esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Experimental Results Obtained with the FT-CFNN
The S/N ratio for each factor and level combination was obtained from the results of the 36 experiments based on larger-the-better characteristic. Table 7 presents the difference of S/N ratio, significance ranking, optimal level, and optimal parameter combination for each factor. If the difference value of S/N ratio for each factor was higher, the corresponding factor had a stronger influence. The significance ranking represents the ranking of the influence degree of each factor. A higher difference of S/N ratio for P2 indicated that a factor had a stronger influence (Table 7). The final optimal parameter combination was as follows: F1 = 16, K1 = 5, P1 = 0, F2 = 16, K2 = 6, and P2 = 0. The results of analysis of variance (ANOVA) are presented in Table 8, and these results indicate whether the screened experimental factors are significant with respect to the experimental results. In Table 8, the degree of freedom is the number of levels minus 1, SS is the error sum of squares, the size of the \(F\) value determines the significance of the factors affecting the overall experiment, and the contribution degree repres \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**No.** & **F1** & **K1** & **P1** & **F2** & **K2** & **P2** & **Expt. 1** & **Expt. 2** & **Expt. 3** & **Expt. 3** & **Expt. 3** & **Expt. 3** \\  & & & & & & & & & & & \\  
\hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Level** & **F1** & **K1** & **P1** & **F2** & **K2** & **P2** \\ \hline
1 & \(-\)0.2405 & \(-\)0.2582 & \(-\)0.2258 & \(-\)0.2382 & \(-\)0.2517 & \(-\)0.1938 \\
2 & \(-\)0.2338 & \(-\)0.2295 & \(-\)0.2567 & \(-\)0.244 & \(-\)0.2399 & \(-\)0.2887 \\
3 & \(-\)0.2494 & \(-\)0.2359 & & \(-\)0.2415 & \(-\)0.2322 & \\ \hline Difference & 0.0147 & 0.0281 & 0.03 & 0.0057 & 0.0194 & 0.0937 \\ Significance ranking & 5 & 3 & 2 & 6 & 4 & 1 \\ Best level & 2 & 2 & 1 & 1 & 3 & 1 \\ Optimal parameter combination & 16 & 5 & 0 & 16 & 6 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results obtained for each factor. each factor to the overall experimental results. Table 8 indicates that the factor P2 (i.e., the padding in second layer) has the strongest influence on and highest contribution to the experiments. Table 9 presents the optimal parameter combinations obtained using the Taguchi method. These parameters were set for the FT-CFNN, which was then used for malware image classification. The accuracy and sensitivity values obtained with the CFNN and FT-CFNN are presented in Table 10. Experimental results show that the accuracy and sensitivity of the proposed FT-CFNN with CGAP fusion method are 98.61% and 97.96%, which are 0.48% and 0.45% higher than CFNN, respectively.
## 3 Design of Adaptive Nonlinear PID Controller
### Enhanced Nonlinear PID Controller  
The integral action of the above NPID controller nonlinearly scales down the error signal. This minimizes the offset for small errors and prevents excessive accumulation and overshoot for large errors. In this work, the integral action uses an enhanced nonlinear function as follows:  
\[g(e)=aexp(-\frac{e^{2}}{2\Delta r^{2}}), \tag{22}\]  
where \(a\) is a constant that determines the shape of \(g(e)\). This nonlinear gain slightly increases the error to reduce the offset by increasing the accumulated error when the error is small. Substituting (22) into (20) yields  
\[v(t)=ae(t)exp(-\frac{e^{2}}{2\Delta r^{2}}). \tag{23}\]  
The constant a is chosen such that the function \(v(t)\) converges to the value of e when e equals \(\Delta r\). Applying this condition to (23), the following relationship is obtained:  
\[a=exp(\frac{1}{2}). \tag{24}\]  
Substituting (24) into (23) gives the nonlinear gain function as  
\[g(e)=exp(\frac{1}{2}-\frac{e^{2}}{2\Delta r^{2}}). \tag{25}\]  
Figure 6 shows the scaled error \(v(t)\) for different \(\Delta r\) values.  
The integral action of the standard PID controller uses \(e(t)\) as is, but as seen in Figure 6, the new \(g(e)\) allows minor errors to be slightly amplified, thereby diminishing the steady-state error, and conversely, significant errors are abruptly scaled down to prevent excessive overshoot caused by the accumulated error.  
To evaluate the tracking and disturbance rejection capabilities of the standard PID and NPID controllers, their performance is analyzed on a first-order plus time delay (FOPTD) system as  
\[T\frac{dy(t)}{dt}+y(t)=Ku(t-L), \tag{26}\]  
where \(T\) is the time constant, \(K\) is the steady-state gain, and \(L\) is the time delay. Adjusting a controller's settings impacts both its responsiveness to the desired setpoint and its ability to manage disturbances. These two settings often conflict. This trade-off is explored by simulating an FOPTD system with \(K=1\) and a dimensionless ratio (\(L/T\)).  
Figure 6: Scaled error plots for different \(\Delta r\) values.  
To evaluate a controller's performance, the following integral absolute error (\(IAE\)) performance criteria are used:  
\[IAE_{SP}=\int_{0}^{\infty}|y_{r}(t)-y(t)|dt\,, \tag{27}\]  
\[IAE_{DIS}=\int_{0}^{\infty}|y(t)|dt\,, \tag{28}\]  
where \(IAE_{SP}\) is a measure of the error between the setpoint and the output over time while applying \(y_{r}=1\) and \(d=0\), and \(IAE_{DIS}\) the same measure while applying \(y_{r}=0\) and \(d=1\). The disturbance \(d\) is considered to be added to the input side. A non-dominated sorting genetic algorithm (NSGA-II) [36; 37] is applied to minimize the two performance metrics simultaneously for four distinct ratios of \(L/T\): 0.01, 0.1, 0.5 and 1. Figure 7 shows the optimal Pareto fronts.  
Figure 7 illustrates the fundamental trade-off between achieving good setpoint tracking and strong disturbance rejection. Enhancing one performance metric often necessitates compromising on the other. Our analysis reveals that the NPID controller outperforms the linear PID controller in this regard.
# Cross-Layer MAC/Routing Protocol for Reliability Improvement of the Internet of Things
## 4 Performance Evaluation
This is because it transmits control packets in a flood manner for route discovery and generates many control packets to maintain the set path, resulting in network congestion and packet drop. However, since MeshMAC and TSMR do not generate control packets for routing and do not transmit control packets for routing, the probability of collision between the two protocols is lower than that of the other protocols. As a result, the delivery ratio of TSMR and MeshMAC is superior to that of the IEEE 802.15.4 standard and the RPL. Figure 14: Energy consumption of the network as a function of the number of source devices. Figure 16 shows the end-to-end delay as a function of network size. In this simulation, all leaf devices in the network transmit data every second. As the number of devices in the network increases, traffic in the network increases. In the IEEE 802.15.4 standard, devices on the routing path transmit many control packets to set and maintain paths, and since all devices on the network participate in competition for data transmission, devices on the routing path have limited opportunities for data transmission and increase the probability of data collision. The end-to-end delay in the RPL is clearly longer than the delay in the mesh topology because it causes a bypass problem in which packets are passed to the root along a predefined path until they reach a common ancestor or root. Figure 17 illustrates the packet loss rate when the number of mobile leaf nodes is varied. Figure 16: End-to-end delay as a function of the network size. Figure 15: Delivery success ratio as a function of the number of source devices. In this simulation, the mobile leaf device moves at a speed of 1 m/s. In Figure 17, the MeshMAC and RPL methods exhibit the worst performance because they do not consider the mobility of router devices. In the IEEE 802.15.4 standard, the device performs a path recovery process in the AODV protocol to reduce packet loss. TSMR experiences some packet loss during the handover process, but the impact of device mobility is minimal. Figure 18 shows the control overhead according to the number of devices moving. In Figure 18, the control overhead represents the total number of packets used for route discovery. This overhead shows the degree of energy consumption of the protocol because control packets cause energy consumption of the device and shorten the network life. When the device moves, a large number of devices must send control packets and reset the path to find the moving device. Thus, as the number of mobile devices increases, more control overhead occurs. TSMR also shows that as the number of devices moving increases, the control overhead increases, but the control overhead decreases because the number of control packets for resetting the routing path can be reduced. Figure 17: Packet loss ratio as a function of the number of mobile nodes. Figure 18: Control overhead as a function of the number of mobile nodes.
## 2 Proposed FT-CFNN
### Convolutional Fuzzy Neural Network
#### 2.1.4 FNN Layer  
An FNN combines the logical reasoning of humans and the learning abilities of neural networks. FNNs have been successfully used in classification systems [30; 31]. Figure 4 illustrates the architecture of an FNN, which primarily contains a fuzzification layer, fuzzy rule layer, and defuzzification layer. Fuzzy rules are inferred using IF-THEN representations. These rules are expressed as follows:  
\[Rule\text{ : IF }x_{1}\text{ is }A_{1j}\text{ and }x_{2}\text{ is }A_{2j}\text{ }\dots\text{ and }x_{n}\text{ is }A_{nj}\text{, THEN }y_{j}\text{ is }w_{j}\]  
Figure 4: Architecture of an FNN.  
Figure 3: Feature fusion methods: (**a**) GMP fusion and GAP fusion as well as (**b**) CGMP fusion and CGAP fusion.  
In the aforementioned rule, \(x_{i}\) is the input, \(A_{ij}\) is the membership function, and \(w_{j}\) is the output of the \(j\)th rule.  
In the fuzzification layer, the Gaussian membership function used by each input is fuzzified to obtain the corresponding membership values. The relevant formula is as follows:  
\[\mu_{ij}(x)=exp\left(-\frac{\left[x_{i}-m_{ij}\right]^{2}}{\sigma_{ij}^{2}}\right) \tag{1}\]  
where \(x_{i}\) is the input, \(m_{ij}\) is the mean value, and \(\sigma_{ij}\) is the deviation.  
Subsequently, the AND operation is completed for the attribution values corresponding to each input to obtain the excitation intensity of each fuzzy rule. The most commonly used AND operations are minimum and product. To facilitate the derivation of the back-propagation algorithm, the product operation is used in this study. The excitation intensity of each fuzzy rule is calculated as follows:  
\[R_{j}=\prod_{i=1}^{n}\mu_{ij} \tag{2}\]  
where \(\mu_{ij}\) is the excitation value of each membership function.  
Finally, each rule is input into the defuzzification layer to obtain a crisp output. The relevant formula is as follows:  
\[y_{k}=\sum_{j=1}^{r}R_{j}w_{jk} \tag{3}\]  
where \(y_{k}\) is the \(k\)th output, \(R_{j}\) is the excitation intensity of the \(j\)th fuzzy rule, \(r\) is the number of fuzzy rules, and \(w_{jk}\) is the weight of the \(j\)th fuzzy rule and \(k\)th output.
# SOLIOT--Decentralized Data Control and Interactions for IoT
## 7 Architecture and Prototypical Implementation
### Use Case Scenario  
Following the scenario from Section 3, the Manufacturer and Data Analyst connect their local machines via CoAP or MQTT. The SOLIOT instances are the only providers, waiting for incoming requests. These requests must follow SOLID specifications, and are therefore easy to implement for any connecting party.  
The administrators responsible for A and B initialize pods for all devices, hosted sensors, machines or even complete facilities relevant for their operation. They create according profiles, deposit the master data and configure the security regime. One part is the storage of identification information to let the SOLIOT server know the addresses of the incoming data. Furthermore, the.acl files are adjusted so that all administrators obtain full control, internal systems gain read and write permissions where needed, and external applications only can access the negotiated information. They do this through the same interaction patterns (Read: GET, Update: PUT etc.) as the clients at runtime.  
As soon as the system has been set up, the local sensors push their observations via their preferred protocol to the SOLIOT server. For supplier A, the devices send CoAP PUT messages with the measured values in JSON. The SOLIOT server validates the token, maps and validates the content. If the server can process the message, the according attribute in the sensor profile is updated.
# A Novel Inverse Simulation Method of Helicopter Maneuvering Flight
## 2 Degeneration Method  
The conventional simulation can be written as a system of first-order differential equations:  
\[\dot{\mathbf{x}}=\mathbf{f}(\mathbf{x},\ \mathbf{u}) \tag{1}\]  
where \(\mathbf{x}\) and \(\mathbf{u}\) are the state and control vectors, respectively. To conduct an inverse simulation, an output vector \(\mathbf{y}\), which is a function of the state vector \(\mathbf{x}\), needs to be prescribed in the course of the simulation:  
\[\mathbf{y}_{\text{DES}}=\mathbf{g}(\mathbf{x}) \tag{2}\]  
Thus, the inverse simulation finds the control time histories \(\mathbf{u}(t)\) under the action of Equation (1) to generate a prescribed output vector \(\mathbf{y}_{\text{DES}}\). To make progress, Equation (2) is usually differentiated to obtain:  
\[\dot{\mathbf{y}}_{\text{DES}}=\mathbf{g}_{\mathbf{x}}(\mathbf{x})\dot{\mathbf{x}} \tag{3}\]  
Substituting Equation (1) into Equation (3) gives:  
\[\dot{\mathbf{y}}_{\text{DES}}=\mathbf{g}_{\mathbf{x}}(\mathbf{x})\mathbf{f}(\mathbf{x},\ \mathbf{u}) \tag{4}\]  
Some numerical algorithms are adopted to solve this equation, such as the differentiation method and the integration method. However, the functional matrix \(\mathbf{g}_{\mathbf{x}}(\cdot)\) needs to be evaluated when using these methods. This inevitably suffers some numerical errors (e.g., low-amplitude, high-frequency oscillations superimposed on the low-frequency waveform) and makes the results dependent on the simulation time step to some extent. These numerical instabilities have been studied thoroughly by [PERSON] and [PERSON] [26], [PERSON] and his colleagues [12; 20], and others.  
In the process of inverse simulation, to avoid evaluation of this \(\mathbf{g}_{x}(\cdot)\) and to achieve better versatility and robustness, a novel method called the degeneration method is developed here. The basic principles of our method consist of the following two main aspects: degeneration based on a transitional vector, and determination of the transitional vector.
# A Circularly Polarized mmWave Dielectric-Resonator-Antenna Array for Off-Body Communications
## 2 Design Methodology
### Single DRA Next to a Human Body  
To investigate the impact of human body on the antenna performance, the DRA was mounted on a three-layer tissue model. The modeled layers of the human-body tissues, measuring 100 \(\times\) 45 \(\times\) 13 mm\({}^{3}\), are illustrated in Figure 2. Separation distances of 1 mm and 5 mm between the DRA and the body phantom were considered [25]. The characterizations of the body parameters were used according to those given in [26], as summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value (mm)** & **Description** \\ \hline \(w_{dr}\) & 5.2 & DRA width \\ \hline \(l_{dr}\) & 5.2 & DRA length \\ \hline \(h_{dr}\) & 2.9 & DRA height \\ \hline \(L\) & 19 & Length of ground plane \\ \hline \(W\) & 21 & Width of ground plane \\ \hline \(h_{s}\) & 0.254 & Substrate height \\ \hline \(l_{t}\) & 10.5 & Length of microstrip line \\ \hline \(w_{t}\) & 0.45 & Width of microstrip line \\ \hline \(l_{stub}\) & 1 & Stub length \\ \hline \(l_{s1}\) & 1.7 & Length of the first slot \\ \hline \(l_{s2}\) & 3.2 & Length of the second slot \\ \hline \(w_{s}\) & 0.35 & Width of the cross slot \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the proposed single DRA element.  
Figure 1: Antenna configuration: (**a**) top view, (**b**) front view, (**c**) back view.  
Figure 3 illustrates the reflection coefficient when the DRA is positioned in free space, as well as at distances of 1 mm and 5 mm above the phantom. For both distances, the achieved impedance bandwidth is \(\sim\)31%, which is close to that of \(\sim\)31.5% for a DRA in free space. This demonstrates the marginal impact of the human tissues on the impedance bandwidth, which can be explained by the presence of a ground plane that minimizes the interaction between the DRA and tissues [14; 27].  
The same was also confirmed for the circularly polarized radiation. As shown in Figure 4, almost the same axial-ratio bandwidth, of 14%, was achieved in free space, as well at various separation distances between the antenna and the phantom. This was also the case for the achieved gain of \(\sim\)8.7 dBic at 28 GHz. The gain is illustrated in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tissue** & **Skin** & **Fat** & **Muscle** \\ \hline Relative Permittivity & 16.55 & 6.09 & 25.43 \\ \hline Density (kg/m\({}^{3}\)) & 1109 & 911 & 1090 \\ \hline Conductivity (S/m) & 25.82 & 5.04 & 33.6 \\ \hline Thickness (mm) & 1 & 2 & 10 \\ \hline \end{tabular}
\end{table}
Table 3: Human-body-tissue parameters at 28 GHz.  
Figure 2: A single DRA element above the body phantom.
# Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology
## 3 Materials and Methods
### Histogram Analysis  
Histogram analysis is a fundamental technique in image processing used to understand the distribution of pixel intensities within an image [40]. A histogram is a graphical representation that shows the frequency of pixel intensity values in an image, typically ranging from 0 (black) to 255 (white) in grayscale images. In the context of color images, separate histograms are generated for each color channel (e.g., red, green, and blue). The histogram provides insights into the overall distribution of pixel intensities in an image. Peaks and valleys in the histogram reveal the predominant intensity levels, helping to understand the contrast and brightness of the image. Histogram analysis is often used to enhance the contrast of an image. By stretching or equalizing the histogram, the full range of intensity values can be utilized, resulting in a visually more appealing image. Histogram analysis aids in adjusting the brightness of an image. Shifting the histogram to the left or right can make an image darker or brighter, respectively. Histogram analysis is a versatile tool that forms the basis for many image-processing algorithms. Understanding and manipulating the pixel intensity distribution through histogram analysis can significantly improve the visual quality and interpretability of images.
## 1 Introduction
To overcome this limitation, another fall detection study brings forth the possibility of using estimated 3D head motions with a single camera [20], but the system works only with a pre-calibrated camera and an initialized head position. These facts indicate that fall detection systems based on a single camera have their own drawbacks. The fall detection systems based on multiple cameras can track 3D motions using 3D reconstruction of foreground objects [21; 22], but the calibration of multiple cameras is problematic and causes computational complexity. Moreover, the need for additional cameras increases the cost and gives rise to various hassles in an outdoor environment. Recently, multiple studies have been conducted using kinetic cameras or depth sensors [23; 24; 25; 26]. These sensors generate accurate depth images in real-time that improve the performance of human detection and tracking in the 3D space. However, this approach increases the additional cost for existing surveillance systems. Moreover, a depth camera does not work accurately in an outdoor environment and there are restrictions in terms of distance [27]. In most vision-based fall detection studies conducted to a date, the focus has been to automatically detect falls of the elderly in the indoor environment. Fall detection in an outdoor environment is more difficult since human motion and surrounding scenarios are more dynamic and complex in general, as compared to the indoor environment. Nonetheless, there has been very little research on noninvasive methods, which is our research direction. It is different from the few studies conducted on the effectiveness of using wearable sensors [28; 29; 30]. In this study, we propose a single camera-based fall detection method to be used in an outdoor environment. In spite of using a single camera, this approach employs 3D movement by analyzing depth images estimated by a machine learning algorithm. Furthermore, our study presents the possibility of outdoor fall detection which is not a well-studied topic yet. To summarize the process, first, a depth image is generated from an input image captured by a RGB camera and then a global optimization method is applied to detect a human form in the depth image. Our method does not employ the initialization process generally required for human tracking in other methods. After a human form is detected, prediction and update steps are performed continuously to track the detected human form using a particle filter. The measurement information used in the update process is computed by applying the graph-cut method. This rules out the need of the background subtraction method (i.e., no use of background reference image), which is frequently used in other detection methods. Therefore, we can say that this approach facilitates the use of existing surveillance systems and provides a novel and universal fall detection method that enables fall detection in various outdoor environments. In the subsequent sections, we introduce the methods used and present the proposed method in Section 2, show our experimental results in Section 3, and present the conclusions in Section 4.