esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanistic Insights into Fish Spoilage and Integrated Preservation Technologies
## 2 Mechanisms of Spoilage
### Enzymatic Autolytic Spoilage
#### 2.1.3 Post-Rigor Autolysis  
Once rigor resolves, a cascade of endogenous enzymes degrade muscle and connective tissues. Nucleotide-degrading enzymes (ATPases, AMP deaminase, 5\({}^{\prime}\)-nucleotide, nucleoside phosphorylase) convert residual ATP through ADP, AMP, and IMP into inosine and hypoxanthine, the latter imparting bitterness [13; 14]. Proteases--including calpain [15], cathepsin B/D/L [13; 15], collagenases, trypsin, and chymotrypsin--then cleave myofibrillar proteins (myosin, actin, tropomyosin, troponins) and collagens, weakening fiber integrity and reducing water-holding capacity [16]. Concurrently, lipases and phospholipid  
Figure 2: Mechanistic flowchart of post-mortem enzymatic autolysis in fish. The upward arrow (\(\uparrow\)) indicates an increase in lactic acid concentration, while the downward arrow (\(\downarrow\)) signifies a decrease in pH.  
pases hydrolyze triglycerides and phospholipids into free fatty acids, which oxidize into aldehydes and ketones, producing rancid off-odors [17; 18].  
The combined accumulation of ammonia, trimethylamine (TMA), hypoxanthine, volatile bases, free fatty acids, aldehydes, and small peptides leads to pronounced off-odors (fishy or ammonia-like), off-flavors (bitterness, rancidity), and progressive muscle softening, markedly compromising fish freshness, sensory quality, and safety [16; 19].  
The following methods can mitigate fish spoilage by targeting the enzymatic autolysis mechanism. Rapid chilling or super-chilling immediately after harvest slows enzyme kinetics by reducing temperature below activity thresholds [20]. High-pressure processing (HPP) inactivates endogenous proteases and lipases without thermal damage [21]. Modified atmosphere packaging (MAP) limits oxygen-driven reactions [20]. Edible coatings or nanoemulsions provide physical barriers to moisture and oxygen while delivering antioxidants or specific enzyme inhibitors [22; 23]. Finally, chelating agents (EDTA) or natural antioxidant extracts (rosemary, green tea) sequester essential metal cofactors and scavenge free radicals, further suppressing spoilage metabolite formation [24].
## 2 Theoretical Background
### Genetic Algorithm
#### 2.1.1 Hybrid Parallel Selection  
The selection operation is based on the survival of the fittest concept, picking high-quality children and discarding low-quality individuals based on fitness. Its primary goal is to avoid gene deletion while improving global convergence and computing efficiency.  
In this paper, a hybrid parallel selection operator is adopted: Preserving the Optimal Strategy and Tournament Strategy. New individuals are constantly produced in the GA through genetic operations such as crossover and mutation. Although an increasing number of excellent individuals will be built with the evolution of the population, they may also destroy the most adaptable individuals in the current population because of the randomness of the selection, crossover, mutation, and other operations [31]. Therefore, Preserving the Optimal Strategy is adopted to ensure that the final result of the iteration is the individual with the highest fitness all the time. There will be no degradation, and it can make the algorithm converge quickly. Tournament Strategy is a selection method based on the relationship between individuals' fitness. The basic idea is to randomly select several individuals at a time and select the individual with the highest fitness to inherit to the next generation population.  
* Tournament Strategy  
\(M\) individuals were randomly selected from the population to compare their fitness, and the best individual was chosen as the parent, repeated \(N\) times.  
* Preserving the Optimal Strategy  
The best parent from the current population is selected and copied directly to the next generation without crossover or mutation.
## 6 Evaluation
### Simulation Results
In these cases, the agent is able to find alternative paths whose delay is more significant but below the maximum bounds established in service requests, which constitutes an important reliability feature for the developed solution. Regarding scheduling, the agent is able to perform similarly for any background traffic level, achieving 97% of optimal schedules for 300 background traffic data flows, 94% for 600 flows, and 93% for 900. A larger topology with 24 nodes is used as a secondary scenario, depicted in Figure 9, where arrows represent unidirectional links between TFCs and TMSs, while the numbers next to the edges represent the delays \(d_{i,j}\) in milliseconds. The objective is to analyze the performance of both routing and scheduling models regarding scalability. Specifically, a new training sequence is launched, generating 1000 background data flows with random source and destination pairs at each episode. Regarding scheduling, no new models are required, as the ones that are trained for the smaller scenario are also valid for the big one: the routing decisions are transparent to the scheduling agent--it just learns  
Figure 8: Distribution of relative end-to-end delays compared with the optimal ones. how to schedule data frames over time given the resource availability information of a link, without knowing at which link it is scheduling frames. Regarding routing, there are 552 combinations of source and destination TSN switches, representing 552 optimal routes to learn. Among these, 464 are successfully established (representing 84% of cases). On the contrary, the remaining 88 routes (16%) can not be established. This scalability problem results in the proposed model performing less optimally for large scenarios. However, to solve this problem, a geographical multi-agent approach (where a routing and a scheduling agent are in charge of managing a subset of the network) could be addressed, which could be a possible future research topic. It is crucial to note that, while end-to-end delays in the proposed solution may vary between different data flows originating from the same source and destination nodes, the periodicity of frame arrivals at the destination is guaranteed, ensuring the provision of synchronous services across the network. The guarantee is achieved by the definition of positions, which forces the scheduler to allocate the required resources to all designated slots to maintain the expected periodicity. Furthermore, since time is slotted and periodicity is preserved, the maximum jitter in this solution is limited to the duration of a slot, which is 100 microseconds. The proposed MARL solution demonstrates that the two agents manage the TSN network automatically and in real time, creating optimal routes for synchronous flows with an end-to-end latency variation of less than 6 ms and a jitter of fewer than 100 microseconds, achieving an effectiveness of over 95%.
# Single Trace Side Channel Analysis on NTRU Implementation
## 4 Countermeasure
### Countermeasure against NTRU Open Source Implementation  
Since the advantage of the original implementation is that computes both modular reduction \((x^{N}-1)\) and polynomial multiplication, simultaneously, the countermeasure we propose also process both of the operation at the same time. Furthermore, the modified implementation has the same number of polynomial coefficient addition as the original implementation. The Algorithm 4 is a countermeasure for the polynomial multiplications described in Algorithm 2.  
The Algorithm 4 is a method that precomputes the index \(i\), where the cipher-text polynomial \(e_{i}\) will be added. For example, let 9 be the degree of the polynomial and let 2 be a coefficient of degree 0, then the original cipher-text polynomial coefficient addition performs as in Figure 6a. During the original iteration, the additions when \(i=0\) to 6 operate with different loop when \(i=7\) to 8. However, the addition in the proposed method operates in the same loop so there is no leakage for side channel analysis. The proposed method first finds the index of cipher-text which is added to the middle index of the result array, then the addition operates simultaneously as in Figure 6b.  
Figure 5: The Average of 10 Power Consumption Traces of Polynomial Multiplication using Grace School Multiplication Method.  
```
1:cipher-text polynomial \(e\in R\) and coefficient location indices of private key \(b\)
2:\(H=F\cdot e\pmod{q}\)
3:for\(i=0;i<N;i++\)do
4:\(t_{i}\gets r\)\(\triangleright\)\(r\) is a random value
5:endfor
6:for\(j=d_{f}+1;j<2d_{f}+1;j++\)do
7:\(k\gets b_{j}\)
8:\(x\leftarrow\frac{N-1}{2}-k,y\gets N-k\)
9:\(t_{\frac{N-1}{2}}\gets t_{\frac{N-1}{2}}+e_{x}\)
10:\(x\leftarrow x+1\)
11:for\(i=0;i<\frac{N-1}{2};i++,x++,y++\)do
12:\(t_{\frac{N-1}{2}+i+1}\gets t_{\frac{N}{2}+i+1}+e_{x}\)
13:\(t_{i}^{2}\gets t_{i}+e_{y}\)
14:endfor
15:endfor
16:for\(i=0;i<N;i++\)do
17:\(t_{i}\leftarrow-t_{i}\)
18:endfor
19:for\(j=0;j<d_{F}+1;j++\)do
20:\(k\gets b_{j}\)
21:\(x\leftarrow\frac{N-1}{2}-k,y\gets N-k\)
22:\(t_{\frac{N-1}{2}}\gets t_{\frac{N-1}{2}}+e_{x}\)
23:\(x\gets x+1\)
24:for\(i=0;i<\frac{N-1}{2};i++,x++,y++\)do
25:\(t_{\frac{N}{2}+i+1}\gets t_{\frac{N}{2}+i+1}+e_{x}\)
26:\(t_{i}^{2}\gets t_{i}+e_{y}\)
27:endfor
28:endfor
29:for\(i=0;i<N;i++\)do
30:\(H_{i}\gets t_{i}-r\pmod{q}\)
31:endfor
40:return\(H\)
```  
**Algorithm 4** Countermeasure of NTRU Open Source Proeject  
Figure 6: Iteration of Addition.
# Pruning Deep Neural Network Models via Minimax Concave Penalty Regression
## 3 Comparison Experiment
### Comparative Experiments with the VGG19 Model
#### 3.2.1 Single-Layer Pruning Analysis  
Similar to Figure 10, Figure 12 depicts the impact of individually pruning layers 3 to 11 of the convolutional layers in the VGG19 model trained using the CIFAR100 dataset. The vertical axis represents the decrease in Top1 accuracy, with smaller values indicating better performance. The horizontal axis denotes the specified pruning ratio, where 0.1 corresponds to pruning 10% of the current channel.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ratio** & **Flop** & **Flop Ratio** & **Param** & **Param Ratio** \\ \hline
0.1 & \(3.37\times 10^{8}\) & 15.52\% & \(1.86\times 10^{7}\) & 8.23\% \\
0.2 & \(2.78\times 10^{8}\) & 30.25\% & \(1.70\times 10^{7}\) & 16.39\% \\
0.3 & \(2.25\times 10^{8}\) & 43.75\% & \(1.55\times 10^{7}\) & 23.76\% \\
0.4 & \(1.80\times 10^{8}\) & 54.94\% & \(1.42\times 10^{7}\) & 30.02\% \\
0.5 & \(1.39\times 10^{8}\) & 64.99\% & \(1.30\times 10^{7}\) & 35.88\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter counts and floating-point calculations of VGG19_Simple model with different pruning ratios.  
Figure 12: VGG19 single-layer pruning diagram. Subfigures(**a**–**i**) represent the results of pruning layers 3 to 11 respectively.  
Redundancy exists in the VGG19 model trained on the CIFAR100 dataset, and pruning a certain percentage of channels leads to improved Top1 accuracy compared with the original network. In Figure 12a, when the pruning ratio is set to 0.15, 0.2, and 0.25, the MCP regression method enhances the Top1 accuracy by 0.02%, 0.11%, and 0.04%, respectively, upon pruning the model. In Figure 12b, at a pruning ratio of 0.1, both MCP regression and Lasso regression improve the accuracy by 0.13% and 0.08%, respectively. Figure 12i exhibits the most prominent redundancy phenomenon. At a pruning ratio of 0.1, when using MCP-L1 to directly crop the model, the accuracy improves by 0.15%. At this point, the MCP regression and Lasso regression methods are less effective than MCP-L1, although they also enhance the model's performance after pruning.  
In its entirety, the direct pruning of the model using the MCP-L1 method significantly diminishes the model's performance, with a Top1 accuracy reduction exceeding 10% in shallower convolutional layers at a pruning ratio of 0.4. In contrast, MCP regression and Lasso regression involve an output reconstruction process, resulting in a more moderate degradation of the model's performance compared with the MCP-L1 method. Notably, the MCP regression method outperforms Lasso regression in most cases, particularly in the middle layer of the network. At this layer, with fewer redundant parameters and more informative channels, channel selection becomes crucial. Consequently, the performance loss of the model after MCP regression is considerably smaller than that observed with the Lasso regression in Figure 12e-g.
## 2 Materials and Methods
### Validation Method and Evaluation Metrics  
This study applied a 10-fold cross-validation technique to test the model overfitting and predictive ability [51]. The training and validation data were run in 10 random iterations. In each iteration, the entire data set was randomly divided into ten equal-sized portions. One of the copies is used as the validation sample, and the remaining nine copies are used as the training sample for one iteration. In the next iteration, one of the previous training samples is used as the validation sample, and the remaining nine are used as the training samples in the next iteration. Repeat this step nine times until ten iterations are completed, and we will get the prediction results for the whole data set of soil moisture. The model cross-validation results are obtained by averaging the ten results. These averaged cross-validation results provide a good check of whether the model is overfitted. When the model is poor, the cross-validation results are also poor, and the model with the most significant correlation coefficient is selected as the best-fit model for subsequent predictions.  
To verify the validity of each model, we quantitatively evaluated the training and test sets using the Pearson correlation coefficient R, root mean square error (RMSE), unbiased root mean square error (ubRMSE), and mean bias (bias). R describes the degree of model convergence between +1 and 1. Where +1 indicates a perfect positive linear correlation, 0 indicates no linear correlation, and 1 indicates a perfect negative linear correlation. RMSE and bias measure the deviation between the inverse soil moisture values and the measured values. RMSE of 0 indicates no deviation. The bias of 0 indicates an unbiased estimate, more remarkable bias than 0 is an overestimate, and less than 0 is an underestimate. In general, the smaller the two, the better. ubRMSE is the random error. ubRMSE eliminates possible additional bias when the measured value is considered the actual value, and the smaller the value is, the better the model performance is.  
Figure 4: GA-BP model calculation process.
# Provoking Actual Mobile Payment Use in the Middle East
## 3 Research Methodology
### Data Collection and Respondent's Demographic Profile  
For a preliminary distribution of the questionnaire, a sample of 10 university doctors and students was chosen to conduct a questionnaire trial-and-error test. Pilot test participants commented on some of the questionnaires and accordingly evaluated the comments and enhanced. Based on the feedback received, four questions were reworded to improve their clarity and understanding. The data in the pilot test were collected using Google forms. The survey link was shared via different applications such as WhatsApp groups, emails, and social media platforms to achieve an adequate sample size for the study. The questionnaire content validity went through the pre-test stage. One hundred and one respondents received and answered the questionnaire. The demographic profile of respondents is shown in Table 1. The respondents voluntarily answered the questionnaire. The first page in the survey was for selecting the preferred language. Then, complete information about the purpose of the survey and confidentiality of the data was provided. In general, the respondents were between the ages 15 and 55 years old, which are a potential group of users of mobile payment applications; 43.5% of the respondents were graduates (bachelor;s degree), 23.7% were postgraduates or above, 13.8% held diplomas, and 18.8 were secondary school or lower graduates. In terms of using mobile payment applications, 5.94% of respondents previously used mobile payments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Characteristics**} & **Answer** & **Number** & **Percentage (\%)** \\ \hline \multirow{2}{*}{**Language**} & Arabic & 64 & 63.37 \\ \cline{2-4}  & English & 37 & 36.63 \\ \hline \multirow{6}{*}{**Age**} & Less than 15 & 0 & 0.00 \\ \cline{2-4}  & 15 to 25 & 20 & 19.80 \\ \cline{2-4}  & 26 to 35 & 30 & 29.70 \\ \cline{2-4}  & 36 to 45 & 31 & 30.69 \\ \cline{2-4}  & 46 to 55 & 20 & 19.80 \\ \cline{2-4}  & Above 55 & 0 & 0.00 \\ \hline \multirow{6}{*}{**Education Level**} & Secondary school or lower & 19 & 18.81 \\ \cline{2-4}  & Diploma & 14 & 13.86 \\ \cline{2-4}  & Undergraduate (Bachelor’s) & 44 & 43.56 \\ \cline{2-4}  & Postgraduate & & \\ \cline{2-4}  & (Master’s and Above) & 24 & 23.76 \\ \hline \multirow{2}{*}{**Do you use a mobile payment application?**} & Yes & 6 & 5.94 \\ \cline{2-4}  & No & 95 & 94.06 \\ \hline \multirow{6}{*}{**If yes, since when did you start to use**} & Less than 1 Year & 4 & 3.96 \\ \cline{2-4}  & 1 to 2 years & 31 & 30.69 \\ \cline{2-4}  & 3 to 4 Years & 30 & 29.70 \\ \cline{2-4}  & More than 5 Years & 30 & 29.70 \\ \cline{2-4}  & N/A & 6 & 5.94 \\ \cline{2-4}  & Yes & 76 & 75.25 \\ \cline{2-4}  & No & 25 & 24.75 \\ \hline \multirow{6}{*}{**If yes, since when did you start to use**} & Network Issue & 4 & 3.96 \\ \cline{2-4}  & Login Issue & 31 & 30.69 \\ \cline{2-4}  & Transaction Issue & 3 & 2.97 \\ \cline{2-4}  & Unfamiliar with application & 8 & 7.92 \\ \cline{2-4}  & Other & 55 & 54.46 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Respondents’ demographic profiles.
## 5 Experimental Simulation and Analysis
### SLP-MACGT Comparison with Other SLP Schemes
#### 5.3.3 Packet Delivery Ratio  
Figure 10 shows that SLP-MACGT demonstrates the highest data packet transmission rate among the compared protocols. SLP-MACGT implements a secure data transmission strategy based on fountain codes, which enhances the success rate of data transmission while reducing the need for data retransmission. In PP-SLPP, AUVs primarily handle most of the network functionalities, and the data packet transmission rate in PP-SLPP is influenced by the collaboration between leading AUVs and their followers, as the leading AUVs gather data from all the following AUVs.  
Figure 9: End-to-end delay: (**a**) network side length vs. end-to-end delay; (**b**) number of nodes vs. end-to-end delay.  
SSLP utilizes AUVs to collect data and necessitates periodic data relaying, potentially leading to higher node energy consumption, thereby influencing data packet transmission rates. As the number of nodes increases, AUVs collect data from more nodes along fixed trajectories, impacting data packet transmission rates. MRGSLP partitions the network into static and dynamic layers, with nodes in the static layer requiring assistance from AUVs for data transmission. This layering scheme may influence data packet transmission rates.
# Predicting Student Grades Based on Their Usage of LMS Moodle Using Petri Nets
## 2 Related Work
### The Basic Concept of Petri Nets  
Petri Net (PN) is a graphical and mathematical modeling tool that has the advantages of graphical notation and simple semantics [24]. Petri nets designate a wide class of mathematical models that allow modeling and describing control flows and information dependencies within a system.  
A PN is a 5-tuple [24; 25]: PN = (P,T,A,W,M\({}_{0}\)), where * P = {p1,p2,...,pm} is a finite set of places. A place represents a circle, such as p1,p2 and p3 in Figure 1.
* T = {t1,t2,...,tn} is a finite set of transitions. A transition represents a bar, such as t1 in Figure 1. The intersection of P and T is an empty set, while the union of P and T is not an empty set, i.e., P\(\cap\)T = \(\emptyset\) and T\(\cup\)P \(\
eq\emptyset\).
* A\(\subseteq\)(P\(\times\)T)\(\cup\)(T\(\times\)P) is a set of arcs connecting places and transitions, such as the arrowhead from p1 to t1 depicted in Figure 1.
* W:A\(\rightarrow\){1,2,3,... } is a weight function, whose weight value is positive integers. Arcs, i.e., arrowhead, are labeled with weights. For example, in Figure 1, the arrowhead from t1 to p3, which is labeled with \"2\", is denoted as W(t1,p3) = 2. When the weight is unity and/or \"1\", the label of arc is usually omitted, e.g., W(p1,t1) = 1 is omitted in Figure 1.
* M\({}_{0}\):P\(\rightarrow\){0,1,2,3,... } is the initial marking. If there are k tokens inside place p1, it is said that p1 is marked with k tokens. For example, in Figure 1a, p1 is marked with one token, which is denoted as M(p1) = 1. p2 is marked with two tokens, which is denoted as M(p2) = 2. If Figure 1a is the initial status, the initial marking is denoted as M0(p1,p2,p3) = {1,2,0}.  
A transition t is said to be fired if all its input places p1 are marked with at least W(p1,t) tokens, where W(p1,t) is called the firing condition of transition t. For example, in Figure 1, the firing conditions of t1 are W(p1,t1) = 1 and W(p2,t1) = 2.  
A firing transition t removes W(p1,t) tokens from each input place p1 and adds W(t,p1) tokens to each output place p1. For instance, since M(p1) = 1 and M(p2) = 2 have satisfied the firing conditions of t1 in Figure 1a, t1 is fired. After t1 is fired as Figure 1b depicts, t1 has removed W(p1,t1) = 1 token from input place p1 of t1 and W(p2,t1) = 2 tokens from input place p2 of t1, respectively, and then added W(t1,p3) = 2 tokens to output place p3 of t1 [16].