esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### TRPO versus Open Loop Basal-Bolus Treatment-Horovka Patient and Carbohydrate Counting Errors
We test the performance of the treatments in the case where the patient forgets to take the bolus insulin during a meal in order to conclude the comparison of the TRPO agent and the basal-bolus controller. To simulate this, we keep the 100 test meal scenarios, but let each meal during testing have a 0.1 probability of containing a skipped bolus. Table 1 shows a summary of all the performance measures for the experiments with the random skipped boluses (RSB) for both TRPO and basal-bolus treatments. We also include additional experiments, as shown in the Table 1, where the agent, denoted now as TRPOe, was trained with an extended action space (from zero to three times the optimal basal rate) and tested on the RSB scenarios as well as the ordinary 100 test scenarios. Figure 5: Kernel density estimation plot comparing the distribution of the results when comparing basal-bolus control to the TRPO agent. (**a**) shortly after a large carbohydrate intake, (**b**) between meals, (**c**) during nighttime (close to steady state). Observing the Table 1, we again see that the TRPO agent is superior to the basal-bolus treatment, increasing time-in-range while decreasing time spent in hypoglycemia. It has lower variation and risk indices, and it is overall more robust towards skipped boluses. We note that the low LBGI for the skipped bolus experiment is most likely an artifact due to the blood glucose level being higher in general when there are skipped boluses involved. The same goes for the overall percentage of time spent in hypoglycemia. When it comes to the results using the extended action space TRPOe, we found that the results using 100 policy gradient iterations are inferior to the other results. Therefore, we extended the number of training iterations to 300, which lead to an improvement over the original action space. The extended action space also leads to a treatment that is more robust to skipped boluses. However, the effect of increasing the number of policy gradient iterations from 100 to 300 represents a significant increase in data used for training the policy. There is a trade-off between the size of the action space and the number of training data/simulations needed.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 7 Application Fields of Electrospun Chitosan Nanofibers and Chitosan Derivatives
### Food Storage
#### 7.3.2 Meat Shelf Life Prolongation  
The antibacterial ability of chitosan/PEO nanofibers to increase the shelf life and prevent _E. coli_ infection of meat was evaluated by [PERSON] et al. in situ under refrigeration at 4 \({}^{\circ}\)C [140]. For antibacterial experiments, a chitosan/PEO ratio of 80:20 (\(w/w\)) was selected as a compromise between the ratio of 90:10 (\(w/w\)), which had the maximum antibacterial effect but low electrospinning ability, and the 70:30 (\(w/w\)) ratio, which had less bactericidal impact but greater nanofiber production capacity. Fresh meat cubes were sliced, immersed in a suspension of _E. coli_, and enclosed in the electrospun mats. Additionally, samples were wrapped and sealed in a standard multilayer food packaging to support the weak barrier properties of chitosan-based nanofibers. Bacteria in the conventionally packaged sample lacking chitosan-based nanofibers grew in concentration as they were fed by the nutrients in the meat, but their increase was moderate compared to the uncovered sample. This proves that the commercial meat package has strong barrier properties for oxygen and moisture necessary for bacterial survival and can slow down the bacterial growth but cannot completely eradicate it. On the other hand, 92% of the bacterial population was eliminated when infected meat was wrapped in chitosan-based nanofiber mats plus commercial packaging, allowing the meat's microbiological quality and safety to be preserved while also extending its shelf life to 7 days at 4 \({}^{\circ}\)C [188].  
Figure 9: The results of the antibacterial assessment of five strawberry groups: (**a**) initial state; (**b**) no cover; (**c**) plastic cover; (**d**) coating of chitosan solution; (**e**) covered with chitosan-derivative electrospun nanofiber membrane. Adapted from [187], originally published under a CC-BY 4.0 license.
# Exploring and Modeling the Incentive Strategies of New Energy Ship Application in Yangtze River
## 2 Methods
### Model Assumptions  
Game theory is a mathematical framework used to describe the interaction between different actors (also known as actors) and to analyze their strategic behavior [35]. British scholar [PERSON] for the first time proposed an evolutionary game method, which is finite rational and can learn from rivals to adapt to the market environment [39]. [PERSON] [40] pointed out that most games, especially those on environmental protection issues, are constantly developing games.  
The research object is the application of new energy in the Yangtze River ships, involving the interests of both government and shipping enterprises. Shipping enterprises actively adopt the application of NESs for social responsibility, achieving carbon reduction and even decarbonization. The government actively promotes the application of NESs, implements reasonable incentive policies including both direct economic rewards and tax incentives, and vigorously supports shipping enterprises to develop NESs. If the government does nothing, shipping enterprises can only choose whether to realize the application of NESs by themselves and actively meet the requirements of the green development of the Yangtze River shipping. The difference between these two ways is in the timing of strategy evolution. Therefore, it is the key to find a mutually satisfactory stability strategy to promote the application of NESs through the evolutionary game model. Figure 1 demonstrates the structure of this research.  
We can make the following assumptions from the above analysis:  
1. When the goal is to promote the application of NESs, only the government and shipping enterprises participate, and the behavior of both sides is limited and rational. Under the assumption of information asymmetry, self-interest is the basis. The strategy  
Figure 1: The structure of this research problem.  
choice of both parties is influenced by their own information, personal preferences, and other factors.
2. The strategies of shipping enterprises and the government are as follows: Shipping enterprises make a choice between the construction of NESs or Traditional Fuel Ships (TFSs), and the behavioral strategies of shipping enterprises are {NESs, TFSs}, and the corresponding selection probability is {x, \(1-x\)} and \(0\leq x\leq 1\). The government can choose whether to launch incentive policies to promote the NES application. The government's behavioral strategies are {Incentive, Non-Incentive}, and the corresponding selection probability is {y, \(1-y\)}, \(0\leq y\leq 1\), respectively. The government and shipping enterprises adjust their own strategies according to the changes in each other's decisions, and the game behavior is repeated and dynamic.
3. According to rational people's pursuit of profit maximization, the net income of shipping enterprises choosing NESs is greater than TFSs.
## 4 Experimental Results
### Determining the Digital Predistorter Model
In the learning stage, the coefficients of the linearization stage are calculated based on a least-squares estimation linear regression, and a comparison is made with the AM-AM characteristic of the device. We implement a Saleh model to set a nonlinearity parameter, with an input scaling (dB) whose parameter scales the input signal before the nonlinearity is applied [28]. The AM-AM parameters, \(\alpha\), and \(\beta\) are used to compute the amplitude gain for an input signal as follows  
\[\mathrm{F}_{\mathrm{AM-AM}}(u)=\alpha_{1}(u)/(1+\beta_{1}u^{2}), \tag{18}\]  
where \(u\) is the magnitude of the scaled signal, the AM-AM parameters, \(\alpha\), and \(\beta\), are used to compute the phase change for an input signal using the function depicted by Equation (19). \[\mathrm{F}_{\mathrm{AM-PM}}(u)=\alpha_{2}(u)/(1+\beta_{2}u^{2}). \tag{19}\]  
where \(u\) is the magnitude of the input signal. Note that the AM-AM and amplitude to phase conversion (AM-PM) parameters, although similarly named alpha and beta, are distinct. For the linearization of the MATLAB virtual amplifier (Saleh model), ILA is used in Figure 13; the spectral growth of the received signal is observed as a result of the use of the amplifier where DPD is not used as before the PA, while in Figure 13 we observe the spectrum of the received signal where its power values decrease in the bands thanks to using the DPD with ILA with \(m=2\) and \(k=6\), that is, three memory stages with a depth of 6, that is, with 18 coefficients the polynomial model. The behavior modeling for derive the predistorter stage is carried out starting from the device characteristics, measurement, and modeling of an RF PA, which considers memory effects and nonlinearity; where the function of the input and the output are interchanged as shown in Equation (14), to model the nonlinear function of the linearization as is represented in Equation (15). For the modeling design, the sweep of the input tone is taken into account, the measurements of the modeling of the AM-AM conversion curve is the basis for the calculation of the coefficients \(d_{k,m}\), which generates a new input \(y_{d}(n)\) based on the DUT peak gain \(y(n)\) is represented by the Equation (14) where the even and odd coefficients based on the LSE method are taken into account. The predistorter effectiveness for two configurations of parameters \(k\) and \(m\) are summarized in Figure 11:0  
Figure 11:1 Spectral regrowth of the QPSK 18–MHz; (**a**) PA Output (Saleh model), (**b**) with PD (\(K=4,M=1\)), (**c**) with PD (\(K=6,M=2\)).
###### Abstract  
Three commercially available extractants (tri-butyl-phosphate (TBP), di-(2-ethylhexyl) phosphoric acid (HDEHP) and Aliquat 336 (a mixture of quaternary ammonium bases)) were tested for separation of Pr(III) and Nd(III) in both static and dynamic modes. In the case of HDEHP, phase stability and influence of nitric acid were considered. Extraction isotherms were constructed, and influence of water-soluble complexing agents on the separation factor was investigated for Aliquat 336. In the case of TBP, influence of calcium nitrate in aqueous phase on the extraction efficiency was investigated. Model countercurrent experiments were conducted for TBP and Aliquat 336. It was shown that TBP is the best choice due to its high capacity and cation extraction order (Nd \(>\) Pr).  
rare earth elements; solvent extraction; separation; neodymium; praseodymium; TBP; HDEHP; Aliquat 336
# Deep Learning-Based Algorithm for Recognizing Tennis Balls
## 3 Experimental Method of Recognition Algorithm
### Cascade Classifier-Based Recognition of Tennis Balls  
Cascade classifier as an object detection method cleverly uses the Boosting algorithm, which is very good at detecting rigid objects from a specific viewpoint, and is a method to improve the accuracy of the algorithm by learning multiple times, boosting weak classifiers to strong classifiers, and iterating by providing weights to the training samples in each round of learning to finally reach a predetermined, sufficiently small error rate [18].  
The cascade classifier is used to generate its own object classifier, firstly, positive and negative samples of tennis balls are collected (the tennis balls occupy the main pixel part in the images of positive samples, while the images of negative samples do not contain tennis balls), as shown in Figure 7 for the positive and negative samples of tennis balls collected in this paper; then the sample images are grayed out and normalized, using two executable files under opencv, opencv_ createsamples.exe and opencv_ traincascade.exe to generate the image description file, that is, the positive and negative training samples; finally, enter the command in the terminal, set the corresponding parameters for training, and generate the training file.  
As shown in Figure 8, the camera is turned on to acquire the image, and the trained detection model can be used to identify the tennis balls in the image.  
Figure 8: Cascade classifier-based recognition of tennis balls.  
Figure 7: Positive and negative sample description files. (**a**) Positive Sample, (**b**) negative sample.  
The use of cascade classifier has a higher recognition rate than only using color and contour features to identify tennis balls and will not easily regard objects with similar color or contour as tennis balls; however, there are strict requirements on recognition distance and ambient light, and factors such as far distance, strong or weak light, and the proximity of other objects with similar color or contour can easily lead to poor recognition effect of cascade classifier. Therefore, it is also necessary to consider the design of a scheme that is less affected by the environment, i.e., stronger anti-interference ability, and on this basis the recognition distance of the robot can be farther to ensure that the robot can obtain the location information of the tennis ball smoothly and efficiently in the actual tennis ball picking operation.
# Fraud Detection Using Neural Networks: A Case Study of Income Tax
## 5 Conclusions  
The aim of this study was to employ Artificial Neural Networks to detect income tax fraud. It was found that there is no difference in income tax fraud between businesses that import and export goods (customs) and domestic businesses, except that customs businesses are more related to tax fraud than domestic businesses. The time of the business that shows the difference in time from when a business was registered to the time of audit was also revealed to be a feature that is related to tax fraud. Small businesses are also related to tax fraud, and this is because there are a large number of people with inadequate information and capital for their businesses and almost all with unstable businesses. Non-individual businesses (corporations) and individual enterprises were also proven to be tax fraud. The location variable shows that businesses located in Kigali City districts are more related to income tax fraud than other districts. This study's findings are consistent with the previously submitted paper that covered all tax types by using different machine learning models where it revealed that businesses that import and export goods and services, small taxpayers and time of the business are among the variables that are more related to tax fraud.  
It is worth mentioning that the model's accuracy reduced as the number of layers increased, which is why the most accurate model had only an input and output layer.  
The current study will reduce the auditing time and cost because the model is applied to a big data set and shows all features related to tax fraud, not one by one. This research will help to inform decision-makers in tax administrations and governments about potential factors of income tax fraud, allowing them to implement evidence-based and effective policy measures. This will also help in recovering money lost to income tax fraud as well as help the revenue authorities maintain the level of compliance of businesses registered for the income tax.  
For future research, it will be interesting to use Deep Convolutional Neural Networks (DCNN) to see if they will perform better than Multilayer Perceptron (MLP).  
It would also be useful to automate the model and create a real-time dashboard to help in the detection of tax fraud in general.  
**Author Contributions:** Conceptualization, B.F.M., O.T., J.N. and D.H.; methodology, B.F.M.; software, O.T.; validation, D.H. and J.N.; formal analysis, B.F.M.; data curation, O.T.; writing--original draft preparation, B.F.M.; writing--review and editing, J.N. and D.H.; supervision, D.H. and J.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Hybrid ANN-Based and Text Similarity Method for Automatic Short-Answer Grading in Polish
## 5 Hybrid Model  
### Hybrid ANN-Based Method  
The hybrid method integrates the strengths of the revised Split Algorithm and the enhanced ANN-based method. By combining these approaches, the proposed system achieves superior accuracy, scalability, and fairness in evaluating short answers. Figure 3 depicts the architecture of the hybrid method. It depicts the structure of the hybrid ANN-based method. STUD stands for the student's answer, TECH is the teacher's answer, DICT is the dictionary, Lev is Levenshtein distance, and J is the Jaccard measure.  
The process begins with preprocessing, including text splitting, lemmatization, and normalisation. The student's response is then evaluated using two parallel modules:  
1. Split Algorithm: The revised method from Section 3.2 calculates a similarity score (S1) using the Jaccard index and length-based adjustments.
2. ANN-Based Algorithm: The lemmatized ANN model from Section 4.2 computes a separate score (S2).
3. The final grade (S) is determined by combining these scores using heuristic decision rules. The decision rules have been formulated taking into account the following factors:  
* The new Split algorithm has no False Positive errors and achieves 100% True Negatives but has a 25.6% False Negative error, correctly classifying only 74.3% of the correct answers.
* The new ANN algorithm has a higher False Positive error of 18.6% but a lower False Negative error of 13.74%, correctly classifying 86.26% of the correct answers. Therefore, the decision rules in the hybrid ANN-based method are as follows:  
1. If the new Split score (S1) is 5, the final grade (S) is also 5.
2. Otherwise, the new ANN score (S2) is used. Thus, from rules 1 and 2, it follows that the hybrid score S is S = Max (S1, S2).  
Figure 2: Confusion matrices: comparison for ANN-based methods, where TP—True Positives, FN—False Negatives, FP—False Positives, and TN—True Negatives.
## 3 Results and Discussion
### Modeling and Optimization of the Malt Mixture
#### 3.2.1 Addition of CaCl\({}_{2}\) and Lactic Acid  
The results of these studies have been shown in Figures 4 and 5.  
The addition of CaCl\({}_{2}\) in doses up to \(52.5\,\mathrm{mg/50}\) g of malt mixture had a positive effect on the extract yield (Figure 5). The calcium ions had a positive influence on the enzyme activity, increasing the stability of \(\alpha\)-amylase, lowering the pH (i.e., the zone of the pH optimum for the respective enzymes was reached) and improving yeast cell growth during fermentation, etc. The chloride ions affected the organoleptic profile of the beer. Calcium  
Figure 5: Investigation of the influence of CaCl\({}_{2}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor** & **Low** & **High** & **Optimum, \%** & **Optimum, g** \\ \hline \multicolumn{5}{c}{**Factors**} \\ \hline Pilsner & 0.0 & 1.0 & 93.6 & 46.8 \\ \hline Caramel Pils & 0.0 & 1.0 & 5.2 & 2.6 \\ \hline Caramel Munich & 0.0 & 1.0 & 1.2 & 0.6 \\ \hline \multicolumn{5}{c}{**Optimum responses/calculated/**} \\ \hline Color & EBC & 8.0 & - \\ \hline Extract & \({}^{\circ}\)P & 13.22 & - \\ \hline FC & mg/dm\({}^{3}\) & 925.24 & - \\ \hline DPPH & & 320.69 & - \\ \hline FRAP & \(\mu\)mol TROLOX/dm\({}^{3}\) & 767.07 & - \\ \hline CUPRAC & & 1922.62 & - \\ \hline ABTS & & 2472.58 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimized malt mixture.  
dichloride is frequently found in combination with carbonates and sulfates; therefore, it was necessary to dose it separately at the beginning of the mashing [7; 38]. In this case, the word pH decreased slightly (from 0.05 to 0.1 unit), which was the reason for the relatively weak influence of the additive.  
Lactic acid dosing is another approach to correcting the word pH and reaching the optimum pH for the respective enzymes. In this case, there are requirements that the lactic acid should be of biological origin; hence, breweries most often produce lactic acid concentrate from word or add lactic acid obtained by cultivating different cultures of lactic acid bacteria [7]. The effect of the lactic acid addition has been shown in Figure 6. The increase in the lactic acid dose provoked a decrease in the word pH from 5.7 to 5.4 to 5.5, which, in turn, was favorable for the enzyme activity and led to an increase in the word extract by 0.8-0.9 \({}^{\circ}\)P. As the dose of lactic acid increased, the extract yield started to decrease, the word viscosity changed and the mash lautering became more difficult.
# Research on the Compound Jamming Method against FDA-MIMO Beamforming
## 1 Introduction
The study in [27] constructed a novel clutter canceller that was not affected by blind velocity by introducing FDA in SAR ground moving target indication (GMTI) to alleviate the problems of blind velocity and doppler blur. [PERSON] proposed a scattered wave deceptive jamming method based on the FDA to interfere with SAR imaging in [28; 29]. The study in [30] combined FDA with scattered wave jamming and proposed a jamming method for inverse synthetic aperture radar (ISAR). A range dimension deceptive jamming identification method based on FDA-MIMO was proposed in [31]. [PERSON] proposed a robust adaptive beamforming anti-jamming method based on FDA-MIMO covariance matrix reconstruction in [32]. The studies in [33] and [34] proposed deceptive jamming suppression methods based on non-uniform sample detection and sample selection, respectively. The study in [35] proposed a simulated annealing algorithm based on FDA-MIMO radar against main lobe deceptive jamming in the range dimension. The study in [36] proposed a weight design method based on the FDA to suppress the main lobe jamming by controlling the zero-notch distribution of the range dimension. However, [33; 34; 35; 36] are susceptible to signal mismatch caused by direction error [37], signal gain [38], incoherent scattering [39], etc. The study in [40] established a very high frequency FDA-MIMO (VHF-FDA-MIMO) radar deceptive jamming signal model and proposed a robust suppression method for deceptive jamming based on Capon reconstruction. Ge proposed a non-uniformly spaced FDA adaptive cognitive anti-jamming method based on the phase center in [41]. Due to the unique range dimension freedom of FDA-MIMO, the existing range dimension deceptive jamming methods against FDA-MIMO are easily identified by radar and cannot form an effective jamming effect. Based on this, this paper studies the compound jamming method against FDA-MIMO beamforming, and the main contributions are as follows:  
1. An accurate understanding of the FDA-MIMO range dimension jamming method is proposed, and its jamming model is analyzed;
2. A compound jamming method of suppression and range deceptive against FDA-MIMO beamforming is proposed. This method can reasonably utilize the beam gain and the minimum variance distortionless response (MVDR) adaptive beamforming algorithm cannot significantly reduce the power of jamming, causing a significant compound jamming effect to radar in the range dimension;
3. For the proposed compound jamming method, it is analyzed at the range bin estimation level and its signal to interference plus noise ratio (SINR) is compared and analyzed. The results show that it can form an effective compound jamming effect. The rest of this paper is arranged as follows: Section 2 introduces the basic principle and jamming model of FDA-MIMO. Section 3 analyzes the MVDR adaptive beamforming algorithm. In Section 4, a suppression and range deceptive compound jamming method against FDA-MIMO beamforming is proposed. Section 5 simulates and analyzes the method. Section 6 summarizes the whole paper.