esa-sceva/satcom-chunk-collection huggingface dataset:
# Entity-Alignment Interaction Model Based on Chinese RoBERTa
## 4 Experiments
### Datasets  
We conducted experiments on the MED-BBK-9K datasets. This dataset was first proposed in 2020 as a new benchmark for entity-alignment evaluation and was extracted from two actual medical-domain knowledge graphs. Specifically, the MED subgraph was constructed from a medical resource that covers fine-grained knowledge such as diseases, symptoms, and drugs, supporting applications such as question-answering systems and medical assistants. The BBK subgraph was extracted from the information boxes of Baidu Baike through natural-language-processing technology. A total of 9162 one-to-one entity mapping connections were annotated between these two subgraphs through manual and heuristic rules. More details of the MED-BBK-9K datasets are shown in Table 1.  
Generally speaking, the more data are used for training, the better the model performs. However, in the entity-alignment task, it was necessary to use a smaller proportion of total data for the training set to verify the model performance on unknown entity pairs; the model showed maximal efficiency of data utilization with 30% of the data used for training, and most existing entity-alignment models have a training-to-testing ratio of 37. Therefore, in order to make a fair comparison with other existing models, we also set 30% of pre-aligned entity pairs to be used for training, and the rest were used for testing.
## 4 Simulation Results and Discussion
### N-Way GPR Target and N-Way Detection  
Figure 10 shows the BER performances for the 2, 3, and 4-way GPR target and its associated 2, 3, and 4-way MVA, respectively. In this experiment, we did not use the feedback path, and the \(\varepsilon\) coefficient for the N-way MVA was set to the GPR target coefficient such that \(\varepsilon=\max\left(\alpha_{1},\alpha_{2}\right)\), which is the ITI coefficient from the estimated GPR target. We found that the two-way method, which corresponds to the conventional VA algorithm, achieved the best BER performance. This shows that our signal had additional ITI information when using the three and four-way GPR targets. When the three and four-way MVA were used by themselves, we were not able to take full advantage of the ITI information. For the two-way GPR target, there was no ITI information, whereas for the four-way GPR target, there was too much ITI information, which resulted in a large alias. Therefore, we chose a three-way GPR target with a three-way MVA so that our model would be able to utilize the ITI information in the detection process.  
In addition, the accuracy of the detection of the first row is very important owing to the addition of a feedback path in the model. If the first row has many errors, these errors will be propagated through to the next row of the algorithm. Therefore, we monitored the accuracy of the detection of the first rows in all data pages by calculating the BER performance for the first rows on each N-way GPR target and N-way MVA method. In other words, we take the first row of data in each page, then we apply the 2,3,4-way GPR and 2,3,4-way MVA. The results are shown in Figure 11. The three-way GPR target and three-way MVA achieved the best BER performance. This is because the data in the first row are affected by three-way interference. Consequently, the three-way GPR target and three-way MVA were well suited for this condition. Unlike the two-way GPR target, wewere able to extract all ITI with the three-way GPR target. Meanwhile, the four-way GPR target and four-way MVA did not appear to affect the BER performance. This is due to the alias effect.  
Figure 11: Combined N-way GPR target and N-way MVA for the data in the first row.  
Figure 10: Combined N-way generalized partial response (GPR) target and N-way MVA.  
Based on the results, the four-way GPR target has the largest alias due to the overlap of the lower branches to the negative side and vice versa. In addition, as the data in the first row are affected by three-way interference, the three-way GPR target and three-way MVA was the most suitable scheme.
# Fault Diagnosis of a Solenoid Valve Based on Multi-Feature Fusion
## 3 Current Signal Feature Extraction
### Multi-Feature Fusion Analysis  
The time-frequency analysis method provides the joint distribution information of the time and the frequency domain, clearly describing the relationship between signal frequency and time. This type of analysis can detect small signals without omitting them. However, when the difference between the signal in the time and the frequency domain is not apparent, the feature vector constructed by the time-frequency analysis scheme attains insufficient recognition accuracy, presenting low credibility for pattern recognition. Time-domain analysis directly analyzes the system in the time-domain offering the most direct signal analysis method and thus has the advantages of a large amount of information, intuitiveness and accuracy. However, the actual signal is more complicated. Therefore, it is difficult to directly use time-domain analysis to reveal the signal's frequency structure, the amplitude (or energy) of each frequency component and the relationship between the contained information and potential faults.  
Spurred by the shortcomings of the time-frequency and time-domain analysis methods, this paper proposes combining wavelet packet decomposition and PCA. Specifically, the time-frequency analysis features and the time-domain parameter features of the current signal were fused, and the resulting multi-feature feature vector was then exploited for fault diagnosis.
###### Abstract  
The use of energy efficient structures in the local construction industry assists in promoting green building concepts, leading to economical and eco-friendly solutions for self-sustained structures. The main aim of this study was to examine and compare the energy performance of various local buildings. Detailed 3D building models (house, office, and warehouse buildings) were constructed and investigated for their cost and energy savings using building energy simulation tools (green building studio and insight). Moreover, the effects of various building materials for walls, window panels, and roof construction were explored, and a life-cycle cost analysis was performed. It was observed that the effect of the window-to-wall ratio was less severe in term of energy use in office buildings compared to normal houses due to the larger amount of space available for air circulation. Furthermore, the most efficient location for windows was found to be at the middle of the wall in comparison with the top and bottom positions. The effect of the orientation mainly depended on the symmetry of the building. More symmetric buildings, i.e., tested warehouse buildings (rectangular structure), showed an energy use difference of around 7 MJ/m\({}^{2}\)/year for a 360\({}^{\circ}\) orientation change. Tested house buildings exhibited an energy use difference of up to 25 MJ/m\({}^{2}\)/year. Three-pane glass windows also showed major improvements, and the total energy consumption for houses was reduced to 14%. Furthermore, wood walls showed comparable energy performance with brick walls without the use of insulation. According to US-LEED guidelines, the tested house, office, and warehouse buildings achieved 79, 89, and 88 points, respectively. The cost recovery period for house, office, and warehouse buildings was estimated to 54, 13, and 14 years, respectively, including running and maintenance costs. It can be argued that the Insight and Green Building Studio packages can assist construction stakeholders to determine the energy efficiency of the modeled building as well as to help in the selection of materials for optimized and improved design.  
energy-efficient structures; ASHARE; US-LEED guidelines; building materials; cost recovery period +
Footnote †: journal: Applied sciences
# Socioeconomic Impact on Urban Resilience against Flood Damage
## 2 Methodology
### Evaluation of Socioeconomic Cost
#### 2.2.1 Valuation of Productive Activities  
The valuation of productive activities (VPA) refers to the economic value generated when labor activities are carried out [24]. It is evaluated based on the average wage and labor hours. The economic loss is calculated through the difference in the VPA at various flood stages, representing the loss incurred due to the inability to participate in labor because of flood damage.  
\[PL_{t}^{d}=\sum VPA_{max}^{id_{b}}-\sum VPA_{t}^{id_{b}} \tag{11}\]  
\[VPA_{max}^{id_{b}}=p^{id_{b}}\times W^{ci}\times T_{max}^{id_{b}} \tag{12}\]  
\[VPA_{t}^{id_{b}}=p^{id_{b}}\times W^{ci}\times T_{t}^{id_{b}} \tag{13}\]  
where _PL_ is loss in production activities, _VPA_ is valuation of productive activities, _Wci_ is average hourly wage of city (USD/h), \(T\) is working hours (h) and \(p^{id_{b}}\) is population capable of production per building.  
The average hourly wage by region is not publicly available, so it was estimated using the available data. Based on the land value by public announcement by the Seoul Open Data Plaza [25], which is operated by the Seoul Metropolitan Government, each city was categorized into deciles. Subsequently, to estimate the average wage by decile, salary income data by percentile from the National Tax Service [26] was used to classify labor income by decile. Based on the classified labor income, the hourly wage by decile was estimated, and this was mapped to each city in Seoul to estimate the average hourly wage by city. The annual average salary income and the estimated hourly wage in the Table 1 below are based on the 2023 standards in South Korea.
## 4 Fractal Reflectarray Interference Analysis in Satellite Simulation Environment Scenario
The reflectarray demonstrates its effectiveness in sharing a substantial amount of signal strength with ground stations from LEO satellites. Similarly, it exhibits stable performance characteristics in scenarios where both LEO and MEO satellites transmit simultaneously without any impact on the orbit. This real-time simulation envi  
Figure 19: Downlink closure status from the MEO satellite with and without interference after replacing the ground station antenna. Figure 18: Radiation patterns of the rectangular array adjusting in reference to the position of LEO satellites at different time frames, (**a**) 18:00 UTC, (**b**) 18:02 UTC, (**c**) 18:04 UTC, and (**d**) 18:06 UTC. ronment evaluates the usefulness of reflectarrays in satellite communication by successfully estimating theoretical concepts.
## 3 Sensor Node Position Estimation
### Uncertainties in GPS Positioning  
Although trilateration is a viable solution for the localization problem, GPS uncertainty makes position estimation more complex. When receiving position packets and calculating the intersection between them, there is no common intersection point among the packets due to the GPS error. Instead, each intersection will generate two intersection points and the sensor node will have to decide which one to use as a reference point to estimate its position. Figure 4 shows an example of how a sensor node calculates the intersection points. It receives three position packets from the sink at times \(t1\), \(t2\), and \(t3\). The intersection between \(t1\) and \(t2\) generates the intersection points \(c\) and \(e\). The intersection between \(t1\) and \(t3\) generates the intersection points \(a\) and \(d\). Similarly, the intersection between \(t2\) and \(t3\) generates the intersection points \(b\) and \(f\). We propose two algorithms to find out which intersection points to use as reference points: distance and closeness algorithms. Afterward, the sensor node can simply estimate its position, calculating the average of the reference points \((a,b,c)\) obtained according to Equation (1).  
\[P(x,y)=\left((x_{a}+x_{b}+x_{c})/3,(y_{a}+y_{b}+y_{c})/3\right) \tag{1}\]  
Figure 3: Overview of how the position of the sensor node is estimated. (**a**) With one position packet source. (**b**) With two position packet sources. (**c**) With three position packet sources.
# A Nonlinear Optimization Design Algorithm for Nearly Linear-Phase 2D IIR Digital Filters
## 4 The Constrained Optimization Problem  
To solve the design problem, the group delay deviation is minimized under hard and soft constraints. Hard constraints are the requirements that the filter must satisfy. These requirements must include at least the stability constraints of the filter. On the other hand, soft constraints are the requirements that the filter should try to satisfy. These can, for example, include the passband ripple constraint, transition-band gain. and stopband attenuation.  
The general constrained optimization problem at hand can be stated as follows:  
\[\min_{x} \max_{w_{1},w_{2}\in\Omega_{pb}}\{|e_{g1}(x,w_{1},w_{2})|,|e_{g2} (x,w_{1},w_{2})|\}\] (10) subject to : \[\text{passband error}\leq\Gamma_{pb},\] \[\text{transition-band gain}\leq\Gamma_{tb}, \tag{11}\] \[\text{stopband gain}\leq\Gamma_{sb},\] \[\text{stability margin}\geq\varepsilon_{s},\]  
where \(\Gamma_{pb},\Gamma_{tb}\), and \(\Gamma_{sb}\) are the maximum prescribed levels for the passband error, transition-band gain, and stopband gain, respectively. The stability margin, \(\varepsilon_{s}\), is defined as \(\varepsilon_{s}=1-r_{max}^{(p)}\) where \(r_{max}^{(p)}\) is the maximum pole radius allowed [11]. The above general optimization problem for the nearly linear-phase 2D IIR digital filters can now be expressed as follows:  
\[\min_{x} \max_{w_{1},w_{2}\in\Omega_{pb}}\{|\tau_{1}(x,w_{1},w_{2})-\tau_{ 10}|,|\tau_{2}(x,w_{1},w_{2})-\tau_{20}|\}\] (12) subject to : \[|e_{h}(x,w_{1},w_{2})|\leq\Gamma_{pb},\forall(w_{1},w_{2})\in \Omega_{pb},\] \[|H(x,w_{1},w_{2})|\leq\Gamma_{tb},\forall(w_{1},w_{2})\in\Omega_{ tb},\] \[|H(x,w_{1},w_{2})|\leq\Gamma_{sb},\forall(w_{1},w_{2})\in\Omega_{ sb},\] \[|\tau_{1}-\tau_{10}|\leq\Gamma_{S1},\forall(w_{1},w_{2})\in\Omega _{pb}, \tag{13}\] \[|\tau_{2}-\tau_{20}|\leq\Gamma_{S2},\forall(w_{1},w_{2})\in\Omega _{pb},\] \[\textbf{B}_{1}x\leq 1-\gamma,\] \[\textbf{B}_{2}x\leq 1-\gamma,\]  
where \(\Gamma_{S1}\) and \(\Gamma_{S2}\) are the maximum errors between the group delays of the actual and the initial filters. \(\gamma=1-(1-\varepsilon_{s})^{2}\), **B1** and **B2** are the stability matrices for \(D_{1}(z_{1},a_{1})\) and \(D_{2}(z_{2},a_{2})\), respectively [11, 44]. The 2D IIR filter design problem can be readily solved using interior-point method. This is because the interior-point method is efficient from a computational point of view and also highly efficient in practice [50, 51]. To make our implementation easier, we used \(\Gamma_{g}=\Gamma_{g_{1}}=\Gamma_{g2}\). By varying \(\Gamma_{g}\), and therefore \(\Gamma_{g_{1}}\) and \(\Gamma_{g2}\), we obtain different filters from which we choose the best filter by looking at the quality factors discussed in the next section. The proposed framework is implemented using the interior-point (IP) method in a MATLAB environment and the flowchart of the optimization process is shown in Figure 1.
# Using a GPU to Accelerate a Longwave Radiative Transfer Model with Efficient CUDA-Based Methods
## 4 GPU-Enabled Acceleration Algorithms  
### Parallel Strategy  
To run the subroutines \(inatm\), \(cldprmc\), \(setcoef\), \(taumol\), and \(rtnmc\) on a GPU, they are each implemented with a separate kernel. \(taumol\) and \(rtnmc\) are too large to run efficiently as a single kernel, so they can each be split into more than one. The RRTMG_LW uses a collection of three-dimensional (3-D) cells to describe the atmosphere. The computation in the RRTMG_LW is independent in the horizontal direction, so for these kernels, each CUDA thread is assigned the workload of one \"column\" shown in Figure 2, where the \(x\)-axis represents longitude, the \(y\)-axis represents latitude, and the \(z\)-axis represents the vertical direction. To further exploit the fine-grained data parallelism, the computational amount in the vertical dimension is even divided. Moreover, the parallelization between kernels should be also considered in addition to the one within kernels.  
In the CAS-ESM, the IAP AGCM4.0 is with a \(1.4^{\circ}\times 1.4^{\circ}\) horizontal resolution and 51 levels in the vertical direction, so here, the RRTMG_LW has \(128\times 256=32,768\) horizontal grid points. If one GPU is applied, the GPU will finish computing the 32,768 grid points in the horizontal direction at each time step. Due to the limitation of global memory on a GPU, a K20 GPU only can compute for \(ncol=2048\) horizontal grid points and start 2048 CUDA threads. Thus, the 32,768 grid points will be  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specification of Central Processing Unit (CPU)** & **K20 Cluster** & **K40 Cluster** \\ \hline CPU & E5-2680 v2 at 2.8 GHz & E5-2695 v2 at 2.4 GHz \\ Operating System & CentOS 6.4 & Red Hat Enterprise Linux Server 7.1 \\ \hline Specification of GPU & K20 cluster & K40 cluster \\ \hline GPU & Tesla X20 & Tesla K40 \\ Compute Unified Device Architecture (CUDA) Cores & 2496 & 2880 \\ Standard Memory & 5 GB & 12 GB \\ Memory Bandwidth & 208 GB/s & 288 GB/s \\ CUDA Version & 6.5 & 9.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configurations of graphics processing unit (GPU) clusters.  
Figure 2: Three-dimensional spatial structure of RRTMG_LW: Its physical dimensions include longitude, latitude, and model layers.  
divided into 32,768/2048 = 16 chunks, each having 2048 points. In other words, a K20 GPU will finish all the computations of 32,768 points by 16 iterations at each time step.
## 1 Introduction
This approach offered distinct advantages: rapid detection speed and accurate positioning, demonstrating significant potential for grain monitoring applications. This study employed long-grained rice as the test subject, which, after drying, exhibited a moisture content of approximately 10.5%. Generally, moisture levels exceeding 5% above that of dried grain were considered abnormal and unsuitable for long-term storage. Therefore, the primary objective of this research was to locate regions with moisture content greater than 15% within a cubic experimental bin containing long-grained rice dried to 10.5% moisture content. This localization aimed to facilitate subsequent treatment and prevent grain deterioration. The remainder of this paper was organized as follows: Section 2 presents the detection system based on electromagnetic wave DMAS beamforming techniques, the fundamental principles of DMAS, and the algorithm for localizing abnormal moisture regions within grain piles. Section 3 shows the impact of electromagnetic transmitter and receiver layouts on the detection area's resolution capability and determination of the detectable region. Section 4 discusses the method for assessing the reliability of localization results and localization tests for areas with 15.4% abnormal moisture content within the grain pile. Section 5 provides a conclusion and insights into potential future research directions.
## 4 Results and Discussion
### Comparison of the Steam-CO\({}_{2}\) Mixture Condensation Features with Steam-Air
(**a**) Variation in \(T_{i}\) with subcooling; (**b**) Variation in \(\Delta NDT_{i}\) with subcooling; (**c**) Variation in \(\alpha_{i}\) with subcooling; (**d**) Variation in \(\Delta ND\alpha_{i}\) with subcooling; (**e**) Variation in \(\overline{h}\) with subcooling; (**f**) Variation in \(\Delta\overline{h}\) with subcooling. low pressures or concentrations is small, and the difference between mixture condensation is mainly reflected by the difference in the physical properties of air and CO\({}_{2}\). The physical parameters of the mixture gas considered in the boundary layer model are viscosity, thermal conductivity, specific heat capacity, density, and the diffusion coefficient. Moreover, the low solubility of CO\({}_{2}\) at high pressures and concentrations is one of the most influential physical properties. The results in Figure 8 show that different gas components also have a significant effect on the condensation features, which can explain why some existing correlations cannot be used to predict the condensation of steam-CO\({}_{2}\)[27]. Figure 8e,f show the \(\overline{h}\) and \(\Delta\overline{h}\) of the mixtures of steam-CO\({}_{2}\) (both the NG model and the CG model) and steam-air on the horizontal plate surface. The heat transfer coefficient of the steam-CO\({}_{2}\) mixture is higher than that of the steam-air mixture. Moreover, the difference in the average HTC becomes more obvious with an increase in the subcooling and concentration of CO\({}_{2}\) or air; when the subcooling is 20 K and \(\alpha_{\infty}\) is 30%, the \(\Delta\overline{h}\) reaches 16%.
# Fuzzy Neural Networks--A Review with Case Study
## 2 Fuzzy Neural Network Description
### Papers Review on Defined Criteria
In [66], the authors proposed the adaptive optimization of fuzzy membership functions and fuzzy rules for controllers used in hybrid electric vehicles. In [67], a self-organizing interval type-2 fuzzy neural network was employed to a motion control system. The use of a fuzzy network allowed for the suppression of disturbances in the data, including those caused by base vibrations. In [68], the authors developed a deep neural network incorporating fuzzy wavelets to forecast energy consumption patterns in urban buildings. In [69], gated recurrent fuzzy neural networks were employed in micro gyroscopes characterized by high data uncertainties. The proposed solution enabled the elimination of disturbances and provided data without delays. In [70], a fuzzy neural network was also used in gyroscopes. However, in this solution, a network architecture with feedback connections was employed. This approach addressed scenarios where lumped parameter uncertainties were unmeasurable, and system parameters were unknown. In [71], the authors introduced a hesitant fuzzy graph neural network model. This model was used for text classification tasks because it enabled the analysis of relationships between samples. In [72], a fuzzy neural network architecture was utilized to design a reliable voltage controller for buck DC/DC converters. This model approximated unknown nonlinear dynamics caused by variations in input voltage and load conditions. The network weights were updated in real time using an adaptive law, enhancing system robustness and its ability to handle uncertainties. In [73] a neuro-fuzzy model was adjusted using the Artificial Bee Colony algorithm for predicting results of consulting services in construction.
# Space Node Topology Optimization Design Considering Anisotropy of Additive Manufacturing
## 3 Results
### Anisotropic Optimization Analysis of Space Nodes
#### 3.4.1 Anisotropic Analysis of Optimization Results for Isotropic Materials  
In this Section, the optimization results of the isotropic materials in Section 3.1 are re-analyzed, the six parameters in Table 4 are substituted as engineering elastic constants, and the deformation analysis is carried out in Abaqus.  
The analysis results in all directions are shown in Figure 14.  
Figure 14 shows the effect of anisotropy during actual manufacturing, which was relatively large. It can be seen in the displacement cloud map that the node deformation of the Y-direction print with Z-scan was the smallest. In addition, the deformation of the node printed in the Z direction of this node was significantly larger than that of the other two directions. The difference between the maximum deformation and the minimum deformation was about 21.4%; hence, the anisotropy problem of additive manufacturing could not be ignored.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Printing** & **Scan** & **E1 (MPa)** & **E2 (MPa)** & **E3 (MPa)** & **G12 (MPa)** & **G13 (MPa)** & **G23 (MPa)** \\ \hline X & Y & \(1.26\times 10^{5}\) & \(1.66\times 10^{5}\) & \(1.58\times 10^{5}\) & 60,769 & 63,846 & 48,462 \\ X & Z & \(1.26\times 10^{5}\) & \(1.58\times