esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Architecture
### Dynamic Parameter-Learning Module (DPLM)  
Existing approaches usually pre-calculate the graph based on geographical distances between different nodes or the similarity of the flow sequences [29; 43]. However, predefined graphs may not capture the complete information about spatial dependencies [44]. To better capture the hidden dependencies between each node, some scholars have proposed graph learning methods. For example, Graph WaveNet [45], AGCRN [39], and MTGNN [46] use learnable node embeddings to construct static graph structures. Building on these works, our dynamic parameter-learning module (DPLM) employs a method that combines daily information, weekly information, and node embeddings to construct a dynamic graph, thereby learning the hidden interdependencies between different parking lots.  
First, a node embedding \(E_{N}\in R^{N\times d}\) is randomly initialized to represent node-specific features common to all time steps, where each row of \(E_{N}\) represents the embedding of a node and \(d\) denotes the dimension of node embedding. Then, we perform an elementwise product operation on node embedding \(E_{N}\), daily information \(X_{T}^{D}\in R^{T\times N}\), and weekly information \(X_{T}^{W}\in R^{T\times N}\) to obtain a new space-time embedding \(E_{N}^{T}\in R^{T\times N\times d}\), whose expression is:  
\[E_{N}^{T}=X_{T}^{D}\odot X_{T}^{W}\odot E_{N} \tag{5}\]  
where \(X_{T}^{D}\) and \(X_{T}^{W}\) represent the position of the parking data \(X_{T}\) of the current time step \(t\) every day and the position of each week, respectively. \(N\) represents the number of parking  
Figure 2: Overall framework of DSTAGCRN.  
lots, \(d\) represents the embedding dimension, and \(\odot\) represents the elementwise product operation. The specific process is shown in Figure 3.  
In time step \(t\), the input \(X_{T}\) of the current time step and the hidden state \(H_{T-1}\) of the previous step are passed through the MLP layer, and the dynamic signal is extracted and multiplied in an elementwise manner with the spatio-temporal embedding \(E_{N}^{T}\) to generate a dynamic graph embedding \(E_{N}^{D}\in R^{T\times N\times d}\), whose expression is:  
\[E_{N}^{D}=\text{MLP}(\text{Concat}[X_{T},H_{T-1}])\odot E_{N}^{T} \tag{6}\]  
Similar to the node similarity definition graph, the spatial dependence can be inferred by multiplying \(E_{N}^{D}\) by \(\left(E_{N}^{D}\right)^{T}\). Therefore, the dynamic matrix \(A_{T}^{D}\in R^{T\times N\times N}\) we generated is substituted into Equation (1) to obtain the dynamic convolution formula, which can be expressed as:  
\[A_{T}^{D}=E_{N}^{D}(E_{N}^{D})^{T} \tag{7}\]  
\[\begin{array}{ll}Z_{t}&=(I_{N}+D^{-\frac{1}{2}}A_{T}^{D}D^{-\frac{1}{2}})X \Theta+b\\ &=(I_{N}+D^{-\frac{1}{2}}(\text{ReLU}(E_{N}^{D}(E_{N}^{D})^{T}))D^{-\frac{1}{ 2}})X\Theta+b\end{array} \tag{8}\]
## 4 Conclusions  
In conclusion, the mechanism of ultrashort pulses generated by nonlinear inverse Thomson scattering of collision between the high-energy electron and a tightly focused intense laser pulse was studied. Under the framework of classical electrodynamics, this paper explores the effects of laser intensity and electron initial energy on the time-space properties of electron radiation. As the laser intensity and electron initial energy increase, the radiation peak power increases and the pulse width becomes shorter. However, the collimation of radiation is negatively correlated with laser intensity, while positively correlated with the initial intensity of the electron. We further researched the time-space joint distribution of electron radiation and found that the time scale of the radiation is several orders of magnitude shorter than the time scale of electron motion. The time-compression effect is used to explain this phenomenon, which is also the cause of zepto-yoctosecond light pulses. The degree of the time-compression effect is also different in different observation directions. When a laser pulse with intensity \(I=1.384\times 10^{20}\) W/cm\({}^{2}\) collides with an electron with initial energy \(\varGamma=51.1\) MeV, yoctosecond (10-24 s) pulses can be generated. These results enrich the research on ultrafast and nonlinear optics and provide theoretical and numerical supports for the experimental production of zepto-yoctosecond light pulses.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14167038/s1](https://www.mdpi.com/article/10.3390/app14167038/s1)]([https://www.mdpi.com/article/10.3390/app14167038/s1](https://www.mdpi.com/article/10.3390/app14167038/s1)), Video S1: Angular distribution of NITS radiation under different laser intensities; Video S2: Angular distribution of NITS radiation under different initial energies of the electron; Video S3: The spatiotemporal distribution of NITS changes periodically with the initial phase of laser.  
Conceptualization, Y.T. and Y.Z.; methodology, Y.Z.; software, J.W. and X.G.; validation, Y.Z., Y.T. and Q.Y.; formal analysis, Y.Z. and J.W.; investigation, Q.Y. and X.G.; resources, Y.T.; data curation, Q.Y.; writing--original draft preparation, Y.Z. and Q.Y.; writing--review and editing, Y.T. and X.G.; visualization, Q.Y. and J.W.; supervision, Y.T.; project administration, Y.T.; funding acquisition, Y.T. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Materials and Methods  
Although theoretical calculations in the previous chapter can estimate the amplitude of solar array current variations, satellite resource limitations mean that not every sub-array is equipped with temperature telemetry. As a result, it is challenging to calculate total current changes using only theoretical methods. Instead, algorithms can be employed to derive these variations and serve as a basis for determining whether the solar array current is operating normally. This section establishes a prediction model for solar array output fluctuations by leveraging load current as the core short-period characteristic factor. Through comparative analysis of the predictive performance of LSTM, Recurrent Neural Network (RNN), ARIMA, and Feedforward Neural Network (FFN), the suitability of different algorithms for short-period time-series modeling is systematically evaluated. Experimental results show that both LSTM and RNN models can effectively capture the dynamic nonlinear relationship between load current and battery array output, with prediction accuracy superior to other comparative algorithms. However, LSTM has no significant advantage over RNN and has the problem of increasing resource occupation.
## 5 Conclusions  
In the study presented herein, remote sensing data sets were established using Landsat-8 data and water quality monitoring data of the Yellow River basin in Ningxia, a new deep learning PCWA-ResCNN model was developed, and multiple indicators were used to test the architecture to verify its performance in water quality inversion. The same data set was compared with the traditional CNN and LSTM models. The results of water quality inversion show that the PCWA-ResCNN model has the ability to perform high-precision inversion in all water quality parameters and has more significant advantages than traditional CNN and LSTM models. In addition, the spatial variation map of water quality inversion shows that the water quality of the Ningxia Yellow River basin is mainly Class I and Class II, which accords with the actual situation of the study area. Therefore, the PCWA-ResCNN model structure can be used as a low-cost and effective method to estimate the concentration of conventional pollutants and water quality parameters in the Yellow River basin of Ningxia and can also be applied to the inversion of water quality parameters in any water quality sampling area. It is suggested that future research should seek to enhance simulations of climatic and environmental factors by expanding data sets, optimizing calculations, interpreting models, and ensuring adaptability across regions and time periods. This will enhance the application breadth and reliability of the model to better meet the needs of water quality monitoring in different regions and different time periods.  
Conceptualization, Q.L.; methodology, Q.L.; software, Q.L.; validation, Z.G., J.L., and X.L.; formal analysis, Z.G.; investigation, Q.L. and B.B.; resources, Q.L.; data curation, Q.L.; writing--original draft preparation, Q.L.; writing--review and editing, Z.G., J.L., B.B., and X.L.; visualization, Q.L. All authors have read and agreed to the published version of the manuscript.  
The research presented herein was funded by the National Natural Science Foundation of China (Grant No. 62365016). The research presented herein was funded by the Central Government supported the Local Special Fund Project of China (Grant No. 2023 FRD05034).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The remote sensing data sets referenced herein were provided free of charge by the geospatial data cloud platform.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Novel Dual-Strategy Approach for Constructing Knowledge Graphs in the Home Appliance Fault Domain
## 4 Experiments  
### Data Preparation  
The data in this study was primarily collected through a Python web scraper from home appliance repair forums, including popular platforms such as \"Home Appliance Maintenance Information Network\" and \"Home Appliance Maintenance Technology Forum\". The initial dataset comprised 5496 entries. To ensure the authenticity and reliability of the data, the collection process followed these steps:  
* Data Source Selection  
We targeted forums and communities where professional technicians and users frequently share repair cases. These platforms were chosen for their high activity levels and the technical depth of discussions.  
* Web Crawling  
Using Python libraries like BeautifulSoup and Scrapy, we extracted textual repair cases, including fault descriptions, causes, and solutions.  
* Initial Data Cleaning  
Automated scripts removed duplicate entries, irrelevant content such as advertisements, non-repair-related posts, and incomplete cases such as missing fault descriptions or solutions. Special characters, HTML tags, and emojis were also filtered out.  
* Manual Verification and Screening  
To ensure case authenticity, two domain experts independently reviewed a random subset of 1000 entries. They verified the technical accuracy of fault descriptions and solutions, flagging ambiguous or unrealistic cases such as \"the refrigerator exploded due to overcharging\". Discrepancies were resolved through discussion, and low-quality entries were discarded. This step resulted in the removal of 12% of the initial dataset.  
* Final Dataset Assembly  
After cleaning and verification, the dataset consisted of 3156 high-quality repair cases. Each case was manually annotated, categorizing entities into five types: appliance category, components, fault cause, fault phenomenon, and solution method. The dataset was split into training, testing, and validation sets in an 8:1:1 ratio for subsequent model experiments. Table 4 provides examples of some entity categories.
## 2 State-of-Art Review
### The Effect of Varying RFA Incorporation Ratio on Concrete Performance  
#### 2.2.1 Workability  
Figure 4 presents the relationship between the concrete slump and the incorporation levels of RFA, sourced from these studies [64; 65; 67]. A reasonable explanation for this may be that RFA absorbs more water from the mix than NA [47; 68; 69]. Moreover, the minimum particle size of RFA affects concrete's workability because smaller RFA have higher water absorption than the bigger ones from the same source concrete. Greater surface-area and higher water absorption of the fine particle sizes of RA have more potential for higher water-demand and decrease workability as a result [67]. The RFA fineness level and its effect on workability are other aspects that should be considered.  
[PERSON] et al. [64] obtained slump values between 85 and 165 mm for fresh concrete that were made with different incorporation levels of RFA. The data of this study confirm that the slump of RFA concrete decreases as the incorporation level of RFA increases. This discrepancy might be due to the texture, shape, water absorption and dust content of the crushed RFA when compared to the natural sand. [PERSON] and [PERSON] [67] kept the water/binder (w/b) ratio constant at 0.40 and reported that RFA based concrete has a higher water adsorption leading to poor slump and workability at higher RFA incorporation ratios.  
For normal RFA concrete without using superplasticizers (SP), these studies [54; 70; 71] showed that the w/c ratio needs to be increased as the incorporation ratio of RFA increased in the concrete mixes to get the same target slump. This could be achieved by adding 15% extra water to the RFA concrete [72]. The following studies by [73; 74; 75; 76] confirmed this trend. Nevertheless, [PERSON] [50] reported that the workability of concrete with high fineness of RFA may not decrease. According to Figure 4, it could be concluded from the literature that given a concrete mix has a starting value for slump equal to Si, the incorporation of a X% of RFA as a replacement to fine NA would result in a slump value of Y\({}_{\text{slump}}\) in Equation (1).  
\[\text{Y}_{\text{slump}}=\text{Si}\times(1-0.05\text{X}) \tag{1}\]
## 3 Results and Discussion  
The results of the measurements of the uniformity of the air velocity distribution using diffusers in an over-row sprayer are presented in Table 1. As expected, the fan's rotational speed and, consequently, the airflow velocity increased when the fan was operated in second (II) gear. The difference in the airflow velocity between the second and the first gears ranged from around 10% (diffuser No. 3) to around 25% (when the diffuser was positioned 50 cm above the patternator). The airflow velocity decreased with an increase in the diffuser height above the patternator, and the observed differences were statistically significant. At the minimum diffuser height (10 cm), the airflow velocity was 140% (first gear) or 125% (second gear) higher on average relative to that noted at the maximum diffuser height (50 cm). These differences could be attributed to the diffusers' conical shape, which caused the supplied air to expand. As a result, the air became diluted over the distance from the diffuser. Smaller differences in the airflow velocity were noted when the fan was operated in second gear, which could be explained by the fact that the airflow was attenuated by the fan's mechanical components, central plenum, and air ducts due to the higher airflow rate and velocity [33; 34]. During spray treatments in orchards, the boom is positioned at a distance of 1 m from tree canopies and the airflow velocity at the diffuser outlet should be around 10 m s\({}^{-1}\). Upon reaching the canopy, the airflow velocity should be reduced by nearly 50% [4]. Field crops can form dense stands, especially in phenological stages characterized by rapid plant growth, which is why the airflow velocity should not be reduced below the value applied to orchards. According to [PERSON] [35], an airflow velocity of up to 15 m s\({}^{-1}\) is sufficient to distribute the spray liquid evenly throughout the crop stand while reducing the loss of chemicals and minimizing soil pollution. Therefore, the analyzed diffusers were positioned at a minimum height of 20-30 cm above the stand. It should be noted that two fan-speed settings can facilitate spraying operations because the airflow velocity can be adjusted to the height of the sprayer boom and the plant's growth stage. The airflow velocity at the diffuser outlet can also be modified to change the size of the droplets reaching the treated plants [36; 37].
## 4 Tree Flash Fault Traveling Wave Model and Attenuation Analysis
The attenuation of tree flash fault current waves propagating along the hybrid line is the result of various interacting factors, including electromagnetic interactions between the line and trees, frequency-dependent characteristics, electromagnetic wave loss through trees, and environmental conditions. Combining Table 3 and Figures 12 and 13, it can be observed that the attenuation of the fault current wave increases with the transmission distance. Consequently, the transition of the fault waveforms becomes smoother, and their amplitudes monotonically decrease with the transmission distance. For instance, at a distance of 1 km from the fault point F, the amplitude is 213.48 A, while at 4 km, it reduces to 101.69 A. Considering the significant attenuation of the tree flash fault current waves, this attenuation may decrease the signal strength, complicating localization. However, the traveling wave method exhibits adaptability to different attenuation environments, relying primarily on time differences in propagation rather than absolute signal strength.
## 4 Hybrid Check Matrix Construction (HC) Algorithm
### Hybrid Construction (HC) Algorithm
In order to test the performance of the designed non-binary iterative detection receiver, a non-binary LDPC-CPM system with different parameters is simulated. The check matrix construction algorithm, code length, code rate, number of outer iterations and number of inner iterations of the non-binary LDPC code are designed. In the simulation, the Log-FFT-BP algorithm is used for decoding. The distribution of variable node degrees for irregular non-binary LDPC code is \(\lambda(x)=0.38354x+0.04237x^{2}+0.57409x^{3}\); the CPM signal is for complex base band mapping. Each symbol is sampled at 8 to 10 points and the modulation index \(h\) is 1/2. The correlation length \(L\) is 2, the pulse function \(g(t)\) is Resistance-Capacitance (RC) type, that is, the specific signal form of CPM is OM2 RC; the channel model adopts Additive White Gaussian Noise (AWGN) model. Error correction performance is detected using an Error-Rate Detector (ERD). ERD is a test instrument that generates a pseudo-random analog signal and compares it with the transmitted-received signal to calculate the error correction performance of the transmission medium. In this paper, a pseudo-random function  
Figure 4: Non-binary LDPC-CPM system model. is used in the simulation to generate a series of pseudo-random numbers, which are input into the encoder to obtain the coded code word and the corresponding probability information is obtained under the analog channel and input to the decoder for decoding. As a result, the decoded result is compared with the originally input code word to obtain the bit error rate of this paper.
## 3 Model
### Modeling of SASCHCS
#### 3.1.1 ETC and Storage Tank  
The efficiency of ETC is calculated by the following expression [28]:  
\[\eta_{et,i}=Q_{et,i}/AI_{i}=F_{R}[\tau\alpha-U_{L}(T_{et,i}-T_{a,i})/I_{i}] \tag{1}\]  
where \(F_{R}\) is the heat removal factor and equal to:  
\[F_{R}=\frac{mc_{p}}{A_{c}U_{L}}\bigg{[}1-\exp\bigg{(}-\frac{U_{L}F^{\prime}A_{ c}}{mc_{p}}\bigg{)}\bigg{]} \tag{2}\]  
\(U_{L}\) is the overall heat loss coefficient and can be expressed by:  
\[U_{L}=U_{e}+U_{t} \tag{3}\]  
where \(U_{e}\) is header pipe edge loss coefficient and is expressed by:  
\[U_{e}=\frac{2\pi\lambda_{ins}}{L_{T}\ln(D_{ext}/D_{\text{int}})} \tag{4}\]  
\(U_{t}\) is the loss coefficient from the absorber tube to the ambient and can be defined as:  
\[U_{t}=\frac{1}{\frac{1}{h_{g-a}}+\frac{1}{h_{p-g^{\prime}}+h_{p-g^{\prime}}}} \tag{5}\]  
The collector efficiency factor, \(F^{\prime}\), is derived by [29]:  
\[F^{\prime}=\frac{1/U_{L}}{L\bigg{[}\frac{1+U_{L}/C_{b}}{U_{L}(d+(L-d)F)}+\frac {1}{C_{B}}+\frac{1}{h_{f}\tau d}\bigg{]}} \tag{6}\]Furthermore, the thermal efficiency of collectors based on the operational period is:  
\[\eta_{tec}=\frac{\int_{\Delta t}Q_{etc,i}dt}{\int_{\Delta t}Al_{i}dt} \tag{7}\]  
The storage tank has three layers, and the energy balance of each layer is calculated below:  
\[\frac{1}{3}\rho V_{st}c_{p}\frac{dT_{st,i}}{dt}=m_{c}c_{p}(T_{st,i-1}-T_{st,i}) +m_{g}c_{p}(T_{st,i+1}-T_{st,i})-h_{st}A_{st}(T_{st,i}-T_{a,i})/3 \tag{8}\]  
The initial temperature of storage tank is equal to the surrounding temperature. It is stated that layer 1 donates the top layer of the storage tank and layer three is the bottom layer.
## 1 Introduction  
The laboratory usually determines the quality-related material properties of ferrous materials. Here, several measuring techniques are available, allowing for the microstructure to be directly analyzed, e.g., by x-ray diffraction and optical or electron microscopy. In addition, destructive testing methods are available for measuring the mechanical properties, such as hardness, tensile strength, etc., of metallic materials. However, today's industry production is characterized by a high degree of automation, which is why it is also necessary to automate quality inspection. The quality characteristics of raw materials, semi-finished, and final products should be determined not only in material laboratories but also parallel to or embedded into production. Critical material characteristics should be continuously and completely registered in order to monitor or even control the manufacturing processes with the aim of optimizing them in terms of quality, efficiency, and costs. These objectives can only be achieved by the application of appropriate nondestructive testing (NDT) methods. In the beginning, NDT techniques have been almost exclusively used for defect detection in order to prevent the catastrophic failure of safety-relevant structures after they have been in service [1]. However, nowadays the role of NDT is extended and it includes all phases of the product's life-cycle, from raw material over production to operation, disposal, and recycling [2].  
Meanwhile, the application of NDT in production has become an indispensable part of modern industry and characterizing materials in terms of microstructure, condition, and properties are important tasks for today's techniques of production-integrated NDT [3]. In this context, the so-called \"micromagnetic\" methods are probably the most prominent representatives of such techniques. Micromagnetic methods allow for the nondestructive characterization of all ferromagnetic materials, showing magnetic interactions with the microstructure and stresses on a microscopic level.