esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Human Glucose-Insulin Regulatory System
### A Mathematical Model for HGIRS
#### 2.1.2 The Exogenous Insulin Equation  
Our approach adopted the pharmacokinetics data of four types of insulin (glargine [22], degludec [23], lispro [24] and aspart [25]) to support this modeling based on the approximation of polynomial functions. Since industry information is not enough for producing a viable approximation, we combined them with information from a dataset of seven real Brazilian volunteers that use such insulins to derive the polinomyal functions related to each of them. The dataset is described in Section 5.  
For instance, for glargine, an insulin of slow action, we obtained values from [26; 27; 28; 29; 30], as well as the FDA report [22] and the industry representative information [31]. On the other hand, for lispro, an insulin of fast action, we obtained values from [32; 33; 34]. The insulin lispro report produced by FDA [35] was used to confirm the time-of-action and pharmacokinetic information. Having such values, we were able to build a polynomial function \(p(t)\) that provides an approximation for the exogenous insulin compartment. Each point of \(p(t)\) represents the concentration of insulin prescribed at a given time (\(t\)), considering the parameters of the T1D individual.  
Figure 1 presents an example of a polynomial curve \(p(t)\) for glargine to cover a 24 h period of time. It can be observed that \(p(t)\) is a piecewise combination of polynomials of degrees four, two and three, as described in Equation (6).  
\[p(t)=\begin{cases}-0.0259t^{4}+0.4255t^{3}-2.5787t^{2}+7.213t+9.5966,&0\leq t\leq 6 \\ 0.105t^{2}-2t+26.655,&6<t\leq 8\\ -0.0996t^{4}+3.8587t^{3}-57.701t^{2}+379.78t-910.29,&8<t\leq 12\\ -0.0808t^{3}+3.5536t^{2}-52.254t+269.54,&12<t\leq 16\\ -0.011t^{3}+0.6492t^{2}-13.24t+102.98,&16<t\leq 24\end{cases} \tag{6}\]  
Figure 2 presents an example of a polynomial curve \(p(t)\) for lispro that covers a period of time of 300 min. It can be observed that \(p(t)\) is a piecewise combination of polynomials of degrees three and two, as described in Equation (7).  
\[p(t)=\begin{cases}-0.0015t^{3}+0.0752t^{2}+0.0473t+0.082,&0\leq t\leq 35\\ -0.0022t^{2}+0.2445t+23.638,&35<t\leq 45\\ -0.0097t^{2}+0.9403t+7.5778,&45<t\leq 60\\ 0.00002t^{3}-0.0059t^{2}+0.3791t+23.134,&60<t\leq 120\\ 0.0004t^{2}-0.2612t+42.8774,&120<t\leq 220\\ 0.000006t^{2}-0.0822t+22.163,&220<t\leq 300\end{cases} \tag{7}\]  
Figure 1: The piecewise polynomial for Glargine insulin (slow-acting) considering 0.36 U/kg concentration.
## 3 Results
### Antibacterial Properties
The observed differences in performance, as compared to certain studies, result from our deliberate choice of a direct testing method. We intentionally excluded active ingredients or external influences such as light or ultraviolet irradiation [44, 45, 46]. While this approach may lead to a relatively lower recorded performance, it ensures a more authentic evaluation of the intrinsic antibacterial capabilities of the CQDs. One significant finding from our research is that a mere 30 s contact time between bacteria and the CQD sample is enough to inhibit the growth of _E. coli_. This rapid onset of antibacterial activity sets our study apart from investigations involving hydrophobic carbon quantum dots/polydimethylsiloxane nanocomposites. In those studies, bacterial growth was effectively suppressed after a longer 15 min irradiation period [47,48]. The highlighted speed and effectiveness of the CQD sample in inhibiting bacterial growth make it a potentially rapid and efficient strategy, particularly when used against the _E. coli_ bacterial strain. The estimated values indicate that these results are slightly lower compared to some reported findings [49,50]. However, this outcome could be explained by the experiment using CQDs directly without the addition of any other active substances such as doped or additional various amide groups, as well as the absence of external factors such as exposure to light or ultraviolet radiation [14,51], resulting in a lower observed efficiency.
## 3 Results  
### Socio-Demographic and Anthropometric Data  
A total of 925 responses were obtained after the dissemination of the questionnaire in an online environment, of which 917 were valid answers (the answers with incorrect anthropometric data were eliminated). Overall, 83.2% of the valid answers belonged to people from urban environments and 16.8% to people from rural settings, and of these, 77.5% were women, and 22.5% were men.  
The anthropometric data (weight and height) were used to calculate the BMI by using the Quetelet equation (body mass (kg)/height (m\({}^{2}\))) and interpreted according to the criteria of the World Health Organization ([PERSON] et al., 2017). Most of those who participated in the survey had normal weight (54%), while 6.6% were underweight, 26% overweight, and 13.4% were obese ([PERSON] et al., 2017). In Table 1, the socio-demographic characteristics of the participants are presented.  
The anthropometric data showed higher percentages of overweight (41%) and obese (18%) among the male respondents than their female counterparts (Figure 2a), for whom the ratio of overweight people was 22%, and that of obese people was 12% (\(p\) = 0.001). On the other hand, underweight people were found to be more among the female respondents (8%) than the male participants (1%).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & \(n\) & \% \\ \hline Gender & & \\ Male & 205 & 22.5 \\ Female & 711 & 71.6 \\ Geographical region & & \\ Banat & 10 & 1.1 \\ Crisana & 6 & 0.7 \\ Maramures & 5 & 0.5 \\ Bucovina & 5 & 0.5 \\ Moldavia & 62 & 6.8 \\ Dobrogea & 235 & 25.6 \\ Muntenia & 530 & 57.8 \\ Oltenia & 28 & 3.1 \\ Transylvania & 36 & 3.9 \\ Age (years) & & \\
18–30 & 317 & 34.6 \\
31–50 & 333 & 36.3 \\ \(>\)50 & 267 & 21.1 \\ Residence areas & & \\ Urban areas & 763 & 83.2 \\ Rural areas & 154 & 16.8 \\ Level of education & & \\ General/primary studies & 30 & 3.3 \\ Secondary education & 151 & 16.5 \\ (paccalaureate degree) & 58 & 6.3 \\ Post-secondary studies & 326 & 35.6 \\ Higher education (bachelor’s degree) & 326 & 35.6 \\ Postgraduate studies (master’s degree, & & \\ residency, doctorate, & 352 & 38.4 \\ other specializations) & & \\ Body mass index (BMI) & & \\ Normal limits (18.5–24.9) & 495 & 54 \\ Overweight category (25–29.9) & 238 & 26 \\ Underweight category (<18.5) & 61 & 6.6 \\ Obese (\(\geq\)30) & 123 & 13.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Socio-demographic characteristics of participants (\(n\) = 917).  
Additionally, from the processing of the anthropometric data (Figure 2b), the highest percentages of overweight (38%) and obese (19%) people were noted among the elderly respondents (over 50 years old), compared with the young (overweight 15%, obese 10%) or middle-aged respondents (26% overweight, 12% obese). Instead, most of the underweight (12%) or regular weight (62%) people were found among the young respondents aged  
Figure 2: Weight status by (**a**) gender and (**b**) age group.  
under 30, compared with the middle-aged respondents (underweight 6%, normal weight 56%) and the older ones (underweight 1%, normal weight 42%), \(p=0.001\).
## 3 Proposed HEMOGWO for SCOS
### Proposed Hemogwo  
Combined with the above improvement strategy, the HEMOGWO algorithm is proposed to solve the SCOS problem. The pseudo-code of HEMOGWO is shown in Algorithm 1.  
```
Input: MaxIt, Population size, Archive size, nGrid, Inflation rate, Leader selection pressure, Deletion selection pressure, Stagnation threshold, \(\beta\) Output: Archive
3 Initialize DE and GWO parameters
4 The gray wolf population is initialized by Equation (12) using Tent chaotic map
5 Evaluate objective functions for all individuals
6 Extract non-dominated solutions and initialize Archive
7 Construct the initial hypercube grid
8 Compute grid indices for Archive individuals
9 for it = 1 to MaxIt
10 Update convergence factor \(a\) using Equation (11)
11 Adaptively update \(F\) and \(CR\) using Equations (20) and (21)
12 for i = 1 to PopulationSize
13 Select \(\alpha\), \(\beta\), \(\delta\) leaders from Archive based on grid fitness and selection pressure
14 Update gray wolf \(i\) using Equations (8)-(10)
15 Apply Levy-based mutation using Equation (19)
16 Perform binomial crossover using Equation (14)
17 Update position of individual i using greedy selection Equation (15)
18 if stagnation_counter(i) \(\geq\) stagnation_threshold
19 Apply Levy-Gaussian perturbation using Equation (23)
20 Reset stagnation_counter(i)
21 else
22 Increment stagnation_counter(i)
23 endif
24 endfor
25 Merge new non-dominated solutions into Archive
26 Reconstruct or update hypercube grid
27 Update grid indices for Archive members
28 if size(Archive) > Archive_size
29 Delete excess individuals based on grid crowding
30 endif
31 endfor
32 Return archive
```  
**Algorithm 1** HEMOGWO for SCOS problem
## 3 Results and Discussion
### Insights from Piper Diagram and SOM Clustering Comparison  
The majority of the samples can be found in the upper left side of the Piper diamond, with just a couple of samples plotted on its right side. The large amount of data points together with its similarity in terms of percentage composition would have made it extremely difficult to differentiate between the water types, as can be seen in Figure 8. Thus, the groups obtained after the SOM clustering were plotted in a traditional Piper diagram (Figure 9).  
Figure 7: Clustered SOM. Each color represents a different cluster. The black circles correspond to the individual samples that were included in each neuron. From C1 to C3: result of the three clusters obtained.  
As can be seen from Figure 9B, almost all of our data-points fell within the zone associated with the _mixed-water_ facies. This result was expected if we take into consideration that samples included in this facies do not show a clear predominance of a particular ions, a characteristic attributed to C1, the most populated group obtained. Waters related to C2 showed a more mixed chemistry and a tendency to be more enriched in Cl\({}^{-}\) and F\({}^{-}\). The majority of the waters associated to C3 plotted on the _magnesium-bicarbonate_ facies presenting low concentrations of Na\({}^{+}\) and K\({}^{+}\). The only point plotting on the right side of the Piper diamond, is the one associated to the one sample of Tuscany rainwater reported by [39]. This would seem to indicate that there are no similarities between rainwater and those present in the aquifer but the use of the SOM has made it possible to highlight that, even if they do not fall within the same hydrogeochemical facies, these waters have similarities in the _codebook vectors_ which allow them to be inserted in neurons belonging to C3. In the rainwater sample, the Ca\({}^{2+}\) and SO\({}_{4}\)\({}^{2-}\) ions represented about 80% of the total composition, therefore, the influence of the other major ions is not considered important in the definition of the hydrogeochemical facies. Following this multivariate approach, makes  
Figure 8: Mean chemical composition of each cluster obtained (columns). Mean concentration is included within each bar. Error bars represent standard deviation. The number of individuals per cluster is represented by the points.  
Figure 9: Piper diagram of the data. The diagram on the left is grouped by well (**a**). The diagram on the right is grouped according to the SOM clustering results (**b**). MW represents the meteoric water of the area [39].  
it possible to evaluate all the variables simultaneously, and after standardization highlight the presence of similarities on the basis of the trend of the other ions, not considered in the construction of the traditional diagram.
# Local Community Detection in Dynamic Graphs Using Personalized Centrality +
## 6 Guaranteed Ranking
### Methods
#### 6.1.1 New Stopping Criterion  
In previous sections to identify the top vertices in a graph, we have run the iterative solver to a predetermined tolerance to obtain an approximation of \(\epsilon_{PR}^{*}\) or \(\epsilon_{Katz}^{*}\). In this section, we introduce an alternate stopping criterion for an iterative solver that determines when to terminate based off of ranking accuracy of the highly ranked vertices instead of numerical accuracy of the solution using Theorem 1. If a user desires a set of the top \(R\) vertices, our stopping criterion returns a set of \(S\) vertices with at least some preset user-desired precision \(\phi^{*}\). The precision for these values of \(R\) and \(S\) is defined as \(\phi=\frac{R}{S-1}\). We show in practice that this is faster than running a solver to machine precision, about \(10^{-15}\), which is the most precise numerical solution possible on a computer. Since our eventual goal is local community detection using the personalized centrality vector, we simply desire the set of the highly ranked vertices without concern for the internal ordering of the set, and we want only that the top \(R\) vertices are contained somewhere within the returned set of \(S\) vertices. Therefore, this new stopping criterion is particularly apt for this purpose.  
We can implement this new stopping criterion as a part of the [PERSON] iterative method. At each iteration of the iterative solver, the current solution \(\epsilon^{(k)}\) is organized in decreasing order to produce the vector \(\mathbf{d}^{(k)}\). To sort \(\epsilon^{(k)}\), we use the method described in Section 4.1. We find the first position \(S>R\) in \(d^{(k)}\) where we have the necessary gap of \(|d^{(k)}_{R}-d^{(k)}_{j}|>2\epsilon_{k}\) for whichever centrality metric we are currently calculating. If, for this value of \(S\), we have at least the user-desired preset precision meaning \(\phi>\phi^{*}\), we terminate; otherwise, we iterate again to iteration \(k+1\) to obtain a more accurate approximation \(c^{(k)}\). Intuitively, the precision shows how far past position \(R\) we must travel down the vector to find the necessary gap to ensure we are returning the top \(R\) vertices in the graph.
###### Abstract  
The convergence of Operational Technology (OT) and Information Technology (IT) networks has become increasingly prevalent with the growth of Industrial Internet of Things (IIoT) applications. This shift, while enabling enhanced automation, remote monitoring, and data sharing, also introduces new challenges related to communication latency and cybersecurity. Oftentimes, legacy OT protocols were adapted to the TCP/IP stack without an extensive review of the ramifications to their robustness, performance, or safety objectives. To further accommodate the IT/OT convergence, protocol gateways were introduced to facilitate the migration from serial protocols to TCP/IP protocol stacks within modern IT/OT infrastructure. However, they often introduce additional vulnerabilities by exposing traditionally isolated protocols to external threats. This study investigates the security and reliability implications of migrating serial protocols to TCP/IP stacks and the impact of protocol gateways, utilizing two widely used OT protocols: Modbus TCP and DNP3. Our protocol analysis finds a significant safety-critical vulnerability resulting from this migration, and our subsequent tests clearly demonstrate its presence and impact. A multi-tiered testbed, consisting of both physical and emulated components, is used to evaluate protocol performance and the effects of device-specific implementation flaws. Through this analysis of specifications and behaviors during communication interruptions, we identify critical differences in fault handling and the impact on time-sensitive data delivery. The findings highlight how reliance on lower-level IT protocols can undermine OT system resilience, and they inform the development of mitigation strategies to enhance the robustness of industrial communication networks.  
operational technology; modbus TCP; DNP3; industrial protocols; communication security; OT protocols; mitigation strategies +
Footnote †: journal: Future Internet
## 3 Results and Discussion
### Total Phenolic and Anthocyanin Contents of the Bread Prepared with the Flour-Bran Blends  
The total phenolic and anthocyanin contents of the bread made from flour-bran blends with different bran blending ratios are shown in Figure 3. The total phenolic content of the bread prepared with the flour-bran blends was in the range of 110.6-258.9 mg GAE/100 g for the bread crumb and of 298.8-487.4 mg GAE/100 g for the bread crust. The total anthocyanin content of the bread prepared with the flour-bran blends was in the range of 2.68-4.07 mg C3 GE/100 g for the bread crumb and of 1.97-2.61 mg C3 GE/100 g for the bread crush. As the blending ratio increased, the total phenolic and anthocyanin contents significantly increased in both the bread crumb and crust. The total phenolic content of the bread crust was higher than that of the bread crumb, which is consistent with the results reported by [PERSON] and [PERSON] [12]. During the baking of wheat-based products, amino acids and reducing sugars of wheat flour form phenolic compounds via the Maillard reaction. More phenolic compounds are formed in the bread crust than in the bread crumb because of the formation of pigments by the Maillard reaction [39; 40]. However, the total anthocyanin content was higher in the bread crumb than in bread crust, showing the opposite trend for the total phenolic content. The results were consistent with those of previous studies, which found that more anthocyanins were destroyed in the crust of bread. Anthocyanins are relatively unstable to heat and lead to easy destruction during processing [41]. In particular, the bread crust was much hotter during baking than the bread crumb [42]. [PERSON] et al. [41] reported that anthocyanins in muffins prepared with purple wheat were destroyed entirely during baking because of their high sensitivity to heat.  
Phenolic compounds are mainly distributed in plants and have been reported to have antioxidant, antibacterial, and anti-inflammatory activities. These can be used as indirect indicators of the antioxidant activity [43; 44]. The purple-colored wheat bran used in this study contained anthocyanin pigment, unlike regular wheat bran. When bran was blended at 30%, the total phenolic and total anthocyanin contents were the highest among the blending ratios tested. The antioxidant activity of the bread can be enhanced by blending with bran.
## 3 Results
### Continuous Measurement of Different Concentrations  
In practical applications, continuous monitoring capability and response ability are two important indexes to evaluate the system. In the experiment, the calibration curve determined by the data in Figure 5 was used to retrieve the known concentrations of chloroform gas. The flow rates of the two gas paths were controlled by the MFCs. One path was for pure nitrogen, while the other path was for chloroform. By changing the flow rate of the two paths, we gradually increased the concentration of chloroform gas inside the PA cell from 104 ppm to 625 ppm (104 ppm, 156 ppm, 208 ppm, 313 ppm, 417 ppm, 469 ppm, 521 ppm, 563 ppm, 625 ppm), while the total airflow velocity was always kept to less than 0.4 L/min to reduce the impact of the airflow sound during the whole process, and the gas was continuously flowing through the PA cell. Figure 6 shows the continuous measurement results for more than half an hour. Apparently, the system has the ability to quickly respond to the change in gas concentration. The numerical instability may be caused by airflow disturbance. The calculation results indicate that compared with the theoretical proportioning concentration, and the measurement error is controlled within \(\pm\)3% for all concentrations, which demonstrates the high measurement accuracy of the system.  
Figure 5: Relationship between the chloroform concentration and the measured signal intensity. Ten different concentrations of chloroform gas were measured in the experiment.  
Figure 6: Continuous measurement of the chloroform gas with the concentration gradually increasing from 104 ppm to 625 ppm. The process lasted for over 40 min.
## 2 Design of Low-Pass Filter
### Calibration and Bandwidth Tuning  
In the proposed filter structure, PVT induced variation and bandwidth tuning is implemented via digitally controlled resistor and capacitor tuning banks. Resistor banks are primarily used for IC process and temperature compensation, while capacitor banks are used to tune the bandwidth and to compensate for the residual, mostly constant, IC process parameter spread.
## 6 Simulation Results and Evaluation of Proposed Methods
### Simulation Results of RAF Method  
Figure 16 shows the simulation results when we used our newly proposed fusion method that was presented as the RAF method in Section 5.2. The maximum square error was reduced to \(10^{-11}\)and no incorrect target tracking was encountered in 400 seconds of simulation run. In this method, each set of simultaneous equations was composed of sensing information of three sensor nodes. But for accurate target tracking we needed four sensing coverage. Four sensing coverage caused at least two different sets of simultaneous equations to be generated and using a proper fusion method, accurate spatio-temporal information of a target object to be computed.  
AFCT and RAF methods compute the spatio-temporal information of target object without iteration. Time and memory usage complexity of both methods are _\(\Theta\)(1)_. Other famous methods for acoustic  
Figure 15: The square error of target tracking using AFCT method.  
target tracking like particle filtering has time and memory usage complexity of _O(n)_ which \(n\) is the number of particles that is used in each step. Typical number of sample particles is about 1000 [4]. Numeric methods like Newton method for solving simultaneous nonlinear equations of Equation (2) are iterative and number of iteration depends on the required accuracy of results and have high computation overhead for application on WSN [51]. In comparison with other methods, our methods are more efficient than other methods form time and memory usage complexity.
# Engineering Design of the European DEMO HCPB Breeding Blanket Breader Zone Mockup
## 6 Performance Analysis
### Determining the Number of Stiffening Rings  
In order to hold the tube insert in position, its rear part is supported by the supporting rods connected via the supporting ring, see Figure 15. In the original design, as shown in Figures 3 and 4, there is only one supporting ring. However, after discussion with the manufacturing partner, it was suggested to have additional stiffening rings for reinforcement. Therefore, FEM analysis was done to check the deflection of the front part of the tube insert due to self-weight. Two cases were studied. The first one (case 1) with one additional stiffening ring, the second one (case 2) with three additional stiffening rings. The boundary conditions for case 2 are shown in Figure 16. Similar boundary conditions for case 1 were used.  
Figure 12: Velocity profile at lines 1–10.  
Figure 14: Temperature contours of the mockup of different TCC values.  
Figure 13: Velocity profile at lines 4 vs. 9 and 5 vs. 10.  
The deflection of the tube insert is shown in Figure 17.  
The resulting deformations for case 1 and case 2 are respectively shown in Figures 18 and 19.  
Figure 16: Boundary conditions case 2.  
Figure 17: Assembly of the tube insert.  
Figure 15: Supporting ring and rods.  
It can be seen that with three additional stiffening rings, the deformation due to self-weight is smaller. To be conservative, case 2 is chosen for the final design.