corresponding theoretical results are also presented for Pt/S-(CH2)n-SH/graphene (red, theory) and Au/S-(CH2)n-SH/graphene (green, theory) junctions respectively. The theoretical conductance of the Pt molecular junctions at the Fermi level is extracted from the electronic transmission plot shown in Figure 5, where 𝑮 = 𝑻(𝑬 𝑭 ). One can clearly notice the proximity of the HOMO level to the Fermi level of the Pt electrodes. Consequently, as in the case of the Augraphene electrodes, the charge transport in the junction is dominated by the HOMO level.

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As expected, the measured conductance decreases with the molecular length for both Pt and Au tips when forming nonsymmetric molecular junctions with graphene as the other contact. The conductance values measured for the two junctions types (Pt/S-(CH2)n-SH/graphene and Au/S-(CH2)n-SH/graphene) are similar. Although the individual theoretical conductance value is slightly overestimated (well-known overestimation of DFT calculations [34][35][36] ), the overall simulation results are in good agreement with the experimental conductance values. A linear fit to the conductance points and the slope of the plot of ln (G) versus the number of -CH2 units gives the value of the attenuation factor 𝜷 𝒏 , since the conductance can be expressed as

𝑮 = 𝑨𝐞𝐱𝐩(-𝜷 𝒏 𝑵) (1)
where G is the conductance, N is the number of methylene groups in the alkane chain and A is a constant inversely proportional to the contact resistance between the molecular bridge and the contacting electrode. Noted from this expression, a low value of 𝜷 𝒏 indicates a slow conductance decay, leading to a more effective electronic transport at longer lengths. In agreement with our previous results [22][23][24][25] , molecular junctions with Pt/graphene electrodes result in a low decay constant when compared to the analogous MJs with Pt/Pt contacting electrodes. These leads to a higher conductance for MJs with N > 6 with Pt/graphene contacts, than those achieved for either Au/Au or Pt/Pt contacting. 27 This significantly smaller value of 𝜷 𝒏 for Pt/graphene junctions is similar to the Au/S-(CH2)n-SH/graphene junctions (0.41) and both also are in good agreement with the theoretical computations of the 𝜷 𝒏 values. This smaller value is attributed to the symmetry breaking induced when using graphene as the second electrode. Indeed, the electrostatics of the molecular junction is unbalanced between the strong dipole at the Pt-S interface and the weak dipole at the graphene-SH interface. 22 Indeed, the alkanedithiol is physisorbed on graphene, leading to no orbital hybridization between the SH anchoring group and graphene, in contrast to the Au-S interface. This is confirmed through DFT calculations and contact resistance measurements. This results in a shift of the HOMO level toward the Fermi level, with respect to a standard Pt-Pt junction, which reduces the electronic barrier at the interface and consequently the attenuation factor. This is exactly the same mechanism as for the Augraphene electrodes. 22 Hence, this behaviour is not dependent on the metal used for the electrodes, but more on the coupling difference, strong at the metal/S interface and weak at the graphene/SH interface. For further comparison, the findings from our earlier study on carbon fibre/S-(CH2)n-SH/graphene junctions are discussed next. 37 As discussed previously carbon fibre "behaves like a gold tip" for such junctions, leading to a similar attenuation factor (0.38), but also a slightly reduced conductance. Again, the conductance depends dramatically on the symmetry breaking and the weak coupling between the molecule and the carbon fiber electrode. Overall, we find no significant difference for the value of 𝜷 𝒏 for all graphene hybrid molecular junctions using Pt, Au or carbon fiber tips. In the same manner, Engelkes et al 38 obtained no notable differences in 𝜷 𝒏 for 18 different symmetric junction types employing three metals (Pt, Au, or Ag) for the substrate and tip, with alkanethiols and alkanedithiols as bridging molecules. The previously reported study 39 involving a series of amine-terminated oligophenyls and polymethylene (alkane) bridges also shows a higher conductance with gold electrodes than with silver. In that study 𝜷 𝒏 is generally independent of whether the contacts are Au or Ag.

The fits in Figure 4 can be extended to zero length, with the resulting intercept of those plots giving the contact resistances (R0). For Pt/S-(CH2)n-SH/graphene junctions, the contact resistance is 5.8 MΩ, which is slightly larger than that obtained for Au/S-(CH2)n-SH/graphene junctions (3.9 MΩ) in our previous study 22 and much higher than that obtained for Au/Au symmetric contacts processing two Au-S-alkane contacts (27 kΩ). This difference points to the electronic coupling between molecular bridge and electrodes being significantly weaker at the graphene-molecule interface. At the other electrode there is a large bond dipole associated with the metal-S contact resulting from the electron donation from the sulfur to Pt. A study reported by Kim et al 19 also found that the R0 is three orders of magnitude higher for Au/molecule/graphite junctions than that of Au/molecule/Au junctions. They proposed a weak electronic coupling at molecule-graphite contact. Also, in agreement with previous results, the bond dipole is profoundly dependent on the metal type for alkanedithiols, 40 Please do not adjust margins Please do not adjust margins eV for Au, which self-consistently explains the behaviour in terms of the junction transport characteristics.

Conclusions

We have experimentally determined the electrical conductance and tunnelling decay constant in Pt/S-(CH2)n-SH/graphene molecular junctions using the STM I(s) method. The conductance of Pt/graphene molecular junctions decreases exponentially with the increase of the molecular length and is higher than equivalent Pt/Pt junctions for longer junctions. Importantly, the tunnelling decay constant of about 0.30 is much lower than that of symmetric metal electrode. configurations. This is in good agreement with previous studies for polymethylene molecular bridges terminated with either thiol, amine or carboxylic acids, where replacement of a metal bottom electrode with graphene considerably reduces the length attenuation factor of the electrical conductance. These results are confirmed by DFT calculations, which show that the HOMO orbital (which is closer to the Fermi level than the LUMO for these systems) mediates the charge transfer. As a consequence, this study confirms the role of the coupling asymmetry between Pt, Au or even carbon fibre on one side of the molecular bridge and the graphene electrode on the other side. This coupling asymmetry leads to the reduction of the length attenuation factor of the molecular junction current.

  Figure 1. Schematic illustration of forming a Pt/alkanedithiol/graphene hybrid molecular junction.

Figure 2 .

 2 Figure 2. Typical I(s) curves of Pt/S-(CH2)n-SH/graphene molecular junctions, with different alkanedithiol length. These are n = 2 (black solid line), 4 (purple lines), 6 (red lines), 8 (light green lines) and 10 (light blue lines).

Figure 3 .

 3 Figure 3. 1D histograms for the conductance of Pt/S-(CH2)n-SH/graphene molecular junctions with different alkanedithiol length: n = 2 (filled solid black area), 4 (purple area), 6 (red area), 8 (light green area) and 10 (light blue area). The coloring is consistent with that used in figure 2.

Figure 4 .

 4 Figure 4. Plots of the natural logarithm of the conductance as a function of junction length for the following systems: Pt/S-(CH2)n-S/graphene (experiment, yellow), Pt/S-(CH2)n-S/graphene (theory, red), Au/S-(CH2)n-S/graphene (experiment, blue) and Pt/S-(CH2)n-S/graphene (theory, green). The length is expressed as the number of methylene groups in the alkanedithiol molecular bridge.

Figure 5 .

 5 Figure 5. a) Atomic configurations of the different molecular junctions used for DFT calculations and b) corresponding calculated transmissions for Pt/S-(CH2)n-SH/graphene molecular junctions (n=2, 4, 6, 8, 10).

  namely the bond dipole increases with the work function. The work function for Pt is 5.65 eV and 5.20 This journal is © The Royal Society of Chemistry 20xx

Table 1

 1 collects together the conductance and 𝜷 𝒏 values of the

	MJs (SH-(CH2)n-SH) with Pt/graphene electrodes, Au/graphene
	electrodes, and Au/Au electrodes. This summary shows that the
	decay constant (0.30) of Pt/S-(CH2)n-SH/graphene junctions is
	less than that of the Au/S-(CH2)n-SH/Au junctions (1.08) and also
	less than Pt/S-(CH2)n-S/Pt junctions (1.05).

Table 1 .

 1 Experimental and theoretical conductance values for the following molecular junction systems: Pt/S-(CH2)n-S/graphene, Au/S-(CH2)n-S/graphene and Au/S-(CH2)n-S/Au molecular junctions. Tunneling decay constant (𝜷 𝒏 ) are tabulated on the right-hand side.

	Molecular	Number	Conductance (nS)	Decay constant (𝛽 𝑛 )
	junctions	of CH2
			Experiment Theory Experiment Theory
	Pt/S-(CH2)n-	2	99	148
	S/graphene	4	45	75
		6	29	31	0.30	0.34
		8	18	21
		10	7.5	9.5
	Au/S-	2	98	152
	(CH2)n-	4	53	46
	S/graphene	6	24	25	0.41	0.37
	22	8	9	18
		10	4	6
	Au/S-	6	28.2		1.08
	(CH2)n-S/Au	8	3.9
	35	10	0.2
	Pt/S-(CH2)n-	6	30.2	341.0	1.05
	S/Pt 27	8	6.6	65.8
		10	0.6	11.7

Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC Grants 21503169), Suzhou Industrial Park Initiative Platform Development for Suzhou Municipal Key Lab for New Energy Technology (RR0140), Key Program Special Fund in XJTLU (KSF-E-28) and the XJTLU Research Development Fund (RDF-14-02-42, RDF-16-01-33 and REF-19-01-05).

Conflicts of interest

There are no conflicts to declare.


Damien Bouchabou
email: damien.bouchabou@imt-atlantique.fr

Sao Mai Nguyen

Christophe Lohr
email: christophe.lohr@imt-atlantique.fr

Benoit Leduc
email: bleduc@deltadore.comnguyensmai@gmail.com

Ioannis Kanellos
email: ioannis.kanellos@imt-atlantique.fr

Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes

Keywords: Human Activity Recognition, Smart Homes, Embedding, Word Encoding, Fully Convolutional Network, Automatic Features

Activity recognition in smart homes is essential when we wish to propose automatic services for the inhabitants. However, it is a challenging problem in terms of environments' variability, sensory-motor systems, user habits, but also sparsity of signals and redundancy of models . Therefore, end-to-end systems fail at automatically extracting key features, and need to access context and domain knowledge. We propose to tackle feature extraction for activity recognition in smart homes by merging methods of Natural Language Processing (NLP) and Time Series Classification (TSC) domains. We evaluate the performance of our method with two datasets issued from the Center for Advanced Studies in Adaptive Systems (CASAS). We analyze the contributions of the use of embedding based on term frequency encoding, to improve automatic feature extraction. Moreover we compare the classification performance of Fully Convolutional Network (FCN) from TSC, applied for the first time for activity recognition in smart homes, to Long Short Term Memory (LSTM). The method we propose, shows good performance in offline activity classification. Our analysis also shows that FCNs outperforms LSTMs, and that domain knowledge gained by event encoding and embedding improves significantly the performance of classifiers.

Introduction

Human Activity Recognition (HAR) has been the focus of research efforts due to its key role for different ambient assisted living (AAL) domains as well as the increasing demand for home automation and convenience services in daily

The work is partially supported by project VITAAL and is financed by Brest Metropole, the region of Brittany and the European Regional Development Fund (ERDF).

activities. The main task of HAR is to recognize human activities from the data collected through environmental sensors and Internet of Things (IoT) devices. They use different sensor technologies such as cameras, wearable or low-level smart sensors to track human activities, as described in [START_REF] Hussain | Different approaches for human activity recognition: A survey[END_REF].

Recent advances in IoT technologies and the reduction of the cost of sensors are leading to the proliferation of these ambient devices and the development of smart homes. This is why in this work we will focus more on IoT-based HAR, as opposed to video or wearable-based HAR.

Along the development of the hardware, the HAR algorithms also need to solve the challenges of HAR in smart homes. Indeed, the number, the type but also the placement of sensors can significantly influence the performance of HAR systems. A system suitable for a given home may be completely inadequate in some other, due to different house configuration or user habits. The algorithms thus need to be robust to the variability of environments. Besides, while videobased HAR can leverage rich and redundant information from images and video streams, IoT based HAR faces the challenges of sparse and incomplete information and redundant models. In contrast to videos where objects and people appear on several pixels and over several video frames, the IoT network only detects changes in the environment that are within their range of detection and in their field of view, and is oblivious to most changes in the environment, which occur outside these ranges. When a change is captured, this detection often translates into a signal with a single value from one sensor. This sparsity entails the redundancy challenge: a set of signals from the same set of sensors can be caused by different activities. Thus, algorithms for HAR in smart homes need to address the challenges of variability, sparsity and redundancy.

To adapt to variations of environments and uses, algorithms for HAR have turned to machine learning methods, and more specifically Deep Learning (DL) algorithms. To deal with sparsity and redundancy, first, algorithms that can learn long-term dependencies have been developed so as to understand the context of sensor signals. Second, studies have also tried to introduce domain knowledge and contextualization of sensors signals, through a good feature representation of sensor events. But handcrafted features need a lot of pre-processing and reduce its adaptability to various environments. Therefore HAR algorithms need to automatically extract domain relevant representations.

In recent years, there have been significant improvements of DL techniques. They have been successfully applied to Natural Language Processing (NLP) and Time Series Classification (TSC). Respectively for automatic extraction of good feature representations through word embedding techniques and classifiers.

Our contributions are the following: 1) We apply for the first time the Fully Convolutional Networks (FCN) classifier from TSC on activity recognition in smart homes. 2) We propose to use frequency-based encoding with word embedding from NLP to improve automatic feature extraction. 3) We design an end to end framework to automatically extract key features and classify daily activities in smart homes by merging TSC classifier and NLP words encoding. 4) Finally, we show that domain knowledge gained by event encoding and embedding improves significantly the performance of classifiers.

We propose in the following section to review the state-of-the-art HAR smart home classifiers, a TSC classifier and the existing feature representation methods, in particular those used in NLP applications. In Section 3, we will propose a framework combining a TSC algorithm and a NLP sequence features extractor method. In Section 5, we will report on the performance of our proposed framework before concluding.

Related Works

In this section, we describe the algorithms developed for HAR, and more generally for Time Serie Classification. We then examine how TSC can be bootstrapped by incorporating domain knowledge in feature encoding as in Natural Language Processing.

Traditional HAR Approaches

To recognize human activities based on sensor traces, researchers used various machine learning algorithms as reviewed in [START_REF] Sedky | Evaluating machine learning techniques for activity classification in smart home environments[END_REF]. These can be divided into two streams: the algorithms exploiting a spatiotemporal representation, with Naive Bayes, Dynamic Bayesian Networks, Hidden Markov Models; and the algorithms based on features classification, with Decision Tree, Support Vector Machines, or Conditional Random Fields.

Most of these traditional HAR approaches commonly use handcrafted feature extraction methods. Automatic feature extraction is one of the challenges addressed by DL.

Deep Learning Approaches

Recently, a variety of DL algorithms have been applied for HAR to overcome those limitations and improve the performance of HAR. DL methods learn the features directly from the raw data hierarchically, to uncover high-level features. Long Short Term Memory (LSTM) can be seen as a very successful extension of the Recurrent Neural Networks (RNN), explicitly designed to deal with longterm dependencies. LSTMs allow automatic learning of temporal information from the sensor data without the need of handcrafted features or kernel fusion approaches, and have led to good performance in HAR in smart homes, as reported in [START_REF] Singh | Human activity recognition using recurrent neural networks[END_REF][START_REF] Liciotti | A sequential deep learning application for recognising human activities in smart homes[END_REF]. [START_REF] Liciotti | A sequential deep learning application for recognising human activities in smart homes[END_REF] evaluated different LSTMs structures for HAR in smart homes. They show that the LSTM approach outperforms traditional HAR approaches in terms of classification score without using handcrafted features. LSTMs leads to a viable solution to significantly improve the HAR task in the smart home but suffers from training time.

Another DL approach, focusing more on pattern detection is Convolutional Neural Networks (CNNs). They have three advantages for HAR. They can capture local dependencies, that is, the importance of neighboring observations correlated with the current event. They are scale invariant in terms of step difference or event frequencies. In addition, they are able to learn a hierarchical representation of data. Researchers used 2D [START_REF] Gochoo | Unobtrusive activity recognition of elderly people living alone using anonymous binary sensors and dcnn[END_REF][START_REF] Mohmed | Employing a deep convolutional neural network for human activity recognition based on binary ambient sensor data[END_REF] and 1D [START_REF] Singh | Convolutional and recurrent neural networks for activity recognition in smart environment[END_REF] CNNs on HAR in smart homes. The 2D CNN obtained good classification results. But this approach is not robust enough to deal with unbalanced datasets, unlabeled events, and is not suitable for online recognition. 1D CNNs are competitive with LSTMs on sequence problems [START_REF] Singh | Convolutional and recurrent neural networks for activity recognition in smart environment[END_REF]. In general LSTMs obtain better performances due to their capacity to use long-term dependencies. But CNNs are faster to train and get accuracy levels close to LSTMs.

The FCN is a particular CNN, with only convolutional layers and no dense layers. FCN has shown compelling quality and efficiency for semantic segmentation of images [START_REF] Long | Fully convolutional networks for semantic segmentation[END_REF]. Due to its performance on feature extraction, researchers transferred the FCN on TSC problems [START_REF] Wang | Time series classification from scratch with deep neural networks: A strong baseline[END_REF]. [START_REF] Fawaz | Deep learning for time series classification: a review[END_REF] compared the FCN against other TSC algorithms and obtained high classification performances. FCNs ranked first on 18 datasets out of 97 and in the top five on the others. However, no application of FCN for HAR in smart homes has been reported. For this reason we propose to apply the FCN to HAR in smart homes as a high-level extractor of features and classifier.

NLP and TSC coupling

Works such as [START_REF] Tahir | Key feature identification for recognition of activities performed by a smart-home resident[END_REF][START_REF] Yan | Using latent knowledge to improve realtime activity recognition for smart iot[END_REF] have shown the importance of a good feature representation, but designing features for HAR applications is a tedious task.

DL algorithms can automatically extract features, they have widely shown to improve feature representation with words pre-processing for text classification in NLP. Researchers have devised many language models and different encoding of words. They proposed encodings such as n-gram, term frequency, term frequency-inverse document frequency, bag-of-words. Recently, they use DL algorithms such as word2vec, GloVe ELMo and more recently Transformers, coupled with the aforementioned encoding to achieve meaning word encoding [START_REF] Kowsari | Text classification algorithms: A survey[END_REF][START_REF] Li | A survey on text classification: From shallow to deep learning[END_REF]. DL algorithms infer features from the current input and to a lesser extent from past inputs, these encodings incorporate more general domain knowledge from the whole corpus. Their strong capacity to generate features from raw data and model word sequences increases the performance of DL classifiers. We propose to transpose previously cited NLP techniques on smart homes HAR problems in order to automatically generate key features.

Thus, we introduce in this article a DL methodology for HAR in smart homes inspired by the NLP and the TSC. We propose to combine the term frequency encoding and embedding with FCN, respectively : incorporate domain knowledge of event encoding in the first level of extraction features ; and realize a higher-level of extraction features and the activity classification. The choice of the FCN algorithm from TSC is led by the output of the NLP embedding, which transforms the sequence classification problem into a multivariate TSC problem.

To our knowledge this is the first time that a study has used FCN in smart-home activity recognition, and has combined it with embedding techniques to perform an end-to-end system that automatically extracts key features and classifies activities in smart homes.

Methodology

We merge NLP encoding and FCN classifier from TSC to deal with smart homes HAR. This coupling allows generating automatic key features and classify activities.

The framework architecture of the proposed method is shown in Figure 1. First raw data from sensors are encoded into a sequence of indexes (section 3.2), then are split using a sliding window (section 3.4). The sliding windows are then processed through an embedding to extract a first level of features, and finally classified by the FCN (section 3.3).

Problem Definition

The activity recognition problem is a classification problem. The goal is to attribute an activity label on sensors events sequences. We model our problem as follows. A set of sensors S = {s 1 , ..., s |S| } produces events e i ∈ E. An event is the value or the state returned by a sensor when the sensors emit a signal: e i = (s i , v i , t i ), where s i is the sensor id, v i the value returned by the sensor and t i the time when the sensor changes its state or value. A sequence L i is a trace of activity. L i is a list of events Seq i = (e i , ..., e n ). Each L i can be associated to an activity label a i ∈ A, by semantic segmentation.

In this paper we did not take in consideration the timestamp t i when an event occurs. We simply ignore the parameter t for our experiments. We want to be able to recognize an activity regardless of the time of the day. For example, the activity "Sleeping" appears in general during the night but this activity can appear at any time during the day. Some people can work during the day and sleep by night and vice versa some people can work during the night and sleep during the day.

NLP Encoding

Our hypothesis is to process sensor events like words and activity sequences as text sentences; these sentences describing the activities carried out by the inhabitants.

First, each sequence of activity is extracted from the dataset as sentences in NLP. Thanks to the label provided by the dataset, it is possible to know the beginning and the end of each activity. As previously described, an event e i is composed of the sensor ID s i , the value v i and the timestamp t i . By concatenating the sensor ID s i with his value v i and by ignoring the timestamp t i , for the reasons explained previously, a sensor word is created, e.g., s i = M 001 and the value v i = ON becomes M 001ON . All these different text words define the smart home vocabulary to describe activities.

Then, as in NLP, each word in the sequences are transformed into an index to be usable by a neural network. In NLP the index starts at 1, the 0 value is reserved for the sequence padding. Indexes are assigned based on word frequency, e.g., if the word M 001ON has the highest occurrence in the dataset, the assigned index is the lowest one i.e 1.

Sequences are then passed through an embedding layer which transforms index tokens (words) into auto learned features vectors. This creates a simple word embedding that helps the network to get an internal representation of each word in our cases each sensor event.

FCN Structure

The FCN is a particular CNN. Its structure only contains convolutional layers e.g., no fully connected layers for the classification part. The same structure as [START_REF] Wang | Time series classification from scratch with deep neural networks: A strong baseline[END_REF][START_REF] Fawaz | Deep learning for time series classification: a review[END_REF] is used in this paper. This structure (Figure 2) is composed of three blocks described by EQ 1. Where x is the input, W the weight matrix, b the bias and ⊗ the convolution operator and h the hidden representation. Each block consists of a 1D convolutional layer with Batch Normalization (BN) [START_REF] Ioffe | Batch normalization: Accelerating deep network training by reducing internal covariate shift[END_REF] and a rectified linear unit (ReLU) activation to speed up the convergence and help improve generalizations.

y = W ⊗ x + b z = BN (y) h = ReLU (z) (1)
After the three convolution blocks, features are fed into a Global Average Pooling (GAP) layer [START_REF] Lin | Network in network[END_REF]. GAP is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding category of the classification task. The resulting vector is fed directly into the softmax layer to realize the final classification.

One advantage of GAP over the fully connected layers is that it is more native to the convolution structure by enforcing correspondences between feature maps and categories. Thus, the feature maps can be easily interpreted as category confidence maps. Another advantage is that there is no parameter to optimize in the GAP thus over fitting is avoided at this layer. Furthermore, GAP sums out the spatial information; thus it is more robust to spatial translations of the input.

One of the advantages of FCNs is the invariance in the number of parameters across time series of different lengths. This invariance due to using a GAP layer enables the use of a transfer learning approach where one can train a model on a certain source dataset and fine-tune it on the target dataset [START_REF] Fawaz | Transfer learning for time series classification[END_REF].

Sliding Window

Contrary to LSTMs, CNNs must have a fixed input size and activity sequences can have different lengths, between 1 and more than 5000 events. To tackle this issue, a sliding window is applied over sequences. Using a sliding window also allows anticipating an online HAR. To fill windows with fewer events than the window size, a zero padding is used. The zero padding can impact the final result. To avoid too much zero in the sliding windows, a fine-tuned window size must be found.

For experiments, the Sensor Event Windows (SEW) [START_REF] Quigley | A comparative analysis of windowing approaches in dense sensing environments[END_REF] was used. The SEW approach divides the data into equal sensor event intervals. The size of a SEW is defined by a number of events. Therefore, the duration of the windows may vary. Authors of [START_REF] Quigley | A comparative analysis of windowing approaches in dense sensing environments[END_REF] compared different windows types and conclude that Time Windows (TW) provides the best accuracy and F-Measure score. They consider SEW as the second-best window method because SEW are able to classify more activities than TW. We assume this is because SEWs keep a fixed context size while it is variable for TWs. A stable context size allows the neural network to keep the same amount of information regardless of the window.

In this work, SEWs were used for two reasons. First, we want to evaluate the method for its ability to learn automatic features from the window context. The intuition being to train a network onto bounded activity sequences to extract features and then use them on streaming sensor data for online recognition. Second, this avoids too many zeros inside windows by controlling the number of events.

Experimental Setup

LSTMs provide very good results on sequence problems and go beyond traditional advanced HAR methods in Smart Homes [START_REF] Liciotti | A sequential deep learning application for recognising human activities in smart homes[END_REF]. In order to evaluate the method, LSTMs and FCNs were compared with two dataset ARUBA and MI-LAN from the widely spread CASAS [START_REF] Cook | Casas: A smart home in a box[END_REF] benchmark datasets.

Datasets Description

Two datasets, ARUBA and MILAN (Table 1) from CASAS were selected for the experiments. The CASAS datasets were introduced by Washington State University. Daily activities data collected, comes from real apartments and houses with real inhabitants, who live in their own houses. The houses are equipped with temperature and binary sensors, as motion or doors sensors.

A single person carried out activities in both the datasets. The MILAN dataset was selected for the noise on the dataset produced by the pet, which increases the difficulty of classification. They contain several months of labeled activities and are unbalanced, i.e., some activities are less represented than others. In addition these two datasets contain common and different activities with approximately the same number of sensors.

An unbalanced dataset increases the classification challenge. Indeed if some classes are less represented the system gets fewer examples to find the discriminating features. Moreover some events are unlabeled or unidentified and are tagged under the class name "Other". This class appears between 45% and 50% into these datasets. In the literature most researchers remove the class "Other" and balance the dataset by reducing the number of examples for each class.

This method creates a drawback, by ignoring unlabeled events it becomes a fixed classification problem. The system cannot make the difference between a known and an unknown class. This does not allow the system to be able to discover new sequences of activities.

Here the original distribution was kept. The objective was to evaluate the robustness of the method and the models.

SEW Parameters

As previously described, sequences of activities were segmented in SEWs. Different SEWs sizes, 100, 75, 50, 25, with a stride of one was studied. This stride size allows the HAR process each time a new event is triggered. The goal is to find the best SEW size e.g., the minimal SEW size with the maximal information that allows to discriminate activities sequences with a high F1-score and high-balanced accuracy. The smaller the size of SEW, the faster an activity can be recognized in the case of online HAR.

Networks Parameters

FCNs parameters are the same as [START_REF] Fawaz | Deep learning for time series classification: a review[END_REF]. All convolutions have a stride equal to one with a zero padding to preserve the exact length of the time series after the convolution. The first convolution contains 128 filters and a length equal to 8, followed by a second convolution of 256 filters with a length equal to 5, which in turn is fed to a third and final convolutional layer composed of 128 filters, with a length equal to 3. LSTMs parameters are the same as [START_REF] Liciotti | A sequential deep learning application for recognising human activities in smart homes[END_REF]. The LSTM cell is composed of 64 neurons and then followed by a softmax layer for the final classification.

As it is usually made in NLP an embedding layer was added between the raw data and the neural network. The number of neurons was fixed to 64 as it was defined in [START_REF] Liciotti | A sequential deep learning application for recognising human activities in smart homes[END_REF].

Hardware and Software Setup

Experiments were made on a server, with an Intel(R) Xeon(R) CPU E5-2640 v3 2.60 GHz, with 32 CPUs, 128 Go of RAM and a NVIDIA Tesla K80 graphic card. Keras and Tensorflow frameworks were used for the algorithm's implementation. The source code can be found at https://github.com/dbouchabou/Fully-Convolutional-Network-Smart-Homes.

Evaluation Method

To evaluate the proposed method, datasets were split into two parts: 70 % for the train and 30% for the test. These two parts contain a shuffled stratified (over class) number of SEW of each activity. e.g., if the dataset contains 100 windows labeled "Sleeping" after shuffling, the 100 windows are split into two parts: 70 windows for the train set, 30 windows for the test set. The random shuffle helps the algorithm to get a better generalization and representation. The stratified forces both subsets to contain representations of each class.

A stratified (over class) threefold cross-validation procedure is performed on the training set. These three trains and three validation subsets are then used to train and validate algorithms.

During the training phase on each train set, early stop and best model selection methods proposed by the Tensorflow framework was used. These methods stop the training before overfitting and saves the best model of each train. The early stop condition is based on the validation loss value. If the current loss doesn't decrease after n epochs since the last, best model selected (here n = 20) the training is interrupted.

The three best trained models (one for each training subset) were evaluated on the test set to calculate the average balanced accuracy and the average weighted F1-score, because datasets are unbalanced.

To accelerate the training time by epoch and because the number of SEW is big, a batch size of 1024 was used for all experiments. No differences were noticed between the batch size evaluation during the tests, the results were similar except in training time.

Experimental Results

FCNs and LSTMs Performances

Table 2 and Table 3 show the performances of two FCNs and two LSTMs on raw sensor data for the two datasets. Vanilla LSTM, FCN and LSTM, FCN with an embedding layer on different windows size were evaluated. The average balanced accuracy and the average weighted F1-score was computed. FCN appears to obtain the best weighted F1-score with and without the embedding onto both datasets. The LSTM is close to or equal to the FCN on a large SEW, greater than 50. Compared to the FCN the LSTM looks to need more events to realize the classification.

From the balanced accuracy point of view, FCNs get best values except on the MILAN dataset when the window size is higher than 50. This decrease in performance is due to the zero padding. Indeed the average sequence length on the MILAN dataset is around 88 events. When the window size is close to or over this average, the performances of the FCN decrease. Some small sequences like "Bed to Toilet" or "Eve Meds" are not classified. This results in a drop in the balanced accuracy score.

As an online HAR is expected in our future work, it is interesting to observe the performance of the method on the small SEW size. The goal is to achieve HAR in as little time as possible, with as few events as possible, to get the most responsive system possible. In this case, the FCN obtained the best values with SEWs of sizes 50 and 25. Performances decrease as the SEW size decreases, but the FCN maintained a high score for balanced accuracy and the F1-score. Performance drops less with FCNs than with LSTMs. It seems that FCNs can generate more relevant automatic features than LSTMs on small sequences, therefore with less information.

Training Time

Table 4 and Table 5 show the average training time and the average amount of training epochs by SEWs size. On both datasets FCNs realized the shortest time on every SEWs size. The embedding layer allows to reduce the number of epochs and the total training time in the majority of cases. The training time is divided by 2 to 6 with the FCN depending on the window size compared to LSTM. This time saving is explained by the ease of parallelization of calculations of convolutional networks.

Encoding Impact

Tables 2 to 5 show that the embedding layer improves network performances. Indeed, with the embedding layer networks gain significant performance, 10 percentage points on balanced accuracy in average. Sensor events are transformed into vectors of 64 automatically learned features that allow networks to maintain a high score on small SEWs.

During our experiments, we noticed that the frequency encoding strategy improved performance, unlike random or arbitrary index allocation. We think this ordering helps networks generate discriminators on important events or rare events.

Conclusion

We have proposed a new method that coupled for the first time FCNs and embedding based on frequency encoding for HAR in smart homes. Our assessment on two datasets shows that:

-The embedding based on frequency encoding significantly improves the performance of LSTM and FCN in all cases. This means that the domain knowledge incorporated in the embedding can improve the understanding of events by LSTM and FCN. -With the same encoding, FCNs obtain the same or better performance than LSTMs, with the exception of only two configurations and are quicker to train. -Moreover, FCNs outperform LSTMs when the window size decreases. This means that FCNs have a shorter delay in recognizing activities, and are more suitable for real-time activity recognition.

The proposed framework is pure end-to-end without any heavy pre-processing on the raw data or feature crafting, thanks to frequency-based encoding and the embedding. This method appears to be relevant for HAR problems in smart homes with low-level sensors.

Discussion and Future Directions

The results presented in this paper show that the applied DL approach based on NLP encoding and FCN is a relevant solution to significantly improve the smart homes HAR task.

We used a naive embedding based on frequency encoding that improved classification results. We plan to explore more word embedding techniques [START_REF] Li | A survey on text classification: From shallow to deep learning[END_REF] such as Word2Vec or ELMo to improve the latent knowledge space and in the process enhance classification performances. Indeed these techniques take into account the context of words.

In addition, we are only experimenting with offline HAR. But the usage of SEWs in our assessment showed relevant results so we want to apply this to online HAR applications.

Moreover we plan to evaluate other windowing methods as TW or Fuzzy Windows [START_REF] Hamad | Efficient activity recognition in smart homes using delayed fuzzy temporal windows on binary sensors[END_REF] with this method. To study which window methods produce the fastest and most accurate online HAR in smart homes.

Fig. 1 .

 1 Fig. 1. Framework architecture of the proposed method

Fig. 2 .

 2 Fig. 2. Fully Convolutional Network (FCN) model core

Table 1 .

 1 Details of datasets.

		Aruba Milan
	Habitants	1 1 + pet
	Number of sensors	39	33
	Number of activities	12	16
	Number of days	219	82
	Average sequence length	133	87.3

Table 2 .

 2 Weighted F1 Score and Balanced Accuracy in Aruba's dataset

	Model	100	75	50	25
		Weighted avg F1 Score (%)
	LSTM	96.67 94.67 90.67 85.00
	FCN	99.00 98.00 97.67 92.33
	LSTM + Embedding 100.00 99.67 98.00 90.00
	FCN + Embedding 100.00 100.00 100.00 99.00
		Balanced Accuracy (%)
	LSTM	81.45 76.09 71.05 83.30
	FCN	88.85 87.41 87.08 80.32
	LSTM + Embedding 94.55 93.61 90.20 74.81
	FCN + Embedding	95.37 95.07 94.89 92.44

Table 3 .

 3 Weighted F1 Score and Balanced Accuracy in Milan's dataset

	Model	100	75	50	25
		Weighted avg F1 Score (%)
	LSTM	84.00 85.67 75.33 64.00
	FCN	77.33 93.67 88.33 83.67
	LSTM + Embedding 98.00 97.00 93.00 73.67
	FCN + Embedding 99.00 98.00 97.00 94.33
		Balanced Accuracy (%)
	LSTM	62.15 64.95 55.70 43.29
	FCN	42.24 76.41 71.82 71.34
	LSTM + Embedding 88.52 86.77 82.05 59.35
	FCN + Embedding 84.23 86.64 87.83 90.86

Table 4 .

 4 Training time performance and number of epochs training in Aruba's dataset

	Model	100	75	50	25
		Average epoch number
	LSTM	242	278	335	256
	FCN	77	71	111	108
	LSTM + Embedding	161	191	210	161
	FCN + Embedding	67	62	71	98
		Average training time (HH:MM:SS)
	LSTM	06:28:42 06:43:08 06:29:58 03:00:26
	FCN	00:58:00 00:52:15 01:20:35 00:51:27
	LSTM + Embedding 04:45:56 04:45:38 04:14:35 02:02:53
	FCN + Embedding 01:12:37 00:59:42 00:57:27 00:52:15

Table 5 .

 5 Training time performance and number of epochs training in Milan's dataset

	Model	100	75	50	25
		Average epoch number
	LSTM	274	385	365	324
	FCN	45	101	87	145
	LSTM + Embedding	255	290	320	183
	FCN + Embedding	65	51	52	55
		Average training time (HH:MM:SS)
	LSTM	02:03:43 02:11:07 01:44:06 01:00:10
	FCN	00:09:39 00:20:17 00:15:08 00:16:50
	LSTM + Embedding 01:57:52 01:49:35 01:36:56 00:35:26
	FCN + Embedding 00:16:24 00:11:42 00:10:00 00:07:67