In the Renaissance, to say that an Italian was cunning -"furbo" -was a compliment. At that time, cunning, like mischief and mischief, were the hallmarks of great minds, or as we would say today, gifted minds. Leonardo da Vinci may be famous for the excellence of his pictorial achievements, but the intelligence of his works seen through the prism of trickery is only just beginning to be recognised... That of Hieronymus Bosch, his contemporary and northern alter ego, the "devilmaker", is little known, if at all. The aim of this study is not to offer yet another reading of the Flemish masterpiece known today as the Garden of Earthly Delights, but to explore the possibility of a deliberate inversion of the order of this triptych in order to obscure its underlying messageheterodox and sacred to the initiated -under the guise of an apparently more orthodox reading to the uninitiated. This reversal lends further credence to Wilhelm Fraenger's earlier re-characterisation of the central panel of this triptych as a "Feast of Metamorphoses" 3 . Closed, the work by Hieronymus Bosch -reduced to a diptych format -depicts the Birth of the World in grisaille. A cosmogony very clearly developed in the ancient mode. We are at the precise moment when, freed from the original fog -ed -the earth, in the form of a primitive hill, emerges from the matrix of water. Hieronymus Bosch depicts this moment when Nature reaches its second wind, after the long bubbling of the Work in the crucible. The tranquil force of the elements completes this slow natural incubation, from which all manner of mineral, plant and animal figures gradually emerge. Some of the forms in the foreground are already reminiscent in their appearance and plasticity of the strange buildings in the background of the central panel inside the work. This aerial perspective -in the pure Leonardo-like style -shows us the other side of the stage. The outline of the scene that will be played out for centuries to come is represented inside this work in triptych format. Breaking with the vision accepted and defended by the Church of a Genesis developing in successive, static stages, Hieronymus Bosch resorts to a cosmogony long abandoned: the orbis of the archaic philosophy of Nature, which floats like a disc inside the sphere. By way of prologue, he offers us the foundation of this theosophy: the eternal morning of Creation. Above this incubating sphere, on the left -at the heart of a cloudy breakthrough -appears a majestic figure in the posture of seated philosopher, with a pontifical tiara on his head, a pendant medal or seal, and a book held open in his left hand. In fact, in the extension of this celestial breakthrough, following a right oblique, a hole in the heaviest clouds in the right-hand shutter allows a ray of light to filter through. Fiat Lux. Light gives way to darkness. Original chaos is replaced by order. The work is born of primitive chaos. Above this crystal ball, equally divided between the two panels, is this autograph inscription (?) taken straight from Psalm 33: Ipse dixit et facta sunt. Ipse mandavit et creata sunt. According to Aristotelian thought, Ipse is the thinking being. Hence this possible translation: The thinking being verbalised it and the great work was accomplished. Psalm 33 continues: The Lord looks down from heaven and sees all the sons of Adam. He sees all the sons of Adam. From where he stands, he observes all the inhabitants of the earth. He alone can read their hearts. He discerns all their deeds. Right-hand panel, Hell, which threatens sinners and unbelievers.

The closed triptych of the Garden of Earthly Delights by Hieronymus Bosch

In the centre, the Original Paradise where man, after his sin, must choose between Good and Evil.

However, a closer look at the work raises questions about the lack of spatial and temporal coherence in the theme treated by _________________________________________________________________________________ Hieronymus Bosch, and by the same token about the pseudocontinuity of the left-to-right reading we are "naturally" offered... On the one hand, it is somewhat surprising to find between the Garden of Eden and Hell such an apology for hedonism, such a polymorphous and unbridled expression of pleasure. The pleasure of the senses in all its guises. Where in fact one would expect to find a more nuanced -even Manichean -illustration of human behaviour balanced between Good and Evil. A display of human behaviour in which pious scenes and venial scenes, some of them even fatal, at best oppose each other and at worst overlap. On the other hand, it is surprising to note that while there is a pseudo-continuity in terms of space, topography, style and colour between the left-hand panel and the central panel, the right-hand panel is a bit of a mismatch. A complete stylistic and colour contrast. But also a spatial and topographical break.

Certainly, it could be a projection of the threat of Hellpromised to sinners and infidels -brandished by the Holy Inquisition. Like a palisade on which the image of Evil at its most perverse is plastered and castigated. But therein lies the paradox. There is no reason to believe that this triptych could have been intended for any kind of church altarpiece. Too much apparent nudity. Too much impropriety in relation to orthodoxy, too much effusiveness in relation to dogmatism. Men, women, whites, blacks -naked as worms -mixed literally and figuratively, treated as equals, without distinction of sex, class or condition. A manifest "swinger". A liberated and open libertinism... Inconceivable in these troubled times of witch-hunting! So we came up with this incredible idea: shouldn't this triptych be read from left to right? Like a Japanese manga! A reversed reading, which would enable us to recover the spatial and temporal coherence of the subject treated by Hieronymus Bosch:

The right-hand panel is no longer a scene from Hell, but the hell of the human condition. An authentic manifesto of perverted and corrupt human behaviour in this lowly existence on Earth.

The central panel then becomes the site of metamorphosis. A happening. An event. A Pythagorean-inspired feast of metamorphoses come to life.

_________________________________________________________________________________

And then, in our still-bubbling minds, this idea of "reverse reading" is transformed into pure and simple material inversion. A physical, structural inversion of the two side panels. An inversion that would appear to have been deliberately created to blur the lines of this manifesto that was originally impious, sacrilegious and blasphemous. The manifesto of the Men of Intelligence, disciples of the Free Spirit and Modern Devotion, or of that Spanish variant, the Alumbrados. A late resurgence of the ancient Gnostic cults. From the Gnosis of the worshippers of the Mother Goddess, alias Alma Mater, alias Isis, the Egyptian. This simple structural inversion would have succeeded -or almost succeeded -in keeping this triptych in a straitjacket of theological and moral asceticism in perfect accord with Vatican orthodoxy for nearly five centuries (half a millennium). In short, to impose, by a simple sleight of hand, a politically correct inverted reading! Now it's time for us too to catch our second wind after this incredible 'retroversion'. Let's look at the recomposed triptych. Hieronymus Bosch's work has regained all its original spatio-temporal coherence. A perfect continuity of reading from left to right is now "artificially" offered to us. We won't dwell on deciphering each scene in this triptych in detail, one after the other. Others have done so before us. And even if none of them held the keys to this inverted triptych, their remarkable work of "cryptanalysis" has nonetheless helped to re-establish a Boschian grammar that is very close to the historical truth: Wilhelm Fraenger, Jacques van Lennep, Charles Prost, Frédéric Elsig and more recently Annick Drevet-Tvermoès, as far as the most perceptive are concerned. To put it succinctly, the triptych by Hieronymus Bosch can be read on two levels. These two levels of reading are very subtly intertwined. On the one hand, there is a first level of reading based on the so-called alchemical register, more

The inverted open triptych of the Garden of Earthly Delights by Hieronimus Bosch

The left-hand panel is no longer a representation of the Garden of Eden, the Original Paradise, but of the Return to the Sources. To the sources of ancient civilisations. A return to the sources of the Nile, illustrated on the left by this tree of Life that looks like a dragon tree, an explicit reference to Martin Schongauer's Flight into Egypt.

_________________________________________________________________________________

Love between the sexes thus becomes a Gnostic mystery, a modus specialis -called angelic love or the ascending path (acclivity), or even the joy of Paradise -that is clearly distinct from the ordinary animal act of copulation and in no way "offends" Nature. The Children of Adam in the central panel of Hieronymus Bosch's triptych should be understood in this sense, as a return to the original purity of Man.

At the heart of this Feast of Metamorphoses, a veritable symbiosis seems to have taken place between these Adamites and the various plants, to the point where we can speak of a vegetable humanity that tends to rediscover the sexual innocence of plants. Men and women seem to have grown there like flowers or mushrooms in a vast, lush green meadow. The emblem of this naturist, Orphic universe -free and uninhibited -is probably this butterfly (Panthea coenobita, aka Small Tortoiseshell ) in the centre, gathering a Milk Thistle that looks like a stallion flying the Adamite flag on this strange beached totemic vessel. At the risk of repeating ourselves, it has to be said that the work of Hieronymus Bosch "retroverted" in this way regains all its original spatio-temporal coherence, and that we are now "artificially" offered a perfect continuity of reading from left to right. There is now a real spatial, topographical, stylistic and colour continuity not only between the central panel and the right-hand pane, but also between the latter and the left-hand pane. Even more subtly, the left-hand panel, the central panel and the right-hand panel are interpenetrated by successive pictorial layers, so that the action extends beyond the margins of the two side panels into the central panel, and vice versa. The two side panels and the central panel behave like communicating vessels in which we can broadly described as Hermetic (including the language of the Birds). On the other, a second level based on the shamanic modus operandi (including the ingestion of mushrooms and other hallucinogenic substances). These two levels, alchemical and shamanic, both symbolic and gestural, served as the ritual basis for the Adamite ceremonial. A synthesis of the old Platonic, Augustinian, Neo-Pythagorean and Gnostic heritage had been achieved. It gave rise to a spirituality imbued with philosophy (known as theosophy) which saw in the Original Man the symbol of an inner renewal and of man's accession to a pure and free humanity. This new vision of the Original Man found its most magnificent expression in the Mediterranean contemporaries of Hieronymus Bosch, in the neo-Platonic Academy of Florence, whose members included Marsilio Ficino and Pico della Mirandola. Adamsituated between animality and divinity -is the Proteus of universal metamorphosis. Grouped into esoteric communities organised rather like lodges, the Men of Intelligence, disciples of the Free Spirit and Modern Devotion, or even of that Spanish variant, the Alumbrados, were thus followers of a singular gnosis, a cross between Platonic Eros and Johannine agape. They all considered themselves to be sons of Adam, united by effective bonds of kinship. Women entered these circles as equal partners, freed at last from the condition of inferiority and the label of Satan's doors to which they had been condemned by the Church. According to the Platonic myth, the original Man unites within himself, in a pure androgynous unity, the masculine solar (celestial) principle and the feminine chtonian (earthly) principle. The divine spiritual part of man has retained the ability to return to the light thanks to the reincorporation of the lost chtonian principle: woman. The right (now left) panel of the triptych by Hieronymus Bosch, known as the Return to the Sources, is an exact representation of this. So the theosophical message could not be clearer: even if our world is a world of depravity -perverted and corrupt -no one is banished or even condemned from it. This doctrine of Apokatastasis (or Universal Redemption), first enunciated by Origen in the 3rd century and taken up again by John Scotus Erigena in the 9th century, is very explicitly illustrated and expressed here: "All Christians, Jews, pagans and even the Devil will be saved and brought back to God". The same goes for the knights errant, the bawdy monks, the street musicians, the acrobats, the hucksters, the swindlers, the gamblers and other minions of Satan detailed in the left-hand (now right-hand) panel: All will be saved and brought back to God! But the Church formally condemned this doctrine. Hence the subterfuge of inverting the side panels to cut off the main thread of this heterodoxy... And to further muddy the waters of a reading now reserved for only a handful of great initiates. Now duly recomposed and renamed the Feast of the Metamorphoses, this work by Hieronymus Bosch expresses the Orphic-Pythagorean heritage of the ancient archaic philosophy of Nature with a force that we have never encountered in any work of art from the Renaissance.

All that remains now is to gently close this inverted triptych...

The inverted closed triptych of the Garden of Earthly Delights by Hieronymus Bosch

__________________________________

  The inverted Triptych by Hieronymus BoschXavier d'Hérouville 1 & Aurore Caulier 2

Figure 1 |

 1 Figure 1 | The Garden of Earthly Delights: Closed tryptic

_________________________________________________________________________________1

  PhD, Art expertise consultant, Saint-Cyr-sur-Loire, France 2 PhD, Scientific Director, Richelieu Museum, France 3 Le Royaume Millénaire de Jérôme Bosch, translated from the German and introduced by Roger Lewinter, Paris, Denoël editions, 1966

Figure 2 |

 2 Figure 2 | King Solomon However discreet he may be -above the fray -in this cloud, this figure has a message for us. The finger pointing upwards testifies to the importance of this message and the assurance of its messenger. Historically, the finger pointing upwards signifies that one has been entrusted with a divine mission. The open book held in the left hand recalls the tablets of the law as depicted in the canonical representations of Moses descending from Mount Sinai. This figure of quiet strength could well be King Solomon incarnating Wisdom. The instrument of the Demiurge. This figure and his pontifical tiara are also to be found quite frequently in the work of Hieronymus Bosch. In the Temptation of Saint Anthony and the Adoration of the Magi, for example. The open book symbolises the Book of Wisdom. King Solomon represents the Logos.

Figure 3 |

 3 Figure 3 | Ipse dixit et facta sunt. Ipse mandavit et creata sunt

Figure 5 |

 5 Figure 5 | Pytagorean "Y" in the central panel of the Garden of Earthly Delights by Hieronymus BoschThe Fountain of Youth in the background is clearly the central element of this celebration. Around it, in a ritual ballet -a perpetual round movement -the human, animal and hybrid nebula is articulated in all its states.

Figure 6 |

 6 Figure 6 | The Round of the human, animal and hybrid nebula in the central panel of the Garden of Earthly Delights by Hieronymus Bosch

Figure 8 |

 8 Figure 8 | Open inverted tryptic of the Garden of Earthly Delights, aka the Feast of Metamorphoses by Hieronimus Bosch

Figure 6 |

 6 Figure 6 | The Dragon tree (dracaena) in the right-hand section of the Garden of Earthly Delights by Hieronymus Bosch and the Dragon tree (dracaena) in the Flight into Egypt by Martin Schongauer Autre référence, un peu plus haut sur la droite : Cet élément de paysage anthropomorphe matérialisant la source du Nil, profil grotesque " arcimboldesque ", aux allures de Vieux (Sage) de la Montagne. Au sommet de son crâne, en guise de couvre-chef, s'élève un autre arbre mythique : l'arbre de la Connaissance du Bien et du Mal. Mais cet arbre autour duquel s'enroule un serpent, évoque aussi la forme symbolique du caducée. Attribut d'Hermès. Hermès trismégiste. Le messager des dieux. Le passeur d'âme. Le guide de l'initiation sacrée. Le maître des métamorphoses...

Figure 7 |

 7 Figure 7 | Anthropomorphic landscape element from the right-hand panel of the Garden of Earthly Delights by Hieronymus Bosch

Figure 10 |

 10 Figure 10 | The Milk Thistle & the Small Tortoiseshell (Panthea coenobita) from the central panel of the Garden of Earthly Delights, also known as the Feast of the Metamorphoses by Hieronymus Bosch.

Figure 9 |

 9 Figure 9 | The Union of the Male and Female Principle in the left panel of the Garden of Earthly Delights, aka the Feast of Metamorphoses by Hieronymus Bosch

Figure 11 |

 11 Figure 11 | "Garden of delights, spins like a helix..." (Gérard Mansset)

Figure 12 |

 12 Figure 12 | The Garden of Earthly Delights: closed inverted tryptic


Franco Terranova

Abdelkader Lahmadi

Isabelle Chrisment

Deep Reinforcement Learning for Automated Cyber-Attack Path Prediction in Communication Networks

Background & Motivation

Traditional methods of manual attack path discovery struggle to scale with the dynamic nature of potential threats and time-varying communication networks. Deep Reinforcement Learning (RL) [START_REF] Sutton | Reinforcement Learning: An Introduction[END_REF] can be leveraged to realize an automated network security analysis. The RL agent will aim to learn an effective attack policy to exploit vulnerabilities and compromise the largest amount of nodes within the network. The cyber-attack paths learned can be used to assess and increase the situational awareness of the network security. This work aims to improve existing studies with a topology-independent neural network (NN) structure and a comprehensive evaluation of generalization, particularly across various topology sizes [START_REF] Li | Innes: An intelligent network penetration testing model based on deep reinforcement learning[END_REF][START_REF] Guo | Automated penetration testing with fine-grained control through deep reinforcement learning[END_REF]. The experimental scenarios has been generated with the following procedure: 200 different chains with a number of nodes in the range [100, 300] divided in training set (60%), validation set (20%), and test set (20%) by size. Deep Q-Network (DQN) [START_REF] Mnih | Human-level control through deep reinforcement learning[END_REF], Proximal Policy Optimization (PPO) [START_REF] Schulman | Proximal policy optimization algorithms[END_REF], and Actor-Critic (A2C) [START_REF] Mnih | Asynchronous methods for deep reinforcement learning[END_REF] algorithms with default hyper-parameters [START_REF] Raffin | Stable baselines3: Reinforcement learning in python[END_REF]. Multiple runs of 1,000,000 steps with episodes' cut-off set to 2 * optimal number of steps: optimal steps' number = (3 * (chain size + 1))

Conclusions

Our experiments of the value-based, policy-based, and actor-critic methods showed convergence results on a chain-based topology environment being able to generalize among chain sizes.

The DQN method has converged to the optimal policy required to solve the deterministic environment, also on reserved sets of larger chains. Future work will aim to explore new environments and leverage Graph Neural Networks (GNNs) capabilities.

Figure 1 .

 1 Figure 1. The agent receives a state or observation S(t), takes action A(t), receives reward R(t), and transitions to a new state S(t + 1).

Figure 2 .Figure 3 .

 23 Figure2. Representation of the POMDP with the distinction of owned, discovered, and not discovered nodes. The observation vector at a given moment t will be the concatenation of the feature vectors of the source and target nodes.

  Average reward on the validation set during training. (b) Owned nodes' percentage on the validation set during training. (c) Success rate percentage of the actions performed on the validation set. ±0.00 (d) Average owned nodes' percentage on the test set.

Acknowledgment

This work has been partially supported by the French National Research Agency under the France 2030 label (Superviz ANR-22-PECY-0008). The views reflected herein do not necessarily reflect the opinion of the French government.


domain: chim sde

text:
D Boulesteix
email: david.boulesteix@centralesupelec.fr

A Buch

Interpretation of SAM results simulating the Martian surface at SOLEIL synchrotron under X-ray irradiations -Abscicon 2022

Introduction

The Sample Analysis at Mars (SAM) instrument onboard NASA Curiosity rover revealed organic matter indigenous to a Martian sample with the detection of chlorohydrocarbons [1]. These molecules are thought to originate from a reaction of unidentified higher molecular mass organic matter with Martian perchlorates. The reaction may occur at Mars' near-surface induced by the harsh radiative environment, e.g. direct radiation (UV, high energy X-and γrays) and secondary photons produced by the interaction of direct radiations with the martian regolith (low energy secondary X-rays).

To address the influence of high energy X-rays directly reaching Mars surface on the degradation of organic molecules, a synchrotron session was undertaken on the PSICHE beam line. The reactivity and chemical characterization of L-alanine and benzoic acid exposed to hard X-rays (25-50 keV) did not show any degradation of organics nor formation of chlorohydrocarbons. In conclusion, the X-rays of the PSICHE beam line may have been too energetic for degrading efficiently organic molecules [3,4]. This why we have investigate the impact of soft X-ray irradiation on the LUCIA beam line at SOLEIL at the 2.6 kv energy which is the energy range for the majority of secondary emissions and monitoring the evolution of perchlorate Cl by X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS).

Materials and Method

Pure molecules (L-phenylalanine, D/L-alanine, and trimesic acid) deposits have been done by sublimation on an optical window or mixed with perchlorate (Mg, Fe) (added by evaporation from liquid solutions and dried out). We have also studied the molecules within mineral matrices (JSC-Mars1 analog) in the form of pellets, in absence and in presence of perchlorates (max 3 % weight). Thanks the Cl K-edge XANES experiments we were able to study the in situ evolution of organic molecules under the X-ray beam such as a signal shift towards low energies (change in the degree of oxidation) or a variation in its intensity (influence of the atomic environment). EXAFS measurements have been also performed to help identifying the new products. For all the sample we have performed infrared (IR) and micro infrared (mIR) spectroscopy at regular irradiation intervals to estimate the kinetic parameters of the degradation of the organic matter. Lastly, some irradiated samples have been analyzed in our laboratory, with pyrolysis and derivatization-gas chromatography mass spectrometry (GC-MS).

Microscopy Characterization of Irradiated Samples

By using Interferometer, Microtomograph and OCT we have evaluated the thinkness and the roughness of our pure organic deposits with and without perchlorate before and after they have been irradiated. From those experiments we obtain the value of the rougness and the thikness of the deposits before and after irradiation (table 1).

Table 1. Thickness and roughness evaluated by OCT and interferometry analyses on pure organics and organics mixed to calcium perchlorate (CaP) matrix.

Infrared and micro-Infrared Characterization of Irradiated Samples

Infrared spectra of irradiated and non-irradiated regions were studied to quantify the chemical evolution of the irradiated deposits at increasing X-ray doses or time of exposure to 2.7 keV. We used a FTIR spectrometer and an Agilent microscope coupled to a micro-FTIR spectrometer. A polynomial baseline was subtracted from the infrared spectra to normalize the continuum arising from rough surface scattering. The transmission spectra are not linearly proportional to the concentration of the sample. The intensity is logarithmically proportional to the concentration though. Thus, we made a first conversion in absorbance to subtract the reference (irradiation window without any organic or matrix deposit). After this treatment, we corrected the baseline and conducted a normalization to compare controls (non-irradiated samples) to samples with standard organics, or with a matrix.

Figure 4 shows the evolution of the infrared vibrational bands from the initial (non-irradiated) alanine to the increasingly irradiated regions on a same irradiation window. Indeed, different spots where irradiated at different time of exposure to 2.7 keV (corresponding to different irradiation doses). We observe the loss in intensity of all infrared vibrational bands belonging to the alanine when the energy doses arise. The quantification in the loss of alanine was conducted thanks to GC-MS analyses . Analogue results were obtained for phenylalanine and trimesic acid showing the same tendency when the irradiation doses are increased. However, alanine and phenylalanine seem more sensible to soft X-rays than trimesic acid. This could be explained by the p-conjugated and very stable aromatic system present in the trimesic acid and not in alanine, or even with phenylalanine where amine and carboxylic acid functions are not p-conjugated to the aromatic cycle, and so more degradable. The loss of intensity of vibrational band could be attributed to the degradation of the organic deposited on MgF2 windows.

GC-MS Characterization of Irradiated Samples

We confirm the semi-quantitative results obtained with infrared analyses with the decrease in intensity of infrared vibrational bands. We add also quantitative measurement between the different irradiation doses and the non-irradiated samples. We thus observed that alanine has a loss factor of about 10 after only 30 minutes of irradiation (figure 5) as phenylalanine. Then, compared to phenylalanine in presence of calcium perchlorates, this loss factor of about 10 is observed only after 1h of irradiation. Concerning the stable p-conjugated trimesic acid system, we observe this loss of factor of 10 after 3h of irradiation (or … irradiation dose).

We noted also on the L-Phenylalanine deposit, no racemization or chiral transformation. Indeed, we have an enantiomeric excess of 100% after an irradiation of 1h, 3h, or 1h in presence of perchlorates. Neither the matrix or the irradiation doses impact the chiral conformation of the phenylalanine below 3h. For alanine (figure 4), we deposited a racemic mix, and sorted out the same racemic mix 0% ± 10% after 0-2h irradiation. Any of the two enantiomeric form deposited is promoted by irradiation doses below 2h.

Conclusion

Infrared spectroscopy revealed the degradation of organics in possible highly volatile compounds, such as CO2 for high irradiation doses (2h and 3h exposure), not visible by IR or GC-MS analyses as the product is spread out the LUCIA infrastructure. Second, we observed a transformation of the organic matter at low irradiation doses (lower than 1h exposure) with a preferential loss of singly bonded groups (N-H, C-H, and O-H), or the formation of sp3 (carbon defects with signatures at 3000-3400 cm-1), or ammonium Zwitterion forms.

GC-MS analyses confirmed quantitatively the degradation and loss in originally introduced organics observed qualitatively, and semi-quantitatively, by infrared and micro-IR analyses. As in infrared, no new products or new bands in IR (or peaks in GC-MS) were detected despite the quantitative degradation of organics. Irradiations lead to volume rather than just surface modifications.

Figure 1 :

 1 Figure 1 : Alanine deposit on MgF2

Fig 2 :

 2 Fig 2 : (a) The squared red area is the analyzed region by OCT of alanine deposit, with at its center the black point representing the irradiated area by LUCIA beamline; (b) the 3D representation of the surface deposit in organic, the gradient color goes from yellow to blue when we decrease the deposit thickness; and (c) a zoom in 2D of this irradiated area.

Figure 3 :

 3 Figure 3 : Thickness measurement by OCT across the x-axis on the irradiation spot way (a) on a pure organic deposit, (b) on an organic-calcium perchlorate 30-minute mixture deposit.

Figure 4 :

 4 Figure 4: Evolution of infrared spectra for irradiated alanine standard at increasing X-ray doses at 2.7 keV: (a) infrared spectrum, and (b) is the integrated area showing the kinetic evolution of infrared vibrational bands through increasing irradiation doses.

Figure. 5 .

 5 Figure. 5. GC-MS analyses of alanine (compared to an internal standard, methyl laurate) irradiated during 0, 5, 15, 30, 45, 60, and 120 minutes on the irradiation F6 window.


Stanley Knut

Vincent Jacobsen

Igor Fabbro

Matthew Mainvis

Giorgio Zakharov

Lundahl Angling

Michael Savastano

Arnlaug Høgås Thomsen

Jonas Bregnhøj Linden-Vørnle

Mohammed Skjaeveland

Nielsen

Aymeric Fabbro

Igor Mainvis

Matthew Zakharov

Giorgio Angling

Per Lundahl Savastano

Michael Thomsen

Arnlaug Høgås Linden-Vørnle

Jonas Bregnhøj Skjaeveland

Mohammed Nielsen

Ouassou

The Space Weather impact on Arctic Navigation (SWAN) project Development of operational forecasts of space weather impacts on GNSS

published or not. The documents may come

Introduction

There are several factors which influence the accuracy of a position estimated by a Global Navigation Satellite System (GNSS) device [START_REF] Jeffrey | An introduction to gnss: GPS, glonass, galileo and other global navigation satellite systems[END_REF]. However, during strong ionospheric activity, the ionosphere is the dominant error source for GNSS signals.

Ionospheric scintillation refers to the random amplitude and phase fluctuations observed in radio signals propagating through electron density irregularities in the ionospheric plasma and most commonly occurs in equatorial, auroral, and polar regions [START_REF] Aarons | Global morphology of ionospheric scintillations[END_REF], [START_REF] Aarons | Ionospheric amplitude and phase fluctuations at the gps frequencies[END_REF]. The scintillations are caused by the presence of a wide range of scale size irregularities in the ionosphere. The irregularities causing scintillation may be considered as an irregular, spatial distribution that is drifting but also evolving in time. The amplitude scintillations come from irregularities having a scale size of the first Fresnel zone, which depends on the signal wavelength and average height of the irregularity layer [START_REF] Shagimuratov | High latitude tec fluctuations and irregularity oval during geomagnetic storms[END_REF], [START_REF] Yeh | Radio wave scintillations in the ionosphere[END_REF]. For GPS, this scale is about 370-400 m. Large-scale plasma density structures in the ionosphere with scale sizes greater than the first Fresnel zone do not cause significant amplitude scintillation but may still cause fluctuations in the signal phase [START_REF] Yeh | Radio wave scintillations in the ionosphere[END_REF]. Under such conditions, refraction effects must be taken into account and the phase fluctuations are due to the optical path changes of a radio wave [START_REF] Pi | Monitoring of global ionospheric irregularities using the worldwide gps network[END_REF]. The low-frequency GPS phase fluctuations may be directly due to electron density changes along the radio ray path.

The occurrence of scintillation at high latitudes is related to the auroral oval, cusp, and polar-cap patches, through the formation of small-scale plasma structures [START_REF] Aarons | Global positioning system phase fluctuations at auroral latitudes[END_REF]- [START_REF] Prikryl | GPS phase scintillation and proxy index at high latitudes during a moderate geomagnetic storm[END_REF]. It has been observed that phase scintillation occurs more often than amplitude scintillation at high latitudes, and that scintillation is more common on geomagnetically disturbed days in the auroral oval region and close to noon and midnight [START_REF] Tiwari | Investigation of high latitude ionospheric scintillations observed in the canadian region[END_REF], [START_REF] Aquino | Ionospheric scintillation and impact on gnss users in northern europe: Results of a 3 year study[END_REF], [START_REF] Spogli | Climatology of gps ionospheric scintillations over high and mid-latitude european regions[END_REF]- [START_REF] Prikryl | GPS tec, scintillation and cycle slips observed at high latitudes during solar minimum[END_REF]. Phase scintillations at high latitudes are associated with particle precipitation and plasma patches [START_REF] Jin | GPS scintillations associated with cusp dynamics and polar cap patches[END_REF]- [START_REF] Clausen | GPS scintillations in the high latitudes during periods of dayside and nightside reconnection[END_REF] and the scale of the processes (i.e. the spatial extent of the region of irregularities) can be substantially enlarged during geomagnetic storms and substorms [START_REF] Krankowski | Study of tec fluctuations in antarctic ionosphere during storm using gps observations[END_REF], [START_REF] Watson | GPS tec technique for observation of the evolution of substorm particle precipitation[END_REF].

Calculating disturbance indices from GNSS measurements is a proven method for the detection and near real-time monitoring of the occurrence and dynamics of ionospheric irregularities. High-rate (sampling rates of 50 Hz or more) GNSS receivers can be used to calculate the variations of the signal phase and amplitude, quantifying them by the phase and amplitude scintillation indices (𝜎 𝜑 and 𝑆 4 [START_REF] Kintner | GPS and ionospheric scintillations[END_REF]. The Rate Of Total electron content Index (ROTI) is another measure of disturbances in the phase measurements and can be calculated based on lower-rate data. Thus it can be based on data from normal geodetic GNSS receiver networks, of which there exists substantial data archives. ROTI has been shown to be a useful tool for characterizing the ionospheric disturbance level of GNSS measurements [START_REF] Cherniak | Dynamics of the high-latitude ionospheric irregularities during the 17 march 2015 st. Patrick's day storm: Ground-based gps measurements[END_REF]- [START_REF] Fabbro | GNSS positioning error forecasting in the arctic: ROTI and precise point positioning error forecasting from solar wind measurements[END_REF].

Tracking errors imposed in the presence of ionospheric scintillation can exceed receiver thresholds and result in signal loss of lock. Loss of satellite signals will degrade the positioning solution. In severe cases, the receiver may be unable to maintain lock on at least four GNSS satellites and a temporary loss of positioning capabilities can occur. The duration and frequency of such outages depend on the duration and severity of the disturbances, the geometry of the satellites in view and the signal reacquisition time [START_REF] Najmafshar | Modeling high-latitude ionospheric scintillations for radio occultation gps receiver performance analysis[END_REF]. Even if the signal is not completely lost rapid phase variation may cause cycle slips, which are discontinuities in a receiver's phase lock on a satellite's signal. [START_REF] Yang | Global view of ionospheric disturbance impacts on kinematic gps positioning solutions during the 2015 st. Patrick's day storm[END_REF] quantified the global-scale impacts of ionospheric disturbances on GPS Precise Point Positioning (PPP) solutions during an extreme space weather event on the 17 𝑡ℎ of March 2015 by taking advantage of more than 5,500 GNSS stations installed worldwide. The overall impact was more severe at high latitudes. Specifically, the results show that kinematic PPP solutions degraded following an intensified auroral particle precipitation during the storm's main phase (06-23 UTC) when up to ∼ 70% of the high-latitude stations experienced degraded position solutions in the range of several meters at 16-18 UTC. Around magnetic noon and midnight, the storm-induced plasma irregularities caused notable PPP errors (> 10 m) at high latitudes.

[34] demonstrated how the PPP 3D position error correlates with ROTI at high latitudes. The risk of having several satellites observing enhanced ROTI values simultaneously is greater at higher latitudes. Several receivers (FOLC, VEGS, TRO1, HAMC, LYRS, and NYA1) were analyzed and 3D position error up to 15 cm was observed.

Occurrence of scintillation is difficult to predict and model due to the variability of its numerous influencing factors, which include solar activity, interplanetary magnetic field orientation, local electric field and conductivity, ionospheric convection processes, and wave interactions [START_REF] Jiao | Comparison of the effect of high-latitude and equatorial ionospheric scintillation on gps signals during the maximum of solar cycle 24[END_REF].

The ESA Space Weather Service Network (https://swe.ssa.esa.int/) provides several space weather monitoring services. Available types of data that may be relevant for GNSS users include TEC maps, ROTI maps, scintillation indices, and limited forecasted TEC maps. In addition, other data are available that could be used as an input for algorithms to estimate impacts on PNT/GNSS systems (Positioning, Navigation and Timing GNSS systems), broadly categorized as solar monitoring data, solar wind monitoring data, geomagnetic conditions monitoring data and solar flare forecasts (in the form of current probability of occurrence).

Examples of other space weather services providing global and/or local monitoring are the US Space Weather Prediction Center (SWPC) (https://www.swpc.noaa.gov/) , Norwegian Centre for Space Weather (NOSWE) (https://site.uit.no/spaceweather/) , Space Weather Canada (https://spaceweather.gc.ca/indexen.php) , Russian space weather services (http://ipg.geospace.ru/) , the UK Met Office Space Weather Operations Centre (MOSWOC) (https://www.metoffice.gov.uk/weather/specialist-forecasts/spaceweather) , International Space Environment Service (ISES) (http://www.spaceweather.org/). The types of ionospheric monitoring data are similar for many of these services, but as they are based on different measurement networks their coverage and resolution varies.

In these operationally available services there is little focus on measures of impact on GNSS end users. Also, forecasts of ionospheric variations (𝜎 𝜑 , 𝑆 4 , 𝑅𝑂𝑇𝐼 or similar) are very limited.

Consequently, the main objective of the SWAN project was to "... develop and demonstrate new techniques, which are expected to improve on the current state-of-the-art Space Weather forecasting techniques and products related to Space Weather effects for Positioning, Navigation and Timing (PNT) systems in the Arctic, with particular attention to the Greenland area.". To achieve this, two approaches have been investigated:

•

Empirical modelling of ROTI statistics, by modifying and extending the HAPEE model [START_REF] Fabbro | GNSS positioning error forecasting in the arctic: ROTI and precise point positioning error forecasting from solar wind measurements[END_REF].

• Forecasting ROTI time series from solar wind inputs, through a machine learning algorithm.

At the end of the project, the machine learning approach is still considered as not mature enough to be used for operational services. Therefore, a forecast service was established using the HAPEE model.

Data sources

This section describes the data sources and the steps taken to check and prepare the data before using it for algorithm development.

Solar wind data

High-resolution solar wind magnetic field, density and velocity data was extracted from NASA/GSFC's OMNI data set through OMNIWeb [START_REF] King | Solar wind spatial scales in and comparisons of hourly wind and ace plasma and magnetic field data[END_REF].

We applied a 10-minute running window average to the solar wind data, in order to reduce the impact of rapid solar wind variations and data gaps. Then we calculated the coupling function

𝑑𝛷 𝑀𝑃 𝑑𝑡
as described in [START_REF] Newell | A nearly universal solar windmagnetosphere coupling function inferred from 10 magnetospheric state variables[END_REF]. The value of this coupling function will be used as the input to the forecast algorithms, as a proxy index of space weather activity in the ionosphere at high latitudes. After quality checks, including clock jump and cycle slip detection, ROTI has been calculated every 5 minutes for each observed satellite. To detect cycle slips, the change of the Melbourne-Wübbena linear combination [START_REF] Melbourne | The case for ranging in gps-based geodetic systems[END_REF], [START_REF] Wübbena | Software developments for geodetic positioning with gps using ti 4100 code and carrier measurements[END_REF] from the previous to the current timestep is compared to a threshold. If the threshold is exceeded, it is considered as a cycle slip. A description of this cycle slip detection method can be found in [START_REF] Liu | A new automated cycle slip detection and repair method for a single dual-frequency gps receiver[END_REF].

ROTI from GNSS receivers

The elevation angle of a ROTI data record is defined as the average elevation angle of the satellite during the time period used for the ROTI calculation. Only measurements with elevation above 30 𝑜 have been kept for the model derivation, as the data from satellites at low elevation is often contaminated with e.g. multipath effects or other disturbances from vegetation and structures on the ground. The remaining ROTI data have been corrected for other elevation-dependent effects based on the theoretical behavior proposed by [START_REF] Carrano | On the relationship between the rate of change of total electron content index (roti), irregularity strength (ckl), and the scintillation index (s4)[END_REF], assuming an isotropic ionosphere. The data quality has been carefully checked by analyzing the skyplots of each station per year. Examples of skyplots for the GNET stations "dmhn" and "sisi" for the years 2014 and 2015, respectively, are shown in Figure 2. Using such an analysis, persistent unwanted disturbances that are not related to space weather (e.g. multipath) can be removed from the data. In the skyplot graphs, the mean value of ROTI is plotted as a function of azimuth and elevation angles. The skyplots illustrate the need to exclude low elevation data. This filtering allows removing the multipath visible at low elevations for station "dmhn". However, other errors can be observed for station "sisi", tracing large arcs related to some satellite paths. This type of disturbance, which is likely caused by measurement or processing errors in the receiver, can be difficult to remove when it is too dominant. For those cases we have removed the entire year of data. This analysis and filtering has been applied to the whole database to generate the final database that will be used in the continuation of the project.

Another check of ROTI data has been performed analyzing the CDF (Cumulative Distribution Function) of the ROTI index for each station, per year. We expect higher levels of ROTI for years close to the maximum of the solar cycle (2013-2015) and lower values close to the minimum (2008-2009 or 2019-2020). Also, the CDF should be smooth (i.e. without any significant jumps or breaks in the graph). These points have been taken into account and years with too high level of noise or unphysical breaks in the graph have not been included in the final database.

The final ROTI database contained more than 95 million ROTI data records.

Empirical forecasts -HAPEE extension and validation

HAPEE extension

The High lAtitude disturbances Positioning Error Estimator (HAPEE) model is described in [START_REF] Fabbro | GNSS positioning error forecasting in the arctic: ROTI and precise point positioning error forecasting from solar wind measurements[END_REF]. It is an empirical, statistical model that can forecast the probability distribution of ROTI for the near future, using solar wind measurements as its real-time input. The original HAPEE model described in the paper is based on data from GNSS receivers in Norway and provides a forecast for the coming hour. It is parameterized by the magnetic latitude, magnetic longitude, and the value of the Newell coupling function

𝑑𝛷 𝑀𝑃
𝑑𝑡 ([37]). An optional extension of the algorithm transforms the output from ROTI distributions to distributions of PPP positioning error, using an empirical model.

In the frame of the SWAN project, the HAPEE model was modified/extended in two ways. As a 3-hour forecast was requested, all statistical regression had to be re-run with a different configuration. Also, the database used to build the model was extended by the addition of data from the GNET network in Greenland. A new realization of the HAPEE model was regressed as described in [START_REF] Fabbro | GNSS positioning error forecasting in the arctic: ROTI and precise point positioning error forecasting from solar wind measurements[END_REF], using the extended dataset and providing parameters for 3-hour forecasts.

Validation of ROTI forecasts

For the validation of the HAPEE model a statistical approach has been used. The ROTI datasets consist of the data measured by NMA and GNET networks measured during period [2007][2008][2009][2010][2011][2012][2013][2014][2015][2016][2017][2018][2019].

During the regressions of the HAPEE model, 80% of the whole database of ROTI data has been considered for the regression, while 20% of the database was kept for the validation. The results of correspondence between modeled and measured 𝑃(𝑅𝑂𝑇𝐼 > 𝑇 𝑅𝑂𝑇𝐼 ) are presented in Figure 3. In those plots, the percentages computed by HAPEE model and obtained from the data have been binned every percent. The colors represent the number of points in each 1 x 1 % bin. This representation shows the spread between modeled and observed percentages. An ideal model would provide a perfect straight line from the origin to the point 100% in abscissa and ordinate. The model results closely follow the actual observations, with the forecasted occurrence rates generally staying within 10 percentage points of the actual occurrence.

One can observe a decay in the percentage correspondence whereas the ROTI threshold is increasing. This phenomenon is due to the lower percentages of occurence to exceed higher values of threshold. The number of points is also lower, as the high levels of scintillation become rarer.

Machine Learning forecasts

Adapting Machine Learning to the problem

In parallel to the statistical approach, based on classification and regressions, Machine Learning techniques have been investigated. Machine learning is normally defined as the group of algorithms trained using an example database in order to find rules, which in time allow them to perform the same task for new data. Three types of machine learning tasks can be classified based on the feedback signal used: supervised learning, unsupervised learning and reinforcement learning. In the case of supervised learning, a database including examples of inputs and the corresponding desired output is presented to the machine learning model with the objective to learn the mapping between the inputs and the outputs. On the contrary, when unsupervised learning is performed the inputs given to the model are not labelled and its task is to find hidden patterns or structures in the data. Another type of machine learning approach is called reinforcement learning, in which the main idea is to maximize a reward in a particular situation by interacting with the environment to solve a problem in a game-like way.

Recurrent Neural Networks (RNN) are a class of artificial neural networks that has applications in all three types of machine learning tasks. RNN are specifically designed to process data for which the temporal behavior is important. In fact, they create a memory of the system by processing each element of the sequence and conserving information regarding what it has encountered up to that time epoch. [START_REF] Lundstedt | Operational forecasts of the geomagnetic dst index[END_REF] used RNN to forecast the Dst index. In this project we implemented a variant of the RNN, called Long Short Term Memory (LSTM) [START_REF] Hochreiter | Long Short-Term Memory[END_REF], in order to forecast ground-based ROTI from solar wind parameters at L1 through the Newell coupling function [START_REF] Newell | A nearly universal solar windmagnetosphere coupling function inferred from 10 magnetospheric state variables[END_REF]. The LSTM is designed to be able to retain past information for many time steps and has previously been successfully applied in the field of space weather (See e.g. [START_REF] Tan | Geomagnetic index kp forecasting with lstm[END_REF] , [START_REF] Srivani | A deep learning-based approach to forecast ionospheric delays for gps signals[END_REF], [START_REF] Liu | Forecasting global ionospheric tec using deep learning approach[END_REF]). [START_REF] Gruet | Intelligence artificielle et prévision de l'impact de l'activité solaire sur l'environnement magnétique terrestre[END_REF] implemented a LSTM NN to predict the am index. By improving the internal structure of basic RNN, the LSTM avoids the gradient issue that can occur with RNN and has the ability to retain long term memory for significant information. Important properties of LSTM NN are:

•

It captures the long-term dependencies.

• It models the time series temporal structure.

• It allows variable length inputs and/or outputs.

• It can address multivariate problems and multi-step prediction.

In addition to the processing of the ROTI, for each epoch at each receiver a super observation is created by taking the median of ROTI from the observed satellites as it has been done previously in [START_REF] Mcgranaghan | New capabilities for prediction of high-latitude ionospheric scintillation: A novel approach with machine learning[END_REF]. Gaps in the solar wind coupling function time series have been filled by Nearest-neighbor interpolation, as it is important for the machine learning algorithm to have continuous time series. As the ROTI data have a 5minute time resolution, the solar wind coupling function data is resampled to 5-minutes in order to have a matching timeline for all data. The dataset is then split into three parts:

• 60% of the total dataset is used to train the LSTM NN, this is the "Train" dataset.

• 20% of the total dataset is used to evaluate the loss function of the LSTM NN during the training phase, this is the "Validation" dataset.

• 20% of the total dataset is used to test the LSTM NN, this is the "Test" dataset.

The first step for building the machine learning forecaster is to provide local time series of ROTI in an appropriate magnetic latitude interval that is more likely to contain the auroral oval at different magnetic local times. In order to select this relevant interval, we analyzed the geographical distribution of ROTI with respect to the magnetic local time. Based on our data, the interval of 70 𝑜 to 85 𝑜 MLAT is the most frequently disturbed and so it is chosen.

The length of the history of the input features has been set to twelve samples, corresponding to one hour of data with a time step of five minutes. The predicted value is set at twelve steps ahead, as it has been done previously by [START_REF] Mcgranaghan | New capabilities for prediction of high-latitude ionospheric scintillation: A novel approach with machine learning[END_REF]. In summary, this model predicts the value of the ionospheric scintillation index one-hour in advance based on the values of the solar wind coupling function at Earth's bow shock nose during the past hour and the MLT position of the IPP.

The LSTM NN was implemented using the Python libraries "Tensorflow", "Keras", "Pandas" and "Scikitlearn". To illustrate this in another way, we compute the standard skill scores using the entire Test dataset. A confusion matrix has been used to evaluate the quality of the prediction system. To classify the ROTI measurements, 8 intervals containing the same number of measurements have been defined. The limits corresponding to these intervals are [0, 0.424, 0.454, 0.49, 0.539, 0.612, 0.735, 0.988, +inf], in units of TECU/minute. Figure 4 shows these ROTI thresholds, represented as dashed horizontal lines. The last of the bins ([0.988, +inf]) is the most important for predicting events. From the confusion matrix, the different skill scores used are the Probability of Detection (POD) which shows what fraction of "true" events is correctly predicted, the False Alarm Rate (FAR) which shows the fraction of the predicted event that actually did not occur, and the True Skill Score (TSS) which accounts for random chance. Other skill scores used are the Accuracy (ACC) and the Root-Mean-Square Error (RMSE).

Validation of Machine Learning forecasts

Table 1 shows the skill scores when considering the condition ROTI > 0.988 TECU/minute (i.e. the detection of disturbed ionosphere). A perfect model would have POD = 1 and FAR = 0. As can be seen in the table, the current ML model has a substantial number of failed detections and false positives when tested in this way. While the model has some prediction skill, it needs to be improved before it can be considered for operational use. For this reason the forecast service has been based on the empirical statistical approach.

POD FAR ACC TSS RMSE 0.19 0.61 0.86 0.15 0.38 Table 1: ML model prediction skill scores, considering the condition ROTI > 0.988.

Forecast service

A major objective of the project was to demonstrate usage of the forecast method by establishing a prototype of a forecast service. The service was built on the forecasts supplied by the extended HAPEE algorithm, using archive data to provide forecasts for previous times (hindcasts), but also designed in a way that allows integration of real-time data feeds in the future.

Figure 5: Overview of service components and data flow

Figure 5 shows an overview of the components of the service and the data flows. Solar wind data and GNSS almanacs are the dynamic inputs to the processing, while the model parameters are static sets of coefficients. The AACGM coefficients are a part of the AACGM library [START_REF] Shepherd | Altitude-adjusted corrected geomagnetic coordinates: Definition and functional approximations[END_REF]. The HAPEE model coefficients define a realization of the HAPEE model. The HAPEE model analysis modules reads the various input data, perform the calculations to provide forecasts, and transmits the output to the database of the forecast service. The model is forecasting the percentage of occurence to exceed a given threshold of ROTI during the following three hours. Note that it also proposes the percentage of occurence to exceed a given PPP error, applying the approach described in [START_REF] Fabbro | GNSS positioning error forecasting in the arctic: ROTI and precise point positioning error forecasting from solar wind measurements[END_REF]. The service modules comprise a database which holds the forecasts, data transformation code to transform the output into a format suitable for visualization, APIs for both internal and external data access, and a webpage. External users can connect to the human interface (webpage), or directly to data APIs (intended for machine-to-machine communication). For a future real-time service, real-time solar wind measurements and almanac data would be processed on a periodic basis. The processing time for a single set of data is less than one minute. We note that solar wind data is available in near-real-time from the NOAA SWPC data service (currently available at https://services.swpc.noaa.gov/products/solar-wind/). The Prediction Engine module uses the input modules to acquire input data, and to perform some calculations (notably, calculating geographic IPP coordinates). For the calculation of geomagnetic coordinates, it uses the AACGM library. The Prediction Engine module will, given a date&time and solar wind parameters, calculate maps of forecasted probabilities to exceed thresholds as follows:

• From Solar Wind Data, calculate coupling function value [START_REF] Newell | A nearly universal solar windmagnetosphere coupling function inferred from 10 magnetospheric state variables[END_REF] • For each grid point of the map:

-Calculate geomagnetic coordinates for IPPs seen from that grid point -Retrieve HAPEE model parameters for the current coupling function and set of geomagnetic coordinates -For each defined threshold, calculate the probability to exceed it • Package results and send it to the output module

The dataflow and major processing steps of the Prediction Engine are shown in Figure 6.

Figure 6: Overview of major processing steps inside the Prediction Engine

The output module transmits data in NetCDF format (See https://www.unidata.ucar.edu/software/netcdf/) via http to an API endpoint that ingests the data into a PostgreSQL database (See https://www.postgresql.org/). Note that this is purely an internal data flow (not exposed externally to users).

In order to best represent the data structure, we can consider it from the point of view of the database. Figure 7 describes three tables, related by foreign key relationships where the data array is stored in the "rast" column in the "coverage" table. Each "coverage" represents one forecast map. In short, one metadata entry, which contains the timestamp, can be related to many coverages, which in turn can be one of many use-cases. This allows us to keep track of the use-cases and uniquely identify the correct coverage (data array) for a given timestamp, use-case and layer (threshold type). Note that the external users do not directly access the database. The external users can request data via an API. An example of an API call is:

https://swan.kartverket.no/api/collections/PPP01/area?parameter_name=pR OTI&datetime=2015-01-01T00

In the URL for the API call we have specified the use case (for example "PPP01" for Precise Point Positioning user type), the parameter "pROTI" (corresponding to the ROTI probability to exceed 1 TECu/mn) and the time "2015-01-01T00". The API returns the forecast map data to the user in the "CoverageJSON" format (See https://covjson.org). The data access APIs are used to provide content to the service webpage. Figure 9 and Figure 10 show examples of forecast maps and actual observation maps, during a space weather event in 2015 known as the "St. Patrick's day storm" [START_REF] Jacobsen | Overview of the 2015 st. Patrick's day storm and its consequences for rtk and ppp positioning in norway[END_REF], [START_REF] Prikryl | GPS phase scintillation at high latitudes during the geomagnetic storm of 17-18 march 2015[END_REF]- [START_REF] Zakharenkova | Features of storm-induced ionospheric irregularities from ground-based and spaceborne gps observations during the 2015 st. Patrick's day storm[END_REF]. The forecasting provided by the service are represented in the top left panel, with probability of occurence to exceed 1 TECu/mn represented in colorscale. The NMA network ROTI measurements are represented by the mean ROTI value at IPP position, and are reported in the other panels for different time corresponding to the interval of the forecasting. The position of the auroral oval appears to be well reproduced by the forecasting.

Summary and Road ahead

The SWAN project has successfully implemented a statistical method (HAPEE) for predicting the occurrence of GNSS disturbances in the polar region. Furthermore, an alternative method using a more sophisticated machine learning (ML) model has been investigated.

The ROTI database was extended by the addition of data from the GNET GNSS receiver network in Greenland (i.e. 13 stations, measuring during the period [2010][2011][2012][2013][2014][2015][2016][2017][2018][2019][2020]. The output of the HAPEE model is probability distributions of ROTI or PPP position error, which can be combined with a threshold to quantify the risk of exceeding that threshold within a period of time. A realization of the HAPEE model for a 3-hour forecast was generated, and a prototype of an operational service based on this approach was created. The service provides forecasts both via a machine-to-machine API, and as a graphical view on a webpage. The provided forecasts were validated, with very good results for the ROTI forecasts.

In parallel to the HAPEE statistical model, a machine learning approach using a LSTM NN was investigated. This method is complementary to the statistical one, providing one-hour forecasted time series of ROTI index, valid for a limited area of MLAT. The results are very promising regarding the timing of the disturbed periods, but are struggling to correctly predict the amplitudes of the disturbances. Further work on this topic is expected to improve the performance.

Future work on the HAPEE-based service

The prototype service is a candidate for inclusion into the ESA Space Weather service network. The model and service are capable of real-time operation, by providing a reliable real-time data flow of solar wind and almanac data and setting up a continuously operating processing server.

Currently, it provides ROTI index forecasts for several use cases but position error forecasts only for the PPP use case. To define position error forecasts for other use cases requires studies to characterize their response to the ionospheric disturbance. More work is needed on validation of position error forecasts.

The spatial coverage of the service may be further extended to fully cover the northern polar cap, and potentially to also cover the southern hemisphere auroral region if sufficient data from that region can be acquired. However, due to the way the model is built it is not suitable for modeling equatorial disturbances.

Future work on machine learning

The Machine Learning approach is promising and its performances have been quantified. The model paves the way for a new kind of operational service based on data driven methods. Even with a simple approach linking solar wind data, magnetic local time and the ROTI, the LSTM NN should be able to blindly learn a coarse regular pattern, which is the strength of this approach. One of the main improvements that can be imagined is to consider additional data sources as inputs for the model to extend the environment description.

Additional data sources for future work

The current SWAN system provides a statistical link between the solar wind at L1 and the ROTI (rate of TEC index) or position error experienced by a GNSS receiver on the ground. However, in the future it may be beneficial to include additional ground-and space-based measurements which can improve the forecasts by providing information about intermediate parts of the physical system (i.e. physical processes taking place after the L1 solar wind measurements but before the ground based ROTI measurements). In particular, space-based GNSS measurements can provide information on the state of small scale irregularities in the ionosphere. Furthermore, space-based magnetometers can provide information on the large scale features of the high latitude ionosphere (i.e. the position of the auroral zone) that have a direct impact on the likelihood of scintillation occurring.

Space-based measurements of the high latitude ionosphere can be provided by low cost LEO satellites in sun-synchronous near polar orbits. One way to use these platforms is to deploy GNSS receivers that can be used to sense the ionosphere in a variety of geometries (Figure 11):

• Towards zenith, usually making use of a receiver designed for precise orbit determination (GNSS-POD). The measurements are generally made with 1 Hz sampling and at positive elevation angles (i.e., >30°), effectively sensing only the topside region of the ionosphere above the satellite's orbit [START_REF] Zakharenkova | GPS and in situ swarm observations of the equatorial plasma density irregularities in the topside ionosphere[END_REF], [START_REF] Zakharenkova | Topside ionospheric irregularities as seen from multisatellite observations[END_REF].

• In a radio occultation (GNSS-RO) geometry (i.e. pseudo horizontally). As the LEO satellite moves in its orbit, the GNSS satellites are seen to rise above or set below the horizon. Thus, the GNSS signals traverse the atmosphere at a range of heights and the direction of the line integral of the electron density (i.e., the sTEC) has mainly an horizontal component. The high rate (50Hz) data can be used to detect and characterise phase and amplitude scintillation [START_REF] Angling | Sensing the ionosphere with the spire radio occultation constellation[END_REF]. Space based magnetometers in LEO can also be used to help determine the location of the Auroral boundaries through the effects of field aligned currents [START_REF] Burrell | AMPERE polar cap boundaries[END_REF].

Given such diverse measurement regions and types, and the naturally irregular sampling that comes from LEO space-based measurements, it is likely that a combined data assimilation (DA) and machine learning (ML) approach will be required. One possible, multi-step approach might be to • predict the Auroral boundary using L1 data (ML)

• Update the boundaries using data (DA)

• Update an irregularity model using data (DA)

• Produce a short term forecast of scintillation (ML)

The use of an ionospheric irregularity model (i.e. using height-integrated strength of irregularities, 𝐶 𝑘 𝐿) [START_REF] Secan | An improved model of high-latitude f-region scintillation (wbmod version 13)[END_REF] rather than a ROTI model allows data at different frequencies, with different geometries, and with different sampling rates to be used self-consistently.

Figure 1 :

 1 Figure 1: Maps showing the location of the GNSS receivers used in this work, on Greenland (GNET) and in Norway (NMA). The red lines show the geomagnetic latitude.

Figure 1

 1 Figure 1 shows the locations of the GNSS receivers used to provide ROTI data for the project, which are subsets of the Norwegian Mapping Authority (NMA) network and the Greenland GNSS Network (GNET). The receivers are geodetic quality, multi-frequency, and sampling observations at 1 Hz. The ROTI data from NMA used in the framework of this project corresponds to the period [2007-2019], covering the 24 𝑡ℎ solar cycle. The ROTI data from GNET used in the framework of this project corresponds to the period [2010-2020], covering most of the 24 𝑡ℎ solar cycle.

Figure 2 :

 2 Figure 2: Example of skyplot of mean ROTI, used to identify multipath or other persistent disruptions. On the left is "dmhn" station in 2014. On the right is "sisi" station in 2015, which has signs of bad data.

  3 different thresholds of ROTI index (𝑇 𝑅𝑂𝑇𝐼 ) have been defined for low (0.75 TECU/min), moderate (1.5 TECU/min) and high (3.0 TECU/min) levels of ionosphere disturbance. The probability of exceeding these thresholds, 𝑃(𝑅𝑂𝑇𝐼 > 𝑇 𝑅𝑂𝑇𝐼 ), has been investigated by comparing forecasts from the HAPEE model with the actual observations in the validation dataset.

Figure 3 :

 3 Figure 3: Comparison of forecasted (abscissa axis, HAPEE model) and measured (ordinate axis, validation dataset) probability to exceed the low (left), moderate (middle) and high (right) thresholds.The problem is binned into different intervals of solar wind conditions (i.e. coupling function), MLT time and Magnetic latitudes. 41 intervals in MLAT have been defined (from 50 to 90 𝑜 of magnetic latitudes, sampled per degree, and an initial bin [0,50] for potential low latitude data), 12 intervals of coupling function levels (from 0 to 19000 Wb/s and a last bin for all upper solar wind conditions), and 48 MLT time intervals from 0 to 24 hours. In each interval combination, 𝑃(𝑅𝑂𝑇𝐼 > 𝑇 𝑅𝑂𝑇𝐼 ) has been compared between validation dataset and HAPEE model. This way HAPEE model is tested for all levels of coupling function (i.e. solar wind condition), all possible MLT time and Magnetic latitudes.

  To obtain the architecture of the LSTM NN model used in this study, several sets of hyperparameters have been tried. Thus the number of LSTM NN layer used to extract representative feature from historical input data is 512. Those layers are then connected to a 3 fully connected layers with 128, 32 and 1 nodes respectively. The batch size used during the training step is 288. The LSTM NN layers are combined with Dropout sequence to prevent overfitting. A patience of 100 epochs has been added to further reduce the overfitting, while the maximum number of training epochs is 1000. The loss function corresponds to the mean square error.

Figure 4 :

 4 Figure 4: Observed ROTI vs predicted ROTI from the LSTM NN model

Figure 4

 4 Figure 4 shows a comparison of the observed ROTI the with 1-hour predicted ROTI from the LSTM NN model. Note that the figure shows only a part of the validation dataset, and thus must be considered as an example. The LSTM NN forecasting correctly reacts to the disturbed periods, generating forecasted ROTI values larger during these periods. The main difference is that the LSTM NN forecasts time series present lower values than the amplitudes of the measured peaks.

Figure 7 :

 7 Figure 7: Database Entity Relationship diagram of the normalized data model

Figure 8 :Figure 8

 88 Figure 8: Screenshot of the SWAN service Figure 8 is a screenshot of the prototype forecast service webpage. The user selects the use case ("PPP01") and threshold type ("pROTI") in the dropdown boxes, and the date and time, and a forecast map is displayed to the left. The map is dynamic, supporting panning and zooming. At the top right are links to information pages about the service itself, the machine learning study, and the APIs for direct data access.

Figure 9 :

 9 Figure 9: Forecast of chance to exceed ROTI threshold (top left), issued at 2015-03-17 13:00 UTC, and ground ROTI maps for Fennoscandia at three points in time during the forecasted time period.

Figure 10 :

 10 Figure 10: Forecast of chance to exceed ROTI threshold (top left), issued at 2015-03-17 22:30 UTC, and ground ROTI maps for Fennoscandia at three points in time during the forecasted time period.

• • Figure 11 :

 11 GNSS-RO and GNSS-POD observation geometries. Fs is the measurement sampling frequency; T is the measurement duration; SLTP is the straight line tangent point.

Acknowledgements

This work was funded by the ESA project "Forecasting Space Weather Impacts on Navigation Systems in the Arctic (Greenland Area)" (Ref. ESA AO/1-9961/20/NL/AS).

We acknowledge use of NASA/GSFC's Space Physics Data Facility's OMNIWeb service (https://omniweb.gsfc.nasa.gov/), and OMNI data.

The original HAPEE model was developed in a cooperation between NMA, ONERA, CNES (Centre National d'Études Spatiales) and NOSA (Norwegian Space Agency).

For GNSS data from Norway, contact NMA. NMAs website is https://kartverket.no/en. For GNSS data from Greenland, contact DTU Space. See the GNET website http://go-gnet.org/ for information.


domain: chim sde sdu sdv

text:
David Boulesteix
email: david.boulesteix@hotmail.com

Arnaud Buch

Amy J Williams

Cyril Szopa

Victor Abrahamsson

Bryana L Henderson

Lydia L Kivrak

Bronwyn L Teece

Trinity L Hamilton

Jeff R Havig

Geochemical and Metabolomic study of few Yellowstone spring systems over a range of pH

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Yellowstone National Park (WY, USA) contains silica-saturated hot springs [1] with chemistry driven by subsurface processes: either minimum subsurface boiling hot springs fed by the deep hydrothermal source, or hot springs in regions where extensive subsurface boiling feeding springs that are predominantly residual liquid phase, predominantly condensed vapor phase, or a mixture of the two [2]. These processes determine the geochemistry of the surface evaporates/precipitates collected for this study: i) minimum phase separation hot springs represented by alkaline (pH 8.7) at 'Similar Geyser' hot spring, named W1L (or SG Digitate), ii) subsurface boiling residual liquid phase enriched in chloride and sulfate represented by pH 5.1 Dante's Inferno L1L (or DI Active), and iii) subsurface boiling vapor phase rich in hydrogen sulfide (oxidizing to sulfuric acid) represented by pH 2.4 at 'Goldilocks' I1L (or Gl Digitate).

Each of these spring systems presented different water temperatures (25 -80 °C) and elemental composition (e.g., Si, O, C, S, Al, Na, Fe, Ca, K, Mg, As, P, and N). These geochemical parameters impact the microbial community composition. The overall mineralogy remains similar between precipitated analyzed samples with more than 90% being composed of an amorphous silica phase (Opal A). Within the remaining 10% we identified by XRD, SEM-EDS, and Raman analysis: crystalline silica forms (quartz for all and cristobalite for I1L), elemental sulfur, phosphorus, and iron. Crystalline pyrite was also present in the majority of each sample, but no sulfates or chlorine-bearing salts) [3]. pH is driven by the subsurface processes and water composition and drives microbial community composition, but does not seem to drive the mineral-organic, (a)gnostic (bio)signatures, or the sheltering of the organic matter (OM) content over time.

Metabolomic studies were conducted using different extraction protocols and analytical instruments [4,5], and revealed a wider diversity of OM and distinct biomarkers from modern systems compared to few thousand to hundred-thousand-year spring systems (10% of OM left in the ancient system compared to the modern). We detected more than 162 organic compounds, 122 were successfully identified with a flight-like pyrolysis-gas chromatograph coupled with a commercial mass spectrometer. Liquid extraction with water or MeOH/DCM and SPME enabled identification of 30 volatile analytes, including ketones, aldehydes, and carboxylic acids. Instrumentation under development (SCHAN and Orbitrap) for future spaceflight missions were used to detect additional bioindicators or biosignatures and enhance our metabolomic microbial community profiling reconstruction and microbial identification. These biogeochemical and metabolomic studies allowed us to target and identify bioindicators and biosignatures relevant for Mars using these analog samples to describe what OM we could observe if life appeared on Mars (similarly to [6,7]. Critically, identifying organic compounds associated with in situ microbial communities that can be preserved through early diagenesis are needed given potential for alteration of organic carbon material [8]. Through MSL and Mars 2020 missions, we will likely analyze ancient hydrothermal systems similar to Yellowstone and perhaps with similar Early-Earth and Early-Mars chemistry [9,10]. Regardless of whether life emerged or evolved differently on Mars, the microscopic (coupled to geochemical/elementary/isotopic analysis presented in Figure 1) and mass spectroscopy analysis from agnostic biosignatures in our Yellowstone samples may be relevant to samples from Perseverance that are being returned to Earth through the Mars Sample Return mission. Despite the absence of microscopic imaging data from Mars landers and rovers, Fig. 1 shows that a rich abundance of sulfur and/or phosphate and/or iron combined with bioindicators' or biosignatures' detection might ensure the robustness of potential past trace of life on Mars, and better inform sample selection for analysis and return moving forward.

Figure 1 .

 1 Figure 1. Agnostic signatures observed in these samples by Scanning electron microscope (SEM) and locally analyzed by energy dispersive X-ray spectroscopy (EDS) and completed with external isotopic and elementary analysis in isotope ratio mass spectrometry (IRMS). References: 1. S. Hurwitz & J.B. Lowenstern, Reviews of Geophysics (2014) 2. J.R. Havig, J.E. Kuether, A.J. Gangidine, S. Schroeder, and T.L. Hamilton, Astrobiology. 21(12):1526-46 (2021). 3. C.L. Zhang, et al., Geochim. Cosmochim. Acta. 68(15):3157-69 (2004). 4. D. Boulesteix et al., Talanta. 257 : 124-283 (2023). 5. C. Freissinet et al., J. Chromatogr. A. 1217 : 731-740 (2010). 6. P.L. Finkel, D. Carrizo, V. Parro, and L. Sánchez-García, Astrobiology, 23(5):563-604 (2023). 7. R.E. Summons, P.V. Welander, and D.A. Gold, Nat. Rev. Microbiol., 20(3):174-85 (2022). 8. B.L. Teece et al., Astrobiology 23(2), 155-171 (2023). 9. J.R. Havig & T.L. Hamilton, Life, 9(3):64 (2019). 10. T. Djokic, M.J. Van Kranendonk, K.A. Campbell, M.R. Walter, and C.R. Ward, Nat. Commun., 8(1):15263 (2017).