Changes for page f. Test CFT3: Two Detailed Use Cases

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30	30	== 2.6 Material ==
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32		-Materials included scenario descriptions, interface mockups (e.g., traffic-light health dashboards), and conceptual visualisations Use case flows. They were shown in the program Mentimeter, to create an interactive session. These were used to prompt discussion and elicit targeted feedback on system behavior and user interaction.
	32	+Materials included scenario descriptions, interface mockups (e.g., traffic-light health dashboards), and conceptual diagrams of drone and sensor workflows. These were used to prompt discussion and elicit targeted feedback on system behavior and user interaction.
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34	34	= 3. Results =
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36	36	Across the three sessions, participants expressed strong interest in the potential of both technologies, while also identifying critical usability and integration challenges.
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38		-**Use Case 1 (Autonomous indoor drones):**
	38	+In the **first session** (Use Case 2), participants emphasized the importance of minimal setup time, discreet personal alerts, and role-based interfaces. Firefighters preferred default configurations and automation, while USAR personnel were more open to detailed monitoring. The traffic-light status concept and a two-stage escalation model for critical alerts were well received, balancing the need for timely warnings with human oversight. Participants also supported the idea of a dedicated tech or medic role to manage sensor readiness and monitoring.
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40		-Feedback centered on the need for **rapid drone deployment**, **minimal interference with human responders**, and **reliable victim detection**. First responders highlighted the importance of **parallel deployment protocols** and **geofencing** to avoid human-drone conflicts. Drone pilots emphasized the value of having **dedicated operators and analysts**, and supported the integration of **friend-or-foe identification** to improve AI recognition.
	40	+In the **second and third sessions** (Use Case 1), feedback centered on the need for rapid drone deployment, minimal interference with human responders, and reliable victim detection. First responders highlighted the importance of parallel deployment protocols and geofencing to avoid human-drone conflicts. Drone pilots emphasized the value of having dedicated operators and analysts, and supported the integration of friend-or-foe identification to improve AI recognition. Trust in the system was closely tied to familiarity with the operator and the ability to override autonomy when needed.
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42		-Participants agreed that drones are a valuable **additional tool**, not a replacement for dogs or human responders. Deployment timing depends on the situation: in urgent cases, responders may enter without waiting for a drone; in international USAR missions, there is more time to prepare and deploy drones. Regulations were a recurring theme—drones are not allowed to fly near personnel, requiring coordination and certification.
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44		-Participants noted that **drone teams are often separate from firefighting teams** and may not fit into standard vehicle deployments. In USAR, drone operation could be an **additional role** for technical search specialists. There was consensus that drones should **continue scanning after detecting a victim**, unless they can provide direct aid. Drone pilots highlighted technical preferences, such as combining **RGB and LIDAR feeds**, and noted that **indoor noise and sensor interference** are real concerns.
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46		-Trust in the drone team varied, especially among participants unfamiliar with previous CFTs. Some responders were initially skeptical but acknowledged that **standard operating procedures (SOPs)** could help integrate drone teams more effectively. Participants also emphasized the need for **flexible information filtering**, allowing HQ to suppress irrelevant data and team leaders to focus on their operational area.
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48		-**Use Case 2 (Physiological and environmental sensors​​​​​​​):**
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50		-Participants emphasized the importance of **minimal setup time**, especially for firefighters who need to deploy immediately. USAR personnel, operating in longer shifts, were more tolerant of a 10–15 minute setup window. There was strong support for **default configurations**, **discreet personal alerts**, and **role-based interfaces**. Firefighters preferred simple, glanceable indicators (e.g., traffic-light status), while USAR medics wanted access to more detailed data.
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52		-Participants stressed that **not everyone needs to see everything**—information should be filtered based on role and task. For example, a smoke director or safety officer might monitor vitals instead of the team leader. The system should provide **yes/no answers** to task-relevant questions rather than raw data. There was consensus that **team leaders must retain control**, including the ability to mute alerts when needed to maintain situational awareness.
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54		-Views on escalation varied: some (e.g., Polish participants) preferred **automatic escalation to HQ**, while others (e.g., Dutch participants) favored **team leader confirmation first**. Participants also supported the idea of a **dedicated tech or medic role** to manage sensor readiness and monitoring. The system was seen as a way to **increase resilience**, but participants warned against over-reliance or information overload.
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56	56	= 4. Discussion =
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58	58	The feedback underscores a central theme: technology must adapt to the tempo and structure of emergency response, not the other way around. Both systems were seen as valuable additions, but only if they respect the cognitive and operational constraints of their users. For drones, this means fast, autonomous reconnaissance that complements rather than delays human action. For sensors, it means providing actionable insights without overwhelming users or requiring excessive configuration.