esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Learning Based Positron Range Correction of PET Images
## 1 Introduction  
Positron emission tomography (PET) is a molecular imaging technique that provides information about biochemical or physiological processes in the body in vivo [1]. PET images are obtained by detecting the radiation emitted by radionuclides bound to molecules (radiotracers) of interest administered to patients or animals under study [2]. PET currently provides very valuable information in clinical fields such as oncology, cardiology, and neurology, as well as in many preclinical studies [3].  
PET is based on the detection of the two collinear 511 keV \(\gamma\) rays resulting from the annihilation of the positrons emitted by the unstable radionuclides. After being emitted, a positron will lose its initial kinetic energy through multiple interactions (mostly inelastic collisions [4]) with the electrons of the surrounding tissues. Positron-electron annihilation is most likely to occur when the positron has lost all its kinetic energy [5]. The distance between the emission and annihilation points is known as positron range (PR), and it is one of the main limiting factors of the spatial resolution of PET scanners [6; 7; 8; 9; 10; 11]. PR makes the spatial distribution of the annihilation points a somewhat blurred version of that of the emission points (see Figure 1b). PR modeling is not straightforward, as it depends on both the kinetic energy of the emitted positrons (Figure 1a) and the electron density (i.e., the density and composition) of the surrounding tissues.  
\({}^{18}\)F is by far the most widely used PET radionuclide, accounting for about 90% of clinical studies [12]. Nevertheless, many other radionuclides have been proposed for use in PET imaging, and hundreds of PET radiotracers based on them have been developed. Achallenge associated with the use of some of these radionuclides, such as such as \({}^{68}\)Ga, \({}^{82}\)Rb, \({}^{124}\)I, \({}^{76}\)Br, or \({}^{86}\)Y, is that they emit positrons with a large initial kinetic energy (see Figure 1), which results in large PR in tissues, yielding images with reduced spatial resolution and lower contrast in small volumes due to partial volume effects [7, 11, 13]. This effect is even more important when using high-resolution scanners designed to image small animals, which are able to achieve millimeter resolution [14].  
The quantitative accuracy of PET depends on an accurate positron range correction (PRC). As \({}^{18}\)F has a relatively small PR in soft tissue, accurate PRC for different radionuclides has been largely neglected, and to the best of our knowledge is not yet performed in standard PET image reconstruction. Nevertheless, the improved resolution of current scanners and the use of radionuclides with large PR such as \({}^{68}\)Ga requires this correction to be improved.
# Development of Wearable Textile MIMO Antenna for Sub-6 GHz Band New Radio 5G Applications
## 4 Results
### SAR Calculation  
To better understand how radiation impacts our bodies, HFSS has integrated an anatomically accurate body model into its antenna array for the easy simulation analysis of its effects. This configuration enables simulations of radiation's potential effects. The SAR value was determined using Equation (2).  
\[SAR=\sigma\frac{|E|^{2}}{\rho} \tag{2}\]  
Figure 9: Two-port irregular octagon antenna (**a**) radiation pattern and (**b**) gain and efficiency.  
Here, the \(\sigma\), \(E\) and \(\rho\) refer to conductivity (s/m), field intensity (V/m), and the density of biological tissue as a measure of mass in kg/m\({}^{3}\). Below, Table 2 lists the properties of tissues of the human body:  
Because antennas emit backward radiation, it is imperative to assess their specific absorption rate (SAR). To achieve this, a flat phantom representing body wear is used, in line with the SAR research methodology; antennas are then mounted approximately 2 mm above this human phantom. Figure 9(a) depicts cross-sectional views of human three-layer models consisting of skin, fat, and muscle layers, while Figure 9(b) presents SAR analysis conducted on proposed textile irregular octagonal patch MIMO antenna when placed onto human bodies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tissue Type** & **Skin Layer** & **Fat Layer** & **Muscle Layer** & **Bone** \\ \hline Permititivity (\(\varepsilon_{r}\)) & 42.1 & 5.3 & 52.8 & 18.5 \\ Conductivity (S/m) & 1.6 & 0.1 & 1.7 & 0.83 \\ Loss tangent & 0.28 & 0.15 & 0.25 & 0.26 \\ Density (Kg/m\({}^{3}\)) & 1108 & 910 & 1092 & 1010 \\ Thickness (mm) & 2 & 8 & 23 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of tissues of the human body.  
Figure 10: Irregular octagonal patch antenna SAR analysis on (**a**) a human phantom and (**b**) an antenna.
# A Blockchain Copyright Protection Model Based on Vector Map Unique Identification
## 2 Related Work  
Research on Copyright Protection Technology for Vector Maps Combining Blockchain and Digital Watermark  
With the rapid development of the internet, the widespread dissemination of digitized content over networks has exacerbated the issue of digital piracy. In terms of addressing the challenges of copyright protection for digital works, the integration of blockchain technology with digital watermarking has emerged as a promising solution [16; 17]. Unlike conventional methods, blockchain offers inherent advantages such as tamper-resistant on-chain records and irrefutability. On-chain records refer to data entries that are permanently stored within a blockchain network, ensuring their immutability and verifiability. These records are instrumental in securely storing watermark-related information, including timestamps, copyright details, and transaction histories, thereby providing a decentralized and transparent framework for copyright protection [18; 19].  
While blockchain-based copyright protection methods have been extensively explored for common data formats, like images and videos, research focusing on vector maps remains relatively nascent. [PERSON] et al. pioneered the integration of vector map watermarking with blockchain by proposing a framework that constructs zero-watermark information based on the angular features of vector maps. This framework stores both the zero-watermark information and associated copyright details on the blockchain, enabling secure and immutable copyright registration [12]. [PERSON] et al. advanced this field by introducing a geographic data trading and copyright protection model that combines zero-watermarking, InterPlanetary File System (IPFS), and smart contract technologies. This model eliminates dependence on third-party intermediaries and ensures permanent proof of transaction information on the blockchain through smart contract design, thereby facilitating timestamp authentication for watermark registration [20]. These studies indicate that the combination of digital watermarking and blockchain technology can effectively protect the copyright of vector maps. However, vector map data have a large scale and high confidentiality requirements [21], making them unsuitable for direct deployment on blockchain platforms. Further research is needed to design blockchain-based copyright protection methods tailored to the characteristics of vector map data.
## 4 Analysis and Results
### Comparison  
Accuracy measures of logistic regression and CART models for the validation datasets are shown in Figure 8. It can be observed that they are almost the same. Hence, one model does not have an advantage over the other in terms of accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Dataset** & **Value** \\ \hline \multirow{2}{*}{Accuracy} & Development & 0.74 \\ \cline{2-3}  & Validation & 0.74 \\ \hline \multirow{2}{*}{Precision} & Development & 0.74 \\ \cline{2-3}  & Validation & 0.73 \\ \hline \multirow{2}{*}{Recall} & Development & \(1\) \\ \cline{2-3}  & Validation & \(1\) \\ \hline \multirow{2}{*}{Error} & Development & 0.26 \\ \cline{2-3}  & Validation & 0.26 \\ \hline \multirow{2}{*}{FDR} & Development & 0.26 \\ \cline{2-3}  & Validation & 0.27 \\ \hline \multirow{2}{*}{FNR} & Development & 0 \\ \cline{2-3}  & Validation & 0 \\ \hline \multirow{2}{*}{F-1} & Development & 0.85 \\ \cline{2-3}  & Validation & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy measures for the CART model.  
However, the CART model has several other advantages over the logistic regression model. First of all, variables for the reason and type of traffic crashes are not required to be converted into multiple binary variables because the CART model detects the best breakup of values/categories for each node. Secondly, the CART model does not have the statistical restriction of the significance of coefficients, therefore, all variables that have any impact on the outcome can be kept in the model. Lastly, the CART model is more efficient in displaying the interrelationships of independent variables. It can be seen from Figure 5 that variables for the reason and type of traffic crashes appear more than once in the tree, and their child nodes are linked with other variables, for example, the number of deaths. Therefore, one can see clearly which crash impact types/reasons are linked, and which ones are not linked, to the involvement of the target age group. This is not possible with the utility function provided by the logistic regression model, which is unable to consider interrelations among the independent variables. Another important factor for researchers is the time required to develop and run the model. In this regard, the CART model needs more time as compared with the logistic regression model. For the available dataset of this study, the processing times are 1 and 5 s for the logistic regression and CART models, respectively. It is expected that the difference in processing may be even higher for a larger dataset in favor of the logistic regression model.
# Study on the Wellbore Flow for Carbon Dioxide Fracturing
## 2. Mathematic Model of Wellbore Flow
### Temperature Field Model  
The differential element of the wellbore is shown in Figure 2. The origin of coordinates is at the wellhead and the forward direction is downward along the axis. The steady-state mass conservation equation and energy conservation equation [28] in the \(z\) direction are as follows:  
\[\frac{d(\rho v)}{dz}=0 \tag{1}\]  
\[\frac{\partial}{\partial z}\bigg{[}\rho c\bigg{(}h+\frac{v^{2}}{2}\bigg{)} \bigg{]}=\frac{1}{A}\frac{dq}{dz}+\rho vg\cos\theta \tag{2}\]  
where \(h\) is the specific enthalpy of the fluid, J/kg; \(v\) is the fluid velocity, m/s; \(t\) is circulation time, s; \(z\) is the depth, m; \(\rho\) is density of CO\({}_{2}\), kg/m\({}^{3}\); A is cross-sectional area, m\({}^{2}\); \(q\) is the heat transfer per unit time, W; \(\theta\) is the deviation angle, rad; \(g\) is acceleration of gravity, m/s\({}^{2}\).  
Substituting the continuity equation into the energy equation, Equation (2) can be simplified as:  
\[\frac{dh}{dz}=\frac{1}{\text{M}}\frac{dq}{dz}+g\cos\theta-v\frac{dv}{dz} \tag{3}\]  
where \(M\) is mass flow rate, kg/s.  
According to the fundamental laws of thermodynamics, the expression of enthalpy change is  
\[dh=c_{\text{p}}dT-c_{\text{p}}J_{\text{T}}dp \tag{4}\]  
where \(c_{\text{p}}\) is the specific heat capacity of fluid, J/(Kg-K); \(J_{\text{t}}\) is Joule-Thomson coefficient, K/MPa; \(p\) is the pressure of fluid, MPa.  
Figure 2. A finite element in wellbore.  
The temperature increment in a finite element can be obtained by combining Equations (3) and (4):  
\[\frac{dT}{dz}=\frac{1}{c_{p}}\Bigg{(}\frac{1}{\text{M}}\frac{dq}{dz}+g\cos\theta -v\frac{dv}{dz}\Bigg{)}+J_{T}\frac{dp}{dz} \tag{5}\]  
The heat transfer between CO\({}_{2}\) and the stratum outside the wellbore is  
\[dq=\Bigg{(}\frac{T_{e}-T_{f}}{R_{ef}}\Bigg{)}dz \tag{6}\]  
where \(T_{e}\) is the formation temperature, K; \(T_{f}\) is fluid temperature in the wellbore, K; \(R_{ef}\) is thermal resistance of formation to the wellbore fluid. The expression of \(R_{ef}\) is shown in Appendix B.
## 2 Theoretical Foundation and Sybil Attack Landscape
### Unleashing Defense Strategies: Safeguarding against Sybil Attacks
In this scenario, the sensor network's communication becomes compromised when a new sensor is introduced under the guise of legitimacy but is, in fact, malicious. Such deceptive nodes can discriminate against fake results, distort weather forecasts, and propagate misleading data to other nodes. Furthermore, malicious nodes might persistently introduce seemingly legitimate nodes by mimicking the behavior of genuine ones. In both scenarios, successful infiltration by a node can profoundly disrupt the network's functionality. In critical infrastructure, e.g., traffic systems, such a disruption can result in city-wide chaos, causing severe accidents. Similarly, a swarm of compromised military drones could result in significant casualties. To safeguard against such threats, constant network monitoring for Sybil attacks is crucial. Hence, this study aimed to comprehend the Sybil attack detection techniques within P2P networks.
# Review of Recent Type-2 Fuzzy Controller Applications
## 3 Review of IT2 FLSs
### General Control Problems  
IT2 FLCs were designed for other applications than robotics, communication, industrial automation, a few of which were mentioned above. For example, in [89], an IT2 FLC was designed to handle data with uncertainties for nonlinear multiple-input multiple-output (MIMO) systems. Simulations were performed, and it was shown that the IT2 FLC was able to handle unpredicted internal disturbances and data uncertainties very well. It was also shown that a T1 FLC was also capable of handling the errors; however, this controller has more control effort than the IT2 FLC.  
In another work [90], an adaptive IT2 fuzzy sliding-based mode controller was designed for a chaotic system. This controller consisted of a fuzzy control design and a hitting control design. An IT2 FLC was designed to imitate the feedback linearization, whereas the hitting controller was designed to control the error between the feedback linearization and the IT2 FLC. From the simulations, it was concluded that the control system was robust while achieving good performance.  
An inverted pendulum on a cart system was proposed as a model to be controlled using an IT2 PID system in [91]. This controller was designed using the simplified TR method, which was able to handle the effects of structure uncertainties. The results were compared to a similar controller that used the uncertainty bound method. Results showed improved performance using the TR method. In addition, the proposed controller outperformed a T1F-PID controller.  
In [92], a new observer-based indirect adaptive IT2 FLC was developed for a mass-spring-damper system in a tracking application. In the modeling of this system, Coulomb friction, non-linear spring forces, non-linear friction forces and non-linear disturbances were considered. The controller outputs the external forces to control the system position. It was shown in the simulations that the IT2 FLC handles the unpredicted internal disturbance very well. The performance of the IT2 FLC showed better results compared to a T1 FLC. Moreover, the T1 FLC required more control effort.  
In [93], an IT2 FS with actuator faults was used to design an IT2 state-feedback sampled data controller. This controller was constructed to guarantee stability and effective performance for possible actuator failures. The IT2 state-feedback sampled data controller was designed such that the closed-loop system is asymptotically stable for all actuator failures using the Lyapunov stability theory. It was then tested using the inverted pendulum model, which was found to be effective. It was found to be reliable because the controller is able to guarantee the asymptotic stability and \(\mathrm{H}_{\infty}\) performance if the actuator fails. Combining T2 FLC and neural networks can be used in developing controllers with learning capabilities while they handle uncertainties [94].
## 3 Results and Discussion
### Characterization of the Prepared SPME
#### 3.1.3 Surface Morphology of Coating  
SEM was used to characterize the morphology of the prepared SPME fiber [25]. Figure 3a is a photo of the fiber with a uniform and complete surface. The coating does not fall off under external force extrusion. Figure 3b,d shows images taken from both the surface and the cross section of the fiber (400\(\times\)). The coating thickness of the prepared SPME was measured at 90 \(\upmu\)m, which is higher than the conventional SPMEs (such as PDMS, 75 mm). Moreover, the surface of the synthesized SPME was rougher in comparison to SPME synthesized using the traditional approach, resulting in an extension of surface area and consequently a higher adsorption efficiency. In addition, the cross-section evaluation (Figure 3e) emphasized a porous structure of the inner part of the sorbent coating. Due to the loss of solvent in the rapid thermal fixation process, some micron openings were formed on the surface of PDMS film, rendering the surface of PDMS film rougher (Figure 3c). The DVB and carbon particles in the PDMS were evenly distributed, and no separation layer or aggregation was observed, which ensures the reproducibility of the coating. This result can be attributed to sufficient stirring during membrane preparation. Moreover, the coating prepared by chemical bonding one-pot method can better overcome the defect of the poor mechanical strength of commercial coatings due to their multi-layer coatings.  
Figure 2: TGA of prepared DVB/Carbon/PDMS SPME fiber: (**a**) temperature programming: initial temperature 50 \({}^{\circ}\)C, heating rate 20 \({}^{\circ}\)C/min, 20–70 min, constant temperature 250 \({}^{\circ}\)C; (**b**) temperature range: 50 \({}^{\circ}\)C–500 \({}^{\circ}\)C.
## 5 Discussion
### Validation of the Results
#### 5.1.4 Validation of Steady-State Heat Results  
The steady-state heat analysis was performed on DFF and PERIIH to investigate the temperature variation within the interior and exterior surfaces. There are no standards governing the required temperature variation; however, this information can be used to approximate the energy cost of the structure, using the total heat flux.  
The total flux of DFF was obtained as 64.4 J/s, equivalent to 64.4 W. Assuming constant heat flux, the total flux for one week can be calculated by multiplying the total flux by 0.168. This results in a total flux of 10.8 kWh per week. Assuming an energy rate of AUD 0.28/kWh, this equates to AUD 3.02 per week to heat the corner shown in Figure 13.  
Similarly, PERIH had a total flux of 75.1 J/s or 75.1 W. Following the above procedure, it results in a weekly energy cost of AUD 3.53 to heat the corner represented in Figure 20.
## 3 Results
### Comparison of Validation Multi-Scale GPP Products at Field Scale and at Footprint Scale  
The MODIS GPP and downscaled GPP was validated at field scale and at footprint scale, as shown in Figure 6. Although studies have demonstrated that MODIS GPP was underestimated in the Heihe River Basin [(49)], we found that the accuracy of MODIS GPP was higher when validated at footprint scale (R\({}^{2}\) = 0.56, RMSE = 3.07 g C m\({}^{-2}\) d\({}^{-1}\)) than at field scale (R\({}^{2}\) = 0.51, RMSE = 3.34 g C m\({}^{-2}\) d\({}^{-1}\)). The same situation occurred in the validation of downscaled GPP. The precision of downscaled GPP was higher when validating in the footprint scale (R\({}^{2}\) = 0.85, RMSE = 1.34 g C m\({}^{-2}\) d\({}^{-1}\)) than validating GPP using field data directly (R\({}^{2}\) = 0.82, RMSE = 1.47 g C m\({}^{-2}\) d\({}^{-1}\)). This indicated that the representativeness of the observed carbon flux data provided some important information for the validation of remote sensing GPP products at different scales. This may be useful when taking into account the footprint of field data in the validation of multiple-scale remote sensing products [(50; 51)].
## 6 Establishment of Semi-Active Control System  
### Determination of Weight Matrix Coefficient  
In the LQR algorithm, the power matrices Q and R are two important control parameters, and the control force and vibration absorption effect of the structure are determined by the selection of Q and R. From the literature [31], it is known that the vibration absorption effect of the structure is only related to the ratio of \(\alpha\) and \(\beta\). Therefore, the values of \(\alpha\) and \(\beta\) are determined by conducting parameter analysis in this section. Through preliminary trial calculations, \(\beta\) is firstly determined as \(10^{-5}\), and the value of \(\alpha\) is set to vary from 0 to 100, and a program was written using MATLAB to study the active optimal vibration absorption effect of PC continuous girder bridge with CSWs.  
Figure 3: Adjusted El Centro Seismic Wave.  
Figure 2: Schematic diagram of the finite element model.  
After determining the coefficient \(\alpha\) of the weight matrix, the value of the active optimal control force can also be determined. The peak value changing of the optimal control force is analyzed when the value of \(\alpha\) is 40 and 100 in Table 1. In the table, Fc_2# and Fc_3# represent the peak active control force of pier #2 and pier #3, respectively, and \(\gamma\) is the growth rate caused by the change in the weight coefficient \(\alpha\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(\alpha\) & \(X_{\text{max}}\) & \(X_{\text{rms}}\) & \(M_{\text{max}}\) & \(M_{\text{rms}}\) & \(F_{\text{max}}\) & \(F_{\text{rms}}\) & Fc\_2\#/N & Fc\_3\#/N \\ \hline
40 & 0.57 & 0.72 & 0.57 & 0.72 & 0.52 & 0.72 & 894.6936 & \(2.53\times 10^{5}\) \\
100 & 0.60 & 0.74 & 0.60 & 0.74 & 0.55 & 0.74 & \(1.92\times 10^{3}\) & \(2.98\times 10^{5}\) \\ \(\gamma\) & 5.26\% & 2.78\% & 5.26\% & 2.78\% & 5.77\% & 2.78\% & 114.64\% & 17.65\% \\ \hline \end{tabular}
\end{table}
Table 1: Variation of active optimal control power with weight coefficient.  
Figure 4: Response of the composite structure with the weight coefficient: (**a**) displacement of main beam; (**b**) bending moment of pier #2; (**c**) shear of pier #2 bottom; (**d**) control effect; (**e**) control force.  
As is seen in Table 1, when the value of the weight coefficient \(\alpha\) is increased from 40 to 100, the increasing of each control index is basically between 3% and 6%, while the active control forces of pier #2 and pier #3 have a larger increase, which indicates that after the value of the weight coefficient \(\alpha\) exceeds 40, with the increase in its value, more input is required in the control and the corresponding increase in vibration absorption effect cannot be obtained. Therefore, it is determined that when the weight coefficient \(\alpha\) is 40, the active optimal control force of composite bridge control system can be calculated.