esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Organic Field-Effect Waveguides (OFEWs)
Dependence of photoluminescence intensities on gate voltage ((**a**), mode I) and source-drain voltage ((**b**), mode II). (**d**,**e**) Modulation of field-effect performance by optical waveguides. Transfer characteristic variations under different laser illuminations in mode I (**d**) and mode II (**e**), respectively. (**f**) Proposed working mechanism. The resonant-energy transfer of photo-generated excitons is hindered by the mismatch in energy gap in Mx induced by hole trapping resulting from external voltage control. Figure taken from reference [54]. application in the novel hybrid OFEWs such as D-A naphthalene derivatives (**6**) or multidonor benzothiadiazole derivatives (**7**) (Figure 12) [55; 56]. In summary, hybrid devices between OFETs and waveguides to create OFEWs represent an exciting area of research in organic electronics. These devices combine the control capabilities of Organic Field-Effect Transistors (OFETs) with the light-guiding properties of waveguides, opening up new possibilities in applications for optical communications and integrated sensors. OFEWs offer significant advantages in terms of flexibility, low power consumption, and the ability to integrate with other electronic and optical components on a single substrate. This makes them suitable for a wide range of applications, such as short-range optical communications, light sensors, and imaging systems. However, the path to widespread implementation of OFEWs is not without challenges. One of the main challenges is optimizing the efficiency and stability of light transmission along the waveguide, especially in large-scale devices. Additionally, the precise integration of electronic and optical components into a single device presents technical challenges in terms of alignment and efficient coupling of light with the field-effect transistors. Furthermore, further research is required to understand and mitigate the effects of signal loss, dispersion, and light absorption in the organic materials used in OFEW construction. Improving uniformity and reproducibility in the manufacturing of these devices is also crucial for their commercial-scale application. Despite these challenges, rapid advancements in research and development of organic materials, as well as manufacturing techniques, offer hope for overcoming these obstacles in the future. With continued progress in these areas, OFEWs are well-positioned to become a foundational technology in a variety of optical and communication applications in the future.
# A Sustainable Composite Cementitious Material Manufactured by Phosphogypsum Waste
## 3 Results and Discussion
### Hydration Products of Composite Cementitious Material  
Figure 6 shows the hydration products of a composite cementitious material paste containing 10% to 90% phosphogypsum after 3 days. The hydration products of paste containing 0% phosphogypsum are mostly calcium silicate hydrate (amorphous, cannot be confirmed by XRD), portlandite, quartz, calcite, and haturrite, as illustrated in Figure 6a, with no clear distinctive peak of ettringite. When phosphogypsum incorporation increased from 0% to 90%, the characteristic peak of portlandite clearly decreased and even vanished, while the characteristic peaks of gypsum, haturrite, and calcite clearly increased and even  
Figure 4: Porosity of composite cementitious material.  
reached their strongest. When phosphogypsum incorporation is 90%, the characteristic peak of gypsum reached the strongest among all mixers. Similarly, the distinctive peak of calcium oxide fluoride grew steadily as phosphogypsum incorporation climbed from 0 to 90%; however, the characteristic peak was not visible. These observations showed that phosphogypsum was involved in the secondary hydration reaction, but its activity was minimal. When phosphogypsum incorporation was quite high, the reaction degree of phosphogypsum was limited, and most of it was still filled, resulting in a prominent gypsum characteristic peak.  
In comparison with the curing age of 3 days, the hydration products of cement paste are primarily calcium silicate hydrate, portlandite, quartz, calcite, and haturrite after 28 days of curing. With 10% to 90% phosphogypsum mixed into cement paste, the relative intensity of typical hydration product peaks should be modified. The relative intensity of the distinctive peak of portlandite, for example, was somewhat increased, while the relative intensity of the characteristic peak of quartz and haturrite was clearly increased, and the relative intensity of the characteristic peak of gypsum was greatly lowered. The fundamental cause of this anomaly is that when the curing time is extended,  
Figure 5: Pore size distributions of composite cementitious material: (**a**) 3 days; (**b**) 28 days.  
Figure 6: Hydration products of composite cementitious material: (**a**) 3 days; (**b**) 28 days.  
the cement reaction accelerated and the hydration products (such as portlandite and calcium silicate hydrate) increased, resulting in a small rise in the distinctive peak of portlandite. Furthermore, phosphogypsum, despite its modest activity, can participate in the secondary hydration reaction under the excitation of portlandite. As a result, the gypsum's unique peak is noticeably weakened.
## 5 Parametric Study
### Concrete Strength  
Although the current design codes neglect the contribution of concrete to the torsional strength of RC members, the experimental results show the increasing torsional strength  
Figure 6: Parametric study using autoencoder: (**a**) beam width; (**b**) beam height; (**c**) closed beam width; (**d**) closed beam height; (**e**) concrete strength; (**f**) yield strength of longitudinal bar; (**g**) amount of longitudinal bar; (**h**) yield strength of transverse bar, (**i**) amount of transverse bar; (**j**) spacing of transverse bar.  
with increasing compressive strength of concrete. As shown in Figures 5 and 6e, the ANN with PCA and autoencoder can effectively take into consideration the effect of concrete compressive strength. The calculated torsional strength with ANN tends to increase as the concrete strength increases, whereas the design codes predict no effect of concrete strength on the torsional strength of RC beams.
# A Novel Malware Detection Model in the Software Supply Chain Based on LSTM and SVMs
## 4 Proposed SSC Malware Detection Model
### The Construction of the LSTM-BO-SVM Model  
To construct the LSTM-BO-SVM model, a staged methodology was adopted in this paper. Firstly, LSTM was introduced as a pre-classifier. In the SSC environment, malware behaviors are often time-dependent, so it is feasible to choose LSTM models to capture these time-dependent features. Its unique cycle mechanism could effectively capture the long-term dependence between different versions of software, so as to improve the accuracy of detection. Then, the BO algorithm was used to optimize the hyperparameters of the SVM model, aiming to further enhance the detection ability of the model. Additionally, the Principal Component Analysis (PCA) technique was adopted to reduce the dimension of the original dataset to enhance the computational efficiency. The LSTM-BO-SVM model flowchart is displayed in Figure 3.  
The steps to build the LSTM-BO-SVM model are detailed as follows:  
1. Input: The T1, T2, and S2 datasets are input into the model.
2. Build the LSTM model: An initialized LSTM model is first built, and then the LSTM model is trained using the training set T1. The specific establishment process of the LSTM model is shown in Section 4.3.
3. Classify T1 and obtain the pre-classification set R: Use the trained LSTM model to pre-classify the training set T1 and obtain the pre-classification set R. The R is defined as a \(J*2\) matrix, where each line is denoted as \(\{id,pc\}\) and wherein \(pc\) represents the pre-classification label.  
Figure 3: Flowchart of the LSTM-BO-SVM model’s establishment.  
4. Build a new dataset D1: Merge the training set T2 with the pre-classification set R to form a new training set D1. The training set D1 is defined as a \(J*Q\) matrix, where each line is denoted as \(\{id,f_{1},f_{2},\cdots,f_{i},\cdots,f_{n},pc,c\}\). Specifically, the \(pc\) column in R is added to the training set T2 as a new feature column.
5. Build the SVM model: Build an SVM model with default hyperparameters and initialize the BO algorithm.
6. Optimize the SVM hyperparameters with BO: The BO algorithm is used to optimize the Gaussian kernel parameter \(\gamma\) and penalty factor \(C\) in the SVM model to obtain the optimal parameters, and the optimal parameters are transferred to the SVM model to obtain the BO-SVM model.
7. Train the LSTM-BO-SVM model with optimized hyperparameters: The LSTM-BO-SVM model is trained using the training set D1, and the training is saved.
8. Evaluate the model: The test set S2 is used to evaluate the classification performance of the LSTM-BO-SVM model. If the requirements (the accuracy is up to the requirement) are met, proceed to the next step; otherwise, go back to step 7 to retrain the model.
9. Obtain the optimal LSTM-BO-SVM model: After the above steps, the optimized model LSTM-BO-SO-SVM is achieved.
## 4 The Proposed Two-Step Access and Handover Strategy
### Importance-TOPSIS  
The second step of the decision-making method is when the users choose to connect to LEO satellites, we use a modified TOPSIS scheme to sort the available LEO satellites and select the best satellite to access.
# Tunable Plasmonic Talbot Effect Based on Graphene Monolayer
## 3 Calculation Results and Discussion  
### The Imaging of Plasmonic Talbot Effect  
Figure 2 depicts the \(\left|E^{2}\right|\) field distributions of the plasmonic Talbot patterns supported by the graphene monolayer, where the chemical potential is 0.3 eV, the period of the grating is 160 nm, the input wavelength in free space is 7 \(\upmu\)m, 8 \(\upmu\)m, 9 \(\upmu\)m, 10 \(\upmu\)m and 11 \(\upmu\)m respectively and the effective wavelength in graphene is 64.3 nm, 86 nm, 111 nm, 137.9 nm and 168.2 nm respectively. All of the \(\left|E\right|^{2}\) profile is obtained from COMSOL. The Talbot image arrays reproduce periodically along the propagation direction. In contrast to the conventional optical Talbot effect, the pattern of the plasmonic Talbot effect demonstrates the attenuation property, and eventually disappears at the propagation direction due to the propagation loss of graphene. However, from Figure 2a-e, it is clear that the Talbot image arrays disappear in different speeds with respect to the wavelength. Longer incident wavelength attenuates faster than the shorter ones. When incident, the wavelength is 11 \(\upmu\)m and there is even no Talbot effect observable, as shown in Figure 2e. Figure 2f plots the imaginary part and real part effective index of the plasmon as a function of the wavelength with a given chemical potential. It is shown that the ratio of the imaginary part to the real part of the plasmon increases with the wavelength, which indicates that longer wavelength suffers higher attenuation. On the other hand, the effective wavelength \(\lambda_{n}\) increases linearly with the increase in \(\lambda_{0}\). When \(\lambda_{n}\) is comparable to period \(D\), the Talbot effect gradually fades away because the diffraction effect becomes weaker. Consequently, the higher order diffraction wave is suppressed by sub-wavelength grating. At this point, even if the wavelength continues to increase, the Talbot effect disappears, as shown in Figure 2e, where \(\lambda_{n}\) is 168.2 nm, which is comparable to the period \(D\).
## 5 Discussion
### Comparison of the QPRM and CLRM  
In this study, the QPRM was used to replace the most commonly used CLRM for water depth inversion of GF data. The comparison results in Table 2 indicate that the water depth results obtained using the CLRM, when compared to ICESat-2 data, yielded an average \(R^{2}\) of 0.9199, an average RMSE of 0.825 m, and an average MAE of 0.65 m. Under the  
Figure 6: Comparison of the bathymetric data results obtained from GF-1 data and from Sentinel-2 data, where the red dashed line represents the 1:1 line and the blue dashed line indicates the fitted line.  
same conditions, the inversion results of the QPRM demonstrated superior performance across all evaluation metrics, generally outperforming the CLRM. Notably, in the study areas of Onotoa Island and Ant Atoll, the QPRM showed particularly outstanding results, with RMSE improvements of 10.89% and 27.08%, respectively, and MAE improvements of 11.63% and 30.99%. These results indicate that the application of the QPRM in this study offers significant advantages, providing more accurate water depth inversion results.  
Advantages of Using Gaofen-1 Fusion Data as a Substitute for Traditional RGB Imagery Acquisition Methods for High-Resolution Data  
The RGB imageries directly available in traditional application platforms (e.g., Google Earth Pro) usually contain only red, green, and blue bands, which cannot effectively eliminate the sun glint signals in the images, and the interference of sun glint on the bathymetric inversion results has been confirmed by several studies [32,38,39]. Therefore, in this study, we adopt the method of fusing GF-1 data by ourselves instead of the traditional direct download of RGB imageries to obtain high-resolution image data, and introduce a deglint step in the pre-processing of GF-1 multispectral images, and then fuse them to obtain high-resolution image data after removing the sun glint signals.  
Furthermore, [PERSON] et al. noted in their research [10,20] that commercially available RGB imagery data often come from various satellite sensors. This not only leads to inconsistencies in the resolution of the obtained bathymetric data but also introduces uncertainties due to spectral differences between different sensors, which may affect the accuracy of the inversions. In light of this, this study focuses on water depth inversion using data from the GF-1 satellite series exclusively. This strategy not only avoids the uncertainties introduced by multiple sensors but also ensures consistency in the resolution of the final inversion results. It can provide a reference for the future application and promotion of GF-1 satellite data in water depth mapping.
# A Review of Sustainable pavement Aggregates
## 5 Industrial and Mining Waste
### Waste Foundry Sand (WFS)  
Industrial processes deliver waste foundry sand (WFS) to the environment through open-air reservoirs polluting soil and groundwater; therefore, plant recovery (Figure 6) of this waste in civil engineering has become relevant in hot mix asphalt (HMA), hydraulic concrete, and pavement subbases [102]. The implementation of sustainable concrete with industrial waste has potential in pavements due to its non-structural volumes [103]. The WFS consists mostly of fine grains of adhered quartz sand that constitute the mold for the liquid metal and that after the casting process is disintegrated and discarded [34]. Foundry sand is classified according to the type of binder used when manufacturing the molds, sand agglomerated with clay or green sand and sand agglomerated with chemicals [104]. Waste foundry sand (WFS) is incorporated with alternative processes to concrete and asphalt for implementation in the construction industry such as pavements [105]. The workability of a hydraulic concrete mixture is not committed to the partial replacement of sand by WFS; however, the demand for water gradually and steadily increases as the percentage increases to a maximum equal to 30% replacement of this solid waste in concrete [106]. When the percentage of WFS as a fines substitute does not exceed 20%, the flexural and compressive strength does not vary significantly against an equal control mixture of respective 4.087 N/mm\({}^{2}\) and 40 MPa values; however, the abrasion resistance increases if the WFS content in the hydraulic concrete increases [107]. In addition, WFS is part of the alternative stabilizing materials for subgrade floors with expansive soil problems as they improve their resistance [108]. On the other hand, concrete formed with 30% WFS as a fine aggregate, 40% ground granulated blast furnace slag (GGBFS) cement mix, and 30% crumb rock as a coarse aggregate achieve an unconfined compressive strength of 12.7 MPa after 90 days of curing, with the limiting use in areas undergoing constant flooding or extreme droughts [109]. However, additives for hydraulic concretes with WFS that reduce their high pozzolanic activity at 28 days should be applied [15]. Thus, the reuse of WFS in road projects could generate significant environmental and economic benefits worldwide [110].  
Figure 5: Soil degraded by mining action, adapted from [22].  
Figure 6: Schematic representation of sand reclamation process, adapted from [102].
## 3 Results
### Evaluation of the EMGPP Models under All Sky Conditions
#### 3.2.2 Evaluation of the EMGPP Model for S/Q \(\geq\) 0.5
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Time**} & \multirow{2}{*}{\(\delta_{\rm avg}\)} & \multirow{2}{*}{**NMSE**} & \multirow{2}{*}{\(\sigma_{\rm cal}\)} & \multirow{2}{*}{\(\sigma_{\rm obs}\)} & \multirow{2}{*}{**cal/obs**} & \multirow{2}{*}{\(n\)} & \multicolumn{2}{c}{**MAD**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{5-10} \multicolumn{1}{c}{**Period**} & & & & & & & **(mg CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\))** & **(\%)** & **(mg CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\))** & **(\%)** \\ \hline
2013–2014 & 137.05 & 0.522 & 0.368 & 0.293 & 1.39 & 5759 & 0.282 & 67.83 & 0.531 & 127.66 \\
2015 & 148.56 & 0.557 & 0.366 & 0.287 & 1.50 & 3617 & 0.306 & 73.83 & 0.554 & 133.34 \\
2016 & 146.65 & 0.576 & 0.353 & 0.312 & 1.26 & 3552 & 0.328 & 69.25 & 0.573 & 120.95 \\
2013–2016 & 143.08 & 0.545 & 0.364 & 0.298 & 1.38 & 13,031 & 0.302 & 69.71 & 0.375 & 86.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Same as Table 7 but for hourly GPP estimates using 2-factor EMGPP for S/Q \(\geq\) 0.5.
## Materials and Methods
### Analysis of Grape Volatiles by HS-SPME-GC-Ms  
Volatile quantification was performed via HS-SPME-GC-TOF-MS (Pegasus 4D, LECO Corp., St. Joseph, MI, USA). Although the instrument is capable of two-dimensional GC analyses, all work was carried out in 1-D GC mode with the modulator and secondary oven turned off. A 2 cm divinylbenzene/Carboxen(r)/polydimethylsiloxane (DVB/CAR/PDMS) fiber was used for all HS-SPME extractions, with an incubation temperature of 40 \({}^{\circ}\)C, a pre-extraction incubation time of 10 min, and 30 min for HS-SPME extraction. A split/splitless injector was used with a constant temperature of 250 \({}^{\circ}\)C. SPME injections were splitless with a flow rate of 50 mL/min and purge time of 3 min. Helium was used as a carrier gas at a flow rate of 1.5 mL/min. The GC column was a DB-5 ms (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Varian, Walnut Creek, CA). The initial GC oven temperature was 50 \({}^{\circ}\)C and held for 5 min, then ramped to 180 \({}^{\circ}\)C at 5 \({}^{\circ}\)C per min, then ramped to 240 \({}^{\circ}\)C at 15 \({}^{\circ}\)C per min and held at 240 \({}^{\circ}\)C for 15 min. The TOF-MS was operated in EI mode with an ionization energy of 70 eV. The electron multiplier was set to 1700 V. MS data from \(m/z\) = 20\(-\)400 was stored at 5 Hz. Data processing was carried out by the LECO ChromaTOF software. The qualifier ions were as follows: for [U\({}^{13}\)C]hexanal, \(m/z\) 46, 60, 76, 88; for [U\({}^{13}\)C]hexanol, \(m/z\) 60, 74, 90; for [2H3]eucalypt, \(m/z\) 114, 142, 157; for [2H3]methyl anthranilate, \(m/z\) 95, 122, 154; for [2H3]IBMP, \(m/z\) 127, 154, 169; and for [2H3]IPMP, \(m/z\) 127, 140, 155. The quantifier ions for [U\({}^{13}\)C]hexanal, [U\({}^{13}\)C]hexanol, [2H3]eucalypt, [2H3]methyl anthranilate, [2H3]IBMP, and [2H3]IPMP were \(m/z\) 76, 74, 157, 154, 127, and 140, respectively.
# The Role of Correlation in the Performance of Massive MIMO Systems
###### Abstract  
Massive multiple-input multiple-output (m-MIMO) is considered as an essential technique to meet the high data rate requirements of future sixth generation (6G) wireless communications networks. The vast majority of m-MIMO research has assumed that the channels are uncorrelated. However, this assumption seems highly idealistic. Therefore, this study investigates the m-MIMO performance when the channels are correlated and the base station employs different antenna array topologies, namely the uniform linear array (ULA) and uniform rectangular array (URA). In addition, this study develops analyses of the mean square error (MSE) and the regularized zero-forcing (RZF) precoder under imperfect channel state information (CSI) and a realistic physical channel model. To this end, the MSE minimization and the spectral efficiency (SE) maximization are investigated. The results show that the SE is significantly degraded using the URA topology even when the RZF precoder is used. This is because the level of interference is significantly increased in the highly correlated channels even though the MSE is considerably minimized. This implies that using a URA topology with relatively high channel correlations would not be beneficial to the SE unless an interference management scheme is exploited.  
massive MIMO; channel estimation; spectral efficiency; spatial correlation; mean square error; ULA; URA +
Footnote †: journal: Applied System  
**Keywords:** massive MIMO; channel estimation; spectral efficiency; spatial correlation; mean square error; ULA; URA
# Proactive Scheduling and Routing of MRP-Based Production with Constrained Resources
## 6 Computational Experiments
### Scalability and Performance Evaluation  
In the final phase, we tested the computational performance of the model on larger datasets by varying the number of orders and the planning horizon. We recorded:  
* Time to first feasible solution (T-FIRST);  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Q** & **Constraints** & **Objective Function** & **Cost\_1** & **Cost\_2** & **Cou\_1** \\ \hline Q4A & 1.12,15–18 & Min (Cost\_1 + Cost\_2) & 2840 & 2675 & 73 \\ Q4B & 1.12,15–18 & & & & \\  & W2 unavailable & Min Cou\_1 & 3010 & 2760 & 55 \\ Q4C & 1.12,15–18 & & & & \\  & R2 unavailable & Min Cou\_1 & **2310** & **2435** & 55 \\ Q4D & 1.12,15–18 & & & & \\  & R1 unavailable & Min (Cost\_1 + Cost\_2) & — & — & — \\ Q4E & 1.12,15–18 & & & & \\  & R2 unavailable & Min (Cost\_1 + Cost\_2) & 2840 & 2675 & 73 \\ \hline \end{tabular}
\end{table}
Table 5: _Cont._* Time to optimal solution (T-OPTIM). This was for both the full MILP model and the MILP model enhanced with the PR model-size reduction procedure (see Table 6).  
Observations:  
* PR consistently accelerated solution times--by more than 20\(\times\) in some cases.
* For instance, when solving Q3 with 50 orders over 150 time periods: \(\bigcirc\) Without PR: T-OPTIM exceeded 7500 s. \(\bigcirc\) With PR: Reduced to ~2187 s.
* For moderate-sized instances (10-20 orders), PR reduced solution time from hours to minutes without sacrificing solution quality. These findings show that the PR algorithm significantly improves scalability, making the model computationally feasible for real-world-sized problems.