esa-sceva/satcom-chunk-collection huggingface dataset:
# State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks
## 5 Experimental Setup of the Proposed Modified LSTM-Based State Estimator
### WLS-Based Loss Function for the Modified LSTM-Based State Estimator  
In order to evaluate the candidate solution in an optimization algorithm, a function called the objective function is used. The objective function is often referred to as the cost function or loss function, and the value calculated by the loss function is referred to as loss [26]. For the regression problem, the most commonly used loss function is the mean squared error (MSE) loss. Another loss function, such as the mean absolute error (MAE) or mean squared logarithmic error loss (MSLE), can also be used, depending on the need.  
In this paper, the author proposed a WLS-based loss function for the training of the LSTM state estimator network. WLS loss error is chosen to represent the conventional WLS SE method in the deep-learning-based SE. The loss function equation is written as  
\[LOSS_{WLS}=\sum_{i=1}^{n}W_{i}\big{(}y_{actual,i}-y_{predicted,i}\big{)}^{2}, \tag{20}\]  
where the square of the differences between the actual \(i\)th output \(y_{actual,i}\) and the predicted \(i\)th output \(y_{predicted,i}\) based on the estimated DC voltages are multiplied by the corresponding weight of each different measurement type.  
The weight \(W\) can also be represented as  
\[W_{i}=\frac{1}{\sigma_{i}^{2}}. \tag{21}\]  
In building the weight matrix \(W\), the \(\sigma_{i}^{2}\) is calculated using Equation (19), based on the measurement type \(i\). The weight matrix \(W\) dimension should be made to be identical to the prediction or the actual output, which in this case is the 9-bus voltage magnitude. The diagonal matrix \(W\in\mathbb{R}^{9\times 9}\) is denoted as  
\[\begin{array}{The weight for the voltage output on bus 1, bus 2, and bus 3 is calculated in regard to the error variance of the power injection measurement, since in the conventional WLS state estimator, the pseudo-measurement of the power injection is used to estimate the voltage magnitude of the buses. For the same reason, the weight for the voltage output on bus 4, bus 7, and bus 9 is calculated in regard to the error variance of the current flow measurement, and the weight for the voltage output on bus 5, bus 6, and bus 8 is calculated in regard to the error variance of the voltage measurement.
## 3 General 3-Stage ASA Switch Architecture
Suppose two signals from different inputs using the same wavelength are carried in a link in stage 2, (i.e., an input of an \(N\times N\) AWG in the last stage). According to Equation (3), these two signals are from inputs with the same member field and will be routed to the same output of the \(N\times N\) AWG in the last stage. However, this scenario cannot happen since we consider that signals from inputs with the same member field are destined to distinct outputs. Thus, two different signals using the same wavelength will not pass through the same link in stage 2. Consequently, two signals using the same wavelength do not collide at a link. Theorem 1 holds if signals from inputs with the same member field are destined to distinct outputs. In other words, from the proof of (ii) in Theorem 1, an A(\(N\), \(N\), \(N^{t-1}\)) switch is nonblocking if signals from inputs with the same member field do not use the same wavelength. This implies that each port in an A(\(N\), \(N\), \(N^{t-1}\)) switch cannot transmit up to \(N\) wavelengths simultaneously. Thus, the total capacity of the general 3-stage \(N^{t}\times N^{t}\) ASA switch architecture is close to, but not exactly, \(N^{t+1}\times\) the bandwidth of a wavelength channel. From the topology of a general 3-stage \(N^{t}\times N^{t}\) ASA switch, (i.e., an A(\(N\), \(N\), \(N^{t-1}\)) switch), we can see that \(N\)\(N^{t-1}\times N^{t-1}\) space switches are required. Considering the scalability of space switches, we propose a general multi-stage \(N^{t}\times N^{t}\) ASA switch architecture using only \(N\times N\) AWGs and \(N\times N\) space switches in Section 4.
## 3 Results and Discussion
### ATR-FTIR Analysis  
ATR-FTIR spectra were used to assess the intermolecular interactions and structural changes of the pectin film after the introduction of TC nanoemulsion. Figure 2 shows the ATR-FTIR spectra. The films exhibited broad absorption regions at approximately 3400 cm\({}^{-1}\), which are attributed to the stretching vibration of -OH bonds within the pectin monomers [61, 62]. The peak appearing around 2927 cm\({}^{-1}\) refers to C-CH stretching vibration of methylene groups in pectin chains and methyl groups of methyl ester [61, 63]. The peak at 1740 cm\({}^{-1}\) corresponds to the C=O and C-O of ester bonds. It refers to the presence of the ester group formed by the esterification of pectin with a degree of esterification ranging from 22% to 28%. The vibrational peaks around 1670-1600 cm\({}^{-1}\) and around 1400 cm\({}^{-1}\) correspond to the asymmetric and symmetric vibrations of carboxyl groups, respectively [62, 64]. The peak around 1010 cm\({}^{-1}\) refers to C-O-C stretching vibrations of the polymer chain structure [65].  
No new absorption peak was found in the pectin film spectra by adding the nanoemulsion of TC, indicating minor or no interactions between pectin and the nanoemulsion. Nevertheless, the O-H stretching vibration peak at approximately 3400 cm\({}^{-1}\) underwent a slight shift from 3281 cm\({}^{-1}\) to 3283 cm\({}^{-1}\) upon the addition of TC. This could be caused by binding interactions between TC and pectin, which reduced the stretching of the free O-H bonds. The presence of TC also leads to changes in the band region around 2900 cm\({}^{-1}\), showing the shift of the band at 2929 cm\({}^{-1}\) to 2926 cm\({}^{-1}\), which is related to the stretching vibration of the aliphatic C-H group (CH\({}_{2}\)) [66]. The amplitude of the peak around 1740 cm\({}^{-1}\) decreased slightly from the control sample to the film incorporated with nanoemulsion of TC. This peak corresponds to the C=O and C-O of ester bonds, which can be described by the presence of the carbonyl radical in the  
Figure 2: ATR-FTIR spectra of film samples with different concentrations of _trans_-cinnamaldehyde: Control (0%), P/NE:1 (5.00%), P/NE:2 (3.33%), P/NE:3 (2.50%) and, P/NE:4 (2.00%).  
triglycerides due to the presence of TC [66]. Similar results were reported by [57] who investigated the effect of incorporating different concentrations of clove bud essential oils into pectin films.
# Triaxial Test of Coarse-Grained Soils Reinforced with One Layer of Geogrid
## 2 Experimental Setup
### Experimental Program  
In total, 42 consolidated drained (CD) triaxial compression tests were conducted for six different types of soils. Initially, tests were performed on soils without geogrid reinforcement, followed by tests on geogrid-reinforced soils. Three different cell pressures \(\sigma_{3}\) were applied during the consolidation and loading phases: 20, 50, and 70 kPa, which were based on stress calculation for road construction by [PERSON] [48]. The conditions for the test were selected according to LST EN ISO 17892-9:2018 [54]. Additional tests at  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample No. *} & **Water Content Determined by** & **Dry Density of the Soil Determined** & \multirow{2}{*}{**Sample Density (Prepared)**} \\  & **Standard Proctor Compaction Test** & & **by the Standard Proctor** & \multirow{2}{*}{**\(\rho\), g/cm\({}^{3}\)**} \\  & **w, \%** & **Compaction Test \(\rho_{d}\), g/cm\({}^{3}\)** & \\ \hline
1 & 3.00 & 1.90 & 1.96 \\
2 & 7.50 & 2.08 & 2.23 \\
3 & 3.50 & 1.83 & 1.90 \\
4 & 14.30 & 1.65 & 1.86 \\
5 & 8.80 & 1.98 & 2.16 \\
6 & 8.00 & 1.75 & 1.90 \\ \hline \hline \end{tabular}  
* Sample numbering coincides with the numbering given in Table 1.  
\end{table}
Table 2: Physical properties of soils.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Unit** & **Value** \\ \hline Mesh size & mm & 25 \(\times\) 25 \\ Mass per unit area & g/m\({}^{2}\) & 260 \\ Ultimate tensile strength & kN/m & \(\geq\)40 \\ Strain at nominal tensile strength & \% & \(\leq\)10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical and mechanical properties of the geogrid.  
cell pressures of 100, 200, and 300 kPa were conducted for one soil type to investigate the impact of cell pressure on geogrid-reinforced samples. The test conditions were selected in accordance with LST EN ISO 17892-9-2018 [54].  
The absence of a dedicated saturation stage aligns with the aim of simulating real-world scenarios, where soils may not always be fully saturated. The acceptable B-value for the tests was maintained by adjusting the water content during mixing to ensure optimal compaction and representative mechanical behavior without a specific saturation phase. The consolidation duration was determined in accordance with the guidelines of the standard LST EN 17892-9-2018. However, in all cases, the consolidation duration was less than 30 min. As per the standard, the rate of vertical displacement of the load frame is dependent on the consolidation duration. To ensure consistency, the decision was made to standardize the consolidation duration and select a uniform rate of strain. In all cases, the soil underwent a 30 min consolidation period, with the rate of vertical displacement of the load frame set at 0.950% per minute. The tests were carried out by loading the specimen up to 15% of the vertical deformation. The results were interpreted using the Mohr-Coulomb criterion \(\tau=\sigma^{\prime}\tanh\dot{\ }+\mathrm{c}\)[55].
# High-Accuracy Entanglement Detection via a Convolutional Neural Network with Noise Resistance
###### Abstract
The sources of noise are diverse, ranging from fluctuations in the environment to imperfections in hardware components and imprecise measurements. Currently, various advanced statistical methods have been applied to QST, such as Bayesian inference, variational quantum eigensolver, and quantum neural networks, which alleviate the problems of noise handling and low computational efficiency in traditional linear inversion and maximum likelihood estimators [27]. Given their robustness in handling noisy image and signal processing [28], the exploration of CNNs for quantum state classification represents an exciting avenue of research. They can perform more accurate classifications by extracting characteristic patterns from noisy data, although they still face challenges in high-noise environments. In this paper, we primarily focus on the entanglement characteristics of quantum states by simplifying the regression task of quantum state tomography (QST) into a classification problem. While improving computational efficiency, we achieve good resistance to noise by leveraging the inherent generalization capabilities of CNNs. Furthermore, we can maintain considerable noise resilience while reducing the number of measurement operators that relate to the information we can obtain. The results show that the model achieves an accuracy rate exceeding 97.5% in determining entanglement for two-qubit quantum states in the absence of noise, and it also exhibits resilience to a specific degree of noise interference.
# Key Issues on the Design of an Offshore Wind Farm Layout and Its Equivalent Model
## 6 Wind Farm Reliability  
The wake effect influences the output of a wind farm. The failure of one or more wind turbines may change the wind speed distribution in the farm and consequently its power output. Therefore, the output of an offshore wind farm depends on both wake effect and wind farm reliability [43; 44; 45]. Several reliability indices have been proposed to capture the effectiveness of generating systems, including loss of expected energy not supplied (EENS), loss of load probability (LOLP), load expectation (LOLE), and expected energy produced (EEP). These reliability indices can be also applied to wind farm reliability.  
Important aspects of a realistic assessment of the reliability of wind generation include wake effects, the offshore environment, wind turbine technology, the types of wind turbines in the installation site, the power collection grid, the output powers of different types of wind turbines, and hub height. For example, the choice of wind turbine technology and its components affect the values of the some of the parameters in the analysis--especially on the failure/repair rate and maintenance for wind turbines. However, few data on the maintenance and failure/repair rates of offshore wind turbines are available owing to their relatively recent development. Power collection system availability also affects the reliability of an offshore wind farm. Numerous investigations have used Monte Carlo simulations to evaluate the reliability of wind farms with various internal cable connections [41; 46].
## 1 Introduction
The influence of crosslink density on the thermo-mechanical properties of the graphite fiber/epoxy matrix interface was examined with molecular dynamics simulations, and the results showed that a stronger interaction and larger density are presented near the interface [41; 42; 43; 44]. [PERSON], [PERSON], [PERSON], [PERSON] have analyzed the effect of interfacial compatibility and microstructure on the performance of polypropylene composites [45; 46]. These researches showed that interface feature plays a dominant role in the properties of nanocomposites. Meanwhile, the interfacial structure of composites filled with treated nanomaterials is different from that of untreated ones [29; 47; 48]. But there are few reports on the direct analysis of interfacial characteristics of surface functionalization nanocomposites from atomic level. As a popular nanofiller, boron nitride sheets (BNNS) have been widely applied to improve the properties of EP [6; 39; 50; 51]. Consequently, the BNNS/EP composites are adopted to investigate the interfacial characteristics in this work. BNNSs are functionalized by CH\({}_{3}\)-(CH\({}_{2}\))\({}_{4}\)-O- radicals, and molecular dynamics models for BNNS/EP composites with different functional density are built. The radial distribution density (RDD) and interfacial binding energy (IBE) of these compound models are computed to explore the structure and bonding strength of nanocomposites interface. Besides, the interface compatibility and molecular chain mobility (MCM) are analyzed by comparing cohesive energy density (CED), free volume fraction (FFV), and radial mean square displacement (RMSD) of different nanocomposites.
## 5 Branch-and-Bound Algorithm for \(\epsilon\)-Proper Equilibrium Computation
### The \(\epsilon\)-Proper Branch-and-Bound Algorithm
**  
**Set** binary variable vectors \(V\) and \(U\) at their values in \(S\). For every \(\hat{x}_{ik}>0\in\hat{X}\): Set \(\hat{x}_{ik}=0\) and **Go to step 5. **  
**Step 5: Solving subprogram. **  
Solve \(Q(\hat{X})\) to optimality. * If the program \(Q\) is infeasible or the optimal objective \(\sigma\) is equal to 0; **STOP. **
* Else, if the optimal objective \(\sigma\) of \(Q(\hat{X})\) is less than \(\bar{\sigma}\), **STOP. **
* If the optimal objective \(\sigma\) of \(Q(\hat{X})\) is \(\geq\bar{\sigma}\), **Update**\(\bar{\sigma}=\sigma\), **Add**\(\hat{X}\) to \(E\) and \(X\) to \(E_{\epsilon}\). **Go to step 4. **  
The sets \(E\) and \(E_{\epsilon}\) are non-empty and contain respectively all Nash equilibria and all \(\epsilon\)-proper equilibria encountered. The maximum value of \(\sigma\) is \(\sigma(\epsilon)=\bar{\sigma}\). The last element entering \(E_{\epsilon}\) corresponds to \(\sigma(\epsilon)\). Theorem 4.1: _The \(\epsilon\)-proper Branch-and-Bound algorithm generates at least one \(\epsilon\)-proper equilibrium for any polymatrix game._  
Proof: The algorithm searches for all possible ways to satisfy the Nash equilibrium, complementary slackness, and properness conditions through the principal branching tree and the secondary trees. If \(X(\epsilon)\) is the unique \(\epsilon\)-proper equilibrium, there exists at least one path in the algorithm's generated branching tree leading to it. The complexity of the \(\epsilon\)-proper Branch-and-Bound algorithm is certainly non-polynomial. It increases exponentially when the size of the polymatrix game increases.
## 4 Results and Discussion
### Spectral Polarization Image Clustering Rendering
#### 4.2.2 Spectral Polarization Image Rendering
This difference is greater than the difference in polarization properties between the two coatings, which leads the fusion algorithm to group the two coatings together in one class, and the edge as one class. The \"edge effect\" of the polarization properties was also confirmed by this finding. This effect is brought about by the thickness of the material. Since this effect is caused by the thickness of the material, it is obvious that the edge circle also belongs to the material, so the image is re-rendered, and the result is shown in Figure 10. The clustering results of six space debris materials and one background material in the image are summarized in Table 3. After denoising the image, the overall false alarm rate of the fusion algorithm is only 3.08%, and the accuracy rate can reach 96.92%. The algorithm still has a good operation effect on pixel clustering, and its un  
Figure 8: (**a**) Physical picture, (**b**) color map, (**c**) color map after denoising. Figure 9: Coloring result of clustering. have been tested. It can be observed from Figures 9 and 10 that the edges of the four other materials are erroneously classified as aluminum materials, and a circular region inside the thermal insulation cladding material is identified as coating material. This is because the edges of the materials have a significant effect on the polarization characteristics, causing these points to be misclassified as other materials. The relatively high false alarm rate of the aluminum and coating materials in Table 3 is also indicative of this issue. Recognizing these patterns will be the focus of our future work to enhance the fusion algorithm.
## 5 Discussion  
Vector polygon overlay analysis is one of the basic functions of GIS spatial analysis. Its algorithm data structure is complex, and the calculations are difficult. Traditional serial algorithms are time-consuming, whereas parallel optimization by a GPU based on CUDA can effectively improve the calculation efficiency of vector polygon overlay analysis algorithms. The Greiner-Hormann algorithm is one of the most commonly used polygon overlay analysis algorithms. It uses a double-linked list structure to store vertex data. The intersection calculation step in the algorithm has a significant effect on the overall operation efficiency of the algorithm. Therefore, this paper uses the multithreaded parallelism of CUDA to improve the intersection calculation step, which is the most time-consuming step in the Greiner-Hormann algorithm. Two kernel functions are designed to implement a GPU parallel improvement algorithm based on CUDA multithreading. Additionally, eight subject polygons of different shape complexities and one clipping polygon are selected, and overlay analysis is performed on them via the serial algorithm and the method proposed in this paper. The results show that the computational efficiency of the polygon overlay analysis algorithm is improved by parallelizing the Greiner-Hormann algorithm, and a satisfactory acceleration ratio is achieved via the GPU hardware platform. This method is a valuable reference for improving the spatial analysis of complex spatial geographic data.  
Although this paper improves the Greiner-Hormann algorithm through GPU parallelization on the basis of CUDA multithreading, it does not improve other polygon overlay analysis algorithms and does not apply other parallel architectures. In the future, we will conduct more in-depth research and analysis of the Greiner-Hormann algorithm, aim to find parts that are appropriate for parallel GPU improvement to increase the algorithm's operating efficiency, and consider implementing parallel GPU transformation of other polygon  
Figure 6: Parallel algorithm acceleration analysis for different datasets.  
overlay analysis algorithms on the basis of CUDA. Moreover, our future work will examine the hybrid parallelization of CPUs and GPUs to more effectively improve the efficiency of the polygon overlay analysis algorithm. Adjusting this method to solve computational geometry problems such as 3D polygon overlay is also our key research direction.
# Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation
## 3 Test Results
### Comparison of ANN, MIX, SPM and RNN Model Performance
Another indication that the four models complement each other well is that only about 5% of forecasting days have MAPEs greater than 2.5 for all four models. This 5% compares to the 18% of days where the MAPEs of both ANN and MIX are both greater than 2.5, and the 14% of days where this is true for both SPM and RNN-a reduction of more than 9% in these cases. Among those days where all four models had large errors, about two thirds had explainable unexpected circumstances that caused the forecasting errors, such as TPC executing electric demand bidding, extreme weather conditions, or special events. A new model can, in fact, be created by using an optimal weighted average of the ANN, SPM and RNN forecasts as such a hybrid model might further reduce the forecasting errors with proper time-varying weightings. How to choose appropriate optimal weights is a topic worthy of further investigation.
# Dynamic Simulation Model and Performance Optimization of a Pressurized Pulsed Water Jet Device
## 5 Performance Optimization of the PPWJ Device
### Experiment on Rock Breaking by PPWJ  
#### 5.2.1 Experimental Apparatus and Procedures  
The experiments were carried out using our team's independently developed four-dimensional water jet platform, with the PPWJ device and rock holder secured on the platform as depicted in Figure 13. As a representative of hard rock, granite was used in this work; for the convenience of fixing, the rock samples were processed into standard 100 mm \(\times\) 100 mm \(\times\) 100 mm (\(\pm\)1 mm) cube specimens with a cutting machine.  
The pulse pressure, stand-off distance, and erosion time were set to be 80 MPa, 100 mm, and 5 min, respectively, in accordance with the results from the original PPWJ device for granite erosion in Ref. [38]. The nozzle was adjusted to align with the center of the rock surface after setting the standoff distance. A rigid baffle was inserted between the nozzle and rock sample to ensure a stable pulse pressure. Once the pulse pressure reached the set value and remained steady, the baffle was swiftly removed. After the experiment, a high-precision 3D topography scanner (Chotest, Shenzhen, China) was used to scan the samples, and 3D point cloud data was obtained and imported into Solidworks software (v2021) to reconstruct the erosion pit image, at which point the diameter and volume of erosion pit could be extracted.  
Figure 12: Comparison of the jet curves before and after optimization.