kamrulkonok/evalllm2026-french-defense huggingface dataset:
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Figure 5: External experiment: experimental radar parabolic antenna and drone 3DR Y6. 
 
Figure 6: External experiment: military sensors ReVISOR and KBV-PZLOK Pandur II CZ. 
 
Figure 7: External experiment scenario: detectability, and UAV (red) and observer post (blue) positions. 
As shown on the Fig. 7, special radar and drone flight configuration was created for determine radar 
capability to detect small drones.
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2.2 
Radio frequency detection 
Radio-frequency analysis of UAS signals is now very common in UAS detection. The principle is based on 
the evaluation of the signal transmitted by the control station (RC controller signals—“uplink”) or the signal 
transmitted by the UAV (“downlink”), e.g., telemetry data and video signals. 
Popular commercial drones use WiFi frequencies (2.4 or 5.8 GHz). In accordance with national frequency 
allocation tables (e.g., in Czech Republic, that of the National Radio Frequency Agency—NARFA), other 
frequencies can be also used, e.g. UHF 433 or 434 MHz. National laws also regulate the maximum output 
power of a transmitted signal.  
One of the most important aspects in the detection and identification of UAS is the used data protocol. RF 
detectors are designed based on knowledge of the system topology and structure, primarily knowledge of the 
transport layer. For precise identification, a database (library) of UAV frequencies and protocols is needed. 
A library allows a user to detect and identify the airborne target (drone) “online” based on its data signals. 
The disadvantage of this approach is the possibility of detection and identification for only the drones with 
known parameters (which are included in the database). Detection of non-standard drones in urban 
environments may not be successful. 
The main aim of the experiment was to evaluate the abilities and the possibility of RF drone detection by 
commercial RF detection devices using allowed frequencies. 
We used for experiments an RF detection device produced by Dedrone Inc. [USA & Germany], the 
DroneTracker Event Kit in Demo configuration; this kit consists of a Drone Tracker Multi Sensor and a 
DroneTracker RF Sensor. The multi sensor contains an RGB camera and a pair of microphones too. 
Measurement was performed at distances from 70 m to 1.7 km, and successful/unsuccessful detection and 
identification were evaluated. 
 
Figure 8: Dedrone Tracker Event Kit. 
2.3 
Infra-Red detection 
The method described here is based on other characteristic attributes of the UAV. The infrared spectrum is 
part of the electromagnetic spectrum, with a wavelength from 760 nm to 1000 μm. The surveillance of the 
aerial area in the IR domain is used mainly for the detection of low altitude aerial targets, and it is part of an 
additional surveillance method whereby “blind” areas in the local air picture (LAP) are covered. 
The thermal imaging camera (FLIR A40M) connected to PC and the appropriate control and evaluation 
software were used for this experiment. For evaluation of the thermal image from the camera, ThermoCAM 
Researcher Professional 2.9, ThermoCAM Explorer 9.9, and MATLAB r2011a were used. 
The laboratory experiments consisted of two parts. The first part was focused on measurement of a thermal 
image of the isolated UAV electric motor, and the second part was focused on a thermal image of the entire 
UAV hovering in the laboratory.
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The isolated electric motor was mounted on a static platform, and measured by the thermal imaging camera. 
When measuring the flying UAV, there was a critical point: to keep the position of the UAV in the field of 
view of the thermal camera. For this reason, the total power of the electric motors and their RPM values 
depended on the UAV position and it could not be adjusted exactly. 
 
Figure 9: Thermal image of the electric motor at output power (RPM) 80%  and hexacopter after 140 sec 
hovering. 
External detectability of UAVs was evaluated first on a terrain background and then on a sky background. 
The flight direction was towards the observer. Accurate data of the UAV position were obtained from UAV 
telemetry, and correlated with the actual situation. 
 
Figure 10: Thermal IR image of the flying hexacopter from the FLIR A40M: a) 10 m away; b) 70 m away. 
2.4 
Optical detection 
Elementary visual detection of the UAV can be done by human sight or with optical devices. 
Human optical detection depends both on external and subjective factors. The series of UAV detection 
experiments using human senses (sight and hearing – see in the next subchapter) in the real environment 
were performed in these conditions: 
• 
number of evaluators/observers: 6; 
• 
age distribution of evaluators: 21–43 years; 
• 
term of experiment: July and September; 
• 
climate zone and place: temperate; Czech Republic; 
• 
weather and temperature: sunny, 25–30 °C; 
• 
visibility: 30+ km; 
• 
wind speed: 0–2.5 ms-1; 
• 
humidity: 45–50%. 
The investigated flying object: drone DJI Phanton 2. An example of the visibility test is shown in the Fig. 11.
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Figure 11: Human eye visibility on the sky background [32]: DJI Phantom 2; distance: 100 m. 
Optical detection using technical devices depends not only on environmental conditions, but – naturally, 
on quality these devices and operator/observer’ skills as well.  Both commercial and military devices were 
used, when these experiments were performed. 
List of evaluated technical devices: 
• 
ReTOB (RETIA co.) - a military air observer device, maximal optical zoom -- 8×[27]; 
• 
LEICA GEOVID 7x42 BD - binoculars, maximal optical zoom 7× [28]; 
• 
NIKON D5100 - a digital camera, maximal optical zoom 6× [29]; 
• 
PANASONIC HC-WX979 - a digital 4K camera, maximal optical zoom 20× [30]; 
• 
TZK - air defence military binoculars, maximal optical zoom 10× [31]. 
The commercial devices are commonly known, for notion about military ones – see Fig. 12. 
 
Figure 12: Optical devices: TZK binoculars. 
The level of involved observers’ skills:  
o 
beginner (researchers from UoD Brno); 
o 
in case of the ReTOB, professional (an air observer crew was involved from Czech Army). 
 
Figure 13: Scanning methods for plain terrain, distant targets.
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2.5 
Acoustic detection 
As much as optical detection, the acoustic one can be done similarly using human senses – ears, and with the 
aid of technical acoustic devices.  
Acoustic detection by human senses. The series of UAV detection experiments using human ears in the 
real environment were performed in the same place, time and conditions like the Human optical detection 
was done (see above).  
The structure of these experiments was a little simplified: The UAVs were flying away from the evaluators 
who did knew the time and the direction of the flight. In a real situation, the direction of the flight would 
probably be the reverse one and acoustic observer will not exactly know the flight azimuth and altitude. 
Acoustic detection using technical devices. This set of experiments was performed in a real environment. 
The TAROT F650 quadcopter and the DJI S800E hexacopter were the representatives of the mini UAV 
category. The UAV noise was recorded by microphones. These data were post-processed and evaluated 
according to the noise level of the background. 
The experiment condition was as follows: 
• 
climate zone and place: temperate; Czech Republic; 
• 
weather and temperature: sunny, 5 °C; 
• 
wind speed: 4 ms-1; 
• 
humidity: 70%; 
• 
background noise level: 55 dB (measured by SONY F-V120 device); 
• 
UAV distance from the sensor post: 8 m; 
• 
UAV flight altitude: 5 m AGL; 
• 
flight method: 
o 
stabilized altitude hold (constant motor RPM); 
o 
manoeuvre (variable motor RPM). 
Noise data were acquired by the SONY F-V120 microphone and post-processed in MATLAB r2011a. 
During post processing, data were transformed from time to frequency domain. 
3.0 RESULTS 
The main aim of the previous experiments, related study and then this paper was to verify, clarify, and 
summarize the real-time detection of commercial drones. Real-time UAV detection is essential for possible 
counter-action or elimination. Human senses without technical devices are the least effective method of 
detection. However, in a terminal phase of UAV flight, human senses are usually the main means for 
weapon aiming and drone elimination. 
The first detection sentinel line is usually executed by X-band radars, which can provide early warnings. The 
effectivity of this kind of system significantly depends on the skills and training of radar operators’ crew. 
However, the availability and usage of radar are limited, and radars are used mostly by military or air traffic 
control organizations. At present, several studies about those methods have been published, and the research 
is in progress [33], [34], [35], [36]. 
The average RCS of the evaluated UAVs was in the range from 0.05 to 0.2 m2 (in certain directions, the 
value of the RCS peak was 0.5 - 0.75 m2). Measured values correlate with values from similar experimental 
measures [37].  
Fig 14 represents the illustrational example of RCS directional diagram.
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Figure 14: RCS directional diagram: DJI Phantom 4. 
Radars, which are currently in operational use, are usually able to find a flying UAV (made “scan”), but they 
are not able to “track” this type of target continuously, especially at longer distances, or can only do so for 
a limited time, intermittently, with interruptions, etc. The probability of detection therefore depends on the 
target RCS, terrain configuration, and other aspects. Generally, we can say, that radar and IR detection is 
possible at distances of units kilometres. 
Acoustic detection is strongly depending on the distance from a signal source and on the background noise 
level as well.  
For example, at the given distance, the acoustic signal level of the quadcopter was 72 dB, and that of the 
hexacopter was 92 dB. The frequency peaks for the quadcopter were 22.86, 302, 969, 2113, 2551, 3756, and 
5884 Hz, and those for the hexacopter were 2985, 82.18, 157, 464, 8800, and 9605 Hz. The noise frequency 
spectrum of hexacopter is shown in the Fig. 15. 
 
Figure 15: Hexacopter noise frequency spectrum. 
General results of small UAV optical visibility (typal DJI Phantom 2) by human sight are possible to be 
describe as follows: 
• 
100 m: The UAV is visible against a sky background and against an urban (forest) background with 
minimal effort.
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• 
200 m: The UAV with no motion is hardly visible against an urban (forest) background; however, 
during motion with low contrast, the UAV is visible with some effort; against the sky background the UAV 
is visible. 
• 
300 m: The UAV is visible during motion with a high amount of effort against the sky background. 
With knowledge of the probable position, it is visible against the urban (forest) background.  
• 
400 m: The UAV is visible during motion with a high amount of effort against the sky background. 
With knowledge of the probable position, it is visible against the urban (forest) background. 
• 
500 m: The UAV is visible with a very high amount of effort and with knowledge of the probable 
position against the sky background (with no clouds).  
• 
>500 m: 
The UAV is almost invisible during motion. Against the sky background, it is 
possible to detect the UAV, but persistent tracking is impossible. 
Visibility results are graphically presented in the Figure 16. The mean value of each observation was 
processed via fuzzy scoring. Blue marks represent the visibility against the sky background of the sky, 
orange stands for the urban (forest) background, and grey represents the visibility on a dry grass background. 
We concluded that the distance of effective UAV detection by human sight is about 200–300 m, which is 
very close to the IR detection limits when using commercial IR detection devices. 
The overview of the all results of the performed experiments is depicted in the Fig. 17. 
 
Figure 16: . Linear regression of the results of subjective visibility.
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Figure 17:Overview of the results of the performed experiments. 
 
4.0 DISCUSSION AND FUTURE DIRECTIONS 
The results of the study show that X-band radars are usually able to detect LSS targets (micro and mini 
UAV) reliably at distances about 3000 m. However, detection is mostly in the form of radar scans or plots 
and not radar tracks. Based on these results, we recommend focusing on multispectral detection with a 
combination of radar, IR, visual, acoustic, radio, etc. Initial detection (scan, plot) is executed by X-band 
radar (or a radio receiver), and more precise detection at closer distances is achieved by other methods. Once 
initial detection and localization of the UAV is achieved, further action can be taken (lethal or non-lethal 
elimination). 
Authors performed several experiments with standard radars (not specialized to detect small RCS objects), 
however we do not have mandate to publish them in this paper. Radar, that is not primarily designated to 
detect small RCS objects, could be used just as the early warning or alarm device. This early warning may be 
triggered through the scan or several scans or short track on the radar scope before the track disappear due to 
the radar threshold parameters. Authors admit that it would not be effective to implement radars, which are 
not designed to detect small RCSs, to multi-sensor fields. Authors also think that producers should work on 
innovations to cope with such disadvantage. There are some radars now, for example C-RAM radar Arthur, 
which would be suitable for UAV detection when innovated. 
Experiments in the IR, visual, and acoustic domains achieved approximately equal detection limits. All of 
these domains (IR, visual, and acoustic) are appropriate for terminal UAV detection at closer distances (up to 
300 m). However, this distance is usually not sufficient for the non-autonomous reaction procedures (a UAV 
with a flight speed of about 10–15 ms-1 can pass this distance within 20–30 s). For this reason, we 
recommend a C-UAV approach and the implementation of autonomous defence systems with a maximum 
reaction time up to 20 s [42], [43].  
For the crucial objects, complex defence a combination of various types of sensors and the corresponding 
real-time data exchange with a command and control system (C2) or with a surveillance control system is 
necessary in non-military applications. Such a system autonomously fuses the data from multiple sensors and 
evaluates possible threats. The operator (the person in the loop concept) chooses which effectors should be
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used. In the case of a lack of time or during a high-density attack, effector use can also be decided 
autonomously, but lethal elimination (weapons) should be controlled only by a human operator (for ethical 
and safety reasons) to avoid friendly fire or collateral damage. 
Research on the C-UAV domain is now focused on active defence against the UAV. There are works on 
lethal or nonlethal elimination by AD C-UAVs [19], [44], but this research is still in progress. This topic has 
been discussed and researched intensively [45], [46], [47], [48]. These reflect recent UAV incidents 
(attacks). The final step of this research will consist of result fusion (detection and elimination) and 
implementation into real systems. 
5.0 CONCLUSIONS 
This paper covers several years of research and supports the design concept of future defence and protection 
systems against UAS threats. Together with the development of radar for LSS targets, there is a great 
potential for the development of robust and reliable systems that are able to detect and eliminate UAV 
threats. The key attribute of the suggested system is its modularity. Potential users (military, air traffic 
control, security services, etc.) can adjust the system to their needs. However, many conceptual and technical 
aspects of the C-UAV systems still need to be concerned. Of course, a general solution that reflects all 
possible situations does not exist - hence the phrase, “there is no silver bullet.” 
Hypothesis, confirmed by this study defines a detection area of up to 2000 m and a terminal area of up to 300 
m around a guarded location. These are key areas for sensor network installation and for the use of counter 
UAS equipment - effectors. 
The last phase of the work will consist of an analysis of current detection methods of small-sized targets, 
from mini or micro UAS categories, across the entire alert system. This last step concludes the logical link 
among the three basic pillars of air defence—sensors, assessment systems (command and control systems), 
and effectors (lethal or nonlethal engagement against UAS). 
ACKNOWLEDGEMENTS 
The work presented in this article was supported by the Czech Republic Ministry of Defence—the 
University of Defence Brno development program “Research of sensor and control systems to achieve 
battlefield information superiority,” DZRO FVT ELEKTRO. 
We thank members of the Department of Weapons and Ammunition and the Department of Communication 
Technologies, Electronic Warfare and Radiolocation, University of Defence, for the special equipment 
provided and the technical support. We would also like to express our gratitude to Mr. Pavel Šedivý (Retia 
Inc.) for cooperating during the research and experiments.
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Sébastien Lecornu annonce une commande de 2 000 drones kamikazes _ Ministère des Armées et des Anciens combattants.pdf — page 1

Sébastien Lecornu annonce une
commande de 2 000 drones kamikazes
Direction : Ministère des Armées / Publié le : 01 mars 2024
En visite jeudi au sein de l’entreprise Delair à Labège, à côté de Toulouse, le ministre
des Armées, Sébastien Lecornu, a salué le passage de l’entreprise à l’économie de
guerre. Il a également annoncé la commande de 2 000 munitions téléopérées à
plusieurs industriels à destination des armées françaises et de l’Ukraine.
Sébastien Lecornu en visite chez l’entreprise Delair, 29 février 2024 - © Thomas Michard/ ECPAD
Dans le grand atelier de l’entreprise Delair, autour des étagères métalliques où sont
classées par taille des ailes de drones, le ministre des Armées, Sébastien Lecornu, a
Voir le fil d'ariane
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annoncé une commande de la Direction générale de l’armement (DGA) de
2 000 munitions téléopérées auprès de plusieurs industriels. Aussi appelés drones
kamikazes, ces appareils sont à destination des armées françaises, mais aussi des
forces ukrainiennes. Ils constituent un véritable « game changer pour l'armée
ukrainienne », a affirmé le ministre, essentiels dans les conduites des opérations
ukrainiennes.
Sur ces 2 000 drones, les 100 premiers ont été commandés à l’entreprise Delair et ils
seront livrés en urgence à l’Ukraine d’ici à cet été. Il faut dire que l’industriel connaît
bien les besoins des forces ukrainiennes : ces dernières avaient repéré en juin 2023 ses
drones de renseignement pour leurs capacités de brouillage. Un contrat de
150 appareils financés par la France avait été signé le mois suivant, pour des livraisons
commencées dès septembre 2023. L’entreprise a annoncé un nouveau contrat pour en
livrer 150 supplémentaires à l’Ukraine prochainement.
Renforcer la filière drone
Face à l’utilisation massive des drones en Ukraine, la DGA et l’Agence de l’innovation
de défense ont lancé en mars 2022 deux appels d’offres sur des projets de munitions
téléopérées.
Le premier projet, Colibri, a pour cahier des charges la neutralisation de cibles non
blindées au-delà de 5 km, avec des coûts limités. Le second, Larinae, devra opérer à
plus de 50 km du point de mise en œuvre. Deux consortiums ont été retenus pour
réaliser les premiers prototypes : Delair et Nexter d’un côté, et Novadem et MBDA de
l’autre.
Une entreprise en économie de guerre
Passée de start-up à PME en 13 ans, l’entreprise Delair continue de se développer. La
cadence de production s’accélère pour répondre à la demande toujours plus
importante. « Nous produisons environ 50 drones de différentes tailles par mois. Sur les
plus gros, nous sommes passés de 4 à 12 par mois. », indique le PDG de l’entreprise,
Bastien Mancini. Résultats : « Plus d’embauches et une augmentation de l’activité pour
toute la chaîne de sous-traitance. »
Cet accroissement de l’activité permet aussi le développement de nouvelles
technologies. Dans un open space d’une quinzaine de bureaux jonchés de maquettes,
Thomas, le directeur technique, explique la volonté de développer l’intelligence
artificielle (IA) embarquée. « L’analyse de la vision par l’IA sur le terrain est déjà la
norme en Ukraine », relève-t-il. L’expérience des drones Delair sur le terrain ukrainien
prend ainsi toute son importance : elle permet d’avoir un retour technique sur le
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Sébastien Lecornu annonce une commande de 2 000 drones kamikazes _ Ministère des Armées et des Anciens combattants.pdf — page 3

matériel pour procéder à des améliorations. « Quinze évolutions ont ainsi été réalisées
sur les modèles de drones livrés à l’Ukraine entre septembre et novembre », affirme
Bastien Mancini.
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Espace : pourquoi la France veut accélérer sa défense
orbitale ?
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Guide_sensibilisation_factcheckeurs_vf.pdf — page 3

3
Qu’est-ce que la 
Composante à part entière des menaces dites 
« hybrides », la menace informationnelle en ligne 
impliquant des acteurs étrangers, se traduit par des 
manœuvres d’ingérence ou des campagnes numériques 
de manipulation de l’information, avec pour objectifs 
de porter atteinte aux intérêts de l’entité ciblée et/ou 
promouvoir les revendications d’un acteur.
Prenant la forme d’opérations planifiées ou d’actions 
opportunistes, ces manœuvres informationnelles 
cherchent à diffuser de fausses informations ou à 
amplifier des contenus malveillants déjà présents dans 
le débat public.
S’appuyant sur un contexte géopolitique marqué 
par l’exacerbation des tensions internationales et 
la recherche du rapport de force, les campagnes 
numériques de manipulation de l’information sont 
devenues un véritable instrument de déstabilisation des 
démocraties.
Du 26 juillet au 8 septembre 2024, la France accueillera 
les Jeux Olympiques et Paralympiques de Paris 2024 
(JOP24). Suivi par près de 4 milliards de téléspectateurs, 
cet événement planétaire offrira une surface 
d’exposition informationnelle inédite aux acteurs 
étrangers malveillants.
Guide_sensibilisation_factcheckeurs_vf.pdf — page 4

4
La considérable couverture médiatique prévue pour les 
JOP24 offre aux acteurs étrangers malveillants un terrain 
d’action exceptionnel dans le champ informationnel, avec 
pour objectifs :
porter atteinte à l’image et à la réputation de la France 
en dénigrant sa capacité à accueillir, organiser et 
sécuriser de grandes compétitions internationales ; 
produire des effets dans le champ économique en 
s’attaquant aux parties prenantes des évènements, aux 
sponsors ou tout autre acteur économique français 
générer ou amplifier du trouble à l’ordre public dans la 
sphère physique afin de perturber le bon déroulement 
des épreuves sportives et cérémonies associées.
VIGINUM a déjà identifié plusieurs manœuvres 
informationnelles impliquant des acteurs étrangers et 
diffusant des contenus hostiles à l’organisation des JOP24 à 
fin d’atteinte réputationnelle. 
Etat de la 
pour les JOP24
Guide_sensibilisation_factcheckeurs_vf.pdf — page 5

5
Exemples de 
Modes opératoires observés lors de manœuvres 
informationnelles détectées par VIGINUM
La publication de photos ou de vidéos instrumentalisant des 
symboles olympiques visant à dénoncer la participation de 
certains athlètes aux compétitions sportives ; 
La création de hashtags hostiles aux JOP24 et leur amplification 
par des comptes inauthentiques ;
La création de sites inauthentiques : des sites se faisant passer 
pour de vrais médias ;
La création et la publication de faux reportages sur des chaines 
Telegram : ces reportages usurpent l’identité de médias français 
en reprenant notamment leur charte graphique et en diffusant 
des contenus trompeurs ;
La publication en ligne de faux graffitis, ajoutés de manière 
numérique pour laisser penser qu’ils se trouveraient réellement à 
Paris ou dans d’autres villes de France. 
Le typosquatting 
Mode opératoire consistant à usurper l’identité de sites web connus 
en enregistrant un nom de domaine très proche du nom de domaine 
officiel officiel (ex :lefigaro[.]me au lieu de lefigaro.fr ou encore 
le washingtonpost[.]ltd au lieu de washingtonpost.com ). Cette 
technique sert à tromper les internautes peu avertis.
Le raid numérique
Mode opératoire visant à créer ou amplifier un bad buzz autour d’un 
sujet polémique, généralement via l’utilisation d’un mot clé ou de 
plusieurs hashtags, dont les acteurs vont chercher à en augmenter la 
visibilité. Le recours à la création d’images et à leur partage massif, 
telles que des détournements de logos, permet une meilleure 
diffusion des narratifs.
Modes opératoires pouvant apparaitre lors de manoeuvres 
informationnelles
Guide_sensibilisation_factcheckeurs_vf.pdf — page 6

6
Cas concrets de manoeuvres
Le 26 juillet 2023, VIGINUM a détecté une campagne 
numérique de manipulation de l’information visant les JOP24 
désignée sous le nom «OLIMPIYA ».
Plusieurs modes opératoires furent détectés : 
L’amplification artificielle d’hashtags appelant à 
boycotter les JOP24 par un faible nombre de comptes 
(spamming)
La création de faux comptes sur les réseaux sociaux, 
reprenant des symboles français ou des manifestations 
françaises ; 
Un montage vidéo mettant en parallèle des images de 
compétitions sportives et des scènes de violence pour 
instiller le doute sur la capacité de la France à organiser 
un événement sportif de grande ampleur, tout en 
dénonçant l’hypocrisie du pays ; 
La création d’un faux média se présentant comme un 
média spécialiste de la ville de New-York, afin de tromper 
les internautes sur l’origine réelle de la publication.
La campagne numérique OLIMPIYA
Guide_sensibilisation_factcheckeurs_vf.pdf — page 7

7
Depuis la fin de l’année 2023, VIGINUM observe et 
documente une campagne de manipulation de l’information 
ciblant les médias et la communauté des fact-checkers. 
Connu en sources ouvertes sous le nom de « Matriochka », 
ce mode opératoire est actif depuis au moins septembre 
2023 et s’appuie sur la publication de faux contenus 
(reportages, graffitis, mèmes, etc.) sur X.
Ces faux contenus ont été diffusés de manière coordonnée 
dans l’espace réponse des publications de comptes X de 
médias, de personnalités et de cellules de fact-checking de 
plus d’une soixantaine de pays.
Certains de ces faux contenus ont notamment ciblé 
l’organisation des JOP24 en usurpant l’identité de médias 
et d’institutions dans le but de diffuser l’idée, auprès 
d’audiences aussi bien françaises qu’internationales, selon 
laquelle les JOP24 seront un échec. 
Ainsi, des publications ont notamment affirmé, en usurpant 
la charte graphique de la CIA, que le risque terroriste était 
trop grand pour le bon déroulement des évènements, ou 
encore diffusé un faux document de la Mairie de Paris pour 
inviter les Parisiens à ne pas activer leur climatisation, car 
celle-ci émettrait des ondes qui pourraient perturber les 
drones sécurisant les infrastructures des JOP24. 
La campagne numérique MATRIOCHKA
Le système de lutte anti-mines marines futur (SLAMF) _ Ministère des Armées et des Anciens combattants.pdf — page 1

Le système de lutte anti-mines marines
futur (SLAMF)
L’opération SLAMF prépare le renouvellement de la capacité de guerre des mines de la
marine nationale. Ce système est destiné à remplacer les moyens actuels (chasseurs
de mines, bâtiments remorqueurs de sonars, bâtiments base de plongeurs démineurs).
Le système de lutte anti-mines marines futur (SLAMF)
Les objectifs du programme sont de sécuriser la mise en œuvre des SNLE et du porte-
avions Charles de Gaulle, protéger l’accès aux ports français, soutenir le déploiement
d’une force d’action navale, l’évacuation de ressortissants et la prévention de crises.
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Ce nouveau système permettra aux marins d’opérer à distance de la zone de danger.
Le programme comprend d’une part des systèmes de drones constitués de drones
navals de surface et de drones sous-marins. Il comprend par ailleurs des bâtiments de
surface porteurs (bâtiments de guerre des mines), des bâtiments pour plongeurs
démineurs ainsi que le renouvellement du système d’exploitation des données de
guerre des mines.
Une démarche incrémentale est prévue pour le programme. Le système de drones,
première étape du programme, fait l’objet d’une coopération franco-britannique
appelée MMCM « Maritime Mines Counter Measures ». Ces travaux ont été confiés à
l’OCCAR. Ce système pourra être mis en œuvre depuis la terre ou à partir des
bâtiments de guerre des mines, notamment pour les déploiements en opérations
extérieures.
L’étude de définition du système de drone a été confiée à Thales Underwater Systems.
Les principaux sous-traitants sont Thales UK, BAé systems et ECA.
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BROASA-8v1.pdf — page 1

Annals of the Japan Association for Philosophy of Science Vol.28 (2019)
9～32
9
Aphantasia, SDAM, and Episodic Memory
Lajos Brons∗
Abstract
Episodic memory (EM) involves re-experiencing past experiences by means of
mental imagery. Aphantasics (who lack mental imagery) and people with severely
deﬁcient autobiographical memory (SDAM) lack the ability to re-experience, which
would imply that they don’t have EM. However, aphantasics and people with
SDAM have personal and aﬀective memories, which are other deﬁning aspects of
EM (in addition to re-experiencing). This suggests that these supposed aspects
of EM really are independent faculties or modules of memory, and that EM is
a composite faculty rather than a natural kind. Apparent varieties of (normal
and “defective”) EM (as well as some closely related kinds of memory) are diﬀer-
ent combinations of these modules, and the EM construct itself adds little if any
explanatory value to these modules.
Key words: episodic memory, imagery, aphantasia, SDAM, mental kinds
1
Most of the research on episodic memory of the last half century assumes (im-
plicitly or explicitly) that episodic memory is a natural kind. Recently, Sen Cheng
and Markus Werning (2016) made this claim explicitly. According to the “homeo-
static cluster view” they adopt something is a natural kind if the entities belonging
to that kind share a large set of inductively and explanatorily relevant properties
and that kind is the largest class whose members share those properties due to some
underlying causal mechanism1. Episodic memory satisﬁes these criteria they claim,
∗Nihon University & Lakeland University
E-mail: mail@lajosbrons.net
I’d like to thank Daniel Greenberg, Kourken Michaelian, Nicholas Watkins, Adam
Zeman, and two anonymous referees for their helpful comments on earlier versions of
this paper.
1 This “weak” notion of natural kinds contrasts with the older and much stronger, es-
sentialist view, and was developed mostly in response to criticism of that stronger
view. (See Dupr´e 1993 for a noteworthy example of such criticism.) One could, of
course, conceive of an even weaker view by giving up (or relaxing) one of the two cri-
— 9 —
BROASA-8v1.pdf — page 2

10
Lajos Brons
Vol. 28
and therefore, is a natural kind.
Since Endel Tulving ﬁrst distinguished episodic memory from semantic memory
in (1972), the concept of episodic memory has undergone a number of conceptual
changes resulting in a rather large set of loose deﬁnitions and operationalizations.
Essential in more recent understandings of the concept is what Tulving (1983) called
the “recollective experience” or what is now more commonly called “autonoetic con-
sciousness”. Tulving (1983) writes that “when a person remembers a past event, he
has a memory image of it and he is consciously aware of its being a mental replay
of what happened once before” (185), and that “ ‘memory image’ could serve as an
acceptable synonym for ‘recollective experience’ ” (186). Hence, episodic memory in-
volves mental imagery. Furthermore, the notion of episodic memory as “replay” (or
re-experiencing or reliving) of what happened once before implies that episodic mem-
ory involves a subjective identiﬁcation of the remembering subject with the subject
that experienced the original episode.
If episodic memory is a natural kind, then the involvement of mental imagery
and subjective identiﬁcation and other explanatorily relevant properties of episodic
memory should be properties of that kind and should have a shared underlying causal
mechanism. There are people, however, who claim not to experience mental imagery.
If those “non-imagers” do have memories with many of the other relevant proper-
ties of episodic memory (such as the involvement of subjective identiﬁcation), then
that would imply that non-imagers have severely deﬁcient episodic memory or no
episodic memory at all, or alternatively, that episodic memory is not a natural kind
but a composite faculty. The latter would be the case if episodic memory consists of a
number of more or less independent components such that the composite (i.e. episodic
memory) has no inductively and explanatory properties and/or no underlying causal
mechanism in addition to the properties of and causal mechanisms underlying its
components.
Recent research on aphantasia, lack of mental imagery (Zeman, Dewar, & Della
Sala 2015), and on severely deﬁcient autobiographical memory (SDAM; Palombo et
al. 2015) suggests that the latter is the case indeed, or so I will argue in this pa-
per. People with aphantasia or SDAM do not relive or re-experience episodes, but do
have memories and lead otherwise normal lives, and that—in conjunction with other
empirical evidence—implies that episodic memory is a composite faculty.
The aim of this paper is to reassess the nature and status of episodic memory
in the light of what we currently know about SDAM and aphantasia (and related
matters).
Towards that end, section 2 looks into the evidence for and nature of
SDAM and aphantasia, and section 3 discusses the main skeptical argument (that
teria of the “homeostatic cluster view”, but such an understanding of “natural kinds”
would be so weak that it is rather doubtful whether it still would be appropriate to
speak of natural kinds at all.
— 10 —
BROASA-8v1.pdf — page 3

Aphantasia, SDAM, and Episodic Memory
11
is, Schwitzgebel’s) against the existence of aphantasia. After that, sections 4 to 6
assess the implications of aphantasia for the notion of episodic memory, and section 7
concludes that episodic memory is not a natural kind (in the sense identiﬁed above)
and suggests an alternative, “modular” conceptualization of the semantic/episodic
distinction.
2
Usually, Francis Galton (1880) is credited with being the ﬁrst to recognize the ex-
istence of people who claim not to experience imagery. Research on the phenomenon
has been scarce, however, and mostly of dubious quality, until very recently. (Watkins
2018 reviews some of the earlier literature.) In 2010, Adam Zeman and colleagues
published a paper reporting on a 65-year old man who had lost the ability to expe-
rience imagery but who otherwise functioned normally2. 21 people contacted Zeman
after that, writing that they didn’t experience imagery either, but that their “con-
dition” had been lifelong. These 21 individuals were sent a questionnaire, which all
of them ﬁlled in. The results were published in Zeman, Dewar, & Della Sala (2015),
which coined the term “congenital aphantasia”. (Until the publication of that pa-
per “non-imager” was the most common term for someone lacking mental imagery,
but it has since been replaced by “aphantasic”.) Of the participants in this initial,
small study, about half reported that they did not have imagery in any modality;
half experienced involuntary ﬂashes of imagery; the majority experienced imagery
in dreams; the majority had diﬃculties with autobiographic memory; and about a
quarter reported having family members with aphantasia.
The 2015 publication received a lot of public attention (albeit mostly in English-
speaking countries)—it was mentioned in the popular press, popular science maga-
zines, and on TV — leading to over 10,000 emails to the research group from people
who believe that they have (something like) aphantasia, or the opposite, hyperphan-
tasia (extremely vivid imagery). More than 2000 of those have ﬁlled in complete
questionnaires. The results thereof have been analyzed mostly, but have not been
published yet, except for a few remarks in a post on the research group’s website
(Zeman 2017). In that short report, Adam Zeman writes that the results conﬁrm
the relation with autobiographical memory, as well as the expected relation with
prosopagnosia (a deﬁciency with regards to facial recognition), and suggests that
“imagery vividness can run in families”.
Aphantasia is deﬁned in Zeman, Dewar, & Della Sala (2015) as “a condition of
reduced or absent voluntary imagery”. This rough deﬁnition is ambiguous in three
2 This was not the ﬁrst report of such a case. For an earlier example, see Basso, Bissiach,
& Luzzatti (1980), which is discussed in section 5 below.
— 11 —