esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
Figure 5 shows the characteristics of the SGRSO and HDSGRSO UMOSFETs for same bias condition; the structural parameters adopted in the simulations are shown in the Table 1. The breakdown voltage for the two structures is shown in the Figure 5a, and the limit of drain current density is 1 \(\upmu\)A/\(\upmu\)m\({}^{2}\). It is obvious that the breakdown voltage of both is about 240 V. The relationship between the \(R_{\text{SP}}\) and gate voltage is shown in the Figure 5b for the two structures at \(V_{\text{DS}}\) = 0.3 V. It can be found that the \(R_{\text{SP}}\) of the HDSGRSO UMOSFET is much smaller than that of the SGRSO UMOSFET, and the gap between them increases with the increase of the gate voltage. The \(R_{\text{SP}}\) of the HDSGRSO UMOSFET can be reduced by 76%-79%, in contrast to the conventional structure at the same gate voltage. The drain I-V characteristics of the SGRSO and HDSGRSO UMOSFETs with same bias condition are shown in Figure 5c when the breakdown voltage is 240 V. It is obvious that the HDSGRSO UMOSFET has a relatively large current than that of SGRSO UMOSFET when the \(V_{\text{GS}}\) is 2 V, 5 V, 10 V and 15 V, respectively. Moreover, the current of the HDSGRSO UMOSFET when the \(V_{\text{GS}}\) is 5 V even exceeds the current of the SGRSO UMOSFET at 15 V especially. The transconductance of HDSGRSO and SGRSO UMOSFETs is shown in the Figure 5d. It is similar to the output characteristics of the two structures that the transconductance of HDSGRSO UMOS at the \(V_{\text{GS}}\) of 0.5 V is bigger than that of SGRSO UMOSFET when the \(V_{\text{GS}}\) is 1.5 V. The transconductance of HDSGRSO UMOSFET increases by approximately 178% contrast to the SGRSO UMOSFET, when the \(V_{\text{GS}}\) is 1.5 V.  
An extensive analysis of the HDSGRSO UMOSFET has been given in contrast with SGRSO UMOSFET with the same structure parameters. The model of bandgap narrowing, concentration dependent mobility model, parallel electric field-dependent mobility model, Shockley-Read-Hall (SRH) model and impact-ionization model have been adopted in the simulation work [16]. Figure 6a shows the BV and FOM (BV\({}^{2}\)/\(R_{\text{SP}}\)) at different doping concentrations of the n-drift region. Table 1 shows the parameters we adopted in the simulations. The BV of the two structures increases with the decreasing of the doping concentration of n-drift within the range of the \(1\times 10^{15}\)/cm\({}^{-3}\)-\(6\times 10^{15}\)/cm\({}^{-3}\). When the doping concentration of the drift region is \(5\times 10^{15}\)/cm\({}^{-3}\), the FOM of HDSGRSO UMOSFET achieves the optimal value, which improved by 106% contrasted with the SGRSO UMOSFET, and the BV of HDSGRSO and SGRSO UMOSFETs were 239 V and 191 V respectively, which improved by 25.1%. It is worth noting that when the BV of the two structures was about 240 V, the FOM of HDSGRSO UMOSFET
# A 1D-2D Coupled Lattice Boltzmann Model for Shallow Water Flows in Large Scale River-Lake Systems
## 4 Simulation of Surge Waves in the Reservoir of a Run-of-River Hydropower Station  
### Computational Setup  
The surge wave propagation and reflection phenomena caused by a load rejection in a run-of-river hydropower station were simulated to further demonstrate the capability of the proposed 1D-2D coupled model. The hydropower station, the reservoir, and the computational domain are depicted in Figure 8. The reservoir was divided into three parts (Channel 1 (C1), Channel 2 (C2), and channel 3 (C3)) by two dikes (Dike 1 (D1) and Dike 2 (D2)). Two powerhouses--Powerhouse 1 (PH1) and Powerhouse 2 (PH2)--were located at the end of Channel 2. Powerhouse 1 included 14 turbines, with a total discharge of 11550 m\({}^{3}\)/s, and Powerhouse 2 included seven turbines, with a total discharge of  
Figure 6: Computational domain for the surge waves in the tailrace canal of a hydropower station.  
Figure 7: Surge wave histories in the tailrace canal for load rejection: (**a**) water depth at the upstream end and discharge at the downstream end; (**b**) water depth and discharge histories at the coupling interface (\(x\) = 50 m).  
6385 m\({}^{3}\)/s. Channel 1 and Channel 3 were used for sand washing and shipping. There were two side branches: Branch 1 was 7.7 km, and Branch 2 was 2.2 km. Several monitoring points were set to record the flow parameters. Point A was located at the inlet of Powerhouse 1, Point B was located at the inlet of Powerhouse 2, Point C was located at the head of Dike 1, Point D was located at the head of Dike 2, Point E was located at the estuary of Branch 1, Point F was located at the estuary of Branch 2, and Point G was located at the coupling interface.  
The simulation scope of this example was about 40 km, in which the 2D part of the reservoir (about 5 km starting from the dam to Point G) was treated by the 2D submodel, and the upstream 35 km river was handled by the 1D submodel. The upstream boundary of the 1D domain was set to constant discharge. The downstream boundary of the 2D domain was the dam, at which the discharge values of each powerhouse were given. Other boundaries of the 2D domain were set to no-slip wall boundary conditions. The Manning roughness coefficient of the river bed was 0.025. The time steps of 1D and 2D submodels were both set to 0.0864 s, and the corresponding uniform lattice length was 4.32 m. The mesh nodes were 8102 for the 1D domain and 1600 \(\times\) 1200 for the 2D domain.
## 4 Results
### Prediction Performance  
Through causal structure learning, causal structure graph of three components were obtained. In order to apply this causality to prediction, we transformed prediction into a graph to graph problem. The directed acyclic graph learned through causal structure can be described by matrix, which is causal matrix. We put causal matrix into GCN as adjacency matrix. When predicting the traffic through GCN, the ratio of training set to test set was 3:1. The data of 10 days was used to predict. The batch size in GCN was set to 128 and the number of iterations was 300. After obtaining prediction results of three components, we finally integrated these results.  
In order to verify impact of causal structure learning on prediction performance, BIC-ST(no causality), which means prediction method based on decomposition integration and GCN, was also used to predict traffic. ARIMA, as an algorithm often used for traffic prediction [40; 41; 42] was also used for algorithm comparison. The prediction results of three base stations in the area were used as examples as shown in Figures 11-13.  
Furthermore, in order to more clearly describe performance of different algorithms, RMSE and MAPE were calculated according to Formulas (21) and (22) for the results obtained by different methods. The verification results are given in Table 2. Through these results, it can be found that causal structure learning plays a role in improving prediction performance. At the same time, proposed method can be well used for network traffic prediction. In addition to this, Figure 14 showed training loss function versus training iterations. It can be seen from the figure that when the number of iterations is 150, the curve tends to be stable. When the number of iterations is close to 300, the loss function no longer fluctuates.  
Figure 10: Causal graph for three components.  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(Y_{i}^{\prime}-Y_{i}\right)^{2}} \tag{22}\]  
\[MAPE=\frac{100\%}{n}\sum_{i=1}^{n}\left|\left(Y_{i}^{\prime}-Y_{i}\right)/Y_{i}\right| \tag{23}\]  
Figure 11: Prediction performance comparison for base station 1.  
Figure 12: Prediction performance comparison for base station 2.  
Figure 13: Prediction performance comparison for base station 3.
## 4 Proposed SFO Estimation Scheme
### Complexity  
To make a fair comparison between the conventional and proposed frequency estimation schemes, the computational complexity is defined as as the number of real floating point operations (flops) used to perform one-shot estimation. For this purpose, we count a complex multiplication as six flops, whereas a complex addition as two flops [28]. At the receiver, the frequency-domain equalization (FEQ) used to obtain \(\hat{X}_{l}(k)\) is assumed to be already executed so that the complexity of the conventional estimation schemes is analyzed in the case of known CSI. Calculating \(\Lambda_{m}\) in Equation (3) for \(m=0,1\) needs \(20N_{c}-4\) flops since QPSK modulation is used. Finally, two flops are additionally needed to acquire the SFO estimate \(\hat{\Delta}_{s}\) in Equation (6). Thus, the total computational complexity used in the conventional estimation scheme A is \(20N_{c}-2\). Meanwhile, the total number of flops used in the conventional schemes B and C are \(29N_{c}-2\) and \(N_{c}(12N_{o}^{2}-13N_{o}+98)/2+7\), respectively. In the proposed approach, taking the frequential correlation \(C_{l}(k)\) in Equation (13) needs \(3N_{c}\) flops, whereas \(3N_{c}\) flops are additionally involved to compute the temporal correlation \(\hat{C}_{l}(k)\) in Equation (17). In order to obtain block-wise partial correlation Equation (22), \(2\sum_{m=1}^{N_{s}}N_{m}-2N_{s}\) flops are needed,which is equal to \(N_{c}-2N_{s}\). Lastly, \(2N_{s}-1\) flops are required to get the SFO estimate \(\hat{\Delta}_{s}\) in Equation (23). The total number of flops of the proposed estimation scheme is counted as \(7N_{c}-1\) for each estimate.
## 5 Experimental Verification
### Experimental Results and Validation of Theoretical and Simulation Models
The superior accuracy observed in copper loss prediction (3.1%) reflects the more deterministic nature of resistive losses, though skin and proximity effects in the end-winding regions still present challenges for precise modeling. These identified discrepancy sources have informed our ongoing model refinement process, particularly through the implementation of stochastic material property variations and enhanced end-effect modeling techniques that promise to further reduce the gap between simulation and experimental results in future iterations.
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
## 5 Performance Evaluation
### Average Call Distance of Transmitted Packets  
Figure 6 shows the average call distances of packets transmitted between 10,000 randomly selected pairs of earth stations in 6 topologies at the beginning of each different time slice.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Network Topology** & **Description** \\ \hline \multirow{4}{*}{Baseline topology} & MHD & Manhattan Network defines the mesh connection method \\  & Distance & Dynamic allocation strategy based on shortest distance \\  & Time & Dynamic allocation strategy based on maximum visibility time \\ \hline \multirow{4}{*}{The optimized topology designed in this paper} & STLS & 2 intra-OP links plus 2 AOPLs \\  & DTLS1 & 2 intra-OP links plus 1 AOPL plus 1 \\  & DTLS2 & dynamic link \\ \cline{1-1}  & \multirow{2}{*}{DLTS2} & 1 intra-OP link plus 2 AOPLs plus 1 \\  & & dynamic link \\ \hline \hline \end{tabular}
\end{table}
Table 3: Topology classification and description.  
Figure 6: Comparison of average packet call distances over the cycle of the six topologies.  
The dynamic topology generated by the Time strategy has the longest average call distance. Comparing the static topology generated by STLS and MHD policies, STLS has a shorter average call distance while the MHD strategy generates a longer distance for the topology. When comparing the dynamic topology with the static topology, the DTLS1 strategy reduces the average call distance by 7.26%, and the DTLS2 strategy reduces the average call distance by 3.81% compared to the STLS strategy if STLS is used as a benchmark. In the STLS strategy, the distance variation is minor, indicating a more stable link, while all three dynamic policies require reconnection of the link with the switch of the time slice, so the distance variation is more significant.  
Comparing several dynamic topologies, the DTLS1 strategy proposed in this paper is superior to all other strategies except for the Distance strategy. When using the Time strategy as a baseline, DTLS1 reduces the distance by 35.97% compared to it, while DTLS2 reduces the distance by 33.59%; when using the Distance strategy as a baseline, DTLS1 increases the distance by 20.28% compared to it, while DTLS2 increases distance by 24.75%.  
The simulation results demonstrate that for the 2 combinations of DTLS1 and DTLS2, it is evident that the DTLS1 strategy reduces the average distance by 3.58% compared to the DTLS2 strategy. As the average call distance of data transmission packets decreases, network performance improves. Therefore, the DTLS1 strategy performs better.
## 5 Results and Discussion
### Pilot Plant Analyses
#### 5.2.3 Energy Profile  
Because of the non-linear evolution of the energy accumulated/released by the PCM during full charging and discharging processes, and in order to extrapolate the results for other processes which are more likely to happen in a real installation, such as partial charging and discharging processes, an important parameter to be evaluated is the energy rate. This parameter is a ratio between the energy accumulated along the specific process and the final energy that the system has accumulated at the moment when the process is considered being finished. Hence, this rate always achieves 100% at the end of the process. This information is useful to understand how the geometry of the tank influences the charging and discharging processes.  
Figure 9 shows the energy rate, bearing in mind the distribution of the PCM inside the tank (Figure 2b), during the charging and discharging processes for an HTF mass flow rate of 1500 kg/h. It can be observed that the PCM located at the main part accumulates/releases the energy faster than the other parts because it exchanges the energy with the HTF easier as they are in direct contact with the tubes bundle, while the PCM located at the corners takes much more time to start storing/releasing  
Figure 8: Comparison of the HTF power profiles with three different mass flow rates. (**a**) charging process and (**b**) discharging process.  
Figure 7: HTF power profiles and average PCM and HTF temperature profiles at an HTF mass flow rate of 1500 kg/h: (**a**) charging process and (**b**) discharging process.  
the energy. This situation shows a non-optimized design of the heat exchanger and, in order to enhance the heat transfer, the corners should be avoided. In both processes, the PCM took around 50% of the process time to accumulate/release 90% of the energy, which is a good reference for partial load processes such as the ones driven by intermittent renewable energies because it can lead on one hand to a redesign of the charging and discharging process, and on the other hand to a redesign of the storage tank.  
In the present study, if compared to the processes at a mass flow rate of 500 kg/h, the process at a mass flow rate of 2500 kg/h was 62% faster during the discharge and 124% faster during the charge, while the process at a mass flow rate of 1500 kg/h was 29% faster during the discharge and 60% faster during the charge times faster. The differences observed between the charging and discharging are mainly due to the dominant heat transfer mechanisms--while, during the charging process, it is mainly the convection, and during the discharging process it is the conduction.
# Evaluation of GNSS-TEC Data-Driven IRI-2016 Model for Electron Density
## 2 Data and Methodology
### Experimental and Evaluation Methods  
[PERSON] et al. [26] minimized the difference between the TEC output from the IRI model (the upper height for TEC integration is 2000 km and the topside-plasmasphere option is the Nequick model) and the high-precision TEC extracted from the GNSS data by adjusting the IG12 index to improve the accuracy of the TEC output from the IRI model and ultimately computed the IG-up value gridded maps for the users in the China region. It was verified that the accuracy of the TEC calculated by the IRI-2016 in the China region can be significantly improved by using the IG-up values provided by it. In this paper, we use the GNSS-TEC driver to update the IG12 values and carry out a detailed accuracy assessment of the IED output from the IRI model driven by the IG-up values. Firstly, we qualitatively and quantitatively evaluate the electron density and NmF2 outputs of IRI-2016 models driven by IG12 and IG-up values, respectively, using ionosonde data from the Beijing and Wuhan stations in January 2015 and January 2019 as reference values, and analyze their optimization under different solar activities, as well as different latitudes with the IG-up value-driven IRI-2016 model optimization. Secondly, the detailed assessment of the electron density and NmF2 of the IRI-2016 model output driven by the IG-up values in January 2015 and January 2019 in the China region is carried out using the IED from the COSMIC inversion as a reference. The validity of the reference data and assessment results are demonstrated by comparing the assessment results using ionosonde and COSMIC data as reference, respectively. Finally, the evaluation and analysis of the IED and NmF2 outputs from the IRI-2016 model driven by IG-up values for the whole year of 2015 (one day per week) is done using the COSMIC data as a reference and comparing the results using the whole year's data with those using one month's data. Since the amount of COSMIC data available in the China region for the whole year of 2019 is very small, the results of comparing the whole year of 2019 with COSMIC are out of the scope of this paper.  
In order to quantitatively describe the performance of the IRI-2016 model before and after the improvement, root-mean-square error (RMSE), mean absolute error (MAE), and percent precision improvement (PI), are calculated as shown in Equations (1)-(3).  
\[RMSE=\sqrt{\frac{\sum_{k=1}^{n}|NeFO(k)-NeFiri(k)|^{2}}{n}} \tag{1}\]  
\[MAE=\frac{1}{n}\sum_{k=1}^{n}|NeFO(k)-NeFiri(k)| \tag{2}\]  
\[PI=\frac{X_{UPDATE}-X_{ORINGIN}}{X_{ORIGIN}}\cdot 100[\%]. \tag{3}\]  
where, \(NeFO(k)\) denotes the IED or NmF2 of ionosonde (or COSMIC) used for reference, \(NeFiri(k)\) denotes the IED or NmF2 output from IRI-2016 (or upda-IRI-2016), \(k\) denotes the number of the statistic, and \(X_{ORINGIN}\), \(X_{UPDATE}\) denote the RMSE or MAE obtained by calculating the IED or NmF2 output from the IRI-2016 model before and after the improvement, respectively. PI denotes the RMSE or MAE corresponding to the percentage improvement.
# A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation
## 2 Fault Detection and Isolation Scheme
### Offline Model Training  
#### 2.1.1 Data Pre-Processing  
As shown Figure 1, the monitoring data are firstly pre-processed and applies to both the offline training and online fault detection phases. This process filters out the erroneous monitoring data, which are common in practical measurement data caused by communication or instrumentation failures [28]. Note that the pre-processing rules can be different for different data and the rules for our experimental data are explained in Section 3.  
Moreover, a data standardization process is applied to the monitoring data to scale the data to equal range. Assume that \(\hat{X}=\{\hat{x}_{i};i=1,\ldots,D\}\) (\(\hat{x}_{i}\in\mathbb{R}^{n}\) for each measurement \(i\)) with \(D\) different measurements, is the monitoring dataset, \(X=\{x_{i};i=1,\ldots,D\}\) (\(x_{i}\in\mathbb{R}^{n}\) for each measurement \(i\)) is the dataset after standardization process. The transformation from \(\hat{X}\) to \(X\) is given in Equation (1).  
\[x_{i}=\frac{\hat{x}_{i}-\mu_{i}}{\sigma_{i}},\ \ (i=1,\ldots,D) \tag{1}\]  
where \(\mu_{i}\) is the mean, and \(\sigma_{i}\) is the standard deviation of the input \(\hat{x}_{i}\). Note that \(\mu_{i}\) and \(\sigma_{i}\) were calculated form training data, and then applied to both training and monitoring data.
# SiamCAM: A Real-Time Siamese Network for Object Tracking with Compensating Attention Mechanism
## 3 Approach
### The Attention Model of Siamese Network
Given the channel-refined feature map \(F^{i}_{z}\in\mathbb{R}^{H\times W\times C}\), We first apply max-pooling and average-pooling operations through the channel axis to get two 2D spatial feature descriptors denoted as \(F^{i}_{max}\in\mathbb{R}^{H\times W\times 1}\) and \(F^{i}_{max}\in\mathbb{R}^{H\times W\times 1}\). Spatial feature descriptors are then concatenated and fed into a single convolutional layer with sigmoid activation. We can get a 2D spatial attention map \(W^{i}_{sz}\in\mathbb{R}^{H\times W\times 1}\),  
\[W^{i}_{sz}=sigmoid(conv([F^{i}_{max},f^{i}_{agg}]))\]  
where \([F^{i}_{max},f^{i}_{agg}]\in\mathbb{R}^{H\times W\times 2}\) denotes the concatenation of global average-pooled and max-pooled feature descriptors. \(conv\) means the single convolutional layer with a sigmoid activation, which is a \(7\times 7\) convolutional layer. Finally, the attention-refined feature map \(F^{i}\in\mathbb{R}^{H\times W\times C}\) weighted with both the channel attention and spatial attention can be calculated sequentially as,  
\[F^{i}=Z^{i}\oplus(Z^{i}\otimes W^{i}_{cz}\otimes W^{i}_{sz})\]  
where \(\otimes\) and \(\oplus\) indicate multiplication and element-wise addition, respectively. The structure of the attention model is shown in Figure 3.  
Figure 3: The Attention Model of Siamese Network.
# Progress in Electrodeposition of Zinc and Zinc Nickel Alloys Using Ionic Liquids
## 8 Summary and Outlook  
ILs have gained significant attention in the past decade and are expected to enter the commercial market as a possible candidates to replace the existing aqueous technology for the deposition of Zn and Zn-Ni alloys. This review highlighted the progress in the development in the electrodeposition of Zn and Zn-Ni alloys covering a wide range of electrolytes, from traditional chlorozincate-based ionic liquids to acetate and propionate-based ones. In addition, the review also focused on the ILs employed so far for corrosion-resistant coating applications in the electroplating sector. Much of the work covered in this review is based on the Zn and Zn-Ni alloy electrodeposition from choline chloride-based ILs such as ChCl/urea/ethylene glycol/malonic acid/glycerol on different substrates (and their characterization). Studies on the profile ILs and other IL alternatives to ChCl-based ones for the deposition of Zn and Zn-Ni alloys are scarce, and these needs to be explored further. Furthermore, the influence of the chain length of ionic liquid cation on the quality of the deposit; deposit characteristics such as grain size, nucleation, and growth mechanism; Zn-Ni alloy formation; and corrosion performance of the newly developed ILs needs detailed study. While other systems such as acetate- and propionate-based ones have been gaining significant attention as an alternative to chloride-based systems, the regulating effects of parameters such as the concentration of metal salts, additives, and water content on the electrodeposition process in such ILs, in relation to mass transport, speciation, and interfacial structure, need detailed study. Many of their properties need to be understood sufficiently to exploit them in industrial applications. Nevertheless, many challenges remain. In addition, studies on the development of ionic liquids (ILs) involving simple and lineal cations and anions to improve the electrodeposition of zinc are scarce. The development of halide-free ionic liquids as electrolytes for the electrodeposition of Zn and Zn-Ni alloys is likely to address some challenges in the electroplating industry, such as environmental toxicity owing to the release of chlorine gas and formation of chlorinated compounds, and support the automotive and aerospace sectors in becoming more environmentally friendly.  
[PERSON] wrote the draft paper and [PERSON] reviewed the draft and provided supervision. 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.