esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Validation
### Experimental Results of Related Algorithms
#### 5.2.3 Pulse Type Data Generation  
For pulse type data, the four comparison algorithms use SGD as the optimizer. As shown in Figure 9, compared with DCGAN, Bilstm-CNN GAN and TimeGAN, which have better effects in image generation and time-series data generation, the algorithm in this paper is more similar to the original data in terms of the change trend and jump position of the generated data in terms of the ladder type characteristics of the negative sample of the industrial control underlying business data.  
In addition, as shown in Table 3, this paper expands all the features of the three datasets and compares the similarity of the five algorithms on each dataset by calculating Manhattan distance.  
As shown in Table 3, the evaluation standard of the algorithm is the average value of each characteristic Manhattan value in the dataset. Smaller Manhattan values indicate more similarity to the original dataset. In the expansion of sample data of industrial control underlying business data attack, the effect of this algorithm is better than the other four comparison algorithms.  
Figure 8: (**a**) Original data of the 1 filterdifferentialpressure feature of the real underlying business dataset of the oil depot; (**b**) generation results of the algorithm in this paper; (**c**) generation results of GAN; (**d**) generation results of DCGAN; (**e**) generation results of BiLSTM-CNNGAN; (**f**) generation results of TimeGAN.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Names** & **Mississippi Gas Dataset** & **Singapore Water Plant Dataset** & **Real Oil Depot Dataset** \\ \hline GAN & 42,160,32 & 23,403,22 & 9228.57 \\ DCGAN & 329,862.87 & 23,509.43 & 5351.57 \\ BiLSTM-CNN & 29,691.46 & 17,290.25 & 5693.17 \\ TimeGAN & 72,557.52 & 57,903.48 & 5550.95 \\
**Ours** & **24,019.34** & **14,213.40** & **4886.39** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of similarity between generated data and seed data (Manhattan distance).
## 1 Introduction
### Research Significance  
With the rapid development of traffic data collection technology, traffic data collection methods have become increasingly diversified, and both the types and volume of traffic data have been greatly enriched. Supported by abundant traffic data, the entire traffic control field has evolved from the data-sparse era to the data-rich era. Control methods are gradually transitioning from statistical data-based modeling approaches to data-driven directions. Model-free intelligent control methods have naturally become a hot topic in the field. Therefore, more and more scholars are beginning to abandon traditional thinking and address a series of traffic control problems within unified intelligent control algorithm frameworks, bringing unprecedented possibilities to traffic control.  
Vehicle trajectory data, represented by GPS-equipped taxis, ride-hailing vehicles, connected and automated vehicles, and digital map navigation vehicles, serves as an emerging traffic data source with incomparable advantages over traditional detector data. The complete physical trajectory of vehicles during travel not only reflects the vehicle's path on the road network but also reveals the spatiotemporal variation characteristics of vehicle speed. It represents the most comprehensive and complete expression of traffic flow operational status, containing extremely rich traffic flow information such as travel time and speed, queue length, delay, and multiple stops. This information holds significant value for understanding both microscopic and macroscopic traffic flow characteristics and optimizing signal control strategies.  
However, trajectory data is essentially sampled data, characterized by limited penetration rates, varying data quality, and uneven spatiotemporal coverage. How to accuratelyextract traffic parameters from limited trajectory samples and effectively apply these parameters to signal control optimization is a technical problem with significant theoretical and practical value. Particularly in the context where traditional traffic detection equipment yields poor data quality at high costs, traffic parameter extraction methods based on trajectory data provide a new technical pathway for traffic signal control optimization.
# RFID Based Embedded System for Sustainable Food Management in an IoT Network Paradigm
## 3 RFID Food Management System
### RFM App
In addition, nutritional information such as energy, proteins, carbohydrates, and fat, is included, it refers to 100 g of product (8). Besides product information, recipe and offer menus (c.f. Figure 4) are described. Figure 5 presents screenshots from the recipe menu, in Figure 4(a)-c and the offer menu in Figure 4(d). Figure 4(a) shows the main section from the recipe menu, the presentation Table (1). The recipe option is also composed of three other tabs: Ingredients (2), recipe preparation text (3), and information (4). The presentation tab also displays a picture of the recipe (5). Figure 4(b) introduces the ingredients tab, providing information about the needed products to prepare the recipe. The ingredients tab displays the quantity of ingredients that is needed, depending on the number of people (1). Ingredients are shown as a list with different background colors: Red for non-available ingredients (2), red with a yellow border for non-available ingredients with available supermarket offers (3), and green for available ingredients (4). Note, that the ingredient can also change its availability (from green to red) depending on the number of portions we want to prepare (1). For instance, if we want to prepare stuffed eggplant with 500 g of chopped meat for four people, the app will color the background in red, because 960 g are needed. Figure 4(c) shows the information tab which explains the nutritional information per portion (1) and allergens (2). The offer menu is shown in Figure 4(d), in which the product name is displayed (2), together with the price per unit (1). Moreover, the user can access to the offer by clicking on the product name (2). Figure 3: Main page and registration form. Figure 4: The user can access many options from the main menu. Figure 5: _Cont_. Note, that all images shown above were captured from different devices. Thus, Figures 2(a),2(b) and 3(a) were captured from a personal computer. Figure 3(b) captured from a tablet with Android. Figure 4(a),3(c) from a smartphone with iOS, and Figure 4(b),4 from a smartphone with Android. We decided to insert captures from different devices to show how RFM App adapts to any type of device and platform.
# Characterization of the Aroma Profile of Food Smoke at Controllable Pyrolysis Temperatures
## 3 Results and Discussion
### Correlations of Different Substance Classes with the Pyrolysis Temperature  
In the next step, correlation analysis was performed after the Pearson test. To the best of our knowledge, there are no comparable studies in food smoke available so far. The results confirmed the observations of the previously performed one-way ANOVA with temperature as a factor variable. The peak areas of the substance classes lactones and phenols and the sum of all target aroma compounds increased significantly with increasing temperature, as indicated by a highly significant, positive linear Pearson correlation (\(r_{\text{T-lactones}}=0.90\), \(r_{\text{T-}phonols}=0.94\), \(r_{\text{T-}total}=0.92\); \(p<0.01\) for all) (Figure 3). The substance class of carbonyls showed a medium positive, linear Pearson correlation with increasing  
Figure 2: Food smoke composition at different wood pyrolysis temperatures (\(T_{p}\)) represented as relative peak areas [%] from individual substance classes. (1) \(T_{p}=150\) \({}^{\circ}\)C, (2) \(T_{p}=300\) \({}^{\circ}\)C, (3) \(T_{p}=450\) \({}^{\circ}\)C, (4) \(T_{p}=600\) \({}^{\circ}\)C, (5) \(T_{p}=750\) \({}^{\circ}\)C, (6) \(T_{p}=900\) \({}^{\circ}\)C.  
temperature (\(r_{T\_carbonyls}=0.59\), \(p=0.05\)). Only for the substance class of furans, no linear relationship with temperature was found. Observing the correlation of the individual substance classes with each other, a highly significant, positive linear correlation is found between all substance classes, apart from furans. These showed no linear correlation with any other substance class.  
Knowing these temperature dependencies is helpful in developing products with desired flavors. Higher pyrolysis temperatures should be used for products with stronger smoky and spicy notes coming from increased phenol levels. Products with a strong smoky note should therefore be smoked at higher temperatures to obtain the desired smoky and spicy notes form the phenols. For samples in which an additional sweetish or even buttery/oily flavor note is present, it is better that they are smoked at lower temperatures to use the higher relative shares from lactones and carbonyl. However, the effect of the smoke on the aroma of the final product still needs to be evaluated.  
Figure 3: Correlation matrix between pyrolysis temperature and substance classes obtained via [PERSON]’s linear correlation analysis; total = sum of all target aroma compounds. Significance levels: *_p_ \(<\) 0.05, **_p_ \(<\) 0.01, ***_p_ \(<\) 0.001.
## 4 Training Method of COMA Policy Network Based on Differential Evolution
### Optimization of COMA Policy Network with DE
#### 4.2.1 Individual Encoding  
In DE-COMA, population individuals serve as the smallest units for operations.  
\[x_{i}=\{\text{fc}_{1}\text{weight},\text{fc}_{1}\text{bias},\text{rnn}_{1} \text{weight},\text{rnn}_{1}\text{bias},\text{rnn}_{2}\text{weight},\text{rnn}_{2} \text{bias},\text{rnn}_{3}\text{weight},\text{rnn}_{3}\text{bias},\text{fc}_{2} \text{weight},\text{fc}_{2}\text{bias}\} \tag{2}\]  
The individual coding information is given in Table 1, and the individual coding is as Formula (2). In Figure 4, the role of the CONVERTER is to transform each row of the strategy network listed in Table 1 into an individual in the population during the evolution process. On the left side of the CONVERTER is the representation of the network structure of the strategy network, while on the right side is a visual representation of an individual. The content of each row in Table 1 corresponds to one dimension within the individual. In summary, the CONVERTER encodes the strategy network into individuals, which then form a population to facilitate multi-agent reinforcement learning training.  
In Table 1, Index 1 (fc1 weight) and Index 2 (fc1 bias) represent a set of parameters for a fully connected layer, i.e., weights and biases, mapping the input features to the hidden layer. Indices 3-8 pertain to a gated recurrent unit (GRU) [27], a type of gated recurrent neural network cell that involves three weight matrices corresponding to the reset gate, update  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Num** & **Definition** & **Size (frow, cell)** & **Symbol** \\ \hline
1 & fc1.weight & [input\_shape,rnn\_hidden\_dim] & fc1 weight \\
2 & fc1.bias & [rnn\_hidden\_dim] & fc1 bias \\
3 & reset\_gate\_weight & [rnn\_hidden\_dim] & rnn1 weight \\
4 & reset\_gate\_bias & [rnn\_hidden\_dim] & rnn1 bias \\
5 & update\_gate\_weight & [rnn\_hidden\_dim, & rnn2 weight \\
6 & update\_gate\_bias & [rnn\_hidden\_dim] & rnn2 bias \\
7 & con\_hidden\_status.weight & [rnn\_hidden\_dim] & rnn3 weight \\
8 & con\_hidden\_status.bias & [rnn\_hidden\_dim] & rnn3 bias \\
9 & fc2.weight & [rnn\_hidden\_dim, n\_actions] & fc2 weight \\
10 & fc2.bias & [n\_actions] & fc2 bias \\ \hline \hline \end{tabular}
\end{table}
Table 1: Individual coding information.  
Figure 4: Policy network coding method.  
gate, and calculation of the candidate hidden state. Specifically, Index 3 (rnn\({}_{1}\)weight) and Index 4 (rnn\({}_{1}\)bias) represent the weights and biases for the reset gate, Index 5 (rnn\({}_{2}\)weight) and Index 6 (rnn\({}_{2}\)bias) those for the update gate, and Index 7 (rnn\({}_{3}\)weight) and Index 8 (rnn\({}_{3}\)bias) the weights and biases related to the candidate hidden state. Index 9 (fc\({}_{2}\)weight) and Index 10 (fc\({}_{2}\)bias) similarly denote the weights and biases for another fully connected layer, which maps the hidden state to the probability distribution of the output actions.
# Point-Kernel Code Development for Gamma-Ray Shielding Applications
## 3 Gamma Flux Solution for Simple Source Geometries
### Spherical Surface Gamma Source  
The gamma flux received from a spherical surface source at point detector can be interpreted as a response from two infinite gamma planes. Figure 3 shows a geometrical arrangement, where surface gamma source of radius \(r\) and specific strength of \(S_{A}\) (\(\gamma/\mathrm{cm}^{2}/\mathrm{s}\)) is surrounded inside and outside by uniform absorption medium. The radius of surface annulus is \(r\cdot\sin\theta\) and width is \(r\cdot d\theta\), so surface element \(dA\) becomes \(2\pi r^{2}\sin\theta d\theta\), allowing the calculation of differential flux \(d\phi\) by PK definition:  
\[d\phi=(S_{A}\cdot dA)K(R)=2\pi S_{A}r^{2}\sin\theta d\theta\cdot K(R) \tag{21}\]  
The total flux received at point detector is equal to integrating the total surface of the sphere, and by changing integration variable from \(\theta\) to \(R\), one obtains detector response as a difference of infinite planes located at \((r_{0}-r)\) and \((r_{0}+r)\) as follows:  
\[\phi=\frac{r}{r_{0}}\left[2\pi S_{A}\int\limits_{r_{0}-r}^{\infty}RK(R)dR-2\pi S _{A}\int\limits_{r_{0}+r}^{\infty}RK(R)dR\right] \tag{22}\]  
For practical applications, the analytical form of kernel function \(K(R)\) must be known to proceed with integration. Using the previous results of infinite gamma plane and Taylor \(B\)-factor form, one can derive expressions for uncollided and buildup gamma flux as  
\[\phi_{u}=\frac{r}{r_{0}}\cdot\frac{S_{A}}{2}[E_{1}(\mu(r_{0}-r))-E_{1}(\mu(r_{ 0}+r))], \tag{23}\]  
\[\phi_{b}=\frac{r}{r_{0}}\cdot\frac{S_{A}}{2}\left\{\begin{array}{l}A[E_{1}( \mu(r_{0}-r)(1+\alpha_{1}))-E_{1}(\mu(r_{0}+r)(1+\alpha_{1}))]+\\ (1-A)[E_{1}(\mu(r_{0}-r)(1+\alpha_{2}))-E_{1}(\mu(r_{0}+r)(1+\alpha_{2}))]\end{array} \right\}. \tag{24}\]  
The modification of this geometry by inserting a large slab shield leads to a useful practical result: the sphere is now filled with a \"black\" (or opaque) material for the gamma radiation, while the shield thickness is \(b\) with the linear attenuation coefficient \(\mu\). The point detector is placed on the opposite side of the slab and has distance of \(r_{0}\) from the center of the source sphere. If the geometry arrangement fulfills the condition \(r/r_{0}\ll 1\), meaning the surface source is point-like, the uncollided gamma flux becomes approximately  
\[\phi_{u}=\frac{S_{A}}{2}\bigg{(}\frac{r}{r_{0}}\bigg{)}^{2}e^{-\mu b}. \tag{25}\]  
The buildup flux can be calculated by merely multiplying this result by Taylor or alternatively the Berger form of the \(B\)-factor.  
Figure 3: Point detector response from the spherical gamma source [23].
## 5 Numerical Study  
In order to perform a detailed analysis of the goods distribution process and CGA performance in logistic networks, a dedicated, multithreading application has been developed. As the first step in the program, a state-space model was created. The model was parametrized with:  
* A simulation interval.
* The number of nodes.
* The type of inventory control strategy used to steer the goods distribution process.
* The type of statistical distribution used for generating demand requests.  
Once the fitness function coefficients were input, the optimizer sought the best vector of RSLs using either a full search method or a CGA. Owing to the large space of potential solutions, which grows exponentially with the number of nodes involved:  
\[\frac{I_{1}^{r}}{\delta}.\frac{I_{2}^{r}}{\delta}.\ldots.\frac{I_{N}^{r}}{ \delta}, \tag{18}\]  
where \(\delta\) denotes the iteration granularity, the full-search method is feasible only for small networks.
## 2 Materials and Methods
### Analysis of Polycyclic Aromatic Hydrocarbons (PAHs)  
The porewater samples were filtered through a 0.22 um filter membrane (syringe filter, diameter 13 mm, PVDF) which was followed by solid phase extraction (LiChroult RP18(r) 500 mg, 6 mL Merck, Darmstadt, Germany). Related to the concentration of the solvent, the PAHs concentration was measured by Ultra performance liquid chromatography (UPLC) (Waters, Milford, MA, USA) coupled with a photodiode array detector (PDA) and a fluorescence detector for analysis and quantification. The separation was carried out on a UPLC(r) BEH Shield RP18 (2.1 \(\times\) 150 mm, 1.7 um) column, with ultra-pure water and acetonitrile as the mobile phase for gradient elution. For acenaphthylene, the UV detector wavelength was set at 228 nm. The elution conditions to separate the target compounds by fluorescence detection, and method detection limits (MDL) are shown as supplementary material (Tables S1-S3). The linear correlation coefficient (R) for all compounds was >0.995.
# Neural Network-Based Analysis and Its Application to Spectroscopy for Mango
## 3 Conclusions  
This study integrates insights from previous research to develop a predictive model for non-destructive testing of mangoes using near-infrared spectroscopy (NIRS), establishing the correlation between brix levels and spectral data acquired through NIR diffuse reflectance. Under controlled conditions, suitably sized mangoes were selected for experimental data collection. Within the 1300 nm to 2300 nm wavelength range, the BP-PLS mango brix level prediction model exhibits a R-squared prediction set of 0.9055, with a root-mean-square error (RMSE) of 0.2192. The elevated RMSE value suggests potential overfitting issues necessitating further optimization to enhance prediction accuracy. Addressing this concern, this study employs the simulated annealing algorithm for model refinement. The resultant SA-BP-PLS mango brix level prediction model showcases a correlation coefficient of 0.9854 and an improved root-mean-square error of 0.0431, surpassing the performance of the BP-PLS model. This research underscores the efficacy of spectral acquisition technology in conjunction with stoichiometric measurement methods, appropriate spectral systems, and refined spectral pretreatment techniques for mango brix content determination, offering valuable insights for the development of online non-destructive testing instruments.  
Conceptualization, Z.Z. and T.W.; Methodology Z.Z. and H.F.; Formal Analysis, Z.Z. and H.F.; Investigation, T.W. and H.F.; Resources, H.F. and Z.Z.; Data Organization, H.F. and T.W.; Writing--Original Draft Preparation, Z.Z. and T.W.; Writing--Review and Editing, Z.Z., T.W. and H.F.; Visualization, H.F.; Supervision, H.F., Z.Z. and T.W.; Project Management, H.F. and Z.Z.; Funding Acquisition, H.F. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Residual plots of SA-BP-PLS model data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Types** & **Fitting Equations** & **Corrected R Square** &
\begin{tabular}{c} **Root-Mean-Square** \\ **Error** \\ \end{tabular} \\ \hline BP-PLS & Y = 0.9202 + 1.873x & 0.9055 & 0.2192 \\ SA-BP-PLS & Y = 0.7912 + 3.447x & 0.9854 & 0.0431 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of results of the two models.  
**Funding:** This work was supported by the National Students' Innovation and Entrepreneurship Training Program (Project Number: 202311415082).  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** Data used in the manuscript and analytic code will be made available upon request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Field Measurement Analysis
### Comparison of Existing Rail Deformation Results  
In order to more intuitively see the consistency between the monitoring results and the numerical simulation results, the simulated values of the final deformation of the existing magnetic levitation rails in the monitoring range are compared with the measured values, as shown in Figures 19 and 20.  
Figure 19: Comparison between simulation values and monitoring values of the deformation of the existing left track.  
Figure 20: Comparison between simulation values and monitoring values of the deformation of the existing right track.  
From the diagram, it can be seen that the maximum vertical deformation value of the existing left line rail row measured in the field is \(-1.19\) mm and the maximum vertical deformation value obtained by numerical simulation is \(-1.56\) mm; the measured value of the maximum vertical deformation of the right rail is \(-1.22\) mm and the simulated value is \(-1.52\) mm. The deformation simulation value is slightly larger than the field-measured value. The numerical simulation results of the existing magnetic levitation rails are basically consistent with the final vertical deformation trend of the field-measured data. Therefore, it can be considered that both numerical simulation and field monitoring can better reflect the deformation trend of the existing structure.
## 3 Results and Discussion
### Effect of BTH Treatment on the Activities of Key Enzymes of LOX Pathway
#### 3.4.1 C6 Aldehydes  
In BTH-treated and CK grapes, the most abundant C6 aldehyde was free E-2-hexenal, with concentrations ranging from 1852.08 to 3779.17 ug/L. Hexanal was the second most abundant, while Z-3-hexenal was relatively the least abundant (Table S3). Free E-2-hexenal increased sharply before veraison, peaked at end-veraison (week 11), and declined in ripe grapes (Figure 4). Nevertheless, BTH treatment did not change the trend of accumulation of free E-2-hexenal. Specifically, there was a notable 31.83% increase in end-swelling BTH-treated grapes compared to the CK, while ripe grapes (week 15) treated with BTH experienced a significant decrease of 15.80%. The enzyme 3Z,2E-enal isomerase plays a crucial role in converting Z-3-hexenal to E-2-hexenal and is active in grape berries [1]. In this study, a decrease in the concentration of free E-2-hexenal was observed in BTH-treated ripe grapes, which was speculated to be due to the inhibition of 3Z,2E-enal isomerase activity by BTH, which reduced the cleavage of Z-3-hexenal to E-2-hexenal. The concentration of bound E-2-hexenal during grape ripening varied from 3.23 to 95.60 ug/L. Ripe grapes treated with BTH showed a significant reduction of 56.68% in bound E-2-hexenal compared with CK (Table S3). The concentration of free hexanal initially increased and subsequently decreased. BTH treatment resulted in an increase in the concentration of free hexanal during the end-swelling and mid-veraison stages (weeks 7 and 9, respectively). However, during the end-veraison to ripe grape stages (weeks 11-15) the concentration of free hexanal decreased with BTH treatment. Notably, the concentration of free hexanal in BTH-treated mid-ripening grapes (week 13) was significantly reduced by 18.91%. This could be attributed to the inhibitory effect of BTH on 13-HPL activity, resulting in a decrease in the conversion of 13-hydroperoxy-octadecadienoiconic acid to hexanal. The trends of bound and free hexanal were basically the same. Due to the large proportion of E-2-hexenal and hexanal concentration in C6 compounds, changes in the levels of E-2-hexenal and hexanal directly affect the overall aroma of grapes, and the reductions in E-2-hexenal and hexanal directly diminish the green, apple, and fruity aroma of grapes.