esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussions
### Evaluation of the Forecasting Accuracy  
To evaluate the forecasting accuracy, measures of performance such as _MAD_ and _MSE_ are used. In this context Equations (10) and (11) are used to determine the values of _MAD_ and the _MSE_ respectively for all the three cases. As presented in Table 6, the _MAD_ and _MSE_ obtained for the forecast values yielded by the proposed Coll-MHW method outperform all other methods. It is to be noted that _MAD_ and _MSE_ values for Case-1 are evaluated by using the conventional multiplicative Holt-Winters method (CMHW) whereas for Case-2 and Case-3 the values of _MAD_ and _MSE_ are obtained from literature. As stated in the published papers, the forecast values for Case-2 were determined by CMHW and improved multiplicative Holt-Winters method (IMHW), while those for Case-3 were evaluated by ARIMA and collaborative adjusted (Coll. Adj.) models.  
It would also be interesting to demonstrate the forecasting performance of the proposed approach for each of the individual periods of time series. In this respect, Figure 11a-c portray a comparative scenario for the three cases. The comparative scenarios as presented in Table 5 and plots in Figure 11a-c consistently reveal the superior performance of our proposed collaborative approach in comparison to the conventional MHW, improved MHW, ARIMA and Coll. Adj. forecasting models.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Cases**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Forecast Error Obtained**} & \multicolumn{2}{c}{**Forecast Error Obtained**} & \multirow{2}{*}{**Error Reduction**} \\  & & & **by Earlier Method** & & & **by Proposed Coll. MHW** & \\ \cline{3-8}  & & _MAD_ & _MSE_ & _MAD_ & _MSE_ & _MAD_ & _MSE_ \\ \hline
1 & CMHW & 1.71 & 0.65 & 0.27 & 0.01 & 84.21\% & 98.5\% \\ \hline
2 & CMHW & 9555.75 & 51,199.94 & 4972.62 & 13,757.55 & 48\% & 73\% \\  & IMHW & 12,269.11 & 52,261.69 & 4972.62 & 13,757.55 & 59.5\% & 73.6\% \\ \hline
3 & ARIMA & 112.2 & 16,821 & 12.35 & 216.92 & 89\% & 98\% \\  & Coll. Adj. & 33.91 & 1836.08 & 12.35 & 216.92 & 63.5\% & 88\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of forecasting accuracy.  
Figure 10: The rule editor in the MATLAB platform.
## 4 Conclusions  
The current research reports the influence of plasma-treated water obtained in different atmospheres (air, nitrogen) on the rehydration and technological parameters of brewing yeast--top- (_S. cerevisiae_) and bottom-fermenting (_S. pastorianus_). Plasma-treated water (PTW) slightly modified the parameters of the fresh yeast; however, the changes observed in the poorer quality yeast cells were statistically significant. We proved that plasma-treated water under air atmosphere (PTWAir) significantly improves the technological features of the dried brewer's yeast such as their viability, flocculation, glycogen and trehalose content. These parameters are very important for brewer's yeast, which is used many times in subsequent fermentation cycles. Between successive cycles, the conditions and the medium used to store the yeast slurry are extremely important for its vitality and viability. Some brewers also use cell washing before the next inoculation, and the use of PTWAir at this stage may help to improve the characteristics of reused cells. A more beneficial effect of PTWAir was found for _S. cerevisiae_ than for _S. pastorianus_ yeast strains. This relationship can be used in other branches of the fermentation industry, for example, in winemaking, where dried _S. cerevisiae_ yeast is used. It should be noted, however, that the effectiveness of PTWAir may depend on the yeast strain it acts on, as well as resistance to various stress factors, which among representatives of the genus _Saccharomyces_s is very different. In the case of plasma-treated water under nitrogen (PTWN), almost all yeast characteristics deteriorated. Further research should be carried out in this regard as new antimicrobial agents that could be used in food and leave no residues in them are still being searched for.  
Figure 2: SEM analysis of the _Saccharomyces cerevisiae_ (_Sc_) and _Saccharomyces pastorianus_ (_Sp_) yeast strain after rehydration with plasma-treated water.  
**Author Contributions:** Conceptualization, A.P., P.S. and M.Z. methodology, A.P., P.S. and K.K.; software, A.P.; validation, A.P., P.S. and M.Z.; formal analysis, A.P., M.M. and K.K.; investigation, A.P. and M.M.; resources, A.P.; data curation, A.P.; writing--original draft preparation, A.P.; writing--review and editing, P.S. and M.Z.; visualization, A.P.; supervision, P.S.; project administration, P.S. and M.Z.; funding acquisition, P.S. and M.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research was funded in part by the Ministry of Science and Higher Education of Poland as scientific project 070013-D020.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data on the compounds are available from the authors.  
**Conflicts of Interest:** There is no conflict of interest for the authors to declare.
calculated the thermodynamic properties and transmission coefficients of C\({}_{5}\)F\({}_{10}\)O/CO\({}_{2}\) mixtures and found that as the concentration of C\({}_{5}\)F\({}_{10}\)O increases, most thermophysical properties, including thermal conductivity, viscosity, and electrical conductivity, of C\({}_{5}\)F\({}_{10}\)O/CO\({}_{2}\) mixtures become closer to those of SF\({}_{6}\)[14]. [PERSON] et al. calculated the stability and possible decomposition path of C\({}_{5}\)F\({}_{10}\)O molecules via a density functional theory simulation and detected decomposition products, such as CF\({}_{4}\), C\({}_{2}\)F\({}_{6}\), C\({}_{3}\)F\({}_{8}\), C\({}_{3}\)F\({}_{6}\), C\({}_{4}\)F\({}_{10}\), C\({}_{5}\)F\({}_{12}\), and C\({}_{6}\)F\({}_{14}\), by performing discharge experiments [15]. [PERSON] et al. calculated the plasma behavior of C\({}_{5}\)F\({}_{10}\)O/N\({}_{2}\) gas mixture under the assumption of local thermodynamic equilibrium. It is found that when the temperature is less than 7500 K, as the concentration of C\({}_{5}\)F\({}_{10}\)O increases, the enthalpy of gas mixture increases, and when the temperature exceeds 7500 K, the result is the opposite [16]. Although studies have investigated the insulation properties and arc extinguishing characteristics of the C\({}_{5}\)F\({}_{10}\)O gas mixture, only few have examined the compatibility of this gas mixture with metal materials. The insulating medium used in electrical equipment should show good compatibility with the metal materials inside this equipment. Copper is widely used as a conductive material in various equipment, including GIS. When using this equipment, the temperature of the conductive copper material increases due to the thermal effect of the current, whereas the poor contact of the conductive contacts may cause partial overheating [17; 18]. Therefore, the compatibility of the C\({}_{5}\)F\({}_{10}\)O gas mixture with metal materials at different temperatures is of great significance in extending the service life and ensuring the safe and stable operation of equipment. In this paper, a gas-solid compatibility experiment platform was designed to investigate the compatibility of C\({}_{5}\)F\({}_{10}\)O/N\({}_{2}\), C\({}_{5}\)F\({}_{10}\)O/air gas mixture, and copper materials when aging at different temperatures [19]. The surface morphology and element content of the gas decomposition products and copper samples after the experiments were studied by using a gas chromatography mass spectrometer (GC-MS), field emission scanning electron microscope (FESEM), and X-ray photoelectron spectroscopy (XPS). The interaction principle of the C\({}_{5}\)F\({}_{10}\)O on the Cu\({}_{13}\) nanocluster surface was calculated via simulation. The related findings provide a reference for the engineering application of C\({}_{5}\)F\({}_{10}\)O gas mixture instead of SF\({}_{6}\).
# Effect of Load Transfer Section to Toughness for Steel Fiber-Reinforced Concrete
## 4 Analysis and Discussion
### Analysis of the Correlation between the Load Transfer Sections and the Toughness  
The analysis of the regression curves showed that the smooth load transfer in the load transfer section would increase the location of the starting point of the subsequent nonlinear section, which would raise the overall height of the load displacement graph and increase the toughness. In this section, the toughness index was proposed to derive a quantitative correlation between the energy transfer capacity of the steel fiber in the load transfer section and the overall toughness. Considering that the overall toughness increased as the discharged energy was small while it was being transferred from the concrete matrix to the steel fiber, and as the area (toughness) of the section where the load reascended after the transfer of energy to the steel fiber was large, the load transfer factor (LTF) in Equation (3) was proposed based on the area of the second section (S\({}_{2}\)) and that of the third section (S\({}_{3}\)).  
\[\text{L.T.F.}=\sqrt{\frac{S_{3}}{S_{2}}} \tag{3}\]  
where  
L.T.F.: Load transfer factor (LTF)  
\(S_{2}\): Area of the third section (kN\(\cdot\)mm)  
\(S_{3}\): Area of the third section (kN\(\cdot\)mm)  
Shown in Table 8 are the LTF and toughness derived by steel fiber type and fiber content, and shown in Table 9 are the results of the regression analysis on the correlation, which resulted in the correlation equation shown in Figure 12, which can predict the overall toughness using the LTF. Reliability analysis of this regression equation was performed with the degrees of freedom, F-statistic, significance probability, and coefficient of determinant (\(R^{2}\)).  
The regression equation of the overall toughness based on the distribution of the LTF was verified by Equation (4), and from the analysis results on the degrees of freedom, F-statistic, significance probability, and coefficient of determinant (\(R^{2}\)), and based on the fact that the coefficient of determinant (\(R^{2}\)) was 0.798, it is believed that the proposed correlation model offers good accuracy and reliability.  
\[T_{b}=17.64\left(\sqrt{\frac{S_{3}}{S_{2}}}\right)^{0.45} \tag{4}\]  
where  
\(T_{b}\): Toughness (KN\(\cdot\)mm)  
\begin{table}
\begin{tabular}{c c c} \hline
**Specimen** & **Load Transfer Factor (LTF)** & **Overall Toughness (Toughness)** \\ \hline H-SF 0.5\% & 0 & 13.86 \\ H-SF 1.0\% & 5.84 & 28.23 \\ SF 0.5\% & 3.34 & 21.91 \\ C-SF 1.0\% & 24.06 & 41.61 \\ C-SLF 0.5\% & 26.09 & 30.88 \\ C-SLF 1.0\% & 40.57 & 41.22 \\ \hline \end{tabular}
\end{table}
Table 8: LTF and overall toughness.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Degree of** & **F-Statistic** & **Significance** & **Coefficient of** \\
**Freedom (df)** & **F-Statistic** & **Probability (Sigf)** & **Determinant (\(R^{2}\))** \\ \hline
3 & 11.84 & 0.041 & 0.798 & \(y=17.64x^{0.45}\) \\ \hline \end{tabular}
\end{table}
Table 9: Regression model of LTF and overall toughness.  
\(S_{2}\): Area of the second section  
\(S_{3}\): Area of the third section
# A Method for 5G-ICN Seamless Mobility Support Based on Router Buffered Data
## 5 Performance Evaluation
### Performance Evaluation Indicators  
Average packet delay: Referring to [41], the average packet delay is the average difference between the time the UE receives a packet at its new location after moving and the time the Content Server sends the packet.  
Weighted average packet loss rate: To evaluate the overall packet loss rate of different types of mobile data flows, we introduce the weighted average packet loss rate. The calculation formula of the weighted average packet loss rate is shown in Formula (7), where \(w_{i}\) is the weight of the mobile data flow, and \(w_{i}\) can be the priority of the business type of the mobile data flow. \(PL_{i}\) is the packet loss rate of mobile data flow \(i\), which is the ratio of the total number of packets of data flow \(i\) received by the UE at the new and old locations relative to the total number of packets of data flow \(i\) sent by the Content Server.  
\[\overline{PL}=\frac{\sum_{i=1}^{F}w_{i}\times PL_{i}}{\sum_{i=1}^{F}w_{i}} \tag{7}\]Average handover delay: Referring to [42], we define the average handover delay as the average handover delay of multiple mobile data flows on the UE. We also define handover delay of mobile data flow \(i\) as the difference between the time when the UE receives the first packet of mobile data flow \(i\) at the new location and the time when the UE receives the last packet of mobile data flow \(i\) at the old location.  
Network overhead: The amount of mobile buffer resource required to buffer all packets during the handover.
## 3 Experiments and Results
### Measurement of Energy Surface Density Using a Piezoelectric Vibrometer
We repeated the experiments for each frequency offered by the apparatus and for different powers and versions of the transmitter (15F, 6A, etc.). Figure 7 shows the relationship between the operating frequency of the generator and the obtained shock wave (relative mean of Newton-Cotes quadratures of pulses). Here, the average quadrature of the pulse at the set maximum power (i.e., 5 bar for a pneumatic solution and 240 V for a solenoid of an electromagnetic generator) and frequencies in the range of 1-7 Hz. It is presented in a relative way, i.e., the value of quadrature for the frequency of 1 Hz is a reference, and the subsequent results refer to the first value because our goal was to compare the obtained results. The obtained results confirm the hypothesis presented in the introduction, i.e., the increase in the frequency of operation of the generator results in the decrease in energy of the shock wave at the same set power of the device. Similar results we obtained for the other powers offered by the devices and for all delivered versions of the transmitter. A completely different approach is discussed in the next section because we wanted to unquestionably confirm the anomaly that occurred. Figure 6: Test bench with piezoelectric sensor—vibrometer.
# Conditional Dependencies and Position Fixing
## 8 Application Example  
I present four pieces of evidence with four hypothesis points in Figure 7. I present the raw data on the left side of the illustration. On the right side, I present their converted probability density diagrams. I also present illustrations of the corresponding rectangles that cover the span of the raw datasets.  
I present the conditional dependencies calculated based on those read from the diagrams in Table 2. For every hypothesis point, I obtained the belief assignments using Equation (8) by referring to each piece of evidence. The conjunctive combination of all the structures resulted in the cumulative measures. I present the final plausibility on the representation of a fixed position by each hypothesis point in the last column of the table. The first point represents the fix with the highest plausibility measure (0.472). The doubtfulness of the statement is close to zero. The last row of the table contains the relative uncertainties calculated for each piece of evidence. The best observation is o2, and it was used as the reference in the evaluation of the other uncertainties within the presented pool of observations.
## 5 Discussion
The laser scanner is an ibeo LUX that has an update rate of 32 Hz. The proposed method should satisfy the computation limit derived from the sensor system. Considering the capability of the equipment used, the computation of the proposed method should be finished within 30 ms. The whole calculation time of the proposed method was tested, and the specification of the testing machine is described as follows; Intel Core i7-8700, 3.20 GHz CPU. The maximum calculation time of the proposed method was 11 ms using the entire testing dataset. This means that the proposed method is able to satisfy the system requirement. The proposed method has two limitations. The first limitation is caused by the assumption that the operation characteristics may not drastically change in a short time period. If the operation characteristics are drastically changed, the precision of trajectory prediction may degrade. The second limitation is that the proposed method cannot handle abnormal behaviors of adjacent vehicles. The proposed method assumes that a driver is in a normal state. If the drivers is in abnormal states, such as sleepiness or inattention, it may lead to deterioration of the overall performance. Therefore, the proposed system is for SAE level 3 automation. Although the driving is fully automated under normal conditions, the driver should be ready to substitute the operation. For level 4 or 5 automation, the development to detect abnormal drivers and anticipate their behaviors is required, and that point is our future work.
# Analysis of the Sugarane Biomass Use to Produce Green Hydrogen: Brazilian Case Study
## 6 Discussion
### Brazil's Growing Role in Biohydrogen Research  
Recent analyses highlight Brazil's increasing contributions to biohydrogen research, driven largely by the abundant availability of sugarcane biomass. The strategic use of sugarcane biomass for biohydrogen production aligns with global efforts to utilize agricultural residues for sustainable energy, positioning Brazil as a competitive player in this rapidly evolving sector.  
Brazil's focus on sugarcane biomass aligns with global trends in biohydrogen research, where the use of diverse lignocellulosic materials has gained traction. Countries like South Korea and Malaysia, with its emphasis on biomass-based renewable energy, highlight the opportunities for emerging players to contribute to a rapidly evolving landscape. Brazil's efforts stand out for leveraging one of the world's most abundant biomass resources in innovative ways.
## 3 Results and Discussions
### Manual Training  
Figure 6 shows the relation between datasets and the overall accuracy of the created model for manual training. For the additional learning of every 5000 images, the overall accuracy when additional learning started from 5 K to 20 K increased continuously up to 25 K. However, after exceeding the 30 K dataset, the overall accuracy fluctuated. For the additional learning of every 10,000 images, the overall accuracy increased continuously up to 40 K. However, the overall accuracy of the dataset of 40 K slightly declined compared to that of 30 K.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset Type**} & \multirow{2}{*}{**Group**} & \multicolumn{8}{c}{**Dataset**} \\ \hline  & & 5 K & 10 K & 15 K & 20 K & 25 K & 30 K & 35 K & 40 K & 45 K & 50 K \\ \hline \multirow{3}{*}{Validation dataset} & A & 0.6028 & 0.6532 & 0.7293 & 0.7914 & 0.8334 & 0.8369 & 0.8615 & 0.8947 & 0.8986 & 0.9033 \\  & B & 0.4833 & 0.5352 & 0.5713 & 0.6166 & 0.6789 & 0.7056 & 0.7693 & 0.7877 & 0.7884 & 0.8422 \\  & C & 0.4927 & 0.5101 & 0.5899 & 0.613 & 0.7121 & 0.7397 & 0.8208 & 0.8472 & 0.8633 & 0.8974 \\ \hline \multirow{3}{*}{} & mean & 0.5263 & 0.5662 & 0.6302 & 0.6737 & 0.7415 & 0.7607 & 0.8172 & 0.8432 & 0.8501 & 0.881 \\ \cline{1-1} \cline{2-10}  & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall accuracy for each dataset.  
Figure 5: Confusion matrix for evaluating the overall accuracy, which was calculated using the validation dataset A with 50 K dataset.
# Feature-Selection-Based DDoS Attack Detection Using AI Algorithms
## 5 Experiment Result
### Evaluation of ML
#### 5.2.2 Precision, Recall, and F1 Score  
Figure 4 shows the F1 score and recall for a collection of five machine learning models--Random Forest, Decision Tree, CNN, SGD, and NGBoost Classifier--are shown side by side in this line graph. These metrics are essential for assessing each model's accuracy in data classification. The three metrics for the Random Forest and NGBoost Classifier models are in the range of 0.95 to 1.00, which indicates a high degree of accuracy with a minimal number of false positives or negatives. The CNN and Decision Tree models, on the other hand, exhibit a sharp decline in all measures, with a maximum at 0.85, suggesting a greater percentage of incorrect classifications. While recall and F1 score are comparable to the best models, the SGD model's precision is slightly below 0.90, indicating that it is still fairly good at recognizing true positives despite its reduced accuracy. Furthermore, we have also included data in Table 3 for ease of understanding.
# Energy-Efficient IoT-Based Light Control System in Smart Indoor Agriculture
## 4 Grow Lights Monitoring System
### Light Control  
LEDs are a sustainable and effective source of artificial light and are continuously gaining ground in indoor farming, to the detriment of the other artificial light sources [66]. Moreover, some LEDs can produce wavelengths that are used in temporary treatments to improve product quality or stimulate the production of important substances with pharmacological properties in medicinal plants. The recent industrial LEDs used in lighting applications can provide higher illuminance than what plants require. In this case, the illuminance of LED lights used in indoor farming has to be dimmed and the light treatments' duration also has to be controlled to precisely match plants' requirements. The proposed method to control the grow lights is given in the next sub-sections.
## 4 Empirical Research
### Questionnaires and Surveys  
The questionnaire items come from published literature (see Appendix A). To be consistent with the literature source, all measurement items (relative advantage, compatibility, complexity, trialability, observability, top management support, organizational readiness, government support, vendor support, competitive pressure, and adoption intention) were measured with the Likert scale, from \"strongly disagree\" (1) to \"strongly agree\" (5). They have been revised to fit the research context--that is, to evaluate the intention to adopt collaborative robots from the perspective of SMEs. All questions were mandatory. If the question was not answered, the questionnaire cannot be submitted. Since the questionnaire was conducted in China, the English version of the questionnaire was translated into Chinese. To ensure the validity of the questionnaire, three language experts proficient in English and Chinese and two experts in this research field were responsible for the review. After that, we conducted a preliminary pre-test on the managers of 50 SMEs.  
The questionnaire of this study is distributed online through social networks. Before issuing the questionnaire, we consulted the Scientific Ethics Committee of Yeungnanm University to ensure that there were no ethical issues in the questionnaire. The questionnaire was filled out anonymously, and the content and purpose of the survey were explained to the participants at the beginning of the questionnaire, which did not involve personal privacy information, and the data collected from the survey was only used for this study, and the participants were free to choose to answer or not to answer the questionnaire. The International Federation of Robotics (IFR) predicts that cooperative robots will continue to replace traditional industrial robots and take the lead in the robot industry. The IFR report shows that since 2016, China's industrial robot market has become the world's largest industrial robot market. In 2019, China's industrial robot market holdings nearly tripled from 2016, accounting for about 48% of the world's top 10 markets [87, 88]. Therefore, China will dominate the market for collaborative robots [89], and Guangdong Province is the largest industrial manufacturing base in China [90]. Thus, in the field survey, the sample of this study is from the SMEs in Guangdong Province, China. The survey lasted two months from March 2022 to May 2022. A total of 373 questionnaires were distributed, and 242 responded effectively, with a response rate of 60.5%.