Sustainability-Driven Green Innovation : Revolutionising Aerospace Decision-Making with an Intelligent Decision Support System

This is a sel -a chi ed e sion o an o iginal a icle. This e sion may di e om he o iginal in pagina ion and ypog aphic de ails. Au ho (s): Ti le: Yea : Ve sion: Copy igh : Righ s: Righ s u l: Please ci e he o iginal e sion: CC BY 4.0 h ps://c ea i ecommons.o g/licenses/by/4.0/ Sus ainabili y-D i en G een Inno a ion : Re olu ionising Ae ospace Decision-Making wi h an In elligen Decision Suppo Sys em © 2023 by he au ho s Published e sion Mu ano , Galimkai ; Omi beko a, Zhana ; Shaikh, Aijaz A.; Issaye a, Zhansaya Mu ano , G., Omi beko a, Z., Shaikh, A. A., & Issaye a, Z. (2024). Sus ainabili y-D i en G een Inno a ion : Re olu ionising Ae ospace Decision-Making wi h an In elligen Decision Suppo Sys em. Sus ainabili y, 16(1), A icle 41. h ps://doi.o g/10.3390/su16010041 2024 Ci a ion: Mu ano , G.; Omi beko a, Z.; Shaikh, A.A.; Issaye a, Z. Sus ainabili y-D i en G een Inno a ion: Re olu ionising Ae ospace Decision-Making wi h an In elligen Decision Suppo Sys em. Sus ainabili y 2024,16, 41. h ps:// doi.o g/10.3390/su16010041 Academic Edi o s: Da jan Ka abase ic and F ancesco Tajani Recei ed: 8 Sep embe 2023 Re ised: 22 No embe 2023 Accep ed: 8 Decembe 2023 Published: 20 Decembe 2023 Copy igh : © 2023 by he au ho s. Licensee MDPI, Basel, Swi ze land. This a icle is an open access a icle dis ibu ed unde he e ms and condi ions o he C ea i e Commons A ibu ion (CC BY) license (h ps:// c ea i ecommons.o g/licenses/by/ 4.0/). sus ainabili y A icle Sus ainabili y-D i en G een Inno a ion: Re olu ionising Ae ospace Decision-Making wi h an In elligen Decision Suppo Sys em Galimkai Mu ano 1, Zhana Omi beko a 1,2 , Aijaz A. Shaikh 3,* and Zhansaya Issaye a 4 1Ins i u e o In o ma ion and Compu a ional Technologies, P.O. Box 050010 Alma y, Kazakhs an; [email p o ec ed] (G.M.); [email p o ec ed] (Z.O.) 2 Depa men o Compu e Science, Al-Fa abi Kazakh Na ional Uni e si y, P.O. Box 050040 Alma y, Kazakhs an 3Depa men o Ma ke ing, Jy äskylä Uni e si y School o Business and Economics, Uni e si y o Jy äskylä, P.O. Box 35, FI-40014 Jy äskylä, Finland 4Facul y o O ien al S udies, Al-Fa abi Kazakh Na ional Uni e si y, P.O. Box 050040 Alma y, Kazakhs an; [email p o ec ed] *Co espondence: [email p o ec ed] Abs ac : G een inno a ion e e s o de eloping and implemen ing new echnologies, p ac ices, p oduc s, and p ocesses ha p omo e sus ainabili y and educe en i onmen al impac s. This a icle pos ula es he concep ualisa ion and implemen a ion o an in elligen decision suppo sys em (IDSS) ailo ed o he ae ospace echnology sec o . The da a we e collec ed om open sou ces such as social media and analyzed using he na u al language p ocessing ool. The en isaged IDSS is a comp ehensi e and seamlessly in eg a ed pla o m designed o unde gi d decision-making, p oblem- sol ing, and esea ch ini ia i es wi hin he ae ospace indus y. Ca e ing o he sec o ’s enginee s, echnicians, and manage ial cad es, i aims o un a el complex da ase s, p o e incisi e analyses, and u nish p uden ad ice and ecommenda ions. I s mul i ace ed capabili ies ange om da a sea ch and op imisa ion o modelling and o ecas ing. Wi h an emphasis on ha monious in eg a ion wi h ex an ae ospace sys ems, i s i es o p o ide enginee s and echnicians wi h en iched da a insigh s. Mo eo e , i s design e hos is cen ed on use - iendliness, unde sco ed by an in ui i e g aphical in e ace ha expedi es seamless access and u ilisa ion. Ul ima ely, he en isioned IDSS will augmen he ae ospace indus y’s analy ical p owess and will se e as a po en ins umen o e ec i e decision-making. Keywo ds: sus ainable g een inno a ions; ae ospace; in elligen decision suppo sys em 1. In oduc ion G een inno a ion e e s o de eloping and implemen ing new echnologies, p ac ices and p oduc s ha p omo e sus ainabili y and educe en i onmen al impac s. I encom- passes many ields, om enewable ene gy and clean anspo a ion o was e educ ion and eco- iendly manu ac u ing. G een inno a ion is essen ial o add essing he u gen challenges o clima e change and ensu ing a sus ainable u u e o ou plane . By le e - aging he la es ad ances in a i icial in elligence (AI), machine lea ning (ML) and o he cu ing-edge echnologies, we can accele a e g een inno a ion and c ea e a be e wo ld o u u e gene a ions [1,2]. As he ae ospace indus y con inues o e ol e and g ow, especially a e he launch o SpaceX by Elon Musk in 2002, he e is an inc easing need o in elligen decision-making sys ems ha can help s eamline p ocesses and imp o e o e all e iciency. Mo eo e , ecen ad ancemen s seen in he shape o he p oli e a ion o a i icial in elligence applica ions, ools, and sys ems, he need o de eloping a new in elligen decision suppo sys em (IDSS) was el mo e ecen ly. This is whe e an imp o ed e sion o IDSS comes in. By ha nessing Sus ainabili y 2024,16, 41. h ps://doi.o g/10.3390/su16010041 h ps://www.mdpi.com/jou nal/sus ainabili y Sus ainabili y 2024,16, 41 2 o 16 he powe o AI and ML, his sys em can e olu ionise how ae ospace p o essionals make decisions, p o iding eal- ime insigh s and ecommenda ions o help iden i y p oblems and oppo uni ies be o e hey become c i ical. Whe he one is wo king in ai c a design, logis ics, o any o he a ea in he ae ospace ield, he IDSS can help one s ay ahead o he cu e and make be e , mo e in o med decisions e e y s ep o he way. The ae ospace sec o , cha ac e ised by a dynamic landscape, has emb aced decision suppo sys ems (DSSs) as ins umen s o disce ning and acking eme gen echnological pa adigms poised o shape i s ajec o y. These sys ems a e pi o al in s ee ing s a egic e- sou ce alloca ion decisions by co po a ions and go e nmen en i ies. The p esen discou se on such sys ems is ocussed on delinea ing hei concep ual unde pinnings, pa icula ly in he con ex o o esigh esea ch wi hin he ae ospace indus y. Ou esea ch conside ed bu geoning ends and echnologies wi hin space o assess hei imminen and ans o ma- i e po en ial. Following his e alua ion, a s uc u ed amewo k was de ised o moni o and manage he ajec o ies o hese nascen pa adigms. Cen al o his endea ou was he impe a i e o u nish an a ay o s akeholde s wi h cogen insigh s equisi e o in o med s a egic de e mina ions. Re olu ionising ae ospace decision-making wi h an IDSS is c ucial o ensu ing he sa e y and e iciency o ai a el. The e is a signi ican esea ch gap in his a ea, pa icula ly in Kazakhs an, whe e he de elopmen and implemen a ion o such sys ems a e s ill in hei ea ly s ages. By implemen ing ad anced echnologies such as AI and ML, we can imp o e he decision-making p ocess and enhance he o e all pe o mance o ae ospace sys ems. This is essen ial o mee ing he g owing demand o ai a el while educing en i onmen al impac s and p omo ing sus ainabili y. The e o e, he p esen s udy was conduc ed o de elop an in o ma ion sys em u ilising AI echnology ha would suppo decision-making in he ae ospace indus y. Speci ically, he sys em ocusses on enhancing sa e y and e iciency in ai a el. The ollowing esea ch ques ions we e conside ed, which a e essen ial when de eloping an IDSS u ilising AI echnology o suppo decision-making in he ae ospace indus y: 1. Wha a e he p ima y sa e y conce ns in he ae ospace indus y, and how can AI echnology be used o add ess hem? 2. How can AI-powe ed sys ems be used o op imise ai a ic managemen and enhance ai a el e iciency? 3. How can ML algo i hms be used o imp o e he pe o mance and eliabili y o ae ospace sys ems? 4. Wha e hical conside a ions mus be conside ed when de eloping an AI-powe ed IDSS o he ae ospace indus y? The p esen s udy has made signi ican con ibu ions. By add essing he esea ch ques ions ou lined abo e, i has p o ided insigh s in o how AI echnology can add ess sa e y conce ns in he ae ospace indus y, op imise ai a ic managemen , and minimise en i onmen al impac s. Addi ionally, i has p o ided aluable in o ma ion on how ML algo i hms can imp o e he pe o mance and eliabili y o ae ospace sys ems and he e hical conside a ions ha need o be conside ed when de eloping an AI-powe ed IDSS o he ae ospace indus y. These indings can bene i he indus y and he academic communi y by allowing hem o be e unde s and how AI echnology can be e ec i ely u ilised in he ae ospace indus y, leading o sa e and mo e e icien ae ospace echnologies. The es o he pape is o ganised as ollows. The Sec ion 1p o ides an o e iew o he esea ch ques ions and objec i es o he p esen s udy. The Sec ion 2examines p e ious s udies on AI echnology in he ae ospace indus y, including i s po en ial bene i s and challenges. Sec ion 3desc ibes he esea ch design, da a collec ion, analysis me hods, and e hical conside a ions. The Resul s sec ion p esen s he s udy’s indings, including insigh s in o how AI echnology can add ess sa e y conce ns in he ae ospace indus y, op imise ai a ic managemen , and minimise en i onmen al impac s. Las ly, he Sec ion 6summa ises he main indings, discusses hei implica ions o he indus y and academic communi y, and p o ides di ec ions o u u e esea ch. Sus ainabili y 2024,16, 41 3 o 16 2. Li e a u e Re iew 2.1. G een Inno a ion As he global communi y becomes inc easingly conce ned abou he impac o human ac i i ies on he en i onmen , a ious subsec o s o he economy and indus y, including he ae ospace subsec o , a e ecognising he impo ance o implemen ing sus ainable p ac ices and educing hei ca bon oo p in s. G een inno a ions, which encompass a ange o solu ions, om enewable ene gy sou ces o eco- iendly packaging op ions, ha e eme ged as an impo an opic in he business wo ld [3]. Resea ch has shown ha companies ha implemen g een inno a ions no only bene i he en i onmen bu can also see a posi i e impac on hei bo om line. Consume s a e becoming mo e en i onmen ally conscious and a e o en willing o pay a p emium o sus ainable and en i onmen ally iendly p oduc s. Mo eo e , sus ainable p ac ices can lead o long- e m sa ings by educing ene gy and was e managemen cos s [ 4 ]. Fo example, he au ho s o Re . [ 5 ], while examining sus ainabili y in he ae ospace indus y, sugges ed a no el decision suppo me hod ha uses bo h quali a i e and quan i a i e analysis. The ex ac i e app oach is deemed easie as i achie es good g amma and accu acy le els by copying la ge chunks o ex om a sou ce documen ; howe e , he complex abili ies essen ial o high-quali y gene aliza ion, including pa aph asing, gene aliza ion, and inco po a ing eal-wo ld knowledge, a e easible only wi hin an abs ac amewo k, and despi e he inc eased challenges, no able ad ancemen s ha e been made, pa icula ly wi h ecen de elopmen s in deep lea ning. In his con ex , he de elopmen o a no el decision suppo me hod ha in eg a es bo h quali a i e and quan i a i e analysis aises he p ospec o achie ing long- e m sa ings in he ae ospace indus y, p o iding a mo e comp ehensi e and e icien app oach o decision-making Companies ha p io i ise sus ainabili y a e also iewed mo e a ou ably by con- sume s, leading o inc eased b and loyal y and a posi i e epu a ion. Mo eo e , imple- men ing g een inno a ions is essen ial o mee ing global sus ainabili y goals and ensu ing a habi able plane o u u e gene a ions [6]. 2.2. In elligen Decision Suppo Sys ems IDSSs ha e ecen ly gained signi ican popula i y in he ae ospace indus y. An IDSS is a so wa e sys em ha u ilises AI and o he cu ing-edge echnologies o assis decision- make s in sol ing complex p oblems and making in o med decisions. IDSSs collec and analyse da a om di e en sou ces o c ea e meaning ul insigh s o analys s. AI-based DSSs hen make ecommenda ions and communica e hem o use s in an unde s and- able way [ 7 ]. A p esen , mos o ganisa ions can easily ob ain enough ele an da a, bu analysing hese da a and de e mining wha o do wi h he insigh s de i ed om hem ha e become he mos di icul and ime-consuming asks. One o he p oblems ha any o ganisa ion may ace is knowing how o ob ain he igh documen classi ica ion due o a sho age o da a [8]. IDSSs can be used in a ious ae ospace ields, such as ai c a design and ai a ic con ol. A i s nucleus, an IDSS in he ae ospace domain epi omises a cogni i e com- pu ing mechanism le e aging AI o augmen he decision-making capaci ies o expe s, esea che s, and enginee s. By seamlessly amalgama ing da a, models, and analy ical ools, his sys em can es ablish a holis ic a senal o dissec ing in ica e quanda ies. The sys em’s u ili y mani es s in di e se ae ospace domains, encompassing s a egic planning, launch logis ics, sa elli e ope a ions, and mission command, and u he ex ends o de elopmen s a egy bluep in ing, esou ce alloca ion, and scheduling. I s capabili ies also ex end in o modelling scena ios o e alua e he pe o mance o no el echnologies and o gauge he epe cussions o p oposed modi ica ions wi hin he space ecosys em. The accele a ing ajec o y o space echnologies is inex icably linked o he need o e ec i e in o ma ion e ie al mechanisms. Se e al esea che s ha e explo ed he applica ion o IDSS in ae ospace. Fo example, Re . [ 9 ] in es iga ed he bene i s o using an IDSS in ai c a design. They highligh ed how Sus ainabili y 2024,16, 41 4 o 16 an IDSS can help designe s c ea e mo e e icien and sa e ai c a designs by explo ing he po en ial o an IDSS in imp o ing ai a ic con ol ope a ions and educing he isk o acciden s. They no ed ha an IDSS can assis ai a ic con olle s in making mo e accu a e and imely decisions. These s udies demons a ed he e sa ili y and po en ial o IDSSs in he ae ospace indus y. 2.3. A i icial In elligence and E hical Issues The p e alence o AI in ou daily li es has g own signi ican ly in ecen yea s [ 10 ], pe haps due o he Fou h Indus ial Re olu ion. De ined as he abili y o machines o ca y ou asks h ough in elligen , human-like beha iou [ 11 ], AI has undoub edly e olu ionised indus ies, o e ing unp eceden ed capabili ies in da a analysis, au oma ion, and decision-making. F om heal hca e o inance, AI-powe ed solu ions ha e become an in eg al pa o ou li es. Howe e , wi h he inc eased popula i y and use ulness o AI, a wide ange o e hical ques ions ha e a isen, such as how AI can be augh o make mo al decisions, how i s decision-making p ocesses can be made adequa ely isible o humans, and who should be held accoun able o he conclusions i a i ed a [ 10 ]. AI’s exponen ial expansion (e.g., Cha GPT) and pe asi e in luence make i e en mo e impo an and p essing o esol e hese mo al dilemmas [ 11 ], bu he cu en empi ical esea ch landscape and he s a e o e hical discou se a e oo concep ually diso ganised and agmen ed o each a conclusion. This will pe haps be mo e e iden in he coming days, when gene a i e AI and simila ools a e expec ed o become mo e common, hus p o iding a mo e s able opinion on he e hical issues su ounding AI. 3. Me hods Today, one o he bes ways o ack echnological ends is o moni o open da a in he global in o ma ion en i onmen . In o he wo ds, all he necessa y in o ma ion is con ained in scien i ic publica ions, pa en s, analy ical epo s, news media, and social ne wo ks o expe s in he ae ospace indus y. Due o he decen alised s uc u e o science and echnology, he global p ocess o inno a i e de elopmen is e lec ed in openly published documen s. The analysis will help iden i y new echnological ends and ack he p og ess o exis ing ones [6]. 3.1. Da ase s Ou de eloped IDSS uses a ious da a collec ion me hods o p o ide comp ehensi e and eliable in o ma ion o ae ospace decision-making. I u ilises web c awle s o sea ch o and ex ac in o ma ion ela ed o scien i ic esea ch and echnological inno a ions in he ae ospace indus y. I also employs a esea ch ub ic o classi y and o ganise da a based on hei hema ic a ilia ions, making i easie o analyse and use he da a. The sys em is in eg a ed wi h academic lib a y applica ion p og amming in e aces (APIs), which p o ide access o au ho i a i e scien i ic a icles, publica ions and esea ch. Addi ionally, i uses pa en da abases and in eg a es wi h he U.S. Pa en and T adema k O ice o ack new pa en s, in en ions, and echnical de elopmen s ela ed o he ae ospace indus y. By equipping he de eloped IDSS wi h hese unc ionali ies, we s i e o p o ide he mos up- o-da e and comp ehensi e in o ma ion o suppo decision-making in he ae ospace sec o [9]. To de elop an in elligen decision-making sys em, we collapsed da a and di ided hem in o se e al ca ego ies, as ollows: • Online da abases: The NASA Technical Repo Se e , he NASA As ophysical Da a Sys em, he Eu opean Space Agency’s space science po al, and o he simila online da abases a e eposi o ies ha g an access o an expansi e collec ion o documen s spanning he space echnology spec um. • Sea ch engines: Google, Bing, and o he con en ional sea ch engines o e a enues o scou ing space echnology- ela ed in o ma ion ha may p o ide pe inen insigh s. Sus ainabili y 2024,16, 41 5 o 16 • Jou nals and publica ions: Specialised jou nals and publica ions, such as he Jou nal o Space Technology, In e na ional Jou nal o Space Science and Technology, and Space Technology In e na ional Fo um, u nish g anula insigh s in o he dynamic lux cha ac e ising he e e -e ol ing landscape o space echnology. • Social media pla o ms: Twi e , Reddi , Quo a, and o he social media pla o ms ha e eme ged as al e na i e o ums o p obing and ex ac ing in o ma ion pe inen o he space echnology na a i e. • Specialised da abases: The In e na ional Space S a ion Expe imen Da abase, he Space Resea ch Ne wo k, and o he da abases me iculously ailo ed o he space domain o e an a ay o da ase s ge mane o he ealm o space echnologies. 3.2. Da a Collec ion Me hods o collec ing ele an in o ma ion and c ea ing a pilo egis y o sea chable sou ces o in o ma ion a e essen ial s eps in he esea ch p ocess. This app oach allows one o ensu e he e iciency and accu acy o da a collec ion and o assess he po en ial alue o he collec ed da a o a speci ic esea ch ask. One ype o da a collec ion me hod is he a ge -o ien ed da a collec ion me hod, which in ol es collec ing ele an in o ma ion and c ea ing a egis y o sou ces ha allows esea che s o ocus on speci ic aspec s o he s udy. This app oach helps minimise in o ma ion noise and ocusses e o s on key sou ces. When using da a collec ion me hods, da a quali y imp o emen is applied, which allows o op imising he selec ion o sou ces based on hei ele ance and con ibu es o he collec ion o be e in o ma ion, which inc eases he eliabili y o he esea ch esul s. An example o a me hod o c ea ing a pilo egis y is lis ing keywo ds and e ms ela ed o he esea ch opic and using hem o sea ch o in o ma ion in da abases, on- line a chi es, and o he esou ces. Collec ing expe opinions o conclusions is also e y impo an . Domain expe s mus be consul ed o iden i y he mos ele an and au ho i a- i e sou ces. I is also impo an o c ea e a small pilo egis y om se e al sou ces selec ed based on hei ele ance. This will enable he e alua ion o da a quali y and he o mula ion o a la ge plan o collec ing in o ma ion. The main app oach is a sys ema ic li e a u e e iew— ha is, analysing and summa ising he esul s o p e ious s udies o iden i y he mos signi ican sou ces and assess hei ele ance [12]. 3.2.1. Me ada a and Ra ing Analysis Me ada a (e.g., ci a ions, a ings, e iews) can be used o iden i y popula and ep- u able sou ces. Collec ing ele an in o ma ion and c ea ing a egis y help ocus esea ch, imp o e da a quali y, and op imise esou ce usage. Rega ding exis ing moni o ing p oblems and he ele ance o collec ing ele an indus y in o ma ion, he ae ospace indus y aces challenges in eal- ime moni o ing due o i s dynamic na u e, and he ele ance o collec ing indus y-speci ic in o ma ion lies in add essing echnological ad ancemen s, supply chain complexi ies, and egula o y changes o enhance decision suppo sys ems o op imizing p ocesses, ensu ing compliance, and main aining compe i i eness. The basis o all analy ical sys ems is he da a ha a e inc easingly a ailable in he public domain on he global in e ne , and he e ec i eness o he de eloped AI-based expe DSS o he space indus y is p ima ily de e mined by he quali y o he p ocessed da a. Howe e , he exponen ial g ow h o in e ne esou ces, he apid de elopmen o social ne wo ks, and he ansi ion o almos all media ou le s o he in e ne lead o he epea ed duplica ion o in o ma ion and in o ma ion noise, which signi ican ly complica es he sea ch o ele an in o ma ion bo h o use s and sea ch obo s. Sus ainabili y 2024,16, 41 6 o 16 3.2.2. Sea ch Engine Da a The Google sea ch engine, which accoun s o mo e han 62% o he global sea ch ma ke , gene a es millions o billions o esponses o space- ela ed que ies in a spli second (see Table 1). Table 1. Ou pu o esponses o que ies in he Google sea ch engine on space opics. Sea ch Que ies Numbe o Responses Sea ch Time Space echnologies in he wo ld 6.31 million 0.5 s Space echnologies in he wo ld 4.31 billion 0.65 s Sa elli e manu ac u e s 11 million 0.48 s Sa elli e manu ac u e s 149 million 0.58 s Spacepo s in he wo ld 1.47 million 0.56 s Cosmod omes in he wo ld 6.68 million 0.59 s P oduc ion o space ocke s 355 housand 0.45 s P oduc ion o space ocke s 8.54 million 0.64 s Space ocke manu ac u e s 127 housand 0.45 s Space ocke manu ac u e s 18.3 million 0.67 s Space echnology inno a ions 1.56 million 0.45 s Space echnology inno a ions 186 million 0.74 s Space echnology ma ke 2.14 billion 0.81 s Space echnology ma ke 829 housand 0.53 s Space echnology ma ke echnology 10.55 million 0.59 s No e: Own esea ch as o 5 No embe 2021. The numbe o sea ch esul s o esponses o space- ela ed que ies as o 12 Oc obe 2021 di e s signi ican ly om ha which shows in Table 1as o 5 No embe 2021. The numbe o esponses o he que y ega ding he space echnologies in he wo ld on 12 Oc obe 2021 was 1.26 billion, while on 5 No embe 2021, he sea ch engine issued 4.31 billion esponses, abou 3.3 imes mo e, which indica es sea ch p oblems. In con as , o he space echnology ma ke echnology que y, he esul s we e op imised in 2021, amoun ing o only 10.55 million esponses, compa ed o 452 million esponses in Ap il 2018. Google is cons an ly imp o ing i s algo i hms and is one o he wo ld leade s in AI de elopmen in es men . Howe e , he p oblem o p o iding ele an in o ma ion in esponse o que ies has no ye been sol ed. The sea ch speed o que ies and keywo ds does no p o ide ad an ages as he esul s con ain much ‘ga bage’. Wi h he g ow h o in o ma ion on he in e ne on he one hand and inc easing ans- pa ency equi emen s o he ac i i ies o p i a e companies and go e nmen agencies on he o he , companies spend signi ican unds o make hemsel es known o he wo ld. Almos e e y company has an o icial websi e and social media accoun s whe e news is duplica ed ( he i s ound o duplica ion). Fu he mo e, all signi ican ma ke playe s p esen o icial p ess eleases o he media, which also ha e websi es and social media accoun s. Thus, eade s (in e ne use s) a e connec ed and epea edly duplica e he publica- ions o companies and mass media on hei own social media accoun s (see Figu e 1). As a esul , he e a e usually a dozen o a housand iden ical publica ions o a single e en . The numbe o publica ions in business media depends on he impo ance o he news, pe son, o e en ( o he egion, coun y, o wo ld) co e ed. The mo e signi ican he pe son o e en is, he g ea e he duplica ion o he publica ions on i . Quan i y does no ansla e in o quali y. The a he away om he o iginal sou ce a publica ion is, he mo e dis o ed he in o ma ion. Acco ding o expe s, e e yone’s media ac i i y does no inc ease bu educes he a ailabili y o in o ma ion. Sus ainabili y 2024,16, 41 7 o 16 Sus ainabili y 2024, 16, x FOR PEER REVIEW 7 o 16 news, pe son, o e en ( o he egion, coun y, o wo ld) co e ed. The mo e signi ican he pe son o e en is, he g ea e he duplica ion o he publica ions on i . Quan i y does no ansla e in o quali y. The a he away om he o iginal sou ce a publica ion is, he mo e dis o ed he in o ma ion. Acco ding o expe s, e e yone’s media ac i i y does no inc ease bu educes he a ailabili y o in o ma ion. Figu e 1. Simpli ied scheme o o ma ion o indus y-speci ic in o ma ion lows on he in e ne . The global issue o in o ma ion noise has been a subjec o discou se o some ime. Ini ially, he e was op imism ega ding he use o au oma ion and AI o add ess his p ob- lem. Howe e , an examina ion o he in e na ional li e a u e e ealed ha e en well- unded wes e n AI de elope s a e g appling wi h challenges akin o escala ed in o ma ion noise and subpa inpu da a quali y. As highligh ed in he quo e below [9], his phenom- enon dis o s he ou comes gene a ed by AI sys ems. … Da a se es as he li eblood o a i icial in elligence models; i canno me ely se e as a means o an end in he modelling p ocess… Wha is ed in de e mines wha is p oduced—a longs anding p inciple o he modelling pa adigm… In he e a o con empo a y AI and he concu en deluge o da a ha machine lea ning models mus con end wi h, deciphe ing lawed ou comes has become a mo e in ica e ask. Ou in es iga ion e ealed ha he quan i y o esponses ob ained is con ingen upon mul iple ac o s, including he speci ic wo ding o he que y, he language used, he geo- g aphic egion, and he empo al aspec o he eques . The obse ed a ia ions a e con- spicuous e en wi hin a single diu nal cycle, le alone o e a span o a week o a mon h. Seman ic analysis is a me hod ha has p o en efficacious in o ches a ing he in lux o incoming da a. To ope a ionalise his app oach, i is impe a i e o i s delinea e he pi o al concep s and nomencla u e ge mane o he subjec domain. This was conduc ed by in e acing classi ie s and in e na ionally ecognised ub ica o s ha ha e ga ne ed consensus wi hin he scien i ic and echnical communi ies. The p esc ibed classi ie s a e used o sc u inise esh publica ions wi hin he ae ospace sec o , wi h he objec i e o elu- cida ing salien concep s and e minologies and disce ning schola ly ajec o ies o encap- sula e he con empo a y landscape o he indus y. This no only be i s he economy o ime and effo bu also u nishes a means o igilan ly moni o ing consequen ial in elli- gence. Figu e 2 p o ides a comp ehensi e schema ic ep esen a ion o elucida ion. Figu e 1. Simpli ied scheme o o ma ion o indus y-speci ic in o ma ion lows on he in e ne . The global issue o in o ma ion noise has been a subjec o discou se o some ime. Ini- ially, he e was op imism ega ding he use o au oma ion and AI o add ess his p oblem. Howe e , an examina ion o he in e na ional li e a u e e ealed ha e en well- unded wes e n AI de elope s a e g appling wi h challenges akin o escala ed in o ma ion noise and subpa inpu da a quali y. As highligh ed in he quo e below [ 9 ], his phenomenon dis o s he ou comes gene a ed by AI sys ems. . . . Da a se es as he li eblood o a i icial in elligence models; i canno me ely se e as a means o an end in he modelling p ocess . . . Wha is ed in de e mines wha is p oduced—a longs anding p inciple o he modelling pa adigm . . . In he e a o con empo a y AI and he concu en deluge o da a ha machine lea ning models mus con end wi h, deciphe ing lawed ou comes has become a mo e in ica e ask. Ou in es iga ion e ealed ha he quan i y o esponses ob ained is con ingen upon mul iple ac o s, including he speci ic wo ding o he que y, he language used, he geog aphic egion, and he empo al aspec o he eques . The obse ed a ia ions a e conspicuous e en wi hin a single diu nal cycle, le alone o e a span o a week o a mon h. Seman ic analysis is a me hod ha has p o en e icacious in o ches a ing he in lux o incoming da a. To ope a ionalise his app oach, i is impe a i e o i s delinea e he pi o al concep s and nomencla u e ge mane o he subjec domain. This was conduc ed by in e acing classi ie s and in e na ionally ecognised ub ica o s ha ha e ga ne ed consen- sus wi hin he scien i ic and echnical communi ies. The p esc ibed classi ie s a e used o sc u inise esh publica ions wi hin he ae ospace sec o , wi h he objec i e o elucida ing salien concep s and e minologies and disce ning schola ly ajec o ies o encapsula e he con empo a y landscape o he indus y. This no only be i s he economy o ime and e o bu also u nishes a means o igilan ly moni o ing consequen ial in elligence. Figu e 2 p o ides a comp ehensi e schema ic ep esen a ion o elucida ion. Sus ainabili y 2024,16, 41 8 o 16 Sus ainabili y 2024, 16, x FOR PEER REVIEW 8 o 16 Figu e 2. Visual depic ion o he lowcha delinea ing he p ocess o seman ic analysis p edica ed upon a uni e sally acknowledged ub ica o . 3.2.3. Da a Collec ed om Public Sou ces The da a collec ed by he de eloped IDSS om open sou ces om 28 Oc obe o 5 No embe 2021 we e news a icles and pos s on social media pla o ms (Vk.com, Ins a- g am, Facebook, and Twi e ) wi h publica ion da es om 1 Janua y 2016 o 5 No embe 2021. The e we e 4538 a icles in all. Table 2 shows he wo d and cha ac e s a is ics om he collec ed da a. Table 2. Da a collec ed om public sou ces om 1 Janua y 2016 o 5 No embe 2021. Wo d Resou ce Wo d Symbols pe Wo d Symbols pe Wo d Facebook 80.00 634.04 8.50 Ins ag am 139.77 1126.81 8.03 Twi e 16.03 118.04 7.41 VK.com 26.67 219.79 8.69 News po als 230.10 2013.24 9.01 A icles we e selec ed acco ding o he lis o keywo ds (space science, space indus- y, ocke , and sa elli e). S a is ics on symbols and wo ds in publica ions show ha , on a e age, publica ions on news po als consis o 230 wo ds and 2000 symbols. The second place was aken by Ins ag am pos s, wi h 140 wo ds and 126 cha ac e s pe pos . On Fa- cebook, Twi e , and Vk, he wo ds/symbols we e 80/634, 16/118, and 27/220, espec i ely (see Table 2). 3.3. P ep ocessing In eg al o he ma u a ion o a obus decision-making appa a us, an a ay o nume - ical expe imen s was me iculously conduc ed, ocussed on elucida ing ends, clus e ing pa e ns, and seman ic ci a ion maps wi hin he milieu o he space indus y. The lynchpin o enqui y e ol es a ound he de elopmen al ajec o y o space echnologies, as eluci- da ed wi hin he scien i ic and echnical li e a y s a um. This endea ou is imbued wi h he explo a ion o algo i hmic pa adigms o a icula ing ends, clus e ing pa e ns, and seman ic ci a ion maps, unde pinned by g aph- heo e ical cons uc s and a cohe en o - mal a chi ec u e inhe en o scien i ic documen s. The efficacy o he algo i hms was igo ously examined, mani es ing affi ma i e ou - comes in delinea ing disc e e subdomains wi hin he o e a ching subjec a ea. This dis- ce nmen o nuanced sub opics bea s signi ican u ili y in he ealm o scien i ic and ech- nological moni o ing o bu geoning space indus ies, alongside acili a ing in o ma ion e ie al, abs ac ing, and o he language p ocessing asks [13–18]. Figu e 2. Visual depic ion o he lowcha delinea ing he p ocess o seman ic analysis p edica ed upon a uni e sally acknowledged ub ica o . 3.2.3. Da a Collec ed om Public Sou ces The da a collec ed by he de eloped IDSS om open sou ces om 28 Oc obe o 5 No embe 2021 we e news a icles and pos s on social media pla o ms (Vk.com, Ins ag am, Facebook, and Twi e ) wi h publica ion da es om 1 Janua y 2016 o 5 No embe 2021. The e we e 4538 a icles in all. Table 2shows he wo d and cha ac e s a is ics om he collec ed da a. Table 2. Da a collec ed om public sou ces om 1 Janua y 2016 o 5 No embe 2021. Wo d Resou ce Wo d Symbols pe Wo d Symbols pe Wo d Facebook 80.00 634.04 8.50 Ins ag am 139.77 1126.81 8.03 Twi e 16.03 118.04 7.41 VK.com 26.67 219.79 8.69 News po als 230.10 2013.24 9.01 A icles we e selec ed acco ding o he lis o keywo ds (space science, space indus y, ocke , and sa elli e). S a is ics on symbols and wo ds in publica ions show ha , on a e age, publica ions on news po als consis o 230 wo ds and 2000 symbols. The second place was aken by Ins ag am pos s, wi h 140 wo ds and 126 cha ac e s pe pos . On Facebook, Twi e , and Vk, he wo ds/symbols we e 80/634, 16/118, and 27/220, espec i ely (see Table 2). 3.3. P ep ocessing In eg al o he ma u a ion o a obus decision-making appa a us, an a ay o nume i- cal expe imen s was me iculously conduc ed, ocussed on elucida ing ends, clus e ing pa e ns, and seman ic ci a ion maps wi hin he milieu o he space indus y. The lynchpin o enqui y e ol es a ound he de elopmen al ajec o y o space echnologies, as eluci- da ed wi hin he scien i ic and echnical li e a y s a um. This endea ou is imbued wi h he explo a ion o algo i hmic pa adigms o a icula ing ends, clus e ing pa e ns, and seman ic ci a ion maps, unde pinned by g aph- heo e ical cons uc s and a cohe en o mal a chi ec u e inhe en o scien i ic documen s. The e icacy o he algo i hms was igo ously examined, mani es ing a i ma i e ou comes in delinea ing disc e e subdomains wi hin he o e a ching subjec a ea. This disce nmen o nuanced sub opics bea s signi ican u ili y in he ealm o scien i ic and echnological moni o ing o bu geoning space indus ies, alongside acili a ing in o ma ion e ie al, abs ac ing, and o he language p ocessing asks [13–18]. Sus ainabili y 2024,16, 41 15 o 16 6. Conclusions The a icle discussed he use o classi ica ion models and clus e ing echniques in he ae ospace indus y in ela ion o g een inno a ion. The esea ch indings highligh how hese models can e ec i ely iden i y pa e ns and ends in da ase s using keywo ds. I sugges s ha hese echniques can help ack de elopmen s and p omo e p ac ices hus accele a ing inno a ion wi hin he ae ospace indus y. These me hods a e also seen as ools o decision-making in a eas such as in es men , esea ch, and s a egic planning. Howe e , he a icle acknowledges some limi a ions. I ecognizes ha ocussing solely on keywo d-based analysis may no p o ide an unde s anding o he da a. The e o e, u u e esea ch should explo e echniques like na u al language p ocessing and sen imen analysis o gain a nuanced pe spec i e. The a icle also emphasizes conside a ions in AI, pa icula ly add essing biases in algo i hms and s essing he impo ance o ai ness ML echniques and s a egies o elimina e bias. In conclusion, while his s udy highligh s he applica ions o classi ica ion models and clus e ing echniques, i encou ages esea ch o imp o e hei accu acy and dependabili y. Addi ionally, i unde sco es he need o conside a ions o mi iga e biases wi hin AI sys ems, p omo ing an inclusi e in eg a ion o a i icial in elligence ac oss a ious indus ies. Au ho Con ibu ions: Concep ualisa ion, G.M. and Z.O.; da a collec ion, A.A.S. and Z.I.; me hods, G.M. and Z.I.; da a analysis, A.A.S. and Z.O.; w i ing—o iginal d a p epa a ion, G.M. and Z.O.; w i ing— e iew and edi ing, all he au ho s; supe ision, G.M. All au ho s ha e ead and ag eed o he published e sion o he manusc ip . Funding: This esea ch was unded by he Ae ospace Commi ee o he Minis y o Digi al De- elopmen , Inno a ions and Ae ospace Indus y o he Republic o Kazakhs an, g an numbe : BR11265420. Ins i u ional Re iew Boa d S a emen : No applicable. In o med Consen S a emen : No applicable. Da a A ailabili y S a emen : Da a a e con ained wi hin he a icle. Con lic s o In e es : The au ho s decla e no con lic o in e es . Re e ences 1. Zhao, P.; Gao, Y.; Sun, X. 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