esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Enhanced Autoencoder for Multi-Carrier Wireless Systems
## 4 Simulation Results
The results are shown in Figure 12b. Figure 11: Comparison of BLER performance between the proposed model and other models in imperfect channel conditions, with a SE of 1 bps/Hz. Figure 5.0 BER performance comparison of the proposed model with other models under perfect channel conditions at a SE of 1 bps/Hz. configurations of \((N,M)\): (4, 16), (4, 32), and (4, 64). As SNR increases, SE improves for all configurations, indicating that higher SNR values support higher data rates. Among the configurations, \((N,M)\) = (4, 64) achieves the highest SE, followed by (4, 32) and (4, 16), suggesting that the increase of M enhances SE. In Figure 12b on the right, EE is plotted against SNR for the same configurations. EE also improves with SNR, showing that the system becomes more energy-efficient at higher SNR levels. Here, the configuration \((N,M)\) = (4, 64) shows superior EE performance across most SNR values, particularly at higher SNRs. This suggests that larger values of \(M\) contribute to higher EE as well. The (4, 32) configuration follows in EE performance, and (4, 16) has the lowest EE across the SNR range. These plots provide insights into how adjusting the \((N,M)\) parameters and SNR affects the SE and EE, helping to optimize the system's performance based on specific requirements.
## 2 Results and Discussion
### RelBe-XRE-ReIE Composite
#### 2.6.2 RelBE of G. thermolevorans Gts  
The gene with the locus tag *_34810 codes for a putative RelE domain-harboring protein (Table 1). Therefore, it is likely that this gene codes for a putative RelE toxin. *_34820, which is upstream of and overlapping with the putative RelE-coding gene by 32 nt, most likely codes for a putative antitoxin. The putative antitoxin encoded by the gene locus *_34820 lacks a conserved domain, as is the case with those of strains HTA426 and ZGt-1, and it has been annotated as a hypothetical protein in both of the strain genome records (Table S1). However, it is orthologous to the same \"XRE family transcriptional regulator\" proteins mentioned above, AGT33452, A0V43_13420, ALA70040, AMX82334, to which it is \(\sim\)99%, \(\sim\)99%, \(\sim\)98%, and \(\sim\)93% identical. Moreover, the NCBI blastp results showed that the protein product of *_34820 is 55% identical to the \"XRE family transcriptional regulator\" (WP_066367164) mentioned above, with an e-value of 4e-26 and query coverage of 97%. Furthermore, as is the case with the other two geobacillus strains mentioned above, the secondary structure of the putative XRE protein in this strain has repeated alternation between coils and helices (Figure S11d). These results indicate that the protein product of *_34820 could as well represent a putative XRE family transcriptional regulator acting as an antitoxin (Table 2).  
According to our analysis, the protein products of *_34820 and *_34810 represent a putative XRE-ReLE TA pair (Table 1).  
While the aa sequences of the putative RelE toxins and XRE antiotoxins are 99% and 98% identical, respectively, in strains HTA426 and ZGt-1, the aa sequence of the putative RelE toxin of strain Gts is 92% identical to that of strain HTA426 and 93% identical to that of ZGt-1, and the aa sequence of the putative XRE antitoxin is \(\sim\)98% and \(\sim\)96% identical to the putative XRE antioxins of strains HTA426 and ZGt-1, respectively (Figure S8).  
Our analysis indicated that G. _thermodenitrificans_ NG80-2 does not code for the RelBE TA family.
## 3 Results and Discussion
### MEL Production Forecasting: Day 4  
Given the small dataset, consisting of four distinct experiments with different initial conditions, the train-test split inevitably caused the under-representation of some initial conditions, making certain samples more challenging to predict. Nevertheless, the overall results were generally satisfactory.  
The results of the MEL production forecasting for day 4 using the NN model, combined with the correlation-derived feature subset, achieved the lowest error among all tested configurations. This combination obtained an MSE of 0.69 and an MAE of 0.58, which can be considered high-accuracy. The MAE of 0.58 suggests that the average deviation of the predictions is only 0.58 \(g_{MELs}/L\). A closer examination of Table 3, which compares the values predicted by each model, shows that the NN prediction errors are below 1 g/L in 9 out of the 10 samples.  
The RF achieved its lowest MSE when the ANOVA feature selection method was used, with an MSE of 3.45 and an MAE of 1.49 g/L. Although the RF model's best MSE result was higher than most of the NN model's MSE results, it provided the most accurate MEL concentration estimate for sample 1 on day 4. The experimental value was 0.08 g/L, while the model predicted 0.14 g/L.  
The SVM consistently produced MSE values around 30, regardless of the feature selection method applied. This poor performance is likely due to a mismatch between the data's complexity and the model's capacity to capture it. The predictions made by the SVM were extremely narrow, ranging from 1.24 g/L to 1.91 g/L, while the actual target values varied significantly from 0.08 to 13.08 g/L. This indicates that the SVM failed to identify the optimal hyperplane for separating the data and instead settled on a region in the feature space that represented the true relationship between the features and the target variable poorly.  
The success of the NN can be attributed to its capacity to capture non-linear relationships within the data and its adaptive learning method. Furthermore, the NN also benefited from the feature reduction techniques since when comparing the MSE obtained using all of the features to that achieved after removing the correlated features, there was a 61% reduction.  
Figure 3 is a visual representation of the predictions made by the three models, highlighting their overall accuracy in forecasting the experimental values. The plot further supports earlier observations regarding the performance of the models. Interestingly, sample 44 not only poses a forecasting challenge for the NN but also the RF, potentially due to the small size of the dataset, causing the under-representation of some initial conditions, which can lead to difficulties in the forecasting.  
Figure 3: Scatter plot of experimental values for MEL production on the fourth day of fermentation with RF, NN, and SVM predictions.
## 3 Results and Discussion
### Adsorption Kinetics
#### 3.4.6. Harkin-Jura Isotherm  
The Harkin-Jura isotherm [56] takes into account multilayer adsorption and can be interpreted by the existence of a heterogeneous pore distribution. It can be represented as Equation (18):  
\[\frac{1}{q_{e}^{2}}=(\frac{B}{A})-(\frac{1}{A})\text{lg}C_{e} \tag{18}\]  
where \(A\) and \(B\) are the Harkin-Jura isotherm parameters [53]. Fitting curves for the Harkin-Jura isotherm are illustrated in Figure 8g.  
It is observed that the Harkin-Jura isotherm has a relatively low \(r^{2}\) value. It follows multilayer adsorption, in accordance with the conclusion made with the Langmuir isotherm that the adsorption process is uniform and monolayer.  
It is clear from Table 3 that Langmuir, D-R, and Temkin models represent the sorption results well. The description of P adsorption onto Al-Ti oxide points to the following: (i) P adsorption occurs on the homogeneous surface of Al-Ti oxide; the number of identical adsorption sites is finite. \(R_{\text{L}}\) values ranging from 0 to 1 indicate the favorable nature of adsorption. (ii) The value of the mean free energy (\(E\)) of adsorption is 4.41-6.28 kJ/mol from the D-R model, suggesting that the type of P adsorption on the sorbent can be defined as physiochemical adsorption. (iii) The Temkin isotherm suggests that the maximum value of \(K_{\text{T}}\) and minimum value of B are 5.21 L/mg and 6.63 kJ/mol at 313 K, respectively, which explains the equilibrium binding energy and maximum heat of adsorption at 313 K. The higher temperature of 323 K shows irregular adsorption energy; it can be inferred that the adsorption mechanism had changed at high temperature.  
The monolayer adsorption capacity of P on the similar sorbents were listed in Table 4. Compared with the reported sorbents, the Al-Ti bimetal oxide composite exhibited a competitive capacity.
# Eco-Efficient Cement-Based Materials Using Biomass Bottom Ash: A Review
## 3 Biomass Bottom Ash in Mortars
### Mechanical Properties
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Compressive Strength (MPa)**} \\  & & \multicolumn{4}{c}{**Days**} \\ \cline{3-6}  & & **1** & **7** & **28** & **90** & **180** \\ \cline{3-6}  & & \multicolumn{4}{c}{**Mortar (Sand, Cement, Water, and BBA)**} \\ \hline \multirow{4}{*}{[PERSON] et al. [6]} & Control-1 & - & 6.8 & 10.2 & - & - \\  & BBA-2 & - & 7.5 & 10.2 & - & - \\  & BBA-3 & - & 7.8 & 10.3 & - & - \\ \hline \multirow{4}{*}{[PERSON] et al. [18]} & Control-4 & 27.83 & 51.22 & 58.26 & 60.12 & - \\  & BBA-5 & 24.91 & 41.5 & 49.95 & 52.12 & - \\  & BBA-6 & 21.2 & 36.65 & 43.59 & 45.25 & - \\  & Control-7 & 26.26 & 50.5 & 54.89 & 57.25 & - \\  & BBA-8 & 21.98 & 36.42 & 46.37 & 48.47 & - \\  & BBA-9 & 19.24 & 31.32 & 37.96 & 39.98 & - \\  & BBA-10 & 22.72 & 40.74 & 52.06 & 54.14 & - \\  & Control-11 & 24.41 & 47.39 & 50.74 & 52.44 & - \\  & BBA-12 & 18.7 & 34.17 & 43.01 & 45.85 & - \\  & BBA-13 & 18.19 & 28.9 & 37.9 & 40.25 & - \\  & BBA-14 & 21.02 & 41.89 & 45.54 & 47.69 & - \\ \hline \multirow{4}{*}{[PERSON] et al. [21]} & Control-15 & 20.31 & 48.29 & 58.03 & 62.09 & - \\  & BBA-16 & 10.08 & 19.54 & 25.12 & 28.46 & - \\  & * BBA-17-CO-NL & 15.58 & 27.06 & 33.21 & 38.87 & - \\  & * BBA-18-C & 11.01 & 29.56 & 36.18 & 41.87 & - \\  & * BBA-19-C-CO-NL & 19.18 & 44.12 & 51.96 & 54.34 & - \\  & BBA-20 & 5.26 & 17.51 & 20.63 & 23.47 & - \\  & * BBA-21-CO-NL & 15.54 & 25.49 & 30.47 & 34.36 & - \\  & * BBA-22-C-CO-NL & 17.06 & 39.42 & 47.28 & 49.64 & - \\ \hline \multirow{4}{*}{[PERSON] et al. [11]} & Control-23 & - & - & 78 & - & 83 \\  & BBA-24 & - & - & 82 & - & 56 \\  & BBA-25 & - & - & 74 & - & 49 \\  & BBA-26 & - & - & 66 & - & 38 \\  & BBA-27 & - & - & 57 & - & 34 \\ \hline \multirow{4}{*}{[PERSON] et al. [28]} & Control-28 & - & - & 68.22 & - & - \\  & BBA-29 & - & - & 65.35 & - & - \\  & BBA-30 & - & - & 58.35 & - & - \\  & BBA-31 & - & - & 64.26 & - & - \\  & BBA-32 & - & - & 59.01 & - & - \\  & BBA-33 & - & - & 67.84 & - & - \\  & BBA-34 & - & - & 58.17 & - & - \\ \hline \multicolumn{6}{c}{**Mortar (Cement, Water, and BBA)**} \\ \hline \multirow{4}{*}{[PERSON] et al. [30]} & Control-35 & - & - & 58.11 & - & - \\  & BBA-36 & - & - & 66.58 & - & - \\  & BBA-37 & - & - & 63.46 & - & - \\  & BBA-38 & - & - & 63.44 & - & - \\  & BBA-39 & - & - & 61.63 & - & - \\  & BBA-40 & - & - & 61.11 & - & - \\  & BBA-41 & - & - & 54.16 & - & - \\  & BBA-42 & - & - & 45.82 & - & - \\  & BBA-43 & - & - & 39.30 & - & - \\  & BBA-44 & - & - & 29.86 & - & - \\  & BBA-45 & - & - & 12.41 & - & - \\ \hline \multirow{4}{*}{[PERSON] et al. [29]} & Control-46 & 18.6 & 45.4 & 59.1 & - & - \\  & BBA-47 & 13.4 & 37.3 & 47.9 & - & - \\ \cline{1-1}  & BBA-48 & 13.6 & 36.9 & 45.6 & - & - \\ \cline{1-1}  & BBA-49 & 14.1 & 33.4 & 43.4 & - & - \\ \cline{1-1}  & BBA-50 & 14.2 & 33.3 & 42.6 & - & - \\ \cline{1-1}  & BBA-51 & 15.1 & 32.1 & 40.05 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Compressive strength.
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
## 5 Simulation Test
### Stability under Frequency Fluctuation  
In the power system, frequency fluctuation is possible due to the large load switching on or off. To evaluate the stability under system frequency fluctuation, local load is defined as 400% rated active power of SM in the simulation model. The local load is suddenly switched off, which could cause the frequency fluctuation shown in Figure 10. The frequency is fluctuated in the range from 49.85 Hz to 50.20 Hz (the peak frequency value at the instant of load switching is casued by the angle jump and should be ignored). Then the frequency is converged on 50 Hz. The instant ROCOF value during the decay oscillation is within the range from \(-2\) Hz/s to 1.5 Hz/s (the transient ROCOF values due to the angle jump should be ingored), as shown in Figure 11a. Then the ROCOF value decays to zero. During the frequency fluctuation, the maximum accumulated phase angle drift is about \(15^{\circ}\), as shown in Figure 11b. Under this test case, the default settings \(\Delta Ang\_set=1.0^{\circ}\), \(\sum Ang\_set=18^{\circ}\), \((df/dt)\_set=0.5\) Hz/s can ensure the stability of the proposed PAD solution.
## 3 Material and Methods
### Spuriousness in the Analysis Results
#### 3.2.1 Heteroskedasticity  
Heteroskedasticity arises when the variance (\(u_{i}\)) of the error terms is not constant and varies with the value of one or more independent variables (\(X_{i}\)) in the model. As the number of independent variables increases, the variance of the error terms tends to rise as well. This can be represented as follows [47]:  
\[var(u_{i})=\sigma_{i}^{2} \tag{1}\]  
From Equation (1), heteroskedasticity arises from two primary causes: (1) the inappropriate selection of independent variables, where the model fails to select suitable independent variables, causing these variables to correlate with the variance of the error terms; and (2) the use of cross-sectional data, in which the non-constant variance of error terms is exhibited across observations, and each observation may have varying error term variances, leading to instability in the model.  
Impacts of Heteroskedasticity  
The variance of \(\hat{\beta}\) cannot be reliably used for confidence interval analysis. This is represented in Equation (2):  
\[var\left(\hat{\beta}\right)=\frac{\sum x_{i}^{2}\sigma_{i}^{2}}{\left(\sum x_{i} ^{2}\right)^{2}}A=\pi r^{2} \tag{2}\]  
Lacks the property of efficiency, even though it possesses the properties of unbiasedness and consistency.  
Hypothesis testing results are more likely to produce non-significance than normal.  
Forecasted values exhibit higher-than-normal errors.  
Detecting Heteroskedasticity  
Heteroskedasticity can be tested using two methods: (1) The Graphical Method, where this method involves using graphs to examine if the variance changes as observations change. If the variance changes in the same direction as the observations, it indicates the presence of heteroskedasticity. (2) The Statistical Method, where this method includes tests, such as the Spearman rank-correlation test, the Goldfeld-Quandt test, the Gleiser test, and [PERSON]'s heteroskedasticity test. The most commonly used method is [PERSON]'s heteroskedasticity test. As for this research, we have chosen to use [PERSON]'s heteroskedasticity test to check the problem.  
Methods for Resolving Heteroscedasticity  
Heteroscedasticity is a common issue that can often be hidden within many models without researchers being aware of it. In reality, it can lead to significant errors. The method used in this research to detect heteroscedasticity is [PERSON]'s heteroskedasticity test, which considers the Chi-square test \(\left(nR^{2}\right)\). If significance is found, it indicates the presence of heteroscedasticity, meaning that the variance of the error term is not constant. Therefore, if this issue arises, it is crucial to revise and improve the model. This research employs the Weighted Least Squares (WLS) method and further examines the value of \(nR^{2}\) to check whether it remains significant. If it is found to be non-significant, it indicates that the model has been successfully weighted to resolve the heteroscedasticity problem.
## 2 Materials and Methods
### Simulation Datasets
For satellite-based SIF retrieval, the SNR of the instrument is also taken into consideration. The noise added to each radiance signal can be expressed as [30]:  
\[\mathrm{noise}(\mathrm{L}_{\mathrm{TOA}},\lambda)=\mathrm{noise}_{r}\frac{ \mathrm{L}_{\mathrm{TOA}}(\lambda)}{\mathrm{SNR}(\mathrm{L}_{\mathrm{TOA}}, \lambda)} \tag{2}\]  
Figure 1: Fluorescence signal at the top-of-canopy (TOC) simulated by SCOPE. Figure 2: Simulated signals with 0.3 nm spectral resolution and 0.1 nm sampling interval derived from the SCOPE and MODTRAN 5 models, including the top–of–atmosphere (TOA) radiance over the vegetated surface (Rad_Veg), solar irradiance (Solar_Irr), upward transmittance of the atmosphere (T_u), SIF signal at the top of the canopy (TOC_SIF) and atmosphere (TOA_SIF). where \(\lambda\) is the wavelength, noise, is a base noise that obeys a standard normal distribution across the spectra, and SNR is a function of the radiance and wavelength, denoted as [14]:  
\[\text{SNR}(\text{L}_{\text{TOA}},\lambda) =\text{SNR}_{\text{ref}}\sqrt{\frac{\text{L}_{\text{TOA}}(\lambda)} {\text{L}_{\text{TOA}_{\text{ref}}}}} \tag{3}\]  
where \(\text{SNR}_{\text{ref}}\) is the reference SNR at the reference radiance level \(\text{L}_{\text{TOA}_{\text{ref}}}\). Based on the specifications of SIFIS, we set the \(\text{SNR}_{\text{ref}}\) (denoted as SNR below) of 300, 350, 400, 450, and 500 to analyze the influence of noise on empirical parameter optimization. In addition, an SNR of 322 was set to select optimal parameters for SIFIS data.
# Cascaded Frequency Selective Surfaces with Matryoshka Geometry for Ultra-Wideband Bandwidth
## 1 Introduction
### Review
In [35], a tri-band-pass FSS is presented that achieves high-order filtering responses in different frequency ranges through a complementary FSS structure, also called negative FSS. The simulation results indicate that the developed FSS can present three passbands around 3.79 GHz, 8.34 GHz, and 12.52 GHz. The fractional bandwidths at 3 dB are 52.8%, 13.7%, and 19.7%, respectively. The frequency responses, in terms of transmission coefficient, show rapid transfer from the passband to the rejection band and there is significant out-of-band suppression between adjacent passbands. In [36], a new procedure is proposed for designing topologies with many degrees of freedom (DoFs). The authors propose an inverse topological design method (ITDM) based on machine learning for FSS structures. An efficient training dataset construction strategy is proposed based on several classical FSS structures. Machine learning is also considered in [37] in the efficient design of FSS structures with many degrees of freedom (DoFs). A spatial inverse design method (SIDM) is proposed based on machine learning technology. This SIDM takes advantage of inverse modeling and topological design to spatially design the FSS. Unlike simple parametric or topological modeling, which involves only one type of variable, i.e., binary or continuous, the proposed SIDM contains binary and continuous variables to model FSS flexibly and at a lower cost. A multilayer perceptron (MLP) neural network is employed to capture the characteristics of both types of variables and perform the mapping of the EM response onto the corresponding FSS structure.
## 2 Materials and Methods
### Measurement Tools
#### 2.3.3 Electroencephalography (EEG) Recording  
Brain activity was recorded using a 64-channel ActiCap system (actiCHamp Plus, Brain Products, Gilching, Germany), which uses high-quality Ag/AgCl active electrodes. Conductive gel (SuperVisc, EasyCap GmbH, Worthsee, Germany) was applied to enhance electrode-skin contact and to maintain low impedances. Active electrodes were chosen due to their reliability in capturing electro-cortical activity during intense exercise while minimizing movement artifacts [29].  
Data were recorded from all 64 electrode sites, but the analysis focused on 15 sites in the extended 10-20 system: F3, F1, F2, F4, C3, C1, Cz, C2, C4, P3, P1, Pz, P2, and P4. These sites were chosen based on both the quality of the recorded signal and their relevance to the motor, sensory, and cognitive processes associated with exercise [21, 38]. Some other electrode sites were excluded due to poor signal quality or noise artifacts. The ground electrode was positioned at AFz, and all electrodes were referenced to FCz. Impedances were maintained below 5 k\(\Omega\) for all sensors to ensure high-quality signal acquisition [39]. Data were amplified using the BrainVision amplifier, sampled at 512 Hz, and recorded with BrainVision Recorder software (version 1.25.0204, Brain Products GmbH, Gilching, Germany).
## 5 Discussion
### Performance Result in Terms of Performance Measurement  
Figure 19 depicts the methods of a '_CarModule_' with performance indicators such as execution time and the number of executions. We show performance measurements before and after refactoring methods of the same _CarModule_, which represented the method name, execution frequency, and execution time. The arrows connect between before and after refactoring the methods. The arrow value denotes the changed execution time. For example, a comparison was drawn for the _vehicleCtrl_ method before and after refactoring. The difference value between these methods was \(-2347.4297\) [ms], which indicated that the performance speed has improved after refactoring of the code.  
Figure 19: Detailed class view with performance indicators on _CarModule_.  
Table 7 shows the performance measurement results. The total execution time before improvement is 24,801.455 [ms]. The total run time after improvement is 23,966.994 [ms]. The difference of the execution time of the _CarModule_ from after to before is \(-\)834.4615 [ms]. Thus, we reduce the execution time of the most complex _CarModule_ through refactoring.
## 3 Evaluation of the Energy Conversion Subsystem and Energy Storage Subsystem
### Conversion Process
#### 3.1.4 Methanol  
Methanol (CH\({}_{3}\)OH) is another option as an energy carrier. It is liquid at room temperature and has advantages due to its compatibility with existing infrastructure--including tanks, pipelines, and fuelling stations--as well as its suitability for application in combustion engines [27]. The carbon dioxide (CO\({}_{2}\)) for the production of methanol can be sourced from carbon capture from processes releasing it, such as cement manufacturing, steel-making, or biomass and power plants, or even obtained through direct air capture (DAC), which can be either onshore or locally at the SailREC System (there are containerised solutions available for DAC, which might be well suited for application in the SailREC System [28]). For nitrogen, separation from air is competitively easier than for carbon dioxide due to the higher concentration of the former in the atmosphere (78% for N\({}_{2}\), <0.5% for CO\({}_{2}\)). DAC of carbon dioxide is still under development and is not competitive enough with flue gas capture on a commercial level [29]. If DAC at the SailREC System is not used, then the transport of carbon dioxide to the system and its storage will need to be addressed. The conversion reaction is not flexible enough to account for the power intermittency and it follows that buffers for the feed-stocks will be required just as in the case of the Haber-Bosch process for ammonia.