esa-sceva/satcom-chunk-collection huggingface dataset:
# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
###### Abstract  
On the one hand, the Internet of Medical Things (IoMT) in healthcare systems has emerged as a promising technology to monitor patients' health and provide reliable medical services, especially in remote and underserved areas. On the other hand, in disaster scenarios, the loss of communication infrastructure can make it challenging to establish reliable communication and to provide timely first aid services. To address this challenge, unmanned aerial vehicles (UAVs) have been adopted to assist hospital centers in delivering medical care to hard-to-reach areas. Despite the potential of UAVs to improve medical services in emergency scenarios, their limited resources make their security critical. Therefore, developing secure and efficient communication protocols for IoMT networks using UAVs is a vital research area that can help ensure reliable and timely medical services. In this paper, we introduce a novel Stackelberg security-based game theory algorithm, named Stackelberg ad hoc on-demand distance vector (SBAODV), to detect and recover data affected by black hole attacks in IoMT networks using UAVs. Our proposed scheme utilizes the substantial Stackelberg equilibrium (SSE) to formulate strategies that protect the system against attacks. We evaluate the performance of our proposed SBAODV scheme and compare it with existing routing schemes. Our results demonstrate that our proposed scheme outperforms existing schemes regarding packet delivery ratio (PDR), networking load, throughput, detection ratio, and end-to-end delay. Specifically, our proposed SBAODV protocol achieves a PDR of 97%, throughput ranging from 77.7 kbps to 87.3 kbps, and up to 95% malicious detection rate at the highest number of nodes. Furthermore, our proposed SBAODV scheme offers significantly lower networking load (7% to 30%) and end-to-end delay (up to 30%) compared to existing routing schemes. These results demonstrate the efficiency and effectiveness of our proposed scheme in ensuring reliable and secure communication in IoMT emergency scenarios using UAVs.  
secure medical healthcare network; internet of things; unmanned aerial vehicles; stackelberg game theory; black hole attacker
## 4 Results
### FPCS of the Landsat-8 Data
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Visible and**} & & & & & & \\ \cline{3-8} \multirow{2}{*}{**ASTER**} & \multirow{2}{*}{**Near-Infrared**} & **Near-Infrared** & **Red Band 2** & **NIR Band 3N** & **TIR1 Band 10** & **TIR2 Band 11** & **TIR 3 Band 12** \\  & & **(VNIR) Band 1** & **(630–690 nm)** & **(780–860 nm)** & **(8120–8470 nm)** & **(8470–8820 nm)** & **(8920–9270 nm)** \\ \hline  & \multirow{2}{*}{**TIR1**} & Raw & \(-0.117\) & \(-0.127\) & \(-0.064\) & \(-0.505\) & \(-0.575\) & \(-0.616\) \\ \cline{2-8}  & & Str & \(0.337\) & \(-0.257\) & \(-0.075\) & \(-0.611\) & \(0.661\) & \(-0.061\) \\ \cline{2-8}  & & Raw & \(-0.624\) & \(-0.736\) & \(-0.117\) & \(-0.033\) & \(0.122\) & \(0.196\) \\ \cline{2-8}  & & Str & \(-0.877\) & \(0.148\) & \(-0.097\) & \(-0.422\) & \(0.094\) & \(-0.110\) \\ \cline{2-8}  & & Raw & \(0.014\) & \(0.128\) & \(-0.981\) & \(0.122\) & \(0.042\) & \(-0.067\) \\ \cline{2-8}  & & Str & \(0.246\) & \(0.683\) & \(0.367\) & \(-0.473\) & \(-0.233\) & \(0.255\) \\ \cline{2-8}  & & Raw & \(-0.102\) & \(-0.126\) & \(0.120\) & \(0.756\) & \(0.036\) & \(-0.621\) \\ \cline{2-8}  & & Str & \(0.236\) & \(0.278\) & \(-0.647\) & \(-0.152\) & \(-0.280\) & \(-0.590\) \\ \cline{2-8}  & & Raw & \(0.030\) & \(0.007\) & \(0.017\) & \(-0.395\) & \(0.807\) & \(-0.437\) \\ \cline{2-8}  & & Str & \(-0.027\) & \(0.607\) & \(-0.119\) & \(0.445\) & \(0.647\) & \(-0.016\) \\ \cline{2-8}  & & Raw & \(-0.765\) & \(0.640\) & \(0.072\) & \(-0.009\) & \(0.016\) & \(-0.001\) \\ \cline{2-8}  & & Str & \(0.000\) & \(0.000\) & \(0.650\) & \(0.060\) & \(0.059\) & \(-0.756\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Eigenvector loadings after applying FPCS on the raw (Raw) and stretched (Str) ASTER LIT data.
## 5 Simulation Examples
### Resolution Performance Simulation
#### 5.1.1 The Spatial Spectrum of the R-MUSIC Algorithm  
In order to verify the effectiveness of the proposed method, some spatial spectrum figures are shown in this section. Assume there are several incoherent signals impinging on the array, the signal frequencies are 6 GHz, SNR (signal to noise ratio) is 20 dB and the snapshots are 100. Figure 3 shows the spatial spectrum of two, three, and four incident signals, respectively. It can be seen that the proposed method has better resolution and estimation accuracy performance. In addition, it can estimate more than two sources successfully.
# Rapid Laser Reactive Sintering Synthesis of Colossal Dielectric CCTO Ceramics
## 2 Experimental Details  
Powders of Cu (99.9%, 5 \(\upmu\)m), CuO (99.9%, 5 \(\upmu\)m), CaO (99.9%, 5 \(\upmu\)m), and anatase-TiO\({}_{2}\) (99.9%, 0.5 \(\upmu\)m) (Aladding Industrial Corporation, Shanghai, China) were used as precursors without further purification. Stoichiometric amounts of Cu, CuO, CaO, and TiO\({}_{2}\) were weighed in a molar ratio of 2:1:1:4 and mixed in a ball milling machine with the medium of absolute ethyl alcohol. After continuously grinding for 12 h with rotating speeds of 400 rpm, the mixture was transferred into a drying oven, and dried at 70 \({}^{\circ}\)C for 2 h. An appropriate amount (8%) of binder polyvinyl alcohol (PVA) was added to the dried powder. After being well mixed by grinding, the powders with binder were put into a steel die for tableting. Under a pressure of 5 MPa for 3 s, a tough pellet measuring 9 mm in diameter and 2 mm in thickness was obtained. The pellet was heated slowly (at a heating rate 5 \({}^{\circ}\)C min\({}^{-1}\)) to 500 \({}^{\circ}\)C to burn off the binder. After removing binder, the pellet was put into a copper crucible for laser sintering. A multimode diode laser (Opttools, 980 nm, CW, 1200 W) was directed to a quartz convex lens and a solid-state reaction occurred for the precursors in pellet when irradiated with a laser spot size of 10 mm in diameter. The focused laser energy on the sample was so intense that chemical reaction was finished instantaneously. Then, the pellet was cooled down to room temperature naturally and CCTO ceramics were obtained. The experimental process of synthesizing CCTO ceramics by rapid laser reactive sintering is shown in Figure 1.  
X-ray diffraction (XRD) patterns for CCTO samples were collected on a Rigaku Ultima VI X-ray diffractometer (Cu-K\(\alpha\) radiation, \(\lambda\) = 1.5418 A) over the 2\(\theta\) range of 20-80 with a scanning speed of 5 per minute. The microstructure of the CCTO was measured by field emission scanning electron microscope (FESEM, ZEISS MERLIN Compact). For dielectric measurements, the electrodes of silver paste were painted on both sides of thin CCTO cylindrical piece. The dielectric and impedance spectra of CCTO ceramics depending on temperature were measured by a high-precision LCR meter (HP 4990A; Agilent, Palo Alto, CA, USA).
## 3 Network Dynamic Evolution and Empirical Analysis
### The Model on the DQF  
#### 3.2.1 Model Descriptions  
The network is partitioned into three adjacent subregions on the DQF. Each one connects to the external system, which means each subregion has external inflow and outflow. The transfer ratio between subregion 1 and 3 is 0 (\(P_{13}=P_{31}=0\)), as shown in Figure 11.  
Figure 11: The illustration of the model on the day of Qingming Festival.  
Figure 10: The network evolution of: (**a1**) subregions 1 and 2 on the weekday before the holiday, (**a2**) subregions 2 and 3 on the weekday before the holiday, (**b1**) subregions 1 and 2 on the ordinary weekday, (**b2**) subregions 2 and 3 on the ordinary weekday. The blue line is the simulation result of the model, and the arrow indicates the direction of system evolution. The blue scatters are the empirical data. Points A and B represent the initial state and final state of the simulation, respectively.  
The network state evolution process can be described as follows:  
\[\frac{dk_{1}}{dt}=\frac{f_{1,in}+f_{21}-f_{12}-f_{1,out}}{L_{1}} \tag{8a}\] \[\frac{dk_{2}}{dt}=\frac{f_{2,in}+f_{12}+f_{32}-f_{21}-f_{23}-f_{2,out}}{L_{2}}\] (8b) \[\frac{dk_{3}}{dt}=\frac{f_{3,in}+f_{23}-f_{32}-f_{3,out}}{L_{3}} \tag{8c}\]  
Substituting Equation (5) into Equation (8):  
\[\frac{dk_{1}}{dt}=\frac{P_{1A}q_{1c}+P_{21}Q_{2}(k_{2})-(P_{12}+P_{1E})Q_{1}(k _{1})}{L_{1}} \tag{9a}\] \[\frac{dk_{2}}{dt}=\frac{P_{2A}q_{2c}+P_{12}Q_{1}(k_{1})+P_{32}Q_{3}(k_{3})-(P_{ 21}+P_{23}+P_{2E})Q_{2}(k_{2})}{L_{2}}\] (9b) \[\frac{dk_{3}}{dt}=\frac{P_{3A}q_{3c}+P_{23}Q_{2}(k_{2})-(P_{32}+P_{3E})Q_{3}(k _{3})}{L_{3}} \tag{9c}\]
# Secure and Privacy-Protected Bioinformation Implementation in Air Passenger Transport Based on DLT
## 6 Discussion and Future Work
In practical applications, airlines should agree on the storage format in advance with border control agencies to achieve faster matching speed. The study also expects the relevant organizations to introduce unified standards to better serve the air transport industry. Facial recognition technology is developing rapidly. With the wide application of deep learning, the combination of facial recognition and deep learning has led to a great improvement in the efficiency and accuracy of recognition. In addition to the techniques used in this research, learning a compact feature vector to represent a face could make it more efficient in the storage and comparison stages [38]. As well as high performance on large-scale datasets was achieved by embedding face images into a high-dimensional feature vector [39]. In the future, this study will continue to explore the application of deep learning on facial recognition to implement the technology in more scenarios in the aviation services industry. This research used the LSH scheme as part of the facial recognition technique; however, this technique still has limitations. Future work will focus on improving the accuracy of LSH for recognizing minor differences in facial features as well as setting better parameters to achieve stronger performance. Since different methods and algorithms may be used for storing the hash values of facial features in various border control agency databases, the possibility of developing a standardized matching format for validating the facial features delivered by this framework will be a future research endeavor. This could lead to the integration of digitization of aviation information on a wider scale. In addition, special attention needs to be paid to the relevant laws and regulations and privacy protection issues. The emergence of new technologies requires improved laws and regulations to accompany them.
## 4 Experimental Verification  
In this section, we verify the effectiveness of the developed sensor network system with the proposed synchronization of clock and data acquisition. The following five experiments were conducted:  
1. Evaluation of clock synchronization (Section 4.1);
2. Experiment 1 in high-speed tactile and vision sensor network system (Section 4.2.1);
3. Experiment 2 in high-speed tactile and vision sensor network system (Section 4.2.2);
4. Experiments in tactile and acceleration network sensor system with various sampling times (Section 4.3); and
5. Experiments in tactile, gyro, and acceleration sensor network system (Section 4.4).
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 3 Protocol Design and Description
### Description of the Protocol Phases
#### 3.3.1 Phase 0--Authentication of Corporal Sensors  
The security levels and protocol parameters are agreed upon in the initial phase. In the same way, the sensors and actuators must be registered with the coordinator node and later with the server. In this way, both the server and the coordinating node know the identities of all the sensors and actuators participating in the WBAN. From this moment, the communications of A with C and D must be through B. However, it is known that one of the most vulnerable aspects of WBANs is wireless communication since it can allow unwanted third-party entities to transmit information passing through authentic sensors. Therefore, it is necessary to provide a method to authenticate corporal sensors. The proposed method is based on the use of the acceleration vector and gyroscope correlation-based authentication proposed in [36]. This method uses a coordinator node (CN) and different sensor nodes; each has a 3D-axis accelerometer and a 3D-axis gyroscope. Sensors are placed on different parts of the patient's body to measure patient-specific physiological data and transmit them to the CN that is placed on the chest. The location of the sensors is shown in Figure 3. The authentication process begins with the sending and receiving of information, where the CN analyzes the received data. For this, the information provided by the accelerometers of both parties is used. To carry out authentication, this method calculates the correlation between the accelerometer data flow of the new sensor and the coordinator node. If the sensor and the coordinator node are in the same body, the magnitude of the accelerometer vector will be highly correlated. The method will approve the connection if the correlation value is high between them, otherwise, it will reject it. Figure 4 presents the flow diagram of the proposed authentication method.  
Algorithm 1 presents the authentication process described in [36], where \(b\) is the new detected sensor. \(A_{CN}\), \(A_{b}\), \(G_{CN}\), and\(G_{b}\) are the accelerometer and gyroscope readings of the CN and the sensor, respectively. \(C_{ACC},C_{GYR}\), are the results of the calculation of the correlation between the data of the CN and the sensor using the accelerometer and the gyroscope measurements, respectively. \(LD\) is the decision limit to accept the sensor or reject it. If the sensor is accepted, it can freely send data to the CN; if rejected, the sensor is deleted, and access to the network is denied.  
Figure 3: Sensors’ location within a WBAN.  
Nodes and actuators register with the coordinator node and then with the server so that the server knows all WBAN participants. From this point on, C and D communicate with A via B. All entities have an \(ID_{x}\) identifier stored during this phase by the server. Figure 5 shows the diagram referring to phase 1 of the protocol.  
Figure 4: Data flow of authentication method.
## 6 Results
### Influence of Temporal Traffic Variation
Results for other scaling factors are similar, however we chose the highest scaling factor due to the peculiarity of high energy consumption and low \(AF_{avg}\) for the instance where \(|T_{inc}|=|T_{dec}|=0\) and the conservative EWA setting is used. This is explained by the fact that (small) traffic loss (1%) occurs in this (and only this) instance. The empty set \(T_{inc}\) corresponds to an immediate transition from \(A_{min}^{ab}\) to \(A_{max}^{ab}\) (and vice-versa) for all \((a,b)\in V^{2}\). Focusing on energy consumption, the longer the transition period is, the lower \(E_{total}^{EWA}\) is. However, differences between energy consumption values are very small. Regarding the AF, it remains stable over all considered sets \(T_{inc}\) and \(T_{dec}\) unless traffic loss occurs. Eventually, we come back to the variation of ratio \(A_{min}^{a,b}/A_{max}^{a,b}\) keeping the total load fixed (as explained in Section 5.1). Energy consumption \(E_{total}^{EWA}\) and \(AF_{avg}\) are shown in Figures 7 and 8, respectively. First, we point out that the figures are similar to Figures 3 and 4. This is due to the fact that although the total load is fixed for the results shown in Figures 7 and 8, it is only slightly different than the load for the results shown in Figures 3 and 4. The original ratio \(A_{min}^{a,b}/A_{max}^{a,b}\) based on measurements is equal to 0.41. Regarding energy consumption, the most significant difference can be observed in Figure (b)b for \(A_{min}^{a,b}/A_{max}^{a,b}\) equal to 1.0 (compare with Figure (b)b). Not only is the energy consumption lower with both EWA settings, but also the more conservative EWA setting does not encounter capacity problem in Figure (b)b. Furthermore, when we move to the analysis of AF, we can see that the corresponding \(AF_{avg}\) achieves values below 1.0 in Figure (b)b. This is explained by the fact that there is no temporal traffic variation (\(A_{min}^{a,b}/A_{max}^{a,b}=1.0\)) and effectively the maximum traffic demand \(A_{max}^{ab}\) takes lower value than the one from measurements in order to keep total demand over the whole set \(T\) fixed. Eventually, we point out also the increased values of \(AF_{avg}\) for \(A_{min}^{a,b}/A_{max}^{a,b}=0.25\) (Figure 8). This is a result of increased traffic amplitude without decreasing total load. Lower load in Figure 4 leads to lower values of \(AF_{avg}\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(|T_{inc}|\) & \(|T_{inc}|\) \(\cdot\)\(\Delta t\) & \(\quad\)\(W_{L}=0.1\), \(W_{H}=\psi=0.5\) & \(\quad\)\(W_{L}=0.1\), \(W_{H}=\psi=0.9\) \\ \cline{3-6}
**(Units)** & **(min)** & \(\quad\)\(E_{total}^{EWA}\) **(MWh)** & \(AF_{avg}\) **(Units)** & \(\quad\)\(E_{total}^{EWA}\) **(MWh)** & \(AF_{avg}\) **(Units)** \\ \hline
0 & 0 & 3780.00 & 0.81 & 2065.50 & 2.19 \\
9 & 45 & 2516.58 & 2.11 & 2069.38 & 1.83 \\
18 & 90 & 2502.71 & 2.07 & 2069.00 & 1.85 \\
27 & 135 & 2496.38 & 2.06 & 2068.63 & 1.83 \\
36 & 180 & 2497.25 & 2.06 & 2068.38 & 1.85 \\
45 & 225 & 2498.50 & 2.06 & 2
# Pose-Driven Body Shape Prediction Algorithm Based on the Conditional GAN
## 3 Materials and Methods
### Development and Implementation of the Pix2 Body Algorithm
#### 3.2.6 Loss Function  
In this study, we used two loss functions to train the architecture. First, the GAN loss was trained using the probability value of whether the image was identical to the correct image. Equation (1) expresses the GAN loss, where x represents the input image and y represents the correct image. The discriminator expresses the relationship between the correct image and the created image using 0 and 1. Specifically, it seeks to output 0 when the image is generated by the generator and 1 when it is a proper image. The purpose of the generator is to persuade the discriminator that the created image is the proper image. Consequently, in the context of Equation (1), the generator learns to minimize, while the discriminator learns to maximize.  
\[L_{GAN}(G,D)=\ E_{x,y}[\log D(x,y)]+E_{x,z}[\log(1-D(x,G(x,z))] \tag{1}\]  
When designing the loss function of the generator, we added a GAN loss to show the similarity to the correct image and a reconstruction loss to ensure that the image was similar to the correct image. In this study, we used the L1 loss to quantify the similarity (distance) between the correct image and the data generated by the generator. The L1 loss is defined in Equation (2). As shown in this equation, we find the absolute value of the error between the correct image and the generated image and sum the errors.  
Figure 8: Architecture of the Pix2 Body’s discriminator patch GAN.  
Figure 7: Architecture of the Pix2 Body generator.  
\[L_{L1}(G)=\ E_{x,y,z}[\left\|y-G(x,y)\right\|_{1}] \tag{2}\]  
The final loss function is expressed by Equation (3). In this study, the loss function combined the GAN loss and the L1 loss to limit the tendency of the L1 loss to create blurred results and of the GAN loss to provide results that differ from the correct value. As indicated earlier, the discriminator is trained to maximize, while the generator is trained to minimize. Furthermore, by conditioning the generation process on body key point information, we introduced a spatial prior that helped guide the generator toward anatomically coherent outputs. This integration alleviates the limitations of texture-only supervision, which often fails to preserve correct body proportions under occlusion.  
\[G^{*}=arg\ min\ max\ L_{cGAN}(G,D)+\lambda L_{L1}(G) \tag{3}\]
# A Comprehensive Survey on Knowledge-Defined Networking
## 5 Control Plane
### Controller Functions
#### 5.4.2 Path Computation  
Path computation involves determining the optimal path that data packets should take through the network based on various factors such as network topology, traffic flow, and network policies. As the control plane has a global network view, that information can be used to compute the most efficient path that a data packet can take to reach its destination using an algorithm (such as shortest path algorithms) [381]. However, some paths may be congested. Thus, making a decision only using the topology may not be ideal. Therefore, the controller can consider the network traffic flow to identify congested links in order to avoid them when computing the paths [382]. Furthermore, when routing, the controller can enforce network policies such as encrypting sensitive data and forwarding it through secure channels or giving priority to one type of traffic over others [383]. Instead of proactively computing paths, paths may be computed on demand, such as due to changes in a network topology or traffic flow. For instance, if a link fails or becomes congested, the control plane can compute paths for the affected data packets reactively [384].
## 3 Methodology
### POD-MCNN Model  
Combining the advantages of POD and CNN, we propose a fast computation method for the flow field, which enables the digital twin to provide a wider range of operating conditions than the simulation and uses it for the real-time monitoring of the temperature and pressure fields of the push-plate kil. The process is shown in Figure 4. The method can be summarized in three steps: POD dimensionality reduction, MCNN prediction, and POD reconstruction. First, the physical field features of the simulation database are extracted by POD to map the high-dimensional flow field to the low-dimensional space. Then, a surrogate model of input parameters and low-dimensional flow field features is established by a multiscale convolutional neural network to respond to the new input parameters. Finally, data are reconstructed by the basic modes and MCNN prediction data.