domain: spi phys

text:
Vincent Nimier
email: vincent.nimier@onera.fr

Joachim Biermann
email: joachim.biermann@fkie.fraunhofer.de

Kellyn Rein
email: kellyn.rein@fkie.fraunhofer.de

Jesus Garcia

Lauro Snidaro
email: lauro.snidaro@uniud.it

Ksawery Krenc
email: ksawery.krenc@wat.edu.pl

Challenges in automated HUMINT processing for situational assessment: Experiences from NATO CIMIC Joint Cooperation

Keywords: sense-making, multi-level fusion, context, domain-modelling, uncertainty, situation awareness, intelligence

The role of the military has been continuously evolving and expanding beyond traditional warfare. In addition to an increasingly asynchronous battlefield, today's military is involved in supporting peacekeeping, crisis management, and disaster relief, to name just a few. Such activities require intense contact with diverse non-military organizations such as government, police, emergency services, relief agencies, and religious leaders. As a result, the information needed for commanders and other decision-makers must come from a wide variety of sources in a wide variety of formats. Thus, any attempt at fusion of this information requires an underlying system concept that is able to standardize information from a variety of input sources, including device-derived information (sensors) and (multiple) natural language(s), as well as to understand complex context, both static and dynamic, and to be able to deal with measures of uncertainty which may vary from source-type to source-type. This paper presents several lessons learned from the ongoing work of a series of NATO Research Task Groups (which have focused on the changing data and information incorporating structured and unstructured human generated information with device-generated data needs for fusion in increasingly complex scenarios requiring interaction between

devices, human-generated information and complex contextual backgrounds. We discuss the differing challenges facing the synergic information processing as scenario complexity grows.

I. INTRODUCTION

Over the past decades, the role of the military has been continuously evolving and expanding beyond traditional warfare. In addition to an increasingly asynchronous battlefield, today's military is involved in supporting peacekeeping, crisis management, and disaster relief, to name just a few. Such activities require intense contact with diverse non-military organizations such as government, police, emergency services, relief agencies, and religious leaders. Additionally, an understanding of cultural, political and other context is needed for decision-making. And now, more than ever before, the Internet plays a role in the dispersal of both information and disinformation, which can affect operations.

As a result, the information needed for commanders and other decision-makers must come from a wide variety of sources in a wide variety of formats. Thus, any attempt at fusion of this information requires an underlying system concept that is able to standardize information from a variety of input sources, including device-derived information (sensors) and (multiple) natural language(s), as well as to understand complex context, both static and dynamic, and to be able to deal with measures of uncertainty which may vary from source-type to source-type.

In this paper we examine issues of integrating HUMINT for both context and intelligence into the fusion process. This paper presents several lessons learned from the ongoing work of a series of NATO Research Task Groups (IST-106, IST-132 and IST-165). In IST-106, the focus was on requirements for fusion of information from diverse, predominantly sensor, sources, and assumed a scenario with a relatively short (2-3 minutes) timespan and a geographic area of interest which was relatively limited (1-2 hectares) and which was well covered by sensors. The second task group (IST-132) focused on analysis of a scenario with a longer time span (24 hours) and a larger geographic area (a town and surrounding areas) which were only partially covered by sensors. The second scenario required the integration of both static information (locations of buildings, landmarks, etc.) as well as dynamic information (e.g., movements of insurgents) and an understanding of political and social dynamics (context) to make sense of the actions which were observed or inferred. In the current on-going task group (IST-165), the geographic scope is changed to a region, with multiple countries and towns, and a timeline which extends over weeks and months. The information sources include local government and news agencies, social media, and sources within the local population. Here, the ability to deal with dynamic context stands in the middle of the sensemaking process. This requires analysis of the data and information requirements when incorporating structured and unstructured human generated information with devicegenerated data needs for fusion in increasingly complex scenarios requiring interaction between devices, humangenerated information and complex contextual backgrounds.

The scenario which has been used as the focus of the ongoing work is a NATO military training exercise, Joint Cooperation (JOCO), which has taken place annually for over 10 years, based at the Clausewitz Barracks in Nienburg, Germany with a focus on civilian-military cooperation (CIMIC). Section II describes this exercise in more detail and explains its usefulness for a study of requirements for information fusion in a complex operation. Sections III through VI discuss some of the issues which have come out of the research of this task group and Section VII summarizes and looks ahead to areas for further work.

II. DESCRIPTION OF JOCO EXERCISE

A. Civil-military cooperation (CIMIC)

The current strategic environment typically involves a multitude of ethnic, religious, ideological and capability drivers, which require sustainable solutions in societies ravaged by conflicts, disasters or humanitarian catastrophes. Solutions to these serious events are impossible to achieve by military means alone. North Atlantic Treaty Organization's (NATO) contribution to a comprehensive approach, as one of its military facilitators, is a link to the civil environment, with civil-military cooperation (CIMIC) as one of the military facilitators [START_REF]NATO AJP-3.4.9 ALLIED JOINT DOCTRINE FOR CIVIL-MILITARY COOPERATION[END_REF].

CIMIC is first and foremost a military capability, with the core mission of producing an assessed civilian situation picture. The ability of the armed forces to engage in civilmilitary cooperation has developed continuously and comprehensively, both in practice and in training [2].

The provision of the civilian situation picture is the core of the work of CIMIC forces, with the aim of advising the military leadership in the context of general mission fulfilment and the 'Comprehensive Approach' and providing targeted support as a link to non-military actors.

This situation picture contains extensive information on, for example, the population, infrastructure and important players. It serves as a basis for decisions on the actions of the military leadership, which must always include the nonmilitary environment in its planning. CIMIC forces must pay particular attention to the complex interactions between the armed forces and the non-military environment in an operation and defuse them where possible.

The situation picture is built up by CIMIC soldiers who are deployed as "field workers" and collect relevant information in direct contact with suitable contact persons. The so-called "Staff Workers" then prepare this information, evaluate it and make it available for the decision-making process. The work and procedures are carried out according to defined NATO standards [START_REF]NATO AJP-3.4.9 ALLIED JOINT DOCTRINE FOR CIVIL-MILITARY COOPERATION[END_REF]. Actively shaping civilmilitary relations enables the assessment of the mutual influences of civil and military action, which is indispensable for effective planning and execution of operations. This enables the military to help reaching the desired end state by coordinating, synchronizing and de-conflicting military activities with civil actors, thus linking military operations with political objectives. The influence of the vast variety of civil contributions to stabilise a dysfunctional society must continue to be considered by the military. This will enable the smooth transition from offensive/defensive operations to security/stability operations, thus reaching a status of development where Alliance forces can leave a secure area behind much sooner.

CIMIC as a military function that is an integral part of modern multidimensional operations, addresses all cooperating parties within a conflict situation and facilitates mutual support of civilian capabilities to military forces and vice versa. The governing idea in all those interactions is reaching the defined and commonly desired end state, for the best of the local population, the civil actors and the Alliance, which will be, under the best of circumstances, hard to achieve.

B. Joint Cooperation exercise JOCO

Since 2014, an international CIMIC exercise, the Joint Cooperation (JOCO), is organized annually by the German CIMIC Command in Nienburg, Germany [3]. JOCO 2019 took place from 28 October -08 November 2019, including one day of academic lectures and a one-week preparation phase. In total 369 soldiers and civilians from 23 countries participated in JOCO 19. The aim of the exercise was: "Apply CIMIC Core Competencies in a multinational environment in order to strengthen interoperability and cooperation within the CIMIC-Community". This aim included the following objectives:

• Contribution of a multinational G9 (CIMIC)-Branch to the Military Planning Process within a NATO Article 3 Scenario;

• Apply fieldworker's knowledge and skills within a multinational CIMIC Support Unit;

• Establish relationships between CIMIC and nonmilitary actors (NMA);

•

Provide an environment for military and civil partners to create mutual understanding.

Joint Cooperation is principally a field training exercise (FTX) for multinational CIMIC Support Units. The training area includes the districts of Nienburg (Weser) and Verden as well as the region Hanover (Germany). In addition to the multinational Training Audience (TA), more than 150 role players from local administrations, private sector, emergency and rescue services and the local population of the districts of Nienburg, Verden and the region of Hanover supported the exercise. JOCO 19 FTX was based on a modified version of the NATO exercise scenario SKOLKAN which is geographically located in Scandinavia. It assumes the presence of NATO Forces in the virtual country of FRAMLAND (FRM). As part of the so named Resilience Support Mission (RSM) the exercise participants are practicing the CIMIC Core functions: Support to the Forces, Civil Military Liaison and Support to Civil Actors and their environment. In this context, it is important not to lose sight of the various actors as for instance Governmental Organizations and Non-Governmental Organizations as well as the military and several civilian key players. The training audience has to establish a liaison matrix during the exercise, in order to create a civil assessment and to draw conclusions for further action.

All scripted events were closely related to real-world topics. Within this context, the Training Audience (TA) had to assess the civil environment. During the exercise, CIMIC has been scripted to be the appropriate facilitator of this assessment due to its close link to civil structures, local population and a diverse range of organizations.

During the exercise, the TA was split into two CIMIC Support Units (North and South) including two CIMIC Centers, Liaison-, Project-, Reconnaissance-and Assessment Teams, and a G9 function. The primary objectives of the exercise have been:

• Assess the civil environment of a NATO member country.

• Conduct reporting and manage the reporting system.

• Maintain and adapt the liaison matrix.

• Apply media awareness.

• Take cross cutting topics (protection of civilians; women, peace and security; children and armed conflict; cultural property protection; building integrity) into account in situation assessment and give advice to the commander.

All in all, for the interest of NATO Task Group IST-165, the complexity of the operation, including its cooperation with the local residents, emergency services, government officials, as well the continuously political and social changing background, provided a rich simulated environment. Additionally, the production of supplemental materials such daily newscasts, daily newspapers and social media activity which was a part of the exercise provided a chance to do daily updates to a fusion process. Further, since we know the ground truth of the activities, we will have a chance to verify the validity of any tested fusion processes as each day's reporting is added to the mix of information and data we will be using for our research on information fusion for sensemaking.

III. ISSUES OF HUMINT DATA COLLECTION

On a variety of the collection of natural language is similar to that of sensor data -a signal is received (in written or audio form), processed via algorithm (e.g., text analytics) or human, and then analyzed for its relevance to ongoing operations. As with sensor data, the information which is received is assigned uncertainty values based upon the reliability of the source and the credibility of the content. However, there are differences inherent in natural language information and human sources which affect the quality of HUMINT.

A. Humans as information sources

Humans as information sources are in numerous ways very different from sensors. One of the obvious ways is that, while a sensor is developed to detect a specific type of physical phenomenon (light, heat, vibration, movement, etc.), a human being is able to react and to report on many different types of physical events, a human is able to go well beyond this, by reporting on not only historical (actual events and phenomenon) directly witnessed by the source, the source may relate information received from other sources (second hand), as well as relating inferences, speculation, and even potentially useful information about future events or states, i.e., things which have not yet taken place. These latter abilities are a significant departure from sensors. All sensor data is historical: it is a recording of physical phenomena which have taken place. A fusion algorithm working upon such sensor data may project future events or states (e.g., where an airplane may be five minutes from now) based upon known laws of physics. Human speculation or inference is not necessarily governed by physical laws or properties, making testing of any algorithm or analysis a difficult activity.

In contrast to a neutral sensor, a human, however, unconsciously, does a certain amount of preprocessing of information. For example, given a specific event, different sources will provide different information based upon experience, focus and background knowledge. A military observer is trained to look for activities and events which have a military significance, thus ignoring, for instance, some activity that a policeman, also a trained observer, would find of note within his framework of law enforcement. A casual observer will not have the skills or background to notice certain types of detail, and may thus filter different details and conclusions of exactly the same events.

Thus multiple observers of the same event may give dramatically different reports of these events. This does not necessarily mean one observer is more reliable than the other, indeed the accuracy of observation of all observers may be relatively high -they simply focus on different details, and may draw quite different conclusions.

Thus assignments of reliability of a source may be difficult to assess from situation to situation. Even trained military observers may miss significant details when involved in non-military engagements that a non-military observer will recognize as important information.

B. Language issues

The "signal" of HUMINT consists of information which is formulated in natural language as audio or in written form. There are literally thousands of natural languages, and any information received will need to be processed depending upon which language was used for the conveying of that information. This requires specialized analytical tools, such as parsers, taggers, part-of-speech recognizers, as well as knowledge of the grammatical and lexical characteristics of the language in question.

But even with such tools, there are still problematic areas. For example, in many CIMIC operations, there may be a number of different languages being used --local language, languages of the non-government organizations (NGOs) like Red Cross or Medecins sans Frontieres involved, as well as other supporting actors. For example, in JOCO, the language of the exercise and the reporting which takes place is in English, although the local inhabitants speak German. Additionally, most of the soldiers producing the reports during any JOCO exercise do not have English as a mother tongue; thus, there may be variations from a standard English. And, of course, there is variation between the US, Canadian and British English "mother tongues" while can result in confusion or ambiguity.

Further, forms of communication such as social media have resulted in non-standard usages of language, including abbreviations, emojis, and often "code switching", in which words or abbreviations from a second language appear embedded in a text (e.g., "cu" as an abbreviation for "see you" may just as easily appear in a German text message as in an English one).

And, of course, there are all of the garden variety issues of misspellings, use of confusing homonyms, and text fragments.

C. (Linguistic) Uncertainty

Human communications are more than just the passing on of information about observations of events. Along with content information, there may information about where an observation originated i.e., was it direct observation or second-hand reporting (i.e., hearsay). The source may indicate that they have doubts or confidence about this information by including hedges in the form of downtoners (expressions of doubt) or boosters (expressions of confidence). Expressions such as "in my opinion" or "we surmise" indicate that the information being passed along is the result of belief or inference. Additionally, human sources pass along information about possible future events which are necessary for intelligence decision-making.

In summary, in natural language utterances there are two types of uncertainty: uncertainty within the utterance itself (inexactness) and uncertainty about the content of the message (evidentiality). Examples of these two categories can be seen in Figure 1: Fig. 2. Uncertainty in natural language utterances [START_REF] Rein | I believe it's possible it might be so… Exploiting Lexical Clues for the Automatic Generation of Evidentiality Weights for Information Extracted from English Text[END_REF] Evidentiality is, in many ways, tied to the reporter: "I am telling you this, but I myself am not certain that this information is true." In particular, when we are informed that the informational content is the result of hearsay or inference, we need to consider that the actual source is not the person conveying the information to us, but another person or process. In other words, even the most reliable source may pass on information of weak or dubious quality, perhaps on the chance that it may be confirmed or disproved by other sources.

In addition, reporting often contains imprecise, ambiguous and vague formulations which leave us uncertain as to the precise nature of the observation: how many is "many", when is "soon", how far away is "nearby", etc. Many of these are context-dependent and can be resolved with background knowledge, others are less easy to resolve.

When dealing with multiple natural languages, it is even more important to be aware of existence of some linguistic ambiguities present in natural languages over the world. That may be significant, especially during automatic translation process, where these seemingly innocent subtleties may lead to serious misunderstanding. For the clarity: at this point we are not discussing dialects, slangs or any idiomatic expressions.

Syntactic ambiguity is an ambiguity where the same sequence of words is interpreted as having different syntactic structures [START_REF] Layman | Some Uses of Symbolic Logic in Law Practice[END_REF] and [START_REF] Allen | Modern Logic and Judicial Decision Making: A Sketch of One View[END_REF].

As an example of that one may consider the following sentence from Shakespeare's Henry VI: "The duke yet lives that Henry shall depose. The ambiguity resides in that we do not really know who shall do the deposing: Henry or the duke?

Similarly, in the following sentence in Polish: "Samochód wyprzedził autobus" -"The car overtook the bus" or "The bus overtook the car". The sentence is very simple, it describes observations with only two nouns and one verb in substitute for English Simple Past tense, but in Polish it is totally ambiguous due to some unfortunate inflection subtleties.

Semantic ambiguity on the contrary to syntactic one is the type of ambiguity where the structure remains the same, but the individual words are interpreted differently [START_REF] Layman | Some Uses of Symbolic Logic in Law Practice[END_REF] and [START_REF] Allen | Modern Logic and Judicial Decision Making: A Sketch of One View[END_REF].

As an example of that one may consider Polish "nieopodal" (pretty close, not far away) (sometimes spelled as "nie opodal" vs "opodal" (in Old Polish meaning: far, now it means: close) "nie" -prefix denoting an antonym, like a-, im-, in-, none-. Here we have a synonym and antonym in one.

Except the above mentioned one can also distinguish:

• global ambiguity, and

• local ambiguity

Global ambiguity is when in one sentence we have at least two different interpretations, like "The woman held the baby in the green blanket". In such it is genuinely unknow if the blanket was just for wrapping the baby, or if it was a tool for holding the baby.

Local ambiguity, i.e. so-called garden path sentences, when the sentence has only one interpretation but contains ambiguous phrases. These ambiguous phrases are typically resolved at the end of the sentence. For example: "The old man the boat".

One of the solutions applied in order to avoid all these above problems is using a controlled language. However, due to the NATO CIMIC assumptions we should be able to deal with structured data and information as well as with non-structured information like public speeches, interviews etc. Notice that the presented examples (with the exception of the Shakespeare one, unless occurred in the public speech) refer to situation which are pretty likely to happen during the exercises (observations on the road or descriptions of what is far or close).

IV. CONTEXT FOR HUMINT

This section presents the main concepts behind the use of context for information fusion [START_REF] Snidaro | Context-based information fusion: a survey and discussion[END_REF][17] [START_REF] Snidaro | Recent Trends in Context Exploitation for Information Fusion and AI[END_REF] and their application to the Intelligence domain. It is focused on processing the available text messages from the JOCO exercise to extract context in order to facilitate the understanding of situations.

The context implicit in messages needs to be highlighted with an appropriate separation with respect to the data contained in the messages. In other words, observations are specific aspects reported in a certain moment which must be integrated to infer the situation of interest, while context is a background knowledge which can be implicitly used in the messages assuming common understanding between emitter and receiver.

The second aspect of interest is the analysis of inference processes done by analyst to show the role of context in the deductions taken from the available messages. An "automatic" processing of data sources is discarded at this level since in this approach the main goal is building a model of information modelling and use of knowledge by human experts. Therefore, the goal is not developing a detailed proposal of algorithmic processes to emulate the experts in situation understanding, but an analysis of the types of information and their role in deduction processes which can be useful to support the analyst decisions to identify relevant data and processes to be done in two directions:

• Highlight context in data/messages (separate the context from the observations)

• Show how certain analyst deductions came from context Fig. 2 shows illustrates an example of context that was explicitly provided to the participants of the 2018 JOCO exercise. The context provides background information that the JOCO analysts and decision makers should keep in consideration while processing incoming reports and assessing the situation as it develops. It is interesting to note that the contextual information is broken down in the diagram accordin to the PMESII (Political, Military, Economical, Social, Infrastructural, Information) model [START_REF] Hillson | The DIME/PMESII model suite requirements project[END_REF].

Even more interesting for the discussion here is that for each factor a gross estimate of the impact to the JOCO forces deployed in the scenario and the population. The relevance of an information is a key quality measure [START_REF] Rogova | Considerations of context and quality in information fusion[END_REF] to assess how contextual is that information to the situation observed depending on mission goals.

A. Representation of context information

A very important aspect is the distinction of context with respect to the rest of information in the analysis of the target intelligence problem. Typically, the analysis to do in order to develop a model of available contextual information begins with two steps:

• define context variables

• model the context data

Context variables are those constraining or influencing problem variables [START_REF] Snidaro | Context-based information fusion: a survey and discussion[END_REF], so we can emphasize the "relativity" of contextual information, with a definition which can be considered relative with respect to the task of developing focused in problem variables. This is the key point for structuring the context of the messages. We should start from the goals of information fusion process: in this problem understanding what intentions of several groups/factions are (since HUMINT came in regarding the possibility that something could happen between them). Then, from these goals, we should partition what is relevant contextual knowledge that could help reaching the objectives/goals. The available contextual sources in the JOCO scenario should be identified in the first place, although it would be necessary an additional step in order to shape this material accordingly to the relevance with respect to the inference tasks of the information fusion engine, something which is beyond this current analysis. Some key aspects to consider are mentioned below.

B. Context sources

The problem addressed here is useful to illustrate how contextual knowledge can be identified, extracted and related with the sensemaking process, assimilated to the reasoning done by experts to build the explanation from observations and considering the background knowledge. In the available dataset with intelligence messages, two main types of available context sources should be identified: Lower-level context:

• Sensor/source models (e.g. sensor model, reliability of human source, etc.) • Physical, like geographic aspects Higher-level context:

• Biographic data of involved persons

• Political background / situation
• Structures of involved factions

• Relations between actors / groups / …

• Intentions and plans of possible actions/threats At low level (JDL level 1) we can identify information about location of entities (persons and groups), and identification of geographic areas inhabited by the different communities.

At higher levels, we can identify relations, belonging to groups, behaviors, etc. (JDL level 2) and at intentions and plans associated to groups (JDL level 3).

V. UNCERTAINTY (FORMAL DESCRIPTIONS)

Whenever solving any Information Fusion problem the question of uncertainty is always relevant. What we really expect after combination of data, information or/and context is not only increasing adequacy and precision of the elaborated results, but also (and in some cases even: above all) reducing the uncertainty of the elaborated results. Therefore, a formal description, and in a consequence, modelling of the uncertainty is required for the purpose of automated (or even semi-automated) HUMINT processing.

In our previous works [START_REF] Biermann | Mulit-level Fusion of Hard and Soft Information for Intelligence[END_REF] we have proposed solutions applicable to systems which were communicating with usage of BML. In these cases information certainty has been modelled as a function of Credibility, Reliability, and Commonness, as (1) shows.

Certainty  Credibility(Reliability, Commonness) (1)

When processing HUMINT information we cannot expect that the communication, in general, will be performed with usage of BML or any other controlled language. Therefore, the above solutions must be revised in order to be able to deal with problems described in section III C (on linguistic uncertainty), and uncertainties introduced to context information as well.

Kennedy and O'Hagan [START_REF] Kennedy | Bayesian calibration of computer models[END_REF] in their uncertainty categorization distinguish the following uncertainty sources:

• parameter uncertainty -their values are not known or cannot be exactly inferred

• parametric variability -variability of input variables of the model

• structural uncertainty -model inadequacy, model bias coming from the lack of knowledge of the underlying physics

• algorithmic uncertainty -numeric uncertainty coming from numerical errors and numerical approximation

• experimental uncertainty -observation errors coming from variability of experimental measurements

• interpolation uncertainty -coming from a lack of available data collection.

Notice that each of the lower-level and higher-level context information described in the section IV B undergoes some or multiple types of uncertainty. For example: sensor/source models  parametric uncertainty, parametric variability, and experimental uncertainty, the same as physical context. As for the higher-level context, the structural and the algorithmic uncertainties are relatively much more common.

Another taxonomy of uncertainty distinguishes two categories: epistemic (also known as systematic), and aleatoric (also known as statistic). The former basically refers to ignorance (something that one could know, but does not), while the latter refers to unknowns which differ each time they run. That distinction is effectively used in multiple evidence theories like Dempster-Shafer, Transferable Belief Model, or Dezert-Smarandache Theory.

When deciding about the formal representation of uncertainty for the purpose of HUMINT processing the real challenge resides in a fact, that none of the known solution applicable to structured data or information is adequate enough to meet the requirements for processing of multiple (in general) natural languages. That means the exact representation must be tailored to fit the needs of systems which not only process numerical data and attribute information, but also deal with multiple language subtleties like ones described in the section IIIC.

VI. SENSEMAKING

A. The objective and character of sensemaking

The major objective in "processing HUMINT for situational assessment" can be described as sense-making. This is the challenge to understand the meaning and consequences of incoming new (semantic) information for the development of a current situation, taking into account its broader information context and background knowledge which are relevant for the fulfillment of own intensions and actions as well as for those third parties and actors in the theatre who are able and willing to infer with our interest.

Sensemaking in typical application areas like CIMIC missions or military intelligence usually is not about recognizing real objects and actions which are usually directly observable by technical sensors. It is going far beyond that by fusing new information with the available knowledge about the current situation, including functions like situation awareness and impact assessment. Sensemaking provides results on an abstract cognitive level modeling the real world under observation, describing complex non observable abstract structures and relations, and reasoning about possible developments.

To explain the kind of reasoning which is necessary and common in intelligence to finally make sense out of the incoming information and data and the knowledge about the situation known so far, here is a simple but realistic example from the IRA conflict in Northern Ireland during the 1990 years. The British intelligence analysts knew from the police reports that some empty beer barrels had been stolen. This theft alone is nothing which is really exciting in itself. However, some days later there was information about a person seen in town who was known to be an IRA expert in bomb building but who did not belong to the local IRA members. This was a quite unusual behavior as different IRA groupings normally did not directly cooperate in their operations. Thus the fact that the empty barrels had been stolen acquired a new and threatening meaning. Barrels are perfect containers for really large bombs which are transported to their destination by car. Putting this information (and some other which cannot be given here) together, it became very probable that a bomb attack on one of the British barracks was to be expected. This attack was prevented by appropriate measures. This sensemaking from knowing about stolen barrels and having seen a hostile bombing expert is based, among others, on knowledge about organization structures, profiles of persons, patterns of behavior and other relevant context of the conflict situation. Link analysis and pattern matching belong to the methods in intelligence results looking for significant facts indicating a threat. The result of such reasoning usually is a set of hypotheses on threats and their impacts which are the bases for further evaluation and decision by human operators.As mentioned in Section II, during CIMIC operations military personnel, the so called "field workers", are sent out to gather information with respect to the current situation and potential influences on its development. This information is documented in a structured way but essentially it is free text concerning own observations of the field workers as well as the answers, statements, and opinions of their interview partners. Furthermore, all media are scanned for additional information. So, there are plenty of different and heterogeneous input streams of unstructured text which have to be exploited for establishing a most comprehensive and consistent situation picture relevant for the fulfillment of the CIMIC mission.

The challenge is to deduce the new situation picture and situation assessment from a set of fragmentary semantic information not knowing about its quality, consistency, trustworthiness, completeness or relevance at all and, more than that, not necessarily having sufficient knowledge about the structure and specification of the part of the reality we are interested in.

From an abstract point of view it is a kind of pattern matching problem: input data has to be matched with a world-model, the pattern, to be put at its correct place, thus developing an updated version of the concrete instantiation of the abstract model. Unfortunately, the seeming similarity to the methods of machine learning by neural networks (NN) does not help.

B. Sensemaking is not a machine learning problem

NN are dedicated to detect real objects contained in their sensor data input. Sensemaking however does not merely aim at the recognition of things that are directly contained in individual input data, but rather to aggregate and integrate the stream of input information, context and background knowledge, to deduce deeper insight into general aspects and hidden structures behind the observable reality.

NN algorithms are based on the assumption that the class of objects of interest (i.e. faces, cars, blackbird singing, etc.) has some statistical characteristics inherently given by the constituting features of the elements of the class. These factors of variation [START_REF] Goodfellow | Deep Learning[END_REF], which explain the observed data, might not be obvious to humans, but they define a statistical pattern representing these objects in a general way. Training a neural network, i.e. learning such patterns and testing them, is done by using appropriate "correct" input data from a sufficient numerous and representative set of elements of the respective class. As a result a NN algorithm will recognise a specific element of that class, for which it had been trained, with a known statistical probability [START_REF] Goodfellow | Deep Learning[END_REF].

Different to that sensemaking for use cases like CIMIC, or military intelligence, or intelligence for fighting against organised crime does not mean to only match individual HUMINT input to a fixed pattern.

Appropriate patterns for a "general" situation assessment rich enough to cover all relevant and significant aspects (features [START_REF] Goodfellow | Deep Learning[END_REF]) usually are not known in advance and the required context and background knowledge will vary in each individual mission. HUMINT, the input data in sensemaking, does not have known statistical properties, regularity in content or quality, and frequency. This means that the basic assumptions and preconditions for a machine learning approach like neural networks in general or, more particular, deep learning do not apply to sensemaking in the area of HUMINT processing for situation assessment.

C. Discussion of alternative solutions

Sensemaking requires ".. some form of higher-level cognition, i.e., explicitly understanding the encountered state of affairs, which is typically realized by using representations supporting explicit reasoning and planning." [START_REF] Salfinger | Reinforcement Learning meets Cognitive Situation Management: A Review of Recent Learning Approaches from the Cognitive Situation Management[END_REF] Thus, CogSiMa has traditionally been addressed with knowledge representation and reasoning approaches from symbolic Artificial Intelligence (AI) [START_REF] Blasch | High Level Information Fusion (HLIF): Survey of models, issues, and grand challenges[END_REF], i.e., ontologies and deductive reasoning, as in [START_REF] Kokar | Ontology-based situation awareness[END_REF], [START_REF] Matheus | Lessons learned from developing SAWA: a situation awareness assistant[END_REF].

Unfortunately, former AI methods based on knowledge bases did not work well either. [a] They allowed reasoning over a fixed set of hard coded knowledge about the world (closed world assumption) which is not appropriate for sensemaking. The lack of flexibility in building up the knowledge about the world under observation prevents to gain deeper insight. Salfinger states in [START_REF] Salfinger | Reinforcement Learning meets Cognitive Situation Management: A Review of Recent Learning Approaches from the Cognitive Situation Management[END_REF] that:

•

Deep Neural Networks' capability for performing comprehension/reasoning is not fully understood yet, representing an area of active research. Despite hopes that Deep Learning may provide a means for overcoming the symbol grounding problem, the gap between learning and reasoning still persists, demanding future research on how to integrate these paradigms [START_REF] Battaglia | Relational inductive biases, deep learning, and graph networks[END_REF], [START_REF] Garnelo | Reconciling deep learning with symbolic artificial intelligence: representing objects and relations[END_REF].

•

Reinforcement Learning (RL) increasingly incorporates human experience, by "bootstrapping" RL agents with imitation learning from human experts' recorded behavior traces.

•

The interpretation of learned higher-level representations and behavior is still largely unsolved.

We share her point of view that a kind of combined effort from cognitive sciences and machine learning might be a promising way ahead. Sensemaking or cognition in domains like intelligence remains a challenge in AI. For the time being any attempt to solve this task may be supported by a NN approach like deep reinforced learning but it requires human-in-the-loop for model building, classification, interpretability and tellability [START_REF] Chakraborty | Deep Learning for Situational Understandig[END_REF].

VII. CONCLUSIONS / WAY FORWARD

The complexity of operations which cover larger geographical areas and take place over longer time periods with reliance on both sensors and human sources is an ongoing challenge. Fusion and modelling approaches and methodologies which work well for lower level data are not necessarily sufficient when the scope of the problem expands to weeks, months or years. For example, whereas a tracking algorithm fuses individual data points to deliver a single track, a long term intelligence operation may require that each track itself becomes a single data point in a more complex process. Additionally, over time context may change (regime shifts due to elections, legal context modified by lawmakers, etc.) and in the area of HUMINT or OSINT, information may prove to be inaccurate or is no longer valid due to any of a variety of reasons.

Over the course of a number of years and three research task groups which have gradually increased scope and complexity, we have tried to identify the important elements which need to be taken into consideration to build a fusion system which is robust enough to deal with a changing and dynamic situation. Using a series of ever more complex scenarios, we have tried to find solutions to many of the questions and challenges of larger-scale complex operations with many diverse sources of information.

While we have found many answers, most of our work is theoretical, and there is still much that needs answering. Reconciling and incorrect or invalid intelligence is an area which is still important; what process does one go through to remove erroneous data or erroneous conclusions from the system? Sometimes making sense of complex information requires not tweaking of existing theories, but discarding previous assumption and results in light of new information: how does one go about this in a systematic way. When new context information is available, how are past conclusions updated?

The fusion of data and information from multiple heterogeneous sources, over time and space will continue to be a challenging and interesting area of endeavor.

Fig. 2 .

 2 Fig. 2. Example of background context regarding a fictional country similar to the one provided to JOCO forces during the 2018 exercise.

ACKNOWLEDGMENT

This work was partially funded by Spanish MINECO, ref. TEC2017-88048-C2-2-R


Zhuxin Li
email: li@cenbg.in2p3.fr

Sébastien Incerti
email: incerti@lp2ib.in2p3.fr

Daniel Beasley
email: daniel.beasley@kcl.ac.uk

Hao Shen
email: haoshen@fudan.edu.cn

Shimei Wang
email: shimeiwang0819@163.com

Hervé Seznec
email: seznech@cenbg.in2p3.fr

Claire Michelet
email: michelet@lp2ib.in2p3.fr

Accuracy of three-dimensional proton imaging of an inertial confinement fusion target assessed by Geant4 simulation

Keywords:

The Geant4 toolkit was used to perform benchmark Monte Carlo simulations of proton microtomography imaging. A phantom of inertial confinement fusion (ICF) target was designed, based on experimental data. Simulations of STIM (Scanning Transmission Ion Microscopy) and PIXE (Particle Induced X-ray Emission) tomography were performed. Quantitative images were obtained from the TomoRebuild and JPIXET software packages, chosen for their ability to handle large solid angles of Xray detection. The tomographic images were compared with the original phantom, used as a reference. For STIM-T, the accuracy of the calculated mass density was ≤ 2% for TomoRebuild and ≤ 14% for JPIXET. Corrections of X-ray production cross section and X-ray absorption were tested for the quantification of Ge, used as a dopant in the ICF target. The accuracy on the obtained Ge density was ≤ 2.9% for TomoRebuild and ≤ 6.4% for JPIXET, whereas the error was about 40% without correction.

Introduction

Particle Induced X-ray Emission Tomography (PIXE-T) in combination with Scanning Transmission Ion Microscopy Tomography (STIM-T) has been used to quantitatively produce the elemental concentration and mass density distributions of microscopic samples of a few tens or hundreds of micrometers in size [START_REF] Michelet | Development and applications of STIM-and PIXEtomography: A review[END_REF]. These techniques have been applied to the three-dimensional (3D) imaging of microscopic inertial confinement fusion (ICF) targets at Fudan University [START_REF] Lu | Reconstruction of Ge spatial distribution in ICF target using PIXE-T[END_REF][START_REF] Guo | A dual-PIXE tomography setup for reconstruction of Germanium in ICF target[END_REF][START_REF] Wang | Application of silicon drift detector in PIXE tomography system of Fudan University[END_REF]. The ICF target is a small spherical shell, a few hundred micrometers in diameter, made of a polymer of uniform composition and mass density, doped with a high Z element, such as Ge. The dopant's distribution should be uniform as it is intended to avoid laser imprinting and limit hydrodynamic instabilities in ICF ignition. Several 3D imaging experiments have been carried out at Fudan University to quantify the Ge content in ICF targets and check its uniformity. For this, a reconstruction program in MATLAB, using Filtered Back Projection (FBP), has been developed. Two physical phenomena need to be considered when reconstructing PIXE-T data: i) Non linear X-ray production (NLXP), caused by the decrease of proton energy in the material, leading to a variation of ionization cross section; ii) X-ray absorption (XA) in the material. However, in this reconstruction program, the correction to compensate for X-ray loss does not take into account the solid angle of the detector, which is assumed as a point. Assuming this would require having a small solid angle in experiments, leading to an increase of collecting time. In the present study, we propose to use reconstruction methods able to take into account a large detection solid angle. This would be an advantage, because it could significantly reduce the duration of experiments. At present, two reconstruction software packages, TomoRebuild [START_REF] Michelet | An implementation of the NiftyRec medical imaging library for PIXE-tomography reconstruction[END_REF] and JPIXET [START_REF] Beasley | A comparison of quantitative reconstruction techniques for PIXE-tomography analysis applied to biological samples[END_REF][START_REF] Beasley | Fast simulation of Proton Induced X-Ray Emission Tomography using CUDA[END_REF], are able to take into account large solid angles. Their performances have been compared in previous studies on experimental data of biological specimens [START_REF] Michelet | An implementation of the NiftyRec medical imaging library for PIXE-tomography reconstruction[END_REF][START_REF] Michelet | Quantitative reconstruction of PIXE-tomography data for thin samples using GUPIX X-ray emission yields[END_REF]. The mass densities obtained by the two packages were found similar. However, this was only a rough comparison of one package relative to the other, due to the lack of reference data. In the present study, we define a numerical phantom with given density and composition that will be used as a reference sample, so that accurate tests can be performed. The main purpose of our study is to demonstrate that the different reconstruction methods implemented in TomoRebuild and JPIXET are able to produce accurate quantitative images of ICF targets using a large solid angle.

For this, we propose to use Geant4 as a benchmark [START_REF] Allison | Recent developments in Geant4[END_REF][START_REF] Allison | Geant4 developments and applications[END_REF][START_REF] Agostinelli | GEANT4-a simulation toolkit[END_REF]. Geant4 is the most widely used open source Monte Carlo toolkit to simulate particle-matter interactions. Thanks to its advanced functionalities, Geant4 has been applied to various domains, such as high-energy physics, astrophysics and medical applications. More specifically, Geant4 provides users with complete examples, which are ready-to-use applications. Considering tomographic imaging, the doiPET so-called "advanced example" is available to simulate positron emission tomography (PET) imaging [START_REF] Hirano | Performance evaluation of a depth-of-interaction detector by use of position-sensitive PMT with a super-bialkali photocathode[END_REF][START_REF] Tsuda | A four-layer depth of interaction detector block for small animal PET[END_REF][START_REF] Yoshida | Development of a whole-body dual ring OpenPET for in-beam PET[END_REF]. Specific applications related to PIXE experiments are also provided. For example, the xray_fluorescence advanced example reproduces various setups for PIXE or XRF experiments [START_REF] Mantero | Simulation of X-ray fluorescence and application to planetary astrophysics[END_REF]. The TestEm5 extended example can be used to test the low energy physics processes in the energy range of a few MeV or below, typically involved in PIXE. For instance, Geant4 was used to simulate X-ray spectra that were compared with experimental spectra obtained from 1.5 MeV proton irradiation of thin foils of Au, Pt, GdF3 and Fe. This comparison showed the ability of Geant4 to predict X-ray spectra for these high-Z samples [START_REF] Incerti | Comparison of experimental proton-induced fluorescence spectra for a selection of thin high-Z samples with Geant4 Monte Carlo simulations[END_REF]. A new advanced example for the specific application of 3D imaging of microscopic samples is being developed at CENBG/LP2I Bordeaux. For this, several phantoms dedicated to biological applications were developed, including the upper part of a Caenorhabditis elegans nematode derived from experimental data [START_REF] Michelet | A Geant4 simulation for three-dimensional proton imaging of microscopic samples[END_REF][START_REF] Michelet | A Geant4 simulation of X-ray emission for three-dimensional proton imaging of microscopic samples[END_REF]. In the present study, we constructed a numerical ICF target phantom with uniform composition and mass density. Geant4 simulations of STIM-T and PIXE-T were performed on the numerical phantom to generate simulated projection data. The principle of our study is to use these simulated projections as an input to the TomoRebuild and JPIXET software packages, and to compare the reconstructed images to the original phantom, used as a reference. Our first aim is to compare the quality of the reconstructed images obtained by different reconstruction methods. A second aim is to quantify the accuracy of the two corrections for NLXP and XA effects implemented during the reconstruction process using a large solid angle. For this, a detailed study was performed using TomoRebuild, taking advantage of specific features of Geant4, which gives access to data that are impossible to get from experiments.

Materials and methods

Numerical phantom

Our simulation code was adapted from the TestEm5 "extended example" of Geant4 10.7 version, which is generally used to study the transport of particles through a single layer of material. We modified this code to simulate STIM-T and PIXE-T experiments on more complex objects. The principle of STIM-T is, for each beam position, to measure the residual energy of protons after passing through the object. PIXE-T consists of collecting the X-rays emitted by the atoms of the object. We designed a numerical phantom of an ICF target according to typical samples analyzed at Fudan University [START_REF] Lu | Reconstruction of Ge spatial distribution in ICF target using PIXE-T[END_REF][START_REF] Guo | A dual-PIXE tomography setup for reconstruction of Germanium in ICF target[END_REF]. The phantom is a spherical shell with outer radius 196 µm and thickness 25 µm. It is composed of a polymer of uniform composition (stoichiometric ratio C:H = 5:7) and mass density (1.08 g/cm 3 ), doped with Ge (5% in mass). The mass percentage of each element is indicated in Table 1.

General set-up for STIM-T and PIXE-T simulations

The phantom is centered at the origin of the Geant4 coordinate system, and placed in a "vacuum" environment. Fig. 1 shows the set-up at the first projection: the proton beam is directed along the x-axis. Just as for real 3D tomography experiments, in the simulation the beam scans over the region of interest (ROI) at a given projection. When it finishes scanning the ROI, it rotates to the next projection and performs the scan again. This procedure is repeated until all projections are simulated. It should be mentioned that in experiments it is the sample that rotates, while in the simulation it is more convenient that the beam rotates, because the positions and directions of the proton beam are easy to parameterize.

Scan parameters

Scan parameters and number of incident protons are crucial points as the simulations can last several days. The simulations were carried out with a proton beam of 4 MeV (point source). For STIM-T, the entire phantom was scanned over a square, each side of length 500 µm. Similar to typical experimental conditions, a scan of 128 slices × 128 pixels was performed for 100 projections over a range of 180°. As the scan involves ~10 6 beam positions, the scan parameters are defined in a C script, which is used to generate automatically the positions and directions of the proton beam into a macro file. The macro file is then read by Geant4 to run the simulation. Considering the long duration of the simulation, the number of incident protons has been optimized to reduce the calculation time whilst keeping a good precision. For each beam position, 100 protons were used, which is sufficient to give a good quality of image [START_REF] Michelet | A Geant4 simulation for three-dimensional proton imaging of microscopic samples[END_REF]. For PIXE-T simulation, only the central slice was scanned. Indeed, PIXE-T requires much more incident protons than STIM-T because of the low X-ray production cross-sections. A PIXE-T experiment usually requires 10 8 to 10 9 protons for each beam position. If the same conditions were applied in the simulation, it would be unrealistic in terms of duration. In order to minimize the simulation duration while maintaining a good quality of image, we reduced the number of protons to 2×10 6 protons. At the same time, to compensate, we took a very large solid angle for detecting the X-rays. This will be discussed in detail in the section 2.6. Besides, some simulation parameters have been optimized to minimize the duration of the simulation, as explained in the following section.

Simulation configuration

The simulation configuration was validated in previous studies for steady beam PIXE analysis, based on the Geant4 TestEm5 example [START_REF] Incerti | Comparison of experimental proton-induced fluorescence spectra for a selection of thin high-Z samples with Geant4 Monte Carlo simulations[END_REF][START_REF] Michelet | A Geant4 simulation for three-dimensional proton imaging of microscopic samples[END_REF][START_REF] Michelet | A Geant4 simulation of X-ray emission for three-dimensional proton imaging of microscopic samples[END_REF]. We used the same physics models in our codes for STIM-T and PIXE-T simulations: Livermore models and Bearden's X-ray energy database were applied, as they were found more accurate for simulations at low energy. The tracking step was set to ensure at least 10 effective steps along the particle transportation. In order to minimize computing time, the simulation code was designed for multithreading. In addition, a low energy cutoff 900 eV was applied for the generation of secondary particles including X-rays. Thus, the X-rays were not generated if their energy was below 900 eV. This threshold corresponds to the typical lowest energy limit of most X-ray detectors used in PIXE experiments. Indeed X-ray absorption by the entrance window of the detector becomes too high for detecting X-ray at lower energies. It was also checked that, as expected, this energy limitation did not affect the proton energy loss. This threshold was shown to significantly reduce the simulation time [START_REF] Michelet | A Geant4 simulation of X-ray emission for three-dimensional proton imaging of microscopic samples[END_REF]. In these conditions, it took about 5 days for a PIXE-T simulation of 100 projections × 1 slice × 128 pixels on a multicore computer with 100 threads, 2.7 GHz, 768 Gb RAM. If the cutoff was not taken into account, it would take 11 days. For STIM-T of 128 slices, it took about 2.2 hours with the cutoff, while 3 hours without the cutoff.

Simulation output data

The information required for tomographic reconstruction is recorded and contains: i) beam position, i.e. indices of projection, slice and pixel; ii) energy and momentum of the collected particles, which are transmitted protons for STIM-T and emitted X-rays for PIXE-T. A very practical question in terms of saving data is that a tomography simulation involves ~10 6 beam positions, and many events to collect. To address the problem of large data, we designed a custom struct variable type to store the information of particles in a compact way. The data are saved in a binary file in append mode. In this way, the output file is about a few megabytes per projection. In addition, remedial measures are implemented in case of interruptions during the simulation, for example power failure. A specific C script was developed to identify where the interruption occurred in order to resume the simulation without need to restart it from the beginning.

One of our objectives is to verify whether the corrections of X-ray attenuation implemented in TomoRebuild are accurate. As these corrections depend on the position of the detector, our tests require the collection of X-rays at different angles. Considering the long duration of a PIXE-T simulation, it would not be realistic to repeat it every time we change the position of the detector. Therefore, the detector is not modelled in the simulation. Instead, we collect the particles of interest in all directions (4π solid angle). The detector is then modeled after the simulation by making a selection of the collected particles.

Selection of simulation output data

The detector is modelled by making a selection of simulation output data after finishing the simulation. The selection of events is based on the following conditions: i) the angular position of the detector with respect to beam direction; ii) the detector solid angle, which depends on the distance of the detector to the sample and the size of entrance window. A C script is used to perform the selection by simply parameterizing the two conditions. In this script, we assume that the detector entrance window is circular, thus, the solid angle is calculated by defining the half apex angle, indicated as 𝐴𝑂𝐵 ̂ in Fig. 2. We chose a configuration similar to the experiments [START_REF] Guo | A dual-PIXE tomography setup for reconstruction of Germanium in ICF target[END_REF]: i) For STIM-T, the detector is placed after the sample, i.e. the angular position of detector is 0° with respect to beam direction, with a half apex angle of 10.2°; ii) For PIXE-T, the detector is placed at 135° with respect to beam direction. However, as mentioned before, only 2×10 6 protons were applied in the present PIXE-T simulation to minimize the duration, leading to a theoretical reduction of the number of detected events. To overcome this difficulty, we chose to take a large half apex angle of 70° in order to obtain sufficient events. In this case, the solid angle is about 4.134 sr. We should note that such a half apex angle would not be possible in experiments, because the detector would block the oncoming beam.

Specific simulation conditions for PIXE-T

One of the objectives of the present study is to evaluate the accuracy of the NLXP and XA corrections implemented in the image reconstruction process. For this, we performed PIXE-T simulations in conditions similar to experiments, as explained in the previous sections. We collected the energy and momentum of X-rays going out of the phantom. In order to study independently the effects of NLXP and XA corrections, we took advantage of the flexibility of Geant4, to perform simulations in specific conditions, which are impossible to realize in experiments:

-We collected X-rays in the object, at the position where they are emitted. In this case, the XA effect is excluded. -We artificially turned off the process of proton energy loss. In this way, the proton energy was kept at the initial energy of 4 MeV along its track in the material. Therefore, the NLXP effect is excluded.

Voxelized phantoms

Quantitative reconstructions based on STIM-T and PIXE-T simulation results were performed by different reconstruction methods. In order to verify both quantitatively and qualitatively the reconstructed images, we need to compare them with the original images of the phantom used in the simulation, as shown in the Results section. For this, we needed to construct two voxelized phantoms: the first one to compare with STIM-T images; the second one to compare with PIXET-T images. The only difference between the voxelized phantoms used for image analysis is the density values set at each voxel. The density is 1.08 g/cm 3 for STIM-T and 0.054 g/cm 3 for PIXE-T, corresponding to the Ge content. Both voxelized phantoms were made of 128 slices × 128 × 128 voxels (Fig. A1). The voxel size is defined by dividing the scan size (here 500 µm in both y and z directions) by the number of pixels of the scan. One difficulty in building the voxelized phantom is to handle the density distribution for the voxels at the border of object to make sure the density decreases gradually from the object to the vacuum (FA. 1). Indeed, the object is a spherical shell, so its border is not parallel to the grid of voxels. Therefore, we first built a phantom of the same geometry, but with a resolution higher by a factor of 8, i.e. 1024 × 1024 ×1024 voxels. Then, the density of each voxel of the final 128 × 128 × 128 voxels phantom was obtained by taking the mean of the 8×8×8 neighbouring voxels of the high-resolution phantom.

Reconstruction of tomographic images

The tomographic images were reconstructed using two software packages, TomoRebuild [START_REF] Michelet | An implementation of the NiftyRec medical imaging library for PIXE-tomography reconstruction[END_REF] and JPIXET [START_REF] Beasley | A comparison of quantitative reconstruction techniques for PIXE-tomography analysis applied to biological samples[END_REF][START_REF] Beasley | Fast simulation of Proton Induced X-Ray Emission Tomography using CUDA[END_REF], performing quantitative reconstructions of STIM-T and PIXET-T images. Their advantage is that they provide NLXP and XA corrections, taking into account a large X-ray detection solid angle. The calculation of the corrections is based on the knowledge of the 3D distribution of mass density obtained from STIM-T. However, the algorithmic approach is different for the two codes:

-For TomoRebuild, the STIM-T reconstruction assumes a global uniform composition in major chemical elements, which is suited to a wide variety of specimens such as ICF targets or biological samples. For this reason, the STIM-T reconstruction is performed independently from PIXE-T. From the 3D STIM-T image, the NLXP and XA corrections for each X-ray emitting element are then computed at each projection angle. They are stored in correction matrices that are incorporated during the iterative process of PIXE-T reconstruction. In this way, each chemical element of interest is reconstructed separately from one another. -For JPIXET, the reconstruction associates the information from STIM-T and PIXE-T at each iteration and for all elements together. The distributions of element content obtained from PIXE-T are used to calculate the stopping power and X-ray absorption of the material at each iteration.

The element content (major and minor elements) is normalized at each iteration, so that the sum of all mass fractions is 1. In this way, JPIXET can be used for samples that are not homogeneous in composition. However, a special attention should be paid to the reliability of each element content, as an error occurring on a specific chemical element would propagate to the other elements as well. Here, of course, as there is only one element in the PIXE-T data (Ge), we do not have this concern.

The TomoRebuild (TR) software package is developed in C++, at CENBG/LP2I Bordeaux. We used two methods in this study:

-Filtered Back Projection (TR_FBP), based on the exact analytical solution of the continuous description of the tomography problem. FBP is often preferred as a simple and fast method suited for not too noisy data, especially transmission tomography, i.e. in our case STIM-T data.

Various filters can be applied to smooth the data, at the cost of a slight loss in spatial resolution.

Here we present the results using a slight smoothing Hann filter (with 0.5 frequency cut-off). -Maximum Likelihood Expectation Maximization (TR_MLEM), an iterative method based on a discrete formulation of the tomography problem. MLEM is often used for PIXE-T as a robust algorithm even with noisy and/or incomplete data [START_REF] Beasley | A comparison of quantitative reconstruction techniques for PIXE-tomography analysis applied to biological samples[END_REF][START_REF] Takahiro | Particle induced X-ray emission-computed tomography analysis of an adsorbent for extraction chromatography[END_REF][START_REF] Ng | A PIXE micro-tomography experiment using MLEM algorithm[END_REF]. The images presented in this study were obtained from 32 iterations, which was the optimal number determined from Normalized Average Absolute Deviation (NAAD) and Normalized Root Mean Squared Deviation (NRMSD) calculations [START_REF] Michelet | A Geant4 simulation of X-ray emission for three-dimensional proton imaging of microscopic samples[END_REF].

JPIXET is a GPU (Graphics Processing Units)-accelerated software package, devised at IST/ITN in Lisbon, for the reconstruction of STIM-T and PIXET-T with a choice of iterative algorithms [START_REF] Beasley | Fast simulation of Proton Induced X-Ray Emission Tomography using CUDA[END_REF], including a GPU version of the Discrete Image Space Reconstruction Algorithm (DISRA) [START_REF] Sakellariou | Three-dimensional ion micro-tomography[END_REF], which was used in the present study. The images presented in this study were obtained from 6 iterations.

The reconstruction time does not constitute a severe limitation. For example, for TomoRebuild, using a very basic PC 3.4 GHz, 8 Go RAM, it takes 1 minute to reconstruct a full 3D STIM-T image of 128x128x128 voxels using TR_FBP, and 25 minutes using TR_MLEM. For PIXE-T, it takes 2 minutes for one slice, including the calculation of the NLXP and XA correction matrix, for which the large detection solid angle is modelled by finely discretizing the entrance window of the detector [START_REF] Michelet | A Geant4 simulation of X-ray emission for three-dimensional proton imaging of microscopic samples[END_REF]. For JPIXET, both STIM-T and PIXE-T reconstructions are performed together. The reconstruction takes about 11 minutes on a computer 3.7GHz, 32GB RAM with a NVIDIA GeForce GTX 1080 Ti GPU. The images were saved as binary files in "raw data" format, which is suited to most graphics software packages. The images presented here were displayed using the Amira graphic software package (https://www.fei.com/software/amira-3d-for-life-sciences), which is particularly suitable for 3D rendering, and the public domain software ImageJ, developed at the National Institutes of Health (https://imagej.nih.gov/ij).

Results

STIM-T analysis of the mass density

We compared the mass density obtained by different methods, i.e. TR_FBP, TR_MLEM and JPIXET.

Quantitative reconstruction was performed on 128 slices, we present here the results for the central slice of the phantom (Fig. 3a andA2), which corresponds to the slice simulated in PIXE-T. Firstly, we compared the reconstructed images to the original phantom, used as a reference, by calculating the difference images (Fig. 3c-e):

Difference image = Reconstructed image -Reference image.

Difference images allows one to reveal small reconstruction inaccuracy that would not be clearly visible directly in the reconstructed images, such as reconstruction artifacts appearing as a moiré pattern, or slight differences at edges for example, as discussed in section 4. Secondly, we compared quantitatively the mass density obtained from the different reconstruction methods (histograms in Fig. 4). For this, we defined four regions of interest (ROI) inside the shell, to check whether the reconstructed mass density was uniform (Fig. 4 ROI 1-4). We also defined two ROI in the background to verify whether the calculated density was close to 0 (Fig. 4 ROI 5-6). ROI 6 was not included in the histogram, because the calculated densities using all methods were very close to 0, as it will be discussed in Section 4.1.

Considering each reconstruction method, we observed there was no significant difference between the calculated density of ROI 1-4, thus, we considered the mean density of ROI 1-4 to compare the accuracy of the different reconstruction methods (Table 2).

PIXE-T analysis of the Ge mass density

Validation of NLXP and XA corrections for TomoRebuild

One of our objectives in the present study is to evaluate the accuracy of the two corrections implemented in TomoRebuild. Fig. 5 shows an example of correction matrices calculated by TomoRebuild (Fig. 5a for NLXP effect, Fig. 5b for XA effect, Fig. 5c for total) for Ge at a specific (arbitrary) projection angle. The total correction factor at a given voxel is calculated as the product of NLXP and XA factors, according to a procedure detailed in a previous publication [START_REF] Michelet | A Geant4 simulation of X-ray emission for three-dimensional proton imaging of microscopic samples[END_REF]. The correction matrix is calculated for each projection angle, since the direction of the beam and position of the detector are related to the projection angle. As explained in section 2.7, Geant4 allows one to perform PIXE-T simulations using standard experimental conditions and non-standard conditions, which are impossible to realize in experiments. In this way, we simulate without either the XA or the NLXP effects so that we can validate the corrections calculated by TomoRebuild. The accuracy of reconstructed density values was evaluated in terms of Relative Difference (RD) as follows:

𝑅𝐷 (%) = 𝑅𝑒𝑐𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑒𝑑 𝑣𝑎𝑙𝑢𝑒 -𝑅𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑣𝑎𝑙𝑢𝑒 𝑅𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑣𝑎𝑙𝑢𝑒 × 100
For this, four types of PIXE-T data were obtained and compared with the reference data defined for the phantom (Table 3):

1. X-rays were collected at the creation points and the proton energy loss process was turned-off. In this case, NLXP and XA effects were both excluded. Therefore, no correction was required and it was expected that the calculated density should be close to the original density defined in the phantom. 2. X-rays were collected at the exit of the object and the proton energy loss process was turnedoff. In this case, only the NLXP effect was excluded. Thus, in the reconstruction process, we applied XA correction to check independently its accuracy. When implementing XA correction, the RD was -1.3 % whereas the error was -3.7% without the correction. 3. X-rays were collected at the creation points but the proton energy loss process was not turnedoff. In this case, only the XA effect was excluded. We applied NLXP correction to check independently its accuracy. The accuracy was dramatically improved when NLXP was applied.

The RD was -3.3% with the correction, wher[eas the error was 39% without. This shows that NLXP correction is here prominent compared to XA, as expected for a high Z element such as Ge. 4. X-rays were collected at the exit of the object and the proton energy loss process was not turnedoff. In this case, both NLXP and XA corrections are required. When implementing both corrections in the reconstruction process, the RD was -2.9%, whereas the error was -40% without any correction.

Ge mass density

Based on the PIXE-T reconstructed images (Fig. A3), we compared the calculated Ge mass density obtained by TR_MLEM and JPIXET. We followed a similar analysis procedure as for STIM-T: we firstly compared the difference images obtained by the two methods (Fig. A4). Then for each method, we compared the calculated Ge mass density in the different ROI (Fig. A5). As for STIM-T, we observed there was no significant difference for Ge mass density in ROI 1-4, thus, we considered the mean density of ROI 1-4 for the comparison of the two reconstruction methods (Table 4).

Discussion

STIM-T analysis

It was observed that JPIXET and TR_MLEM produced, as expected, a density of 0 for the outer vacuum background (Fig. 3c and3d). This is inherent to these reconstruction methods, as they are iterative methods, for which the calculated densities are constrained to be positive or null. For TR_FBP, the density obtained in the outer background was close to 0 on average, with slight fluctuations between positive and negative density values (moiré pattern visible in Fig. 3b). This behavior was expected for FBP, as an analytical method based on Fourier transform. For the inner background, JPIXET still gave exactly 0 (Fig. 3d and Fig. 4 ROI 5), and for TR methods the densities were close to 0 (Fig. 4 ROI 5).

For the shell region (Fig. 4 ROI 1-4), TR methods produced more accurate results than JPIXET, with an accuracy respectively ≤ 2.2 % for TR and ≤ 14 % for JPIXET (Table 2). Moreover, the density obtained by TR_MLEM was more uniform than JPIXET: the density fluctuations were found about 20% larger with JPIXET than for TR_MLEM (Fig. 4 ROI 1-4). In contrast, the definition of borders was better with JPIXET, whereas a slight density discontinuity was observed for TR methods (Fig. 3b-d). This phenomenon has been already discussed in a previous study, from line profiles on sharp edges [START_REF] Michelet | A Geant4 simulation for three-dimensional proton imaging of microscopic samples[END_REF].

For this reason, we avoided the borders of the shell when we defined the ROI.

PIXE-T analysis

The results obtained for the distribution of Ge mass density show similarities with STIM-T analysis. For the outer background, both TR_MLEM and JPIXET produced the expected density 0 (Fig. A3 andA4).

For the inner background, the density obtained by TR_MLEM was close to 0 (Fig. A5 ROI5), while exactly 0 for JPIXET. For the shell region, TR_MLEM gave a more accurate calculated density (RD: -2.9%) than JPIXET (RD: 6.4%) (Table 4). Moreover, the density obtained by TR_MLEM was more uniform than JPIXET: the density fluctuations were found about 40% larger with JPIXET than for TR_MLEM (Fig. A5 ROI 1-4). However, the definition of borders was better with JPIXET.

Conclusion

This study demonstrates the application of the Geant4 simulation toolkit as a benchmark for proton tomography of a typical ICF target. Geant4 simulations produce simulated results that are completely independent of the reconstruction algorithm, so the results can be used to check the accuracy of reconstruction methods. Moreover, our results show the ability of the TomoRebuild and JPIXET software packages to reconstruct quantitative STIM-T and PIXE-T images in the case of large detection solid angle. For both STIM-T and PIXE-T images, TomoRebuild produced more accurate mass density than JPIXET compared to the original phantom used as a reference. However, a particular attention should be paid to the borders of the shell, which were better defined with JPIXET. The present study also validates the NLXP and XA corrections implemented for the compensation of X-ray attenuation.

The detailed study of NLXP and XA effects shown from TomoRebuild reconstruction results reveals Geant4's flexibility. Indeed, Geant4 allows one to have access to data that are impossible to obtain in experiments: i) X-rays at the position where they are emitted, for which the XA effect is excluded; ii) X-rays generated considering no proton energy loss, for which the production cross section is constant, i.e. the NLXP effect is excluded. It is worth mentioning that the Geant4 simulation code that we developed to assess the accuracy of micro-tomography imaging is also suited to other applications. For example, it enables to simulate experiments of "classical" (2D) STIM and PIXE imaging, to check their accuracy. Moreover, Geant4 already contains all physical processes for many types of particles, including ions and X-rays in a wide energy range. Thus, other particle sources can be directly used in our simulation code. A public release of our code is planned in the coming months, in the set of advanced examples of Geant4.

Figure captions

All figures should be in black and white in the printed version

Appendix -Supplementary data captions

Fig. 1 .

 1 Fig. 1. Schematic view of the phantom in the Geant4 coordinate system. The positions of the beam correspond to the first projection.

Fig. 2 .Fig. 3 .

 23 Fig. 2. Position of detectors for STIM-T (a) and PIXE-T (b). The 𝐴𝑂𝐵 ̂ angle is used to parameterize the selection of the simulation output data.Fig. 3. Reconstruction of the 3D STIM-T image of the phantom (a). Analysis of the central slice, corresponding to the slice in PIXE-T simulation, by comparison to the phantom used as a reference. The difference images were obtained using TR_FBP (b), TR_MLEM (c) and JPIXET (d). The same linear color scale was used for all images for comparison purpose.

Fig. 4 .

 4 Fig. 4. Histograms of STIM-T mass densities in ROI-1-5, obtained using different reconstruction methods, compared to the reference density of the original phantom. The error bars correspond to the standard deviation.

Fig. 5 .

 5 Fig.5. Correction matrices obtained for Ge at the 18 th projection (30.6°) arbitrarily taken as an example. Proton energy loss causes NLXP when passing through the sample (a); XA correction depends on the position of the emitting voxels according to the X-ray detector (b). The combination of NLXP and XA gives the final correction matrix (c). A linear grey scale was used to code correction values, from black (maximal correction) to white (1, i.e. no correction).

Fig. A1 .

 A1 Fig. A1. Voxelized phantom for STIM-T image analysis: 3D view (a) and the central slice (b), corresponding to the slice in the PIXE-T simulation Fig. A2. Reconstructed STIM-T images using TR_FBP (a), TR_MLEM (b) and JPIXET (c).The images are displayed here with no additional filter (except the han filter used for FBP) nor threshold, so that they can be directly compared to the voxelized version of the original phantom, as shown in Fig.3. Fig.A3. Reconstructed PIXE-T images using TR_MLEM (a) and JPIXET (b). The images are displayed here with no additional filter nor threshold, so that they can be directly compared to the voxelized version of the original phantom, as shown in Fig.A4.

Fig. A4 .

 A4 Fig. A4. PIXE-T difference images obtained using TR_MLEM (a) and JPIXET (b), compared to the original phantom. The same linear color scale was used for the two images for comparison purpose.

Fig. A5 .

 A5 Fig. A5. Histogram of the Ge mass density in ROI 1-5, obtained using different reconstruction methods, compared to the density of the original phantom. The error bars correspond to the standard deviation.

Fig. 1 Fig

 1 Fig. 1

  Fig. 5

Table captions Table 1 .

 captions1 Composition of the numerical spherical shell phantom used for the STIM-T and PIXE-T simulations.

Table 2 .

 2 Mean mass density calculated by different reconstruction methods from STIM-T simulation results. The mean density is the mean value of ROI 1-4. The indicated uncertainty only corresponds to the resulting standard deviation.

Table 3 .

 3 Mean Ge density values obtained with TomoRebuild from four types of PIXE-T data with and without taking into account the NLXP and/or XA corrections, and compared with the reference data. The mean density is the mean value of ROI 1-4. The indicated uncertainty corresponds to standard deviation.

Table 4 .

 4 Mean Ge density comparison, calculated by different reconstruction methods for PIXE-T simulation results. The mean density is the mean value of ROI 1-4. The indicated uncertainty corresponds to standard deviation.

Table 1

 1

	Element	Mass percentage (%)
	C	85.0
	H	10.0
	Ge	5.0

Table 2

 2

		Mean mass density	Relative
		(mg/cm 3 )	difference (%)
	TR_FBP	1062 ± 12	-1.7
	TR_MLEM	1057 ± 28	-2.2
	JPIXET	1222 ± 34	14
	Reference value 1080

Table 3 PIXE

 3

	type	-T data	Correction implemented	Mean Ge mass density (mg/cm 3 )	Relative difference (%)
	Case 1: both
	NLXP and XA	No correction required	53.2 ± 2.9 -1.7
	canceled
	Case 2: NLXP canceled	XA correction required and applied Test without XA correction 52.1 ± 2.9 -3.7 53.4 ± 2.9 -1.3
	Case 3: XA canceled	NLXP required and applied 52.3 ± 3.4 -3.3 Test without NLXP correction 33.5 ± 3.7 -39
	Case 4: NLXP and XA not	Both NLXP and XA required and applied	52.5 ± 3.4 -2.9
	canceled	Test without any correction 32.9 ± 3.6 -40
	Reference value		54.0

Acknowledgements

This work was financially supported by China Scholarship Council, through a four-year funding (N° 201906680103) of Z. Li's PhD at University of Bordeaux, France, in collaboration with Fudan University. Developments of the reconstruction code were carried out thanks to the financial support of "Mission pour les Initiatives Transverses et Interdisciplinaires" (MITI) of CNRS, in the frame of the "Appel à Projets Interne Inter-Institut 2018", "Simulation Numérique pour le Traitement des Images de tomographie par émission X" -NUMETRIX project. The authors are grateful to P. Desbarats, from Laboratoire Bordelais de Recherche en Informatique (LaBRI), and J.-F. Giovannelli, from Laboratoire de l'Intégration du Matériau au Système (IMS), for their helpful and friendly advice on image reconstruction and analysis techniques. We would also like to thank the Geant4 collaboration, especially Susanna Guatelli, Center for Medical and Radiation Physics, University of Wollongong, Australia, for her technical support and advice as the Geant4 advanced example working group coordinator.

Table 4

Ge mean density (mg/cm 3 ) Relative difference (%) TR_MLEM 52.5 ± 3.4 -2.9 JPIXET 57.5 ± 4.