# Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform
## 6 Results
### Non-Parametric Tests
#### 6.2.3 Interpretation of the Non-Parametric Test Results
ARECH_002 and ARECH_003, both related to MIMO recharging antennas, SOCI_001 and SOCI_002, addressing real-time social media sensing and text analytics, PRE_002, focused on hazard footprint prediction, and REL_001, which requires the inclusion of a tethered UAV communication relay, were ranked higher by end-users than by academics. End-users have rated ARECH_002 and ARECH_003 higher than academics on account of their recognition of the practical benefits of MIMO recharging antennas in real disaster scenarios, where uninterrupted communication is crucial. Academics, on the other hand, may have considered these requirements from a broader technological standpoint, taking into consideration the intricate technical complexities and challenges, such as interference management, alignment and beamforming, etc., involved in deploying such technology in real-life disaster scenarios. Similarly, academics approached PRE_002 with an emphasis on the challenges and complexities associated with combining multiple data sources, including real-time meteorological data, topographic data, and real-time sensor information, for precise hazard footprint prediction. This stance is further reinforced by a comment provided by an academic. The requirement of tethered communication relay (REL_001) raised controversy among end-users and academics, in contrast to the non-tethered relay proposed in REL_002, where no significantly different responses were observed across participant groups. This discrepancy can be attributed to end-users, who, having encountered situations where traditional communication methods failed or were unreliable, have a greater appreciation for the practical significance of a tethered communication relay, as emphasized by an end-user's comment. End-users prioritize SOCI_001 and SOCI_002 due to their immediate impact on real-time situational awareness and operational effectiveness during disasters. Academics may assess these requirements with a more research-oriented perspective, beyond immediate disaster response. As expressed by an academic, the extraction of real-time observations from online social media users (SOCI_001) requires sophisticated web scraping or data crawling techniques, robust APIs, and mechanisms to ensure data accuracy and integrity. They also added that multilingual text analysis (SOCI_002) poses complex challenges requiring natural language processing (NLP) expertise and access to comprehensive language resources. Both requirements may involve the use of ML models for tasks such as sentiment analysis, topic modeling, and content classification. Building, training, and fine-tuning these models, as well as ensuring their scalability and adaptability to different disaster scenarios, can also present technical complexities. Additionally, the extraction and analysis of social media data for disaster response must address concerns related to data privacy and ethics. Last but not least, TF_005, i.e., the integration of identity verification procedures for transparent funding, received higher ratings from disaster management professionals compared to academics, a discrepancy reasonably attributed to the fact that disaster management professionals likely have a heightened awareness of potential fraudulent activities within the HL domain, thanks to their practical experience in frameworks and strategies across all disaster phases. This familiarity offers a plausible explanation for their elevated rating of this security requirement--compared to academics' rating.