## 3 Materials and Methods  
In this study, bibliometric and text-mining analyses were used. Bibliometric analysis, which describes patterns in publications within a given period or body of literature using quantitative analysis and statistics, provided an international research perspective. The text-mining process extracted hidden information from non-structured to semi-structured data [33]. The stages of text mining were: information retrieval (Boolean search rules applied), text analysis (stop words elimination, automatic clusters identification, word frequency queries, and manual coding), information extraction, visualization (presentation of tables and word clouds), and summarization. It is worth mentioning that bibliometrics was possible to perform after the information retrieval stage. The first step, information retrieval, allowed for preparation of materials for further analyses. The material used in the research comprised text data (titles, abstracts, keywords), which were retrieved from the Scopus database. The period of analysis was from 2017 to 1 April 2022. Specific keywords and Boolean operators \"AND\" and \"OR\" were used to extract the desired content. The formulas were: (TITLE-ABS-KEY (innovation*) AND TITLE-ABS-KEY (CBRN) OR TITLE-ABS-KEY (weapon AND of AND mass AND destruction) OR TITLE-ABS-KEY (chemical AND hazards) OR TITLE-ABS-KEY (biological AND hazards) OR TITLE-ABS-KEY (nuclear AND hazards) OR TITLE-ABS-KEY (radiological AND hazards)) AND (LIMIT-TO (PUBYEAR, 2022) OR LIMIT-TO (PUBYEAR, 2021) OR LIMIT-TO (PUBYEAR, 2020) OR LIMIT-TO (PUBYEAR, 2019) OR LIMIT-TO (PUBYEAR, 2018) OR LIMIT-TO (PUBYEAR, 2017)). All documents were considered (articles, reviews, conference papers, book chapters, conference reviews, notes, books, editorials). Before achieving the final data, thematic accordance was manually checked as an essential criterion for record inclusion or exclusion. However, there were no records removed. The number of identified records was 156. This set was used for the bibliometric analysis that involved the year of publication, publications by country or territory, Scopus subject area, and type of documents. In a further step, the final text data set (.txt), comprised titles, keywords, and abstracts from 156 records, was created. It comprised the material for analyzing the most frequent words, portmateau words, and clusters. The tools for the task were the VOSViewer application--for clusters and most frequent portmateau words--and NVivo Word Frequency Query (WPQ)--for single words only. Both applications automatically created the most frequent categories. In these two processes, stop words (irrelevant words) such as \"also\", \"unit\", \"example\", \"study\", article\", \"ref.\", etc. were eliminated. Additionally, the text data file was coded manually (assigning a label to a data section). Coding turned qualitative data (texts) into quantitative data (codes) [34]. The process of coding was descriptive. This means that the codes summarize the topic of the excerpt, i.e., a researcher asks what the topic is about [35]. It should be noted that one text snippet could belong to more than one code. This means that, finally, the codes became interconnected. While the automatic analyses aimed at the identification of basic trends that emerged from the explored texts, the manually created codes were used to verify the clusters, portmateau words, and word frequency query results; they provided a more complex view of the innovations' directions. Furthermore, the codes were used to analyze the innovations' contributions to SDG's realization. All the processes required the researcher's interpretation of words and clusters. Results of automatic analyses were visualized in the form of word clouds, tables, and diagrams. Summarization was the last stage of the research. The detailed research design is presented in Figure 1.  
With mentioning is the fact that the Scopus database provides only a limited set of publications.