## 4 The Proposed Scheme: Architecture and Components
### The COVID-19 Layer
#### 4.3.3 COVID-19 Thermal Inspection  
For thermal inspection, we consider \(E_{UAV}\) to be equipped with two sets of cameras, one is the normal camera, and the second is the thermal imaging camera, which is connected to ground networks through a 6G communication channel. Each UAV has a unique distance measurement standard; for instance, the UAV with the MEDICAS thermal imaging system can capture the thermal image at a distance of 5 m with an area of 4.1 \(\times\) 3.3 m and a spot size of 6.5 mm/2 cm. Furthermore, with a distance of 20 m, it can capture an image area size of 16.8 \(\times\) 13.6 m with a spot size 2.6  cm/7.8 cm. Here, the MEDICAS system offers an image resolution of 640 \(\times\) 512 pixels, which works well for any computer vision algorithm to process thermal images [50]. Similarly, other UAVs have their specifications to capture thermal images, but formally, all imaging systems use spot size ratio (SSR) to determine how far you can measure. \[\text{SSR}=\frac{\text{distance}}{\text{spot size}} \tag{3}\]  
For example, a thermal imaging system with an SSR of 39:2 implies that the imaging system could measure an object of 2 feet at 39 feet away. The thermal imaging cameras mounted on any \(k^{th}\) UAV measure the skin temperature (in a contactless manner) of any person. The normal body temperature is \(\approx\)98.6 \({}^{\circ}\)F. Thus, any temperature higher than this value is flagged as a potential case. However, the temperature measurement consideration has the inherent limitation of fixation on a particular region, and we consider the face as our region of interest (RoI). Thus, in the first part, we consider video frame sequences, denoted by \(\{V_{1},V_{2},\dots,V_{l}\}\), which are collected and labeled with tags (frame number), denoted by \(\{F_{1},F_{2},\dots,F_{l}\}\). For human face detection, a histogram of the oriented gradient (HOG) encoding is used to form reduced labeled versions of the images [51]. Next, we consider an identification algorithm embedded with thermal cameras mounted on UAVs. We consider a user registration portal where users in the region \(R\) are registered with \(E_{NC}\). The registration of any user \(P_{c}\) at \(E_{NC}\) is depicted as follows. \[R(P_{c})=\{ID(E_{MC}),P_{name},P_{har},P_{i_{1}},P_{i_{2}},\dots,P_{i_{t}}\} \tag{4}\]  
where \(ID(E_{MC})\) is the identity of the registering MC, \(P_{name}\) is the person name, \(P_{ha}\) is the home (residential) address, and \(\{P_{i_{1}},P_{i_{2}},\dots,P_{i_{t}}\}\) are \(t\) pictures of \(P_{c}\) captured from different orientations (normally \(t\in[1,5]\)) to train the machine learning algorithm. We apply HOG to form image labels and send the face embeddings 128 \(\times\) 1 to be trained via computer vision algorithms such as YOLOv3 and F-RCNN. The UAVs capture the normal and thermal images in real time and send the videos at a specified frame rate over the channel [52]. In case the forehead region in the thermal image (captured in real time) has a higher temperature, then the personal details are fetched from \(R(P_{c})\), and \(E_{MC}\) visits the person and performs COVID-19 detection tests (RT-PCR, blood tests, and others). Algorithm 3 presents the thermal inspection algorithm. In this algorithm, every \(UAV_{k}\) is assisted by two cameras, one normal camera and one thermal camera. In any given region \(R\), real-time video feeds are captured by both cameras, and frames are depicted as \(F_{norm}\) and \(F_{therm}\), which are stored in database \(D\). Lines 1-6 present these conditions. Now, we label the RoI in the stored images (forehead) and capture essential attributes on both \(F_{norm}\) and \(F_{therm}\). The HOG algorithm is utilized for this purpose. Lines 7-12 present these conditions. On the HOG images, we execute our models \(M_{n}\) and \(M_{t}\) with the YOLOv3 algorithm (per-frame basis) and note the temperature values. Now, the difference between normal temperature and increased temperature is noted. Lines 13-14 present the same.