esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyclic Consistent Image Style Transformation: From Model to System
## 6 Conclusions  
This study introduces the C3 GAN model, which was designed to address the issues of unstable training and limited pattern generation in traditional Generative Adversarial Networks (GANs). The core principle of C3 GAN is the incorporation of cyclic consistency loss, which ensures the consistency of content during the image style transfer process while maintaining the style information of the input images. By leveraging cyclic consistency, C3 GAN effectively stabilizes the training process and enhances the diversity and quality of the generated images.  
Experimental results demonstrate that C3 GAN significantly outperforms existing image style transfer models, such as CoGAN, BiGAN/ALI, and SimGAN, in terms of Per-pixel Accuracy (ACC), Per-class Accuracy (ACC), and Mean Intersection over Union (mIOU). These improvements highlight the effectiveness of cyclic consistency loss in enhancing the performance of GAN-based image style transfer systems.  
Additionally, we designed a comprehensive image style transfer system that integrates the C3 GAN model. This system, developed using Python, Flask 2.0.1, and MySQL 5.7, provides a user-friendly interface for uploading and processing images, leveraging the advanced capabilities of C3 GAN to deliver superior style transfer results. This system not only demonstrates the practical applications of our model but also sets a new standard for future developments in the field of image style transfer.  
Conceptualization, Y.G. and J.P.; methodology, B.S. and T.Z.; software, J.P. and T.Z.; validation, B.S. and J.P.; formal analysis, B.S. and Y.G.; investigation, T.Z. and B.S.; resources, Y.G., J.P. and K.C.; data curation, Y.G., T.Z. and K.C.; writing--original draft preparation, J.P., B.S. and K.C.; writing--review and editing, B.S. and J.P.; visualization, Y.G., B.S. and K.C.; supervision, J.P., T.Z. and K.C.; project administration, B.S. and Y.G.; funding acquisition, J.P., K.C., Y.G. and B.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the 2024 Macao Foundation Project (MF2342); the 2022 Research Topic of Online Open Course Guidance Committee of Undergraduate Universities in Guangdong Province (2022 ZXKC041; 2022 ZXKC561); and the \"Four New\" Experimental Teaching Curriculum Reform Project of Jinan University (SYJG202317).  
Not applicable.  
The data used to support the findings of this study can be obtained from the corresponding author upon request.  
The authors declare that there are no conflicts of interest regarding the publication of this paper.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Time(ms)** \\ \hline CoGAN & 8260 \\ BiGAN/ALI & 7032 \\ SimGAN & 7900 \\ cGAN & 6260 \\ L1+CNN & 6840 \\ L1+cGAN & 6480 \\ C3 GAN(ours) & **6240** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time-efficiency comparison of various image style transfer methods.
# Experimental Setup and Graphical User Interface for Zero-Length Column Chromatography
## 4 Program Design
### Data Acquisition and Processing  
The code performs a series of actions that guarantee the acquisition of high-quality data and the precise determination of physico-chemical parameters that characterize the adsorbate-adsorbent system under study. This section describes the actions implemented in the code. They also allow the efficient determination of the operating conditions. The actions implemented in the GUI are the following:  
**Normalization and extraction of the Signal:** The FID signal was recorded continuously through a DAQ card with a sampling rate of 250 samples/second and saved in an Excel file for normalization. The code then normalizes the signal using the Equation (S16) of supplementary information.  
**Digital processing of the signal:** A digital signal processing stage is applied to the normalized desorption response. In this step, high-frequency fluctuations of the ZLC curve are reduced through a smoothing average filter with a 1000 sample window and a translation in time for compensating the effects of the smoothing filter. The sample window can be modified by the user.  
**Linear regression in the long time domain:** The linear region of desorption curve log-linear representation is fitted by the least-squares method and the slope and intercept are estimated. The criterion for determining the beginning of the linear region was as follows. The derivative of the ZLC curve is obtained and the beginning of the linear domain is identified with the point whose slope is a certain percent of the maximum of the derivative curve. This percentage can be chosen by the user and in this work it was 5 %.  
**Determination of kinetic and equilibrium parameters:** The time constant ratio (\(L\)) is calculated from the intercept calculated above. If this parameter is greater than 1, then the diffusion time constant (\(D/R^{2}\)) is estimated from the slope also calculated previously. Otherwise, the slope allows to estimate Henry's constant (see Section S2.2 of the Supplementary Materials). To achieve the condition of \(L>1\) it is necessary to perform experiments at different flow rates. The GUI allows the identification of kinetic conditions in an efficient way by reducing analysis times.
# Plant Electrical Signal Classification Based on Waveform Similarity
## 3 Materials and Methods
### Svm  
SVM is a supervised learning algorithm for classification and regression analysis. It is a non-probabilistic binary classifier. It supports both minimizing the empirical classification error and maximizing the geometric margin [87]. It is applied into in various applications such as ECG and EEG signal classification, image classification, text document categorization, etc.  
SVM model maps the sample data into another feature spaces so that they can be separated by a hyperplane. A good separation is achieved by the hyperplane that has the largest distance to the nearest training-data point of any class. Kernel functions can be used to measure the relative nearness between the data points to be discriminated to produce maximum-margin hyperplane [87; 88; 89].  
SVM includes linear and nonlinear classifiers which are supported by kernel functions. The common kernel functions are as following [88].  
Linear kernel function: \(K(x,x_{i})=x\cdot x_{i}\);  
Polynomial kernel function: \(K(x,x_{i})=\left[\left(x\cdot x_{j}\right)+1\right]^{b}\);  
Gaussian radial basis function: \(K(x,x_{i})=\exp(-\frac{\left|x-x_{j}\right|^{2}}{2\sigma^{2}})\);  
Sigmoid kernel: \(K(x,x_{i})=\tanh(\gamma\times(x-x_{j})+c)\).  
Classification performance is determined by the selection of kernel function and a kernel function's parameters. In this paper, we compared four kernel functions' performance to get a proper classifier. Moreover, we tested the results of different kernels with a range of parameters.
## 5 Experimental Results
### Experiments Comparing the Accuracy of CNN and ViT  
We conducted an experiment comparing the accuracy of a model with four encoders in the ViT and a CNN based on VGG16 across three datasets: Dataset 1, Dataset 2, and Dataset 3. We present the results in Figure 6. In the comparison between the ViT and CNN, data from Dataset 1 resulted in high accuracy at epoch 25, data from Dataset 2 and Dataset 3 resulted in high accuracy at epoch 15, and no significant improvement was observed even after training up to epoch 45. For the CNN model, the highest accuracy was obtained with Dataset 3, recording an accuracy of 99.6%. The datasets following this were Dataset 1 with 96.7% accuracy and Dataset 2 with 99.3% accuracy. Additionally, the training times for each method were as follows: For the ViT model, it was 37 s per epoch for Dataset 1, 37 s for Dataset 2, and 101 s for Dataset 3. For the CNN model, it took 4 s per epoch for Dataset 1, 4 s for Dataset 2, and 5 s for Dataset 3. Table 3 summarizes the recognition results compared to CNN when the ViT's encoder was four layers.
## 3 Antenna Characteristics
### Effect of the Size of the Slot on the Edge of the Radiating Patch  
The presence of left and right arc cuts along the middle edge of the radiating patch has a notable impact on the reflection coefficient characteristics of the second resonant frequency. Moreover, these arc cuts introduce a third resonant frequency and enhance the antenna's bandwidth. Figure 6 demonstrates that a larger arc slot produces a band notch ranging from 7 GHz to 8 GHz with only two resonant frequencies. On the other hand, an exceedingly small, slotted arc results in a poor reflection coefficient despite having two resonant frequencies. The desired outcome is achieved when the radius of the arc is set at 1.6 mm. This radius yields a bandwidth of 10.69 GHz at the resonant frequencies of 4.03 GHz, 9.61 GHz, and 11.48 GHz.
## 4 Experiments and Performance Evaluation
### Performance on Score Calculation  
The score calculation table is shown in Table 6. We compared this result with the Hadoop MapReduce framework. There was a little time variation after submitting the job every time. Therefore, we repeated our job five times and took the average result. For automatic repetition, we used a corntab [36] schedule. The result is shown in Figure 3. We see that there is a constant time delay with respect to the total data volume from 50K to 500K. However, if the data volume becomes larger, there would be a slight increase in the processing time.
## 5 Numerical Experiments  
In this section, numerical experiments are presented to demonstrate the accuracy and efficiency of the AG algorithm. First, we compare the AG algorithm with the FG algorithm for various randomly generated BAPs. Second, we test the performance of the AG algorithm on existing benchmark problems. Finally, a real-life production line is investigated to show that the AG algorithm can efficiently solve large-scale BAPs.  
In the experiments, simulation was used as a performance evaluation tool to calculate throughput and was integrated with the IPA technique to evaluate the throughput gradient [44]. The simulation procedure is based on the pseudocodes in [44; 46]. In each simulation run, the first 1,000,000 time units are used as the warming-up period and the subsequent 9,000,000 time units are used for statistical calculation of the average performance. To compare the quality of solutions obtained by different algorithms, 50 replications were run for each solution to obtain a good throughput estimate. The algorithms have been implemented in the C# language and the experiments have been carried out on a computer equipped with a 2.7 GHz Intel Core i7 processor and 8 GB of RAM.
## 4 Testing the Performance of Measurement Matrix Using Simulated Underwater Echo  
### Parameters Used in Performance Testing  
Let \(X\) be the original signal and \(\hat{X}\) be the recovered signal. The following evaluation indices are defined:  
Figure 2: Flowchart for the construction of the measurement matrix by the cyclic direct product and QR decomposition.  
Signal-to-noise ratio: \[SNR=10\text{lg}\Bigg{(}\frac{\|\hat{X}\|_{2}^{2}}{\|X-\hat{X}\|_{2}^{2}}\Bigg{)},\]
2. Matching rate: \[Matchingrate=1-\frac{\|\hat{X}^{\prime}-X^{\prime}\|_{2}}{\|\hat{X}^{\prime}+X^{ \prime}\|_{2}},\]  
In which \(\hat{X}^{\prime}\) and \(X^{\prime}\) are the absolute values of \(\hat{X}\) and \(X\).
## 1 Introduction
### Methods for Detecting Damage to Civil Engineering Structures
In addition, FBG and OFDR sensor systems require special optical fibers with embedded Bragg gratings, which have an estimated cost of USD 500-1000 per 100 m), as well as the need for specially trained personnel to work with these systems and interpret the results of the strain measurements. Given the high cost and complexity of the said optical fiber sensor systems, FBG, BOTDA, OFDR, and FPR-based sensor systems are mainly used to monitor new original structures and precarious buildings to ensure that they behave properly and safely in real-world conditions [41; 42]. For mass application in permanent damage-detection systems, which this work targets, only a much simpler and cheaper method of detecting structural damage is suitable.
## 4 The Calculation of Reliability Based on Fuzzy Mathematics  
In traditional reliability analysis theory, the calculation of structural failure probability is from the perspective of certainty, which is unreasonable due to the definite limit value being used to determine structural failure and the fuzziness of structure failure being ignored. Therefore, this paper introduced fuzzy mathematics to calculate fuzzy failure probability, which is needed to solve fuzzy loss expectation in terms of life-cycle cost and the multi-objective optimization model of SRHPC frame structures.  
The performance function to calculate performance-based seismic reliability of SRHPC frame is shown in Equation (17):  
\[f(u_{0},X,P)=u_{0}-u(X,P), \tag{17}\]  
where \(u_{0}\) is the random variable used to express the structural resistance term that represents inter-story drift, whose standard value has been shown in Table 3; \(u(X,P)\) is the load effect term, which also represents the story drift (only horizontal earthquake action is considered in this paper), where \(X\) is a random variable vector related to the attribute of the structure itself (e.g., component size, material characteristics, etc.) and \(P\) is a random variable vector related to loading.
## 2 Unanticipated Obstacle Scenario and Obstacle Avoidance Principle  
### Unanticipated Obstacle Scenario  
Figure 1 illustrates a realistic and common unanticipated obstacle scenario at the intersection inlet lane. The global path planning for the ego vehicle is to turn left. According to the road sign requirements, the vehicle enters the left-turn guidance lane delineated by a solid white line on its right side and a double solid yellow line on its left side, as shown in Figure 1a. Since the traffic light is red at this point and there are already other vehicles waiting in the lane, the ego vehicle queues up, as shown in Figure 1b. When the traffic light turns green, vehicles enter the intersection in order. At this moment, the vehicle in front of the ego vehicle suddenly breaks down and cannot move. It becomes a road obstacle that blocks the passage of the ego vehicle, as shown in Figure 1c.  
This scenario involves multiple driving requirements, including safety, mobility, and traffic law compliance. Different decision priorities lead to different behaviors. One engineering design option is to program the ego vehicle to prioritize the ability to obey traffic laws. This means continuous waiting behind the obstacle vehicle, which negatively impacts the mobility and safety of surrounding vehicles. Another approach is to program the vehicle to prioritize the ability to continue moving. This means entering adjacent lanes and proceeding around obstacles while remaining safe. This action will cause the ego vehicle to briefly violate the lane marking rules. Unanticipated obstacles could lead to a conflict between traffic law compliance and obstacle avoidance. AVs have to weigh the tension between safety, mobility, and traffic law compliance in this scenario [29].
## 2 Literature Review  
Scheduling problems in production systems have been widely studied, starting with the formulation of the Flow-Shop Problem (FSP), proposed by [PERSON] (1988). This model is particularly relevant to industries with structured production processes, such as bottling. Several extensions have been formulated to represent more complex production layouts. [PERSON] (1988) introduced the Hybrid Flow-Shop Problem (HFSP) which models a scheduling environment where \(n\) jobs must be processed through \(m\) stages, each containing \(k\) machines denoted by \((M_{k})\) with all jobs follow the same stage order, as opposed to the fixed machine sequence in the FSP.  
Additionally, time-dependent scheduling models, for which an extensive review is found in [PERSON] (1988), classify processing times as either monotonic or non-monotonic, fluctuating based on job characteristics and machine performance.  
To further address the limitations of traditional models, researchers have introduced family setups which group similar jobs and reduce transition times. Models for unrelated parallel machines, as explored by [PERSON] (1988), handle heterogeneous capacities. Finally, the inclusion of sequence-dependent setup times, as highlighted by [PERSON] et al. [PERSON] et al. (2000, 2001), underscores that setup times can vary significantly with the order of job processing, making sequencing a critical factor in optimizing production efficiency.  
However, a significant limitation in the literature involves properly accounting for job ordering in multi-machine scenarios, especially when setup tasks require extensive reconfiguration processes. This is particularly significant in industries such as wine bottling, where changing from one wine type to another demands substantial cleaning and adjustment. Our work addresses this gap by proposing a continuous-time scheduling model that integrates hydraulic considerations and complex sequence-dependent setups.
# Dual-Band Rectifier Circuit Design for IoT Communication in 5G Systems
## 2 Materials and Methods  
### Related Work  
RF-to-DC rectifier research can be widely categorized into integrated designs [CMOS and monolithic microwave integrated circuits (MMICs)] and discrete designs utilizing diodes or transistors. The use of CMOS technology can be found in several interesting works in the literature [36; 37; 38; 39; 40; 41].  
In [42], the authors fabricated a receiving antenna system that can harvest energy from Wi-Fi and WiMAX bands. This half-wave rectifier (HW) is on a Rogers RT/Duroid 5880 substrate and works at 3.5 GHz with a 0 dBm input with an efficiency of 44%. Another half-wave rectifier can be found at [43]. This rectifier achieves an efficiency of 42.5%, by using the SMS-7630 diode and having an input of \(-10\) dBm.  
Full-wave (FW) rectifiers are the best choice for achieving the best results since we can take advantage of both halves of the RF signal. In [44], the authors designed a full-wave rectifier that achieved 39.6% power conversion efficiency (PCE) at 3.8 GHz using an HSMS-2820 Schottky diode. Another full-wave rectifier was designed in [45] with a power conversion efficiency of 29.7% for an input power of 6 dBm at 3.5 GHz. At a frequency of 3.5 GHz, the authors in [46] designed a Villard voltage doubler that reaches 42% power conversion efficiency with 14 dBm input power. The authors in [47] manufactured a full-wave rectifier on Rogers RO3003 substrate with 0 dBm as input power and two Schottky diodes, SMS/630 \(-\) 079 LF. They achieved a power conversion efficiency of 42% at 3.5 GHz. In [48], the authors designed a full-wave rectifier that operates at sub-6 GHz in 5G bands with a central frequency of 3.5 GHz. This rectifier achieves a maximum PCE equal to 42.5% when the output load is 1.1 k\(\Omega\) and the input power reaches 9 dBm. The authors in [49] using SMS7630 diodes designed a rectifier that works at GSM900, GSM1800, UMTS2100, WiMax 3500, and Wi-Fi (2.4 and 5 GHz). They achieve 33.5% maximum PCE at \(-15\) dBm when the frequency is 1.8 GHz. In [50], the designers fabricated a bridge rectifier that operates at 5 GHz. The circuit's conversion efficiency is up to 36.4%. A dual-band rectifier was designed in [51], which achieves 49.9% power conversion efficiency with 1.1 k\(\Omega\) load resistance and 0 dBm input power at 5 GHz. The authors of [52] created a rectifier with a PCE of 51.3%, a frequency of 5.1 GHz, and an output load of 350 \(\Omega\).
# Using Markov Random Field and Analytic Hierarchy Process to Account for Interdependent Criteria
## 4 Numerical Example
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Weights** & \(\mathbf{C_{1}}\) & \(\mathbf{C_{2}}\) & \(\mathbf{C_{3}}\) & \(\mathbf{C_{4}}\) \\ \hline AHP & 0.150 & 0.400 & 0.150 & 0.300 \\ \hline MRF-adjusted weights (1) & 0.273 & 0.109 & 0.360 & 0.258 \\ FCM-adjusted weights (1) & 0.244 & 0.217 & 0.280 & 0.259 \\ \hline MRF-adjusted weights (2) & 0.291 & 0.200 & 0.256 & 0.253 \\ FCM-adjusted weights (2) & 0.267 & 0.231 & 0.250 & 0.253 \\ \hline MRF-adjusted weights (3) & 0.063 & 0.343 & 0.324 & 0.270 \\ FCM-adjusted weights (3) & 0.192 & 0.272 & 0.281 & 0.254 \\ \hline MRF-adjusted weights (4) & 0.213 & 0.256 & 0.285 & 0.246 \\ FCM-adjusted weights (4) & 0.223 & 0.261 & 0.254 & 0.261 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Weights for different methods. Figure 1: The dependency and pairwise potential between criteria. example, we have demonstrated how discrete MRF can be incorporated into the AHP framework to account for the interdependencies between criteria using concrete values. The proposed method enhances decision making and can be applied to various real-world decision-making problems where criteria interdependencies play a crucial role.
# Separable Data-Hiding Scheme for Encrypted Image to Protect Privacy of User in Cloud
## 3 Proposed Scheme
### Formatting of Mathematical Components  
Data extraction and image recovery are very easy in our scheme. When the receiver obtains the stego-image, if he/she has the data-hiding key \(K_{2}\), the LSBs of the encrypted image can be obtained and converted into the decimal number \(d_{t}\). Then the hidden bit can be computed by \(s_{t}=d_{t}\)mod2. But if the receiver has an encrypted key \(K_{1}\), he/she can recover the protected image directly by BCS reconstruction. Because the compressive sensing has a certain resilience to error, the small modification of the measurements will not have a significant effect on recovering the image. If the receiver has both an encrypted key \(K_{1}\) and a data-hiding key \(K_{2}\), he/she can obtain the hidden bits and the recovered image.  
Figure 5: Process of encoding and hiding.
# Web3 DGIS-Based System for Reservoir Landslide Monitoring and Early Warning
## 1 Introduction
Some progress has been made on the development of an early warning system; however, there are issues that remain to be resolved: (1) expression and analysis of three-dimensional spatial information; (2) real-time collection, transfers, storage, analysis and integration of multi-spot and multi-type sensor statistics; and (3) connecting the landslide early warning modal with a decision-making model and with information publishing functions, so as to effectively achieve the functional integration of the system. In this study, the Danjiangkou Reservoir area was used as a case study to develop a system monitoring and modeling landslides based on Web3 DGIS (Web3D Geographic Information Systems), which features three-dimensional spatial expression and function analysis of the ground surface environmental and monitoring spot data. The developed method, which is based on a real-time data transport network among sensors, on-spot monitoring sites, and monitoring early warning centers, is able to collect and store real-time monitoring data, and to integrate monitoring, early warning, and information publishing functions.
## 3 Proposed Method
### Overall Architecture  
The overall architecture of the proposed AFFDet is shown in Figure 1, which consists of three parts: Backbone, MFFM, and Detection Head. The backbone outputs the feature maps of \(R3\), \(R4\) and \(R5\). MFFM fuse the multi-scale feature maps through these feature maps and output a final feature map after weighted multi-level feature fusion. The detection head module consists of three branches, which are the branch of center point prediction, the branch of the offset prediction, and the branch of the scale prediction on the final feature map.