esa-sceva/satcom-chunk-collection huggingface dataset:
## Nomenclature  
Indices and Sets  
\(t\) & 5-min time step in \(T\).  
\(T\) & Set of time steps.  
\(Off\)_peak_\(\subset\)\(T\) & Subset of off-peak period.  
\(Peak\)\(\subset\)\(T\) & Subset of peak period.  
Parameters  
\(smp_{t}\) & System marginal price at time, \(t\), (Won/kW-5 min).  
\(REC_{t}\) & Price of renewable energy certificate at time, \(t\) (Won/kW-5 min).  
\(P_{t}^{Wind}\) & Wind farm power generation at time, \(t\) (kW-5 min).  
\(CapWind\) & Capacity of the wind farm (kW).  
\(o^{Var}\) & Variation criteria of the point of common coupling (PCC).  
\(Cap^{ESS}\) & Capacity of the energy storage system (kWh).  
Appendix A The Linearization Process of Nonlinear Constraints for Determining the Surplus Variable for Power Exceeding Upper-Lower PCC Variation Criteria  
Equations (13) and (14) show the nonlinear constraints that determine the amount of power exceeding the upper and/or lower variation criteria. The right-side variables of these equations can be expressed as follows:  
\[\left\{\begin{array}{ll}P_{t}^{Excess}\geq P_{t}^{wind}-P_{t-1}^{PCC}-P_{t}^{ th}-\sigma^{\text{var}}\times Cap^{Wind}-\left(1-\delta_{t}^{Excess}\right)\cdot Z &t=2,3,\cdots,t^{max},\\ P_{t}^{Excess}\leq P_{t}^{wind}-P_{t-1}^{PCC}-P_{t}^{th}-\sigma^{\text{var}} \times Cap^{Wind}+\left(1-\delta_{t}^{Excess}\right)\cdot Z&t=2,3,\cdots,t^{max}, \end{array}\right. \tag{13}\]  
\[\left\{\begin{array}{ll}P_{t}^{Excess}\geq-\delta_{t}^{Excess}\cdot Z&t=2,3, \cdots,t^{max},\\ P_{t}^{Excess}\leq\delta_{t}^{Excess}\cdot Z&t=2,3,\cdots,t^{max},\end{array}\right. \tag{14}\]  
\[\left\{\begin{array}{ll}P_{t}^{Short}\geq P_{t}^{wind}-P_{t-1}^{PCC}+P_{t}^{ th}+\sigma^{\text{var}}\times Cap^{Wind}-\left(1-\delta_{t}^{Short}\right)\cdot Z &t=2,3,\cdots,t^{max},\\ P_{t}^{Short}\leq P_{t}^{wind}-P_{t-1}^{PCC}+P_{t}^{th}+\sigma^{\text{var}} \times Cap^{Wind}+\left(1-\delta_{t}^{Short}\right)\cdot Z&t=2,3,\cdots,t^{max},\end{array}\right. \tag{15}\]  
\[\left\{\begin{array}{ll}-P_{t}^{Short}\geq-\delta_{t}^{Short}\cdot Z&t=2,3, \cdots,t^{max}.\end{array}\right. \tag{16}\]  
For the linearization process, either \(\delta_{t}^{Excess}\) or \(\delta_{t}^{Short}\) is equal to 0 if the power deviation at the PCC, expressed by \(\left(P_{t}^{wind}-P_{t-1}^{PCC}-P_{t}^{th}\right)\) or \(\left(P_{t-1}^{PCC}+P_{t}^{th}\right)\), does not exceed the variation criteria, expressed by \(\left(\sigma^{\text{var}}\cdot Cap^{Wind}\right)\). Then, the variable for the excessive power (\(P_{t}^{Excess}\) or \(P_{t}^{Short}\)) is 0, as shown in Equations (14) and (15). Otherwise, either \(\delta_{t}^{Excess}\) or \(\delta_{t}^{Short}\) is equal to 1. The power variable has a non-zero value as shown in Equations (13) and (15).
# Analytical Framework of CP-Free Multiuser OFDM System for Coordinated Multi-Point at mmWave
## 2 System Model  
### Scenario Descriptions  
A scenario of CoMP-JT-based mmWave signal transmission is presented in Figure 2, where two base stations (BSs) and four users are considered. Each BS and user is equipped with multiple antennas. Here, CoMP communication is chosen to combat some of the key challenges associated with mmWave signal-transmission-like severe path loss, the sensitivity of blockage, directivity, and narrow beamwidth [24].
# Fluorescence Hyperspectral Imaging for Early Diagnosis of Heat-Stressed Ginseng Plants
## 5 Conclusions  
In the present study, hyperspectral fluorescence imaging in the spectral range of 400-1000 nm was used to detect and predict relative chlorophyll composition in sensitive and heat-resistant ginseng plants. ANOVA-determined wavelengths of 683 nm and 731 nm were used for the generation of a ratio image that was completed with 98% discrimination accuracy. Furthermore, individual quantitative PLSR models with appropriate preprocessing approaches were established based on the full-band spectral data, yielding good results with a coefficient of R\({}^{2}\)val = 0.90 and a RMSEV = 3.59%. Additionally, using stepwise regression (SR) analysis on the observed bands resulted in a R\({}^{2}\)val of 0.81 and a RMSEV of 4.74%. By applying the obtained beta coefficient in the PLSR regression model, chemical visualization maps were generated to predict the level of chlorophyll composition relative to each pixel in the images. This chemical map created with a pixel-wise approach helped to evaluate the greenness level or relative chlorophyll concentration patterns of the control, sensitive, and resistant plants. The sensitive ginseng plants were affected more strongly than the resistant varieties, and the same pattern was observed in the PLS image of each variety. These findings indicate that the observed influential bands by ANOVA and SR could be applicable for developing low-cost and portable instruments for the early-stage evaluation of ginseng plants.  
Conceptualization, B.-K.C. and E.P.; methodology, M.A.F.; software, M.S.K. and I.B.; validation, E.P., M.A.F. and R.J.; formal analysis, M.A.F. and J.K.; investigation, E.P.; resources, B.-K.C., T.K. and M.A.F.; data curation, E.P.; writing--original draft preparation, M.A.F.; writing--review and editing, B.-K.C.; visualization, M.A.F. and E.P.; supervision, B.-K.C.; project administration, B.-K.C.; funding acquisition, B.-K.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry (IPET) through the Smart Farm Innovation Technology Development Program, funded by the Ministry of Agriculture, Food, and Rural Affairs (MAFRA) (421030-04).  
Not applicable.  
The authors declare no conflict of interest.
# Risk Assessment of Landfalling Tropical Cyclones in China Based on Hazard Risk Theory
## 3 Results and Discussion  
### Analysis of the Characteristics of Tropical Cyclone Hazards in China  
#### 3.1.1 Temporal Distribution Characteristics of Landfalling Tropical Cyclones  
A polynomial fit was applied to the tropical cyclone landfall data from 1949 to 2023, with the comparative data shown in Figure 4a. The data indicate that the frequency of tropical cyclone landfalls in China exhibited an oscillatory pattern. Over the 75-year period,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Primary Index** & \begin{tabular}{c} **Single Sort** \\ **Weight** \\ \end{tabular} & \begin{tabular}{c} **Positive/Negative** \\ **Indicators** \\ \end{tabular} & **Secondary Index** & \begin{tabular}{c} **Single Sort** \\ **Weight** \\ \end{tabular} & \begin{tabular}{c} **Positive/Negative** \\ **Indicators** \\ \end{tabular} & \begin{tabular}{c} **Total Sort** \\ **Weight** \\ \end{tabular} \\ \hline \begin{tabular}{c} The Hazard of \\ Hazard-Causing \\ Factors \\ \end{tabular} & 0.67 & Positive & \begin{tabular}{c} Average Daily Wind \\ Speed \\ \end{tabular} & 0.19 & Positive & 0.13 \\ \cline{2-6} \begin{tabular}{c} The \\ Hazard-Causing \\ Factors \\ \end{tabular} & 0.67 & Positive & \begin{tabular}{c} Average Daily \\ Maximum Wind \\ Speed \\ \end{tabular} & 0.13 & Positive & 0.09 \\ \cline{2-6} \begin{tabular}{c} Sensitivity of \\ Natural Carriers \\ \end{tabular} & 0.33 & Positive & \
\begin{tabular}{c} Terrain Elevation \\ \end{tabular} & 0.83 & Negative & 0.28 \\ \cline{2-6}
\begin{tabular}{c}  Natural Carriers \\ \end{tabular} & 0.33 & Positive & Vegetation Coverage & 0.17 & Negative & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selection of Evaluation Indicators and Weight Calculation.  
a total of 661 tropical cyclones made landfall, with an average of 8.8 landfalls per year. The overall trend shows a fluctuating decline around this average frequency, with an oscillation period of approximately four years. Since 1986, the frequency of tropical cyclone landfalls in China has generally remained below the average. Between 2011 and 2023, except for 2013 and 2018 (which saw 10 and 12 landfalls, respectively), the number of landfalling tropical cyclones each year ranged from 6 to 9. This result is consistent with previous research on historical tropical cyclone landfalls [33].  
A monthly frequency analysis of tropical cyclone landfalls in China from 1949 to 2023 is shown in Figure 4b. The frequency distribution indicates that landfalls were concentrated between June and October, with August having the highest number of landfalls at 189, representing 28.0% of the total; July follows with 164 landfalls, accounting for approximately 24.2% of the total. Therefore, from a seasonal perspective, there are no tropical cyclone landfalls in China during the winter. Tropical cyclones predominantly make landfall in summer and autumn, which are the peak seasons for tropical cyclone hazards, necessitating enhanced risk prevention measures for such events.
###### Abstract  
Lipids play an essential role in providing energy and other physiological functions for insects. Therefore, it is important to determine the composition of insect lipids from cuticular and internal tissues for a better understanding of insect biology and physiology. A novel non-derivatization method for the analysis of lipids including fatty acids, hydrocarbon waxes, sterols in _Tribolium castaneum_ ([PERSON]) and _Rhyzopertha dominica_ ([PERSON]) was explored using the direct immersion solid-phase microextraction (DI-SPME) coupled with gas chromatography-mass spectrometry (GC-MS). Nine extraction solvents, acetonitrile, methanol, hexane, ethanol, chloroform, acetonitrile and ethanol (1:1 _v/v_), acetonitrile and water (1:1 _v/v_), ethanol and water (1:1 _v/v_) and acetonitrile and ethanol and water (2:2:1 _v/v/v_) were selected and evaluated for the extraction of insect lipids with DI-SPME fiber. Acetonitrile extraction offered the best qualitative, quantitative, and number of lipids extracted from insects samples results. Acetonitrile extracted high-boiling point compounds from both species of tested insects. The range of hydrocarbons was C25 (pentacosane) to C32 (dotriacontane) for _T. castaneum_ and C26 (11-methylpentacosane) to C34 (tetratriacontane) for _R. dominica_. The major compounds extracted from the cuticular surface of _T. castaneum_ were 11-methylheptacosane (20.71%) and 3-methylheptacosane (12.37%), and from _R. dominica_ were 10-methyldotriacontane (14.0%), and 15-methyltritriacentane (9.93%). The limit of detection (LOD) for the n-alkane compounds ranged between 0.08 (nonacosane) and 0.26 (dotriacontane) \(\mu\)g/g and for the fatty acids between 0.65 (arachidic acid) to 0.89 (oleic acid) \(\mu\)g/g. The study indicated that DI-SPME GC-MS is a highly efficient extraction and a sensitive analytical method for the determination of non-derivatized insect lipids in cuticular and homogenized body tissues.  
Keywords:DI-SPME; GC-MS; insect lipids; insect hydrocarbons; _T. castaneum; R. dominica_
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 4 Experimental Method
### Metrics  
The peak signal-to-noise ratio (_PSNR_) is typically used to assess the quality of an image transmitted over a network. As image quality is assessed quantitatively, this based on the difference between the pixels of the image reconstructed following transmission and the original image [24].  
The _PSNR_ of a transmitted image can be calculated as  
\[PSNR=10\log\frac{S^{2}}{MSE} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline _BW_ & _SF_ & **PT** & **CR** \\ \hline
125 kHz & From 7 to 12 & 14 & 4/8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Settings of the LoRa physical layer during testing.  
where, for an 8-bit image, \(S\) is 255 and \(MSE\) is the mean squared error, which is the average of the squared difference in the intensity pixels in the original image and the output image [24]. This is calculated as  
\[MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=1}^{n-1}[I(i,j)-K(i,j)]^{2} \tag{4}\]  
where \(m\) and \(n\) are the respective length and width of the image in pixels \(I(i,j)\) and \(K(i,j)\) are functions describing the intensity of individual pixels in the transmitted and received image, respectively.  
Studies comparing reconstructed images with original images using PSNR and MSE have reported that these two metrics are not sufficient for image quality assessment. It is advised that another metric called the'structural similarity index' (SSIM) is used in addition to these, to measure the degree of similarity between the original and the transmitted image. Hence, in testing the ability of LoRa to transmit images using the proposed method, we used these three metrics to define the transmission quality.
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 5 Performance Measures  
This section discusses the various performance measures used for chaotic time series forecasting approaches. According to the literature review summary in Table 1, it can be concluded that there are many approaches for chaotic time series forecasting. However, it is challenging to choose one proposed method that performs better based on the performance measures. Table 1 also shows that the researchers have evaluated the performance of the forecasting approach using various statistical errors. The different classifications of statistical performance measures used for chaotic time series forecasting are mean, relative, percentage, prediction, and coefficients. The classification and its subcategories are shown in Figure 6. The formula for computing these performance measures is demonstrated in Figure 7. In Figure 7, \(n\) denotes the number of samples, and \(Y_{a,i}\) and \(Y_{p,i}\) are the actual and predicted outputs by the chaotic time series forecasting model. Further, \(\bar{Y}_{a}\), and \(\bar{Y}_{p}\) are the averages of \(Y_{a,i}\) and \(Y_{p,i}\).  
As shown in Figures 6 and 7, most of the performance measures used for chaotic time series forecasting are mean errors. Further, the review summary in Table 1 shows that MSE and its variants are the most widely used performance measures for chaotic time series forecasting. The MSE and its variants measure the error between \(Y_{a}\) and \(Y_{p}\), and the closest value to zero indicates a better estimation of the forecasting approach [186; 187]. After mean errors, the percentage errors are the second most used performance measure for chaotic time series forecasting. The percentage errors measure the percentage error between \(Y_{a}\) and \(Y_{p}\). The closer values of percentage error to zero also indicate a better estimation of the forecasting approach. On the other hand, the coefficient of determination R\({}^{2}\) is most widely used to indicate the forecasting approach's predictive ability in fitting the actual  
Figure 6: Classification of various performance measures used for chaotic time series forecasting.  
Figure 7: Formula for computing the various performance measures.  
data \(Y_{a}\)[188; 189]. Thus, the values of R\({}^{2}\) range from zero to one, and the value equal to 1.0 indicates a perfect fit. The summary in Table 1 also shows that most of the researchers used a combination of different performance measures for evaluating the forecasting approach. The combinations are MAE, MAPE, and RMSE; MSE, MAPE, and RMSE; MAE and RMSE; MSE and RMSE; R\({}^{2}\) and MSE; R and MSE, etc.
# Ultra-Low Interfacial Tension Foam System for Enhanced Oil Recovery
## 3 Results and Discussion
### Foaming Properties and Oil-Liquid IFT of Binary Surfactants  
Corrosion and blockage can be caused by chemical reactions between cationic surfactants and the formation. Nonionic surfactants often have weak solubility. Therefore, anionic and amphoteric surfactants were used for compounding in this study. Based on the results shown in Table 7, THPB, DHPB, AOS, AES and SDS were selected for compounding. The compounding scheme and test results are shown in Table 8. The total concentration of each surfactant was 0.3 g/L.  
The compound of DHPB and SDS (mass ratio: 1.5:1 to 1:1.5) had both an ultra-low oil-liquid IFT and high foaming properties (Table 8). A strong electrostatic attraction existed between the positive charge of the hydrophilic group of DHPB and the negative charge of the hydrophilic group of SDS [23]. This attraction impacted on the diffusion behavior of surfactant molecules at the interfaces and made foam generation easier. Meanwhile, electrostatic attraction facilitated the formation of a tight adsorption membrane at the interface, which led to higher foam stability and lower oil-liquid IFT.  
According to the results shown in Table 8, the compounding of surfactants with the same carbon number showed a better synergistic effect. Because the carbon number of the surfactant molecule could affect the diffusion rate of the surfactant, the diffusion of surfactant molecules with the same carbon number may be more balanced, and the arrangement of the surfactant molecules on the interfaces more even. This ensures the adsorption of two surfactants on the interfaces and leads to two surfactants with similar diffusion rates. Thus, local IFT imbalance was minimized.
## 1 Introduction
### Low-Area Hardware Implementations with Limitations
Instead of the ECC-based PKC schemes, NIST is progressing with a quantum-resistant cryptographic protocols contest to standardize new standards for securing future communications [20]. Even if we are not dealing with quantum-resistant algorithms in this work, it is essential to highlight that the respective community is progressing well in this domain, and it seems to be a part of the systems being developed shortly. Some recent examples include quantum processors designed by IBM [21] and Google [22]. Another example consists of an experimental quantum secure network presented in [23], where digital signatures and encryption operations are operated at the same time. Moreover, from a hardware perspective, a systematic study of several quantum algorithms is performed in [24; 25], where the cost of various building blocks is evaluated. Although several low-area realizations of ECC PM designs have already been reported [15; 18], these hardware architectures utilize lower binary field 163 bit key length. Other low-area PM designs of [12; 13; 14; 16; 17] are impressive, but they consider different design parameters (such as throughput and area) for realizations at the same time. Although flexibility allows multiple cryptographic algorithms to operate in a same design as implemented in [18], the design uses considerable hardware resources and also reduces the performance. Therefore, this work aims to realize area-efficient implementation of PM computation of ECC for various constrained cryptographic applications.
## 5 Discussion
cious cases that could be considered benign. Therefore, a higher recall (i.e., sensitivity) of malicious cases is prioritized. The Transformer model achieved the highest recall of 87.80% with a strong balance between minimizing false negatives and false positives. Overall, when we take account of the recall and F1 score of malicious cases, the optimal model achieved 87.80% and 92.54%, respectively, using the MalCode2 Vec embedding at the word level. We also observed a lower rate of false negatives compared with the model using integer encoding. The proposed model can be applied to PDF malware. A PDF file consists of four main sections: Header, Body, Cross-Reference table, and Trailer. Header contains the document version information in 8 bytes. Body comprises objects that contain the actual document information. The object types include the Boolean value that presents TRUE or FALSE, elements of an array, or entries in a dictionary, an array that can hold a combination of various types of objects, a dictionary that consists of key-value pairs, and a stream. The stream means a sequence of bytes (binary data) with no length restrictions, typically used for large image files or page composition objects. It is composed of a dictionary object, followed by the keyword'stream' and 'endstream', enclosing multiple bytes. Malicious JavaScript can be encoded and embedded within a PDF stream. This is similar to how a malicious code is embedded in HWP documents. However, since PDFs use a different syntax, directly applying a model trained on Hangul document malware to PDFs is challenging. Nevertheless, since both document formats follow an OLE-based structure, the approach of identifying streams and analyzing their content remains the same. Figure 15 shows an example of a PDF file that includes objects. In future work, we will build a model that works across malware documents that have an OLE structure, including RTF word, PDF, and so on. Deep learning models are highly susceptible to adversarial examples [38], and even minute alterations can lead to misclassification. While we utilize the SeqGAN model to address the issue of sample imbalance, we believe that incorporating our proposed sample augmentation technique can yield a model that is more robust to such perturbations. Prior research [39] has already demonstrated that adversarial training makes models less sensitive to adversarial examples and improves their generalization capabilities.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed using the open-source SciPy v1.2.1. software [28]. Parametric and non-parametric data are expressed as mean \(\pm\) standard deviation (SD) or median (Q1-Q3), respectively. The Shapiro-Wilk test was used to verify the normality of the data. To assess the intra- and interobserver reproducibility, Bland-Altman analysis was performed, and the coefficient of variation (COV), Spearman rank correlation coefficient (r), and intraclass correlation coefficient (ICC) (two-way mixed effects, absolute agreement) were calculated. For the Bland-Altman analysis, the mean difference and limits of agreement (LoA = 1.96 \(\times\) SD) were computed [29]. In addition, the relative difference in LoA between the training and validation cohort was calculated (LoA\({}_{\text{diff}}\) = ((validation LoA-training LoA)/training LoA) \(\times\) 100%). The COV was classified as low (\(\leq\)10%), intermediate (11-20%), high (21-30%), or very high (>30%). The r and ICC were classified as poor (<0.50), moderate (0.50-0.69), good (0.70-0.84), very good (0.85-0.94), or excellent (\(\geq\)0.95) [25]. A \(p\)-value of <0.05 was considered statistically significant.