esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Hydration and Thermodynamic Considerations  
[PERSON] et al. [33] described how oxide compositions are converted into molar quantities of the clinker constituents, proportioning the molar amounts of K\({}_{2}\)O, Na\({}_{2}\)O, MgO and SO\({}_{3}\) in C\({}_{3}\)S, C\({}_{2}\)S, C\({}_{3}\)A, and C\({}_{4}\)AF. The remaining clinker phases (lime, calcite, gypsum, periclase, arcanite and thenardite) are provided in the first time-step/solution and allowed to reach equilibrium with the pore solution to contribute to the formation of hydrate solids. Consideration is also paid to the initial oversaturation of C-A-S-H, gypsum, portlandite, syngenite and ettringite in the first 12 hrs of hydration. An oversaturation factor (=0.15\(n\)) is added to the input for these phases to obtain improved predictions of the pore solution data, where \(n\) is the number of charged species involved in the hydration reaction (\(n\) = 2, 3, 4.95, 5, 3 and 15 for gypsum, portlandite, C-A-S-H, syngenite, brucite and ettringite, respectively [34]). Finally, to accurately predict the changing pH and pore solution chemistries, alkali (K and Na) distribution between the pore solution and the C-A-S-H previously developed by [PERSON] et al. [35] and shown in Figure 3 were added. Figure 2: **(Left)** Degree of reaction and (**Right**) dissolution of GGBS and RCGP. The thermodynamic input for the slag and glass powder were provided through phase equations derived from their oxide content. This method was developed by [PERSON] et al. [36] to describe the glass phase of fly-ash in addition to the other crystalline phases. This provides a comprehensive way to include the glass phase, which will provide accurate predictions of phase assemblages, pore solution chemistry and pH. For the slag and recycled glass powder used here, it was assumed they constitute 100% amorphous glass with no crystalline phases with a 50:50 split between active and inactive glass. The reaction stoichiometries shown are based on a 1 kg = 1 mole stoichiometry, which makes it convenient to add into the required molar quantities of the PHREQC input file. The thermodynamic phase equations for the slag and recycled glass powder used in the analysis, following the methodology described in [36], are provided in Table 1.  
The hydration of four paste formulations was performed by HYDCEM. Table 2 shows the relative oxide composition of the blending components and of the four blends used in the simulations (Table 3). A water-to-binder (w/b) ratio of 0.5 was used throughout, and the simulations were made on a thermodynamically closed system (no mass loss or gain).
###### Abstract
Once developed, this model can predict wave states--such as wave direction, height, and period--based on measured structural responses [12; 14; 16; 17]. However, conventional artificial neural networks often struggle to capture the phase information necessary to fully represent the directional characteristics of waves. To handle wave direction and phase differences, wave direction data must be expressed in the form of complex numbers in polar coordinates. In traditional artificial neural networks, representing polar coordinates typically requires the application of both sine and cosine functions or separating the complex-valued wave direction data into real and imaginary components for learning. While these approaches are possible, they are inefficient in terms of learning efficiency. To address this limitation, this study applies complex-valued neural networks (CVNNs). The use of complex numbers allows the network to model both the amplitude and phase of wave motion, providing a more comprehensive understanding of the wave dynamics affecting the floating structure [18; 19; 20]. In this study, we focus on a specific floating structure, generating response data through high-fidelity numerical simulations under various wave conditions. These data are used to train a complex-valued neural network model capable of predicting wave conditions--such as wave direction, height, and period--based on the observed motion responses of the floating structure. By leveraging phase information captured by the complex-valued network, we can achieve more accurate and robust predictions of the wave environment surrounding the structure, leading to better insights into the floating system's behavior under different conditions.
## 7 Observations and Recommendations
### Recommendations and Research Challenges
#### Hybrid Schemes  
Combining multiple detectors to create hybrid and ensemble schemes has the advantage of creating improved schemes that counteract each other's limitations. This improvement in accuracy comes at the cost of a higher computational complexity which could render them infeasible for resource constrained IWSN applications. The authors in [58] however found that these schemes are in fact feasible in these applications depending on how they designed to function in this setup. Future work into this topic should look into innovative ways to combine multiple algorithms to produce schemes that perform better than the individual components but that also ensure that the resultant overhead would not render them impractical in a IWSN scenario.
###### Abstract  
Plastic/rubber materials used as fasteners in equipment for analyzing or removing organic pollutants in water treatment technologies form an essential part of the device. Micropollutants in water are typically present at very low concentrations (ng/L to ug/L). Therefore, when designing, for example, units for advanced oxidation processes (AOPs) or planning sample handling, it is necessary to assess whether the material is compatible with the usually hydrophobic nature of the pollutants. As a model example, the possible interactions of estrogens, namely, estrone (E1), 17P-estradiol (E2), estroid (E3) and 17\(\alpha\)-ethirestradiol (EE2) with six commonly used plastic and rubber materials were investigated at environmentally relevant concentrations (100-500 ng/L). In the first phase, we proved that polyvinyl chloride (PVC), polytetrafluoroethylene (PTFE), polyvinylidene fluoride (PVDF) and ethylene propylene diene monomer (EPDM) materials adsorbed only negligible amounts of estrogens, while significant amounts of E1, E2 and EE2 were adsorbed onto Tygon S3(tm) material. Another unsuitable material was styrene butadiene rubber (SBR), sorting a considerable quantity of estrone. A detailed test of EPDM at higher concentrations (300 and 500 ng/L) and prolonged soaking time showed significant sorption of EE2 after 12 h of soaking in both deionized and tap water matrices. Thus, EPDM, PTFE and PVDF are suitable materials for sample handling or producing devices for AOP treatment due to their chemical inertness and mechanical flexibility. The results suggest that plastic materials that come into contact with contaminated water must be carefully selected, especially when working at environmentally relevant concentrations.  
sorption; desorption; plastics; estrogens; hormones; environmentally relevant concentrations; tubing materials; sampling materials +
Footnote †: journal: Applied Sciences
## 3 Results
### Correlation between Resilience and Variables Associated with COVID-19  
Table 2 shows the mean scores obtained by the teachers in the resilience variable according to the presence or absence of perceived stress in the face of COVID-19 infection and the significance (bilateral) of Student's \(t\)-test.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & & \(N\) & \(M\) & \(SD\) & Mean Standard Error & \(p\)-Value \\ \hline \multirow{4}{*}{Exhaustion} & & \multicolumn{4}{c}{Family contagion} \\  & Absence & 36 & 2.15 & 1.296 & 0.216 & 0.000 \\  & Presence & 383 & 3.12 & 1.632 & 0.083 & 0.000 \\ \multirow{4}{*}{Cynicism} & Absence & 36 & 1.35 & 0.977 & 0.163 & \\  & Presence & 383 & 1.88 & 1480 & 0.076 & 0.005 \\ \multirow{4}{*}{Efficiency} & Absence & 36 & 4.58 & 0.835 & 0.139 & \\  & Presence & 383 & 4.58 & 0.759 & 0.039 & 0.963 \\ \multirow{4}{*}{Exhaustion} & Absence & 127 & 2.37 & 1.46 & 0.13 & 0.000 \\  & Presence & 292 & 3.33 & 1.61 & 0.09 & \\ \multirow{4}{*}{Cynicism} & Absence & 127 & 1.61 & 1.41 & 0.13 & 0.038 \\  & Presence & 292 & 1.93 & 1.46 & 0.09 & \\ \multirow{4}{*}{Efficiency} & Absence & 127 & 4.57 & 0.78 & 0.07 & 0.777 \\  & Presence & 292 & 4.59 & 0.76 & 0.05 & \\ \multirow{4}{*}{Exhaustion} & Absence & 172 & 2.45 & 1.49 & 0.11 & 0.000 \\  & Presence & 247 & 3.45 & 1.60 & 0.1 & \\ \multirow{4}{*}{Cynicism} & Absence & 172 & 1.71 & 1.46 & 0.11 & 0.154 \\  & Presence & 247 & 1.91 & 1.44 & 0.09 & \\ \multirow{4}{*}{Efficiency} & Absence & 172 & 4.52 & 0.76 & 0.06 & 0.136 \\  & Presence & 247 & 4.63 & 0.76 & 0.05 & \\ \multirow{4}{*}{Exhaustion} & Absence & 131 & 2.37 & 1.472 & 0.13 & 0.000 \\  & Presence & 288 & 3.34 & 1.606 & 0.1 & \\ \multirow{4}{*}{Cynicism} & Absence & 131 & 1.66 & 1.454 & 0.13 & 0.098 \\  & Presence & 288 & 1.91 & 1.444 & 0.09 & \\ \multirow{4}{*}{Efficiency} & Absence & 131 & 4.53 & 0.784 & 0.07 & 0.299 \\  & Presence & 288 & 4.61 & 0.756 & 0.05 & \\ \multirow{4}{*}{Exhaustion} & Absence & 187 & 2.45 & 1.45 & 0.11 & 0.000 \\  & Presence & 232 & 3.51 & 1.61 & 0.11 & \\ \multirow{4}{*}{Cynicism} & Absence & 187 & 1.67 & 1.40 & 0.10 & 0.038 \\  & Presence & 232 & 1.96 & 1.48 & 0.10 & \\ \multirow{4}{*}{Efficiency} & Absence & 187 & 4.55 & 0.76 & 0.06 & 0.362 \\  & Presence & 232 & 4.61 & 0.77 & 0.05 & \\ \multirow{4}{*}{Exhaustion} & Absence & 87 & 2.3 & 0.44 & 0.15 & 0.000 \\  & Presence & 332 & 3.23 & 1.62 & 0.09 & \\ \multirow{4}{*}{Cynicism} & Absence & 87 & 1.63 & 1.28 & 0.14 & 0.146 \\  & Presence & 332 & 1.88 & 1,49 & 0.08 & \\ \multirow{4}{*}{Efficiency} & Absence & 87 & 4.43 & 0.76 & 0.08 & 0.041 \\ \cline{1-1}  & Presence & 332 & 4.62 & 0.76 & 0.04 & \\ \hline \hline \end{tabular} Note: \(N\): participants; \(M\): mean; \(SD\): standard deviation; \(p\)-value: bilateral significance in Student’s \(t\)-test.  
\end{table}
Table 1: Descriptive statistics and significance of Student’s \(t\)-test for burnout dimensions and stress associated with COVID-19.
## 1 Introduction
### Background and Purpose of the Study
More specifically for damage detection algorithms, the data from photogrammetry, laser scanners [23], and microwave [24] have been utilized, and the associated imaging processing methods such as edge detection techniques [25], histogram matching, image filtering and change detection [26], and automatic thresholding [27] have been proposed by many researchers. Also, in conjunction with image processing, a logical classification has been applied to find and identify various types of defects for the reduction of inspection time and efforts. [PERSON] et al. [28] used classification of neuro-fuzzy networks and [PERSON] et al. [24] applied the fuzzy logic technique to find concrete structural defects. [PERSON] and [PERSON] [29] used neural networks to detect cracks from images, and [PERSON] et al. [30] used genetic algorithms to extract crack patterns from digital images. In many papers, the classification methods show a diversity of algorithms, which means that the operational environment is different from place to place. For example, there are many stains, sediments, joints, efflorescence, and micro-cracks on the surface of structures, and it is not easy to distinguish them from the major structural cracks.
## 2 Optimization Methodology
### Artificial Potential Field
The repulsion force is first calculated from the starting position of the robot by definition and then recalculated as the robot moves to the final position. It is a way of ensuring that the inertia of movement will be overcome and also that there will be no local minimum in the starting position. The repulsion potential field of the starting position is represented by the following equation [27]:  
\[U_{rep}(x,y)=\left|\frac{k_{r}}{\sqrt{\left(y-y_{0}\right)^{2}+\left(x-x_{0} \right)^{2}}}\right| \tag{12}\]  
where \(k_{r}\) is the repulsion intensity parameter in the initial position, which defines the intensity of the repulsion field at the starting position, \(x\) and \(y\) are reference points for the APF and \(x_{0}\) and \(y_{0}\) are the coordinates of the starting position of the robot in the planned trajectory. The attraction force is first calculated from the final position of the robot by definition and then recalculated as the robot moves from the starting to the final position. The potential field of attraction for the final position is represented by the following equation [27]:  
\[U_{att}(x,y)=\frac{1}{2}k_{a}\left|\left(y-y_{f}\right)^{2}+\left(x-x_{f} \right)^{2}\right| \tag{13}\]  
where \(k_{a}\) is a parameter of the intensity of attraction which defines the intensity of the attraction field at the final position, \(x\) and \(y\) are the coordinates of reference position for the APF and \(x_{f}\) and \(y_{f}\) are the coordinates of the final position of the generated trajectory. The repulsion force due the obstacle is first calculated from the position of the obstacle by definition and then recalculated as the robot moves from the starting to the final position. The equation that represents repulsion potential field due to the presence of an obstacle is given by [27]:  
\[U_{0}(x,y)=\left|\frac{k_{0}}{\sqrt{(y-y_{0i})^{2}+(x-x_{0i})^{2}}}\right| \tag{14}\]  
where \(k_{0}\) is a parameter that controls the intensity of the field potential to repel obstacles, \(x\) and \(y\) are coordinates of reference position for the APF and \(x_{0i}\) and \(y_{0i}\) are the coordinates of the position of the obstacle's center.
## 2 Materials and Methods  
### Sensor Design and Fabrication  
The underlying idea behind the design of the sensor was to develop a device that could be affixed to the surface of a structural component subjected to stress; thus, its de sign incorporated a non-conductive flat and flexible surface with an appropriate sensing piezoelectric element on top, as shown in Figure 1a. The non-conductive layer was made of Acrylonitrile Styrene Acrylate (ASA, BASF B.V.) while the conductive layer, which served as the sensing element, was made of Fiber Force Nylforce Carbon Nanotubes (CNT) Conductive, a Polylactic Acid (PLA) biopolymer compound enriched with carbon nanotubes. The piezoresistive element geometry was chosen to be as simple as possible because the goal of this initial investigation was to validate the feasibility of fabricating 3D printed strain sensors employing commercially available polymer-based building materials and their performance characteristics. The device was fabricated by Fused Deposition Modeling (FDM) additive manufacturing technology using a commercial desktop 3D printer, Ultimaker S3 [26]. First, the non-conductive layer was printed as a substrate, followed by the printing of the sensing element on top, which incorporated the appropriate electrical contact pads. Specifically, a 80 mm \(\times\) 20 mm (L \(\times\) W) layer with a thickness of 0.6 mm was printed as a substrate using ASA. On top of this layer, the sensing element with the geometry presented in Figure 1a was built employing CNT-enriched PLA. The sensing element comprises a printed resistor with dimensions of 50 mm \(\times\) 4 mm (L \(\times\) W) and two rectangular electrical pads situated at both ends, each with an area of 225 mm\({}^{2}\). The thickness of the sensing element, including the contact pads, is 0.2 mm. Figure 1a illustrates the geometry of the device, while Figure 1b presents two printed devices. It is significant to notice that the necessary connection with the readout circuit can be easily achieved through directly connecting suitably sized conductors using standard soldering. This is well demonstrated in reference [9] where the same building material was employed as contact pads
## 4 Discussion  
To our knowledge, this study was the first interventional clinical experimental study to investigate the impact of the consumption of chicken eggs fortified with four nutrients on microvascular and macrovascular reactivity, serum free fatty acid and serum lipid levels, oxidative stress levels, and anti- and proinflammatory cytokine concentrations in participants with CCS. The most significant outcomes of our study are as follows: (a) the consumption of Nutri4 eggs improved endothelium-dependent vasodilation (i.e., microvascular PORH and AChID, FMD of brachial artery) in both micro- and macrocirculation and increased the serum protein concentration of iNOS; (b) the consumption of Nutri4 eggs decreased serum triglyceride levels; (c) the consumption of Nutri4 eggs reduced serum concentrations of the proinflammatory cytokines IL-17A and TGF-1\(\beta\); and (d) the consumption of Nutri4 eggs did not significantly affect biomarkers of oxidative stress and antioxidant protection (TBARS, OxLDL, 8-iso-PGF2\(\alpha\), AOPP, FRAP, serum CAT, SOD, and GPx activity) in patients with CCS. In our study, a significant increase in the serum concentration of n-3 PUFAs was observed in the Nutri4 group, as well as a decrease in the ratio of n-6/n-3 PUFAs. Also, increased serum concentrations of vitamin E and selenium in the Nutri4 group indicate adherence to the dietary protocol and an important opportunity for the physiological modification of serum lipid composition via diet. Overall, the consumption of nutrient-enriched chicken eggs in participants with CCS resulted in a more favorable lipid profile, improved microvascular and macrovascular endothelial function, and reduced proinflammatory potential. In addition, the consumption of chicken eggs, whether normal or Nutri4, had no deleterious effects on serum lipid levels or on other measured parameters. Dyslipidemia stands out as a crucial risk factor in atherosclerosis development, particularly elevated levels of LDL cholesterol and triglycerides [29]. Despite improved outcomes in atherosclerotic cardiovascular disease with statin therapy, a residual risk remains. There are numerous studies with conflicting results about the role of n-3 PUFAs in changes in triglyceride and LDL levels. On the one hand, supplementation with n-3 PUFAs (EPA and DHA) can reduce serum lipids (especially levels of triglycerides) in individuals with hyperlipidemia [30]. Treatment with EPA or DHA is associated with a reduction in triglyceride levels and with differential effects on LDL and HDL cholesterol [31]. On the other hand, in patients at high cardiovascular risk treated with statins, the addition of n-3 PUFAs compared with corn oil has not resulted in a significant difference in the composite
# Long-Term Forecasting Using MAMTF: A Matrix Attention Model Based on the Time and Frequency Domains
## 3 A Matrix Attention Model Based on the Time and Frequency Domains
### The Matrix Attention Mechanism
#### 3.3.1 Independent Matrix Attention Mechanism  
We view a multivariate time series as consisting of multiple univariate time series. That is, the multivariate time series can be split into multiple univariate time series. Each component series in the multivariate is weighted using an independent matrix. For example, a variable a of length n in a multivariate can be represented as a row matrix \(X_{n,a}=\big{(}x_{1}^{a},x_{2}^{a},\cdots,x_{n}^{a}\big{)}\), which corresponds to a weight matrix \(U_{nl,a}=\left(\begin{array}{c}u_{11}^{a},u_{12}^{a},\cdots,u_{11}^{a}\\ u_{21}^{a},u_{22}^{a},\cdots,u_{21}^{a}\\ \cdots,\cdots,\cdots,\cdots\\ u_{n1}^{a},u_{n2}^{a},\cdots,u_{nl}^{a}\end{array}\right)\). The output of the independent matrix attention mechanism is a row matrix \(\mathrm{E_{i,a}=\big{(}e_{1}^{a},e_{2}^{a},\cdots,e_{1}^{a}\big{)}\) of length l. The specific calculations are shown in Equation (11):  
\[\mathrm{E_{i,a}=MAATT(X_{n,a})=X_{n,a}U_{nl,a}.} \tag{11}\]  
The other variables in the multivariate are calculated in a similar way. Figure 2 visualizes the whole calculation process, where m represents the number of multivariates.
## 4 Experimental Results
### Evaluation Metrics  
The models were evaluated using the dataset described in Section 3.1, with the main conclusions drawn based on these metrics. To comprehensively assess the performance of the ensemble scheme and the basic methods, six commonly used regression evaluation metrics were employed: mean relative error (MRE), mean absolute error (MAE), symmetric  
Figure 4: Stacking ensemble model architecture.  
mean absolute percentage error (SMAPE), coefficient of determination (\(R^{2}\)), root mean square error (RMSE), and mean absolute percentage error (MAPE), defined as follows [38]:  
\[MRE\ =\ \frac{1}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}}\right| \tag{21}\]  
\[MAE\ =\ \frac{1}{n}\sum_{i=1}^{n}\left|y_{i}-\hat{y}_{i}\right| \tag{22}\]  
\[SMAPE\ =\ \frac{1}{n}\sum_{i=1}^{n}\frac{\left|y_{i}-\hat{y}_{i}\right|}{|y_{i}|+ |\hat{y}_{i}|/2} \tag{23}\]  
\[R^{2}\ =\ 1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}- \hat{y})^{2}} \tag{24}\]  
\[RMSE\ =\ \sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{25}\]  
\[MAPE\ =\ \frac{1}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}} \right|\times 100\% \tag{26}\]  
where \(y_{i}\) represents the actual value, \(\hat{y}_{i}\) denotes the predicted value, \(\hat{y}\) represents the average of the actual values, and \(n\) denotes the sample size. MRE reflects the degree of deviation of the predicted value relative to the actual value and is a dimensionless indicator. MAE ranges from 0 to \(+\infty\), indicating the absolute difference between the predicted value and the actual value, with smaller values indicating higher prediction accuracy of the model. SMAPE combines the absolute values of the predicted and actual values, providing a more reasonable reflection of prediction errors. \(R^{2}\) ranges from 0 to 1, with values closer to 1 indicating a stronger explanatory power of the model for the data. RMSE provides the square root of the average squared prediction errors, with smaller values indicating higher prediction accuracy of the model. In comparison to MAE, RMSE is more sensitive to large errors. MAPE expresses the accuracy as a percentage and is useful for comparing the performance of different models on the same dataset [39].