esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Mapping of Power Semiconductors in H-Bridge Circuit +
## 5 Conclusions  
In this paper, a H-bridge platform is presented with the aim of emulating various loading conditions of a power converter (e.g., resistive, inductive and capacitive load). The basic operation principle and modulation scheme have been presented, both in the case of inverter and rectifier operations. Loss and thermal models of power semiconductors have been presented and discussed, including the key influential factors like power factor, loading current, fundamental frequency, and switching frequency. An experimental validation of the predicted losses is performed via measuring the semiconductors' junction temperature via optical fibers. As an important outcome, loading conditions can be adjusted in order to obtain an arbitrary thermal stress. Furthermore, the test leg of the converter is able to operate with the desired power factor, operational frequency, and current loading. It can serve as an  
Figure 11: Experimental results in terms of the loading current and junction temperature of power semiconductors. (**a**) Inverter mode. (**b**) Rectifier mode.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**IGBT**} & \multicolumn{3}{c}{**Diode**} \\ \hline  & & **Sim.** & **Exp.** & **Error** & **Sim.** & **Exp.** & **Error** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Inverter \\ mode \\ \end{tabular} } & Junction & \multirow{2}{*}{53.8–67.7} & Junction & \multirow{2}{*}{43.9–46.9} & \multirow{2}{*}{/} & \multirow{2}{*}{50.1–56.1} & \multirow{2}{*}{40.8–41.3} & \multirow{2}{*}{/} \\  & temperature swing & & & & & \\ (\({}^{\circ}\)C) & & & & & & \\ \cline{2-7}  & Mean temperature & & & & & & \\ (\({}^{\circ}\)C) & & & & & & & \\ \cline{2-7}  & Temperature deviation (\({}^{\circ}\)C) & 13.9 & 3.0 & 10.9 & 6.0 & 0.5 & 5.5 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Rectifier \\ mode \\ \end{tabular} } & Junction & \multirow{2}{*}{52.2–60.2} & Junction & \multirow{2}{*}{42.8–45.2} & \multirow{2}{*}{/} & \multirow{2}{*}{53.0–63.5} & \multirow{2}{*}{41.2–42.2} & \multirow{2}{*}{/} \\  & temperature swing & & & & & \\ (\({}^{\circ}\)C) & & & & & & \\ \cline{1-1} \cline{2-7}  & Mean temperature & & & & & & \\ (\({}^{\circ}\)C) & & & & & & \\ \cline{1-1} \cline{2-7}  & Temperature deviation (\({}^{\circ}\)C) & 10.0 & 2.4 & 7.6 & 10.5 & 1.0 & 9.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thermal Stress Comparison between Simulation (Sim.) and Experiment (Exp.).  
emulator to mimic the power converter oriented for the adjustable speed drive of machinery, grid-tied applications of PV/wind power inverter, etc.  
Conceptualization, D.Z. and F.I.; methodology, D.Z. and F.I.; validation, D.Z. and Y.P.; writing--original draft preparation, D.Z.; writing--review and editing, F.I., M.H., and F.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Nonuniform Background Elimination
### Self-Adaptive Size Adjustment  
Surveillance images contain targets of different sizes. A structural operator with a fixed size will influence the background elimination effect and even reduce the information of faint targets. Before performing morphological processing, we block the original surveillance image and find the optimal size of the structural operator suitable for each image block by the minimum bounding rectangle method. To solve the problem of surveillance images being sensitive to the fixed-size structural operators used in other methods, the self-adaptive size adjustment of the structural operator is conducted as follows:  
An original surveillance image \(F_{0}\) with 1024 \(\times\) 1024 imaging pixels is divided into several image blocks \(f_{c}\) with 32 \(\times\) 32 pixels, where c is the index of the image blocks. Each block is binarized with the threshold obtained by the following equations:  
\[Th_{c}=\mu_{c}+\alpha\sigma_{c}\quad(1\leq c\leq k) \tag{7}\]  
\[\mu_{c}=\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}f(i,j) \tag{8}\]  
Figure 4: Defined structural operators used in the IBGSM method. (**a**) structural operator \(\Delta B\); (**b**) structural operator \(B_{b}\).  
\[\sigma_{c}=\sqrt{\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}(f(i,j)-\mu_{c})^{2}} \tag{9}\]  
where \(k\) represents the number of image blocks, \(\mu_{c}\) and \(\sigma_{c}\) are the mean and standard deviation of each block, respectively, \(M\) and \(N\) refer to the rows and columns of each image block, respectively, and \(\alpha\) is a coefficient, where \(\alpha=2\) is selected according to the features of the surveillance images.  
The binarized image \(b_{c}\) can be obtained by:  
\[b_{c}(i,j)=\begin{cases}0,&f(i,j)<Th_{c}\\ 1,&f(i,j)\geq Th_{c}\end{cases} \tag{10}\]  
In order to determine the size (\(K\) and \(L\)) of the optimal structural operator \(\Delta B\) for each image block \(f_{c}\), we find the set of the minimum bounding rectangle sides of the connected domain in each binary image block \(b_{c}\). As shown in Figure 5, the value of the largest side in each set is used as the \(K\) value of the structural operator \(\Delta B\), while the value of parameter \(L\) can be obtained by the value of \(K\). Since the sizes of the largest targets differ in different image blocks, we use self-adaptive adjustable structural operators to perform morphological operations.
## 7 Conclusions  
1. A BP-ANN model taking the deformation temperature (\(T\)), strain rate (\(\dot{\epsilon}\)) and strain (\(\epsilon\)) as input variables and the true stress (\(\sigma\)) as output variable was constructed for the compression flow behaviors of superalloy, nimonic 80A, which presents desired precision and reliability.
2. A strain-dependent Arrhenius-type model is developed to predict the flow behavior of superalloy nimonic 80A under the specific deformation conditions. A sixth order polynomial is adopted to reveal the relationships between variable coefficients (including activation energy Q, material constants \(n\), \(\alpha\), and \(A\)) and strain with good correlations.
3. A series of statistical indexes, involving the relative error (\(\delta\)), mean value (\(\mu\)), standard deviation (\(w\)), correlation coefficient (\(R\)) and average absolute relative error (\(ARRE\)), were introduced to contrast the prediction accuracy between the improved Arrhenius type constitutive equation and BP-ANN model. The mean value (\(\mu\)) and standard deviation (\(w\)) of the improved Arrhenius-type model are 3.0012% and 2.0533%, respectively, while their values of the BP-ANN model are 0.0714% and 0.2564%, respectively. Meanwhile, the correlation coefficient (\(R\)) and average absolute relative error (\(ARRE\)) for the improved Arrhenius-type model are 0.9899 and 3.06%, while their values for the BP-ANN model are 0.9998 and 1.20%, which indicate that the BP-ANN model has a good generalization capability.
4. The true stress data within the temperature range of 950-1250 \({}^{\circ}\)C, the strain rate range of 0.01-10 s\({}^{-1}\), and the strain range of 0.1-0.9 were predicted densely. According to these abundant data, a 3D continuous interaction space was constructed by interpolation and surface fitting methods. It significantly contributes to all the research requesting abundant and accurate stress-strain data of superalloy nimonic 80A.  
Figure 10: The 3D relationships among temperature, strain rate, strain and stress: (**a**) 3D continuous interaction space, 3D continuous mapping relationships under different (**b**) temperatures; (**c**) strain rates and (**d**) strains.  
**Acknowledgments:** This work was supported by the National Natural Science Foundation of China (51305469). The corresponding author was appreciated from the Chongqing Higher School Youth-Backbone Teacher Support Program.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Smart Inverter Control Architecture
### Control Architecture of Three-Phase Photovoltaic Full-Bridge Inverter
\[C\leq\frac{Q}{\omega_{o}V_{rms}}^{2}=\frac{0.05P_{out}}{\omega_{o}V_{rms}}^{2} \tag{7}\]  
where \(\omega_{o}\) is the angular frequency of the mains, and \(V_{rms}\) is the effective value of the output voltage. By substituting \(L_{1}\), \(L_{2}\) and \(C\) into Equation (8), the resonant angular frequency \(\omega_{res}\) is obtained. \[\omega_{res}=\sqrt{\frac{L_{1}+L_{2}}{L_{1}L_{2}C}} \tag{8}\]In order to avoid the occurrence of resonance phenomenon, the capacitance and inductance values obtained must be substituted into Equation (9) to confirm whether the resonant angular frequency \(\omega_{res}\) is within a reasonable range. \[10\omega_{o}\leq\omega_{res}\leq\frac{1}{2}\omega_{sw} \tag{9}\]  
where \(\omega_{sw}\) is the angular frequency of the switch switching. Finally, the passive damping resistor can be obtained by substituting the obtained \(L_{t}\) and \(C\) into Equation (10) [19]. \[R_{d}=\sqrt{\frac{L_{t}}{C}} \tag{10}\]  
In this paper, the switching frequency of the switch is 20 kHz, the output sine wave frequency is 60 Hz, the output power is 1.6 kW, the output current ripple percentage is 5%, the amplitude modulation ratio is 0.9, the converter output voltage RMS is 220 V, and the DC-link voltage is 400 V. Therefore, the values of selected filter elements calculated by Equations (4)-(10) are \(L_{1}\) = 12.86 mH, \(L_{2}\) = 2.57 mH, \(C\) = 4 \(\mu\)F, and \(R_{d}\) = 23.1 \(\Omega\), and \(\omega_{res}\) = 10,503.5 rad/s is within the reasonable range of Equation (9).
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 3 The Proposed Scheme
### Image Preprocessing  
The image owner performs the pre-processing of the ROS of the extract, including image segmentation, bit plane separation, data cross-reorganization, data compression, and data filling.
# Enhancing Neural Network Interpretability Through Deep Prior-Guided Expected Gradients
## 2 Related Work
### Gradient-Based Explanation Methods  
Gradient-based explanation methods are widely used to interpret the decision-making processes of machine learning models, particularly DNNs. These methods compute the gradient of the output with respect to the input features, quantifying how sensitive the model's predictions are to changes in each feature [32]. For a neural network with an input vector \(x\) and an output scalar \(y\), the relationship is expressed as in Equation (1).  
\[y=f(x;\theta) \tag{1}\]  
where \(\theta\) represents the model parameters. The influence of each input feature \(x_{i}\) is measured by the partial derivative \(\frac{\partial y}{\partial x_{i}}\).  
Gradients are typically computed using the backpropagation algorithm, which efficiently calculates gradients by propagating errors backward through the network. For a loss function \(L(y,\hat{y})\), the gradient with respect to each parameter \(\frac{\partial L}{\partial\hat{y}}\) is computed, and the gradient with respect to the input feature \(x_{i}\) can be expressed as in Equation (2)  
\[\frac{\partial L}{\partial x_{i}}=\frac{\partial L}{\partial y}\cdot\frac{ \partial y}{\partial x_{i}} \tag{2}\]  
This gradient information provides a foundation for interpreting model predictions. A higher magnitude of \(\frac{\partial y}{\partial x_{i}}\) indicates that the input feature \(x_{i}\) significantly influences the output \(y\). This approach is particularly valuable in image classification tasks, where gradients can generate sensitivity maps that highlight important pixels.  
Building on traditional gradient-based methods, researchers have proposed enhancements such as layer-wise relevance propagation (LRP) [20] and the layer-wise relevance framework (LRF) [21]. LRP analyzes the contribution of features at each layer by propagating relevance scores layer by layer, aiding in the interpretation of complex models. Conversely, LRF leverages the hierarchical structure of DNNs to clarify the importance of input features across different layers, thereby improving the accuracy of model interpretations. Despite these advancements, gradient-based methods remain susceptible to issues such as vanishing or exploding gradients, which can complicate feature importance assessments and reduce interpretability.
# Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia
## 2 SAR Sensing of Hazards
InSAR methods have been used to identify precursory behaviour prior to hazardous events (e.g., slow slip prior to landslides: [17]) and to monitor and understand a multitude of natural and human-made hazards (for reviews, see [18; 19]). In Australia, this includes subsidence arising from the extraction of resources, such as groundwater (Section 3.2) and hydrocarbons (Section 5), the stability and subsidence of slopes and other structures in open-pit mines (Section 5.2), and earthquakes (Section 6). If the phase response of scatterers within a pixel varies significantly between images, as expected when fires burn vegetation or floods inundate the land, the phase difference becomes incoherent, causing gaps in interferograms (for further details, see [14]). Mapping spatial and temporal changes in interferometric coherence can therefore be used to map events that cause changes to the surface environment (Figure 2B). Loss of coherence is also caused by large magnitude displacements when the LoS displacement measured across a single pixel exceeds half the sensor wavelength [14]. In these instances, and if the displacement has a component perpendicular to the LoS, it may be possible to use pixel offset tracking to measure larger magnitude displacements (>tens cm) by applying 2D cross-correlation to quantify sub-pixel offsets (e.g., [20]; Figure 2B). Figure 2: Uses of SAR amplitude and phase measurements for hazard monitoring in Australia. (**A**) Locations of the case studies and figures presented here. (**B**) Schematic showing approaches taken with SAR amplitude/phase data for hazard monitoring focusing on applications that are discussed in this study. Illustrative images are Sentinel-1 Interferometric Widespread (IW) amplitude images acquired over Melbourne, Victoria.
## 2 Materials and Methods
### Heat Meters
#### 2.1.1 Maintenance of Heat Meters  
Heat meters are generally considered as reliable. However, like all devices, they sometimes break down and require technical maintenance. Premature meter failures affect all parties, i.a., tenants, building managers, and the billing company, significantly disturbing the scheduled maintenance and causing losses. The consequences include incorrect readings, understated invoices, and customer complaints.  
The replacement of meters is a complex logistical process, which must be planned in detail. The old meters have to be removed from the network and delivered to a service center or recycled. The installation of new meters is usually done in stages, and it is convenient to carry out the service works in a limited area before proceeding to the next one. It is necessary to arrange meetings with building managers or tenants.  
The whole procedure can generate high costs related to the work of qualified installers, planning and delivering of components, final inspection of the installed devices, and updating the operational data regarding meters. It is often more efficient to replace a functioning device during a scheduled maintenance check if there are any indications of a possible failure in the next billing period. Developing a method for predicting the occurrence of the meter failure in the subsequent period is the subject of this article.  
Heat meters are as a rule the subject of planned maintenance. In the majority of the European countries, their verification should take place every five years due to metrological requirements. During this time, heat meters accumulate mineral deposits from water (especially warm and hot), which results in wear of mechanical elements due to being moved by the stream flowing through the equipment. To mitigate this problem, regular checks are done, aimed at investigating the quality of water used for heating.  
Reverification is time consuming. Heat meters have to be dismounted from the network and delivered to the verification point. For the verification period, the older heat meters are replaced with the new ones, which have a valid heat meter verification marking. However, currently, the majority of the companies plan only one verification visit due to logistical costs. Once the verification is done, the dismounted heat meters are installed in other locations. Before the verification, the heat meters are cleaned, regulated, and sealed so that they could work for another five years, meeting metrological requirements. It is not uncommon that heat meters are verified two or even three times.  
The users decide to replace them due to three reasons: new equipment has a longer warranty than the older verified one (five years instead of two years); the new heat meter is much more technologically advanced; or the old heat meter was broken, and repair is impossible or useless.  
Figure 1: Principle of the heat meter.
# 5G/B5G Internet of Things MIMO Antenna Design
## 5 Conclusions  
In this work, a multi-band and multi-beam antenna for 5G MIMO Internet of Things (IoT) has been presented. The antenna structure consists of four tapered slots and has been placed on the corner of a smartphone to realize pattern diversity. The tapered slot has dual functionality: It operates as an open-ended slot antenna at band 3.6 GHz with a measured impedance bandwidth of 300 MHz, and as an end-fire tapered slot antenna at 28 GHz, with a measured impedance bandwidth of 4 GHz. The measured realized gains at Sub-6 GHz and mm-wave bands are 3.5 dBi and 8 dBi, respectively. The measured results show that the proposed design fulfills the requirements of multiple wireless communication standards (5G/B5G) at both the Sub-6 GHz and mm-wave bands.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The author declares no conflict of interest.
## 1 Introduction
Stochastic switching topologies are assumed to follow a Markov chain. The proposed framework demonstrates its effectiveness in managing the high nonlinearity and coupled dynamics of AUVs. This paper investigates the problem of achieving time-varying formations based on the consensus for multi-AUVs. First, the focus is on time-varying formations, which are essential in practical applications where certain formations require agents to have different speeds, such as rotation formations. Only time-varying formations can effectively address this scenario. Second, we present an analysis of AUV formation and the design protocol for multi-AUV systems, aiming to establish time-varying formations. To do so, we propose the necessary and sufficient conditions for these formations. Third, the explicit formula of the AUV formation center functions is provided. Lastly, the theoretical results of simulations are presented. The rest of this paper is organized as follows. In Section 2, basic concepts and the problem are presented. In Section 3, the main theoretical analysis is presented. In Section 4, three typical working conditions verified by a simulation are presented. Furthermore, Section 5 gives the conclusions and discussion.
## 3 Methodology
### Data Preprocessing  
The data preprocessing phase consists of two main phases: data cleaning and preparation, and data transformation. In the data cleaning and preparation (Section 3.2.1), we ensure the dataset's quality and readiness for analysis. The subsection elucidates the criteria for cleaning the NBI dataset and provides a detailed description of selected variables, presented in Table 1. Furthermore, the section offers summarized results for both numerical (Table 2) and categorical variables (Table 2). The subsequent subsection (Data Transformation, Section 3.2.2), addresses the challenges encountered in constructing time-series data for bridges. Here, we outline the obstacles faced and detail our approach to overcoming them to create meaningful time-series data for the analysis.
## 5 Conclusions  
In this paper, SIMO and MISO micro-scale site diversity systems are evaluated in terms of capacity and compared to the conventional SISO case for both downlink and uplink scenarios, respectively. Using the 4-year experimental rainfall data-bank from two stations at NTUA campus, the SST is used to obtain rain attenuation time series. From the rain attenuation time series the outage capacity is calculated assuming receive (SIMO) and transmit (MISO) diversity schemes with perfect channel state knowledge at the user terminal; a gain seems to always be present for both cases and at both Ka- and Q-band. A multi-dimensional time series synthesizer based on Gaussian copulas is proposed for the generation of spatio-temporally dependent time series of rain attenuation. Using the rain attenuation time series from the SST, the parameter of Gaussian copula is calculated for micro-scale site diversity systems. Making use of the proposed synthesizer, micro-scale site diversity systems with multiple ground stations are evaluated, concluding that a considerable gain is always present for both clear-sky and rainy conditions. Experimental propagation campaigns for micro-scale site diversity scenarios must be conducted in order to validate the presented results.  
It has been shown that even small separation distances in the order of a few hundred meters can significantly improve system performance. Micro-scale site diversity systems can therefore be exploited by enterprise users to increase data rates, ensure high system availability and therefore allow for trouble-free operation of time-critical, high data volume applications. Part of the pending future work on the subject is the assessment of the channel capacity within the framework of real modulation schemes (e.g., QAM) comparing the results with the ones based solely on the [PERSON]'s Gaussian channel.  
Conceptualization, A.Z.P. and A.D.P.; Formal analysis, A.Z.P. and A.D.P.; Investigation, A.Z.P.; Methodology, A.Z.P., A.D.P. and C.I.K.; Software, A.Z.P. and C.I.K.; Supervision, A.D.P.; Validation, A.Z.P.; Visualization, A.Z.P. and C.I.K.; Writing--original draft, A.Z.P. and A.D.P.; Writing--review and editing, A.Z.P. and A.D.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Edge-Aided Reliable Data Transmission for Heterogeneous Edge-IoT Sensor Networks
## 4 Evaluation  
In this section, we conduct simulation experiments to study the performance of the proposed edge-aided data transmissions in sensor networks.