esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Source Heterogeneous Kernel Mapping in Software Defect Prediction
## 2 Related Works
### Multi-Source Transfer Learning  
For the multi-source heterogeneous problem, there have been some studies in other fields. [PERSON] et al. [30] proposed multiple graphs and low-rank embedding (MGLE) for the learning of representations of data points in a graph which employs a low-rank embedding to acquire a shared representation for the data across multiple graphs. Specifically, the method learns a low-dimensional embedding for each graph separately, then combines these embeddings into a shared representation, using a weighted sum.  
[PERSON] et al. [31] put forward a method for predicting future price fluctuations, using a combination of heterogeneous data sources. The method utilizes natural language processing techniques to extract relevant information from multiple heterogeneous textual data sources, then combines this information with numerical data from financial statements. The combined data are then used to train a machine learning model to predict future price fluctuations.  
[PERSON] et al. [32] proposed an ontology and mapping heterogeneous data from multiple sources, using a combination of a hybrid neural network, composed of a convolutional neural network (CNN), a recurrent neural network (RNN), and an autoencoder, which is employed to learn a low-dimensional representation of the data that can be used to construct an ontology and map data from different sources.  
The aforementioned method is primarily used to address multi-source heterogeneous problems in other fields, and there are also some studies on the application of this method for multi-source CPDP.  
[PERSON] et al. [33] evaluated the effectiveness of different strategies, with respect to defect prediction, including single-source and multi-source approaches, feature selection, and transfer learning. They concluded that HDP methods based on metric transformation usually have better predictive performance, while HDP methods based on metric selection have better interpretability. [PERSON] et al. [34] evaluated the effectiveness of different multi-source cross-project defect prediction (MSCPDP) models for defect prediction across multiple projects.  
However, the above studies considered homogeneous CPDP, rather than heterogeneous. For multi-source HDP, there are two related studies in the literature: [PERSON] et al. [35] presented a multi-source selection-based manifold discriminant alignment (MSMDA) approach to address the challenges of using multiple sources of data for heterogeneous defect prediction while preserving privacy, while [PERSON] et al. [21] proposed a multi-source heterogeneous cross-project method (MHCPDP), which is implemented using multi-source transfer learning and autoencoder techniques.  
Despite the significance of multi-source HDP, there remains a limited number of studies addressing this topic, with associated models requiring further refinement. Furthermore, the impact of varying numbers of source projects on model performance has received little attention in the literature.
# Single-Phase, Bidirectional, 7.7 kW Totem Pole On-Board Charging/Discharging Infrastructure
## 2 Brief Detail and Parameter Design  
Figure 2 depicts the detailed structure of the BDCI. This type of BDCI is adopted for personal as well as commercial use with the range of 3.3 kW to 11 kW. This system consists of two stages. The first stage includes the grid and a grid-side bidirectional (GSBD) converter. The GSBD converter operates as a rectifier and an inverter in charging and discharging mode, respectively, to maintain the power balance between EV and the grid. On the other hand, the second stage consists of a battery-side bidirectional (BSBD) DC-DC converter connected with the EV batteries. Both stages are interfaced with the DC-link capacitor and operate in charging and discharging mode for G2V and V2G applications. In both charging and discharging modes, the design guideline and sizing of the parameters for the proposed system depend upon the system rating, which is presented in Table 1. Firstly, in the charging mode, the calculation of design parameters for CCM totem pole PFC is presented as follows.
## 4 Results and Discussions
### Geographical Exploration of Spatial Differentiation of Water Conservation in Yunnan Province
#### 4.4.1 Construction of an Impact Factor Index System
In the three periods, the changes in precipitation, evapotranspiration and NPP are the most powerful ones, which further explains the strong explanatory power of climate and ecological factors on water conservation. During the study period, the explanatory power of land use change, population density change and precipitation change on the spatial differentiation of water conservation in the study area was enhanced, while the explanatory power of the other four factors was weakened. It can be seen that the \(q\) value of the population density change factor increases with the passage of years, indicating that the intervention intensity of human activities on water conservation function is increasing. The single factor explanatory power of static years and dynamic periods reflects that natural environmental factors are the dominant factors affecting the spatial differentiation of water conservation in the study area. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Factor**}} & \multicolumn{2}{c}{**2000**} & \multicolumn{2}{c}{**2010**} & \multicolumn{2}{c}{**2018**} \\  & \multirow{2}{*}{\(q\)-Value} & \multicolumn{2}{c}{\(q\)-Value} & \multirow{2}{*}{\(q\)-Value} & \multirow{2}{*}{\(q\)-Value} & \multirow{2}{*}{\(q\)-Value} & \multirow{2}{*}{\(q\)-Value} & \multirow{2}{*}{\(q\)-Value} \\  & & & & & & & \\ \hline \multirow{8}{*}{\
\begin{tabular}{c} Natural Environment \\ \end{tabular} } & Precipitation & 0.766 & 1 & 0.822 & 1 & 0.784 & 1 \\  & Evaporation volume & 0.279 & 5 & 0.233 & 5 & 0.192 & 5 \\  & DEM & 0.434 & 4 & 0.402 & 4 & 0.532 & 2 \\  & NPP & 0.603 & 2 & 0.590 & 2 & 0.530 & 3 \\  & NDVI & 0.579 & 3 & 0.442 & 3 & 0.446 & 4 \\ \hline \multirow{8}{*}{
\begin{tabular}{c} Socio-economic \\ \end{tabular} } & GDP & 0.143 & 7 & 0.114 & 7 & 0.028 & 9 \\  & Population density & 0.028 & 9 & 0.025 & 9 & 0.035 & 8 \\ \cline{1-1}  & Distance from town & 0.103 & 8 & 0.054 & 8 & 0.071 & 7 \\ \cline{1-1}  & Land Use & 0.152 & 6 & 0.120 & 6 & 0.112 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Static year factor detection for geographic detectors.
## 4 Results Analysis
### Effect of Crack Size and Location on Wave Propagation Properties  
To ascertain the effects of size and location of crack on wave propagation, the mix type AC-13 was selected. All parameters required for the development of model remained the same with the inclusion of the crack at different location and sizes. Figure 9a-d shows wave propagation at a time point of 2e-4 (s) for cracks A, B, and C, as designated above. It can be seen that the ultrasonic waves were continuous until they reached the crack area, at which point the ultrasonic waves started to split up, were refracted, reflected back, and superimposed onto each other after passing through the crack.  
Figure 8: Effects of different load pressures on wave propagation.  
Time-displacement curves of ultrasonic wave propagation through these models are presented in Figure 10a for cracks A, B, C, and D (similar to C, but with 40 mm crack width). Figure 10b shows the maximum displacement observed for the model. The results indicate that the wave propagation was sensitive enough to the location of the crack depth and size. This feature of the ultrasonic wave was very useful in detecting where the crack initiated within the pavement structure.  
Figure 9: Wave propagation on asphalt concrete with a crack size of 30 mm at distance (**a**) 1/3 and (**b**) 1/2 from the top of the model at a time point of 2e–4 (s). (**c**) 30 mm at distance 2/3 from the top of the model at a time point of 2e–4 (s). (**d**) 40 mm at distance 2/3 from the top of the model at a time point of 2e–4 (s).  
From this analysis, it is implied that there is an indirect relationship between ultrasonic wave propagation and cracks, which is also consistent with the experimental study that the void ratio and the wave velocity are indirectly proportional to each other. With the increase in void ratio, the wave velocity passing through the material decreases.
## 2 Data Collection from the Target Area
### Study of the Monthly Average Energy Use of 905 Households  
Table 1 depicts the average energy consumption of the 905 households for every month of 2017. The maximum value of energy consumption took place in August, showing 262 kWh, whereas the minimum value took place in March, showing 205 kWh. The total and average power usage of the 905 samples in 2017 were 2712 kWh and 226 kWh respectively. When compared with the average energy consumption of a metropolitan area, which is around 1000 kWh, the number is quite low. This is because the characteristics of the people living in rural areas--such as the size of a family, which generally does not exceed three, and that people in rural areas tend to avoid owning and using electrical devices--show very few activities after sunset, resulting in a very small power consumption for lighting purposes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Month\_Households** & **1** & **2** & **904** & **905** & **Average** \\ \hline January & 55 & 489 & & 130 & 144 & 232 \\ February & 88 & 249 & & 179 & 107 & 236 \\ March & 44 & 162 & & 135 & 103 & 205 \\ April & 66 & 148 & & 142 & 117 & 222 \\ May & 207 & 184 & & 137 & 114 & 216 \\ June & 77 & 254 & & 149 & 127 & 213 \\ July & 78 & 349 & \(\sim\) & 158 & 186 & 230 \\ August & 97 & 428 & & 165 & 198 & 262 \\ September & 76 & 400 & & 130 & 151 & 220 \\ October & 103 & 246 & & 142 & 116 & 225 \\ November & 43 & 368 & & 149 & 115 & 225 \\ December & 45 & 315 & & 158 & 121 & 226 \\ Total & 979 & 3592 & & 1.774 & 1599 & 2712 \\ \hline \end{tabular}
\end{table}
Table 1: Monthly energy consumption of the 905 households in 2017 (unit: kWh).  
Figure 1: Family distribution in the western coast of Chungnam Province.  
Table 2 and Figure 2 show the proportion of energy usage of the 905 target households. Out of the 905 households, 304 (33.59%) had an average energy usage between 200 and 300 kWh in 2017, which was the biggest portion of the whole samples. Households using less than 100 kWh accounted for only 1.66%, and those using over 700 kWh were only 0.22%, which made them the minority cases.
# Dynamical Manipulation of Surface Plasmon Polaritons
## 4 SPP Vortex Generation  
With the helical wavefront, optical vortexes in free space are usually generated with spiral phase plates or SLMs [89; 90]. Similarly, imposing spiral phases on SPP can also synthesis SPP vortexes. SPP generated by Archimedes spiral slits which are defined by \(r_{m}(\theta)=r_{0}+\frac{\lambda_{sp}\text{mod}(m\theta,2\pi)}{2\pi}\) are imprinted with a spiral phase along the slits because of the different optical path during propagation. The radius \(r\) of slit changes with azimuthal angle \(\theta\) and \(m\) determines the partial slit segments and the topological charge of the generated SPP vortex. With the illumination of LCP and RCP light, Figure 5a schematically gives the phase of SPP generated by \(m=2\) spiral slit [21; 70]. It should be pointed out that there exists another spin-determined spiral phase besides the spiral phase induced by a different optical path. Therefore, the topological charge of the SPP vortex can be expressed by \(j=m+\sigma_{\pm}\). For an \(m=4\) spiral slit, the topological charge of the SPP vortexes generated by LCP and RCP light are \(j=5\) and \(j=3\), respectively. The simulated SPP distributions in Figure 5a show that the diameter of the SPP vortex for the LCP light is larger than the one for RCP light, which is consistence with the theoretical analyses. When the spiral slits are illuminated by optical vortex beams as shown in Figure 5b, the spiral phase of incident vortex beam can be transferred to the SPP vortex. Hence, the total topological charge of the SPP vortex is given by \(j=l+m+\sigma_{\pm}\)[44; 68]. Numerical results in Figure 5b give the profiles of SPP vortexes generated in different cases, which verifies that SPP vortex can be dynamically modulated by changing the polarization and the topological charge of the incident light. However, the maximum topological charge modulation of SPP vortex obtained by switching the handedness of the incident light is limited to \(\Delta j=\pm 1\), because the spiral phases induced by the spiral structure and the incident vortex beam are polarization independent. As shown in Figure 5c, replacing the spiral slits with subwavelength nanoslits can overcome this restriction [73], considering that spin-dependent PB phase can be introduced by changing the orientation angles of the slits. The topological charge of the generated SPP vortex is \(j=\sigma_{\pm}(2n-1)\), where \(n\) is the ratio between the orientation angle \(\alpha\) and the azimuthal angle \(\theta\). Figure 5c schematically shows the structure of the \(n=1.5\) SPP vortex generator, and the simulated phase distributions show that the topological charges are \(j=2\) and \(j=-2\) for LCP and RCP incident light, which agrees with the theoretical value.
# A Model Based Framework for IoT-Aware Business Process Management
## 1 Introduction
The proposed metamodel is used for deriving the following software artifacts:
* An _IoT-aware UML profile_ for annotating SysML models that provide the _structural view_ of the IoT system;
* An _IoT-aware extension of the Business Process Model and Notation (BPMN)_, the standard notation for BP modeling, for specifying the _operational view_ of the IoT system;
* A _methodology_ that illustrates how the framework exploits the UML profile and the BPMN extension to effectively support the development and analysis of IoT-aware BPs is introduced, along with appropriate model transformations for automating the generation of both the system and its digital twin;
* An _architecture_ that describes the framework in terms of its components (i.e., ABBs) and the capabilities they shall provide is specified. As regards the modeling of BPs, it is worth noting that several formalisms are available, e.g., Petri-Net [6], Yet Another Workflow Language (YAWL) [7], or Event-Driven Process Chain (EPC) [8]. Different from the various modeling languages that focus on specific levels of abstraction (e.g., business level or technical level), BPMN aims to provide a notation that is easily readable by all the different roles involved in a business process management effort, bridging the gap between the BP design and the process implementation. Over the years, BPMN has gained an ever-growing popularity, until it has become a de facto standard in the BPM field [9]. Therefore, as this work deals with BP modeling from both operational and technical perspectives, BPMN has been considered the most suitable option to achieve the research objectives. Finally, in order to evaluate the feasibility of the proposed approach, this work also includes an experimentation dealing with a Water Monitoring System (WMS). The rest of this paper is structured as follows. Section 2 reviews the existing literature and clarifies the novelty of the proposed contribution. Section 3 provides the concepts at the basis of this work, by briefly outlining the DT paradigm, the Sensor Web Enablement framework, the Model-driven Engineering principles and the PyBPMN/eBPMN approach for supporting the simulation-based analysis of BPs. Section 4 introduces the proposed IoT-aware framework for BP analysis. Section 5 provides details on the framework implementation. Section 6 discusses an example application to monitor the risk of flooding of a basin, and finally Section 7 provides concluding remarks.
## 4 Experiments
### Comparison with Other Methods
#### 4.3.1 Quantitative Comparison  
On RESIDE-indoor and RESIDE 6K datasets [48], CCD-Net is compared with with other supervised, unsupervised, and unpaired dehazing methods, including GridDehazeNet, DehazeFormer-T for supervised comparison, DCP for unsupervised methods, and D4, ODCR for unpaired methods. The results shown in Table 3 indicate that CCD-Net achieves the best dehazing effect on the evaluation metrics PSNR, SSIM and CIEDE. For CCD-Net, the PSNR values on the two datasets are 35.90 dB, 30.71 dB, and SSIM metrics of 0.9589 and 0.9850, respectively. These values are improved compared to the sub-optimal values obtained. The CIEDE values of CCD-Net, on the other hand, are reduced by 1.3 and 1.88 compared to the sub-optimal values, which is a more significant improvement.  
Figure 8 visualizes the performance of multiple dehazing models on the RESIDE-indoor and RESIDE-6K datasets on three metrics, PSNR, SSIM, and 1/CIELDE, using radar plots. The relationships of multiple metrics and comparisons between different methods or models are shown. Each dimension extends along a different axis and the data points are connected by lines to form polygons, thus showing the performance of different methods on each metric. From this radar plot, our method has a larger coverage area, which represents a better overall performance. The higher PSNR value indicates a better quality of the reconstructed image, SSIM is close to 1, which indicates that the structural information of the image is well preserved after dehazing, and the 1/CIELDE is also higher, which indicates a more accurate color restoration.  
To evaluate the deployment efficiency of our method, we compare the number of parameters and inference time of CCD-Net with state-of-the-art dehazing methods, as shown in Table 3. While CCD-Net is not the most lightweight model in terms of computational complexity and runtime, it achieves an effective balance between performance and efficiency. On the RESIDE-6K dataset, CCD-Net outperforms other methods in both dehazing quality and color fidelity while maintaining a reasonable computational cost. Furthermore, the model achieves an inference speed of approximately 29 frames per second on an RTX 3080 GPU for 256 \(\times\) 256 images, demonstrating its practical applicability for high-quality dehazing and color-correction tasks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Patch size & 128 \(\times\) 128 \\ Batch size & 12 \\ Learning rate & 0.0003 \\ Iterations & 300,000 \\ Optimizer & AdamW \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration of model hyperparameters.
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 1 Introduction  
In the dynamic realm of vehicular technology, inter-vehicular communications serve as a foundational pillar, driving advancements in road safety, traffic management, and passenger experience. Enabled by Vehicular Ad-Hoc Networks (VANETs) [1], these communications are facilitated through On-Board Unit (OBU) devices that vehicles are equipped with. The primary communication paradigms include Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). The V2I paradigm relies on strategically placed Roadside Units (RSUs) to ensure uninterrupted data packet transmission. However, the dynamic nature of VANETs, marked by frequent topological changes due to vehicular speeds and mobility, poses challenges in data transmission. These challenges include intermittent connection disruptions, interference in high-traffic areas, and signal attenuation due to obstacles. A significant concern is the potential for delay in data packet transmission from an emergency vehicle, which could jeopardize a patient's timely arrival at a medical facility. While there is extensive literature on channel characterization across urban, rural, and suburban areas [2; 3; 4; 5; 6], only a few have explored the potential of ANN, stochastic models, and machine learning for channel modeling [7; 8; 9; 10]. For example, Ref. [10] presents a detailed physical layer simulation tool in Simulink, specifically designed for vehicular communication modeling.
# Timing and Frequency Synchronization Using CAZAC Sequences for OFDM Systems
## 4 Materials and Methods
### Timing Synchronization Method
#### 4.2.1 Continuous Correlation Peak Detection Algorithm  
The correlator output signal \(Corr(n)\) is the result of the correlation between the received signal \(r(n)\) and the ZC sequence, which can be described as  
\[Corr(n)=\sum_{k=0}^{N-1}r(n-N+k)\cdot a^{*}(k), \tag{10}\]  
where the received signal \(r(n)\) is shown in Equation (2) and the ZC sequence \(a(n)\) is described by Equation (3).  
The synchronization position is obtained by detecting the peak of \(Corr(n)\). The amplitude variation of \(Corr(n)\) is shown in Figure 4, where \(|Corr(n)|\) is the absolute value of \(Corr(n)\).  
Figure 3: The proposed timing synchronization method.  
Figure 2: The structure of the new preamble based on the ZC sequences.  
The operation period of the correlator is the length \(N\) of the ZC sequences in the preamble, and one operation period is called a correlation window. As the correlator continuously outputs the signal, the continuous correlation peaks of \(|Corr(n)|\) can be detected, and their number is \(m\), which is the number of ZC sequences in the preamble. At the same time, the intervals between these correlation peaks are identical and equal to the ZC sequence length \(N\). The position of these correlation peaks from the starting point of the corresponding correlation window is _MaxPos_.  
Based on the regular positions of these correlation peaks, we propose a new timing synchronization algorithm, called the continuous correlation peak detection (CCPD) algorithm. The process of the CCPD algorithm is as follows:  
1. Perform correlation operation on the received signal \(r(n)\) and the ZC sequence to obtain the correlation value \(Corr(n)\);
2. Set a correlation window with a period of \(N\), where the parameter _Counter_ (\(1\leq Counter\leq N\)) indicates the relative position of each point in the correlation window, and record the relative position _MaxPos_ of each correlation peak in the corresponding correlation window and its peak value (denoted as _PeakValue_);
3. If the _MaxPos_ of the current correlation window is the same as that of the one before (denoted as _MaxPosBefore_), then add one to the parameter _MaxPosTimes_; otherwise, set it to zero;
4. If _MaxPosTimes_ is greater than \(m-1\), the received signal has been synchronized successfully. Next, calculate the frame header position of the received signal according to the _MaxPos_. It is easy to calculate the distance between the synchronization decision point and the frame header as \((m+1)*N-MaxPos\).  
In order to understand the process of the CCPD algorithm more intuitively, the algorithm flowchart is drawn as shown in Figure 5.