esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Noncellular In Vitro Antioxidant Activity
The ORAC activity observed herein for the BWSPHs was slightly lower and higher than that reported for protein hydrolysates derived from salmon muscle (587.41 \(\pm\) 26.50-882.58 \(\pm\) 105.72 umol TE/g sample) and mussel meat (66.40 \(\pm\) 2.27-121.56 \(\pm\) 3.96 umol TE/g sample), respectively [41; 42]. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Sample Code** & **GSH Concentration** & **CAT Activity** & **ROS Production** \\
**(0.5\% w/o dw)** & **(\% BCOOH)** & **(\% H\({}_{2}\)O\({}_{2}\))** & **(\% H\({}_{2}\)O\({}_{2}\))** \\ \hline Control & 100.0 \(\pm\) 0.0 & 100.0 \(\pm\) 0.0 & 100.0 \(\pm\) 0.0 \\ BW-SPH-A & 140.3 \(\pm\) 14.1 * & 137.6 \(\pm\) 7.4 ** & 86.4 \(\pm\) 1.8 * \\ BW-SPH-A-GI & 138.5 \(\pm\) 12.7 * & 133.2 \(\pm\) 4.6 & 81.0 \(\pm\) 3.7 * \\ BW-SPH-B & 79.7 \(\pm\) 11.1 & 123 \(\pm\) 1.2 & 89.9 \(\pm\) 2.1 \\ BW-SPH-B-GI & 124.8 \(\pm\) 14.4 \# & 97.8 \(\pm\) 4.2 & 90.7 \(\pm\) 5.7 \\ BW-SPH-C & 102.3 \(\pm\) 8.0 & 125.7 \(\pm\) 3.1 & 89.7 \(\pm\) 3.2 \\ BW-SPH-C-GI & 94.3 \(\pm\) 13.2 & 138.3 \(\pm\) 16.9 & 90.2 \(\pm\) 3.0 \\ BW-SPH-D & 82.5 \(\pm\) 12.2 & 125.6 \(\pm\) 9.1 & 92.4 \(\pm\) 3.0 \\ BW-SPH-D-GI & 108.7 \(\pm\) 13.8 & 116.6 \(\pm\) 9.3 & 104.3 \(\pm\) 7.6 \\ BW-SPH-E & 108.6 \(\pm\) 8.8 & 110.6 \(\pm\) 11.6 & 94.7 \(\pm\) 7.3 \\ BW-SPH-E-GI & 113.0 \(\pm\) 9.4 & 146.4 \(\pm\) 11.4 * & 91.7 \(\pm\) 3.5 \\ BW-SPH-F & 97.2 \(\pm\) 13.4 & 128.4 \(\pm\) 10.4 & 104.1 \(\pm\) 6.5 \\ BW-SPH-F-GI & 113.7 \(\pm\) 4.5 & 128.5 \(\pm\) 13.8 & 96.1 \(\pm\) 4.0 \\ \hline \hline \end{tabular}  
* Antioxidant potential of BWSPHs (BW-SPH-A–F) and SGID BWSPHs (BW-SPH-A–GI–F–GI) at 0.5% (\(w/v\) dw) (dry weight) as assessed by their ability to increase glutathione (GSH) concentration in retributed hydroperoxide (IBOOH)-treated RAW264.7 cells, increase catalase (CAT) activity in hydrogen peroxide (IBO-2)treated RAW264.7 cells and reduce reactive oxygen species (ROS) production in H\({}_{2}\)O\({}_{2}\)-treated RAW264.7 cells. For GSH and CAT assays, RAW264.7 cells (1 \(\times\) 10\({}^{5}\) cells/mL, 2 mL/well) were exposed to BWSPHs and SGID BWSPHs for 24 h, followed by a tboOH (1 mM, 3 h) or H\({}_{2}\)O\({}_{2}\) challenge (1 mM, 3 h), respectively. ROS production was measured in RAW264.7 cells (1 \(\times\) 10\({}^{5}\) cells/mL, 200 mL/well) exposed to BWSPHs and SGID BWSPHs for 24 h, followed by a \(\mathrm{H_{2}O_{2}}\) challenge (1 mM, 1 h). Values were expressed as a percentage relative to positive control (100%). Values represent the mean \(\pm\) SEM of at least three independent experiments. Statistical significance between samples and the control was measured using ANOVA followed by Dummett’s test.
* denote statistically significant differences between samples and the control at \(p\) < 0.05 and \(p\) < 0.01, respectively. # indicates a significant difference at \(p\) < 0.05 between pre- and post-SGID values measured by \(t\)-test.  
\end{table} 
Table 3: Cellular antioxidant activity of blue whiting (_Micromessitis poutassou_) soluble protein hydrolysates (BWSPHs) pre- and post-simulated gastrointestinal digestion (SGID).
## 2 Materials and Methods
### Proposed Method
#### 2.5.1 Shares Construction Phase  
In this section, we explain the process of generating or constructing the features, key shares and secret shares. The features share is constructed for all of the image blocks using WHT, LBP and DWT. The pseudo code for this phase is described below, in Algorithm 1.  
WHT is used to extract directional features from each image block to assure the robustness of our proposed approach against geometrical attacks [37]. Then, LBP is used to extract the texture feature representation of image blocks that are robust to false positives and geometrical attacks [33]. Lastly, DWT is used to extract multiscale features from image blocks that represent the important features of each block at the different scales [41]. These features are combined together to form the block features, and then all of these block features are combined together to obtain the final feature share. The key share is generated randomly using a pseudo-random generator, and it is given secretly to the original image owner. Now, the secret share is constructed by applying the XOR operation between the original watermark, feature share and key share, as in the following equation:  
\[S_{S}=W\ \oplus F_{s}\ \oplus K_{s} \tag{11}\]  
where \(S_{S}\), \(W\), \(F_{s}\) and \(K_{s}\) represent the secret share, watermark, feature share and key share. Here, the key share is generated randomly for each tested image. Then, the secret share is constructed by exclusive OR among the watermark, feature share and key share.  
Figure 4: Block diagram of the proposed method: (**a**) Shares construction phase; (**b**) Image forgery verification.
# A Wideband Hybrid Envelope Tracking Supply Modulator with Slew-Rate-Enhanced Linear Amplifier
## 2 Circuit Design
### LA Design with an SRE Structure  
This paper proposed an LA design assisted by a nonlinear SRE structure, the diagram of which is shown in Figure 4. The nonlinear SRE structure is controlled by the input voltages, \(V_{INP}\) and \(V_{INN}\), whose inputs are connected to nodes 1 and 2. The input voltage difference, \(V_{INP}-V_{INN}\), is converted into nonlinear SRE currents, \(I_{SRP}\) and \(I_{SRN}\), through the SRE structure. Due to the Miller compensation, the gates of the output transistors, nodes 3 and 4, become the slew-rate limiting points. Therefore, the output currents of the SRE structure, \(I_{SRP}\) and \(I_{SRN}\), are then injected into nodes 3 and 4, accelerating the charging or discharging process of the Miller capacitors (\(C_{1}\) and \(C_{2}\)).
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 6 Carbon-Based Nanomaterials
### Carbon Nanotubes  
The applications of carbon nanotubes (CNTs) for the removal of indoor pollutants have been extensively studied due to their thermal and mechanical stability, hydrophobicity, and hollow structure, which contribute to a large surface area. CNTs also exhibit high electrical conductivity and structural tunability; however, their high production costs limit their use in photocatalysis. The adsorption capacity of CNTs is influenced by several factors. Notably, there is a direct correlation between the diameter of the nanotubes and their adsorption affinity for volatile organic compounds (VOCs). Research has demonstrated that CNTs possess a higher affinity for slightly polar molecules such as acetone, toluene, and methanol. Additionally, the electrical charge of CNTs can affect their adsorption capacity towards various pollutants [155].  
When coupled with photocatalysts, CNTs serve two primary roles: they act as photosensitizers and function as electron sinks, facilitating the transfer of electrons to adsorbed pollutant molecules. These actions enhance the photocatalytic activity of the semiconductor, leading to increased degradation of pollutants [141].  
In a study examining TiO\({}_{2}\)/CNT composites for the photocatalytic removal of acetone (Figure 19) [163], it was found that introducing CNTs into the photocatalyst resulted in higher photocatalytic activity for certain samples compared to P25. Specifically, increasing the amount of CNTs in the composite led to a reduction in anatase crystallite size, an increase in the anatase content, and a decrease in brookite content within the TiO\({}_{2}\). These changes contributed to enhanced photocatalytic efficiency. However, it was also observed that the introduction of CNTs negatively impacted the adsorption properties of TiO\({}_{2}\) under UV light when the amount of CNTs exceeded the optimum level. Interestingly, some samples exhibited higher photocatalytic activity compared to the TiO\({}_{2}\)/AC composite, despite CNTs having a lower surface area than activated carbon [163].  
The presence of CNTs in the composite can also alter the morphology of TiO\({}_{2}\). Specifically, an increase in CNT content results in a transition from spherical particles to agglomerated particles and ultimately to bulk structures. Furthermore, photoluminescence spectra indicated that the TiO\({}_{2}\)/CNT composites exhibited lower photoluminescence intensity than untreated TiO\({}_{2}\), suggesting a reduced rate of charge carrier recombination. As the CNT content in the TiO\({}_{2}\)/CNT composites increased, the photoluminescence intensity for the various samples decreased [163].  
Figure 18: Adsorption of methyl ethyl ketone using various photocatalytic air filters [162].
# Evaluation of Non-Encapsulated and Microencapsulated Lactic Acid Bacteria
## 4 Discussion
[45], who reported that bacteria growth depends on the drying temperature processes. To confer health benefits to the host, bacteria used as a probiotic source must present a viability >7 Log CFU/g [1]. A log reduction of two--to four-fold was observed after spray drying (Figure 2), signifying that strains' survivability diminished. Furthermore, the parameters such as air flow, mechanical processing, storage conditions, and encapsulated material can influence the survivability of bacteria [46]. Generally, obtaining _Lactobacillus_ probiotic powder by spray drying implied a carrier media protection based on maltodextrin and protein sources [47]. Probiotic bacteria combined with the carrier matrix, should maintain some aspects (i.e., viability during storage, low pH, resistance to bile salts and the digestive enzymes of the GIT), in order to reach the colon and to confer positive effects to the host [46]. A good selection of carrier matrix can significantly improve the number of viable bacteria conferring protection during severe GIT conditions. A previous study affirmed that bacteria cultures prepared with a matrix based on maltodextrin/skim milk powder (1:1) involve significant viable cell count (8 Log CFU/g); data are similar to _L. salivarius_ strains from our report at 28 days of storage at room temperature. The viability of _L. acidophilus_ IBNA 64 had an obvious decrease during spray drying in the presence of the maltodextrin/dextrin matrix. Similar data were reported, when the survival rates of _L. acidophilus_ NCIMB 701748 were significantly (\(p<0.001\)) affected by the composition of the carrier [45]. Unfortunately, the viability after the drying process is between <1 and 100% due to the drying parameter setting, different media conditions, and microorganism type [48; 49]. The Coliforms bacteria represent a major concern in controlling the probiotic LAB safety [46]. Our results are in accordance with [PERSON] et al. [50], who affirmed that a quantity of coliforms less than 100 cell/mL was considered acceptable, but a count of less than 10 cell/mL is achievable and desirable. It suggests that after spray drying, the coliforms remained undetectable. Spray drying is the most common dehydration method, as it has a low cost and is not time-consuming [51]. The decline and mortality of bacterial cell viability during the spray drying processing is probably caused by stress treatments [8], culture media, drying temperature, and water activity that impact not only the extent of thermal inactivation but also the loss of water bound on the cell surface [48]. Furthermore, the stability of microencapsulated bacteria is critically influenced by storage temperature, the exponential or stationary growth rate of the culture strains, physical state, growth stimulants, and thermoprotectant agents [46]. It was previously reported that different polysaccharides used for the microencapsulation of probiotics were proven to be an excellent way to conserve and protect the bacteria cells from harmful factors during spray drying processing [12; 52].
## 4 Result and Discussion
### Recovery of Ancient Gas Content
#### 4.1.5 The Content of Ancient Free Gas  
According to Figure 6, the bottom boundary of the Silurian stratum in Weiyuan area was buried to a depth of 1800 m in the middle period of the Silurian, with the \(R_{\text{O}}\) value reaching 0.5%, entering the hydrocarbon generation threshold. By the late Silurian, the stratum was buried to a depth of about 2100 m, and then underwent uplift and erosion, causing the hydrocarbon generation process to stop. At the end of the Permian, the stratum was rapidly buried again, and the Ro value of the stratum reached 0.5% again, and the organic matter officially entered the hydrocarbon generation threshold. In the middle Jurassic, with the rapid increase in geothermal gradient, the thermal cracking intensity of organic matter was significantly increased, and the pore pressure was about 125 MPa, ensuring the enrichment of gas. In terms of evolution of shale gas content, affected by factors such as overpressure and rapid heating of strata, the free gas content increased rapidly to 11.4 m\({}^{3}\)/t by the middle Jurassic, and reached the historical maximum of 12.5 m\({}^{3}\)/t at the middle Cretaceous. In the later period of the Cretaceous, the stratum entered the initial stage of the last uplift, and the intensity of stratum deformation slightly increased, forming two adjacent syncline and anticline areas. At this time, due to the cessation of hydrocarbon generation, the gas supply disappeared, and the gas reservoir pressure and gas content decreased rapidly. By the middle and late Neogene, the gas content in the positive position of well Wei201 was always higher than that in the negative position of well Wei204; however, as the stratum continued to uplift, the squeezing stress significantly changed the structural style of the stratum, and the positive position ruptured before the negative position, greatly accelerating the loss of vertical gas.  
The numerical simulation results showed that the current free gas volume for well Wei201 is 1.8 m\({}^{3}\)/t; for well Wei204, it is 3.0 m\({}^{3}\)/t (Figure 6). Considering that the proportion of free gas in the Longmaxi shale gas reservoir in the southern Sichuan region is around 60% to 80% [41], the simulated total gas content for well Wei201 is between 2.3 m\({}^{3}\)/t and 3.0 m\({}^{3}\)/t; for well Wei204, it is between 3.8 m\({}^{3}\)/t and 5.0 m\({}^{3}\)/t. The results of exploration practice show that the measured total gas content for well Wei201 ranges between 2.1 m\({}^{3}\)/t and 4.8 m\({}^{3}\)/t; for well Wei204, it ranges between 3.8 m\({}^{3}\)/t and 7.3 m\({}^{3}\)/t [43]. The lower limit of the simulation results is similar to that of the measured results, while the upper limit of the simulation results is less than that of the measured results. Overall, the simulation results are consistent with the actual exploration results (Table 2).
# On the Performance of LDPC-Coded Large Intelligent Antenna System
## 3 System and Signal Model for the Receivers  
In LIS systems scenarios, various receiver design strategies are possible. First, linear feedforward non-iterative FDE receivers such as ZF, MMSE, MRC, and EGC, and second, nonlinear feedback equalization iterative MRC and EGC receivers are possible (Figure 2).  
Although their BER results can be excellent, the ZF and MMSE algorithms, which are based on matrix inversions, are plainly detrimental in this sort of system since the computational cost increases dramatically with the number of transmitting and receiving antennas. Contrarily, the MRC and EGC procedures are simple, leading to a decrease in processing and, consequently, energy saving.  
Assuming a non-iterative receiver, the estimated frequency domain data symbols \(\widehat{S}_{k}=\left[\widehat{S}_{k}^{(1)},\ldots,\widehat{S}_{k}^{(R)} \right]^{T}\) is:  
\[\widetilde{S}_{k}=F_{k}Y_{k} \tag{2}\]  
Depending on the algorithm, \(F_{k}\), the detection coefficient corresponding to the \(k\)th subcarrier, can be computed [20] as:  
\[F_{k}=\frac{1}{H_{k}} \tag{3}\]  
For the evaluation of these different receivers, we need information about the feed-forward and feedback matrices where \(F_{k}\) denotes feedforward and \(B_{k}\) shows feedback matrices, respectively.  
Figure 2: Block diagram using SC-FDE receiver.
## 4 Results Validations
### In Free Space Environments  
In this section, the proposed antenna is tested experimentally to evaluate S\({}_{11}\) and gain spectra at two LDR values. As shown in Figure 8a, the proposed antenna S\({}_{11}\) spectra are found to show an excellent response to the switching process; when all LDR resistance values are changed from OFF status, logic_0, to ON status as logic_1. Such a process is achieved by covering all LDR resistances with lids for OFF status. For ON status, LDR resistances are lighted all without cover lids to be activated ON. Consequently, it is found that the proposed antenna gain spectrum realizes a gain of 11.6 dBi when all LDR resisters are switched ON with light; however, when all LDR resistors are switched OFF, the gain decreases to 2 dBi at 4.5 GHz as seen in Figure 8b. It is found that the measurements agree well with simulated results with insignificant errors.  
Figure 8: Antenna performance measurements with different switching sceneries: (**a**) S\({}_{11}\) spectra and (**b**) gain.
# Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model
## 2 Motivation and Main Methodology
### Construction of a Data-Driven Failure Level Classification Model for Multirotor  
3.1 Real-Time Monitoring of Multirotor Flight Status and Establishment of a Condition Assessment Information Source  
It is difficult and costly to actually collect fault data of different types of multirotors. Therefore, based on the Rflysim platform, this paper used an existing dynamic model of a multirotor to build the fault injection module, simulate different types of fault injections, and collect the flight data under each fault condition as a source for the condition assessment information. Based on the Rflysim platform, real-time monitoring of the flight status of the multirotor was carried out, and the information source of the condition assessment was established. The collected flight data include basic measurement data and control deviation data. The basic measurement data include the velocity, angular velocity, acceleration, attitude angle of the \(X\)-axis, \(Y\)-axis and \(Z\)-axis, namely \(\delta_{\mathbf{1}}=\left\{V_{x},V_{y},V_{z},W_{x},W_{y},W_{z},a_{x},y_{a},a_{ z},\psi,\theta,\gamma\right\}\), in which \(\left\{V_{x},V_{y},V_{z}\right\}\) are the triaxial speeds, \(\left\{W_{x},W_{y},W_{z}\right\}\) are the triaxial angular velocities, \(\left\{a_{x},a_{y},a_{z}\right\}\) are the triaxial accelerations, and \(\left\{\psi,\theta,\gamma\right\}\) are the yaw angle, roll angle, and pitch angle. The deviations between the actual flight status of the multirotor and the set target constitute the control deviation data. The control deviation data include position deviation and speed deviation data on the \(X\)-axis, \(Y\)-axis, and \(Z\)-axis, namely \(\delta_{\mathbf{2}}=\left\{\Delta h_{x},\Delta h_{y},\Delta h_{z},\Delta V_{x}, \Delta V_{y},\Delta V_{z}\right\},\) where \(\Delta h_{x}=h_{tx}-h_{x},\Delta h_{y}=h_{ty}-h_{y},\Delta h_{z}=h_{tz}-h_{z};h _{x},h_{y},h_{z}\) are the set positions on the \(x\)-axis, \(y\)-axis, and \(z\)-axis, respectively, \(h_{tx},h_{ty},h_{tz}\). They are the real-time positions on \(X\)-axis, \(Y\)-axis, and \(Z\)-axis, respectively; \(\Delta V_{x}=V_{tx}-V_{x},\Delta V_{y}=V_{ty}-V_{y},\Delta V_{z}=V_{tz}-V_{z}, V_{x},V_{y},V_{z}\) are the set speed on the \(x\)-axis, \(y\)-axis, and \(z\)-axis, respectively; \(V_{tx},V_{ty},V_{tz}\) is the real-time speed on the \(x\)-axis, \(y\)-axis, and \(z\)-axis, respectively. Therefore, the basic measurement data and control deviation data together constitute the information source of the condition assessment. \(\theta=\left\{\delta_{1},\delta_{2}\right\}\), including 12 types of basic measurement data and 6 types of control deviation data variables, with 18 groups of variables.
## 3 Methodology
### Feed-Forward Neural Network  
In addition to the aforementioned algorithms and statistical approaches, a feed-forward neural network was applied to the dataset. The neural network was trained on the dataset that included the dependent and independent variables. To test the hypotheses, we can use the trained network with different combinations of values for the independent variables to analyze the relationship between each independent variable and the dependent variable. Python and Tensorflow were used for the feed-forward neural network. The Python code for the neural network training is presented in Algorithm 3.  
```
1. import pandas as pd
2. import tensorflow as tf
3. from sklearn.model_selection import train_test_split
4. from sklearn.preprocessing import StandardScaler
5. # Load the survey data
6. df = p.read_csv('enterprisesict_data.csv')
7. # Split the data into training and testing sets
8. X_train, X_test, y_train, y_test = train_test_split(
9. df['Manager Age', 'Manager Gender', 'Manager Education', 'Enterprise Size',
10. 'Business Performance', 'Awareness of ICT Importance', 'Intellectual Capital', 'Market Assessment']],
11. df['Intentions to Implement ICTs']],
12. test_size = 0.3,
13. random_state = 42)
14. # Normalize the data
15. scaler = StandardScaler()
16. X_train = scaler.fit_transform(X_train)
17. X_test = scaler.transform(X_test)
18. # Define the model architecture
19. model = tf.keras.models.Sequential([
20. tf.keras.layers.Dense(8, activation ='relu'),
21. tf.keras.layers.Dense(16, activation ='relu'),
22. tf.keras.layers.Dense(1, activation ='sigmoid')
23. ])
24. # Compile the model
25. model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
26. # Train the model
27. history = model.fit(X_train, y_train, epochs = 100, batch_size = 32, validation_data = (X_test, y_test))
28. # Evaluate the model on the test set
29. test_loss, test_acc = model.evaluate(X_test, y_test)
30. print('Test accuracy:', test_acc)
31. # Make predictions on the test set
32. y_pred = model.predict(X_test)
33. # Convert predictions from probabilities to binary outputs
34. y_pred = (y_pred > 0.5).astype(int)
35. # Print precision, recall, and F1-score for each class
36. print(classification_report(y_test, y_pred))
```  
**Algorithm 3. Python Cozde for Neural Network Training.**  
The dependent variable in this study was the intention to implement ICTs. As this is a binary classification, a binary cross-entropy was used as the loss function, and the Adam optimizer was used for compiling the model. Categorical variables were encoded using one-hot encoding, and feature values were standardized with StandardScaler. Figure 3 presents the architecture schematics of the neural network used in this study.  
The feed-forward neural network has 2 layers, with 8 and 16 neurons for hidden layers and sigmoid activation function for the output layer. The model was trained with a batch size of 32 and for 100 epochs. The accuracy of the model is 85.8%; precision is 91.5; recall is 82.1; and F1-score is 86.8.