## 3 Results  
### Difference in DC for Internet Activities  
We remind the reader again that in this work we calculated a DC sample using Wi-Fi 6 signals recorded per second, taking a total of 6 min of signal duration, which is the time specified by the ICNIRP guidelines for estimating the average exposure level. Figure 5 presents the DC versus time for four representative internet activities conducted on an 80 MHz Wi-Fi 6 network: YouTube 4K video streaming, WhatsApp video calling, FTP file downloading, and web browsing. The results demonstrate that Wi-Fi 6 DC is highly dependent on the type of internet service, as different applications exhibit significant variations in traffic demands and transmission patterns. * YouTube 4K video streaming follows a cyclic behavior characterized by periods of preloading (buffering) and silence. As shown in Figure 5, DC peaks during buffering, reaching approximately 40%, followed by periods of minimal activity with low DC as only control frames are transmitted;
* FTP file downloading, as a high-bandwidth, continuous transmission application, exhibits the stable and highest DC, with an average of approximately 57%;
* WhatsApp video calling: This real-time interactive application requires continuous up and downlink traffic. However, since the data are transmitted in small packets (i.e., low data rate), it maintains a relatively low yet stable DC of around 6%;
* Web browsing, on the other hand, alternates between AP active and idle due to the intermittent nature of the user's activity, and DC exhibits burstiness with a CF of 12.9 dB, reflecting the sporadic nature of the traffic. Table 5 details the statistics of DCs recorded at 20 MHz and 80 MHz for different network applications with mean, median, 95 th percentile (P\({}_{95}\)), maximum values (Max), and standard deviation (SD). To intuitively present the data distribution, the DC box plots at 4 bandwidths are shown in Figure 6. We remark that at 20 MHz bandwidth, high-throughput applications such as FTP upload demonstrate the highest mean DC, reaching 94.3%, which is attributed to its continuous and stable data transmission demands. A closer examination reveals that FTP upload consistently exhibits higher DC values than download across all bandwidths, primarily due to the fact that upload traffic is more concentrated, with data being sent directly from the UD to the AP and the acknowledge (ACK) frames taking up less time; in contrast, download traffic needs to switch directions frequently, and the AP needs to wait for the ACK response and buffer assembly of the data, which results in longer waiting time between frames, thus resulting in a lower DC for downloads. Figure 4: Layout of UDs (including a laptop, a mobile phone, a tablet and a laptop with UWiA installed), measurement points (center) and the AP for assessing the exposure level of Wi-Fi 6 in a multi-user scenario (The brown boxes show tables where devices were placed). Pre-buffered internet services (e.g., video and audio stream) show positively skewed DC distributions, which are manifested by short bursts of high-intensity data, as shown in Figure 5. Buffering behavior is the main contributor to the overall DC; hence, the maximum value better characterizes the impact of network bandwidth on the DC compared to the mean value for such applications. For YouTube 4K and 1080p video streaming, the mean DC values were 5.3% and 1.2%, respectively, while the maximum values reached 89.2% and 15.4%. Applications with continuous up and downlink traffic, such as WhatsApp video calls, Twitch streaming, and online gaming, showed varying mean DC values depending on their traffic demands: 5.6%, 7.8%, and 2.8%, respectively. In contrast, web browsing demonstrated bursty traffic patterns due to user-triggered activity, resulting in a positively skewed DC distribution with an average of 1.6% and a P\({}_{95}\) of 6.1%.