esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In the work, defatted muscle proteins of monkfish (_Lophilus litulon_) were separately hydrolyzed by pepsin, trypsin, and in vitro gastrointestinal (GI) digestion methods, and antioxidant peptides were isolated from proteins hydrolysate of monkfish muscle using ultrafiltration and chromatography processes. The antioxidant activities of isolated peptides were evaluated using radical scavenging and lipid peroxidation assays and H\({}_{2}\)O\({}_{2}\)-induced model of HepG2 cells. In which, the cell viability, reactive oxygen species (ROS) content, and antioxidant enzymes and malondialdehydedehyde (MDA) levels were measured for evaluating the protective extent on HepG2 cells damaged by H\({}_{2}\)O\({}_{2}\). The results indicated that the hydrolysate (MPTH) prepared using in vitro GI digestion method showed the highest degree of hydrolysis (\(27.24\pm 1.57\%\)) and scavenging activity on a 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical (\(44.54\pm 3.12\%\)) and hydroxyl radical (\(41.32\pm 2.73\%\)) at the concentration of 5 mg protein/mL among the three hydrolyses. Subsequently, thirteen antioxidant peptides (MMP-1 to MMP-13) were isolated from MPTH. According to their DPPH radical and hydroxyl radical scavenging activity, three peptides with the highest antioxidant activity were selected and identified as EDIVCW (MMP-4), MEPVW (MMP-7), and YWDAW (MMP-12) with molecular weights of 763.82, 660.75, and 739.75 Da, respectively. EDIVCW, MEPVW, and YWDAW showed high scavenging activities on DPPH radical (EC\({}_{50}\) 0.39, 0.62, and 0.51 mg/mL, respectively), hydroxyl radical (EC\({}_{50}\) 0.61, 0.38, and 0.32 mg/mL, respectively), and superoxide anion radical (EC\({}_{50}\) 0.76, 0.94, 0.48 mg/mL, respectively). EDIVCW and YWDAW showed equivalent inhibiting ability on lipid peroxidation with glutathione in the linoleic acid model system. Moreover, EDIVCW, MEPVW, and YWDAW had no cytotoxicity to HepG2 cells at the concentration of 100.0 \(\upmu\)M and could concentration-dependently protect HepG2 cells from H\({}_{2}\)O\({}_{2}\)-induced oxidative damage through decreasing the levels of reactive oxygen species (ROS) and MDA and activating intracellular antioxidant enzymes of superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px). These present results indicated that the protein hydrolysate and isolated antioxidant peptides from monkfish muscle, especially YWDAW could serve as powerful antioxidants applied in the treatment of some liver diseases and healthcare products associated with oxidative stress.  
morkfish (_Lophilus litulon_); muscle; antioxidant peptide; HepG2 cell; cytoprotective activity
# A Learning Game-Based Approach to Task-Dependent Edge Resource Allocation
## 4 Incentives under Information Sharing Conditions
### Participant Utility Functions  
This subsection first discusses the utility function of ESs (the followers). We let \(f_{m}\) denote the resources contributed by ES \(m\), and let \(R_{1}\) represent the funds spent by the user to calculate the current dependent task. Given that ESs incur additional energy consumption when processing external tasks, this extra energy consumption is considered to be the inconvenience that is caused by handling these tasks. Therefore, for each ES, its utility is the reward gained from the contributing resources subtracted by its internal inconvenience [34]. Consequently, the utility of ES \(m\) by selling computational resources is as follows:  
\[u_{1,m}=\frac{R_{1}f_{m}}{\sum_{m=1}^{M}f_{m}}-\alpha_{m}D\eta_{m}\kappa_{m}f_ {m}^{2}, \tag{9}\]  
where \(\alpha_{m}\) is the unit cost expense of computing energy consumption, \(D\) is the average data size for each sub-task, and \(\kappa_{m}\) is the effective switching capacitance.  
For communication resource \(r_{m}\), this paper uses communication speed as a measure. If the user purchases communication resources with amount \(R_{2}\), then the utility of ES m by selling communication resources is as follows:  
\[u_{2,m}=\frac{R_{2}r_{m}}{\sum_{m=1}^{M}r_{m}}-\beta_{m}r_{m}, \tag{10}\]  
where \(\beta_{m}\) is the unit transmission rate cost. From this, the total utility of ES \(m\) can be obtained as follows:  
\[u_{m}=u_{1,m}+u_{2,m}. \tag{11}\]  
When designing utility functions for the user, two factors need to be considered: price satisfaction and resource acquisition satisfaction. Since these two aspects are in conflict with each other, the satisfaction gained from resource acquisition follows a law of diminishing returns. This principle can be represented by a continuously differentiable, concave, strictly increasing function [35]. Therefore, a user's utility can be modeled as follows:  
\[U=\delta ln(\sum_{m=1}^{M}f_{m}+1)+(1-\delta)ln(\sum_{m=1}^{M}r_{m}+1)-R_{1}- R_{2}, \tag{12}\]  
where \(\delta\) is the compromise factor.
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 5 Discussion
### About Application of the Proposed System  
The proposed system was designed for tunnel deformation monitoring in construction, but it is also suitable for the tunnel that has already been built. However, because of the heavy traffic or trains' passage, an unavoidable noise is produced in operating tunnels, and the noise intensity is different and relates to traffic volume, vehicle speed and vehicle type.  
In order to solve this problem in the application of operating tunnels, a noise prediction model based on traffic volume, vehicle speed and vehicle type needs to be studied [41]. With this prediction model, a data compensation method should be proposed to compensate for the measurement error caused by unavoidable noise.
## 4 Optimization Problem Modeling
### System Energy Consumption Model  
Let \(E_{t}^{c}\) denote the energy consumption for executing tasks at time \(t\), comprising energy spent transmitting tasks from IoT devices to relay satellites, relay satellites computing tasks, and transmitting tasks to ground stations. The energy consumed by ground stations is neglected due to their ample power. \(E_{t}^{c}\) is given by the following:  
\[E_{t}^{c}=\sum_{m=1}^{M}\sum_{k=1}^{K}E_{m,t,k}^{t}+\sum_{m=1}^{M}\sum_{k=1}^{K}E _{m,t,k}^{c}+\sum_{m=1}^{M}\sum_{k=1}^{K}E_{m,t,k}^{g} \tag{9}\]  
In Equation (9),  
* \(E_{m,t,k}^{t}=z_{m,t,k}D_{m,t}^{o}P_{m,t}^{i}/R_{m,t,k}^{i}\) is the energy to transmit a task \(\theta_{m,t}\) from IoT device \(ID_{m}\) to relay \(RS_{k}\) at \(t\);
* \(E_{m,t,k}^{c}=y_{m,t,k}e_{k}\big{(}D_{m,t}^{o}/\varphi_{k}\big{)}^{2}\) is the energy for \(RS_{k}\) to compute \(\theta_{m,t}\) at \(t\) where \(\epsilon_{k}\) is the energy coefficient for \(RS_{k}\);
* \(E_{m,t,k}^{g}=\lambda_{m,t,k}D_{m,t}^{o}P_{m,t}^{g}/R_{m,t,k}^{g}\) is the energy to send \(\theta_{m,t}\) from \(RS_{k}\) to ground stations at \(t\).  
Define \(E_{t}^{o}\) as the energy needed for data transmission at time \(t\), incorporating data transfer from observation satellites to relay satellites, among relay satellites, and from relay satellites to ground stations. The formula for \(E_{t}^{o}\) is  
\[E_{t}^{o}=\sum_{n=1}^{N}\sum_{k=1}^{K}E_{n,k,t}^{o}+\sum_{k=1}^{K}\sum_{k^{ \prime}=1,k^{\prime}\
eq k}^{K}E_{n,k^{\prime},t}^{r}+\sum_{n=1}^{N}\sum_{k=1 }^{K}E_{n,k,t}^{g} \tag{10}\]  
where the following holds:  
* \(E_{n,k,t}^{o}=\gamma_{n,k,t}^{o}D_{n}^{o}P_{n}/R_{n,k}^{o}\) denotes the energy for observation satellite \(OS_{n}\) to send the data \(D_{n}^{o}\) to the relay satellite \(RS_{k}\) at \(t\);
* \(E_{n,k^{\prime},t}^{r}=\gamma_{n,k,t^{\prime},t}^{r}D_{n}^{o}P^{r}/R_{k,t^{ \prime}}^{r}\) quantifies the energy for relay satellite \(RS_{k}\) to transmit the data \(D_{n}^{o}\) to another relay satellite \(RS_{k^{\prime}}\) at \(t\);
* \(E_{n,k,t}^{g}=\gamma_{n,k,t}^{g}D_{n}^{o}P^{g}/R_{k,g}^{g}\) defines the energy for \(RS_{k}\) to transmit the data \(D_{n}^{o}\) to ground stations at \(t\).
# Evaluation of Hazelnut Cake Flour for Use as a Milk Powder Replacer in Ice Cream
## 3 Results and Discussion
### Fat Destabilization Index  
The fat destabilization process, i.e., partial coalescence of the fat during the freezing and air incorporation, participates in forming the ice cream structure [67]. In addition, the level of partially coalesced globules directly influences the melting properties of ice cream [62]. Table 4 shows the fat destabilization index levels for all ice cream treatments. The replacement of SMP with HPCF had a significant (\(p<0.05\)) effect on this parameter, by decreasing it from 43.11% for the control sample to 7.50% for the sample with a 100% replacement.  
The fat destabilization index can be connected with the milk protein content in ice cream. Namely, each milk protein possesses different adsorption properties and functions, which somehow affect emulsion stability and the tendency to partially coalesce during the churning [62]. Additionally, fats from hazelnuts may contribute to the decrease of the fat destabilization index due to the possible stabilization of milk fat globules.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Fat Destabilization** & **Overrun** & **First Drop** & **Melting Rate** & **Melting Time** \\  & **Index** & **(\%)** & **(min)** & **(\%/min)** & **(min)** \\ \hline C & 43.11 \(\pm\) 0.20 \({}^{\text{a}}\) & 26.99 \(\pm\) 1.32 \({}^{\text{a}}\) & 19.26 \(\pm\) 1.62 \({}^{\text{a}}\) & 2.18 \(\pm\) 0.01 \({}^{\text{a}}\) & 69.85 \(\pm\) 2.50 \({}^{\text{c}}\) \\ HN1 & 40.59 \(\pm\) 0.16 \({}^{\text{b}}\) & 21.33 \(\pm\) 1.90 \({}^{\text{b}}\) & 22.17 \(\pm\) 1.93 \({}^{\text{a}}\) & 1.96 \(\pm\) 0.02 \({}^{\text{b}}\) & 73.58 \(\pm\) 1.07 \({}^{\text{c}}\) \\ HN2 & 33.51 \(\pm\) 0.20 \({}^{\text{c}}\) & 19.64 \(\pm\) 0.35 \({}^{\text{b}}\) & 26.79 \(\pm\) 4.14 \({}^{\text{a}}\) & 1.92 \(\pm\) 0.06 \({}^{\text{b}}\) & 74.78 \(\pm\) 6.11 \({}^{\text{b}}\) \\ HN3 & 28.09 \(\pm\) 0.14 \({}^{\text{d}}\) & 18.60 \(\pm\) 0.77 \({}^{\text{b}}\) & 26.89 \(\pm\) 3.95 \({}^{\text{a}}\) & 1.82 \(\pm\) 0.04 \({}^{\text{c}}\) & 87.37 \(\pm\) 5.50 \({}^{\text{ab}}\) \\ HN4 & 7.50 \(\pm\) 0.12 \({}^{\text{e}}\) & 15.85 \(\pm\) 1.66 \({}^{\text{c}}\) & 27.08 \(\pm\) 4.20 \({}^{\text{a}}\) & 1.84 \(\pm\) 0.03 \({}^{\text{c}}\) & 89.83 \(\pm\) 0.09 \({}^{\text{a}}\) \\ \hline \hline \end{tabular} The results are expressed as mean \(\pm\) SD. Means in the same column with different superscripts are significantly different according to the Tukey HSD test (\(p<0.05\)). C—control ice cream; HN1, HN2, HN3, HN4—samples with 25%, 50%, 75%, and 100% replacements of SMP with HPCF.  
\end{table}
Table 4: Physical properties of ice cream samples.
# Analysis of the Dynamic Stability of Tailing Dams:
## 5 Conclusions  
In this study, a series of dynamic triaxial tests with the same CSR and different confining pressures were carried out on tailing silt from a tailing dam in Sichuan, China. The main conclusions are as follows:  
1. Limit equilibrium theory was used to investigate the effect of various conditions on the sample's critical pore pressure, \(\mu_{cr}\), under limit equilibrium. With an increase in confining pressure, the increase in sample density and the decrease in dynamic load can lead to an increase in \(\mu_{cr}\) and enhancement of the liquefaction resistance of the sample.
2. At the same CSR, the liquefaction resistance of the sample does not rise monotonically as confining pressure rises over time, but changes variably at values near a specific  
Figure 11: Energy dissipation curves under different confining pressures.  
low confining pressure. The specific confining pressure value dramatically reduces when the sample density is merely raised under the same conditions, and the anti-liquefaction strength improves significantly. Therefore, in engineering, the dynamic stability of a tailing pond can be improved by increasing the compactness of tailing silt.
3. The pore pressure rising trend of the sample is the same under different confining pressures and densities, it can be matched using the BiDoseResp function, and the fitting effect is good.
4. The development of hysteresis curves of tailing silt can be divided into the elastic strain stage, plastic failure stage, and softening stable stage. The area of each hysteresis curve can be accurately calculated using the matrix formula. With an increasing number of vibrations, the area of the hysteresis curves and the dissipated energy increase gradually. With an increase in confining pressure, the overall dissipation area of the hysteresis curves gradually increases and shows cyclic activity.  
Conceptualization, F.K. and G.W.; methodology, F.K.; formal analysis, Y.L.; investigation, F.K.; resources, F.K.; data curation, B.C.; writing--original draft preparation, S.L.; writing--review and editing, L.Z.; visualization, X.L.; supervision, F.K.; project administration, F.K.; funding acquisition, G.W. All authors have read and agreed to the published version of the manuscript.  
This project was supported by the National Natural Science Foundation of China (no. 52174114) and the Yunnan International Technology Transfer Center for Mineral Resources Development and Solid Waste Resource Utilization (202203 AE140012).  
Our research does not involve humans or animals.  
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results and Discussion
### Reactive Azo Dye Oxidation  
#### 3.2.1 Effects of Reaction Time and Reactive Azo Dye Loading  
Each of the experimental parameters affecting the kinetics of the reaction was investigated, starting with the experimental reaction time. In this experiment, the influence of the oxidation system was examined in terms of the photo-Fenton oxidation reaction based on [PERSON]'s earth natural clay. In order to examine the optimal reaction time and its impact on the oxidation process, experiments were undertaken with illumination times ranging from 2 to 60 min. All other parameters, including the initial concentrations of Fuller's earth and H\({}_{2}\)O\({}_{2}\) at 1.0 g/L and 800 mg/L, respectively, were kept constant, and the solution pH was maintained at 3.0. Figure 4 demonstrates the influence of the reaction time on the profiles of various reactive azo dye loadings. An investigation of the results in Figure 4 indicated that the dye oxidation rate reached 73% within only the initial two minutes of the illumination time. However, afterward, it steadily declined, achieving an accumulative oxidation efficacy reaching 97% within 15 min when the initial azo dye concentration was 50 mg/L. According to the previously cited literature [16; 20], azo dye molecules compromise aromatic rings. The hydroxyl radicals generated from the reaction of [PERSON]'s earth with hydrogen peroxide attack such rings and open them. Then, this generates reaction intermediates and eventually oxidizes them to harmless end products (CO\({}_{2}\) and H\({}_{2}\)O). Initial rapid oxidation was dominant for all the dye concentrations. As the reaction proceeded, the generated hydroxyl radicals in the reaction medium gradually declined, corresponding to a reduction in the H\({}_{2}\)O\({}_{2}\) concentration. Moreover, radicals other than hydroxyl radicals were produced, and they inhibited the oxidation reaction rate rather than improving the dye removal rate. The previous conclusion by [13] confirmed this investigation by treating various dyes contaminating wastewater effluent.  
Figure 3: FTIR spectrum of the [PERSON]’s earth clay.  
While a similar rapid reaction rate in the initial reaction time period was observed for all the treated dye concentrations, an assessment of Figure 4 found that the oxidation rate declined with the increase in the dye loading. The removal efficiencies were 97, 95, 91, and 79% for the 50, 100, 150, and 200 mg/L azo Levafix Dark dye concentrations in the aqueous effluent, respectively. Moreover, the reaction time also increased from 20 to 100 min with the increase in the dye loading. At higher dye concentrations, the concentration of the exerted 'OH radicals was insufficient for completing dye oxidation. This conclusion of increasing the oxidation activity with a reduction in the initial pollutant concentration was also previously investigated and recorded by [PERSON] et al. (2011), who used the Fenton reagent for the oxidation of methyl-pesticide-containing wastewater.
## 5 Results and Discussion
### Experiment II: Litchi Spray Hydrocooling Performance
#### 5.2.3 Total Power Consumption and Average Power Consumption  
Figure 13 shows the total power consumption and average power consumption of litchi spray precooling at different experimental conditions. Figure 13a shows that charging and precooling power consumption increases with charging time, and the power consumption during charging was significantly higher than that during precooling. In contrast, the average power consumption of litchi decreased significantly with increases in charging time. The average power consumption of litchi precooling was 437 kJ kg\({}^{-1}\), 349 kJ kg\({}^{-1}\), 250 kJ kg\({}^{-1}\), and 193 kJ kg\({}^{-1}\) for charging times of 3 h, 4 h, 5 h, and 6 h, respectively. The average power consumption can be reduced by increasing the TES capacity within the limits of the hydrocooler capacity. Figure 13b,c shows a few examples of power consumption difference during 4 h of charging in different tests. There was likely an environmental effect under natural conditions. The maximum difference in energy consumption during the charging process was 2.52 \(\times\) 10\({}^{6}\) J.  
From Figure 13b, it can be seen that the precooling energy consumption significantly increases with low litchi load and average energy consumption also significantly increases with low litchi load. When the litchi load reaches 8 kg, the maximum average energy consumption is 383 kJ kg\({}^{-1}\). The minimum average power consumption is 250 kJ kg\({}^{-1}\) when the litchi load is 23 kg. As seen in Figure 13c, the minimum average power consumption of litchi was at a spray flow rate of 30 L min\({}^{-1}\), and a 40 L min\({}^{-1}\) spray flow rate was a little higher. The average power consumption of 30 L min\({}^{-1}\) and 40 L min\({}^{-1}\) spray flow rates was 243 kJ kg\({}^{-1}\) and 253 kJ kg\({}^{-1}\), respectively. The optimal spray flow rate is 30 L min\({}^{-1}\) in litchi spray precooling, followed by 40 L min\({}^{-1}\).  
Figure 12: Variation of precooling capacity and precooling rate under different (**a**) charging times, (**b**) litch loads, and (**c**) spray flow rates.
# Coordinated Control of Virtual Power Plants to Improve Power System Short-Term Dynamics
## 2 Control of VPPs
### VPP Control Modes  
In this subsection, we consider different coordinated control methods for ESS and DERs in VPP.  
* **Mode 1**: DERs and ESSs regulate the frequency but are fully independent (\(y_{p}=0\)).
* **Mode 2**: Only ESSs regulate the frequency. DERs do not include a frequency controller.
* **Mode 3**: DERs do not include a frequency control. The ESS is regulated to keep constant the power injection \(p_{\text{inj}}\) of the VPP into the POC. This is a typical VPP operation mode, where TSO schedules the VPP output every 15 min.
* **Mode 4**: In [5], the weather-driven DERs such as WGs and SPVGs are considered to be non-dispatchable resources due to the stochastic nature of the wind and clouds. The ESS is the only device that regulates frequency. Therefore, in this mode, only the ESS is fed with the signal \(y_{p}\).
* **Mode 5**: In [31], it is proposed that wind farms and VPPs can be used for emergency frequency control in smart super grids. Hence, in this mode, both ESS and DERs are coordinated with the signal \(y_{p}\).
* **Mode 6**: As with Mode 5, both the ESS and DERs are coordinated in this mode. However, the feedback signal \(y_{p}\) is used differently for ESSs and DERs. ESSs are always fed with \(y_{p}\) and, thus, their primary frequency regulation acts immediately after the contingency. On the other hand, DERs are included in the coordinated control and receive the signal \(y_{p}\) after a given time after the occurrence of the contingency, e.g., 15 s. The timer that activates the feedback signal for the DERs is triggered by the magnitude of the variation of the frequency \(\Delta\omega_{\text{POC}}\).
## 4 Multi-Antenna Frequency-Selective Channels
### Meta-Learning for LSTD-Based Prediction
```
Input: previous data \(\{\{h_{l,f}\}_{l=1}^{L+N+\delta-1}\}_{f=1}^{F}\); training set size \(L^{\text{tr}}\); test set size \(L^{\text{te}}=L-L^{\text{tr}}\); window size \(N\); prediction lag size \(\delta\); number of total features \(K\); step size for gradient-based update \(\kappa\); hyperparameter \(\lambda_{1}>0,\lambda_{2}>0,\alpha\ll 1\) Output: meta-learned hyperparameters \(\bar{b}^{\text{1,meta}},\ldots,\bar{b}^{K,\text{meta}},\bar{v}^{\text{1,meta }},\ldots,\bar{v}^{K,\text{meta}}\)
```  
**Algorithm 3** LSTD-based meta-learning for channel prediction for \(S\geq 1\) \begin{table}
\begin{tabular}{c c c} \hline \hline
**Learning Type** & \(\boldsymbol{O(\cdot)}\) **for Naïve Approach** & \(\boldsymbol{O(\cdot)}\) **for LSTD-Based Approach** \\ \hline Conventional learning & \(O(S^{3}N^{3}+S^{2}N^{2}L^{\text{tr}})\) & \(O(KI_{\text{ALS}}(L^{\text{tr}}(S^{2}+S^{2})+N^{3}+S^{3}))\) \\ Transfer learning & \(O(S^{3}N^{3}+S^{2}N^{2}L^{\text{tr}})\) & \(O(KI_{\text{ALS}}(L^{\text{tr}}(S^{\text{NL}})+S^{2})+N^{3}+S^{3})\) \\ Meta-learning & \(O(S^{3}N^{3}+S^{2}N^{2}L^{\text{tr}})\) & \(O(KI_{\text{ALS}}(L^{\text{tr}}(S^{\text{NL}}+S^{2})+N^{3}+S^{3}))\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Computational complexity analysis at deployment (meta-testing).
## 4 Experimental Results and Analysis
### Discussion of the Depth of Deep Residual Network Layers  
The depth of the deep residual network refers to the number of layers it contains. Increasing the number of layers can potentially increase the model's capacity to learn complex patterns and representations from the data, which may lead to improved performance. To determine the relatively optimal depth of the deep residual network, we conducted experiments with different depths and assessed the model's performance, with the embedding rate set to 100%. The experimental results in Figure 5 illustrate the impact of varying the depth on the performance of the steganalysis models.  
Indeed, increasing the number of layers in a model can increase its complexity. Figure 5 demonstrates that increasing the number of layers initially enhanced model performance. However, as the number of layers increased from 2 to 3, performance plateaued. This phenomenon occurs because an excessively complex model is prone to overfitting. To strike a balance, setting the number of hidden layers to two is a good choice across the three steganography datasets.