esa-sceva/satcom-chunk-collection huggingface dataset:
# Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network
## 3 Methods  
Firstly, this section presents the definition of the prediction problem. The MDST-GNN model consists of a graph-learning module, a temporal convolution module, a graph convolution module and a down-sampling convolution module. In this paper, an attention mechanism is introduced into the graph convolution module to filter spatiotemporal information. A down-sampling convolution module is proposed to extract deep spatiotemporal information to improve model performance. Finally, the overall frame diagram and algorithm steps of the MDST-GNN model are given.
# Dynamic Energy Management Strategy of a Solar-and-Energy Storage-Integrated Smart Charging Station
## 2 Description
### Taipower Electricity Tariff of EV Charging and Swapping Facility  
The Taipower Electricity Tariff for the EV Charging and Swapping Facility is shown in Table 1. It applies to high electricity demand users of charging and swapping facilities with contracted capacities. It contains three main features: a minimal basic fee, substantial peak-off peak price differential (exceeding TWD 6 per kilowatt-hour, three times the regular electricity tariff), and extended off-peak periods.  
smart charging station, adhering to the guideline proportions, is further illustrated in Table 2.  
The EV charging station in this study is meticulously designed to feature eight 60 kW DC fast charging piles, a configuration that aligns with the current dominant trend in Taiwan's EV charging infrastructure. This specific choice of 60 kW charging piles is informed by their prevalence and effectiveness in meeting the current demands of electric vehicles in the region. Moreover, the overall contracted capacity of the station is capped at 499 kW [15], in strict compliance with Taipower's regulations for low-voltage supply contracts. This limit is a standard practice in Taiwan, reflecting a balance between the operational capacity of charging stations and the constraints imposed by local grid infrastructure. By adhering to this standard, the station ensures optimal compatibility with the existing electrical systems and regulatory frameworks while also catering to the practical needs of EV users. The architectural layout and the integration of the solar-and-energy storage system with the EV charging infrastructure are illustrated in Figure 2.  
The charging station operates under the control of a Smart EMS. Upon an EV's arrival at the station, the vehicle owner is prompted to set the departure time and target state of charge (SOC). The EMS is capable of autonomously adjusting charging strategies based on factors such as electricity tariffs, solar energy generation levels, energy storage system status, and vehicle charging demands. These energy management strategies aim to achieve optimal economic benefits.
## 2 Numerical Method  
### The Coupled Eulerian-Lagrangian (CEL) Method  
During near-field underwater explosion simulation, the traditional Lagrange and Euler grid algorithms have limitations. The Lagrange algorithm would like severe distortion in the elements. The Euler algorithm struggles to accurately capture the material boundary. Therefore, the coupled Euler-Lagrange (CEL) method is proposed to solve the above weaknesses. The CEL method combines the advantages of the two traditional grid algorithms, processing the structure and fluid separately, and carrying out the coupling calculation on the contact surface to better solve the complex fluid-structure coupling problem and large deformation problem. In calculation, the Lagrange method was adopted to solve the structure. The movement and deformation of the elements could be generated, along with the flow of materials. The governing equations can be expressed as [24]:  
\[\frac{d\rho}{dt}+\rho\
abla\cdot v=0 \tag{1}\] \[\rho\frac{dv}{dt}=\
abla\cdot\sigma+\rho b\] \[\rho\frac{de}{dt}=\sigma:(\
abla\otimes v),\]  
where \(\rho\), \(v\), \(e\), \(\sigma\), and \(b\) are the density, velocity, specific internal energy, stress, and mass forces of the Lagrangian medium, respectively.  
The fluid is solved by the Euler method. When a response occurs, the mesh is fixed without movement, and the material is transported among the elements. The governing equations can be written as follows [24]:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho v)=0 \tag{2}\] \[\frac{\partial\rho v}{\partial t}+\
abla\cdot(\rho v\otimes v)= \
abla\cdot\sigma+\rho b\] \[\frac{\partial e}{\partial t}+\
abla\cdot(ev)=\sigma:(\
abla \otimes v),\]  
where \(\rho\), \(v\), \(e\), \(\sigma\), and \(b\) are the density, velocity, specific internal energy, stress, and mass forces of the Euler fluid medium, respectively. At the interface, the velocity on the Lagrangian boundary provides the constrained kinetic energy for the Euler calculation, and the Euler material's pressure provides the Lagrangian region's loading force. Lagrangian and Euler solvers interact through the coupling surface.  
It should be noted that the CEL method requires high computational power support to simulate underwater explosions. Although Abaqus provides the setting of non-reflection boundaries, it can only reduce the influence of wave reflection to a certain extent. Therefore, a large Euler domain is still needed, and a high-accuracy mesh division is required around the explosive. When calculating the long analysis time of explosion bubbles, the problem of high calculation consumption becomes more prominent.
# Email Campaign Evaluation Based on User and Mail Server Response
## 5 Conclusions  
The article presents artificial neural network models that can be used by an ESP company as part of the evaluation of the sending of email campaigns or a group of transaction emails sent by a given client. The key element of the proposed solutions are the conditional attributes used, i.e., instead of analysing the content of an email, we use statistics collected by the ESP system on the response of servers and subscribers (recipients). Such an approach allows us to use the same model regardless of the language of an email. The purpose of using the proposed models is to evaluate an email campaign (or a set of transaction emails) and identify bad cases. Since the bad label is associated with negative phenomena such as hard and soft bounces, users complaints, or resignations, it can be identified with the problem of detecting emails that we qualify as spam or phishing.  
The solution presented is not intended to replace existing methods of analysing the quality of emails sent. Rather, it complements existing mechanisms. Experiments with data have shown that even as an independent method of evaluating email campaigns (transaction emails), the proposed solution is highly effective (the F1-score is above 0.95 for any used sample). In addition, the approach is rather simple, easy to implement, and reliable.  
Conceptualization, M.S., P.S., S.O. and G.J.N.; methodology, M.S. and P.S.; software, M.S., P.S. and S.O.; validation, M.S. and P.S.; investigation, M.S. and P.S.; resources, M.S.; data curation, P.S. and M.S.; writing--original draft preparation, M.S.; writing--review and editing, M.S. and G.J.N.; project administration, G.J.N. and S.O. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
0 & 0.98 & 0.98 & 0.98 & 250,123 \\
1 & 0.98 & 0.98 & 0.98 & 249,877 \\ accuracy & & & 0.98 & 500,000 \\ macro avg & 0.98 & 0.98 & 0.98 & 500,000 \\ weighted avg & 0.98 & 0.98 & 0.98 & 500,000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification report for model shown in Figure 3.  
Figure 3: ANN structure for transaction emails classification model.  
**Funding:** This research was funded by The National Centre for Research and Development, Poland, grant number NCBiR POIR.01.01.01-00-0202/19.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Determining the Environmental Parameters  
Several studies have proven that the clearness index (CI) is a significant meteorological parameter that can characterize the proportional intensity of solar incident radiation, which is associated with the ratio of direct to diffuse light [48; 49]. The CI can be used to differentiate between weather conditions, such as sunny and cloudy days. A greater CI value indicates a sunnier day. To gain a deeper comprehension of how various incident light conditions affect GPP estimation using SIF and PRI, we calculated the CI based on the observed canopy PAR (\(\text{PAR}_{\text{TOC}}\), \(\text{\mu mol}\text{\textbullet}\text{m}^{-2}\text{\textbullet}\text{s}^{-1}\)) and the PAR above the atmosphere (\(\text{PAR}_{\text{TOA}}\), \(\text{W}/\text{m}^{2}\)):  
\[\text{CI}=\frac{\text{PAR}_{\text{TOC}}}{\text{PAR}_{\text{TOA}}} \tag{9}\]  
\[\text{PAR}_{\text{TOA}}=\text{S}_{0}\times(1+0.033\times\cos\,(2\pi\times \frac{\text{DOY}}{365}))\times\cos\,(\text{SZA}) \tag{10}\]  
where \(\text{S}_{0}\) represents the solar constant, which canopy be set to a value of 1367 \(\text{W}/\text{m}^{2}\); DOY stands for the day of the year; and SZA indicates the solar zenith angle. Finally, the unit of \(\text{PAR}_{\text{TOA}}\) was converted from \(\text{W}/\text{m}^{2}\) to \(\text{\mu mol}\text{\textbullet}\text{m}^{-2}\text{\textbullet}\text{s}^{-1}\) by multiplying it by 4.6.  
The vapor pressure deficit (VPD) was determined by [PERSON]'s formula based on the air temperature and RH [50]. The VPD was used to indicate the dryness of the air. Other environmental parameters (e.g., PAR and Ta) could be obtained from the open-access dataset.
# Investigation of Electro-Elastic Properties for LN Single Crystals at Low Temperature
## 3 Results and Discussion
### Temperature Dependence of Elastic Compliance  
Based on the resonant and anti-resonant frequencies, the temperature dependent behaviors of elastic compliances were calculated. Figure 3 gave the elastic compliances of LN crystals as a function of temperature. The elastic compliance \(s_{44}\) exhibited a large value, which was 16.0 pm\({}^{2}\)/N at \(-150\)\({}^{\circ}\)C, increasing to 16.9 pm\({}^{2}\)/N at 150 \({}^{\circ}\)C. The \(s_{11}\), \(s_{13}\), \(s_{33}\) and \(s_{44}\) all presented positive increases with increasing temperature, where the variations were 5.0%, 8.2%, 4.6% and 5.4%, respectively, showing a good temperature stability.
## 4 Results and Discussions  
### Environmental Setup  
For a compilation of the VGG16 model, we used 'binary_crossentropy' as a loss function because of the binary classification. We used Stochastic Gradient Descent as an optimization function with a learning rate of 0.001 and accuracy is used as the correctness measure. In the first step of training, we trained the model on different batch sizes (16, 32) and epochs (50, 100, 150, 200). However, during validation, we analyzed that training on the 16-batch size and 100 epochs performed better than the other settings. The dataset comprised 3000 images in total out of which 20% of data were used for testing and the remaining 80% were used for training and validation. In the literature, we observed that the performance of models was mostly tested through accuracy [24]. We used correctness measures including Accuracy, Precision, Recall, and F-Measure for the testing of our model. Our proposed model predicts the engaged and non-engaged frames with 90% accuracy and classified 93% of frames into positive class (recall); 93% of frames were correctly classified into both positive and negative classes (precision). Table 3 presents some important correctness measures for the computation of the affective states of students in a real classroom environment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Optimal Value** \\ \hline Learning Rate & 0.001 \\ Batch Size & 16 \\ Epochs & 50 \\ Weight Decay & 0.0005 \\ Dropout Rate & 0.3 \\ Activation Function & ReLU \\ Optimizer & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimal hyperparameters for the proposed VGG16 model.
## 3 Improved Geometric Particle Filtering Method
### Improved Gaussian Resampling  
The improved Gaussian resampling algorithm deals with the high-weight and low-weight particles. First, the Gaussian distribution is established for the high-weight particle set to form an intermediate particle set. Then, the low-weight particles are combined with this set to form the final particle set. The correspondence between the final particle set and the low-weight particle set is reduced. After incorporating the information of the low-weight particles, the particle diversity is expanded to achieve the intended purpose of reducing the difference in particle probability distributions before and after resampling.  
The main steps of the improved Gaussian resampling algorithm are as follows:  
* Divide the particle sets according to weight  
According to the accumulation of particle weights and screening results of the resampling algorithm, the particle sets are divided into a low-weight particle set \(\{x^{i}\_{min,k}\}\) and a high-weight particle set \(\{x^{i}\_{max,k},\,\omega^{i}\_{max,k},\,n^{i}\}\). The principle of high and low weight particle poem selection is based on an accumulation and resampling algorithm. A set of random numbers \(u^{i}\)\(\sim\)\(U\) [0, 1] is used to determine whether the corresponding particles are copied. If \(u^{i}\) falls into a sub-region \(r_{i}\), the corresponding particle \(x^{i}\) is screened out as the resampled particle. After resampling, since high-weight particles have a longer subregion \(r_{i}\), \(u^{i}\) is more likely to fall into the region. The number of times that high-weight particles are copied increases.
* Gaussian sampling By taking the \(x^{i}_{max\_k}\) as the mean and the estimated variance \(\epsilon^{2}_{k-1}\) in the \(k-1\) step as the variance, the distribution is sampled \(n^{i}\) times to generate a preliminary resampling intermediate particle set \(\{x^{i}_{first\_k},\omega^{i}_{max\_k}\}\).
* Optimized combination A linear optimized combination is performed on the intermediate particle set \(\{x^{i}_{first\_k},\omega^{i}_{max\_k}\}\) and the low-weight particle set \(\{x^{i}_{min\_k}\}\) to generate the final particle set \(\left\{\hat{x}^{j}_{k},\ \hat{\omega}^{j}_{k}=\frac{1}{N}\right\}\).  
The linear combination is carried out according to Equation (24):  
\[\hat{x}^{j}_{k}=x^{i}_{first\_k}+KL\left(x^{i}_{\min\_k}+x^{i}_{first\_k}\right) \tag{24}\]  
where \(\hat{x}^{j}_{k}\) are the new particles that are generated. The \(K\) is the step size coefficient. The \(L\) is the step size which is calculated by Equation (25):  
\[L=\left[\frac{1}{N\omega^{i}_{\max\_k}}\right]^{\frac{1}{dim}} \tag{25}\]  
where \(dim\) is the dimension of the state vector represented by the particle.  
The observation weight of the finally generated new particles needs to be higher than the weight \(\omega^{i}_{max\_k}\) of the corresponding height-weight particle. Otherwise, the step size \(L\) needs to be halved and the new particles recalculated.
# Small-Sample Bearings Fault Diagnosis Based on ResNet18 with Pre-Trained and Fine-Tuned Method
## 2 Relevant Theoretical Approaches
### Sdp  
The SDP analysis method is a technique that converts time-domain signals collected by sensors from Cartesian coordinates to polar coordinates using a specific equation. The texture features of images transformed by SDP can reflect the amplitude and frequency variations in the original vibration signal. Owing to significant differences in the SDPimages of different bearing fault types, this method allows for the visualization of faults. Figure 1 displays a schematic representation of the SDP transformation. For the vibration signal, \(X=\{X_{1},\ X_{2}\ldots X_{i}\}\) converts the amplitude of time \(i\) in the bearing vibration signal to \(S[r_{i},\varphi_{i},\varXi_{i}]\) in polar coordinates by means of symmetric points, and the calculation equation is as follows [35].  
\[r(i)=\frac{x_{i}-x_{min}}{x_{max}-x_{min}} \tag{5}\]  
\[\varvarnothing(i)=\theta+\frac{x_{i+l}-x_{min}}{x_{max}-x_{min}}g \tag{6}\]  
\[\varphi(i)=\theta-\frac{x_{i+l}-x_{min}}{x_{max}-x_{min}}g \tag{7}\]  
where \(t\) and \(\Delta t\) are the abscissa axis of time and sampling time, \(x_{min}\) and \(x_{max}\) are the minimum and maximum amplitude of vibration signal time \(i\), \(r(i)\) represents the radius of a point in polar coordinates, \(\varvarnothing(i)\) and \(\varphi(i)\) are the counterclockwise and clockwise angles of the points relative to the initial line. \(\theta\) represents the degree of rotation for the mirror symmetric plane, generally \(60^{\circ}\)[36]. \(g\) is the angular amplification factor, generally \(g\leq\theta\). \(l\) represents a parameter indicating a time interval. \(l\) is used to reflect the difference between vibration signals, usually \(1<l<10\).
## 4 Solution of the Problem  
To solve the original joint optimization problem involving UAV altitude, beamforming vectors, and STAR-RIS phase shifts, we adopt an optimization framework. The original problem is challenging and includes coupled variables and multiple constraints, making direct joint optimization intractable. Therefore, we decompose the problem into two subproblems. This approach is a widely used technique in solving non-convex optimization problem [17]. In particular, when the UAV altitude is fixed, the beamforming and STAR-RIS phase optimization becomes more tractable. The decomposition significantly reduces computational burden and simplifies implementation. Although a theoretical convergence guarantee for the proposed scheme is difficult, our simulation results in Section 5 confirm that the proposed algorithm converges consistently across different sets of simulation parameters.
###### Abstract  
Predicting crop yield throughout its development cycle is crucial for planning storage, processing, and distribution. Optical remote sensing has been used for yield prediction but has limitations, such as cloud interference and only capturing canopy-level data. Synthetic Aperture Radar (SAR) complements optical data by capturing information even in cloudy conditions and providing additional plant insights. This study aimed to explore the correlation of SAR variables with soybean yield at different crop stages, testing if SAR data enhances predictions compared to optical data alone. Data from three growing seasons were collected from an area of 106 hectares, using eight SAR variables (Alpha, Entropy, DPSVI, RFID, Pol, RVI, \(V\!H\), and \(VV\)) and four speckle noise filters. The Random Forest algorithm was applied, combining SAR variables with the EVI optical index. Although none of the SAR variables showed strong correlations with yield (r < 1.0351), predictions improved when SAR data were included. The best performance was achieved using DPSVI with the Boxcar filter, combined with EVI during the maturation stage (with EVI:RMSE = 0.43, 0.49, and 0.60, respectively, for each season; while EVI + DPSVI:RMSE = 0.39, 0.49, and 0.42). Despite improving predictions, the computational demands of SAR processing must be considered, especially when optical data are limited due to cloud cover.  
precision agriculture; SAR vegetation index backscatter coefficient; polarimetric decomposition; EVI; machine learning Article
# Overview of FEM-Based Resistance Models for Local Buckling of Welded Steel Box Section Columns
## 3 Finite Element Model
### Material Model
#### 3.2.1 Material Model Used for Normal-Strength Steel  
The material model proposed by the prEN 1993-1-14 [39] is a quad-linear material model that captures the yield plateau and the strain-hardening of the NSS until the ultimate strength is reached. The general shape of the curve is presented in Figure 2. This model was developed by [PERSON] et al. [40] through a combination of experimental and numerical analyses, and its validation was performed in accordance with the European standards. The parameters required to establish the strain-stress relationship for materials with varying yield strengths are presented in Equations (1)-(6).  
\[\varepsilon=\left\{\begin{array}{ll}\mathrm{E}_{\varepsilon}&\text{for }\ \varepsilon\leq \varepsilon_{\mathrm{y}}\\ \mathrm{f}_{\mathrm{y}}&\text{for }\ \varepsilon_{\mathrm{y}}\leq \varepsilon\leq\varepsilon_{\mathrm{sh}}\\ \mathrm{f}_{\mathrm{y}}+\mathrm{E}_{\mathrm{sh}}(\varepsilon+\varepsilon_{ \mathrm{sh}})&\text{for }\ \varepsilon_{\mathrm{sh}}\leq\varepsilon\leq\mathrm{C}_{1} \varepsilon_{\mathrm{u}}\\ \mathrm{f}_{\mathrm{C}_{1}\varepsilon\mathrm{u}}+\frac{\mathrm{f}_{\mathrm{ u}}-\mathrm{f}_{\mathrm{C}_{1}\varepsilon\mathrm{u}}}{\varepsilon_{\mathrm{u}}- \mathrm{C}_{1}\varepsilon_{\mathrm{u}}}(\varepsilon-\mathrm{C}_{1}\varepsilon _{\mathrm{u}})&\text{for }\ \mathrm{C}_{1}\varepsilon_{\mathrm{u}}\leq\varepsilon\leq \varepsilon_{\mathrm{u}}\\ \end{array}\right. \tag{1}\]  
\[\mathrm{E}_{\mathrm{sh}}=\frac{\mathrm{f}_{\mathrm{u}}-\mathrm{f}_{\mathrm{y}}} {\mathrm{C}_{2}\varepsilon_{\mathrm{u}}-\varepsilon_{\mathrm{sh}}} \tag{2}\]  
\[\mathrm{C}_{1}=\frac{\mathrm{\varepsilon}_{\mathrm{sh}}+0.25(\varepsilon_{ \mathrm{u}}-\varepsilon_{\mathrm{sh}})}{\varepsilon_{\mathrm{u}}} \tag{3}\]  
\[\mathrm{C}_{2}=\frac{\mathrm{\varepsilon}_{\mathrm{sh}}+0.4(\varepsilon_{ \mathrm{u}}-\varepsilon_{\mathrm{sh}})}{\varepsilon_{\mathrm{u}}} \tag{4}\]  
\[\mathrm{\varepsilon}_{\mathrm{sh}}=0.1\frac{\mathrm{f}_{\mathrm{y}}}{\mathrm{f }_{\mathrm{u}}}-0.055\ \text{for }0.015\leq\varepsilon_{\mathrm{sh}}\leq 0.03 \tag{5}\]  
\[\mathrm{\varepsilon}_{\mathrm{u}}=0.6\bigg{(}1-\frac{\mathrm{f}_{\mathrm{y}}} {\mathrm{f}_{\mathrm{u}}}\bigg{)}\ \text{but }\mathrm{\varepsilon}_{\mathrm{u}}\geq 0.06 \tag{6}\]  
where  
\(\mathrm{C}_{1}\) \"cut-off\" strain to avoid over-prediction;  
\(\mathrm{C}_{2}\) coefficient to compute strain hardening modulus;  
\(\mathrm{\varepsilon}_{\mathrm{sh}}\) strain-hardening;  
\(\mathrm{\varepsilon}_{\mathrm{u}}\) ultimate strain;  
Figure 2: Material model characteristic used for normal-strength steel [39].  
E\({}_{\rm sh}\) strain hardening modulus.
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 4 Results
### HDdtb Database  
With our proposed HDdtb (cf. Figure 10), for color-blind metrics, the color space \(J_{2}a_{2}b_{2}\) provides slightly lower performances for all metrics, except with FSIM. For this metric, the performances with \(J_{2}a_{2}b_{2}\) are higher. The best performing color-blind metrics for this database are FSIM\({}_{J_{2}a_{2}b_{2}}\), FSIM\({}_{HDR\)-\(Lab_{1000}}\) and MS-SSIM\({}_{HDR\)-\(Lab_{100}}\). For the color metrics, the metrics based on color difference metrics (\(\overline{\Delta E}\), \(\Delta\overline{E}^{5}\) and CSSIM) have very low performances. This is partially due to the presence of the gamut mismatch artifact. As noticeable on Table 4, discarding this artifact increases the performances of these metrics. For the participants of our subjective test, the distortions on the images are clearly visible but are not directly associated with a loss in quality perception.  
Figure 8: SROCC performances for the 4 Kdtb database for color-blind quality metrics (**a**) and for color quality metrics (**b**).  
Figure 9: SROCC performances for the Zerman et al. database for color-blind quality metrics (**a**) and for color quality metrics (**b**).