esa-sceva/satcom-chunk-collection huggingface dataset:
# Text-Guided Customizable Image Synthesis and Manipulation
## 3 Our Proposed Method
### Loss Function  
**Generator Loss.** The loss function of the generator in the network structure includes two aspects: one is the GAN's adversarial loss, and the other is the perceptual loss. The specific adversarial loss of the generator is as follows:  
\[L_{G\_adv}=\underbrace{-\frac{1}{2}\sum_{I_{g}\sim P_{G}}\log D(I_{g})}_{ unconditional loss}\underbrace{-\frac{1}{2}\sum_{I_{g}\sim P_{G}}\log D(I_{g},T)}_{ conditional loss} \tag{21}\]  
The unconditional loss is used to determine whether the generated image is real, and the conditional loss is used to determine whether the synthesized image matches the text's semantic information.  
The specific calculation of the perceptual loss is as follows:  
\[L_{per}(I_{g},\ I_{r})=\frac{1}{CHW}\big{\|}\varphi(I_{g})-\varphi(I_{r}) \big{\|}_{2}^{2} \tag{22}\]  
where \(C\), \(H\), and \(W\) represent the channel number, height, and width of the image, respectively, and \(\varphi\) stands for the pretrained VGG model [31]. Following the work of [PERSON] et al. [32], we also used the second layer's feature output of VGG to calculate the perceptual loss.  
In summary, the generator's loss function is as follows:  
\[L=L_{G\_adv}+L_{per} \tag{23}\]  
**Discriminator Loss.** The discriminator's loss function includes the adversarial loss, and the corresponding equation is as follows:  
\[L_{D\_ture}=-\frac{1}{2}\sum_{I_{t}\sim P_{data}}[\log D(I_{r})+\log D(I_{r},T)] \tag{24}\]  
\[L_{D\_false}=-\frac{1}{3}\{\sum_{I_{g}\sim P_{G}}[\log D(I_{g})+\log D(I_{g},T )]+ \tag{25}\]  
\[\sum_{I_{t}\sim P_{data}}\log D(I_{r},T_{mis})\}\]  
\[L_{D}=L_{D\_true}+L_{D\_false} \tag{26}\]  
where \(L_{D\_true}\) and \(L_{D\_false}\) represent the cases where the discriminator needs to be judged as true and false, respectively. There are two cases that the discriminator needs to distinguish as true: the original image (\(I_{r}\)) and the original image with matched text (\(I_{r},T\)). There are three cases that need to be judged as false: the generated image (\(I_{g}\)), the generated image with matched text (\(I_{g},T\)), and the original image with mismatched text (\(I_{r},T_{mis}\)).
## 2 An Overview of Published Articles
Reference [23] applies data-driven methods to detect faults in power systems and proposes an effective load frequency control strategy to address potential fault scenarios. Reference [27] employs real-time process monitoring techniques to assess fluctuations in the production process and combines these with statistical analysis to provide early warnings for fault detection, ensuring production stability. References [18; 24; 25] all involve the application of system control strategies. Reference [18] proposes an effective battery management strategy that uses controllers to mitigate battery aging and manage high-frequency transients. Reference [24] explores the application of intelligent control strategies in piezoelectric structures, employing smart piezoelectric patches and advanced control methods to effectively suppress vibrations in engineering structures. Reference [25] introduces the application of data-driven, model-free sliding mode control techniques. References [20; 21; 22] focus on the application of decision support strategies. Reference [20] presents a data analysis method based on the work sampling technique, studying work activities and their interrelationships in production management and providing decision-makers with more accurate production optimization tools. Reference [21] helps production managers optimize workflows by discovering the process choreography. Reference [22] proposes a sequential three-way classification ranking method based on the VIKOR method, aiding decision-makers in making optimal choices. In addition, Reference [19] introduces real-time monitoring in maritime rescue missions through path planning and intelligent scheduling methods. Reference [26] utilizes finite element modeling to simulate surface quality and stress distribution in the ultrasonic vibration-assisted milling process, optimizing material processing. Reference [17] presents a hybrid crow search arithmetic optimization algorithm for solving the weighted combined economic emission dispatch problem in power systems, optimizing emissions and cost issues in the power scheduling process. Finally, the industries covered in these studies include manufacturing, power systems, maritime rescue, and aerospace. The authors are mainly from China, but scholars from Slovenia, Mexico, Morocco, Brazil, South Africa, Colombia, and Greece also contributed to our Special Issue.
# Junction Temperature Optical Sensing Techniques for Power Switching Semiconductors: A Review
## 3 Junction Temperature Optical Sensing Techniques
### TSOP Sensing Technique
The spectral shifts of the two peaks at 510 nm and 390 nm were used to determine junction temperature at a mean error of \(\pm\)3 \({}^{\circ}\)C within a temperature range of 50 to 130 \({}^{\circ}\)C. A similar approach was also implemented in [96] with similar major and minor spectral peaks, but an improved sensing performance was reported with an error of \(\pm\)1.2 \({}^{\circ}\)C in a temperature range of 10\({}^{\circ}\) to 90 \({}^{\circ}\)C and using multiple sensors for photodetection. However, this approach employed a high-resolution spectrometer for measurement, since the peak wavelength shifts noted were only a few nanometers. In addition, optical measurement with a high gain was required since the emission from the body diode of SiC is not very efficient due to its indirect bandgap. Light-sensing circuits such as a photodetector typically detect the spectral characteristics of light emission through EL. In the case of multiple parallel devices, multiple detectors of different wavelength bands may be implemented for spectral sensitivity; otherwise, the guided light has to be filtered for proper detection [83]. Also, a major limitation of EL application in SiC MOSFET is that the chip is packaged in plastic and covered by metal on the top, which makes it challenging to obtain a direct measurement. When a direct current is applied to the body of diode, the light emitted around the chip cannot be fully extracted. Thus, the ratio of weighted spectral information obtained by the photodiode does not reflect the actual junction temperature. In addition, the cross-sensitivity of junction temperature and current is another challenge in SiC MOSFET TSOP sensing. Although TSOP is not as sensitive as a thermal camera, they are still usable for online junction temperature measurement and have been implemented for SiC MOSFET in the literature. Other challenges related to the setup are discussed in Section 4 of this article, while the summary of this technique's key advantages and challenges is highlighted in Table 5. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Key Benefits** & **Challenges** \\ \hline \(\bullet\) & Online junction extraction. & \(\bullet\) \\ \(\bullet\) & Low-cost photodetection circuits can be employed for detection. & \(\bullet\) \\ \(\bullet\) & Noninvasive to the device operation. & \(\bullet\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of key benefits and underlying challenges of TSOP sensing technique.
## 3 Benefits of Remote Health Monitoring  
Remote health monitoring ensures the constant delivery of quality care to patients who are not proximal to traditional healthcare service delivery. IoT healthcare is becoming more sophisticated and available for diverse applications, amid the new generation of Internet communications enabled by fiberoptic and 5G Internet connections. Remote monitoring allows HPs to reach out to potential patients, including those otherwise excluded from healthcare access by financial barriers, as well as to keep track of conventional patients and perform monitoring (e.g., pre-surgery check-ups). It thus offers better quality of life, improved mobility, and decreased mortality rates for unprivileged and disadvantaged healthcare service users. In addition, it reduces healthcare expenditure by curtailing prolonged hospitalization and reducing the frequency of re-hospitalization, as well as helping prevent or mitigate deteriorating health conditions through continuous monitoring, utilizing advanced and sophisticated devices.  
The pervasive access of patient data in electronic health records allows interdisciplinary collaboration and consultation, which further helps optimize health resource allocation efficiency and patient outcomes, increasing patient satisfaction and confidence. Faster access to relevant patient data is safer and enables quicker treatment initiation, associated with shorter treatment duration and lower costs. Both HPs and patients can easily access health information, with up-to-date values of the vital parameters as we focus on our proposed algorithm. It is, therefore, easier for HPs to check all data collected from monitoring devices in real-time, with the help of visualization tools such as charts and diagrams.  
The rest of this paper is organized as follows. Section 4 presents an overview of Cloud Computing-healthcare and IoT-healthcare integration, the evolution of key Cloud Computing (CC) and IoT technologies, and recent applications of CC and IoT in healthcare services. Section 5 describes related works on incentive schemes, including previous work about the IoT and how it relates to improvements in network QoS. Section 6 describes our proposed technique and its mechanisms, with some mathematical modeling and theoretical analysis concerning this proposal. The results are then reported, and the performance of the proposed technique is evaluated in Section 8. Finally, a conclusion is given and future work is discussed in Section 9.
# A Study on the Applicability of Bio-Sulfur as a Cement Admixture
## 2 Results
### Evaluation Outcomes of the Specimen Characteristics  
#### 2.2.1 Compressive Strength  
Figure 5 shows the compressive strength evaluation results for each substitution method. Figure 5a shows bio-sulfur used instead of cement, and Figure 5b shows bio-sulfur added to cement. There were no significant differences in the compressive strength at 2.5% and 5% content for the substituted binder and substituted aggregate. At 5% and 10% content, the substituted aggregate case showed slightly higher strength. In the substituted aggregate case, which contained a relatively large amount of cement as a whole, the degree of strength reduction showed a similar tendency.  
The reference standards for compressive strength for KS L 5201 Portland cement are 12.5 N/mm\({}^{2}\), 22.5 N/mm\({}^{2}\), and 42.5 N/mm\({}^{2}\) or more, respectively, for 3 day, 7 day, and 28-day strength levels. The seven-day compressive strength levels of the specimen containing 2.5% bio-sulfur were equivalent to or greater than the KS standard but were lower than the standard for 28 days. However, even though the KS standard criteria were met at ages of three, seven, and 28 days, the overall strength decreased in the specimens with bio-sulfur compared to the reference specimen (OPC 100%). A typical cement mixture showed a black (dark gray)-based fracture surface, whereas the specimen containing bio-sulfur showed a yellow fracture surface, confirming that the bio-sulfur was mixed homogeneously. According to these results, it is inferred that the fine particles of bio-sulfur are helpful for mixing, whereas bio-sulfur does not have a positive effect on improving the degree of hydration. This appears to be due to the pH of the bio-sulfur. The hydration reaction of cement increases its compressive strength at pH 11-12, but with the added amount of bio-sulfur (pH of 8-9), which has a pH lower than that of cement, it becomes neutralized, and the specimen's strength decreases [23].  
Figure 4: XRD results of the dry bio-sulfur (**a**) and petroleum sulfur (**b**). The sulfur format in (**a**) is S8, with Na also present, and the sulfur format in (**b**) is S\({}^{\$}\).  
Figure 5: Relative comparison of the compressive strength ratios for binder (**a**), aggregate (**b**) by OPC (100%). The figure shows that, as the mixing ratio of bio-sulfur increased, the compressive strength decreased, and the binder had lower compressive strength than the aggregate.
## Appendix E Upper Bound on the Volume of the Hamming Ball  
**Lemma A2** (see (72, Lem. 16.19)).: _Let integer \(n\geq 1\) and \(0<\varepsilon\leq 1/2\) with \(n>\lfloor n\varepsilon\rfloor\geq 1\). Then, volume of the Hamming ball in the binary alphabet is upper bounded as follows:_  
\[\text{Vol}\big{(}\mathcal{B}_{\mathbf{x}_{0}}(n,r)\big{)}\triangleq\sum_{j=0}^ {\lfloor nr\rfloor}\binom{n}{j}\leq 2^{nH(\varepsilon)}, \tag{101}\]  
Proof.: Note that \(0<\forall\varepsilon\leq 1/2\), the logit function \(H(\varepsilon)\triangleq\log\left(\frac{\varepsilon}{1-\varepsilon}\right)\) is non-positive, i.e.,  
\[H(\varepsilon)=\log\!\left(\frac{\varepsilon}{1-\varepsilon}\right)=\log \varepsilon-\log(1-\varepsilon)\leq 0. \tag{102}\]  
Next, notice that for \(i\in[0,\lfloor n\varepsilon\rfloor]\) we obtain the following:  
\[i\log\varepsilon+(n-i)\log(1-\varepsilon)\geq-nH(\varepsilon), \tag{103}\]  
where \(H(\varepsilon)\) is the binary entropy function. Hence, \(\varepsilon^{i}(1-\varepsilon)^{n-i}\geq 2^{-nH(\varepsilon)}\). Now,  
\[1=(\varepsilon+(1-\varepsilon))^{n}=\sum_{i=0}^{n}\binom{n}{i}\varepsilon^{i}( 1-\varepsilon)^{n-i}\geq\sum_{i=0}^{\lfloor nr\rfloor}\varepsilon^{i}(1- \varepsilon)^{n-i}\geq 2^{-nH(\varepsilon)}\sum_{i=0}^{\lfloor nr\rfloor} \binom{n}{i}. \tag{104}\]  
\(\square\)  
Therefore, we obtain  
\[\text{Vol}\big{(}\mathcal{B}_{\mathbf{x}_{0}}(n,r)\big{)}\triangleq\sum_{j=0}^ {\lfloor nr\rfloor}\binom{n}{j}\leq 2^{nH(\varepsilon)}. \tag{105}\]
# Acquisition of Weak BDS Signal in the Lunar Environment
## 2 Materials and Methods
### BDS B1I Signal  
The BDS B1I signal is received by the antenna, then down-converted and filtered by the front end. Thus, the received signal before the analog-to-digital conversion can be written as follows:  
\[S(t)=\sum_{i=1}^{N_{s}}s_{i}(t)+n(t), \tag{11}\]  
where \(S(t)\) is the sum of the transmitted signals from \(N_{s}\) different satellites and \(n(t)\) is the additive white Gaussian noise (AWGN) term. The expression of the intermediate frequency (IF) signal \(s_{i}(t)\) of the \(i\)-th satellite without noise usually assumes the following structure [33]:  
\[s_{i}(t)=A_{i}d_{i}(t-\tau_{i})c_{i}(t-\tau_{i})NH_{i}(t-\tau_{i})\cos[2\pi(f_ {IF}+f_{d,i})t+\varphi_{i}], \tag{12}\]  
where:  
* \(A_{i}\) is the amplitude of the \(i\)-th received signal, and the signal power is given by \(P_{i}=A_{i}^{2}/2\).
* \(d_{i}\) is the binary phase shift key (BPSK)-modulated navigation data.
* \(c_{i}\) is the spreading sequence generated by two shift registers in a certain way, and it is a pseudo random noise (PRN) code of length 2046.
* \(NH_{i}\) is the Neumann-Hoffman (NH) code modulated on the navigation data.
* \(\tau_{i}\), \(f_{d,i}\), and \(\varphi_{i}\) are the code phase, the Doppler shift, and the initial carrier phase offset for the \(i\)-th signal, respectively.
* \(f_{IF}\) is the receiver intermediate frequency.  
In this research, we wrote a MATLAB-based software for generating BDS BII digital IF signals. According to the performance of the GNSS signal at LLO receivers in Section 3.1, the relevant parameters of the software are presented in Table 3.
## 2 Materials and Methods
### Geographic Polarization Suppression Model
#### 2.2.2 Orientation Information Solution Method
The post-rotation orientation matrix is shown in Equation (16). \[ \begin{array}{l}C_{E}^{b}=\begin{array}{c}\begin{bmatrix}1&0&0\\ 0&\cos\theta&\sin\theta\\ 0&-\sin\theta&\cos\theta\\ \end{bmatrix}\begin{bmatrix}\cos\gamma&0&-\sin\gamma\\ 0&1&0\\ \sin\gamma&0&\cos\gamma\\ \end{bmatrix}\begin{bmatrix}\cos\psi&\sin\psi&0\\ -\sin\psi&\cos\psi&0\\ 0&0&1\\ \end{bmatrix}\\ =\begin{bmatrix}\cos\gamma\cos\psi&\cos\gamma\sin\psi&-\sin\gamma\\ \sin\theta\sin\gamma\cos\psi+\sin\theta\sin\psi&\sin\theta\sin\gamma\sin\psi+ \cos\theta\cos\psi&\sin\theta\cos\gamma\\ \end{bmatrix}\end{array}\end{array}  \tag{16}\]  
Since the coordinate system remains Cartesian during the rotation from the geographical coordinate system to the object coordinate system, \(C_{b}^{E}\) is an orthogonal matrix, implying \(C_{b}^{E}=\left(C_{E}^{b}\right)^{T}\). By relating \(C_{b}^{E}\) and \(C_{E}^{b}\), the expressions for the rotation angles of each axis can be derived, where these rotation angles represent the orientation angles. The expressions for the orientation angles are shown in Equation (17). \[ \begin{array}{l}\left\{\begin{array}{l}q_{0}^{2}+q_{1}^{2}-q_{2}^{2}-q_{3}^ {2}=\cos\gamma\cos\psi\\ 2(q_{1}q_{2}+q_{0}q_{3})=\cos\gamma\sin\psi\\ 2(q_{1}q_{3}-q_{0}q_{2})=-\sin\gamma\\ 2(q_{2}q_{3}+q_{0}q_{1})=\sin\theta\cos\gamma\\ q_{0}^{2}-q_{1}^{2}-q_{2}^{2}+q_{3}^{2}=\cos\theta\cos\gamma\\ \end{array}\right.\Rightarrow\left\{\begin{array}{l}\gamma=\arcsin2(q_{0}q_ {2}-q_{1}q_{3})\\ \theta=\arctan(\frac{2(q_{2}q_{3}+q_{1}q_{2})}{q_{0}^{2}-q_{1}^{2}-q_{2}^{2}+q_ {3}^{2}})\\ \psi=\arctan(\frac{2(q_{1}q_{2}+q_{0}q_{3})}{q_{0}^{2}+q_{1}^{2}-q_{2}^{2}-q_ {3}^{2}})\\ \end{array}\right.\end{array} \end{array} \tag{17}\]  
By combining Equations (13) and (17), the angle values of the orientation angles can be calculated. Therefore, the orientation information solution is presented as shown in Equation (18). \[ \begin{array}{l}f_{2}\big{(}w_{x},w_{y},w_{z}\big{)}=\left\{\begin{array}{ l}\arcsin2(q_{0}q_{2}-q_{1}q_{3})\\ \arctan\left(\frac{2(q_{2}q_{3}+q_{1}q_{2})}{q_{0}^{2}-q_{1}^{2}-q_{2}^{2}+q_ {3}^{2}}\right)\\ \arctan\left(\frac{2(q_{1}q_{2}+q_{0}q_{3})}{q_{0}^{2}+q_{1}^{2}-q_{2}^{2}-q_ {3}^{2}}\right)\\ \end{array}\right.\end{array}  \tag{18}\]
## 2 Experiment Description
### Experimental Equipment and Test Design  
#### 2.2.1 Experimental Equipment  
The experimental equipment consists of seamless steel pipe, flange blind plate, bolt, and pressure cell. Specifically, a seamless steel pipe with a diameter of 150 mm, a length of 300 mm, and a thickness of 20 mm was used. Flanges with thickness of 20 mm were welded at both ends of the seamless steel pipe, and both ends were sealed with a flange blind plate with a thickness of 20 mm. The pressure box was mounted in a circular groove with a diameter of 26.5 mm on the bottom flange blind plate, as shown in Figure 1. Four bolts with diameter of 15 mm and length of 360 mm were used to fix and seal flange plates to form grouting containers for test. In order to prevent the pressure box from contacting the grouting material directly, a layer of high-temperature resistant silica gasket was needed between the lower flange and the cylindrical steel plate.  
The pressure box produced by Nanjing Danmo Electronic Technology Co., Ltd. is used to measure the force, which has a diameter of 58 mm and a thickness of 12 mm, and the maximum range is 8 MPa. The pressure cell a strain full bridge circuit is adopted in the earth pressure box, which can accurately eliminate the influence of temperature changes on the instrument.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Grout** & **Density (g-cm\({}^{-3}\))** & **Viscosity (Pa-s)** & **Gel Time (s)** & **Gravity (mg/m\({}^{3}\))** \\ \hline Polymer & 0.21 & 2.2 & 60 & 3.14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measured properties of the tested grouts.
## 2 Experimental Apparatus and Methods
### The Corona-Stabilized Switch
#### 2.1.3 Operating Pressure and Gap Spacing  
The pressure (\(p\)) and gap spacing (\(s\)) are important parameters of the gas switch that determine the motion characteristics of corona-generated charge during switch breakdown and the recovery characteristics of neutral gas density during insulation recovery. The breakdown stability of the corona-stabilized switch was experimentally studied in a pressure range of 0.05-0.4 MPa and gap spacings ranging from 1.5 mm to 12.5 mm.
# Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes
## 4 Sensitivity Analysis
### Emulators-Based Sensitivity Analysis
As stated in Reference [36], we can use the GP emulator developed to probabilistically estimate \(f(\cdot)\), to make inference about the main and interaction effects of \(f(\cdot)\), since they can be considered as linear combinations of \(f(\cdot)\). Furthermore, since the posterior distribution of \(f(\cdot)\) is a multivariate Gaussian distribution (c.f. Equation (4)), the resulting posterior distribution for the main and interaction effects is also a multivariate Gaussian distribution. Specifically, if the posterior mean for \(f(\cdot)\) is given by Equation (5), then the posterior mean of the following quantity  
\[E(f(\mathbf{X})\mid\mathbf{x}_{p})=\int_{\chi_{-p}}f(\mathbf{x})dG_{-p\mid p }(\mathbf{x}_{-p}\mid\mathbf{x}_{p}), \tag{17}\]  
(recalling that \(\chi_{-p}\) denote the input space associated with \(\mathbf{x}_{-p}\), and \(G_{-p\mid p}(\mathbf{x}_{-p}\mid\mathbf{x}_{p})\) is conditional distribution of \(\mathbf{x}_{-p}\) given \(\mathbf{x}_{p}\) under the input parameters distribution, \(G\)), can be written as  
\[E_{post}\{E(f(\mathbf{X})\mid\mathbf{x}_{p})\}=T_{p}(\mathbf{x}_{p})K^{-1} \mathbf{y}\,, \tag{18}\]  
where  
\[T_{p}(\mathbf{x}_{p})=\int_{\chi_{-p}}t(\mathbf{x})^{T}dG_{-p\mid p}(\mathbf{ x}_{-p}\mid\mathbf{x}_{p})\,. \tag{19}\]  
The posterior mean of main effect or interaction can be similarly obtained as follows:  
\[E_{post}\{z_{i}(X_{i})\}=\{T_{i}(x_{i})-T\}K^{-1}\mathbf{y} \tag{20}\]  
Similarly, we can derive the standard deviations of the main effects and interactions. For further details see References [36; 37]. After evaluating Equation (20) for all inputs \(X_{i}\), we can visually assess the main effects by plotting their posterior means \(E_{post}(z_{i}(X_{i}))\) against \(x_{i}\), typically with bounds of plus and minus two posterior standard deviations. In order to see the influence of each variable, the input variables can be standardized and \(E_{post}(z_{i}(X_{i}))\) drawn in a single plot for \(i=1,\ldots,d\). The results of the SA of the two logical scenarios of interests in this paper will be presented in Section 6. Direct posterior inference for the variance-based measures introduced in Section 4.1, \(V_{i}\) and \(V_{T_{i}}\), is more complex as these measures are quadratic functionals of \(f(\cdot)\). See Reference [36] for detailed discussion on mathematical approached of dealing with quadratic functional forms of \(f(\cdot)\).
## 6 Results and Discussions
### Simulation of the Monitoring and Control of Parking Lots in the North of a City
#### 6.2.6 Context III: Simulation of Parking Lots in the North of a City in Real Time
We estimated the system's recovery time causing failures in the Pod that contains the microservices of the reservation service. Once we caused the failure, we calculated the time it took for the Pod to return to normal operation. We executed continuous deletions of the Pod using Kubernetes commands to simulate Pod failures and observe the Pod restart time in the cloud platform. The pod restart time was on the order of milliseconds as it is a light resource. Moreover, the recovery time of the microservices that control the reservations depends on the recovery of the state of the system (the total number of available reservations) in real time. The WSAN keeps the value of the number of available reservations, and the system recovers it by the synchronization operation when the web application restarts. Synchronization time was approximately 1 s.