esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
## 5 Experiment and Analysis  
### Layer Number Optimization  
We conduct simulation experiments to evaluate our denoising autoencoder, and we also compare it with a set of typical models including RLS, LMS, PCA, and the deep learning method DnCNN proposed by [PERSON] et al. [18]. The evaluation metrics are SNR, Error Vector Magnitude (EVM), and demodulation BER before and after noise reduction. In addition, the modulation recognition accuracy is considered and the modulation recognition model is the CNN [34].  
The SNR is defined as the ratio of noiseless signal power to noise power, which can intuitively measure the quality of signal. The calculation formula is given by the following:  
\[SNR_{dB}=10\lg(\frac{||s||_{2}^{2}}{||s^{\prime}-s||_{2}^{2}}) \tag{13}\]  
where \(s\) represents the noiseless modulation signal, and \(s^{\prime}\) represents the noisy modulation signal. The parameter EVM can comprehensively measure the amplitude error and phase error of the modulation signal, which is defined as the ratio of the root mean square value of the error vector signal and the root mean square value of the ideal signal, and expressed in the form of a percentage. The calculation formula is given by the following:  
\[EVM=\sqrt{\frac{\frac{1}{N}\sum\limits_{i=1}^{N}|s_{i}-s_{i}^{\prime\prime}|^{ 2}}{\frac{1}{N}\sum\limits_{i=1}^{N}|s_{i}|^{2}}}\times 100\% \tag{14}\]  
where \(s\) is the same as before, and \(s_{i}^{\prime\prime}\) represents the signal-reconstructed value (predicted value). In the first experiment, we determine the optimal depth of encoder-decoder modules. We design three encoder module structures, whose encoder-decoder depths equal 2, 3, and 4, respectively, and the corresponding neural networks are called DRdA-CA2, DRda-CA3, and DRda-CA4, respectively. The training loss and validation loss of the three network models are shown in Figure 5. The detailed value of the training loss with different epochs and the numbers of parameters are listed in Table 3. In terms of the training loss, DRda-CA3 is 1.4-1.9% lower than DRda-CA2 on average when the epoch is larger than 10, and the validation loss presents a similar trend. It should be noted that the performance ofDRdA-CA3 and DRdA-CA4 is almost the same. When it comes to the complexity, the size of the model parameters of DRdA-CA2, DRdA-CA3, and DRdA-CA4 are 181 k, 263 k, and 345 k, respectively. Therefore, considering both the loss and model complexity, we choose DRdA-CA3 as the neural network model structure for the denoising tasks. Although the loss values have been small at the epoch equaling 10, they continue to decline and basically stabilize at 40 rounds. In this paper, the model parameters adopt the training results of 40 rounds.
## 3 Results and Analysis
### Effects of Additives and Their Proportions on the Nutritional Quality of Corn Stover
#### 3.1.2 Effects of Different Additive Treatments on the Nutritional Quality of Corn Straw Silage  
The dynamic changes in the nutritional quality of corn straw silage are shown in Table 2. No significant differences were observed in the nutritional quality of the corn straw silage between different additive treatments and silage days (\(p>0.05\)). However, significant differences were found in the DM and WSC contents of the corn straw among different additive treatments (\(p<0.05\)), while silage time significantly affected the CP, ADF, NDF, and WSC contents (\(p<0.05\)). After 60 days of ensiling, the DM content of the corn straw in the LP and LPXE2 treatment groups was 39.84% and 39.06%, respectively; these were significantly lower than that in the CK treatment group (\(p<0.05\)). The CP content in the CK group was significantly lower than in all other additive treatment groups (\(p<0.05\)). After 30 days of ensiling, the ADF contents of LPC1, LPC2, LPXE1, and LPXE2 were 44.21% DM, 41.17% DM, 45.02% DM, and 43.84% DM, respectively, which were significantly lower than those in the CK and LP groups (\(p<0.05\)). The addition of cellulase and xylanase significantly reduced the ADF content in corn straw silage, and the cellulase concentration had a significant effect on the ADF content (\(p<0.05\)). After 30 days of silage, the NDF contents in the LPC1, LPXE1, and LPXE2 treatment groups were 68.36% DM, 65.84% DM, and 64.82% DM, respectively, significantly lower than those in the CK and LP groups (\(p<0.05\)). No significant differences were found between the CK and LP treatment groups (\(p>0.05\)). After 60 days of ensiling, the NDF content ranged from 69.57% to 65.21% DM. The NDF contents in the CK and LP treatment groups were significantly higher than in the LPXE1 and LPXE2 treatment groups (\(p<0.05\)). A high concentration of xylanase significantly reduced the NDF content after 60 days of ensiling. Compared with the CK and LP groups, the WSC contents in the LPC1, LPC2, LPXE1, and LPXE2 groups were significantly higher (\(p<0.05\)). High concentrations of cellulase and xylanase significantly enhanced the WSC content, while silage duration had no significant effect on WSC content (\(p>0.05\)).
## 3 Results
### Results of Modal Analysis  
Vibration theory indicates that higher-order modes have minimal contribution to the dynamic response and decay rapidly, so only low-order modes need to be considered [32]. Modal analysis of the RSS base was performed using ANSYS Workbench, extracting the first six natural frequencies and mode shapes of the rotor through the Block Lanczos method. The complete mode shape contour diagram is shown in Figure 5, with the natural frequencies and mode shapes described in Table 2.  
In practical operating conditions, with the M701F working at 3000 RPM, the object's natural frequency must be more than 20% away from the rotor's operating frequency (50 Hz) to ensure no resonance occurs. Therefore, the first and second natural frequencies of the RSS flexible base must be more than 20% away from the operating frequency, ensuring that the first natural frequency is below 40 Hz and the second is above 60 Hz. At this stage, the first critical frequency of the RSS base exceeds the operating frequency, causing the dynamics of the RSS base to be rigid, which does not correspond to the dynamics of the actual large RSS base. Therefore, based on the above dimensions of the RSS base, the dimensions of the tangential support plate were optimized using an equivalent static load model to identify the specific size of the tangential support plate that ensures the first natural frequency of the RSS base is lower than the operating frequency and sufficiently away from the resonance frequency.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode Order** & **Frequency/Hz** & **Mode Shape** \\ \hline
1 & 61.3 & Axial elongation of the bearing seat \\
2 & 127.73 & Bending of the outer casing in the XZ plane \\
3 & 278.67 & Bending of the bearing plate \\
4 & 327.83 & Twisting of the entire system in the XY plane \\
5 & 354.97 & Torsional deformation of the bearing seat \\
6 & 377.55 & Torsional deformation of the bearing seat \\ \hline \hline \end{tabular}
\end{table}
Table 2: First six modal frequencies of RSS pedestal with 11.5 mm width support plate.  
Figure 5: The first six vibration modes of RSS pedestal with 11.5 mm plate.
## 3 Results and Discussion
### Oxidative-Reduction Potential  
In both sausage treatments the increase (\(p<0.05\)) of redox potential was observed during storage (Table 2). During the whole storage period a significantly higher (\(p<0.05\)) redox value was observed in the C treatment, which means that adding _L. plantarum_ SCH1 could inhibit the oxidation processes in the final product. Fine grinding, tissue structure decay, and aeration during the separation process caused the MSPM to be more prone to oxidation [6]. LAB belongs to organisms relatively anaerobic but in unfavorable conditions they might use oxygen as a final electrons acceptor. Nevertheless, oxygen might have a negative effect on the lactic acid bacteria cells. During oxygen reduction in the respiratory chain they appear damaging for the cells hydroxyl radicals, hydrogen peroxide and peroxide radicals. Reactive oxygen species (ROS) attack proteins, lipids and nucleic acids contribute directly to cells' death [35; 36] Some LAB respond to oxygen stress with increased anti-oxidative enzymes synthesis [37]. In the case of _L. plantarum_ the pseudocatales containing manganese is produced (manganese-containing catalase) [38]. The other enzyme catalyzing hydrogen peroxide decomposition is glutathione peroxidase [39]. Moreover, the activity of the _L. plantarum_ SCH1 anti-oxidative enzymes in raw MSPM batter during cold storage before cooking could have an influence on the redox potential value in the product after thermal treatment.  
The highest redox potential in the C and L treatments was observed 1 week after storage. After 3 weeks of storage, the redox potential in the C treatment was similar (331.7 mV), but in sausage with the addition of _L. plantarum_ SCH1 the redox potential was significantly lower (\(p<0.05\)) and lowered from 322.4 mV to 311.6 mV. It should be pointed out that the significant anti-oxidative activity of _L. plantarum_ SCH1 in the L treatment after 3 weeks of storage could have an effect on lower redox potential in this period [37].
# Designing Trojan Detectors in Neural Networks Using Interactive Simulations
## 3 Methods
### Design of Neural Network Measurements
#### 3.2.2 Representation Power Defined via Neural Network States  
The histogram of states is viewed as a probability distribution that indicates the utilization of a layer. To quantify the NN utilization, the parallels between neural network and communication fields are leveraged in terms of: (a) NN representation power/capacity (channel capacity in communications); (b) NN efficiency (channel efficiency); and (c) the universal approximation theorem [36] (source coding theorem [37]). According to the universal approximation theorem, we view the NN representation power (also denoted as expressiveness or model capacity or model complexity) as its ability to assign a training class label to each training point and create accurate class regions for that class. For instance, a NN must have at least two nodes (\(nnodes=2\)) in the final layer in order to assign four class labels (i.e., \(C=4\leq 2^{modes}=4\rightarrow\{00,01,10,11\}\)).  
Once the layer node outputs (i.e., the state information shown Figure 4 (top)) are gathered, one can categorize the states across all nodes of a layer into four categories:  
1. One state is used for predicting multiple class labels.
2. One state is used for predicting one class label.
3. Multiple states are used for predicting one class label.
4. States are not used.  
The first category is detected when a NN layer does not have enough nodes (insufficient representation power). It could also occur when a NN layer does not contribute to discriminating class labels (poorly trained NN). The second and third categories suggest that a subset of data points associated with the same class label is represented by one or multiple states (efficient or inefficient representation). The number of states representing a class label could correlate with the within-class variability. The last category implies that a NN layer might have a redundant (inefficient) node in a layer for representing a class label. Thus, states at NN layers provide information about NN representation power as: (1) insufficient; (2) sufficient and efficient; or (3) sufficient and inefficient. An ideal NN is sufficient and efficient. Figure 5 shows an example of a NN with a sufficient capacity and inefficient encoding in Layer 1 of Label P (blue).  
Figure 4: The computation of KL divergence from NN state information at each layer per class label: (**Top left**) states 0100, 110 and 10 at the three layers for an input point; (**Top right**) tabular summary of state information for a set of points \(d_{k}\); (**Bottom right**) combined histogram of states for all layers and both class labels (one color per layer); and (**Bottom left**) KL divergence computed per layer and class label. The KL divergence values can be used for comparison purposes.
# The Calculated Circadian Effects of Light Exposure from Commuting
## 2 Materials and Methods
### Calculations of the Circadian Effects of Lights
#### 2.2.1 \(\alpha\)-Opic Irradiance  
The first method is recommended by the International Commission on Illumination (CIE) in CIE S 026/E:2018 [16]. This international standard provides the spectral sensitivity functions (action spectra), which describe the extent to which radiation stimulates each of the five photoreceptor types that contribute to the non-visual effects of light in humans [16]. The sensitivity function for the intrinsically-photosensitive retinal ganglion cells (ipRGCs) is based on the work of [PERSON] et al. [22]. The cone sensitivity function and rod sensitivity function recommended in CIE S 026/E:2018 are based on a previous CIE publication [16]. With the SID and spectral sensitivity functions, the weighted irradiance (\(\alpha\)-opic irradiance) for each human photoreceptor type can be calculated with Equation (1):  
\[E_{\epsilon,\;\alpha}=\int\;E_{\epsilon,\;\lambda}(\lambda)S_{\alpha}(\lambda )d\lambda \tag{1}\]  
where \(E_{\epsilon,\;\alpha}\) is the \(\alpha\)-opic irradiance, \(E_{\epsilon,\;\lambda}(\lambda)\) is the spectral irradiance, and \(S_{\alpha}(\lambda)\) is the \(\alpha\)-opic action spectrum [16]. A toolbox to support the use of this metric has been developed and is available on the CIE website [23].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multirow{2}{*}{**Date**} & \multirow{2}{*}{**Weather**} & \multicolumn{2}{c}{**GPS Coordinates/**} & \multirow{2}{*}{**Time**} & \multirow{2}{*}{**Note**} \\ \cline{3-4} \cline{6-9}  & & & & **Train Stations** & & & & \\ \cline{3-4} \cline{6-9}  & & **Start** & **End** & **Start** & **End** & **Start** & **End** & \\ \hline \multirow{2}{*}{**Driving**} & 12 Dec. & Mostly cloudy & Mostly cloudy & \((-33.882898,\;(-33.879560,\;151.121124)\) & \(151.198464)\) & 8:40 & 9:11 & Heavy traffic, medium to high-density housing areas \\ \hline \multirow{2}{*}{**Train**} & 18 Dec. & \multirow{2}{*}{Sunny} & \multirow{2}{*}{Sunny} & Ashfield railway & Parramatta railway & \multirow{2}{*}{7:39} & \multirow{2}{*}{8:10} & facing direction of travel, lower level, no tunnel \\  & 2019 & & & & station & & & \\ \hline \multirow{2}{*}{**Walking**} & 19 Mar. & \multirow{2}{*}{Sunny} & \multirow{2}{*}{Sunny} & \((-33.922654,\;(-33.922073,\;151.190043)\) & \multirow{2}{*}{8:08} & \multirow{2}{*}{8:34} & High-density housing areas \\  & 2020 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Examples of trip itineraries for three commuting modes.  
Figure 2: Examples of the GPS route for driving and walking: (a) driving from S point to E point; (b) walking from S point to E point.
# Discovery and Flavor Characterization of High-Grade Markers in Baked Green Tea
## 3 Materials and Methods
### Taste Components' Detection
#### 3.6.2 HPLC Detection of Amino Acids  
The amino acids in the samples were analyzed by HPLC (Agilent 1200, Agilent Technologies, Palo Alto, CA, USA) with a Zorbax Eclipse-AAA column (4.6 mm \(\times\) 150 mm, 5 \(\upmu\)m; Agilent Technologies, Santa Clara, CA, USA) through pre-column derivatization. Two standard solutions used were a mixture of 16 amino acids (i.e., Asp, Glu, Ser, His, Gly, Thr, Arg, Ala, Tyr, Cys, Val, Met, Phe, Ile, Leu, and Lys) and a L-theanine standard solution, and their concentrations were both 1 nmol/\(\upmu\)L. HPLC parameters were as follows: mobile phase A, 0.04 mol/L NaH\({}_{2}\)PO\({}_{4}\) H\({}_{2}\)O (pH 7.8); mobile phase B, methanol/acetonitrile/H\({}_{2}\)O (45/45/10, _v/v/v_); temperature, 40 \({}^{\circ}\)C; flow rate, 1.5 mL/min; and DAD detection wavelength, 338 nm. Automated derivatization procedure: 2.5 \(\upmu\)L of borate buffer solution was mixed into 0.5 \(\upmu\)L of standard solution or sample solution; then, 0.5 \(\upmu\)L portions of OPA and FMOC were added, respectively, and they were all mixed uniformly 6 times; finally, 32 \(\upmu\)L of ultrapure water was pipetted and 20 \(\upmu\)L of above-mixed solution was injected. The following gradient was used (mobile phase A in B): 100% isocratic elution for 1.9 min, from 100 to 43% (1.9-18.1 min) and then from 43 to 0% (18.1-18.6 min); 0% isocratic elution (18.6-22.3 min) from 0 to 100% (22.3-23.2 min); and 100% reconditioning (23.2-26 min). The external standard method was adopted for quantitative analysis.
# DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework
## 6 Conclusions  
A novel distributed and dynamic fog (DD-fog) framework has been proposed in this paper for deploying services based on MEC and fog infrastructure in accordance with user dynamics. In addition, migrating the microservices onto the necessary fog-node is considered as one of the key feature of this framework which can provide with a stable quality level for each service. Moreover, two K-means and SWARM-based algorithms were investigated for determining the centers of high demand and finding the fog-node that is more consistent with the service requirements. Furthermore, the fog separation on microclusters was suggested which is based on the fitness-function metrics. Finally, simulation results proved that the proposed framework could reduce the execution time of the microservice function due to the rational allocation of resources by up to 70%.  
Figure 14: Micro-clusters separating.  
Figure 13: Result of PSO modeling.  
For our future work, we plan to build a new predictive model of user moving and service dynamics, in which the results of this paper are considered. On the next step of this project, expected to build the predictive model of user routes and service dynamics, taking into account presented results. For example, the following tasks monitoring of the consumer demand (AI), taking into account their routes monitoring of the load on the services and their components (microservices) prediction of user routes and their demand (AI) based on the monitoring prediction of routes for microservices migration (via edge network and D2D) (AI) based on the biological algorithms.  
Conceptualization, V.A. and K.V.; methodology, V.A.; software, A.M.; validation, I.A.E. and A.K.; formal analysis, K.V.; investigation, A.M.; resources, A.K.; writing--original draft preparation, V.A.; writing--review and editing, I.A.E.; supervision, A.M. and A.K. All authors have read and agreed to the published version of the manuscript.  
This research is based on the Applied Scientific Research under the SPbSUT state assignment 2021.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 4 Simulation and Analysis  
In conclusion, we have designed a protocol stack that is suitable for a space-based optical backbone network. Next, we simulated it in a simulation software and tested it in a hardware platform, before analyzing the results. Most of the systems used in current satellites of space-based optical backbone networks are based on VxWorks. So, in order to be as close to the actual space environment as possible, we used VxWorks as the system to run the protocol stack, both in the simulation software and hardware platform.
## 3 Life Evaluation for High-Temperature Steam Pipes  
### Creep Life Prediction  
Many methodologies are based on time-temperature parameters that relate the stress and temperature to time-to-failure. One advantage of using time-temperature parameters is the possibility to create master curves, which allows easy interpolation and extrapolation of data. In this paper, the Larson-Miller parameter was used to calculate the creep life for the high-temperature piping systems. The Larson-Miller parameter (LMP) is the most universally used parameter today to assess creep damage. As shown below, creep damage is a function of temperature, stress, and time. Creep damage was calculated by curve fitting through the Larson-Miller parameter using the National Institute for Materials Science (NRIM) creep test data [37]. The NRIM test results for calculating creep and the LMP curve fitting equation and graph are shown in Figure 4. LMP is a function of temperature and design life, and it can be expressed as follows:  
\[LMP=(T+273)(C+\log t_{r})=B_{0}+B_{1}(\log\sigma)+B_{2}(\log\sigma)^{2} \tag{3}\]  
Figure 4: NRIM test results for creep life prediction: (**a**) Larson–Miller parameter, and (**b**) cumulative failure probability curve of P91 [37].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Location** & **Types** & **3 DDMS** & **TRD** & **Allowable** & **Maximum** \\  & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **Stress (MPa)** \\ \hline
1 & & 112.35 & & 95.57 & 112.35 \\
2 & & 94.76 & & 95.11 & 99.47 \\
3 & & 49.39 & & 93.24 & 99.47 \\
4 & & 45.64 & & 93.71 & 99.47 \\
5 & & 76.96 & & 95.57 & 99.47 \\
6 & & 72.40 & & 95.11 & 99.47 \\
7 & & 58.84 & & 95.57 & 99.47 \\
8 & & 13.69 & & 94.18 & 99.47 \\ \hline H-joint & T-piece & 113.76 & 103.91 & 96.47 & 113.76 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the stress calculation evaluated by three different methods.  
In the case of P91, the LMP is presented in Table 6. From the estimated LMP, the creep rupture time, which is also called the creep design life, is expressed as:  
\[t_{r}=10^{(\frac{1000\text{LMP}}{T+273}-C)} \tag{4}\]  
Creep life, which is also called the creep life consumption rate \(\phi_{creep}\), is calculated as follows:  
\[\phi_{creep}=\frac{t_{op}}{t_{r}} \tag{5}\]  
where \(t_{op}\) and \(t_{r}\) indicate the operating hours and the creep rupture time, respectively. Finally, the maximum creep life among the three results with the 3 DDMS, TRD, and allowable stress methods was selected and used to calculate the total life of the target component, i.e., the main steam piping.
## 3 Results and Discussion
### The Composition and Changes of VOCs in the Ripening Process of _R. roxburghii_ Fruit
#### 3.2.3 Dynamic Changes of Volatile Esters during the Ripening Process of _R. roxburghii_  
Esters in fruits are generally derived from straight or branched-chain carboxylic acid esters originating from fatty acid and amino acid pathways [43]. As illustrated in Figure 2, there were notable differences in the accumulation of esters in _R. roxburghii_ at various ripeness levels. The ester content at stage I was significantly lower than that in the other four stages. The highest ester content was observed at stage II, measuring approximately 283.97 mg/kg, which was about five times greater than the content at stage I (52.69 mg/kg). Additionally, the proportion of esters at stage II was the largest, accounting for around 66%, whereas the proportion at stage V was the lowest, at approximately 10%. The accumulation of esters in the _R. roxburghii_ fruit ripening process contrasted with the accumulation of aldehydes, and this finding aligned with the conclusions of [PERSON], [PERSON] et al. [49], who noted similar variations during the ripening of strawberries. In the fatty acid pathway, unsaturated fatty acids, such as linoleic and linolenic acids, can be converted by lipoxygenase (LOX) and hydroperoxide lyase (HPL) to form aldehydes, which are subsequently reduced by alcohol dehydrogenase (ADH) to yield the corresponding alcohols. These alcohols then undergo acylation to form esters, a process catalyzed by alcohol acyltransferase (AAT) [50; 51]. As shown in Table 1, hexyl acetate was detected at all stages, likely resulting from the esterification of acetic acid with hexanal. (Z)-3-hexenyl acetate was the most abundant ester, derived from (3E)-3-hexen-1-ol, which involves a series of reactions, including isomerization, acyltransferase activity, and the involvement of acetyl coenzyme A [8; 52]. This indicates that hexyl acetate and (Z)-3-hexenyl acetate are the primary components contributing to the aroma of _R. roxburghii_ esters.